; .
rhidakn:
" 9 (In "'I u
it: 1.1..
Ivit 79
l. .

J... ‘
:.
‘1539525.
; . :- at!
. . .

3..
3‘ x~ la!
1:) It?

 

 

 

 

 

 

THESIS

3

gom

 

 

LTB-‘r “av
Michiga:. State
University

 

 

 

This is to certify that the

dissertation entitled

REGIONAL ECONOMIC DEVELOPMENT AND MAQUILADORA PRODUCTION:

AN INTEGRATED MODEL OF YUCATAN'S SPACE-ECONOMY

presented by

James John Biles

has been accepted towards fulfillment
of the requirements for

 

 

 

 

Ph.D. degree in Geography
Major er “
£44, /j I M0 /
Date fl /

MS U is an Affirmative Action/Equal Opportunity Institution 0-12771

 

 

 

 

 

PLACE IN RETURN BOX to remove this checkout from your record.
TO AVOID FINES return on or before date due.
MAY BE RECALLED with earlier due date if requested.

 

DATE DUE

DATE DUE

DATE DUE

 

 

 

 

 

 

 

 

 

 

 

 

 

 

6/01 cJCIRC/DateDuopss-sz

 

 

REGIDNAL ECO
AX [\"TE

REGIONAL ECONOMIC DEVELOPMENT AND MAQUILADORA PRODUCTION:
AN INTEGRATED MODEL OF YUCATAN’S SPACE-ECONOMY

By

James John Biles

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
Department of Geography

2001

ABSTRACT

REGIONAL ECONOMIC DEVELOPMENT AND MAQUILADORA PRODUCTION:
AN INTEGRATED MODEL OF YUCATAN’S SPACE-ECONOMY

By

James John Biles

Maquiladoras are export-oriented assembly plants based on the labor-intensive
manufacture of imported components. Traditionally, these firms have located along the
U.S.-Mexico border. However, during the past decade maquiladora production has
spread to other regions of Mexico. Perhaps the most dramatic shift in the location of these
export-oriented industries has been experienced by the state of Yucatan. A decade ago,
maquiladora production in Yucatan was insignificant; by early 2001, however, more than
145 export-oriented firms, employing more than 37,000 persons, generated more than $6
billion (US) in output. The majority of these firms produce clothing and apparel,
primarily for the US. market.

The proliferation of maquiladora production in Yucatan is the consequence of
profound changes in the global economy, as well as a series of national and regional
economic development strategies implemented since the mid-19803. In general, recent
policy initiatives have identified maquiladoras as a "key" sector in redressing geographic
disparities in economic development and bringing about a more equitable spatial
distribution of economic opportunity in Yucatan.

Given the tremendous locational changes in maquiladora production during the past
decade and the prevailing lack of integration of export-oriented industries with Mexico's

economy, this dissertation provides a critical appraisal of export—oriented industrialization

wxfl'w‘. -,v- f'.‘ L
rzxtuscikli 1L H“
of Yucaién. Furti".

some a: the re:-

as a regional development strategy in the case of Yucatan, Mexico. An integrated input-
output/spatial econometric model is developed and implemented in order to carry out
policy analysis at different spatial scales. The model is a fully operational planning tool
that may be employed for economic impact assessment and simulation in the case of
Yucatan. Furthermore, the methodology upon which the model is based may be utilized
to develop similar models for other locations.

In general, the input-output component of the model reveals that maquiladora
production has had a positive impact on economic growth in both urban and rural regions
of Yucatan. Furthermore, the E01 strategy has brought about a moderate redistribution of
income at the regional scale (urban/rural) during the past decade. Spatial econometric
analysis indicates that maquiladora production at the municipio scale creates additional
employment not only locally, but among other locations throughout Yucatan. The
propensity to generate "spillover" employment in other municipios is a function of both
geographic distance and economic importance.

Notwithstanding the positive impacts of maquiladora production, the E01 strategy
has not induced a more geographically-balanced distribution of economic development in
Yucatan. The benefits of export-oriented industrialization have been highly concentrated
at the local scale and employment creation effects are particularly weak among the highly

impoverished and economically marginal municipios in the southernmost part of state.

Copyright by
JAMES JOHN BILES
2001

Para los que ya se fueron y los que ar’m no llegan

.. ,
Twas). t.»

$437.14“ in \K.

‘ T "9
“073?; 3a

2 ”.1
ug\ H»

1" 0315 few :7.

‘iiiifi and R

ACKNOWLEDGMENTS

Typically, dissertations and theses are prefaced by a trite, and usually superfluous,
statement in which the student recognizes the support offered by friends, family
members, professors, and colleagues. Such acknowledgments are invariably superfluous,
at least in my opinion, because those providing assistance rarely expect public
recognition. In a nutshell, one’s appreciation should be shown not in black-and-white, but
by one’s actions.

This dissertation differs from most others. Had I desired to acknowledge those
persons who played prominent roles in facilitating completion of my graduate studies and
this research project, I would undoubtedly mention Prof. Bruce Pi gozzi, my advisor, and
committee members Assefa Mehretu, Robert Wittick and René Hinojosa. In addition, I
would recognize the assistance provided by Mexican colleagues Juan Carlos Gonzalez
Avila and Roberto Vallejo. Most importantly, I would express my gratitude to my wife,
Loly, and children, Alex and Monica, for enduring the sacrifices required to complete this
dissertation.

Rather than profess public appreciation for the contributions of numerous persons
who have no desire to see their names in print, I prefer to "acknowledge" my debt to the
region that forms the focus of this dissertation - Yucatan, Mexico.

I was first introduced to Yucatan in 1985 as a participant in the Latin American
Studies Semester at Temple University. As a young man from Bristol, PA who had never
been outside the United States, the experience changed my life. In 1986, I dropped out of

Temple to take up residence in Mérida, Yucatan for the next four years. Quite literally, it

vi

‘
0L1-

a; in Y

.79

u.n'.',,‘.
i.“ L. '
Him WI

7337.3 .17.

a“
.5,

A.ns-~\

.,"..V09
st ' 3

im

 

was in Yucatan where I grew up. I was enchanted by the region from the first time I
visited and I remain so today.

The purpose of this preliminary statement, then, is to recognize the critical role that
Yucatan has played in my personal and professional life. The research that follows
represents an initial attempt at repaying the debt I owe Yucatan and its people, a debt that

I am comitted to repay.

vii

L157 OF TABLES...........
'L‘ST OF FIGURES .

CHEER ONE
NRDDI’CIION
1.1 S'uismrnl of pr}?
1.: Research {Its-5E.”
1.3 Objectiies. l’t’x‘s'
I 3 .1 Regan}:
13.. Rrswgh
i 3.. Rascal".
13-1 Rest-.1111“.
13-51R::‘.rrgh
1W4Ei .....
15 FOI'IIDiSOi SIUJ\ .

  

L’J

CIWTER DI 0
REGION II Ecoxom

1m ”1111:: TC‘I In 1.

1‘
.'-11 chirlF.‘
‘I 1 - n:
:13. DCII, JFZT:
‘ t
"1'3 'An Upgrd'

ThfiTKXLR\ Hf r1
“L“‘idi‘idl'v oh:

:1 anomic dL ‘
3
3.1.1 Rcsounc
313 Import-s“-
3: ..r, IIPIITI‘OI'
;1)duc110n of If
3:1 Impctus I
3‘: . ‘onomi.
. ’9 r"' ‘ I
324 l :' Jr?
3 ‘5 Nil 1"...
3:6 Impdui Hi
Th6 "NHL:

 

 

TABLE OF CONTENTS

LIST OF TABLES ................................................................................................................... xi
LIST OF FIGURES ................................................................................................................. xiv
CHAPTER ONE
INTRODUCTION
1.1 Statement of problem .................................................................................................. 1
1.2 Research problem ....................................................................................................... 3
1.3 Objectives, research questions and hypotheses .......................................................... 4
1.3.1 Research question 1 ....................................................................................... 5
1.3.2 Research question 2 ....................................................................................... 5
1.3.3 Research question 3 ....................................................................................... 6
1.3.4 Research question 4 ....................................................................................... 6
1.3.5 Research question 5 ....................................................................................... 7
1.4 Expected results .......................................................................................................... 8
1.5 Format of study ........................................................................................................... 9
CHAPTER TWO
REGIONAL ECONOMIC DEVELOPMENT AND REGIONAL POLICY
2.1 Defining regional economic development .................................................................. 11
2.1.1 Defining region .............................................................................................. 12
2.1.2 Defining economic development ................................................................... 14
2.1.3 An operational definition of regional economic development ...................... 20
2.2 The process of regional economic development ......................................................... 21
2.3 Understanding the role of regional policy .................................................................. 24
CHAPTER THREE
AN ECONOMIC GEOGRAPHY OF MEXICO’S MAQUILADORA INDUSTRY
3.1 Economic development strategies in Mexico ............................................................. 28
3.1.1 Resource-based exports ................................................................................. 28
3.1.2 Import-substitution industrialization (ISI) ..................................................... 31
3.1.3 Export-oriented industrialization (EOI) ......................................................... 36
3.2 Introduction of the maquiladora strategy ................................................................... 40
3.2.1 Impetus for the maquila program .................................................................. 41
3.2.2 Economic impacts of the maquila strategy .................................................... 43
3.2.3 Geographic distribution of maquila impacts ................................................. 48
3.2.4 Locational shifts in maquila production ........................................................ 51
3.2.5 Impact of NAFTA on export-oriented industries .......................................... 53
3.2.6 The maquila strategy and regional development ........................................... 55

viii

 

(PAPER FO'LR
41%;‘111’L4DOR4 PRU?
11641.45. MEX 1C0
4.1 Back-ground 1.111»
4.1.1 51753.":
4.2 sttonsai preccd
4.3P011c5 unsurcs .
440L5611D£3n1130
4.5 Pr31zfsraixon of n
45.1 Creepy?
4.6 91er econam;
4.6.1 Sun—143:1
4.6.2 Empigct
4.6.3 5.11.5255:
4.6.4 (than;

WAFER FIVE
.1VBTEGR4TED MOI
5.1 ngmna1m’dcf:
5.1.1 lnzcgriza

3.3 Research dsstzr. .

{-4

...-. Acqusn.
. , ,
3...- Confirm:

' a
3.3 [XKCIU'FT

SN'ER SK
151E NPACT OF 4H1)?
SP,4CE-ECO.\'O.\1Y ‘ i
6.1 Introduction .......
.: sglona1 econm‘.
..~ bar-regional 1m:
4 mpactsof mam;
.3 Impach of "11111.11.
6 Impacts of "1121;;1..
.1 alpaca 01mm

(”In
I

'4: (31151211:
"~ 8114114111.:

ESIHT‘ut;

7

 

 

CHAPTER FOUR
MAQUILADORA PRODUCTION AS REGIONAL DEVELOPMENT STRATEGY IN
YUCATAN, MEXICO

4.1 Background information ............................................................................................. 59
4.1.1 Structure of regional economy ....................................................................... 62
4.2 Historical precedents .................................................................................................. 63
4.3 Policy measures and political environment ................................................................ 66
4.4 Other incentives .......................................................................................................... 69
4.5 Proliferation of maquiladoras in Yucatan .................................................................. 71
4.5.1 Geographic distribution of maquiladoras ...................................................... 72
4.6 Direct economic impacts of the maquiladora strategy ............................................... 73
4.6.1 Start-up costs ................................................................................................. 74
4.6.2 Employment ................................................................................................... 75
4.6.3 Salaries and benefits ...................................................................................... 75
4.6.4 Other expenditures ......................................................................................... 76
CHAPTER FIVE
AN INTEGRATED MODEL OF YUCATAN'S SPACE-ECONOMY
5.1 Regional modeling and regional science .................................................................... 79
5.1.1 Integrated modeling in regional science ........................................................ 79
5.2 Research design .......................................................................................................... 80
5.2.1 Acquisition of primary and secondary data ................................................... 80
5.2.2 Construction of inter-regional input-output (IRIO) model ............................ 83
5.2.3 Development of spatial multipliers ................................................................ 101
CHAPTER SIX

THE IMPACT OF MAQUILADORA PRODUCTION ON YUCATAN'S
SPACE-ECONOMY

6.1 Introduction ................................................................................................................ 118
6.2 Regional economic impacts of maquiladora production ............................................ 118
6.3 Inter-regional impacts of maquiladora production ..................................................... 121
6.4 Impacts of maquiladora production on gross state product ....................................... 127
6.5 Impacts of maquiladora production on the inter-regional distribution of GSP .......... 130
6.6 Impacts of maquiladora production on regional economic structure ......................... 136
6.7 Impacts of maquiladora production on local economies ............................................ 143
6.7.1 Calibration of OLS model ............................................................................. 145
6.7.2 Spatial lags model .......................................................................................... 148
6.7.3 Estimation of local impacts ........................................................................... 150
CHAPTER SEVEN
A CRITICAL ASSESSMENT OF YUCATAN'S MAQUILADORA STRATEGY
7.1 Initial assessment of research hypotheses ................................................................... 157
7.1.1 Overall impacts of the E01 strategy .............................................................. 157
7.1.2 Comparison of export-oriented and domestic industries ............................... 158
7.1.3 Geographic distribution of economic impacts ............................................... 159
7.1.4 Economic impacts at the municipio level ...................................................... 160
7.1.5 Viability of the E01 strategy .......................................................................... 161
7.2 A more detailed assessment ........................................................................................ 161
7.3 Alternative impacts ..................................................................................................... 167
7.4 Maquiladoras as a "ratchet" for regional economic development .............................. 171
7.5 Final remarks .............................................................................................................. 175

APP-331655
Ap-pcndit .4 Sum;
AppendixB CLVVI
AppendixC Egon."-
App-cnditD Rt; \r.
AggvzniixE Inter-r;
ApxndixF Taxing

53110011413111 ....... .

 

APPENDICES

Appendix A Survey Instruments ..................................................................................... 181
Appendix B CINVESTAV Regional Input-output Table. . . . . . . .. .................................... 185
Appendix C Economic Potential by Municipio ................................................................ 196
Appendix D Regional Input-output Tables and Multipliers ............................................. 199
Appendix E Inter-regional Input-output Tables and Multipliers ...................................... 205
Appendix F Traditional Economic Base Multipliers ....................................................... 218
BIBLIOGRAPHY .................................................................................................................... 221

 

151631 S'iI'. .:.'
I913 3.: III... 4?
Tab-264.1 Strum-
I31: 4.: 1995.2

Iibkil Industr

- . . , ‘
1431‘}- Dzstfi‘

:91; Sf, AC" ‘.

T ‘ .

4-‘~‘is:‘1,.....
tn‘l.

LIST OF TABLES

Table 3.1 Shift and share analysis of maquiladoras in metal products .................................. 52
Table 3.2 Shift and share analysis of maquiladoras in textiles industries .............................. 52
Table 4.1 Structure of employment in Yucatan, 1993 and 1998 ............................................ 62
Table 4.2 1995-2001 State Development Plan planning regions ........................................... 68
Table 5.1 Industrial sectors included in integrated model ...................................................... 89
Table 5.2 Distribution of household expenditures .................................................................. 90
Table 5.3 Estimated distribution of expenditures for rural households .................................. 91

Table 5.4 Estimation of inter-regional employment in personal and professional services 91

Table 5.5 Inter-industry transactions of maquiladora industries ............................................ 95

Table 5.6 Actual and projected output by sector, 1988 .......................................................... 98

Table 5.7 Imaginary data for five regions ............................................................................... 111
Table 5.8 Disaggregation of economic potential .................................................................... 112
Table 5.9 Spatial-economic weights matrix ........................................................................... 112
Table 5.10 Traditional spatial weights matrix based on simple contiguity ............................ 113
Table 6.1 Regional multipliers for maquiladora industries — 1988, 1993 and 1998 .............. 119
Table 6.2 Disaggregation of output multipliers for maquila industries .................................. 119
Table 6.3 Employment generation effects for every 1000 maquiladora jobs ........................ 120
Table 6.4 Inter-regional input-output multipliers for maquiladora industries (Mérida) ........ 121

Table 6.5 Inter-regional input-output multipliers for maquiladora industries (rural areas) 121

Table 6.6 Intra-regional distribution of maquiladora impacts (Mérida) ................................ 122
Table 6.7 Intra-regional distribution of maquiladora impacts (rural areas) ........................... 122
Table 6.8 Inter-regional distribution of maquiladora impacts on output (Mérida) ................ 123
Table 6.9 Inter-regional distribution of maquiladora impacts on output (rural areas) ........... 124

xi

.,. \7‘

1:51:6161ritcr-rc, ..
116156.11 Inter-regzcn.
1331: 6.1: Direct {0527:
115156.13 ms cont: '7
1:31: 6.14 Inter-reg: 3:.
116136.15 DiSITlI‘bILiT’.
115156 16 101.11 contrtt
1352611 10141 Cunt,

T ' 1". "
f .\ ‘v ~ UH.
1‘5}; 6‘3 .AUJ! JPIL‘d Aloo'

19.6126 19 Impact of n..
T_..
14615 6 26 Scam! is:

1261: 6.21 4!

l 9
w. 1. v ‘ '
“914...“: 'r4

136136.33 Empiti} men:

T,‘ '_) ' 11
1.61.6-3. Emplomrr:

13' 3 ‘
61. 6.4 Impact of m.

1:666“ '
-~. Ct 135.104.",
1.614 '
BI TCCIImCdI on
1:61“ ‘
.B.. Sectoral me?

Is '.
461. C1 Measures of c

1:61:

figitina1 inp;

$1011.11 mg

141:1)? -
‘ Csmnai mp...
111-1:

DAERHmwr

Iii-1:
7 . “1
£361:

6 Type 11 my
7.5;. I

Table 6.10 Inter-regional distribution of jobs created (per 1000 maquila jobs in Mérida) 125

Table 6.11 Inter-regional distribution of jobs created (per 1000 jobs in rural maquilas) ....... 126

Table 6.12 Direct contribution of maquiladora production to GSP, 1990 to 2000 ................ 128
Table 6.13 Total contribution of maquiladora production to GSP, 1990 to 2000 .................. 128
Table 6.14 Inter-regional distribution of gross state product .................................................. 130
Table 6.15 Distribution of Yucatén's gross state product by region, 1990-2000 .................... 131
Table 6.16 Total contribution of maquiladora production to GRP in Mérida ........................ 131
Table 6.17 Total contribution of maquiladora production to rural GRP ................................ 132
Table 6.18 Adjusted impacts of maquila production on GRP due to leakage effects ............ 133
Table 6.19 Impact of maquiladora production on regional distribution of income ............... 135
Table 6.20 Sectoral distribution of employment by region .................................................... 136
Table 6.21 Maquiladora employment by region, 1990-2000 ................................................. 137
Table 6.22 Employment generation by sector, region and year - rural maquiladoras ........... 138

Table 6.23 Employment generation by sector, region and year - maquiladoras in Mérida... 139
Table 6.24 Impact of maquiladora production on economic structure (1998) ....................... 141

Table 6.25 Contribution of maquiladora industries to employment change, 1988-1998 ....... 142

Table B.1 Technical coefficients, CINVESTAV regional input-output table ........................ 186
Table 8.2 Sectoral multipliers, CINVESTAV regional input-output table ............................ 191
Table C.l Measures of economic potential by municipio ...................................................... 197
Table D.1 Regional input-output table, 1988 ......................................................................... 200
Table D2 Regional input-output table, 1993 ......................................................................... 201
Table D3 Regional input-output table, 1998 ......................................................................... 202
Table D4 Type II multipliers for Yucatan, 1988 ................................................................... 203
Table D5 Type II multipliers for Yucatan, 1993 ................................................................... 203
Table D6 Type II multipliers for Yucatan, 1998 ................................................................... 204
Table E.1 Inter-regional input-output table, 1988 .................................................................. 206

xii

 

 

.. '\ v - P“ I.“- "
m‘aE 111.51 1“” -
‘ 1b ‘- -

 

Tab-15L: Ifliff'l'i‘é‘ ‘7'"

1:51:54 T}pe II 0.3?4
1332355 Type II 041??
?_i' T‘ | [I . 3.
1:31: Lb! T33 .1.st .«
1151: E‘- Type 11 incur.
1361255 Type 11 empf.
1:151:59 Type II emf]
1451: E10 T_\pe II 0.1;
TabieEII Ilipc 1101.17
133155.12 Inc 11 men
TEN.) E 7‘ .

Ai\'l\ I: T)R II ZnCI'
11'-.)

46.1.14 TVpe Il err.“

tame E15 13p: II err;

1abieE16 Tme II 011'

T "I
'k"
.404: E

17 1);»: 11 out;
1361: E18 T)pe 11 int\'

161215.19 Tm: II intx!
.¢.~e E20 Type 11 em;

1361:

E21 Type II em;

1».

161:1: ‘ '
I Tradititiinai e
\\

Table E.2 Inter-regional input-output table, 1993 .................................................................. 207

Table E.3 Inter-regional input-output table, 1998 .................................................................. 208
Table E.4 Type H output multipliers for Mérida (1988) ......................................................... 209
Table E.5 Type II output multiplier for rural areas (1988) ..................................................... 209
Table E.6 Type H income multipliers for Mérida (1988) ....................................................... 210
Table E.7 Type H income multipliers for rural areas (1988) .................................................. 210
Table E.8 Type II employment multipliers for Mérida (1988) ............................................... 211
Table E.9 Type II employment multipliers for rural areas (1988) .......................................... 211
Table E. 10 Type H output multipliers for Mérida (1993) ...................................................... 212
Table E.11 Type H output multiplier for rural areas (1993) ................................................... 212
Table E. 12 Type H income multipliers for Mérida (1993) ..................................................... 213
Table E.13 Type H income multipliers for rural areas (1993) ................................................ 213
Table E.14 Type II employment multipliers for Mérida (1993) ............................................. 214
Table E.15 Type H employment multipliers for rural areas (1993) ........................................ 214
Table E. 16 Type H output multipliers for Mérida (1998) ....................................................... 215
Table E. 17 Type H output multiplier for rural areas (1998) ................................................... 215
Table E.18 Type H income multipliers for Mérida (1998) ..................................................... 216
Table E.19 Type H income multipliers for rural areas (1998) ................................................ 216
Table E.20 Type H employment multipliers for Mérida (1998) ............................................. 217
Table E.21 Type H employment multipliers for rural areas (1998) ........................................ 217
Table F.1 Traditional economic base multipliers, 1998 ......................................................... 219

xiii

LIST OF FIGURES

Figure 1.1 Location of study area ........................................................................................... 2

Figure 2.1 Regional economic development and the role of regional policy ......................... 24
Figure 3.1 Number of maquiladoras, 1980-2000 ................................................................... 42
Figure 3.2 Maquiladora employment, 1980-2000 .................................................................. 43
Figure 3.3 Distribution of maquiladoras by state, 2000 ......................................................... 44
Figure 3.4 Maquiladora employment by state, 2000 .............................................................. 45
Figure 3.5 Annual maquiladora salaries, 1990-1999 ............................................................. 45
Figure 3.6 Mexican inputs as a share of total inputs .............................................................. 46
Figure 3.7 Purchase of commodity inputs and producer services, 1990-2000 ....................... 47
Figure 3.8 Location of maquiladoras by region ..................................................................... 48
Figure 3.9 Change in maquiladora employment by region, 1991-2000 ................................. 49
Figure 3.10 Average maquiladora wages in border and interior regions of Mexico .............. 50
Figure 3.11 Maquiladora employment vs. total manufacturing employment ........................ 54
Figure 3.12 Mexican "share" of maquiladora benefits ........................................................... 55
Figure 3.12 Distribution of income by region, 1970-1998 ..................................................... 56
Figure 3.14 Per capita income by region, 1970-1998 ............................................................. 57
Figure 4.1 Municipio boundaries — State of Yucatan ............................................................. 60
Figure 4.2 Level of marginality at municipio level (1995) ..................................................... 61
Figure 4.3 Number of maquiladoras in Yucatan, 1985-2001 ................................................. 66
Figure 4.4 Maquiladora employment in Yucatan, 1990-2001 ............................................... 67
Figure 4.5 Regional planning districts, 1995-2001 State Development Plan ........................ 67
Figure 4.6 Number of maquiladoras by municipio, 2001 ...................................................... 70
Figure 4.7 Maquiladora employment by municipio, 2001 ..................................................... 71

xiv

Figure 4.8 Maquiladora employment as a percentage of total formal employment ............... 72

Figure 5.1 Stylized regional input-output table ...................................................................... 87

Figure 5.2 Stylized inter-regional input-output table .............................................................. 88

Figure 5.3 Traditional economic base model (two regions) ................................................... 106
Figure 5.4 Incorporating space into the economic base relationship ...................................... 107
Figure 5.5 Economic potential by municipio .......................................................................... 110
Figure 5.6 Imaginary study area comprised on five regions ................................................... 111
Figure 6.1 Overall contribution of maquiladoras to Yucatan GSP, 1990-2000 ..................... 129
Figure 6.2 Overall contribution of maquiladora production to GSP in rural areas ................ 133
Figure 6.3 Overall contribution of maquiladora production to GSP in Mérida ..................... 134
Figure 6.4 Traditional economic base multipliers (1998) ....................................................... 144
Figure 6.5 Distribution of OLS residuals ............................................................................... 146
Figure 6.6 Plot of residuals against predicted values (OLS model) ....................................... 147
Figure 6.7 Geographic distribution of OLS residuals ............................................................. 148
Figure 6.8 Geographic distribution of spatial lags model residuals ........................................ 149
Figure 6.9 Spatial multiplier effects of 1000 basic sector jobs in Mérida .............................. 151
Figure 6.10 Spatial multiplier effects of 1000 basic sector jobs in Motul, Yucatan ............... 152
Figure 6.11 Spatial multiplier effects of all maquila jobs created since 1999 ........................ 153
Figure 6.12 Spatial multiplier effects of all maquila jobs ...................................................... 154
Figure 6.13 Direct and indirect non-basic jobs generated by maquiladoras, 1990-2000 ....... 155

Figure 7.1 Total non-basic employment created by the E01 strategy by planning district 162

Figure 7.2 Ratio of total noon-basic employment to population by planning district ............ 163
Figure 7.3 Total employment created by maquiladora production by planning district ........ 164
Figure 7.4 Ratio of total employment to population by planning district ............................... 165

Figure 7.5 Overall employment created by maquiladoras as a share of total employment 166

Figure 7.6 Geographically-balanced employment generation scenario .................................. 168

XV

Egan '7 Lorenz cum-
1.ng Geography;
Figure ".9 Lorenz eur» e
125627111 Impm of s!

Egre‘Il "Locks; :r.

Ears .41 Sum} of

-
V

Egan .42 Sum of d.

 

 

Figure 7.7 Lorenz curve — geographically balanced employment generation ........................ 169

Figure 7.8 Geographically-imbalanced employment generation scenario ............................. 170
Figure 7.9 Lorenz curve - geographically imbalanced employment generation .................... 171
Figure 7.10 Impact of structural change on regional multiplier effect ................................... 172
Figure 7.11 "Locking in" of regional economic development ................................................ 173
Figure A.1 Survey of maquiladoras ....................................................................................... 182
Figure A.2 Survey of domestic firms ...................................................................................... 183
Figure A.3 Survey of households ........................................................................................... 184

xvi

1.1 Statement 01 Pm

1": ‘
Danni.

the pm
Eastfidltulton (ISI)

omitted industnflm:

exponents tMaeLa

,9.
x.-

 

anented firms hue 11‘

has: 90 percent of .111

56:25. Dunng the pas'

Chapter One

INTRODUCTION

1.1 Statement of Problem

During the past two decades, Mexico has abandoned import-substitution
industrialization (ISI) policies in favor of maquiladora production, a form of export-
oriented industrialization (EOI) based on the labor-intensive manufacture of imported
components (MacLachlan and Aguilar, 1998; Sagawe, 1996). Traditionally, these export-
oriented firms have located along the U.S.-Mexico border. In 1990, for example, more
than 90 percent of all maquiladoras and corresponding employment were found in border
states. During the past decade, and especially since inception of the North American Free
Trade Agreement (NAFTA) in 1994, export-oriented production has spread throughout
other regions of the country. At present, more than 3600 maquiladoras employ more than
1.3 million persons throughout Mexico (INEGI, 2001). Although more than 70 percent of
these assembly plants and 80 percent of all employment remain concentrated in the
border region, almost 40 percent of maquiladoras established since 1990 (and one-third

of resulting employment) are located in non-border states (INEGI, 2001).

 

Figure 1.1 Location of study area

At the regional scale, perhaps the most dramatic shift in maquiladora location has
been experienced by the state of Yucatan (Figure 1.1 above). In 1990, only 13
maquiladoras employing about 2500 persons were operating in this marginal region of
southeastern Mexico. By early 2001, however, more than 145 export-oriented firms
provided direct employment to more than 37,300 persons (Secretan’a de Desarrollo
Industrial, 2001). A decade ago, virtually all maquiladoras were located in Mérida, the
state capital. Today, however, the majority of employment in export-oriented production
is found in rural areas of the state. About three-quarters of these plants produce clothing
and apparel, primarin for markets in the United States. In a peripheral region such as
Yucatan, with little indigenous industry, maquiladoras now represent about one-third of
all manufacturing employment and almost two-thirds of total exports. Furthermore, the

output of these 145 export-oriented firms is equivalent to almost 10 percent of all goods

 

and seniees procue;

11111.

 

The erp1osne gr
n1) 6} profound .1".

\Y‘ '4

\ u.»-

.4

0. fl ‘ ‘
ram 4nd re

no

The most recent of 1
the: 'ba1anced 5..
onented production .

mgr-ore lining cond i

 

 

 

ant insatin. specific
:ne‘ pTOHdt‘d scarce
onented production -

. f, ,1
411..n.tl production ~

such a detelnprnent s

’4 mate to 111183;!

.998. Brannon and I.

‘3 Research pruhl.

G1\‘en 1116 Heme;

and services produced in the state (INEGI, 2001; Secretarz’a de Desarrollo Industrial,
2000).

The explosive growth of maquiladora production in Yucatan has been fomented not
only by profound changes in the global economy, but also as a consequence of a series of
national and regional economic development policies implemented since the mid-19803.
The most recent of these initiatives, the 1995-2001 State Development Plan, seeks to
achieve "balanced sustainable regional development" by channeling as much export-
oriented production as possible to rural areas in order to generate employment and
improve living conditions (Estado de Yucatan, 1996). In the case of Mexico, generally,
and Yucatan, specifically, the maquiladora strategy certainly has generated employment
and provided scarce foreign exchange earnings. However, the very nature of export-
on'ented production — importing virtually all intermediate inputs and exporting virtually
all final production - constrains the potential linkages and overall economic impacts of
such a development strategy. As a result, the E01 strategy has been widely criticized for
its failure to integrate more fully with Mexico's economy (MacLachlan and Aguilar,

1998; Brannon and James, 1994; Pradilla, 1993).

1.2 Research problem

Given the tremendous locational changes in maquiladora production during the past
decade and the prevailing lack of integration of export-oriented firms with the Mexican
economy, this doctoral dissertation research project will provide a critical appraisal of
EOI as a regional development strategy in the case of Yucatan, Mexico. Due to the rapid

proliferation of export-oriented firms in Yucatan, the proposed study area offers a unique

1112312 1aborator} in
" ' > 4.. u

minimum prw....

etnnennc relations -..

mei- nattems in th

nnft for Yucatan. but

 

 

means of promoting e

1.1 Objectites. reset
In a general 5e
consequences of cf".

tiger-anon anahzes

temperinxe aux anta-

5211 mosaic of r91.

|

Speezficalh. the ‘
:nternetinn of aloha'

embedded prettier;-

1113071185) In X'UCJI .r

6.,

e“ 1990 and

03114 e

living laboratory in which to evaluate the potential consequences of the spread of
maquiladora production to interior regions of the country. Furthermore, given the uneven
economic relations that prevail between rural and urban areas of the state, the study will
reveal patterns in the geographic distribution of economic impacts that are relevant not
only for Yucatan, but also for other peripheral regions that adopt the E01 strategy as a

means of promoting economic development.

1.3 Objectives, research questions and hypotheses

In a general sense, this study provides an empirical assessment of the local
consequences of changes in the world economy. Paraphrasing Scott (1998), the
dissertation analyzes the implications of policy efforts designed to maximize Yucatan's
competitive advantages (low-cost labor and proximity to the US.) in an increasingly
global mosaic of regional economies.

Specifically, the primary objective of the project is to understand more completely the
interaction of global economic forces (proliferation of the E01 strategy) on sub-regionally
embedded production and service systems (economic structure, employment and
incomes) in Yucatan, Mexico (Vellinga, 2000). In the context of this study, analysis will
focus on changes in urban and rural areas of the state and at the local (municipio) scale
between 1990 and 2000. Furthermore, the study will assess the viability of the E01
strategy in the case of a peripheral region of a developing country with little indigenous
manufacturing. In light of these objectives, the following research questions and working

hypotheses will be addressed.

1.3.1 Research question I

What is the overall impact of export-oriented industrialization on Yucatan's economy
in terms of output, employment, and income? To date, the bulk of previous research has
focused only on the direct effects of maquiladora production in terms of job creation and
purchase of Mexican inputs (MacLachlan and Aguilar, 1998; Garcia and Pérez; and
Pradilla, 1993). However, according to Okuyama et al. (1999), in a regional context
household consumption may have a far greater impact on economic outcomes than
traditional backward or forward linkages among firms. Therefore, a more complete
assessment of the E01 strategy may be obtained only if the indirect impacts of secondary
purchases made by firms and households are considered.

With respect to associated hypotheses, it is expected that maquiladoras in Yucatan
will have "non-trivial" effects on income, output and employment generation. This
hypothesis will be tested by contrasting regional multipliers that incorporate the impact of
maquiladora production with a "counterfactual" baseline analysis that excludes the
presence of export-oriented production. Specifically, the contribution of maquiladora
production to Yucatan's gross state product (GSP) from 1990 to 2000, as well as the

impact of changes in maquiladora production on changes in GSP, will be quantified.

1.3.2 Research question 2

How do the economic impacts of maquiladora industries compare with those of
domestic firms? According to Guajardo (1998), the overall impacts of export-oriented
firms are about 50 percent smaller than those of domestic industries at the national level.

Therefore, it is hypothesized that export-oriented industrialization in Yucatan has a more

limited effect on job creation, income growth, and output than domestic manufacturing
due to relatively weak forward and backward linkages. However, it is also expected that
regional economies will become more economically self-sufficient over time due to
structural change. Consequently, it is hypothesized that multipliers for both domestic

firms and export-oriented industries will increase between 1990 and 2000.

1.3.3 Research question 3

What is the geographic distribution of these economic impacts within the state of
Yucatan? In other words, given the recent proliferation of export-oriented production
within rural areas of the state, what are the effects on rural areas and urban areas of
Yucatan? Based on the classic literature on dependent development (Evans, 1979) and
regional economic development (Friedmann, 1966) and the asymmetries that prevail
between urban and rural regions in the developing world, the benefits of the E01 strategy
are unlikely to be evenly distributed in the case of Yucatan.

In general, then, it is expected that the majority of direct economic impacts will be
concentrated within rural areas of the state where most maquiladora employment is
located. However, it is hypothesized that the bulk of indirect benefits will accrue to
residents of Mérida, the state's urban core. Given the structural changes mentioned above,
however, a greater and greater share of the indirect benefits is expected to remain within

rural areas of Yucatan.

1.3.4 Research question 4
How strong is economic interaction within and between urban and rural areas

(municipios) of Yucatan's economy? How do changes in maquiladora production impact

upon these interdependencies? These research questions are concerned with the
geographic extent of economic impacts, termed a "spatial multiplier" in the context of this
study. Richardson (1985) was among the first to suggest the need to incorporate space
into multiplier analysis. However, in the intervening decade and a half, only a handful of
scholars have attempted to move from traditional sectoral multipliers to more dynamic
geographic multipliers (Olfert and Stabler, 1999; Robinson, 1997).

Specifically, it is hypothesized that export-oriented industrialization in a given
location will have positive effects on economic activity in surrounding locations in terms
of job creation. In addition, these spatial multiplier impacts are expected to decline with
distance and level in the central place hierarchy (economic importance) within the study

area.

1.3.5 Research question 5

Is export-oriented industrialization a viable economic development strategy in the
case of Yucatan? Specifically, what are the impacts of this strategy on regional
development? This research question will be evaluated based on the impact of
maquiladora production on economic growth (a necessary condition for development),
inequality (distribution of income), and structural change. It is hypothesized that the
maquiladora strategy is a catalyst for some degree of economic growth and structural
change at the regional (urban/rural) level. However, as expressed in the previous research
question, export-oriented industrialization is expected to increase regional income
disparities. As a consequence, the general working hypothesis of this dissertation is that

the form of EOI adopted in Yucatan is not a viable regional economic strategy.

1.4 Expect?d ”7
This dissertail
model of Yucatan
’gpagteeonom} "
defines space-est
economic process
al‘n. remains part:
ms of mteraetir‘.
The model de
robust methodotoé
country. In additio
mfttoith limited 5
tnede‘: is termed

3310. the spatial mu?

,.
1,11".

the form of em

tnfltipl

ier model.

71320

unts not onlx

1303111110Vers and
in

CiLdIEd In Cth

niedel is a fullv 0“:
- t"

if)” I
Rival.
11.th the model I.
5

1.4 Expected results

This dissertation will result in the construction and implementation of an integrated
model of Yucatan's space-economy. In the context of this study, the definition of the term
"space-economy" follows Isard (1956) and Friedmann (1966). In general, Isard (1956)
defines space-economy as the "spatial as well as dynamic character of interrelated
economic processes." Given the objectives of this study, Friedmann's (1966) definition
also remains particularly relevant — "...the economy in its spatial dimension, defined in
terms of interacting flows of goods, capital, labor and information."

The model developed and implemented in subsequent chapters offers a relatively
robust methodology to carry out policy analysis at different spatial scales in a developing
country. In addition, model calibration is fairly straightforward and data demands may be
met with limited survey data collection and incorporation of existing secondary data. The
model is termed "integrated" because it links inter-regional input-output (IRIO) analysis
and the spatial multiplier concept discussed above. In essence, the output of IRIO analysis
(in the form of employment multipliers) becomes one of the primary inputs of the spatial
multiplier model. By incorporating basic spatial econometric techniques, the model
accounts not only for the linkages between different sectors of the state's economy, but
also spillovers and interdependencies among Yucatan’s 106 municipios (local scale). As
indicated in Chapters Six and Seven, the integrated input-output/spatial econometric
model is a fully operational planning tool, which may be employed for policy analysis and
economic impact assessment in the case of Yucatan. Furthermore, the methodology upon
which the model is based (Chapter Five) may be utilized to develop similar models for

other locations.

1.5 Format of study

Export-oriented industrialization represents the most recent economic development
strategy pursued by Latin American nations since independence in the 19th century. The
following section (Chapter Two) operationalizes "regional economic development" as the
term is applied in this study and provides an overview of the role of regional development
policy.

Maquiladora production is a particularly Mexican manifestation of the export-
oriented production strategy. Chapter Three begins with an overview of other economic
development strategies followed by Mexico and many other Latin American nations.
Subsequently, the chapter traces the general evolution of the Mexican maquiladora
industry since inception in the 19603. In addition, prominent locational changes since
1990 and the geographic distribution of economic impacts are assessed.

Chapter Four focuses on the recent proliferation of export-oriented firms in the study
area — Yucatan, Mexico. This section identifies the historical and economic impetus and
policy initiatives that have induced the rapid establishment of such firms in Yucatan.
Furthermore, the direct economic implications of such a strategy are assessed.

In Chapter Five, an integrated model of Yucatan's space-economy, consisting of inter-
regional input-output and spatial econometric components, is developed. The model is
designed to facilitate identification of overall economic impacts, as well as the geographic
implications of the E01 strategy at regional (urban/rural) and local (municipio) scales in
Yucatan.

The results of the integrated model are presented in Chapter Six. A detailed

assessment of the overall impacts of maquiladora production on Yucatan's economy

  
  

teneen1990 and I
nexnrnes is identxie

Chepzer Sexen ;
hirntheses introdne.
streeg) is offered. .

net the eeonorr.

 

between 1990 and 2000 is offered initially. Subsequently, the geographic distribution of
outcomes is identified.

Chapter Seven provides a formal evaluation of the research questions and working
hypotheses introduced above. In addition, a detailed assessment of the viability of the E01
strategy is offered. Finally, several policy alternatives that may serve to optimize or

increase the economic benefits of maquiladora production are discussed.

10

Chapter Two

REGIONAL ECONOMIC DEVELOPMENT AND REGIONAL POLICY

2.1 Defining "regional economic development"

As mentioned above, this dissertation serves as a formal assessment of export-
oriented industrialization (EOI) as regional development policy in the case of Yucatan,
Mexico. Before proceeding, therefore, it is necessary to operationalize the terms "region,"
"development," and "regional economic development as applied in this study." In
addition, a general understanding of the process of regional economic development, as
well as the role of regional policy, is required.

Although numerous examples of economic development policy analysis may be found
in the contemporary literature, the vast majority of these studies have been carried out at
national or international scales (for example, Farmer, 1999, and Barro, 1997). Within the
geography and regional science literatures, regional income convergence/divergence has
become the primary focus of much theoretical and applied work during the past decade
(Rey and Montouri, 1999; Fujita and Hu, 2001). Nonetheless, these studies overlook

assessment of income distribution at the regional level as defined below. In addition,

11

i ;
1 v

.
0"...»

*D.‘)’.'
un‘ka‘

3:213?

contemporary textbooks on development policy analysis (see Sadoulet and de Janvry,
1995, for an example) abdicate regional issues at the expense of national or sector-
specific concerns. Furthermore, much of the recent scholarly work on economic
development — both applied and theoretical — fails to provide a formal definition of the
concept. Prominent examples of this shortcoming include Krugman’s (1995) treatise on
geography, development and economic theory and Haddad’s (1999) analysis of regional
disparities in Brazil.

Lack of a formal definition of "economic development" is troubling for (at least) two
reasons. On the one hand, this oversight suggests that some tacit agreement or consensus
exists regarding the meaning of the term. Judging from the wide variety of definitions that
appear in the literature, however, the more likely case is that an explicit definition of
economic development is lacking because no real agreement exists with respect to its
meaning. To paraphrase Malecki (1991), the definition of economic growth is

unambiguous, whereas development has meant almost all things to all people.

2.1.1 Defining "region "

Traditionally, geographers have defined regions: 1) on the basis of some social,
economic or physical characteristic; 2) drawing on the concept of nodality — a functional
relationship between the city and its hinterlands; or 3) based on administrative or political
boundaries (Malecki, 1991). In these each of these instances, there is no premise that
these regions represent the optimal division of space (Hewings, 1977).

Notwithstanding these (apparently) clear-cut definitions, the regional concept

becomes murky because it has been applied at a variety of spatial scales, from the very

12

Mk

0‘:
.atb

Id»-

 

local to the international. In addition, regional boundaries are not static — they inevitably
depend on the research problem in question and they may change over time as a result of
geographic and economic forces (Friedmann, 1966).

In the context of this study, "region" is defined following Chisholm (1990) and Scott
(1998). Chisholm (1990) identifies a region as an area within a nation that enjoys certain
powers of government or administration. Scott (1998) offers a similar definition - a
geographic area of sub-national extent characterized by some minimal level of
metropolitan development together with an associated tract or hinterland. Chisholm
(1990) also offers several salient differences between regional and national economies
that are relevant to the analysis that follows:

1) a region has fewer powers than a national government;

2) consequently, policy instruments available to regional governments are fewer;

3) and, regional policy decisions tend to have limited effects on the nation;

4) however, national decisions have more profound effects on regions;

5) in addition, regional data, by comparison with national data, are limited or non-

existent.

These distinctions are important because they reflect the limits of regional
development policy and the limitations of regional development policy analysis. In
general, regional policymakers have access to a relatively limited array of economic
development tools. For example, state and local governments lack the ability to limit the
volume of trade or movement of national citizens. In addition, with respect to policy
analysis, it is often difficult to disentangle the effects of regional policies from those of

national policies. Furthermore, regional policy analysis tends to be relatively less

sophisticated than national policy analysis due to the dearth and limited quality of data.

13

 

n
o'b~
h: ii

I

1* :r 7‘

Cl. 1':

‘3“.
Kn. .o’:
.

in.
In)”.
““5. 4'91
1 Vs:

Consequently, regional economic models frequently must be derived from national data

using ad hoc rules or mechanical techniques (Round, 1983).

2.1.2 Defining "economic development"

With respect to "development," Mabougunje’s (1981) warning about the term’s
ambiguity remains relevant — in much of the literature, economic development is
frequently confused with economic growth. A further complication arises because both
words are often treated as synonymous with standard of living. Todaro (1997), for
example, states that the "traditional" definition of economic development refers to the
capacity of an economy to generate and sustain annual increases in aggregate income.
Even a cursory perusal of the social sciences literature, however, reveals a stunning array
of alternative definitions. Other connotations range from: increases in the quality of life
associated with changes in the composition of population, quality and nature of local
jobs, and quantity and prices of goods and services produced locally (Malecki, 1991); a
process in which the potential of an organism is released to achieve its mature form or a
more advanced state (Franko, 1999); change, improvement or progress in some direction
(Smith, 1986); a reduction in absolute poverty, greater equality, equity and social stability
(Sadoulet and de Janvry, 1995); use of the productive resources of society to improve
living conditions of the poorest people (Feet and Hartwick, 1999); and a social
scaffolding capable of sustaining effective networks of production and exchange based on
the accumulation of physical and human capital (Scott, 1998).

Based on a synthesis of earlier work, Mabougunje (1981) identifies four

interpretations of development: increases in per capita output (productivity); changes in

14

the quality of human capital; reduction of poverty and satisfaction of basic needs; and
socio-economic transformation (production and consumption of "modern" goods and
services). Mabougunje’s synthesis forms the basis for the definition of economic
development applied in this study. For the purposes of this dissertation, then,
development is defined as a social and economic process consisting of three (interrelated)
components: economic growth; greater equality in the distribution of income; and
technological change. Each of these three components is discussed in greater detail

below.

2.1.2.1 Development as economic growth

Economic growth may be defined as the expansion of income at a rate greater than
population growth (Malecki, 1991). Typically, national accounts or other aggregate data
are used to measure economic growth. However, growth does not necessarily lead to a
qualitative improvement in welfare and, as such, is not a good measure of development
(Malecki, 1991). As a case in point, the Mexican economy grew at an annual rate of more
than seven percent between 1960 and 1970. However, poverty levels among the
population (income accruing to the poorest 20 percent of the population) actually
increased during this period (Panuco-Laguette and Szekley, 1996).

According to Chisholm (1990) and Peet and Hartwick (1999), growth consists solely
of replicating existing economic structure — producing more of the same for everyone in
the context of a lot more for a few. In other words, economic growth does not presuppose
structural transformation of the regional economy. Consequently, development must

incorporate far more than mere economic growth. As Mabougunje (1981) concludes,

15

 

«‘3'
his.)

r-e

l
m .n.’

3......

a" -'
.
“in...

1‘ ~_:—

Liv; .
.

.A‘ "a

“3.4

 

v“
i. ‘= 'r

however, economic growth is certainly a necessary, though not sufficient, condition for

development.

2.1.2.2 Development as greater equality

As implied above, the relative distribution of benefits constitutes another important
distinction between simple economic growth and economic development. Inequality in
Latin America and the developing world tends to be driven by initial endowments of
natural resources and human capital (Franko, 1999). However, policymaking since
independence in the 19‘h century has brought about even greater levels of inequality
(Franko, 1999). This uneven development or dualism (an economy in which the rich
become richer and the poor more destitute) has been justified on the grounds of economic
rationality. In fact, some scholars (for example, Kuznets, 1955; Myrdal, 1957;
Hirschman, 1958; and Williamson, 1965) have identified increasing inequality as a likely
(and perhaps necessary) consequence during the initial stages of development.

Kuznets (1955) hypothesized that inequality follows an inverted U—shaped path as
economic development expands. An analogous model proposed by Alonzo (1980)
suggests a "bell-shaped" pattern. In both cases, it is posited that disparities are likely to
worsen during the early stages of economic growth. However, as economic development
proceeds, inequality is expected to decline. This increasing inequality is attributed to
structural changes that take place in the economy - growth may be concentrated in the
industrial sector and regional income disparities may be exacerbated (Alonso, 1980). Ever
the economist, Kuznets failed to consider space explicitly. However, the geographic

ramifications of such a pattern of growth were obvious, at least to Friedmann (1966).

16

11

~_._¢- ‘

‘Lii;s

DJ'
.2.»-

P;
f A.

b has

In”- -
4b.).h

am“,

.IL'.

tux.

i.“.\:

.
O
n: N

r."

1.

6
11;“.
“we:

. .
. m;

.,,\
.

According to Friedmann (1966), growth is a cumulative process that brings about
shifts in existing spatial patterns. He affirms that industrial growth tends to be
concentrated in one or two metropolitan regions, which results in a "center" of rapid,
intensive development and a "periphery" whose economy is stagnant or declining. This
pattern of geographically uneven development has also been suggested by Smith (1986)
and Scott (1998), among others. Friedmann also asserts that the inter-regional terms of
trade will continue to favor the center of rapid development as long as the periphery
remains a producer chiefly of primary products and raw materials. Although Kuznets
suggests that regional disparities will decline with economic development, he fails to
provide any insights into the process(es) driving these eventual changes. In addition,
empirical evidence of the hypothesized "Kuznets curve" has proven contradictory in both
developed and developing countries (Todaro, 1997; Franko, 1999).

The relationship between inequality and development has also been expressed by the
concepts of convergence and divergence (Williamson, 1965). In general, convergence
refers to the decline in disparities associated with economic development; divergence
indicates increasing inequality. In the economic geography and regional science
literatures, two forms of convergence have received substantial attention during the past
two decades. The first type of convergence, o-convergence, refers to decreasing variation
in regional incomes. Typically, the standard deviation or coefficient of variation is
employed to analyze the distribution of income. The second form of convergence, B-
convergence, occurs when per capita incomes in poor regions increase faster than
incomes in wealthy regions. In general, econometric and spatial econometric models have

been employed to test for this type of convergence (Rey and Montouri, 1999).

17

o.
i

30'

Myrdal’s and Hirschman’s theories of development follow the modernization
paradigm associated most closely with Rostow (1960), based on rapid industrialization
and the transfer of an underemployed rural population to the productive urban sector
(Hodder, 2000). Furthermore, both theories are predicated on the necessary existence of
polarization — concentration of a very large share of economic activity in a small number
of locations (Storper, 1991).

Myrdal’s (1957) concept of cumulative and circular causation was gleaned from the
experience of developing countries at the turn of the 20th century. He noted a tendency
for the operation of market forces (trade between developed and developing nations, for
example) to increase inequalities in incomes and productivity, contrary to classical
economic theory (Friedmann, 1966; Higgins and Higgins, 1979). According to Myrdal, at
the regional scale initial growth of certain locations is the result of historical accident.
However, these economic advantages are self-reinforcing and concomitant with decline
or stagnation in other areas. As economic development takes place, though, a process of
polarization reversal is expected to occur. Polarization reversal refers to the redistribution
of economic activity outside the central region into other areas, leading to convergence or
the eventual decline in regional disparities (Storper, 1991).

Like Myrdal, Hirschman (1958) affirmed that trade can serve as an instrument of
domination, rather than of mutual benefit (Farmer, 1999). In response, he introduced a
development strategy based on the notion of "unbalanced growth" in which investment of
scarce resources is concentrated in relatively few sectors (and regions) of the economy
(Friedmann, 1966; Storper, 1991; and Franko, 1999). Hirschman’s model of "controlled

disequilibrium" targets initial investment to points of rapid urban industrial expansion

18

I... 1;».
was. “A «Q

E; i);
it: .

(Friedmann, 1966). According to Dussel (1997), Hirschman believed that only a process
of industrialization would be capable of attacking the bottlenecks to regional
development and sustaining a process of economic transformation. The development of
forward and backward linkages was proposed as the means of reducing imbalances and
promoting development. Furthermore, Hirschman suggested establishment of new core
regions in peripheral locations as a means of reducing disparities in regional economic
development.

In each of the above cases, inequality is purportedly efficient for growth and
distributional consequences are not relevant so long as the economy grows at a rapid pace
(Friedmann and Weaver, 1979). However, as Peet and Hartwick (1999) state, economic
development differs from economic growth in that it pays attention to conditions of
production and social consequences, including distribution of incomes and human
welfare. As Gilbert and Goodman (1976) assert, reduction of regional income differences,
as well as integration of regions with the national economy, must be major goals of

economic development policies.

2.1.2.3 Development as structural change

According to Franko (1999), the primary distinction between development and
economic growth is that the latter presupposes technological (or structural) change.
Technological change may be defined as the impact of technology on economic structure.
Malecki (1991) asserts that these (qualitative) structural changes are more important than
mere (quantitative) growth. Schumpeter (1934) distinguishes between growth as a process

of gradual change and development as a process rapidly impelled by innovations.

l9

 

7":
5.»

‘q‘
leu

”A...

.C‘tv;
- dl.

.
\nt

bL,‘ L~

 

Friedmann and Weaver (1979) assert that economic development results in increasing
specialization and greater interdependency (among different sectors, as well as regions, of
the economy). This technical change is characterized by shifts in the mix of products,
industries, firms and jobs that make up an economy (Malecki, 1991). As Pred (1966)
affirms, the functions performed by a region change radically as economic development
takes place. Therefore, the stage of development of any region should be evident in the
industrial and occupational distribution of the labor force. Accordingly, as development
proceeds, employment in agriculture and other extractive industries declines, while the
share of employment in manufacturing and services rises. Subsequently, the share of
employment in manufacturing declines and the service sector increases in importance
(Chisholm, 1990). This structural change also consists of a qualitative change in
technological skills of the population and the technological capabilities of firms and

institutions, as well as increasing diversity and specialization of regional economies.

2.1.3 An operational definition of "regional economic development"

Based on the preliminary information above, regional economic development must
contemplate the reduction of poverty, unemployment and inequality within the context of
a growing economy (Todaro, 1997). Specifically, for the purposes of this dissertation,
regional economic development is defined as a three-fold process consisting of the
following components — growth in regional income at a rate greater than population
change; increasing equity in the distribution of regional income; and structural change in
composition of the regional economy. Although this definition is drawn from the
literature cited above, it has been chosen for practical purposes — each of the three

components is amenable to quantitative analysis.

20

l.
\l.\

a“,

$51

;lv- H'-

ugl

ham»

aLI‘

. A21.

3

In general, growth in regional income may be assessed by comparing changes in gross
state product with population growth between 1990 and 2000. In the case of Yucatan, two
distributional issues are relevant — the region’s overall contribution to national income
and the distribution of income within the region. Structural change within the region may
be assessed from a variety of perspectives. Possible measures include changes in
employment, total output and productivity by sector, as well as changes in self-sufficiency

and input—output technical coefficients by sector and region.

2.2 The process of regional economic development

Notwithstanding the detail of the previous sections, the definition of regional
economic development is relatively straightforward. Defining and understanding the
process of regional economic development, however, is a sOmewhat more complicated
endeavor.

In general, two complementary bodies of theory exist with respect to regional
economic development. During the past two or three decades, endogenous growth
theories, in which savings and investment are the keys to self-sustaining economic
growth, have become increasingly popular (Hartman and Seckler, 1967). Within the field
of regional science, the seminal work of Tiebout (1956) provided initial impetus for the
notion that endogenous forces, such as business and government investment and
construction (migration), may serve as important determinants of regional income.

In contrast, export-base theories attribute regional economic growth to external
demand for a region’s goods and services. This export or "staples" model was initially
proposed by North (1955) to describe the process by which regional economic

development takes place. In the simple export-base model, regional and local markets are

21

 

0’.

rr‘
.«JS

Av
’

'Q..

”an.

\fi‘

l:
¥AL

5».
h.
"b
.

 

‘
' bu

"w,
.

_.“

too small to generate scale economies and other positive extemalities. As a consequence,
only significant external demand can generate the forward and backward linkages and
increased division of labor necessary to promote development (T opik and Wells, 1998).

In the real world, both exogenous and endogenous factors coexist. In general, the
importance of the export base in determining regional income depends on the size of the
region - the larger the region, the smaller the role of exports. Therefore, notwithstanding
the likelihood for endogenous growth, the export-based explanation of regional economic
development has been reiterated by Friedmann (1966) and Malecki (1991) and will be the
focus of this dissertation (Figure 2.1 below).

By their very nature, regional economies are open to the outside world and subject to
external influence (Friedmann, 1966). As a consequence of this interdependency, the
initial impulse for economic development usually comes from outside and is based on the
combination of resources the region has to offer (comparative advantage).

According to Malecki (1991), regional economic growth is fundamentally a function
of demand for a location’s goods and services and a process of multiplier effects.
Production and distribution of goods and services in response to external demand creates
employment and income. This new economic activity expands the local economy through
forward and backward linkages. Forward linkages refer to the sales of output to different
sectors of the regional economy as intermediate inputs and final demand. Backward
linkages correspond to purchases of intermediate inputs from different sectors of the
regional economy. Ultimately, the size of the multiplier effect depends on regional
economic structure. As mentioned above, increased forward and backward linkages may

alter the re ion’s occu ational, and hence, economic structure (Pred, 1966).
g P

22

 

Ji"'

vs).

“_ul
ELL L1

{—5 _-

)Jksl

' r
‘1' v.‘
U; I

u.

3]“
3‘5.)
st‘

4a.

“1231

h

’eClr‘r'

--‘

1,,
7

.7

wfi
I;

‘e

Consequently, promotion of greater forward and backward linkages represents a possible
method of inducing structural change in regional economies.

The success of export-led development in the staples model is dependent on the
existence of these linkage or spillover effects between export activities and the regional
economy (Brannon and Baklanoff, 1987). Conversion of export sector growth, however,
into regional economic development is not automatic. As Friedmann (1966) states, the
success of an export-based development strategy depends on the socio-political structure
of the region, as well as the distribution of income and pattern of expenditures. Socio-
political structure refers (in part) to control of markets and factors of production. In
extreme cases of external control, dependent development is likely to result.

Evans ( 1979) defines dependent development as a predicament in which one region’s
economic development is conditioned by the expansion of another region. Dependent
development occurs when involvement in the global economy leads a region to specialize
in the export of few products. As a result, local entrepreneurship is surpressed, investment
in infrastructure is not undertaken, wages are kept to a minimum, and profits are taken
out of the region (Friedmann, 1966).

In this case, the regional economy becomes disarticulated — firms must import their
equipment and other capital goods. Consequently, increases in the output of the export
sector do not feed back into the local economy and the purported multiplier effects and
"spir " of development associated with manufacturing do not take place (Evans, 1979).

According to Friedmann (1966), investment in the export sector must be converted

into structural change — the "transformation of markets and productive facilities," in his

23

[Rn]

l'__ Tl

Hon

D

U!

h".
31%

words — in order for regional economic development to take place. Promotion of these

structural changes is one of the primary objectives of regional development policy.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

GROWTH DEVELOPMENT
I
I
External I
demand for I
reglon'c goods I
and services |
. I
l
I .
I
Creation of In : 3
employment *— $3“:ng l‘ E
1 : 1|
0| trlbutlo
8.00an l : oI.boneflten I :
muItIplIer InItIeI I a
eflect ' —
:1 “2:2: . T o
: Structural E
| change
I
Expanelon ' T 3
:b at local ' =
economy 7
r 0
I I‘
l
I
I
: Adapted from Meleckl (1991)
I

Figure 2.1 Regional economic development and the role of regional policy

2.3 Understanding the role of regional policy
Economic development is not driven solely by factor endowments or geographic
variation in resources (comparative advantage), but also by economic policy (Franko,

1999). In fact, those economists ascribing to the "neo—classical" paradigm would assert

24

that a substantial portion of (national) economic development since World War H has
been the direct result of policy (trade liberalization is a case in point).

Some scholars contend that regional economic growth is essentially determined by
national policies (Friedmann, 1966). Although national policy decisions, such as tariffs,
interest rates, and subsidies certainly have important regional consequences (Biles and
Pigozzi, 2000), such a statement is misleading. In fact, since many developing nations
(such as Mexico) have no explicit regional policy at the national level, individual
locations have no other recourse but to implement their own policies (OECD, 1997).

According to Friedmann (1966), regional policy deals with the locational aspects (the
"where") of economic development. In similar fashion, Malecki (1991) asserts that
regional policies are intended to alter geographically uneven development by creating a
system of economic and spatial linkages simultaneously. This definition emphasizes the
two primary roles ascribed to regional development policy in the context of this
dissertation — facilitating structural change and assuring a more equitable geographic
distribution of the benefits of economic growth within the region.

As shown in Figure 2.1 above, regional growth is comprised of a multiplier process in
which exogenous forces generate demand for goods and services produced within a given
region. This demand results in an initial multiplier effect as a consequence of increased
output of inputs needed to meet external demand. In addition, the employment generated
by external demand results in a secondary multiplier effect by way of household
consumption. Both multiplier effects result in the expansion of the local economy.

If left simply to market forces, the benefits of these exogenous forces merely feed

back into the local economy in the form of growth — expansion of aggregate output and

25

1*"
II»5

.1.
‘1‘

’v

L-L ..

r‘fi

”1.:
I

.3“.

5 um

.-

\ p.

-

 

income. In addition, as discussed above, impacts typically will be concentrated both
spatially and socially. Economic development, however, results only if regional policy or
some other form of concerted intervention is employed to induce the qualitative changes
mentioned above — structural change and a more equitable distribution of income and
other benefits.

According to Miemyk (1965), structural change may include the appearance of new
industries and the effects of technical change on the production process (as reflected in
input-output technical coefficients). In the case of Yucatan, regional development policy
addresses both forms of structural change. Objectives include promoting job creation and
greater self-sufficiency in rural areas of the state, while diversifying the economy and
improving productivity.

As mentioned above, the export-based model of economic development is likely to
exacerbate disparities between rural and urban communities in the absence of regional
policy. Friedmann (1966) confirms this, stating that the export of manufactured products
from core areas tends to grow more rapidly than agricultural exports from the
countryside. Although rapid growth of the urban core is expected to generate investment
in surrounding rural areas (through the multiplier effect discussed above), regional policy
is needed to achieve an optimal spatial distribution of the outcomes of the export-based
model.

Frequently (as exemplified by Hirschman), regional policy options have focused on
the choice between concentrated or balanced development strategies. According to
Friedmann (1966), these options represent a false dichotomy. On the one hand, some

redistribution of income and other benefits is necessary to promote economic

26

 

«N

N
i

.16‘

I.

-a:

a~ 1
n.

development in peripheral areas. On the other hand, the emphasis on balance creates
conflict as any number of a host of different criteria may be chosen for the purposes of
policy (income, investment, productivity, and output, for example).

In response, Friedmann (1966) proposes a "systems-wide" approach. He affirms the
need to recognize that problems of economic development in different regions are
"mutually contingent" on each other. Therefore, in order to be truly effective, regional
policies must consider the impacts of economic transformation in all regions
simultaneously. Consequently, Friedmann (1966) asserts that the ultimate objective of
regional policy is to choose the optimal geographic location in which concentrated
investment is likely to trigger the rapid expansion and full articulation of the space-
economy. Though the integrated input-output/spatial econometric model proposed in this
study is implemented in an ex post fashion, it also may be utilized in an ex ante fashion as
suggested by Friedmann to evaluate the potential spatial implications of competing
regional development policy decisions. Several hypothetical policy scenarios are assessed
in Chapter Seven to demonstrate the utility of the integrated model as a regional

economic development planning tool.

27

.r 11 r
. sc~u
u. rile
1.l UL
G .l
u u. 1
16:6

 
 

Chapter Three

AN ECONOMIC GEOGRAPHY OF MEXICO’S MAQUILADORA INDUSTRY

3.1 Economic development strategies in Mexico

Export-oriented industrialization (EOI) represents the most recent attempt to foster
greater levels of development in third world nations. The maquiladora program is a
particularly Mexican manifestation of the E01 strategy. According to Bulmer-Thomas
(1994), the economic development strategies pursued by Mexico (and other Latin
American nations) since independence fall into three general phases: "traditional"
resource-based exports; import substitution industrialization (ISI); and export-oriented
industrialization (EOI). This chapter offers a relatively brief overview of these
development strategies, as well as a detailed assessment of the inception and the

geographic and economic implications of Mexico’s maquiladora industry.

3.1.1 Resource-based exports

Mexico and many other Latin American countries followed the traditional resource-
based development policy until the first part of the 20th century. Upon independence in
the early 19th century, these countries confronted a global economic system dominated by

capitalism. One of the tenets of capitalism is the notion of comparative advantage.

28

3.13.?

63‘)“;
~5 .5. ..
~

According to this theory (advanced by Ricardo), countries — and regions for that matter —
should specialize in the economic activities in which they are most efficient.
Furthermore, they should trade with each other on the basis of this comparative
advantage in order to improve their overall welfare.

Since industrialized countries were purportedly more efficient in the production of
manufactured goods, Mexico and other newly independent nations in Latin America
chose to specialize in agricultural and resource-based products as a means of achieving
greater levels of economic development (Sklair, 1993; Topik and Wells, 1998). Among
the most common export items were mineral products (silver, tin and copper, for
example), as well as agricultural commodities such as coffee, bananas and sugar. In
general, economic development was expected to result from growth in commodity
exports and the establishment of linkages between the export sector and the domestic
economy. In theory, this greater integration with the world economy would also enhance
productivity, bring about structural change and ultimately, result in higher levels of
economic development (Bulmer-Thomas, 1994).

By the late 18003, resource-based exports had become the consensus economic
development strategy in Latin America — during the second half of the 19th century
exports grew almost four percent annually throughout the region (Bulmer-Thomas,
1994). Overall, Latin American exports grew by 1000 percent between 1850 and 1913
(Topik and Wells, 1998). In the case of Mexico, exports grew by about three percent per
year during the same period. Silver and copper, alone, comprised more than 40 percent of

Mexico's total exports by the beginning of the 20th century (Bulmer-Thomas, 1994).

29

 

ED
.28 '

any“

n’.‘..

5.

I

1...).

 

"A‘

 

By the 19203, however, the resource-based strategy had been abandoned because it
exacerbated structural problems inherited from the colonial period and created a host of
new contradictions that inhibited the development of a productive market economy
(Topik and Wells, 1998). In general, three (related) reasons are offered for the failure of
the traditional export strategy — slow growth of internal markets; a reliance on single
commodities and single markets; and the relative inelasticity of demand for primary
products.

Since traditional resource-based policies were concerned with the needs of the export
sector, countries did not invest adequately in their productive infrastructures and weak
linkages existed between the export sector and the domestic economy (Franko, 1999). As
a consequence, internal markets failed to grow and no substantial economic development
took place (Bulmer-Thomas, 1994).

The emphasis on traditional exports also failed because countries tended to specialize
in a single dominant commodity. As late as 1913, for example, one commodity accounted
for more than 50 percent of exports in the majority of Latin American countries (Bulmer-
Thomas, 1994). In extreme cases (such as that of Yucatan), a single commodity
(henequen) comprised more than 90 percent of a region's total exports (Brannon and
Baklanoff, 1987). Dependence on a single commodity export was an unstable basis for
development as it exposed the domestic economy to violent shocks brought about by
change in external demand (T opik and Wells, 1998). The strategy was exacerbated
further by dependence on a single market (Franko, 1999). In the case of Mexico, for

example, the United States accounted for three-quarters of its exports in 1913.1 In Latin

 

' In passing, it should be noted that the US. now accounts for an even greater share of Mexico’s imports
and exports (approximately 80 percent).

30

HM...

‘1‘

.3“;

 

America, exports to only four countries (the United States, United Kingdom, France and
Germany) averaged almost 71 percent of total exports around the turn of the 20th century.
The resource-based development strategy also failed because primary products are
relatively price and income inelastic. When prices decline (or incomes increase), demand
for these commodities does not proportionately. Consequently, as world income grows,
international demand for agricultural products does not keep pace. Demand for
manufactured goods, on the other hand, is relatively income elastic — as incomes increase,
demand increases at an even greater rate. Due to these declining terms of trade, demand
for agricultural products did not keep up with demand for manufactures and the primary
sector failed to serve as a catalyst for economic growth in Latin America (Franko, 1999).
As a result of these limitations, export pessimism — the belief that exports would not
be the engine of growth — became prevalent by the early 19005 and the resource-based
strategy was abandoned by Mexico and most other Latin American countries by the

19208 (Franko, 1999; Bulmer-Thomas, 1994).

3.1.2 Import-substitution industrialization (ISI)

In addition to the three reasons offered above, Mexico and its neighbors in Latin
America abandoned the resource-based development model due to the turmoil in
international markets during World War I and the Great Depression (Vellinga, 2000;
Bulmer-Thomas, 1994). The Great Depression, in particular, created a great deal of
instability in world commodity markets and left developing countries without stable
markets for their products. Due to the dramatic decline in exports, Latin American
countries were forced to reduce imports of manufactured goods from industrialized

nations. Ultimately, countries such as Argentina, Brazil and Mexico sought to replace

31

P‘ .

I“.

. \

.I

L.

I .

\

.A.

Bk.-

 

manufactured imports with domestic production. This strategy eventually became known
as import-substitution industrialization (ISI).

The formal ISI strategy, however, was not advocated and implemented until shortly
after World War II. A group of policymakers at the United Nations Economic
Commission on Latin America (ECLA) had made note of the robust economic growth of
Latin America during the relative isolation of the World War II period. Led by Rail
Prebisch, these scholars, later known as the "dependency school," concluded that
international capitalism was the primary cause of underdevelopment in Latin America.
As such, they sought and established a theoretical basis for the import substitution
strategy that had been (informally) adopted earlier by the larger nations of Latin America.
The Prebisch-Singer theorem provided the theoretical basis for the ISI strategy (Dussel,
1997). This theory is based on the assumption that the elasticity of demand for primary
products is relatively inelastic with respect to income. However, demand for
manufactured goods is relatively elastic. Therefore, in the long-term countries that
specialize in agricultural products and raw materials face a lower level of economic
growth and a decline in their terms of trade.

Dependency theory is part of a long tradition of theoretical work that has viewed the
primary obstacle to economic development in Latin America as unequal relations with
foreign powers (Chisholm 1990). It views economic development as a "zero-sum" game
in that underdevelopment is the result of the same process that produces economic
development - the march of capitalism. Dependency theorists suggested that Latin
American countries were not falling behind; they were being pushed behind by the

exploitative development process in the powerful industrialized countries. Accordingly,

32

the dependency school represents a rejection of the optimistic view of economic growth
as an inevitable (internally induced) process advanced by Rostow (1960) and others
during the post-war period. In other words, developing countries are not developed
countries in the making. Rather, industrial countries have brought about
underdevelopment in other nations in the process of economic expansion.

In order to explain the origin, persistence and exacerbation of dependency, Prebisch
and his followers emphasized the unequal relations of exchange that prevail between
developed nations (center) and less developed nations (periphery). These unequal
relations are manifest in industrialized nations’ access to cheap inputs through the
extraction of resources, export of minerals, and exploitation of cheap labor in
underdeveloped countries. According to dependency theorists, powerful industrial
countries were draining developing nations of their wealth. Internal dynamism was
lacking due to weak linkages between the export-oriented agricultural sector and the
industrial sector. In addition, shifts in terms of trade provided a mechanism for systematic
transfer of resources from poorer to richer countries (Chisholm, 1990).

Some dependency theorists argued that autonomous development was possible within
the periphery. However, to move beyond dependent development, the state was identified
as the "necessary" agent of change. According to Franko (1999), a powerful state acting
in the national interest could counteract the unfettered operation of market forces and
promote genuine development in the periphery. Consequently, the policy of import-
substitution industrialization treats the state as the primary developmental actor.

Import-substitution was also predicated on the need for state intervention to correct

the failures of domestic markets and the need for trade policy to transform the structure

33

L,

32.

r‘i

of exports and domestic production. The strategy was based on two propositions —
markets in Latin American countries were inefficient and the international trading system
was biased against the products that Latin America exported (Weeks, 1996). The ISI
strategy depends on the intervention of the state, as well as trade and industrial policies in
order to achieve balanced growth, increase capital accumulation and increase private
investment. The state also plays a critical role by protecting infant industries. According
to Dussel (1997), the challenge is to integrate these industries with the national economy.
As conceived by dependency theorists, the ISI development strategy uses a variety of
policy measures to encourage domestic production of manufactured goods previously
supplied by imports (Cravey, 1998). ISI policies were designed to create domestic
industries capable of producing substitutes for expensive imports while promoting
industrial growth and the expansion of internal economies (Franko, 1999). Among other
purported benefits, manufacturing would result in greater scale economies and achieve
higher levels of efficiency (Dussel, 1997). The ISI strategy is premised on protecting
internal markets and development of manufactured goods with greater elasticities
(Dussel, 1997). Policy measures include high trade and non-trade barriers, tax incentives,
subsidies, public credit, and provision of infrastructure (Dussel, 1997; Vellinga, 2000).
At first, the benefits of the import substitution strategy appeared substantial. By the
end of World War 1], Mexico, Brazil and Argentina had all established a role as exporters
of manufactured goods (Sklair, 1993). During the war years, manufacturing grew at an
annual rate of 5.7 percent throughout Latin American and 9.4 percent in Mexico. In
general, the Mexican economy grew at 5.6 percent a year between 1950 and 1960 and 7.1

percent annually from 1961 to 1970 (Bulmer-Thomas, 1994).

34

J-

n

"3'

,-

~‘

.0

on
o
It.

T“

a

In Mexico, import substitution was in place from about 1930 to 1976 (Cravey, 1998).
As several scholars have noted, the ISI strategy induced the dramatic growth of the
country’s industrial core in the Mexico City region (Sklair, 1993; Hanson, 1994; and
Cravey, 1998). By the time the strategy was abandoned, Mexico City alone accounted for
about 13 percent of total population and more than 25 percent of gross national product.

The ISI strategy ultimately failed for several reasons. As competition from imports
vanished, pressure to improve quality and design of domestic production dissipated.
Furthermore, instead of importing finished goods, countries began to rely heavily on
imports of machinery and intermediate goods. In reality, substitution was not between
domestic and imported goods. Rather, substitution occurred between imported finished
goods and imported capital and intermediate goods (Sklair, 1993). Imports of
intermediate and capital goods rose faster than imports of finished goods fell, causing
balance of payment problems. In addition, ISI policies were implemented in the hopes of
promoting domestic industrial development. By the time the strategy was abandoned,
however, many ISI industries had fallen under the control of foreign-owned transnational
companies (Dussel, 1997). Furthermore, as imports grew (and balance of payment
problems worsened), exchange rates worked against exports. In addition, global interest
rate hikes brought about a massive debt crisis in Latin America and consumption and
investment imports could no longer be financed. Finally, balance of payments shocks
driven by the oil crisis of the 1970s prompted export promotion to relieve current account
pressures (Franko, 1999).

The ISI strategy did little to change the fundamental pattern of asset ownership in

Latin America, reinforcing the dual economy and highly unequal pattern of growth

35

(Mabougunje, 1981; Franko, 1999). Furthermore, the protectionism of the ISI strategy
had a negative impact on the allocation of resources, resulted in a new privileged urban

class, and largely neglected poor, abandoned rural areas of Latin America (Dussel, 1997).

3.1.3 Export oriented industrialization (E01)

The debt crisis of the 19808 brought about the end of the ISI strategy in Latin
America. A need existed to generate a trade surplus in order to accommodate debt service
payments (Bulmer-Thomas, 1996). As a consequence, new development strategies were
adopted to shift resources back toward exports. Trade was liberalized and firms were
forced to compete against imports (Bulmer-Thomas, 1994).

Inward-looking development was replaced with export-led growth; state intervention
was replaced with market forces. In contrast with the resource-based export strategy of
' the 19lh century, however, the new export-oriented focus emphasized the role of
manufactures. The focus of the manufacturing sector was shifted from the domestic
economy to the global economy because the small size of markets in developing
countries limited the potential success of import substitution (Amara, 1994). The strategy
was also favored and imposed (though structural adjustment programs) by multilateral
agencies such as the IMF and World Bank. The shift to the E01 phase was accompanied
by a set of related policy measures including trade liberalization, inflation stabilization,
privatization of state-owned enterprises (Bulmer-Thomas, 1996; Dussel, 1997).

The concepts of the production chain and the product life-cycle are essential to
understanding the justification of E01 (and ability of developing countries to adopt such
as strategy). A production or commodity chain may be defined as transactionally linked

sequence of business functions in which each stage adds value to the process of

36

 

are
I

as:

["33
I

n;
..

n$l
‘ t

r.»

production of goods or services (Dicken, 1998). Typically, the stages of a basic
production chain include procurement of materials, transformation, marketing and sales,
distribution, and service.

The product life-cycle (PLC) is a model that describes the development of a product
through a series of five stages — initial innovation, growth, maturity, decline and
obsolescence (Dicken, 1998). Accordingly, at any given stage of the life-cycle, the
product's requirements of labor, capital and technology will vary substantially (Malecki,
1991). By the maturity stage, the production process has become standardized and
manufacturers are confronted with greater competition and diminishing profit margins.
Furthermore, in order to maintain (or expand) its market share, a firm must continually
innovate. Innovation may take a number of different forms, including development of
new products, improvement of existing products, or reorganization of the production
process in order to maintain the profitability of existing products.

At this point, the production chain and product life-cycle concepts converge. In
essence, certain segments of the production chain and product life-cycle favor particular
geographic locations. Starting in the 19503, trans-national corporations (TNCs) began to
divide production processes not just according to the traditional division of labor, but also
in a geographical sense. Certain activities, such as unskilled and semi-skilled processing
and assembly operations, were moved to plants abroad as a form of geographic hedging
(Sklair, 1993). Furthermore, an additional impetus for "offshore assembly" was fostered
by the need to maintain profitability — innovation within the PLC concept, then, could
take the form of TNCs shifting production on a large scale overseas in order to reduce

labor costs (Sklair, 1993).

37

are“

l
a.
l

. fi..h§
V\ .r .v
Ad 5 I... .hhlU 11:23“

a
6..
3
“I
e
P
et-

 

From an alternative perspective, the E01 strategy was promoted because
manufacturing purportedly possesses unique characteristics for economic growth — its
potential for creation of value added, employment generation, technology transfer and
productivity growth, for example. According to Dussel (1997), manufactured exports
provide a greater source of demand for domestic inputs, and (as a result) generate greater
income for internal consumption items. In addition, they provide a source of foreign
exchange and facilitate additional imports of intermediate or capital goods needed to
increase levels of production.

The export-oriented industrialization strategy was initiated in the 19503 and 19603 in
a group of East Asian countries (South Korea, Hong Kong, Singapore and Taiwan) that
eventually became known as the newly industrializing economies (NIEs). These
countries adopted a development strategy based on import substitution, as well as
promotion of manufactured exports (Sklair, 1993). In addition, national governments
played a prominent role in guiding the economy and controlling the vulnerabilities of the
external sector in order to maximize the benefits of trade.

The initial application of the E01 strategy in Latin America took place in Puerto Rico.
Puerto Rico exploited its unique relationship with the US. and resorted to manufactured
exports as the engine of growth by the 19503. In the 19603, responding to the success of
manufactured exports in Southeast Asia, policymakers in Latin America began to take
export promotion seriously (Bulmer-Thomas, 1994). The EOI strategy eventually
adopted throughout Latin America during the 19803, however, was not a carbon-copy of
the policies implemented among the NES. Asian countries, for instance, had not been

large-scale exporters of agricultural products and natural resources. Furthermore, the

38

BICEOTL

an'
bbkétyru.‘

mats
:2: to r
53751221:
.5.” SC:

I: g:
0 ..' ll]:
'Pmc-‘o

ig“
rm
avdllli‘r

policy reforms of 19803 and 19903 were much more dramatic than those implemented by
the NIEs in the 19603 and 19703.

The movement from the state-led ISI strategy to export-oriented development has
also had profound implications on the role of national governments. With the neo-liberal
economic reforms of the 19803, the state was "delegitirnized" as the primary agent of
economic change. Instead, following the neo-classical economic development doctrine,
markets were viewed as the primary allocators of resources. Government spending was
cut to maintain a fiscal balance and state-owned firms were privatized to promote
efficiency. Tariffs were reduced, exposing firms to competition. The external sector was
again seen as the engine of development (Franko, 1999).

In general, economic liberalization was expected to have positive effects on standards
of living and bring about high growth rates and more efficient allocation of resources
(Panuco-Laguette and Szekely, 1996). However, the neo-liberal policies that
accompanied the shift to the E01 strategy are unconcerned with income equality and
redistributive objectives in favor of rapid economic growth and higher levels of average
per capita income (Gilbert and Goodman, 1976; Bulmer—Thomas, 1996). Consequently,
divergence, either in terms of increased variation in regional incomes or growth of per
capita income, is more likely than convergence. These distributional consequences are
readily apparent in the case of Mexico — according to Panuco-Laguette and Szekely
(1996), income received by the wealthiest five percent of the population increased from
24 percent to 29 percent during the initial stages of the E01 strategy (1984 to 1992).

According to Evans (1979), the ultimate consequence of export-oriented

industrialization is the exclusion of most of the population from the potential benefits of

39

ariuztraz:
c.7211). the r
cinnamon
2125:1222."
EYWSECOE

Accordz:
3986 was t1
:rszzzor. fr.
:5. in lo.
Outlaws“;
misses. ll
[aspirin of
it mania

.9305,

3‘2 1011‘ 0dr

1: def}
mated as
"Element:
M 10 m:
“Lined me
Hm mos: 1‘ ,
tlpgn l‘lnu,

A-..“
tumfmmcd

industrialization. Although the export-oriented production was not adopted to improve
equity, the reforms associated with such a strategy have non-neutral effects on income
distribution and level of poverty (Bulmer-Thomas, 1996). Therefore, the export-oriented
industrialization strategy is unlikely to reduce inequalities in the absence of government
intervention.

According to Panuco-Laguette and Szekely (1996), Mexico’s entry into GATT in
1986 was the first step towards formal adoption of the export-oriented strategy. The
transition from ISI to the E01 strategy in Mexico has also been marked by a geographic
shift in location of manufacturing from industrial core regions (Mexico City and
Guadalajara) to the U.S.—Mexico border region (Cravey, 1998). As the following section
indicates, these dramatic locational changes are attributable, at least partially, to the
inception of the maquiladora program in the 19603 and (more importantly) adoption of
the maquiladora strategy as the country’s de facto regional development policy in the

19803

3.2 Introduction of the maquiladora strategy

As defined in Chapter One, a maquiladora (or maquila, for short) is an export-
Ol‘iented assembly plant based on the labor-intensive manufacture of imported
COmponents. The word maquiladora is derived from the Spanish verb "maquilar" which
means to mill or process. In practice, the term was used to refer to the portion of grain
retained in exchange for milling (processing). The formal definition above highlights the
t“we most important characteristics of maquiladoras. By their very nature, these firms
export virtually all of their production. In addition, maquiladoras are typically

concentrated in labor-intensive manufacturing activities, such as clothing and apparel

40

gram“. of
Harrier.
21:15:32:
11:: ll. 1;
From.
.
writers (
Egon an

Juarez. N

moral
PROM

The l
W Brat.
131E116;
“01m
Wins:
‘South, 1

20.2”;me

assembly and electronics industries. In general, "cheap" labor is the primary input.
Furthermore, maquiladoras rely on a variety of subsidies and incentives allowing

temporary import of virtually all inputs duty-free.

3.2.1 Impetus for the maquiladora program

The maquiladora strategy was conceived as an imitation of the export-oriented
growth of the newly industrializing economies (NIEs) of East Asia (Cravey, 1998).
However, from the Mexican perspective, the initial stimulus for the E01 strategy was the
implementation of the Border Industrialization Program (BIP) in 1965. During World
War II, labor shortages in the United States led to the implementation of the Bracero
Program, allowing millions of Mexican laborers to enter the US. legally as seasonal
workers (South, 1990). The program attracted large numbers of migrants to the border
region and resulted in rapid population growth in border cities such as Tijuana, Mexicali,
Juarez, Nuevo Laredo and Matamoros. In order to improve infrastructure in the border
region, broaden its economic base, and promote integration of the border region with the
national economy, the Mexican government initiated the National Border Program
(PRONAF) in 1961 (South, 1990).

The Border Industrialization Program was Mexico’s response to US. elimination of
the Bracero Program in 1964, which affected almost 200,000 seasonal workers and their
families. The primary objective of the BIP was to provide employment to seasonal
Workers who had migrated to the border region. Attracting labor-intensive U.S.
manufacturers to the border region was envisioned as the means of generating jobs
(Sonth, 1990). The BIP subsidized construction of industrial parks and allowed foreign

Companies to own and operate factories in Mexico and import equipment and inputs

41

much
“mat

‘3‘ ’ n‘
Ti~\\\§5‘r . .

hxon

7"“ '7;
hu‘lri“ ng

"133.51 '61
L, -. . ,m
”03* £0.31.

twang
N

an. . .
‘JM h.._
*‘Us .
I'
A.,!
‘3: '5: s‘_
v.‘

duty-free if final products were exported (Cravey, 1998). Initially, the BIP restricted such
activity to a 20-kilometer strip along the U.S.-Mexico border.

The success of the maquiladora program was also facilitated by US. tariff provisions
undertaken in the early 19603 in an effort to promote "offshore assembly" (South, 1990).
Specifically, U.S. tariff items §806.30 and §807.00 permit duty-free entry of goods
processed and assembled abroad. Import duties were assessed only on the value-added
abroad. In essence, these reforms allowed US. firms to export components duty-free to
Mexico (and export-processing zones in other countries), carry out assembly or
processing abroad, and re-import the final product paying taxes only on value-added.
Since only U.S.-made inputs are exempt from tariffs, enterprises have an incentive to
minimize local purchases of goods and services. Consequently, value-added is comprised
almost exclusively of the low wages paid to unskilled and semi-skilled workers in the
host country. Needless to say, these practices have represented the major impediment to

increasing integration between maquiladoras and the local economy (Gereffi, 2000).

 

 

 

4000 ,
3500 S
3000 ,
2500 .
2000 i
1500
1000
500

o

 

 

 

 

    

o '1. .9- 9? ‘b 9 '1' 9" Q? Q’
to o e o o o
,9 ,9 .9 .9 ,9 ,9 ,9 ,9 ,9 ,9

o
o
'19

 

 

 

Figure 3.1 Number of maquiladoras, 1980-2000

The maquiladora program began as a regional development policy. However, by the

early 19803, it had become Mexico's de facto economic development strategy. By 1973,

42

most geographic restrictions on the location of maquiladoras had been eliminated (Sklair,
1993; South, 1990). In addition, national legislation enacted in 1983 encouraged location
of maquiladoras outside of major manufacturing centers as a means of national industrial

decentralization (Wilson and Kayne, 2000).

 

1400000
1200000
1000000
800000
600000
400000
200000
0

Q '1’
‘b %
(a (3

 

a? a? 6" 9° <2?’ 9" 01° 99’ 0°
,9 ,9 ,9 .9 ,9 ,9 ,9 ,9 .19

 

 

 

Figure 3.2 Maquiladora employment, 1980-2000

3.2.2 Economic impacts of maquiladora strategy

During the first couple of decades of its existence, the maquiladora program grew
slowly, but steadily. By 1970, only 120 maquiladoras had been established (Galhardi,
1998). In 1980, around 600 plants employing almost 125,000 workers were operating.
The growth of Mexico’s maquiladoras accelerated rapidly with the country’s economic
crisis and debt debacle of the early 19803 (inception of the formal EOI strategy).
Structural adjustment reforms adopted by the Mexican government at this time included
reduction of trade barriers, privatization of state-owned enterprises, and dramatic
currency devaluations. These reforms also resulted in lower real wages, a particularly
important factor for the promotion of export-oriented development. As Figure 3.1 above

indicates, the number of maquiladoras tripled between 1980 and 1990 (from 600 to more

43

1' i'
Ir“

'J.‘
‘10
V
‘

Number of Mane-II-dorau

tumor
(it Est
305.;
100:5;

05011;:

11:31.6.

than 1700) and more than doubled between 1990 and 2000 (from 1700 to more than

3500).

 

 

 

 

 

 

 

 

 

Number of Mequlladoras

 

 

 

 

 

 

 

 

 

 

Figure 3.3 Distribution of maquiladoras by state, 2000

Currently more than 3600 export—oriented firms employ about 1.3 million Mexicans
(Figure 3.2). More than 75 percent of all maquiladoras are located in border states,
making the U.S.-Mexico border region the largest export-processing zone in the world.
As a source of foreign exchange, maquiladoras are now more important to the Mexican
economy than petroleum and tourism. Based on data provided by the Instituto Nacional
de Estadz’stica, Geografi’a e Informatica (INEGI), these firms accounted for more than
$60 billion (US) in output in 1999.2 In addition, maquiladoras employed 26 percent of all
industrial workers in Mexico and accounted for 68 percent of gross industrial product and
one-half of total exports in 1998 (Vellinga, 2000). Figures 3.3 and 3.4 show the total

number of maquiladoras and maquila employment by state, respectively.

 

2 Unless otherwise stated, discussion of economic impacts in this chapter refers to US. dollars

44

opu0pp.)a~.n=:uu —I~C.—

Fig:

30-

51

‘ is

\

ngr

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

350000
300000 ham- . . ._ 4. -- . .. _ -..._. -.- P— ....- .-- . ... .. . . « _._..-_ ._._ _...__-.. _ . _. _____~_.__--.... - ..
on .
r: .. j
E 250000 ......-..-.....- ..-.__..+E,~...__.._. _ -. -. “my”.-- - up-..» .Ek.qfi- .. . -.-. .3..- . . ..-. ....._- . ........._ . ....- .._...-_... -.... i
2 200000 ...___._.__., "’"””""‘ ‘Efi’ """"" ’ "’ ’ ’ ' "W" *4" ""’ ’ ' ’ ""“ ”""‘" “"‘ "' ‘ "‘ l
g- ‘ r" .
Ill 150000 -W —---~---- -—-—-—-+ --—-«—---—-—~--—---—-~-» ---—»—-~---—-—» -—~—~—4 :
3 WW, . m. _ M____.__ __ . _- __ I F
0 100000 ‘ .
50000 .f _' ..__._.__ - [W M i ~—- - l
0 .mg . .--. ' fall] mgfflffi ' gm
9 5s .9 09 o ‘o d“ o \o o .0 o
«o \{k 6‘ ‘99 $0 060 0‘0 6*) 59$ch 9'“ f Qf °o° 0'99 0“?"
,2 (.9 5° 0° ,5 0° 5.3 3° 9
'990 .\. 0&0 0 o 0 e0 «9
at +‘
i __1 l

 

 

Figure 3.4 Maquiladora employment by state, 2000

In 1999, salaries of maquiladora employees comprised almost $7 billion (US). On
average, salaries were about $7000 (US) annually. However, as Figure 3.5 below shows,
a huge variation exists in wages among laborers and non-production workers (managers,
technicians, etc.). Historically, production workers have earned about one-third as much
as non-production workers. For example, the average laborer made less than $5000 in

1999, whereas the average non-production worker earned more than $19,000.

 

 

25000

1 5 0 0 0
g / \/ 10+"— _-—W—;rko—n—-

5+9 -9 L"! tr.- '1.

 

 

 

10000

 

 

5000 W W

 

 

 

1990 1991 1992 1993 199‘ 1995 1990 1997 1990 1999

Figure 3.5 Annual maquiladora salaries, 1990-1999

 

 

 

45

51020173
'1" l

IBZCL

203.15 I

52023. 1

frames“

The purchase of domestic inputs also has a significant impact on the Mexican
economy. In 1999, sales of goods and services to maquiladoras totaled about $5 billion
(INEGI, 2001). However, as many sources have documented, the purchase of Mexican
inputs as a share of total inputs historically has been very limited. As Figure 3.6 below
shows, purchase of Mexican goods and services averaged about 10 percent of total inputs
between 1990 and 1999. However, the share of domestic content —- purchases of goods
and services within Mexico — appears to have declined slightly since inception of

NAFTA in 1994.

 

loomesrrc
IIMPORT_ED

 

 

 

. . .. ...,_, . _.... i

1990 1991 1992 1993 1994 1995 1998 1997 1998 1999

 

 

 

Figure 3.6 Mexican inputs as a share of total inputs

With one notable exception (Fuentes et a1., 1993), previous studies have suggested
that domestic content of maquiladora production amounts to only about two percent of
total inputs (MacLachlan and Aguilar, 1998; Guajardo, 1998; Garcia and Pérez, 1996;
Brannon and James, 1994; Carrillo, 1994; and Hanson, 1994). Although their study
focuses on "material" inputs, Fuentes et al. (1993) included relationships with sub-
contractors and supporting services among the backward linkages of export-oriented

firms in Mexico. Other studies, however, have overlooked the importance of linkages

46

pg. - -- queen-n-p

’71:“.
unri-
V1.
"hi/1"!
:4.

Fla,
5! "
Mr

between maquiladoras and local service industries that are essential for export-oriented
production. These "producer services," including financial services and real estate, public
utilities, wholesale and retail trade, transportation and communications, and personal and
professional services, are consumed by firms as intermediate inputs in the production
process (Malecki, 1991). The economic importance of "producer" services has been
confirmed by Beyers (1992) and Hansen (1990), among others. Historically, the purchase
of these services among Mexican maquiladoras far exceeds the value of domestic
commodity inputs. As Figure 3.7 below confirms, between 1990 and 2000 their value

averaged about three times that of commodity inputs (INEGI, 2001).

 

 

/

/

 

 

 

 

 

0:522”: : a:

/ -.
/ :23;
/

 

 

 

 

 

1m 191 1a 1% 194 1% 1% 1W 1% 1% 20m

 

 

 

Figure 3.7 Purchase of commodity inputs and producer services, 1990-2000

A review of geography, regional science, and economics literatures reveals that only
one scholar has quantified the indirect impacts of export-oriented production on Mexico’s
national economy. In general, Guajardo (1998) estimates that the economic effects of
maquiladoras are more than 50 percent smaller than those of domestic firms. On average,

every dollar of maquiladora production generates about 34 cents of additional output;

47

n r :'v 9" "r‘
.1 I \
l\- bu. . .

my ‘
M3301

\. h,“
til-”Ln

FlE’Ure

every dollar of income produces another 15 cents in salaries and benefits. Based on
Guajardo’s estimated weighted output multiplier of 1.34, export-oriented firms accounted

for almost three percent of Mexico’s gross national product in 1999.

3.2.3 Geographic distribution of maquiladora impacts

Historically, maquiladoras have been concentrated in the U.S.- Mexico border region.
The Border Industrialization Program discussed above provided the initial impetus for
concentration of export-oriented firms in this region. The proximity to headquarters,

"twin plants,"3

and markets in the United States explains, to some degree, the continued
clustering of maquiladora plants. Some scholars, however, have recently asserted that the
geographic advantages of the border and poor quality of infrastructure within Mexico’s

interior will continue to reserve the economic advantages of the maquila industry for the

country’s northern border (OECD, 1997; Hanson, 1994).

 

 

1990 2000 91139199)

 

 

 

Figure 3.8 Location of maquiladoras by region

 

3 Twin plants are assembly operations and input suppliers in the United States, frequently in close
proximity to the U.S.-Mexico border, that coordinate production with maquiladoras in Mexico.

48

During the past decade, however, a fairly dramatic change has occurred in the
location of these export-oriented production facilities. In 1980, more than 90 percent of
all maquiladoras were located in Mexican border states (Figure 3.8). Although 75 percent
of maquiladoras remain in this region, about 40 percent of plants established since 1990
are located in Mexico’s interior. Shifts in individual maquila sectors are even more
striking. For example, by 1992 almost 60 percent of employment in clothing and apparel
maquiladoras was concentrated in non-border locations (Gereffi, 2000). The state of
Yucatan represents perhaps the most dramatic example of this abrupt locational shift. As
shown below in Figure 3.9, Yucatan (29 percent) and other interior states (23 percent),
display the greatest rates of growth in maquila employment between 1991 and 2000. The

case of Yucatan will be explored in depth in the next chapter.

 

60.0%"

   
  

Vrmdn
om III-Io! "as
MalcoCIIy clou
Bolas Ivan
1097 . ‘ 101d

  

 

 

 

Figure 3.9 Change in maquiladora employment by region, 1991—2000

49

0:

Several explanations have been offered for the recent changes in maquiladora
location. Purported reasons include increasing negative extemalities (congestion,
pollution and crime, for example) in the border region. Other explanations are high
turnover rates (greater than 100 percent in some industries/locations) and associated labor
shortages.

Perhaps the most important reason for the shift of maquiladora production from the
border region, though, are wage differentials. Historically, the Mexican government has
maintained a three-tiered minimum wage system. The lowest salaries, approximately 30
percent less than those in Mexico City and the border region, are found in southern and
southeastern states. As Figure 3.10 below reveals, similar disparities in maquiladora
wages were apparent between border and interior regions between 1990 and 2000. In
general, if wage differentials exceed the additional transportation costs and lower
productivity rates in interior states, export-oriented firms will be acting "rationally" by

shifting operations away from the border region.

 

 

 

eoooo . -L-..-___-.--

 

70000

60000 /
50000 /
I.“ ._. _
/. I+Border
40000 / / 1* Ant-gig]
30000 / -/

20000 - /—

10000 W

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

 

 

 

MW)

 

 

 

 

 

 

 

 

 

Figure 3.10 Average maquiladora wages in border and interior regions of Mexico

50

3.2.4 Locational shifts in maquila production

Shift-and-share analysis (SSA) provides a relatively simple method for assessing
locational changes in maquiladora production. SSA decomposes these shifts, typically on
the basis of readily available employment data, into three components: national growth
(NS), industrial growth (1M), and regional growth (RS). National growth, also called
national shift, corresponds to the overall change in economic growth between two time
periods. Industrial growth, also termed industrial mix, identifies patterns of change in
specific sectors of the economy relative to the national growth component. Regional
growth, also referred to as the regional shift, indicates the extent of industrial growth in a
particular location with respect to industrial growth at the national scale. The general

formula for shift-and-share analysis may be expressed as follows:

R

E =NS +IM +RS (34>

1' (t+l)

where: E refers to employment
R is a given region
i indicates the industry
t+1 refers to some future time period
NS accounts for national growth
[M refers to the industrial mix
RS indicates the regional shift

The computational formula is:

[EA/0+1) I E010}-
R
_. _ 3.2
E ’ (EN MI EN (0 EN(:+1)/ E100} *Efrr) ( )
[ER (2+1), ER 0ny (1+1), EN 0)) J

1 (1+1)

 

 

where: E, R, i, and H] are as above
N refers to the nation
t indicates the present time period

51

 

 

n—J/

 

 

In Tables 3.1 and 3.2 below, shift-and-share analysis has been utilized to assess
changes in two of Mexico’s most important maquiladora industries — metal products and
clothing and apparel. Two time periods — pre-NAFT A (1990-1993) and post-NAFT A

(1994-1998) — and two regions (border states and non-border states) are considered.

 

 

 

 

 

 

 

 

 

 

 

 

METAL 1990-1993 1994-1998

PRODUCTS NS IM RS Total NS IM RS Total
Border 44959 -1 6401 -2553 26005 254770 -39621 -631 5 208834
Interior 1 1 51 -420 2553 3284 8384 -1 304 6315 13395

 

 

 

Table 3.1. Shift and share analysis of maquiladoras in metal products

In the case of maquiladoras producing metal products, the vast majority of new jobs
during both time periods were created in border states. Between 1990 and 1993, almost
90 percent of new jobs were created in the border region (26,005 of 29,289 jobs). During
the post-NAFI‘ A period, employment growth was even more concentrated among border
states (94 percent of 222,229 jobs). However, as indicated by the industrial mix
component (IM), employment growth in the metal products sector is inferior to average
national growth of maquiladora employment during both time periods. Interestingly,
though, a relatively pronounced shift appears to have taken place in regional comparative
advantage between 1990 and 1998. During the pre-NAFT A period, a net shift of 2553
jobs takes place between the border region and interior. Between 1994 and 1998, a net
shift of 6315 jobs results. In the pre-NAFI‘ A period, the shift in comparative advantage

represents almost nine percent of total jobs; in the post-NAFT A period it represents about

 

 

 

 

three percent of employment.
TEXTILES 1990-1993 1994-1998
INDUSTRIES NS lM RS Total NS IM RS Total
Border 7782 13926 -91 51 12557 49070 4461 6 -26375 6731 1
Interior 21 18 3791 9151 1 5060 26997 24547 26375 7791 9

 

 

 

 

 

 

 

 

 

 

 

Table 3.2. Shift and share analysis of maquiladoras in textiles industries

52

(I)

,3...
5198“

v“
.p'

I"
I‘

Shift-and-share analysis also indicates that a large portion of the employment gains in
clothing and apparel industries (during both time periods) is the result of overall national
growth in maquiladora employment. In addition, the industrial mix component indicates
that employment in clothing and apparel maquiladoras has increased at a substantially
greater rate than overall national growth, suggesting some degree of comparative
advantage in this sector. Results also indicate a fairly significant shift in regional
comparative advantage (RS). During both time periods, the majority of new jobs are
created among interior states. Furthermore, between 1990 and 1993 this shift in regional
comparative advantage accounts for more than 60 percent of employment creation (9151
of 15060 jobs) in clothing and apparel maquiladoras in interior states. During the post-
NAFTA period, the ongoing shift in regional comparative advantage from the border
region to Mexico’s interior is responsible for more than one-third of the new jobs (26,375
of 77,919 jobs).

Overall, then, shift-and-share analysis suggests that the locational changes in
maquiladora employment are attributable, at least partially, to an ongoing shift in
regional comparative advantage. Since export-oriented industrialization depends almost
exclusively on low-wage Mexican labor, it is fair to conclude that wage differentials are

among the primary driving forces of these locational changes.

3.2.5 Impact of NAFTA on export-oriented industries

Enactment of the North American Free Trade Agreement (NAFTA) in 1994 signifies
disappearance of the formal maquiladora program in 2001 (Vellinga, 2000). However,
some scholars assert that NAFTA represents a de facto expansion of the maquila strategy

to the whole country (Kopinak, 1995; OECD, 1997). The most significant outcome of

53

 

NAFTA is the eventual access that export-oriented firms will gain to Mexico’s domestic
market. On the one hand, NAFTA will permit increasing sales to Mexican consumers
(Wilson and Kayne, 2000). In addition, U.S. customs provisions that discourage purchase
of Mexican goods and services will be eliminated. As a result, NAFTA may actually
strengthen forward and backward linkages between maquiladoras and the domestic

economy.

 

I Maquila

 

 

 

 

 

 

Figure 3.11 Maquiladora employment vs. total manufacturing employment

Another perspective on the impact of NAFI‘ A has been offered by scholars such as
Cravey (1998) and Kopinak (1995). Cravey asserts that the maquiladora regime was the
model for NAFTA. In other words, she believes that the Mexican government has
accepted the notion of export—oriented industrialization as the country’s industrial
development model. Kopinak has coined the term maquilization to refer to this scenario.
Maquilization refers to the growth in the number of maquiladora plants and people
employed in export-oriented industries as a proportion of the Mexican economy. The
term also may be applied to describe the adoption of maquiladora characteristics by

industries that are not legally maquiladoras. Evidence of this purported maquilization

54

 

”at.

L?

may been seen in Figure 3.11 above. In less than a decade, maquiladora employment as a
share of total manufacturing employment increased from 13 percent to more than 27

percent.

 

100%
90%
80%
70%
60%
50%
40%
30%
20% 2
10%

.34

 

 

{ I Maquila
Domestic

 

    

 

    

  

 

_:.
. . a. .
_ .' ,- “.' '.-‘ 2 1'.

0°A, -‘ "I -' . , z.
1990 1991 19921993 1994 1995 1996 1997 1998 1999

 

 

 

 

 

 

 

 

 

 

Figure 3.12 Mexican "share" of maquiladora benefits

3.2.6 The maquila strategy and regional development
As defined above, regional development must be defined to include economic growth

(a necessary condition), reduction in regional income disparities, and structural
transformation of the regional economy. Among previous researchers, only Sklair (1993)
has attempted to assess the developmental implications of maquiladora production.
Sklair identifies six criteria in order to assess the impact of the maquila strategy on
development:

1) linkages between export-oriented firms and the Mexican economy;

2) retention of value-added within Mexico;

3) upgrading of human capital, as exemplified by greater employment of

Mexican managers and technicians;
4) transfer of technology;

5) labor conditions;
6) distribution of costs and benefits between foreign investors, Mexican

government and local population.

55

In general, Sklair concludes that the "developmental impacts" of the maquila strategy
have been mixed. The policy has resulted in greater employment of "indigenous"
managers and brought some prosperity to (urban) zones where firms have been
established. With respect to working conditions, he asserts that export-oriented industries
are no worse than domestic firms. However, the strategy has not brought about any
substantial linkages with the domestic economy nor resulted in meaningful technology
transfer. Furthermore, outcomes with respect to the "retention" of the benefits within the
country are particularly disappointing. In 1980, value-added retained within Mexico
amounted to about 44 percent of the value of imported inputs (Sklair, 1993). As Figure
3.12 above attests, the Mexican share of maquiladora benefits had fallen to about 30
percent in 1990. Moreover, by 1999 they had declined to less than 17 percent of imported

inputs (INEGI, 2001).

 

 

 

El Interior
I Mex. City
Border

 

 

 

 

 

1970 1975 1980 1985 1988 1993 1998

 

 

 

Figure 3.13 Distribution of income by region, 1970-1998

As a regional development strategy, it is also appropriate to assess the impacts of the

maquila strategy on the regional distribution of income. In other words, since Mexico’s

56

maquiladoras historically have been concentrated in the border area and maquiladoras
represent a significant share of Mexico’s national income, the E01 strategy may have
brought about a change in the distribution of income at the regional level.

Figure 3.13 above was derived from national accounts data. In general, this figure
suggests that the income accruing to the country's core region — the Mexico City area —
has declined from about 30 percent of the national total in 1970 to slightly more than 20
percent in 1998.4 The share of national income corresponding to residents of the border
region has averaged between 15 and 20 percent of the total between 1970 and 1998.
Furthermore, a moderate increase in regional income appears to have taken place among
border states since 1980. The most notable "change" in regional income is found among
the states that comprise Mexico's interior. Between 1970 and 1985, this region's share of
national income rose from 50 percent to about 60 percent of the total. However, since

1985 this share appears to have stagnated or fallen slightly.

 

 

 

 

 

 

150 +Irterior

 

 

 

i +
g MBKOIY
i

 

 

 

 

1970 1990 1% 1%

 

 

 

Figure 3.14 Per capita income by region, 1970-1998

 

4 The "Mexico City area" in this case is defined as the federal district (Distrito Federal). Unlike other
studies, this region does not include the state of Mexico.

57

The data in Figure 3.13 are somewhat misleading. They are aggregate regional totals
and do not account for population change between 1970 and 1998. Figure 3.14 above
displays per capita income by region between 1970 and 1998. If regional population
change is considered, income growth among interior states has remained stagnant during
the past 30 years (about 75 percent of the national average). The border region displays a
slight improvement in per capita income since 1995 (slightly more than 25 percent above
average national income). Contrary to popular opinion, the concentration of income in the
Mexico City region has not weakened greatly during the past three decades. Per capita
incomes grew from about two times the national average in 1970 to more than 2.5 times
by 1995. Although average incomes have fallen slightly since 1995, the average
capitalino (resident of Mexico City) still earns almost 250 percent of the national
average. Consequently, it appears that the purported dispersal or "deconcentration" of the
Mexican economy during the past two or three decades is more the result of population
growth than any real concrete changes in the distribution of income.

It is tempting to assert that the maquiladora strategy has not brought about a
significant change in the regional distribution of income in Mexico. However, such a
statement would be misleading. Although the regional income gap (at the national level)
has not diminished during the past 30 years, we have no way of knowing how large these
disparities would have been had the maquila strategy not been adopted. The methodology
proposed and implemented in this dissertation, however, will facilitate analysis of the
impacts of maquiladora production on the regional distribution of income within Yucatan

between 1990 and 2000.

58

Chapter Four

MAQUILADORA PRODUCTION AS REGIONAL DEVELOPMENT
STRATEGY IN YUCATAN, MEXICO

4.1 Background information

As Figure 1.1 reveals, the state of Yucatan, Mexico is located at the crown of the
Yucatan Peninsula. At present, the state's population is slightly less than 1.7 million;
about 40 percent of all inhabitants and more than one-half of total employment are
concentrated in the capital city and municipio of Mérida (INEGI, 2000). Locations that
are peripheral geographically also tend to be peripheral economically and Yucatan is no
exCeption. In 1996, Yucatan's gross state product per capita was slightly more than $2300
(US), less than 75 percent of the national average. Additional evidence of the state's
Inarginality may be found in its below average literacy rate (85 percent) and poor average
1eVels of educational attainment (approximately six years). Based on these and other
soCio—economic data, Mexico's Consejo Nacional de Poblacion identified Yucatan as one
of 15 states with high or very high levels of marginality in 1990.1 In 1995, Yucatan
re“rained one of only 12 such states (CONAPO, 2001b).

\

1
Variables used in calculating marginality rates included literacy and education levels, income, and quality
of housing.

59

 

Not surprisingly, Yucatan also lacks a strong indigenous industrial presence. In 1996,
for example, gross state product (GSP) in manufacturing comprised less than one percent
of Mexico's total gross national product (GNP) in manufacturing. Notwithstanding the
state's limited industrial base, Mérida is the regional center for goods and services for
southern and southeastern Mexico; as a consequence, the city displays a relatively strong

presence in wholesale and retail trade and other service industries (INEGI, 2001).

 

 

 

 

 

 

 

 

50 Kilometers

 

Figure 4.1 Municipio boundaries — State of Yucatan

As shown in Figure 4.1 above, the state of Yucatan is comprised of 106 municipios,
r0llghly equivalent to US. counties. Large disparities in basic socio-economic measures
may also be found at the local scale in Yucatan. For example, average literacy levels in
l'lll‘al areas (77 percent) lag far behind those in the state capital (95 percent). In addition,
more than 50 percent of total employment and almost 80 percent of value-added in the

State's economy is concentrated in the municipio of Mérida. A3 a consequence, 1996 per

60

capita income averaged approximately $4400 (US) in the state capital and less than $1000

(US) in the rest of the state (INEGI, 2001).

   

- Very low
- Low
- Average
1:] High
:1 Very high

40 0 40 80 Kilometers

   

Figure 4.2 Level of marginality at municipio level (1995)

The CONAPO study mentioned above revealed that only one municipio (Mérida) in
Yucatan displayed very low levels of marginality in 1990 (CONAPO, 2001a). Seventy
locations, representing almost 30 percent of the state's population, however, were subject
to high or very high levels of marginality. Based on 1995 data, CONAPO identified two
Municipios with very low levels of marginality (Mérida and the neighboring port of
Iz’l'ogreso). A substantial decrease, however, was apparent in the number of locations
exhibiting the highest levels of marginality. Only 38 municipios, accounting for about 15
Percent of Yucatan's total population, displayed high or very high levels of marginality in

1995 (CONAPO, 2001b). Somewhat troubling, however, is that the number of municipios

61

exhibiting very high levels of marginality increased from two to eight during this time.

The map of 1995 marginality levels for Yucatan is found above in Figure 4.2.

4.1.1 Structure of regional economy

Some of the differences and disparities that prevail within Yucatan are further
exemplified by the differences in economic structure between urban and rural areas of the
state. Employment in Mérida is dominated by three sectors (food and textiles products,
commerce, hotels and restaurants, and other services) which account for more than three—
quarters of all jobs. In general, employment in service industries and wholesale and retail
trade account for almost two-thirds of employment in the state's urban core. In rural areas,
the primary sector remains far and away the most prominent source of employment.
However, some structural change occurred during the 19903, as employment in
agriculture and mining decreased by more than 30 percent (in relative terms). In addition,
service sector employment more than doubled in rural communities between 1993 and
1998. Also of note in the context of this dissertation is the substantial increase in the
importance maquiladora employment in only five years. In 1993, export-oriented firms
represented about two percent of all jobs. By 1998, these firms accounted for almost

Seven percent of employment in Mérida and nine percent in rural locations.

 

F‘

 

 

 

 

 

 

 

& 1993 1998
\ Sector Mérida Rural Mérida Rural
Agriculture and mining 3.7% 64.6% 2.4% 45.2%
Food and textile products 17.4% 8.5% 10.7% 11.9%
other manufacturing 8.2% 2.9% 6.2% 1.7%
Construction 3.2% 0.8% 6.7% 1 .9%
Commerce, hotels and restaurants 38.1% 16.1% 24.2% 13.3%
Other services 26.6% 6.3% 43% 17.4%
filquiladora industries 2.7% 0.9% 6.8% 8.6%

 

Table 4.1 Structure of employment in Yucatan, 1993 and 1998

62

 

4.2 Historical precedents

Until the 19803, Yucatan's economy was based on the production of a single
agricultural commodity — henequen. Henequen is a natural fiber, indigenous to the region,
used primarily to make rope and twine. Upon independence in the early 19th century,
henequen production served a limited local demand for nets, sacks, hammocks and r0pes.
During the second half of the 18003, production of henequen grew rapidly, first due to the
demand for ship riggings, and subsequently as a result of the growth of mechanized
agriculture in the United States (Wells, 1985; Brannon and Baklanoff, 1987).

Production of henequen expanded rapidly between 1880 and World War 1 due to the
introduction of the McCormick reaper and the rapid expansion of grain production in the
United States. With the financial backing of International Harvester, entrepreneurs in
Yucatan developed an efficient system and necessary machinery for processing henequen
fiber on a large scale and the region became the sole reliable source of henequen to
supply demand (Chardon, 1963). After 1880, the United States (International Harvester,
specifically) accounted for 90 percent of the state's henequen production (Brannon and
Baklanoff, 1987; Topik and Wells, 1998). More than 85 percent of US. binder twine was
manufactured with fiber from Yucatan (Wells, 1985).

By the turn of the 20th century, henequen production and processing occupied the
dominant role in Yucatan's economy. During the 60-year "henequen boom," the crop
Comprised more than 95 percent of the region's exports and more than 70 percent of
agricultural land was devoted to its production (Brannon and Baklanoff, 1987). Between
1 870 and 1920, henequen comprised almost 20 percent of Mexico's total exports

(Castilla, 1991). By the eve of the Mexican Revolution, Yucatan was exporting more

63

than 100,000 tons of henequen fiber annually, making it Mexico’s second most important
export commodity following precious metals (Wells, 1985; Castilla, 1991). At this time,
Yucatan had the largest share of its workforce employed in industry in all of Mexico. In
contrast with most other plantation economies in Latin America, local entrepreneurs
maintained control over land, physical capital, and local transportation infrastructure that
were essential to henequen production (Brannon and Baklanoff, 1987). The state also had
an indigenous industry of machine shops and foundries that built steam engines and
machinery necessary to remove fiber from henequen plants. In the 19203, a local cordage
industry was established to manufacture twine, cables, and rope for domestic and US.
markets (Brannon and Baklanoff, 1987).

The henequen boom transformed Yucatan from one of the poorest regions in Mexico
to the most wealthy and industrialized state in the entire Republic in a span of only three
decades (Wells, 1985; Topik and Wells, 1998). However, Yucatan’s rapid economic
growth was a textbook case of dependent development — the region's economic future was
completely reliant on foreign investment and tied directly to a single foreign market
(Evans, 1979; Wells, 1985). Furthermore, the henequen boom created social unrest as a
local oligarchy (a group of 30 families known as la casta divina) emerged to control as
much as 90 percent of total fiber production (Wells, 1985; Brannon and Baklanoff, 1987).

Similar disparities in the distribution of land and income existed throughout much of
the country (latifimdia), giving rise to the Mexican Revolution in 1910. As a consequence
of the Revolution, more than 70 percent of henequen haciendas were expropriated and

di stributed to peasants in 1934. Cordage factories were eventually nationalized in 1964.

 

 

Although the failure of Yucatan's henequen industry is frequently blamed on the
Mexican Revolution and break up of large estates, the ultimate explanation of its demise
lies in its inability to generate sufficient linkages with the local economy. Yucatan's rail
system provides a prime example - although almost 900 kilometers of rails moved
henequen from the countryside to the port of Progreso and markets in the United States,
secondary cities were not linked and Yucatan was not connected to the national railroad
system until 1957. Furthermore, as Brannon and Baklanoff (1987) state, failure of
Yucatan's henequen industry also resulted from its "operating outside the discipline of the
market system and without the benefit of serious efforts to coordinate production and
planning."

Although the importance of henequen to Yucatan's economy "peaked" in the first 25
years of the 20th century, the crop remained the region's primary economic activity until
the 19703. In 1970, more than 55 percent of the economically active population in the
state remained employed in agriculture. At this time, henequen still represented more than
50 percent of farmed land and 60 percent of total agricultural production (Castilla, 1991;
Bar'ios, 1996). However, the introduction of synthetic fibers and emergence of new
competitors in South America (Brazil), Africa (Kenya and Tanzania) and Asia
(Philippines) brought about a dramatic decline in the region's henequen industry. By
1990, only one-quarter of the population was employed in agriculture and henequen
l‘epresented less than 10 percent of total production (Bafios, 1996). The most obvious
I‘esult of the demise of the henequen industry has been its impact on unemployment levels
in rural areas of Yucatan. For example, by 1990 only 42 percent of the working age

POpulation in rural areas was classified as "economically active" (INEGI, 1990).

65

 

 

160 Mg.... -----._..s.~..._.-,-.,, -.... _ . ,--.--.- -._--..-.-. -- ....- --.-.. --.
l

140

 

 

 

 

100

 

 

80

 

607

 

40 -

207

 

 

 

 

 

0 , . , 4 . .
1985 1980 1987 1980 1989 1990 1991 1992 1993 1994 1995 1996 1997 1999 1999 2000 2001

Figure 4.3 Number of maquiladoras in Yucatan, 1985-2001

 

 

 

4.3 Policy measures and political environment

In the 19803, state and federal governments began to come to terms with the
inevitable demise of the henequen industry. As a consequence, the Henequen Zone
Restructuring Program was implemented in 1984 to "diversify" the regional economy
and promote economic development by focusing on three key industries — tourism,
fishing and maquiladoras (Wilson and Kayne, 2000). This policy, and the eventual
privatization of the state-owned fiber processing plant (Cordemex) in 1990, provided the
initial impetus for adoption of the E01 strategy. As displayed above in Figure 4.3,
offshore producers responded rapidly; by 1988, ten maquiladoras had been established in
Yucatan. In 1990, these export-oriented industries employed about 5000 people and were
evenly distributed between clothing and apparel and "other manufacturing" plants.

The most recent policy initiative, the 1995-2001 State Development Plan, has played
an even more important role in promoting the proliferation of export-oriented firms in
Yucatan. According to Wilson and Kayne (2000), the rapid expansion of the maquiladora
industry in Yucatan is "the result of a state policy to revitalize a declining local economy

by reinSerting it in the global economy on the basis of cheap manufacturing labor." As

66

 

mentioned above, this policy seeks to redress disparities and achieve "balanced
sustainable regional development" by channeling maquiladora production to rural areas

of the state in order to generate employment and improve living conditions.

 

 

1990 1991 1992 1993 1994 1995 1999 1997 1999 1999 2090 209‘

Figure 4.4 Maquiladora employment in Yucatan, 1990-2001

 

 

 

This policy identifies the excessive concentration of economic activity in Mérida as
the primary cause of disparities in income, employment and economic opportunity in
Yucatan. In addition, the plan seeks to redress the high levels of marginality that persist in

many areas of the state (Estado de Yucatan, 1996).

 

Figure 4.5 Regional planning districts, 1995-2001 State Development Plan

67

 

In response to these inequities, state government proposes to bring about a more
equitable spatial distribution of employment by providing small cities with basic
infrastructure to serve as growth poles and slow migration from the countryside to
Mérida. The plan identifies the need to develop an effective method of regionalizing the
state as an indispensable planning tool in achieving these objectives. The proposed
regionalization scheme represents a traditional grouping of Yucatan's municipios based
on historic, economic and other traditional linkages; economic activities of each region,
including production, available resources, environmental conditions and socio-economic
conditions. Figure 4.5 above identifies the nine planning districts established by the 1995-

2001 State Development Plan. Table 4.2 below provides further detail about each region.

 

 

 

 

 

 

 

 

 

 

 

 

Name 01 region Number of munlclplos Population (1995)
Eastern coast 9 104,202
East 13 117,051
Central coast 17 83,896
Central 1 5 79,783
South central 7 43,224
Metro 1 4 873,078
Western coast 9 68,286
Southwest 1 4 87,986
[@0111 8 1g304

 

 

 

Table 4.2 1995-2001 State Development Plan planning regions

In order to provide a suitable environment for export—oriented production, the
development plan also calls for concentrated investment in order to enhance the quality of
infrastructure. Some of the most prominent improvements carried out since 1995 include:
BXpansion of runway facilities to accommodate larger planes; construction of a new
airport in the interior of Yucatan; deepening the harbor at the port of Progreso to

aCcommodate larger ships; construction of a gas pipeline from the coast to industrial

68

parks; construction of a new electricity generating plant; expansion of existing highways;
and expansion of industrial parks, especially in interior regions of the state.

Anecdotal evidence, gleaned from fieldwork in Yucatan, indicates that the policy
initiatives and concentrated investment of the past decade have played an important role
in attracting export-oriented firms to Yucatan. In addition to a favorable geographic
location and low-cost labor, maquiladora representatives identified infrastructure
enhancements and the stable social and political climate as important factors in their
decision to locate in Yucatan. Notwithstanding recent policy initiatives, maquiladora
representatives also recognized the importance of social capital — the role of personal
relationships — in conducting business in Yucatan. Apparently, these personal
relationships play an important role in locating qualified labor, gaining support of local

government officials, and accessing needed resources.

4.4 Other incentives

From the perspective of trans-national corporations (TNCs), several other more
"practical" considerations also make Yucatan an attractive setting for export-oriented
production. The state boasts a fairly well-trained workforce; for example, by the time
most women enter formal employment in maquiladora firms, they have had some
experience in sewing, embroidery and weaving. A large pool of idle labor also contributes
to the attractiveness of the state. As implied above, high levels of unemployment exist,
Particularly in rural areas, as a result of the collapse of the henequen industry. The
existence of this "surplus labor" has played a significant role in depressing wages in the

r€=gion and extremely low labor costs have been an especially important factor in the

69

 

proliferation of export-oriented production. As Dicken (1998) observes, in the mid-19903
Yucatan displayed the lowest costs in the world per standard minute in the clothing and
apparel industry.

In addition, workers in Yucatan are less politically organized and the "threat" of
unionization is extremely limited (Wilson and Kayne, 2000). Furthermore, as mentioned
in Chapter Three, Mexico continues to maintain a three—tiered minimum wage system; the
lowest salaries, approximately 30 percent less than those in Mexico City and border
states, are found in Yucatan and other southern and southeastern states. Finally, although
peripheral to Mexico City, Yucatan is relatively well positioned with respect to Central
America and the Caribbean (location of many other export-oriented production facilities)

and the southern United States (origin of most intermediate inputs and destination of the

Vast majority of final production).

 

Figure 4.6 Number of maquiladoras by municipio, 2001

70

4.5 Proliferation of maquiladoras in Yucatan

As mentioned above, ten maquiladoras, employing approximately 2000 persons had
been established in Yucatan by 1988. By early 2001, though, approximately 145
maquiladoras were in operation, employing more than 37,300 persons throughout the
state (Figures 4.3 and 4.4). The vast majority of these plants produce clothing and apparel
for markets in the United States (about 80 percent). Other prominent sectors also depend
on labor-intensive assembly — electronics, jewelry, and orthopedic and prosthetic devices.
Less than a decade ago, maquiladoras played an insignificant role in the regional
economy (Table 4.1 above). Currently, however, these firms comprise more than one-
third of total manufacturing employment in Yucatan and more than 80 percent of total

eXports (Secretaria de Desarrollo Industrial, 2000).

     

0
gem-1-249
250-999
-1000 — 5414

- 15261

Figure 4.7 Maquiladora employment by municipio, 2001

71

4.5.1 Geographic distribution of maquiladoras

As displayed in Figure 4.6 above, more than 60 percent of export-oriented firms in
Yucatan are located in Mérida. In general, these firms are smaller and "older" than those
in rural areas. About 60 percent of total employment in maquiladoras, however, is found
in rural areas of the state. As shown above, export-oriented firms in rural areas are
clustered in Yucatan's traditional "henequen zone," the most heavily populated area of the
state within a radius of 80 kilometers around Mérida. In general, rural maquilas are "more
recent" and larger than those in Mérida. As the map indicates, maquiladoras are found in
relatively few municipios in Yucatan (only 30 of 106 municipios). Most locations have
only one maquiladora, though export-oriented firms account for substantial employment

in these communities due to their size (about 395 employees on average).

.712 - 13.35
.1385 - 21.18
.2118 - 28.65

    

I“igure 4.8 Maquiladora employment as a percentage of total formal employment

72

Figure 4.7 above indicates current maquiladora employment by municipio. Mérida,
obviously, accounts for the greatest share of export-oriented employment (15,261).
Notwithstanding the small number of firms found in individual municipios, several other
locations also have significant maquiladora workforces, including Motul (5413),
Valladolid (3781), Maxcanu (1904), and Uman (1508). Figure 4.8 above reinforces the
prominent role that maquiladoras play in local economies. Although the largest
maquiladora workforce, by far, is found in Mérida, export-oriented industries account for
less than seven percent of the city's total employment. In several locations, however,
maquiladora employment accounts for a significantly greater share of formal

employment.

4.6 Direct economic impacts of the maquila strategy

A survey of maquiladoras was conducted in the summer of 1999 in order to assess
the direct impacts of export-oriented industrialization on Yucatan's economy. Based on a
liSt provided by the Secretaria de Desarrollo Industrial, approximately 60 firms were
contacted either by telephone or in person. Of the maquiladoras contacted, 45 agreed to
t8tke part in the survey. Participants, usually plant managers or other representatives of
management, were asked to provide basic information on start-up costs, employment,
er'nployee turnover, and other expenditures. In some instances respondents refused or
were unable to provide requested information; therefore, the sample size used to make
inferences regarding economic impacts sometimes is less than the total number of firms

taking part in the survey. Nonetheless, the collection of firm-level data allows inferences

73

to be drawn about the maquiladora sector as a whole. A copy of the survey is included in
Appendix A.l.

In addition, aggregate secondary data on maquiladoras were obtained from the local
office of the Instituto Nacional de Estadz’stica, Geografi’a e Infonnatica (INEGI). These
unpublished data were employed to confirm the accuracy and supplement survey data as
needed. INEGI data were drawn from monthly reports submitted by clothing
maquiladoras. It should be noted, however, that the data provided by INEGI are
incomplete, consisting of aggregate information from only 42 firms. Of the 45 plants
surveyed for this study, only 23 were reporting to INEGI at the time. The list provided by
the Secretaria de Desarrollo Industrial listed 145 maquiladoras as of March 2001.

The direct impacts of maquiladoras on Yucatan’s economy are comprised of at least
five distinct components: the initial expenses (start-up costs) incurred within the state; the

number of direct jobs created; the salaries associated with these jobs; the purchase of
lOcally-produced commodity inputs; and the purchase of a variety of services associated
With operating a plant in Yucatan. In order to calculate the total direct impacts of all
maquiladoras on the state's economy, survey data were collected and used to estimate
Several relatively simple models that facilitate prediction of missing data from firms that

did not participate in the survey.

4.6.] Start-up costs
Analysis of survey data from 35 firms indicates that, on average, maquiladoras
invested about $350 thousand (US) in start-up costs. Since much of the machinery used in

e)lrport-oriented production is imported duty-free under the maquiladora program, only 40

74

percent of these initial expenditures, about $140 thousand (US) per plant, were incurred

in Yucatan.

4.6.2 Employment

According to survey data and information provided by INEGI and Yucatan's
Secretaria de Desarrollo Industrial, export-oriented firms directly employed
approximately 37,327 men and women as of March 2001. A common criticism of
maquiladoras is their tendency to exploit a female workforce almost exclusively. In the
state of Yucatan, however, between 25 and 30 percent of production workers in
maquiladoras are men. In addition, the annual employee turnover rates of firms
participating in the survey were substantially lower than those of maquiladoras in other

parts of Mexico. On average, annual turnover was less than 35 percent.

4. 6.3 Salaries and benefits

Several firms were reluctant or unable to provide information on total salaries and
benefits. Therefore, available data and ordinary least squares (OLS) were employed to
eStimate this information. Using survey data from 34 firms (that provided both
employment and salary information), a simple OLS model was estimated, regressing total
Salaries and benefits on the number of employees. Intuitively, this relationship makes
Sense - the number of persons employed by each firm should be the single most

important determinant of salaries and benefits. The results of the model are displayed

below:2

¥

2 The first line of OLS model below (and all subsequent regression models) contains estimated coefficients.
The second line provides corresponding standard errors.

75

Salarles = -55.613 + 2.351 Employment
34.242 0.113
Ad]. R” = 0.929 F(1,321 = 432.030 p = 0.0000

As the model indicates, the number of employees accounts for almost 93 percent
of the variation in total salaries and benefits at the firm level. In addition, the coefficient
of the independent variable (measured in thousands of US. dollars) is highly statistically
significant and takes theexpected sign. Since the model explains a substantial amount of
the variation in the dependent variable and reliable employment data are readily available
for all firms, total salaries and benefits can be calculated with some degree of confidence
for the entire maquiladora sector. Based on the results of the OLS model above, overall
salaries and benefits accruing to the 37,327 employees of maquiladoras in Yucatan are

estimated at more than $87 million (US).

4. 6.4 Other expenditures

The survey also indicates that of every dollar spent in Mexico, the average firm
allocates more than 50 cents to salaries and benefits, 33 cents to producer services, and
about five cents to locally purchased commodity inputs. These estimates compare
favorably with the data collected by INEGI.

Of the 45 maquiladoras participating in the survey, 36 provided information on their
gross annual operating expenses. Excluding salaries and benefits and commodity
Purchases, the remaining operating expenses of these plants — producer services - totaled
n‘lore than $11 million (US), an average of roughly $310 thousand (US) per firm. The
data collected by INEGI are comparable, indicating about $18 million total among 42

Plants, or somewhat more than $400 thousand (US) in operating expenses on average. In

76

light of substantial missing data, a simple OLS model also may be used to estimate the
total value of producer services consumed by all 145 maquiladoras. Again, total firm
employment may be used as the explanatory variable since it is readily available for all
maquiladoras and is associated with the "scale" of plant operations. The dependent
variable corresponds to total expenditures on producer services (PrdSrv). The results of

the model are found below:

PrdSrv = 30.908 + 1.229Employment
30.405 0.107
Ad]. 11’ = 0.788 F034) = 130.793 p = 0.0000

Results reveal that employment accounts for a substantial amount of the variation in
expenditures on producer services (almost 79 percent). Again, the overall model and
independent variable are highly statistically significant. Consequently, the model above
Can be used to forecast the expenses of firms that failed to provide information on
producer services. Based on regression coefficients, the total value of production-related
services consumed by the 145 maquiladoras is approximately $50 million (US).

The 45 firms taking part in the survey also reported purchasing almost $2 million
(US) of commodity inputs in the state of Yucatan. These purchases are essentially retail
and wholesale transactions since the goods purchased — thread, fabric, elastic, etc. — are
not produced within the state. According to INEGI data, more than $8 million (US) of
Commodity inputs were purchased domestically by maquiladoras in Yucatan in 2000.
INEGI data, however, do not differentiate between purchases made in Yucatan and those
made in other parts of Mexico. Initial data analysis indicated that the value of commodity

inputs purchased in Yucatan was not strongly associated with any of the other survey

Variables (percent of final product exported, percent of raw materials imported,

77

employment, etc.). Therefore, no attempt has been made in this dissertation to estimate
the total value of commodity purchases made in the state.

Nonetheless, it can be concluded with a substantial degree of confidence that
Yucatan's maquiladoras are injecting at least $140 million (US) directly into the state's
economy each year in terms of salaries and benefits ($87 million), purchase of producer
services ($50 million), and commodity inputs ($2 to $3 million). This figure does not
include the impact of initial investment in new plants, which represents an additional $2
to $3 million annually (based on an average of about 20 new plants per year since 1995).

The results presented above represent merely the direct impacts of maquiladoras on
Yucatan's economy. A more complete indication of the economic importance of export-
oriented firms can be obtained only if the impact of secondary purchases of goods and
services by firms and households is considered. Estimation of these indirect and induced
effects (as well as distributional consequences) comprises the primary focus of this study
and is taken up formally in Chapter Six. Before addressing the inter-regional distribution
of overall economic impacts, however, it is necessary to develop a methodology that
facilitates such an analysis. Development of this methodology is the subject of the

following chapter.

78

Chapter Five

AN INTEGRATED MODEL OF YUCATAN’S SPACE-ECONOMY

5.1 Regional modeling and regional science

Regional analysis deals with the study of sub-national territories in which issues of
location, distance, contiguity and interaction within and between regions are the primary
focus. Based on this definition, Anselin and Madden (1991) offer a methodology that will
guide analysis in the context of this dissertation. From their perspective, the point of
departure for regional modeling is identification of the appropriate regions and collection
of necessary data. Once regions have been defined, data must be organized in a consistent
system to facilitate analysis. Typically, an accounting system — of which the input-output
table is an example — is the preferred framework. After these accounts have been

developed, it is possible to generate models, carry out data analysis, and test hypotheses.

5.1.1 Integrated modeling in regional science

In the regional science literature, a model is termed "integrated" if it considers more
than a single process in a regional context, focuses on multiple regions or spatial scales,

Or combines more than one modeling technique (Rey, 2000). Typically, the results from

79

one component of the integrated model serve as inputs in subsequent stages of analysis.
According to this definition, then, the model proposed below may be defined as
integrated since it focuses on multiple spatial scales (regional and local) and combines
more than one modeling technique (input-output analysis and spatial econometrics).
Specifically, the employment multipliers derived from inter-regional input-output

analysis will be utilized to calibrate spatial econometric models and estimate economic

impacts at the municipio level in Yucatan.

5.2 Research design

The analysis portion of this dissertation consists of three inter-related activities:
acquisition of needed primary and secondary data; construction of an l8-sector inter-
regional input-output (IRIO) model; and development of spatial multipliers at the local
scale in Yucatan based on spatial econometric techniques. As mentioned above, the
primary objective of this study is to understand more fully the impact of export—oriented
industrialization (EOI) on economic structure, employment and incomes in Yucatan. The

integrated model of Yucatan's space-economy will be employed to achieve this objective

and test the hypotheses specified in Chapter One.

5 - 2.] Acquisition of primary and secondary data

In order to develop an integrated model of Yucatan's space-economy, a combination
of primary and secondary data will be used. Available secondary data include information

from the 1989, 1994 and 1999 Economic Censuses, the 1990 and 2000 Censuses of

80

 

Population and Housing, and the Banco de Informacio’n Econo’mica.l All data are
published by Mexico’s Instituto Nacional de Estadz’stica, Geografz’a e Informatica
(INEGI).

The 1989, 1994 and 1999 Economic Censuses offer data on employment, total wages
and benefits, total output, total inputs, and value added by sector. The 1990 and 2000
Population Censuses provide information on employment by sector and economically
active population. In both instances, data are available at the municipio level. Among
other data series, the Banco de Informacién Econémica (BIE) provides annual
information on the maquiladora industry at the state level. Data include number of
establishments, total employment, wages, profits, intermediate consumption, value of
production, and overall value added.

Additional secondary data resources include the regional input-output table for the
state of Yucatan and social accounts data for two rural municipios. The regional input-
output table was developed by researchers at the Centro de Investigaciones y Estudios
Avanzados del Instituto Politécnico Nacional (CINVESTAV, 2000). The table consists of
72 sectors and was compiled from a variety of primary and secondary sources (for 1993).
Technical coefficients and sectoral multipliers from this regional 10 table are included in
Appendix B. As discussed below, the regional table will provide many of the baseline
teChnical coefficients and other needed components of the inter-regional model. Social

accounts data also have been compiled for two rural municipios (Hocaba and Chabihau)

\
1

Data from Mexican economic censuses refer to economic activity during the previous year. Therefore, the
1 989 census provides information for 1988, the 1994 census offers data on 1993, etc. As a result, input-
output models developed in this study correspond to 1988, 1993 and 1998.

81

 

by two recent graduates of the Universidad Autonoma de Yucatan (Ortiz, 1999;
SEDESOL, 2000). This information will be integrated with primary data to estimate the
sectoral and regional distribution of income and expenditures of rural households.

Information from these secondary sources will be supplemented with primary data
collected from four sources — maquiladoras, domestic firms, officials of trade
organizations that represent firms in different sectors of Yucatan's economy, and
individual households.

As mentioned in the previous chapter, representatives of individual maquiladoras
were surveyed during the summer of 1999 in order to determine total value of production,
total employment, salaries and benefits, cost and origin of raw materials, intermediate
inputs and producer services, and final destination of production. Approximately 60
maquiladoras were contacted and asked to participate in the survey; three-quarters of
these firms (45 firms) agreed to take part. A copy of the survey is included in Appendix
A.1.

In addition, a small sample of 44 domestic firms was surveyed in February and March
2001 to identify the origin of their intermediate inputs and raw materials and the final
destination of their finished products. The purpose of the survey was to determine what
types of raw materials and intermediate goods different firms consume and the
destination -— both geographically and sectorally — of their output. A copy of this survey is
also included in Appendix A.2.

Due to its limited size, the survey of domestic firms has been supplemented with
expert opinion from officials of state and national trade organizations that represent firms

in three different sectors of Yucatan's economy. In general, representatives of these

82

groups were asked to respond hypothetically to the survey of domestic industries as if
they operated a "typical" or "average" firm in urban or rural regions of Yucatan.
Incorporation of expert opinion in the development and calibration of input-output
coefficients has been suggested as an alternative to a "full-blown" survey by Miemyk
(1965) among others. Trade organizations that took part include the Chambers of the
Clothing and Apparel, Manufacturing, and Baking industries.

Furthermore, a small sample of 25 private households in Mérida and rural areas was
interviewed to gain a first-hand indication of the distribution of their monthly expenses.
The purpose of the survey was to determine what kinds of goods and services households
consume and the percentage of household income spent in different sectors of Yucatan's
economy (housing, food, clothing, entertainment, etc.). The information obtained from
this survey will be integrated with data from the regional input-output table and rural
social accounts to calculate the sectoral and regional distribution of household
expenditures in rural and urban areas of Yucatan. Again, a copy of this survey is included

in Appendix A3.

5 .2.2 Construction of inter-regional input-output (IRIO) model

The primary data collected in the surveys above will be combined with existing
secondary data in order to develop a hybrid or mongrel (survey/non-survey) inter-regional
input-output model (IRIO) of Yucatan's economy.

Input-output (IO) analysis is a modeling technique that divides the economy into two
Components — consumption and production - and accounts for the direct and indirect

iIlterdependencies or linkages among the different sectors of the economy (firms,

83

 

households, etc.). The technique was introduced by Leontief in the 1930s and adapted for
the purposes of regional analysis by Isard in the 19505 (Isard et al., 1998). 10 analysis
remains an extremely useful tool for planning and regional analysis due to its flexibility.
Furthermore, it is an empirical model that is not associated with any particular economic
or geographic "paradigm" (Hewings, 1985).

Traditionally, the 10 model has been employed to assess the economy-wide
implications of changes in "exogenous" factors such as household demand, government
spending or international trade (Sadoulet and de Janvry, 1995). Since changes in export-
oriented production, by their very nature, respond to forces external to Yucatan's
economy, the technique is well-suited for policy analysis in this proposed application. In
the vast majority of cases, 10 analysis is used in an ex ante fashion to estimate the
potential outcomes of a given policy decision. In this instance, however, the technique
will be applied in an ex post manner to assess the regional implications that have been
brought about by Yucatan's adoption of the export-oriented industrialization strategy.

The primary objective of the inter-regional input-output model for Yucatan is holistic
accuracy, as defined by Jensen (1980). Rather than pursuing the impossible accuracy of
each cell of the 10 table (partitive accuracy), holistic accuracy emphasizes representation
of the main features of the regional economy in a descriptive sense while preserving the
importance of these features in an analytical sense. In other words, the goal of the IRIO
table is to accurately reflect the relative size and overall structure of Yucatan's urban and
rural economies.

In a seminal paper, Alonso (1968) distinguishes between errors of specification and

errors of measurement. As implied in the preceding paragraph, some degree of

84

measurement error is inevitable in a model that relies heavily upon secondary data and
statistical and mechanical methods for adjusting such information. However, even in the
event of considerable measurement error, holistic accuracy is possible. With respect to
errors of specification, the proposed integrated model is a substantial improvement over
previous studies in several respects. This study, for example, incorporates explicitly the
role of "producer" services and the importance of household consumption.

The emphasis on holistic accuracy will become readily apparent in the discussion of
the development of the IRIO model that follows. In several instances, relatively simplistic
assumptions have been made to facilitate analysis of distributional consequences. In all
cases, justification of these assumptions is argued on practical grounds - in many
instances data are simply unavailable and could not be obtained with limited resources.
Hannon and Ruth (1997) assert that models built on uncertain parameters (assumptions)
may be of value in providing a “picture of a particular process, rather than exact
information.” Furthermore, they affirm that models cannot be verified completely by
comparing results with the real world. Instead, verification must be made in terms of
model consistency — logical accuracy of its internal structure (Hannon and Ruth, 1997).
Consequently, the acceptability and viability of model assumptions should be judged on
their reasonableness and the pursuit of holistic accuracy mentioned above.

Notwithstanding its utility, the input-output model has its limitations. In general, 10
analysis makes three assumptions that violate basic economic theory — proportionality,
constant returns to scale, and no substitution. First and foremost, the model assumes that
each sector of the economy consumes inputs in fixed proportions. In other words, the

amount of a particular intermediate input needed by a given industry is a "fixed

85

proportion" (linear function) of its output. A second shortcoming is the model’s
assumption of constant returns to scale, which ignores the possibility of economies of
scale in different industries. Finally, the model assumes no substitution between different
inputs. No matter how much of an intermediate input becomes available, the quantity a
sector can produce is limited by the availability of other inputs.

Other serious criticisms also have been voiced, including assumptions of perfectly
elastic supply and fixed prices (failure to incorporate prices explicitly). As Partridge and
Rickman (1998) note, as a result of these assumptions, all predicted change in the IO
model derives from exogenous change in final demand. Furthermore, predicted change in
the regional economy is always proportionate to the exogenous change.

Recently, researchers have favored computable general equilibrium (CGE) models as
a means of assessing policy impacts more completely. Although these models offer many
advantages over the traditional 10 model — ability to incorporate increasing returns to
scale and explicit economic theory (impact of prices) — they require massive amounts of
data and depend greatly on the calibration of parameters that are frequently unknown or
unavailable.2 Furthermore, the results obtained from CGE models may not differ
significantly from those obtained from traditional input-output analysis (Partridge and
Rickman, 1998). Notwithstanding the potential gains of CGE analysis and the criticisms
mentioned above, many researchers believe that the insights provided by input-analysis
into the workings of the regional economy outweigh its potential shortcomings.

Figure 5.1 below provides a highly stylized representation of a basic regional input-

output table. In general, rows of the table correspond to sales from a given sector to all

 

2 Haddad’s (1999) three-region CGE model for Brazil, for example, includes more than 240,000 equations.

86

sectors of the economy; columns represent purchases of intermediate inputs, raw
materials, labor, etc. made by each sector of the economy. Data needed for construction
of a basic regional 10 table include sectoral estimates of output, inputs, value added,

imports, exports and other components of final demand.

 

 

 

 

 

 

 

 

 

 

Sector 1 . . . n Final Demand ’Y) Total Output m)
1
Inter-Indus try Total Production
Transactions HH Other Exports In Each Sector
- (X1) (Y1) (Y2) (Y3)
11
Imports ('1)
Salaries (VA 1)
Value Added (VA) Other VA (VA2)
Total Production in
Total Inputs ()_(‘) Economy (X)

 

 

 

Figure 5.1 Stylized regional input-output table

The regional 10 table is essentially a double-entry accounting system. The table is
employed for the purposes of input-output analysis by making the assumptions listed
above, simulating exogenous changes to final demand, and estimating the resulting
impacts on output, income and employment.

Although the data requirements are considerably more demanding, the basic regional
input-output framework can be extended readily to an inter-regional context in order to
account for linkages between different locations, as well as different sectors of the
economy. In this proposed dissertation, an inter-regional input-output (IRIO) model will
be developed in order to assess the impact of the E01 strategy in urban and rural areas of
Yucatan. Therefore, primary and secondary data must be ordered not only by sector, but
also by region of the state. In the case of each region, the IRIO table partitions
transactions into intra-regional and inter-regional economic activity. A highly stylized

representation of the components of an IRIO table is shown in Figure 5 .2 below.

87

As in the regional table, the rows of the IRIO table correspond to sales and the
columns represent purchases of inputs. However, the shaded portions of the table indicate
inter-regional transactions — sales and purchases that take place between different regions
of the economy. The diagonal quadrants account for intra-regional economic activity. In
addition to the basic data required for the regional input-output model, some estimate of
the inter-regional distribution of inter-industry transactions and final demand must be

obtained to determine the inter-regional portion of the IRIO table.

 

Mien MERIDA RURAL
Sector 1...nHH1...NHHOtherFDTotaIOutput

 

 

 

Employment
Wagee
Other VA

 

 

N
Employment

Wagee ’

Other VA

Imports
Total Outlay:

Figure 5.2 Stylized inter-regional input-output table

FDJCI

 

 

 

 

 

 

 

 

 

 

 

 

 

5.2.2.1 Derivation of IRIO model components

The baseline inter-regional IO model will be derived by expanding the existing
regional 10 table for Yucatan based on available primary and secondary data. Derivation
of each of the necessary components of the IRIO model is discussed in detail below.

Data on total output/inputs, value added, employment and wages and benefits are
available at the municipio level from the 1989, 1993 and 1999 Economic Censuses for the

first 17 sectors shown in Table 5.1 below. Similar data are available for maquiladora

88

industries (Sector 18) at the state level through the Banco de Informacio’n Econo’mica.
With respect to maquiladora industries, total output/inputs, value added, employment and
wages and benefits will be allocated to each region (urban/rural) based on secondary
information provided by Yucatan's Secretaria de Desarrollo Economico and INEGI.
Based on this information, 74 percent of output, 84 percent of employment, and 100

percent of other value added were concentrated in Mérida in 1993.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Sector Industry
1 Ajri:culture
2 Mining
3 Food products
4 Textile products
5 Wood products
6 Paper products
7 Chemical products
8 Non-metallic products
9 Basic metal products
10 Machinery and equipment
11 Other manufacttflg
12 Construction
13 Public utilities
14 Commerce, hotels and restaurants
15 Transportation and communications
16 Financial services and real estate
17 Personal and professional services
18 Maquiladora industries

 

 

 

Table 5.1 Industrial sectors included in integrated model

Data on imports, exports and other non-household components of final demand
will be derived for all non—maquiladora industries by aggregating data from the 72-sector
regional 10 table compiled by CINVESTAV. Again, in the case of maquiladoras, import
and export data may be obtained from the Banco de Informacion Econémica. For the
purposes of this dissertation, it will be assumed that imports and exports of export-

oriented firms at the regional level are proportional to total output.

89

Household demand for residents of Mérida will be derived primarily from the
regional input-output table. As shown below in Table 5.2, the sectoral distribution of
household expenditures drawn from the small sample of households in Mérida differs
somewhat from the regional IO table. However, since the data from regional table are

based on a significantly larger sample, it was decided that this information is likely more

 

 

 

 

 

 

 

 

 

 

reliable.

SECTOR Survey IO Table
Food 20.5% 1 0.1 %
Housing 10.9% 17.6%
Shgplng/Restaurants 21 .7% 15.5%
Transportation 14.1% 15.1%
Other Services 24.6% 31.1%

 

Table 5.2 Distribution of household expenditures

Household demand for rural areas will be drawn from two sources. On the one
hand, it will be assumed the 50 percent of rural households (particularly residents of more
"urban" municipios in close proximity to Mérida) have the same household consumption
patterns as residents of Mérida. In the case of remaining rural areas (representing the
poorest and most marginalized households), expenditure data from social accounts for the
municipios of Hocaba and Chabihau will be employed. The integration of these two data
sources yields the (distribution of income for rural households displayed in Table 5.3
below. Not surprisingly, the average rural household spends a considerably greater share
of its income (41 percent versus 27.8 percent) on food and shelter than residents of
Mérida.

As mentioned in Chapter Two, economic growth is initially concentrated in urban
locations. Rapid growth of urban areas purportedly induces growth in the surrounding

rural hinterlands. Among other "spillover" effects, providing employment opportunities

90

for rural residents in one way in which the urban core promotes economic growth in the
countryside. As such, it is quite reasonable to expect that residents of rural areas around
Mérida make up a share of urban employment. In addition, in some cases economic

activity in rural areas may provide job opportunities for city residents.

 

 

 

 

 

 

 

 

 

SECTOR Expenditure
Food 26.6%
Housing 14.4%
Shopping/Restaurants 1 2.7%
Transportation 12.4%
Other Services 25.4%

 

Table 5.3 Estimated distribution of expenditures for rural households

Employment information from the 1990 Population Census and the 1989 Economic
Census may be utilized to estimate this inter-regional employment. In this study, inter-
regional employment is defined as employment of a resident of a given region (rural
Yucatan, for example) in another region (Mérida). The 1990 Population Census identifies
region of residence and sector of employment of the resident — it does not identify the
location of the workplace. On the other hand, the 1989 Economic Census identifies total
employment in a given sector in a particular region — it does not identify the residence of
worker. Therefore, if employment rates from both censuses are compared, it is possible to
estimate the number of persons who live in one region, but work in another region. An

example is shown in Table 5.4 using actual data for Yucatan.

 

Rgglon 1989 Economic Census 1990 Population Census
Mérida 72.5% 67.3%
Rural 27.5% 33.7%

Table 5.4 Estimation of inter-regional employment in personal and professional services

 

 

 

 

 

 

 

As displayed above, the 1989 Economic Census indicates that 72.5 percent of all

employment in personal and professional services is concentrated in Mérida. However,

91

the 1990 Population Census reveals that only 67.3 percent of all employment in personal
and professional services is comprised of residents of Mérida. The remainder, therefore,
must be comprised of inter—regional employment - persons who reside in rural areas and
work in the state capital. Therefore, of the 21,209 persons employed in personal and
professional services in Mérida in 1989, it is estimated that 1845 workers reside in rural
municipios and work in the state capital. Although somewhat crude, this technique yields
surprisingly acceptable results. For 1993, a total "net surplus" of 14,749 jobs exists in 14
of 17 sectors or Mérida's economy. This total represents slightly more than 12 percent of
total employment in the urban region. A total surplus of 611 jobs (filled by residents of
Mérida) is identified in two rural sectors. For 1998, the results are similar — inter-regional
employment comprises more than 13 percent of total jobs in Mérida and less than one
percent of positions in rural areas.

In the case of maquiladora industries in Mérida, survey data suggest that
approximately 18 percent of employees come from rural areas of the state. With respect
to export-oriented firms in rural municipios, it was assumed that all management and
technical staff (about five percent of total employment) was comprised of Mérida
residents. This assumption coincides with impressions gleaned from fieldwork —
generally speaking, plant managers and other skilled employees reside in the state capital
and travel to work in rural municipios on a daily or weekly basis.

As mentioned above, a survey of 44 domestic firms and representatives of three local
trade organizations was carried out in February and March 2001. Unfortunately, such a
small survey precludes any ability to derive reliable technical coefficients for the 18

industrial sectors that make up Yucatan's economy. However, the survey does permit

92

some general insights into consumption and distribution patterns of firms in urban and
rural areas of the state.

On average, domestic firms in Yucatan purchase almost 60 percent of their inputs
within the state. The remainder is comprised of imports. About 93 percent of inputs
purchased within Yucatan come from Mérida. Slightly less than seven percent of
purchases are made in rural areas. With respect to output, 77 percent of total production is
sold within Yucatan.3 Almost one-half of all sales within the state are made to rural
municipios. Finally, direct sales to consumers (households) make up more than 23 percent
of total sales.

Firms in urban and rural areas display marked differences in their sales and purchases.
The average rural firm acquires 74.5 percent of its inputs in Yucatan. More than 75
percent of these purchases are made in the state capital. The typical business in Mérida is
slightly less likely to import — 65.1 percent of its inputs are sourced within the state. With
respect to sales, rural firms sell a greater share of their output directly to the public (30.8
percent vs. 17 percent). Firms in Mérida report selling about 89 percent of their output in
Yucatan; almost 50 percent of their in-state sales are made to rural municipios. Firms in
rural areas, on the other hand, sell only about 3.5 percent of their total in-state sales to
businesses and households in Mérida.

Due to the relatively small sample size of the survey of domestic firms, a
"mechanical" technique was sought to estimate inter-regional inter-industry

transactions. Consequently, in a fashion analogous to the Kendrick and Jaycox (1965)

 

3 This information provides an indication of Yucatan's position as a net importer — the state imports 40
percent of its inputs but exports only 23 percent of its output.

93

method of deriving gross state product, inter—regional flows were apportioned by region
based on total production. For example, in the case of agriculture, rural areas account for
about 95 percent of total production in Yucatan. Therefore, it was assumed that firms and
residents of Mérida purchase 95 percent of needed agricultural goods from producers in
rural areas. Remaining purchases would be made of firms in Mérida.

This assumption is certainly simplistic. However, it allows regional and inter—regional
purchase coefficients to vary over time based on changes in output and economic
structure. For example, rural municipios accounted for only 12.4 percent of total
production in textile industries in 1988. By 1998, however, rural production made up
about one-third of total output. As a consequence, it seems reasonable to expect rural
areas to be somewhat more self-sufficient in meeting intra-regional demands for clothing
and textiles. The apportionment technique adopted above facilitates analysis of such
changes.4

Technical coefficients for maquila industries will be based on survey data. This
information will be utilized to "augment" the inter-regional 10 table for the state of
Yucatan - in essence, an additional sector corresponding to export—oriented industries
will be added to each region. Since maquiladoras in Yucatan are concentrated in two
industries — clothing and apparel and other manufacturing - and firms purchase very few
commodity inputs locally and sell no production in Yucatan, little is sacrificed by

grouping all export-oriented firms together as an industry.5

 

‘ Although this ad hoc approach is similar in design to allocation based on regional shares of employment
(location quotient approach), it represents an improvement in that it allows for "crosshauling."

5 Survey results indicate that maquiladoras purchase about five cents of commodity inputs locally for every
dollar of output. Since these goods are not produced in Yucatan, however, only the trade margin - in this
instance assumed to be 25 percent of the total spent ( 1.25 cents) — accrues to commercial firms in Yucatén.

94

As mentioned above, previous studies contend that maquiladoras purchase less than
two percent of their inputs within Mexico. However, these studies account for commodity
inputs exclusively and fail to consider demand for producer services. In the case of
Yucatan, none of the commodity inputs consumed by these firms are produced within the
state. However, as shown in Table 5.5 below, the survey of maquiladoras reveals that
these firms spend more than eight cents for every dollar of production on producer
services within the state of Yucatan. With respect to the inter-regional distribution of
maquiladora purchases, surveys indicate that essentially all of these purchases are made

in Mérida (for export-oriented firms in both rural and urban areas).

 

 

 

 

 

 

SECTOR Expenditure
Public utilities 0.0094
Commerce, hotels and restaurants 0.0125
Transportation and communications 0.0239
Financial services and real estate 0.0114
Personal and professional services 0.0316

 

 

 

 

Table 5.5 Inter-industry transactions of maquiladora industries

In this IRIO model, these inter-industry transactions actually correspond to regional
and inter-regional purchase coefficients. Regional purchase coefficients reflect the
proportion of a given sector's input needs that can be met by production within the region.
Inter-regional purchase coefficients represent the portion of inter-industry demands
provided by firms in the other region. The purchase coefficients quantify a given region's
ability to meet its input demand, as well as dependence on in-state and out-of-state
imports. Since urban and rural areas possess considerably different industrial structures,

their ability to meet their own demands for goods and services will vary. The differences

95

in industrial structure at the regional scale will be reflected in the magnitude and sectoral

break-down of these purchase coefficients.

5.2.2.2 Construction of IRIO tables for 1988, 1993 and 1998

Several mechanical techniques have been developed to facilitate adjustment or
updating of input-output technical coefficients. Among these techniques are the well-
known RAS bi-proportional matrix adjustment technique and a related methodology
proposed by Hinojosa and Pigozzi (1986, 1988).

In general, the RAS method uses more recent information on intermediate inputs,
intermediate output, and total output to adjust the technical coefficients of an existing
input-output table (Miller and Blair, 1985; Isard et al., 1998). Typically, the technique has
been employed to update 10 tables on the basis of new data or to develop regional input-
output models based on "borrowed" technical coefficients. The Hinojosa and Pigozzi
(1986, 1988) RDS methodology uses a similar adjustment process, but is based on
partitioning employment data in a fashion analogous to the traditional economic base
model (Hewings, 1985).

Although these methods are useful in adjusting input-output tables, they require
information on intermediate outputs (RAS) or make assumptions (RDS) that cannot be
justified in the case of Yucatan. Several authors, including Conway (1979, 1990) and
Israilevich et al. (1997) have proposed econometric methods to facilitate updating of
technical coefficients. Basically, a two-step procedure employing time-series and cross
sectional (industrial sectors) data is utilized. The first step uses existing technical

coefficients to generate an estimate of output for each sector. Except for the base year,

96

 

projected output by sector will differ to varying degrees from actual output. The second
step employs generalized least squares and regresses actual output for each sector on
expected output to capture changes in technical coefficients over time (Conway, 1990).
This technique has been widely adopted during the past decade as a means of constructing
integrated input-output/economic models (Rey, 2000).

Unfortunately, in this instance the necessary time-series data are lacking. As a
consequence, an alternative solution must be sought in order to "backcast" (1988) and
forecast (1998) technical coefficients. The framework introduced below is similar in
design and spirit to the econometric adjustment process discussed above, however
coefficients are updated using a non-linear optimization technique since no time-series
data are available.

In order to adjust the technical coefficients of the 1993 regional 10 table for Yucatan,
data on total output by sector were obtained from the 1989 Economic Census.6 The inter-
industry coefficients of the 1993 regional table were employed to obtain an initial
estimate of inter-industry transactions for each sector.7 It was assumed that total final
demand as a percentage of total output remained constant over time. Estimates were next
summed across the row (for each "selling" sector) to obtain an overall estimate of total
output (sales) by sector. Needless to say, projected output by sector will differ to varying

degrees from actual output.

 

6 This discussion makes reference to the procedures used to estimate the regional IO coefficients for 1988.
Coefficients for 1998 were derived in identical fashion.

7 Estimated inter-industry transactions were derived by multiplying each of the technical coefficients for a
given purchasing (column) industry by its corresponding total output.

97

Table 5.6 below displays actual output, projected output, and absolute percentage
error (APE) by sector for 1988. In general, errors are relatively small, with a weighted
average (based on total output by sector) of about 6.7 percent. In general, the largest
errors are found in relatively small sectors of Yucatan's economy (mining, chemical
products, wood products, etc.). In the case of financial services, output is severely
overestimated, indicating fairly prominent technical change (much greater reliance on

financial services as an intermediate input) between 1988 and 1993.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Sector Actual Output Projected Output APE
Agriculture 375784 420897 12.0%
Mining 43002 32747 23.8%
Food products 357283 355725 0.4%
Textile products 300887 299262 0.5%
Wood products 41057 35465 13.6%
Paper products 39906 39787 0.3%
Chemical products 66704 53661 1 9.5%
Non-metallic products 129428 111930 13.5%
Basic metal products 16405 15820 3.6%
Machinery and equipment 31576 31526 0.2%
Other manufacturing 13270 13204 0.5%
Construction 254440 254440 0.0%
Public utilities 49026 52430 6.9%
Commerce, hotels and restaurants 1390462 1355344 2.5%
Transportation and communications 363193 384578 5.9%
Financial services and real estate 308303 453182 47.0%
Personal and professional services 807399 815459 0.9%

 

Table 5.6 Actual and projected output by sector, 1988

Next, mean absolute deviation (MAD) was calculated for the vectors of actual and
projected output. Subsequently, a non-linear optimization (generalized reduced gradient)
procedure was employed to adjust technical coefficients iteratively so that the MAD
between the two vectors was minimized. In basic terms, the generalized reduced gradient
(GRG2) technique is designed to solve continuous functions when the objective function
and/or problem constraints are non-linear. In this instance, the objective is a linear

function; the constraints, however, are not.

98

This optimization procedure offers a distinct advantage over adjustment techniques
such as RAS and RDS. In general, mechanical techniques adjust rows and columns of
technical coefficients uniformly. Each iteration of the RAS method, for example, reduces
or increases technical coefficients of each sector by a scaling factor. In addition, any
technical coefficient that had an original value of zero will be assigned the same value in
subsequent matrix adjustments (Miller and Blair, 1985).8 The alternative method
proposed above does not suffer from these limitations.

The solution to the non-linear optimization problem is subject to a variety of
constraints common to all input-output models. In addition, several other constraints may
be imposed based on available secondary data. In updating the technical coefficients for
all 17 non-maquiladora sectors, the non-linear optimization problem consists of 289
variables (technical coefficients from the 17x17 matrix) and 579 constraints. The

objective function and basic model constraints are listed below.

Objective Function: MIN 2 IX. - X,*|
n
Constraints: Z,X.* = XIX. for each sector (i)
a" Z 0
a" S 1
2| 8" S 1
Where: Xi refers to actual output for a given sector (i)

X,-* represents predicted output for a given sector (i)
a,,- indicates individual technical coefficients
n corresponds to the (289) elements of a 17x17 matrix

The objective function states that the goal of the optimization process is to minimize
the mean absolute deviation (MAD) between actual and predicted output for all 17

sectors. The first set of constraints, however, requires that the predicted output equal the

 

8 By the same token, any aij with an original non-zero value will necessarily be assigned a non-zero value.

99

actual output for each sector. This requirement means that the MAD will be minimized at
a value of zero. The second and third constraints set bounds on possible values of the
technical coefficients. The final constraint assures that the sum of the technical
coefficients for any given purchasing sector (column) does not exceed feasible values.
These final constraints are commonly applied in input-output analysis.

The non-linear optimization procedure converges after approximately 40 iterations. In
the vast majority of instances, adjustments to technical coefficients are relatively minor.
The most prominent changes are found in agriculture. For example, the intra-industry
purchase coefficient for agriculture shifts from 0.148 in 1993 to 0.099 in 1988. Once
estimates of regional purchase coefficients have been obtained, the techniques presented
in section 5.2.1 above may be employed to apportion economic activity between urban

and rural regions and estimate updated inter-regional inter-industry purchase coefficients.

5.2.2.3 Objectives of IRIO analysis

Once complete, the IRIO tables for 1988, 1993 and 1998 will be used to calculate
output, income and employment multipliers for both urban and rural areas. In the context
of this study, only Type H multipliers, which incorporate the induced effects of wages and
household consumption, will be estimated (Isard et al., 1998; Hewings, 1985). The output
multiplier will serve to quantify the direct, indirect and induced impacts of maquiladoras
on production of goods and services within each region of the state. The income
multiplier will facilitate assessment of the effects of the E01 strategy in terms of salaries
and benefits. The employment multiplier will quantify the job creation effects of export-

oriented production.

100

The IRIO model also facilitates analysis of the distributional consequences of export-
oriented industrialization. In addition to the general multipliers mentioned above, intra-
regional and inter-regional multipliers may be calculated to disaggregate impacts
according to region. The intra-regional multipliers will serve to quantify impacts that
remain within a particular region; inter-regional multipliers identify leakages or spillovers
between regions. For instance, IRIO employment multipliers will reveal the impact of a
change in maquiladora production on job creation in both rural and urban areas of the
state - in other words, the total employment multiplier may be decomposed into an
impact on rural employment and an impact on urban employment.

In addition, the IRIO model will be employed to assess the dynamic effects of the E01
strategy on Yucatan's economy. The output multipliers derived from the IRIO models will
be used to estimate the overall contribution of maquiladora industries to Yucatan's gross
state product (GSP) on an annual basis between 1990 and 2000.9 In addition, the impact
of annual changes in maquiladora production between 1991 and 2000 on changes in GSP
will be identified. Finally, similar changes in gross regional product (GRP) will be

quantified for both Mérida and rural municipios.

5.2.3 Development of spatial multipliers
According to Anselin and Bera (1998), spatial econometrics is comprised of a variety
of techniques that deal with the peculiarities caused by spatial effects — distance, spatial

interaction, and location, for example — in statistical models. Specifically, these

 

9 The 1988 multipliers will be used to estimate impacts on GSP for 1990; 1993 multipliers will be used for
1991, 1992, 1993, 1994 and 1995; 1998 multipliers will be used for 1996, 1997, 1998, 1999 and 2000.

101

techniques focus on two particular concerns — spatial dependence (spatial autocorrelation)
and spatial structure (spatial heterogeneity).

Following calculation of IRIO models, spatial econometric techniques will be

employed to calibrate a spatially disaggregate economic base model. Hinojosa and
Pigozzi (1988), among others, have demonstrated the fundamental equivalence of
economic base and input-output multipliers. In a sense, the spatial economic base model
is the geographic analogue of the traditional input-output model. Whereas the input-
output mode] quantifies linkages between sectors of the regional economy, the spatial
economic base model quantifies basic/non-basic sector linkages between regions.

The spatial economic base model will serve to estimate spatial economic base
multipliers at the municipio level in Yucatan. Given a change in maquiladora
employment or output for a given location, the spatial multiplier quantifies the
(geographic) extent of economic impacts (Sonis et al., 1994). In other words, if
employment in export-oriented firms increases by 1000 jobs in a particular municipio, the
spatial multiplier reveals the corresponding impacts on employment and income in
surrounding locations (as well as locally). Since it would be extremely difficult

(impossible given availability of data) to develop a 106-region IRIO model at the local
scale in Yucatan, the method proposed below offers a relatively parsimonious framework
for estimating basic employment and economic outcomes at the local scale. As in the case

of the RIO model discussed above, the ultimate objective of spatial multiplier analysis is

holistic accuracy.

102

 

 

5.2.3.1 Traditional economic base model

Spatial multipliers may be developed by using basic spatial econometric techniques to
model the economic base relationship stochastically for a set of sub-regions (i)
comprising a larger region (A). A non-spatial econometric approach to the economic base
model was initially proposed by Mathur and Rosen (1972). The traditional economic base
model distinguishes between two kinds of economic activities — basic and non-basic. As

indicated in the identity below, total regional economic activity is merely the sum of basic

and non-basic components.

ET = E3 + ENB (5.1)

where: ET refers to total regional economic activity
E3 indicates basic activity
ENB represents non-basic activity
Basic (or export) activities serve demands beyond the boundaries of the region. As
Hewings (1985) states, these activities are derived from a combination of locational
factors, comparative advantage, and historical accident. The second type of economic
activity is termed non-basic or local. These activities serve demands within regional
boundaries. As Equation 5.2 below shows, the economic base model is premised on the
fundamental assumption that non-basic economic activity depends on basic activities.

Consequently, total regional economic activity may be modeled as a function of basic

activity and any change in the export sector would be expected to bring about a change in

total regional economic activity.

if: ENB = I(E3), then
ET = E3 + I(EB), and
AENB = I(AEB), SO
AET = AEB + f(AE3)

(5.2)

103

 

 

 

The relationship between basic activity and total activity is specified by the economic
base multiplier. In essence, the multiplier reveals the overall impacts within the regional
economy of a change in the basic sector. Assuming that non-basic activity is a constant
proportion of total activity, the economic base multiplier may be derived in the following

fashion.

r = ENE/ET, so 0 < r < 1 (5.3)
ENB = r(ET). and
E7 = I'(E'r) + E3

E7 - [(ET) = E3
E7“ - f) = E3

E = E; / (1 — r)
E7 = (1 — r)"EB

5.2.3.2 Calculating basic and non-basic employment

In this study, sub-regions are defined as the 106 municipios making up the state of
Yucatan, Mexico. Sectorally disaggregate employment data from the 1999 Economic
Census will be utilized to estimate basic, non-basic and total economic activity at the
local level.10 In general, the location quotient (LQ) approach will be employed to

calculate these values. The basic formula for the LQ method is shown below.

El
l g] (5.4)

LQir= .
Es

where: E refers to employment
subscript r refers to a given municipio
superscript i refers to a particular sector or industry
subscript S refers to the state of Yucatan

 

'0 Employment data at the municipio scale are available for 18 sectors (including maquiladora data from
Yucatan's Secretan’a de Desarrollo Econémico).

104

 

 

 

ll

In essence, the location quotient is simply the ratio of two ratios. The numerator
expresses the percentage of the workforce employed in a given sector for a particular
municipio. The denominator displays the same relationship at the state level. If the
percentage of the workforce employed in a given sector at the municipio level exceeds the
state average, the location quotient will be greater than 1. If the LQ is greater than 1, it is
assumed that the municipio is self-sufficient and that "excess" employment serves
demand outside the region. If the LO is less than 1, it is assumed that the municipio is not
self-sufficient and that no basic employment exists. Though this approach has its
limitations, its use remains common in the literature (Isard et al., 1998). The LQ must be
carried out and summed over all sectors for each of Yucatan's municipios in order to

derive estimates of basic and non-basic employment for each location.

5.2.3.3 Criticisms of the traditional economic base model

Although the traditional economic base model remains a useful tool for regional
economic impact assessment, it has been criticized on several grounds. Among the most
notable criticisms are the model's failure to account for spillovers and feedback effects
between regions and its inability to explain why the size of economic base multipliers
varies from region to region.

As shown in Equations 5.1, 5.2 and 5.3 above, economic base multipliers are derived
from a simple identity in which total regional activity is a function of basic activity within
the region. By default, then, the model assumes that all impacts of basic activity remain
within the region. From another perspective, the model assumes that basic economic

activity in surrounding regions has no impact upon economic activity in a given location.

105

 

As Haining (1987) notes, however, in the real world income generated in a particular
sector and place will be spent not only locally, but in neighboring areas. Consequently,
output of the local (non-basic) sector in any location will depend not only on income and
consumption levels in that area, but in neighboring areas.

Furthermore, since economic base models are typically applied to individual regions,
no consideration is given to explanation of the variation in the size of multipliers. As
Hewings (1985) points out, non-basic activities are comparable with central place
functions. Since the number of central place activities is a function of a region’s size, the
economic base multiplier also can be expected to increase with the region’s size (as a

proxy for economic importance). These criticisms may be addressed, however, by using

the basic spatial econometrics techniques proposed below.

 

rest of the world

 

 

EXPORT SECTOR

 

 

 

EXPORT SECTOR

 

 

 

i

LOCAL SECTOR

i

LOCAL SECTOR

 

 

 

 

 

 

 

 

 

realm ‘ feglon b

 

 

 

Figure 5.3 Traditional economic base model (two regions)

5.2.3.4 Introducing space into the economic base model

As mentioned in the previous paragraphs, the simple economic base model does not

account for the impact of economic activity in one region on neighboring locations. In

106

 

 

 

 

 

 

 

 

essence, it is a two-region/two—sector (basic/non-basic) model that treats the local sectors
of each regions as autonomous entities (Figure 5.3 above). Consequently, economic
activity in one region has no impact on economic activity in other locations. Such an
assumption seems untenable in the case of Yucatan and the context of this dissertation.
As discussed at some length in Section 5.2.2.1 above, economic activity in Mérida
provides employment for a substantial segment of the rural population. In addition, a
significant share of maquiladora workers in Mérida resides in rural areas. Therefore, it is
quite reasonable to conclude that basic economic activity in Mérida (and other
municipios) will generate employment for residents of neighboring locations. A portion
of the income earned by these "commuters" will be spent in the home community;
however, some income likely will be spent in Mérida and the place of employment, as
well as other locations. As a result, basic employment in one municipio may be expected

to generate non-basic employment not only locally, but also in other locations (Figure 5.4

below).

 

rest of the worid

 

 

EXPORT SECTOR

 

 

EXPORT SECTOR

 

 

 

 

1

LOCAL SECTOR

1

LOCAL SECTOR

 

 

 

 

 

 

 

 

 

"9'0“ 9 region b

 

1

Figure 5.4 Incorporating space into the economic base relationship

 

107

 

 

 

 

Given the openness of local economics, the economic base multiplier may be recast
by incorporating interaction among locations that comprise the space-economy. In

essence, the traditional economic base multiplier may be "expanded" as shown in

Equation 5.5 below.

ETl = ENBi + EBl (5-5)

ENBi = (rETi + Wi/EBI)

E7; = I'ETi -i- E31 + Wi/Eaj
Err - 1‘51"“: EBi + WaEBj
ETlU‘r) = EB; + Wi/‘Eaj
ET; = (1/(1-1f))E3i + Wi/Eej
E7] = (14‘) *[E81 + wijEBj]

In the expanded economic base multiplier proposed above, total economic activity
within a particular region (i) is a function not only of local basic sector activity (E31), but
also basic sector activity in other locations (WijEBj). The term Wij expresses the propensity
for basic economic activity in others locations (1) to create non-basic economic activity in

location i. In the context of spatial econometrics, wij is called a spatial weights matrix.

Derivation of the spatial weights matrix utilized in the integrated model of Yucatan's

space-economy is discussed in the following section.

5.2.3.5 Calculating a weights matrix based on Y ucatcin’s space-economy

Spatial econometric analysis relies on the specification of spatial weights matrices in
order to quantify the impact of spatial structure on geographic (or economic) processes.
Typically, spatial weights matrices are based on relatively simple concepts such as
contiguity, nearest neighbors, or inverse distance. Notwithstanding the type of spatial
weights utilized, the choice of matrix must be appr0priate for the research problem in

question. As some scholars have noted (Anselin and Bera, 1998; LeSage, 1999), model

108

 

 

 

results are frequently as much a function of the spatial weights matrix employed as data

and other parameters. In this study, a unique spatial weights matrix, based on the concept
of economic potential, will be used.

Economic potential is a measure of accessibility and economic influence that
identifies the likelihood for spatial interaction between locations (Taaffe et al., 1996). The
concept was introduced in the geographic literature by Harris (1954) and Warntz (1964).
Several potential measures exist in the geographic literature; in all cases, the models are
based on concepts similar to those found in gravity models. In the context of this study,
economic potential for a given location i is defined as follows:

vi = PHZ P,/d,,2 (5.6)

where: V, refers to total potential
P, is some measure of economic importance at place i

Pj represents economic importance at place j
dijz is friction of distance or spatial separation between pairs of municipios

In general, many different variables may be utilized to calculate economic potential
for a particular location. In this study, population at the municipio level will be used to
operationalize P,- and P'; dij is defined as simple geographic distance between centroids of
municipios. Measures of economic potential for each of the 106 municipios in Yucatan
are listed in Appendix C and mapped in Figure 5.5 below. Not surprisingly, Mérida (with
40 percent of total population) displays the highest level of economic potential by far.ll

Municipios in close proximity to Mérida also exhibit fairly substantial values.

 

” Economic potential for each municipio was "scaled" based on the municipio with the smallest value. As a
result, the smallest possible measure of economic potential is l. The analysis that follows (and map above)
are based on the natural log of economic potential. Therefore, the smallest measure is zero.

109

 

 

 

 

 

 

 

 

     
 

{-3.6} 1.063- 1.902
- 1.902 - 2.767
- 2.767 — 3.926
- 3.926- 7.159

40 0 40 80 Kilometers

Figure 5.5 Economic potential by municipio

As defined above, economic potential is an aggregate measure that serves to quantify
the economic importance of any given location. However, as applied in this study, the
economic potential for any dyad or pair of municipios also may be calculated. In fact,
based on Equation 5.6 above, the row sum of these dyads of economic potential for any
given location i equals that location’s total economic potential. This matrix of economic
potential is easily calculated. If row standardized to equal 1, it may be used as a weights
matrix — a matrix representing the structure of Yucatan's space-economy — in order to
apply spatial econometric techniques to the economic base model. An example is

presented below to demonstrate how this "spatial-economic" weights matrix was

developed.

110

 

 

 

 

 

 

 

 

 

 

 

Figure 5.6 Imaginary study area comprised of five regions

Figure 5.6 above represents a simple, imaginary study area comprised of five regions.
The equally simple and imaginary data necessary to calculate economic potential for each

location — population and distances between region centroids — are shown in Table 5.7

 

 

 

 

 

 

 

 

 

 

 

below.
Re ion Population d" dig (in d” d5 V.
1 12 0 2 3.5 5 4 164.5
2 50 2 0 2 4 3 298.3
3 8 3.5 2 0 2 3 120.1
4 3 5 4 2 0 3.5 18.5
5 7 4 3 3 3.5 0 52.1

 

 

 

 

 

 

 

 

 

 

Table 5.7 Imaginary data for five regions

The table indicates that region 2, by far, is the most populous location in the imaginary
study area. Furthermore, regions 2 and 3 display the smallest aggregate distances to other
locations. Not surprisingly, due to its large population and relatively central location,
region 2 displays the greatest level of economic potential among the five locations.

Region 4, due to its peripheral location and small population, possesses a level of

economic potential more than 15 times smaller than that of region 2.

111

 

 

 

 

 

 

 

 

Pylon 1 2 3 4 5
1 0 150 7.84 1.44 5.25
l 2 150 0 100 9.38 38.89
3 7.84 100 0 6 6.22
4 1.44 9.38 6 0 1.71
5 5.25 38.89 6.22 1.71 0

 

 

 

 

 

 

 

 

Table 5.8 Disaggregation of economic potential

As mentioned above, economic potential (V,) for any given location is an aggregate
measure of economic influence. Disaggregation of total economic potential for a
particular region is a simple task, however, and is displayed in Table 5.8 above. This table
reveals the relative importance of dyads or interaction between pairs of locations in
accounting for a region’s total economic potential. This matrix is symmetrical because
identical data (particularly d,-,-) have been used in estimating economic potential between
places i and j, as well as j and i. If this matrix is row standardized to sum to one, however,

the relative weights assigned to each dyad change markedly (Table 5.9 below). For
example, although the economic potential between regions 1 and 2 (V12 and V2; in the
table above) is 150, the relative weight assigned it is 80 percent greater in region 1 than
region 2. Also of note is the relative importance of region 2 with respect to other
locations in the study area. Due primarily to’its relative economic importance, this region

accounts more than 50 percent of total economic potential in each of the remaining zones.

 

 

 

 

 

 

 

 

 

 

 

 

Region 1 2 3 4 5
1 0 0.91 0.05 0.01 0.03
2 0.50 0 0.34 0.03 0.13
l 3 0.07 0.83 0 0.05 0.05
l 4 0.08 0.51 0.32 0 0.09
l 5 0.10 0.75 0.12 0.03 0

 

 

Table 5.9 Spatial-economic weights matrix

The spatial-economic weights matrix shown above may be employed in analogous

fashion to more traditional spatial weights matrices. Essentially, this matrix includes not

112

 

 

only the impact of proximity and spatial separation (as in customary weights matrices),
but also an indication of relative economic importance. Conceptually, therefore, this
matrix may be thought of as a combination of two kinds of weights — inverse distance and
relative position in a central place hierarchy (as a proxy for economic importance).
Although weights derived from economic relationships have received limited attention in
the literature (for example, Case et al., 1993), the spatial-economic weights employed in

this study represents the first attempt to integrate geographic location and economic

importance as weights for spatial econometric analysis.

 

Reggrn 1 2 3 4 5
1 0 0.50 0.50 0.00 0.00

2 0.33 0 0.33 0.00 0.33
3 0.25 0.25 0 0.25 0.25
4 0.00 0.00 0.00 0 1.00
5 0.00 0.50 0.50 0.00 0
Table 5.10 Traditional spatial weights matrix based on simple contiguity

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Spatial-economic weights may be compared to traditional spatial weights based on
contiguity to highlight differences between the two approaches (Table 5.10). Region 3
provides a good example of relevant differences in these weights matrices. Based on
simple contiguity, each neighbor exerts a similar effect on this location (0.25 each).
However, the spatial-economic weights matrix (Table 5.9) suggests that the impact of
each neighbor will vary greatly and that this influence will depend on each region's
economic importance, as well as distance. Given that the overall economic potential
(importance) of region 2 is more than 15 times greater than that of region 4, it is unlikely
that both locations will exert an equivalent effect on region 3. In summary, then,
development of a truly spatial economic base model requires components of both

proximity and economic importance in order to account for potential spillovers between

113

regions. In essence, the magnitude of feedback effects among municipios in Yucatan

likely depend not only on relative location, but also functional economic relationships.

5.2.3.6 Spatial economic base model

Once estimates of basic and non-basic employment and the spatial-economic weights
matrix have been obtained, the next step in modeling the economic base relationship
econometrically is calibration of a traditional OLS model. Total employment for 1999 at
the municipio level serves as the dependent variable in this model. Basic employment at
the same scale, as calculated using the employment multipliers from the IRIO and the
location quotient technique, serves as the independent variable. Due to extreme non-
normality (and to evoke a more linear relationship), the natural log of each variable will

be employed in this and subsequent models. The traditional OLS model is specified as

follows:

(ln)ET = or + B1(ln)EB (5.7)

where: ET and EB are defined as above

or is a constant or y-intercept term
B] is a regression coefficient representing the economic base relationship

This initial model will be used not only to estimate the relationship between total
economic activity (dependent variable) and basic economic activity (independent
variable), but to carry out diagnostics for spatial effects on variables and residuals. For the
sake of comparison, diagnostic tests will be performed using the spatial-economic
weights matrix specified above, as well as a traditional spatial weights matrix based on
nearest neighbors. Assuming spatial effects are present, spatial regression techniques will

be employed to correct the model and (hopefully) obtain unbiased, consistent estimates of

114

model parameters. In addition, incorporation of spatial effects will facilitate identification
of spillover effects among municipios and account for the varying magnitude of economic
base multipliers.

Results of the diagnostic tests mentioned above will determine what kind of spatial
econometric model best represents the economic base relationship at the municipio level
in Yucatan. In general, four possible model outcomes exist: no spatial effects; spatial lag
effects; spatial error effects; and a combination of spatial lag and spatial error effects.
Each of these potential outcomes is discussed below.

In the event of no spatial effects, the simple OLS model above (Equation 5.7) may be
employed to represent to economic base relationship at the local scale in Yucatan. In this
instance, no spatial autocorrelation will be found among model residuals. As with all
econometric models, however, issues of heteroskedasticity and non-normality of residuals
must be taken into account.

Spatial lag effects are present in OLS models when a substantive process (spatial
interaction, for example) brings about autocorrelation in model residuals (Anselin, 1988).
In this instance, inclusion of a spatially lagged (dependent or independent) variable
eliminates spatial dependence among the error terms. The spatially lagged dependent

variable model may be specified as follows:

(In)ET = a + B1(ln)E3 + [32W(ln)ET (5.8)

where: ET, or, and EB are defined as above
W represents the spatial-economic weights matrix
31 represents the direct impacts of basic activity within the municipio
B; corresponds to indirect impacts of economic activity in other municipios

115

The model with a spatially lagged dependent variable is analogous to a (mixed)
autoregressive model in time-series analysis. This spatial lags model offers a theoretically
sound, intuitive approach for modeling spatial multiplier (feedback) effects among
municipios in Yucatan. The initial independent variable (ln)EB captures the direct impacts
of basic activity within the region on total regional employment. The lagged dependent
variable (W(ln)ET) captures the indirect impacts of employment in other locations on total
employment within a given municipio.

According to Anselin and Bera (1998), spatial error effects are a form of "nuisance
dependence" in the residuals that frequently results in geographic data when
administrative boundaries (used for data collection) do not coincide with the substantive

process being modeled. The spatial errors model takes the following form:

(ln)ET = or + B1(|n)Ea + AWU + 6 (5.9)

where: E7, or, and EB and W are defined as above
11 represents the spatially correlated component of the residuals
A is an autoregressive error parameter
e is a normally distributed, uncorrelated error term
The possibility exists that both spatial lag and spatial error effects will be present in
OLS residuals simultaneously. The resulting model has been termed SARMA (Spatial
AutoRegressive Moving Average model) by some scholars (Cliff and 0rd, 1981) and

may be expressed as follows:

116

(ln)ET = or + B1(|n)E3 + 02W1(ln)ET + )1qu + e (5.10)
where: W1 is the spatial economic weights matrix

W2 is a spatial weights matrix based on simple contiguity

u represents the spatially correlated component of the residuals

A is an autoregressive error parameter

8 is a normally distributed, uncorrelated error term
5.2.3.7 Objectives of spatial econometric modeling

One of the primary objectives of this dissertation is to determine if proliferation of

export-oriented industrialization in a given municipio affects economic activity in
neighboring locations. In the event that spatial multiplier effects are present, this study
also seeks to quantify potential economic impacts. Spatial econometric techniques
facilitate these goals in three ways: identifying the presence of spatial effects; determining
the causal factors of spatial effects; and correcting the economic base model for these
spatial effects. In identifying the presence of spatial effects, spatial econometrics tests the
hypothesis that maquiladora production in one location influences the economies of
neighboring locations. In determining the causal factors of spatial effects, spatial
econometrics reveals whether spillovers are the result of spatial interaction among
municipios or a consequence of spatial mismatch. Finally, in accounting for these

potential spillovers, spatial econometrics quantifies the impact of export-oriented

industrialization in a given location on neighboring communities.

117

Chapter Six

THE IMPACT OF MAQUILADORA PRODUCTION
ON YUCATAN’S ECONOMY

6.1 Introduction

The direct effects of export-oriented firms on Yucatan’s economy were identified in
Chapter Four. A more complete indication of the economic importance of maquiladora
industries can be obtained only if the impact of secondary purchases of goods and
services by firms and households is considered. In addition, the geographic distribution of
these economic impacts must be estimated in order to assess the viability of EOI as a
regional development strategy in the case of Yucatan. As mentioned above, inter-regional
input-output analysis and spatial econometric techniques facilitate assessment of these
impacts. The detailed results of IRIO analysis, followed by the results of spatial
econometric models, are presented immediately below. The formal hypotheses and

research questions presented in Chapter One will be addressed in the following chapter.

6.2 Regional economic impacts of maquiladora production
Regional input-output tables for 1988, 1993 and 1998, as well as resulting output,
income and employment multipliers for all 18 sectors of Yucatan's economy, are found in

Appendix D. Table 6.1 below provides these data for maquiladora industries.

118

 

 

 

 

YEAR Output Income Employment
1988 1.218 1.359 1.151
1993 1.405 1.511 1.102
1998 1.322 1.591 1.113

 

 

 

 

 

 

Table 6.1 Regional multipliers for maquiladora industries - 1988, 1993 and 1998

In general, multipliers reveal a fair degree of stability during the ten-year period. The
impact of export-oriented firms on output and income has increased somewhat since
1988; employment generation effects, however, have declined slightly. As of 1998, for
every dollar of output, maquiladora industries in Yucatan generated about 32 cents of
additional output within the state. In addition, each dollar of salaries and benefits
produced almost 60 cents of additional income. Furthermore, for every 100 jobs in
maquiladora industries, approximately 11 additional jobs were created in Yucatan's
economy. Therefore, based on these regional multipliers and the direct economic impacts
estimated in Chapter Four, export-oriented industries in Yucatan generated almost $2
billion of additional output, more than $51 million of additional income, and about 4300

jobs in the state economy during 2000.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

SECTOR 1988 1993 1998
rggnculture 0.01282 0.019515 0.010112
Mining 6.27E-05 3.161 E-05 4.268E-06
Food products 0.01046 0.014128 0.011191
Textile products 0.00900 0.004050 0.00345
Wood products 0.00194 0.001372 0.000710
Paper products 0.00185 0.001637 0.000794
Chemical products 0.00192 0.000818 0.000453
Non-metallic products 0.00093 0.00037 8.567E-05
Basic metal goducts 0.00036 6.723E-06 0.000172
Machinery and equipment 0.00086 0.001020 0.001307
Other manufacturing 0.00046 0.0007639 0.000562
Construction 0 0 0
Public utilities 0.00880 0.015045 0.015066
Commerce, hotels and restaurants 0.04832 0.060453 0.041160
Transportation and communications 0.03485 0.066800 0.062506
Financial services and real estate 0.02355 0.088301 0.066255
Personal and Eofessional services 0.06218 0.130490 0.108198
Maquiladora industries 1 1 1
TOTAL 1 .218 1 .405 1 .322

 

Table 6.2 Disaggregation of output multipliers for maquila industries

119

 

Table 6.2 above disaggregates the impacts of export-oriented production on output in
Yucatan's economy by sector. As expected, the largest impact of these firms is
concentrated among the producer services mentioned in Chapter Three. In fact, producer
services account for more than 90 percent of direct, indirect and induced effects for 1998
(29.3 cents out of 32.2 cents). Although maquiladoras do not directly consume goods
from primary and secondary industries (sectors 1 to 12) of Yucatan's economy, they
indirectly consume almost three cents of such commodities for every dollar of output.
Much of this demand is "induced" by household purchases of maquiladora employees.

The disaggregate output multipliers displayed above represent backward linkages
between export-oriented firms and the domestic economy. In general, these linkage
relationships remain relatively "stable" between 1988 and 1998. Service industries,
however, exhibit somewhat stronger linkages with maquiladoras over time; purchases

from most primary and secondary sectors of Yucatan's economy declined slightly.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

SECTOR 1988 1993 1998
Agriculture 58.5 30.2 25.1
Mining 0.0 0.0 0.0
Food products 5.6 5.1 4.2
Textile products 3.6 5.3 6.5
Wood products 1.7 0.8 0.8
Papeljroducts 1 .1 1 .0 0.9
Chemical products 1.1 0.4 0.5
Non-metallic products 0.3 0.1 0.0
Basic metal products 0.0 0.0 0.0
Machinery and equipment 1.1 0.7 0.9
Other manufactum 0.1 0.3 0.4
Construction 0.0 0.0 0.0
Public utilities 9.1 2.9 8.6
Commerce, hotels and restaurants 23.2 24.5 24.3
Transportation and communications 9.4 8.2 9.6
Financial services and real estate 1.1 1.0 3.2
Personal and professional services 35.2 21.4 28.2
TOTAL 151 102 113

 

Table 6.3 Employment generation effects for every 1000 maquiladora jobs

120

 

The employment generation effects of export-oriented industrialization are also
concentrated among service industries (T able 6.3). In 1998, almost two-thirds of all jobs
created in Yucatan's economy were concentrated in the tertiary sector. The maquila
sector also generates a significant number of jobs in agriculture, food products, and
textile products. Again, these impacts are largely the result of the demand created by

households employed in maquiladora industries.

 

 

 

 

YEAR Output Income Employment
1988 1 .247 1 .359 1.139
1993 1.406 1.526 1.096
1998 1.324 1.603 1.111

 

 

 

 

 

 

Table 6.4 Inter-regional input-output multipliers for maquiladora industries (Mérida)

6.3 Inter-regional impacts of maquiladora production

Inter-regional 10 tables for 1988, 1993 and 1998, as well as corresponding sectoral
multipliers for both regions of Yucatan, are also included in Appendix E. Tables 6.4 and
6.5 display IRIO multipliers for maquiladora industries in urban and rural regions,

respectively. As discussed below, differences in input-output multipliers are quite small.

 

 

 

 

YEAR Output Income Employment
1988 1.119 1.421 1.270
1993 1.420 1.556 1.131
1998 1.328 1.618 1.134

 

 

 

 

 

 

Table 6.5 Inter-regional input-output multipliers for maquiladora industries (rural areas)

With respect to output and employment generation, inter-regional input-output
analysis indicates that the multiplier effects of export-oriented firms are slightly greater in
rural areas than in Mérida. Maquiladoras in Mérida also generate somewhat less
additional income than similar firms in rural municipios. Based on IRIO multipliers and
the direct economic impacts identified in Chapter Four, export-oriented firms in Mérida

created about $1 billion in additional output, $18 million in income, and more than 1500

121

new jobs in the state’s economy in 2000. Maquilas in rural areas accounted for about $1.5
billion in additional production, $30 million in salaries and benefits, and about 2800

employment opportunities.

 

 

 

 

YEAR Output Income Employment
1988 82.2% 78.3% 49.6%
1993 86.9% 82.6% 57.7%

1 998 89.4% 73.4% 59.3%

 

 

 

 

 

 

Table 6.6 Intra-regional distribution of maquiladora impacts (Mérida)

Although the aggregate inter-regional input-output multipliers are quite similar for
both urban and rural regions, the geographic distribution of these impacts is very
different. As shown in Table 6.6 above, the vast majority of additional output and income
generated by maquiladoras in Mérida remains within the urban core. About one—half of
the jobs created indirectly by export-oriented firms are occupied by residents of the state

capital (Table 6.6).1

 

 

 

 

 

 

 

 

YEAR Output Income Employment
1988 21.9% 73.0% 63.3%
1993 17.4% 66.0% 55.5%
1998 13.4% 65.7% 50.7%

 

 

Table 6.7 Intra-regional distribution of maquiladora impacts (rural areas)

In the case of rural areas, a much smaller share of output generated by maquiladoras
remains within the region (Table 6.7). This result occurs, in part, because the only direct
purchases made by export-oriented firms in Yucatan are producer services. As mentioned
in Chapter Five, these services are purchased exclusively in Mérida. The bulk of
additional income generated by maquila industries remains within rural areas, though less
than in Mérida. In addition, the majority of jobs created by export-oriented firms in rural

municipios are also found outside Yucatan's urban core.

 

' Analysis in this chapter makes reference to the "indirect" impacts of export-oriented firms. Strictly
speaking, impacts are created "indirectly" by maquiladoras and "induced" by household expenditures.

122

Inter-regional input-output multipliers may also be disaggregated according to sector.
As Table 6.8 below indicates, maquiladora firms in Mérida promote additional demand
for a variety of services within the region, as well as agricultural products and some
limited personal services in rural areas. By and large, the majority of intra-regional
impacts are the result of inter-industry transactions; inter-regional effects occur mainly as
a result of the final demand exercised by households. Backward linkages of export-
oriented firms in Mérida demonstrate substantial stability over time. In general, changes
in linkages with sectors of the urban and rural economies confirm insights gleaned from
Table 6.6 — intra-regional linkages, particularly with service industries, strengthened

between 1988 and 1998. Inter-regional linkages, however, tended to weaken.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

REGION SECTOR 1988 1993 1998
_Agrlculture 0.00031 0.00092 0.0005
Mining 1.62E-07 7.202E-06 1.088E-06
Food products 0.0090 0.00975 0.007727
Textile products 0.00909 0.00316 0.001792
Wood products 0.00044 0.00121 0.000696
M Paper products 0.00211 0.00165 0.001034
E Chemical products 0.0011 1 0.00076 0.000403
R Non-metallic products 0.00088 0.00029 6.455E-05
I Basic metal products 0.00041 6.63E-06 0.000174
Machinery and equipment 0.00089 0.00089 0.000623
D Other manufacturipg 0.00030 0.00070 0.000508
A Construction 0 0 0
Public utilities 0.00924 0.01403 0.014136
Commerce, hotels and restaurants 0.04822 0.05300 0.038162
Transportation and communications 0.03527 0.06096 0.056282
Financial services and real estate 0.02528 0.08809 0.067479
Personal and professional services 0.06048 0.11728 0.099798
Agriculture 0.01301 0.01578 0.011282
Minty 7.058E-05 2.184E-05 2.705E-06
Food products 0.00183 0.00229 0.002461
Textile products 0.00129 0.00100 0.000934
Wood products 0.00180 0.00020 0.000149
Paper products 1.444E-05 3.599E-05 4.291 E-05
B Chemical products 0.00109 6.932E-05 43535-05
U Non-metallic products 0.00018 7.998E-05 8.557E-06
R Basic metal products 4.589E-06 0 1.528E-08
A Machinery and equipment 9.425E-05 0.00014 0.000417
L Other manufacturing 0.0002 8.382E-05 1.034E-05
Construction 0 0 0
Public utilities 0.00074 0.00102 0.000708
Commerce, hotels and restaurants 0.00698 0.00817 0.005656
Transportation and communications 0.0044 0.00685 0.002845
Financial services and real estate 0.00162 0.00177 0.00155
Personal and proiesslonal services 0.01053 0.01548 0.008284

TOTAL 1 .247 1.406 1 .324

 

Table 6.8 Inter-regional distribution of maquiladora impacts on output (Mérida)

123

 

The geographic distribution of inter-regional impacts is quite similar for export-
oriented firms located in rural areas (Table 6.9 below). In general, maquiladoras in rural
municipios generate substantial inter-regional demand for producer services and food
products, and intra-regional demand for agricultural commodities. Backward linkages
also display a fair degree of stability between 1988 and 1998. Although inter-regional
linkages strengthened during this time (confirming results of Table 6.7), intra-regional

purchases, especially among service industries, also increased somewhat over the ten-

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

year period.
REGION SECTOR 1988 1993 1998

_Agrlculture 0.00029 0.00207 0.000905
Mining 8.252E-08 1.202E-05 9.977E-07

Food products 0.00878 0.02219 0.013694

Textile products 0.00358 0.00286 0.001644

Wood products 0.00018 0.00110 0.000636

M Paper products 0.00083 0.00149 0.000947
E Chemical products 0.00048 0.00077 0.000372
R Non-metallic products 0.00040 0.00030 5.892E—05
I Basic metal products 0.00019 6.936E-06 0.000171
Machinery and equipment 0.00039 0.00091 0.000606

D Other manufacturlpg 0.00012 0.00061 0.000458
A Construction 0 0 0
Public utilities 0.00431 0.01369 0.013972
Commerce, hotels and restaurants 0.02094 0.05188 0.037262
Transportation and communications 0.01558 0.05769 0.054331
Financial services and real estate 0.01082 0.08158 0.063752
Personal and professional services 0.02617 0.10947 0.095228
Agriculture 0.01250 0.03546 0.0203486
Minlrlg 35955-05 3.647E-05 2.480E-06

Food products 0.00178 0.00521 0.004362

Textile products 0.00051 0.00090 0.000857

Wood products 0.00072 0.00017 0.000136

Paper products 5.69E-06 3.265E-05 3.930E-05

R Chemical products 0.00046 7.026E-05 4.031 E-05
U Non-metallic products 8.498E-05 8.199E-05 7.810E-06
R Basic metal products 2.105E-06 0 1.495E-08
A Machinery and equipment 4.084E-05 0.00014 0.000406
Other manufacturing 9.324E-05 7.564E-05 9.459E-06

L Construction 0 0 0
Public utilities 0.00030 0.00095 0.000663
Commerce, hotels and restaurants 0.00295 0.00794 0.005423
Transportation and communications 0.00178 0.00628 0.002639
Financial services and real estate 0.00065 0.00162 0.001442
Personal and professional services 0.00423 0.01410 0.007663

TOTAL 1.1 19 1.420 1.328

 

 

 

 

 

 

Table 6.9 Inter—regional distribution of maquiladora impacts on output (rural areas)

124

 

The geographic distribution of employment generation by maquiladoras also displays
similar patterns. In the case of export-oriented firms in Mérida, intra-regional job creation
effects are strongest in service industries. However, a significant number of local jobs are
also created in agriculture, food products and textile products. In general, the most
obvious inter-regional job creation impact of maquiladora production in Mérida is
expansion of the agricultural sector in rural areas. The EOI strategy also has a noticeable
effect on inter-regional employment in commercial establishments, food products,

textiles and personal services.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

REGION SECTOR 1988 1993 1998
_Agrlculture 2.4 1.2 1.2
MInlnL 0.0 0.0 0.0
Food products 3.3 2.9 2.1
Textiltmoducts 2.3 3.4 2.2
Wood products 0.9 0.6 0.6
M Paper products 1.0 0.7 1.1
E Chemical products 0.6 0.3 0.5
R Non-metallic products 0.2 0.1 0.0
I Basic metal products 0.0 0.0 0.0
D Machinery and equipment 0.9 0.5 0.6
Other manufacturipg 0.0 0.2 0.3
A Construction 0.0 0.0 0.0
Public utilities 6.9 2.4 5.0
Commerce, hotels and restaurants 16.1 17.4 18.5
Transportation and communlcations 6.8 7.4 7.6
Financial services and real estate 0.8 0.7 2.6
Personal and professional services 26.7 17.6 23.7
_A_grlculture 49.6 24.7 28.0
Mining 0.0 0.0 0.0
Food products 1.7 1.5 1.7
Textile products 1.3 2.0 2.9
Wood products 0.8 0.2 0.3
Paper products 0.0 0.3 0.1
B Chemical products 0.5 0.1 0.1
U Non-metallic products 0.1 0.0 0.0
R Basic metal products 0.0 0.0 0.0
A Machinery and equipment 0.1 0.2 0.1
L Other manufacturipg 0.1 0.1 0.0
Construction 0.0 0.0 0.0
Public utilities 1.1 0.3 1.0
Commerce, hotels and restaurants 6.0 6.4 5.7
Transportation and communications 1.8 0.9 1.2
Financial services and real estate 0.2 0.2 0.6
Personal and professional services 6.9 3.7 3.6
TOTAL 139 96 111

 

 

 

 

 

 

 

Table 6.10 Inter-regional distribution of jobs created (per 1000 maquila jobs in Mérida)

125

Export-oriented production in rural areas has positive effects on a number of sectors
of the urban economy. Impacts are mainly concentrated in two areas — agriculture and
food products and service industries (particularly, commerce and personal and
professional services). Intra-regional job creation effects as are most prominent in four
sectors — agriculture, food products, commerce and personal and professional services.
Almost one-half of the total employment generated by rural maquiladoras (and about 80

percent of intra-regional employment) is found in agriculture.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

REGION SECTOR 1988 1993 1998
__Agrlculture 7.0 2.7 2.2
Mlnlng 0.0 0.0 0.0
Food products 9.5 6.5 3.7
Textile products 2.7 3.1 2.1
Wood products 1.1 0.6 0.6
M Paper products 1.2 0.7 1.0
E Chemical products 0.7 0.3 0.4
R Non-metallic products 0.3 0.1 0.0
I Basic metal products 0.1 0.0 0.0
Machinery and equipment 1.2 0.5 0.5
D Other manufacturing 0.0 0.2 0.3
A Construction 0.0 0.0 0.0
Public utilities 9.6 2.3 4.9
Commerce, hotels and restaurants 21.0 17.1 18.0
Transportation and communications 9.0 7.0 7.3
Financial services and real estate 1.1 0.7 2.5
Personal and professional services 34.6 16.5 22.6
Agriculture 142.9 55.5 50.6
Mining 0.0 0.0 0.0
Food products 4.9 3.3 3.0
Textile products 1.5 1.8 2.7
Wood products 0.9 0.2 0.3
Paper products 0.0 0.3 0.1
B Chemical products 0.6 0.1 0.1
U Non-metallic products 0.1 0.0 0.0
R Basic metal products 0.0 0.0 0.0
A Machinery and equipment 0.2 0.2 0.1
Other manufactuang 0.1 0.1 0.0
L Construction 0.0 0.0 0.0
Public utilities 1.4 0.3 0.9
Commerce, hotels and restaurants 7.5 6.3 5.5
Transportation and communications 2.1 0.8 1.1
Financial services and real estate 0.3 0.2 0.5
Personal and professional services 8.3 3.4 3.3
TOTAL 270 131 134

 

 

 

 

 

 

 

Table 6.11 Inter-regional distribution of jobs created (per 1000 jobs in rural maquilas)

126

6.4 Impacts of maquiladora production on gross state product

The outcomes above suggest two preliminary conclusions. In general, export-oriented
industries have a substantial overall impact on Yucatan's economy. In addition, although
aggregate multiplier effects are similar in both urban and rural regions of the state, the
geographic distribution of these outcomes differs markedly. These initial results do not
provide any real indication of the impact of maquiladora production on economic growth
in Yucatan, however. In this section, the output multipliers discussed above will be
utilized to estimate the contribution of export-oriented industrialization to Yucatan's
gross state product (GSP) between 1990 and 2000.

INEGI provides gross state product data for Yucatan for 1988 and for 1993 through
1999 (INEGI, 2001). In order to carry out the analysis proposed above, then, GSP must
be estimated for 1990, 1991, 1992 and 2000. In order to calculate GSP, a simple trend
model was calibrated regressing the natural log of GSP against the corresponding year.

The results of this model are presented below.

(mesa = 440.905 + 0.2295Year
9.884 0.0050
Ad]. 3’ = 0.997 For) = 2145.721 p = 0.0000

Although only eight observations were available to estimate the model, the simple
trend explains almost all of the variation in annual gross state product. Therefore, it may
be employed to estimate missing values. The second column of Table 6.12 below shows
gross state product for Yucatan (in thousands of pesos) for 1990 to 2000. While growth in
GSP during this period certainly seems impressive, it is overstated due to the fact that the
Mexican peso was devalued several times during the 1990s — from about 2.8 pesos per

dollar in 1990 to about 9.3 pesos per dollar in 2000 (INEGI, 2001).

127

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Year GSP Maqulla GSP Pct. GSP

1990 7,186,421 41 ,093 0.57%
1991 9,040,251 61 ,454 0.68%
1992 11,372,301 81,250 0.71%
1993 15,029,646 90,008 0.60%
1994 17,218,056 1 10,689 0.64%
1995 20,898,510 179,828 0.86%
1996 29,029,1 50 336,583 1 .16%
1997 36,895,171 547,572 1.48%
1998 45,777,958 1 ,005,856 2.20%
1999 56.71 1,465 1,942,166 3.42%
2000 71,316,656 3,106,954 4.36%

 

 

Table 6.12 Direct contribution of maquiladora production to GSP, 1990 to 2000

Table 6.12 also indicates total value of gross state product corresponding to

maquiladora industries in Yucatan between 1990 and 2000 (INEGI, 2001). These figures

have been termed GSP because they correspond only to the value added of export-

oriented production that remains in Yucatan. The final column indicates the contribution

of the maquila sector to the state's economy. Clearly, export-oriented firms have become

an increasingly important segment of Yucatan's economy, now accounting for more than

four percent of total state income.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Year GSP Maqulla Multiplier Total Maqulla Pct GSP Pct Chang
1990 7,186,421 41,093 1.218 50,051 0.70% . ‘
1991 9,040,251 61 ,454 1 .405 86,343 0.96% 1 .96%
1992 1 1,372,301 81,250 1.405 1 14,156 1.00% 1.19%
1993 1 5,029,646 90,008 1 .405 126,461 0.84% 0.34%
1994 17,218,056 1 10.689 1.405 155,518 0.90% 1.33%
1995 20,898,510 179,828 1 .405 252,658 1 .21% 2.64%
1996 29,029,150 336,583 1.322 444,963 1.53% 2.37%
1997 36,895,171 547,572 1 .322 723,890 1 96% 3.55%
1998 45,777,958 1 ,005,856 1 .322 1 ,329,742 2.90% 6.82%
1999 56,71 1 ,465 1,942,166 1 .322 2,567,543 4.53% 1 1 .32%
2000 71 ,316,656 3,106,954 1 .322 4,107,393 5.76% 10.54%

 

Table 6.13 Total contribution of maquiladora production to GSP, 1990 to 2000

The information above represents only the direct impacts of export-oriented

production on the regional economy. If the output multipliers in Table 6.1 are taken into

128

account, it is possible to estimate the total (direct, indirect and induced) effects of
maquiladora production on Yucatan's economy. These data are presented in Table 6.13
above.

When multiplier effects are considered, export-oriented production contributes almost
six percent of Yucatan's gross state product. However, the final column of the above table
provides perhaps the most telling insight into the relative importance of the E01 strategy.
In general, between 1991 and 2000, maquiladoras have accounted for a greater and
greater share of the annual change in Yucatan's GSP. In 1999 and 2000, these firms
accounted for about 11 percent of the change in the total value of goods and services

produced in the state's economy.

 

70,000,000 _
60,000.000
50,000,000 2

3 +ActuelGSP

40,000,000 . ;_ +_ GSP mlnus E014

30,000,000
20,000,000 ;

10,000,000 '

 

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

 

 

 

Figure 6.1 Overall contribution of maquiladoras to Yucatan GSP, 1990-2000

Based on the data in Table 6.13 above, it is possible to estimate the growth of

Yucatan's economy between 1990 and 2000 had the E01 strategy not been implemented.

129

In basic terms, GSP may be adjusted by removing the total (direct, indirect and induced)
effects of maquiladora production during a given year and the annual change in total
maquiladora effects between 1991 and 2000. This information is displayed in Figure 6.1
above. Had the E01 strategy not been pursued, Yucatan's economy would have been
almost five percent smaller by 2000 — in other words, average income at the state level

would have declined by more than $150 (US) per person.

6.5 Impacts of maquiladora production on the inter-regional distribution of GSP
The inter-regional distribution of GSP impacts may also be identified. The Economic
Censuses of 1989, 1994 and 1999 allow GSP to be disaggregated by region. In addition,
the Banco Nacional de Mexico (Division of Economic and Social Research) provides
information on Yucatan's GSP at the state level and for the municipio of Mérida for 2000

(BANAMEX, 2000). This information is shown in Table 6.14 below.

 

 

 

 

 

Year Pct. Mérida Pct. Rural
1988 75.6% 24.4%
1993 80.4% 19.6%
1998 76.0% 24.0%
2000 74.8% 25.2%

 

 

 

 

 

Table 6.14 Inter-regional distribution of gross state product

The period from 1988 to 1993 corresponds to the presidency of Carlos Salinas de
Gortari (1988 to 1994). As discussed elsewhere (Biles and Pigozzi, 2000), this period was
marked by the implementation of a series of dramatic neo-liberal economic reforms
purportedly designed to promote development in rural areas of Mexico. In reality, these
policies induced greater relative growth in urban locations. The increasing concentration
of economic activity in Mérida during this time may provide some empirical evidence of

the impact of these reforms in the case of Yucatan. Although Mérida still accounts for

130

almost three-quarters of all economic activity in the state, during the past eight years rural
areas have represented a greater and greater share of Yucatan's gross state product.

Using the IRIO multipliers discussed above, it is possible to determine the overall
impact of maquiladora production on urban and rural economies. As indicated in Table
6.14, inter-regional distribution of gross state product information is available only for
1988, 1993, 1998 and 2000. Therefore, missing values must be interpolated. These data

are shown in Table 6.15.

 

 

 

 

 

 

 

 

 

 

 

 

 

YEAR Mérida Rural areas
1990 77.5% 22.5%
1991 78.4% 21.6%
1992 79.4% 20.6%
1993 80.4% 19.6%
1994 79.5% 20.5%
1995 78.6% 21.4%
1996 77.7% 22.3%
1997 76.9% 23.1%
1998 76.0% 24.0%
1999 75.4% 24.6%
2000 74.8% 25.2%

 

 

 

 

Table 6.15 Distribution of Yucatan's gross state product by region, 1990-2000

Using the annual gross state product data from Table 6.12 above, it is now possible to
apportion economic activity by region. Tables 6.16 and 6.17 below reveal gross regional

product for both urban and rural areas of Yucatan, respectively.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Year GRP Mérida Maquila Multiplier Total Maquila Pct GRP Pct Change_1
1990 5,569,476 32,210 1 .247 40,165 0.72% . .

1991 7,087,557 48,169 1 .406 67,726 0.96% 1 82%
1992 9,029,607 63§85 1.406 89,542 0.99% 1.12%
1993 12,083,835 66,547 1 .406 93,566 0.77% 0.1 3%
1994 13,688,355 81,838 1 .406 1 15,064 0.84% 1.34%
1 995 16,426,229 99,841 1 .406 140,376 0.85% 0.92%
1996 22,555,650 183,387 1 .324 242,804 1 .08% 1 .67%
1 997 28,372,386 249,195 1 .324 329,934 1 .16% 1 .50%
1998 34,791 ,248 395,814 1.324 524,058 1.51% 3.02%
1999 42,760,445 738,384 1 .324 977,620 2.29% 5.69%
2000 53,344,859 1 ,221,1 16 1 .324 1 ,616,758 3.03% 6.04%

 

Table 6.16 Total contribution of maquiladora production to GRP in Mérida

131

Although the results for Mérida indicate the increasing importance of export-oriented
production in the city's economy, maquiladoras comprise a relatively small, but
increasingly important, segment of the local economy (about three percent). However, the
E01 strategy has had fairly prominent effects on annual changes in gross regional product
between 1990 and 2000.

As displayed in Table 6.17 below, the relative impacts of export-oriented
industrialization are much greater in the case of rural municipios. By 2000, these firms
accounted for almost 14 percent of all goods and services produced in rural areas. In
addition, more than one-fifth of the change in regional income during the past two years

is attributable to the growth of maquiladora industries in the countryside.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Year GRP Rural Maquila Multiplier Total Maquila Pct GRP Pct Changej
1990 1,616,945 8,883 1.119 9,941 0.61%

1991 1,952,694 13,285 1 .420 18,865 0.97% 2.66%
1992 2,342,694 17,565 1 .420 24,942 1 .06% 1 .56%
1993 2,945,811 23,461 1.420 33,314 1.13% 1.39%
1994 3,529,701 28,851 1.420 40,968 1.16% 1.31%
1995 4,472,281 79,987 1 .420 1 13,582 2.54% 7.70%
1996 6,473,500 153,196 1.328 203,444 3.14% 4.49%
1 997 8,522,785 298,377 1.328 39i245 4.65% 9.41%
1998 10,986,710 610,042 1.328 810,136 7.37% 16.80%
1999 13,951,020 1 203,782 1 .328 1,598,622 1 1 .46% 26.60%
2000 17,971 ,797 1 ,885,838 1 .328 2,504 ,393 1 3.94% 22.53%

 

Table 6.17 Total contribution of maquiladora production to rural GRP

The contribution of maquiladora production to the inter-regional distribution of gross
regional product (as shown in Tables 6.16 and 6.17) is somewhat misleading because it
does not account for leakage effects. As discussed in section 6.3 above, the multiplier
effects of export-oriented firms are much more likely to remain in Mérida than in rural
areas. Therefore, to truly assess the inter-regional implications of maquiladora
production on regional income, "leakage" effects must be taken into account. As shown

in Table 6.18 below, due to these leakage effects, the importance of export-oriented

132

production is underestimated in the case of Mérida and overestimated in the case of rural

municipios.

 

 

 

RURAL fl MERIDA fl ADJUSTED IMPACTS |
6|

gmfil-flfifl
mas-filmmm 11071
WWI—m

1mm
1M!“
IW
“El-MEI mars-Ema
1 968 72 WWI Imam-mm“
Table 6.118858 Adjusted impacts of maquila production on GRP due to leakage effects

 

 

 

 

With these revised multiplier effects, it is now possible to estimate the change in
gross regional product for rural and urban areas between 1990 and 2000 had the export—
oriented industrialization strategy not been implemented. These results are displayed

below in Figures 6.2 and 6.3.

 

20,000,000
1 8.000.000
1 6,000,000
1 4,000,000

1 2,000,000

‘ +Actual G RP l

| +GRP minus EOI .

1 0,000,000

8,000,000

6.000.000

4,000,000

2,000,000

 

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

 

 

 

Figure 6.2 Overall contribution of maquiladora production to GSP in rural areas

133

When leakage effects are accounted for, the overall impact of maquiladora
production on rural economies is considerably weaker (for example, about 50 percent
smaller in 2000). However, export-oriented industries still play an extremely important
role in the growth of rural municipios. As shown above in Figure 6.2, average per capita
income would have been more than 11 percent less in 2000 if the E01 strategy had not

been adopted.

 

60,000,000

50,000,000

40,000,000

20,000,000

10,000,000

 

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

 

 

 

Figure 6.3 Overall contribution of maquiladora production to GSP in Mérida

In the case of Mérida, leakage effects increase the impacts of maquiladora production
on the city's economy. However, since Mérida's economy is about three times larger than
the rural economy, impacts are considerably smaller. Had state government not initiated
the E01 regional development policy, the average per capita income in the state capital

would have been about 3.95 percent smaller in 2000.

134

As discussed in Chapter Two, one of the criteria proposed to assess the viability of
the export-oriented industrialization strategy is its impact on the (geographic) distribution
of income in Yucatan. The analysis carried out in this section facilitates assessment of
changes in the inter-regional income distribution between 1990 and 2000. Table 6.19
below shows actual gross regional product for urban and rural regions, as well as
predicted GRP had the maquiladora strategy not been adopted. In addition, regional

shares of GRP are also indicated.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ACTUAL PREDICTED
Year GRP Mérida GRP Rural Pct Rural GRP Mérida GRP Rural Pct Rural
1990 5,569,476 1 ,616,945 22.5% 5,529,900 1,606,415 22.5%
1991 7,087,557 1,952,694 21.6% 7,017,78 1,935,876 21.6%
1992 9,029,607 2,342,694 20.6% 8,937,359 2,320,458 20.6%
1993 12,083,835 2,945,81 1 19.6% 1 1,985,670 2,917,096 19.6%
1 994 13,688,355 3,529,701 20.5% 13,567,634 3,494,389 20.5%
1995 16,426,229 4,472,281 21 .4% 16,263,41 4,381 ,139 21.2%
1996 22,555,650 6,473,500 22.3% 22,275,629 $307,273 22.1%
1997 28,372,386 8,522,785 23.1% 27,966,257 8,202,736 22.7%
1998 34,791,248 10,986,71 24.0% 34,107,503 10,336,261 23.3%
1999 42,760,445 13,951,020 24.6‘7 41,466,252 12,668,970 23.4%
2000 53,344,85 17,971,797 25.2°/ 51,234,370 15,961,13 23.8%

 

Table 6.19 Impact of maquiladora production on regional distribution of income

The data in Table 6.19 suggest that export-oriented industrialization has had a
moderately positive impact on the inter-regional distribution of income. By 1995,
noticeable differences in income distribution begin to appear — prior to this time the
maquiladora sector was too small to affect aggregate income distribution. If Yucatan had
not adopted the E01 strategy, only 23.8 percent of economic activity would have been
found in rural areas of the state by 2000. Since 25.2 percent of gross state product
accrued to rural municipios in 2000, the maquiladora strategy is responsible for a net
shift of approximately 5.6 percent in the inter-regional distribution of income between

1990 and 2000.

135

6.6 Impacts of maquiladora production on regional economic structure

A second criterion proposed in Chapter Two to assess the developmental implications
of the E01 strategy is impact on regional economic structure. Several variables could be
utilized to assess structural change — total output or value added, for example.
Employment by sector, however, will be employed in combination with the inter-regional
employment multipliers discussed in section 6.3 to estimate the impacts of maquiladora
production on structural change at the regional level.

Employment data by region are available for 1988, 1993 and 1998 (INEGI, 2001).
Table 6.20 below displays the sectoral distribution of employment by region for each of
these years. Some of the basic changes in economic structure were discussed in Chapter
Four. However, with respect to rural areas, a substantial degree of structural change
appears to have taken place in the regional economy since 1988. Although agriculture
still employs almost 50 percent of rural workers, this sector now represents a significantly
smaller share of total employment. Textile products, commerce, hotels and restaurants,
and personal and professional services now comprise a much greater portion of

employment among rural economies (more than 40 percent).

1988 1993

SECTOR Rural Rural

3.61 64
0.91 0 0.64

9.1 4.11

8. 4.44

1.21 0.

1 0.

1.01 0

2.01

0 0

0.
8.
5
1
1
1.
1
0.

A

metal
and 0.24
0.11 0. 0.1
. 2. 0.
utilities . 0. 1 .1 0
hotels and restaurants 9 39.1 1
and communications 1 6.1 1.01
services and real estate . 0.1 1.01 0
and services 19 4.71

Table 6.20 Sectoral distribution of employment by region

 

136

Some structural change has also occurred in Mérida, though it is much less
pronounced than in rural areas. Although commerce, hotels and restaurants and personal
and professional services remain the two most important sectors in the state capital, the
later industry now comprises almost 38 percent of total employment. Food products and
textiles industries continue to generate the most employment among manufacturing
sectors, though neither industry displayed much change between 1988 and 1998. As in
rural areas, relative employment in agriculture declined sharply during the ten-year
period.

In general, then, structural change has taken place in both regions between 1988 and
1998. The objective of this section is to identify the portion of structural change that is
attributable to the growth of maquiladora industries in urban and rural regions of
Yucatan. Some of the techniques applied in section 6.5 above will prove useful in
achieving this objective.

The inter-regional employment multipliers displayed in Tables 6.10 and 6.11
disaggregate job creation effects by sector and region. These multipliers may be used in
conjunction with Table 6.21 below, which displays annual maquiladora employment by

region, in order to estimate changes in regional employment between 1990 and 2000.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Year Rural Mérida

1990 89 2546
1991 135 3966
1992 790 3870
1993 834 4513
1994 1804 6669
1995 6414 8005
1996 7914 9474
1997 12941 10808
1998 17988 11671
1999 20052 12299
2000 22066 1 5261

 

 

Table 6.21 Maquiladora employment by region, 1990-2000

137

  

5535393: Eh: I use» can :2qu .588 3 couneoeuw EoEzoEEm

138

  

nutmeg E 3.533353: I so» can comm»: .588 ‘3 cosmeoeow “conic—afim

sis

139

Table 6.22 above reveals the inter—regional distribution of employment impacts in the
case of rural municipios. Between 1990 and 2000, these firms generated more than 2900
jobs in Yucatan's economy. Almost 1600 of the positions were created in rural locations.
In addition, nearly 40 percent of all jobs generated are found in rural agriculture. Export-
oriented firms in rural areas also promoted notable employment generation effects in
personal and professional services in Mérida, as well as commerce, hotels and restaurants
in both urban and rural locations.

Table 6.23 above displays the employment generation effects of maquiladoras in
Mérida by sector and region. Although the aggregate multiplier effects are somewhat
smaller, the sectoral and geographic distribution of impacts is quite similar. Overall,
export—oriented firms in Mérida generated more than 1600 jobs between 1990 and 2000.
More than 25 percent of these jobs are concentrated in rural agriculture. Other prominent
effects are found among service industries in the state capital, particularly personal and
professional services and commercial establishments. Maquiladoras in Mérida also
induce job creation in food and textiles industries in both regions and, to a somewhat
lesser degree, in rural service establishments.

Total employment generated by export-oriented firms between 1990 and 1998 may be
summed by region.2 Subsequently, it may be "subtracted" from the employment figures
for each region (for 1998) to provide an estimate of the impact of maquiladora
production on structural change. Table 6.23 below shows actual employment for 1998 by
region, as well as projected employment had the E01 strategy not been implemented. In

both urban and rural areas, the impact of export-oriented production on economic

 

2 Employment generation for 1990 includes the effects of all maquiladora employment that existed prior to
1990.

140

structure is very limited. In each case, the jobs created by maquiladoras represent a small
portion (less than one percent) of total employment. As a consequence, the impact of the

E01 strategy on overall economic structure is minimal.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

RURAL AREAS MERIDA
Employment Structure Employment Structure
SECTOR Actual Predicted Actual Predicted Actual Predicted Actual Predicted
Agriculture 101569 99949 49.3% 49.0% 4521 4444 2.1% 2.1%
Mining 763 763 0.4% 0.4% 1040 1040 0.5% 0.5%
Food products 9029 8939 4.4% 4.4% 10782 10642 5.1 % 5.1 %
Textile products 17626 17538 8.6% 8.6% 13499 13408 6.4% 6.4%
Wood products 1 154 1 142 0.6% 0.6% 1910 1887 0.9% 0.9%
Paper products 268 264 0.1% 0.1 % 2377 2343 1 .1 % 1 .1 %
Chemical products 309 307 0.2% 0.2% 2526 2513 1.2% 1 .2%
Non-metallic products 1 134 1 134 0.6% 0.6% 1762 1761 0.8% 0.8%
Basic metal products 0 0 0.0% 0.0% 361 361 0.2% 0.2%
Machinery and equipment 804 801 0.4% 0.4% 3714 3693 1.8% 1.8%
Other manufacturing 82 82 0.0% 0.0% 131 6 1310 0.6% 0.6%
Construction 4370 4370 2.1% 2.1% 15261 15261 7.2% 7.3%
Public utilities 1196 1169 0.6% 0.6% 2016 1856 1.0% 0.9%
Commerce, hotels and restaurants 30015 29797 14.6% 14.6% 55271 54607 26.0% 26.0%
Transportation and communications 1051 1010 0.5% 0.5% 9997 9724 4.7% 4.6%
Financial services and real estate 742 727 0.4% 0.4% 5349 5279 2.5% 2.5%
Personal and professional services 3601 4 35876 17.5% 17.6% 80508 7971 1 37.9% 38.0%

 

Table 6.24 Impact of maquiladora production on economic structure (1998)

 

It is also possible to assess the impact of maquiladora production on employment
change between 1988 and 1998. Table 6.24 below displays total employment change by
region and sector between 1988 and 1998, as well as job creation effects of export-
oriented industries during this same time period. The contribution of EOI to employment
change by sector is also quantified for each region.

Although the jobs generated by export-oriented firms do not represent a significant
portion of Yucatan's total employment, they do account for a relatively important share of
employment change between 1988 and 1998 in both urban and rural regions. In the case
of rural municipios, significant gains are found in service industries, including public

utilities, commerce, hotels and restaurants, and transportation and communications.

141

Though small in absolute terms, substantial employment gains also occur in several
manufacturing industries (food products, textiles, wood products and paper products).
Finally, the most important impact of maquiladora production on employment
generation in rural areas between 1988 and 1998 is found in agriculture. Had the E01
strategy not be adopted, employment in this sector would have declined to fewer than
100,000 persons. However, employment in Yucatan's export-oriented firms generates
income for more than 37,300 persons. Employees spend a portion of this income on
foodstuffs and some of these items are produced in rural areas of Yucatan. By generating
increased demand for agricultural products, maquiladora production indirectly created
almost 1700 employment opportunities for farmers between 1988 and 1998. As such, the
E01 strategy may have reduced rural-to-urban migration during this period by allowing

farmers and their families (and other rural residents, as well) to remain in their local

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

communities.
RURAL AREAS MERIDA
Sector 1988 1998 quulle % Mequllall 1988 1998 Maqulle % Maqulla
Agriculture 104900 101569 1620 48.63% 5157 4521 77 12.11%
Mining 1310 763 0 0.00% 442 1040 0 0.00%
Food products 4147 9029 90 1.84% 8064 10782 140 5.15%
Textile products 2824 17626 88 0.59% 4910 13499 91 1.06%
Wood products 1055 1154 12 12.12% 1264 1910 23 3.56%
Paper products 34 268 4 1 .71 % 1431 2377 34 3.59%
Chemical products 1043 309 2 27.00% 1318 2526 13 1.08%
Non-metallic products 748 1134 0 0.00% 1838 1762 1 1.32%
Basic metal products 5 0 0 0.00% 114 361 0 0.00%
Machinery and equipment 346 804 3 0.66% 2189 3714 21 1.38%
Other manufacturing 166 82 0 0.00% 61 1316 6 0.48%
Construction 3564 4370 0 0.00% 7888 15261 0 0.00%
Public utilities 1016 1196 27 15.00% 2250 2016 160 68.38%
merce, hotels and restaurants 13093 30015 218 1.29% 29722 55271 664 2.60%

Transportation and communications 1952 1051 41 4.55% 4319 9997 273 4.81%
Financial services and real estate 244 742 15 3.01% 712 5349 70 1.51%
Personal and professional services 8086 36014 138 0.49% 21209 80508 797 1.34%

 

 

 

 

 

 

 

 

 

 

Table 6.25 Contribution of maquiladora industries to employment change, 1988 - 1998

142

 

With respect to Mérida, export-oriented production also had some impact on changes
in employment structure between 1988 and 1998. In general, the largest absolute impacts
are concentrated among tertiary industries such as personal and professional services,
commerce, hotels and restaurants, public utilities, and transportation and
communications. The EOI strategy also had a relatively important impact on employment
change in several manufacturing sectors, including food products, textiles industries,
wood products and paper products.

Overall, the employment generated by maquiladora production does not have a
significant impact on the structure of employment in urban and rural regions of Yucatan's
economy. In general, the number of jobs created by such firms (about 4500) is too small
to affect the sectoral distribution of employment at the regional level. However,
substantial structural change has taken place in Mérida and rural municipios since 1988.
When employment created by maquilas between 1988 and 1998 is evaluated as a share of
total employment change by sector and region during the ten-year period, the E01
strategy does exhibit a moderate impact on the structure of employment in both urban and

rural areas of Yucatan.

6.7 Impacts of maquiladora production on local economies

As discussed above, maquiladora production in Yucatan generates substantial inter-
regional effects. In addition, export-oriented firms display relatively strong linkages with
several sectors (especially producer services) of the regional economy. The preceding
analysis, however, has been canied out at a relatively "coarse" geographic scale. In

essence, the input-output models implemented above emphasize the sectoral distribution

143

of economic impacts at the expense of geographic detail. In this section, employment
data at the municipio scale for 1998 will be utilized to estimate spillover effects produced
by maquiladoras at the local level. Consequently, the spatial econometric models
calibrated below focus explicitly on the geographic distribution of economic impacts (at
the expense of sectoral detail).

As discussed in Chapter Five, the employment multipliers for Mérida and rural areas
may be utilized to partition local employment into two shares — the portion serving local
demand (non—basic) and the portion serving demands in other locations (basic). In the
case of rural municipios, the location quotient approach may be employed to apportion
basic and non-basic employment among 105 municipios. Once employment has been
partitioned, traditional economic base multipliers may be estimated for each of the 106
municipios in Yucatan. Economic base multipliers are included in Appendix F and

mapped below in Figure 6.4.

     

:1 1.067 -1.266
1.266 . 1.472
.1472 - 1.782
-1.782 - 2.273
.2273 - 3.447

40 0 40 80 Kilometers

Figure 6.4 Traditional economic base multipliers (1998)

Not surprisingly, the largest multipliers are concentrated in and around Yucatan's
urban core. A handful of geographically isolated municipios in the southern part of the
state, as well as the eastern cities of Valladolid and Tizimr’n, also display relatively large
multipliers. A large area comprised of more than 30 small municipios with very small
multipliers exists in central and southeast Yucatan. The Moran's I statistic (0.128)
indicates weak, though statistically significant (p=0.014), spatial autocorrelation in
economic base multipliers at the local scale. The presence of this spatial dependence
suggests that some degree of spatial interaction or spillover effects may be taking place

with respect to employment generation among neighboring municipios.

6. 7.1 Calibration of OLS model

Following estimation of basic and non-basic employment at the local scale, a
traditional OLS model may be calibrated. The natural log of total employment serves as
the dependent variable; the natural log of basic employment is used as the independent

variable. The results of the model are shown below.

(IrmaT = 0.9753 + 0.9051(In)E3
0.1268 0.0186
0.0000 0.0000

Ad]. a” = 0.9577 Fm“) = 2375.44 p = 0.0000

Not surprisingly, basic employment accounts for more than 95 percent of the
variation in total employment at the municipio level. However, the initial OLS model has
been carried out primarily for diagnostic purposes. As shown in Figure 6.5 below, model
residuals are not normally distributed. This result is confirmed by the Jarque-Bera test

(p=0.000). In addition, the Breusch-Pagan test (p=0.0006) indicates the presence of

145

heteroskedasticity among the error terms. The result is corroborated by the plot of

residuals against predicted values below (Figure 6.6).

 

 

 

 

 

 

 

 

 

 

 

50 I I
~ 0.4
40 —
—‘ 1?
O
H 30 _ - 0.3 ‘8
c 3.
8 — 8
0 20 — ‘ ° 2 <3
a:
__ El
10— ‘01
0 F j l I l ‘ 1 0.0
-0.5 0.0 0.5 1.0

RESD
Figure 6.5 Distribution of OLS residuals

An additional violation of fundamental OLS assumptions (though frequently
overlooked by researchers in other disciplines) is displayed below in Figure 6.7. Visual
inspection of model residuals reveals a cluster of large positive values in and around
Mérida, as well as along the boundaries of the study area. Consequently, the OLS model
underestimates the level of total employment in these locations. In the case of Mérida and
nearby municipios, total employment may be under-predicted due to the spillover effects
mentioned above. In the case of municipios along the edge of the study area, interaction
with municipios in neighboring states (which will not be addressed in this study) may

have a positive impact on total employment.

146

 

1.0 l I I l i l

RESIDUAL

0.0—’6‘0%'&-€&6°--o ————————— _

 

 

 

6 7 8 9 10 11 12
ESTIMATE

Figure 6.6 Plot of residuals against predicted values (OLS model)

Based on the spatial-economic weights introduced in Chapter Five, the Moran’s I test
(1:0.1449) confirms the statistical significance of spatial autocorrelation among model
residuals (p=0.0000). This result is corroborated by Lagrangian multiplier tests
(p=0.0155), which indicate the presence of spatial lag effects among error terms.
Diagnostic tests also suggest a weak degree of spatial error dependence (Moran’s I =
0.0986) among residuals based on nearest neighbors. However, this spatial
autocorrelation is not highly statistically significant (p=0.0542). Since spatial lag effects
are statistically significant, the initial spatial econometric model calibrated below will
account for these effects. Subsequently, diagnostic tests will be performed to determine if
significant spatial error effects remain in model residuals. In the event that spatial

dependence persists, an alternative model (SARMA, for example) will be calibrated.

147

   

|:]-0.287 --0.135
[:]-o.135-0

.0-0.152
. 0.152 - 0.374

- 0.374 - 0.817

40 0 40 80 Kilometers

Figure 6.7 Geographic distribution of OLS residuals

6. 7.2 Spatial lags model
The spatial lags model was calibrated as shown in Chapter Five. Based on the results
of the initial OLS model, the weights matrix based on economic potential was employed.

The results of the spatial lags model are displayed below.

(InnsT = 0.3656 + 0.9066(In)Eg + 0.0667pW(|n)ET
0.2804 0.0178 0.0290
0.1922 0.0000 0.0213

Pseudo a2 = 0.9602 LIK,2_,,,, = -142.489 p = 0.0000

As the results indicate, maximum likelihood estimation was used to calibrate the
spatial lags model. As Anselin and Bera (1998) assert, maximum likelihood methods
must be employed due to the correlation (simultaneity) between the spatial autoregressive
component (W(ln)E1~) and the model residuals. In this instance, traditional OLS is not

consistent.

148

   

|:] -0.266 - -0.146
[3 -0.146 - -0041
. .0041 . 0.135
0.135 - 0.364
. 0.364 - 0.814

40 0 40 80 Kilometers

Figure 6.8 Geographic distribution of spatial lags model residuals

In general, the coefficient corresponding to the independent variable (basic
employment) does not change significantly in the spatial lags model. In addition, the
spatial autoregressive parameter is highly statistically significant and the overall
explanatory power of the model improves slightly. It is also important to consider the
functional form (log-linear) employed in the model above. Due to this transformation,
model coefficients represent elasticities or rates of change (Gujarati, 1995). This
transformation is appropriate since our interest is in the economic base multiplier, which
represents the impacts of a marginal change in total employment. In other words, the
results above indicate that a one percent increase in basic employment generates a 0.91
percent increase in total employment at the municipio level.

Figure 6.8 above displays the spatial distribution of the residuals of the spatial lags
model. Some small pockets of relatively large positive values persist in and around

Mérida. However, diagnostic tests indicate that no significant spatial autocorrelation

149

remains in error terms based on nearest neighbors (p=0.8159) or economic potential
(p=0.719). The map also shows that residuals of the spatial lags model are somewhat
smaller than those in the OLS model. Additional tests reveal that heteroskedasticity is no
longer prevalent among model residuals (p=0.2631). Normality of residuals was not
assessed since maximum likelihood estimation requires the assumption that model error
terms are normally distributed. Since no significant spatial dependence remains in
residuals, no additional spatial econometric models need be calibrated. As a consequence,
the spatial lags model may be employed to estimate the spillover effects generated by

export-oriented employment at the municipio level in Yucatan.

6. 7.3 Estimation of local impacts
As discussed in Chapter Five, the traditional economic base multiplier may be
expanded to account for the possibility of economic interdependence among municipios

in Yucatan. The expanded multiplier takes the following form:

Em) = (1- f)-1*[EB(|) + PwiEBtlil (6.1)

The spatial economic-weights matrix (WU) serves as a measure of the propensity of
basic employment in location i to generate non-basic employment in place j. The spatial
autoregressive coefficient (p) indicates that basic employment in a given municipio has a
positive (and statistically significant) impact on total non-basic employment in "spatial-
econonric neighbors."

The spatial autoregressive component of the model above (own-Ego» can be utilized
to quantify the job creation effects of basic employment (maquiladora production, in this

case) in one location on non-basic employment in another location. Drawing on Equation

150

5.8 above, an increase in basic employment ((ln)EB) in a given municipio will generate a
corresponding change (131) in total employment ((ln)E1-). The resulting increase will also
reverberate through other municipios as a consequence of the spatial autoregressive

portion of the model ((pW(ln)E«r).

 

40 0 40 80 KIlometers

Figure 6.9 Spatial multiplier effects of 1000 basic sector jobs in Mérida

The spatial multiplier model will be employed in five scenarios below. The first two
examples represent hypothetical situations; the remaining examples incorporate actual
data from Yucatan. In the initial case, the indirect job creation effects of 1000 basic
sector jobs in Mérida will be assessed. Subsequently, the impact of 1000 basic sector jobs
in a typical rural municipio will be presented. The next two examples focus on the
employment generation effects of maquiladora employment since 1999, as well as the
overall spatial impacts of Yucatan's 37,327 export-oriented jobs. The final scenario

incorporates direct non-basic employment, as well as indirect non-basic employment, in

151

order to estimate the total job creation effects of the E01 strategy at the local scale. In the
context of this dissertation, direct non-basic employment refers to jobs created by basic
employment within the municipio (Em); indirect non-basic employment is generated by
basic employment in other locations (EBJ‘).

The estimated impacts of 1000 additional basic sector jobs in Mérida are shown in
Figure 6.9 above. In addition to the 478 direct non-basic sector jobs created locally, about
34 jobs are generated indirectly in rural municipios as a consequence of spatial spillovers.
Overall, indirect jobs represent about seven percent of total non-basic employment
generated by basic employment in Mérida. As the map indicates, spillover effects are
modest. The most prominent impacts are found in the nearby port of Progreso (five jobs)

and neighboring municipios of Uman (three jobs) and Kanasr’n (two jobs).

   

[:1

E0205
.o.5-1.1
- 1.1 - 1.9
.1.9-24.4

40 0 40 80 Kilometers

 
 

Figure 6.10 Spatial multiplier effects of 1000 basic sector jobs in Motul, Yucatan

152

The potential spatial implications of the proliferation of export-oriented
industrialization in a typical rural area are displayed in Figure 6.10 above. In general, an
increase of 1000 basic sector jobs in the municipio of Motul results in 179 non-basic
positions locally. In addition, about 57 jobs are generated as a consequence of spillover
effects. Overall, spillovers represent almost one-quarter of resulting non-basic
employment. Compared with the previous example, basic sector employment in rural
areas appears to have a greater likelihood of generating non-basic employment in spatial-
economic neighbors. This outcome is due to the greater openness of rural economies and
confirms some of the insights gleaned from IRIO analysis above. Not surprisingly, about
40 percent of indirect non-basic employment is concentrated in Mérida (24 jobs).
However, many other locations, including Progreso (1.3) and the neighboring municipios

of Sinanché (1.5) and Dzemul (1.5) also benefit as a result of spillover effects.

   
 

:10-1
2-4

-5-7
-8-27
-28-276

40 0 40 80 Kilometers

Figure 6.11 Spatial multiplier effects of all maquila jobs created since 1999

153

Figure 6.11 above indicates the spatial distribution of non-basic employment
spillovers generated by maquiladora employment since 1999. Since the spatial
econometric models were calibrated with 1998 data, this graphic reveals the potential
impacts of the 4666 export-oriented jobs created since 1999. Overall, more than 529
indirect non-basic jobs result. Although the bulk of spatial spillovers are concentrated in
Yucatan's most populated municipios (Mérida (276), Tizirm’n (l7), and Progreso (16), for
example), smaller locations also experience fairly prominent job creation effects. The
average rural municipio, for example, experiences an increase of almost 2.5 non-basic
sector jobs. A large area in the central part of the state, however, exhibits very few job
creation benefits as a result of increases in maquiladora production. In general, this area
is comprised of small municipios that are relatively distant from the largest cities in
Yucatan. In addition, as mentioned above, these locations display particularly small

economic base multipliers.

 

-
- 111 - 1152

40 0 40 80 Kilometers

Figure 6.12 Spatial multiplier effects of all maquila jobs

154

It is also possible to estimate the total spillover effects of the 37,327 export-oriented
jobs in Yucatan's economy. These impacts are shown in Figure 6.12 above. In general,
maquiladora production generates more than 3000 indirect non-basic jobs thoughout the
state. Although more than 35 percent of all impacts are concentrated in Mérida, all
municipios experience some benefits. On average, almost 18 jobs have been produced in
each rural community as a result of spillover effects. Several rural municipios, such as
Tizimi’n (86), Motul (90), and Progreso (110), display especially prominent job creation

effects.

    
 

l:
D4o-1o4
-105 - 273
-274-815
-816-2846

40 0 40 80 Kilometers

Figure 6.13 Direct and indirect non-basic jobs generated by maquiladoras, 1990-2000

Finally, both forms of non-basic employment — direct local employment and indirect
spatial spillovers — may be combined in order to estimate the overall impacts of export-
oriented industrialization strategy on employment generation at the local scale in

Yucatan. As Figure 6.13 above shows, direct and indirect non-basic employment totals

155

almost 7600 jobs. About 37 percent of all jobs are generated in Mérida. However, rural
municipios on average gain more than 45 direct and indirect non-basic sector jobs. Rural
locations displaying particularly prominent job creation effects include Motul (815),

Valladolid (537), Maxcanu (273), and Uman (264).

156

Chapter Seven

A CRITICAL ASSESSMENT OF YUCATAN’S MAQUILADORA STRATEGY

7.1 Initial assessment of research hypotheses

Several formal hypotheses and associated research questions were introduced in
Chapter One. The methodology developed in Chapter Five and applied in the previous
chapter facilitates assessment of the consequences of the E01 strategy and, hence, allows
these research questions to be addressed. Each specific hypothesis is evaluated below.
Subsequently, a more detailed assessment of the viability of export-oriented

industrialization, as well as alternative policy measures, will be offered.

7.1.1 Overall impacts of the E01 strategy

As stated above, economic growth is a necessary condition for regional economic
development. Controlling for inflation, Yucatan's gross state product increased by about
23 percent between 1993 and 1999 (about 3.4 percent annually). Between 1990 and 2000,
the state's population grew approximately 21.5 percent, or an average of about 1.95
percent per year (INEGI, 2001). Consequently, even when population growth is taken

into account, Yucatan's economy demonstrates a fair degree of economic growth.

157

Furthermore, the analysis canied out in sections 6.4 and 6.5 above confirms that
maquiladora production has had a notable positive impact on regional economic growth.
In addition, the strategy appears to have benefited both urban and rural regions of the
state.

The impact of export-oriented production on economic growth may also be assessed
by examining Yucatan's overall contribution to national income. According to INEGI
(2001) data, Yucatan's gross state product comprised 1.3 percent of total national income
in 1993. In 1995, the state accounted for only 1.24 percent of gross national product. By
1999, this share had increased slightly to 1.35. With respect to manufacturing activity,
Yucatan's share of GNP rose from 0.83 percent to 0.97 percent during the same period.
Though Yucatan still accounts for a relatively small portion of Mexico's economy, its
contribution to national income has increased somewhat during the past decade. In
addition, maquiladora production has played an important role in generating regional
income growth. Therefore, these hypotheses cannot be rejected. Maquiladoras have had
significant impacts on economic growth (in the form of production, income, and

employment generation) in Yucatan, Mexico.

7.1.2 Comparison of export-oriented and domestic industries

Tables 6.4 and 6.5 above reveal the impacts of maquiladora production on output,
income and employment in both urban and rural regions of the state. Complete sectoral
multipliers for other industries are included in the appendices. With respect to impacts on
output and employment, multipliers for export-oriented firms are smaller than virtually
all other sectors of Yucatan's economy. This result is hardly surprising given the dearth of

purchases made locally. However, with respect to income generation, maquiladoras

158

compare favorably with many other sectors of Yucatan's economy. Therefore, in terms of
employment generation and impacts on output, the formal hypothesis stated in Chapter
One cannot be rejected. Export-oriented firms have a more limited effect on job creation
and output growth than domestic firms. However, with respect to income creation, the
hypothesis may be rejected.

An associated hypothesis concerns changes in the magnitude of sectoral multipliers
between 1990 and 2000. As a consequence of structural change, the regional economy
purportedly should become more self-sufficient and productive. If regional output
multipliers for 1988 and 1998 are compared, increases are found in 11 of 18 sectors.
Several sectors, such as commerce, restaurants and hotels, personal and professional
services, and textile products exhibit substantial increases in terms of their impact on
regional output. However, in only eight of 18 instances, aggregate regional purchase
coefficients exhibit increases between 1988 and 1998. Consequently, in terms of
structural change, Yucatan's experience has been mixed. This associated hypothesis may

neither be rejected nor accepted.

7.1.3 Geographic distribution of economic impacts

Since 60 percent of total employment in export-oriented firms is found in rural areas
of Yucatan, the majority of direct income and job generation effects are concentrated in
this region. In addition, a greater and greater share of maquiladora employment has
become concentrated in rural municipios during the past decade. Consequently, an
increasing share of the direct income and employment benefits of the E01 has accrued to

rural residents.

159

With respect to direct impacts on output, maquiladoras purchase virtually all needed
goods and services in Mérida. Therefore, these impacts are not felt (directly) in rural
areas of the state. In the case of rural maquiladoras, the vast majority of indirect effects
on output flow back to the state capital (Table 6.7). Furthermore, the share of indirect
benefits has declined since 1988. Although the majority of income and employment
generation benefits remain in rural areas of the state, a greater and greater share of these
indirect impacts accrues to residents of Mérida. In the case of urban maquiladoras, an
increasing share of indirect benefits remains within the region. Consequently, the
hypothesis regarding the distribution of indirect economic impacts may not be rejected —
the bulk of additional income and output generated by export-oriented firms is
concentrated in Mérida. However, the hypothesis concerning changes in the inter-
regional distribution of economic effects may be rejected — in general, rural areas of
Yucatan have not experienced a greater share of indirect benefits. Consequently, it may
be concluded that most of the gains that have accrued to rural areas during the past
decade result from an increasing share of direct, rather than indirect, benefits. In general,
the greater share of direct gains (in terms of jobs and income) concentrated in rural

municipios more than compensates for the decreasing retention of indirect benefits.

7.1.4 Economic impacts at the municipio level

The spatial multiplier analysis above confirms that basic economic activity (such as
export-oriented production) in a given municipio has positive effects on economic
activity in surrounding locations. As discussed in Section 6.7, spillovers account for
about seven percent of job creation effects in the case of Mérida and around one-quarter

of employment impacts in rural municipios. In addition, the spatial-economic weights

160

matrix implemented above (accounting for both distance and economic importance)
proves statistically significant in accounting for these spillover effects among locations.
Therefore, the hypotheses presented in Chapter One may not be rejected — at the
municipio scale, basic economic activity has positive impacts not only locally, but also
within other locations. Furthermore, the geographic extent of these spillover effects is a

function of a combination of both distance and economic importance.

7.1.5 Viability of the E01 strategy

The final hypothesis concerns the viability of export-oriented industrialization as a
development strategy in the case of Yucatan. This issue will be evaluated at greater
length in the remainder of this final chapter. However, both inter-regional input-output
analysis and spatial econometric models confirm that maquiladora production has had a
positive impact on economic growth, income distribution, and economic structure at
regional and local scales. On the one hand, export-oriented production has had a positive
impact on economic growth in both rural and urban areas of the state. In addition,
inception of the maquiladora strategy has brought about a modest redistribution of
regional income since 1990. Furthermore, on the basis of shifts in sectoral employment,
EOI has promoted a degree of structural change in both regions of Yucatan's economy.
Therefore, the maquiladora strategy fulfills the three formal development criteria
introduced in Chapter Two and appears to be a viable economic development strategy in

the case of Yucatan, Mexico.

7 .2 A more detailed assessment
The ultimate assessment of the viability of export-oriented industrialization, however,

must be based on how the strategy contributes to the purported objectives of regional

161

development policy in the case of Yucatan. Specifically, the role of maquiladora
production in fulfilling the goals of the 1995-2001 State Development Plan must be
considered. Some of the concepts related to regional economic development and the role
of regional policy introduced in Chapter Two will prove helpful in extending the analysis
of the policy implications of the maquiladora strategy.

As discussed at length in Chapter Four, Yucatan's 1995-2001 State Development
Plan was implemented with the express purpose of achieving "balanced sustainable
regional development." Although Friedmann (1966) affirms that the pursuit of balance
creates potential conflict from a policy perspective, it is readily apparent that
policymakers in Yucatan have sought a more geographically-balanced distribution of
economic development as a means of redressing disparities in income, employment and
marginality. In fact, the grouping of municipios into nine planning districts as shown in

Figure 4.5 above provides a clear indication of this objective.

     
   

143 - 384
- 385- 573
‘ $574- 1091
- 1092- 3671

Figure 7.1 Total non-basic employment created by the E01 strategy by planning district

162

The results of the spatial multiplier analysis may be employed to determine the extent
of geographic balance achieved by the EOI strategy. In general, municipio level data on
direct (local) and indirect (spillover) non-basic employment resulting from maquiladora
production may be summed for each of the planning regions. These job creation impacts
are displayed above in Figure 7.1.

In general, the E01 strategy promotes substantial job creation effects in several
planning districts. Not surprisingly, almost 50 percent of all employment generation is
concentrated in and around Mérida. However, the Central Coast area gains 1091 jobs and
the East region exhibits an increase of 856 positions. Unfortunately, the southern cone
planning districts, historically among the most impoverished and economically marginal

areas of Yucatan, display the most limited job creation benefits.

   
  
 

Eastern Coast

E0515 - 0.598
. 0.598 - 0.698
.0698 -1
.1 - 1.492
- 1.492 - 2.654

Figure 7.2 Ratio of total non-basic employment to population by planning district

 

A more complete understanding of job creation effects may be obtained if change in

non—basic employment is assessed with reference to population. This population-to-

163

employment ratio technique has been suggested by Blakely (1994) as a means of
assessing the distribution of job creation efforts. A ratio greater than one indicates a
greater relative share of employment (with respect to population); a ratio less than one
reveals a relatively smaller share of employment.l

Figure 7.2 above displays the ratio of job creation to population for each planning
region. For example, the metropolitan region accounts for 47.7 percent of total non-basic
employment generated by the maquiladora strategy between 1990 and 2000. However,
based on 1995 population figures, this planning district represents 55.6 percent of
Yucatan's population. Therefore, the ratio of non-basic employment to population is

0.858.

  

Eastern Coast

  

I: 416 - 716
[:1 717 - 2778
- 2779 - 4637
a 4638 - 7297
- 7298 - 22955

Figure 7.3 Total employment created by maquiladora production by planning district

 

' The following discussion makes the implicit assumption that population-to-employment ratios "close" to
one indicate greater levels of balance in the distribution of economic impacts. However, this "rule of
thumb" was chosen on an ad hoc basis; any number of other reasonable criteria may be employed.

164

Based on this alternative representation, the export-oriented strategy has preformed
relatively well in generating economic opportunity in four planning districts — Central
Coast, Central, East, and Western Coast. Its impact in the Metropolitan region is also
acceptable — although the policy has created substantial employment in the capital region,
economic opportunities are not overly concentrated in these municipios. However, even
when population is taken into account, the policy has not generated sufficient
employment in the southern cone and Eastern Coast planning district. In general, the
share of non-basic employment in these regions is approximately 30 to 40 percent less

than their share of population.

   

Eastern Coast

   

1:] 0.237 - 0.336
[:3 0.336 - 0.553

- 0.553 -1
1 - 1.631

-1.631 - 3.033

40 0 40 80 Kilometers

Figure 7.4 Ratio of total employment to population by planning district

Maquiladora employment, as well as resulting non-basic employment, also may be
combined as an alternative method of assessing the geographic balance of the E01
strategy. The resulting distribution of economic impacts, shown above in Figure 7.3, is

somewhat similar to the distribution of non-basic employment. Not surprisingly, the

165

greatest absolute employment gains are concentrated in the Metropolitan area. Once
again, substantial employment generation is found in the Central Coast, Central and East
planning districts. In addition, the weakest impacts remain in the Eastern Coast and
southernmost regions.

Even when population is accounted for, impacts in the southern part of the state and
the Eastern Coast planning district are far inferior to those in other regions of Yucatan
(Figure 7.4). In general, the share of total employment generated in these areas by
maquiladoras is approximately 50 to 75 percent less than their share of population. From
another perspective, export-oriented industrialization is directly or indirectly responsible
for 25.8 percent of total employment in the Central Coast region, 18.1 percent in the
Central planning district, 14.9 percent in the Western Coast area, and 12.8 percent in the

East planning district (Figure 7.5).

  
   

Eastern Coast

3.8 - 5.6
- 5.6 - 8.3
8.3 -18.1

- 18.1 - 25.8

40 0 4O 80 Kilometers

   

Figure 7.5 Overall employment created by maquiladoras as a share of total employment

In the case of remaining (rural) districts, however, EOI accounts for less than six
percent of total employment. As displayed in Figure 4.2 above, marginality levels are not
especially high in the Eastem Coast region. In general, fishing and cattle ranching are
among the most important economic activities in this region. The southernmost planning
districts, whose economies are dependent on milpa agriculture and some citrus
production, exhibit particularly worrisome levels of marginality. The EOI strategy (as
currently configured in Yucatan) does not appear to offer any substantial economic

benefits to this region of the state.

7.3 Alternative impacts

The spatial multiplier methodology also may be employed to assess the potential
consequences of alternative strategies. For example, if geographic balance is the ultimate
objective of Yucatan's regional development policy, the spatial multiplier model above
may be linked with non-linear optimization to maximize overall employment creation
effects while assuring that the distribution of jobs is equitable. In essence, this proposed
alternative is similar to a MINIMAX problem in that we are attempting to maximize

economic impacts while minimizing geographic disparities.

Objective Function: MAX 2E1“,
where: E1") = E30) + Eng“) + pwnEw)

Constraints: 2E3“) = 37,327
53(1). 51930) 1 30d PWIIEBG) 2 0
R") I Pa) 2 0.9
R“) I P“) S 1.1
where: R“, refers to a region’s proportion of total employment
Po, refers to a region's proportion of total population

167

     

I: 1390
1390 - 1799
- 1799 - 2979
- 2979 - 3476
- 3476 - 27298

40 0 40 80 Kilometers

Figure 7.6 Geographically-balanced employment generation scenario

In the geographically-balanced employment generation simulation presented below,
the spatial multiplier technique will be utilized to determine the optimal geographic
location of Yucatan's 37,327 maquiladora jobs (in order to maximize overall job creation
impacts), while accounting for population distribution among the nine planning districts.
As shown in the constraints above, in each region the ratio of total employment to total
population must fall between 0.9 and 1.1.

The results of this simulation are displayed above in Figure 7.6. Overall, the pursuit
of geographic balance has increased total basic and non-basic employment (47,950) by
about 6.5 percent over actual job creation effects (Figure 7.3).2 Also, employment

generation impacts in the southernmost planning districts are far superior. Furthermore,

 

2 Although not shown, non-linear optimization was also utilized to determine the optimal location of
maquiladora employment in order to maximize overall employment creation without considering
geographic balance. In this scenario, total employment creation was 48,177 jobs, less than one percent
greater than the outcome of the balanced alternative.

168

as expected, the distribution of job creation benefits is somewhat smoother (for example,
the coefficient of variation has decreased from 0.705 to 0.645). In addition, as displayed
in the Lorenz curve below (Figure 7.7), remaining disparities in the distribution of basic
and non-basic employment are trivial. This outcome is confirmed by the corresponding

Gini coefficient (0.027).

 

Total Employment

 

D 0.1 0.2 0.3 0.4 0.5 0.6 0,7 0.8 0.9 1
Population

 

 

 

Figure 7.7 Lorenz curve — geographically balanced employment generation

As shown in Figure 7.6, the bulk of job creation impacts remain concentrated in the
Metropolitan planning district. This outcome is hardly surprising since the capital region
accounts for more than one-half of Yucatan's population. An alternative scenario may be
envisioned in which the objective is to maximize overall employment in rural areas while
limiting employment impacts in and around Mérida. This hypothetical situation is
analogous to two concepts introduced in Chapter Two — Hirschman's (1958) notion of
"unbalanced growth" and Friedmann's (1966) goal of choosing the optimal geographic
location in which investment is likely to promote "the rapid expansion and full

articulation of the space-economy."

169

In the geographically-imbalanced employment generation scenario presented below,
the objective function is to maximize total employment while restricting the ratio of
employment to population in the Metropolitan district to 0.75 or less. In the case of rural
planning districts, the total employment to population ratio must be at least one. The

results of this simulation are displayed below in Figure 7.8.

     

l: 1840 — 1990
I: 1990 - 3295
fl 3295 - 3962
- 3962 — 7116
- 7116 - 19725

40 0 40 80 Kilometers

 

Figure 7.8 Geographically-imbalanced employment generation scenario

At first glance, the results of this strategy do not appear markedly different from the
previous scenarios. However, only 40 percent of total job creation impacts are
concentrated in the Metropolitan planning district. Consequently, rural planning districts,
which comprise about 45 percent of Yucatan's population, share approximately 60
percent of total basic and non-basic employment. The "disequilibrium" of this strategy is
evidenced by the Lorenz curve below and the corresponding Gini coefficient (0.147).

Overall, in terms of job creation impacts, the geographically-imbalanced strategy

170

(47,735) compares favorably with the balanced alternative (47,950) and the "non-

geographic" optimal location scenario (48,177).

 

 

Total Employment

 

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Population

 

 

 

Figure 7.9 Lorenz curve — geographically imbalanced employment generation

In all instances, the above simulations result in substantially greater overall
employment generation effects than the current configuration of maquiladoras in
Yucatan. In general, the existing job creation impacts of export-oriented firms in Yucatan
are about five percent smaller than the results of the three scenarios discussed above.
Consequently, from a variety of (normative) policy perspectives, it is fair to conclude that
the geographic distribution of maquiladoras is not optimal. However, as demonstrated in
this section, the spatial multiplier model and basic non-linear optimization techniques
may be employed in a policy context to maximize the impacts of the E01 strategy while

"balancing" any number of other criteria.

7.4 Maquiladoras as a "ratchet" for regional development
As discussed in Chapter Two, regional policy plays an essential role in converting

economic growth into regional economic development. In general, two functions were

171

ascribed to regional policy — assuring a more equitable distribution of benefits and
promoting structural change (Figure 2.1). As mentioned above, structural change is
characterized by a shift in the mix of products, industries, firms and job opportunities that
make up the regional economy (Malecki, 1991). Furthermore, promotion of stronger
forward and backward linkages among firms and households represents a means of

bringing about structural change.

103:1:13 Hal'ldllflnfl

 

 

 

DEVELOPMENT

Figure 7.10 Impact of structural change on regional multiplier effect

Based on these definitions, then, structural change is ultimately a function of (changes
in) multiplier effects. As Pred ( 1966) asserts, the regional multiplier is not a constant — it
varies not only from place to place, but also from time to time. Krugman (1995) and
Fujita et al. (1999) provide a relatively coherent explanation of how changes in multiplier
effects and economic structure purportedly induce regional development within an export
base context. In general, as regional exports increase, total regional income also
increases. At some (unspecified) critical point, the regional economy grows to such an

extent that goods that were imported previously may be replaced with local production.

172

Consequently, the share of income spent locally also increases.3 As a result of increased
local spending, a qualitative change occurs in which multiplier effects increase.
Eventually, a cumulative process of regional development takes hold — structural change
promotes expansion of the regional multiplier, further increasing income, inducing
additional growth of the local market and concomitant structural change, etc.

Expansion of the regional multiplier effect may be envisioned as shown in Figure
7.10 above. The initial solid upward sloping line (starting at the origin) corresponds to
economic growth as a consequence of multiplier effects. At some point (indicated by the

disjunctures), structural changes are locked in place and the magnitude of the regional

  
  

multiplier increases.
3
C
S Decline in Economy
5 . ’
c . , ‘ ’
g Multiplier Stage 2 ,«’
m Subsequent
I: multiplier effect
0
.q
Multiplier Stage 1

 

 

DEVELOPMENT
Figure 7.11 "Locking in" of regional economic development
The process described above was termed the "ratchet" effect by Thompson (1965) in
referring to the stages of urban economic growth. Essentially, changes in the structure of

the regional economy are viewed as cumulative and the ratchet effect serves to lock in

 

3 Based on central place theory, the share of income spent locally is assumed to depend on the size of the
local market.

173

structural change at each stage of development. Even in the event of a decline in
economic growth, the "locking in" of structural changes precludes any decline in regional
multiplier effects (Figure 7.11).

In the case of Yucatan, policymakers have implemented the maquiladora strategy
with the express purpose of ratcheting the regional economy to greater levels of
development. As discussed in Chapter Four, the proliferation of export-oriented
industries in Yucatan is a direct consequence of the 1995-2001 State Development Plan,
which especially targets rural areas of the state. In the context of the "ratchet" model
introduced above, maquiladoras are expected to create employment opportunities and
provide a steady source of income for an historically underemployed rural population.
Although a portion of rural incomes will undoubtedly flow back to Mérida, it is expected
that some of these wages will be spent locally (at the municipio level). As mentioned
above, increases in regional income and local spending purportedly will trigger greater
investment in rural municipios as entrepreneurs establish businesses to meet the demands
of maquila workers and their families. Furthermore, the local (non-basic sector)
employment generated by maquiladora industries will result in a secondary multiplier
effect due to additional purchases. Eventually, regional multiplier effects will expand as
residents of rural municipios are able to meet a greater and greater share of their demand
for goods and services locally.

Based on the analysis in Chapter Six, the export-oriented industrialization strategy
may produce the ratchet effects desired by policymakers in Yucatan in some (isolated)
instances. The municipio of Motul provides a good case study of the potential for

promoting structural change by means of maquiladora production. In 1990, agriculture

174

employed more than 37 percent of the local workforce. Manufacturing made up slightly
more than 21 percent of all jobs; service industries and commerce accounted for
remaining employment (almost 42 percent). Between 1990 and 2000, more than 5000
jobs were created by export-oriented firms in Motul. According to the 2000 Census of
Population, jobs in agriculture had declined to less than 18 percent of total employment.
Manufacturing now comprises more than 32 percent of employment and service
industries employ almost one-half of the overall workforce.

However, analysis in the preceding chapter also suggests that the prospects for
widespread structural change at the municipio level in Yucatan are somewhat bleak, at
least in the short-term. As documented in Section 6.6, export-oriented industrialization
has had a relatively limited impact on structural change, at least in terms of employment,
since 1990. Although export-oriented industrialization has brought about a modest
redistribution of income between urban and rural regions of Yucatan, results of spatial
multiplier analysis (Section 6.7) suggest that the benefits of the E01 strategy have been
highly concentrated at the local scale. Among rural municipios only a handful of
locations (Motul, Tizimr’n, and Valladolid, for example) display especially prominent
increases in local sector employment. These isolated cases of potential structural change,

however, suggest the need for additional analysis at the municipio level in Yucatan.

7.5 Final remarks

Like other regions of the developing world, Yucatan has adopted the export-oriented
industrialization strategy as a means of inducing economic development. As discussed in
Chapter Two, previous development strategies — whether traditional resource-based

exports or state-led import substitution — have ultimately failed. Unfortunately, the

175

outlook for the maquiladora strategy may be equally pessimistic. Topik and Wells
(1998), confirm the potential limitations of the current EOI strategy, noting its uncanny
(unfortunate) resemblance to the resource-based development strategy of the 19th and
early 20th centuries.

Notwithstanding the limitations of maquiladora production, policymakers in Yucatan
have resorted to the E01 strategy in the pursuit of a "ratchet" for regional economic
development. Although the 1995-2001 State Development Plan officially concluded with
the gubernatorial elections earlier this year, Yucatan’s new (opposing party) governor
appears equally comrrritted to export-oriented industrialization. In light of the decision to
link Yucatan's economy to maquiladora production, several policy prescriptions may be
offered in order to augment the potential economic impacts of such a strategy.

Despite relatively robust linkages with "producer" services, maquiladoras in Yucatan
are not strongly integrated with primary and secondary sectors of the state's economy.
Needless to say, increased backward linkages with these industries would generate
further benefit for the state's residents and economy by hastening structural change and
expansion of the regional multiplier effect.

Furthermore, since Yucatan's economic development strategy emphasizes a single
export-oriented industry (clothing and apparel maquiladoras), local policymakers should
promote expansion of domestic sectors that traditionally supply commodity inputs to
these firms. As Mexican border states have demonstrated, backward linkages could also
be enhanced by requiring maquiladoras to purchase a greater share of local content.
Eventually, even if export-oriented firms relocated, manufacturing in Yucatan would

become more autonomous and the domestic economy would benefit from the resulting

176

structural change. Preliminary simulations using the integrated model developed above
indicate that a two-cent domestic content requirement would increase benefits to
Yucatan's economy by ten percent. Furthermore, if export-oriented firms purchased local
inputs at the same level as domestic firms, indirect economic impacts would expand by
about 70 percent.

In addition, because consumption and production are intertwined, consumers in
Yucatan would benefit from greater variety and lower costs if maquiladoras were
induced to sell a limited amount of their final production locally. As discussed in Chapter
Three, this recommendation is quite feasible since full-scale implementation of NAFTA
eventually removes the existing disincentives that prompt export-oriented firms to export
virtually all of their output.

Ultimately, the success of Yucatan's EOI development strategy is beyond the control
of local officials. Yucatan is participating in a global economic process that operates at
international, national, regional and local scales simultaneously. This internationalization
of manufacturing as a consequence of the new international division of labor includes
multinational enterprises, supranational political bodies, international financial
institutions, national governments, regions of specific countries, and individual laborers
and consumers. Yucatan's only "stable" comparative advantage lies in its relative
proximity to markets in the United States and cheap wages relative to other locations in
Mexico. The state's ability to expand maquiladora production and promote the desired
ratchet effect depends on its ability to continue providing low-wage labor and subsidized

infrastructure and training.

177

In a peripheral region such as Yucatan, export-oriented industrialization may be a
viable, short-term economic development strategy. Since many sectors of the state's
economy depend heavily on imported commodity inputs, maquiladora industries do not
differ radically from domestic producers in terms of backward linkages. In addition, as
income multipliers reveal, maquiladoras generate larger direct and indirect impacts than
many other sectors of Yucatan's domestic economy. Furthermore, because the economic
impacts of export-oriented firms are derived globally, rather than locally, maquiladoras
may be capable of expanding output (and increasing salaries and employment, as a result)
more easily than domestic firms.

However, because global capital is increasingly mobile and the locational decisions of
trans-national firms more and more volatile, Yucatan may not be able to emulate the
successes of other regions. Maquiladoras may relocate long before the state is able to
improve wages, effect technology transfers, upgrade the skills of its workers, and
integrate export-oriented production more fully into the local economy. In fact, anecdotal
evidence exists of several maquiladora closings in Yucatan during the past six months as
a result of the recent economic slowdown in the United States.

As the analysis above indicates, export-oriented industrialization has had significant
positive effects on both urban and rural economies in Yucatan since 1990. However,
local policymakers must recognize that the direct benefits of such a strategy are
ephemeral. Consequently, state government must strive not only to attract maquiladora
industries to Yucatan; officials must take advantage of their presence to expand output of
domestic firms and promote structural change of local economies, particularly in rural

areas of the state.

178

In conclusion, the export-oriented industrialization strategy offers Yucatan a short-
terrn opportunity to effect a fundamental structural transformation of the state's economy.
Regional policy plays a critical role in converting the benefits of maquiladora production
into structural change. However, this regional economic alchemy — transformation of
growth into development — is anything but automatic. Yucatan's current policy initiatives
do not specifically contemplate the indirect impacts of the export-oriented production or
the role of maquiladora industries in bringing about the desired ratchet effect. In
addition, maquiladoras are not creating the local supply networks needed to promote
structural change and resulting convergence. Consequently, the export-oriented
development strategy runs the risk of emulating the failures of Yucatan's henequen boom

of the late 19th and early 20‘h centuries.

179

APPENDICES

180

APPENDIX A

SURVEY INSTRUMENTS

181

Survey 01‘ Apparel and Clothing-based Maquiladoras in Yucatan, Mexico

When did this plant initiate operations in Yucatan?

What were the total costs (construction, equipment, etc.) of initiating Operations?
What portion of these expenses were incurred in Yucatan?

Did the plant owner (parent company) receive any special considerations or

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

incentives from local and/or state governments? Y N 7 3
If yes, what incentives were offered? [ 33
What was the total value (5) of incentives? l '3b
Has the plant made any improvements/investments in local infrastructure? Y N 7 4
If yes, what kinds of improvements were made? r 43
What was the total value (5) of improvements? | 4b
Has the plant made any other contributions to the local community? Y N 7 5
If yes, what kinds of contributions were made? [ So
What was the total value (5) of contributions? | 5b
Is this maquiladora owned by a: Mexican corporation Other 6
US corporation Joint venture
Does the plant owner operate other maquiladoras in Yucatan? Y N 7 7
Does the plant owner operate other non-maquiladoras in Yucatan? Y N 7 8
11 yes, when were other plants established? Ba
11 yes, what types of goods do these plants produce? 8b
Does the plant owner operate other maquiladoras in Mexico? Y N 7 9
11 yes, where? [ ]9a
Does the plant owner operate other non-maquiladoras in Mexico? Y N 7 10
if yes, where? [ 10a
If yes, when were other plants established? 10b
11 yes, what types of goods do these plants produce? 10c
Has the plant owner relocated maquiladora production in the past decade? Y N 7 11
if yes, from what state to what state? 11a
If yes, why was production relocated? 11b
How many persons did this plant employ in 1998? 12

How many perons were employed when the plant opened?
What was your total payroll (salaries and benefits) in 1998?
Where do the majority of plant employees live?
What percentage of your employees are women?
What was your annual employee turnover rate in 1998?
What type of products does this plant manufacture?
What percentage of final production is exported?
Does this plant sell any final production to consumers/companies in Yucatan?
If yes, what percentage of total production is sold in Yucatan?
What are the three primary destinations (cities) of the plant's final production?

What percentage of the plant's total raw materials and direct inputs is imported?
If less than 100%, does the plant purchase raw materials and inputs in Yucatan?
Annually, what is the value (5) of raw materials and direct inputs bought in Yucatan?

   
    
  

 

128
12b
12c
12d
129
13
138
Y N 7131:
13c
14

15
Y N 7158
15b

 

What kinds of goods does the plant purchase in Yucatan? r

If yes, what is the name of one of your main suppliers in Yucatan? L

11‘ 08 and/or Q10 yes, does plant purchase inputs from company's non-maquiladoras?

Annually, what is the value of indirect inputs and operating expenses bought in Yucatan?
What was the plant's total gross operating budget for 1998?

Figure A.1 Survey of maquiladoras

182

15c
15d
Y N ?156
151

C:
12::116

 

Survey of Domestic Firms
Yucatan, Mexico

 

 

 

 

What major goods and/or services does this firm produce? [ ] 1

During 1999, what types of raw materials and goods and services did this firm purchase? 2

What percentage of these goods/services were imported (supplied by firms outside Yucatan)? 2a

What percentage of these goods and services were supplied by firms in Mérida? 2b
Goods and Services Amount (3 or °/o) Pct Imported Pct Mérida

 

 

 

What percentage of your expenses was comprised of salaries/benefits? 3
What percentage was comprised of local, state and federal taxes? _ 4
How many people worked for this firm in 1999? — 5
What percentage of these workers lived outside Mérida? — So
What was your gross operating budget for 1999? 6
Who are the primary customers (industries) for your products? 7
What percentage of your output was exported (sold outside Yucatan)? 7e

What percentage of your output was sold in Mérida? 7b

Customers Amount (5 or %) Pct Exported Pct Mérida

 

 

 

 

Did you sell any of your output directly to consumers? Y N 7 8

if yes, how much? I I 9

Did you sell any of your output to government agencies? Y N 7 10
If yes, how much? I l 11

 

Figure A.2 Survey of domestic firms

183

 

Survey of Households
Yucatan, Mexico

 

Where do you live (municipio)? I

 

Number of household members

 

Number of household members employed
Total monthly household income Less than $5000 (pesos) 4
Between $5000 and $15000
More than $15000

Percentage of HH income spent on following 5
EXPENSE Percent % Spent In Mérida

 

Food

Housing
Clothing
Transportation
Utilities

Education & medical expenses

 

 

 

 

 

 

Eating out

 

Other entertainment

 

Help around house

 

Other services

 

Taxes

 

 

 

 

Savings

 

Figure A.3 Survey of households

184

APPENDIX B

CINVESTAV REGIONAL INPUT-OUTPUT TABLE

185

SILVICUL
Y PESCA
Y DERIV
DE

8
8

MINERAL DE
MINERALES MET
CANTERAS.ARENA, GRAVA Y ARCILLA
OTROS MINERALES NO METALICOS
PRODUCTOS CARNICOS Y LACTEOS
ENVASADO DE FRUTAS Y

DE Y
MOLIENDA DE NIXTAMAL Y

13.09.099.09

8
8

.0000 0.0000

coccooopooop

AZUCAR Y SUBPRODUCT
Y VEGET
PARA

(a)
_e

BEBIDAS ALCOHOLICAS

CERVEZA

REFRESCOS EMBOTELLADOS

TABACO Y SUS PRODUCTOS
DE

HILADO Y DE

coocoocccoocooocpcccppcc
.0999999999999

VESTIR
CUERO Y SUS PRODUCT
ASERRADEROS INCLUSO TRIPLAY
INDUSTRIAS DE LA MADERA
PAPEL Y ON
32 IMPRENT Y EDIT
REFINACION DE PETROLEO
PETROQUIMICA BASICA
QUIMICA BASICA
Y

9.0.0
80
8§

OTRAS
PRODUCTOS DE HULE
DE
Y SUS
CEMENTO
PRODUCTOS DE
lNDUSTRIAS BASICAS DEL Y
BASICAS DE METALES

Y NO
Y APARATOS
APARATOS ELECTRO-DOMESTICOS
Y ACCESORIOS
EOUIPOS Y APARATOS

Y MATERIAL DE
MANUF

Y

REST Y
TRANSPORTE
COMUNICACIONES

DE

70
71 DE
1

Table B.l Technical coefficients, CINVESTAV regional input-output table

 

186

 

10

11

14

15

16

20

21

22

23

24

25

 

0.0000

0.0000

0.1600

00000

00000

00000

00000

0.0000

0.0273

0.0200

0.0001

0.0000

00005

00000

0.0000

0.1675

 

0.0000

0.6819

0.0078

0.0185

00000

0. 0000

0.0000

0.0000

0.0000

0.0237

0 0003

0, 0000

0.0000

0.0000

0 0001

0.0000

 

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0 . 0000

0. 0000

0. 0000

0 0000

0. 0000

0.0000

0.5140

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0 , 0000

0.0000

0. 0000

0.0000

0 0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0,0000

0 0000

0. 0000

0 0000

0 . 0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0 0000

0.0000

0 0000

0 0000

0.0000

0 0000

0.0000

0.0000

0 0000

0 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0 . 0000

0. 0000

0.0000

0 , 0000

0.0000

0.0000

0.0000

0 0000

0 0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

00000

01276

0.0000

0 0000

0, 0000

0 0000

0 0000

0.0000

0.0003

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0035

0.0051

0 0009

0. 0000

0.0000

0.0000

0.0000

0.0046

0.0253

0.0001

0.0000

0.0002

0.0000

0.0000

0.0003

 

0.0000

0.0000

0.0268

0.0032

0. 0000

0 0000

0 . 0000

0.0000

0 0008

0.0063

0. 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

 

00000

0.0000

0.0005

0.1291

0 0000

0.0000

0 . 0000

0 0000

0.0137

0.0003

0 0000

O. 0000

0.0000

0.0000

0.0000

0.0000

 

0 0000

0 0000

0.0000

0 . 0000

0 2934

0.0000

0 0000

00000

0.0000

0.0000

0 . 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0. 0000

0 0000

0.0000

0 0000

0 0000

0. 0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

00000

00000

0.0000

0.0000

00000

00000

0 . 0000

00000

0. 0000

0.0000

0 0000

0 0000

0.0000

0.0000

0. 0000

0.0000

 

0.0000

0.0005

0.0080

0.0440

0 0000

0.0000

0.0000

0 0002

0.0339

0.0023

0.0000

0. 0000

0.0000

0.0000

0. 0000

0.0000

 

0,0000

0. 0000

0.0000

0 . 0000

0. 0000

0 0000

0.0000

0.0000

0.0003

00000

0 . 0000

0 . 0000

0. 0000

0.0000

0.0000

0.0000

 

0.0000

0.0001

0.0019

0 0039

0.0003

0.0026

0 0000

0.0000

0 0022

0.0030

00040

00017

0. 0365

0.0000

00000

0.0000

 

0.0000

0.0000

0.0000

0 0000

0. 0000

0 . 0000

0 0000

0 0000

0.0000

0.0000

0.0191

0.0000

0.0000

00000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0 0000

0.0000

0 0000

00000

00000

0.0000

0 . 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0 0000

0. 0000

0. 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

00000

00000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0 0000

0 0000

0 0000

0.0000

0.0000

0.0000

0 . 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0 . 0000

0.0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0001

0. 0000

0.0000

0.0005

0 0000

0. 0006

0 . 0000

0 0000

0.0000

0.0001

0.0000

0. 0000

0.0000

0.0000

0.0001

0.1350

 

0.0000

0. 0000

0 . 0000

0.0000

0.0000

0 0000

0.0000

0.0000

0.0000

0. 0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0. 0000

0. 0000

0.0000

0.0000

0.0000

0 0000

0.0003

0.0000

0.0000

0.0000

0.0001

 

0.0000

0.0000

0 . 0000

0 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0 0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0,0000

0 0000

0.0000

0.0000

00000

00000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

00016

00000

0.0000

0.0000

0.0000

0.0000

0.0001

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0017

0.0038

0.0003

0.0016

0 0000

0.0002

0.0000

0.0005

0 . 0063

0. 0000

0.0017

0.0000

0.0002

0.0008

 

0.0000

0.0000

0.0000

0 . 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0. 0000

00000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0 0000

00000

0. 0000

0.0000

00000

00000

0. 0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0 . 0000

0.0000

0 0000

0 0000

0.0000

0.0000

0.0000

0.0000

0 0000

0.0000

0.0000

0.0000

00000

0.0000

 

0.0000

0.0000

0.0001

0 . 0000

0. 0000

0.0000

0.0000

0.0000

0.0001

0.0001

0.0470

0.0003

0.0003

0.0000

0.0000

0.0006

 

0.0000

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

0.0000

0,0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0. 0000

0.0000

0.0000

0. 0000

0.0000

0 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

00000

00000

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0 . 0000

0. 0000

0. 0000

0 0000

0.0000

0.0000

0. 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0 , 0000

0. 0000

0 . 0000

0.0000

0 . 0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0. 0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

 

0.0003

0.0003

00184

00151

0 0030

0. 0000

0.0000

0.0016

0.0008

00046

00000

0.0000

0.0171

0.0000

00000

0.0000

 

0.0000

0.0000

0.0002

0.0000

0 . 0000

0.0000

0 0000

0.0000

0.0000

0.0000

0.0001

0. 0002

0.0001

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0, 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0004

0. 0000

0.0686

0.0000

0 . 0000

0.0000

0.0000

0.0000

0 . 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0004

0.0000

00000

0.0000

0,0000

0.0000

0.0000

0.0000

0.0000

0.0075

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0,0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0054

0.0001

0.0036

0.0005

0.0002

0.0001

0.0000

0.0002

0.0000

0.0002

0.0004

0.0016

0.0023

0.0000

0.0002

0.0009

 

0.0097

0.0001

0.0004

0 . 0009

0. 0004

0.0003

0.0000

0.0002

0.0000

0.0005

0.0008

0.0024

0.0012

0.0000

0.0005

0.0016

 

0.0000

0.0000

0.0000

0.0000

00000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

00000

00000

00000

0.0000

0.0000

0.0000

0 0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0001

0.0000

0.0000

0. 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0007

0.0020

0.0121

0.0151

0.0179

0.0056

0.0000

0.0109

0.0088

0.0165

0,0094

0.0412

0.0088

0.0000

0.1994

0.0332

 

0.0248

0.2402

0.0762

0.0488

0.0380

0.1174

0.0000

0.1533

0.1657

0.1388

0.0858

0.0459

0.0498

0.0000

0.0542

0.0661

 

0.0014

0.0000

0.0010

0 . 0002

0.0000

0.0001

0.0000

0.0001

0.0015

0.0000

0.0001

0.0107

0.0019

0.0000

0.0002

0.0046

 

0.0227

0.0111

0.0175

0.0040

0.0017

0.0053

0.0000

0.0065

0.0090

0.0079

0.0061

0.0237

0.0096

0.0000

0.0028

0.0098

 

0.0186

0.0000

0.0001

00000

00000

00000

0.0000

0. 0000

0.0004

0.0000

0.0000

0.0030

0.0005

0.0000

00000

0.0006

 

0.0692

0.0000

0.0008

0.0001

0.0000

0.0001

0.0000

0.0001

0.0022

0.0000

0.0001

0.0154

0.0027

0.0000

0.0001

0.0035

 

0.0000

0.0000

0.0014

0.0003

0. 0000

0.0001

0.0000

0.0000

0.0003

0.0005

0.0003

0.0005

0.0012

0.0000

0.0000

0.0011

 

0.0017

0.0000

0.0004

0.0003

0. 0000

0.0001

0.0000

0.0000

0.0024

0.0007

0.0011

0.0288

0.0146

0.0000

0.0001

0.0021

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

00000

00000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0079

0.0002

0.0000

0.0000

0.0000

0.0000

0.0001

0.0006

0.0009

0.0022

0.0010

0.0000

0.0000

0.0001

 

 

0.0187

 

0.0000

 

0.0021

 

0.0004

 

0.0000

 

0.0001

 

0.0000

 

0.0001

 

0.0006

 

0.0003

 

0.0002

 

0.0027

 

0.0017

 

0.0000

 

0.0002

 

0.0047

 

187

 

 

26

27

28

29

30

31

34

35

36

37

40

41

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

00000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0003

0.0001

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0 .0000

0. 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0. 0000

0.0000

0 .0000

0. 0000

0 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0 . 0000

0.0000

0 . 0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0 . 0000

0.0000

0. 0000

0.0000

0.0471

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0528

0.0000

0.0001

0 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0034

0.0103

0.0026

0.0000

 

0.0000

0.0000

0.0015

0.0000

0.0000

0. 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0004

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0 . 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0001

0. 0000

0 . 0000

0.0000

00000

0.0000

0.0005

0.0000

0.0000

0.0010

0.0036

0.0021

0.0000

 

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0001

0. 0000

0.0000

0.0000

0.0001

0.0000

0.0000

0.0023

0.0000

0.0002

0.0000

 

0.0000

0.0000

0.0000

0 . 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0. 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

00000

00000

0 . 0000

0. 0000

0.0000

0. 0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

00000

00000

0.0000

0.0000

0.0000

 

0.0495

0.0008

0.0007

0 . 0000

0 0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0. 0000

0.0000

0 . 0000

0.0001

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0002

0.0059

0.0003

0.0000

0.0001

0.0000

0 0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0005

 

0.0000

0.0125

0 . 0000

0.0001

0.0000

0. 0000

0.0001

0.0003

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0001

 

0.0000

0.0000

0 . 0000

0. 0000

0 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0002

0.0064

0.0270

0.0007

0.0001

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0003

0.0000

 

0.0001

0.0000

0.0002

0.0000

0.0042

0.0001

0.0001

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0002

0.0005

0.0000

0.0003

0.0000

0.0000

0.0001

0.0000

0.0023

0.0000

0.0000

0.0047

0.0000

0.0017

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0 . 0004

0.0001

0.0001

0. 0002

0.0000

0.0001

0.0000

0. 0008

0.0000

0.0000

0.0002

0.0009

0.0004

0.0002

 

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0028

0.0001

0.0208

0.0001

0.0423

0.0006

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0004

0.0044

0.0073

0.0000

0.0035

0.0005

0.0010

0. 0006

0.0000

0.0222

0.0000

00000

0.0571

0.0002

0.0169

0.0000

 

0.0000

0.0000

0.0000

0 . 0000

0.0015

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0002

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0095

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0002

0.0000

0.0000

0.0000

0.0000

0.0207

0.0000

0.0009

0.0000

0.0000

0.0001

0.0000

0.0068

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0010

0.0000

0.0002

0.0003

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0002

 

0.0000

0.0000

0.0000

0 . 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0003

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0001

0.0001

0.0003

0.0002

0.0043

0.0001

0.0003

0.0001

0.0000

0.0001

0.0000

0.0000

0.0002

0.0007

0.0009

 

0.0003

0.0000

0.0003

0.0003

0.0007

0.0000

0.0006

0.0002

0.0000

0.0002

0.0000

0.0000

0.0001

0.0014

0.0015

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0001

0.0006

0.0000

0.0000

0.0000

0.0004

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

00000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0278

0.0149

0.0097

0.0123

0.0138

0.0132

0.0145

0.0268

0.0000

0.0484

0.0000

0.0000

0.0124

0.0131

0.0082

 

0.0798

0.0573

0.0825

0.1149

0.1124

0.0295

0.0690

0.0421

0.0000

0.1030

0.0000

0.0000

0.0485

0.0645

0.0861

 

 

0.0001

0.0030

0.0000

0.0032

0.0023

0.0006

0.0108

0.0022

0.0000

0.0009

0.0000

0.0000

0.0005

0.0001

0.0001

 

0.0041

0.0109

0.0149

0.0145

0.0138

0.0027

0.0124

0.0094

0.0000

0.0133

0.0000

0.0000

0.0183

0.0065

0.0094

 

0.0000

00004

0.0000

0.0004

0.0003

0.0001

0.0013

0.0010

0.0000

0.0001

0.0000

0.0000

0.0012

00000

0.0000

 

0.0001

0.0023

0.0000

0.0024

0.0017

0.0005

0.0082

0.0043

0.0000

0.0007

0.0000

0.0000

0.0004

0.0001

0.0001

 

0.0002

0.0021

0.0020

0.0007

0.0028

0.0002

0.0063

0. 0003

0.0000

0.0004

0.0000

0.0000

0.0026

0.0003

0.0005

0.0017

 

0.0001

0.0017

0.0007

0.0014

0.0013

0.0003

0.0053

0.0052

0.0000

0.0004

0.0000

0.0000

0.0008

0.0008

0.0003

0.0033

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0028

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

 

0.0000

0.0004

0.0006

0.0000

0.0003

0.0000

00006

0.0000

0.0000

0.0001

0.0000

0.0000

0.0005

0.0007

0.0002

00004

 

 

0.0001

 

0.0019

 

0.0008

 

0.0026

 

0.0017

 

0.0008

 

0.0067

 

0.0042

 

0.0000

 

0.0017

 

0.0000

 

0.0000.

 

0.0017

 

0.0004

 

0.0005

 

0.0052

 

 

188

 

42

45

46

47

49

51

53

57

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0,0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

00000

00000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0. 0000

0.0000

0 . 0000

0.0000

0. 0000

0.0000

0.0000

0. 0000

0. 0000

0.0000

0.0000

0,0000

0.0000

0.0000

 

0.0000

0. 0000

0.0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0 0000

0. 0000

0.0000

0. 0000

0 0000

0.0000

0.0000

0 . 0000

0 0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

00000

0 . 0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0,0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0044

00937

00000

0.0000

0. 0000

0.0000

0.0003

0.0000

0.0000

0.0000

0.0000

0. 0000

0. 0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0 . 0000

0. 0000

0.0000

0.0000

0. 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

00000

 

0.0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0. 0000

0 0000

0.0000

0,0000

0 0000

00000

0.0000

0. 0000

0 0000

0.0000

0.0000

0.0000

0 0000

0.0000

 

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0 0000

0.0000

0.0000

0, 0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0 0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0,0000

0 . 0000

0.0000

0.0000

0.0000

0 0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

00000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0. 0000

00000

0.0000

0.0000

0.0000

0. 0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

00000

00000

 

0.0000

0.0000

0 0000

0 0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0 0000

0.0000

0.0000

00000

00000

0 0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0. 0000

00000

0.0000

0 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0. 0000

0 . 0000

0.0000

0.0000

0. 0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0 . 0000

0. 0000

0 0000

0.0000

0 . 0000

0. 0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0 0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0. 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0 0000

0.0000

0.0000

0.0000

0 . 0000

0 0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0004

00000

0 0000

0 . 0000

0.0000

0. 0000

0.0003

0.0000

0.0000

0.0001

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0001

00000

0.0002

0.0000

0.0000

00000

00001

0.0000

0.0001

0.0001

0.0001

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

 

0.0002

00000

00000

0.0000

0.0000

00000

0 0050

0.0000

0.0001

0.0002

0.0007

0.0000

0.0000

0.0001

0.0000

0.0000

 

0.0001

0.0000

0.0000

0.0000

0.0000

0.0000

0.0011

0.0007

0 0005

0.0012

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0002

0.0000

0.0000

0.0004

0.0000

0 0000

0.0000

0.0000

0.0000

0.0164

0.0002

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0. 0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0. 0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0,0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0002

0.0014

0. 0000

0.0003

0.0000

0.0000

0.0000

0.0057

0.0001

0.0012

0.0001

0.0000

0.0000

0.0004

0.0000

0.0000

 

0.0000

0.0000

0.0000

0 . 0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0. 0000

0.0000

0.0000

0 . 0000

0 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0. 0000

0.0000

00000

00000

0.0000

0,0000

0.0000

0.0000

0.0000

0.0000

 

0.0032

0.0226

0 . 0000

0.0002

0 . 0000

0 0000

0.0525

0 0205

0.0005

0.0033

00017

0.0000

0.0000

0.0572

0.0000

0.0000

 

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0062

0.0004

0. 0000

0.0043

0 0000

0. 0000

00056

00004

0.0010

0.0012

0.0057

0.0000

0.0000

0.0067

0.0000

0.0000

 

0.0000

0.0002

0.0000

00000

0.0000

0.0000

0, 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0040

0.1132

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0006

0.0001

0.0000

0.0677

0.0001

0 . 0000

0.0036

0.0000

0.0009

0.0012

0.0030

0.0000

0.0000

0.0063

0.0000

0.0000

 

0.0001

0.0000

0.0003

0. 0090

0.2303

0.0000

0.0196

0.0217

0.0055

0.0178

0.0174

0.0000

0.0000

0.0075

0.0000

0.0000

 

0.0000

0.0000

0 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0007

0.0000

0.0000

0.0000

0.0215

0.0000

0.0033

0.0031

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0005

0.0004

0.0022

0.0017

0.0005

0. 0000

0.0089

0.0048

0.0005

0.0020

0.0051

0.0000

0.0000

0.0006

0.0000

0.0000

 

0.0009

0. 0009

0.0035

0.0016

0.0002

0.0000

0.0014

0.0008

0.0010

0.0074

0.0027

0.0000

0.0000

0.0004

0.0000

0.0000

 

0.0002

0.0000

0.0000

0.0004

0.0001

0.0000

0.0000

0.0002

0.0004

0.0001

0.0000

0.0000

0.0000

0.0001

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0001

0.0000

0.0000

0.0000

0.0000

0.0000

0.0003

0.0000

0.0000

0.0000

0.0001

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

00000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0351

0.0277

0.1070

0.0177

0.0301

0.0000

0.0157

0.0114

0.0145

0.0119

0.0139

0.0000

0.0000

0.0203

0.0000

0.0000

 

0.1090

0.0653

0.0301

0.0769

0.1854

0.0000

0.1336

0.1149

0.0641

0.1000

0.0411

0.0000

0.0468

0.0000

0.0000

 

0.0057

0.0024

0.0131

0.0016

0.0001

0.0000

0.0036

0.0025

0.0040

0.0048

0.0082

0.0000

0.0000

0.0017

0.0000

 

0.0191

0.0059

0. 0098

0.0112

0.0093

0.0000

0.0168

0.0124

0.0102

0.0207

0.0019

0.0000

0.0000

0.0208

0.0000

0.0000

 

0.0007

0.0003

0. 0036

0.0002

0.0000

0.0000

0.0004

0.0003

0.0005

0.0006

0.0010

0.0000

0.0000

0.0002

0.0000

0.0000

 

0.0044

0.0018

0.0187

0.0012

0.0002

0.0000

0.0027

0.0019

0.0030

0.0036

0.0062

0.0000

0.0000

0.0013

0.0000

0.0000

 

0.0034

0.0005

0. 0009

0.0013

0.0002

0.0000

0.0039

0.0017

0.0020

0.0038

0.0032

0.0000

0.0000

0.0040

0.0000

0.0000

 

0.0034

0.0011

0.0163

0 . 0009

0.0001

0.0000

0.0020

0.0011

0.0023

0.0026

0.0038

0.0000

0.0000

0.0026

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0008

 

0.0000

0.0010

0.0002

0.0000

0.0000

0.0004

0.0000

0.0005

0.0005

0.0002

0.0000

0.0000

0.0016

0.0000

0.0000

 

 

0.0043

0.0037

 

0.0139

 

0.0029

 

0.0009

 

0.0000

 

0.0031

 

0.0019

 

0.0041

 

0.0055

 

0.0051

 

0.0000

 

0.0000

 

0.0037

 

0.0000

 

0.0000

 

189

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

58 59 60 61 62 63 64 65 66 67 68 69 70 71 rg]
0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000 0,0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.000g
0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0,0000 0 0000 0.0005 0.0006 0.0002 0.0%
0.0000 0 0000 0.0000 0.0000 0 0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0003?]
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00m]
0.0000 0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0 0000 0 0000 0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000
0.0000 0,0000 0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0 0502 0.0000 0 0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0,0000 0.0000 0 0000 0.0000 0.0000
0.0000 0.0000 0,0000 0.0000 0 0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0004 0.0062 0.0000 0.0000
0.0000 0.0000 0 0000 0.0000 0 0000 0.0000 0,0000 0.0000 0.0000 0 0000 0.0000 0.0001 0.0010 0.0000 0,0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0002 0.0018 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0 0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0 0000 0 0000 0.0000 0,0000 0.0000 0.0000 0.0004 0.0000 0.0000
0.0000 0 0000 0.0000 0,0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0,0000 0 0001 0.0000 0.0002 0.0000
0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0014 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0 0000 0 0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0,0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0,0000 0 0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.000g
0.0000 0.0000 0.0000 0.0000 0 0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0009]
0.0000 0.0003 0.0000 0.0000 0.0002 0 0000 0.0000 0.0000 0,0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00%]
0.0001 0.0002 0.0000 0.0000 0.0000 0.0001 0.0000 0 0002 0.0000 0.0000 0.0000 0.0001 0.0006 0.0000 0.0001|
0.0000 0.0000 0.0000 0.0002 0.0002 0.0001 0.0001 0 0002 0.0000 0.0000 0.0000 0.0003 0.0003 0.0000 0.0000|
0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0,0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000|
0.0002 0.0051 0.0006 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0002 0.0000 0.0001 0.0000 0.0000 0.0001|
0.0001 0.0007 0.0031 0.0001 0 0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0002 0.0001 0.0009]
0.0000 0.0008 0.0000 0.0000 0.0001 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00@
0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0,0000 0.0000 0.0009
0.0000 0.0000 0.0055 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0020J
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0,0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0009]
0.0040 0.0004 0.0000 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0002 0.0002 0.0000 0.0008
0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0 0000 0.0000 0.0000 0.0000 0 0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0,0000 0.0000 0.0000 0.0000 0.0000
0.0059 0.0000 0.0000 0.0000 0.0000 0.0000 0,0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0 0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0003 0.0017 0.0152 0.0000 0.0050 0.0000 0.0000 0.0002 0.0000 0.0001 0.0000 0.0004 0.0000 0.0000 0.0001
0.0016 0.0001 0.0000 0.0000 0,0000 0.0000 0.0000 0,0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0300 0.0001 0.0000 0.0000 0.0000 0.0007 0.0000 0.0001 0.0000 0.0015 0.0008 0.0000 0.0001
0.0000 0.0011 0.1870 0.0003 0.0000 0.0000 0.0000 0.0008 0.0000 0.0068 0.0000 0.0008 0.0014 0.0000 0.0057
0.0205 0.0014 0.1106 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0003 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0003]
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0030]
0.0000 0.0001 0.0611 0.0002 0.0000 0.0000 0.0001 0.0006 0.0000 0.0004 0.0000 0.0002 0.0000 0.0000 0.0037]
0.0013 0.0006 0.0116 0.0013 0.0003 0.0001 0.0007 0,0004 0.0000 0.0003 0.0000 0.0002 0.0001 0.0001 0.0003]
0.0018 0.0010 0.0007 0.0027 0.0002 0.0001 0.0015 0.0004 0.0000 0.0000 0.0000 0.0002 0.0001 0.0001 0.0003]
0.0001 0.0000 0.0002 0.0007 0.0000 0.0000 0 0004 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000|
0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000|
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000|
0.0000 0.0000 0.0000 0,0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000|
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000|
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000|
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000|
0.0000 0.0006 0.0000 0.0001 0.0000 0.0001 0.0000 0.0002 0.0000 0.0003 0.0000 0.0011 0.0001 0.0001 0.0003
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0003
0.0056 0.0238 0.0000 0.0197 0.0092 0.0057 0.0046 0.0076 0.0000 0.0171 0.0006 0.0487 0.1440 0.0752 0.0373]
0.0695 0.0689 0.1340 0.0089 0.0043 0.0033 0.0100 0.0044 0.0000 0.0021 0.0013 0.0063 0.0045 0.0027 0.003]
0.0016 0.0019 0.0000 0.0016 0.0174 0.0060 0.0082 0.0146 0.0001 0.0063 0.0107 0.0080 0.0030 0.0062 0.0003]
0.0113 0.0178 0.0028 0.0051 0.0317 0.0025 0.0142 0.0159 0.0001 0.0021 0.0103 0.0107 0.0034 0.0020 0.00_7§|
0.0010 0.0002 0.0000 0.0016 0.0132 0.0088 0.0042 0.0266 0.0001 0.0013 0.0123 0.0129 0.0062 0.0086 0.001fl
0.0024 0.0015 0.0000 0.0080 0.0469 0.0193 0.0108 0.0102 0.0001 0.0030 0.0118 0.0178 0.0063 0.0168 aofl
0.0004 0.0035 0.0000 0.0001 0.0112 0.0120 0.0013 0.0130 0.0001 0.0000 0.0037 0.0072 0.0029 0.0132 0.01 g
0.0030 0.0017 0.0000 0.0033 0.0770 0.0319 0.0293 0.0314 0.0003 0.0012 0.0383 0.0192 0.0097 0.0471 0.0mm
0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0013 0.0000 0.0003
0.0448 0.0000 0.0000 0.0011 0.0008 0.0000 0.0224 0.0000 0.0000 0.0000 0.0000 0.0259 0.0121 0.0030 0.003]
0.0001 0.0007 0.0000 0.0000 0.0050 0.0196 0.0005 0.0001 0.0000 0.0012 0.0151 0.0047 0.0018 0.0962 0.0003]
0.0020 0.0020 0.0000 0.0054 0.0111 0.0107 0.0106 0.0256 0.0000 0.1316 0.0084 0.0178 0.0088 0.0167 0.003]

 

190

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

SECTOR 1 2 3 4 5 6 7 8 9
1 AGRICULTURA 1.1198 0.0106 0.0000 0.0010 0.0000 0.0000 0.0000 0.0000 0.0000
2 GANADERIA 0.0000 1.0582 0.0000 0.0007 0.0000 0.0000 0.0000 0.0000 0 0000
3 SILVICULTURA 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
4 CAZA Y PESCA 0.0000 0.0004 0.0000 1.0281 0.0000 0.0000 0.0000 0.0000 0.0000
5 CARBON Y DERIVADOS 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000
6 EXTRACCION DE PETROLEO Y GAS 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000
7 MINERAL DE HIERRO 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000
8 MINERALES METALICOS NO FERROSOS 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000
9 CANTERASARENA. GRAVA Y ARClLLA 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000
10 OTROS MINERALES NO METALICOS 0.0000 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
11 PRODUCTOS CARNICOS Y LACTEOS 0.0000 0.0016 0.0000 0.0004 0.0000 0.0000 0.0000 0.0000 0.0000
12 ENVASADO DE FRUTAS Y LEGUMBRES 0.0000 0.0013 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000
13 MOLIENDA DE TRIGO Y SUS PRODUCTOS 0.0000 0.0114 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
14 MOLIENDA DE NIXTAMAL Y PRODUCTOS DE MAIZ 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
15 PROCESAMIENTO DE CAFE 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
16 AZUCAR Y SUBPRODUCTOS 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
17 ACEITES Y GRASAS VEGETALES COMESTIBLES 0.0000 0.0120 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
18 ALIMENTOS PARA ANIMALES 0 0000 0.3382 0.0000 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000
19 OTROS PRODUCTOS ALIMENTICIOS 0.0000 0.0008 0.0000 0.0166 0.0000 0.0000 0.0000 0.0000 0.0000
20 BEBIDAS ALCOHOLICAS 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
21 CERVEZA 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
22 REFRESCOS EMBOTELLADOS 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
23 TABACO Y SUS PRODUCTOS 0,0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
24 HILADO Y TEJIDO DE FIBRAS BLANDAS 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
25 HILADO Y TEJIDO DE FIBRAS DURAS 0.0000 0.0001 0.0000 0 0030 0.0000 0.0000 0.0000 0.0000 0.0001
26 OTRAS INDUSTRIAS TEXTILES 0.0000 0.0000 0.0000 0.0003 0.0000 0.0000 0.0000 0.0000 0.0000
27 PRENDAS DE VESTIR 0.0000 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
28 CUERO Y SUS PRODUCTOS 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
29 ASERRADEROS INCLUSO TRIPLAY 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
30 OTRAS INDUSTRIAS DE LA MADERA 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
31 PAPEL Y CARTON 0 0003 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001
32 IMPRENTAS Y EDITORIALES 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
33 REFINACION DE PETROLEO 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
34 PETROOUIMICA BASICA 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
35 OUIMICA BASICA 0.0000 0.0000 0.0000 0.0003 0.0000 0.0000 0.0000 0.0000 0.0000
36 ABONOS Y FERTILIZANTES 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
37 RESINAS SINTETICAS Y FIBRAS ARTIFICIALES 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
38 PRODUCTOS MEDICINALES 0.0000 0.0004 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
39 JABONESDETERGENTES. PERFUMES Y COSMETICOS 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
40 OTRAS INDUSTRIAS OUIMICAS 0.0050 0.0001 0.0000 0.0004 0.0000 0.0000 0.0000 0.0000 0.0000
41 PRODUCTOS DE HULE 0,0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
42 ARTICULOS DE PLASTICO 0.0006 0.0029 0.0000 0.0005 0.0000 0.0000 0.0000 0.0000 0.0001
43 VIDRIO Y SUS PRODUCTOS 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000
44 CEMENTO 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
45 OTROS PRODUCTOS DE MINERALES NO METALICOS 0.0001 0.0002 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 0.0002
46 INDUSTRIAS BASICAS DEL HIERRO Y ACERO 0.0004 0.0000 0.0000 0.0019 0.0000 0.0000 0.0000 0.0000 0.0002
47 INDUSTRIAS BASICAS DE METALES NO FERROSOS 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
48 MUEBLES Y ACCESORIOS METALICOS 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
49 PRODUCTOS METALICOS ESTRUCTURALES 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001
50 OTROS PRODUCTOS METALICOS 0.0003 0.0001 0.0000 0.0324 0.0000 0.0000 0.0000 0.0000 0.0025
51 MAOUINARIA Y EOUIPO NO ELECTRICO 0.0163 0.0002 0.0000 0.0002 0.0000 0.0000 0.0000 0.0000 0.0056
52 MAOUINARIA Y APARATOS ELECTRICOS 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
53 APARATOS ELECTRO-DOMESTICOS 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
54 EOUIPOS Y ACCESORIOS ELECTRONICOS 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
55 OTROS EOUIPOS Y APARATOS ELECTRICOS 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
56 VEHICULOS AUTOMOVILES 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
57 CARROCERIAS Y PARTES AUTOMOTRICES 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
58 OTROS EOUIPOS Y MATERIAL DE TRANSPORTE 0 0000 0.0000 0.0000 0.0605 0.0000 0.0000 0.0000 0.0000 0.0000
59 OTRAS INDUSTRIAS MANUFACTURERAS 0.0000 0.0001 0.0000 0.0005 0.0000 0.0000 0.0000 0.0000 0.0000
60 CONSTRUCCION E INSTALACIONES 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
61 ELECTRICIDAD. GAS Y AGUA 0.0110 0.0093 0.0000 0.0062 0.0000 0.0000 0.0000 0.0000 0.0407
62 COMERCIO 0.0565 0.0735 0.0000 0.0608 0.0000 0.0000 0.0000 0.0000 0.0131
63 RESTAURANTES Y HOTELES 0.0014 0.0021 0.0000 0.0015 0.0000 0.0000 0.0000 0.0000 0.0204
64 TRANSPORTE 0.0228 0.0085 0.0000 0.0056 0.0000 0.0000 0.0000 0.0000 0.0043
65 COMUNICACIONES 0.0010 0.0022 0.0000 0.0028 0.0000 0.0000 0.0000 0.0000 0.0031
66 SERVICIOS FINANCIEROS 0.0045 0.0063 0.0000 0.0219 0.0000 0.0000 0.0000 0.0000 0.0168
67 ALQUILER DE INMUEBLES 0.0008 0.0012 0.0000 0.0009 0.0000 0.0000 0.0000 0.0000 0.0083
68 SERVICIOS PROFESIONALES 0.0054 0.0119 0.0000 0.0056 0.0000 0.0000 0.0000 0.0000 0.0113
69 SERVICIOS DE EDUCACION 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
70 SERVICIOS MEDICOS 0.0006 0.0026 0.0000 0.0030 0.0000 0.0000 0.0000 0.0000 0.0002
71 SERVICIOS DE ESPARCIMIENTO 0.0005 0.0007 0.0000 0.0005 0.0000 0.0000 0.0000 0.0000 0.0008
72 OTROS SERVICIOS 0.0013 0.0141 0.0000 0.0014 0.0000 0.0000 0.0000 0.0000 0.0237

 

Table B.2 Sectoral multipliers, CINVESTAV regional input-output table

191

 

 

 

 

10

11

12

13

14

16

17

18

19

21

23

24

25

 

0.0000

0.0073

0.1891

0.0012

0.0000

0.0001

0.0000

0.0000

0.0310

0.0247

0.0002

0.0001

0.0015

0.0000

0.0000

0.2190

 

0.0000

0.7252

0.0128

0 0237

0.0000

0.0001

0.0000

0. 0000

0.0038

0.0441

0.0005

0.0001

0.0018

0,0000

0.0000

0.0002

 

0.0000

00000

00000

00000

0.0000

0.0000

0.0000

0. 0000

0.0000

0 , 0000

0.0000

0.0000

0.0000

0 0000

0.0000

0.0000

 

0.0000

0.0003

0.0011

0.0024

0. 0002

0.0013

0.0000

0.0000

0.0012

0.5322

0.0022

0.0009

0.0196

0.0000

0.0000

0.0000

 

0.0000

0.0000

0 0000

0. 0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0,0000

0.0000

0.0000

0.0000

 

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0 0000

0.0000

0 0000

0.0000

0 . 0000

0.0000

0 0000

0.0000

0.0000

 

0.0000

0.0000

0. 0000

00000

00000

00000

0.0000

00000

0.0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0. 0000

0.0000

0,0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0001

0 0000

0. 0099

0.0000

0. 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

1 .0000

0.0001

0.1346

0.0005

0.0000

0.0000

0.0000

0.0000

0.0001

0.0012

0.0000

00000

00000

0.0000

0.0000

0.0000

 

0.0000

1.0047

0 0055

00013

0 0000

0.0001

0. 0000

0.0000

0.0047

0.0259

0.0002

0.0001

0.0011

0.0000

0.0000

0.0003

 

0.0000

0.0009

1.0283

0.0039

0 . 0000

0.0000

0. 0000

0.0000

0. 0009

0.0067

0.0000

0.0000

0.0002

0.0000

0.0000

0.0000

 

0.0000

0.0078

0.0008

1.1494

0. 0000

0.0000

0 . 0000

0.0000

0.0158

0.0008

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

00000

0.0000

0.0000

0 . 0000

1.4157

0.0000

0. 0000

00000

0.0000

0. 0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

00000

0. 0000

0.0000

1 .0000

0.0000

0 0000

0.0000

0.0000

0.0000

0 . 0000

0 . 0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0 . 0000

1.0000

0 0000

0.0000

0.0000

0.0000

0. 0000

00000

0.0000

0.0000

0.0000

 

0.0000

0.0088

00087

00511

0.0000

0.0000

0.0000

1 0002

0.0348

0 0029

0.0000

0.0000

0.0001

0. 0000

0.0000

00000

 

0.0000

0.2318

00041

0.0076

0.0000

0.0000

0.0000

0.0000

1.0016

0.0141

0. 0002

0.0000

00006

00000

0.0000

0.0001

 

0.0000

0 0006

0.0021

0 0046

0 0004

0.0024

0.0000

0 0000

0.0023

1.0116

0.0041

0.0018

0.0373

0.0000

0.0000

0.0000

 

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0 0000

0.0000

0 0000

1.0197

0 . 0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0 . 0000

0 0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

00000

1.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0 0000

0 0000

0.0000

0 . 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0 . 0000

1.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0 . 0000

0.0000

0. 0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

1.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0,0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

00000

1.0000

0.0000

 

0.0001

0.0001

0.0000

0.0007

0 , 0000

0 0000

0.0000

0.0000

0.0001

0.0018

0.0000

0.0000

0.0001

0.0000

0.0000

1.1576

 

0.0000

0.0000

0.0000

00000

00000

0.0000

0.0000

0.0000

0.0000

0.0002

0.0000

0.0000

0.0000

0.0000

00000

00000

 

0.0000

0.0002

0.0001

00000

0.0000

0.0000

0. 0000

0 0000

0.0001

0.0001

0.0000

0.0003

0.0001

0.0000

0.0001

0.0001

 

0.0000

00000

0. 0000

0.0000

0 0000

0.0000

0 0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0 .0000

0.0001

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0001

0.0000

0.0018

0.0000

0.0000

0.0000

0.0000

0.0000

0.0001

0.0000

0.0001

00000

0.0000

0.0000

0.0000

 

0.0002

0.0001

0 0020

0.0045

0.0005

0.0024

0.0000

0.0002

00001

00005

0.0066

0.0001

0.0018

0.0000

0.0000

0.0010

 

0.0000

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

00000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0002

0.0000

0.0000

0.0000

0. 0000

0 0000

0.0001

0.0002

0.0484

0.0003

0.0003

0.0000

0.0000

0.0008

 

0.0000

0.0000

0. 0000

0.0000

0. 0000

0 0000

0. 0000

0.0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0,0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0003

0.0000

00000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

00000

0.0000

0 . 0000

0.0000

0 0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

00000

0.0000

0.0009

0.0001

0.0000

0.0000

0.0000

00000

0.0001

0.0003

0.0000

0.0000

0.0001

0.0000

0.0000

0.0010

 

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0004

0.0035

0.0202

0.0182

0.0050

0 . 0006

0.0000

0.0024

0.0020

0.0060

0.0016

0.0003

0.0178

0.0000

0.0003

0.0005

 

0.0000

0.0000

0.0003

00000

00000

0 0000

0.0000

0.0000

0.0000

0.0001

0.0001

0.0002

0.0001

0.0000

0.0000

0.0000

 

0.0000

0.0000

0 0001

0.0000

0.0119

0.0000

0.0000

00000

0.0000

00000

00000

0.0000

00000

0.0000

0.0000

0.0000

 

0.0002

0.0002

0.0006

0.0001

0.1044

0.0000

0.0000

0.0000

0.0001

0.0001

0.0001

0.0001

0.0001

0.0001

0.0001

 

0.0003

0.0000

0.0007

0.0001

0.0013

0.0000

0.0000

0.0000

0.0000

0.0010

0.0000

0.0099

0.0001

0.0000

0.0001

0.0002

 

0.0000

0.0000

0. 0000

0.0000

0.0000

0 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0001

0.0000

0. 0000

0.0000

0.0001

0 . 0000

0. 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0001

0.0000

 

0.0055

0.0002

0 0047

0.0007

0.0006

0.0001

0.0000

0. 0002

0.0002

0.0171

0.0006

0.0017

0.0030

0.0000

0.0003

0.0012

 

0.0099

0.0004

00047

00012

0.0009

0.0001

0.0000

0.0003

0.0006

0.0011

0.0010

0.0026

0.0014

0.0000

0.0006

0.0053

 

0.0000

0.0000

0 . 0000

0.0000

0.0001

0 . 0000

0.0000

0 0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0002

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0. 0000

00000

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

00000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0001

0.0001

0.0000

0.0001

0.0000

0.0000

0.0001

0.0313

0.0001

0.0001

0.0012

0.0000

0.0000

0.0000

 

0.0002

0.0001

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0003

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0017

0.0113

0.0181

0.0204

0.0305

0.0075

0.0000

0.0130

0.0121

0.0229

0.0137

0.0440

0.0115

00000

0.2041

0.0429

 

0.0270

0.2934

0.1011

0.0704

0.0641

0.1183

0.0000

0.1547

0.1770

0.1843

0.0952

0.0500

0.0600

0.0000

0.0565

0.0897

 

0.0025

0.0062

0.0037

0.0018

0.0019

0.0023

0.0000

0.0031

0.0051

0.0039

0.0021

0.0126

0.0035

0.0000

0.0015

0.0074

 

0.0248

0.0253

0.0297

0.0083

0.0063

0,0089

0.0000

0.0119

0.0162

0.0175

0.0105

0.0269

0.0129

0.0000

0.0062

0.0191

 

0.0198

0.0052

0.0045

0.0012

0.0011

0.0018

0.0000

0 0024

0.0032

0.0039

0.0015

0.0046

0.0017

0.0000

0.0012

0.0023

 

0.0714

0.0164

0.0162

0.0041

0.0040

0.0059

0.0000

0.0079

0.0112

0.0194

0.0051

0.0193

0.0066

0.0000

0.0045

0.0095

 

0.0009

0.0039

0.0032

0.0014

0.0011

0.0015

0.0000

0.0019

0.0025

0.0029

0.0016

0.0016

0.0022

0.0000

0.0008

0.0026

 

0.0056

0.0286

0.0108

0. 0066

0.0060

0.0100

0.0000

0.0131

0.0176

0.0169

0.0094

0.0359

0.0210

0.0055

0.0109

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0006

0.0025

0.0008

0. 0003

0.0002

0. 0003

0.0000

0.0004

0.0005

0.0021

0.0003

0.0007

0.0004

0.0000

0.0004

0.0006

 

0.0003

0.0023

0.0102

00008

0.0005

0.0009

0.0000

0.0012

0.0015

0.0021

0.0018

0.0036

0.0019

0.0000

0.0004

0.0010

 

 

0.0203

 

0.0134

 

0.0075

 

0.0021

 

0.0020

 

0.0018

 

0.0000

 

0.0025

 

0.0036

 

0.0042

 

0.0020

 

0.0047

 

0.0033

 

0.0000

 

0.0020

 

0.0077

 

192

 

 

26

27

28

29

30

31

32

34

35

36

37

39

40

41

 

0.0000

0.0000

0.0007

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0001

0.0001

0.0000

0.0000

 

0.0000

0.0000

00391

00000

0.0001

0 . 0000

0.0001

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0030

0.0076

0.0019

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

 

0.0000

0 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0001

0.0000

0.0000

0.0012

0 0000

0.0001

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0. 0000

0 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0. 0000

0.0000

 

0.0000

0.0000

0 . 0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0. 0000

0. 0000

0. 0000

0 . 0000

0.0000

 

0.0000

0.0000

0. 0000

0 0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0. 0000

0.0000

0 0000

0.0000

0.0000

0.0000

0.0494

0.0000

0.0001

0.0000

0 0000

0. 0000

00000

0.0007

0.0000

 

0.0000

0.0000

0.0002

00000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

00000

0.0000

0.0000

 

0.0000

0. 0000

0.0541

0 . 0000

0.0001

0.0000

0.0001

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0036

0.0104

0.0026

0.0000

 

0.0000

0.0000

0.0016

0 0000

00000

0. 0000

0.0000

0.0000

0.0000

00000

0. 0000

0.0000

0. 0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0004

0 0000

0.0000

0 0000

0.0000

0.0000

0.0000

00000

00000

0. 0000

00005

00001

0.0000

0.0000

 

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0. 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0 .0000

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

00000

00000

0. 0000

00000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0005

0. 0000

0.0000

0.0000

0.0001

0.0000

0. 0000

0.0005

0.0000

0. 0000

0.0012

0 0037

0.0021

0.0000

 

0.0000

0.0000

0.0125

0. 0000

0.0000

0.0000

0 . 0000

0.0000

0. 0000

0.0000

0 . 0000

0.0000

0.0010

0.0024

0.0006

0.0000

 

0.0000

0. 0000

0.0000

0. 0000

0 . 0000

0.0001

0.0000

0.0000

0.0000

0.0002

0.0000

0.0000

0.0023

0.0000

0.0002

00000

 

0.0000

0.0000

0.0000

0 0000

0.0000

0.0000

0 . 0000

0 . 0000

0. 0000

0.0000

0.0000

0.0000

0 0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0. 0000

0 0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

 

00000

0. 0000

0.0000

0 0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0495

0.0011

0.0007

0.0000

0,0000

0 0000

0.0000

0.0000

0 0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0 0000

0. 0000

0. 0000

0. 0002

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

1.0002

0. 0060

0.0003

0 . 0000

0.0002

0.0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0005

 

0.0000

1.0129

00001

00000

0.0001

0.0000

0.0002

0.0004

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0002

 

0.0000

0.0000

1 .0000

0 . 0000

0.0000

00000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

 

00000

0.0002

1 .0066

0.0277

0.0007

0.0001

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0003

0.0000

 

0.0001

0.0000

0.0002

0 0000

1.0043

0.0001

0,0001

0.0000

00000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0003

0.0005

0.0000

0 0003

1.0001

0.0001

0 . 0000

0.0000

0.0024

0.0000

0.0000

0.0050

0.0000

0.0018

0.0000

 

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

1 .0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0. 0000

0 . 0000

0.0000

0.0000

0.0000

1.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

00000

0 0000

0 . 0000

0.0000

0.0000

1.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0001

0. 0004

0.0000

0.0001

0 0002

00000

0.0002

0.0000

1.0009

0.0000

0.0000

0.0000

0.0010

0.0004

0.0002

 

0.0000

0. 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

1 .0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0 0000

0 0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

1.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

1.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0. 0000

0. 0000

0.0000

0 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

1.0000

00000

0.0000

 

0.0000

0.0000

0.0029

0.0000

0.0212

0.0001

0.0429

0.0006

0.0000

0.0001

0.0000

0.0000

0.0002

0.0000

1.0001

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

1.0000

 

0.0007

0.0048

0.0082

0. 0006

0.0045

0.0007

0.0021

0 .0009

0. 0000

0.0232

0.0000

0.0000

0.0597

0.0006

0.0177

0.0005

 

0.0000

0.0000

0.0000

0.0000

0.0016

0.0000

0 .0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0 0000

0 0000

0.0001

0.0121

00000

0.0001

0.0000

0. 0000

00000

0.0000

0.0008

0.0000

 

0.0000

0.0001

0.0003

00001

0.0002

0.0001

0.0004

0.0223

0.0000

0.0010

0.0000

0.0000

0.0001

0.0000

0.0074

0.0001

 

0.0000

0.0000

0.0001

0.0000

0.0014

0.0000

0.0003

0.0008

00000

00000

0.0000

0.0000

0.0000

0.0000

0.0001

0.0003

 

0.0000

0.0000

0 0000

0.0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0003

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0002

0.0001

0.0004

0.0003

0.0045

0.0001

0.0004

0.0004

0.0000

0.0001

0.0000

0.0000

0.0005

0.0007

0.0009

00008

 

0.0005

0.0001

0.0005

0.0003

0.0008

0.0001

0.0008

0.0007

0.0000

0.0004

0.0000

0.0000

0.0002

0.0015

0.0016

0.0013]

 

0.0000

00000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0001

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0001

0.0000

0.0000

0.0000

 

0.0000

0.0001

0.0006

0.0000

0.0000

0.0000

0.0004

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

 

0.0396

0.0170

0.0124

0.0144

0.0168

0.0138

0.0168

0.0324

0.0000

0.0522

0.0000

0.0000

0.0163

0.0145

0.0106

0.0309

 

0.0836

0.0604

0.1022

0.1181

0.1217

0.0301

0.0750

0.0459

0.0000

0.1081

0.0000

0.0000

0.0594

0.0694

0.0918

0.0899

 

0.0018

0.0045

0.0022

0.0057

0.0050

0.0012

0.0128

0.0046

0.0000

0.0033

0.0000

0.0000

0.0021

0.0016

0.0021

0.0089

 

0.0074

0.0136

0.0200

0.0192

0.0193

0.0039

0.0161

0.0121

0.0000

0.0181

0.0000

0.0000

0.0226

0.0093

0.0133

0.0208

 

0.0014

0.0014

0.0017

0.0024

0.0023

0.0006

0.0028

0.0021

0.0000

0.0020

0.0000

0.0000

0.0023

0.0011

0.0015

0.0025

 

0.0046

0.0057

0.0055

0.0087

0.0082

0 . 0022

0.0127

0.0082

0.0000

0.0067

0.0000

0.0000

0.0040

0.0038

0.0049

0.0102

 

0.0013

0.0030

0.0034

0.0023

0.0045

0.0006

0.0076

0.0016

0.0000

0.0020

0.0000

0.0000

0.0037

0.0012

0.0018

0.0032

 

0.0073

0.0074

0.0100

0.0119

0.0122

0.0029

0.0128

0.0105

0.0000

0.0102

0.0000

0.0000

0.0068

0.0070

0.0083

0.0119

 

0.0000

0.0000

0.0000

0.0000

0.0000

0,0000

0.0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0003

0.0004

0.0006

0. 0006

0.0006

0.0001

0.0005

0.0032

0.0000

0.0006

0.0000

0.0000

0.0006

0.0003

0.0004

0.0006

 

 

00006

0.0010

0.0015

0.0010

0.0013

0.0002

0.0016

0.0006

0.0000

0.0009

0.0000

0.0000

0.0010

0.0013

00009

0.0013

 

 

0.0016

 

0.0035

 

0.0034

 

0.0049

 

0.0044

 

0.0014

 

0.0094

 

0.0068

 

0.0000

 

0.0040

 

0.0000

 

0.0000

 

0.0035

 

0.0018

 

0.0023

 

0.0075]

 

193

 

 

42

45

46

47

49

51

52

55

57

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0 0001

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0001

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

00000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

00000

0 . 0000

0. 0000

0.0000

0. 0000

00000

0.0000

0.0000

0. 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

00000

0. 0000

0.0000

0 . 0000

0.0000

0. 0000

0.0000

0. 0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0 0000

0.0000

0.0000

0.0000

0 0000

0.0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

00000

00000

0.0000

00000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0 0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0001

0.0000

0.0045

0.1018

0.0000

0.0000

0.0004

0.0000

0.0004

0.0001

0.0003

0.0000

0.0000

0.0007

0.0000

0.0000

 

0.0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0 . 0000

0.0000

00000

00000

0.0000

0.0000

 

0.0000

0.0001

0.0000

0.0000

0.0000

0.0000

0.0001

0.0001

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0002

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0 0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0. 0000

0.0000

0.0000

0,0000

0 . 0000

0 . 0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0,0000

0. 0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0 . 0000

0.0000

 

0.0000

0.0000

0.0000

0 . 0000

0.0000

00000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0 . 0000

0.0000

 

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0001

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0001

0.0000

0.0000

 

0. 0000

0. 0000

0.0000

0 . 0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0. 0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

0,0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0001

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0004

0.0000

0.0000

0 . 0000

0.0000

0.0000

0 0002

0.0000

0.0000

0.0001

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0001

0.0000

0.0003

0.0001

00001

00000

0.0000

0.0001

0.0001

0.0001

0. 0002

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

00000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0002

0.0000

0 . 0000

0.0001

0.0000

0 0000

0 0053

0.0000

0.0002

00003

0.0008

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0001

00000

00000

0.0000

0.0000

0.0000

0.0011

0.0008

0.0005

0.0013

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0002

0.0001

0.0001

0.0005

0.0001

0.0000

0.0002

0.0001

0.0001

0.0170

0.0002

0.0000

0.0000

0.0001

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

0.0000

 

0.0000

00000

00000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0002

0.0015

0.0001

0.0003

0.0000

0.0000

0.0000

0,0059

0.0001

0.0013

0.0000

0.0000

0.0000

0.0004

0.0000

0.0000

 

0.0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0 0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0 . 0000

0. 0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

 

0.0032

0.0219

0.0000

0.0002

0.0000

0.0000

0.0539

0.0212

0.0005

0 . 0035

0.0018

0.0000

0.0000

0.0587

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

0.0000

 

1.0070

0.0008

0.0002

0.0051

0.0012

0.0000

0.0075

0.0016

0.0013

0.0019

0.0061

0.0000

0.0000

0.0084

0.0000

0.0000

 

0.0000

1.0000

0.0000

0 . 0000

0.0000

0 0000

0.0000

0 . 0000

0.0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0001

0.0000

1.0041

0.1228

0.0001

0. 0000

0.0005

0.0000

0.0001

0.0002

0.0004

0.0000

0.0000

0.0008

0.0000

0.0000

 

0.0008

0.0002

0. 0002

1.0732

0.0002

0. 0000

0 0045

0.0002

0.0011

0.0015

0.0033

0.0000

0.0000

0.0074

0.0000

 

0.0003

0.0001

0.0005

0.0129

1.3000

00000

0.0263

0.0291

0.0072

0.0240

0.0229

0.0000

0.0000

0.0099

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

1.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0 . 0000

0.0000

0.0000

00000

1.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0001

0.0008

0.0001

0.0000

0.0000

1 .0222

0.0000

0.0035

0.0033

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0007

0.0001

0.0024

0.0025

0.0008

0.0000

0 0093

0.0051

1.0005

0.0021

0.0053

0.0000

0.0000

0.0007

0.0000

0.0000

 

0.0011

0.0016

0.0040

0.0029

0.0005

0.0000

0.0017

0.0010

0.0011

1.0077

0.0029

0.0000

0.0000

0.0006

0.0000

0.0000

 

0.0003

0.0000

0.0001

0.0005

0.0001

0.0000

0.0000

0.0002

0.0004

0.0002

1.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0. 0000

0.0000

0.0000

0.0000

0.0000

1.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

1.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

1.0000

0.0000

0.0000

 

0.0000

0.0000

0. 0000

0 . 0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

1.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

0.0000

1.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0001

0.0000

0.0000

0.0000

0.0000

0.0000

0.0004

0.0000

0.0000

0.0000

0.0002

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0385

0.0295

0.1111

0.0390

0.0429

0.0000

0.0203

0.0151

0.0162

0.0154

0.0162

0.0000

0.0000

0.0238

0.0000

0.0000

 

0.1137

0. 0689

0.0326

0.0926

0.2435

0.0000

0.1507

0.1281

0.0672

0.1106

0.0482

0.0000

0.0000

0.0575

0.0000

0.0000

 

0.0084

0.0044

0.0146

0.0075

0.0050

0.0000

0.0070

0.0052

0.0055

0.0074

0.0094

00000

0.0000

0.0033

0.0000

0.0000

 

0.0241

0.0088

0.0123

0.0176

0.0207

0.0000

0.0239

0.0180

0.0130

0.0260

0.0043

0.0000

0.0000

0.0247

0.0000

0.0000

 

0.0028

0.0012

0.0049

0.0026

0.0038

0.0000

0.0030

0.0024

0.0017

0.0025

0.0019

0.0000

0.0000

0.0013

0.0000

0.0000

 

0.0108

0.0053

0.0221

0.0104

0.0127

0.0000

0.0108

0.0086

0.0068

0.0098

0.0092

0.0000

0.0000

0.0049

0.0000

0.0000

 

0.0051

00010

0.0019

0.0037

0.0033

0.0000

0.0061

0.0035

0.0030

0.0055

0.0040

0.0000

0.0000

0.0051

0.0000

0.0000

 

0.0141

0.0076

0.0213

0.0125

0.0209

00000

0.0155

0.0124

0.0086

0.0130

0.0084

0.0000

0.0000

0.0085

0.0000

0.0000

 

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

 

0.0007

0.0003

0.0004

0.0005

0.0007

0.0000

0.0007

0.0005

0.0004

0.0007

0.0002

0.0000

0.0000

0.0006

0.0000

0.0000

 

0.0020

0.0006

0.0020

0.0012

0.0018

0.0000

0.0017

0.0011

0.0012

0.0015

0.0008

0.0000

0.0000

0.0023

0.0000

0.0000

 

 

0.0072

 

0.0058

 

0.0161

 

0.0094

 

0.0052

 

0.0000

 

0.0065

 

0.0044

 

0.0058

 

0.0085

 

0.0068

 

0.0000

 

0.0000

 

0.0059

 

0.0000

 

0.0000

 

194

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0003 0.0000 0.0000
0.0003 0.0000 0 0000 0 0000 0.0000 0 0000 0 0001 0.0000 0.0000 0.0000 0.0000 0.0010 0.0054 0.0003 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0. 0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0 0000 0 0000 0 0000 0.0000 0.0000 0.0000 0.0001 0.0008 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0 0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0001 0.0697 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0008 0.0000 0.0001 0.0002 0.0000 000$]
0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000]
0.0003 0.0000 0.0000 0.0000 0.0000 0 0000 0.0001 0.0000 0.0000 0.0000 0.0000 0.0005 0.0064 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0 0000 00000 0.0000 0.0000 0.0000 0.0001 0,0011 0.0000 0.0000
0.0001 0.0000 0.0000 0.0000 0.0000 0 0000 0 0000 0 0000 0.0000 0.0000 0.0000 0.0003 0.0022 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0006 0.0000 0.0000
0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000 0 0000 0.0000 0.0004 0.0017 0.0003 0.0000
0.0001 0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0002 0.0014 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0 . 0000 0.0000 0.0000 0.0000 0 . 0000 0.0000 0.0000 0.0000 0. 0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0 0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0 0000 0 0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0003 0.0001 0.0000 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0 0001 0.0002 0 0000 0.0000 0.0000 0 0001 0.0001 0 0003 0 0000 0.0000 0.0000 0.0001 0.0006 0.0000 0.0001
0.0000 0.0001 0 0001 0.0002 0.0002 0.0001 0.0001 0.0002 0.0000 0.0000 0.0000 0.0003 0.0004 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0003 0.0052 0.0007 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0002 0.0000 0.0001 0.0000 0.0000 0.0001
0.0001 0.0008 0.0032 0 0001 0 0001 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0003 0 0001 0.0000
0.0001 0.0009 0.0002 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0055 0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0 0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0041 0.0004 0.0004 0.0002 0.0000 0 0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0002 0.0002 0.0001 0.0008'
0.0000 0.0000 0,0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 00000'
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000'
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000'
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000'
0.0060 0.0000 0.0015 0.0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000'
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000]
0.0009 0.0022 0.0173 0.0001 0.0051 0.0000 0.0001 0.0003 0.0000 0.0002 0.0000 0.0005 0.0002 0.0000 0.00011
0.0016 0.0001 0.0000 0.0000 0.0000 0 0000 0.0000 0 0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000]
0.0001 0.0002 0.0531 0.0002 0.0000 0,0000 0.0001 0.0008 0.0000 0.0011 0.0000 0.0017 0.0010 0.0001 0.0008]
0.0002 0.0013 0.2009 0 0004 0.0002 0 0002 0.0002 0.0011 0.0000 0.0081 0.0001 0.0011 0.0017 0.0003 0.00631
0.0271 0.0019 0.1481 0.0003 0.0001 0.0000 0.0001 0.0000 0.0000 0.0001 0.0000 0.0004 0.0001 0.0000 0.0001'
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0 0000 0.0000 0.0000'
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000'
0.0000 0.0001 0.0626 0.0003 0.0000 0 0000 0.0001 0.0006 0.0000 0.0005 0.0000 0.0003 0.0001 0.0001 000$
0.0014 0.0007 0.0128 0,0013 0.0004 0.0001 0.0007 0.0005 0.0000 0.0003 0.0000 0.0003 0.0003 0.0003 0.0003]
0.0020 0.0012- 0.0019 0.0028 0.0003 0.0002 0.0016 0.0004 0.0000 0.0001 0.0001 0.0004 0.0005 0.0004 0.0003]
0.0002 0.0000 0.0003 0.0008 0.0000 0.0000 0.0004 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0001 000$]
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00M
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000]
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0009
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0009
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0009]
1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000]
0.0001 1.0007 0.0000 0.0001 0.0001 0.0001 0.0000 0.0002 0.0000 0.0003 0.0000 0.0011 0.0001 0.0001 0.0002'
0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000'
0.0148 0.0263 0.0209 1.0206 0.0112 0.0081 0.0087 0.0089 0.0000 0.0190 0.0025 0.0550 0.1497 0.0882 0.0079'
0.0780 0.0732 0.1918 0.0099 1.0058 0.0038 0.0108 0.0054 0.0000 0.0039 0.0018 0.0081 0.0090 0.0044 0.0%]
0.0035 0.0037 0.0066 0.0019 0.0192 1.0069 0.0091 0.0159 0.0001 0.0066 0.0116 0.0090 0.0038 0.0080 0.000‘7]
0.0151 0.0214 0.0153 0.0058 0.0339 0.0034 1.0155 0.0176 0.0001 0.0037 0.0113 0.0122 0.0051 0.0039 0.0086'
0.0027 0.0015 0.0035 0.0019 0.0151 0.0098 0.0052 1.0282 0.0001 0.0017 0.0135 0.0141 0.0071 0.0108 0.0015'
0.0070 0.0057 0.0124 0.0089 0.0494 0.0207 0.0124 0.0119 1.0001 0.0039 0.0132 0.0197 0.0085 0.0206 0.0024'
0.0016 0.0046 0.0035 0.0004 0.0124 0.0129 0.0019 0.0141 0.0001 1.0018 0.0046 0.0081 0.0035 0.0154 0.0121'
0.0107 0.0087 0.0183 0.0046 0.0832 0.0353 0.0327 0.0353 0.0004 0.0025 1.0421 0.0226 0.0122 0.0557 0.0033
0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 0.0014 0.0000 0.0009
0.0464 0.0006 0.0006 0.0013 0.0016 0 0002 0.0230 0.0004 0.0000 0.0001 0.0003 0.0266 1.0126 0.0035 0.0002'
0.0010 0.0015 0.0016 0.0002 0.0074 0.0225 0.0015 0.0011 0.0000 0.0016 0.0177 0.0059 0.0024 1.1077 QM
0.0040 0.0042 0.0065 0.0060 0.0148 0.0136 0.0120 0.0292 0.0000 0.1334 0.0103 0.0206 0.0109 0.0221 1.0103]

 

195

 

APPENDIX C

ECONOMIC POTENTIAL BY M UNICIPIO

196

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

MUNICIPIO Potential
ABALA 2.9259
ACANCEH 4.2738
AKIL 2.6881
BACA 3.2853
BOKOBA 2.2767
BUCTZOTZ 1 .9019
CACALCHEN 3.2754
CALOTMUL 1 .1773
CANSAHCAB 2.6618
CANTAMAYEC 1 .0339
CELESTUN 1.5479
CENOTILLO 1 .3000
CONKAL 4.2364
CUNCUNUL 0.2947
CUZAMA 2.6428
CHACSINKIN 0.9016
CHANKOM 1.3406
CHAPAB 2.2697
CHEMAX 1.9995
CHICXULUB PUEBLO 2.7490
CHICHIMILA 2.1490
CHIKINDZONOT 0.7329
CHOCHOLA 2.7195
CHUMAYEL 1 .8356
DZAN 2.9204
DZEMUL 2.6939
DZIDZANTUN 2.4221
DZILAM DE BRAVO 0.3792
DZILAM GONZALEZ 1.6938
DZITAS 1.4943
DZONCAUICH 1 .3896
ESPITA 2.2627
HALACHO 2.9988
HOCABA 3.0338
HOCTUN 3.0484
HOMUN 2.8919
HUHI 2.2495
HUNUCMA 4.1228
IXIL 2.3837
lZAMAL 3.8946
KANASIN 5.9425
KANTUNIL 2.2401
KAUA 0.8037
KINCHIL 2.2415
KOPOMA 1.8404
MAMA 2.0278
MANI 2.5048
MAXCANU 3.2117
MAYAPAN 1 .4520
MERIDA 7.1588
MOCOCHA 2.8244
MOTUL 4.5891
MUNA 3.2541
MUXUPIP 2.7670
OPICHEN 2.3398

 

 

197

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

OXKUTZCAB 3.3679
PANABA 1 .5962
PETO 2.4346
PROGRESO 5.0477
QUINTANA ROO 0.3020
RIO LAGARTOS 0.3633
SACALUM 2.4362
SAMAHIL 2.6092
SANAHCAT 1 .8244
SAN FELIPE 0.0000
SANTA ELENA 1 .2506
SEYE 3.5257
SINANCHE 2.0322
SOTUTA 2.5868
SUCILA 1 .0628
SUDZAL 0.8691
SUMA 2.0514
TAHDZIU 1 .0597
TAHMEK 2.8663
TEABO 2.0346
TECOH 3.8363
TEKAL DE VENEGAS 1.6415
TEKANTO 2.7017
TEKAX 3.1336
TEKIT 2.8389
TEKOM 0.8569
TELCHAC PUEBLO 2.6701
TELCHAC PUERTO 1.1376
TEMAX 2.3872
TEMOZON 2.0873
TEPAKAN 2.0168
TETIZ 2.4000
TEYA 2.0068
TICUL 4.1667
TIMUCUY 3.9264
TINUM 2.0662
TIXCACALCUPUL 1.1421
TIXKOKOB 4.3730
TIXMEUAC 1 .5082
TIXPEUAL 3.8148
TlZlMlN 2.8880
TUNKAS 1 .4291
TZUCACAB 2.0664
UAYMA 1 .0321
UCU 3.0917
UMAN 5.5564
VALLADOLID 3.3693
XOCCH EL 2.4277
YAXCABA 2.4595
YAXKUKUL 2.8012
YOBAIN 1 .5920

 

 

 

Appendix C.l Measures of economic potential by municipio

198

 

 

APPENDIX D

REGIONAL INPUT-OUTPUT TABLES AND MULTIPLIERS

 

 

199

 

 

 

$3 .033 39:03:35 Renamed ad 039—.

 

 

 

 

 

 

08 {ON c F vac — 828 noooon mm 58 as F mNomv ogN oanp cum 5 move oNvap 3&8 momma hmo 3 bass 8N5? Noemi 352.0 .5350 305.
nvaNp 34310 m 58— mmONON muss mo EN 0 5: 61: :3— vnov NNmnN omomN 2"th SON OnhN—w Nam 5— 38p 35 9:9: 8:09:—
33 8mg 5mm» mNnnv 3 EN N EN 00219 ooop as 5:. QNN «v a 5: can moms hog w $8— 88 80mm 98:. 300. 35h
ovap NVNN FN mnNm : 1 N C. 5: 5 $02.. tom: NOON :10 8NN 8 «on mNhap 3N3 =th 5680 mm 30 0:9 can i 0008 33>
«Nmop ENm— omoNV om— : 0'85 3N hvmov N—ON comm Nvt who: 08o Sam ammN 820 :58 o: 13': c02¢...
mnmN mmNmN omm {Nu m 3N? 8N0 va: hNN meN a: §N EON no: mpnN «at. :N w thw hmoO: EoEonw
936— SE? a o o o o o o o o o c o o o c o o o a 8.5.6465 92.2563. up
$058 038’ 500% ch mthM 3:.” mo 5 — p88 cum 5’ mv 8N @N mooN 93 km P: a 5' 1 SN 80 38 00035.6. 3:29.205 can 3:089. hr
common 0N0: mNogN over vomm o 2N0 Pang oN: 0 var 1mm 3 when 090— ovo— Nmn o o aNmn o 288 38 9.3 80.2.0». 38:05”. Or
858 now: 35 SN NmpN ono m: 0000 once an” th P 3N Nov mNn §N oNN — m8 mom §N mmpN 0.3. g acouaoEEoo 9.3 Stages; mp
Novomnp ommvnm oo— pom w: p SQON mmmv 008 8% N8 3N 5 00°F mama no; 00va SN gm thv ONQVN mNo 3 Row gmNN 9.3583. 0:6 £20... .oegtoo Cr
@Nomv 88'. as NM Now now: com— o 0 com 0 NF 12. who 59 has EN 8' SN «8 3 SH @055: 0.3:.“ up
94va ovvva o o o o o o o o o o o o o o o o o o o 8:038:00 Np
onan nNNo 88 o m: an o— m N o v p o o — h o m— o _. m — oat—435:9: .050 w r
cum 5 0012 new 0 mom m3 mmN vomN mm 050 n on v amp ON 5 9 3w th 3 ommp E5560 0:0 >552 Or
wow? 8?. o o 3%— mNN N5 03% o :Nm p N Pom mm C v m v—N mom v mom 90385 .89: 033 a
chmN— mamas mm: 0 53 N8 cam 80» m— pnONN v 3 N Q33 mNp h n— m ; new a Nmo 301605 2:395:02 o
358 SMON mac? 0 CNN m 8 uni anon Np sow Np QNN p 3N NQN 58 at Nmn m8 p h at? p 9028.5 30.5050 h
momma va—p Xuth 0 o o o 3 o o u ow o 5 t — n on F S." o o 9260.6 Local 0
~00 3 mmmv 83N 0 RN— 3N 8N ammN ON Nmmv om — 3 c on NM I 09: cup mun 0 EN 99.605 uoo>> u
588...” vavm— wnNhN— o C F p 19 mNN 2.9... p r mm m v w ; mN h h Nmoz. VON m 80 3809a 9.86... v
Sthn $69 comm: o 89 mNP mt mmmN n 8 o P c NN 0N N 0 pic :8 N 509 8260.5 noon 0
N081 88p 0 o o o o o c m 5: o o o 50% 0mm 0 o o 3N o o 0552 N
3350 x. 5': owns? 0 o o o o c o o o c o o o o m :3 :n C. o amok" 05230.5( F
g ..
30h E0 1... up hr up up cw op Np we or a o h o m c n N p #5302.

 

 

 

 

 

 

 

200

 

80. .033 5050-505 .28.qu NA. 030...

 

 

 

 

 

030005 ~00an gmgmN Nmmwo0N 00059 8085 mN5mNN NwmoFm 880 0055 508F QNoFFn 85°F 3N8 8N55 NNanF n5FOFa 0000: Q55NmNF 0>850 1.0L"
0Nm¢NN $5005 55m F8 F ON580 5waN0 @80ON 0FN00 mom5 F 050 FN FNno 0305 5080 N08N 0N0 F0 5580 mNNcNN soNN mNN5m F 0.30:. 00:09...
0FO0N 8800 F005; 008NN F0mF0o QFO0F 8553 50.00 $0NF 855 mmoFoF N055F 88F 88F 55000 00850 0F F FN 8008 050:. .000. 3.05
a F 5 F 5805 N088 $0555 F530: 0mg”. 05000 F 0N0nF ONmON 000 F 50Noo F F550N FomNN mono F 05m FM 8 FonN 005m5 8500N 00000 03.0)
80mm 58009 N058 F m5amoN 0853 N8 FoF 0N F00 F m5OON 8Nm F NNOF 80 F0 flown F mow FN 0 F8 F ammoN 05Nm5 FannN 80000 .000.—
500... $000 08F 0 FOOF mmN Fe m0 FN mam... com 580 FNO 0 F00 0 SN 0 FON omnN 5omwF 83F 08F oomSF .Eoltw
Nnmm Fm Nmmc FM 0 o o o o o o o o o c 0 o o o c o o c 0050305 0.000.500: 0 F
gammaN 050550 0000055 800 000m F F 0800N 0008 a Fem: 005 F o 8N mam 0N 8 F0 N50 mNm FON n Fm 0 F00. 8NN 508 F 0003.00 302000.90 0:0 3:00.00 5F
N0080N m FmNn F @5530 F 88 5NONmF QwoNN N085 nN F000 0590. o 08 050 F 8 F553 Nam F 080 80 005 F N58 Fn FN F 0555 F 0.0.00 W0. 0:0 0005.00 3.9.05“. OF
0859 N88 @5009 095 895 F58 330 00050 35F 5000 mm: FNFF an” 058 58F 0aNF 0n: F55F 0N8 N5oF 0°5FN 000002020000 0.5 0000000000; nF
50088 5008.: 808: oF5n 8F Fm 000FN O50mN F800 ooQN anNNo 800 085 5000 N500 35°F FF05 m5mm FowoF 8009 80m 0008 0.005300. 0:0 0.0.0: 690:»..00 0F
mN5mNN 8000. 0058 F 85N mmmN5 ommm F 0009 n FN5 85 F 0 00.... N F0 Nan FNM F F NmNF mom can own F 5000 8 F F @000 002:5 0.330 ”F
Nome Fm N80 Fm o o o o o o c o o o o O o o o o o o 0 8:03.080 NF
0800 585N mmNon 0 N8 0 Fa 0m mm FF 0 5F N o o N 0 F o 5 o N am 0:03.00F3cg .050 FF
85F5 0000N wNQON o oNN OON 80 8F 00F 50NFN nF 8F 0 mNm FN FF mN NN onN 00F 050 58.0300 0:0 5.00.3002 0F
5§F moon 0 0 N— o o N F FmoFF 0 mm F0» QFF F F N o 5 o F 803005—0203 a
oNoF Fm 0508 F ONn0 o 0 F5 F 800 55 F N o F 5N8 0F FN F 0059 5... F o F F F 8NN o on 0.03090 05305002 0
no 5.: 000mm meN 0 com co F on a F Nn ON 996 F 00 8m 0 000 N00 000 F mNn 0 FN 800 n m5nN 0.03005 30.208 5
0°30 NmNmN 005 F0 0 o o o on F o o 5N Q: o 05 «N F 0 F0 85 o o 0.03090 .0000 0
ENE. an F0 NN80 o 0N0 85 F0 an F 05 mwomF 80 an F o 5N 5n 5N m0mN 0F 050 0 F5 9030.0 0003 m
NN Fan F 0 F0 F5 030m 0 08 an RN F50 5n 0 NN 5 o 00 0N FF 0 F 0000 05 m 80 0.03090 0:03... 0
95 F0 F0 000050 5Nnn5n 0 N00 F o o o F o o o o o 0N o F FON moN5 F o m F F00 30300.0 000“. n
0000: 088 o o o o o o o 0005 o 0 o NmNmF mom 0 o o FFFN o o 0552 N
055NON F 000000 wNmN50 0 N00 F o o o o o N o o o F o o w5oNF 0FvnNN c 0008 F 05:39.2 F
50 on.
.30... ICINIFO I... 0F 5F 0F mF 0F nF NF FF OF 0 a 5 c m 0 n N F >¢|bmaaz

 

 

 

 

 

 

 

201

 

mam. 6...... 598-5%. .2...me n: 2......

 

 

1550 So..—

 

 

 

.6808 .823.” 83.8. «.82... 8g 82.8 899 882... 8.9. «.88 «88 W098 0.38 .8m. 0.8... 883. 89.3 8.8. 282..

.838 .88... 3683 923. .928. 83m“ 383 2.3m 28... km; S. .88. 8a.. 8.8 03.8 2&3. .3: 98.... 8353.59...

8.8.. 8.34. :83 88$ .89... 893 88k. «m3. 808 8.9 8..qu 8.3 Sfim 88m 838 Gas... 83.. pg... 2.6.... .82 .80»

888 «3.2.. 338.. 898. mg: .88 83k 25 «58 £8 E 89. a...» has. 2.8. 8!... .33 98k... 002802;

888 :8!» £88 2.8% 90.3. 88. .89.. 88» 8o; 93 81:. 2.3a 8.3 98.. 838 888 88. 588. .33

88. 8a: 2.3 88. «93 83 88. 8v. t5 .8 t8 .8» 3..“ 98“ 8o.» 9.8 8.. 2.8. Eogm
598.. 838.. o o o o o o o o o o o o o o o o o o o 8.535 903.532 a.
8&6... 3:8. 0882 98¢. 8:9 BS... :32. .039 38 88 8m 88 3. 83. 8! .3. Ev 89 v38 8... 88... 303.3 3538.0... uca 3:33. 3
«.82.. 389 .23.... 58 868 2.5 g «98... 0.8. 89 9a 3.: 8. 33s 88 88 m... 38. 3:... 1.8 808 0.93.3.9... 89.5» anger. 3
88¢? .89... 58.9 368. 88a. 8: 88m. :28 R8 ~85 88 8... to. at... 88 38 a8“ 88. 38“ men. 93% 82.8.5888 new Santana...» 3
82.8 8.85 0893 «93 o o 88 o 39 ~88: 88. «39 .88 83a m8... 8.... 3.». 81K «.88 2mm 88.. 9:83.38. 9... m3.o:.oo..oEEoo 3
899 .38. .82... own; «33. o o o 8.... o «8. 8.... 88 ~88 83.. as a... £8 89 m8 2.. 8.5.... 23:... n.
8..ch 88...,» o o o o o o o o o o o o o o o o o o 0 3.82.260 up
8.9. «9.8 can: o .6. 8. 8 0 on o 9 .. o o n on o «m o . 3 cacaoasgc. .050 S
8.88 88». 88.. 0 ~98 58. 98 85. 8o 89... 8 no. on .8 8 8 S. 9... 8R 3. 88 29.5.33 new €05..an op
.88 8..» c o .m... 2; m8. 8?. m. 983 .. a... 8... 8.. v o m. z. a... o o3 90:3... .99.. ofiwm o
838 «3.8 5.... o o o o o o 8.8... o o o 8..“ 8 o o . o o o 903.65 2.32-82 o
988 8.8 .88 o o o o o o .33 8 88 o 2. 8o 23 3.. 8o 88 o o 80335 .3395 h
.85.. m8: 389 o o o o o o o 8 3m . 8 9 o a 3. 9.... o 0 90:35.33 a
0.8... 88. v83 o o o . o o «88 m6. 8... o o n v 8... o o o o 90305302. m
.303. 8.3.... .203 o :8. 8.... own. :9 I .5 3 we a 9. 8 .~ .~ .63 So o wow. 33.65 05.6.. v
39.3 g. 838. o £03 88. 8?. 88. «v 38 m 8 o 8. 8 o o «8.. 8.8 o 808 233.5 noon a
.28. v.8. o o o o o o o 81... o o o 2.8 a.» o o o o o o 9:53. u
282... 838 288 o o o o c o o o o o o o o o .68 898 o «88“ 9250...? F
5 So 9.
13h £930 :1 3 3 or m. 3 up up 2 o. a o h o m o a u p 5.5302.

 

 

 

 

 

 

 

 

 

 

202

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

YUCATAN OUTPUT INCOME EMPLOYMENT
Agriculture 1 .949 1.438 1 .248
Mining 1.209 12.759 1.129
Food products 1.855 2.079 3.734
Textile products 1.633 1.528 2.645
Wood products 1.355 1.555 1.248
Paper products 1.441 1.434 1.463
Chemical products 1 .378 1 .533 1 .414
Non-metallic products 1.618 1.819 2.169
Basic metal products 1.778 1.811 4.861
Machinery and equipment 1.388 1.471 1.187
Other manufacturing 1.410 1.382 2.008
Construction 2.082 1 .670 1 .884
Public utilities 1.939 1.259 1.673
Commerce, hotels and restaurants 1.306 1.849 1.349
Transportation and communications 1.335 1.441 1.768
Financial services and real estate 1.393 1.471 6.628
Personal and professional services 1.458 1.408 1.569
Maquiladora industries 1 .218 1 .359 1 .151
Table D4 Type II multipliers for Yucatan (1988)
YUCATAN OUTPUT INCOME EMPLOYMENT

Agriculture 2.177 1 .696 1 .369
Mining 1 .478 1 .552 1 .343
Food products 1.946 3.755 2.807
Textile products 1.801 1.878 1.326
Wood products 1.572 1.652 1.291
Paper products 1.665 1.529 1.292
Chemical products 1 .499 1 .727 1.302
Non-metallic products 1.684 2.309 1.721
Basic metal products 1.847 3.614 2.913
Machinery and equipment 1.625 1.580 1.247
Other manufacturing 1.764 1.453 1.581
Construction 2.253 2.41 5 4.809
Public utilities 1.804 1.372 2.138
Commerce, hotels and restaurants 1.606 1.606 1.319
Transportation and communications 1.481 1.566 1.934
Financial services and real estate 1.316 2.358 7.540
Personal and professional services 1.754 1.502 2.133
Maquiladora industries 1 .405 1 .51 1 1 .102

 

 

 

 

 

Table D.5 Type II multipliers for Yucatan (1993)

203

 

.- I
I

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

YUCATAN OUTPUT INCOME EMPLOYMENT
Agriculture 1.946 1 .504 1 .238
Mlnlng 1.390 1.570 1.176
Food products 1.649 3.495 2.791
Textile products 1.831 1.596 1.301
Wood products 1.562 1.546 1.230
Paper products 1.505 1.549 1.186
Chemical products 1 .501 1 .552 1.174
Non-metallic products 1.592 2.042 1.937
Basic metal products 1.762 3.820 2.827
Machinery and equipment 1.413 1.703 1.265
Other manufacturing 1.764 1.385 1.514
Construction 2.051 1.817 1.951
Public utilities 1.346 1.376 1.227
Commerce, hotels and restaurants 1.523 1.593 1.260
Transportation and communications 1.380 1.534 1.719
Financial services and real estate 1.357 1.729 3.521
Personal and professional services 1.634 1.418 1.871
Maquiladora industries 1 .322 1.591 1.1 13

 

 

 

 

 

Table D.6 Type D multipliers for Yucatan (1998)

204

 

APPENDIX E

IN TER-REGIONAL INPUT-OUTPUT TABLES AND MULTIPLIERS

205

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

M E R 1 D A R u R A L
INDUSTRY 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 HH 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1G 17
1 Agriculture 14 0 1359 299 0 0 0 0 0 o 0 0 0 0 0 0 0 0 998 838 0 282 55 0 0 o 0 0 0 0 o 0 0 0 0 o
2 Minmg 0 0 0 0 0 0 0 11 0 0 0 32 0 0 0 0 0 11 0 0 0 0 0 o 0 0 3 0 0 0 7 0 o o 0 0
3 Food products 188 0 4784 450 1 2 11 15 0 1 0 59 2 1801 115 98 940 0 38315 10871 2 987 83 1 0 9 4 0 0 0 12 1 328 30 9 193
4 Text1le products 5 0 148 10414 3 8 12 28 1 3 2 84 8 2138 159 107 807 11 80140 287 4 31 1922 3 0 10 8 0 0 2 13 2 387 42 10 188
5 wood products 1 0 81 29 134 3 3 8 0 15 15 745 3 498 32 43 200 0 3444 45 1 13 5 132 0 3 2 0 2 15 154 1 90 8 4 41
8 Paper producrs 0 0 258 74 2 1 9 24 0 41 3 0 0 37 o 0 0 0 19943 0 0 53 14 2 0 7 7 0 4 3 0 0 7 0 0 o
M 7 Chemical products 10 0 834 322 45 338 87 113 1 104 3 2255 5 3278 175 148 943 0 8888 571 4 173 59 45 2 58 31 0 11 3 487 1 594 47 14 194
E 8 Non-metallic products 9 0 1004 289 8 8 57 2958 2 10 2 19190 10 8157 417 851 2352 :1 1088 529 7 208 53 5 0 47 801 0 1 2 3974 3 1115 111 81 484
R 9 Bas1cme1a1 products 6 0 250 179 2 4 8 89 323 24 1 2855 5 3834 244 204 1289 U 0 325 4 52 33 2 0 5 19 4 2 1 550 1 894 85 19 281
l 10 Machlneryand equ1pmen1 21 0 192 125 7 6 10 113 3 49 1 4504 25 ‘964 185 123 680 0 5980 1201 40 40 23 7 0 8 31 o 5 1 933 7 356 49 12 140
D 11 Olhermanufactunng 0 0 0 7 0 4 0 0 0 0 1 0 1 3 4 20 87 11 2718 8 0 0 1 0 0 0 0 0 0 1 0 0 0 1 2 14
A 12 Construcuon 0 o 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 o 0
13 P110118 011I1t1es 4 1 585 1550 74 178 353 2981 308 130 49 0 235 0 0 982 9919 7511 18997 205 282 117 288 73 1 293 802 4 13 50 0 83 0 0 92 2042 104 8817 8040 40285
14 Commerce, 1101615 and restaurants 327 2 29403 17888 2033 2885 3379 9851 5393 2738 420 84472 408 40498 5951 3553 14718 100: 307180 18904 917 8094 3298 2014 20 2801 2887 89 278 433 13353 108 7335 1582 332 3029 138 110225 500128 1185788
15 Transponation and communlcations 87 1 1473 1987 241 484 549 1734 284 380 104 855 220 12578 5385 112 4718 1927 172237 3878 443 305 387 239 3 455 489 3 38 107 177 58 2278 1432 10 971 285 81808 19709 298292
16 F1nanc1al sen/ices and real 951319 0 8 0 0 182 1503 529 4103 16 482 88 0 818 41458 8885 0 7238 919 134849 0 3234 0 0 181 11 438 1111 0 47 90 0 217 7509 1777 o 1490 126 48319 19907 283071
17 Personal and professmna] sewlces 45 1 1589 885 44 297 199 1830 21 172 18 99 238 48147 9489 22890 21594 2549 309905 2582 547 329 183 44 2 185 441 0 17 18 21 83 8358 2522 2140 4445 350 111210 90843 841871
18 Maqulladoramdustnes 0 0 0 0 0 0 0 0 0 0 0 0 0 n 0 0 0 ’1 0 0 0 0 0 0 0 0 0 0 o 0 0 0 0 0 0 0 0 0 129857 129857
Employment 5157 44 8731 4283 1284 808 1301 1425 88 1397 81 4842 1901 ,’7855 2554 832 19384 19". 0 880 0 0 14 0 0 0 0 0 84 0 0 0 0 o 0 11
Labor 3315 18 44827 47952 1718 3097 3571 9239 1000 1802 1101 21103 20123 80373 21307 34101 123089 1202: o 50 o 0 17 0 o 0 351 0 0 0 0 0 228
Value added 1850 26 25999 59828 1032 10174 7977 43897 2288 5890 1718 38284 -6612 175139 57708 111515 188503 14135 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 2292
1 Agriculture 815 0 57710 12714 0 0 0 0 0 0 0 o 0 0 0 0 0 0 1W90 0 11981 2347 0 0 0 0 0 o 0 o 0 o 0 0 0 0 111545 szzeefi
2 Mining 0 0 202 0 0 0 194 4938 0 0 0 14084 0 0 0 0 0 3 o 0 0 42 0 0 0 181 1337 0 0 0 291; 0 o 0 0 0 0 0 19055 42904
3 Food producis 38 0 988 91 0 0 2 3 0 0 0 12 0 385 23 19 191 0 7788 2204 0 200 17 0 0 2 1 0 0 0 2 0 88 6 2 39 0 20438 27785 80222
4 Tex11|e products 1 0 21 1474 0 1 2 4 0 0 0 9 1 302 22 15 '14 1) 11344 41 1 4 272 0 0 1 1 0 0 0 2 o 55 8 1 24 0 4071 19519 37311
5 Wood products 3 0 252 121 548 12 14 23 0 82 80 3055 13 2041 131 177 822 :1 14129 185 2 52 22 543 0 11 8 o 8 81 833 5 370 35 17 189 0 5070 4381 33010
6 Paper products 0 0 1 0 0 0 0 0 0 0 0 0 0 0 o 0 J 138 0 0 0 0 0 0 o 0 0 0 0 0 0 0 0 0 o 0 49 82 271
.7 Chemical products 10 0 810 313 44 327 85 110 1 101 3 2191 5 3185 170 144 918 2 6893 555 3 188 58 44 2 54 30 0 10 3 454 1 577 45 13 189 0 2402 13178 32873
R 8 Non-metallic products 2 o 211 81 1 1 12 821 0 2 o 4031 2 1294 88 137 494 -) 224 111 1 44 11 1 0 10 188 0 o 0 835 1 234 23 13 102 0 81 13853 22470
U 9 Basic metal products 0 0 3 2 0 0 0 1 4 0 0 30 o 43 3 2 14 1) o 4 0 1 0 0 c 0 o 0 0 0 8 0 8 1 0 3 0 o 57 182
R 10 Machineryand equipment 2 0 20 13 1 1 1 12 o 5 0 474 3 207 19 13 72 0 828 128 4 4 2 1 0 1 3 0 1 0 98 1 37 5 1 15 o 225 1012 3009
A 1101hermanufac1uring 0 0 o 6 0 3 0 o 0 0 1 0 1 2 4 18 54 1‘ 2172 7 0 0 1 0 o 0 0 0 o 1 0 0 0 1 1 11 0 779 2835 5898
L 12 Construcmn o o o 0 0 0 0 o 0 0 0 0 0 o 0 o o .1 0 o 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 45488 45488
13 Publlc utilities 1 0 123 337 16 38 77 844 87 28 11 0 51 0 0 214 2158 19 4133 45 81 28 82 18 0 84 174 1 3 11 3 14 0 o 20 444 0 1483 -1562 8781
14 Commerce, hotels and restauranls 58 0 5078 3084 351 495 583 1701 931 473 73 11129 70 8991 1027 813 2540 11 53024 3283 158 1052 589 348 4 483 480 12 48 75 2305 19 1288 273 57 523 0 19028 88533 204894
15 Transportation and communications 15 o 320 432 52 101 120 377 57 78 23 188 48 2737 1172 24 1027 'J 37475 844 96 88 80 52 1 99 102 1 8 23 39 13 498 311 2 211 0 13448 4785 84901
16 Financial sen/Ices and real 951319 0 1 0 0 14 134 47 388 1 41 8 0 73 3895 598 0 845 J 12002 0 288 0 0 14 1 39 99 0 4 8 0 19 889 158 o 133 0 4307 1888 25232
17 Personal and professional services 12 o 410 228 11 77 51 420 5 44 5 28 81 11901 2447 5903 5589 0 79919 888 141 85 42 11 1 43 114 0 4 5 5 18 2155 851 552 1148 0 28879 24123 185528
18 Maqunadora industries 0 0 0 0 0 o 0 0 0 0 o 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 o 0 o 0 0 0 0 0 35759 35759
Employmeni o 0 1333 847 0 823 17 413 48 792 o 3048 349 1887 1885 80 1845 4 11 104900 850 4147 2824 1041 34 1043 748 5 348 102 3584 1018 13093 1952 244 8088 211
Labor 0 0 8877 7278 0 2388 47 2878 728 1022 0 13278 3894 4047 13387 4317 11728 288.1 140829 50 3013 8454 1283 55 2889 2158 14 478 580 8589 4551 8429 8485 4440 17980 1580
Value added 0 0 o o o 0 0 0 0 0 0 0 0 0 0 0 0 a 70088 18750 12180 3441 28482 44 11752 5411 12 520 291 8720 ~827 38282 13508 7720 45739 0
Total local inputs 1451 11 108028 53357 3795 6861 8353 32834 7398 4948 888 130137 2300 193150 34543 38207 80057 718I 83885 8242 22390 9849 3780 49 5288 8890 95 501 915 28953 811 34984 9183 3385 18479 983
Imported inputs 2227 44 109331 95183 1502 17115 15884 18510 4812 14907 3889 8193 20759 753080 171370 98932 280495 9384' 72341 19811 22880 17587 1488 122 13188 5012 82 1511 3778 3226 4428 127019 35725 9887 85350 30878
Total Outlays 8843 98 297061 263576 8047 39635 33831 108958 18223 28587 7374 208972 40285 1185788 298292 283071 841871 12985"T 1374480 387141 42904 80222 37311 33010 271 32873 22470 182 3009 5898 45488 8781 204894 84901 25232185528 35759 898548

 

 

Table E. 1 Inter—regional input—output table, 1988

206

 

 

 

 

MERIDA

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

|NDUSTRY 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 HH 1 2 3 4 5 6 7 8 10 HH OTHER Total
FD Out U!
1 Agriculture 502 0 10410 911 0 0 0 0 0 0 0 0 0 0 0 0 95 0 778:1 7888 0 2177 331 o 0 0 0 0 0 o 0 0 0 0 0 20 0 14382 22808 888
2 Mining 0 0 232 0 o 0 52 1817 0 1 0 7105 0 0 0 o o 0 0 0 0 49 0 0 0 5 435 0 0 0 1254 0 0 0 0 o 0 0 7173 17922
3 Food products 2316 0 12824 283 1 0 18 0 0 0 0 0 0 0 0 0 2024 0 112408 35488 0 2882 103 0 0 1 0 0 0 0 o 0 0 o 0 433 0 207953 380913 737442
4 Textile products 25 1 80 7727 7 8 18 32 1 8 12 0 27 543 178 44 929 0 69163 390 3 12 2805 1 0 2 9 0 1 2 0 8 117 29 1 199 0 17450 92179 191986
5 Wood products 3 0 307 20 1507 18 20 16 0 183 321 10791 47 153 20 748 878 0 25857 48 o 84 7 211 0 2 4 0 31 58 1904 11 33 3 15 188 o 6524 7113 57102
6 Paperproducts 0 0 480 39 3 1 12 43 1 93 12 0 0 98 0 0 0 0 38774 0 0 100 14 o 0 1 12 0 18 2 0 0 21 0 0 0 o 9783 14420 63927
M 7 Chemical products 94 o 2294 192 170 781 183 303 o 433 21 10951 11 2304 34 92 449 0 13887 1434 1 480 70 24 21 15 81 0 74 4 1933 3 498 8 2 98 o 3499 25768 66162
E 8 Non-metallic products 1 0 1356 1 6 0 79 8175 2 1B 8 61860 11 1 108 3050 1512 0 2060 19 0 284 0 1 0 7 1860 0 3 1 10918 2 0 17 62 324 o 520 122474 212537
R 9 Basic metal products 0 0 17 o 4 2 2 295 2942 219 7 29994 3 7 0 0 45 0 0 3 0 4 0 1 0 0 79 0 37 1 5293 1 2 0 0 10 0 0 13737 52703
| 10 Machineryand equ1pment 308 39 259 36 26 11 19 351 16 223 11 24547 156 182 451 343 369 0 17513 4723 72 54 14 4 0 2 94 0 38 2 4332 38 39 74 7 79 o 4419 29105 87953
D 11 Othermanufacturing 2 0 0 5 o 6 1 0 0 1 5 0 4 28 21 192 332 0 16038 25 0 0 2 0 0 0 0 0 o 1 0 1 6 4 4 71 0 4046 15380 36174
A 12 Construction 0 0 0 0 0 0 0 0 0 0 0 0 0 o 0 0 0 0 0 0 0 0 0 0 0 0 o 0 o 0 0 0 0 0 0 0 0 0 809012 809012
13 Public 01111085 144 134 2095 966 150 213 484 4895 707 279 182 0 785 4080 1500 10931 48050 584 56082 2210 252 438 351 21 8 42 1318 0 47 33 0 176 878 248 223 10284 206 14150 41580 181540
14 Commerce, hotels and restaurants 3529 647 74929 12807 8084 5188 6713 24774 19596 8980 2472 250834 1854 41322 21942 24416 34260 777 803571 54068 1215 15687 4577 850 138 814 6859 0 1524 444 44284 427 8894 3803 499 7332 274 202747 1123310 2821802
15 Transportation and communicatlons 789 245 4744 1472 547 638 839 3475 701 886 453 3819 854 58154 23898 7285 39738 1485 797650 12089 480 992 534 78 17 77 934 o 150 81 874 196 12517 3891 148 8505 524 201253 18373 1208945
16 Financtal services and real estate 793 1945 3660 1779 491 2315 1042 9779 72 1338 435 0 810 288453 48481 22272 154280 708 1425456 12145 3854 785 846 89 62 95 2829 0 227 78 0 831 61658 7957 455 33015 250 359653 377191 2828250
17 Personal and professional services 477 384 3654 547 125 521 379 3998 89 543 101 0 1114 123510 46361 250729 114760 1963 1639158 7314 721 784 198 17 14 35 1075 0 92 18 0 258 28584 7812 5121 24561 692 413572 324293 3001335
18 MaqUiiadpra industnes 0 0 o 0 0 0 0 0 0 0 0 0 0 0 o 0 0 0 0 0 0 0 0 0 o 0 0 0 0 0 0 0 0 0 o 0 0 66547 86547
Employment 5088 339 10697 10753 1316 1334 1111 2215 329 2424 496 2488 1248 51671 8340 1424 23121 3432 0 515 0 0 0 0 0 0 0 0 0 0 o 0 o 98 o 68
Labor 22982 3824 62117 38414 7710 12218 7201 15404 2488 15171 10227 73841 54987 519514 180890 153545 884854 9831 0 3993 0 0 0 0 0 0 0 0 0 0 o 0 0 1500 0 630
Value added 14114 8399181745 42382 12890 18576 18787 70087 5738 25984 8050 135913 57859 1325918 535429 1048928 791095 2114 0 0 0 0 0 0 0 0 0 0 0 0 0 o 0 0 0 745
1 Agnculture 10205 0173274 15165 0 0 1 0 0 0 1 0 0 0 o 0 1578 0 129210 158338 0 36231 5505 0 0 0 0 0 0 0 0 0 0 0 o 338 0
2 Mlning 0 0 705 0 0 0 158 4905 0 2 0 21551 0 o 0 0 0 0 0 0 0 147 0 0 0 14 1319 0 0 0 3803 0 o 0 0 0
3 Food produCts 544 0 3010 88 0 0 4 0 0 0 0 0 o 0 0 0 475 0 28378 8327 0 629 24 0 0 0 0 0 0 0 o 0 0 0 102 0
4 Textile produCts 8 0 19 2440 2 2 8 10 0 .2 4 0 8 172 58 14 293 0 21839 123 1 4 886 0 0 1 3 0 0 1 0 2 37 9 0 83 0 5510 29107 60822
5 Wood produCts 1 0 49 3 238 3 3 3 o 29 51 1707 7 24 3 118 139 0 4091 8 0 10 1 33 0 0 1 0 5 9 301 2 5 1 2 30 0 1032 1125 9035
6 Paperproducts 0 0 10 1 0 0 0 1 0 2 0 0 o 2 0 0 0 0 849 0 0 2 0 0 0 0 0 0 0 0 o o 0 0 o 0 0 214 316 1399
7 Chemlcal products 9 0 210 18 16 72 15 28 o 40 2 1004 1 211 3 8 41 0 1271 132 0 44 8 2 2 1 7 0 7 0 177 0 45 1 o 9 0 321 2363 8067
R 8 Non-metallic products 0 o 370 0 2 o 21 1684 o 5 2 18870 3 0 29 832 412 o 562 5 0 77 0 0 0 2 453 0 1 0 2977 1 o 5 17 88 o 142 33401 57981
U 9 Basicmetal products 0 0 0 0 0 0 0 o 0 0 0 o 0 0 0 0 o o 0 0 0 0 0 o 0 0 0 0 0 0 0 0 0 o 0 0 0 0 0 0
R 10 Machineryand equipment 46 6 40 6 4 2 3 55 o 35 2 3818 24 28 7o 53 57 o 2724 735 11 8 2 1 o 0 15 0 6 0 874 8 6 12 1 12 0 887 4529 13879
A 11 othermanufactunng 0 0 0 1 0 1 o 0 0 0 1 o 1 3 3 23 40 0 1923 3 0 0 0 0 0 0 o 0 0 0 0 0 1 0 o 9 0 485 1844 4337
L 12 Construction 0 0 0 0 0 0 0 0 0 o 0 0 o o 0 0 0 0 o 0 0 0 o 0 0 0 0 0 o 0 o o 0 0 0 0 0 0 142393 142393
13 Public utilities 30 28 435 201 31 44 98 1017 0 58 38 0 159 847 312 2270 9980 0 9871 459 52 91 73 4 1 9 273 0 10 7 0 37 182 51 46 2138 o 2491 -2907 28432
14 Commerce, hotels and restaurants 897 128 14804 2491 1202 1025 1328 4895 0 1774 488 49558 366 8184 4335 4824 8769 0 158764 10882 240 3095 904 168 27 121 1316 0 301 88 8745 84 1757 712 99 1449 0 40057 228015 557472
15 Transponation and communications 147 45 882 274 102 119 156 848 o 185 84 710 159 10806 4404 1350 7384 0 140393 2248 85 184 99 14 3 14 174 0 28 15 125 38 2326 723 28 1580 0 35422 1505 212432
16 Financial services and real estate 18 44 84 41 11 53 24 223 0 31 10 0 82 8543 1107 509 3524 0 32580 277 83 17 15 2 1 2 60 0 5 2 o 19 1408 182 10 754 0 8215 8639 64557
17 Personal and professional services 84 68 644 96 22 92 67 705 0 96 18 0 196 21775 8174 44205 20233 0 288991 1289 127 135 35 3 2 8 189 0 16 3 0 45 4687 1342 903 4330 0 72915 57655 529149
18 Maquiladoraindustn‘es 0 0 0 0 0 o o 0 0 0 0 0 o o o 0 0 1 o 0 0 0 o o 0 0 0 0 o o 0 0 0 0 0 0 o 0 0 23481 23461
Employment 0 0 2118 1632 384 389 310 619 92 677 139 2138 385 3504 2382 o 3675 521 103495 512 6590 7123 687 898 594 1185 0 1298 285 1259 512 26114 1617 349 7580 1326
Labor 0 0 12302 5829 2251 3567 2009 4302 695 4237 2858 63275 18939 35230 59936 0 137433 1342 440867 3970 7677 72061425 204 586 7615 0 2704 417 16946 11605 82015 25432 5423 143670 3309
Value added 0 o o 0 0 0 0 o o 0 0 0 0 0 0 0 0 0 216223 31934 60564 15159 2825 356 1987 17348 0 3752 1218 30827 -17112 272929112998 24468 126909 0
Total local inputs 20775 3714311859 47387 10750 11111 11899 89924 24107 15444 4740 495118 9484 585415 161288 374286 448827 5518 318283 6979 85208 172031502 295 1071 18795 0 2821 851 87373 2178 121700 28480 7645 98015 1945
imported inputs 8992 3984189419 57994 23501 20458 26467 52840 19677 27118 10301 41085 138030 375527 289423 1249490 759526 47743 137758 7488 39607 21054 3283 544 2423 14203 0 4802 1850 7247 31762 80828 47523 25521 162554 16831
Total Outlays 68884 17922 737442191988 57102 63927 86182 212537 52703 87953 38174 809012 161540 2821602 1206945 2826250 3001335 86647 5844784 1112908 54382173057 60622 90351399 6087 57961 0 13679 4337142393 25432 557472 212432 84557 529149 23481 1915263

 

 

Table E. 2 Inter—regional input—output table, 1993

207

 

 

 

 

 

 

 

 

 

 

 

 

_ 4.
MERIDA
: =
INDUSTRY 1 2 8 4 5 B 7 B 9 10 11 12 13 14 15 1C 17 .11 _j1_
1 Agriculture 430 0 10210 2202 o o o o o o 0 o 0 0 o 0 1:4:
2 Mining 0 0 o 0 o o 131 7000 0 0 o 7035 0 0 0 0 ' 1'1
3 Food products 3101 0 30352 1101 5 0 42 00 0 10 4 2440 25 11040 3003 10252 2:11 2131;
4 Textile products 35 2 340 23002 11 13 35 70 2 10 313 30 2400 007 1307 4151 1:11;
5 Wood products 0 o 0 o 3510 3 1 o 0 374 1270 31070 0 o 0 o 1 11‘
0 Paper products 0 o 023 104 7 o 30 74 1 312 00 0 0 0 o o 1.1-'35
M 7 011011002! products 0 1400 200 474 2102 523 521 o 1402 05 50744 0 0 0 11 my;
E . Nonmemum products 0 0 o 1 0 o 20 17200 0 0 0 250400 0 3 0 I1 ti
R 9 Basic metal products 21 0 032 55 4 3 4 204 1003 100 10 43007 12 4044 1512 4013 110;: 1‘1
I 10 Machinery and equipmnt 245 25 1202 152 27 13 20 403 14 270 20 50010 142 0021 2030 0200 11011 F:
D 11 Other manufacwring 4 1 0 30 0 24 3 o o 0 30 0 10 0 50 753 1111': rm]
A 12 Construction 0 o 0 0 0 o 0 0 0 0 0 0 0 0 0 0 11 1’1
13 pubuc aunties 57 200 2021 5202 403 040 1727 10010 1704 001 000 o 1017 0 o 0 014111 “‘1
14 001111110100.hoteis and restaurants 4005 705 200351 45747 11000 0003 15550 50010 30045 20742 0300 053442 2000 0 0222 0 31 1’41
15 Transportation and commmuons 1034 407 20234 0044 1725 1054 3110 13200 1700 3204 2473 10040 2100 105200 4174 111090316
1‘ Financial services and real estate 1830 3402 18582 97% 1368 8246 3422 33426 190 “15 2091 41” 8340 “13% 100400 ”935 5.. “It
17 Pm] and professional services 937 583 17669 2809 300 1228 1091 12025 1‘0 1583 428 0115 2281 338358 1“ 702637 31 .:_»1
1. Maquiladoraindusmes 0 0 0 0 0 0 O 0 0 0 0 0 0 0 0 O . I 4'
swam: 4521 205 0021 13500 1240 1203 1000 1452 132 1020 022 0020 1002 50304 W 4000
L000: 44152 2021 117705250003 17532 11002 14347 47527 1052 10012 24000 101000 51004 1105013 457202 703332 21
Value added 24571 4070 437145 00570 20352 32055 17435 307255 0000 47731 40451 203720 71450 3137701 1521001 3415010 2003:
1 Agriculture 0002 0 432050 40512 0 0 0 0 0 0 0 o o 0 0 0 0\j
2 Mining 0 0 0 0 o 0 327 17500 0 0 0 10474 0 0 0 0 111 .;
3 Food proauas 000 0 12535 370 2 0 14 31 0 5 1 700 0 3774 1231 3200 7:151 [.5
4 Textile mums 10 1 102 12354 0 7 10 40 1 0 10 103 20 1250 400 723 21311 iu
5 Wood products 0 o 0 0 752 1 0 0 0 00 273 0041 o o 0 0 1i ,4
0 Paperpmducts 0 o 30 4 0 0 2 3 0 13 2 0 o 0 0 0 11 n1
7 011mm pfoducts 0 o 101 20 51 237 50 50 0 151 0 5402 o o o 0 0 ,4
R I Non-metallic products 0 o o o 0 o 4 2201 0 0 0 33000 0 o o o 0: 4
u 0 Basic metal products 0 0 o 0 0 o o 0 0 o 0 4 o 0 o 0 11
R 10 Machineryand equ1pmen1 104 10 000 102 10 0 10 323 0 101 14 34040 05 4037 1002 4100 7242. M
A 11 Other nanufactun‘ng 0 0 o 1 0 1 0 o 0 0 1 o 0 0 1 10 201 1:
L 12 ccnstmcuon o o 0 0 0 0 o o 0 0 0 0 0 o 11
13 pubuc games 10 73 700 1435 120 170 471 5130 400 207 272 0 523 0 0 0 05401 O
14 Comm, hotels and restaurants 1013 107 51004 11320 2001 2451 3040 14505 7057 5133 2075 235030 003 0 1540 0 1; 0.
15 Transponaum and mnjmuons 173 51 2137 913 182 200 329 1403 189 847 201 2001 231 17457 1M7 441 1173111 :4-
10 Financial sennoes and real 5131.3 47 00 530 201 40 101 00 000 0 120 01 121 242 24303 5770 2510 1504:" 4
17 Personal and professional services 127 79 2309 300 42 167 148 1633 19 215 58 830 310 45673 2”“ $115 5&1 i
18 Maquiladora industrig 0 O 0 0 0 0 0 0 0 0 0 0 0 0 O o 0.
swarm-11 0 0 2320 0 503 1274 1700 573 100 1542 722 14034 0 4077 2054 0 m ;
Labor 0 0 31072 0 7004 12554 14030 10753 2403 10037 20020 270001 0 100200 202715 0 0270101
Value added 0 o 0 0 o o 0 0 0 0 o 0 o o o 0 0:
Total 10ml inputs 24300 0070 035400170000 24057 25050 31001205030 45237 40110 10040 1705010 10732 1400040 520177 022050 12030401
"flooded inputs 21003 10402 1100201150050 55170 00075100042 07753 41275 114307 23002 1227720101202 1410300 1000100 2430007 2010030
romomy. mm

 

 

 

\

Table E.3 Inter-regional input-output table, 1998 1

208

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2 3 4 5 C 7 8 9 10 11 12 18 14 15 1O 0 HER Total
F0 0 mu!
0 5055 740 0 0 o o o o 0 o 0 0 o— 0 27000 1151
o 0 0 0 o 17 1070 0 0 0 40 0 0 o o 1 w 3017
0 11501 305 1 o 0 23 o 11 o 14 2201 407 205 500744151~u~ 250001
2 103 0002 2 1 5 10 0 11 1 2 10 404 100 30 41 ... 00302
0 o o 700 0 o o o 257 54 103 0 0 0 0 13101
0 272 35 1 o 5 10 0 214 3 o 0 0 0 0 530 1 .1.
o 430 01 103 130 00 123 o 003 4 200 o 0 0 0 74431.3
0 o 0 0 o 4 4070 o o o 1500 0 1 0 0 737
o 100 10 1 0 1 70 o 130 o 250 3 004 103 104 311 m:
30 300 52 0 1 4 114 o 100 1 200 30 1332 343 101 10141 23
1 o 13 o 2 o o 0 2 o 4 o 10 74 14310
0 o o o o 0 0 o 0 o 0 0 o . 374024 374024
411 030 1701 101 40 220 4402 0 074 42 0 521 o 0 .25531 .
1220 01012 15500 2000 014 2000 13000 1 14251 350 5011 720 o 752 0 5004 271 , 722002
747 5050 2041 370 121 413 3151 o 2250 100 112 505 31014 12020 100 71711 451534
5222 5400 3302 200 307 453 7025 0 3034 00 25 m 101000 24100 2251 1177
005 5200 070 07 70 144 2051 o 1000 10 30 020 04741 20405 10104 50200 1251“. 13014 001
0 o o o o o o o o o 0 o o 0 o o 3007 3057
0 0 0 o o o 0 0 o 0 500 0 o 1100 0
2575 o 0 0 o o 0 0 o 0 o 0074 o 0 30700 0
o 0 0 0 o 0 o o 0 o o o o 0
0127155 10047 0 o o o o o o 0 0 o 0 0 0 .. 250707
0 o o 0 o 43 4100 o 0 o 115 0 o 0 o 0 1 . 3331
o 3000 120 o o 2 7 0 4 o 5 2 720 140 05 077 1 101107 403071 02400
1 54 4204 1 0 2 0 o 0 1 1 5 242 57 10 207 1 rs: 210401
o o 104 o 0 0 o 55 12 30 o o 0 o 0 - 11
0 11 o 0 o o 0 o o o 0 o 0 0 1110- 22 025
0 47 1o 11 15 7 13 0 104 o 32 0 o 0 0 0 ~ 1 . 1 --
0 o o o o o 541 o 0 0 200 0 o 0 0 0 . r. . 1 077v.
0 0 o 0 o o o o 0 0 0 0 o 0 o 0 - .
25 255 35 4 1 2 77 0 124 1 200 20 003 230 100 075 - ~ 0704 1 .
0 0 o o o 0 0 0 o 0 o o o o o 1 1 n .
0 0 0 o o 0 0 0 o 0 o o 0 0 o . - 2537 2537
112 220 400 20 11 02 1210 0 104 12 o 142 0 o 0 3420 . 1041 .3 0000
302 15240 3052 045 152 510 3430 0 3520 00 1300 100 o 100 0 0 1 14007 070201 170003
70 020 311 40 13 44 333 0 230 11 12 03 3300 1270 11 1570 . ”11.7.4.1 47
151 150 00 0 11 13 230 0 00 3 1 00 4003 000 05 2100 . m 21011
122 700 133 0 10 20 307 0 140 2 5 04 0701 2702 2470 0020 - 1000 10011....
0 o o 0 0 o o o o 0 o 0 0 o 0 o - 0 01w.
101040 700 000017501 1041 272 414 1202 1 000 70 132 1300 20701 3000 100 0070 01
002000 7200 00014132004 42001350 4100 13213 0 0040 170 3000 15247 a0103 23000 5000 315740 3 .
500310 30720174202 70035 0525 444 1052 12510 5 40700 050 3057 154431000034170701 00320 4m 1
540540 0330245050 00004 52441501 4110 40020 2 27505 003 10302 5300 202102 03040 23007 172000
530414 25171343027 04100 120202071 10111 23177 2 705021010 7225 40277 271453210350 03147 37042544077
1771431 2507072 75005024001350033 200020257 20077 07732 0 1500002050 25370 00007 mew 470007 210117 . <1

 

 

 

 

 

 

-4444]. “1.. .. - .

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

MERIDA OUTPUT Intra-regional Inter-regional Pct Local
Agriculture 1 .839 1 .594 0.245 70.8%
Mining 1.424 1.344 0.080 81.2%
Food products 1.892 1.520 0.372 58.3%
Textile products 1.627 1.451 0.175 72.0%
Wood products 1.940 1.711 0.229 75.6%
Paper products 1.444 1.348 0.097 78.3%
Chemical products 1 .412 1 .329 0.082 80.0%
Non-metallic products 1.600 1.447 0.153 74.5%
Basic metal products 1.780 1.641 0.139 82.1%
Machinery and equipment 1.382 1.303 0.079 79.4%
Other manufacturing 1.381 1.300 0.080 78.9%
Construction 2.099 1 .819 0.280 74.5%
Public utilities 1.948 1.736 0.211 77.7%
Commerce, hotels and restaurants 1.308 1.249 0.060 80.6%
Transportation and communications 1.335 1.263 0.073 78.3%
Financial services and real estate 1.385 1.301 0.083 78.4%
Personal and professional services 1.492 1.387 0.105 78.8%
Maquiladora industries 1 .247 1.203 0.044 82.2%
Table E.4 Type 11 output multipliers for Mérida (1988)
RURAL OUTPUT lntra-regional Inter-regional Pct Local

Agriculture 1 .997 1 .361 0.637 36.2%
Mining 1 .209 1 .032 0.177 15.2%
Food products 1.721 1.342 0.378 47.5%
Textile products 1.692 1.223 0.469 32.2%
Wood products 1.214 1.058 0.156 27.2%
Paper products 1.574 1.146 0.428 25.4%
Chemical products 1 .349 1 .082 0.267 23.4%
Non-metallic products 1.714 1.194 0.520 27.2%
Basic metal products 1.830 1.150 0.680 18.1%
Machinery and equipment 1.476 1.117 0.359 24.7%
Other manufacturing 1.449 1.109 0.341 24.2%
Construction 2.040 1 .278 0.763 26.7%
Public utilities 1.923 1.270 0.654 29.2%
Commerce, hotels and restaurants 1.291 1.060 0.231 20.6%
Transportation and communications 1.356 1.084 0.271 23.7%
Financial services and real estate 1.474 1.126 0.348 26.6%
Personal and professional services 1.321 1.082 0.239 25.6%
Maquiladora industries 1 .1 19 1.026 0.093 21.9%

 

 

 

 

 

Table E.5 Type H output multipliers for rural areas (1988)

209

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

MERIDA lNCOME lntra-regional Inter-regional Pct Local
Agriculture 1 .373 0.323 0.050 86.7%
Mining 1.356 0.328 0.029 91.9%
Food products 1.993 0.581 0.412 58.5%
Textile products 1.510 0.384 0.126 75.3%
Wood products 1.500 0.448 0.052 89.7%
Paper products 1.448 0.264 0.184 59.0%
Chemical products 1.504 0.448 0.056 88.9%
Non-metallic products 1.778 0.572 0.206 73.5%
Basic metal products 1.821 0.517 0.305 62.9%
Machinery and equipment 1.494 0.319 0.175 64.5%
Other manufacturing 1.352 0.322 0.029 91.7%
Construction 1 .675 0.429 0.246 63.6%
Public utilities 1.252 0.202 0.049 80.3%
Commerce, hotels and restaurants 1.795 0.665 0.130 83.6%
Transportation and communications 1.438 0.274 0.164 62.5%
Financial services and real estate 1.467 0.387 0.081 82.7%
Personal and professional services 1.388 0.328 0.060 84.6%
Maquiladora industries 1.359 0.281 0.078 78.3%
Table E.6 Type H income multipliers for Mérida (1988)
RURAL INCOME Intra-regional Inter-regional Pct Local

Agriculture 1 .497 0.429 0.068 86.3%
Mining 14.804 3.909 9.895 28.3%
Food products 4.109 2.476 0.633 79.6%
Textile products 1.733 0.590 0.143 80.4%
Wood products 1.660 0.492 0.168 74.6%
Paper products 1.426 0.353 0.073 82.9%
Chemical products 1 .604 0.465 0.140 76.8%
Non-metallic products 2.101 0.722 0.378 65.6%
Basic metal products 2.195 0.753 0.442 63.0%
Machinery and equipment 1.437 0.360 0.077 82.5%
Other manufacturing 1.426 0.233 0.193 54.7%
Construction 1 .753 0.549 0.204 72.9%
Public utilities 1.319 0.279 0.040 87.4%
Commerce, hotels and restaurants 2.326 0.811 0.515 61.2%
Transportation and communications 1.562 0.439 0.123 78.1%
Financial services and real estate 1.481 0.387 0.094 80.5%
Personal and professional services 1.540 0.426 0.114 79.0%
Maquiladora industries 1.421 0.308 0.114 73.0%

 

 

 

 

 

Table E.7 Type H income multipliers for rural areas (1988)

210

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

MERIDA EMPLOY lntra-reglonal inter-regional Pct Local
Agriculture 1 .096 1 .027 0.069 28.3%
Mining 1.003 1.002 0.002 50.6%
Food products 4.599 1.608 2.991 16.9%
Textile products 3.220 1.650 1.570 29.3%
Wood products 1.223 1.125 0.098 55.9%
Paper products 1.514 1.228 0.286 44.4%
Chemical products 1 .382 1 .209 0.172 54.9%
Non-metallic products 2.329 1.669 0.661 50.3%
Basic metal products 5.146 3.287 1.858 55.2%
Machinery and equipment 1.201 1.099 0.102 49.2%
Other manufacturing 2.745 1.917 0.828 52.6%
Construction 2.107 1 .581 0.526 52.5%
Public utilities 1.771 1.330 0.440 42.9%
Commerce, hotels and restaurants 1.416 1.230 0.186 55.3%
Transportation and communications 1.987 1.437 0.550 44.3%
Financial services and real estate 7.448 4.344 3.104 51.9%
Personal and professional services 1.628 1.310 0.318 49.4%
Maquiladora industries 1 .139 1 .069 0.070 49.6%
Table E.8 Type H employment multipliers for Mérida (1988)

RURAL EMPLOY Intra-regional Inter-regional Pct Local
Agriculture 1 .320 1 .254 0.067 79.2%
Mining 1.172 1.072 0.100 41.9%
Food products 2.350 2.159 0.190 85.9%
Textile products 1.736 1.561 0.175 76.2%
Wood products 1.309 1.170 0.140 54.9%
Paper products 1.249 1.164 0.085 65.8%
Chemical products 1 .526 1.309 0.216 58.9%
Non-metallic products 1.907 1.499 0.408 55.0%
Basic metal products 2.168 1.543 0.625 46.5%
Machinery and equipment 1.222 1.141 0.081 63.5%
Other manufacturing 1.771 1.455 0.316 59.0%
Construction 1 .559 1 .295 0.264 52.7%
Public utilities 1.548 1.395 0.152 72.2%
Commerce, hotels and restaurants 1.172 1.088 0.084 51.0%
Transportation and communications 1.582 1.361 0.221 62.1%
Financial services and real estate 3.903 2.856 1.046 64.0%
Personal and professional services 1.375 1.238 0.137 63.4%
Maquiladora Industries 1 .270 1.171 0.099 63.3%

 

 

 

 

 

Table E.9 Type II employment multipliers for rural areas (1988)

211

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

MERIDA OUTPUT Intra-regional inter-regional Pct Local
Agriculture 2.163 0.810 0.353 69.6%
Mining 1 .646 0.561 0.086 86.7%
Food products 2.007 0.592 0.415 58.8%
Textile products 1.831 0.604 0.227 72.7%
Wood products 1.582 0.488 0.095 83.8%
Paper products 1 .670 0.563 0.107 84.1%
Chemical products 1.507 0.423 0.083 83.6%
Non-metallic products 1.672 0.549 0.123 81.7%
Basichetal products 1.851 0.796 0.054 93.6%
Machinery and equipment 1.637 0.534 0.103 83.8%
Other manufacturing 1.795 0.661 0.133 83.2%
Construction 2.274 1 .046 0.228 82.1 %
Public utilities 1.814 0.675 0.139 83.0%
Commerce, hotels and restaurants 1.614 0.530 0.083 86.4%
Transportation and communications 1.506 0.426 0.081 84.1%
Financial services and real estate 1.315 0.267 0.048 84.7%
Personal and professional services 1.775 0.653 0.121 84.3%
Maquiladora industries 1.406 0.353 0.053 86.9%
Table E.10 Type 11 output multipliers for Mérida (1993)

RURAL OUTPUT lntra-regional Inter-regional Pct Local
Agriculture 2.244 0.41 1 0.833 33.0%
Mining 1 .432 0.069 0.363 15.9%
Food products 1.828 0.364 0.465 43.9%
Textile products 1.732 0.239 0.493 32.6%
Wood products 1.534 0.105 0.429 19.6%
Paper products 1.576 0.104 0.472 18.1%
Chemical products 1 .446 0.084 0.362 18.8%
Non-metallic products 1.740 0.150 0.590 20.2%
Basic metal products 0.000 0.000 0.000 0.0%
Machinery and equipment 1.643 0.126 0.517 19.6%
Other manufacturing 1.475 0.086 0.389 18.1%
Construction 2.201 0.223 0.978 1 8.6%
Public utilities 1.817 0.183 0.634 22.4%
Commerce, hotels and restaurants 1.577 0.096 0.481 16.7%
Transportation and communications 1.399 0.075 0.325 18.7%
Financial services and real estate 1.386 0.072 0.314 18.6%
Personal and professional services 1.752 0.150 0.601 20.0%
Maquiladora industries 1 .420 0.073 0.347 17.4%

 

 

 

 

 

Table E.11 Type II output multipliers for rural areas (1993)

212

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

MERIDA lNCOME lntra-regionai inter-regional Pct Local
Agriculture 1 .791 0.618 0.173 78.2%
Mining 1 .573 0.516 0.057 90.0%
Food products 3.571 1.330 1.241 51.7%
Textile products 1.826 0.601 0.225 72.7%
Wood products 1.666 0.499 0.167 74.9%
Paper products 1.539 0.404 0.135 75.0%
Chemical products 1 .732 0.552 0.180 75.4%
Non-metallic products 2.423 1.056 0.366 74.3%
Basic metal products 3.706 2.191 0.515 81.0%
Machinery and equipment 1.581 0.439 0.142 75.5%
Other manufacturing 1.452 0.341 0.111 75.4%
Construction 2.390 0.895 0.495 64.4%
Public utilities 1.381 0.283 0.099 74.1%
Commerce, hotels and restaurants 1.603 0.516 0.087 85.5%
Transportation and communications 1.573 0.411 0.162 71.7%
Financial services and real estate 2.426 1.196 0.231 83.8%
Personal and professional services 1.490 0.396 0.094 80.8%
Maquiladora industries 1.526 0.435 0.092 82.6%
Table E.12 Type II income multipliers for Mérida (1993)

RURAL INCOME Intra-regional Inter-regional Pct Local
Agriculture 1 .740 0.591 0.149 79.9%
Mining 1 .570 0.246 0.324 43.2%
Food products 5.896 3.401 1.495 69.5%
Textile products 2.460 1.030 0.430 70.6%
Wood products 1.690 0.504 0.186 73.0%
Paper products 1.778 0.548 0.230 70.5%
Chemical products 1.935 0.626 0.309 67.0%
Non-metallic products 2.126 0.721 0.405 64.0%
Basic metal products 0.000 0.000 0.000 0.0%
Machinery and equipment 1.665 0.490 0.176 73.6%
Other manufacturing 1.994 0.655 0.339 65.9%
Construction 2.854 0.995 0.858 53.7%
Public utilities 1.429 0.351 0.078 81.9%
Commerce, hotels and restaurants 1.763 0.542 0.221 71.1%
Transportation and communications 1.700 0.511 0.189 73.0%
Financial services and real estate 1.861 0.490 0.370 57.0%
Personal and professional services 1.600 0.457 0.143 76.2%
Maquiladora industries 1 .556 0.367 0.189 66.0%

 

 

 

 

 

Table E.13 Type H income multipliers for rural areas (1993)

213

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

MERIDA EMPLOY lntra-regional Inter-regional Pct Local
Agriculture 1 .456 0.123 0.333 27.0%
Mining 1.423 0.246 0.176 58.3%
Food products 3.381 0.476 1.905 20.0%
Textile products 1.395 0.143 0.252 36.1%
Wood products 1.359 0.208 0.152 57.8%
Paper products 1.406 0.221 0.184 54.6%
Chemical products 1 .399 0.227 0.171 57.0%
Non-metallic products 1.793 0.460 0.333 58.0%
Basic metal products 2.676 1.387 0.288 82.8%
Machinery and equipment 1.309 0.173 0.136 56.0%
Other manufacturing 1.765 0.404 0.360 52.9%
Construction 5.283 2.565 1 .719 59.9%
Public utilities 2.301 0.640 0.661 49.2%
Commerce, hotels and restaurants 1.377 0.214 0.164 56.7%
Transportation and communications 2.020 0.533 0.487 52.3%
Financial services and real estate 9.080 4.900 3.179 60.6%
Personal and professional services 2.242 0.657 0.585 52.9%
Maquiladora industries 1.096 0.056 0.041 57.7%
Table E.14 Type II employment multipliers for Mérida (1993)

RURAL EMPLOY Intra-reglonal Inter-regional Pct Local
Agriculture 1 .436 0.326 0.109 74.9%
Mining 1.345 0.178 0.167 51.5%
Food products 1.954 0.774 0.179 81.2%
Textile products 1.232 0.157 0.075 67.7%
Wood products 1.153 0.078 0.074 51.2%
Paper products 1.022 0.011 0.011 51.1%
Chemical products 1 .089 0.044 0.045 49.1%
Non-metallic products 1.647 0.320 0.327 49.4%
Basic metal products 0.000 0.000 0.000 0.0%
Machinery and equipment 1.141 0.074 0.066 52.9%
Other manufacturing 1 .154 0.073 0.081 47.5%
Construction 3.723 1 .126 1 .596 41 .4%
Public utilities 1.985 0.620 0.366 62.9%
Commerce, hotels and restaurants 1.187 0.104 0.083 55.7%
Transportation and communications 1.909 0.511 0.398 56.2%
Financial services and real estate 1.931 0.487 0.444 52.3%
Personal and professional services 1.981 0.582 0.399 59.3%
Maquiladora industries 1 .131 0.073 0.058 55.5%

 

 

 

 

 

Table E.15 Type II employment multipliers for rural areas (1993)

214

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

MERIDA OUTPUT Intra-regional Inter-regional Pct Local
Agriculture 1 .945 1 .71 5 0.230 75.7%
Mining 1 .427 1 .378 0.049 88.5%
Food products 1.658 1.386 0.272 58.6%
Textile products 2.020 1.783 0.237 76.8%
Wood products 1.575 1.479 0.096 83.3%
Paper products 1.501 1.420 0.081 83.8%
Chemical products 1.492 1 .406 0.086 82.6%
Non-metallic products 1.547 1.446 0.102 81.4%
Basic metal products 1.764 1.635 0.129 83.1%
Machinery and equipment 1.481 1.400 0.081 83.2%
Other manufacturing 1.780 1.638 0.142 81.8%
Construction 1 .912 1 .748 0.164 82.0%
Public utilities 1.317 1.274 0.043 86.4%
Commerce, hotels and restaurants 1.548 1.482 0.065 88.1%
Transportation and communications 1.397 1.340 0.057 85.7%
Financial services and real estate 1.353 1.305 0.048 86.4%
Personal and professional services 1.646 1.550 0.096 85.2%
Maquiladora industries 1 .324 1 .289 0.034 89.4%
Table E.16 Type H output multipliers for Mérida (1998)

RURAL OUTPUT lntra-reglonal Inter-regional Pct Local
Agriculture 1 .971 1 .288 0.683 29.6%
Mining 1.380 1.061 0.320 15.9%
Food products 1.644 1.266 0.378 41.3%
Textile products 1.881 1.238 0.643 27.0%
Wood products 1.520 1.101 0.419 19.4%
Paper products 1.716 1.130 0.586 18.1%
Chemical products 1 .635 1 .125 0.509 19.7%
Non-metallic products 1.946 1.190 0.756 20.0%
Basic metal products 1.309 1.051 0.258 16.6%
Machinery and equipment 1.319 1.054 0.265 16.8%
Other manufacturing 1.498 1.084 0.414 16.8%
Construction 1 .865 1 .165 0.700 19.1%
Public utilities 1.460 1.087 0.372 19.0%
Commerce, hotels and restaurants 1.427 1.067 0.360 15.8%
Transportation and communications 1.277 1.039 0.237 14.2%
Financial services and real estate 1.473 1.069 0.404 14.7%
Personal and professional services 1.577 1.111 0.466 19.2%
Maquiladora industries 1 .328 1 .044 0.284 13.4%

 

 

 

 

 

Table E.17 Type II output multipliers for rural areas (1998)

215

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

MERIDA lNCOME Intra-regional inter-regional Pct Local
Agriculture 1 .506 0.431 0.075 85.1%
Mining 1.780 0.696 0.085 89.1%
Food products 3.730 1.317 1.413 48.2%
Textile products 1.577 0.491 0.086 85.0%
Wood products 1.544 0.373 0.171 68.6%
Paper products 1.561 0.311 0.250 55.4%
Chemical products 1 .569 0.318 0.251 55.9%
Non-metallic products 2.030 0.732 0.299 71.0%
Basic metal products 3.845 1.860 0.985 65.4%
Machinery and equipment 1.590 0.351 0.240 59.4%
Other manufacturing 1.395 0.203 0.191 51.5%
Construction 2.243 0.696 0.547 56.0%
Public utilities 1.386 0.355 0.031 92.0%
Commerce, hotels and restaurants 1.595 0.500 0.095 84.0%
Transportation and communications 1.521 0.360 0.161 69.1%
Financial services and real estate 1.743 0.647 0.096 87.1%
Personal and professional services 1.422 0.317 0.105 75.1%
Maquiladora industries 1 .603 0.443 0.161 73.4%
Table E.18 Type 1] income multipliers for Mérida (1998)

RURAL INCOME Intra-regional inter-regional Pct Local
Agriculture 1.531 0.441 0.090 83.1 %
Mining 1.528 0.313 0.214 59.4%
Food products 3.066 1.552 0.514 75.1%
Textile products 1.506 0.415 0.091 81.9%
Wood products 1.637 0.478 0.159 75.0%
Paper products 1.604 0.457 0.147 75.6%
Chemical products 1 .561 0.431 0.129 76.9%
Non-metallic products 2.186 0.747 0.439 63.0%
Basic metal products 0.000 0.000 0.000 0.0%
Machinery and equipment 2.454 0.853 0.601 58.7%
Other manufacturing 2.439 0.849 0.590 59.0%
Construction 1 .973 0.655 0.319 67.3%
Public utilities 1.369 0.225 0.144 61.0%
Commerce, hotels and restaurants 1.627 0.467 0.161 74.4%
Transportation and communications 2.051 0.683 0.368 65.0%
Financial services and real estate 1.511 0.102 0.409 19.9%
Personal and professional services 1.466 0.374 0.092 80.2%
Maquiladora industries 1 .618 0.406 0.212 65.7%

 

 

 

 

 

Table E.19 Type II income multipliers for rural areas (1998)

216

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

MERIDA EMPLOY intra-reglonai inter-regional Pct Local
Agriculture 1 .233 1 .073 0.1 60 31 .5%
Mining 1.281 1.165 0.116 58.7%
Food products 3.576 1.475 2.101 18.4%
Textile products 1.534 1.192 0.342 35.9%
Wood products 1.317 1.173 0.144 54.6%
Paper products 1.216 1 .1 14 0.102 52.9%
Chemical products 1 .189 1 .096 0.093 50.8%
Non-metallic products 2.283 1.663 0.619 51.7%
Basic metal products 2.879 2.138 0.741 60.5%
Machinery and equipment 1.244 1.130 0.113 53.6%
Other manufacturing 1.622 1.282 0.340 45.3%
Construction 2.067 1 .622 0.445 58.3%
Public utilities 1.307 1.170 0.138 55.3%
Commerce, hotels and restaurants 1.325 1.180 0.145 55.4%
Transportation and communications 2.019 1.513 0.506 50.3%
Financial services and real estate 3.967 2.745 1.222 58.8%
Personal and professional services 2.000 1.497 0.502 49.7%
Maquiladora industries 1.1 1 1 1.066 0.045 59.3%
Table E.20 Type H employment multipliers for Mérida (1998)

RURAL EMPLOY intra-regionai Inter-regional Pct Local
Agriculture 1 .289 1 .215 0.074 74.4%
Mining 1.184 1.103 0.081 56.1%
Food products 1.989 1.806 0.182 81.6%
Textile products 1.205 1.143 0.062 69.5%
Wood products 1.123 1.063 0.060 51.3%
Paper products 1.129 1.067 0.062 52.0%
Chemical products 1.247 1 .136 0.1 10 55.3%
Non-metallic products 1.499 1.261 0.238 52.3%
Basic metal products 1.020 1.007 0.012 37.3%
Machinery and equipment 1.470 1.200 0.270 42.6%
Other manufacturing 1.146 1.065 0.081 44.7%
Construction 2.335 1.615 0.719 46.1%
Public utilities 1.160 1.097 0.064 60.2%
Commerce, hotels and restaurants 1.159 1.093 0.066 58.5%
Transportation and communications 1.227 1.118 0.110 51.8%
Financial services and real estate 1.458 1.212 0.246 46.2%
Personal and professional services 1.632 1.393 0.239 62.2%
Maquiladora industries 1 .134 1 .068 0.066 50.7%

 

 

 

 

 

Table E.21 Type II employment multipliers for rural areas (1998)

217

 

 

APPENDIX F

TRADITIONAL ECONOMIC BASE MULTIPLIERS

218

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

MUNICIPIO Multiplier
ABALA 1 .3014
ACANCEH 1 .6387
AKIL 1 .2982
BACA 1 .5643
BOKOBA 1 .2290
BUCTZOTZ 1 .2283
CACALCHEN 2.0715
CALOTMUL 1.1415
CANSAHCAB 2.1217
CANTAMAYEC 1 .1826
CELESTUN 1.1584
CENOTILLO 1 .1630
CONKAL 1 .5589
CUNCUNUL 1.1708
CUZAMA 1 .4581
CHACSINKIN 1.1795
CHANKOM 1 .0674
CHAPAB 1 .2082
CH EMAX 1 .1373
CHICXULUB PUEBLO 2.0495
CHICHIMILA 1.1466
CHIKINDZONOT 1.0873
CHOCHOLA 2.9489
CHUMAYEL 1.3551
DZAN 1 .1780
DZEMUL 1 .6790
DZIDZANTUN 1.3160
DZILAM DE BRAVO 1 .2082
DZILAM GONZALEZ 1.1818
DZITAS 1 .2431
DZONCAUICH 1.1413
ESPITA 1 .1561
HALACHO 1 .8670
HOCABA 1 .8221
HOCTUN 1 .7823
HOMUN 1 .9384
HUHI 3.4467
HUNUCMA 2.6604
IXIL 1 .3278
lZAMAL 1 .8225
KANASIN 1 .2458
KANTUNIL 1 .2134
KAUA 1 .2207
KINCHIL 1.1950
KOPOMA 1 .3736
MAMA 1 .3056
MANI 1 .2325
MAXCANU 1 .7081
MAYAPAN 1 .2001
MERIDA 1 .6299
MOCOCHA 2.0005
MOTUL 1 .3006
MUNA 1 .3108
MUXUPIP 1.4150

 

 

 

219

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

OPICHEN 1.1946
OXKUTZCAB 1 .3496
PANABA 1.1813
PET 0 1.6180
PROGRESO 1 .2297
QUINTANA ROO 1.1468
RIO LAGARTOS 1.2600
SACALUM 1 .2660
SAMAHIL 1.1505
SANAHCAT 1 .3803
SAN FELIPE 1.1472
SANTA ELENA 1.3041
SEYE 2.2730
SINANCHE 1.2102
SOTUTA 1 .2997
SUCILA 1 .2223
SUDZAL 1.1359
SUMA 1 .5021
TAH DZiU 1.1488
TAHMEK 1.5148
TEABO 1 .4348
TECOH 2.5223
TEKAL DE VENEGAS 1.1536
TEKANTO 1 .4637
TEKAX 3.0948
TEKIT 1.1790
TEKOM 1.2176
TELCHAC 1 .5283
TELCHAC PUERTO 1.2432
TEMAX 1 .5951
TEMOZON 1.1331
TEPAKAN 1 .5967
TETIZ 1.1944
TEYA 1 .2341
TICUL 1.2186
TIMUCUY 2.2259
TINUM 1.1733
TIXCACALCUPUL 1 .0969
TIXKOKOB 1.4718
TIXMEHUAC 1.1227
TIXPEHUAL 1.7617
TIZIMIN 1.3837
TUNKAS 1.1374
TZUCACAB 1.2189
UAYMA 1.1630
UCU 1 .3879
UMAN 1.3157
VALLADOLID 1.4130
XOCCH EL 1 .4638
YAXCABA 1 .0852
YAXKUKUL 1 .8624
YOBAIN 1.1730

 

 

Table F.1 Traditional economic base multipliers, 1998

220

BIBLIOGRAPHY

221

BIBLIOGRAPHY

Alonso, W. 1968. Predicting Best with Imperfect Data. Journal of the American
Institute of Planners, 34, 191-227.

Alonso, W. 1980. Five Bell Shapes in Development. Papers, Regional Science
Association, 45, 5-16.

Amara, Y.A. 1994. Externally Traded Services and the Development of Small Economies,
in D.L. McKee, ed, External Linkages and Growth in Small Economies.
Westport, CT: Praeger.

Anselin, 1988. Spatial Econometrics: Methods and Models. Dordrecht: Kluwer
Academic Publishers.

Anselin, L. and M. Madden. 1991. Integrated and Multi-regional Approaches in
Regional Analysis, in Anselin and Madden, eds., New Directions in Regional
Analysis: Multi-regional Approaches. London: Belhaven Press.

Anselin, L. and A.K. Bera. 1998. Spatial Dependence in Linear Regression Models with
an Introduction to Spatial Econometrics, in A. Ullah and D. Giles, eds.,
Handbook of Applied Economic Statistics. New York: Marcel Dekker.

Banco Nacional de Mexico. 2000. Reportes Regionales (Yucatan). Division de Estudios
Econornicos y Sociales. Mexico City: BANAMEX.

Bafios Ramirez, 0. 1996. Neoliberalismo, Reorganizacién y Subsistencia Rural: El
Caso de la Zona Henequenera de Yucatan, 1980-1992. Mérida, Mexico:

Universidad Autonoma de Yucatan.

Barro, R. 1997. Determinants of Economic Growth: A Cross-Country Empirical
Study. Cambridge: MIT Press.

Beyers, W.B. 1992. Producer Services. Progress in Human Geography, 16, 4, 573-583.

Biles, J.J. and B.W. Pigozzi. 2000. Interaction of Economic Reforms, Socio-Economic
Structure and Agriculture in Mexico. Growth and Change, 31,1,3-22.

Blakely, EJ. 1994. Planning Local Economic Development: Theory and Practice.
Thousand Oaks, CA: Sage.

222

Brannon, J. and EN. Baklanoff. 1987. Agrarian Reform and Public Enterprise in
Mexico: The Political Economy of Yucatan’s Henequen Industry. Tuscaloosa:
University of Alabama Press.

Brannon, J .T., D.D. James, and G.W. Lucker, 1994. Generating and Sustaining
Backward Linkages Between Maquiladoras and Local Suppliers in Northern
Mexico. World Development, 22, 12, 1933-1945.

Brannon, J.T and DD. James. 1995. Daunting Challenges Confront Borderlands. Forum
for Applied Research and Public Policy, 10, 2, 34-49.

Bulmer—Thomas, V. 1994. The Economic History of Latin America since
Independence. Cambridge: Cambridge University Press.

Bulmer-Thomas, V. 1996 The New Economic Model in Latin America and its Impact
on Income Distribution and Poverty. New York: St. Martin’s Press.

Buimer-Thomas, V. 1982. Input-Output Analysis in Developing Countries: Sources,
Methods and Applications. New York: John Wiley and Sons.

Carrillo, J. 1994. The Apparel Maquiladora Industry at the Mexican Border, in E.
Bonacich et al., eds., Global Production: The Apparel Industry in the Pacific
Rim. Philadelphia: Temple University Press, 217-229.

Case, A.C., H. Rosen, and J. Hines. 1993. Budget Spillovers and Fiscal Policy
Interdependence: Evidence from States. Journal of Public Economics, 52, 285-
307.

Castilla Ramos, B. 1991. La Ocupacién en Yucatan. Mérida, Mexico: Universidad
Autonoma de Yucatan.

Cervera Pacheco, V. 1996. Primer Informe de Gobiemo. Mérida, Mexico: Estado de
Yucatan.

Chardon, RE. 1963. Hacienda and Ejido in Yucatan: The Example of Santa Ana Cucci.
Annals of the Association of American Geographers, 53, 2, 174-193.

Chisholm, M. 1990. Regions in Recession and Resurgence. London: Unwin Hyman.

CINVESTAV. 2000. Matriz de Insumo-Producto del Estado de Yucatan. Mérida,
Mexico: CINVESTAV.

Cliff, AD. and J .K. Ord. 1981. Spatial Processes: Models and Applications. London:
Pion Limited.

223

Conway, RS. 1979. Simulation Properties of a Regional Inter-Industry Econometric
Model. Papers of the Regional Science Association, 43, 45-57.

Conway, RS 1990. The Washington Project and Simulation Model: A Regional Inter-
Industry Econometric Model. International Regional Science Review, 13, 141-
165.

Conroy, ME. and SE. West. 2000. The Impact of NAFTA and the WTO on Chiapas and
Southern Mexico, in R. Tardanico and MB. Rosenberg, eds., Poverty or
Development: Global Restructuring and Regional Transformations in the
US. South and the Mexican South. New York: Routledge.

Consejo Nacional de Pobiacion. 2001. Grado de Marginacion por Entidad F ederativa
1990. Internet address: http://www.conapo.gob.mx/indicaso.

Consejo Nacional de Poblacion. 2001. Grado de Marginacion por Entidad F ederativa
1995. Internet address: http://www.conapo.gob.mx/indicaso.

Cravey, A. 1998. Women and Work in Mexico’s Maquiladoras. Lanham, MD:
Rowman and Littlefield.

Dicken, P. 1998. Global Shift: Transforming the World Economy. New York:
Guilford Press.

Dussel Peters, E. 1997. La Economia de la Polarizacién: Teoria y Evolucion del
Cambio Estructural de las Manufacturas Mexicanas (1988-1996). Mexico
City: UNAM.

Estado de Yucatan. 1996. Plan Estatal de Desarrollo, 1995-2001. Mérida, Mexico:
Estado de Yucatan.

Evans, P. 1979. Dependent Development: The Alliance of Multinational, State and
Local Capital in Brazil. Princeton: Princeton University Press.

Farmer, BR. 1999. The Question of Dependency and Economic Development: A
Quantitative Analysis. Lanham: Lexington Books.

Franko, P. 1999. The Puzzle of Latin American Economic Development. Lanham,
MD: Rowman and Littlefield.

Friedmann, J. 1966. Regional Development Policy: A Case Study of Venezuela.
Cambridge: MIT Press.

Friedmann J. and C. Weaver. 1979. Territory and Function: The Evolution of
Regional Planning. Berkeley: University of California Press.

224

Fuentes, F. ct al. 1993. Local Sourcing and Indirect Employment: Multinational
Enterprises in Northern Mexico, in P. Bailey, ed., Multinationals and
Employment, Geneva: International Labor Office, 161-187.

Fujita, M. and D. Hu. 2001. Regional Disparities in China, [985-1994; The Efiects of
Globalization and Economic Liberalization. Annals of Regional Science, 35, 3-
37.

Fujita, M., P. Krugman, and A. Venables. 1999. The Spatial Economy. Cambridge: MIT
Press.

Galhardi, R.M.A.A. 1998. Maquiladoras Prospects of Regional Integration and
Globalization. International Labor Organization, Employment and Training
Papers, 12.

Garcia de Fuentes, A. and S. Perez Medina. 1996. Factores de Localizacio’n de la
Industria Maquiladora: El Caso de Yucata’n. Yearbook, Conference of Latin
Americanist Geographers, 22, 17-30.

Gereffi, G. 2000. The Mexico-US. Apparel Connection, in R. Tardanico and MB.
Rosenberg, eds., Poverty or Development: Global Restructuring and Regional
Transformations in the US. South and the Mexican South. New York:
Routledge.

Gilbert, A.G. and DE. Goodman. 1976. Regional Income Disparities and Economic
Development: A Critique, in A. Gilbert, ed., Development Planning and Spatial
Structure, 113-141. London: John Wiley and Sons.

Greytak, D. 1970. Regional Impacts of Inter-regional Trade in Input-output Analysis.
Papers, Regional Science Association, 25, 203-217.

Guajardo-Quiroga, KG. 1998. Assessing the Impact of the Maquiladora Industry in
Mexico: An Inter-industry Analysis. Review of Urban and Regional
Development Studies, 10, 2, 109-122.

Gujarati, D. 1995. Basic Econometrics. New York: McGraw-Hill.

Haddad, E. 1999. Regional Inequality and Structural Changes: Lessons from the
Brazilian Experience. Aldershot: Ashgate.

Haining, R. 1987. Income Diffusion and Spatial Econometric Models. Geographical
Analysis, 19, 1, 57-68.

Harmon, B. and M. Ruth. 1997. Modeling Dynamic Economic Systems. New York:
Springer.

225

Hansen, N. 1990. Do Producer Services Induce Regional Economic Development?
Journal of Regional Science, 30, 4, 465-476.

Hanson, CH. 1994. Industrial Organization and Mexico-US Free Trade: Evidence from
the Mexican Garment Industry, in E. Bonacich et al., eds., Global Production:

The Apparel Industry in the Pacific Rim. Philadelphia: Temple University
Press, 230-245.

Harris, CD. 1954. The Market as a Factor in the Localization of Industry in the United
States, Annals, Association of American Geographers, 44, 315-348.

Hartman, L.M. and D. Secklcr. 1967. Toward the Application of Dynamic Growth Theory
to Regions. Journal of Regional Science, 7, 2, 167-173.

Hewings, G.J.D. 1977. Regional Industrial Analysis and Development. London:
Methuen.

Hewings, G.J.D. 1985. Regional Input-output Analysis. Beverly Hills: Sage
Publications.

Higgins, B. and J .D. Higgins. 1979. Economic Development of a Small Planet. New
York: W.W. Norton and Company, Inc.

Hinojosa, R. and B.W. Pi gozzi. 1986. Updating Input-output Tables Using Employment
Data: A Reformulation. Environment and Planning A, 18, 269-276.

Hinojosa, R. and B.W. Pi gozzi. 1988. Economic Base and Input-output Multipliers: An
Empirical Linkage. Regional Science Perspectives, 18, 2, 3-13.

Hirschman, A. 1958. The Strategy of Economic Development. New Haven: Yale
University Press.

Hodder, R. 2000. Development Geography. London: Routledge.

Instituto Nacional de Estadistica, Geografr’a e Informatica. 2001. Banco de Informacion
Economica. Internet address: http://dgcnesyp.inegi.gob.mx/bie.html-ssi.

Instituto N acional de Estadistica, Geografr’a e Informética. 2000. Censo de Poblacién y
Vivienda. Aguascalientes: INEGI.

Instituto N acional de Estadistica, Geografr’a e Informatica. 1990. Censo de Poblacién y
Vivienda. Aguascalientes: INEGI.

Instituto Nacional de Estadistica, Geografr’a e Informatica. 1999. Censos Econémicos
(1998). Aguascalientes: INEGI.

226

Instituto N acional de Estadistica, Geografia e Informatica. 1994. Censos Econémicos
(1993). Aguascalientes: INEGI.

Instituto Nacional de Estadistica, Geografr’a c Informatica. 1989. Censos Econémicos
(1988). Aguascalientes: INEGI.

Isard, W. 1956. Location and Space-Economy. Cambridge. MIT Press.

Isard, W. et a1. 1998. Methods of Interregional and Regional Analysis. Aldershot:
Ashgate.

Israilevich, P.R., G.J.D. Hewings, M. Sonis and GR. Schindler. 1997. Forecasting
Structural Change with a Regional Econometric Input-Output Model. Journal of
Regional Science, 37, 565-90.

Isserman, A.M. 1980. Estimating Export Activity in a Regional Economy: A Theoretical
and Empirical Analysis of Alternative Methods. International Regional Science
Review, 5, 2, 155-184.

Jensen, RC. 1980. The Concept of Accuracy in Regional Input-output Models.
International Regional Science Review, 5, 2, 139-154.

Kendrick, J .W. and C. J aycox. 1965. The Concept and Estimation of Gross State Product.
Southern Economic Journal, 32, 153-168.

Kopinak, K. 1995. Transitions in the Maquilization of Mexican Industry: Movement and
Stasis from 1965 to 2001. Labour, Capital and Society, 28,1,68-94.

Krugman, P. 1995. Development, Geography and Economic Theory. Cambridge: MIT
Press.

Kuznets, S. 1955. Economic Growth and Income Inequality. American Economic
Review, 45, 1-28.

LeSage, JP. 1999. Spatial Econometrics. Web Book of Regional Science. Regional
Research Institute, West Virginia University. Internet address:
http://www.rri.wvu.edu/WebBook/LeSage/spatial/spatial.htrnl

Mabougunje, A. 1981. The Development Process. New York: Holmes and Meier
Publishers.

MacLachlan, I. and A.G. Aguilar. 1998. Maquiladora Myths: Locational and Structural

Change in Mexico is Export Manufacturing Industry. Professional Geographer,
50, 3, 315-331.

227

Mathur, V.K. and HS. Rosen. 1972. An Econometric Export Base Model of Regional
Growth: A Departure from Conventional Techniques, in A.G. Wilson, ed.,
Patterns and Processes in Urban and Regional Systems, London: Pion.

Malecki, E. 1991. Technology and Economic Development. London: Longman.

Miller, R. and P. Blair. 1985. Input-output Analysis: Foundations and Extensions.
Englewood Cliffs, NJ: Prentice-Hall.

Miemyk, W.H. 1965. The Elements of Input-output Analysis. New York: Random
House.

Myrdal, G. 1957. Rich Lands and Poor: The Road to World Prosperity. New York:
Harper and Row.

Ngo, T.W., A. J azayeri, and H.W. Richardson. 1987. Regional Policy Simulations with

an Inter-regional Input-output Model of the Philippines. Regional Studies, 21, 2,
1 2 1 - 1 30.

North, DC. 1955. Location Theory and Regional Economic Growth. Journal of Political
Economy, 63, 243-258.

Okuyama, Y., M. Sonis and G.J.D. Hewings. 1999. Creating and Expanding Trade
Partnerships within the Chicago Metropolitan Area: Applications Using a
Miyazawa Accounting System (Discussion Paper 99-T-2). Regional Economic
Applications Laboratory, University of Illinois at Urbana-Champaign.

Olfert, MR. and J .C. Stabler. 1999. Multipliers in a Central Place Hierarchy. Growth
and Change, 30, 2, 288-302.

Organization for Economic Cooperation and Development. 1997. Regional
Development and Structural Policy in Mexico. Paris: OECD.

Ortiz, R. 1999. Cuentas Sociales del Municipio de Hocabd, Yucatan. Tesis de
Licenciatura, Facultad de Economia. Mérida, Mexico: Universidad Auténoma de
Yucatan.

Panuco-Laguette, H. and M. Szekley. 1996. Income Distribution and Poverty in Mexico,
in V. Bulmer-Thomas, ed., The New Economic Model in Latin America and its
Impact on Income Distribution and Poverty. New York: St. Martin’s Press.

Partridge, MD. and D.S. Rickman. 1998. Regional Computable General Equilibrium

Modeling: A Survey and Critical Appraisal. International Regional Science
Review, 21, 3, 205-248.

228

Peet, R. and E. Hartwick. 1999. Theories of Development. New York: Guilford Press.

Pradilla Cobos, E. 1993. The Limits of the Mexican Maquiladora Industry. Review of
Radical Political Economics, 25, 4, 91-108.

Pred, A. 1966. The Spatial Dynamics of U.S. Urban-Industrial Growth, 1800-1914.
Cambridge: MIT Press.

Rey, SJ. and B. Dev. 1997. Integrating Econometric and Input-output Models in a Multi-
regional Context. Growth and Change, 28, 2, 222-243.

Rey, SJ. and 8D. Montouri. 1999. US Regional Income Convergence: A Spatial
Econometric Perspective. Regional Studies, 33, 2, 143-156.

Rey, SJ. 2000. Integrated Regional Econometric+Input-output Modeling: Issues and
Opportunities. Papers in Regional Science, 79, 3, 271-292.

Richardson, H. W. 1985. Input-output and Economic Base Multipliers: Looking
Backward and Forward. Journal of Regional Science, 25, 4, 607-662.

Robinson, M.H. 1997. Community Input-output Models for Rural Area Analysis with an
Example from Central Idaho. Annals of Regional Science, 31, 3, 325-351.

Rostow, W.W. 1960. The Stages of Economic Growth: A Non-communist Manifesto.
Cambridge: Cambridge University Press.

Round, J .I. 1983. Non-survey Techniques: A Critical Review of the Theory and the
Evidence. International Regional Science Review, 3, 189-212.

Sadoulet, E. and A. de Janvry. 1995. Quantitative Development Policy Analysis.
Baltimore: Johns Hopkins University Press.

Sagawe, T. 1996. Industrial Free Zones in the Dominican Republic: National vs. Local
Impact. Journal of Geography, 95, 5, 203-210.

Schumpeter, J .A. 1934. The Theory of Economic Development: An Inquiry into
Profits, Capital, Credit, Interest, and the Business Cycle. Cambridge: Harvard
University Press.

Scott, AJ. 1998. Regions and the World Economy: The Coming Shape of Global
Production, Competition and Political Order. Oxford: Oxford University Press.

Secretarr’a de Desarrollo Industrial del Estado de Yucatan. 2000. Bolett’n de Noticias.
Internet address: http://www.yucatan.gob.mx/newsletter/espanol.htm.

229

Secretaria de Desarrollo Industrial del Estado de Yucatan. 2001. Listado de
Maquiladoras, March 2001. Mérida, Mexico: Secretan’a de Desarrollo Industrial.

Secretaria de Desarrollo Social. 2000. Informe Final del Analisis Econémico de la
Microcuenca de Chabihau. Mérida, Mexico: SEDESOL.

Sklair, L. 1993. Assembling for Development: The Maquila Industry in Mexico and
the United States. San Diego: Center for U.S.-Mexican Studies, UCSD.

Smith, D.M. 1986. Geography, Inequality and Society. New York: Cambridge
University Press.

South, RB. 1990. Transnational “Maquiladora ” Location, Annals of the Association of
American Geographers, 80, 4, 549-570.

Storper, M. 1991. Industrialization, Economic and the Regional Question in the
Third World. London: Pion.

Taaffe, E., H. Gauthier and M. O’Kelly. 1996. Geography of Transportation. Upper
Saddle River: Prentice Hall.

Thompson, W.R. 1965. A Preface to Urban Economics. Baltimore: Johns Hopkins
Press.

Tiebout, C. 1956. Exports and Regional Economic Growth. Journal of Political
Economy, 64, 160-164.

Todaro, MP. 1997. Economic Development. Reading, MA: Addison-Wesley.

Topik, SC. and A. Wells. 1998. The Second Conquest of Latin America: Coffee,
Henequen and Oil during the Export Boom, 1850-1930. Austin: University of
Texas Press.

Vellinga, M. 2000. Economic Internationalisation and Regional Response: The Case of
North Eastern Mexico. Tijdschrift voor Economische en Sociale Geografie, 91,
3, 293-307.

Wamtz, W. 1964. A New Map of the Surface of Population Potential for the United
States, 1960. Geographic Review, 54, 170-184.

Weeks, J. 1996. The Manufacturing Sector in Latin America and the New Economic
Model, in V. Bulmer-Thomas, ed., The New Economic Model in Latin America
and its Impact on Income Distribution and Poverty. New York: St. Martin’s
Press.

230

Wells, A. 1985. Yucatan’s Gilded Age: Haciendas, Henequen and International
Harvester. Albuquerque. University of New Mexico Press.

Williamson, J .G. 1965. Regional Inequality and the Process of National Development: A
Description of the Patterns. Economic Development and Cultural Change, 13,
4, 1-84.

Wilson, RA. and T. Kayne. 2000. Local Economic Development and Transnational
Restructuring: The Case of Export-Assembly Manufacturing in Yucatan, in R.
Tardanico and MB. Rosenberg, eds., Poverty or Development: Global
Restructuring and Regional Transformations in the U.S. South and the
Mexican South. New York: Routledge.

231

   
  

  

G T

111111111111111111111111311

S ATE
1 1‘
1 1 1
3 021

         

 

LI_I_I_I_I_I_I_I_I_I_I_I_E_I