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This is to certify that the

dissertation entitled

Effects of Monetized Food Aid on Local Maize
Prices in Mozambique

presented by

Cynthia Donovan

has been accepted towards fulfillment
of the requirements for

Ph.D. degree in Agricultural
Economics

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Major professor

Date March 29, 1996

 

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EFFECTS OF MONETIZED FOOD AID ON LOCAL MAIZE PRICES IN
MOZAMBIQUE

By Cynthia Donovan

A DISSERTATION

Submitted to

Michigan State University

in partial fulfillment of the requirements

for the degree of

DOCTOR OF PHILOSOPHY

Department of Agricultural Economics

1 996

ABSTRACT

EFFECTS OF MONETIZED FOOD AID ON LOCAL MAIZE PRICES IN
MOZAMBIQUE
By

Cynthia Donovan

Yellow maize commercial (monetized) food aid has been a major policy instrument for
meeting food security needs in Mozambique, particularly among the urban poor. This
research studies the effects of this food aid on prices for Mozambique’s domestic white

maize in difl‘erent locations, under current and likely fiiture conditions.

Market studies, including a rapid appraisal of the white maize markets, were completed in
Maputo and other provinces throughout the country. Then, time series analysis was
conducted on weekly price data and weekly food aid deliveries data for the 1990-1995
period. Vector autoregressions were used to investigate the effects of unpredictable
fluctuations in prices and quantities. War/drought and post-war/drought periods were

determined and analyses conducted based on the separate time periods.

In the war/drought pen'od, maize prices in Maputo were volatile and white maize supplies
were highly limited. Heterogeneous preferences for white maize and yellow maize meant
that white maize prices rose very high. The importance of yellow maize food aid in this

period was as a cheaper consumption alternative, for those consumers willing to switch.

In the second period, however, the analysis showed white maize prices would have been

10 -15 percent higher going into the harvest season of 1995, if food aid had not arrived.

With time, the markets in other parts of Mozambique have begin to recuperate. Market
integration analysis based on separate war/drought and post-war/drought periods
demonstrated that the effects of Maputo price shocks on Chimoio prices (in a maize
production area) were significant. Producer and trader incentives were affected by such

shocks.

Finally, the lessons for fiiture food security and food aid policy are presented. As yellow
maize food aid supplies diminish, policy analysts must evaluate the market supplies and
price relationships, recognizing that domestic production may still be insufficient to meet
demand and some imports required. Local purchases of white maize for use in the
emergency distribution programs may provide incentives for market development, but
must be undertaken with care to avoid generating high consumer prices. Without yellow

maize food aid, white maize prices for both rural and urban consumers will be higher.

  
   
   

Copyright by
CYNTHIA DONOVAN

1996

 

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ACKNOWLEDGMENTS

This acknowledgment is designed to say thank you to those who helped to make this
document reality, but in truth, I can never truly thank all of the people involved in the past
four years and one-half years, and all the learning time before that. For those whose
names do not appear here, remember that your ideas, questions, and smiles are still with

me.

There are several names that cannot be absent here, however. I thank Mike Weber, with
his years in the field, knowledge of markets, and drive for the work, encouraged and
supported me through the work in Mozambique. Also thanks to Bob Myers, who
contributed greatly to analysis in this work with ideas, corrections, and comments. He has
said that this work is supposed to be enjoyable, and I find that it is, just not necessarily on
a day to day basis. Dave Tschirley continually pushed me to think about what was
happening and write about it clearly. John Strauss was under-utilized, but very much
appreciated. Tom Reardon provided ideas and valuable suggestions on writing and

publishing, along with fiiendship.

At Michigan State University, the collegial environment fosters good work. Professors,
support staff, and fellow students have knowledge, experience, and mutual respect that

contribute to a positive learning experience.

Thanks to Jennifer Wohl, Karin Stefl‘ens, Cynthia Phillips, Doug Krieger, Julie Howard,
Larry Dyer, Scott Swinton, Sylvia Morse, Amelia Swinton, Peter Swinton, Bonnie Banks,
Valerie Kelly, and all the others who helped provide the food, discussions, laughter, beer,

and other key elements of survival when researching and writing a dissertation.

Obviously, this research relies heavily upon many people in Mozambique. My
Mozambican colleagues, including Higino Marrule, Paula Santos, Rui Benfica, Anabela
Mabote, and Paulo Mole provided ideas, and great food, while we visited to markets and
graphed prices. In the oflice in Maputo, the support of Conceicao, Simao, Lalu, e Chico

was critical to success.

In USAID, the financial support for the Food Security 11 project in Mozambique was

fundamental. I also found people like Julie Born, dedicated to their work there.

Ao final, estou muita agradecida a tudos os intervenientes nos mercados do milho em

Mocambique, que sempre me deram tempo, ideias, e coisas para pensar, alem das sorrisas.

Finally, I thank my parents for their unfailing support and reminders that life should both

work and relaxation, that laughter and enjoyment can always be found. My thoughts of

you both and the beauty of the Minnesota island keep it all in perspective.

CXD

TABLE OF CONTENTS

List of Tables ....................................................... xiii

List of Figures ....................................................... xv

Abbreviations ....................................................... vii
Chapter 1

Introduction ......................................................... l

1.1. Issues ..................................................... 1

1.2 Objectives of this research ...................................... 2

1.3 Background on food aid in Mozambique ........................... 3

1.4 Debate on disincentives ........................................ 8

1.5 Organization of the dissertation ................................. 11
Chapter 2

Previous Evaluations of Food Aid Effects in Developing Countries ............ 12

2.1 Reviews of potential effects of food aid ........................... 12

2.2 Analysis of price disincentives .................................. 14

2.3 Methodologies .............................................. 17

2.4 Market integration in the food aid literature ........................ 19

2.5 Analytical concerns in the present study ........................... 22
Chapter 3

Maize Markets ..................................................... 24

3.1 Introduction ............................................... 24

3.2 Maize production in Mozambique ............................... 25

3.3 Maize consumption in Mozambique .............................. 31

3.4 Rapid Appraisal (RA) Methodology ............................. 34

3.5. Agents in the markets ........................................ 38

3.5. 1 . lntervenientes ...................................... 38

3.5.2 Marketing channels prior to ERP ........................ 40

3.5.3 Marketing channels in the informal sector .................. 43

3.5.4 Marketing channels in the formal commercial sector ........... 45

3.5.5 Marketing channels and the public sector ................... 46

3.5.6 Role of processing industries ............................ 51

3.5.7 Imports and exports ................................... 52

3.6 Additional considerations ...................................... 53

3.6.1 Nonmarketed production ............................... 53

3.6.2 Marketing margins and price risks ........................ 54

3.6.3 Risk-bearing mechanisms ............................... 58

viii

3.7 Brief summary of the observed marketing functions .................. 59

3.8 Characteristics of the markets relevant for food aid analysis ............ 60

3.8.1 Fragility of nascent markets ............................. 61

3.8.2 Need for investments .................................. 61

3.8.3 Bias against the informal sector .......................... 62

3.8.4 High risks due to price volatility .......................... 62

3.8.5 Consumption versus market incentives ..................... 63

3.9 Review of key issues with white maize traders ...................... 64
Chapter 4

Food Aid in Mozambique ............................................. 67

4.1 Structure of food aid ......................................... 67

4.1.1 Making the decision on commercial food aid ................ 67

4.1.2 Consignees and food aid allocations ....................... 72

4.2 Pricing of commercial food aid ................................. 74

4.3 Food aid in the markets ...................................... 77

4.3.1 Agents and channels in the Maputo markets ................. 77

4.3.3 Competitiveness in the markets .......................... 80

4.3.3 Potential diversion to animal feed ......................... 81

4.4 Emergency yellow maize food aid in the markets .................... 81

4.5 Summary of yellow maize in the markets .......................... 83
Chapter 5

Data and Preliminary Analysis ......................................... 85

5.1 Introduction ................................................ 85

5.2 Data sources ............................................... 85

5.3 A structural shift in the data .................................... 89

5.4 Unit root and stationarity tests .................................. 92

5.4.1 Stationarity and unit roots .............................. 92

5.5 Unit root and stationarity tests in the initial war/drought period ......... 94

5.6 Unit root and stationarity tests for the post-war/drought period ........ 102

5.6.] Introduction ........................................ 102

5.6.2 White maize prices and food aid deliveries ................. 102

5.6.2 Yellow maize prices .................................. 107

5.7 Seasonality ................................................ 111

5.8 Implications for time-series modeling ........................... 112
Chapter 6

VARs and the Estimation of the Effects of Food Aid Deliveries ........ . ...... 113

6.1 Introduction ............................................... 113

6.2 Structural and reduced form models ............................. 113

6.2.1 Structural models .................................... 113

6.2.2 Vector Autoregressions ............................... 114

ix

 

6.2.3 VARs in the economic literature ........................ 115

6.2.4 Advantages and Limitations of VARs .................... 116
6.3 Modeling the Mozambique case ................................ 117
6.3.1 Data constraints ..................................... 117
6.3.2 Emergency food aid arrivals ........................... 118
6.3.3 Weekly data and price analysis .......................... 119
6.3.4 Data used ......................................... 120
6.4 VAR model in the war/drought period ........................... 120
6.4.1 Modeling in the war/drought period ...................... 120
6.4.2 VAR order selection ................................. 121
6.4.3 Multivariate Granger causality ......................... 123
6.4.4 VAR Model ........................................ 126
6.4.5 Identification with a recursive structure ................... 129

6.4.6 Selected model: Recursive identification with WP, FA, YP ordering
................................................ 129
6.4.7 Impulse responses and simulations ...................... 131
6.4.8 Forecast error variance decompositions .................. 134
6.4.9 Historical simulations ................................. 136
6.4.10 Alternative identification schemes ...................... 138
6.4.11 Summary of war/drought period results ................. 140
6.5 Post-war/drought period: April 1993 through April 1995 ............ 141
6.5.1 Identification considerations ............................ 141
6.5.2 VAR order selection ................................. 142
6.5.3 Exogeneity testing ................................... 144
6.5.4 Recursive ordering: FA, YP, WP ........................ 144
6.5.5 Seasonality issues and results ........................... 148
6.5.6 Historical simulations with reduced food aid deliveries ....... 150
6.5.7 Alternative identification schemes and orders .............. 157
6.6 Summary of the VAR results .................................. 157

Chapter 7
Market Integration and Transmission of Shocks .......................... 161
7.1 Issues .................................................... 161
7.2 Concepts ................................................. 164
7.2.1 Law of One Price and spatial equilibrium .................. 164
7.2.2 Market integration ................................... 165
7.2.3 Recent additions to the literature ........................ 166
7.2.4 Mozambique research ................................ 167
7.3 Organization of the markets ................................... 169
7.3.1 Maputo, Chimoio, and Beira maize markets . . . .. ............ 169
7.3.2 Static measures of price relationships between Maputo, Beira, and

Chimoio ......................................... 172
7.3.3 Yellow maize ...................................... 175
7.4 Data considerations ......................................... 178
7.4.1 Weekly data ....................................... 178

X

7.4.2 Retail prices as a proxy for producer prices ................ 179

7.4.3 Grain as the selected commodity ........................ 181
7.4.4 Prices and costs of trade .............................. 181
7.4.5 Seasonality ........................................ 182
7.4.6 Stochastic trends in the data ............................ 182
7.4.7 Structural break in the prices ........................... 183
7.4.8 Margins and trade development ......................... 183
7.5 Analysis of the war/drought period .............................. 185
7.5.1 Univariate Analysis of the war/drought period: Maputo and Chimoio
prices ........................................... 186
7.5.2 Simple correlation coefficients .......................... 190
7.5.3 VAR order selection and exogeneity testing ................ 190
7.5.4 Identification considerations ............................ 194
7.5.5 VAR results for the war/drought period ................... 195
7.6 Analyses for post-war/drought period ............................ 197
7.6.1 Univariate analysis of the post-war/drought period: Maputo and
Chimoio prices .................................... 197
7.6.2 Static measures of relationship between Maputo and Chimoio . . 201
..................................................... 201
7.6.4 VAR results for the post-war/drought period ............... 204
7.6.5 Long run dynamics .................................. 204
7.7 Summary of market integration results and conclusions .............. 207
Chapter 8
Conclusions and Implications ......................................... 211
8.1 Introduction ............................................... 211
8.2 Research objectives ......................................... 212
8.3 Research Methods .......................................... 212
8.4 Findings from the Rapid Appraisal .............................. 214
8.5 Findings on commercial food aid effects on Maputo maize prices ...... 216
8.5.1 Effects during war/drought ............................ 216
8.5.2 Effects during the post-war/drought period ................ 219
8.6 Findings on market integration and price transmission ............... 220
8.6.1 Efi‘ects during war/drought period ....................... 221
8.6.2 Effects during the post-war/drought period ................ 222
8.7 Lessons learned ............................................ 223
8.7.1 Yellow maize and monetization ......................... 223
8.7.2 Market integration ................................... 226
8.7.3 Domestic purchases acquired for emergency food aid supplies . . 227
APPENDIX A
Data for Analysis of Food Aid Quantity Effects on Maize Prices ............. 233

APPENDIX B

Alternative Models for the Food Aid Effects Analysis ...................... 238

B] Simultaneous model for the war/drought period .................... 238

B2 Recursive ordering: FA, YP, WP in the war/drought period ........... 244

3.3 Simultaneous identification in post-war/drought period .............. 246

3.4 Recursive model WP, FA, YP for the post-war/drought period ........ 250

B5 Recursive model FA, YP, WP for the post-war/drought period with seven lags

........................................................... 250
APPENDIX C

Data for the Market Integration Analysis .............................. 253
APPENDIX D

Alternative Models for the Market Integration Analysis .................... 257

D.1 Recursive model with Chimoio-Maputo ordering in war/drought period . 257
D2 Recursive model with Chimoio-Maputo ordering in post-war/drought period
........................................................... 259

REFERENCES .................................................... 261

LIST OF TABLES

Table 1.1. Cereal Supplies and Sources in Mozambique: 1989/90 through 1995/96 . . . . 6

Table 3.1 Characteristics of the research sites for the rapid appraisal ............. 36
Table 3.2 Indicative marketing margins for white maize traders observed during rapid
appraisal, July - September 1994 ................................... 55
Table 4.] Yellow maize food aid in total availability and market supplies ........... 68
Table 5.1 Descriptive statistics of war/drought period ......................... 94
Table 5.2 Augmented Dickey Fuller (ADF) t-tests for war/drought period ......... 98
Table 5.3 Phillips Perron test results for the level series for war/drought period ...... 99
Table 5.4 KPSS tests for war/drought period .............................. 100
Table 5.5 Descriptive statistics for post-war/drought period ................... 103
Table 5.6 Augmented Dickey Fuller (ADF) t-tests for post-war/drought period ..... 104
Table 6.1 Testing on lag lengths for early period with trend variable ............. 122
Table 6.2 Exogeneity testing on the war/drought period series ................. 124
Table 6.3 Forecast error variance decomposition for recursive model, war/drought period
........................................................... 135
Table 6.4 Testing on lag lengths for post-war/drought period with mean shifi and trend
variables ..................................................... 143
Table 6.5 Exogeneity testing in the post-war/drought period ................... 145
Table 6.6 Forecast error variance decompositions for recursive order FA,YP,WP, post-
war/drought period, 4 lags ....................................... 149

Table 7.1 Cross-price correlation coefficients for nominal and real white maize grain
prices: levels and first differences for April 1993 - November 1995 period,

Maputo, Chimoio, and Beira ..................................... 173
Table 7.2 Descriptive statistics of real price data for war/drought period .......... 187
Table 7.3 Phillips Perron test results for the real white maize prices: Maputo and Chimoio
in war/drought period ........................................... 189
Table 7.4 KPSS test results for the real white maize prices: Maputo and Chimoio in
war/drought period, with trend .................................... 191
Table 7.5 Cross-price correlation coemcients for real white maize grain prices: levels and
first differences in the war/drought period ........................... 192
Table 7.6 Lag length testing for war/drought period: Maputo and Chimoio prices with
trend variable in VAR .......................................... 193
Table 7.7 Exogeneity testing of Maputo and Chimoio prices, with seven lags and trend in
the war/drought period .......................................... 195
Table 7.8 Descriptive statistics of Maputo and Chimoio prices during post-war/drought
period ...................................................... 198
Table 7.9 Phillips Perron test results for the real white maize prices: Maputo and Chimoio
........................................................... 199
Table 7.10 KPSS test results for the real white maize prices: Maputo and Chimoio in
post-war/drought period, with and without trend ...................... 200

xiii

Table 7.11 Cross-price correlation coefficients for real white maize grain prices: levels

and first differences in the post-war/drought period .................... 201
Table 7.12 Lag length testing for post-war/drought period: Maputo and Chimoio prices
with trend variable in VAR ....................................... 202
Table 7.13 Exogeneity testing of Maputo and Chimoio prices, with 7 lags and trend in the
post-war/drought period ........................................ 203
Table B.l Forecast error variance decompositions for simultaneous model, war/drought
period ...................................................... 242
Table 3.2 Forecast error variance decompositions, based on nonrecursive ordering, late
period ...................................................... 249

xiv

LIST OF FIGURES

Figure 3.1 Map of Mozambique with Maputo, Chimoio, and provincial borders ..... 26
Figure 3.2 Total monthly rainfall at Chimoio station, 1991 - 1995 ................ 28
Figure 3.3 District level maize production in Mozambique, 1993/94 .............. 30

Figure 3.4 Map of Mozambique with rapid appraisal sites, July - September 1994 . . . 37
Figure 3.5 Principal marketing channels for domestic white maize observed during Rapid

Appraisal in 1994/95 marketing year ................................ 39
Figure 3.6 Agents and functions observed in the white maize markets, July - September
1994 ........................................................ 41

Figure 3. 7 White maize grain retail prices in Maputo, Chimoio, and Nampula, and ofiicial
producer prices, Jan. 2, 1993- Nov 25,1995 (nominal meticais/kg) ......... 49
Figure 4. l. Maputo monthly yellow maize food deliveries and nominal yellow and white

maize grain retail price, December 1992 - November 1995 ............... 71
Figure 4.2. Maputo white and yellow maize grain retail prices and official consignee
prices (nominal meticais/kg), April 1990 - December 1995 ................ 75
Figure 4.3. Monthly food aid deliveries in Maputo, March 1990 - April 1995
Figure 5.1 SIMA and USAID maize grain retail prices, April 1990 - April 1995 ..... 88
Figure 5.2 Weekly commercial food aid deliveries and real white and yellow maize prices
in Maputo, April 1990 - April 1995 ................................. 90
Figure 5.3 Real white maize prices, April 1990 - April 1995 .................... 95
Figure 5.4 First difi‘erences: Real white maize prices, April 1990 - April 1995 ....... 95
Figure 5.5 Real yellow maize prices, April 1990 - April 1995 ................... 96
Figure 5.6 First difl‘erences: Real yellow maize prices, April 1990 - April 1995 ...... 96
Figure 5.7 Yellow maize commercial food aid deliveries to Maputo, April 1990-April
1995 ........................................................ 97

Figure 5.8 First difi‘erences: Yellow maize commercial food aid, April 1990-April 1995 97
Figure 6.1 Impulse Responses for the recursive model WP, FA YP, war/drought period

........................................................... 133

Figure 6.2 Simulation in war/drought period, recursive model with no food aid afier

July 31, 1992 ................................................ 137
Figure 6.3 Impulse responses to shocks, based on the recursive model, FA, YP, WP, in

the post-war/drought period ...................................... 147
Figure 6.4 Impulse responses to shocks, based on the recursive model, FA, YP. WP, with

hungry season variable, in post-war/drought period .................... 151
Figure 6.5 Simulation in post-war/drought period with no food aid from >

November 1994 - January 1995 ................................... 153
Figure 6.6 Simulation in post-war/drought period with no food aid from

November 1994 - January 1995, with hungry season variable ............ 154
Figure 6.7 Simulation in post-war/drought period with no food aid in January 1995 . 156
Figure 7.1 Map of Mozambique with Chimoio, Beira, and Maputo .............. 162
Figure 7.2 Real white maize grain prices for Maputo, Chimoio, and Beira ........ 163

Figure 7.3 Qualitative assessment of yellow maize grain availability in Chimoio markets,

April 3, 1993 - November 25, 1995 ................................ 177
Figure 7.4 Qualitative assessment of yellow maize grain availability in Maputo markets,
April 3, 1993 - November 25, 1995 ................................ 177
Figure 7.5 Qualitative assessment of yellow maize grain availability in Beira markets,
April 3, 1993 - November 25, 1995 ................................ 178
Figure 7.6 White maize grain prices at retail, itinerant trader, and producer market levels
in Manica, July 1, 1995 - February 2, 1996 .......................... 180
Figure 7.7 Gross margin between real white maize grain retail prices in Maputo and
Chimoio, April 3, 1993 - November 25, 1995 (in meticais/kg) ............ 185
Figure 7.8 First differences in real white maize grain prices in Maputo ........... 188
Figure 7.9 First differences in real white maize grain prices in Chimoio ........... 188
Figure 7.10 Impulse responses of each price series to shocks, recursive, seven lags,
war/drought period, (in real meticais/kg) ............................ 196
Figure 7.11 Impulse responses from various shocks, based on recursive model, post-
war/drought period, with seven lags (in real meticias/kg) ................ 205

Figure 8.1 Weekly real retail prices for whites and yellow maize grain in Maputo and
weekly monetized yellow maize food aid deliveries in Maputo, April 1990 -
December 1995 ............................................... 217

Figure 3.1 Impulse responses to shocks, based on the simultaneous model, war/drought

period ...................................................... 241
Figure B.2 Simulation in war/drought period, simultaneous model with no food aid after
July 31, 1992 ................................................. 243
Figure B.3 Impulse responses to shocks, based on the recursive model FA,YP, WP, in
war/drought period ............................................ 245
Figure B.4 Impulse responses to shocks, based on the simultaneous model with trend and
mean shift, in the post-war/drought period ........................... 248
Figure B.5 Impulse responses to shocks, based on the recursive model WP, FA, YP, with
a trend and mean shift, four lags, in the post-war/drought period .......... 251
Figure B.6 Impulse responses to shocks, based on recursive model FA, YP, WP, with a
trend and mean shift, seven lags, in the post-war/drought period .......... 252
Figure D.1 Impulse responseof each price series to shocks, recursive model, Chimoio-
Maputo ordering in the war/drought period, with seven lags .............. 258

Figure B.2 Impulse response of each price series to shocks, recursive with Chimoio-
Maputo ordering, with trend, seven lags, and hungry season in post-war/drought
period (in real meticais/kg) ...................................... 260

 

FRELIMO

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IASF
ICM
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KPSS

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S-C-P
SEMOC
SIMA

ABBREVIATIONS

Augmented Dickey Fuller Tests for unit root

Parastatal food crop marketing agency (replaced by ICM in 1994)
Companhia Industrial Matola (industrial grain mill)

National Planning Commission

Consumer Price Index

Directorate of Agricultural Economics, Ministry of Agriculture and
Fisheries

National Directorate of Statistics

Economic Rehabilitation Program

European Union

Frente de Liberacao de Mocambique (political party , currently majority in
government)

Food Security Project-Mozambique

Government do Mozambique

German Technical Assistance Agency

Agricultural Survey of the Family Sector

Cereals Institute of Mozambique (“Instituto de Cereais do Mocambique”)
independently and identically distributed

International Monetary Fund

Kilogram

Kwiatkowski, Phillips, Schmidt, and Shin (tests for unit roots)
Lagrange Multiplier (test statistic)

Monetary unit of Mozambique (plural: meticais)

Ministry of Agriculture and Fisheries (Mozambique)

Michigan State University

Non-Govemmental Organizations

Rapid Appraisal

National Resistance of Mozambique (political party/military organization)
Structure-Conduct-Performance (analytical paradigm)

Seed Company of Mocambique

Market Information System (“Sistema de Informacfio de Mercados
Agricolas”)

World Food Programme

Chapter 1

Introduction

1.1. Issues

After years of civil war and a devastating regional drought, Mozambique is highly
dependent upon foreign assistance both for government budgetary resources and for food
supplies. Foreign aid makes up to 80 percent of GNP and food aid cereals were 30
percent of total cereals’ availability in 1994/95. The United States Agency for
International Development (USAID) has been the principal donor to bring in yellow maize
food aid to be monetized to provide budget resources for the government, while
ameliorating food production deficits and import constraints that threaten the food
security of many Mozambicans.l However, in a market environment undergoing policy
reform and recuperation from civil war, commercial food aid has the potential for

undermining the market development which has been an objective of government policy.

This research uses time series analysis to evaluate the price effects of food aid arrivals on
Mozambican maize markets. There are two time periods for the analysis: 1) a
war/drought period, from April 1990 to February 1993, when the destructive

consequences of civil war limited movement and economic activity, and a devastating

 

' The European Union has also imported commercial yellow maize food aid, but the in much smaller
quantities than USAID.

_¥—_‘

 

2
regional drought decimated maize production; and 2) a postwar recuperation period, fi'om
April 1993 through November 1995, when production increased again and private markets

were expanding.

Retail market prices provide the basis for the analysis, in combination with detailed
information on the structure of markets and the food aid distribution system. The time
series analysis allows an evaluation of the effects of food aid arrivals in Maputo on market
price changes for white and yellow maize. The analysis also looks at issues of market
integration by examining the effects of Maputo price shocks as they spread to Chimoio,

an important market in the central maize production region.

1.2 Objectives of this research

The objectives of this research are the following:

1) to determine the key factors in the structure of Mozambican maize markets that
mediate how commercial food aid affects those markets;

2) to analyze the relationship between yellow and white maize prices, and quantities of
yellow maize commercial food aid arrivals in Maputo during the drought/war
period and in the current environment of market development;

3) to evaluate the extent to which the Maputo maize markets are spatially integrated with
production region markets by examining how price effects in Maputo are
transmitted to producer prices;

4) to identify policy options available to mitigate potential negative effects of food aid

while enhancing positive effects.

3
Four specific research questions arise from these objectives. First, given the market
reforms and consequences of war, what are the key aspects of the market structure that
influence how food aid deliveries affect prices? Second, how do the arrivals of yellow
maize commercial food aid affect prices for white maize in urban and rural markets?
Third, how do Maputo white maize price shocks affect the prices for white maize in maize
producing areas in Mozambique? Fourth, are there differences in the effects of food aid
deliveries and price shocks between the war/drought period in 1992 through early 1994
and the post-war recovery period in 1994 through late 1995? Finally, what lessons can be
learned concerning the potential effects of various policy options for ensuring food

security?

1.3 Background on food aid in Mozambique

Food aid has arrived in Mozambique since Independence in order to help supplement the
nutrition of vulnerable sectors of the population and provide the government with
budgetary support through the sales on the market. During the 1991/ 1992 drought and its
afierrnath, food aid was responsible for providing the means for survival of thousands, if
not millions, of Mozambicans. There are two basic channels for food aid, termed
“emergency food aid” and “commercial food aid.”2 Emergency food aid arrives in

response to specific expressed needs, distributed for free or for work to targeted groups

 

2 The vocabulary used with food aid is not consistent across documents, with terms such as “program
aid”, “project aid”, “monetized food ai ” used to denote various types of food aid. Here, emergency
food aid is defined as aid received by non-govemmental organizations, government ministries, and
international agencies in order to be given to people in need. “Commercial food aid” is used here to
indicate food aid supplies that arrive specifically to be sold on the local markets, by non-
governmental organizations or by the Mozambican government, not to be distributed for free.

4
(e.g., school children in poor areas, demobilized soldiers, recently returned refugees). In

Mozambique, almost all of the free distribution and work projects have been in rural areas.

In contrast, commercial food aid is imported by the donor for delivery to the government
of Mozambique. The govemment can either give or sell the commercial food aid. In the
case of USAID, the government of Mozambique must deposit local currency funds
equivalent to the free-alongside-ship (F AS) price in a special development account. Most
commonly the government sells the maize either through government shops or through
private traders in the major urban centers. Government ration shops ceased firnctioning by
1992, so that the informal sector has been responsible for most of the retail sales since
1991. USAID has used the commercial yellow maize food aid to promote the policy
reform efforts towards private market development, requesting that the qualified list of

consignees be expanded to ensure greater competition.

Commercial food aid has the dual purposes of providing budgetary supplements to the
national government and alleviating food shortages. Food shortages often result in price
spikes that threaten food security, particularly of the urban poor who have little or no
savings to smooth consumption. However, reducing or eliminating these price spikes may
be in conflict with development goals since lower prices diminish incentives to producers
and traders, the so-called disincentive effect. The administration and regulations

governing commercial food aid in Mozambique will be discussed firrther in Chapter 4.

5
In Mozambique, the majority of all food aid cereals have been in the form of yellow maize,
as can be seen in Table 1.1. During the war and drought period, maize food aid was a
significant portion of cereal availability in the country. By 1992/93, yellow maize food aid
reached 60 percent of total cereals availability in the country; commercial yellow maize
food aid alone was 25 percent of the total, whereas emergency yellow maize food aid was
35 percent of the total. In 1994 and 1995, domestic production of white maize and other
staples began to increase, as did the domestic marketing opportunities for those crops, and
food aid cereals became less important in the total. By the 1994/95 marketing year,
yellow maize food aid (commercial and emergency) was only 15 percent of total cereals
availability. Commercial yellow maize food aid contributed only 6 percent of the total

cereals availability in the 1994/95 marketing year.

The 6 percent of total cereals availability arriving in the form of yellow maize food aid
represents a large proportion of the cereals available for consumers to buy on the market,
and populations in urban areas rely heavily upon the market for basic staples. Estimates of
marketed surplus fiom domestic production of white maize are in the range of 13-20%, or
about 70,000 - 100,000 metric tons annually (MAP/UAP 1994; MA/DEA/Estatistica
1994; NDAE/DS 1993) . This means that the yellow maize commercial food aid about
doubled maize supplies on the market in the country, mostly in the urban areas of Maputo

and Beira, where price effects can be expected to be greatest.

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8
There is no domestic yellow maize production, so any production disincentives through
prices would be felt by the producers of the main domestically-produced substitute good,
white maize. On the consumption side, yellow maize is the less-preferred consumption
staple compared to white maize. Yellow maize is prepared for the table in much the same
way as the local white maize. Available at prices below the local white maize, yellow
maize products give the low-income consumers an alternative consumption good at a

lower market price.

1.4 Debate on disincentives

While recognizing the benefits of commercial food aid for consumers, current policy
debate in Mozambique centers around the efl‘ects of food aid arrivals on both producer
and trader incentives. Incentives include not just the level of prices, but also the
anticipated fluctuations in prices (opportunity for arbitrage), risks, and the availability of

the commodity. All of these in turn depend upon the markets’ and traders’ behavior.

As argued in Isenman and Singer (1977), producer disincentives stem from the arrival of
cheap (or free) food aid commodities which reduce demand for locally produced goods,
thus reducing overall prices in the markets. In such a case, farmers would face lower
prices for their commodities and have less incentive to produce the basic consumption
staples. In addition, the food aid arrivals might increase price risks through counter-
cyclical price declines. That is, commodity prices might decline during the hungry season
when food aid arrives. A lower mean along with a higher risk would make risk-averse net

surplus farmers worse off than before, even when the poor urban consumers would benefit

9
from the reduced costs of obtaining food. This analysis depends upon the price
responsiveness of farmers. If a lower price or increased risk reduces their incentives to
produce staples, such price effects mean that Mozambique may suffer the long-term

consequences of increased food aid or import dependency.

The incentives to trade in locally produced commodities could be decreased by lowered
quantities of the local commodity available (thin markets for the domestic good), or from
high price risks for the local commodity with an inability to predict food aid arrivals and
price declines. Volatility in prices itself need not be a disincentive to traders, if they
perceive that the fluctuations present arbitrage opportunities, rather than just an increase
in risk. Food aid arrivals may also limit transport availability for local commodity trade.
Firms allocate their limited transport resources to the highest profit activity, and this is
ofien food aid. This transport constraint is more apparent with emergency food aid
distributions in rural areas with severe transport constraints. The government is
withdrawing from direct market activities in buying and selling of grains and flours,

thereby increasing the importance of motivating private traders to work with local maize.

All of these potential disincentive effects depend upon various factors, including the price
for market sales, the total quantity arriving in relation to quantities available in the market,
and consumer preferences. Decisions on the quantity and pricing for commercial food aid

are made by both the donors and GOM.

1 0
Evaluation of the efl‘ects of food aid must rely upon knowledge of market structure and
organization. Prices may be determined through government administration, base-point
pricing, monopoly or monopsony, collusive action between agents, purely competitive
market forces, or some combination of these mechanisms. In the past the government
played a key role in the purchase and sales of agricultural commodities, including maize. A
minimum producer price is still set, yet there is no effective enforcement mechanism in
most parts of the country. It is also necessary to know who is trading with producers,
where they purchase and sell maize, and other aspects of the rural marketing system. The
price discovery process is dependent upon the market channels available and the
competitiveness of these markets. Given all of the changes within the country since the
Economic Rehabilitation Program (ERP) and Peace Accord, knowledge about the current

structure and performance of maize marketing is critical.

A related problem is the extent of spatial integration of maize markets in Mozambique. If
markets are highly spatially integrated, with price shocks from one market quickly and
fiilly transmitted to other markets, the efl‘ects of food aid arrivals on Maputo prices will be
transmitted to consumers and producers in other regions of the country. On the positive
side, if market integration is improving, localized scarcities in Maputo will serve to draw
on reserves elsewhere in the country and the producers and traders will respond to the
need. Since producer price data is lacking for the relevant historical period, the hypothesis
that food aid arrivals affect producer prices cannot be directly tested. However it will be
shown that retail prices for markets in the production areas serve as a good proxy for

producer prices in order to evaluate those effects.

 

1 1
Spatial market integration is also of interest for policy makers in determining investment
priorities and potential for increasing marketed production. Structural transformation of
the food system is retarded as long as each region (potentially each household) remains in
autarky. Market integration would indicate that price signals are being transmitted from
consumption centers to production or import centers, in order to guide investment. The
lack of market integration would indicate that either transaction costs are too high to

motivate transactions or that other constraints are blocking market development.

1.5 Organization of the dissertation

This dissertation is organized into eight chapters, progressing from a description of market
structure and organization into the econometric models, and then the results and policy
implications. Chapter 2 will present a brief review of previous research on the effects of
food aid in the developing world. Chapter 3 describes the rapid appraisal methodology
used to evaluate the structure and performance of the maize markets in Mozambique, as
well as the findings of that analysis. Chapter 4 will discuss yellow maize food aid in the
context of that market structure, including the administration, delivery, and marketing of

food aid.

Chapter 5 presents the data and their characteristics. Chapter 6 details the food aid VAR
model and simulations. Chapter 7 presents the market integration model. In conclusion,
Chapter 8 brings together the econometric analysis and the rapid appraisal results,

exploring the policy implications of these findings.

Chapter 2

Previous Evaluations of Food Aid Effects in Developing Countries

2.1 Reviews of potential effects of food aid

There are two basic objectives of food aid, as noted by von Braun and Huddleston (1988):
1) a transfer to people who are in need and unable to obtain sufficient food supplies; and
2) a source of funds for the government, reducing foreign exchange constraints or other

resource constraints, and providing resources for development projects.1

The actions to obtain these two objectives may have medium- to long-term effects that
undermine the food security of a recipient country. Research on the effects of food aid is
extensive, varying both in theme and in analytical methods.2 Maxwell and Singer (1979)
review early attempts to evaluate food aid, with examples mainly from the Indian
experience of the late 1950s and 1960s Later, in 1989, Jones surveyed the literature on

food aid disincentive effects in sub-Saharan Afiica. Two recent books provide much of

 

‘ Another basic objective is self-interest, political or economic, on the part of the donor country. The
donors may use food aid deliveries to sustain producer incomes in their own countries through
government purchases and extraction of commodities from the domestic markets of the donor
country, ofien termed “surplus disposal” in the food aid literature. While it is important in
determining the type of food aid available, the self-interest objective will not be discussed further
here.

2 The extensive literature on food aid cannot be covered in this brief chapter. Those interested should
refer to the reviews indicated below, as well as the sources listed in them.

12

 

1 3
the background information and discussion of issues. Edited by V. Ruttan, Why food
aid? (1993) includes excerpts fi'om much of the literature on the effects of food aid. In
Food aid reconsidered: Assessing the impact on third world countries, editors
Edward Clay and Olav Stokke (1991) concentrate on recent food aid efforts, particularly
in sub-Saharan Africa, providing articles that orient the reader on efi‘ects, their

measurement, and possible policy alternatives.

Maxwell (1991) categorizes food aid disincentive scenarios into macro versus micro
scenarios with possible efi‘ects through prices, policies, food habits, and labor. In the
guidelines developed by Shaw et a1. (1991) for the World Bank and the World Food
Programme (WFP), price disincentives are related to the absorptive capacity of the
economy, i.e., “the level of food aid that can be absorbed without causing prices to fall
below the border price” (1991, p.2). This is not the only issue with regard to prices, since
increasing the volatility in prices or adding an additional source of price variability, may

create price risks unacceptable to traders or farmers.

The policy disincentives of commercial food aid stem from the budget subsidies to the
government which may allow the government to ignore agricultural policy needs or food
security policy needs. The budget subsidies are ofien given, however, to provide
additional resources necessary to support policy reform, so there may be both positive and
negative effects on policy reform. Negative effects of food aid on food aid are attributed
to the availability of imported food aid commodities that create new demand for imports,

increasing the dependence upon imported staples, rather than the domestically produced

 

14
staples. Regarding the labor disincentives, the issue is usually raised concerning either
food for work programs and labor market distortions, or free distribution of food reducing
the need to sell labor, neither of which is at issue here. Thus, the remainder of this chapter
will focus on the potential price disincentives from additional supplies brought onto the

market at subsidized prices.

2.2 Analysis of price disincentives

When concerned about the effects of food aid on local markets, the primary focus of
researchers has been on producer price disincentives. The argument is the following: food
aid commodities arrive at subsidized (or zero) prices, adding quantities to the markets,
displacing the consumption of locally produced food commodities, depressing their prices,
and thereby reducing production incentives, eventually leading to lowered production of
food grains (Isenman and Singer 1977). Researchers have thus attempted to determine
trends in food prices and production, and determine how food aid quantities may influence

those trends.

Isenman and Singer (1977) present one of the most well-known analyses of the effects of
food aid on local prices in their work on India in the 19503 and 19605. As the authors
noted, the lack of data precluded a multi-equation econometric model to evaluate effects
and instead they used a “common sense” approach and some basic price analysis. The
food aid effects depend on several factors: 1) the extent to which food prices are

depressed with the anivals of food aid; 2) the aggregate supply response of farmers to

15

price changes; and 3) other nonprice constraints on production and incentives to produce,

such as the lack of inputs.

Using annual data from 1955-1971 for India, Isenman and Singer (1977) found evidence
of possible short-run negative price effects, but positive long-run effects on prices and
consumption. There were declines in producer prices for rice in the short run. However,
in the long run, there were income-induced demand increases that were supported by the
increased nonagricultural investments. Food aid, by keeping the price of food low,
ameliorated difficulties of a food bottleneck, thus bolstering investments and economic
growth (Isenman and Singer 1977). In addition, they cite further research (Mann 1969;
Rogers, Srivastava, and Heady, cited in Isenman and Singer 1977) that looked directly at
production and prices through the 19505 and 19605, with findings that bolster Isenman
and Singer’s arguments that the price effects were short-term. An additional concern was
that food aid was used to support the government in pursuing policies that did not favor
the agricultural sector. The authors stress that food aid can be used to support policies

both favorable and unfavorable to agriculture, just as with monetary aid.

Farzin’s (1991) work in Somalia sought to evaluate various effects of food aid, including
whether or not the food aid displaced commercial imports. This is important for US food
policy since the legislation specifically states that food aid must be additional to, rather
than displacing, commercial imports. In Somalia’s case, Farzin found that donors did not
decrease the amount of food aid arriving after the crisis, resulting in continued dependence

of the government upon counterpart funds as well as continued disincentives for the

 

l 6
domestic crop production. The research does show evidence of disincentive effects of
food aid through lower prices for consumption goods, but the analysis is based on annual

prices and production, thus not lending itself to a more dynamic analysis of the effects.

The role of food aid in price stability has been evaluated by several researchers.3 F arzin
found that food aid programs of the donors in Somalia were uncoordinated, resulting in
unpredictability of food aid flows which “added to local price instability and increased the
risks and uncertainties facing local producers” (p.269). He did not extend the analysis to
evaluate how such variability might have affected traders. With research conducted in
Ethiopia, Buchanan-Smith (1988, as cited by Maxwell 1991) argues that trader incentives
may be undermined by food aid, with unexpected price volatility increasing the risks and
reducing inter-seasonal storage. In general, the effect of food aid on market development
and traders’ activities has not been a focus of researchers in the past. A recent study on
the informal markets in Maputo addressed the relationship between food aid arrivals and
the development of the informal markets (Tschirley et al 1996). The research in this

dissertation continues from that knowledge base.

In a 1989 survey of literature on the impacts of food aid specifically in an African context,
Jones found that the lack of reliable statistical information hampered efforts to evaluate

the direct market disincentive effects of food aid. Jones concluded that existing research

 

3 Some food aid commodities enter directly into commodity price stabilization programs as reserves.
In the current research, the focus is on commodities which enter into the market directly, not through
a commodity reserve program. Discussion of price stabilization programs can be found in
documents such as Pinckney 1989 and Mellor and Ahmed (1988).

 

17

shows that the more important effects of food aid may stem from the policy disincentives
from food aid rather than the direct market disincentives observed in prices. Nevertheless,
Jones writes that “given the lack of reliable statistical information the true extent of [direct

market disincentives] has been unclear” (p.16).

2.3 Methodologies

Researchers have used a variety of tools to reach their conclusions. In the early literature,
basic price and quantity analysis was used, including some correlation coefficients and
ordinary least squares regression analysis. Examples are Isenman and Singer’s (1977)
work in India, Khadka (1989) in Nepal, and Stevens (1979) in four African case studies.
Isenman and Singer did cite two other multi equation studies of food aid in India.
However, in their analysis, based on a “common sense view,” an expected yield was
estimated and then expected prices given that yield. Those prices were compared to the
realized prices and the food aid to see if lower than expected prices could be associated
with years in which food aid was high, and it was found to be true. Then, the authors
looked to see if years of low prices for food grains were followed by declines in

production that could not be explained by rainfall, and the evidence was less clear.

As Stevens did in 1979, Farzin’s (1991) analysis uses aggregate price, import, and
production data, showing trends over time, with narrative on economic relationships
connecting the pieces together. Farzin’s study focused on the issue of import dependence
and changes in consumption habits. He supported his conclusions by comparing

consumption data and relative consumer prices over different time periods during which

 

18

there were food aid arrivals. This follows Maxwell’s (1991) recommended use of both
quantitative and qualitative methods for evaluation, as reflected in Maxwell’s (1986) study

in Senegal, where lack of data frustrated detailed evaluation.

As data and computers have increased technical potential, researchers have developed
general equilibrium models. An example of this type of analysis is Cathie’s (1991)
research on Botswana using a social accounting matrix for the period 1965-1984 and
aggregate data on production, income, imports, exports, prices, labor remittances, and net
resource inflows. The data demands of this analysis make it inappropriate for the current
case. In addition, the annual analysis ignores the intra-annual dynamics of the supply and

price relationships.

Micro-level studies have been used to evaluate food aid, as with Bezuneh, Deaton, and
Norton (1988), but the focus was on food for work programs and the direct effects upon

household resource allocation of the recipients of the food aid.

In the current literature on the effects of liberalization and structural adjustment, time
series techniques are commonly used in price analysis, to determine if real prices have
increased or decreased, and to estimate the variation and possible structural shifls. The
development of market information systems in many countries during the 19805 has
enabled researchers to incorporate time series analysis, previously limited by the data

constraints.

 

19
The literature, while addressing the issue of producer prices and food aid, has not had the
detailed price and arrival data necessary to look at the short-nm dynamics and the
uncertainty that may be added to production and trading within a given year. While the
average annual price may remain the same or even be higher, it may be the distribution of

those prices through the year that is of interest to traders and producers.

Lavy’s (1991) analysis is the only article specifically on food aid found that uses VARs,
the econometric approach that will be used in this research. Farzin used annual data on
aggregate cereal production, imports, and food aid for 33 countries in sub-Saharan Africa
for the period 1970-1987 to evaluate whether food aid had positive or negative effects on
the econonries. The results point to food aid having a significant positive effect on food
production over time. However, given the heterogeneity in the countries, the difference in
relative importance of food aid between the countries, and the different policy and market
environments involved, as well as structural changes during the period (independence,
economic reforms), there are doubts as to the validity of the results. Lavy does attempt in

various ways to deal with the heterogeneity, however data constraints are present.

2.4 Market integration in the food aid literature

The market integration literature contains many examples of time series econometrics
from the early use of price correlation coefficients over time and space, to notions of
Granger-causality, to more recent work with Error Correction Models (ECM) and
cointegration analysis. All of these analyses are based upon the Law of One Price, with

variations due to assumptions about competitiveness and efficiency.

 

20
As was found with studies attempting to assess direct food aid effects on markets, analysis
of the price discovery process involves so many factors that stmctural models are not
possible in a developing country environment. Few countries collect the entire set of data
necessary to correctly specify a structural model. In addition to the data problems,
structural models are identified on the basis of strong restrictions on the parameters and

their interactions.

Time series modeling has added to the recent work with market integration, enabling
analysts to use these techniques to model relationships over time. Sims promoted vector
autoregressions (VARs) for macroeconomic policy analysis. He stressed that the VARs
are able to evaluate the long-term trends between variables, and the responses of different

variables fiom shocks from other sources.

One of the most difficult aspects of this type of econometric analysis is linking the
statistical relationships to sound economic theory. In the early work with VARs, found in
articles by Sims, for example, researchers identified systems assuming that the
simultaneous relationships between the variables followed a strictly recursive structure.
This is a strong assumption, ofien violated in reality. Recent developments in computer
technology and econometrics have enabled analysts to develop other nonrecursive systems

for identification based on economic reasoning.

It is still important to bear in mind the cautions of Harriss and Fanrinow and Benson

regarding what prices can tell, based upon the economic structure and physical reality.

2 1
That two prices move together may indicate competitive markets effectively operating.
However the same comovement can be found where an oligopolist operates or where the
government administers prices. Prices in two spatially separate markets may move
together not because the markets are directly connected, but because both markets are

afi’ected by some common phenomenon.

Sexton, Kling, and Carman (1991) used weekly data to model market integration in the
point-space tradition, with each market having its own supply and demand factors. In
equilibrium, there are three possible arbitrage regimes: efficient arbitrage, relative storage,
and relative glut. The existing regime depends upon the transaction costs, the price in the
markets of the supply region, and the autarky price of that region. There was trade when
the difference between the market price and the autarky price was greater than the

transactions costs.

In an analysis of market integration and the transition from war to peace in Ethiopia,
Dercon (1994) provides an example of the econometric analysis using error correction
methods, an adaptation of VARs in which long-run relationships between markets are key.
This will be discussed firrther in Chapter 6. The author’s comparison of different time
periods is based in part on separate analyses for each period. For some aspects,
regressions are estimated using dummy variables for the war to peace transitions. The
latter method may be questionable when underlying structural changes are ignored in the

durrrrny variable simplification.

22
In addition to the classic spatial market integration, with the same commodity in two
spatially distinct markets, researchers have applied market integration analyses across
commodities. When two commodities are substitutes or complements, as is the case with
white and yellow maize products, the prices for each are expected to move in concert.
Regarding substitutability, Sexton, Kling, and Carman state that if goods are not perfect
substitutes (i.e., there is no stable premium between them), “then their price difference
may systematically range above or below ori (the premium) in response to variations in
each product’s individual supply-demand factors” (p.574). This relationship between
products’ prices over time is part of the analysis needed in cases where the food aid

commodity is distinct from the locally produced commodity.

2.5 Analytical concerns in the present study

Back in 1977, Isenman and Singer point out the three major factors in determining
whether or not food aid has a price disincentive effect on farm production: 1) the extent to
which food prices are depressed with the arrivals of food aid; 2) the aggregate supply
response of farmers to price changes; and 3) other nonprice constraints on production and
incentives to produce, such as the lack of inputs. The present research first looks at prices
and whether they are affected by food aid, using as analytical support recent developments
in econometrics in addition to the common sense qualitative analyses suggested by
Maxwell(l99l ). In addition to the direct price effect, this research addresses the potential
disincentive efi‘ects upon traders that stems from unpredictable variations in prices. Points
2 and 3 of Isenman and Singer’s list cannot be answered with the current research,

although qualitative analysis provides orientation on possible answers for Mozambique.

23
Thus, both the food aid disincentives research and the market integration research ofl‘er
tools that will be helpful for the present study on food aid in Mozambique. In the next
chapter, the structure and conduct of the maize markets will be described in order to set
the stage for the econometric work that will evaluate the market outcome in the changing

economic environment.

 

Chapter 3

Maize Markets

3.1 Introduction

Walking into any market in Mozambique, the observer will see many people selling white
and yellow maize grain or flour. Prices observed in these markets are the end result of the
interaction of many agents, thus information on how supplies arrive in the markets, who is
involved in the process, and the information available to the agents enables the information
in prices to be analyzed. To interpret changes in prices, a thorough knowledge of the
organization of markets is necessary, including the competitiveness, the integration

between markets, and the role of the private and public sector.

Mozambique’s fight for independence and the subsequent years of civil war lefl much of
the production and market infrastructure destroyed or useless. The socialist policies in
the post-Independence era introduced changes and then market reforms initiated in 1987
began a new process of change. However, it was difficult for researchers to follow the
whole process. The landmines and frequent assaults on the roads made research in rural
areas difficult and government funds for research were limited. This has meant a paucity
of research on the currently evolving markets, with a few exceptions (MAARP 1990,

Quezada et al 1990, Billing 1990, MOA/MSU wr>#10 1993).

24

 

25
Thus, in 1994, the Ministry of Agriculture and Fisheries project in connection with
Michigan State University (referred to as MAP/MSU) assisted the Agricultural Econonrics
Directorate (DEA) in the development and implementation of a rapid appraisal (RA) of
the domestic maize markets, focusing on white maize and the constraints on market
development. Earlier research in Maputo (conducted by MAP/MSU in 1992 and 1993)
provided much of the basis for the yellow maize markets and food aid analysis, which will
be detailed in Chapter 4. Figure 3.1 shows the location Maputo (the capital city), Chimoio

(a research site), as well as the provincial boundaries.

This chapter will present a brief description of white maize production and total maize
consumption in Mozambique, followed by a brief review of the organization and
performance of the white maize markets, focusing on the aspects relevant for the current
analysis of food aid and the maize markets. The fragility of the nascent markets, the lack
of market infrastructure, the prevalence of the informal sector traders in spite of the bias
against them, the high risks in production and trade are all factors that contribute to the

effects of food aid on these markets.

3.2 Maize production in Mozambique

Maize production in Mozambique is almost entirely white flint, open-pollinated varieties,
with no yellow maize production. Based upon rain-fed production systems, total white
maize production in Mozambique in any given year depends upon the timing and quantity

of the rains. As can be seen in the rainfall data for Chimoio station in the center of the

 

26

 

Figure 3.1 Map of Mozambique with Maputo, Chimoio, and provincial borders

 

27
country (Figure 3.2), there is a single rainy season (unimodal distribution), thus there is
only one cropping season per year in most parts of Mozambique. In the late 1980s and
early 19905, war limited the production of maize. Roads and even footpaths to distant
fields were dangerous for farmers to travel, so only the fields close to villages could be
cultivated in the areas most affected by the war.1 Transportation problems also limited the
delivery of inputs and consumer goods. Finally, many rural stores were destroyed during
the war and traders were prevented from purchasing surplus production. Afier the
drought, many farmers relied upon the seeds distributed by relief agencies and are

continuing to use the later generations of those seeds, even though some were hybrids.

As Table 1.1 indicates, annual production was below 500,000 metric tons per annum
between 1988 and 1992.2 The 1993/94 and 1994/95 crop years experienced both better
rainfall and an end to war, and production has begun to increase. Family sector farmers
(farmers with less than five hectares of cultivated land) produce approximately 95 percent
of total domestic maize, with private farmers, state cooperatives, and mixed farms

producing the remaining 5 percent (Agricom 1994). During the 1989-92

 

‘ Tschirley and Weber (1994) analyzes rural household production as affected by war and other
factors in northern Mozambique in the recent period before the Peace Accords.

2 All production and consumption figures in Mozambique must be taken as indicators rather than
exact numbers, until new censuses of the population and of rural production can be conducted.

  

82 - 59 .828 29:20 a 352 2585 was 3 2:2..—

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29
period, the large-scale commercial sector (including mixed companies and state
cooperatives) produced an average of 22,000 metric tons annually, with 95 percent sold
on the market. According to AGRICOM records, during the 1989 - 1992 marketing
years, an average of 76,600 metric tons of maize was commercialized per annum
(MA/DEA/Estatistica, 1994).3 By 1994, production was estimated at 527,000 metric

tons, with 158,000 metric tons projected for marketed surplus (ICM 1995).

Per hectare production estimates for the fanrily sector vary. A 1993 farm household
survey of twenty districts nationwide by MA/DEA/Estatistica found that district average
yield per hectare ranged from a low of 259 kg/ha in Buzi District in Sofala to a high of
1,330 kg/ha in Gondola District in Manica (MA/DEA/Estatistica 1995). From the same
1993 survey, average marketed production by the family sector was 14 percent of total
maize production. As indicated in Figure 3.3, production of maize in Mozambique varies
by region. The southern districts are generally less productive for maize due to soil type,
soil exhaustion, and rainfall patterns, with most districts producing less than 5,000 metric
tons annually. In the central and north, production varies by district, with districts in

Nampula, Zarnbezia and Cabo Delgado having more than 10,000 metric tons each.

 

3 The marketed tonnage estimated by AGRICOM and by ICM is based mainly upon the formal
sector trade, thus reflecting the minimum marketed maize quantities. Actual quantities marketed in
the country may be much larger due to the strong participation of the informal sector.

V.’

30

    

 

Production in metric tons
I 10000 to 25000

 

 

 

 

Figure 3.3 District level maize production in Mozambique, 1993/94

31

3.3 Maize consumption in Mozambique

Maize consumption figures are difficult to estimate due to the lack of recent household
consumption surveys, population changes during the war, and no recent census of
population. Maize is a major staple, although some regions of the country consume
manioc or rice as an alternative staple. Across the 10 regional capitals (excluding
Maputo), in a 1992/3 survey of income and expenditures, 30 percent of average monthly
total household expenditures went toward bread and cereals, about half of which (49%)
were expenditures on maize products (Republic of Mozambique, CNP/DNE 1994). Thus,
15 percent of average monthly household expenditures in these urban zones were on
maize. In Maputo, a household expenditure and consumption survey conducted in
Maputo in 1991/92 estimated that for the poorest 20 percent of households, household
expenditures on maize products were 18 percent ‘of total household expenditures at that
time (Sahn and Desai 1993), however the drought was affecting consumption patterns

during the survey period and a new survey is needed.

The income and expenditures surveys from the provincial capitals that were cited above
did not have consumption components. Rural traditions and the past unreliability of the
markets means that many urban households have land plots in the surrounding rural areas
and cultivate basic consumption goods. The DNE 1992 urban household survey indicated
that an average of 73 percent of the households in the provincial capitals own agricultural
land, reaching almost 90% of the households in Tete, and many produced maize for own
household consumption (Republic of Mozambique, CNP/DNE 1994). In the 1994

preference survey in poorer neighborhoods of Maputo, 47 percent of the households

32

indicated obtaining at least some maize from nonmarket sources, including maize from
own fields and fiom fiiends and relatives elsewhere, although 90 percent said that market

purchases were the main source of maize for the household (MAP/MSU WP # 18, 1994).

The Cornell consumption survey in Maputo in 1991/92 was undertaken when regional
drought and war limited consumption alternatives and afl‘ected relative prices, so it is
dificult to estimate actual consumption, yet this study provides the best estimate available.
In terms of total consumption, Dorosh et al. (1993) used the 1991/92 household
consumption survey to calculate Maputo urban area maize consumption of 110,000 metric
tons annually, about 9,000 metric tons monthly. When comparing this to a total
nationwide marketed amount of 76,000 metric tons annually, the need for imports is clear,
at least until production of maize for the markets can increase, and transport can get the

supplies down to the Maputo consumers.

In consumption studies, there has been extensive discussion on the substitutability of white
and yellow maize. Tschirley et al. (1996) present the strong evidence that white and
yellow maize products are imperfect, but close, substitutes“, with yellow maize an inferior

good compared to white maize, but cross-price elasticities would appear to be high.5

 

‘ Perfect substitutes require that consumers would be indifferent to the two commodities at some
fixed margin between their prices; however, in white/yellow maize consumption, approximately one-
quarter of consumers indicated in a 1994 Maputo household survey that they would not consume
yellow maize regardless of the price differential between white and yellow. Thus the goods are
imperfect substitutes.

5 The cross-price elasticity of demand for white and yellow main products remains unknown.
Dorosh et al. (1995) estimated a very low value of 0.013 for the elasticity of demand for yellow
maize with respect to white maize price; however the data were collected at a time of increasing white

33

Women in Mozambican households use either white or yellow maize to make the local
“chima”, a maize porridge eaten daily. There are regional differences on preferences and
substitution of commodities in consumption. In some regions, manioc becomes a hungry
season substitute for maize products, as in the north. Rice is considered the preferred
good (to maize products) for many urban households. In terms of complements for the
consumption of maize products, there are no clear choices, since the types of sauces used
with the “chima” vary greatly, with peanuts and dark green leafy vegetables

predominating.

Consumption of maize products depends upon the household income level, composition,
and preferences, as well as prices. The preparation methods for white and yellow maize
are similar enough, such that preparation costs do not vary greatly. However, for hand-
pounding, the flint maize varieties (including the white maize varieties grown in
Mozambique) are harder and lose less of the endosperm, resulting in a preferred “chima”,
while the dent varieties (including all of the yellow maize that arrives in Mozambique) tend
to become powder with the pounding, resulting in a pastier “chima”.6 Regardless of how
the maize is prepared or the color of the maize, consumers in Maputo and other urban
areas depend upon the markets to supply the basic grain, so logically the structure of those

markets influences the consumers’ options.

 

maize scarcity and rising real and nominal prices. The 1994 Maputo preference study (MOA/MSU
WP#20) suggests that there should be a much higher value for the cross price elasticity of demand
for yellow. The issue will remain unresolved until new consumption analyses can be undertaken in
current market conditions or longitudinal studies conducted.

5 For an in-depth discussion on the characteristics of flint and dent maize in consumption in
Mozambique, see Weber et al. 1992.

34
3.4 Rapid Appraisal (RA) Methodology’
A rapid appraisal (RA) methodology was selected for the market research, using the basic
framework of Structure-Conduct-Performance (S-C-P), focusing on the structure and
conduct aspects. As Holtzman (1986) defined it, a RA is "a broad preliminary overview
of the organization, operation, and performance of a food system or components thereof,
designed to identify system constraints and opportunities". A combination of informal
interviews, formal surveys, and secondary data are used. The choice of activities depends
upon the objectives and prior knowledge of the system. This methodology is a flexible
and rapid way to identify agents and structures (Holtzman 1986), a basic objective of this

research in Mozambique.

The RA consisted of the following aspects: 1) informal interviews with key informants,
fi'om the public and private sectors; 2) formal interviews, based on survey instruments,
with traders in both the formal and informal sectors; 3) direct market observations; and 4)
various types of secondary data, including prices, family sector survey data, and

infiastructure status.

In each research site, the research team went to at least one retail market site, searching
for various types of traders, usually accompanied by enumerators from the market

information system, the SIMA. The SIMA enumerators are generally well known in the

 

7 For an in-depth presentation of research methods and results, see MOA/MSU Working Paper #20.
Working Paper #20 is available only in Portuguese as of February 1996. The report by Coulter
(1995) contains some of the results of that work in English.

35

markets, helping the research team to overcome problems that might arise due to lack of
confidence of the traders in strangers. Survey instruments were designed specifically for
each basic type of trader thought to be operating in the markets, including itinerant
wholesale traders, itinerant retail traders, informal retail traders in markets, and formal
wholesalers. The interviews obtained information on type of products, costs of transport
and of products, sales prices, taxes and fees, units of transactions, suppliers, and clients.
With respect to market observations, the research team noted the number of traders, the
available transport, the products offered for trade, common units, and other aspects of the
basic operations. The language of the interview depended upon the languages of the

participants, preferably the local language, but at times in Portuguese.

Various criteria entered into the selection of sites for the RA, including overall agricultural
production, production of maize, commercial and parastatal activity in maize markets,
access, and population. Variability in the criteria was sought. For example, sites were
selected that had good transport access while other sites were chosen for difficult access.
Based upon the criteria identified by the research team, seven research sites were selected.
Table 3.1 presents basic information on location and characteristics of the sites chosen for
the RA, while Figure 3.4 indicates the location of each district on the map. For the
1993/94 production year, Manica Province had the highest estimated maize production in
the country, although by 1994/95, it was only fourth in total estimated production. The
Beira Corridor highway and railway have been maintained throughout the war and assure

accessibility at least near the Corridor itself. The area has been commercially active in

36

 

 

 

 

 

 

 

 

 

 

 

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37

 

Figure 3.4 Map of Mozambique with rapid appraisal sites, July - September 1994

38

maize markets for years and was the site of market research in 1990. Other sites chosen,
such as Caia and Sena in Sofala province, had very poor access to large urban markets and
also had poor production in the 1993/94 production year. In the north, Malema and
Mutuali had good production but poor access; Mocuba had fairly good production and
has limited access to the Quelimane market, while Ribaue was in a similar position for the

Nampula market.

3.5. Agents in the markets

3 .5. 1. Intervenjentes

In the marketing of agricultural commodities, the term "interveniente" (loosely translated
as ”people who act within the markets") is used in Mozambique to designate all economic
agents who buy and sell in the market. The following types of intervenientes were
identified: 1) farm producers, both family and private sectors; 2) rural traders, both formal
rural store traders ("lojistas" in Portuguese) and informal; 3) retailers, formal and informal,
all in the marketplaces; 4) itinerant traders, retailers and wholesalers; 5) formal
wholesalers; and 6) public sector agents including parastatals and donors. Figure 3.5
presents a stylized view of the major agents involved in the supply of white maize from
farm producers to consumers during the RA period. The informal sector was very active
between the center and the south, serving as the middlemen and middle women between
producers and urban consumers. The formal sector traders supplied the parastatal
Mozambican Cereals’ Institute (ICM), and the donors. That role definition becomes

important in discussing the fragility of the maize markets, as well as the sources of

39

  
  

Smallholder Sector

Producers Consumers

   
    
 
       
 

  
 

Urban
Consumers
ltinerant traders \
(Informal Sector)
Rural Stores
(formal sector)
Private Producers

l
(formal sector)

\ arketing Boar. /
Urban Markets (PUWC sector)
(retailers)

New: ”or in. m groom vellum

  
 
 

 

  

Wholesalers and

Figure 3.5 Principal marketing channels for domestic white maize
observed during Rapid Appraisal in 1994/95 marketing year

4O

dynamism in those markets. Figure 3.6 indicates the strong participation of the informal
sector in accumulation, short-term storage, and assumption of risks, whereas the formal
sector participated only minimally in any of these functions during the 1994/95 marketing

season, according to the assessment of the RA team.

“Formal” and “informal” are used here to indicate the relationship that the trader has with
legal authorities. Formal sector agents are registered as retailers or wholesalers with the
Ministry of Commerce and have legal status. Informal agents do not have legal
recognition for their commercial activities. At the retail level in the marketplaces, there is
little difference between the formal and informal. At the wholesale level, however, the
formal sector wholesalers have more resources and permanent assets than the itinerant

wholesale traders.

3. 59.2 Mmmg channels prior to ERP

In the 1980s, maize was marketed using a combination of private and public sector agents.
The first buyer was the “lojista”, the formal sector rural trader who engaged in a variety of
activities, including buying and selling of smallholder production, provision of production
inputs and consumer products, and provision of credit. Lojistas sold the agricultural
commodities to the state sector or to large-scale formal wholesalers, and then through the
government shops or private retailers, the maize was bought by consumers. A few lojistas
said that they bought limited quantities of white maize to store and then sell back to

consumers in the hungry season, but this did not appear to be common.

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41

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42

During this period, there were official price schedules for maize from the producer level all
the way to the consumer level. With the government as an active buyer and seller on the
markets through its agricultural products’ marketing board AGRICOM, the official prices
were relatively effective. The government granted monopoly trading rights to lojistas for
their zones of influence. The resulting marketing system had the lojistas purchase maize
and other agricultural products fiom the farm producers; the lojistas then sold directly to
the parastatal marketing board or to a wholesaler who traded with the marketing board or
sold maize at the retail level. Thus, private sector traders were active in the white maize

markets at both wholesale and retail levels.

The lojistas were most often of Portuguese or Asian descent and, at the time of
Independence, many of the lojistas left the rural areas, either for the cities or for foreign
destinations. During the ensuing war years, many stores were damaged or destroyed, and
so in some regions, operating stores are rarities. By 1992, the oflicialstatistics registered
more than 10,000 stores, of which 33 percent were either destroyed or non-operational,
the majority in rural areas (Republic of Mozambique, CNP 1993). According to the
Provincial Directorate of Commerce in Manica, a major maize production zone, out of
461 authorized retail outlets in the province, 209 stores were destroyed, 74 were non-
operational for other reasons, and 178 retail stores (39 percent) remain in operation, many
of those in the urban areas of Chimoio and Manica City (personal communication,

Director of Commerce, Manica Province, 1994).

43

The efl‘ects of the war were less severe in the north such that lojistas still buy maize from
the producers for sales to private wholesalers or the parastatal ICM. The informal sector
development has lagged behind in this zone, as well. Throughout the country discussions
on the “commercial network” and market development involve rehabilitating these
multipurpose rural stores. In the meantime, other marketing channels are developing to
fill the void left at the producer level, particularly in the hard hit central zones of the

country.

3.5.3 Mgkging channels in the informal sector

 

The informal sector has grown tremendously in the period since the PRE began and
especially since the Peace Accord took effect. This sector has no legal status and is
occasionally the target of police crackdowns, although the government for the most part
looks the other way. Initially, the informal sector was focused in the urban areas, selling
small quantities of goods obtained through illegal imports, through leaked food aid or
ration shop supplies, or through other means. The informal urban maize traders traveled
wherever transport was available in order to obtain supplies, including the southern
production areas of Gaza and Inhambane as well as Swaziland.8 There was also active
trading in food aid yellow maize supplying towns such as Xai Xai and Inhambane. After
the Peace Accords, the geographical range expanded to include the central production

areas in Manica Province, whose location is shown in Figure 3.1.

 

8 MOA/MSU Working Paper #10 (1993) presents a more detailed presentation of the growth and
operation of Maputo informal markets for white and yellow maize products.

44

The informal traders from Maputo and Beira travel to the production areas with cash to
purchase grain. They go into the rural areas, following the recommendations of other
traders, friends, and family. Traveling with cash for grain purchases, empty sacks, and a
tarp to cover the maize, they live for a week or so at a crossroads or truck stop, buying
white maize that the farmers carry on their heads fiom the farm to the crossroads. Sales
are based on volumes, so no scales are needed. There may be several difl‘erent traders at a
given spot, either fiiends from Maputo or competitors fiom another city. At one
crossroads in Manica provinces, there was observable competition between two women
traders from Maputo sitting together on their sacks at one side, and three male traders
from Beira, with their sacks on the other side. Farmers approached on foot with 30- to

SO-kilogram sacks of maize carried on their heads, seeking the best price.

After purchasing up to the limit of their fimds, the itinerant traders hire transport to the
nearest paved road and then transport to the city from which they came. They sell the
maize in bulk on the fringes of the markets to retailers fi'om the market, who in turn sell in
small, nonstandard units to the consumers. There is a notable shift in gender of the traders
between regions, with women traders from the south and men traders from the center and

north. Cultural and ethnic differences may explain the shift.

The informal sector dominates retail sales of domestic white maize in Maputo and
elsewhere. After the PRE in 1987, informal marketplaces began to appear and grow in
Maputo, often near the official marketplaces. Called “dumbanengues” in the south and

‘tchungamoios” in the north, the informal marketplaces abound with individuals selling

45

basic food stuffs, fuel, and other consumption goods, in small units. Edible oil is sold in
sizes as small as a bottlecap, while maize grain and flours are sold by the bowl, cup, or
can. In Maputo, the most common unit is a used infant formula can which holds about
800 milligrams. Traders group themselves by commodity and competition appears to be
strong, with little price variability or price bargaining for the most common foodstuffs. As
indicated in surveys done in Maputo markets, many white maize retailers obtain white
maize from itinerant traders within the same market or from a nearby larger market. Thus,
the informal traders fulfill most of the marketing functions from rural producer to urban

consumer.

3.5.4 Marketing channels in the form_al commercial sector

As will be seen, the formal sector and the informal sector in the white maize markets do
not have joint transactions. The formal wholesalers obtain white maize in two basic
ways.9 As noted earlier, the formal rural retailers, “lojistas”, provided maize purchased
from producers and sold to the wholesalers, a system still in place in the north.
Alternatively, in areas in which the rural lojas were largely eliminated, the formal sector
wholesalers send out their representatives to purchase white maize from private and family

sector producers. The final purchaser in both cases is either a donor or ICM.

The weak participation of formal wholesale agents in white maize marketing, as shown in

Figure 3.6, stems less fi‘om supply difiiculties than from the perceived lack of a stable

 

9 Formal and informal sector trading of yellow maize commercial food aid will be discussed in
Chapter 4. Formal and informal do have continued direct trading relations, with yellow maize.

46

market for the white maize. During the RA, wholesalers complained in every part of the
country about the “lack of a market” for white maize. Accustomed to dealing with
parastatals that were authorized to buy maize at the official price, wholesalers had not
developed private markets for white maize and were not interested in storing what is
perceived to be a risky commodity. In 1994, wholesalers became active in the white maize
markets only when the donors and ICM began buying white maize, providing the traders
with a guaranteed market for immediate sales of maize. Wholesalers bought white maize

with prior knowledge of the buyer and the price, thus minimizing the price risks.

3.5.5 Marketing channels and the public sector

Over time the public sector role in the maize markets has shifted. As noted earlier, before
the PRE, the government maintained a minimum price system by being an active buyer and
seller on the market. Since 1987, that role has greatly diminished. Urban ration shops
have been eliminated, and the new marketing parastatal ICM was established in 1994
without the substantial subsidies of previous agencies. AGRICOM, ICM’s predecessor,
was declared bankrupt and dissolved in 1994, with its assets turned over to ICM, after

years of government and donor subsidies. ‘°

ICM uses commercial bank loans in order to operate in the marketing of maize and the

general manager speaks of the need to be a financially sound, independent profit-making

 

'0 During the RA, most traders still referred to the parastatal agency as “AGRICOM” rather than
“ICM”, suggesting that there was no perceived difference between the two agencies other than the
name.

 

47

business in order to survive. In the firture, it is believed that ICM will have to respond to

market forces, paying no more than what they can profitably pay.

Occupying the same buildings, sometimes without even a new nameplate, ICM has lost
markets due to the problems that traders and farmers had with the previous parastatal,
AGRICOM. In a liquidity crisis during the 1993/94 marketing season, AGRICOM issued
checks in 1993 that could not be cashed for at least several weeks after maize was
received, discouraging firture trades. In a glaring example in Manica Province in August
1994, one family sector producer with 10 SO-kg sacks of white maize to sell preferred to
deal in cash with a rural trader at 450 meticais/kg rather than travel 2 kilometers further to
the ICM warehouse and possibly receive 550 meticais/kg but in a check. The rural trader
said that AGRICOM’s August 1993 check to that farmer was not honored until January

1994.

Nevertheless, in 1994, ICM purchased 34,000 metric tons from traders and producers
throughout the center and north of the country. Of that, about 16,000 metric tons of
white maize were sold by ICM to private traders. Otherwise, the maize that enters this
marketing channel of formal wholesalers to donors or to ICM is not destined for the retail
markets. ICM purchased 22,000 metric tons for the WFP, while donors and non-
governmental organizations directly purchased up to another 20,000 to 30,000 metric
tons. Thus, around 50,000 (somewhere between 42,000 and 52,000) metric tons was

distributed by non-governmental organizations to people without effective demand,

48

including returned refugees, internally displaced persons, and ex-soldiers.ll As a result,
the local purchasing channel removed 50,000 metric tons of marketed surplus fi'om the
retail trade available to consumers. Considering that total marketed surplus was estimated
to be about 123,000 metric tons, this represents 40 percent of white maize marketed. This
was the maize marketing channel of the formal sector. The donors have made no long
term commitment to continued local purchases, and so this channel may no longer be
significant in a few years time, leaving the formal wholesale sector without a guaranteed
market, likely reducing formal sector participation in white maize markets, unless private

market channels develop.

The government still maintains a minimum producer price policy, with the panterritorial,
panseasonal prices set at the beginning of the marketing season each year (March or
April). Figure 3.7 shows the nominal official producer prices, along with the actual retail
level prices in three markets: Maputo, Chimoio, and Nampula. When the actual retail
price and the announced producer price are close, there is no potential retail margin left.
Farmers can get the retail price by traveling to the markets to sell. Alternatively, if the
parastatal ICM were purchasing from farmers, farmers could get the official price. If
farmers can only sell to local traders due to transport or other constraints and there is no

profit margin for the traders, there will be no maize traded at the official price. Thus

 

“ It is possible that some of the maize supplies distributed in the emergency programs were then
resold by the participants into the market. The extreme scarcity of alternative consumption staples
and the high market prices for them makes it likely that leakages would be small and would not enter
Maputo markets since distributions are conducted elsewhere. During the RA, leakages fi'om the
distributions to demobilized soldiers were found, but this was highly localized.

49

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50

farmers are likely to face lower than official prices when the retail and official producer

prices are close.

In 1994, there were continued difliculties with the producer price policy. The
government declared a minimum of 700 meticais/kg and then reduced that to 550
meticais/kg, in part because the donors indicated reluctance to initiate local purchases of
white maize at the 700 meticais/kg price.12 Without the donors participation, ICM has
limited access to bank credit and would likely have not participated in the marketing
season in any significant way. This would leave the government with no real way to
enforce a minimum price policy. Another problem in 1994 stemmed from the oficial
announcement of the minimum price when the marketing season had already begun. This
late date prevented producers from adjusting their planting or cultivation investments in
accordance with the price. By changing the price after an oficial declaration, the
government further weakened the incentives that an official minimum price might give,

leaving uncertainty about the policy’s efficacy in future years.

Ifthe government is no longer a buyer, the minimum prices become irrelevant or a
hindrance to many producers and traders. The Ministry of Industry, Commerce, and
Tourism (MITC) is responsible for enforcement of the minimum price policy, with very

limited success. One district administrator lamented that the policy may take markets

 

‘2 A similar scenario is being played out in January 1996, as the World Food Programme indicated
that it would not purchase local maize at the official white maize producer price of 1450 meticais/kg
for 1996, about US $132 per metric ton at January exchange rates.

51

away from the farmers, for if he were to enforce the official prices, no traders would buy
maize from the farmers at all, and the farmers would be left with maize rotting in storage.
For example, a mral store in Nampula Province was closed by an official of the MITC in
August of 1994 for purchasing at prices below the oflicial producer price. Yet retail
prices in Nampula City at that time, about two hours away by unpaved road, were at or
slightly below the official 550 meticais/kg (Figure 3.7), leaving traders no potential margin
for covering transport costs if the maize were sold in the market. Traders also cited such
closings as harassment, adding another risk factor for private traders due to discretionary

enforcement.

3.5.6 Role of processing industries

In industrialized countries, one of the main intermediate sources of demand for raw
materials is the processing industry, which needs a reliable supply. The industrial maize
milling sector is now beginning to recuperate in Mozambique. In 1995, after several years
of reliance upon yellow maize food aid imports, the industrial mills began purchasing more
domestic white maize for processing and sales. The Companhia Industrial Matola (CIM)
is a recently privatized industrial mill. It purchased 2,000 metric tons of white maize in the
north for maritime shipment to the south for milling and distribution near Maputo. This
sector could provide a source of demand for the formal wholesalers, but as yet little white

maize is involved in these trades.

In Maputo, small-scale maize processing mills can be found near every market, as retailers

and consumers buy grain in bulk to take to the mills for custom processing. A recent

52

nationwide survey of small-scale mills found that almost 100 percent of mills work solely
on commission basis, processing maize owned by third parties (MAP/MSU Working
Paper #21, 1995). Thus, this sector does not provide a direct source of demand for the

white maize.

3.5.7 Imports and exports

A variety of factors influence imports of white maize. In the past, the country has
imported white maize food aid from Mexico or from countries in the region, particularly
Zimbabwe. These latter are known as triangular transactions, in which a donor purchases
maize from a surplus country in the same region as Mozambique. Generally, the world
markets for white maize are thin, and regional supplies of white maize are covariant with
production variability in Mozambique, making these markets unreliable, particularly in the
times of drought, when the need is greatest. However, the southern region of
Mozambique sufl‘ers from a structural maize deficit and will need supplies even in the best
of production years. Whether traders purchase maize from South Africa or from central
and northern Mozambique depends upon the relative prices and costs of delivery. One
formal trader did import maize fi'om South Afiica in 1995. Since September 1995 and in
previous periods, small scale informal traders have continuously brought in the finely
processed white maize flour from South Africa. The South Afiican price for white maize

products thus puts a ceiling on the prices for domestic maize in Mozambique.

White maize exports are illegal according to the food aid agreements, as long as

Mozambique is receiving maize food aid from USAID, and thus such trade is unregistered.

53

Trade between Malawi and Mozambique has developed in the postwar period, but the
direction of white and yellow maize trade depends upon the relative prices in each. At

times, maize may flow from Malawi to Mozambique; at other times, the flow is reversed.

3.6 Additional considerations

3.6.1 Nonmarketed production

The above details on market channels must be taken in the context of a semi-subsistence
agricultural sector. By all estimates, less than 25 percent of the maize produced in
Mozambique enter these market channels. Home consumption is still the most important
destination of white maize, and many households obtain grain through nonmarket
exchanges, though the volume of these transactions is not known. Development of the
maize markets is needed to provide higher cash income potential for rural households, as
well as reduce the vulnerability of land-poor households (Tschirley and Weber 1994).
Overall market development ensures the availability of consumer goods and services,
providing additional incentives for households to invest in crop production for the market.
As various authors (e. g., Kyle 1991, Barker 1985, Billing 1990) have noted, the serious
lack of consumer goods in rural Mozambique, especially in the past 10-15 years, has

lessened the incentive for farmers to produce for the market.

54

3.6.2 Marketing margins and price risks

Table 3.2 presents indicative marketing margins fi'om the RA in 1994 for various types of
traders in the white maize markets. ‘3 While these margins are based on a small, purposive
sample, they can help to evaluate the potential effects of price variability on different
traders in the system. Consider the case of the formal wholesale traders in Nampula. If
they pay the legal minimum producer price, pay all of the taxes, and then sell to ICM in
Nampula, they obtain negative returns. This is not to say that the formal traders are
participating in a losing activity; rather, if they do buy white maize, they cannot pay the

legal price and cover costs, let alone make a normal profit.

In connection with this, comments from the formal traders throughout the country during
the RA can be expressed in the often heard phrase “No ha mercado” (“there is no
market”). By this, traders meant that there was no satisfactory market, for they would not
buy maize unless they knew the prices and the buyer beforehand. The example
represented by results of the Nampula formal wholesalers assumes that traders do not
store the commodity for any length of time because there was little observed storage by
formal traders in the production regions. “Having a market” means quick turnover,
guaranteed profit, no price risk. During the RA, only two formal traders were interviewed
who stored maize for later sales in the hungry season, and both would sell at the retail

level, in small units at rural lojas, not wholesale.

 

‘3 The table only looks at white maize trade, not at the formal traders in the yellow maize markets.
As will be seen in Chapter 4, the low official consignee price leaves a wide margin for rents when
market prices are high, rents which are captured by the formal traders.

55

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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56

Table 3.2 (continued)

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57

Looking at the Maputo prices as 1994 progressed can give an idea of what would have
happened if a trader had stored white maize for later sales. The purchase price in early
August 1994 in the production zones was close to the government minimum price of 550
meticais/kg, with a Maputo retail price of 1050 meticais/kg, leaving a gross margin of 500
meticais/kg. The average monthly price in the Maputo retail market of Xipamanine
reached its highest level for 1994 in September with 1750 meticais/kg (nominal price),
gross margin of 1200 meticais/kg. The average white maize retail price did not reach or
surpass that level again until May of 1995, so a trader with white maize supplies would
have done best selling the maize long before the hungry season. Note that in real terms,
the retail price of white maize was declining gradually through the September 1994 -
March 1995 period, such that real net returns on any maize stored would have been
declining over time as well, in comparison with the returns to be achieved in early

September, without even including storage costs.

In 1995, a different story emerges, with prices for white maize continuing to rise from
August through mid-January 1996. Traders with maize stored are seeing much higher
returns. The combination of average local production, reduced yellow maize food aid, and
high levels of local purchases of food aid supplies makes the difference between the two
years. The expectation of temporal arbitrage opportunities may encourage investments in
storage facilities for white maize or other marketing infrastructure. Such investments are
unlikely in an environment in which the price risks are high, few risk bearing mechanisms
exist (as will be discussed below), counter cyclical price declines are not uncommon, and

temporal arbitrage has negative expected returns.

58
3.6.3 Risk-bearing mechanisms
Price and other risks are common in agricultural product markets. In order to manage -
price risks, various mechanisms have been developed in markets throughout the world.
One major way to manage risk in the US and other countries is the use of futures markets,
in which traders and producers buy and sell firtures contracts that offset their production

or trade positions. There are no firtures markets in Mozambique.

Instead, producers tend to minimize their risks through diversification of production.
Rather than specialize in the production of a single cash crop, producers have a
combination of cash and subsistence crops. In Mozambique, there is not just the threat of
a low price for the crop, but there is also the possibility that traders will not be present to

purchase maize at all.

The history of a strong public sector presence has conditioned traders to rely upon
administrative pricing to remove price risks, setting pan-territorial, pan-seasonal prices,
and then serving some of the intermediary functions, as well as some retailing functions in
urban areas. In the new market environment, traders reduce their risks through
involvement in other activities. In Mozambique, formal traders may buy other agricultural
commodities, such as beans, cashews or manioc, for sales to processors or retailers.
During the RA, the formal traders had warehouses stocked with processed consumer
goods, including imported rice, wines, beer, sugar, paper products, and batteries, rather

than agricultural goods.

59

These mostly imported goods provide higher expected returns on investment than the
agricultural products. In particular, for maize, the formal traders indicated a reluctance to
become active in maize markets unless there was some kind of guaranteed transaction,
such as a commitment fiom a donor or from the government parastatal to purchase. Lack
of capital to invest and problems with the public sector were cited as well during the RA

as limiting factors for business expansion in white maize markets.

In the months since the Peace Accords were signed, the variability in maize prices has
declined considerably." The remaining variability has three identifiable sources, both on
the supply side: 1) weather variability; 2) seasonality, and 3) food aid arrivals. There may
be additional volatility added to prices by changing transport costs. Weather variability
and seasonality are common sources of risk and have not stopped trading activities and
investments in other countries. Food aid arrivals are different. Administrators can remove
some of the uncertainty by developing a highly transparent process for decision-making
and delivery. Anivals can be gradually released in order to smooth out the flows to the

markets, as will be discussed in Chapter 4.

3.7 Brief summary of the observed marketing functions
Figure 3.5 presents a stylized view of white maize trading as observed during the 1994/95
marketing season. The main marketing outlets for producers were the itinerant informal

traders, particularly in the south and center. Urban consumers relied upon informal and

 

‘4 Chapter 6 will describe the characteristics of prices in the war/drought period and in the post-war
period.

60

formal marketplaces for their purchases, with nonmarket sources still important. The

figure does not capture the routes traveled by the nonmarketed output.

The formal sector, when it operated in white maize markets, dealt with other formal sector
commercial agents or the public sector. The lack of rural stores in many areas of the
center and south limits the effectiveness of traditional trading systems. Itinerant traders do
not generally sell to the formal wholesalers because higher prices can be obtained by
itinerants in the urban markets. As will be discussed in Chapter 4, the wholesalers and
informal sector have traded major quantities of yellow maize, and in that case, the

wholesalers are the supply source.

The government role with ICM and the direct donor purchases were important in
providing the guaranteed price environment for white maize in which formal wholesalers
were willing to operate. How long the formal sector can depend upon such an

environment remains to be seen.

3.8 Characteristics of the markets relevant for food aid analysis
The following characteristics of the Mozambican white maize markets are critical for the
food aid analysis:

- fragility of nascent markets;

- need for investments in marketing infrastructure;

- bias against informal sector;

61
- high risks due to price volatility; and

- consumption versus market incentives

3.8.1 Fggility of nascent markets

In the past, the public sector has served as risk bearer and disciplinarian of the private
sector, such that the formal private traders have not yet developed as independent, risk-
bearing agents in the market. With the public sector withdrawing, there has not been a
strong movement for the formal commercial sector to step into the gap. There are cases
in which private wholesalers have taken advantage of arbitrage opportunities, as occurred
when a southern wholesaler traveled in August 1994 to Beira and Chimoio to purchase
white maize for later retail sales in the south. Price volatility makes this risky, especially

when hungry season food aid shipments arrive, as will be seen later.

3.8.2 Need for investments

The participation of the formal sector in white maize markets has been limited in recent
years to supplying guaranteed markets with fast turnover of stocks. Investment in storage
and transport infrastructure for the maize markets and related agricultural goods markets
is very slow in taking place, in spite of the deterioration and destruction that occurred
during the war. Formal traders state that the maize markets are too unreliable, too volatile
for other than very short-term activities, and the interest rates on bank credit are too high
to justify investments. The informal traders, using their limited available funds for
operating capital, lack the capital for investments in anything more than tarps and sacks,

and have no access to bank credit.

62

3,8,3 Bias against the informal sector

As is true in many parts of the world, the informal sector is disparaged by many people in
Mozambique, including government officials. In Manica Province, one official wanted to
control their activities, accusing informal traders of extracting too much maize from the
province, threatening food security. This is a concern that is reiterated by local authorities
throughout the country. The formal traders want a level playing field, in which the
informal sector becomes formalized, paying taxes and observing rules just as the formal
sector must do. Meanwhile, the informal sector is constantly under the threat of sanctions
or extortion, yet they are active in the markets. To eliminate the informal sector would

take the dynamism and competitiveness out of the white maize markets.

3.8.4 High risks due to price volatility

 

As will be discussed in later chapters, maize price volatility and unpredictable price
fluctuations may be limiting investment and participation in the domestic maize markets.
Sources of the volatility and fluctuations include weather shocks, transport constraints,

food aid arrivals, and changing government policy.

One aspect that contributes to the price instability is the lack of interseasonal storage.
With predictable interseasonal price changes, traders who buy maize at harvest, can store
maize and sell it later in the hungry season, realizing profits above their storage costs. In
Mozambique, food aid arrivals in the hungry season can depress prices such that there are
losses with interseasonal storage. The unpredictable nature and quality of these arrivals

contribute to the perception of risk in storage.

63

Looking at the food aid arrival information, yellow maize arrived in September, October,
and November of 1994, placing a damper on the expected seasonal increase in white
maize prices. The same phenomenon of a counter cyclical decline or stabilization in white
maize prices can be seen in September - November 1992, December 1993 - March 1994,
and again in late 1995, and all can be associated with food aid arrivals of yellow maize.
This is exactly why the formal traders in Maputo, when attending meetings with
government and donor officials, ask quite pointedly, “How much food aid is coming, when
is it coming, and what quality will it be?” This ties in directly with the difficulties of food
aid planning and objectives, in which traders want incentives to operate and consumers

need food at an accessible price.

3 .8.5 Consumption versus market incentives

Food security is defined as “a situation where each person has economical and physical
access at all times to an adequate food supply allowing each person to live a productive
life, in good health, and without malnutrition or undernourishment” (Laval University
1995). One of the objectives of the commercial food aid is food security, to assist low
income consumers, providing a consumption staple at an accessible price. With the war
and drought, extreme scarcity meant that the white maize prices soared out of reach as in
June 1992, when food aid yellow maize became available as a cheaper alternative.
Mozambique did not produce enough food nor generate enough foreign exchange to
import food, so food aid was used to fill the gap in the most difficult period, the hungry

$838011.

64

Now, as local production of the white maize comes back into the markets, finding the
appropriate price and the relevant food aid quantities is the challenge. The food aid
arrivals are deliberately timed to diminish the rising hungry season prices, yet traders need
prices to rise over time in order to cover their storage costs. Policy makers and donors
need to strike a balance between accessibility for poor urban consumers and price

incentives for rural producers and traders to invest in maize production and marketing.

3.9 Review of key issues with white maize traders

Since domestic maize production began to recuperate alter the drought and war,
essentially two white maize market channels have developed in Mozambique. Figure 3.5
indicates the flows of these two channels. The formal sector, including commercial
traders, the parastatal marketing board (AGRICOM and then ICM), and donors, combine
to purchase maize from producers, transport to warehouses in deficit regions, and then
distribute to ex-soldiers, returned refugees, internally displaced persons, and others
identified as vulnerable groups with no efl‘ective purchasing power. Economies of scale in
accumulation and transport may be obtained. Only the donors pay for long term storage,

with little risk-sharing by the formal traders.

Urban consumers use the second market channel, whereby the informal itinerant traders
travel to the production areas, purchase maize directly from the producers, use
backhauling transport to return to the city. At that point, the informal urban retailers buy
the maize, arrange for milling if necessary, and sell in nonstandard units in the local

marketplaces. These informal white maize markets are characterized by small individual

65

scale of operations, small investments of capital, and rapid turnover. Currently, the
informal trading provides the information and physical trading links between surplus and

deficit markets such that market integration may be obtained.

There are signs of continued change in the maize markets in Mozambique. In response to
very high prices in regions hit by a cyclone in 1994 in Nampula Province, private traders in
the north began traveling to Cabo Delgado to purchase maize and then traveling to
Nampula to sell it. In August and September 1994, a formal trader from Gaza Province
was up in Sofala and Manica, buying two truckloads of white maize to then take to Gaza
and sell during the hungry season at retail level. The industrial mill CIM purchased 2,000
metric tons in Cabo Delgado in 1995 to ship to Maputo and mill, selling the flour on the
Maputo market. In 1995, one formal trader brought in commercial white maize imports
from South Afiica. These are all indications that the formal sector is beginning to take

advantage of arbitrage opportunities in the maize markets.

Local purchases provide a major impetus to the development of the formal sector trading.
That is one of the objectives of such transactions. Local purchases, however, do not
represent an addition to domestic supplies. If Mozambique is deficit in producing maize
and no substitutes are available, imports will be needed to assure food security, whether

those are private sector commercial imports or food aid imports.

Actions taken on the yellow maize markets will condition the opportunities available to

both the formal and informal sectors. In the next chapter, the yellow maize food aid

66

management and distribution system will be described, as well as the markets for yellow

maize and their relationship with white maize markets.

Chapter 4

Food Aid in Mozambique‘

4.1 Structure of food aid

The United States and other donors have imported large quantities of yellow maize as a
food aid commodity since the late 1980s, when Mozambique was having production
deficits due to weather, civil war, and the consequences of agricultural policies focused on
the state sector (Kyle 1991). Averaging 58 percent of total marketed maize supplies
between 1989 and 1995, as shown in Table 4.1, commercial yellow maize food aid has
been critical in the food security of low income urban consumers in particular. This
chapter details the decision making and delivery system for food aid and then presents an
overview on how yellow maize markets are organized and how they are linked with white

maize markets.

4.1.1 Making the decision on commercial food aid

USAID and the European Union (EU) have been the major donors involved in commercial

yellow maize food aid, with the Ministry of Industry, Commerce, and Tourism (MICT) as

 

‘ This chapter is based upon Tschirley, Donovan, and Weber 1996, and MOA/MSU Working Paper
#10, 1993. Those works present more analyses and historical detail on food aid in Mozambique.

67

68

Table 4.1 Yellow maize food aid in total availability and market supplies

 

Total yellow maize food
aid g a percentage of

Commercial yellow maize food aid
W

 

...... .... 2:2. :1 2222 22:2 222..

food aid food aid availability supplies

ofmaizel
1989/90 48.5 21.3 17.7 7.7 54.8
1990/91 63.8 27.2 23.1 9.9 57.3
1991/92 70.4 35.0 23.5 11.7 64.8
1992/93 83.3 59.7 35.1 25.1 90.8
1993/94 90.9 24.8 24.6 6.7 ' 41.1
1994/95 61.6 15.7 22.3 5.7 39.2
1995/96 41.1 6.5 24 3.8 26.9

 

 

 

1 The Early Warning Unit of the Ministry of Agriculture (1994) estimated that
23% of white maize produced for human consumption is marketed.

Source: See Table 1.1 for data, sources, and notes.

69

the GOM counterpart.2 USAID has imported yellow maize for two reasons: 1) domestic
US stock availability; and 2) desire to choose a commodity that is self-targeting for low
income consumers. In view of domestic maize production shortfalls and the importance of
maize as a staple, the MICT requests yellow maize. The EU and other donors have

brought additional commercial food aid imports of wheat and rice, but in lower quantities.

When requesting commercial food aid, MICT indicates the quantities and the markets for
which maize is needed. Maputo, the capital and the largest urban market with over one
million inhabitants is high on the list of priorities, but Beira and Nacala are also port cities
that receive commercial yellow maize food aid directly. This research focuses on Maputo
since it is the largest consumption market in the country, receives the largest shipments,

and thus if food aid does have an effect, it will be strongest in these markets.

The GOM requests specific amounts of yellow maize commercial food aid, corresponding
to the anticipated excess demand over domestic production and imports, as well as the
need for funds. There is no formula used in the calculation, just rough assessments on
supplies and needs. The commercial trade sector has not been involved in the decisions on
quantities to import and prices, although during the planning period in 1995, MICT held
meetings with donors and consignees in order to get information, ideas, and concerns

regarding commercial food aid for the October 1995 - March 1996 period.

 

2 The EU also imports white maize from international markets or fi'om regional markets, however
they brought in yellow maize during the drought.

70

In order to reduce the per unit transport and handling costs, USAID and other donors
request shipments in large lots, 10,000 to 25,000 metric tons. Because of the proceeds
fi'om monetization, MICT has strong incentives to immediately sell all supplies upon
arrival. Likewise, MICT stores the unsold quantities of maize in government warehouses
or commercial warehouses at government expense, with high risk of losses through theft,

mishandling, and deterioration.

The release of large quantities in a short period of time has obvious efl‘ects upon the prices
in the markets. Figure 4.1 gives the monthly average retail price of yellow maize and the
monthly total commercial food aid yellow maize deliveries in Maputo for April 1990
through November 1995. There are several periods of interest indicated on the figure. In
November-December of 1991, a large shipments arrived in Maputo and prices for yellow
maize grain decreased, even as white maize supplies were diminishing with the
approaching hungry season, and the same happened in November 1994. In late 1992 and
early 1993, a series of large shipments resulted in retail yellow maize prices decreasing
once again during the hungry season and remaining low over almost a year. In each case,
white maize was a scarce commodity, so only yellow maize own supply can explain the
downward pressure on supply. Demand factors could also be responsible, but there are no
know major demand shifters during the periods being discussed. Tschirley, Donovan, and

Weber (1996) conducted initial analysis of the effects of food aid arrivals on the retail

 

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71

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72

yellow maize prices in Maputo, analysis that will be extended in the empirical chapters of

this dissertation.

4.1.2 Consignees and food aid allocations

“Consignees” is the word used to designate the private traders and parastatals who
receive allocations of commercial food aid stocks for market sales. Originally, parastatal
marketing agencies and industrial mills (including CIM) received the yellow maize grain
either for direct sales in government ration shops or for milling and later distribution
through the ration shops. After a 1991 assessment report (Louis Berger, International
1991) presenting evidence of the poor payment performance of the parastatals, USAID
pressured for private traders to be authorized to receive and market the commercial food
aid stocks. This also accorded with USAID policy reform efforts to reduce the role of

government in the markets.

The wholesale traders who are consignees are often engaged in various activities: import
of foodstuffs such as rice and wheat flour; purchase and resale of domestic agricultural
products including white maize, rice, and cashews; milling of maize and wheat grain; and

export of agricultural goods, particularly cashews, tobacco, and seafood.

The GOM has a set of criteria to determine which traders are eligible to purchase yellow
maize stocks. Among the qualifications are ownership of warehouse facilities, access to
means of transport, adequate liquidity, and good standing on payments for previous

shipments (Austral 1992). USAID has pressured for the number of consignees to be

73

expanded such that collusion is avoided in the markets. The EU experience based on only
two consignees for a yellow maize shipment of 15,000 in 1992 demonstrates the potential
for the exercise of market power in the Mozambican markets. In that case, detailed in
MOA/MSU Working Paper #10 (1993), one of the consignees controlled sales to the
market in order to maintain higher prices. Only when the next USAID shipment was
about to arrive did the warehouse doors open for further sales. Since 1993, MICT has

designated at least 18 consignees for each USAID shipment, avoiding that problem.

Increasing the number of consignees to receive maize at the port and diminishing the
amount received by individual traders resulted in new problems at the port, with possible
delays in unloading shipments. Every day that the ship is docked is costly, and so GOM
and the donors want the ships unloaded rapidly and the delivery to consignees completed
as quickly as possible. Procedures were modified, such that large scale traders were
allowed at the port directly and the smaller scale consignees with less than 1,000 metric
tons, went to warehouses to obtain their allotments, thereby reducing pressure at the port.
However, thefis from port warehouses in December 1995 and January 1996 are putting

this system (and the entire Title 111 program) in jeopardy.

One of the other benefits of selling yellow maize directly to private traders has been the
development of a small-scale milling sector (MOA/MSU Working Paper #10, 1993;
MOA/MSU Working Paper #20, 1995). With the industrial mills in crisis and no longer
receiving special concessions for the yellow maize, the informal traders and urban

households took maize directly to local small hammer mills for grinding into a low cost,

74

straight-run meal. Generating income and employment, the local hammer mills are now

beginning to appear in towns and villages throughout the country.

4.2 Pricing of commercial food aid

The Title 111 Food for Peace Program of USAID is specifically designed to provide
commodities for commercial sale by the receiving government, which then uses the fiinds
generated for development purposes. The price paid by the consignees to the government
is determined by the GOM, through its National Salaries and Prices Commission on the
basis of recommendations from MICT; the donors make recommendations only.
Regardless of the consignee price set by the government, USAID regulations state that the
value (in F AS prices) be deposited by the government of Mozambique for development
firnds. Figure 4.2 shows the nominal consignee price, along with retail market prices in
Maputo. Donors have recommended prices close to the free-alongside-ship (F AS) price.3
The USAID regulations state that the equivalent value for the FAS price be covered in

counterpart fiinds, regardless of the revenues generated by the government sales.

Consignees are charged a fixed metical/kg rate, and generally prices have not changed
during the distribution of a given shipment, except when quality changes necessitate

lowering the price. Prior to 1993, the government set a fixed resale price at which

 

3 The FAS price is the price for the commodity placed at the ship in the Gulf Ports, in the case of US
maize. It does not include the transcontinental shipping costs, the port handling costs, or any of the
other additional costs that are in the import parity price, and so FAS is below the import parity price.

75

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76

consignees could sell the food aid maize. However, since early 1993, traders have been

permitted to sell the yellow maize on the market at the market prices.

The low consignee prices do not necessarily result in lower consumer prices. Tschirley,
Donovan and Weber (1996) provide the analysis to show how low consignee prices may
result in high rents that are captured by consignees rather than by lower prices for
consumers. In a quantity constrained system, demand determines the prices in the market,
so that as long as the price to consignees is below the market price by a “reasonable”

margin, lowering the consignee price even lower makes no change in consumer price.

As Figure 4.2 indicates, the consignee prices are changed infrequently, usually just once
each year with the first arrival for that marketing year’s hungry season, between
September and December. During 1991 and 1992, MICT offered the mills offered a price
discount and longer payment periods than traders for food aid maize. The prices on
Figure 4.2 do not reflect this price discount; the private trader price is shown. Since 1993,

all receivers have been given the same conditions.

Prices have been lowered on a few shipments due to quality problems, some of which was
sold at low prices to animal feed agents.‘ The arrival of damaged maize has caused
dificulties with planning the allocations to consignees. In 1991 and 1992, MICT sold the

shipments immediately to parastatals and wholesale traders, according to the allocations

 

‘ The prices in Figure 4.2 do not reflect either the millers’ discount as mentioned above, or the
discounted price for animal feed agents of the deteriorated maize sold in 1993.

77

that had been pre-determined. In late 1992 and early 1993, with famine threatening, large
quantities of both emergency and commercial food aid arrived, as can be seen in Figure
4.2. Some of the maize was in poor condition, and the consignees would not accept their
allotments. The larger consignees, such as Ignacio de Sousa, Capela, and EURAGEL,
evaluated the quality before accepting firll consignments, because they intended to store
the maize and sell gradually over the hungry season. Because the consignees did not
accept delivery of their allotments, GOM had to store the deteriorating stocks of those
1993 shipments in grain warehouses. As shown in Figure 4.2, yellow maize grain retail
prices in mid-1993 were close to the consignee price. For example, in June 1993, the
consignee prices were 329 meticais/kg (and had been since October 1992) while the

average monthly 1993 retail price was 374 meticais/kg, only 14 percent higher.

For the consignees, the relationship between quantities and prices determines the
quantities that they will accept from food aid shipments. In a 1995 meeting between
MICT, USAID, and consignees, the consignees would not address the issue of a
consignee price level until information was released on quantities and dates for both

commercial and emergency food aid arrivals and distribution.

4.3 Food aid in the markets

 

4.3.1 Agents and chmels in the Maputo markets

In contrast with the white maize markets, there is active trade between the formal and
informal sectors in the yellow maize markets. The formal sector consignees receive

allotments of yellow maize which they pick up at the port or warehouses and then store or

78

immediately sell. Both formal and informal sector redistribution wholesalers arrive at the
traders’ warehouses to purchase sacks of the grain (MOA/MSU WP#10, 1993) at the

market prices, as indicated above.

There is little delay between the arrival of ship in port and the arrival of the yellow maize
in the informal markets. In fact, trucks often leave directly from the port to go to Bazuka
and other markets in Maputo. In 1993, “paper trading” developed.’ This trading enabled
consignees to sell part of their allotment before it arrived in the port to a trader who was
authorized via a paper receipt to pick the maize up fi'om the port directly, as a
“representative” of the consignee. While not legally condoned, oflicials did not take
action against it. It results in very fast delivery of yellow maize to the markets. In July of
1993, a USAID shipment began unloading at the Maputo port at 6:00 am. MOA/MSU
researchers observed the first bags of that shipment arriving in Bazuka market by 10:00

am. on the same day.

Informal traders work in the markets in Maputo and other towns in the southern zone.
They sell the maize in 50 kilogram bags, usually still in grain form, but a few traders take
the maize to local hammer mills for grinding and then sell bags of the flour. The retail
traders in the marketplaces then buy the bags of grain or flour to break down into smaller

nonstandard units for sale. The informal retail and wholesale traders for yellow maize

 

5 Paper trading is described in greater detail in MOA/MSU WP#10, 1993.

 

o!

79
work side by side with the white maize traders in the marketplaces (MOA/MSU Working

Paper #10, 1993).

4.3.2 Agents and channels in other markets

Traders of yellow maize work in many markets throughout Mozambique, either with
commercial food aid or with leaked emergency food aid, as will be discussed further
below. Wholesalers from urban areas in the south, including Xai Xai, Maxixe, and
Inhambane, are on the list of consignees and take supplies fi'om the port directly to the
other cities, although if Maputo prices are high, the bags may never leave Maputo. In
other cases, buses and trucks will leave the area around Bazuka market loaded with sacks
of sugar and yellow maize for sales in those areas. In Maxixe, SIMA enumerators
reported that yellow maize grain was present in the local market in 92 out of the 147

weeks of observations (63 percent) from December 1992 through November 1995.

In the production areas of the center region, SIMA enumerators reported no yellow maize
grain present in the market in Chimoio 72 percent of the time (106 out of 147 weeks) in
the January 1992 to November 1995 period. Further north in Nampula City, SIMA
enumerators reported yellow maize absent in 92 percent of the weeks with recorded data
(136 out of 148 weeks). There were scarcities in Maputo, particularly during 1995, when
there was no yellow maize grain observed in the Maputo markets between July 22 and

October 7. Over the entire 1992 - 1995 period, however, SIMA enumerators reported no

The

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80

yellow maize grain on the Maputo market in only 17 percent (26 out of 153 weeks) of the

weeks recorded.6

The absence of yellow maize in the regional markets is a consequence of high transport
and other transaction costs relative to the prices in Maputo and the prices of the substitute
white maize. The price margins between wholesale in Maputo and retail in Chimoio may
have been too narrow, resulting in an efficient no-trade equilibrium. In addition, as noted
in Chapter 3, demand in the regional markets is weak while supplies of white maize are
relatively abundant. The result is little yellow maize actually moving from urban port
regions to more distant towns. The issue of market integration will be discussed in

Chapter 9.

4.3.3 Competitiveness in the m

Tschirley et al. (1996) present evidence supporting the hypothesis of competitive markets
for yellow maize, at least in Maputo. USAID requested that MICT expand the number of
possible consignees in 1992 in order to avoid market concentration issues, and since then
shipments have been at least 18 consignees per shipment. Tschirley et al. (1996)
determined that the four firm concentration ratio for the seven shipments between August
1992 and January 1993 was 28 percent, an indication of lack of concentration. Recently,

MICT personnel have been more concerned about excessive numbers of consignees,

 

‘5 For comparison, from January 1992 to November 1995, SIMA enumerators reported white maize
grain absent fi'om the Chimoio, Nampula, and Maputo markets for 2 percent, 1 percent, and 1
percent of the weeks, respectively.

81

resulting in problems with certification and unqualified traders causing delays in the

delivery of 1995 shipments.

4.3.3 P tential iversion to animal feed

In other countries, people have diverted food aid supplies from human consumption into
animal rations, particularly when poultry, swine, and beef industries are willing and able to
pay more than poor consumers. In Mozambique, the war decimated small animal
husbandry, such that this problem has not arisen in any large scale. There were sales in
1993 and 1994 of deteriorated maize grain to those industries. Examiners for MICT
declared the yellow maize grain “unfit for human consumption” and so it was sold at a
reduced price to traders and small animal producers, particularly in the poultry industry. It
is possible that some of these supplies made their way to the human food chain. However,
the deterioration of the grain was perceptible both to the eyes and the nose, lessening the

likelihood that large quantities were bought and sold for human consumption.

4.4 Emergency yellow maize food aid in the markets

As Figure 4.3 indicates, large volumes of emergency yellow maize food aid also arrived in
Mozambique during the 1989 to 1996 period, particularly during late 1992 and early
1993. There are two basic ways in which emergency food aid enters the market. First it
may be stolen from ports, warehouses, or railcars before it ever reaches the targeted
receivers or distribution point. Prior to 1993, knowledgable sources indicate that up to
one-third of emergency supplies were leaked this way. MOA/MSU researchers identified

some emergency supplies in the Maputo markets in 1992. Changes in administration and

82

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83

transport lowered these percentages. Nevertheless, these leaked quantities exert
downward pressure on prices in Maputo. Alternatively, recipients of emergency food aid
may sell the food they receive in order to purchase other articles or services. The GOM
and non-governmental organizations targetted internally displaced persons, returning
refugees, and demobilized soldiers for emergency relief. Most of these populations were
in rural areas or in the smaller urban zones of the center and north such that supplies sold

by them are unlikely to have afl‘ected Maputo markets.

4.5 Summary of yellow maize in the markets

Formal and informal traders have developed business relationships based upon the yellow
maize commercial food aid deliveries, with the formal sector as wholesalers and the
informal sector the middlemen and women, as well as the retailers. This is in contrast to
the dual marketing channels in domestic white maize marketing, with the formal sector

serving the public sector needs and the informal sector serving the urban consumers.

GOM sets the consignee prices at levels below the FAS and much below the retail market
prices. By involving at least 18 consignees with each shipment, MICT helps to ensure that
prices to be charged are established through competition, rather than collusion.
Nevertheless, there are substantial rents to be obtained by the consignees due to the
limited quantities. At the retail level, traders of white and yellow maize work in close

proximity in the markets, providing a competitive environment.

U5

lL’

84

When MICT delivers the maize to consignees at the port of Maputo, traders immediately
begin taking supplies to the local markets for sale. The efi‘ects of the deliveries in the
Maputo and Beira markets can be almost immediate. This research uses the weekly SIMA

data in an efl‘ort to capture some of the rapid prices movements.

USAID and other donors bring in yellow maize in order to provide government budget
subsidies and to help temper consumer price spikes for the basic staple of low income
urban consumers. That the yellow maize arrivals dampen yellow maize prices can be seen
in the price graphs. Since the government has strong incentives delivery all the maize as
quickly as possible to avoid losses in storage, large quantities are released onto the
market. These shocks of yellow maize food aid deliveries will be further analyzed in the

empirical chapters to follow.

The lack of yellow maize products on the market has important implications for any white
maize price declines on these markets. Emergency yellow maize supplies are not leaking
onto these markets in large amounts and traders do not bring large quantities of yellow
maize commercial food aid supplies to the production regions. Thus, if there is an effect
of yellow maize food aid on white maize prices, it does not come from yellow maize
supplies introduced directly in those rural markets. This points to the need to assess how
food aid afl‘ects the white maize prices in the main consumer markets and how those

consumer prices then affect the prices in producing regions.

Chapter 5

Data and Preliminary Analysis

5.1 Introduction

This chapter explores the data characteristics and identifies a structural break in the data
which separates the sample into a war/drought period and a post-war/drought period.
Unit root and stationarity tests confirm that the data do not have stochastic trends, once

potential mean and trend shifts are incorporated into the analysis.

5.2 Data sources

Data series were constructed from several different sources. Both white and yellow maize
prices were collected at the retail level in urban marketplaces in Maputo. From April 1990
through December 1992, retail prices in Maputo were collected by USAID in Mucoriama
market on Saturday mornings, with a single purchase of each product.1 The commodity is
then weighed and the price converted to a standard per kilogram price. The price data
fiom January 1993 through April 1995 were collected by SIMA in the Xipamanine market

in Maputo. Each week on Saturdays, enumerators visited Xipamanine and other Maputo

 

‘ As in many markets in developing countries, commodities are sold to consumers in nonstandard
units. In Maputo, the most common unit for retail maize sales is the used 850 milliliter infant
formula can, which is filled for the customer and then the contents emptied into plastic sacks or
paper. There are very few scales in the markets, so sales are not based upon volume, not weight.

85

86

markets to collect three observations on each product for retail and wholesale market
levels. To calculate the per kilogram price for retail sales, the quantity of the nonstandard
unit was measured by volume in a plastic beaker, and then the per kilogram price was

calculated using calculated conversion ratios for each product.

Both Mucoriama and Xipamanine are markets are active in retail maize trade and serve
low-income clientele. The USAID/Mucoriama data begin in 1990, whereas the
SIMA/Xipamanine data collection for basic staples began collection in 1991, adding grain
price collection in late 1992. Figure 5.1 shows the close relationship between the SIMA
and USAID data. The SIMA sampling and measuring methodology is more accurate than
the single sample of the USAID data, and the SIMA enumerators have extensive training
on quality difl‘erentiation, thus the SIMA data may be of higher quality. Rather than lose
the information for the early period, the two sets were merged with January 1993 chosen

as the switching date.

During the 265 week period fi'om April 7, 1990 through April 31, 1995, there were 14
missing values in the white price series and 17 missing values for the yellow price series.
Those values were replaced by the mean of the values for the week before and the week

after the missing week.

Weekly commercial food aid data set were constructed from data sets and information
provided by donors, the Ministry of Finance, Ministry of Industry, Commerce, and

Tourism (MICT), World Food Programme, and non-governmental organizations active in

87

Mozambique. Although a database is being maintained by the MICT in Mozambique for
all food aid arrivals, there are gaps due to lack of firnding for data collection and revisions.
During 1992/93, it was particularly diflicult to update the information because of large
arrivals, swapping between commercial and emergency programs, variations in record
keeping methods by the donors, and delays in shipping. For this analysis, daily off-loading
data for commercial food aid were obtained from USAID and SOCOTEC, the firm
responsible for overseeing the unloading and delivery of shipments. Detailed arrivals
information was also obtained on EU arrivals. The USA and EU account for all of

commercial food aid maize arrivals in Mozambique during the study period.

Weekly data are based on the actual delivery of maize into the hands of commercial
agents. The numbers may not agree with official USAID or GOM statistics because only
commercial sector deliveries are included in the data. If the maize arrived and entered a
government warehouse, it would not be included until it is delivered out of the warehouse.
Parastatals such as the Empresa de Abastecimento da Cidade de Maputo (Maputo
Wholesale Supply Firm, known as EACM) are considered commercial sector agents and
are included. During late 1993 and early 1994, some of the yellow maize in stocks
deteriorated so badly it was deemed unfit for human consumption and subsequently sold
to animal feed operations. Those deliveries were also excluded from the data set on the
belief that the inferior maize did not enter the market for human consumption due to its
obvious deteriorated state. Losses from the warehouses due to thefi are also not included

in the food aid deliveries data.

88

White maize nominal prices

 

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89

The Consumer Price Index (CPI) data were obtained from GOM. The CPI is estimated on
a monthly basis, so conversion to a weekly series was needed. Linear interpolation of the
monthly CPI was used to fill in the missing weeks. Data were not available for March and

April 1995 for the CPI and so linear trend values were estimated for those two months.

5.3 A structural shift in the data

Plots of the data and of their first difl‘erences (y, - y,.,) are presented in Figures 5.2 - 5.7.
Inspection of the real maize price charts, there is an obvious shift in the price series. For
yellow maize, a sustained downward shift began on November 28, 1992 with a high in real
prices of 309 meticais/kg, rapidly reaching a low of 128 meticais/kg by December 12,
1992. This period coincides with the arrival of two large shipments of yellow maize
commercial food aid: 1) the Lash Atlantic/Vitoria on Nov. 2 with 33,000 metric tons; and
2) the OMI Missouri/Argo Explorer on Nov. 12 with 21,250 metric tons. In addition,
over 50,000 metric tons of yellow maize arrived through emergency programs before the
end of December. These arrivals were scheduled to ensure supplies during the hungry
season when domestic food supplies would be critically low due to crop failure in the '

1991/92 cropping season.

For white maize prices, there was a structural break between February 6 and March 20,
1993. The average real white maize price on February 6 was 644 meticais/kg., but by
March 20, 1993, the real price was down to 186 meticais/kg. This period marks the

beginning of the first harvest and marketing after the drought, when forecasts of domestic

91

production were high and regional production prospects were also good. Not only had
production prospects improved, but the Peace Accords had been signed in late 1992 and
travel within the country began to resume. Thus, domestic maize could more easily be
transported from surplus to deficit areas. With all of the yellow maize available and good
prospects for the domestic white maize crop, markets were no longer operating under

severe supply constraints.

Maize prices are less volatile after the structural break. This is indicated by a reduction in
the variance of real maize prices.2 The simple sample variance of real yellow prices is 4.4
times greater in the war/drought period than in the post-war/drought period. For real

white maize prices over the same periods, the sample variance in the earlier period is 9.4

times greater than in the later period.

Given the structural shifts in supplies and the shifts in the prices, the analysis was divided
into two distinct periods, using February 1993 as the separation point. From April 1990
through February 1993, a combination of war and drought limited the domestic and
regional supplies of white maize, as well as the internal transport of maize. From March
1993, weather conditions returned to more normal patterns and the Peace Accords were
firmly in place. The month of March 1993 is left out of the analyses because it was a
transition month in which maize market conditions shifted as both domestic and regional

supplies began to reappear in the markets.

 

2 Tables 5.1 and 5.5 in the following sections report the sample variances along with other basic
descriptive statistics.

92

5.4 Unit root and stationarity tests
The statistical properties of the variables are important in determining how multivariate
time-series models should be specified and estimated. Various tests were implemented to
evaluate whether or not each data series was stationary. Hamilton defines stationarity as
follows:

“If neither the mean Ir, nor the autocovariances yj, depend on the date I,

then the process for Yt is said to be covariance-stationary or weakly

stationary... Notice that if a process is covariance-stationary, the

covariance between Y, and Y,_j depends only on j, the length of time

separating the observations, and not on t, the date of the observation.”

(1994, p.45-46).
The term stationarity here will mean covariance stationarity, using Hamilton’s definition.
If a variable is stationary, we will say that it is integrated of order zero (1(0)), whereas if

the variable is not stationary but its first differences are stationary, we will say that the

variable is integrated of order one (1(1)).

5.4.1 Stationarity and unit roots

Stationarity of the data is necessary for the usual distribution theory to be used in
hypothesis testing and estimation. In some cases, transformation of a variable into log
form may be used to achieve stationarity or seasonal cycles may need to be removed. First
differencing will also induce stationarity in I(l) series. Recent literature abounds in
theoretical and empirical work on cointegration between 1(1) series in which common
stochastic trends can be determined to identify stationary linear combination of I( 1)
variables. Several tests are available to test for these properties, each with its advantages

and disadvantages.

93

Dickey-Fuller tests were designed to test the null hypothesis that a series is 1(1) against the
alternative that it is 1(0), (Hamilton 1994). The Augmented Dickey Fuller test (ADF)
includes lagged first differences in the estimation to account for autocorrelation n the
series. Rejection of the null hypothesis provides evidence that the series is stationary, i.e.
distributed 1(0). Hamilton (1994) provides a summary of the Dickey Fuller and
Augmented Dickey Fuller tests, as well as Fuller’s tables of critical values for the test
statistics. However, these tests are not reliable if the data generating process includes
moving average terms, if the residuals are heteroskedastic, or if there is remaining serial

correlation in the residuals (Myers 1992).

Phillips and Perron (1988) developed additional tests that are more general than the ADF
tests, appropriate when serial correlation and heteroskedasticity is present and allowing for
drifts and trends in the series. The Phillips-Perron (PP) tests for unit roots are based upon
three regressions, with test statistics for difi‘erent null hypotheses on the coeflicients for
level and trend stationarity. A summary of the tests and tables of critical values for the

test statistics can be found in Banerjee et al. (1993).

With the PP and ADF tests, there must be strong evidence against the null hypothesis, so
these tests may have low power. For this reason, Kwiatkowski, Phillips, Schmidt, and
Shin (1992) have developed tests that have stationarity as the null hypothesis. The
number of lags chosen is important because increasing the number of lags results in

decreasing the power of the tests, while helping to correct for autocorrelation. Therefore,

94

the choice of how many lags to include must be balanced with the number of observations

and the resulting power of the tests.

5.5 Unit root and stationarity tests in the initial war/drought period

Table 5.1 gives the descriptive statistics for real white maize prices, real yellow maize
prices, and quantity of food aid deliveries over the initial war/drought period. Figures 5.3
- 5.8 show the data and first differences, with a vertical dotted line to indicate the end of
the war/drought period. Results of the ADF tests, PP tests, and KPSS tests are reported

in Tables 5.2, 5.3, and 5.4, respectively.

Table 5.1 Descriptive statistics of war/drought period

 

 

 

 

Statistic Real White Real Yellow Food aid
Price Price quantity
(meticais/kg) (meticais/kg) (metric tons)
Mean 384.30 234.04 2,276.30
Standard
Deviation 102.08 66.38 3,738.17
Variance 10,420 4,406 13,973,915
Median 386.19 222.30 0.00
Maximum 658.71 413.92 21,660.00
Minimum 191.19 126.50 0.00
Skewness 0.48" 0.49” 2.02'"
Kurtosis -0.21 -0.51 4.77'”
* indicates significance level of 10%

** indicates significance level of 5%
"* indicates significance level of 1%

95

 

700

 

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04/07/90 09/22/90 03/09/91 08/24/91 02/08/92 07/25/92 01/09/93 06/26/93 12/11/93 05/28/94 11/ D194 04/29/95
06/30/90 12/15/90 06/01/91 11/5/91 05/02/92 0/17/92 04/03/93 09/ B/93 03/05/94 08/20/94 02/04/95

IX:

Source: SIMA dataset
Note : Dotted lire 'nd‘nates end of war/drought period.

Figure 5.3 Real white maize prices, April 1990 - April 1995

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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01, i "mi iii. rim/«Ii .rrLlir i i 11:..Irfiin-.vrvrri. agilirvmwv.
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14/90 '09/2'9/90 ' 03/13/91' 08/51/91' 07/8/92' 08/0392 ' Otis/93 ' 0103/93 ' wi/93 toe/0094' 1119/94 '
0107/90 012700 06/08/91 rim/91 05/09/92 0/24/92 04/0/93 09/25/93 03/0/94 08/2194 02/11/95

DI:

Snowman-a
Notclhtted halvwadoful'ldrorghperbd.

Figure 5.4 First differences: Real white maize prices, April 1990 - April 1995

96

 

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04/07/90 09/22/90 03/09/91 08/24/91 02/08/92 07/25/92 01/09/93 06/26/93 12/11/93 05/28/94 11/12/94 04/29/95
06/30/90 WIS/90 06/01/91 W W91 05/02/92 DI 17l92 04/03/93 09/ 18/93 03/05/94 08/20/94 02/04/95

Du

Salaam/Adina
Meznnedlineirxficnaardofwmwghpuiod

Figure 5.5 Real yellow maize prices, April 1990 - April 1995

 

 

 

 

 

 

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no 1 ll I A ;
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07/07l90 12/22/90 06/08/91 11/23/91 05/09/92 D/24/92 04/11/93 09/25/93 03/2/94 08/27/94 02/11/95

Date

Sauce: SIMA dame!

nu ma- auuuduura-‘r page

Figure 5.6 First difi‘erences: Real yellow maize prices, April 1990 - April 1995

 

97

3W0

 

21100

 

 

 

 

 

 

   

 

$001

I. .
i o. lllll1ll II "1 1

04/0190 09/22/90 03/09/91 08/24/91 02/08/92 0125/92 0109/93 0026/93 Mil/93 05/28/94 1112/94 04/‘29/95
06/30/90 011990 06/0791 WIS/91 0502/92 DID/92 04/03/93 09/8/93 03/05/94 08/31/94 OWN/95

 

 

   

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MWhid'nte-cdofwmpubd.

Figure 5.7 Yellow maize commercial food aid deliveries to Maputo, April 1990-April
1995

 

 

 

 

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010190 0122/90 06/08/91 11/23/91 05/09/92 n/24/92 04/n/93 09/25/93 03/12/94 08/27/94 02/1195

 

 

 

 

 

me
Salaam/Adana
hbtclbttedhebwadofvm’dmghpubd.

Figure 5.8 First differences: Yellow maize commercial food aid, April 1990-April 1995

98

Table 5.2 Augmented Dickey Fuller (ADF) t-tests for
war/drought period

 

 

Test White Yellow Food aid

price price quantity
ADF: no trend -3.28” -2.81’ -5.61'"
ADF : trend -3 .3 7° -3 .91" -5 .92'”
Legs 2 2 2

 

 

* indicates significance level of 10%
“ indicates significance level of 5%
"" indicates significance level of 1%

Note: Significance levels using critical values found in Hamilton
1994.

99

Table 5.3 Phillips Perron test results for the level series for

war/drought period

 

 

Food aid
Test statistic White price Yellow price quantity
Z(phi_2) 7 .38'" 7.24'” 1526'"
Z(phi_3) 1106‘” 10.80‘“ 2289'“
Z(t_ alpha~) -4.09"° -4.19’" -6.24°"
Z(phi_l) 10. 13'” 6. 12'" 20.67‘”
Z(t alpha“) -3.76'" -3.13" -5.31°"
Z(t alpha“) -0.66 -1.05 -4.15”'
NW lags 4 4 4

 

 

* indicates significance level of 10%
“ indicates significance level of 5%
*" indicates significance level of 1%

Note: Significance level determined according to Banerjee et al. 1993.

100

Table 5.4 KPSS tests for war/drought period

 

 

 

Trend/ White Yellow Food aid
no trend Lags price price quantity
NO trend 0 1.81.” 4. 3 8m 1 .01 4..
No trend 2 0.79'" 1 .81'“ 0. 54"
No trend 4 0.53" 1.21'” 0.46'
No trend 8 0.36' 0.81'" 0.43'
With trend 0 1.03 m 0.27”. 0. 10
With trend 2 046'" 0.12" 0.06
With trend 4 0.31m 0.10 0.05
With trend 8 0.21” 0.07 0.05

 

 

* indicates significance level of 10%
" indicates significance level of 5%
"* indicates significance level of 1%

Note: Significance level as determined by Kwiatkowski, et al. 1992.

101

For white maize prices over the initial war/drought period, the ADF tests rejected the null
hypothesis of a unit root at the 0.10 significance level when two lags are included with a
trend. Without a trend, ADF tests were rejected at the 0.05 significance level with two
lags included. The PP tests were rejected at the 0.01 significance level with two Newey-
West lags included. These tests provided evidence of weak trend stationarity. The KPSS
tests rejected stationarity around a mean or a trend at the 0.10 significance level or better
when up to 8 lags were included.3 We conclude that these data are weakly stationary

around a trend in the initial war/drought period.

With the yellow maize prices, the unit root tests all indicated that this is a trend stationary
series. The ADF tests rejected the null with from O to 4 lags included, both with and
without the trend component. The PP tests provided strong evidence against the null
hypothesis of a unit root with the lag truncation at 4 lags. With the KPSS tests, the test
statistic failed to reject at the 0.10 significance level or lower with 4 or more lags and a

trend included. Thus, we find that real yellow maize prices are trend stationary as well.

Figure 5.7 shows the bar graphs of the deliveries. A week of very high deliveries in
November 1992 resulted when two ships were simultaneously off-loading bulk maize. In
general, the port capacity limited the weekly deliveries to less than 10,000 metric tons. In

testing the food aid deliveries, the ADF and PP unit root tests reject the null hypothesis of

 

3 As Kwiatkowski et al. (1992) state regarding rejection of both hypotheses (stationarity and
nonstationarity), “it is not clear what to conclude” (p.175).

102

a unit root. The KPSS tests failed to reject the null hypothesis of trend stationarity, at the

0.10 significance level, with one or more lags included.

5.6 Unit root and stationarity tests for the post-war/drought period

5.6.1 Introduction

The data for the post-war/drought period are presented in Figures 5.3, 5.5, and 5.7, shown
with the earlier period as well. An examination of the figures of real prices and food aid
deliveries and their first differences clearly shows that changes have been dramatic. Table
5.5 details descriptive statistics for each series in this later recovery period. Comparing
Tables 5.5 and 5.1, the sample means of all three series have declined along with their
variances. Nevertheless, the mean white price is 63 percent higher than the mean yellow

price in this period, quite close to the 64 percent margin in the war/drought period.

5.6.2 White maize prices and food aid deliveries

For both the white maize prices and the food aid deliveries, unit root tests provide
evidence of stationarity around a trend, although the test results are weak for the white
maize price series (Tables 5.6- 5.8). ADF tests results, reported in Table 5.6, were
stronger for stationarity around a mean rather than around a trend. However, the PP tests
of white maize prices rejected the null hypothesis of a unit root at the 0.01 significance
level, with four Newey-West lags included, both with and without trend terms included.
The KPSS tests failed to reject to null hypothesis of stationarity when four or more lags

were included with a trend.

103

Table 5.6 Descriptive statistics for post-war/drought period

 

 

 

 

Real White Price Real Yellow Food aid
Statistic Price quantity
(meticais/kg) (meticais/kg) (metric tons)
Mean 201.72 123.52 733.86
Standard Deviation 32.99 31.21 1,867.42
Variance 1088.00 974.00 3487257 .00
Median 196.69 113.04 0.00
Maximum 306.70 204.46 10,414.35
Minimum 135.32 78.63 0.00
Skewness 0.64‘“ l .48'” 3. 16’”
‘Kurtosis 0.18 1.17” 10.50'"
"' indicates significance level of 10%
" indicates significance level of 5%

*** indicates significance level of 1%

Note: Significance level determined by Doan 1994.

104

Table 5.7 Augmented Dickey Fuller (ADF) t-tests for post-
war/drought period

 

 

 

 

Test Real Real Food Aid
White Yellow Quantity
Price Price
ADF: no trend -2.75‘ -2.03 -4.40'"
ADF: trend -2.74 -2.06 -4.30”'
Lags 2 4 2
* indicates significance level of 10%

“ indicates significance level of 5%
*" indicates significance level of 1%

Note: Significance levels using critical values found in Hamilton 1994

105

Table 5.8 Phillips Perron test results for the level series for post-

war/drought period

 

 

Test statistic Real Real Food Aid
White Yellow Quantity
Price Price
Z(phi_2) 5 .64'” 1 .36 5 . 78'”
Z(phi_3) 8.45'” 2.04 8.68'”
Z(t_ alpha~) -3 .57” -1.98 -3 .92"
Z(phi_l) 837‘” 1.90 8.35”
Z(t alpha“) -337“ -1.91 -3.81°”
Z(t alpha“) -0.61 -O.50 -3.47”
NW lags 4 4 4

 

 

"' indicates significance level of 10%
" indicates significance level of 5%
“* indicates significance level of 1%

Note: Significance level determined according to Banerjee, et al. 1993

Table 5.9 KPSS tests for post-war/drought period

106

 

 

 

 

 

Trend/ Real Real Food Aid
no trend Lags White Yellow Quantity
Price Price

No trend 0 0.32 395‘” 0.61”
No trend 2 0.15 1.44m 0.29
No trend 4 0.1 0.90'" 0.24
No trend 8 0.07 0.54. 0.2
With trend 0 0.33" 1.48‘” 0.16"
With trend 2 0.15" 056'” 0.08
With trend 4 0.1 0.35'" 0.07
With trend 8 0.07 0.22" 0.06

* indicates significance level of 10%

" indicates significance level of 5%
“* indicates significance level of 1%

Note: Significance level as determined by Kwiatkowski, et al. 1992.

107

The ADF and PP tests (Table 5.7) rejected the null hypotheses for food aid deliveries,
with or without the trend included. In Table 5.8, the KPSS results support that result,
with a failure to reject the null hypothesis of stationarity around a mean or a trend. There
were two main periods of food aid deliveries fi'om April 1993 through April 1995,
designed to meet the hungry season needs of the urban areas. Most shipments arrived in
late October, early November and were distributed through February (in 1994) and
through January (in 1995). Due to months without deliveries, the mean was only 734
metric tons per week, with a median of 0. A linear time trend becomes insignificant as
soon as any lagged own values are included in simple univariate regressions.
Nevertheless, the unit root tests indicate that trend stationarity can be accepted for this

series.

5.6.2 Yellow maize prices

Th yellow maize series demonstrates no significant trend and very strong first order
autocorrelation. In the initial testing with ADF, the null hypothesis of a unit root cannot
be rejected (testing conducted with up to 4 lags) except in the case of the ADF with 4
lags at the 0.10 significance level. Further testing with the PP tests results in failure to
reject the null hypothesis of a unit root. In the KPSS tests, the null hypothesis of mean
stationarity fails to be rejected at the 0.10 significance level with 5 or more lags included,

although trend stationarity is rejected through 8 lags at least.

In evaluating the series and thinking about the maize markets, there was a significant

upward shift in prices during the week of July 23, 1994. In terms of the harvest, this is

108

early for scarcities to occur, yet the traders’ qualitative assessment of the markets in
Maputo, as recorded by the SIMA for that week, showed a decline from “large quantities”
to “moderate” quantities, with some traders indicating “very little”. A rapid appraisal
team was in the center region of the country, the major production zone for the southern
consumption areas at this time. They found that the quantities available for purchase were
diminishing, as evaluated by local officials and itinerant traders. The government
parastatal received funds the last week of July to purchase white maize to be sold to the
donors for their local purchase efforts. The World Food Programme (WFP) signed a
contract with ICM, such that WFP would buy local white maize from the parastatal rather

than from private traders.

The entrance of ICM as a major buyer introduced a new factor into the domestic markets,
a new source of demand for white maize. In the past, regional purchases had been made
to meet Mozambican needs for white maize, with donors buying in Zimbabwe and
importing; however, large quantities of white maize had not previously been purchased
locally. In order to further investigate the efi‘ects of the maize price shifts in July, August,
and September, special unit root tests will be used that incorporate knowledge of

exogenous breaks in evaluating stationarity.‘

 

" In the earlier decision to divide the analysis into two different time periods due to a break, the

differences between the time periods were in mean and in variance. In this case, the difference is
though to be a mean shifi only.

109

Perron (1989) has developed tests that are appropriate to use when a single break in trend
or mean is though to occur in a series. As Perron noted, researchers would find
nonstationarity when the data generating process may actually be stationary fluctuations
around a mean or trend with a single break. Because of the limited number of
observations (before and after the break), testing the period before a break and after a
break separately may have very low power against the null of unit roots. Hence, Perron
developed the appropriate test statistics to test for a unit root using the firll length time
series, including the possible mean or trend shifts. There are two cases developed by
Perron that we will use here: 1) a single exogenous mean shift at a specified point in time;

and 2) that allows for both a shifi in the mean as well as a shift in the slope of the trend.

Both models are based on the premise that the shift is “not a realization of the underlying
data-generating mechanism of the various series” (Perron 1989, p. 1362), but rather fi'om a
rare event that can be considered exogenous. Perron used Monte Carlo techniques to
evaluate the distribution of the coefficient on the lagged dependent variable in each case in
order to determine the appropriate test statistics when the shifi variables are included in
the unit root tests. The critical values vary in accordance with when the shift is thought to
occur. The July 23, 1994 date is around the 60 percentile of the observations, that is,
about 60 percent of the cases occurred prior to July 23, 1994. Once the shifi is
incorporated into the test statistics, Perron found rejection of the unit root hypothesis in

series that were previously thought to be nonstationary.

110

Table 5.9 Perron’s unit root test for the post-war/drought period with a
single exogenous mean shift or mean and trend shifts on July 23, 1994

 

 

Test White price Yellow
price
Model A: mean shift -8.63”' -3.79”

Model C: mean and -8.63’” -4.3 l"
trend shift

 

 

Note: Test is the t-test on the coefficient for the lagged price variable in the
regression including trend, mean shift indicator, and 5 lags of own prices. Trend
shift indicator was also included in Model C. Break occurred at the 60 percent
point of the data.

* indicates significance level of 10%
** indicates significance level of 5%

W“ indicates significance level of 1%

Note: Significance levels using critical values found in Hamilton 1994

111

In the Mozambican case, it was hypothesized that there was a shift in the mean of the
price series when the local purchases began. As indicated earlier, the test statistics must
be modified in the presence of a mean shift. Using the Perron test statistics for a unit root
with a single shift in mean at July 23, 1994, the tests reject the null hypothesis of a unit
root at the 0.05 significance level for yellow maize prices (Table 5.9). The t-statistic for
the coefficient on lagged yellow maize price in the mean shift model was -3.79 compared
to a 0.05 critical value of -3.76 for a shift that is within the first 60 percent of the time
series. Included in the regression were 5 lags of yellow maize price, as well as the
indicator variable for the periods before and after the shift, a time trend, an indicator
variable for the day of the shift, and a constant. Testing the white maize series for non-
stationarity around the shift resulted in at statistic of -8.63, significant at the 0.01

significance level.

Testing based on a break in mean as well as a break in trend also resulted in rejection of
the null hypothesis of a unit root, with the t statistics on the lagged price coeflicient of -
4.31 and -8.63 for yellow and white maize prices respectively. As a result of this testing,
the inclusion of a mean shift variable or of both mean and trend shift variables in the VAR

modeling would be appropriate.

5.7 Seasonality
Seasonality does not appear to be present in data during the war/drought period. Seasonal
elements would not be expected to be important, given limited domestic production and

storage, the lack of regional white maize production, and the arrival of food aid yellow

112

maize during most months of the period at fixed prices to the consignees. In the post-
war/drought period, seasonality may be a concern since there is both regional and
domestic production during a single annual crop cycle. Also, food aid tends to be
delivered during the hungry season, when there is scarcity of domestic supplies. This
means that the food aid tends to exert a counter-cyclical downward pressure on white
maize prices. During the marketing season, the price of white maize drops, as is expected
for storable agricultural commodities. There are too few observations to be able to
thoroughly model seasonality in the present context. By not modeling the seasonality, the
estimated impact of food aid shocks on maize prices will be lessened, since maize prices in
general would tend to be increasing during the hungry period. In the post-war/drought
period estimations, dummy variables were included to at least begin to capture the broad

seasonal movements that might be present.

5.8 Implications for time-series modeling

The data were split into the war/drought period (April 1990 - February 1993) and the
post-war/drought period (April 1993 - April 1995) in accordance with the changes in the
economic structure, apparent in the time series plots. The series were all tested for
stationarity by period. Only the late period yellow maize prices indicated potential
nonstationarity. Further testing incorporated a mean shift for the period when local
purchasing began, and rejected the null hypothesis of nonstationarity around this structural
break in the series. These results are used in the next chapter to help specify an
appropriate VAR model for white maize prices, yellow maize prices, and quantity of food

aid deliveries.

Chapter 6

VARs and the Estimation of the Effects of Food Aid Deliveries

6.1 Introduction

In this chapter, vector autoregressions models (VARs) are used to show that shocks in
food aid quantities delivered on the market significantly affect Maputo white and yellow
maize prices, and that these efi‘ects vary from the war/drought period to the post-
war/drought period. VARs can be interpreted as reduced-form equations, useful when
data are limited or knowledge of underlying economic structure is extremely uncertain or
both (Sims 1980; Fackler 1988; Myers, Piggott, and Tomek 1990). VARs estimated with
weekly data are used to evaluate the effect of food aid quantity shocks as they impact
Maputo maize prices. The following chapter will extend the analysis to study the efi‘ects

of price shocks on markets outside Maputo.

6.2 Structural and reduced form models
6.2.1 Structural models
Structural models are commonly used to determine the relationship between variables in a

economic system. Consider a structural model for the endogenous variable y,:

yr = 130 + B1 x1: + B2 3‘2: + +111: x/a + e, (6.1)

113

114

where B, are structural parameters, x, are predetermined variables (exogenous or lagged
endogenous), and e, is a structural error. This can be generalized to a vector process with
a vector of endogenous variables yt and vectors of 0,, x“ and 6,. In that case, current
values of endogenous variables may enter into each equation. Economic theory is needed
to provide restrictions that identify a unique set of structural parameters. For example, it
is often the case that standard supply equations do not include income, while income does
enter the demand equation. The parameter for income is thus restricted to zero in the
supply equation. In large structural models with many exogenous variables, the models

are frequently over-identified with many zero restrictions on parameters.

6.2.2 Vector Autoregressions

VAR models can be thought of as reduced-form equations for a structural system,
including lagged dependent variables (Hamilton 1994). VARs assume an underlying
relationship that holds through time between the variables in the system. Interest lies not
in the reduced form parameters but in the error terms, or "innovations", i.e. the
components in the variables that are "new" in the sense of not being predicted from past
values of variables in the system. VARs can be viewed as a structural simultaneous
equations system in which there are no truly exogenous variables in the system (Judge et
a1. 1988). The reduced form parameters are unrestricted. The basic VAR model for a

vector y, takes the following form:

y: = C + ‘1’: Y” + 4’2 yt-Z * + (pp yl-p + '3: (6-2)

115

The lagged values (at some chosen lag length p) of all of the endogenous variables appear

in each equation, with the (I)i unrestricted.

6.2.3 VARs in the economic literature

In 1980, Sims wrote of the potential uses of VARs for macroeconomic policy analysis.
Previously VARs had been used primarily for forecasting, but new research developed
applying VARs to policy evaluation (for example, Orden and Faclder 1989; Myers,
Piggott and Tomek 1990; Karnas 1995; Lastrapes 1992). VARs have also been used to
evaluate commodity price relationships in both spatial analysis of homogeneous products
(for example, Ravallion 1986) and in intercommodity research with two commodities in

the same market (for example, Alderman 1993).

In recent literature, many analysts have used error correction models with cointegration to
look at short and long-run price relationships, and the efi‘ects of policy changes (for
example, Goodwin 1992; Fisher, Fackler, and Orden 1995). Cointegration analysis entails
using common stochastic trends between series in order to evaluate long run relationships.
Cointegration analysis has proven usefirl when individual series are not stationary and yet
the series are thought to have some long-run equilibrium relationship connecting them. In
other words, while an individual series may “wander extensively,” some of the pairs may

stay basically together over time (Engle and Granger 1987).

116

6.2.4 Advantages and Limitations of VARs

VARs have been usefirl in economic analysis, particularly in cases where structural models
are diflicult to estimate. When knowledge of the underlying economic structure is weak,
or the required data are not available, formulating a structural model is difficult and entails
arbitrary decisions on variables and lag lengths to include in each equation, resulting in

over-identification of the system.

Typically, all deterministic trends and seasonal components are removed from the series
prior to VAR estimation; or trend and seasonal variables can be included directly in the
VAR. VARs are designed to trace out the efi‘ects of structural shocks to help predict the
efi’ects of a shock in one series on all of the variables in the model. For example, an
unanticipated large delivery of food aid may occur one week; yellow maize prices may
then experience previously unanticipated declines in price which can then be associated

with the food aid shock.

VARs must generally be parsimonious models, with only a few variables included. As the
number of variables included expands, the economic interpretation of the shocks becomes
difficult, identification restrictions increase in number; and data requirements increase

rapidly with the number of variables and their lags.

Identification of VARs has been the source of considerable controversy. Early analysis
focused on using assumptions regarding the contemporaneous relationships between

variables to identify the system. More recent efl‘orts by Blancth and Quah (1989),

117

Lastrapes (1992), and others use information on the long-run effects and the infinite
moving average representation of the VAR to impose identifying restrictions on the
system. When there are nonstationary series and the possibility of cointegrating
relationships, the use of long-run identifying restrictions is key. In the current research,
stationarity of the data series rules out the existence of cointegration and unit root

restrictions.

6.3 Modeling the Mozambique case

6.3.1 Data Enstraints

Data needed for estimating a full structural model for maize supply and demand in
Mozambique are unavailable. Furthermore, as indicated in Chapter 4, the Mozambican
economy is undergoing major shifts and the markets are changing. White maize supply
modeling is the most problematic. Dispersed family-level production and marketing
hinders data collection efforts, and estimates of marketed surplus vary from 13 percent of
total production to 23 percent of total production. After years of controlled producer
prices and active government purchasing on the market, only recently have private
markets expanded and prices deviated from the omcial prices. As discussed in Chapter 3,
interseasonal storage is mainly conducted at the farm level, with little information available

concerning quantities and locations.

Commercial white maize imports are generally unrecorded transactions of the informal

sector, through the southern borders with Swaziland and South Afiica, as well as the

118

center and northern borders with Zimbabwe and Malawi. White maize exports have been

small in recent years, but once again the informal sector activities are unrecorded.

On the demand side, consumption parameters remain a subject of debate, particularly the
cross-price elasticities of demand for white and yellow maize as well as for rice and bread.
The 1991 consumption surveys conducted by the National Directorate of Statistics (DNE)
with Cornell University were conducted in a different market environment than exists
now. Substitution possibilities were limited when the surveys were conducted because of
the scarcity of white maize on the markets. This biased the surveys towards finding a lack

of substitution.

6.3.2 Emergency food aid arrivals

Since a substantial portion of the yellow maize grain arrives as emergency food aid, it is
important to look at the factors which influence emergency arrivals. First, the timing of
emergency food aid coincided more or less with the commercial food aid deliveries, as can
be seen in the monthly chart of arrivals (Figure 3.2). Since all food aid in Mozambique
was programmed to meet needs, it is logical that there be a correspondence in emergency
and commercial arrivals during the hungry season. Without explicitly including emergency
food aid in the models, the models may over-estimate the efl‘ect of commercial food aid,

attributing the full impact of the total food aid shock to the commercial food aid arrivals.

The amount of emergency food aid supplies that enter the Maputo markets is thus

important. Emergency food aid stocks that arrive in Maputo port are intended for

119

distribution in rural areas to targeted populations, and so only stolen quantities of
emergency aid appear in the Maputo markets. In the war/drought period under study,
losses in the emergency distribution system may have been as high as 50 percent, however
by mid-1993, the distribution system had become more efficient in avoiding losses, so the
potential quantity of leakage into the markets has diminished since 1993. Thus, it is
plausible to attribute supply shocks of yellow maize mainly to the commercial food aid
deliveries, recognizing that emergency grain may play a role in part of the effect during

some periods.

6.3.3 Weekly data and price analysis

A unique aspect of this research is the use of weekly data on retail prices and food aid
deliveries. Previous research on food aid using time series methods is limited to yearly or
monthly analysis (for example, Farzin 1991). Since the short-term and medium-term
effects of the prices may be important in determining trader incentives and hence producer

marketing options and prices, the weekly data may provide quite different results.

Price analysis in general has been criticized for interpreting statistical relationships as
representations of economic relationships, without knowledge of the price formation
process (Harriss 1979). Farninow and Benson (1990) indicate the importance of the
underlying market structure in determining what price analysis across markets (or across
products) can reveal. The analyst must evaluate whether or not the markets are
competitive in price setting, and determine the appropriate models for price analysis. As

Chapter 3 details, in the retail markets in Maputo, the retail sellers of yellow and white

120

maize grain act competitively with little evidence of collusion or price fixing. At the
wholesale level, there were signs of market power being used during a brief time in 1992
when only two private traders received maize. However, more maize arrived within two
weeks and market power could no longer be exercised by the two wholesalers because

many more consignees received maize allotments.

6.3.4 Data used

The current research focuses on the potential price effects of commercial food aid delivery
shocks. Thus, the data consist of weekly price data at the retail level for white maize grain
and yellow maize grain in Maputo consumer markets, as was detailed in Chapter 5. To
capture the food aid arrivals, data on the weekly commercial food aid deliveries to

consignees were selected as the best indicator of yellow maize supplies in the market.

6.4 VAR model in the war/drought period

6.4.1 Modeling in the war/drought period

During the war/drought period, fiom April 1, 1990 through February 27, 1993, there was
substantial variability in prices and in food aid, due to a combination of drought, civil war,
lack of private market development, and changing government policies and role. The

VARs will evaluate the sources of fluctuations within the three variable system.

Before estimating the VARs, exogeneity testing presents evidence of the ability of each
series or a pair of series to help forecast other series. The researcher must determine the

appropriate number of lags to include in the system. Then an identification scheme must

121

be selected and the system estimated. Impulse responses and forecast error variance
decompositions indicate how shocks work through the system and trace the fluctuations

back to sources, while historical simulations evaluate alternative scenarios.

6.4.2 VAR order selection

Choosing the appropriate lag length entails determining how many lags are sufficient to
capture the dynamics without the loss of efficiency that occurs when unnecessary lags are
included. Braun and Mittnick (1993) stress that the use of higher order autoregressions
helps to alleviate problems with omitted moving average components. This will have to be
balanced with the limited times series observations available and the need for eflicient

estimation.

There are several different tests to determine the appropriate lag length to use in a VAR.
Each of the tests involves estimating the VAR and then evaluating the improvement in
explanatory power with each additional lag added. Akaike’s Information Criterion (AIC)
and Schwarz’ Criterion (SC) are well known (Judge, et al. 1988). The Chi-squared

Likelihood Ratio test is also used (Hamilton 1994).

As is noted in Kamas (1995), when choosing between the different criteria, the AIC will
tend to over-parameterize as sample size grows, while the SC in small samples tends to
under-parameterize. Hamilton (1994) uses the Sims’ Likelihood Ratio test, which under
the null hypothesis, (no significant difference betweenj lags and j+1 lags), has a x2

distribution. All three of the tests are reported in Table 6.1. The AIC criterion indicates 7

122

Table 6.1 Testing on lag lengths for early period with trend variable

 

 

 

Test Statistic
Akaike’s Schwartz Likelihood Ratio
Number of Lags Information Criterion Test
Criterion 2
X P“
value

1 31.93 32.25 221.21 0.00
2 31.82 32.45 13.33 0.15
3 31.62 32.56 30.35 0.00
4 31.49 32.75 18.80 0.03
5 31.44 33.01 6.37 0.70
6 31.37 33.26 9.92 0.36
7 31.34 33.54 4.09 0.91
8 32.22 33.73 16.45 0.06

 

 

123

lags, while the SC criterion indicates l lag. The LR test suggests 4 lags are needed. In

view of the results, the model was estimated with four lags.

6.4.3 Multivariate Granger causality

Vector autoregressions are used to answer questions about the relationships between
variables over time and, in particular, the ability of different variables to help forecast
other variables. A related concept in a single equation model is “Granger causality,”
developed by Granger (1969). When extended to apply to a multivariate fi'amework, the
term “exogeneity” is used to indicate that, among the relationships of interest, one variable
(or group of variables) can add no firrther information for a forecast beyond what past
values of the other variables already contribute (Hamilton 1994). This is a helpful
diagnostic tool and may provide support for hypotheses concerning relationships among

the variables.

A multivariate version of the Granger-Sims causality tests was used to evaluate possible
exogeneity, based on a likelihood ratio test with a chi-squared distribution in which the
restricted regression excludes the lags of one or more of the variables, and the unrestricted
regression allows the coefficients on those lags to be non-zero (Doan 1992, Hamilton
1994). The results of the tests can be seen in Table 6.2. The null hypothesis of the first
test is that food aid deliveries are exogenous, i.e. that white and yellow maize prices do
not significantly improve forecasts of food aid deliveries. The null hypothesis was
rejected, providing evidence that food aid is not exogenous in the time series sense to

white and yellow maize prices. Further testing indicates that white maize prices contribute

124

Table 6.2 Exogeneity testing on the war/drought period series

 

 

Null hypothesis x2 p-value Results
YP and WP do not help to predict 20.6 0.01 Reject at 1%
FA
FA does not help to predict YP 14.31 0.08 Reject at 10%
and WP
FA and WP do not help to predict 10.88 0.07 Fail to reject at
YP 10%
YP does not help to predict WP 11.13 0.21 Fail to reject at
and FA 10%
YP and FA do not help to predict 9.61 0.29 Fail to reject at
WP 10%
WP does not help to predict YP 11.23 0.19 Fail to reject at
and FA 10%

 

 

125

significantly to improving forecasts of the food aid deliveries, whereas yellow maize

prices are not significant.

This result has important economic implications. One interpretation is that the donors
base their deliveries on the weekly prices, responding quickly to white maize price shifts in
order to avoid large price spikes that threaten food security. However, knowledge of the
food aid distribution system rules out this interpretation. Food aid stocks held by the
government were supplies that formal traders were unwilling to purchase, rather than
stocks retained specifically for later release. Foreign food aid supplies take 3 months or
more to arrive in response to requests. Had the model been based on monthly data, with
sumcient lags, it is logical that food aid deliveries can be predicted from prices, for food
aid officials seek to avoid extreme scarcity and price jumps that threaten the food security
of the urban poor.1 It is possible that the long run relationship between white maize

scarcity and food aid arrivals is reflected in the short-run events.

An alternative interpretation is that the lack of exogeneity of food aid may reflect forward-
looking behavior on the part of traders in the system. Today’s white maize prices serve as
indicators of trader profitability. When white maize prices are rising, traders know that
the white maize price will fall only at harvest or when food aid arrives, because there are

no other downward pressures on white maize prices in this period. While the quantity of

 

' This is related to the seasonality issues that will be discussed further below.

126

yellow maize that arrives indicates the yellow maize supply, only white maize prices are

available to indicate supply of the white maize.

Due to the substitutability between white and yellow maize products, the prices that
traders will face for yellow maize products in the markets will depend upon the white
maize prices. As noted earlier, a preference survey in Maputo in 1994 found that 40
percent of households interviewed would switch fiom white to yellow if the yellow price
was 29 percent lower than the white. Low white maize prices mean that the traders would
face low yellow maize prices in the markets. Since the consignees know the price that
they must pay the government for the commercial food aid, their profitability hinges on the

market prices.

While the commercial food aid is delivered to traders on the basis of allocations
determined by the MITC, traders may not allocate the transport resources to receive
delivery at the port if there are other more profitable activities to pursue. In addition, they
may decide not to use the available bank credit to obtain food aid maize. Current white

maize prices figure into expectations on profitability, guiding these decisions.

6.4.4 VAR Model
The structural form of the VAR model in food aid deliveries (FA) and prices (WP, for

white maize prices and YP, for yellow maize prices) at time t can be written as follows:

127
A0 y, = k + Al y,_l + A2 yr2 + + Ap yrp + u,
where:
y, = (FA, , YP, , WP,)’ (6.3)
u, = (”1: , 1:2, , ”31),

k =(k11 k2 9 k3),

 

 

r1 “12 “13
A 0 = 0121 1 (123 (6.4)
_°‘31 “32 1.
to“ 0 0
E u, u,’ = Q = 0 (4)22 0 (6.5)
.0 0 0033‘

 

 

and the A, are (3x3) matrices, with s=1,2,...p (number of lags). The A0 matrix contains
the coefficients on the contemporaneous values, yL The matrix 11. , containing the

structural disturbances, is assumed to have zero mean and contemporaneous covariance
matrix 0. Time trends are included in the regression. Pre-multiplying both sides of the

equation by A"o results in the VAR reduced form representation:

 

 

where
c = A;1 k “-0
<0, = A," A,
er : AO-l ”r
011 012 013
156.6! = E = 02. 022 023 (6.7)
.031 032 033.
There are three parts (2, A0, and 0), that characterize the identification process.
Equations 6.5, 6.6, and 6.7 combine to give the following relationships:
_ / _ -r / -1 / _ -1 -1 /
ZJ—E(¢s,e,)-A0 E(u,r/,)(A0)-AO Q(A0) (6.8)

One of the usual conditions for identification is that the structural disturbances, ub are
uncorrelated with each other as well as serially uncorrelated. Some further restrictions are
still necessary to identify the A0 and 0 matrices. IfQ is diagonal, it has n parameters
(number of variables, equivalent to the number of diagonal elements in 9). Knowing that
2‘. has n(n+l)/2 distinct values and that Q is diagonal, A0 should have no more than n(n-
l)/2 free parameters in order to be identified. With a 3-variable VAR, A0 must have no
more than 3 free parameters to satisfy this order condition, if only contemporaneous

relationships are used to identify the system.

129

6 4.5 I tification with a recursive structure

The A0 matrix contains the coefficients on the contemporaneous relationships, with the
superscripts referring to time period. With a recursive structure, the 3 coefficients in A0
above the principal diagonal are all set equal to zero:

1'

 

 

1 0 0‘
A0 = “21 1 0 (6-9)
_°‘31 c‘32 1.

A coefficient value of 0 means that there is no contemporaneous effect of that variable in
the specified equation. In A0 above, the first series is unaffected contemporaneously by
shocks from the remaining two; the second series is affected contemporaneously by the
first but not by the third; finally, the third series is contemporaneously afi‘ected by both of
the preceding series. With A0 structured as a lower triangular matrix, the Choleski
decomposition of the 2 matrix enables estimation of Q and A0 using the relationships

between (2, 2, and A0 (Hamilton 1994).

6.4.6 Selected model: Recursive identification with WP, FA, YP ordering

In order to allow white maize price shocks and food aid delivery shocks to
contemporaneously affect yellow maize prices, a recursive model was chosen with the
ordering white price (WP), food aid (FA), and yellow price (YP). A trend term is
included, in accordance with the unit root test results. The exogeneity tests showed that
white maize prices were significant in predicting food aid deliveries, suggesting forward-

looking behavior on the part of traders, based upon white maize prices. The recursive

130

identification system was developed based upon the premise that white maize prices are
the driving force in the relationships between the two maize prices and food aid deliveries,
and that food aid shocks will have contemporaneous effect on yellow maize prices. The
A() matrix can be represented in Equation 6.9, in which the first row refers to the WP,

equation, the second to the FA, equation, and finally the third equation is for YPt .

In this system, yellow maize prices are contemporaneously influenced by both white maize
price shocks and food aid shocks. Food aid is contemporaneously affected by white maize
prices only. Placing the white maize price first in the ordering has implications for the
hypothesis that white maize prices are influenced by food aid deliveries. This recursive
identification system would tend to minimize efi‘ects of food aid shocks and yellow maize
price shocks on white prices. Finding a significant effect of food aid delivery shocks on
white prices over time would therefore be strong evidence of feedback from food aid and

yellow maize prices to white maize prices.

The shocks fiom the white maize prices can be interpreted as the effect of white maize
aggregate supply and demand shocks. The lack of white maize supply data inhibits the
ability to separate the supply and demand effects. The market conditions prevalent in
Mozambique during this period suggest that supply shocks are predominant, since the

drought limited production and the war limited transport and distribution of supplies.

The yellow maize price shocks can be considered aggregate demand-side shocks while the

commercial food aid shocks are the yellow maize supply shocks, given that commercial

131

food aid is the main source of yellow maize for the market. The demand shocks may be
capturing some of the quality differences in the maize available in the markets and the
consumers’ reactions to poor or high quality grain, but also incorporate other shifts in
demand for yellow maize. Ideally, the prices would be collected on a homogeneous good,
however, this source of variability remains in the data and is picked up in the demand
shocks. Thus, when old food aid supplies on the market are of low quality and low price,
the delivery of food aid that is of better quality may result in a rise in price, even though

additional supplies are entering the market.

6.4.7 Impulse responses and simulations

The parameters estimated with the VAR can be used to generate impulse responses, i.e.

the response of a given series after a one unit or one standard deviation structural shock.

For example, if the standard deviation of the food aid delivery structural shock is 2,600

metric tons, what is the effect of that single shock over time on white maize prices? The

moving average representation of the model is obtained by inverting the estimated VAR:
y, = (I—<I>,L -<II>2L2 - -<I>pL ”We,

(6.10)
= C(L)et

C(L) is a polynomial in the lag operator L, where L e, 5 6H (Hamilton 1994). It would
be easy to estimate the change in y at period t+s with respect to a change in a VAR
innovation 6,, , however 6]., = A0”1 u, are the combined effects of different shocks, not

the response of one variable to a single structural shock. What is needed are the

orthogonalized impulse responses “based on decomposing the VAR innovations (en,

132

em) into a set of uncorrelated components (uu, ..., um) and calculating the consequences of
a unit impulse in uj,” (Hamilton, 1994, p.322). For ease of understanding, the shock can
be set to equal one standard deviation of the innovation, and the responses are also stated

in those terms.

Figure 6.1 shows the impulse response plots from the recursive identification scheme with
four lags and a time trend. In this estimation, the one standard deviation values for the
innovations are 2,600 metric tons for the food aid deliveries, 65 meticais/kg (in real 1989
meticais) for white maize prices, and 34 meticais/kg for yellow maize prices. Looking at
the top plot in Figure 6.1, each line represents a single series’ response over time to a
single shock from white maize prices, including the white maize price response to its own
shock. The impulse responses to a one standard deviation positive food aid delivery
shock, as seen in the second plot in Figure 6.1, are negative over time for both price
series. Logically, if the supplies of yellow maize are increased in the market by 2,600
metric tons, prices for yellow maize and its substitute, white maize, would be expected to
drop. White maize prices are not affected contemporaneously because of the identification
assumption. The effect on the yellow maize prices is immediate, a drop of about 0.4
standard deviations of the innovation, lasting for 4 weeks, after which time it gradually
returns to previous levels. White maize prices drop about 0.25 standard deviations, but

they begin to return to the steady state level much faster.

1 .00
0.75
0.50
0.25
0.1”
-0.25
-0.50

I.“
0.75
0.50
0.25
0.”
0.25
050

1.”
0.75
0.50
0.25
0.”
025
-0.50

133

 

  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

J Response to Shocks fro- Whlte Price

0 2 ‘ 4 3 s ' 10 12 ' 14 1s 1s
- Response 14 Shocks rm. rud 4111 (MT) mm” ------
-1 .' ,.
-1 ..I. M"

0 2 4 a? 10 12 14 1s 1s
- ‘3. Response to Shocks from Yellow Price [£3.15ng ------

\ -.-.-.
‘ .\’~
W *' ----..
o 2 4 s 10 12 14 ' 1s 10
Weeks

Figure 6.1 Impulse responses for the recursive model WP, FA, YP, war/drought period

134

The sharp fall-off of food aid deliveries after the 2,600 MT shock in delivery reflects the
compensation that traders, and possibly donors and the GOM, make when a large delivery
occurs that results in high supplies and low prices in the markets. The responses to white
maize price shocks (shown in the top plot) and to yellow maize price shocks (shown in the
bottom plot) also accord with economic logic and observation. A positive white maize
price shock increases food aid deliveries, as traders demand more food aid. Initially, the
yellow maize prices increase with the white maize price shock, but with increased food aid

supplies, the yellow maize price does fall over time.

6.4.8 Forecast error variance decompositions

Another way to use the information from the VARs is to estimate the percentage of the
variance in forecast errors that can be attributed to the difi’erent structural shocks over the
forecast period (F ackler 1988). In the current research, the total forecast error variance
for each series is broken into the three potential sources, estimating the percentage that
can be attributed to each. Table 6.3 shows the forecast error variance decomposition
(FEVDs) for twenty periods forecasted. In all three of the series, by the twentieth period,
approximately 85 percent of the forecast error variance is explained by own shocks, rather
than shocks from the other series. For the white maize price series, food aid shocks reach
about 2 percent of error variance by the fifth period and stay that way through 20 periods,
so the effect of food aid delivery shocks on white maize prices is relatively small.
However, the effect of yellow maize price shocks on white maize prices gradually
increases, accounting for about 10 percent by the thirteenth period. For yellow maize

prices, the food aid shocks gradually reach about 12 percent of error variance by the fifth

135

Table 6.3 Forecast error variance decomposition for recursive model, war/drought period

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Variance Decomposition (% of total variance due to each shock)

White maize price Food aid Yellow maize price

Step Own FA YP WP Own YP WP FA Own

shock shock shock shock shock shock shock shock shock

1 100.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 100.0
2 99.2 0.0 0.7 0.1 99.8 0.1 1.3 2.7 95.9
3 96.4 0.8 2.8 4.1 95.5 0.3 1.9 6.4 91.7
4 95.7 1.5 2.8 4.4 93.2 2.4 2.2 10.3 87.5
5 94.6 1.8 3.6 4.5 89.7 5.7 2.0 11.9 86.1
6 93.4 1.9 4.7 5.3 88.1 6.6 2.1 12.2 85.7
7 92.7 1.9 5.5 5.4 87.2 7.4 2.1 12.3 85.6
8 92.0 1.8 6.2 5.4 86.7 7.9 2.2 12.3 85.5
9 91.0 1.8 7.1 5.4 86.5 8.1 2.3 12.3 85.4
10 90.2 1.9 8.0 5.4 86.4 8.2 2.4 12.3 85.3
11 89.5 1.9 8.6 5.4 86.3 8.3 2.5 12.3 85.2
12 88.9 2.0 9.2 5.4 86.2 8.4 2.6 12.3 85.1
13 88.4 2.0 9.6 5.4 86.2 8.4 2.6 12.3 85.0
14 88.1 2.1 9.9 5.4 86.1 8.5 2.7 12.3 85.0
15 87.8 2.1 10.1 5.4 86.1 8.5 2.7 12.3 85.0
16 87.6 2.1 10.3 5.4 86.1 8.5 2.8 12.3 84.9
17 87.5 2.1 10.4 5.4 86.1 8.5 2.8 12.3 84.9
18 87.4 2.1 10.5 5.4 86.1 8.5 2.8 12.3 84.9
19 87.3 2.1 10.6 5.4 86.1 8.5 2.8 12.3 84.9
20 87.2 2.1 10.6 5.4 86.1 8.5 2.8 12.3 84.9

 

 

136

period and maintain that percentage through 20 periods. Thus food aid shocks account
for error variance in yellow maize prices, while the yellow maize price shocks are

important in the white maize price forecast error variances.

6.4.9 Histgrical simulations

Policy makers are often interested in the question of what paths prices might have taken if
supplies had been different or if multiple shocks are involved, rather than the single shocks
evaluated with the impulse responses. Historical simulations are used to answer those

questions, based upon the appropriate VAR model.

Historical simulations were conducted, using the parameter estimations and the
innovations from the recursive identification scheme. In the these simulations, the path of
innovations for the food aid deliveries was controlled to generate simulated food aid
deliveries of zero. The innovations for white maize prices (white maize demand and
supply shocks) and yellow maize prices (yellow maize demand shocks) were maintained at
the originally estimated values. The effects of the new food aid shocks were then traced

through the system, estimating new paths for each of the price series.

In the war/drought period simulation, all food aid deliveries after July 31, 1992 were set to
zero. In reality a total of 44,800 metric tons of yellow maize commercial food aid was
delivered during the August 1992 through February 1993 period. As demonstrated in
Figure 6.2, the direction of shifts in white maize prices was not affected by the change in

food aid, being driven by the extreme scarcity, instead. However, the magnitude of

137

 

 

 

 

 

 

 

 

 

 

 

 

coo
_ RealV/hitcl’riee —— . . .
"0 Shun,“ m ------ Real Whrte Maize Pnces I: ,1,
m «1- :s’:
1.5. ' ':

K "0 ‘ 51:! “g

m -I

m c-

270 CI

"° Aajdssoao‘rrusufafAsouo'rruAuM/lsouo'rr

Week
000
ltcalYelloa/Priee — . .

soo “Shaun,“ ...... RealYellow Mazean

7“ -1
K m 1

400 - 1.” 1.

,"-‘:'.1': 'a

800 -

200 1

100

 

 

 

amidssouofiruaubfaeouo'rrusufM/rsouo'rr

Wests

Figure 6.2 Simulation in war/drought period, recursive model with no food aid after
July 31, 1992

138

changes in the white prices was affected, with prices that would have been higher in
January and February 1993 by 33 percent on average. For yellow maize prices, the
simulation shows prices increasing throughout the hungry season, with simulated prices an
average 150 percent above the observed prices for January and February 1993. That
yellow maize prices are strongly affected by the removal of food aid is not surprising since
the food aid deliveries are the major supply source. This simulation clearly shows that the
objective of lowering consumer prices for maize products was achieved by the actual

deliveries. Consumers would have faced the high simulated prices otherwise.

The modeling does not have a constraint on the relative prices for white and yellow nor a
ceiling constraint for the white maize and so may over-estimate the upward movement of
prices without the arrivals. White maize grain prices in general should not exceed rice
prices or prices for imported South Afiican fine processed maize flour, for these are the
preferred consumption substitutes, nor should yellow maize grain prices exceed white

maize grain prices for more than brief periods.

6.4.10 Alternative identification schemes

Bemanke (1986) and Sims (1986) developed methods for using non-recursive restrictions
that would still yield information on the A0 and 0 matrices if the rank and order conditions
are met. Rather than the Choleski decomposition, assumptions on selected coefficients in
the A0 matrix are used to then decompose the errors. Since the restrictions used for

identification are just identifying, they cannot be tested, as is true for the recursive scheme.

139

A simultaneous identification scheme was estimated based on the A0 matrix:

 

 

1 0 0113
A0 = 0121 1 0 (6.11)
0 0132 1

This simultaneous identification scheme allows each of the series to have
contemporaneous effects from the shocks of at least one other series. With this
identification, yellow maize prices are allowed to contemporaneously affect white maize
prices, whereas white maize prices affect yellow maize prices with a lag. The informal
traders with white maize must have rapid turnover and hold little stocks to make a profit,
given the high storage costs; whereas, the large wholesalers of yellow maize may wait

before reacting to the shocks.

The impulse response results and simulations are for the most part qualitatively similar to
the recursive estimations and can be found in Appendix B. The FEVDs for this
identification are shown in Appendix B, Table B. 1. As was true with the recursive
identification, yellow maize prices are affected by the food aid delivery shocks within four
periods, whereas the white maize prices are afi‘ected by the yellow maize prices rather than
by the food aid deliveries directly. The strength of the effects is much greater with the
simultaneous estimations, with 30 percent of the error variance of yellow maize prices
accounted for by shocks from food aid, and a similar percentage of white maize error

variance accounted for by yellow maize price shocks.

140

An alternative recursive identification scheme was also estimated with the ordering food
aid deliveries, yellow maize prices, and white maize prices. This computes impulse
responses and FEVDs under the following assumptions: 1) food aid deliveries are not
contemporaneously afi’ected by shocks from either yellow or white prices; 2) yellow maize
price is contemporaneously affected by food aid delivery shocks but not white maize price
shocks; and 3) white maize prices are contemporaneously affected by shocks from both
food aid and yellow maize prices. The results of this model were not good in terms of
reflecting what economic theory and market structure would predict. For example, the
response of white maize prices to a food aid delivery shock (approximately 2600 metric
tons) was to increase, rather than to decrease. The lack of correspondence led the

researcher to reject this identification scheme.

6.4.11 Summm of war/drought period results

During this period, white maize price fluctuations cannot be attributed solely to food aid
deliveries, but this is not surprising given the extreme scarcity of the grain. White maize
prices are used by donors and traders to determine the deliveries, making white maize
prices a key indicator and predictor for the deliveries. Yellow maize prices are strongly
affected by the deliveries, a fact best shown in the simulations of retail prices if the

deliveries had not occurred.

That the results for the recursive and nonrecursive estimations are similar for white maize

price effects provides evidence that the white maize prices are leading indicators in these

14]

markets. That white maize prices are influenced by yellow maize demand and supply
shocks even under these identification restrictions, is strong evidence that food aid can

change prices for substitutes, even when the substitute is a preferred consumption good.

6.5 Post-war/drought period: April 1993 through April 1995

6.5.1 Identification considerations

Under the new, relatively open marketing system of this period, white maize supplies are
more readily accessible and yellow maize commercial food aid supplies are diminishing.
In the past, the white maize price played a key role in expectations, but the same may not
hold in a competitive market environment with greater white maize supplies and the

increasing likelihood of commercial imports, including yellow maize from South Afiica.

Yellow maize supplies may begin to have greater importance than before in forming
expectations. Formal traders make the connection between white maize price variability
and yellow maize food deliveries. When formal traders outside of Maputo were
interviewed and asked about their general unwillingness to engage in white maize trade,
they mentioned price uncertainty and yellow maize food aid arrivals as sources of
problems. Traders in Maputo only accept deliveries when the yellow maize price is high

enough (or expected to be high enough) to ensure profits.

This trend of enhanced private maize markets is expected to continue, provided that risks
from war and other sources do not increase in the markets. There have been commercial

white and yellow maize imports during this period, an indication that markets have

142

recovered and that formal traders are increasingly willing to take actions in both white and
yellow maize markets. With the new market structure, yellow maize food aid deliveries
and weather shocks to white maize production provide the main sources of unpredictable

fluctuations.

As was indicated in the univariate analysis of white and yellow maize, there was an abrupt
upward shift in maize prices in July 1994, corresponding to the announcement of donor
commitment to make local purchases and the entrance of ICM on the maize market to
make most of those local purchases. Thus, the modeling of the later period includes an

indicator variable for a one-time mean shift in the prices in July 1994.

6.5.2 VAR order selection

Testing to determine the appropriate number of lag lengths resulted in differing
recommendations from each test used. Table 6.4 indicates the results of testing for lags in
the three-variable VAR with the mean shift and trend indicator variables. The likelihood
ratio tests indicated four lags might be adequate, although increasing to seven lags might
contribute significantly. The Schwartz Criterion and the Akaike Information Criterion

indicated one lag and eight lags, respectively. The modeling was conducted with four

lags.

143

Table 6.4 Testing on lag lengths for post—war/drought period with mean shift and trend

 

 

 

variables
1251.
Number Of L383 Akaike Schwartz Likelihood ratio test
Information Criterion
Criterion X2 p-value
1 23.5 24.0 237.6 0.00
2 23.4 24.3 23.2 0.01
3 23.3 24.6 17.1 0.05
4 23.1 24.7 19.7 0.02
5 22.7 24.7 13.2 0.16
6 23.5 25.0 3.0 0.96
7 22.9 24.8 21.6 0.01
8 22.5 24.8 13.2 0.16

 

 

144

6.5.3 Exogeneity testing

As white maize production in the region increases with improved rainfall, the extreme
scarcities no longer drive the market and thus white maize may not be the dominant
market force that it was earlier. Exogeneity testing of the three series in the later period,
with the mean shift and trend parameters added, indicates that white maize prices are not

exogenous to yellow maize prices and food aid deliveries in this post-war/drought period.

Market conditions from April 1993 to July 1994 included substantial yellow maize stocks
in the hands of consignees and deteriorating quality of yellow maize, such that the
nominal prices approached the consignee price paid to the government. Additional
exogeneity testing was conducted to evaluate whether or not the yellow maize prices help
to predict the food aid deliveries and white maize prices. As Table 6.5 indicates, in both
the first part of the post-war/drought period and the firll post-war/drought period,
exogeneity testing found that the predictions of food aid deliveries were not significantly
improved by including the yellow and white maize price series although food aid deliveries

do significantly improve white and yellow maize price predictions.

6.5.4 Recursive ordering: FA. YR WP

The exogeneity testing for this period suggests that white maize prices may no longer be
driving the markets. The recursive structure used in the war/drought period, with white
maize prices, food aid, and then yellow maize prices, was based upon the extreme scarcity
of white maize and its critical importance in the maize markets as a whole. With increased

supplies of white maize and decreasing food aid supplies, markets for white maize

145

Table 6.5 Exogeneity testing in the post-war/drought period

 

 

 

Full Early part of
post-war/drought post-war/drought
Null hypothesis period period
x2 p- result x2 p- result
value value
WP, YP do not help to predict 6.31 0.61 fail to 7.40 0.49 fail to
FA ' reject reject
FA does not help to predict 19.90 0.01 reject 28.50 0.00 reject
WP, YP
FA, WP do not help to predict 15.35 0.05 reject 5.50 0.70 fail to
YP ' reject
YP does not help to predict 21.26 0.01 reject 6.12 0.63 fail to
FA, WP reject
Y? and FA do not help to 26.17 0.00 reject 30.27 0.00 reject
predict WP
WP does not help to predict 9.90 0.27 fail to 4.74 0.78 fail to
FA, YP reject reject

 

 

 

1 .00
0.75
0.50
0.25
0.“
-0.25
41.50

1 .00
0.75
0.50
0.25
0.00
41.25
41.50

1.00
0.75
050
025
0.00

025

0.50

147

 

   

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

R bMMFoodm 70°44‘41”) ——
4 m M) Volcanic. -----
WhlePrla - - -
I .-.‘.‘
a W-._._._ ___ _ _,_,_,._... _. _._._._._ ...—.11.:
'1 ‘- .............................................................
0 2 4 8 8 7 10 12 14 18 18
J “, Rssponssbflroeksmvslwm FoodAldM) —
’1 Yoke/Price """
‘- mums - - -
J """" 3 ------
I ‘\. " -------
/ \. ..............
1 ~° ...... J \ ...............................................
null—Avb‘ ------------------- - ------ = . ='-l-l-I- . -- I ---
-l
0 2 4 8 V 8 V 10 12 14 18 18
\
' FoodAHM) ——
\ R to mm
1 _\ espouse Shocks Pubs Yoke/Fries ______
‘\ 11:11qu -----
.1 -\
-l \‘x
0 2 4 - 8 8 10 12 14 18 18
Weeks

Figure 6.3 Impulse responses to shocks, based on recursive model, FA,YP, WP, with
trend and mean shift, post-war/drought period

146

products become more stable. Yellow maize traders may react with a lag to changes in
prices in the less volatile market, such that food aid deliveries would not be affected
contemporaneously by shocks from white maize prices. Instead, food aid deliveries may
respond only with a lag to changes in either of the price series. White maize prices
become more sensitive to the shocks fi'om both food aid deliveries and yellow maize
prices, as the substitutability between white and yellow maize becomes key in the relative
prices of the two commodities. Thus a recursive ordering with food aid, yellow maize,

and then white maize will be used here.

The results of this model, including four lags and the mean shift and trend variables, are
presented in the impulse responses of Figure 6.3 and the FEVDs in Table 6.6. In this
post-war/drought period, a food aid shock of one standard deviation is about 1250 metric
tons; standard deviation of the yellow maize shock is 12 meticais/kg; and the standard

deviation of white maize shock is 20 meticais/kg.2

A food aid delivery shock initially has an unexplained positive efl‘ect on white maize
prices. By the sixth period a slight negative effect on white prices is observed and
continues through the remaining periods. The price shocks of white maize have little
effect on own prices by the sixth period, with prices rapidly falling after the initial shock,
indicating market supply and demand responses to the high prices, in addition to the food

aid delivery response. In contrast, the yellow maize prices are slow to return to steady

 

2 This compares with war/drought period one standard deviation values for innovations in food aid,
yellow and white maize prices of 2600 metric tons, 34 meticais/kg, and 65 meticais/kg, respectively.

148

Table 6.6 Forecast error variance decompositions for recursive order F A,YP,WP, post-
war/drought period, 4 lags

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Variance Decomposition (% of total variance due to each shock)
Food aid Yellow maize price White maize price
Step Own YP WP FA Own WP FA YP Own
shock shock shock shock shock shock shock shock shock
1 100.0 0.0 0.0 2.0 98.0 0.0 1.1 3.6 95.3
2 97.9 0.6 1.6 7.6 92.3 0.1 8.5 4.2 87.4
3 96.9 0.5 2.5 9.9 89.1 1.0 8.3 5.1 86.6
4 96.5 0.6 2.9 10.1 87.4 2.5 7.2 17.9 74.9
5 95.5 0.7 3.8 12.1 83.1 4.9 8.3 18.9 72.8
6 94.8 0.7 4.5 15.2 77.4 7.3 8.5 19.7 71.8
7 94.7 0.7 4.6 17.3 73.0 9.7 8.4 20.6 71.0
8 94.6 0.7 4.7 19.2 69.3 11.5 8.5 21.4 70.2
9 94.6 0.8 4.7 21.1 65.7 13.2 9.0 21.5 69.5
10 94.6 0.8 4.7 22.6 62.9 14.6 9.5 21.6 69.0
11 94.6 0.8 4.7 23.6 60.7 15.7 9.9 21.6 68.5
12 94.6 0.8 4.7 24.3 59.0 16.6 10.3 21.6 68.1
13 94.6 0.8 4.7 24.9 57.7 17.4 10.6 21.6 67.8
14 94.6 0.8 4.7 25.4 56.6 18.0 10.8 21.5 67.7
15 94.6 0.8 4.7 25.7 55.8 18.5 10.9 N 21.5 67.6
16 94.6 0.8 4.7 26.0 55.2 18.9 11.0 21.5 67.5
17 94.6 0.8 4.7 26.2 54.6 19.2 11.1 21.5 67.4
18 94.6 0.8 4.7 26.4 54.2 19.4 11.2 21.5 67.3
19 94.6 0.8 4.7 26.5 53.8 19.6 11.3 21.5 67.3
20 94.6 0.8 4.7 26.7 53.5 19.8 11.3 21.5 67.2

 

 

149

state values after any one of the three shocks. A shock in food aid deliveries results in
decreasing yellow maize prices, and those prices stay around 10 meticais/kg below steady
state through twelve periods after the shock. White maize price shocks have a similar

direct effect on yellow maize prices.

The FEVDs for this model are presented in Table 6.6. Yellow maize prices show strong
influence from food aid with 27 percent of forecast error variance attributed to food aid
shocks by the twentieth period. In comparison with the war/drought period, food aid
becomes important in explaining forecast error variance only gradually over time, with
food aid deliveries accounting for only 10 percent of yellow maize price forecast error
variance in the third period, and only reaching twenty percent by the ninth period. As
would be expected in this period, white maize prices respond more directly to food aid
shocks, with 8 percent of forecast error variance explained by food aid by the second
period. Yellow maize prices gradually become more important in explaining forecast error
variance, explaining about 21 percent by the seventh period and staying at that figure. The
food aid series forecast error variance was mainly explained by own shocks, rather than

shocks from other series, even after 20 periods.3

6.5.5 Seasonalig/ issues and results

As domestic production and marketing has recovered and food aid has arrived during the

hungry season, seasonality of the data series may be present, particularly with the food aid

 

3 A simultaneous model equivalent to the war/drought period simultaneous model was also estimated.
The results were qualitatively similar and are presented in Appendix A.

150

deliveries and the white maize prices. Food aid deliveries during the hungry season are
intended to dampen the increases in white maize prices in this period. During the early
part of the marketing season, the price of white maize drops, as is expected of storable
agricultural commodities. Initial inclusion of a market season dummy and a hungry season
dummy showed that the market season dummy did not help the explanatory power in the
three variable VAR model, and so just the hungry season variable was included, along
with the mean shift variable. As would be expected, the coefficients for hungry season in

both the food aid and the white maize price equations were positive and significant.

In Figure 6.4, changes in the impulse responses with the addition of the hungry season
variable are most obvious in the top box with the responses to a food aid shock. White
maize response becomes negative by the second week, gradually returning to the previous
level over the next six periods. The pronounced oscillation in the food aid response to
own shock may reflect the overshooting and undershooting of supplies over time,or may
reflect underlying problems with the distribution of the food aid series, with arrivals mainly

in the hungry season.

6.5.6 Historical simulations with reduced food aid deliveries
There were two commercial food aid shipments to Maputo during the November 1994 -

January 1995 period, the first with 21,480 MT and the second with 26,100 MT.

151

 

1.00

   

WDMMFWMM Fwd/WM) "
Vela/Prue -----

0.75 "
0.50 ‘
0.25 "

 

 

 

 

-0.25 "
'0.” ' o

 

 

 

 

1.00 1‘ _
0.75 ‘ 4 Y“ m .....

 

 

0.00
-0.25 "

 

 

 

“5° ' 0 TT 2 4 s s 10 12 14 '10 18

 

1.00 \
'\_ Recourse 10 Shocks 1001 Mb Price
0.75 -' \

0.50 " '\.

\
025 4 'x.

 

 

 

 

ooo . a ...
' ~ ‘-‘... .M_.-.—-—-— f
...............
-.-
...........

 

 

 

'°-5° '0' *2' ‘4' '0' '0' '10‘ *12' ‘14‘ '10' '11P

Figure 6.4 Impulse responses to shocks, based on recursive model FA, WP, YP, with
hungry season variable, in post-war/drought period

152

Historical simulations were conducted to understand how the white and yellow maize
prices might have been affected had one or both of the major shipments not occurred for
the hungry season that year. Removal of both major shipments was simulated by setting
the actual food aid arrivals to zero and estimating a new path for the food aid shocks,
while maintaining the previously observed shocks in yellow and white maize prices. As
can be seen in Figure 6.5, yellow maize prices would have been higher throughout the
simulation period. The average price in January was 99 meticais/kg; the simulated average
price was 147 meticais/kg, almost 50 percent higher; for February, the average yellow
maize prices were 94 and 149 meticais/kg for actual and simulated, respectively, almost

60 percent higher in the simulation.

In the November - January period, the simulation shows little difference between the
actual and the simulated white maize prices, but by February and March, the actual
average price was 183 meticais/kg whereas the simulated prices rise to 202 meticais/kg,

about 10 percent higher than the observed.

In the belief that there may be seasonality in white maize prices and food aid deliveries, the
simulation was also estimated with the hungry season variable included. The results are
presented in Figure 6.6. The actual white maize price of 183 meticais/kg compares to 208
simulated, 14 percent higher than the actual. For yellow maize, the simulated prices
averaged 160 for February and March, a 76 percent increase over the observed price

average of 91 meticais/kg during the period.

153

 

 

 

 

 

 

 

 

 

 

 

 

400
Simulatedl’riee m
880 _ pmwmpm —— Real White Maize Prices
m -
z
o.
E m ‘
N
200 ‘ ' ”A“
150 -
100 Arr/jusrssouo'rFusurdssouo'rrus
Weeks
400
Sinulstedl’riee ......
35° " 3681118110" Price " Real Yellow Maize Prices
m -
15
1:. 250 -
3 .. -

 

 

 

 

AflimJASONDJFw:hAI*”tIA80NDJFIA

Figure 6.5 Simulation in the post-war/drought period with no food aid from November 1994 - January
1995 (recursive, 4 lags)

154

 

 

 

 

 

 

 

 

 

 

 

 

 

400
Shulatedl’riee m . . .
:50 _ Rwy/mm“ — Real WhrteMstchrlccs
m .1
I
3.
1' ”° ‘
w
2” - 1 104'". ...
150 -
100 . .
2‘10”" ssouorrusurmr Asouorrus
Weeks
400
_ s“ P110: Real Yellow Maize Prices
35° RcleellowPriee —-
m .11

mam
E
l

 

 

 

 

AlimJASONDJFIAIIu‘JASONDJFIA
Weeks

Figure 6.6 Simulation in post-war/drought period with no food aid from November 1994 - January
1995, with hungry season variable

155

Excluding only the January shipment had a diminished impact, as shown in Figure 6.7. ‘
The impact on white maize prices was felt gradually over time, whereas the impact on
yellow maize prices was immediate, but for both series, the effects extended into March
and April. As all of these simulations demonstrate, consumers in Maputo benefited from
the commercial food aid arrivals through the lower maize prices, with both yellow and

white maize prices lowered by the food aid arrivals.

In the post-war/drought period, with greater availability of white maize products and more
active markets, the food aid effects appear to be important in determining prices in the
Maputo market, more so than in the war/drought period when the white maize prices were
driven by scarcity. Thinking of the underlying market structure, shocks fi'om the white
maize markets would decline in importance as supplies become more available and
production recuperates, providing a more regular flow of maize to the urban markets, with
expected seasonal fluctuations. Food aid arrivals remain a source of shocks in the system,
due to difficulties in planning and lack of information available in the markets concerning
arrivals, quality of product, and government price. Thus, significant price effects,
sustained over time, are probable with large food aid arrivals during the current period of

greater market activity and renewed domestic white maize production and trade.

 

‘ The simulation for excluding the January shipment based on the recursive model with the hungry
season variable included was estimated, with qualitatively similar results, and so the plot is not
included here.

156

 

 

 

 

 

 

 

 

 

 

 

 

 

400
SinulstedPrice m . . .
sso _ wamm _. Real White Maize Prices
3” 1—
z
0
B.
1:- ..
CC
200 ‘ .. .../“x,
"’13... "*
150 -
’°° Aufmrssouo'rFusurmrasouo'rrus
Weeks
400
SirnulstedPricc W . .
35° - Real “ka _ Real Yellow Marze Prices
m 4.1
K
«.1. ... J
i .. .

 

 

 

 

AlimJASONDJI‘WJhAlia‘IASONDJFHA

Figure 6.7 Simulation in the post-war/drought period with no food aid in January 1995 (recursive, 4
lags)

157

6.5.7 Alternative identification schemes and orders

In order to judge the robustness of the model for the post-war/drought period, the impulse
responses and forecast error variance decompositions were estimated for alternative
models. The historical simulations were also estimated with alternative identifications.
The results of the same simultaneous identification used in the war/drought period, as well
as the same recursive identification (white maize price, food aid deliveries, yellow maize
price) are reported in Appendix B. In the case of the simultaneous model, the impulse
responses are very similar for white and yellow maize price responses to a food aid
delivery shock. White maize prices respond negatively to food aid arrivals only after six
periods, with the initial response positive, as was the case with the recursive model

without the hungry season variable included.

The results of a recursive model with white maize price first, then food aid deliveries, and
finally yellow maize price, as was used in the war/drought period, show no dramatic
differences fi'om the chosen recursive model in this period, with food aid quantities forst,
followed by yellow maize price and then white maize price. Estimating the recursive
model with seven lags, rather than the selected four lags, gives results that are qualitatively
similar to the four lag model, with the exception of a more pronounced oscillation of food
aid deliveries to own shocks (Figure B.6). Thus, the results of the alternative
specifications indicate the robustness of the realtionships between food aid deliveries,

yellow maize prices, and white maize prices.

158

6.6 Summary of the VAR results

The VARs presented in this chapter are designed to evaluate the effects of ex ante
unpredictable fluctuations in prices and food aid deliveries, allowing unrestricted dynamic
interactions between the variables. This focuses on market uncertainty, a major concern
for traders in these markets and the policy makers who are trying to foment private market
development. Identification of the shocks with meaningful economic interpretation is
critical and involves judgment and knowledge of the markets. The results may over-
estimate the influence of commercial food aid if emergency food aid supplies were also
contributing to the fluctuations, although leakage of emergency supplies into the markets
is thought to be substantially lower in the later period when greater administrative controls

were placed on supplies.

In the war/drought period, the analysis brought out the importance of white maize prices
in the markets. In a country without a futures market and little large-scale, ofilfarm
interseasonal stocking, white maize prices play an important role and may be the key
factor forming expectations in the maize trade sector. The extreme scarcity of white
maize in the markets enhances its role in the formation of expectations for highly volatile
markets. Yellow maize prices do not form the basis for expectations since yellow maize
prices are influenced by quantities that the formal traders withdraw, whereas the white

’ maize prices establish an observable reference price.

During this war/drought period with high volatility, it is possible that much of the

dynamics between food aid deliveries and maize prices is captured in the structure of the

159

lagged relationships. The forecast error variances for each series explained mostly by each
series’ own shocks rather than by shocks fi'om the other series. The standard deviation in
food aid delivery shocks in this war/drought period is twice the volume (2600 metric tons)
of the later period (1250 metric tons), and prices are also more variable. It would be
inaccurate to assume that the food aid arrivals are the cause of the instability of prices
during this period. The VARs demonstrate that much of the variability in white maize
prices is due to own supply and demand shocks, rather than yellow maize demand and

supply effects.

In the post-war/drought period, food aid takes a more important role in determining
fluctuations in the yellow maize prices, which in turn account for a substantial amount of
white maize price variations. Food aid remains, in the short run, exogenous to the other
two series, determined through the policy process. Given the infrequency of the food aid
shipments during this period, the deliveries in port and the factors influencing those
delivery dates take precedence over domestic supply and demand factors in the short run.
Yellow prices sustain the downward effect of food aid delivery shocks, while white maize

prices become lower only after seven periods with the food aid shocks.

Food aid deliveries do affect the white maize prices in both periods, more strongly in the
post-war/drought period. The simulations indicate that both yellow and white maize
prices would have been higher without the maize food aid deliveries in late 1994 and early
1995. The lower average price with food aid deliveries, though, is coupled with ex ante

unpredictable fluctuations fiom those deliveries. The formal traders view this as the worst

160

of both worlds: lower mean prices and higher downside price risk, when risk is interpreted
as unpredictable fluctuations. The consumers clearly benefit in the short run from the
lower average retail prices for both yellow and white maize when there are positive food
aid shocks.’ In the long run, if formal traders withhold investments in the marketing
system and the government is withdrawing fi'om market participation, marketing costs for
domestic maize will be very high, and there will be no yellow maize food aid arrivals to
dampen the prices. Informal traders are currently operating in this environment with
unpredictable risks, but the per unit marketing costs are relatively high with individual
quantities small. World market prices will place a ceiling on the domestic white maize
prices, as supplies in South Afiica and elsewhere become available for import into
Mozambique, so reducing marketing margins may be important in improving
competitiveness of the domestic white maize in the major urban markets, especially

Maputo.

 

5 Recent events in Mozambique, with the possible suspension of Title III yellow maize shipments
that were expected to arrive in early 1996, may cause great hardship for consumers. Traders, both
formal and informal, may have acted upon the expectation of at least some food aid arrivals, not
engaging in intrascasonal storage of white maize. Unexpected withdrawal of all of the yellow maize
supplies in the hungry season may cause extreme price spikes in markets unprepared for the scarcity.
In this case, donors did announce the arrivals with at least 3 months anticipation.

Chapter 7

Market Integration and Transmission of Shocks

7.1 Issues

The previous chapters have shown that food aid delivery shocks in Maputo, the capital
city, have important effects on market prices for white and yellow maize in Maputo. This
chapter takes that analysis one step further by examining the extent to which prices in the
Maputo market have effects on prices in the markets of Chimoio, a small city in the central

production regions (see Figure 7.1).

The analysis uses weekly white grain retail prices from Maputo and Chimoio fi'om April 3,
1993 through November 25, 1995, shown in Figure 7.2. The earlier analysis of food aid
and prices in Maputo only went through April 1995 because detailed food aid data for the
May through December 1995 period were not available at the time the analysis was
undertaken. Market price data are more readily available, and given the possibility of
incorporating an additional marketing season, the data through November 1995 were
selected for this market integration work. Due to structural changes in the markets, as
evidenced by changes in distribution of the data, the analysis is broken into two periods: 1)
war/drought period, April 3, 1993 - August 6, 1994; and 2) post-war/drought, August 13,

1994 - November 25, 1995. The war/drought period in this case begins later and

161

162

     

Dashed litres indicate paved
roads. East-west road is the
Beira Corridor. Shaded areas
are major maize production
regiors.

Figure 7.1 Map of Mozambique with Chimoio, Beira, and Maputo

163

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164

continues through August 6, 1993. While the Peace Accord was signed in October of
1992, the recovery period in different parts of the country has varied. In the case of
Chimoio, visual inspection of the price data confirms that the structural changes in the
market happenned at a later date. Analysis will show the war/drought period as one of
relatively poor market integration, whereas the post-war/drought period demonstrates

improved market integration.

Stationarity testing of the time series with the more recent months indicates that the price
series may have stochastic trends, indicating that shocks could have permanent efl'ects.

This issue will be addressed in detail later in the chapter.

Market integration issues have been developed since the mid-19705 using a variety of
econometric methods. An integrated market is defined as a “market in which prices of
difi’erentiated products do not behave independently” (Monke and Petzel, 1984, p.482).
That differentiation can take the form of spatial or temporal differences for identical
products or product differences in the same market location. The VAR analysis of white
and yellow maize prices in the previous chapter could be classified as market integration
for the cross-product markets. Clearly the white and yellow maize markets within the
Xipamanine marketplace in Maputo are integrated, at least to some extent. In this
chapter, spatial market integration will be the focus, evaluating how white maize prices in
Maputo and white maize prices in Chimoio are related over time. More specifically, the
reaction of each market to shocks from own demand and supply as well as from the other

market factors, will be evaluated.

165

7.2 Concepts

7.2.1 Law of One Price and spatial equilibrium

The Law of One Price, as stated by F ackler (1994), is an arbitrage condition such that, “in
an efficient market, prices at any two locations will differ at most by the cost of transport
between the two locations and that this difference should be exact if there are any
commodity flows between the two locations” (p. 1). Takayama and Judge (1971)
formalized this condition with the concept of spatial market integration and spatial
equilibrium. Given prices for identical goods in separate market locations between which
there is no trade currently, the margin between prices should be less than the transport

costs, resulting in the no-trade equilibrium.

The transaction cost literature has suggested broadening the margin between the two
prices to include all transaction costs, not just transport costs. When transaction costs are
high, there can be a wide no-trade band in which price differences between the two
markets may be high but the differences do not cover the transaction costs and there is no
trade (Baulch 1994). These market locations may have constant information flows,
leaving open the possibility of actual trade if the margins widen or the costs go down, and
so the markets may be integrated without actual trade. Poor market integration would be
indicated by repeated cases of a margin that would cover or exceed transaction costs,

without any trader taking advantage of the arbitrage opportunities.

166

7 .2.2 Ma_rl_<et integration

Considering market integration as an absolute concept in which markets are classified
either as “integrated” or “not integrated” reduces the value of the concept. The interest
in market integration in the current research is not in whether or not the Chimoio market is
integrated with the Maputo market. We know fi'om field observations that there is some
integration between the two markets, such that a “yes-no” classification would have little
value. Of greater interest is the degree of market integration or the speed and degree to
which price shocks from one market may be transmitted to prices in the other market and
how long those effects may be felt. The important policy question is whether market
integration is improving as investments are made in infrastructure and market institutions,
including the market mics and information systems. This relates to Ravallion’s question:
“how long can an initially localized scarcity be expected to persist?” (1986, p.103). Do
the Chimoio markets respond when there is a scarcity in Maputo, and if so, to what
extent? Has this responsiveness changed over time? Similar questions can be formulated

with regard to yellow maize gluts in Maputo and the response of markets to excess

supply.

As Faminow and Benson (1990) stress, the interpretation of correlation coefficients and
other statistics on prices across markets depends upon market organization. High
correlation between prices may be accounted for by base-point pricing, by oligopoly with
spatially dispersed buyers, by oligopsony with spatially dispersed sellers, by government

administration, or by effective, competitive markets (F aminow and Benson 1990).

167

Knowledge of market structure thus becomes important in interpreting the statistical

relationships found.

Fackler (1993) discusses the issue of direction of causality in price movements, when
researchers attempt to attribute price movements in one market to price changes in
another. “This makes sense in an oligopolistic market structure in which firms are acting
as price setters” but in the competitive framework, prices are endogenously determined
such that the effects of a change in price in one region on prices on other regions depend
upon what caused the price change and how the regions are related. On the cause of the
change, Fackler gives the example of the different effects of lowering transport costs and
of shifiing excess demand. The source of a price change (whether demand, production,
transport, etc.) at one level will afi‘ect the price changes at other levels. Thus, the

conclusions drawn from spatial price analysis may be limited.

7 .2.3 Recent additions to the literature

Barrett (forthcoming) classifies market analyses based upon the data used for analysis, and
the consequent ability to answer key questions. Analyses that use solely prices for
commodities are considered Level I methods, with a limited ability to examine causality.
Much of the early work on market integration and the current work using cointegration
methods is based solely on prices. With the advancement of econometric theory on
nonstationary time series, researchers have used cointegration theory to evaluate
relationships between prices, as in the work of Mendoza and Rosegrant (1994), Goletti

(1993), and Alderman (1993).

168

The second direction of research, Barrett’s Levels 11 and III, is based upon dissatisfaction
with the information that prices alone can give. Level II methods incorporate transport
and other transaction cost information in order to evaluate margins and trade. Finally,
Level III includes price and trade flow data, enabling researchers to use margins as a
reflection of expected trade profitability. Loveridge (1988) included flow information in
his bivariate correlation analyses for market integration in Rwanda. Fackler (1994),
Baulch (1995), and Barrett (forthcoming) include transport or transaction costs along with
prices in determining relative price movements and trade. In particular, Baulch (1994)
develops a parity bounds model, evaluating the price differential between markets and
comparing it to transport and other transaction costs. If the differential is higher than the
transaction costs, there is a lost arbitrage opportunity and the parity bounds are violated.
The parity bounds model allows for periods in which there may be no actual trade because
the differential is insufficient to cover the transaction costs, yet the markets remain

connected in terms of information exchange.

7.2.4 Mozambique research

The VAR approach chosen for this research only requires data on prices and can
investigate the extent to which shocks in one market get transmitted to other markets.
However, the VAR approach cannot address some of the traditional market integration
issues. For example, the flow or direction of trade cannot be determined using only price
data. The supply and demand relationships are left unrestricted over time in the VARs

while the focus is placed upon the ex ante unpredictable fluctuations in prices.

169

- Without restrictions on supply and demand, and using only price data, the VAR
methodology cannot trace back shocks to their specific sources, whether fi'om changes in
transport costs or other transaction costs or from food aid arrivals or weather shocks. For
purposes of the present analysis, the question is confined to studying the degree to which
the ex ante unpredictable price fluctuations are related between products in two key
spatially separated markets. More specifically, we wish to determine if shocks to Maputo

prices influence Chimoio prices, as well as whether Chimoio shocks affect Maputo prices.

Furthermore, if a shock does have some effect, how many periods does it take to be
transmitted from Maputo to Chimoio and in the other direction? The rapid appraisal
found that itinerant informal traders take about two days to leave Maputo, arrive in the
vicinity of Chimoio, and begin buying. It then takes the informal traders at least one to
two weeks in order to purchase suficient quantities of maize and return to the Maputo
markets with the maize. At a minimum then, it should take at least a week for price
shocks from Chimoio to reach Maputo, since traders rely upon personal communication
and observation for their information. It could take much less than that in the Maputo -
Chimoio direction, because traders travel directly from Maputo and begin buying and
communicating the relevant Maputo prices. It is the reliance of the informal traders on
direct observation and personal communication with other trades than may result in lags in

the flow of information.

Another aspect to consider is the price information that farm producers obtain. If there is

no competition between traders for the product, and market information is scarce, farmers

170

may not know about positive price shifts in the urban markets and traders may be able to
capture the difference. This would mean a lack of price movement in the production
areas. With competition between traders or improved market information or both, farmers
may be able to negotiate a better price. When there are price increases in Maputo due to

an increase in demand, farm producers will be able to bargain for a higher price as well.

7.3 Organization of the markets

7.3.1 Maputo, Chimoio. and Beira maize markets

As will be seen, markets in Chimoio and Maputo were chosen for the current analysis,
however a look at the broader market context is needed. Maputo is a consumption
market with strong demand pull for white maize. The population of Maputo is estimated
(at over one million people with only low-potential agricultural land nearby. In the 1994
preference study, almost 95 percent of households said that they rely primarily upon the
market for their maize supplies (MAP/MSU WP#18 1994). As was discussed in Chapter
3, the Maputo markets are active in maize trading, with both white and yellow maize
products available to consumers. The yellow maize commercial food aid arrivals are
greatest in Maputo; 65 to 70 percent of all shipments arrive in Maputo, with the rest
arriving in Beira. Given the large population living in poverty, the political importance of
the capital city, and the reliance of the population on market maize supplies, Maputo will

continue to be the most likely distribution point for commercial food aid.

On the production side of the maize markets, the center region of the country is

recognized as a surplus production area. The map of Mozambique (Figure 7 . 1) shows the

171

small city of Chimoio, located in Manica Province in the center region, near the Zimbabwe
border. Provincial production in the 1993/94 agricultural year for Manica Province was
estimated to be 100,000 metric tons, the largest in the country that year. The Chimoio
region is the nearest major production area to Maputo, but it is still 1,150 kms fi'om

Maputo (at least 18 hours driving time) along paved roads, as shown in Figure 7.1.

The Beira Corridor is the main road transport route fi'om Zimbabwe (on the west) to the
port city of Beira. The road was maintained open throughout the war, but there were
periods when transport had to travel in convoys with a military escort in order to safely
transit, so trade has been limited in the recent past. There is a north-south connecting
highway from the Beira Corridor that ends in Maputo, as noted in Figure 7.1, however
that road was unsafe and unusable during the war due to landmines and attacks. After the
Peace Accords, during late 1992 and 1993, the road was de-mined and repaired, and road

transport began to move again between the Beira Corridor and Maputo.

Although the northern provinces (N ampula, Zambezia, and Cabo Delgado) each produce
larger quantities of maize in most years, the infrastructure linking them to major markets is
weak and transport is costly between the southern consumption zones and the northern
production zones. North-south trade is increasing but infi'astructural investments (roads,
rails, and ports) are necessary for the northern maize to compete in the consumption
markets of the south with imports fi'om South Afiica and elsewhere and with domestic

production from the center.

172

At the east end of the Beira Corridor, Beira is the other large consumption market for
Chimoio maize. Beira is the capital of Sofala province, has major international port
facilities, and has an estimated population of 400,000. It is located only 200 kilometers
from Chimoio, about three hours of travel along the fairly good roads of the Beira
Corridor. According to the household survey of provincial capitals, 67 percent of the
households in Beira have agricultural plots of some kind, however the value of own
consumption was only 3 percent of total estimated household income (Mozambique,
Republic of, CNP/DNE 1992), so reliance on the market is still high to meet maize
consumption needs. Chimoio is the nearest major production region for Beira traders,
because the agriculturally rich regions of Zambezia province are separated from Sofala
province by the Zambezi River, for which only a ferry crossing has been available until

recently.

As noted in Chapter 3, interviews with traders in Beira and Chimoio revealed actual trade
between these two markets, as well as between Maputo and Chimoio. The quantities
traded vary through the seasons, with informal traders still present in the rural areas of
Manica province during the hungry season months. There is no evidence of reversed white
maize trade flows fiom Maputo into Chimoio, although that may change if there are
imports with relatively low prices fiom South Afiica or elsewhere into the ports, and there

may already be such reversalsfi'om Beira.l In the fixture, trade reversals might develop for

 

‘ Yellow maize trade issues will be discussed below.

173

white maize if formal traders begin to take advantage of interseasonal arbitrage

opportunities, buying and storing white maize.

7.3.2 Static measures of price relationships between Maputo, Beira, and Chimoio

As Figure 7.2 shows, prices in the three markets are closely linked. The cross-price
correlations of the nominal series are significant and high for all three possible pairs but
that may be explained, at least partially, by a common inflationary trend so the real series
were evaluated. The bivariate cross-price correlation between Maputo and Chimoio real
prices was still significant, but with a value of0.59, as shown in Table 7.1. The Chimoio

and Beira real white maize prices are highly correlated.

First differences (y, - y,,,) can be thought of as the weekly changes in prices in a given
market, but the correlations only capture contemporaneous cerrelation of those changes.
If there is a lag in the response of one market to changes in the prices in another market,
these simple correlations will not capture it. The cross-price correlation coefficient
between the first differences of the real prices is not significant for Maputo and Chimoio,
nor for Maputo and Beira. Only between Chimoio and Beira is there a significant

correlation, with a value of 0.26.

It is clear that Beira markets influence the prices in Chimoio, in addition to the influence
from Maputo markets. During the rapid appraisal, researchers observed Maputo itinerant
traders competing directly with Beira itinerant traders in these rural markets. Most of the

informal Maputo traders are women who are Changana speakers, while most of the Beira

174

Table 7.1 Cross-price correlation coefficients for nominal and real white
maize grain prices: levels and first differences for April 1993 -
November 1995 period, Maputo, Chimoio, and Beira

 

Market Maputo Chimoio Beira

 

Nominal prices in levels

Maputo 0.67'" 0.92'"
Chimoio 0.67'” 0.97'"
Beira 0.92'" 097’"

Real prices in levels

Maputo 0.59'" 0.52'"
Chimoio 0.59m 0.92m
Beira 0.52'" 092’"

First differences of real prices

Maputo 0.01 -0.13
Chimoio 0.01 0.26'"
Beira -O. 13 0.26'”

 

Note: ... indicates significant at the 0.01 level.

Source: MAP/MSU SIMA database.

175

traders are men speaking Ndau or other languages, forming clear cultural as well as
gender differences between the trading groups. The informal Maputo traders, however,
do not pass through Beira on their way to the Chimoio region and there is no evidence

that the Beira markets are considered an alternative sales point for the Maputo traders.

The Maputo and Chimoio markets were chosen for the analysis in this chapter, focusing
on the arbitrage opportunities of the Maputo traders and Chimoio area producers. This
continues the focus in the earlier chapters, in which food aid effects on Maputo prices are
evaluated. Note that we cannot trace out the specific effects of food aid shocks on the
Chimoio prices for there is limited data available on supply sources to Chimoio.
Commercial food aid arrives in both Beira and Maputo, with each shipment of food aid
partioned such that one-third goes to Beira and the rest to Maputo. Without detailed

Beira arrivals data, food aid specific shocks cannot be identified.

Maintaining the focus on the Maputo-Chimoio link does not exclude the Beira influence,
but leaves it in the underlying dynamics, particularly in the remaining unexplained sources
of shocks in Chimoio after the efi‘ects of the Maputo shocks are identified. If Maputo and
Beira price shocks happen at the same time, the estimated effects of Maputo shocks may
be capturing some of the Beira effects. However, inspection of Figure 7.2 suggests that
own price shocks in Chimoio, rather than Maputo price shocks, may incorporate much of
the influence of Beira. The analysis of the relationship between all three markets (the
production market and its two main consumption markets) would be an additional level of

complexity, a valuable extension of this bivariate analysis.

176

Chimoio is the largest surplus production area most likely to be influenced by Maputo
prices. Maputo is the largest market and receives the greatest quantities of commercial
food aid. Finding no transmission of price effects fi'om Maputo to Chimoio would be a
strong statement that direct producer price disincentives of yellow maize food arrivals
were not felt in the production region, given that yellow maize did not arrive in Chimoio

for much of the period under analysis here.

7.3.3 Yellow maize

Yellow maize was not selected for this integration analysis for various reasons. Yellow
maize products are not regularly available in the markets in Chimoio. Of the 131 weeks of
recorded SIMA data in Chimoio during the April 3, 1993 - November 25, 1995 period,
there was no yellow maize grain available in the Chimoio markets 81 percent of the weeks,
as shown in Figure 7.3. The grain was considered scarce in Chimoio for another 3 percent
of the weeks. This compares to Maputo numbers of 19 and 24 percent of cases for
missing and scarce, respectively, as shown in Figure 7 .4. For Beira, shown in Figure 7 .5,
about 44 percent of the weeks for which information is available, no yellow maize was
found in the market and 22 percent of the time yellow maize grain was scarce in the

market.2

In addition, the yellow maize available in production areas may come fiom several

different sources: 1) commercial food aid sold in Maputo or Beira and transported by

 

2 There was no information available for 20 out of 137 weeks during the period in Beira.

177

3.5

3.0

2.5

  
  

 

0218/95
05/29/93 09/25/93 01/27/94 05/14/94 09/03/94 12/24/94 04/15/95 08/05/95 11/25/95
1]:
Note; 'D'inriatu not avilifle, ’3' indict/ea plauiful in mm.
Source: MAP/LN} EMA m

Figure 7.3 Qualitative assessment of yellow maize grain availability in Chimoio
markets, April 3, 1993 - November 25, 1995

 

 

 

 

 

Availability indicator

 

     

04/03/93 00/31/93 11/20/93 03/19/94 07/09/94 1029/94 - (WIS/95 7 0910/95 0900/95 _
05/29/93 0925/93 01/22/94 05/14/94 09/03/94 12/24/94 04/15/95 08/05/95 11/25/95

Date
Mite; 'O'indicasrnavailble: ”3"inidesplatifill inmdu.
Scum: MAP/MSU SIMAdm.

Figure 7.4 Qualitative assessment of yellow maize grain availability in Maputo
markets, April 3, 1993 - November 25, 1995

178

 

 

 

 

 

 

 

 

Availdility inhaler

 

 

04/03/93 07/31/93 11/20/93 03/19/94 07/09/94 10/3/94 02/18'95 06/10/95 09/30/95
05/3/93 ()9/25/93 01/22/94 05/ W94 09/03/94 1224/94 04/15/95 08/05/95 1105/95
Date
Nae: "U'hldiasmwildilg ”3" irllimesplalifil inmllu
Source MAP/MSU SIMA disc.

Figure 7.5 Qualitative assessment of yellow maize grain availability in Beira markets,
April 3 1993 - November 25, 1995

179

traders to Manica; 2) emergency food aid supplies distributed in the region and sold by
recipients; 3) emergency food aid supplies stolen fi'om distributors; or 4) informal
commercial imports from Zimbabwe or elsewhere of yellow maize, primarily flours. This
is in contrast to Maputo, where most of the yellow maize grain on the market has come
from commercial food aid arrivals, and, in earlier periods, from leakages of emergency

food aid supplies in transit.

7.4 Data considerations

7.4.1 Weekly data
Weekly prices will be used in this analysis. As noted earlier, itinerant traders can easily
make one round trip, and possibly two, in a month fiom Maputo, out to Chimoio to buy
maize, and then back to sell the maize. Therefore, weekly price variations are important
to capture changes in their behavior. As the earlier analysis showed, the food aid arrivals
can influence markets rapidly, and weekly data help to evaluate how that works through

the marketing system.

7.4.2 Retail prices as a proxy for producer prices

As previously noted, producer prices for locally produced white maize are not available
for the period under study. However, retail prices in the markets in producing areas have
been collected on a weekly basis since December 1992. In July 1995, a new producer
price collection system was designed by PSA-MSU researchers taking into account the
different marketing options that may be available to producers. In the center region part

of the country, the region near Manica City (about 50 kilometers west of Chimoio) was

180

 

 

 

 

 

 

 

 

 

 

WWW/AW:

Figure 7.6 White maize grain prices at retail, itinerant trader, and producer market levels
in Manica, July 1, 1995 - February 2, 1996

chosen as the data collection point. As Figure 7.6 shows, retail market prices provide a
good proxy for producer prices based on either the producer marketplace or the itinerant
trader. For the July 1995 - February 1996, retail prices are an average 10 percent higher
than the producer market prices, and for the July - December 1995 period, an average 13
percent higher than the itinerant trader level. The cross-price correlation coefiicients are
0.99, while the correlation coefficients for the first differences are 0.70 between. retail and
informal levels, 0.77 between informal and producer market levels, and 0.87 between
producer market and retail levels, all highly significant. Thus, retail market prices are

reasonable proxies for the prices faced by farmers in these production regions.

181

7.4.3 Grain as the selected commodig

In the current research, the focus will be on white maize grain, the locally produced,
unprocessed product. Since there are no industrial mills based near Chimoio, white maize
destined for other markets leaves in an unprocessed form. The proliferation of small
hammer mills in urban areas and the continued practice of home processing means that
white maize grain can be found readily in the consumption area markets, other than during

drought and economic crisis.

7.4.4 Prices and costs of trade

The analysis of Chapter 6 shows that white maize prices in Maputo are affected by both
food aid supply shocks and yellow maize price shocks fi'om the demand side. In modeling
the relationships between the markets, transport and transaction costs must also be
considered. Ravallion (1986) shows that levels of price data (rather than logs) should be
used if transport data can be considered as a cost per unit rather than an ad valorem fee.
In Mozambique, transport costs are per unit volume or weight (90 kg sack, for example),

not by value of commodity, thus price levels will be used in the analysis.

Following the analysis in Chapter 6, April 3, 1993 was chosen as the starting date for the
analysis. This date marks the beginning of the first harvest in the post-war, post-drought
period. Figure 7.2 shows the two data series over the April 3, 1993 - November 25, 1995
period of analysis, along with the Beira prices. The analysis in Chapter 6 used data
through April 1995. The addition of the May through November 1995 data for the market

integration work allows the analysis to incorporate an additional marketing season.

182

However, the additional months have important consequences for the modeling the entire
series. Both the Maputo and Chimoio price series indicate the possible presence of
stochastic trends in the later part of the period, when markets are thought to be more

integrated. This will be discussed in greater detail below.

7.4.5 Seasonality

Seasonality may be one underlying reason for this finding of nonstationarity. With the
harvest season each year the price declines and then gradually increases over time through
February and possibly March. These data end in November as the price in on the increase,
seemingly to some new level, yet when harvest occurs the prices will once again come
down. As domestic white maize production increases and becomes the dominant maize in
the markets, this typical seasonal pattern for a storable commodity develops with the
prices. As will be seen in the univariate testing, the inclusion of simple seasonal indicator
variables did not result in stationarity. The time series is too short to adequately identify
the seasonal components, so more detailed seasonality modeling will have to await a

longer data series.

7 .4.6 Stochastic trends in the data

The question remains: could there be stochastic trends in real maize prices in Chimoio and
Maputo? What would be the source(s) for such trends? If the economy is now making
adjustments after the war, and with the dismantling of the command-based economic
system, there may be adjustments that compensate for misalignment in prices during those

periods. Nonstationarity would be indicated, as shocks would have long term effects,

183

permanently shifting price levels. Over the long run, these would be captured as single
exogenous shifts rather than stochastic trends. Regardless of the cause of the
nonstationarity results, researchers need to evaluate whether or not traditional hypothesis
tests and parameter estimates based on asymptotic distribution theory are valid in the

presence of such nonstationarity (Sims, et al. 1990).

7.4.7 Structural” break in the prices

The Peace Accord was signed in late 1992, but the roads did not become easily transited
for months, because both demining and rehabilitation of the main routes and feeder roads
was needed. The Chimoio white maize price graph clearly shows a change in mid-1994.
Prior to that period, there is no evidence of significant maize trade between Maputo and
Chimoio. In the Chimoio market, demand was apparently thin while supply was high
relative to demand, so that the price remained basically at a floor price over long periods.
With the marketing season of 1994, the activities of the itinerant traders increased in the
center production zones. ICM began a major purchasing effort in the Chimoio zone in
early August 1994, when firnds become available. The demand for maize in the Chimoio
markets increased, consequently so did the price. Chimoio went from relatively inactive

maize markets to very active markets during this period.

7 .4.8 Margins and trade development

It unclear whether the increase in informal maize trade was due to increased gross margins
between prices, increased transport availability, lowered transport and other transaction

costs, increased information about maize supplies and arbitrage opportunities, or some

184

other factor. Figure 7.7 shows the gross margin (in real meticais/kg) between Maputo and
Chimoio retail real prices for white maize during the period of study.3 The margin for
white maize grain during 1993 was between 100 and 1150 meticais/kg and increased in
early 1994 reaching 200 meticais/kg. The margin fell in April 1994 to about 100
meticais/kg and the margin rose again in August and September 1994. The increase in
informal trade may have been sparked by the increased gross margins in January and
February or August and September of 1994, when white maize prices in Maputo rose.
These periods of increased gross margins also coincide with reduced market supplies of
yellow maize in Maputo. For the informal sector, these periods of high margins may have
motivated the initial search for maize in more distant markets. Once these informal traders
had learned about the markets, continued travel and trade was conducted with lower costs
and known risks. Thus, in the August 1994 to November 1995 period, these traders
became more active in the markets in the center. For that reason, the data are divided

into two periods for analysis, the war/drought period and the post-war/drought period.

As shown in Figure 7 .7, the Beira-Chimoio margin approached zero and then fell below it,
while the Maputo-Chimoio margin narrowed in late 1995 to between zero and 50
meticais/kg. As demand for the white maize increased in Beira and Maputo, prices rose,
but prices also increased in Chimoio. During the post-war/drought period, the Maputo

prices basically hit a ceiling as white maize grain prices approached the prices for rice and

 

3 Rather than gross margin as a percentage of price, the simple gross margin in meticais/kg is a
usefirl indicator of potential arbitrage opportunities because most of the transport and other
transaction costs are based upon weight or volume rather than value. We do not have sufficient
information to estimate average net margins over time.

185

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186

finely processed imported white maize flour, preferred substitutes to the domestic white
grain for many households. Thus, white maize grain prices could not continue to increase
as the households shifted into substitutes. The margins in late 1995 were likely to be
insufficient to motivate trade in either direction (to or fiom Chimoio), and yellow maize

did not reappear until late October 1995.

7.5 Analysis of the war/drought period

7 .5.1 Univariate Analysis of the war/drought period: Maputo and Chimoio prices

The Maputo white maize price data in the April 1993 to August 1994 period are similar in
characteristics to the April 1993 - April 1995 series evaluated in Chapter 6 for the food aid
analysis.‘ The coefficient of variation for white maize prices is 0.15 for this period (Table
7.2). The figure of first differences, Figure 7.3, also portrays the volatility. The partial
autocorrelation plot indicates that only the first lag is significant. There is also a
significant trend in this series. All of the unit root tests provide evidence that Maputo
prices were trend stationary in this period, as can be seen in the Phillips and Perron tests

reported in Table 7.3, and the KPSS test results in Table 7.4.

The mean of the white maize price in Chimoio for this period is only 71 meticais/kg, much
lower than the mean in Maputo of 199 meticais/kg. The plot of the first differences
(Figure 7.3) indicates the lack of volatility of Chimoio price series in the war/drought

period. With a coefficient of variation of 0. 1 3, variability is relatively low. Both the low

 

‘ The August 1994 breaking point was selected based upon a shift in the Chimoio price data, not the
Maputo data.

187

Table 7.2 Descriptive statistics of real price data for war/drought period

 

 

Statistics

April 3, 1993 - August 6, 1994

Maputo Chimoio

Mean (meticias/kg) 198.94 70.96
Standard deviation 29.56 8.95
(meticias/kg)
Coefiicient of variation 0.15 0.13
Median (meticias/kg) 194.64 71.14
Maximum (meticias/kg) 283.76 100.44
Minimum (meticias/kg) 139.19 57.61
Skewness 0.54’" 0.91'"
Kurtosis -0. 1 1" 1 .5"

 

 

Note: “ indicates significant at the 0.05 level.
... indicates significant at the 0.01 level.

Source: MAP/MSU database.

188

 

 

 

 

 

 

:
.00
J» ............................ f ........................... i
01““ 1. . A‘ .-.5 1 J ‘ ‘5
! v v ' v v v ' ' j y " v 1 v‘ V ' v r v v V
13 ............................ . - ..........................
g’ :
-100 i
.3 :
.3 ................................ . .............................. l
” :
2 .200 _ fl _ _ i - 1 _ _ _ _ _
04/ 1003 100203 030604 09/1004 020505 08/ l 205
070303 120503 06/18/94 120304 050005 1 10405
Date

Note: Dashed vertical line indicates Auglst 6, 1994.
Source: MAP/MSU $MA database.

Figure 7.8 First differences in white maize grain prices in Maputo

 

 

200
I
I
<7- ------------------------------ I ------------------------------ 1
I
I
100 _
I
l ............................... I ...............................

 

 

 

 

g ------------------------------- : ------------------------------
I
.. _______________________________ : ______________________________
‘65 :
E -200 _ _ i _ _ - _ _ - - -
04/10/93 1002/93 0306/94 09/10/94 02/25/95 08/12/95
07/03/93 121250.? 06/18/94 12/03/94 05/20/95 1 104/95
Date

Note: Vertical dashed line indicates Auglst 6, 1995.
Source: MAP/MSU SIMA dataset.

Figure 7.9 First differences in white maize grain prices in Chimoio

189

Table 7.3 Phillips Perron test results for the real white maize pn'ces: Maputo and Chimoio

in war/drought period

 

Test statistic Maputo

Z(phi_2) 5.53‘“
Z(phi_3) 8.29‘”
Z(t_ alpha~) 4242'”
Z(phi_l) 5.16“

Z(t alpha“) -2.71'
Z(t alpha“) -O.32

NW lags 4

Chimoio
4.15‘
6.23"
-2.45
5.38"
-3. 19"
-0.35

4

Note: Significance level determined according to A. Banerjee, et al., Co-intcgration,
Error Correction, and the Econometric Analysis of Non-stationary Data, Oxford

University Press, Oxford, England, 1993.

* indicates significance level of 10%
** indicates significance level of 5%
*** indicates significance level of 1%

190

mean and low variability reinforce the perception that, during this period, the Chimoio
market had abundant supply relative to demand, maintaining prices at a floor level. As
with the Maputo series in this period, trend stationarity of the series was supported in the
various tests conducted, as shown in Tables 7 .3 and 7.4. The abundant supply in
Chimoio with low, relatively stable prices is in contrast with the variability in Maputo
prices, suggesting that the markets in this war/drought period were only weakly

integrated, a hypothesis which will be tested below.

7 .5.2 Simple correlation coefficients

 

Table 7 .5 shows the simple cross-price correlation coeficients for the real prices in the
war/drought period. The levels of the real prices are significantly correlated, whereas the
first differences are not. In a static context, then, there is not a strong relationship
between changes in Maputo and changes in Chimoio. It is the dynamic context with lags

that may be more important here.

7.5.3 VAR order selection Mexogeneig testing

Lag length testing of the bivariate VAR system indicates that between three and seven lags
are needed in the models (Table 7.6). Both the likelihood ratio tests and the AIC suggest
7 lags be included. Given the delays in transport between the regions and the potential

bias of omitted variables, the estimations will be conducted with seven lags.

191

Table 7.4 KPSS test results for the real white maize prices: Maputo and Chimoio in
war/drought period, with trend

 

Lags Maputo Chimoio

0 0.37." 0.14.
1 0.23.” 0.01
2 0.18" 0.08
3 0.15” 0.07
4 0.13. 0.06
5 0.12 0.06
6 0.11 0.06
7 0.11 0.06
8 0.1 0.06

 

Note: Significance level determined according to D. Kwiatkowski, et al., “Testing the
null hypothesis of stationarity against the alternative of a unit root,” Journal of
Econometrics 54: 159-178, 1992.

* indicates significance level of 10%
** indicates significance level of 5%
"* indicates significance level of 1%

192

Table 7.5 Cross-price correlation coefficients for real white maize grain
prices: levels and first differences in the war/drought period

 

Market Maputo Chimoio

 

Real prices in levels
Maputo 0.41‘”

Chimoio 041'"

First differences of real prices
Maputo 0.08
Chimoio 0.08

 

Note: ... indicates significant at the 0.01 level.

Source: MAP/MSU SIMA database.

193

Table 7.6 Lag length testing for war/drought period: Maputo and Chimoio prices with
trend variable in VAR

 

 

 

Number of Lags Test
Akaike Schwarz Likelihood Ratio
Information Criterion
Criterion x2 p-value
1 1.54 1.82 225.06 0.00
2 8.85 9.40 5.78 0.21
3 8.39 9.21 6.78 0.15
4 8.34 9.45 6.54 0.16
5 8.29 9.67 1.67 0.80
6 8.03 9.69 7.47 0.11
7 7.94 9.88 8.57 0.07
8 8.93 10.14 1.85 0.76
9 8.90 10.38 2.01 0.73
10 8.79 10.55 6.06 0.19

 

Note: ' indicates significant at the 0.10 level
" indicates significant at the 0.05 level
" indicates significant at the 0.01 level

194

Table 7.7 Exogeneity testing of Maputo and Chimoio prices, with
seven lags and trend in the war/drought period

 

 

Null hypothesis x2 p-value
Maputo helps to predict Chimoio 4.99 0.66
Chimoio helps to predict Maputo 8.89 0.26

 

Using seven lags and a trend in the estimations, exogeneity testing of the this period
supports the characterization of this period as one of weak integration, at least between
Maputo and Chimoio. Neither series “Granger-causes” the other to a significant degree,
as can be seen in Table 7.7.5 Thus in forecasting, knowledge of the lagged price of the

other market would not contribute information beyond what own prices already have.

7.5.4 Identification considerations

\Vrth only two variables in the VAR there are fewer alternative identification schemes.
Should Maputo price shocks be allowed to affect Chimoio prices contemporaneously or
should Chimoio price shocks be allowed to affect Maputo prices contemporaneously?
Given the transportation and communication difficulties during the war/drought period,
lags in responses to shocks from spatially separate markets might be expected. The
recursive identification of Maputo first, then Chimoio, such that Maputo prices unaffected

contemporaneously by Chimoio prices, will be used, however, alternative specifications

 

5 See Chapter 6 for a definition and greater details on Granger-causality.

195

will include the Chimoio-Maputo ordering to assess the sensitivity of the results to the

choice of identification, as reported in Appendix B.

7 .5.5 VAR results for the war/drought period

The VAR results, based on estimates using a trend and seven lags, provide further
evidence that the Chimoio prices remain relatively unaffected by Maputo price fluctuations
during the this period. Figure 7.10 presents the impulse responses for this model in
meticais/kg. The top plot in Figure 7.10 shows the response paths of Maputo prices to
own shocks (solid line) and to shocks fiom Chimoio (dotted line). Maputo prices oscillate
between 0 and 5 meticais/kg in response to a Chimoio price shock, returning to near
steady state values by the fifih week. In the bottom plot, it can be seen that Chimoio
prices fluctuate between 0.5 and 1 meticais/kg in response to a shock from Maputo.6

This effect of Maputo price shocks wears ofi‘ quickly in Chimoio, with a return to near-
previous levels by the fifih period. That Maputo price shocks have little efi‘ect on Chimoio
in this period is not surprising, but the effect of Chimoio price shocks on Maputo is larger

than would be anticipated given the transportation difficulties in this period.

The results were qualitatively similar using the alternative recursive ordering of Chimoio,

then Maputo, as reported in Appendix B.

 

6 The impulse responses in this chapter can be shown in meticais/kg., rather than proportions of
standard deviations as in Chapter 6, because both variables have the same units. The meticais/kg are
in constant December 1989 values. To determine approximate values in January 1996 terms,
multiply by ten.

196

 

2° jfi =
PlotofkespomofM Price Rcswnscpnthtoshockfrom ovnPrice —
W Response path to shock from Chimoio Price """

  

 

 

15‘

10‘

 

 

 

 

3.5

 

‘. PlotofllespomofChhoio Price Response pm to shock from raw m. ——

3.0 - lespoasepathtoshoekhommfriee """

 

 

2.5-1

2.0 "'

1.5"

1.0‘

0.5“

0.0

 

 

 

«0.5

 

 

Figure 7 .10 Impulse responses of each price series to shocks, recursive, seven lags,
war/drought period, (in real meticais/kg)

197

7.6 Analyses for post-war/drought period

7.6.1 Univariate analysis of the postwar/drought period: Maputo and Chimoio prices

 

In Maputo, the white maize prices increased in mean during the post-war/drought period
by 18 percent, going from 199 to 235 meticais/kg (Table 7.8). Similarly, the Chimoio
prices saw a major climb in prices, averaging 163 meticais/kg, 79 percent higher than the
previous mean of 91 meticais/kg. There was little change in the coefficient of variation of
Maputo prices and the plot of first differences (Figure 7.8) reconfirrns the continued
variability. Chimoio prices, however, showed increased variability, with the coefficient of

variation doubling, apparent in an inspection of the plot of first differences (Figure 7.9).

Results for testing stationarity suggest that there may be a stochastic trend in both of these
series. The ADF tests and the PP tests for Maputo and Chimoio prices fail to reject a unit
root, regardless of whether or not a trend and a constant are included, and through
inclusion of eight lags (Table 7.9). The KPSS of stationarity tests are rejected through six
lags for Maputo prices, with and without the inclusion of a trend, and for up to eight lags
for the Chimoio prices (Table 7.10). The Perron models were used, with possible shifts in

trend and in mean in July 1994, and still there was evidence to support nonstationarity.

198

Table 7.8 Descriptive statistics of Maputo and Chimoio prices during post-war/drought
period

August 13, 1994 - November 25,

 

Statistics 1995
Maputo Chimoio
Mean 234.64 163.04
Standard deviation 37.44 51.97
Coeflicient of variation 0.16 0.32
Median 230.22 152.18
Maximum 337.44 287.04
Minimum 173.45 70.01
Skewness 0.62” 0.61”
Kurtosis -0. 12 -0.29

 

Note: “ indicates significant at the 0.05 level.
... indicates significant at the 0.01 level.

Source: MAP/MSU database.

199

Table 7.9 Phillips Perron test results for the real white maize prices: Maputo and Chimoio

 

 

Test statistic Maputo Chimoio
Z(phi_2) 2.64 1 .78
Z(phi_3) 3.25 0.95
Z(t_ alpha~) -1.86 -1 .31
Z(phi_l) 3 .07 1.82
Z(t alpha“) -1.63 -0.67
Z(t alpha") 0.85 1.37
NW lags 4 4

 

(Significance level determined according to Banerjee, et al. 1993)

* indicates significance level of 10%
** indicates significance level of 5%
*** indicates significance level of 1%

Table 7.10 KPSS test results for the real white maize prices: Maputo and Chimoio in

200

post-war/drought period, with and without trend

 

 

We LEM—mag
Lags Without With Without With
trend trend trend trend
0 1.63‘” 1.21‘” 2.87‘” 1.07‘”
1 0.92‘” 0.69‘” 1.51‘” 0.56'"
2 0.67” 0.50‘” 1.05‘” 0.39‘“
3 0.53“ 0.40‘” 0.82‘” 0.3‘“
4 0.45‘ 0.34‘” 0.68'” 0.25‘”
5 0.39‘ 0.29‘” 0.59“ 0.22‘“
6 0.35’ 0.26‘” 0.53“ 0.19“
7 0.31 0.24‘“ 0.48“ 0.18“
8 0.29 0.22‘" 0.45‘ 0.16”

 

 

Note: Significance level determined according to D. Kwiatkowski, P.C.B. Phillips, P.
Schmidt, and Y. Shin, “Testing the null hypothesis of stationarity against the alternative
of a unit root,” Journal of Econometrics 54(1992): 159-178.

* indicates significance level of 10%
** indicates significance level of 5%
*** indicates significance level of 1%

201

Table 7.11 Cross-price correlation coefficients for real white maize grain
prices: levels and first differences in the post-war/drought period

 

Market Maputo Chimoio

 

Real rices in levels
Maputo 0.46m

Chimoio 046’"

First differences of real prices
Maputo -0.03
Chimoio -0.03

 

Note: ” indicates significant at the 0.05 level.
... indicates significant at the 0.01 level.

Source: MAP/MSU SIMA database.

7 .6.2 Static measures of relationship between Maputo and Chimoio

As shown in Table 7.11, the bivariate cross-price correlation between Maputo and
Chimoio real prices was significant, with a value of 0.46, however the cross-price
correlation coefiicient between the first differences of prices was not significant for

Maputo and Chimoio.7

7.6.3 VAR order selection and exogeneity testing in the post-war/drought period

Lag length testing of the bivariate VAR system indicates that a minimum of 3 lags is

needed, with the possibility that seven lags contribute usefirl information (Table 7.12). For

 

7 For comparison, the cross-price correlations coefficients for real prices and first differences of real
prices between Chimoio and Beira are significant, with values of 0.84 and 0.24, respectively, in this

period

Table 7.12 Lag length testing for post-war/drought period: Maputo and Chimoio prices

202

 

 

with trend variable in VAR
m
Number of Lags Akaike Schwarz Likelihood Ratio
Information Criterion
Criterion X2 p-value
l 17.22 17.50 8184.78 0.00
2 14.84 15.40 11.04 0.02
3 10.66 11.50 8.88 0.06
4 10.54 11.66 9.02 0.06
5 10.38 11.78 8.10 0.09
6 10.29 11.97 0.73 0.95
7 10.23 12.19 6.38 0.17
8 11.20 12.44 3.59 0.46
9 11.17 12.69 1.89 0.76
10 11.11 12.91 15.84 0.73

 

203

Table 7.13 Exogeneity testing of Maputo and Chimoio prices,
with 7 lags and trend in the post-war/drought period

 

 

Null hypothesis x2 p-value
Maputo helps to predict Chimoio 19.28 0.01
Chimoio helps to predict Maputo 10.29 0.17

 

the VARs, seven lags will be used, based upon the war/drought period’s estimations and

the desire to avoid bias.

Exogeneity testing results, with the seven lags included as well as a trend, support the
division of the data into periods of weak versus improved integration. Given the large size
of the Maputo market relative to the Chimoio market and the verified existence of direct
trade between the two markets, Maputo prices would be expected to help predict Chimoio
prices. As reported in Table 7.13, testing results accord with that hypothesis. The
Chimoio prices were not useful in helping to predict Maputo prices, as would be expected

given Maputo’s diversified sources of maize supply.

7 .6.4 VARidentification in the post-war/drought period

Once again, identification options in this period are limited, with the bivariate VAR, using
just contemporaneous restrictions. The Maputo - Chimoio ordering was chosen, due to
the relative size of the Chimoio and Maputo markets, but the alternative specification will

also be estimated and reported in Appendix B.

204

7.6.4 VAR results for the pog-war/drought period

The seven lag model of the recursive VAR, with a time trend added, is used in the initial
estimations (Figure 7 .1 1). A one standard deviation shock in the Maputo price series is 15
meticais/kg, slightly lower than the value in the autarky period. In Chimoio, however, the
greater sample variance in this period translates to greater value for the standard
deviations of its innovation than in the previous period, with 12 meticais/kg the value of a
single shock compared to about 4 meticais /kg in the previous period. The estimated
efi‘ect of a Maputo price shock on the Chimoio prices is much greater than in the
war/drought period, with a 15 meticais/kg shock of Maputo prices resulting in a Chimoio
price increase of about 8 meticais/kg, and that shock has long term effects, to be
discussed later. The Chimoio price begins to climb by the second period in response to

the shock in Maputo prices, as would be expected given the lags due to transport.

7.6.5 Long run dmamics

The apparent nonstationarity of the data series leaves open the possibility of a common
stochastic trend between the two prices series in the post-war/drought period. There was
no evidence of nonstationarity in the war/drought period, so we are cancerned only with

the post-war/drought period .

205

 

16

 

 

_ . W
1‘ Plot of Responses of Maputo Pnce Chholo Price ......

 

 

12"

10"

 

 

 

 

 

 

 

 

 

 

 

 

o '2 4' 'o e' to 12 14 '10' 're'
Weeks

Figure 7.1 1 Impulse responses from various shocks, based on recursive model, post-
war/drought period, with seven lags (in real meticias/kg)

206

The work of Sims, Stock, and Watson (1990) shows that the use of a VAR in cases in
which one or more unit roots may be present does not necessitate the use of the two-step
procedures involved in cointegration analysis. They write that “the OLS estimator is
consistent whether or not the VAR contains integrated components, as long as the
innovations in the VAR have enough moments and a zero mean, conditional on past
values of Y,” (1990, pp.113-114). The critical issue is “whether the coefficients and test
statistics of interest have distribution unaffected by the nonstationarity, in which case the
hypotheses can be tested without first transforming to stationary regressors” (Sims, Stock,
and Watson 1990, p.136). Testing does show that the Maputo and Chimoio prices are
first difference stationary, indicating that both are 1(1) series. Difi‘erencing the data and
then estimating the VAR on the stationary first differences would not be appropriate if the

series actually are cointegrated, that is, have a common stationary trend (Hamilton 1994).

Looking at the results, long term effects are shown in all of the impulse responses for this
period. As was discussed earlier regarding the sources of stochastic trends in these series,
the market adjustments of the ERP may cause permanent changes in these series, as they
correct for previous policy distortions and the effects of the war. Over a longer period of
analysis, such changes would be considered as one time shifts in mean or trend or both.
The relatively short period of time included here does not allow us to incorporate or

model these shifts.

207

An additional source of difficulties is the possibility of increasing seasonality in the
Mozambican time series data. As domestic and regional production increases, farmers are
not afi'aid to store on-farm, marketing activities expand, and the potential for profits with
interseasonal storage improve, the usual seasonal trends expected in prices for storable
commodities may be established. As yet however, those seasonal trends may be masked
somewhat by food aid arrivals which come in cycles counter-cyclical to the regional white

maize production and harvest cycles.

7.7 Summary of market integration results and conclusions

The above analysis has demonstrated that the relationships between the Maputo and
Chimoio markets for white maize have changed over time, as the peace became effective
and investments in transport and communications enabled product and information flows.

If food aid arrivals afi'ect the prices in Maputo, Chimoio markets will also feel the efl‘ects.

This analysis cannot look separately at demand and supply shocks, to see if there is a
difi‘erence in response due to the specific source of shock. During recent history in
Mozambique, the significant price changes in the post-war, post-drought period can be
associated with supply side shocks, specifically food aid arrivals and then recovering white
maize production and trade. Thus, the preceding analysis is relevant for the question at
hand, that is the response of prices in maize production areas to prices changes due to
food aid arrivals in the consumption areas. Further research on the Maputo-Chimoio-
Beira trade patterns would be valuable to gain more information concerning price

formation in the production zones.

208

What the analysis shows unambiguously is that maize markets have changed in
Mozambique. During the war/drought period with weak integration, Chimoio prices were
flat, presumably due to high supply relative to demand, so producers faced weak markets
for their maize production. The presence of large stocks of yellow maize in Maputo may
have contributed to the lack of white maize market activity in the production regions,
because the margins between Maputo white maize prices and Chimoio White maize prices
were too narrow to spark trader action. Transport constraints and high transportation

costs exacerbated the inactivity in these markets.

By 1994, transport became more available, larger quantities of maize were produced in the
center region in hopes of a better marketing season, and yellow maize food aid diminished
in the markets. With these developments, more informal traders began to arrive in
Chimoio fi'om Maputo and Beira, and the markets experienced greater integration. In that
environment of integrated markets, a shock in the Maputo markets is found to have a
significant efi‘ect over time in the Chimoio markets. In this analysis, a 15 meticais/kg
positive shock in the Maputo market price was transmitted to the producing region,
resulting in an 8 meticais/kg upward shifi in prices that was maintained over time. It took
only two weeks for the Maputo shocks to begin to affect the Chimoio prices, about the
amount of time it takes informal traders to arrive in the Chimoio markets and buy enough
maize to push prices up. In the other direction, Chimoio price shocks are also transmitted
to Maputo, resulting in Maputo price jumps of up to 6 meticais/kg within four weeks of an

11 meticais/kg price shock in Chimoio prices.

209

That both markets respond fairly rapidly to shocks in the other market indicates that the
scarcities in one market will generate a response in the other market. When scarcities hit
the market in Maputo, Chimoio producers will face higher prices, increasing their price
incentives to produce maize and to sell from on-farm stocks. This is the result that is

I predicted with market integration, that producers will see higher prices as the demand for
their product increases, While consumers will face lower prices than they otherwise would
as the supplies available increase. With lowered transportation and transaction costs,
trade is facilitated, as the margin can be narrowed between producer prices and consumer

prices.

But this also means that a localized glut of yellow maize in Maputo that shocks white
maize prices downward in Maputo will also have a negative effect on Chimoio prices for
white maize in less than one month. If the shock from yellow maize is large and white
maize prices in Maputo fall enough, white maize prices in Chimoio may reach a floor
price, below which farmers are unwilling to sell. At that point, with low Maputo prices,
the margin left between Maputo and Chimoio White maize prices may be insumcient to

cover transport and transaction costs and there will be no trade between the two markets.

Market integration is a key for economic development in Mozambique. Ifthe markets are
integrated, farm producers have greater options for selling their products in a competitive
environment. Consumers demand for goods is communicated to producers through the

prices. Competition keeps the margins between producer prices and consumer prices to a

minimum. Mozambican maize markets are improving and expanding, with more traders,

210

particularly informal traders, active in more regions. While improved market integration
means that food aid shocks in Maputo will result in lower prices in the production regions,
it also means that Maputo markets can signal a maize scarcity with high prices and the

markets will respond, bringing in more maize to lower the price spikes.

Chapter 8

Conclusions and Implications

8.1 Introduction

For some ten years, yellow maize commercial food aid has been a major policy instrument
for meeting food security needs in Mozambique, particularly among the urban poor.

Three major events provided the impetus for large quantities of food aid. First, over a
decade of civil war, ending with the Peace Accords in late 1992, had destroyed many of
the productive assets of the country and left much of the physical infrastructure unusable.
Second, socialist policies put into place at the time of Mozambican Independence in 1975
proved unable to generate strong economic activity, leading to an Economic Rehabilitation
Program (ERP) which was initiated in 1987 . Third, agricultural production and food
availability were limited by a severe drought throughout the Southern Afiica region during
the 1991/92 crop year. The research reported here is designed to determine the effects of
this yellow maize food aid on prices for domestic white maize in different locations, under

these and likely future conditions.

211

212
8.2 Research objectives
This research was directed toward five key research questions. First, what are the key
characteristics of the structure and conduct of the maize markets in Mozambique that
would influence the effects of food aid deliveries and price shocks? Second, how do the
quantities of yellow maize commercial food aid deliveries affect prices for white maize in
the Maputo markets? Third, how do Maputo price shocks for white maize afl‘ect the
prices for white maize in the maize producing area of Chimoio? An important fourth
question is Whether there are differences in the effects of yellow maize food arrivals
between the war/drought period in 1992 and 1993 and the post-war recovery period in
1994 through late 1995. Finally, in thinking about the future, a fifth question arises. What
are the lessons learned from this research about commercial food aid and its effects? That
is, What can this research tell us about the likely efl‘ects of various policy options for
ensuring food security in Mozambique, especially for the low income rural and urban

consumers.

8.3 Research methods

Well-formulated price analysis begins with a solid knowledge of the structure of the
markets, the behavior of the agents involved, and the marketing channels being used.
From that base, time series price analysis can be used to answer questions surrounding the
relationships between commodity prices in spatially distinct markets, or between prices for

different commodities in the same markets.

213

Thus, there were two interrelated methods used in this study: 1) a market structure study
consisting of a rapid appraisal of trader behavior in key white maize markets; and 2)
econometric analyses of yellow and white maize prices and commercial food aid delivery

quantities.

In 1992, the MAP/MSU Food Security project began market surveys on the behavior of
informal markets and traders in Maputo. For the present study, a White maize market
rapid appraisal was designed to complement the previous studies. During July to
September 1994, the author worked with Mozambican analysts to conduct structured
surveys of traders in markets in six provinces of Mozambique, along with informal
interviews of key personnel in the public and private sectors at both local and national
levels. In each site, informal and formal traders were systematically asked questions

concerning their business activities, marketing costs, and pricing behavior.

Dynamic analysis using time series methods is used to examine weekly price and food aid
delivery data that were collected over the April 1990 through November 1995 period.
Initial analysis, including descriptive statistics and stationarity testing, assisted in
determining the characteristics of the data, including the structural break identified in early
1993. Vector autoregressions (VARs) on maize prices and food aid deliveries are then
used to investigate the effects of unpredictable fluctuations in prices and quantities
delivered. VARs are used to examine the price effects of Maputo commercial food aid
deliveries (question 2), and the relationship between white maize price shocks in Maputo

and Chimoio (questions 3). In each case, the data were divided into two periods, based

214

upon difl‘erences in variability which can be traced back to structural changes in the
economy, to determine changes over time (question 4). High frequency (weekly) data
were used to capture the rapid responses of prices, in contrast with previous analyses
using monthly or annual data which can only reflect the net effects of food aid on prices

over time.

Evaluating the efi‘ects of food aid in this way is not merely an exercise to determine
whether or not there were (or are) price and market effects. The research was designed to
help evaluate how the effects may be working through the markets in this developing
market economy and suggest options that may help governments in the firture in using
commercial food aid constructively to support food security and market development. In
addition, the analysis provides a quantitative assessment of the effects, demonstrating the
relative importance of the effects in overall price movements, which is crucial to efl‘ective

policy choice.

8.4 Findings from the market structure study

Rapid appraisal surveys in 1994 found that informal trade was prevalent in white maize in
the center and south regions, including extensive interregional maize trade. Informal
maize trade in the north was limited at the time of the rapid appraisal, but has become
more active over time. In contrast, the formal sector traders took part in white maize
trade on only a limited basis, when an implicit or explicit contract guaranteed immediate
resale of the commodity at known prices. Interseasonal storage was primarily undertaken

at the farm level, with little evidence of large-scale commercial storage of domestic white

215

maize. Private yellow maize trade was primarily in the port cities where the yellow maize
was delivered, and in the nearby deficit areas of the south, with small quantities of yellow

maize appearing in the markets in rural areas under emergency distributions.

The following key market characteristics were found to influence how quantities of food
aid maize afl‘ect the prices of white and yellow maize:

o participation of large numbers of small informal traders and maize millers who
got started in their trade because of yellow maize food aid availability,
increasing market competition;

0 the informal private sector much more active than the formal sector in white
maize markets, buying from rural producers and selling to urban
consumers;

0 lack of marketing infi'astructure significantly increases transaction costs and
limits trade development;

0 government regulations deny legal status to the informal sector, while the formal
sector relies on public sector guarantees (contracts);

0 risks due to price volatility and unpredictable shifts in yellow and white maize
supply and prices;

o policy caught in a conflict between ensuring food security and promoting

domestic production and marketing.

216

8.5 Findings on commercial food aid effects on Maputo maize prices

Weekly retail prices (in constant December 1989 meticais) of white and yellow maize in
Xipamanine and Mucoriama markets in Maputo were combined with weekly food aid
yellow maize delivery data to analyze the effects of food aid deliveries on prices (Figure
8.1). Two periods were evaluated: 1) the war/drought period, April 1990 - February

1993; and 2) the post-war/drought period, April 1993 - April 1995.

8.5.1 Effects during war/drought period

During the war/drought period, there was high volatility in both white and yellow maize
prices caused by extreme domestic and regional scarcity of White maize combined with
limited market development. In this period, the commercial yellow maize food aid
program was designed to dampen the high white maize price spikes which jeopardized
food security for the majority of consumers in Maputo and other urban areas, as well as
provide an alternative, lower cost consumption staple to urban consumers. Impulse
responses fiom the VARs show the responses of white and yellow maize prices to a
typical food aid arrival shock. Based on a positive shock of one standard deviation of
food aid’s own innovation (about 2,600 metric tons in one week), and assuming no further
food aid shocks, prices for yellow maize decline immediately by a value of about 14
meticais/kg, and return gradually to near pre-shock values after eight weeks.1 The impulse
responses for white maize prices to the same food aid delivery shock show an immediate

decline of 12 meticais/kg and return rapidly (within 4 weeks) to the pre-shock price level.

 

' All prices are stated in constant December 1989 meticais throughout this chapter. These prices can be
approximately converted to January 1996 prices by multiplying by a factor of ten. Thus a 14 meticais/kg real price in
constant 1989 values corresponds to about 140 meticais/kg using a base of January 1996.

217

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218

The VAR results were also used to conduct historical simulations of the effects of
removing food aid shipments during this period. Results indicate yellow maize prices may
have been two to three times higher in real terms without the food aid (only commercial
imports would have been available). The simulations suggest that white maize prices
would have been only 10 percent higher because prices were already very high,
approaching the price of rice, a preferred substitute. The food security implications for
Maputo households are clear. During the war/drought period, although food aid had only
a minor impact on White maize prices in Maputo, yellow maize commercial food aid
played a fundamental role in ensuring food supplies at accessible price for poor urban
households at a time when white maize scarcities meant extremely high prices without any

other dampening mechanism.

The minimal effects of food aid on White maize prices during this period appear to be a
result of white maize scarcity and heterogeneous consumption preferences which result in
imperfect substitutability between white and yellow maize products. No matter how high
white maize prices go, there are some consumers who indicated in a MAP/MSU
preference survey that they would not switch to yellow maize, and so some demand is
sustained even at very high white maize prices. For that reason, positive shocks from food
aid do not have strong efl‘ects on White maize prices during the war/drought period.
However, for those consumers willing to substitute yellow maize for white maize, or for
consumers forced to switch to yellow maize to maintain consumption, the commercial

food aid provided an alternative staple.

219

8.5.2 Efl‘egs during the post-war/drought period

With the peace accords and gradual recovery of the markets, an increase in domestic
production has meant that domestic prices have become more sensitive to food aid shocks
during the post-war/drought period. While the direct effect of food aid shocks on white
maize prices is small in absolute amount, as shown in the impulse responses, with a decline
in white prices of 2-3 meticais/kg with a single food aid shock of 1,500 metric tons.
However, comparing the forecast error variance decompositions (FEVD) of this period
with the earlier period, a greater proportion of White maize error variance is explained by
food aid shocks and yellow maize price shocks. In the war/drought period, only 2 percent
of FEVD was attributed to food aid shocks and 11 percent to yellow maize price shocks.
In the post-war/drought period, 11 percent is attributed to food aid shocks and 22 percent

to yellow maize prices.

Yellow maize food aid shocks affect yellow maize prices, with a food aid shock of 1,250
metric tons corresponding to a decline in real yellow maize prices of 5 meticais/kg over
nine weeks. The forecast error variance decompositions for this period indicate that food
aid shocks account for 27 percent of FEVDs for yellow maize prices the 20 periods

evaluated, while white maize price shocks account for 20 percent.

Historical simulations of real white and yellow maize prices were conducted by excluding
the December 1994 - January 1995 commercial yellow maize food aid deliveries of 47,500
metric tons. Comparing the simulated white maize price with the actual price, the results

suggest that white maize prices were held lower during January - March 1994 by the food

220

aid deliveries. Going into the harvest and marketing season of 1995, the observed White
maize prices were ten to fifteen percent lower than what the historical simulations indicate
they would have been without the food aid deliveries. For consumers, this is a benefit, but
for white maize producers and traders, depending upon transmission of prices across

markets, the lower prices may affect incentive for future production and trade.

8.6 Findings on market integration and price transmission

The market integration analysis extended the earlier analysis of Maputo markets to the
Chimoio market, in the central maize production area of Manica Province. Maputo was
included in the study because it is the major consumption market, with high population
and poor agricultural production nearby contributing to a strong demand pull. Large
amounts of commercial food aid enter the Maputo market directly, along with South
African imports when they are available. Chimoio was selected because it is an important
market in the center maize production region, connected by roads to Maputo, and with a

market structure well-known to the researchers.

As in the Maputo analysis, there was an obvious structural break in the data after the
signing of the Peace Accords and the ERP. Based upon the Chimoio prices, the recovery
of the markets was delayed in the rural areas outside Maputo, and so the structural break

for Chimoio was in August 1994. So, for this market integration analysis, the war/drought

221

period extends from April 3, 1993 - August 6, 1994; the post-war/drought period is

August 13, 1994 - November 1995.2

A key finding in the analysis is that yellow maize was not available in Chimoio for long
periods of time and yet the prices for white maize in Chimoio were low with little
variability, indicating high supply relative to demand. Ifthe Chimoio white maize prices
were affected by yellow maize commercial food aid deliveries, it must be through the
reduction in demand for White maize products in Maputo due to supplies of yellow maize
food aid. Given the strong connection between Chimoio and Beira, yellow maize
commercial food aid arrivals in Beira could also depress Chimoio prices, but once again

only through reduced demand for white maize.

8.6.1 Efl‘ects during war/drpught m’pd

During the period from April 3, 1993 through August 6, 1994, identified as the period of
weak market integration, price shocks in Maputo had basically no effect in Chimoio and
vice versa. There are two main explanations: 1) abundant yellow maize supplies in
Maputo depressed white maize prices in Maputo, thus depressing white maize demand in
Chimoio; and 2) transport and other transaction costs remained too high relative to the
gross margin during this period to motivate any active trade. These are not mutually
exclusive explanations for the apparent lack of white maize trade between Maputo and

Chimoio in this period.

 

2 The April 3, 1993 starting date and the November 25, 1995 ending date differ from the earlier analysis due to
data availability. See Chapter 7 for a discussion.

222
8.6.2 Effms drm'ng the post-war/drought period

During the post-war/drought period, identified as the improved integration period, White
maize price shocks were transmitted in both directions. Maputo price shocks (15
meticais/kg) have little effect on Chimoio prices in the first two weeks after a shock, but
by the fourth week, the Chimoio prices increase substantially and continue to climb to

about V2 of the value of the shock, or about 7 meticais/kg higher than before the shock.

Own price shocks in Chimoio may have come fiom demand or supply shifts in Beira or
from more local sources, but they also afi'ect Maputo prices over time. The initial efi‘ects
of Chimoio price shocks are felt in Maputo prices after one week, but the strongest effect
is four weeks after the shock. Overall, the effects of Chimoio prices on Maputo are not
strong, with Maputo prices rising only about 2 meticais/kg for a 12 meticais/kg shock
from Chimoio prices. Since Maputo has other supply sources, this was expected. The
strength of the Maputo price shocks on Chimoio reflects the importance of the Maputo
market for Chimoio maize producers during the later improved integration period.
Finding that there are effects from Maputo price shocks on the Chimoio market prices
(and vice versa) is not surprising; the interesting part is the magnitude of the efi‘ects, how

they work through the markets, and how the effects have changed over time.

In summary, white maize prices in the post-war/drought period are no longer driven
primarily by great scarcity of white maize in urban areas. Instead, White and yellow maize
products compete in the market as substitutes in consumption. The forecast error

decompositions and the impulse response analyses show increased responsiveness between

223

the prices. Preference surveys bring this relationship clearly into focus, as poorer
consumers indicate a willingness to substitute yellow maize for white maize with relatively

moderate price differences.

8.7 Lessons learned to inform future policy options

The government of Mozambique and the donor agencies have been and will continue to
diminish the amount of commercial yellow maize food aid available in the country, as long
as the economy continues to recover from the war and drought, and there no new events
that create extreme shortages of basic consumption staples. In fact, USAID announced in
January 1996 that commercial yellow maize food aid would no longer be brought into
Mozambique. Other donors have started to rely on local purchases of white maize
(40,000 metric tons in 1994), in order to obtain relief supplies for demobilized soldiers and
other targeted vulnerable groups. They continued this approach in 1995 and have
announced additional purchases for 1996. What lessons does the present research provide
regarding the policy options available for meeting food security needs and promoting
domestic production and market development under these evolving conditions of reduced

yellow maize and increased domestic production of white maize?

8.7.1 Effectflf yellow maize; and monefiation

In 1992 and 1993, as the informal sector in Maputo was growing, the arrival of large
quantities of yellow maize commercial food aid spurred informal trading and small-scale
milling sectors of yellow maize by providing a commodity that allowed business activity to

expand when white maize was scarce in Maputo. This food aid provided a low-cost, self-

224

targeting commodity which lowered food prices in urban areas during a period of white
maize scarcity. Unlike other food aid commodities (for example, wheat and rice), yellow
maize is a less-preferred substitute for locally produced domestic white maize. Choosing
yellow maize rather than wheat as the main commodity avoided potential fiiture problems
with demand for an expensive, imported commodity. This experience also shows that
Mozambique has increased food security options in the long run, for world white maize
markets are thin and prices are relatively unstable compared to the world yellow maize

prices.

Eliminating commercial yellow maize food aid does three things. Removal of yellow
maize commercial food aid disengages the policy dialogue that has developed between
USAID and the Government of Mozambique. Second, unless commercial traders begin to
import yellow maize from South Afiica or the world markets, depending upon exchange
rates and prices, urban consumers will no longer have a less-expensive consumption
alternative to domestic White maize. As the price graphs show, there is a persistent 10 -
25 percent margin between white and yellow maize prices when both yellow and white
maize are relatively abundant. Third, in the post-war/drought period, white maize price
increases are dampened by the food aid deliveries, such that removal of the deliveries
means higher white maize prices. Estimation of precise welfare effects requires new
Maputo consumption studies, but for low income consumers, the yellow maize food aid

can be an important contribution to food security.

225

Should another severe drought strike the region and large quantities of yellow maize food
aid be needed again, there are several additional lessons that have been learned. Yellow
maize food aid shocks will not have much effect on prices for the domestic white maize
commodities in times of extreme scarcity, as happened in Mozambique in 1992. Yet the
price of yellow maize will be lower than that of white, and lower income consumers will
take advantage of this price difi‘erential. Of course, some consumers will not substitute
out of white maize to yellow, and so the price of domestic white maize may rise as high as
the next preferred good (rice or finely milled imported white maize flour). During these
periods, the arrivals of the inferior yellow maize did relatively little to lower white maize
prices, but consumers benefitted from access to the alternative, cheaper commodity,

yellow maize.

However, at higher levels of domestic production and under improved market integration,
care should be taken to avoid large unpredictable quantities of food aid, for such shocks
do act to lower prices for the domestic White maize. Both producers and traders will be
affected by the lower prices. In order to avoid such food aid shocks, the government and
donors need to plan carefully the quantities that are delivered to consignees during any
given week. Since shipments tend to be large (10,000 to 15,000 metric tons), it may be
necessary for the GOM to make an efl‘ort to constrain the quantities available to the
market, limiting weekly deliveries to consignees, and possibly storing maize for later
release. If large quantities do arrive, the consignee prices charged may form a floor price
and can be used to hold maize prices up, as long as emergency program leakages are kept

down. Thus, distribution policies can be designed to avoid the large quantities arriving on

226

the market in any given week or over an entire hungry season. This should help to reduce

potential negative price effects.

When food aid yellow maize makes up 25 - 50 percent of total marketed supplies, as it
did in 1994 and 1995, that yellow maize will have significant effects on consumer prices.
For planning the overall quantities of food aid to arrive and the timing of those arrivals,
policy makers need greater knowledge of the domestic production, marketed surplus, and

consumption demand in both urban and rural areas.

8.7.2 Effects of market integgation

The general lack of yellow maize in the Chimoio markets indicates that the food aid
commodity need not be present in production areas to have an effect upon the markets in
those regions. By lowering the prices in the consumption regions, the food aid commodity
competes with the domestic commodity, lowering its price and jeopardizing production
and trade incentives. Yet, this only happens when the markets are more strongly

integrated.

One clear result of the rapid appraisal and the market integration analysis is that the
markets in Mozambique are increasingly connected. In 1993, prices in Chimoio were
relatively unaffected by Maputo price shocks. By 1995, Maputo price shocks rapidly and
significantly affected prices in Chimoio. Government and donor participation in markets
will have more than localized effects in the presence of such integrated markets. Further

research will help to evaluate the extent of market integration between other regions of the

227

country. It is logical to expect that with new investments in major roads connecting the

north and south of Mozambique, markets will continue to become more integrated.

 

8.7.3 Effects of domestic purchases to agguire emergency food aid supplies

Given continued emergency food aid needs for targeted groups, and the increase in
domestic production of white maize, some donors began in 1994 to purchase locally
produced white maize to supply their emergency distribution programs. As was shown
above, the rapid price increases Maputo and Chimoio in August 19943 for white maize can
be attributed to these purchases, as White maize was bought by ICM and others for
delivery to WFP and other donors. The market integration work shows that price shocks
in Chimoio do affect the Maputo prices, and vice versa. White maize local purchases
represent a drain on available marketed quantities, as the supplies are targeted to those
people without effective demand. Essentially, every 10,000 metric tons purchased locally
is 10,000 metric tons less available on the market, logically resulting in higher prices than

would occur otherwise.

Whether local purchases drive up consumer prices depends upon the quantity, the
location, and the procedures used for making these purchases. During the rapid appraisal,
white maize prices went up for consumers in Maputo because formal traders began buying
maize for ICM in the main producing areas. These are the same regions (near Chimoio,

for example) in which informal traders were buying for Maputo consumers, so the local

 

’ The price increase in Maputo was earlier, in July at the time of World Food Programme and ICM’s
announcement of the intention to purchase. In Chimoio, it began in August with the actual initiation of purchases.

228

purchases meant increased competition for white maize. If local purchases were
conducted in zones in which traders were absent, for example, in some of the northern
zones of Mozambique, white maize quantities purchased might otherwise have remained
unsold in farmers’ storage sheds. The local purchases may provide additional production
incentives in otherwise economically depressed areas. Thus, local purchases may assist in
longer term development efforts if they are focused in areas that have future trading

potential.

This increased trade, however, may be only transitory, as long as local purchases are
made, if traders are absent because the transportation and transaction costs are too high to
justify the activity. The local purchases may provide incentives for maize production in
zones that are not financially viable for firture trading without infi'astructure investments.
In this way, local purchases are similar to pan-territorial pricing mechanisms in which the
more distant production zones receive prices that do not reflect market supply and

demand conditions.

The rapid appraisal and market integration work indicate that informal traders in particular
respond to changing environments, and prices can become responsive between markets as
infrastructure improves. Yet, considerable and careful analysis is needed to determine
Whether and how much local purchases will influence white maize prices for all of the
consumers dependent upon the market for their supplies of white maize. More research is
needed to understand the effects on rural households and their maize sales and purchases,

for many rural households are net buyers of maize.

229

Special care is needed to study the potential positive and negative effects of local pur-
chases in 1996. If indeed donors do not bring in quantities of commercial yellow maize
food aid, lower income consumers in Maputo and other urban areas in southern Moz-
ambique will not have the option to purchase yellow maize. Domestic white maize prices
will be at least 10-15 percent higher than they would be when commercial yellow maize
food aid is present in the market. Ifthe local purchases are a relatively large compared to
total marketed surplus available (as was the casein 1994), then market prices could again
be quite high in the hungry season months of 1996/97, as indeed happened in 1995/96

(Figure 8.1).

One source of optimism with the elimination of the commercial yellow maize food aid
shipments is that there will no longer be the unpredictable downward pressures on white
maize prices that came fiom delivery shocks. White maize prices are already beginning to
have the seasonal price movements of falling prices at harvest and gradual increases

through the hungry season which will help to generate interseasonal storage and trade.

However, this also means that increases in white maize prices will no longer be dampened
by the commercial food aid yellow maize supplies and consumers in large urban areas may
face substantially higher prices in the hungry season. Prices for white maize may rise until
they reach an equilibrium based on higher-priced, substitute commodities, such as rice and
imported finely processed white maize flour, at least until marketed supplies of white

maize are suflicient to meet demand with lower prices.

230

Therefore, in fixture years, in order to firlfill its food security objectives, the government
may need to encourage some import of yellow maize from South Afiica or from the world
markets when white maize prices are high. Formal traders do not have experience with
private imports of yellow maize. Special import licenses or exemptions fi'om duties may
encourage the private sector to experiment with imported yellow maize, thus making the
lower priced yellow maize available as a consumption substitute for the poor. In addition,
the yellow maize imports would continue to stimulate the small scale milling sector, as the
food aid supplies did on the past. Yellow maize is more fi'equently milled than yellow

maize in urban areas.

Mozambique is an economy in transition and growth. When the drought hit the Southern
Africa region, Mozambique was already having great economic problems. The yellow
maize food aid did not just temporarily affect the opportunities of existing private traders
and their marketing channels. Instead, Mozambican consumers, farm producers, and pri-
vate traders were learning and adapting in a new economic environment. Yellow maize
food aid brought new opportunities, supporting the private sector commercial activities.
In the long run, consumer, producer, and trader interests will be served by encouraging
continued competition in private markets. As this research shows, there may still be an
important role for commercial yellow maize imports by the public or private sector, as
well as for additional domestic purchases of emergency food aid supplies. But, these
efl‘orts must be managed with care to avoid the negative price efl‘ects on all market par-
ticipants. Combining knowledge of the markets with better production and consumption

information will help policy makers make informed decisions for food security policy.

APPENDIX A

Data for analysis of food aid quantity effects on maize prices in Maputo

APPENDIX A

 

 

YEAR MONTH DAY Nominal Nominal Food Aid Real Real CPI
White Price Yellow Deliveries White Yellow (Dec 1989
Price Price Price = 100)

90 4 7 400.00 357.14 0.00 288.60 257.68 138.60
90 4 14 375.00 285.71 0.00 272.93 207.94 137.40
90 4 21 384.62 266.67 0.00 282.39 195.79 136.20
90 4 28 375.00 266.67 0.00 277.78 197.53 135.00
90 5 5 454.55 312.50 0.00 339.72 233.56 133.80
90 5 12 304.88 273.97 0.00 229.06 205.84 133.10
90 5 19 410.96 273.97 0.00 310.39 206.93 132.40
90 5 26 466.67 334.35 0.00 354.34 253.88 131.70
90 6 2 437.50 394.74 0.00 333.97 301.33 131.00
90 6 9 466.67 266.67 0.00 357.71 204.40 130.46
90 6 16 533.33 416.67 0.00 410.51 320.71 129.92
90 6 23 533.33 400.00 0.00 412.22 309.17 129.38
90 6 30 547.95 400.00 0.00 425.29 310.46 128.84
90 7 7 533.33 479.45 0.00 415.69 373.70 128.30
90 7 14 500.00 479.45 0.00 390.55 374.50 128.03
90 7 21 400.00 466.67 0.00 313.11 365.30 127.75
90 7 28 500.00 400.00 7590.00 392.23 313.79 127.48
90 8 4 466.67 400.00 7410.00 366.88 314.47 127.20
90 8 1 1 600.00 384.62 0.00 468.38 300.25 128.10
90 8 18 473.68 333.33 0.00 367.20 258.40 129.00
90 8 25 562.50 357.14 0.00 433.03 274.94 129.90
90 9 1 597.00 400.00 0.00 456.42 305.81 130.80
90 9 8 466.67 400.00 0.00 354.56 303.91 131.62
90 9 15 466.67 400.00 2530.00 352.36 302.02 132.44
90 9 22 562.50 357.14 7620.00 422.11 268.00 133.26
90 9 29 547.92 41 1.90 0.00 408.65 . 307.21 134.08
90 10 6 533.33 466.67 850.00 395.35 345.94 134.90
90 10 13 571.43 466.67 0.00 419.94 342.95 136.08
90 10 20 615.38 466.67 1265.00 448.37 , 340.01 137.25
90 10 27 533.33 333.33 8855.00 385.29 240.80 138.43
90 1 1 3 571.43 466.67 6985.00 409.33 334.29 139.60
90 11 10 533.33 428.57 1895.00 376.98 302.93 141.48
90 ll 17 571.43 428.57 0.00 398.62 298.97 143.35
90 11 24 875.00 466.67 0.00 602.51 321.34 145.23

231

232

 

 

YEAR MONTH DAY Nominal Nominal Food Aid Real Real CPI
White Price Yellow Deliveries White Yellow (Dec 1989
Price Price Price = 100)

90 12 1 875.00 562.50 0.00 594.83 382.39 147.10
90 12 8 775.34 487.13 5000.00 523.38 328.83 148.14
90 12 15 675.68 411.76 0.00 452.93 276.02 149.18
90 12 22 642.86 428.57 0.00 427.94 285.30 150.22
90 12 29 615.38 500.00 0.00 406.84 330.56 151.26
91 l 5 533.33 400.00 0.00 350.19 262.64 152.30
91 1 12 533.33 400.00 3795.00 346.49 259.87 153.93
91 1 19 533.33 428.57 1646.00 342.87 275.52 155.55
91 1 26 642.86 400.00 8855.00 409.01 254.49 157.18
91 2 2 750.00 342.47 3630.00 472.29 215.66 158.80
91 2 9 666.67 400.00 0.00 414.59 248.76 160.80
91 2 16 625.00 428.57 0.00 383.91 263.25 162.80
91 2 23 666.67 400.00 0.00 404.53 242.72 164.80
91 3 2 857.14 400.00 0.00 513.87 239.81 166.80
91 3 9 866.67 538.46 0.00 513.49 319.03 168.78
91 3 16 666.67 466.67 1265.00 390.41 273.29 170.76
91 3 23 687.50 466.67 8855.00 398.00 270.16 172.74
91 3 30 571.43 583.33 2880.00 327.05 333.87 174.72
91 4 6 533.33 466.67 0.00 301.83 264.10 176.70
91 4 13 500.00 400.00 0.00 283.73 226.98 176.23
91 4 20 500.00 400.00 3000.00 284.50 227.60 175.75
91 4 27 466.67 400.00 0.00 266.25 228.21 175.28
91 5 4 533.33 400.00 2530.00 305.1 1 228.83 174.80
91 5 l 1 466.67 384.62 6470.00 263.69 217.33 176.98
91 5 18 500.00 277.78 0.00 279.10 155.05 179.15
91 5 25 533.33 333.33 0.00 294.13 183.83 181.33
91 6 1 600.00 300.00 7000.00 326.98 163.49 183.50
91 6 8 514.29 333.33 0.00 284.10 184.14 181.02
91 6 15 428.57 333.33 0.00 240.04 186.70 178.54
91 6 22 500.00 333.33 0.00 283.99 189.33 176.06
91 6 29 714.29 333.33 0.00 411.50 192.03 173.58
91 7 6 625.00 312.50 0.00 365.28 182.64 171.10
91 7 13 562.50 333.33 0.00 324.82 192.48 173.18
91 7 20 428.57 333.33 6325.00 244.55 190.20 175.25
91 7 27 666.67 333.33 3675.00 375.96 187.98 177.33
91 8 3 467.00 400.00 0.00 260.31 222.97 179.40
91 8 10 353.00 400.00 0.00 195.59 221.63 180.48
91 8 17 438.00 333.00 0.00 241.24 183.41 181.56
91 8 24 600.00 400.00 0.00 328.52 219.01 182.64
91 8 31 500.00 333.00 0.00 272.15 181.25 183.72

233

 

 

YEAR MONTH DAY Nominal Nominal Food Aid Real Real CPI
White Price Yellow Deliveries White Yellow (Dec 1989

Price Price Price = IDOL
91 9 7 533.00 400.00 0.00 288.42 216.45 184.80
91 9 14 610.00 400.00 0.00 327.25 214.59 186.40
91 9 21 500.00 400.00 0.00 265.96 212.77 188.00
91 9 28 600.00 533.00 0.00 316.46 281.12 189.60
91 10 5 737.50 633.00 0.00 385.72 331.07 191.20
91 10 12 800.00 500.00 0.00 416.83 260.52 191.93
91 10 19 775.00 516.50 0.00 402.28 268.10 192.65
91 10 26 563.00 533.00 0.00 291.14 275.63 193.38
91 11 2 1000.00 667.00 0.00 515.20 343.64 194.10
91 l l 9 928.50 690.50 1265.00 476.01 353.99 195.06
91 1 l 16 1143.00 467.00 21660.00 583.10 238.24 196.02
91 ll 23 750.00 400.00 10960.00 380.75 203.07 196.98
91 11 30 708.50 366.50 10060.00 357.94 185.16 197.94
91 12 7 1067.00 333.00 12305.00 536.45 167.42 198.90
91 12 14 1000.00 357.00 1990.00 492.31 175.75 203.13
91 12 21 714.00 357.00 0.00 344.35 172.17 207.35
91 12 28 667.00 333.00 0.00 315.25 157.39 211.58
92 1 4 714.00 333.00 0.00 330.86 154.31 215.80
92 l l 1 563.00 313.00 0.00 255.76 142. l 9 220.13
92 1 18 563.00 313.00 0.00 250.84 139.45 224.45
92 1 25 500.00 333.00 0.00 218.56 145.56 228.78
92 2 1 588.00 313.00 0.00 252.25 134.28 233.10
92 2 8 635.00 429.00 0.00 271.07 183.13 234.26
92 2 15 643.00 429.00 0.00 273.13 182.23 235.42
92 2 22 667.00 333.00 0.00 281.93 140.76 236.58
92 2 29 556.00 471.00 0.00 233.87 198.12 237.74
92 3 7 643.00 533.00 0.00 269.15 223.11 238.90
92 3 14 529.00 412.00 0.00 218.03 169.81 242.63
92 3 21 471.00 353.00 0.00 191.19 143.29 246.35
92 3 28 625.00 660.00 0.00 249.93 263.92 250.08
92 4 4 750.00 556.00 0.00 295.51 219.07 253.80
92 4 1 1 750.00 586.00 0.00 293.97 229.69 255.13
92 4 18 722.00 706.00 0.00 281.54 275.30 256.45
92 4 25 1000.00 1067.00 0.00 387.94 413.93 257.78
92 5 2 1059.00 1059.00 0.00 408.72 408.72 259.10
92 5 9 1063.00 832.50 440.00 410.46 321.45 258.98
92 5 16 1067.00 606.00 8554.25 412.19 234.10 258.86
92 5 23 923.00 571.00 9723.90 356.73 220.68 258.74
92 5 30 1000.00 571.00 10057.65 386.67 220.79 258.62
92 6 6 1000.00 533.00 2709.50 386.85 206.19 258.50

234

 

 

YEAR MONTH DAY Nominal Nominal Food Aid Real Real CPI
White Price Yellow Deliveries White Yellow (Dec 1989
Price Price Price = 100)
92 6 13 688.00 438.00 133.45 265.38 168.95 259.25
92 6 20 1067.00 643.00 0.00 410.38 247.31 260.00
92 6 27 800.00 571.00 0.00 306.81 218.98 260.75
92 7 4 941.00 500.00 0.00 359.85 ‘ 191.20 261.50
92 7 11 1067.00 667.00 0.00 ' 412.17 257.65 258.88
92 7 18 889.00 667.00 0.00 346.93 260.29 256.25
92 7 25 1250.00 800.00 0.00 492.85 315.43 253.63
92 8 1 1059.00 625.00 3947.37 421.91 249.00 251.00
92 8 8 1000.00 643.00 5526.32 397.58 255.65 251.52
92 8 15 1500.00 533.33 8308.57 595.14 211.61 252.04
92 8 22 1 176.47 533.33 4378.25 465.82 211.17 252.56
92 8 29 1375.00 533.33 13701.75 543.31 210.74 253.08
92 9 5 1333.33 533.33 9381.90 525.76 210.30 253.60
92 9 12 1263.16 466.67 10859.60 490.36 181.16 257.60
92 9 19 1111.11 400.00 4866.30 424.74 152.91 261.60
92 9 26 1250.00 466.67 2778.15 470.63 175.70 265.60
92 10 3 1111.11 466.67 1317.55 412.13 173.10 269.60
92 10 10 1333.33 400.00 2663.65 486.69 146.01 273.96
92 10 17 1833.33 714.29 5519.90 658.71 256.64 278.32
92 10 24 1466.67 400.00 4424.70 518.84 141.50 282.68
92 10 31 1195.22 531.21 3648.45 416.39 185.06 287.04
92 11 7 1333.33 463.58 1423.10 457.56 159.09 291.40
92 11 14 1744.19 392.16 7402.40 590.50 132.77 295.38
92 1 1 21 1428.57 714.29 4107.40 477.22 238.61 299.35
92 11 28 1357.14 937.50 7164.40 447.42 309.07 303.33
92 12 5 1250.00 500.00 6155.80 406.77 162.71 307.30
92 12 12 1529.41 400.00 2489.05 490.59 128.31 31 1.75
92 12 19 750.00 400.00 1071.15 237.19 126.50 316.20
92 12 26 141 1.76 533.33 1568.30 440.28 166.33 320.65
93 1 2 1699.01 534.76 2803.00 522.61 164.49 325.10
93 1 9 1986.25 534.76 10340.51 609.54 164.11 325.86
93 1 16 2005.35 529.67 1 1894.65 613.97 162.17 326.62
93 1 23 1871.66 467.91 3665.08 571.71 142.93 327.38
93 l 30 1604.28 534.76 1480.71 488.90 162.97 328.14
93 2 6 2118.67 534.76 0.00 644.17 162.59 328.90
93 2 13 1986.25 534.76 0.00 597.37 160.83 332.50
93 2 20 1426.03 529.67 1939.58 424.29 157.59 336.10
93 2 27 1292.34 472.64 1385.42 380.43 139.13 339.70
93 3 6 1292.34 472.64 0.00 376.44 137.68 343.30
93 3 13 968.01 524.86 0.00 280.87 152.29 344.65

235

 

 

YEAR MONTH DAY Nominal Nominal Food Aid Real Real CPI
White Price Yellow Deliveries White Yellow (Dec 1989
Price Price Price = 100)
93 3 20 643.69 514.67 0.00 186.04 148.75 346.00
93 3 27 722.92 386.22 0.00 208.12 111.19 347.35
93 4 3 722.92 441.92 0.00 207.32 126.73 348.70
93 4 10 722.92 467.91 0.00 206.06 133.38 350.83
93 4 17 802.14 472.64 0.00 227.27 133.91 352.95
93 4 24 668.45 397.25 0.00 188.26 1 11.88 355.08
93 5 1 802.14 401 .07 0.00 224.56 1 12.28 357.20
93 5 8 802. 14 401.07 0.00 224.76 1 12.38 356.88
93 5 15 655.72 401.07 0.00 183.90 1 12.48 356.56
93 5 22 643.34 397.25 0.00 180.59 1 1 1.51 356.24
93 5 29 757.58 399.16 0.00 212.85 112.15 355.92
93 6 5 794.50 401.07 0.00 223.43 1 12.79 355.60
93 6 12 786.86 305.58 0.00 220.41 85.60 357.00
93 6 19 764.79 405.12 0.00 213.39 113.04 358.40
93 6 26 776.84 384.69 0.00 215.91 106.92 359.80
93 7 3 755.27 392.16 0.00 209.10 108.57 361.20
93 7 10 792.24 384.69 0.00 219.53 106.60 360.88
93 7 17 776.84 384.69 0.00 215.45 106.69 360.56
93 7 24 727.05 392.16 0.00 201.82 108.86 360.24
93 7 31 769.37 384.89 0.00 213.76 106.94 359.92
93 8 7 776.84 457.52 0.00 216.03 127.23 359.60
93 8 14 872.43 440.57 0.00 238.48 120.43 365.83
93 8 21 740.74 440.33 0.00 199.10 1 18.35 372.05
93 8 28 629.39 440.33 0.00 166.38 1 16.40 378.28
93 9 4 769.79 415.05 0.00 200.21 107.95 384.50
93 9 1 1 898.09 377.43 0.00 232.60 97.75 386.10
93 9 18 755.27 453.16 0.00 194.81 116.88 387.70
93 9 25 755.27 415.29 0.00 194.01 106.68 389.30
93 10 2 784.31 377.43 0.00 200.64 96.55 390.90
93 10 9 755.27 380.95 0.00 191.89 96.79 393.60
93 10 16 726.22 384.69 0.00 183.25 97.07 396.30
93 10 23 740.74 377.63 0.00 185.65 94.64 399.00
93 10 30 790.12 432.10 0.00 196.69 107.57 401.70
93 11 6 754.85 448.80 0.00 186.66 110.98 404.40
93 1 1 13 740.74 448.80 0.00 179.07 108.50 413.65
93 11 20 864.20 432.10 0.00 204.35 102.18 422.90
93 11 27 755.27 453.16 0.00 174.77 104.86 432.15
93 12 4 881.14 436.21 1379.50 199.62 98.83 441.40
93 12 11 864.20 503.23 2626.80 191.34 111.42 451.65
93 12 18 898.09 452.67 4028.05 194.43 98.00 461.90

236

 

 

YEAR MONTH DAY Nominal Nominal Food Aid Real Real CPI
White Price Yellow Deliveries White Yellow (Dec 1989
Price Price Price = IOOL
93 12 25 1091.62 533.77 1112.70 231.20 113.05 472.15
94 1 1 740.74 517.90 1005.10 153.55 107.36 482.40
94 1 8 864.20 503.51 1023.10 176.81 103.01 488.78
94 1 15 897.60 556.77 4560.25 181.28 112.44 495.16
94 1 22 1374.86 694.44 2324.20 274.13 138.46 501.54
94 1 29 1258.08 647.74 3541.95 247.69 127.53 507.92
94 2 5 1320.39 601.04 4430.30 256.74 1 16.87 514.30
94 2 12 1258.78 595.62 807.25 241.09 1 14.08 522.13
94 2 19 1258.78 653.59 1710.25 237.53 123.33 529.95
94 2 26 1258.08 560.22 1927.30 233.94 104.17 537.78
94 3 5 1282.98 623.16 1313.00 235.15 114.22 545.60
94 3 12 1035.79 606.45 545.50 189.24 1 10.80 547.35
94 3 19 888.89 641.49 57.50 161.88 116.83 549.10
94 3 26 864.20 641 .49 0.00 156.88 1 16.45 550.85
94 4 2 747.80 617.28 0.00 135.32 111.71 552.60
94 4 9 856.41 647.37 0.00 154.79 117.01 553.26
94 4 16 871.46 653.59 0.00 157.33 117.99 553.92
94 4 23 941.18 549.94 0.00 169.71 99.16 554.58
94 4 30 915.03 653.59 0.00 164.80 117.71 555.24
94 5 7 972.22 694.44 0.00 174.89 124.92 555.90
94 5 14 972.22 694.44 0.00 173.96 124.26 558.88
94 5 21 936.82 694.44 0.00 166.74 123.60 561.85
94 5 28 995.37 694.44 0.00 176.23 122.95 564.83
94 6 4 995.37 617.28 0.00 175.30 108.72 567.80
94 6 11 953.16 694.44 0.00 165.88 120.85 574.63
94 6 18 906.32 663.50 0.00 155.87 114.11 581.45
94 6 25 972.22 694.44 0.00 165.27 1 18.05 588.28
94 7 2 953.16 694.44 0.00 160.17 1 16.69 595.10
94 7 9 953.16 694.44 0.00 157.82 1 14.99 603.94
94 7 16 915.03 653.59 0.00 149.32 106.66 612.78
94 7 23 1045.75 1 176.47 0.00 168.23 189.26 621.62
94 7 30 1045.75 1013.07 0.00 165.87 160.69 630.46
94 8 6 1045.75 1045.75 0.00 163.58 163.58 639.30
94 8 13 1960.78 1176.47 0.00 306.70 184.02 639.33
94 8 20 1437.91 1307.19 0.00 224.90 204.46 639.35
94 8 27 1424.21 1307.19 0.00 222.75 204.45 639.38
94 9 3 1830.07 1288.52 0.00 286.22 201.52 639.40
94 9 10 1795.21 1307.19 0.00 278.81 203.02 643.88
94 9 17 1666.98 1307.19 0.00 257.11 201.62 648.35
94 9 24 1725.49 1307.19 0.00 264.31 200.24 652.83

237

 

 

YEAR MONTH DAY Nominal Nominal Food Aid Real Real CPI
White Price Yellow Deliveries White Yellow (Dec 1989
Price Price Price = 100)
94 10 1 1424.21 1307.19 0.00 216.68 198.87 657.30
94 10 8 1608.47 1270.88 0.00 243.20 192.16 661.38
94 10 15 1624.30 1234.57 0.00 244.09 185.52 665.46
94 10 22 1583.15 1246.33 0.00 236.45 186.15 669.54
94 10 29 1580.73 1244.08 0.00 234.66 184.69 673.62
94 1 I 5 1692.19 1244.08 0.00 249.70 183.57 677.70
94 11 12 1803.64 1241.83 8653.95 258.86 178.23 696.78
94 11 19 1721.13 965.87 10414.35 240.43 134.93 715.85
94 11 26 1373.19 1015.87 2235.30 186.85 138.23 734.93
94 12 3 1483.71 1045.75 172.70 196.78 138.69 754.00
94 12 10 1805.86 1069.96 0.00 237.11 140.49 761.60
94 12 17 1667.88 849.04 0.00 216.83 110.38 769.20
94 12 24 1510.53 820.42 0.00 194.46 105.62 776.80
94 12 31 1583.15 890.19 2232.70 201.83 113.49 784.40
95 1 7 1635.51 816.37 6453.70 206.50 103.08 792.00
95 1 14 1636.41 766.73 5884.50 206.94 96.96 790.78
95 1 21 1699.35 755.27 6993.55 215.23 95.66 789.55
95 1 28 1539.58 775.22 4556.90 195.30 98.34 788.33
95 2 4 1629.37 769.37 0.00 207.01 97.75 787.10
95 2 1 1 1463.33 769.79 0.00 184.26 96.93 794.17
95 2 18 1525.05 727.39 0.00 190.34 90.78 801.24
95 2 25 1454.85 747.80 0.00 179.99 92.51 808.30
95 3 4 1414.34 776.84 0.00 173.46 95.27 815.37
95 3 1 1 1414.88 769.79 0.00 171.98 93.57 822.69
95 3 18 1481.48 708.08 0.00 178.49 85.31 830.01
95 3 25 1497.18 658.44 0.00 178.80 78.63 837.34
95 4 1 1576.02 872.43 0.00 186.59 103.29 844.66
95 4 8 1636.41 976.48 0.00 187.02 1 1 1.60 875.00
95 4 15 1651.69 1235.20 0.00 182.22 136.27 906.42
95 4 22 1620.22 1164.02 0.00 172.55 123.97 938.98
95 4 29 1709.03 1174.05 0.00 175.70 120.70 972.71

 

Appendix B

Appendix B

Alternative models for the Food Aid Effects Analysis

3.1 Simultaneous model for the war/drought period

Although the results of the recursive ordering WP, FA, YP were in correspondence with
economic expectations and allowed for F At to be contemporaneously affected by WPt , it restricts
the coefficient on YP, to be zero in the WP, equation. As an alternative, a nonrecursive
identification scheme was tested, as indicated by the following A0 matrix with the same ordering

of WP, FA, and YP:

 

 

0‘11 0 “13
A0 = 0‘21 0‘22 O (3.1)
_O “32 “33‘

This is based on the following logic: white maize price shocks affect food aid arrivals
contemporaneously since they are the signals of market scarcity of maize products, shown in the
first row. Yellow maize prices are affected contemporaneously by the food aid arrivals (as was
observed), but not by the white maize price shocks. Food aid shocks do not contemporaneously
affect white maize prices, however yellow maize price shocks do. The corresponds to the
interpretation that the yellow maize price shocks are demand shocks. Since white and yellow
maize products are substitutes in consumption, white price would be affected by yellow maize
demand shocks and only indirectly by yellow maize supply shocks.

238

239

The results are in accordance with economic logic. Figure 8.] presents the impulse responses for
a one standard deviation shock, holding all other shocks to zero. A food aid arrival shock has
negative effects on white and yellow maize prices. High supply is followed in 4 weeks by a
reduction in FA arrivals, in compensation for the oversupply and the reduction in prices with
series returning to near steady state by the twelfih week. Yellow maize price shocks result in a
positive response of all three series with each series returning to near steady state by the eleventh

week .

The difference in identification schemes allows contemporaneous effects across the series, such
that yellow maize prices, while not affected directly by a white maize price shock in the initial
period, are affected by the change in food aid arrivals that the shock generates. Food aid arrivals
show an increase with the white maize price shock, as would be expected since the price spike in
white signals scarcity. That increase of yellow maize supplies has a negative influence on the

yellow maize prices, resulting in a net negative effect on yellow maize prices.

The FEVDs for the VAR with this nonrecursive identification can be seen in Table B. 1. With the
nonrecursive structure, white maize price shocks account for almost 17 percent of the forecast
error variance in food aid after one period, and after 20 periods, still account for almost 16
percent. By the ninth period, yellow maize price shocks account for 11.4 percent, and maintain a
level close to 12 percent of the food aid error variance throughout the remaining periods. Thus,
food aid own shocks account for about 72 percent of the error variance, with 28 percent from
other shocks. Looking at the yellow maize price, own shocks account for a high percentage of

the error variance, yet by the 4th period, 30 percent of the error variance can be accounted for by

240

the shocks from food aid. Finally, the white maize prices follow a pattern similar to the yellow
maize prices, initially 80 percent for own shocks, declining to 60 percent, with over 30 percent
accounted for by the yellow maize price shocks, with surprisingly little attributable to the food aid
shocks. This suggests that the food aid delivery shocks affect the yellow maize prices; through

the demand in the markets, yellow prices in turn affect white maize prices.

The simulation was run with this simultaneous identification scheme as well. Figure B.2 appears
almost identical to Figure 6.2, the simulation results with the recursive model, although careful
observation shows a moderate increase in the simulated yellow maize price with the simultaneous

identification scheme.

‘1 .00
0.75
0.50
0.25
0.“
-0.25
41.50

1.00
0.75
0.50
0.25
0.00

415

-o.so

1.00
0.75
0.50
0.25
0.00
4.25
41.50

241

 

Response to Shocks from White Price

 

 

White Price
Food Aid (MT)

 

 

 

 

 

 

2 4 e s 10 12 14 15 111
Weeks
. White Price —
Response to Shocks from Food Aid (MT) Food MS (MT) ._. ._. ._.

 

 

 

 

 

 

f12

14

1B

 

 

l- h
UN~‘O-

 

MOI-“HM - -

 

 

 

Weeks

-12-

14‘

'16'

'“f

Figure 3.1 Impulse responses to shocks, based on the simultaneous model, war/drought period

 

 

 

242

Table B.l Forecast error variance decompositions for simultaneous model, war/drought period

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Variance Decomposition (% of total variance due to each shock)
White maize price Food aid Yellow maize price
Step Own FA YP WP Own YP WP FA Own
shock shock shock shock shock shock shock shock shock

I 80.2 2.9 16.9 17.0 79.5 3.6 3.1 14.4 82.5
2 77.2 3.1 19.7 16.1 80.1 3.9 2.4 21.0 76.5
3 71.8 5.7 22.5 19.0 75.4 5.5 2.3 25.5 72.1
4 70.3 7.2 22.5 18.6 73.5 7.9 2.5 30.0 67.5
5 69.1 6.7 24.2 17.3 72.6 10.1 3.7 31.9 64.4
6 67.5 6.5 26.0 16.3 73.0 10.6 4.3 32.3 63.4
7 66.4 6.5 27.1 16.0 72.8 11.2 4.5 32.5 63.1
8 65.4 6.5 28.1 15.8 72.7 11.5 4.6 32.4 63.0
9 64.2 6.7 29.0 15.8 72.6 11.6 4.8 32.4 62.8
10 63.1 7.1 29.7 15.7 72.5 11.7 5.0 32.3 62.6
11 62.3 7.5 30.2 15.7 72.5 11.8 5.1 32.3 62.5
12 61.7 7.8 30.6 15.7 72.4 11.9 5.3 32.3 62.4
13 61.1 8.0 30.9 15.7 72.4 11.9 5.3 32.3 62.4
14 60.8 8.2 31.0 15.7 72.7 11.9 5.4 32.3 62.3
15 60.5 8.3 31.2 15.7 72.4 11.9 5.5 32.3 62.3
16 60.4 8.3 31.3 15.7 72.4 11.9 5.5 32.3 62.2
17 60.3 8.4 31.3 15.7 72.4 11.9 5.5 32.2 62.2
18 60.2 8.4 31.4 15.7 72.4 11.9 5.5 32.2 ' 62.2
19 60.1 8.5 31.4 15.7 72.4 11.9 5.5 32.2 62.2
20 60.1 8.5 31.4 15.7 72.4 11.9 5.6 32.2 62.2

 

 

243

mamm,mmmmummai.1m
W

 

 

 

W
”numeric. -—-1 .
”0 " ..... an
. .' 1“
no - H : ‘.
I‘ 'i a: i
w " i:'.-'o ‘.
o " '3
m " . 1"" i:
w a!
w d
m -

 

 

 

a

RedYeIodezePrteee

 

 

Real men Pllc —

 

 

W
§§§§§§§§
ll

 

 

 

 

auwasouo'rruauwasouoiFMAHM11301163r
«a

Figure B.2 Simulation in war/drought pe’riod, simultaneous model with no food aid afier July 31,
1992

244

B.2 Recursive ordering: FA, YP, WP in the war/drought period

Initial analysis was conducted with identification based upon the recursive model, ordering the
variables FA, YP, and WP. The first row Of A0 gives the coefficients for the contemporaneous
effects of each variable on FA1 ; the second row, similarly for YP, ; and the third row for WP, .
Thus, an is the coefficient of FA, in the YP, equation. This A0 implies that the FA, equation
would not contain YP, or WP, as explanatory variables, although the lagged values Of those
variables are still in the equation (see equation 5.2). For YP, , the current FA, FA, , would be
included but not WP, . Finally, current values of YP and FA would be included in the equation

for WP.

The results of this recursive model (with F A, YP, WP ordering) were not good in terms of
reflecting what economic theory and market structure would predict. For example, the response
Of white maize prices to a food aid arrival shock (approximately 2600 metric tons) was tO
increase, rather than to decrease. The lack of correspondence led the researcher tO re-examine
the premises Of the identification and the market structure. The first aspect evaluated was
whether or not food aid arrivals were exogenous, in the time series sense, to the white and yellow
maize prices, as was assumed. This led tO the recursive model selected in Chapter 6 for the

war/drought period.

245

 

 

   

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1.00
Food Aid (MT) -—
0.75 _ Response to Shocks from Food Aid (MT) Yellow Pd“ ______
W ‘ Price -'-'-
0.50 -
0.25 -1
o.oo _ _ _
-o.2s ~
'°°5° o 2 4 s ' s 10 12 14 16 14
Weeks
mo “ FoodAid(M‘1‘) —
0.75 - Response to Shocks from Yellow Price Yellow m ______
a w ' Price -----
0.50 - _lme__
025 i
0.00
.025 ~
‘°°5° o 2 4 e s 10 12 14 15 111
Weeks
m roodAidmr) —-
m - ‘-, Response to Shocks from White Price Yen" m ______
'. i w1_m_e' Price —-—~-
0.50 -
0.25 d
0.00
-o.2s -
-o.eo -0- '2' '4' '6 fi‘ *10* ‘12'fi4' '15' 7111'

Figure B.3 Impulse responses to shocks, based on the recursive model FA,YP, WP, in
war/drought period

246

B.3 Simultaneous identification in post-war/drought period

Exogeneity testing suggests that it would be valuable to estimate a model in which at least one Of
the maize prices is allowed to have a non-zero contemporaneous coefficient in the food aid
equation. In order to evaluate this, a simultaneous identification was tested, in which white maize
price shocks were allowed to affect food aid deliveries contemporaneously, as in the earlier
period. Also white maize prices would be affected contemporaneously only by yellow maize price
shocks and not by the food aid delivery shocks. This is the same simultaneous identification

scheme used in the war/drought period, but the parameters estimates and shock paths will differ.

Using the ordering WP, FA, YP, the following Ao matrix was proposed:

1 0 0113
A 0 = a2, 1 O (B.2)
i 0 0:32 1

 

 

The impulse responses for this model are presented in Figure B.3. Comparing Figure B.3 with
Figure 6.3 which presents the recursive model impulse responses, the results are almost identical,
except in the first period. For a single delivery shock Of 1250 metric tons, yellow maize prices
decline about 4 meticais/kg, an effect lasting for 12-14 weeks. White maize price shocks increase
the food aid, resulting in a decrease in yellow maize prices. With the simultaneous identification
scheme, the contemporaneous effect Of white maize prices on yellow maize prices allows yellow
maize prices to increase initially, and then, as food aid increases, the yellow maize prices decrease.
White maize prices come down quickly after a shock, due to the combination Of increased food

aid and lowered yellow maize prices.

247

The similarity between the two estimates indicates that the relationships between food aid shocks
and yellow maize prices and between yellow maize demand shocks and white maize prices are

robust.

248

 

   

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1.00 .
. Foodmmr) —
0.75 _, Responseto Shocks from Food Ard (MT) Y.“ PM ______
WhltePrlce -----
0.50 ~ L—-—k
025 - N
0'00 L‘c ‘ ' i _ §>—.V‘____.:___,_,_,,_.-._._.:_—._-..--—-._......-.:_-.-—.:.-_.....
-o.2s ~ """ “ ...................................
'°-5° 0 7 2 4 s s 10 12 14 1s 10
Weeks
1.00 R onse to Shocks from Yellow Price F°°d A” INT) —
0.15 - “P Vampire. ------
‘~ .............. WhiePriee - - -
0.50 - ’_ ____________
/’ '\, -------------

0.25 "‘ / \ -----------------------

.I \~---"'""-——__--___,___________‘_':_'
0.00 —~ " I
41.25 -
‘°-5° 7 o ‘ 2 4 e a 10‘ 12* '14* 1s 10

Weeks
1.00 \ . ——
. . FoothllT) —
\
0.75 _ .\ ResponsetoShocks from White Price mm PM ______
I‘- “11111er -----
0.50 - \
\,

0.25 - __.- \'\ ..... ‘.
0.00 , “ ...... "“ ———-.._--____.-____-_....1._.-—_---_—_:
-o.2s - ----------------------------
'°-‘° '0‘ '2T 4' 'e' 's' '10' '12‘ '14‘ 715 111'

Weeks

Figure B.4 Impulse responses to shocks, based on the simultaneous model with trend and mean
shift, in the post-war/drought period

Table B.2 Forecast error variance decompositions, based on nonrecursive ordering, late period

249

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Variance Decomposition (% Of total variance due to each shock)
Food Aid Yellow maize price White maize price
Step Own YP WP FA Own WP FA YP Own
shock shock shock shock shock shock shock shock shock
I 92.2 2.7 0.1 0.1 96.2 3.7 1.8 0.0 98.1
2 96.8 1.8 1.4 2.8 93.0 4.2 9.9 0.0 90.0
3 95.8 1.9 2.2 3.9 92.7 3.4 9.6 0.6 89.7
4 95.4 2.1 2.5 3.8 93.0 3.2 9.0 11.6 79.4
5 94.4 2.1 3.5 5.0 91.3 3.6 10.6 11.9 77.5
6 93.7 2.1 4.3 7.5 87.8 4.7 10.9 12.6 76.5
7 93.5 2.1 4.4 9.2 84.9 5.9 10.9 13.5 75.6
8 93.4 2.1 4.4 10.8 82.2 7.0 10.8 14.6 74.6
9 93.4 2.1 4.5 12.5 79.5 8.0 11.1 15.2 73.7
10 93.4 2.1 4.5 13.9 77.2 9.0 11.4 15.5 73.0
11 93.4 2.1 4.5 14.8 75.5 9.7 11.7 15.9 72.4
12 93.4 2.1 4.5 15.5 74.1 10.4 12.0 16.1 71.9
13 93.4 2.1 4.5 16.1 73.0 10.9 12.2 16.3 71.5
14 93.4 2.1 4.5 16.5 72.2 11.4 12.3 16.5 71.2
15 93.4 2.1 4.5 16.8 71.5 11.7 12.4 16.6 71.0
16 93.4 2.1 4.5 17.1 70.9 12.0 12.4 16.7 70.9
17 93.4 2.1 4.5 17.3 70.5 12.2 12.5 16.7 70.8
18 93.4 2.1 4.5 17.5 70.1 12.4 12.5 16.8 70.7
19 93.4 2.1 4.5 17.6 69.8 12.6 12.6 16.9 70.6
20 93.4 2.1 4.5 17.7 69.6 12.7 12.6 16.9 70.5

 

 

250

B.4 Recursive model WP, FA, YP for the post-war/drought period

In the war/drought period, the recursive model estimated was based on the ordering white maize
price, food aid deliveries, yellow maize price. Figure 3.5 shows the results from that same
ordering for the post-war/drought model. Comparing Figure B.5 with Figure 6.3 shows that the

change in ordering makes little difference in the impulse responses.

3.5 Recursive model FA, YP, WP for the post-war/drought period with seven lags

Figure 3.6 shows the impulse responses for the model selected in the post-war/drought period but
includes additional lags, a total of seven compared to the four lags in the model in Chapter 6, to
test the model for sensitivity to the choice of lags. As with the other alternative specifications, the
responses of white and yellow maize prices to shocks fiom food aid deliveries are quite similar

between the four lag and seven lag models, shown by examining Figure B6 and Figure 6.3.

251

 

1.00

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

0,75 - Response to Shocks from White Price :ggiieA’ifi'r) :1
Yellow Price -----
0.50 ‘
0.25 ‘
0.00 . ‘
.0'25 d . ' '~-—._. ——————— . _______ .-4-- -------------------------
41.50
0 2 4 6 fiat: ‘10 12 14 1° 18
1.00 -, -.
0.75 .4 Response to Shocks from Food Aid (MT) 3:11.153};ng '1‘) :2
‘. Ye w Price -----
0.50 "" 4‘
025 ~
‘>v./\ --------------
0.” /\\/ k ........ x --- '-
'025 —4 \‘\.-.‘,.’.o"",‘.‘.‘ ............................................
“0‘50 7° '2 7‘ 5* '8 '10 712 14 1s' '10
Weeks
1.00 \ . .
0.75 4 " \ Response to Shocks from Yellow Price Fogthlfmn 7.7—.-.
i" Yellow Price -----
0“ —1 —.....—._._._,‘.‘ -.-.
025 " [A .....................................
0.00 -c-,._,_ -- - _ ........ _
41.25 d
0.50 0 2 4 6 f - 3 10 12 14 ‘8 ‘3
Weeks

Figure B.5 Impulse responses to shocks, based on the recursive model WP, FA, YP, with a trend
and mean shifi, four lags, in the post-war/drought period

252

 

   

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1.00 ,
, FoodAid(MT) —-
0.75 - Response to Shocks from Food Aid (MT) you", We, ------
White Price -----
0.50 4
I ‘\_.— ......... ‘
0'00 “ ------ V “""-M::._ - — - —
.0.” -1 “" ..... ‘ ..................................
'0'” o ' ' 2 ' 4 s s 10 12 14 is 10
Weeks
10 . .
° x , Food Aid(MT) —
0.75 ~ 2 Response to Shocks from Yellow Price Yellow Price ------
‘r .s- ,,,,,,, wm Price -.-...
0.50 d ....... .- '
0.25 - -. [I \.\l_ I ,\\~~_‘
0.00 Al\’/ _ - - - ‘ fl..._._—.-_--
.025 1
'°‘5° 0 2 4 s s 10 12 ' 14 1s 10
Weeks
1.00 \ .
" a Sh k fro Whi Pri iii“? M) _
°~75 " ' es nseto oc s m te ce e ow rice ------
\i\ P0 WhitePrice -'---
0.50 - -\
\.
0.25 - \
000 ‘-.~‘ I .~ V'v ‘ ._ -'____‘r-:..:-.-: ......
on 4 “ ''''' “an" ‘jjjf:”":::$""
'°-5° TO' '2' '4' ‘s* '03 '10' ‘12fi'14' '16' '10'
Weeks

Figure 3.6 Impulse responses to shocks, based on recursive model FA, YP, WP, with a trend and
mean shift, seven lags, in the post-war/drought period

APPENDIX C

APPENDIX C

Data for the Market Integration Analysis

 

 

CPI Maputo Chimoio Beira Maputo Chimoio Beira
Year Month Dav (Dec. Nominal Nominal Nominal Real Real Real
' 1989:100) win“ win": WP” WI.“ Wt“ WI?“
Price Price Prrce Prrce Prrce Prrce

93 4 3 348.70 640.97 342.86 183.82 98.32

93 4 10 350.83 659.46 352.38 685.71 187.98 100.44 195.46
93 4 17 352.95 806.72 295.24 428.57 228.57 83.65 121.43
93 4 24 355.08 672.27 276.19 428.57 189.33 77.78 120.70
93 5 1 357.20 814.87 323.81 400.00 228.13 90.65 111.98
93 5 8 356.88 806.72 257.14 371 .43 226.05 72.05 104.08
93 5 15 356.56 659.46 257.14 342.86 184.95 72.12 96.16
93 5 22 356.24 647.01 257.14 295.24 181.62 72.18 82.88
93 5 29 355.92 761.90 228.57 342.86 214.07 64.22 96.33
93 6 5 355.60 799.04 257.14 371.43 224.70 72.31 104.45
93 6 12 357.00 791 .36 257.14 419.05 221.67 72.03 117.38
93 6 19 358.40 769.16 247.62 314.29 214.61 69.09 87.69
93 6 26 359.80 799.04 276.19 342.86 222.08 76.76 95.29
93 7 3 361.20 776.84 266.67 342.86 215.07 73.83 94.92
93 7 17 360.80 799.04 285.71 495.24 221.46 79.19 137.26
93 7 24 360.40 747.82 285.71 444.44 207.50 79.28 123.32
93 7 31 360.00 791.36 304.76 393.65 219.82 84.66 109.35
93 8 7 359.60 799.04 285.71 342.86 222.20 79.45 95.34
93 8 14 365.83 897.35 285.71 342.86 245.30 78.10 93.72
93 8 21 372.05 761.90 285.71 342.86 204.79 76.79 92.15
93 8 28 378.28 634.92 285.71 357.14 167.85 75.53 94.41
93 9 4 384.50 791.78 314.29 371.43 205.93 81.74 96.60
93 9 11 386.10 923.75 314.29 371.43 239.25 81.40 96.20
93 9 18 387.70 776.84 285.71 400.00 200.37 73.69 103.17
93 9 25 389.30 776.84 285.71 400.00 199.55 73.39 102.75
93 10 2 390.90 806.72 285.71 342.86 206.38 73.09 87.71
93 10 9 393.60 776.84 285.71 400.00 197.37 72.59 101.63
93 10 16 396.30 746.97 285.71 400.00 188.48 72.10 100.93
93 10 23 399.00 761.90 285.71 400.00 190.95 71.61 100.25
93 10 30 401.70 812.70 285.71 400.00 202.31 71.13 99.58
93 l l 6 404.40 776.42 285.71 342.86 191.99 70.65 84.78
93 11 13 413.65 761.90 285.71 342.86 184.19 69.07 82.89
93 11 20 422.90 906.32 285.71 342.86 214.31 67.56 81.07

253

254

 

 

CPI Maputo Chimoio Beira Maputo Chimoio Beira

Year Month Day (nee. NOW] Nominal Nome“ Real Real Real

1989=100 W“ W“ W” “1"" ““1“" WP”

Price Prrce Price Prrce Prrce Prrce
93 11 27 432.15 776.84 285.71 342.86 179.76 66.11 79.34
93 12 4 441.40 906.32 361.90 342.86 205.33 81.99 77.67
93 12 1 1 451.65 888.89 342.86 400.00 196.81 75.91 88.56
93 12 18 461.90 923.75 361.90 457.14 199.99. 78.35 98.97
93 12 25 472.15 1102.76 342.86 457.14 233.56 72.62 96.82
94 l 8 482.40 888.89 371.43 571.43 184.26 77.00 118.46
94 1 15 490.38 923.25 342.86 571 .43 188.27 69.92 1 16.53
94 l 22 498.35 1414.14 342.86 571 .43 283.76 68.80 114.66
94 1 29 506.33 1294.03 361.90 571.43 255.57 71.48 1 12.86
94 2 5 514.30 1358.12 366.67 571.43 264.07 71.29 111.11
94 2 12 522.13 1294.74 371.43 514.29 247.98 71.14 98.50
94 2 19 529.95 1294.74 371.43 495.24 244.31 70.09 93.45
94 2 26 537.78 1294.03 342.86 514.29 240.63 63.75 95.63
94 3 5 545.60 1319.64 371.43 514.29 241.87 68.08 94.26
94 3 12 547.35 1065.39 371.43 514.29 194.64 67.86 93.96
94 3 19 549.10 914.29 371.43 514.29 166.51 67.64 93.66
94 3 26 550.85 955.72 371.43 514.29 173.50 67.43 93.36
94 4 2 552.60 769.16 342.86 571.43 139.19 62.04 103.41
94 4 9 553.26 888.89 342.86 514.29 160.66 61.97 92.96
94 4 16 553.92 896.36 342.86 457.14 161.82 61.90 82.53
94 4 23 554.58 941 . 18 342.86 571.43 169.71 61.82 103.04
94 4 30 555.24 941.18 342.86 571.43 169.51 61.75 102.92
94 S 7 555.90 1000.00 342.86 571.43 179.89 61.68 102.79
94 5 14 558.88 1000.00 352.38 457.14 178.93 63.05 81.80
94 5 21 561.85 963.59 342.86 457.14 171.50 61.02 81.36
94 5 28 564.83 1023.81 342.86 457.14 181.26 60.70 80.94
94 6 4 567.80 1023.81 361.90 571.43 180.31 63.74 100.64
94 6 11 574.63 980.39 352.38 457.14 170.61 j 61.32 79.56
94 6 18 581.45 932.21 342.86 457.14 160.33 58.97 78.62
94 6 25 588.28 1000.00 342.86 457.14 169.99 58.28 77.71
94 7 2 595.10 980.39 342.86 457.14 164.74 57.61 76.82
94 7 9 603.94 980.39 352.38 571.43 162.33 58.35 94.62
94 7 16 612.78 941.18 361.90 571.43 153.59 59.06 93.25
94 7 23 621.62 1075.63 361.90 571 .43 173.04 58.22 91.93
94 7 30 630.46 1 120.45 371.43 571.43 177.72 58.91 90.64
94 8 6 639.30 1075.63 447.62 571.43 168.25 70.02 89.38
94 8 13 639.33 2016.81 447.62 571.43 315.46 70.01 89.38
94 8 20 639.35 1478.99 476.19 733.33 231.33 74.48 114.70
94 8 27 639.38 1464.91 609.52 857.14 229.12 95.33 134.06
94 9 3 639.40 1882.35 685.71 857.14 294.39 107.24 134.05

255

 

 

CPI Maputo Chimoio Beira Maputo Chimoio Beira

Year Month Day (Dec. ”mm“ Nominal Nominal Real Real Real

1989=100 Wm WI.” Wime WI?“ W19“: “”9”

Price Prrce Prrce Prrce Prrce Prrce
94 9 10 643.88 1846.50 647.62 857.14 286.78 100.58 133.12
94 9 17 648.35 1714.61 685.71 857.14 264.46 105.76 132.20
94 9 24 652.83 1774.79 609.52 971.43 271.86 93.37 148.80
94 10 1 657.30 1464.91 914.29 1200.00 222.87 139.10 182.57
94 10 8 661.38 1654.42 1142.86 1428.57 250.15 172.80 216.00
94 10 15 665.46 1670.71 1142.86 1428.57 251.06 171.74 214.67
94 10 22 669.54 1628.38 1142.86 1428.57 243.21 170.69 213.37
94 10 29 673.62 1625.89 1 142.86 1428.57 241.37 169.66 212.07
94 1 I 5 677.70 1758.14 1104.76 1428.57 259.43 163.02 210.80
94 11 12 696.78 1855.18 1142.86 1314.29 266.25 164.02 188.62
94 11 19 715.85 1770.31 1142.86 1428.57 247.30 159.65 199.56
94 11 26 734.93 1483.71 952.38 1428.57 201.89 129.59 194.38
94 12 3 754.00 1526.10 1028.57 1428.57 202.40 136.42 189.47
94 12 10 761.60 1857.45 1066.67 1428.57 243.89 140.06 187.58
94 12 17 769.20 1715.53 1142.86 1428.57 223.03 148.58 185.72
94 12 24 776.80 1553.69 1428.57 1428.57 200.01 183.90 183.90
94 12 31 784.40 1628.38 1428.57 1428.57 207.60 182.12 182.12
95 l 7 792.00 1682.24 1714.29 1714.29 212.40 216.45 216.45
95 1 14 790.78 1683.16 1428.57 2000.00 212.85 180.65 252.92
95 1 21 789.55 1715.53 1428.57 1857.14 217.28 180.93 235.22
95 1 28 788.33 1583.57 1428.57 1714.29 200.88 181.22 217.46
95 2 4 787.10 1675.92 1219.05 1428.57 212.92 154.88 181.50
95 2 11 798.25 1525.87 1142.86 1657.14 191.15 143.17 207.60
95 2 18 809.40 1718.02 1142.86 1428.57 212.26 141.20 176.50
95 2 25 820.55 1496.42 1257.14 1428.57 182.37 153.21 174.10
95 3 4 831.70 1454.75 1257.14 1600.00 174.91 151.15 192.38
95 3 11 839.05 1455.30 1142.86 1714.29 173.45 136.21 204.31
95 3 18 846.40 1583.57 1142.86 1714.29 187.09 135.03 202.54
95 3 25 853.75 1539.96 1142.86 1428.57 180.38 133.86 167.33
95 4 1 861.10 1621.05 1123.81 1428.57 188.25 130.51 165.90
95 4 8 864.50 1725.49 1028.57 1428.57 199.59 118.98 165.25
95 4 15 867.90 1698.88 990.48 1428.57 195.75 114.12 164.60
95 4 22 871.30 1666.52 914.29 1428.57 191.27 104.93 163.96
95 4 29 874.70 1757.86 914.29 1428.57 200.97 104.53 163.32
95 5 6 878.10 1760.35 1028.57 1428.57 200.47 117.14 162.69
95 5 13 886.68 2221.74 1028.57 1314.29 250.57 116.00 148.23
95 5 20 895.25 2146.92 1028.57 1428.57 239.81 114.89 159.57
95 5 27 903.83 2063.19 1028.57 1428.57 228.27 113.80 158.06
95 6 3 912.40 1979.46 1028.57 1428.57 216.95 112.73 156.57
95 6 10 916.18 1962.30 1028.57 1428.57 214.18 112.27 155.93

256

 

 

CPI Maputo Chimoio Beira Maputo Chimoio Beira

Year Month Day (Dec. Nominal Nominal Nominal Real Real Real

1989=100) WI?” W1.“ “1"“ “”3““ W11“: “1""

Prrce Prrce Prrce Prrce Prrce Price
95 6 17 919.95 1922.90 1142.86 1428.57 209.02 124.23 155.29
95 6 24 923.73 1942.11 1371.43 1428.57 210.25 148.47 154.65
95 7 1 927.50 1871.35 1371.43 1428.57 201.76 147.86 154.02
95 7 8 927.88 1922.90 1371.43 1428.57 207.24 147.80 153.96
95 7 15 928.26 1997.60 1371.43 1428.57 215.20 147.74 153.90
95 7 22 928.64 2285.71 1485.71 1571.43 246.14 159.99 169.22
95 7 29 929.02 2201.06 1485.71 1428.57 236.92 159.92 153.77
95 8 5 929.40 2171.43 1619.05 1542.86 233.64 174.20 166.01
95 8 12 936.73 1997.60 1904.76 1714.29 213.25 203.34 183.01
95 8 19 944.05 2285.71 2095.24 1647.62 242.12 221.94 174.53
95 8 26 951.38 2570.13 2000.00 1714.29 270.15 210.22 180.19
95 9 2 958.70 2460.01 2000.00 1809.52 256.60 208.62 188.75
95 9 9 966.04 2550.78 21 14.29 2285.71 264.05 218.86 236.61
95 9 16 973.38 2412.70 2171.43 2114.29 247.87 223.08 217.21
95 9 23 980.72 2412.70 2152.38 2095.24 246.01 219.47 213.64
95 9 30 988.06 2589.48 2247.62 2285.71 262.08 227.48 231.33
95 10 7 995.40 3005.29 2380.95 2228.57 301.92 239.20 223.89
95 10 14 999.58 2639.28 2361.90 2285.71 264.04 236.29 228.67
95 10 21 1003.75 2695.63 2571.43 2171.43 268.56 256.18 216.33
95 10 28 1007.93 2663.47 2685.71 2285.71 264.25 266.46 226.77
95 11 4 1012.10 3174.60 2666.67 2571.43 313.67 263.48 254.07
95 1 l 1 1 1011.00 3077.85 2742.86 2857.14 304.44 271.30 282.61
95 11 18 1016.47 3062.07 2819.05 2857.14 301.25 277.34 281.08
95 l 1 25 1021.94 3448.49 2933.33 2780.95 337.44 287.04 272.12

 

Appendix D

Appendix D
Alternative Models for the Market Integration Analysis

D.l Recursive model with Chimoio-Maputo ordering in war/drought period

To evaluate the robustness of the VAR results to the identification scheme chosen for the
war/drought period, the VARs were estimated using the reverse ordering. In this case, Chimoio
price shocks are allowed to affect Maputo prices contemporaneously, whereas Maputo price

shocks only have effects with a lag.

The impulse response results of Chimoio-Maputo ordering with seven lags and a time trend
included are reported in Figure D. 1. Visual inspection of this figure in comparison with Figure 7.
10 indicates that the results are robust to the identification scheme for the two sets of figures

show similar trends.

257

258

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

3.5
Response path to own shock —
3.0 4 Plot of Responses of Chimoio Price Response pm, to “mm shock ......
2.5 -
2.0 -1
1.5 ~
1.0 -
0.5 -
0.0
-o.s 0
2o
. Response path to Chimoio shock —
‘. Plot of Responses of Maputo Prrce new” M to own sh oek ______
15 ‘
1o
5
o ----
'5 0 2' T4 11 of '17 12 14f 1or 10

Figure D.l Impulse responseof each price series to shocks, recursive model, Chimoio-Maputo
ordering in the war/drought period, with seven lags

259

D.2 Recursive model with Chimoio-Maputo ordering in post-war/drought period

As in the war/drought period above, to evauate the robustness of the results to the identification
scheme chosen, VARs were estimated using the reverse ordering. Chimoio price shocks are
allowed to affect Maputo prices contemporaneously, whereas Maputo price shocks only have
effects with a lag. The hungry season variable was included along with the time trend and seven

lags.

For this post-war/drought period, the impulse response results of Chimoio-Maputo ordering with
are reported in Figure D.2. Visual inspection of this figure in comparison with Figure 7. 10
indicates that the results are robust to the identification scheme for the two sets of figures show

similar trends.

260

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

12
. . . Responsepsthtoownshoeks —
10 ‘1 Plot of Responses of Chimoio Prrce ”mm p. 111 to “mm shocks ______
. d
o -
4 —
2 —i
0 ...,
.2 - c 4 1 f . - - 4 .
0 2 s 10 12 14 13 10
Weeks
10
_ ', Plot of Responses of Maputo Price Rm W" 1° Chum” “1°“ ______
14 ‘, Response path to own shock
12 - ‘.
10 n ‘.
. - =.
0 '1
, l
2 i
0 fi 7 2f ' '10' izf'u' '1er Tisfi
Weeks

 

Figure D.2 Impulse response of each price series to shocks, recursive with Chimoio-Maputo
ordering, with trend, seven lags, and hungry season in post-war/drought period (in real

meticais/kg)

 

 

 

 

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