lulllllllllll n
I'llllllllllll?

WW?
‘7' LIBRARY

1 3 . .
Sal/22:5] 9 MIChlgal‘. State

University

 

 

 

 

This is to certify that the

dissertation entitled

Time Series Analysis of Famine Early Warning Systems in Mali

presented by

Cynthia A. Phillips

has been accepted towards fulﬁllment
of the requirements for

Agricultural
Economics

Ph.D.

 

 

degree in

Major professor

/
Date 12/6/02

MSU is an Afﬁrmative Action/Equal Opportunity Institution 0-12771

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

 

DATE DUE DATE DUE DATE DUE

 

NOV 1‘8 2008
r0 5 2T 0 E

 

 

 

 

 

 

 

 

 

 

 

 

 

 

6/01 C‘JCIRC/DateDuopss-pjs

Time Series Analysis of Famine Early Warning Systems in Mali

Volume I

Cynthia A Phillips

A DISSERTATION

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

DOCTOR OF PHILOSOPHY

Department of Agrimltural Economics
2002

ABSTRACT
TIME SERIES MODELING OF FAMINE EARLY WARNING SYSTEMS
IN MALI
By

Cynthia A Phillips

Since the mid-eighties, famine early warning systems have been implemented
throughout Aﬁica. This research focuses on expanding the analytical methods used to
evaluate the information collected by these systems. Time series techniques are employed
to explore the vulnerability to food insecurity for eight regions in Mali. The ﬁrst part of
the study develops an econometric model of the dynamic relationship between
socioeconomic and agroclimatic measures, and the state of food security vulnerability.
For each region, a single-index leading indicator model is constructed to identify
signiﬁcant relationships that enhance early warning of supply-side susceptibility to food
insecurity. The indexes distinguish between periods of high and low vulnerability to food
insecurity and are compared to reference cycles derived ﬁ'om qualitative assessments of
vulnerability. The vulnerability indexes produced turning points that tended to provide
early warning of one to three months lead times to the onset of periods of moderate or
high vulnerability to food insecurity.

The second part of the research concerns spatial and temporal market integration
and the econometric tests used to measure such relationships. The vulnerability index
developed in the ﬁrst part is used in the tests of market integration to examine the
ﬁrnctioning of goat markets in Mopti. A model that explores the relationship between

regime shifts, expressing diﬁ‘erent levels of vulnerability to food insecurity, and price
information is developed. Empirical results suggest that there exist highly signiﬁcant
regime shifts in the level of market integration in Mopti goat markets which are triggered
by the state of vulnerability to food insecurity.

Whether markets are integrated and, if so, the degree to which markets are
integrated is important to policy makers. Markets that may have appeared integrated in
less vulnerable times [may cease to ﬁmction well under stress. Rather than moving to a
deﬁcit region, a surplus in one market may remain in that market during a period of food
insecurity. Thus, policy makers should ﬁnd this information useful in their eﬁ‘orts to
mitigate food shortages as they decide whether food aid is necessary, how to distribute
food aid shipments, and whether to purchase imported grain. With this in mind, early
warning also encompasses understanding the manner in which markets respond in times of

crisis.

ACKNOWLEDGENIENTS

A long journey is rarely completed without the grace, assistance, and
encouragement of ﬁiends, colleagues, and guides. I would like to thank the MSU
Department of Agricultural Economics and Eric Crawford in particular for the opportunity
to complete my doctorate and for the supportive environment for professional
development and scholarly pursuits.

I would like to extend my appreciation my dissertation committee. My major
professor, James Oehmke, has been a source of continued support and guidance
throughout this pursuit. His generosity and encouragement have meant the world to me.

I would like to thank Professor Robert Myers for serving as chair of my dissertation
committee. His high standards and intelligence will always inspire me. Many thanks to
Professor Jeﬁ‘ Wooldridge who as teacher and scholar instills in his students the desire to
ﬁnd and achieve their best. Many thanks to Professor John Staatz for his vision, wisdom,
and encouragement. Last, but not least, I would like to thank Professor A Allan Schnrid
for supporting the creative core in his students and encouraging us to honor intuition, the
spirit of community, as well as excellence in scholarship.

My deep gratitude extends to so many friends, colleagues and gifted scholars who
have believed in me when I forgot to and who helped to keep me on this path Thank you
so much Diane Pinkley, Cynthia Donovan, Meeta Punjabi, Julie Howard, Val Kelly, Karl
Longstreth, Pat Cark, Agustin Arcenas, Carol Luce, Andrew Howard, Sharon and David

Bogden, Mitch Bean, Eileen Van Ravensway, Sherry Rich, Scott Swinton, Tom Reardon,

-iv-

Kelly Comier, Sam Batzli, Mike Lipsey, Susan Blitz, Dave and Dorthea Goodwin, Robert
and Kathy Savit, Zvi and Marlene Gitleman, Evie Lichter, Donna Davis, Christina
Espinoza, Janice Stroud, Walter Penaranda, Irene Fisher, Colleen Scyphers, Vinita Kumar,
Rowel Fulinara, John Montgomery, Jennifer Wohl, and Dahnesh Medora.

A special thanks to my mother and brother for their love and support. Most of all,
I thank the Creator for my life and the blessings of health, loving companions, and grace

along this path to completion.

TABLE OF CONTENTS
LIST OF TABLES

LIST OF FIGURES

CHAPTER ONE INTRODUCTION AND PROBLEM STATEMENT

1.1 Introduction and Problem Statement .................................................. 1
1.2 Objectives of the Study ......................................................................... 3
1.2.1 The Construction of Vulnerability Indices ....................... 6
1.2.2 Tests for Market Integration .......................................... 11
1.3 Outline for the Dissertation .................................................................. 13
CHAPTER TWO FAMINE EARLY WARNING SYSTEMS
2.1 Deﬁnitions of Famine and Famine Theories ............................................ 15
2.2 Famine Early Warning Systems .............................................................. 20
2.3 Famine Early Warning Systems in Mali .................................................. 24
CHAPTER THREE A REFERENCE CYCLE FOR THE STATE OF
VULNERABILITY TO FOOD INSECURITY 1N MALI
3.1 Introduction .......................................................................................... 34
3.2 The Regions
3.2.1 Rainfed Agriculture .................................................................... 35
3.2.2 Water Recession Agriculture ..................................................... 36
3.2.3 Irrigated Agriculture .................................................................. 36
3.2.4 Wild Cereals .............................................................................. 36
3.2.5 Regional Descriptions ................................................................. 37
3.3 The Approach: Deriving Reference Cycles for the
State of Vulnerability ............................................................................. 39
3.4 The Reference Cycles by Cercle ............................................................. 43

3.5

3.6

Smoothed Millet Prices .......................................................................... 64

Comparing Smoothed Millet Prices with the Reference Cycles ............... 69

CHAPTER FOUR A Leadining Indicator Model

4.1

4.2

4.3

4.4

4.5

4.6

Introduction

4.1.1 What do Leading Indicators Lead? ............................................. 72

4.1.2 An Application of the Single Index Model to Food Security
Vulnerability ............................................................................... 75

The Model

4.2.1 The Single Index Model for Vulnerability to Food Insecurity ....... 84

4.2.2 State Space Representation of the Single Index Model of
Vulnerability .............................................................................. .87

4.2.3 Construction of the Single Index of the Vulnerability to Food
Insecurity ................................................................................... 89

Estimation

4.3.1 The Kalman Filter Approach ...................................................... 91

4.3.2 State-Space Representation and the Kalman Filter ..................... 98

Data and Preliminary Analysis

4.4.1 Description of Data ................................................................. 100

4.4.2 Univariate Analysis ................................................................. 102

Results

4.5.1 Model Speciﬁcation ................................................................. 116

4.5.2 The Single Index for each Region ............................................ 121

Comparison of the Single Index with the Simple Index and Reference
Cycles ................................................................................................. 135

CHAPTERFIVE VulnerabilitytoFoodInsecruityandRegionalGoatMarket

5.1

5.2

Integration in Mali
Introduction ...................................................................................... 142
Literature Review
5.2.1 Cointegration ......................................................................... 143
5.2.2 Threshold Cointegration ........................................................ 147

 

5.3 Model Speciﬁcation and Estimation...... .............................................. 150
5.4 Data and Preliminary Results

5.4.1 Univariate Analysis of Retail Goat Prices ................................. 156
5.4.2 Cointegration Tests of Retail Goat Princes in Mopti Region ..... 164
5.5 Empirical Results and Discussion
5.5.1 Empirical Results ..................................................................... 172
5.5.2 Implications of Threshold Cointegration Results and Market
Integration in Mapti ................................................................ 188

CHAPTER SIX CONCLUSIONS AND POLICY RECOMMENDATIONS

6.1 Conclusions
6.1.1 The single Index ofVulnerability to Food Insecurity ................... 190
6.1.2 Regime Shifts in Market Integration ............................................ 193
6.2 Policy Recommendations .......................................................................... 194
6.3 Future Research. ....................................................................................... 197
APPENDDI CHAPTER THREE ............................................................................... 199
APPENDIX CHAPTER FOUR ................................................................................. 238
REFERENCES .......................................................................................................... 293

Table 2.1
Table 2.2
Table 2.3
Table 2.4
Table 2.5
Table 2.6
Table 3.1
Table 3.2
Table 3.4.1
Table 3.4.2
Table 3.4.3
Table 3.4.4
Table 3.4
Table 4.1
Table 4.2
Table 4.3
Table 4.1.0

Table 4.1.1
Table 4.2.1
Table 4.3.1
Table 4.4

Table 4.5

Table 4.5.1
Table 4.5.2
Table 4.5.3

LIST OF TABLES

Selected Famines of the Twentieth Century ............................................. 16
A Taxonomy of Famine Theories ............................................................ 19
Typology of Early Warning Systems ....................................................... 23

Mali’s Climate andTopography ............................................................... 26
Principal Droughts in the Sahel since1740 ............................................... 30
SAP Vulnerability Classiﬁcations ............................................................ 32

Characteristics of Reference Periods for Vulnerability Assessments ......... 41

Classiﬁcation of the Severity of Food Secruity Conditions ....................... 42
Summary Clasiﬁcations for Reference Cycles .......................................... 44
Vulnerability Assessments for Gao based on SAP Reports ....................... 49
Vulnerability Assessments for Mopti based on SAP Reports ................... 55

Vulnerability Assessments for Timbuktu based on SAP Reports .............. 61

Comparing the Smoothed Millet Price Index and the Reference Cycles....70
Operating Assumptions used by FEW S ................................................... 79
Descriptive Statistics for Retail Millet Prices ......................................... 105
Descriptive Statistics for Adjusted NDVI .............................................. 106
Critical Values for Augmented Dickey-Fuller

and Perron Unit Root Tests ................................................................... 106
Gao Results from Augmented Dickey-Fuller and Perron Tests ............... 109
Mopti Results ﬁom Augmented Dickey-Fuller and Perron Tests ............ 112
Timbuktu Results ﬁ'om Augmented Dickey-Fuller and Perron Tests ...... 115
Number of Lags of Vt in Adjusted NDVI and Millet Price Equations ..... 121
Comparing the Single Index with the Reference Cycles .......................... 122
Parameter Estimates for the Vulnerability Index, Gao ............................ 127
Parameter Estimates for the Vulnerability Index, Mopti ......................... 131
Parameter Estimates for the Vulnerability Index, Timbuktu ................... 134

Table 4.6
Table 5.1
Table 5.2
Table 5.3
Table 5.4
Table 5.5

Table 5.6

Table 5.7

Table 5.8

Table 5.9

Table 5.10.1

Table 5.10.2

Table 5.10.3

Table 5.11

Comparing the Single Index with the Simple Index

and Reference Cycles ............................................................................ 136
Descriptive Statistics for Retail Goat prices ........................................... 157
Results from Augmented Dickey-Fuller Tests for Retail Goat Prices ...... 158
OLS Estimates of Cointegrating Relationship: P',’ = a + P: .................... 166
Cointegration Testing Results ............................................................... 167
AIC values for SETAR models for cointegrating relationship between

Mopti and DJenne Goat Prices .............................................................. 174

p-values for HCC test of linearity against SETAR alternative for the
cointegrating relationship between Mopti and DJenne Goat

Prices .................................................................................................... 174
SETAR estimates for the cointegrating relationship between Mopti and

DJ enne Goat Prices ............................................................................... 17 5
AIC values for SETAR models for cointegrating relationship between
Mopti and Douentza Goat Prices ........................................................... 178
p-values for HCC test of linearity against SETAR alternative for the
cointegrating relationship between Mopti and Douentza Goat

Prices .................................................................................................... 178
SETAR estimates for the cointegrating relationship between Mopti and
Douentza Goat Prices ............................................................................ 179

SETAR estimates for the cointegrating relationship between Mopti and
Douentza Goat Prices ............................................................................ 180
SETAR estimates for the cointegrating relationship between Mopti and
Douentza Goat Prices ............................................................................ 181
AIC values for SETAR models for cointegrating relationship between
Mopti and Tenenkou Goat Prices ......................................................... 183

Table 5.12

Table 5.13

Table 5.14

Table 5.15

Table 5.16

p-values for HCC test of linearity against SETAR alternative for the
cointegrating relationship between Mopti and Tenenkou Goat

Prices .................................................................................................... 183
SETAR estimates for the cointegrating relationship between Mopti and
Tenenkou Goat Prices ........................................................................... 184
AIC values for SETAR models for cointegrating relationship between
Mopti and Youvarou Goat Prices .......................................................... 186
p-values for HCC test of linearity against SETAR alternative for the
cointegrating relationship between Mopti and Youvarou Goat

Prices .................................................................................................... 186
SETAR estimates for the cointegrating relationship between Mopti and
Youvarou Goat Prices ........................................................................... 187

 

Figure 1

Figure 2
Figure 2.1
Figure 2.2
Figure 3.4.1
Figure 3.4.2
Figure 3.4.3

Figure 3.5.1

Figure 3.5.2

Figure 3.5.3

Figure4.1
Figure4.1.1

Figure 4.1.2
Figure 4.2.1
Figure 4.2.2
Figure 4.3.1
Figure 4.3.2
Figru'e 4.5.1
Figure 4.5.2
Figure 4.5.3
Figure 4.5.1

LIST OF FIGURES

Creating a Single Index to Depict Vulnerability to

Food Insecurity ......................................................................................... 4
Market Integration and Vulnerability to Food Insecurity ............................ 5
Map of Mali ............................................................................................ 27
Mali: Rainfall Isohyets ............................................................................. 28
Geo Reference Cycle Mapping of Vulnerability to Food Insecurity .......... 45
Mopti Reference Cycle Mapping of Vulnerability to Food Insecurity ....... 53
Timbuktu Reference Cycle Mapping of Vulnerability to

Food Insecurity ....................................................................................... 59
Gao Simple Index and Reference Cycle Mapping of Vulnerability to Food
Insecurity ................................................................................................ 66
Mopti Simple Index and Reference Cycle Mapping of Vulnerability to
Food Insecurity ....................................................................................... 67
Timbuktu Simple Index and Reference Cycle Mapping of Vulnerability to
Food Insecurity ....................................................................................... 68
Coping Strategies Model ......................................................................... 78
Geo Retail Millet Prices ......................................................................... 108
Gao Adjusted NDVI .............................................................................. 108
Mopti Retail Millet Prices ...................................................................... 1 10
Mopti Adjusted NDVI ........................................................................... 111
Timbuktu Retail Millet Prices ................................................................. 113
Timbuktu Adjusted NDVI ..................................................................... 114
Gao Vulnerability Index to Food Insecurity ........................................... 124
Mopti Vulnerability Index to Food Insecurity ........................................ 129
Timbuktu Vulnerability Index to Food Insecurity ................................... 133
Gao Vulnerability Index to Food Insecurity , the Simple Index and
Reference Cycle Mappings .................................................................... 137

Figure 4.5.2

Figure 4.5.3

Figure 5.1
Figure 5.2
Figure 5.3
Figure 5.4

Figure 5.5
Figure 5.6

Figure 5.7

Figure 5.8

Figure 5.9

Mopti Vulnerability Index to Food Insecurity the Simple Index and

Reference Cycle Mappings .................................................................... 138
Timbuktu Vulnerability Index to Food Insecurity the Simple Index and
Reference Cycle Mappings .................................................................... 140
Mopti: Retail Goat Price ....................................................................... 159
DJenne: Retail Goat Price ..................................................................... 160
Douentza: Retail Goat Price .................................................................. 161
Tenenkou: Retail Goat Price ................................................................. 162
Youvarou: Retail Goat Price ................................................................. 163
Mopti Vulnerability Index and residuals ﬁom cointegrating regression,
Mopti and DJenne ................................................................................. 168
Mopti Vulnerability Index and residuals ﬁ'om cointegrating regression,
Mopti and Douentza ............................................................................. 169
Mopti Vulnerability Index and residuals ﬁ'om cointegrating regression,
Mopti and Tenenkou ............................................................................. 170
Mopti Vulnerability Index and residuals from cointegrating regression,
Mopti and Youvarou ............................................................................. 171

 

Time-Series Modeling of the Famine Early Warning System
Chapter 1

Introduction

1.1 Problem Statement

This research centers on the econometric analysis of the system used by USAID to
assess vulnerability of a subset of Aﬁican nations to acute food insecurity. The Famine
Early Warning System (FEWS) Project of USAID was developed in 1987 as a response to
the severe famines of the mid-eighties. The FEWS mandate includes a) providing early
warning capacity for decision-makers within USAID, b) providing support for host
governments in the development of national early warning systems; and c) collaboration
with the international community on the development of improved early warning
technologies (Riley, 1993). FEWS analyzes market, socioeconomic, and climatic data
ﬁom various early warning systems and market information systems in numerous African
cormtries. The project also uses information from satellite imagery on crop vegetation and
rainfall levels. FEWS monitors conditions in Mauritania, Mali, Burkina Faso, Niger, and
ChadintheSahelaswellasin Sudan, EthiopiaandKenyainEastAﬁica SinceMay
1992, FEW S expanded to include Malawi, Mozambique, Zambia and Zimbabwe in
Southe'nAﬁica TheseexpansionsarepartoftheFEWS mandatetoinﬂuenceand
enhance food security in Aﬁica (FEWS,1992).

This study will undertake time-series modeling of agroclimatic and socioeconomic
dataforMali. Thebiophysicaldatamcludesrainfaﬂindicesandthenormaﬁzeddiﬁ‘a’ence

1

vegetative index (NDVI) while the socioeconomic data include cereal prices and
inﬁ'astructure information. The ﬁrst part of the study develops an econometric model of
the dynamic relationship between socioeconomic and agroclimatic measures, and the state
of food security vulnerability. A single index leading indicator model is constructed to
identify signiﬁcant relationships that might enhance early warning of supply-side
susceptibility to food insecurity. The index distinguishes between periods of high and low
vulnerabilitytofoodinsecurity. The secondpart oftheresearchconcems spatialand
temporal market integration and the econometric tests used to measure such relationships.
Thesingleindexdevelopedintheﬁrstpartofthis studywillbeusedinthetests ofmarket
integration

Whether markets are integrated and, if so, the degree to which markets are
integrated is important to policy makers. Markets that may have appeared integrated in
less vulnerable times may cease to ﬁrnction well under stress. Rather than moving to a
deﬁcitregiorr, asurplusinonemarketmayremaininthatmarket duringaperiodoffood
insecurity. Thus, policy makers should ﬁnd this information useful in their eﬁ’orts to
mitigate food shortages as they decide whether food aid is necessary, how to distribute
food aid shipments, and whether to purchase imported grain. With this in mind, early
analmenwmpassesmderstmdingmemannermwhichmmketsrespondmﬁmesof

O O .

1.2 Objectives of the Study

Figures land 2 provide an overview ofthe two parts ofthis research. The speciﬁc
objectives of the research are:

1) Construct afood secrnityvulnerabilityindexandevaluate its eﬁ‘ectivenessin
depictingthestate ofvulnerabilityto foodinsecurityforregionsinMaliusingNDVIand
price data.

2) Employ robust econometric tests for market integration which are structural in
nature.

3) Use the vulnerability indicators in the construction of models of market
integrationtoassesstheimpactofvulnerabilityonthedegreeand stabilityofmarket

integration between regions.

Figure 1

Creating a Single Index to Depict Vulnerability to Food Insecurity

 

 

    
 

    
 

     
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

I anUuobservableState
A formal statistical model
FEWS and SAP (Mali) use primary linking the rndrgaltors could
and secondary data as key indicators d prove use as a .

, , escnptive and forecasting
of vulnerability ...assessments are [00' in early warning
made by experienced analysts using actiVities

this information - .
and in program
development
A
1
Secondary data include agro-climaiic and
socioeconomic information: V
. The Model:
‘33:?! _ Vulnerability to food
Prices of staples insecurity rs treated as an
Asset prices unobservable state
Disease outbreaks variable. Use Stock and
Watson’s single-index
framework to develop a
monthly vulnerability index:
based on millet prices and
‘ NDVI.
SAP reports provide qualitative '
assessments regarding /

 

; vulnerability to food insecurity

 

SAP monthly assessments are used
to highlight and compare vulnerable
periods

along with socioeconomic and
agroclimatic information

 

 

 

 

 

 

 
     

Outcome: Construction of Vulnerability ‘
Indexes for Eiolrt Re ions in Mali ' ‘

   

 

Figure 2

Market Integration and Vulnerability to Food Insecurity

 

 

 

 

   
   

 

 

 

 

 

 

 

 

Tests (WW AIC markets integrated?
In tion:
‘98" 1 I l . that
Iﬁstorically, look at price integration: spatial trade and Transactions costs (unobservable),
correlations to infer . WW3 . . lead to a regime shift under
integration (later work M8 M10 PCPOdS 13 prices are not linked to each other in
usescointegration “W““WM? oneregimebutarelinkedinanotha'
recognizing the - regime.
nonstationary data
typically used) Price arbitrage activities are '
' only when localized shocks remit in
Both approaches ignore price diﬁ‘erences which exceed some
transactions costs threshold

, WkwmhanmmmuMcanbeglemwdﬁompdcemfonnaﬁmangmka
i

se tin-allow reintegration to deplore market integration quarters rot/goat markets in
optt, Malt. Mopti as central maker with Jenna, Douentza, Tenenkou and Youvarou
peripheral mar-ken.

 

 

 

 

a relationship to exist between periods of vulnerability as depicted in Part One
the regime slot/ts In market integration.

 

 

1.2.1 The Construction of Vulnerability Indices

FEWS does not conduct primary data collection. Instead, it develops, manages,
analyzes, and presents data from a variety of existing sources. In this sense, FEWS is a
consumer of Aﬁican information systems. In return, the FEWS management and analysis
of the data is helpful to national and regional agencies. With this approach, FEW S is able
to investigate regional patterns of food security indicators across FEWS countries. (May,
1992).

Geographical Information Systems (GIS) are a method for digitally organizing, storing
and analyzing information on the earth's resources in a spatial ﬁamework. Information
ﬁom satellite imagery, population, agricultural and geological characteristics can be
analyzed and simulated for policy analysis. FEWS relies on this tool in its assessments.

Currently, FEWS uses the term vulnerability to describe the ”relative susceptibility of
households to various levels of food insecurity" (Riley, 1993). In practice, FEWS does
not evaluate individual households but relatively homogenous groups of households. The
project focuses on pastorialists, agro-pastorialists, sedentary farming groups, landless
laborers, ﬁshermen, craftsmen, urban poor and reﬁrgees located in speciﬁc areas. The
ﬁequentlyused indicatorsusedinearlywamingandvulnerabilityassessmentare:

1) Crop production, area and yield estimates for both food and non-food crops;
2) livestock productiondata; '
3) NDVI satellite data which reﬂects photosynthetic activity and vigor, or the

"greenness” of vegetation;
4) Meteosat estimates of probable rainfall in a given area;
5) Point rainfall estimates from individual weather stations;
6) Market prices and quantities of throughput of food and cash crops,

6

livestock and other commodities;
7) Data on the outbreak of civil conﬂict in speciﬁc locations;
8) Information on food aid distributions; and
9) Health information such as vaccination programs and disease outbreaks.

The most consistently collected of these indicators are market prices, rainfall estimates and
the vegetative index

At each stage of the assessment process, FEWS analysts use conﬁrming indicators
such as information on household behavior and nutritional status to evaluate the evidence
supplied by the secondary data sources. Using the convergence of evidence approach
(NRC, 1987 in Riley, 1993), the conﬁrming indicators are used to support formal analysis
or to direct ﬁrrther data gathering when contradictions occur (Riley, 1993). Ideally, all
indicators would be available in all regions in some consistent time interval, e. g. on a
monthly basis. This not being the case, the FEW S Field Representatives (FFRs) do not
use a formal model. Rather, they evaluate vulnerability from period to period using the
above mentioned indicators and anecdotal information, placing more weight on some
information in one period than another.

While the FEW S analysis is informative, a precise model used consistently in
assessrnentshasnotyetbeenestablishedevenonacountryorregionspeciﬁcbasis. Itis
unclear how the convergence of evidence approach is operationalized or whether there is
any means to verify its eﬁcacy in any formal way. Mthout a well-deﬁned notion of
vulnerabilityoraformalﬁ’amework, itisdiﬂiculttoassesstheeﬁ‘ectiveness oftheearly
warningcapacityoftheproject. Ananalysiswhichhasthemeansforvalidationof

WacouldMenﬁaﬂyimprovetheeaﬂywamingfadﬁtyoftheanalysis.
7

'I‘heﬁrstpartofthedissertationwillprovideaformal model ofearlywarningto
food security vulnerability in Mali. The dynamic relationship between market indicators
and agroclimatic information will be investigated. The indicators used in this study are
market prices and NDVI. Thus, the model results will reﬂect a notion of supply-side
susceptibility food insecurity. Here we hope to capture the relationship between leading

' and lagging components such that if one indicator implies a potential problem, it is
detected early enough to be conﬁrmed through further targeted research The nature of
these relationships may assist analysts in early warning activities so that vulnerable areas
can be identiﬁed well in advance.

The vegetative indices, rainfall estimates and price data are available on a monthly
basisandadekadalbasis formanyregionsinMali. Evaluation oftheunivariate
clmracteristics of each series as well as their relationship to each other provides some
useﬂﬂinsightsintotheassesmnaﬁofvdnmbﬂhyandeaﬂywarmngcapabiﬁﬁes.

Thegoal ofthis researchistobuildaleadingindicatormodel ofMali'sfamineearly
warningsystem USAIDandthegovernment ofMali developedearlywarning systemsin
the mid-eighties, FEWS and the Systeme d’Alerte Precoce (SAP), respectively. Both use
agroclimactic and socioeconomic variables to detect evidence of famine conditions and
allow for maximal response time to crisis on the part ofdonors and government agencies.
'I'heinformationgatheredbyFEwsandSAPincludethefollowing: normaldiﬂ‘erenced
vegetativeindemeVI, ameasureofcropgreenness),rainfalllevels, andprice
information SAPalsoprovidessomequalitaﬁveandquantitaﬁveassessmentsonhealth,
migrationandinsectinfestation. Monthlydataisusedtoconstructthemodelforthe

 

I
\
II
.M.

period of October/Novemba' 1986 January 1998. The regions within Mali selected in
thisstudyareasubsetofthosemonitoredbySAP: Areasin Segou, Mopti, Gaoand
Timbuktu above the 14‘ parallel.

The model developed here is similar to those used to describe and predict
macroeconomic downturns and turning points of business cycles for developed economies
(Stock and Watson (1991), Necﬁi (1991), Kim and Nelson (1999), Hamilton (1994)). In
this approach, we deﬁne a variable which represents the general "state of vulnerability"
with respect to a region's susceptibility to food insecurity. This notion of vulnerability
refers to the regime during which regional food insecurity could emerge. Given the
observed information in the model, we create a variable which is descriptive of this
unobserved state and serves as an indication of the vulna'ability of agents within regions
to a Food Availability Decline (FAD). The regions monitored by SAP were selected
becausetheyareproneto entitlement failures. WhileFAD theories offamineare
groundedinthelackofavailablefood, entitlementfaihrresemphasizetheinabilityof
agents to acquire resources even when they are available in ample supply. It is assumed
thattheregionsarenotclosed systemsandtradecantakeplacebetweenthem. Thisissue
is explored in the second part of the research Essentially, in regions monitored by SAP, a
bmadgeneralinﬁonworﬂdbethatmtheseareasmeFADmdenﬁﬂememfaﬂuresare
intertwined because real incomes in these areas, even among non-subsistence households,
are largely dependent on agriculture and livestock People, in general, are prone to
enﬁﬂemanfaihnewhichisﬁrrtherexacerbatedbyfoodavaﬂabﬂitydecﬁnes, suchasthose

engardered by poor rainfall conditions and other events which depress crop production.

period of October/November 1986 January 1998. The regions within Mali selected in
thisstudyareasubsetofthosemonitoredby SAP: Areasin Segou, Mopti, Gaoand
Timbuktu above the 14“ parallel.

The model developed here is similar to those used to describe and predict
macroeconomic downturns and turning points of business cycles for developed economies
(Stock and Watson (1991), Necﬁi (1991), Kim and Nelson (1999), Hamilton (1994)). In
this approach, we deﬁne a variable which represents the general "state of vulnerability"
with respect to a region's susceptibility to food insecurity. This notion of vulnerability
referstotheregimeduringwhichregionalfoodinsecuritycouldemerge. Giventhe
observed information in the model, we create a variable which is descriptive of this
mobsavedstatemdsavesummdicaﬁonofmeVMnaabﬂityofagentswithinregiom
to a Food Availability Decline (FAD). The regions monitored by SAP were selected
becausetheyarepronetoentitlement failures. WhileFADtheoriesoffamineare
groundedinthelackofavailablefood, entitlementfaihrresemphasizetheinabilityof
agentstoacquireresourcesevenwhentheyareavailableinample supply. Itisassumed
thattheregionsarenot closed systernsand tradecantake placebetweenthern Thisissue
is explored in the second part ofthe research Essentially, in regions monitored by SAP, a
bmadgenerﬂinﬁonworﬂdbethatmtheseareastheFADandenﬁﬂememfaﬂuresare
intertwinedbecauserealincomesintheseareas, evenamongnon-subsistencehouseholds,
are largely dependent on agriculture and livestock People, in general, are prone to
enﬁﬂememfaihnewhichisﬁutherexacabatedbyfoodavailabiﬁtydecﬁnes, suchasthose

mgeuderedbypoorrainfaﬂwndiﬁommdomaevunswhichdepresscroppmducﬂon

We describe the current "state of vulnerability”, which is a common element of key
variables collected by FEWS and SAP. We build a probability model which creates a
deﬁnition of the unobserved "state of vulnerability”. As in Stock and Watson (1991), we
assume linearity in the unobserved variables. We employ a Kalman Filter to construct the
Gaussian Likelihood ﬁmction and glean estimates of the parameters. From this we get the
mean squared error (MSE) estimate of the unobserved state of vulnerability variable using
the observed information at time t. We are then creating a ”single-index” model in which
the state of vulnerability, V, is an unobserved variable common to multiple time series.

The estimate ofV, can be viewed as an index ofcoincident indicators using a
single index model This altermtive index can be forecasted using leading variables
includingNDVI, pricesofstaple crops, assetprices, curmrlativerainfall, aswellas
qualitative variables. The steps which lead to this vulnaability index involve
characterizingthesysteminastate—spacerepresentation, estimatingparametersusingthe
Kahnanﬁlter, andupdaﬁngparameterestimateswiththemostcurrentinformation. From
thismodel,wecanalsocalculatetheexpected durationofvulnerability states.

10

1.2.2 Tests for Market Integration

There have been mrmerous studies using price series to evaluate the existence of
market integration between regions (Alderman 1992, Timmer 1987, Ravallion 1986).
These studies use various econometric models to assess information ﬂows between
markets. Essentially, this body of work investigates the correlations between prices and
infers that comovement means that markets are integrated. The basis for this approach
restsintheconcept oftheLawofOnePricewhichpositsthatinaneﬁcient market, prices
for homogenous goods in two diﬂ‘erent regions should differ at most by the transportation
costs.

Cnrcial information concerning inﬁastructure (the existence and quality of roads)
andmnsacﬁonscostshasbeennotablyabsentﬁomthesemodelspnmmilydueto
inaccessibility of data Optimally, transport rates between markets would be available and
integration easements would include these. However, this information, especially in
countries with less developed transport sectors, is costly to collect and potentially
unreliable. Essentially, this leaves the researcher with price data only. Still, an issue
remains the appropriateness ofusing prices alone to determine the adstence and nature of
market integration A strong correlation between two or more prices from various
markets is diﬁcult to interpret. Ravallion (1986) constructed a simultaneous system with
many restrictions in order to yield an expression of long-run market integration primarily
using prices.

Fackler (1994) carefully critiques Ravallion's approach as well as the efforts of
other researchers. Fackler develops a spatial equilibrium model and develops testable

ll

restrictions on market eﬁciency and market integration While Fackler achieves testable
restrictions for long-rim market integration similar to those of Ravallion, he uses
econometric techniques such as cointegration and error-correction models which are
better suited to some price data due to non-stationarity. Fackler has developed a model
which lends itself better to interpretation and which does not impose ad hoc restrictions in
order to achieve identiﬁcation of a system.

Fackler argues that there has been much conﬁrsion over the concepts of market
integration and market eﬁciency. If we deﬁne market integration to mean that two
regions are actively trading with each other, then markets could have varying levels of
integration. For example, a natural disaster may make it diﬁcult to transport between
regionsasreﬂectedinhightransportcosts. Iftradersaredeterredbythehighcoststhen
itishardto saythattheyarenotactingeﬁciently. Thesemarketsarestillpotentially
integrated and, most likely, once transport costs decrease, trade will resume. In using only
prices, the shock to transport costs would aﬂ‘ect both markets and thus their prices. It
would be diﬁcult to distinguish just from an investigation of prices whether rmrkets are
integrated.

Recent work by Goodwin and Piggott (2001) uses a model ofthreshold
cointegration to explore the role of transactions costs in inhibiting price adjustments which
can aﬁ‘ect tests ofmarket integration. They evaluate price linkages among several local
North Carolinian corn and soybean markets. Goodwin and Piggott’s analysis employs
nmﬁnearmodeﬁngthquestomcoumforregimesshiﬁswitmnanwnﬂbandmdepia

the eﬂ'ects of transactions costs on long-run equilibrium relationships. Threshold models

12

characterize a regime switch as triggered by a transition variable (or variables) crossing
some threshold value. This threshold parameter delineates alternative regimes. Hitherto,
most tests for market integration express the long-run price adjustment as a linear
phenomenon while threshold cointegration allows for alternative regimes to exist along
with the long-run relationship.

This idea of incorporating regime shifts in tests of market integration leads to
another issue which is the ﬁmctioning of markets during times of food security
vulnerabilityorfamine. ThefactthatmarketAisanet sellerofmaizetomarketBina
non-stress period does not imply that this integration would prevail in times of
vulnerability. Such information would be useful to donors and policy makers with respect
to food aid distribution design and other policy oriented concerns. Thus, it would be

useful to incorporate‘a dynamic measure of vulnerability into a structural model of market
integration. A model that explores the relationship between regime shiﬁs, expressing
different levels of vulnerability to food insecurity, and price information would be useﬁrl
inassessingthestabilityofmarket integration. Inthesecondpartofthisresearch, we
examine the functioning of goat markets in Mopti. We incorporate additional structural
mfomaﬁonmdaﬂowdiﬁ'erenﬁﬂhegraﬁonmﬁmesofsuessusingthevmna’abiﬁty
indicator developed in the ﬁrst part of this study.
1.3 Outline for the Dissertation

The dissertation is organized into six chapters. Chapter One is the introduction

just presented. Chapter Two provides an overview of famine early warning systems and

some background on Mali and its famine early warning program. Chapter Three provides

13

an overview of the regions in the study and presents a reference cycle of vulnerability to
food insecurity for each region. Chapter Four presents a model of leading indicators and
then develops an application for modeling the unobservable state of vulnerability to food
insecurity. Chapter Four also provides empirical results for the leading indicator model
for famine early warning Chapter Five presents an investigation of market
interrelatedness in Mopti’s goat markets and explores nonlinear aspects of these
relationships. Chapter Six provides a summary of the research ﬁndings and the

implications for policy.

14

Chapter 2

Historical Overview of Famine Early Warning Systems

2.1 Deﬁnitions of Famine and Famine Theories

F amines have occurred throughout the history of mankind. The twentieth century
saw many incidences of famine internationally (see Table 2.1). As we embark on the
twenty-ﬁrst century, the risk and occurrence of famine persists globally, but most
predominantly in Aﬁica (Von Braun et al, 1999). Diﬂ‘erent disciplines in the social and
physical sciences each approach famine with particular views, deﬁnitions, and theories.
Deﬁnitions of famine range from a focus on anthropomorphic measures, such as relative
body-weight (Rivers, 1988), to socioeconomic deﬁnitions, such as the lack of available
food in a region causing migration in search of sustenance. Walker (1989) deﬁnes famine
as a socioeconomic process which causes the accelerated destitution of the most
vulnerable, marginal and least powerful groups in a community, to a point where they can
no longer, as a group, maintain a sustainable livelihood. According to D’Souza (1988),
famine can be deﬁned as a reduction in normally available food supply such that
individuals, families, and eventually whole communities are forced to take up abnormal
social and economic activities in order to ensure food. Ifthese activities are unsuccessﬁrl,

then starvation will follow.

15

Table 2.1 Selected famines of the twentieth century (Von Braun et al, 1999)

 

 

 

 

 

 

 

 

 

 

 
 

 

 

 

 

 

 

 

 

  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

India ozambique 1982-83
India (Darbhanga) 1906-7 Shel Region 1982-85
Russia 1913-1915 [Sudan 1984-85
Russia 1921-22 [Horn or Africa Region 1983-85
China 1929 [Mozambique 1985-86
Russia/Ukraine 1933-34 [Sudan 1988
Russia (Leningrad) 1941-43 Somalia 1988
India/Bengal 1943 [Ethiopia (Horn of Africa) 1989-90
Russia/Ukraine 1946-47 Liberia 1992-93
China 1958-61 Somalia 1992-93
Nigeria (Biafra) 1968-69 Sudan 1993
Bangladesh 1971-72 Angola 1993-94
Cambodia 1973 Liberia/Sierra Leone 1995-98
Sahel Region (West 1969-74 ire 1997
Africa) [Z-

Ethiopia 1972-74 orth Korea 1996-98
Bangladesh 1974-75 Sudan 1998
Angola 1974-76

Zaire (Bas Fleure) 1977-78

Uganda 1980

 

 

Sen (1981, p40-41) notes that in analyzing starvation in general, it is important to make
clear distinctions between three diﬁ‘erent issues: (1) lowness of typical level of food
consumption; (2) declining trend of food consumption; (3) sudden collcmse of food
consumption. Famine is chieﬂy a problem of the third kind. As the above suggests,
deﬁnitions offamine range ﬁ'om a state ofcrisis or catastrophe, as in Sen, to that ofa

process, as depicted by Walker and others.

16

To Western observers, famine is commonly thought of as a widespread death ﬁom
starvation. De Waal (1989) and Devereux (1993) note that the crisis state deﬁnition
depicts “outsider” deﬁnitions of famine where famines imply starvation (referring to
perspectives such as donors and international organizations). The “insidef’perspective
incorporates concepts such as the coping strategies engaged, the fact that fanrine mortality
relates to disease outbreaks indirectly tied to undernutrition, and the complexities and
stratum of famine. To illustrate, the Arabic word for hunger is maja ’a. In Darfur, Sudan,
the word takes on a process oriented, or dynamic meaning, incorporating three aspects:
hunger, destitution, and death, with the emphasis on destitution (Devereux, 1993 and
Starr, 2000, personal correspondence). Davies (1996) and others use the terms “food
vulnerability” and “food insecurity” interchangeably, with the understanding that famine is
the last stage in a process of hunger and destitution FEWS (1997) employs the following
deﬁnitions in deﬁning levels of food security which it then uses in its publications such as
Vubrerabiliiy Assessments.

Food Insecurity is the inverse of food security: a condition in which a population

doesnothaveaccessto suﬁcientsafeandmrtritiousfoodoveragivenperiodto

meet dietary needs and preferences for an active life. Possible causes are

insuﬁicient food availability, insuﬁcient food access, and inadequate food

utilization

Current (or transitory) food insecurity occurs when a population suﬂ‘ers a
.temporarydeclineinconsumption Currentfoodinsecuritycanresultﬁom

instability in food production, food prices, household incomes, or health

conditions.

17

Chronic (or long-term) food insecurity occurs when a population has
continuously inadequate consumption. Chronic food insecurity arises ﬁorn
conditions of poor food production, limited incomes, and poor health.

In this work, famine will be deﬁned as the endpoint of a process during which
households and communities move ﬁom a situation of food security through various
stages of vulnerability to food insecurity where the end of the process is famine as deﬁned
in the ‘outsider’ sense. Essentially, this notion of mapping the vulnerability to food

insecurity captures both “insider’ and “outsider” perspectives and terminology.

There have been many attempts to develop comprehensive theories of famine
which tend to be divided along disciplinary lines. Dominant theories have approached
famine as a FoodAvailability Decline (FAD) resulting ﬁ'om population increases or
external shocks, such as drought or war. Alternative explanations include market failure
(Alamgir, 1989) and exchange entitlements faihrre (Sen, 1981). Table 2.2 provides a
taxonomy of famine theories. Von Braun et al (1999) note that the debate over famine
suﬁ‘ers from the following: (1) untestable hypotheses of broad generality, (2) the synthetic
hypotheses of broad generality, (3) a lack of empirical analysis of the actual
socioeconomic processes that occur during famine. Single-cause and general theories fail
to provide lasting insights into the complex and dynamic famine risks facing Aﬁican
nations as well as the rest of the developing world. Political, institutional, organizational,
market, andproductionfailuressetthestageforhouseholdsandcommunitiestobe

vulnerable to food insecurity. Armed conﬂict has been widely acknowledged as a major

18

factor in both famine and chronic food insecurity in Aﬁica (Duﬂield (1990), Von Braun et

al (1999), Harrison (1988), Davies (1996)).

T 2 f ‘ ri m 1 3
l. FoodAvailability Decline (FAD)

1.] Population Increase (Malthus)
1.2 War

1.3 Climatic Factors

2. Economic Theoria
2.1 Market Failure
2.2 Exchange entitlements (Sen)
3. Socioeconomic and Political Dislocation
3.1 Due to Structural Changes
3.2 Due to changes associated with development

4. Government M'rsnranagenrent (Political or Institutional Failure)

5. Anthropological or Sociological Explanations

6. Maui-causal orEclectic Approaches

 

 

 

Drought itself does not immediately translate into famine. Seasonality, climate
shocks, and production failures are not unknown to households in these vulnerable

regions. In response, they have developed coping (short-term) and adaptive (long-term)

19

   

strategies. The relationship between drought and famine, as well as between production
failures and famine becomes strong when poverty is endemic, the resource base is poor,
and the public capacity for prevention and mitigation is weak (Von Braun, 1999). When
drought takes place in consecutive years and other factors combine, a reoccurring
vulnerability prevails that can be deﬁned as ‘not lack or want, but defenselessness,
insecurity, and exposure to risks, shocks, and stress’ (Chambers, 1989). Crow (1986)
notes that climatically-induced reductions in output may aﬂ‘ect the household’s ability to
get food by: (l) reducing their own production (and any sales thereof); (2) reducing their
ability to get cash or kind wages through work, and (3) reducing purchasing power of
what wages they do get (and assets they have accumulated) by raising the price they pay
for food. To survive, households respond to these circumstances with a variety of coping
strategies or adjustments, such as risk minimization, risk absorption, and risk taking.
Persistent challenges that necessitate evoking these strategies exhaust the household’s
asset base and make them more vulnerable to food insecurity. In Chapter 3, this process
is discussed in more detail.

Given the complex process that leads to famine, it may be more constructive to
thinkofthefamineearlywaming asaneﬂ‘ort to identifythetriggersthat compromisefood

securityandmaptheresulting path ofvulnerabilitytofood insecurity.
2.2 Famine Early Warning Systems

In response to the tragic loss of life due to the Aﬁican famines of the 1970's and

20

1980's, the international community established a variety of famine early warning systems.
The creation of these systems stems ﬁom the hypothesis that there had been a lack of
available information for donors and in-country policy makers to equip them to respond in
a timely manner to food security emergencies. These early warning systems (EWS)
gathered information which tended to focus on the food availability issues, such as crop

production All of the EWS have their own approach and objectives for their systems.

There are a variety of early warning systems monitoring conditions in the Sahel,
Horn of Aﬁica, and in southern Aﬁica, each which operate on diﬂ‘erent levels. There is a
global-level EWS , the Global Information and Early Warning System (GIEWS) of the
United Nations’ Food and Agricultural Organization. GIEWS was founded in 1974 and
operates on a very broad national basis. It is geared toward information gathering to
address food shortages and is designed to be used by policymakers in decisions concerning
food aid and emergency measures. The integration of the information concerning food
security conditions generated ﬁom sources such as country food balance sheets and the
use of remote sensing techniques were pioneered by GIEWS. Indicators such as the
“normalized diﬁ‘erenced vegetative index” (NDVI) and methods for monitoring cloud
cover for inference on rainfall conditions were also developed through GIEWS’ Aﬁica
Real Time Environmental Monitoring Satellites (ARIEMIS) and other NOAA satellite
imagery. GIEWS is supply-oriented and is an important source of information for donors
and governments in food security decision-making. GIEWS is not formally linked to

response mechanisms (Buchanan-Smith et al, 1991). A complementary system to GIEWS

21

is the World Health Organization’s Epidemiological Early Warning System (EEW S).

There are also regional early warning systems that include the two early warning
projects operated by the Comité Permanent Inter-Etats de Lutte contre la Secheresse dans
le Sahel (CLISS) in nine countries in West Aﬁica and the Famine Early Warning System
(FEW S) of the United States Agency for International Development (U SAID) that

operates throughout Aﬁica.

On a national level, there are multiple systems in Mali, Chad, Ethiopia, Sudan,
Botswana, Kenya, and Burkina Faso. The Systeme d’Alerte Précoce (SAP) is Mali’s
oﬁcial early warning system Southern Aﬁica’s regional EW S is run by the Southern
Aﬁican Development Coordination Conference (SADCC) in ten countries. There are also
several non-governmental organizations that maintain or have operated targeted early
warning systems, including Save the Children Fund that operated in the Inner Niger Delta
region of Mali (SADS project), and the Red Crescent Society that operates in parts of
Sudan

The Evolution of Famine Early Warning Systems

There has been much criticism to date of the crisis orientation of EWS.
Researchers (Davies (1995), Campbell (1991), Devereux (1993), Walker (1981), Brock
(1998), Rakodi (1999) ) and policymakers point out that, for the system to possess an
truly early warning capacity, there should be an emphasis on sustainable livelihoods as an

approachtominimizethevulnembﬂityofhouseholdsheadingintoadownward spiralto
destitution when a drought or some other external shock occurs. Table 2.3 outlines the

22

diﬂ‘erent approaches to early warning (where conventional refers to systems such as

GIEWS and FEWS, historically). There have been eﬂ‘orts to incorporate the alternative

approaches; however, regional and international early warning systems tend to focus on

the conventional approaches in terms of information gathering and analysis. In recent

years, FEWS (1999) has extended its approach to gather and analyze the sorts of access

and socioeconomic information called for by critics. FEWS still tends to rely on

conventional indicators in its Vulnerability Assessments while developing alternative

indicators (FEWS 1999).

 

 

 

 

 

 

 

 

 

 

 

 

Table 2.3 Typolog of ca: warning systems
Conventional Famine Alternative Food
Early Warning Systems Information Systems

Scope F arnine-orierrted Food security-oriented
Determinants of food Food production Access to food
security ‘
Level of operation Macro-centralized Micro-decentralized
Unit of analysis Geographic Socio-economic

e.g. nation/districts e. g. vulnerable groups
Approach Top-down Bottom-up

Data-centered People-centered
Response F ood-aid oriented Sustainable improvement in

access to food

 

Source: Davies et al. (1991)

Themissionoffamineearlywarningsystemsisstillevolving. ThecurrentEWS

are viewed as state—of-the-art in their collection and dissemination of information.

23

 

However, there exist problems with the link between early-warning and response
mechanisms. As recently as 1996/97, the Sahelian countries of Chad, Mauritania, and
Niger faced problems of food shortages due to production shortfalls. Assistance to
vulnerable communities came late at best. The reasons include the inability to
communicate eﬂ‘ectively the direness of circumstances in some regions, conﬂicts between
donor and recipient countries, and the lack of news media attention as the problems
emerged. As a result, the slow response increased the vulnerability of many communities
who faced even further erosion of their assets (FEW S 1998). As these challenges are
addressed, and the capacity for broader use of the information systems such as FEWS is
recognized, the role for such systems will most likely be expanded in development
planning. In a recent article, Pierstrup-Andersen (2000) summarized the emerging issues
in agricultural development. He noted that incorporation of technological advances
(which include information systems) in economic development planning is an emerging
issue. Early warning systems as a leader in high tech information gathering mechanisms
will certainly have an important role to play as a tool in development policy and program
analysis ( for example, in the analysis of programs such as marketing information systems)
and will likely continue to evolve with an expanded mandate for information collection and

analysis.

2.3 Famine Early Warning Systems in Mali

Mali is a land-locked country situated in West Africa, with Mauritania, Niger,

Burkina Faso, COte d’Ivoire, Guinea (Conkry) and Senegal as its neighbors. Oﬁcial

24

estimates of population are 10.6 million in 2000 with a low growth rate of approximately
1.8% due to emigration Twenty-ﬁve percent of the population is urban and 75% reside in
rural areas of which ﬁve percent is nomadic. Mali is one of the world’s poorest nations,
with a GDP per capita of $270 in 1996. Since independence in 1960, economic growth
and structural change have been slow. In 1965, 90% of the labor force drew their
subsistence level of income ﬁ'om agriculture, while the ﬁgure is estimated between 80 and
90 percent in 1995 (Davies, 1996). Opportunities for wage employment are few. The
main agricultural crops for Mali are millet, sorghum, and cotton Livestock-raising
accounts for approximately one halfof agriculture’s contribution to GDP and is the
principle economic activity in the north. Drought, institutional problems, and low prices
to producers have affected Malian agricultural production. Cotton, gold and livestock are
Mali’s main exports. In 1994 there was a devaluation in the CFA ﬁ'anc which has
stimulated exports. GDP growth is expected with the expansion of the mining of gold and

cotton exports. Cotton accounted for over 50% of export income in 1998.

The Malian transportation system is poorly developed. The road systems are
comprised of a mere 18,000 km of classiﬁed roads, of which 6,000 km are all-weather and
2,000 km are tarred (See Figure 2.1). The inland waterways, which extend over 1,815
km, are essentialto thetransportation infrastructure. TheRiverNiger serves asthekey
transportation route in the rainy season.

This sparsely-populated former bread basket of French West Aﬁica is mostly
covered with desert or semi-desert. Table 2.4 provides some general information on
Mali’s climate and topography.

25

T 124 " ' T h

 
   
  
 

subtropical to‘arid', hot and dry Februaryrtro lune; ' ,
.rainy, humid, and mild June to November; cool and
{dry November to February.

mostly ﬂat torolling northern plains covered by

    

 

Source: ESA-ESARIN, 1998

26

Figure 2.1 Map of Mali

 

“r. .

SounezMilreuMsMichiganStateUnlvenltyaool)

27

The country is 1,240,142 sq km, slightly less than twice the size of Texas, and

spans ﬁve agroecological zo

nes that are diﬁ'erentiated by rainfall levels. About 780 sq km

is irrigated by the River Niger. This research centers on the Sudanic, Sahelian, and

Sahelian-Sahara zones, which receive annual rainfall levels of 350-600 mm, ZOO-350 mm,

and less than 200 mm,
respectively (see
rigme 2.2). Rainfall
and Sahelian zone
ﬂood levels of the
River Niger are the
key determinants of
food production. The
south is the productive
region, whereas the
population of the
Sahelian north suﬁ‘ers
signiﬁcantly lower
average income per
capita of rural

producers, estimated

 

Figure 2.2 Mali: Rainfall Isohyets (Davies, 1996)

 

 

 

   

at 8100 in 1990 (Davies, 1996).

 

100
200
400
800.

1000

 
     
    
  
   

 

 

 

Rainfall isohyets in mm

While there have been numerous accounts of drought in the Sahel throughout

history, the onsets of such have rarely resulted in famine in Mali. Mali has suﬂ‘ered two
widespread famines in the last 50 years, in 1972-3 and 1984-5. Malian Sahel and Inner
Niger Delta are not characterized by acute shortages that lead to famine, but rather by
chronic seasonal food insecurity in most years that can degenerate into acute shortages for
some groups (Davies, 1996). The civil unrest that has perpetuated and supported famine
in other Aﬁican regions was absent in Mali in the 1970's and 80's, which contributed to
fewer onsets of fanrines after drought. In the early 1990's, conﬂicts between the Malian
army and the Tuareg, and between rival groups in the north, were negotiated into
settlements. Further conﬂicts have been sporadic. While famine is a relatively rare
occurrence in Mali, chronic seasonal food insecurity for certain groups is a reality in
Northern Mali. Table 2.5 offers a very rough overview of droughts and the onset of

famines in the Sahel ﬁom 1740 to present.

29

Table 2.5 Principal Droughts in the Sahel since 1740

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1740 Drought, famine
1750 Drought, famine
1790 Drought
1855 Drought
1900-3 Drought
1911-14 Drought, famine
1931-4 Drought, famine
1942 Drought
1950 Drought
1968-73 Drought, famine
1983-5 Drought, famine
1987 Drought
1990 Drought
1992 Drought

 

 

Source: Moorehead (1991) and Davies (1996)

Famine Early Warning in Mali

Since 1986, no famines have occurred in Mali, yet many areas have experienced
food crises. The early warning capacity has improved information ﬂows, and has helped
facilitate national policy makers and donors in their response decisions. The development
of a market information system (SIM), cereal market reforms, and SAP have proved

useful tools for mitigating crises, and have still greater potential as development planning

30

tools. Current fanrine early wanting systems, like FEWS and SAP, tend to be focused on

local food supplies.

Famines, however, do not occur only due to declines in local production Declines
in incomes and other factors prevent households from getting adequate access to food;
thus, famines can result even in the presence of ample food supplies. In Mali, poverty and
weather ﬂuctuations tend to be the primary factors contributing to food insecurity. The
areas selected for monitoring by SAP ( all arrondissments above the 14‘h parallel in the
regions of Mopti, Kayes, Kidal, Gao, Timbuktu, Segou, and Koulikoro) are prone to food
insecurity for a variety of reasons, including livelihood vulnerability, where external events
such as drought can move a household from subsistence to destitution fairly rapidly due to
the household’s dependence on agriculture for income sources. One could say that the

regions are prone to entitlement failures when there are shortfalls in regional production.

Local committees convene monthly to review the food situation in the
arrondissment. The committees are composed of technical experts ﬁom the Ministry of
Rural Development, the weather service, livestock service, hydrological service, NGO’s,
as well as members of civil society. They debate and discuss the food security situation
and come to agreements in order to ﬁll out the standard questionnaire. The questionnaire
is then validated by Cercle-level oﬁcials, and sent on to regional capitals. The regional
SAP representative synthesizes the questionnaires and generates a regional report, which
is sent to SAP headquarters in Bamako. Bamako oﬂicials consider the situation in each
zone, assessing whether any production shortfalls (especially in food-production-deﬁcit
zones) can be oﬂ‘set by acquiring food through markets and other transfers. Monthly

31

reports summarize the ﬁndings and provide an assessment of each arrondissement’s food

situation using the following classiﬁcations (Table 2.6):

Table 2.6 SAP Vulnerability Classiﬁcations

 

 

 

 

 

 

 

 

 

 

Acronym Meaning Government Relief Action Undertaken
through SAP
RAS Nothing to report None
DEL Some economic None
diﬁcultics
DES Severe economic None
difﬁculties
DA Food Deﬁcit 3 months distribution of “half ration” to
everyone in the arrondissement
(9kg/person/mo) during the soudre (June-Sept)
CA Food Crisis 7 months distribution of “half ration” to
everyone in the arrondissement
(9kg/person/mo) ﬁ'om January through
September
FA Famine Full ration (l8kg/person/mo, plus salt and dried
ﬁsh) distributed until the situation improves.

 

 

This classiﬁcation system has been in place since SAP’s inception. It is important to note
the early warning and response connection developed in this early warning system Since
1986, no arrondissement has ever been classiﬁed as FA. Ifan arrondissement is
designated as DA or worse, SAP launches a special socio-economic study that looks at
markets, health, livestock and the area’s nutritional situation Ifthe situation worsens,
SAP sends a team to conduct medico-socio-nutritional studies that include anthropometry

(SAP, 1986-1999 and Staatz, 1997 through personal correspondence) .

32

This research relies on the monthly SAP reports and FEWS for time-series data

and other information. How this information is used to develop a vulnerability index is
discussed in the following chapters.

33

CHAPTER 3
A Reference Cycle for the
State of Vulnerability to Food Insecurity in Mali

3.1 Introduction

The aim of this chapter is to produce a reference cycle that can be used as a
comparison to the more complex indexes developed later in this research. In business
cycle research, reference cycles provide a depiction of actual business or socio-economic
conditions and create a narrative framework with which to compare the results ﬁom
simulating or estimating an econometric model. Mdely accepted notions of reference
cycles in business cycle research are readily available because there exist well-deﬁned
measures of economic downturns such as recessions (deﬁned as two or more quarters of
negative growth in GNP).

Such a widely accepted notion of an ‘objective’ measure of vulnerability to food
insecurity does not exist in the econonric development literature, among international
development organizations, or among donors. However, famine early warning systems
such as Mali’s SAP and USAID FEWS do track certain qualitative and quantitative
measures in an effort to document the factors contributing to periods of vulnerability to
food insecurity. In this research, we develop reference cycles based on the qualitative
assessment of famine conditions using the monthly bullitens published by SAP in Mali. In
this chapter, Section 3.3 details the approach to developing the reference cycles and
Section 3.4 reports the reference cycles for each region in the study. Section 3.5 provides
asimpleindexofsmoothed millet pricestobeused asabenchmarkforthesingleindex

34

developed in the next chapter. Section 3.6 compares the smoothed millet price index with
the qualitative reference cycles developed for each region. The next section provides

some background on the regions examined in this research.

3.2 The Regions

The regions selected for this study are those monitored by SAP because they tend
to be deﬁcit producers of cereals (with the exception of Segou) and more vulnerable to
rainfall variability than the southern regions of Mali. Nicholson (1983) characterizes
regions based on rainfall criteria: 100-200 mm for the Sahelo-Saharan Zone, 200-400 mm
for the Sahel Zone, and 400-600 mm for the Sahelo-Sudanian Zone.

Over time, the length of the rainy season in the Northest has shortened while
variability of precipitation has increased (Steﬂ'en, 1995). In general, Davies (1996) notes
a trend toward the decline in rainfall levels in the region over the past several decades.
Rainfall plays a dominant role in the production of Mali’s main agricultural crops: millet,
sorghum, maize, rice, cotton and groundnuts. There are three main cropping patterns in
regions monitored by SAP: rainfed agriculture, water recession agriculture, and irrigated
agriculture. Livestock trade is also an essential economic activity in the region monitored
by SAP and FEW S in Mali.

3.2.1 Rainfed Agriculture

Millet and sorghum are the principle cereals grown in the areas monitored by SAP.

They have adapted to semi-arid conditions and can produce a minimum yield even in years

of generally poor conditions (Steffen, 1995). Sorghum requires a longer growing season

35

and is thus more vulnerable than millet to rainfall volatility. Planting occurs in June and
July with harvests in October and November.
3.2.2 Water Recession Agriculture

Water recession agriculture in Mali consists of sequential planting along the river
banks of the Niger and other rivers or around seasonal ponds in the desert as water levels
receed in the dry season. River recession sorghum planting takes place in February and
March The crop is harvested in September or October in periods of good rains. Serni-
sedentary nomads plant pond recession sorghum in September or October around
temporary ponds formed in the rainy season with a harvest in January or February
(Steﬂ'en, 1995)
3.2.3 Irrigated Agriculture

Irrigated agriculture is practiced along the Niger, Bani and Dire Rivers and in lake
zones. There are two annual rice crops. The main crop is planted in July and harvested in
December. The oﬁ-season rice crop is planted in January and harvested in June (Steﬂ’en,
1995). The PPIV, established in 1991, operates in the Northeast to promote rice
cultivation and distribution.
3.2.4 Wild Cereals

Wild cereals such as fonio and cram-cram are gathered to ﬁll the cultivated cereals
gapduringthelrrrngryseason ThewildcerealsmatureinAugustandSeptember. Since
the droughts of the 1970's and 1980's the stigma associated with consunrption of wild
cereals has lessened. However, consumption of these cereals serves as a signal of

household food stress and is reported in the SAP reports as such. Fonio can be stored for

36

long periods and is relied upon by nomads and poorer households in the hungry season
(Davies (1995), Steﬂ'en (1995).

The cercles(regions) examined in this research are Gao, Mopti, Timbuktu, Segou,
and four cercles in Mopti Region: DJenne, Douentza, Tenenkou, and Youvarou. For the
analysis in Chapter 3 and Chapter 4, the research discussion and results for Gao, Mopti
and Timbuktu are presented in the main body of the dissertation while the background and

ﬁndings for the remaining regions are presented in appendices.

3.2.5 Regional Descriptions
Goo

The cercle of Gao is located in Northeastern Mali bordered by the regions of
Timbuktu to its west and Kidal to its north with Burkina Faso and Niger along its southern
and eastern border. The annual rainfall level is 185mm. Gao is a cercle which has
experienced tremendous changes due to civil conﬂicts and enviromnental factors. Kidal,
to the north separated from Gao in 1990 while conﬂicts of the early 1990's also
contributed to a great deal of upheaval and uncertainty in the region. Along with
Timbuktu, Gao serves as the northern most boundary of pasture for pastoralists. Gao is
connectedtoBamakoandothereasternareasbyapavedroad andis situatedalongthe
Niger River. These factors support Gao Cercle as an important center of trade in the
Northeast of Mali. A deﬁcit region in the production of cereals, agro-pastoralists and

pastoralists tend to play a dominant economic role in the region.

37

Marti

The cercle of Mopti is the administrative center of the Mopti Region and serves as
a barometer for local, regional and in the case of ﬁsh and livestock, international
commerce, rural-urban migration, and the general economic health of the region (Davies,
1996). Average annual rainfall is under 500mm As a transportation node, all trade to
the north passes through Mopti which is located where the rivers Niger and Bani converge
with the Seno drylands to the east and south. Trade to Timbuktu passes through by road
(when the ﬂood has retreated) or by river and to Gao by road (see Figure 2.1).
Regionally, Mopti is the pathway for trade for imports and exports for the Mopti, Gao,
Kidal and Timbuktu regions. Cereals from southern regions are the most important
imports for subsistence producers along with manufactured household items. Exports
from Mopti include dried and smoked ﬁsh. Livestock markets are less concentrated in the
region such that Fatoma and Douentza are also important trading centers. Trade in Mopti
serves as an indicator for regional prosperity: merchants serve as creditors and cereal
traders in times of deﬁcits and suppliers of productive equipment and consumer goods in
times of surplus. Migrants in search of urban employment, which is diﬁcult to come by in
bad years, pass through Mopti. Since the drought of 1984-5 more displaced persons have

stayed on in the cercle (Davies, 1996).

Timbuktu
Timbuktu is the northern most cercle in Mali and is bordered by Algeria,

Mauritania and Niger Gao, Kidal and Mopti. In the 1990's the region experienced a long

38

period of unrest and armed conﬂict which was peaceﬁrlly defused culminating in an
historic bonﬁre of the armaments from disputing parties (Poulton and Youssouf, 1998).
Due to this regional conﬂict and those in surrounding areas, Timbuktu has experienced
large movements of displaced persons. In the mid-1990's repatriation of refugees who ﬂed
the conﬂict further stressed the resource and administrative capacities. In addition,
environmental factors such as declining rainfall levels and desertiﬁcation have put
enormous pressure on already vulnerable communities. Timbuktu receives approximately
160m of rainfall each year and, like Gao serves as the northern boundary of pasture for
pastoralists.
3.3 The Approach: Deriving Reference Cycles for the State of Vulnerability

The indicators used in this study are transformed such that an increase in their
value is associated with increasing vulnerability. For example, as millet prices increase,
household purchasing power declines, all else being unchanged, thereby negatively
aﬂ'ecting the household’s level of food security. As indicated earlier, there does not yet
exist a formal model such as a leading indicator model of vulnerability nor is a formal
quantiﬁable deﬁnition of vulnerability widely used. Therefore unlike the case with Stock
and Watson’s approach to the NBER Coincident Index, where an index exists and an
accepted deﬁnition of recession also exists, a conventional measure to be used as a basis
for comparison for vulnerability indexes constructed in this research is not available. For
the purposes of this study, reference periods have been developed ﬁ'om the qualitative

assessments made by local and regional oﬁcials in the monthly SAP reports.

39

The SAP reports provide a monthly summary for each monitored cercle consisting
of reports on pasture conditions, the agricultural outlook for seasonal crops such as millet
and sorghum and for contra-seasonal crops such as rice, disease outbreaks, the status of
household reserves (impressionistic rather than from surveys), migration patterns, ﬁshing
outlook, market condition, rainfall levels and the need for food aid. Beyond the broad
status characterizations such as Food Deﬁcit (DA) and Famine (FA) presented in Table
2.6, the summaries provide a considerable amount of data from which to glean information
concerning food security conditions. Reference periods depicting events or circumstances
that can trigger food security vulnerability are presented in Table 3.1. These criteria were
distinguished for the cercle as a whole as well as for localized phenomenon and are used
as benchmarks for characterizing vulnerable periods and relating them to the constructed
vulnerability indices.

The SAP reports are sometimes criticized as overstating and understating food
security conditions (Davies 1996, Buchanan-Smith and Davies 1995) and in failing to
clearly authorize food aid with enough lead time to be eﬂ'ective. However, the reports
provide the most consistent record of food security conditions, both in terms of time ﬂame
and substance, in the region Table 3.1 outlines the events which were identiﬁed as
fostering conditions of vulnerability to food insecurity. Table 3.2 presents the method for
classifying a period as moderately vulnerable or highly vulnerable. An example of
moderate conditions would be a slight increase in prices combined with other factors such
asdiseaseoutbreaks. Aperiod ofhighvulnerabilityisdeﬁnedasonewheretherewasa

sharp and sustained rise in prices and low quantities ofstaples in the market, for example.

40

Table 3.1 Characteristics of Reference Periods for Vulnerability Assessments

 

 

 

‘ Household reserves reported as “weak”

   

General qualitative assessment of vulnerability: _ food secunty summary
DEL: Some economic drﬂicultres (as presented in Table 2. 6)

 

DES: Severe economic drﬂicultres K'
'DA FoodDeﬁcrt :,

CA: Food Cnsrs
FA: Famine

Food aid authorized ‘ ,
Sharp increase in staple prices reported
Low level of staples in market- reported

Disease outbreaks. classiﬁed as epidemics (memngrtrs cholera and/or measles)
Consumption of Wild cereals reported

 

 

41

Table 3.2 Classiﬁcation of the Severity of Food Security Conditions

 

5-;fuv Ay‘f'C; agave”? 11. w... ;. .m:..,.., f“ in}. ”(4,; 1““
.‘r r.‘11"' :..u'frvfmut‘qur‘ ' . .v L‘Ir
- ‘-2~ .. a w .a. .. ,i .
’ " 8 scation " ' “
a ' 1, . -- .—' .
1’" 415.3)“ “' Twins-4' iii-A "r it -' ;~~'« 1‘ ;. "'r- 't' a '-

Description

 

 

Law Vulnerability

Reports of no problems or localized problem areas with a
duration of less than two months

 

Moderate
Vulnerability

Sustained pockets of problem areas

Price increases sustained over more than one month
Low quantities of cereals on the market for one month
Moderate damage from pests

Household Reserves (HHR) reported as fair or average
Consumption of secondary crops

 

High Vulnerability

 

 

Sharp and sustained increase in food prices

Sustained decrease in the availability of cereals in the markets
High levels of disease cited

Sharp increases in oﬂlseason migration

Civil unrest reported

I-IHR reported as weak

Persistent but spread out DES/DA

 

Periods where there were vulnerability is classiﬁed as low, unmarked in the

ﬁgures, tended to include no signiﬁcant reports of problems or reporting of isolated

problem areas that were not noted for more than two consecutive months. The severity

and duration of conditiond are documented such that periods of high vulnerability can be

distinguished ﬁ'om periods of low vulnerability. For example, ‘sharp increase in food

prices’, ‘oﬂlseason migration’, and ‘very low quantities of cereals marketed’ that

persisted for more than one month were depicted as highly vulnerable periods. Whereas

. periods with occurances such as ‘some economic difﬁculties’or ‘some disease

outbreaks’were viewed as moderately vulnerable periods if these conditions persisted for

twomonthsormore. Inthenextsection, wepresentthereferencecyclesonatimeline

42

 

for Gao, Mopti, and Timbuktu as representations of the qualitative information gleaned

ﬁ'om the monthly SAP reports.

3.4 The Reference Cycles by Cercle

This section provides an overview of vulnerability conditions for each region as
expressed as reference cycle mappings. Figures 3.4.1 through 3.4.3 map the qualitative
information from the monthly SAP reports month by month for each region. The lightly
shaded areas represent moderately vulnerable conditions whereas the darker areas depict
periods of high vulnerability to food insecurity. These shaded areas are descriptive and
based on the narratives provided in the SAP reports such that the volume of food aid
distributions or the severity of a disease outbreak are not captured only their presence or
absence or. their persistence. Each regional summary is followed by the reference cycle
mapping for the cercle (Figures 3.4.1 through 3.4.3) as well as detailed chronologies for
each region (Tables 3.4.2 to 3.4.4). Table 3.4.1 provides a summary of reference cycle

classiﬁcations for Gao, Mopti and Timbuktu.

43

Table 3.4.1 Summary Classiﬁcations for Reference Cycles

  
  

 

  

 

 

 

 

 

 

 

 

r... - , ~ Flint: k r - ' . " i 4..
January 1987 to June 1987 November 1986 to December 1986
September 1987 to January 1988 February 1988 to May 1988
November 1989 to February 1990 March 1990 to June 1990
September 1992 June and July 1993
January 1993 to February 1994 March 1994 to September 1995
October 1995toMay 1996 Junel996toAugnst 1996
September 1996 to January 1998 May and June 1997
October 1997

Mopti January 1987 to July 1987 November and December 1986
October 1989 August 1987 to July 1988
January 1990 to May 1990 June 1990 to October 1990
July 1991 April 1992 to July 1992
October 1991 to March 1992 April 1995 to August 1996
February 1993 March 1997 to January 1998
December 1993 to May 1994
March 1995
September 1996
February 1997

Timbuktu January 1988 to August 1988 October 1986 to July 1987
March 1989 to Angst 1990 June 1989
April 1991 February 1990 and March 1990
July and August 1991 May and June 1991
January and February 1992 September 1991 to December 1991
JunetoAnglst 1992 March 1992toMay1992
December 1992 to December 1993 June 1994 to January 1998
January and February 1995

(some areas)
Gao

We turn to Figure 3.4.1 which maps the reference cycle of vulnerability to food

insecurity in Gao. The time period under investigation is November 1986 through

Jamrary 1998.

 

 

 

ummzcucoS.

MM Pom“
ll' In,

10.: I
m._.<mm_00_2 a.

Kuquweum :0 9:vo mares 099

 

 

 

 

 

 

 

 

 

 

wee— baa—an. 8 33 hon—8962
e583 e8... 3 £2.55; .3 ”5.3.5 296 sates. 8e 3..» 2:5

 

 

45

The SAP reports note that October 1986 was a period of signiﬁcant vulnerability,
exacerbated by the hardship of famine during 1984 and 1985 that weakened or exhausted
household resources. The SAP bulletins for November and December 1986 indicate with
migration pressures into Gao cercle from outlying areas in search of food during this
period. In addition, consumption of wild cereals is noted as a strategy to maintain levels
of household reserves. N’Tillit is noted as especially vulnerable in June 1987 but an
isolated case. Food aid deliveries throughout the period (May, June, August and
December) appear to mitigate food security crisis in the cercle although vulnerability
persists with a poor harvest in October/November 1987.

January 1988 is a period of increasing vulnerability with migration pressures and
markets poorly stocked in cereals. February through May 1988 are declared periods of
food insecurity in the cercle. Food aid deliveries are spaced out during this period and
appear to mitigate further deterioration in conditions. Food security conditions improve
with the release of food aid and with harvests in October and November of 1988.

In January, 1989 the SAP‘reports make note of N’Tillit as an isolated problem
where there are increasing prices. April 1989 reports describe the onset of a vulnerable
period. In March and April 1989 high volatility in prices of staples is noted with a trend
ofincreasingpricesaswellasanincreaseintheconsumptionofwild cerealsinthecercle
in general. In May conditions deteriorate with disease outbreaks, high price volatility,
lower levels of cereals in the market and irregular migration. August and September are
found to be moderately vulnerable times with migrant workers returning with low levels

46

of resources, higher prices and a scarcity of cereals in markets. A food aid delivery in
November assists those in the most vulnerable regions. Several areas in the cercle
contimre to be under surveillance. In February more isolated problems are reported with
additional pressures with the arrival of refugees from Algeria. In May and June 1990
food aid distributions and wild cereals mitigate food security problems where a average
harvest is expected.

Alter August 1990, the qualitative assessments do not indicate conditions
detrimental to food insecurity for some time. There are some isolated problems in
Djebock in the ﬁrst part of 1991 and a food aid delivery in July after which the qualitative
reports do not indicate any further problems. No major problems are reported in 1992,
however, the formerly secondary strategy in food insecure periods of consuming wild
foods has become a typical way to supplement household reserves. A food aid delivery
in November 1992 serves to assist the most vulnerable. Djebock and N’Tillit are
declared to be having economic diﬁculties from January through August 1993. In April
and May disease outbreaks are reported cercle-wide. In June pressures mount with an
increase in migration ﬁ'om Algeria due to civil conﬂict. In July, the United Nations High
Commission on Reﬁrgees establishes a camp in Gao. In August 1993, an expected poor
huvestisreaﬂzedmdsharpmcreasesmmiﬂetpﬁcesaroundthecerclereponedm
SeptembermeaoberwchthatthereisanmcreaseMnerabiﬁtytofmdmseauity
throughout the rest of 1993.

47

These conditions of high prices and migration due to civil conﬂict persist
throughout 1994 and 1995 such that populations are growing more and more vulnerable.
The negative eﬁ‘ects of devaluation on millet prices and the increase in the adjusted NDVI
during this period indicate a threat to food security. The decline in vegetative cover may
depict the disruptions to farming due to civil conﬂict and the deterioration of
environmental conditions in the region. The remaining months in the sample are times of
high vulnerability in Gao cercle primarily due to the disruptions caused by civil conﬂicts
and then with repatriation of displaced persons. There were high levels of disease in this
period as well. Signiﬁcant food aid deliveries in April, May, and August 1997 served to

mitigate food insecurity somewhat but only with short-run impacts.

48

Table 3.4.2 Vulnerability Assessments for Gao based on SAP Reports

 

.u.v 1

.'v v -. aI‘..v.’§- ".... {use "v. .. ’«u "- . . ...A . .1 .-‘ .. ramp u a... A, ..r -‘ 2. «...-us" . . wg’..v x-.,a...r. .;«a..:.,41\vi Va ‘. r n .4 a
~. ' 7.. ."..". .4 J' -. . ' '— \. ..r ‘ J'. . ~- '. .7 . ; ,, y _
._Year ., .- ,V 11100"me Assessment based on SAP Reports ,
L :- . , ~ 1...”.Lﬂ-4‘... " :4 its; 1 - ‘ 5.4-S. and, .L .2 ‘i: ' .‘ Agnau .u. . . A... 5‘ "m5. "' ‘..’ lanai” “ca mag“

. L ,r
1 vi nus-.4 a!“ ,

.«,_ a..L-'-q;... Lsmu.u_'...i._..s14 ‘ .2

 

1986

October through December: vulnerable
HHR: weak

 

1987

January through April, October, November: vulnerable due to migration
pressures(those in search of food)

June: N’Tillit especially vulnerable

May, June, August, December: Food aid delivery

September through December: vulnerable due to poor harvest

 

1988

January increasing vulnerability with migration and markets
poorly stocked in cereals

February through May declared DA, June stable

July: Increasing vulnerability

August: HHR wealg consumption of wild cereals noted

 

1989

January through May: N’ Tillit isolated problems
Increasing consumption of wild cereals in cercle
Prices increasing with quantities decreasing
March/April: Volitility in markets noted, Prices increasing
May: Some disease outbreaks, high price volitility, diminished quantities
of cereals marketed, inhabitual migration
August: Return of migrant workers with low levels of resources
September: Prices increasing with low levels of cereals in market
November: N’tillit under surveillance, Food Aid delivered

 

1990

February: Isolated problems reported
March - June: Increased migration from Algeria
May and June: Food Aid
Livestock markets volatile
Fonio and cram-cram (wild cereals) ﬁll HHR gaps

 

1991

February through July isolated problems (Djebock)...not entire cercle
July: Food aid in N’Tillit

 

1992

 

 

November: Food aid distribution
September: Fonio supplements HHR

 

49

 

 

1993

January through August: Djebock and N’Tillit DE
April and May: Food aid delivery
Noted increase in disease outbreaks,
Tabaski Festival in May .
June: Increase in migration ﬁ'om Algeria due to civil conﬂict
July: HRC camp established, Poor Ag outlook, Sharp increase in millet
prices

 

l 994

February: Increases in millet prices
March: Sporadic measles outbreaks
April: Increase in spread of disease, Tabaski Festival
May: Migration ﬁ'om Djebock due to insecurity
June: Increase in disease, sharp price increases, continued migration
July: Markets not necessarily ﬁinctioning well, blocked due to civil
unrest

Food Aid delivery....Errtire cercle AT RISK Sharp increase of
exports to Burkina Faso and Niger...high prices for goats
August and September: Increased migration due to civil insecurity
October and November: Monitoring problems due to insecurity
December: Some areas not reporting due to civil unrest,

high level of displaced persons

 

1995

January: Djebock and N’Tillit no report due to civil unrest
February: Migration back to Djebock, Steep price increases
April through June: Repatriation alter conﬂict

Sharp price increases for millet, sorghum and rice
March through May: Food Aid deliveries

(questionnaires were not distributed at local level)

July: Low levels of Rainfall, declining terms of trade, Food Aid
August: Food Aid, Problems in Djebock and N”Tillit
September: Repatriation esp around Djebock
October: Poor Ag outlook, more repatriation with poor reserves
November and December: DA in N’Tillit (many areas not reporting)

 

 

1996

 

January through August: N’Tillit (DA)

April through September: HHR weak

May: Epidemic menigitis

June: Food aid

June through December: economic problems (DE)
June through August : food insecure (DA)

 

50

 

 

 

 

 

 

1997 March, April, and August: Food Aid Distribution
May and June: Disease outbreak
October: HHR weak
1998 January: Food Aid distribution
Source: SAP

51

 

Mopti
At the beginning of the sample period, October 1986, conditions of food

insecurity in Mopti are ‘high vulnerability’ as the SAP srunmaries report weak household
reserves, high levels of disease outbreaks and a qualitative assessment of generally poor
conditions (see Figure 3.4.2). Post-harvest in January through May 1987 there is a
decline in the severity of vulnerability with food aid deliveries in February, March and
April and June 1987. Beginning in May 1987 there is an increase on demands for foods
with an increase in migration to the area, the impact on prices is dampened with the food
aid distributions. By August, sharp increases in prices are reported followed by an even
higher level of migration ﬁ'om Timbuktu. A poor harvest and increasing migration
pressures cause a steep increase in vulnerability in the region which does not truly
diminish until just before harvest in 1988. From January through July 1988, increasing
vulnerability is reported with household reserves weak or exlmusted, out-migration ﬁ'om
villages and the selling oﬂ‘ of animals. After a signiﬁcant food aid distribution in June,
conditions beg'n to show improvement in August when households improve their
reserves with the consumption of wild cereals and with a good harvest for 1989. There is
localized vulnerability in Korientze where food aid is recommended. There is a sharp
increase in prices in November and December but a good harvest replenishes household
reserves.

The vulnerability conditions are moderate when compared to the period prior to

August 1988 and this seems reﬂected in the index. For most of 1990, Korientze is

52

 

 

LaotcuCO—Z

a w u a m
I. l I.
m.

10:..- a”
f m
m._.<mmn_0_2 _. . , ‘

Museum/no 91vo warez: ndow

 

wee“ Pas—:3. 8 exe— aonﬁo>ez
.3582: age 3 £528....» a. maﬁa: 296 anaes— eaoz «in 25...—

 

 

53

 

declared an area at risk and food aid is requested. Migration pressures persist.
Beginning in September 1990 there are sharp increases in cereal prices with low levels in
the market as well as migration pressures around F atoma, Korientze and Sendegue.
Food aid deliveries arrive in June and July. Conditions stabilize overall after harvest yet
some areas are still vulnerable.

In January 1991 there is a steep increase in prices and many areas are under
surveillance: Korientze, Sendegue, Fatoma, Korma, Soufouroulaye and Soye. This is
reﬂected in the vulnerability index. An April food aid delivery appears as a decline in the
vulnerability index which turns upward again right after the delivery. In July 1991 an
increase in millet prices is noted as well as consumption of wild cereals and the onset of
measles outbreaks. A moderately vulnerable period continues into August of 1992 when
a measles epidemic subsides and signiﬁcant food aid deliveries in June arrest further
deterioration in food security conditions.

The situation stabilizes and improves until post-harvest of 1993 when poor
harvests cause migration and several areas grow increasingly more vulnerable. Problems
persist throughout the ﬁrst part of 1994 and conditions appear to improve after three
signiﬁcant food aid distributions in April, May, and June and with the harvest. March of
1995 is the onset ofa very vulnerable period with high disease and sharp price increases.
Added to these poor conditions is the pressure of migration into the region by reﬁigees as
well as a poor harvest. Food aid deliveries appear to assist in the most vulnerable areas

and the situation improves with the harvest. Food security begins to deteriorate in 1997

54

and problems persist for the rest of the sample period. Sharp price increases, disease, and

weak household reserves characterize most of 1997 and January 1998.

Table 3.4.3 Vulnerability Assessments for Mopti based on SAP Reports

 

- .- ...: :Vulnerabrlity Assessment based on SAP Reports

Krg'rw‘nwra , ,"“'- ‘ _‘ ’4" "; £11..“ fur-(.1111;

October through December: vulnerable, especially Korientze
HHR: weak, high level of disease outbreaks

1987 January through May: somewhat vulnerable, some disease outbreaks
North at risk (Korientze and Doko)
February, March, April and June: Food aid
May through December: vulnerable or food insecure due
to increased migration pressures
August: Sharp price increases
September: Area under watch, high level of immigration ﬁ'om Timbuktu.
Food Aid
October: Poor harvest assessment, only good in Korientze and Senegue
Continued migration ﬁ'om Timbuktu
November: HHR weak. Poor pasture conditions
December: Poor rice harvest, stable but poor conditions overall
Food Aid and increase in migrations ﬁ'om Timbuktu

1988 January through July: increasing vulnerability, HHR weak or exhausted,
out migration from villages and selling oﬂ‘
of small animals

 

 

 

 

June: Food aid
August: HHR weak but stable with consumption of wild cereals

1989 October: Food aid recommended for Korientze,

Sharp increase in prices
Good harvest for 1988/89 mitigates HHR problems in November and
December

 

 

 

 

 

55

 

1990

January and February: Migration pressures, food aid requested for
Korientze, Oua-Mody, Fatoma, and Korma
Migrants enter with poor resources

March through June, October, November: Isolated problems in
Korientze: vulnerable
September and October: Increasing prices, low level of cereals in market.
Migration around Fatoma, Korientze and
Sendegue
June and July: Food aid
November and December: Stabilizing HHR

 

1991

January: Under watch- Korientze, Sendegue, Fatoma, Korma,
Soufouroulaye and Soye. Sharp increase in millet prices

April: Food aid to Korientze

July: Millet Price increases

June and July: Food aid distributions

February through August: Korientze precarious

June and July: consumption of wild cereals in some areas

September: Food aid authorized increase in measles outbreaks

October through December: increase in measles, some migration for food

Korientze still highly vulnerable

 

1992

February: Disease and DEL in Fatoma and Sendegue
April through August: Measles epidemic
June: Food Aid to vulnerable areas, then improvement

 

1993

February: Poor rice harvest causes vulnerability in some areas (DA)
October through December: some areas vulnerable

Poor harvest causes migration

Korma, Diallobou, Korientze at risk

 

 

1994

 

January, February, and March: Isolated areas vulnerable, (DA) in some
areas Rice harvest poor for most of the
cercle
Devaluation causes a slurp increase in
cereal prices, Konna DA

April: Korma DA, Food aid sharp increase in disease

May: Migrant workers arrive with poor resources, HHR weak

Meningitis spreads, Additional food aid requested for Korma

May and June: Food aid

 

56

 

 

1995

March: Meningitis outbreak
April: Increase in diseases and rising prices FOOD AID
May and June: Cholera and meningitis high
July: Food aid, prices increasing, disease prevalent
August: Pest and bird- crop damage
February through September, December: Cholera outbreak
November and December: Food aid, increase in disease
Camp created for Mauritanian refugees

 

1996

January: Prices sharply increasing, increase in disease

February through May: HHR weak

February and July: poor supply of cereals in markets, high prices noted

High level of disease(measles, meningitis, cholera): Worst April through
August

August: Food aid, markets poorly supplied prices increasing

September: prices still increasing, disease outbreaks stabilized

 

1997

March and April: continued disease outbreaks, price increases

March through May: many areas of vulnerability (North)

May and June: increase in disease, HHR weak

July: Sharp prices increases in some areas, HHR weak, FFW in Korma

Disease outbreaks stabilize

August: HHR weak, FFW, migrant workers with poor resources

September: HHR weak, FFW, Poor ag outlook

October: DEL in Fatoma, Korientze, Dialloube, poor harvest overall

June, August, September: Food aid

November and December: Poor agricultural outloob>economic
problems(DE) and some areas severe (DES)
Refugees begin retourn to Burkina F aso

 

1998

 

 

January: Some areas DEL
Food aid

 

Source: SAP

57

 

Timbuktu

The reference cycle for Timbuktu is presented in Figure 3.4.3. For Timbuktu
there are consistent reports of poor to moderate food security conditions from October
1986 through the harvest of 1988. Consistent deliveries of food aid and Food for Work
programs appear to mitigate some problems. There are improvements in the cercle with
consumption of fonio. In is an onset of poor conditions for food security as reported in
March and persist through August 1989 then conditions stabilize with harvest. Migration
pressures with reﬁrgees ﬁ'om Algeria are noted in February 1991 in conjunction with
sharp increases in prices throughout the rest of the year create conditions of vulnerability
through September when household reserves begin to be reconstituted with fonio and
harvested millet. November food aid serves to decrease risks to displaced households.

The problems of high disease and prices are reported in April 1992 as a cercle-
wide phenomenon. By May and June civil unrest prevents ﬁrll reporting of conditions in
the cercle, conditions of food insecurity are reported throughout the year but signiﬁcant
and consistent food aid deliveries seem to mitigate insecurity in the short-run In late
1992, nomad populations are at risk and deterioration in conditions persist as civil
conﬂict persists into 1993 and 1994, again signiﬁcant food aid distributions seem to
stabilizeprices. Asharpincreaseinvulnerabilityto foodinsecurityoccursatMay 1994.
Devaluation and continued civil unrest contribute to household resources being weak as I
noted in June 1994 in the SAP reports. Conditions deteriorate for the rest of the year
and into the next such that by June 1995 the entire cercle is DA Prices drop somewhat

with harvest and food aid but are still relatively high due to a poor harvest.

58

 

 

smotcucoﬁ

 

10.1-
m.._.<mm00_2. .

Murmur/so 9:010 were nunqwu

 

 

 

8a 5.5:. 3 83 .383
e582.— ..8._ e bangs...» ... ”59...: 295 8:29.8— .5.—3.5... 38 25%

 

 

59

Vulnerability conditions are still poor ﬁ'om January through September 1996 with
cholera and measles outbreaks, repatriation of refugees and weak household reserves due
to a poor harvest in 1995, post-harvest conditions of moderate vulnerability persist. By
September 1997 the region is once again declared DA with price increases, high
migration and weak household reserves. A hard hungry season is expected for 1998,
with December 1997 reﬂecting sharp price increase.

In the next section, a simple index is developed as a method to depict turning

points for conditions of vulnerability to food insecurity.

Table 3.4.4 Vulnerability Assessments for Timbuktu based on SAP Reports

 

1 ..1..-z .. ...,r'...“ .--.. A“. .,r. .‘ u. .. '._.‘r. ' .‘ ’.H~-g‘ . new -. .»_'.". i we, v', _ 'v, .v. 1'>’-.‘\”~.'.-~w «Ci-'3 ‘4”i‘7“ J‘,“’~"'1“"-
"'_"J.. _.,, : '. -' . r -g .A' . . .. " 3" . . " '~ "7' ‘nst ~ 1.; . 1'.“ ,_ J. .
r ; a" , ‘9 o ‘ ‘ . . . f
~ ' = VulnerabrlrtyAssessment based on SAP Reports , -
. 3 f '1 .
’ j ‘ \ " ’1' L 1‘1. . i - - ~ , v» . . - v .e’ern‘Iou .;'_1 r s; >.‘. .Lx'w ',~; ' .1__: 4); ". ~'. :.‘ i. x . r ‘ 1.4 4; 'v', 1,. 3.. “r —. w I"-

- n", rrJ-{re‘ .~ L :u‘e: s«t\-,:’x'.'/..r.‘a . ‘-‘1'-'l)""‘-‘, aﬁﬁﬁﬂ‘l‘vﬁtlt‘
"‘1 ' .. ." ..A‘m- L':.' ..~ 7— .s‘. ->.I

. .-4 -‘ .0;
god... «, re 4 -‘;. 412mm

 

 

October through December: At risk
High cereal prices

 

January through June: Out-migration from villages for Food for Work
Increase in infants at feeding centers
February through June: Food aid delivery, prices stable
February and March: Sharp increase in prices of cereals in some areas
UNICEF reports malmrtrition
December: Food Aid distributions

 

1988

January through March: increasing vulnerability, migration ﬁ'om
Groundam

February through August: Increasing food aid distributions

May through July: Decrease in number of meals/day for nomads

May through September: Consumption of wild cereals reported

September and October: improvements in HHR with consumption of

fonio

 

1989

March: Entire cercle under watch
April, May and July: Weak HHR reported for non-ag households
June: Nomads weak HHR, Tabaski
HHR very weak in Ber, Agal, Tin-agielhadj and Timbuktu Central

August through December: Stabilize with good harvest although still

areas under surveillance.

 

1990

 

February: Marked increase in migration ﬁ'om Algeria
July and August: thin markets reported, FOOD AID
Increase in prices, ag outlook poor
September through December: some areas vulnerable
October: Improve HHR with harvest of fonio...mitigates mil/sorg losses
due to crop daruage done by birds
November: better HHR with F onio and rice, FOOD AID to Algerian

 

reﬁrgees

 

61

 

 

1991

February and March: Localized problem Bourem-Inaly at risk
General price increases
April: Price increases, increase in disease
May and June: Many areas of concern, could not gather all reports due
to civil conﬂict
July: Consumption of famine foods, low quantities of staples in markets
March through September: Food aid distributions
June through August: Consumption of wild cereals
June through September: disease outbreaks
November: Civil unrest continues with migration ﬁom Ber as
consequence
Food aid
December: Entire region under surveillance due to civil conﬂict

 

1992

March through May: Problems for displaced persons/refugees (DFJDA)
Consumption of wild cereals in May

March through August: at risk in some areas “pockets of insecurity"

February, May through August: High levels of FOOD AID throughout

region (NGOs)

November through December: Nomads at risk, in very poor health

 

1993

January through October: Nomads’ situation precarious
May: Measles outbreak
Many reports not in due to conﬂicts in the region

 

1994

Reports not in due to conﬂict in region

May: Many displaced persons on food aid especially in Ber and
Timbuktu Central

June and July: Migration due to conﬂict

April through June: Food aid distributions

June: HHR weak in some areas

 

 

1995

 

January: Reports not in as enumerators feel at risk in travel
February through December: Repatriation as conﬂict diminishes
March through September: some vulnerability and disease outbreaks
June: most of cercle DA
July and August: Problems for displaced persons/refugees
September: 186 families are reﬂrgees coming from Burkina Faso and
Mauritania

November: Food aid and increases in disease

Migrations due to poor harvest ﬁ'om Bourem-Inaly and

A8181
December: FOOD AID, more refugees, cholera outbreak

 

62

 

 

 

1996

January through September: Increasing vulnerability and high disease
outbreaks

July, November and December: Increased migration ﬁ'om Burkina Faso
October through December: HHR weak even after harvest

 

1997

April through September: Continued repatriation of displaced persons
Food aid
April through June: HHR weak, FFW
September through December: Food insecure (DA)
November: Pastures poor, prices decline with harvest,
HRC-repatriation of refugees ﬁom Mauritania
December: Price increases- entire region marked as DA

 

1998

 

 

January: Food Insecure (DA)
Food aid
HHR reconstituted with rice harvest although poor harvest
except for PPIV areas

 

Source: SAP

63

 

3.5 Smoothed Millet Prices

One approach to developing an index is to take an existing vulnerability indicator
and run a basic smoothing algorithm on the time series to produce a measure of the
dramatic shiﬂs and investigate how well these shifts correspond to the shifts in the
reference cycles.

Exponential smoothing procedures construct forecast functions based on
discounted past observations. Each time a new observation is available, the forecast
ﬁrnction can be updates in a very straight-forward process which is one of the appealing
features of this approach. The drawback of exponential smoothing procedures is that
- they are ad hoc in that estimates are not generated from a ‘properly deﬁned statistical
model’ (Harvey, 1989). However, these techniques are widely used and are generally
eﬂ‘ective (Hamilton, 1994). ‘

We use exponential smoothing to develop a simple index of smoothed millet
prices as a benchmark with which to compare the more sophisticated vulnerability index
developed in the next chapter. Millet prices are an essential vulnerability indicator
monitored by FEWS and SAP in Mali and neighboring countries. Figures 3.5.1 through
3.5.3 present the smoothed millet price index for Gao, Mopti and Timbuktu, respectively.

These ﬁgures also contain the reference cycle mappings developed in the last section.

The simple index (the smoothed millet price index) for Gao has key upward
turning points at November 1987, May 1990, and June 1993. The simple indexes were

converted to values running ﬁ'om 0 to 1. For Gao, the highest values appear post-1995

and the index increases at an increasing rate during this period. There were three peaks
in the sample period.

The Mopti simple index has upward turning points at July 1987, April 1990, May
1993, and August 1995. The Gao and Mopti simple indexes are very similar with three
peaks and similarly timed turning points. In addition, the highest values for the Mopti
simple index appear at the end of the sample period. The simple index for Timbuktu is
less smooth than the Gao and Mopti simple indexes but possesses similar turning points
and its highest values also appear after 1995.

In the next section we compare the simple indexes to the reference cycles.

65

 

 

..nukﬁcoS.

 

 

 

 

 

 

 

 

 

 

 

w%%%%%mmwmmmmmm
.....III .___L________L__ _ .....m m.
m._.<mm_n_0_2. . v .... We.
XOUC_ 0.05.:w .N. AnIWI
II m
,. _ .... u
U
... . . .... WW
.._ .2. I . m.-
. ...... ... m m
a .. A. u
,, m
./ . , m m.

0. r

:3 i252. 3 33 5:59.52
mat—585 tech
8 mum—3.22:...» a: game: 296 85.5qu can .39.— oE-Em 25 ﬁmd enema

 

 

 

 

.uotcucoﬁ

IO_I'
wk<mm002 _ ..

X00:— Dachw

 

33 5.3:. ... 8.: .3532
5258.: 68m
8 3:35.05...» he means: 295 ooeaouom use now..— 9355 mane:

 

alolo aouararaa pue xapul qdwgs ndow

"an 2:5

 

 

67

 

 

emote—«:05.

 

0.0

_____L___ .

I0_I I _
m._.<mm_00_2 .M,

x00...— 0_n_c..__nw

JV.

 

epio asuarajsu pus xapur aldrugs nmnqurrl

.32 5.5.... ... 83 .285
3.38..— .xxﬁ 3
banshee—.5 he means: 296 8:95qu was 5::— 9355 3:385. m.m.n «...—«E

 

 

68

3.6 Comparing Smoothed Millet Prices to the Reference Cycle

For the index to be valuable in the predictive sense, the turning points should
appear a minimum of one month prior to a period of moderate or high vulnerability to
food insecurity (as outline in Section 3.4). Figures 3.5.1 to 3.5.3 present the Gao,
Mopti, and Timbuktu simple indexes and their corresponding reference cycles. Table 3.4
provides a summary of the reference cycle classiﬁcations and the turning points for each
simple index

In Figure 3.5.1, we ﬁnd the Geo simple index to capture the increasingly
vulnerable conditions at the end of the sample period, commencing in mid-1995. The
earlier periods of moderate and high vulnerability are not signaled by the other turning
points of the simple index.

The Mopti simple index captures 1990 food security vulnerability conditions with
aturningpointinApril1990, precedingtheonset ofaperiod ofhighvulnerabilitybytwo
months. The other turning points do not provide such early warning signals. The later
turning point in August 1995 is not as distinct. However, the highest values for the
index appear near the period of highest vulnerability at the end of the sample period.

The Timbuktu simple index fails to provide key turning points that correspond to
the onset of vulnerable conditions in the region. The highest values for the index appear
in late 1996 and 1997 which correlate with conditions of high vulnerability.

Overall, the simple indexes did not consistently provide clear and timely turning points
that signaled the onset of moderate or high vulnerability to food insecurity. The

69

 

32 8.25m 3a baa—an

 

 

 

 

 

88.5853s8.8=2 82358988.;
82822822.: «82.88328...
.8. Essen... .8. .3598 «8. 58.23.52...
8. 383. .8. a... ...... 22 .8. 5.8.3 ...... a...
M8. 8.2 82 new: a... 82 .538.— .82 in...
82 .532 82 2.... 82 283. a 82 5...:

82 has. 82 ...... a 82 58.8 82 .398 s 82 5.55 .5.—3.5.5.
8. 538.—
82 2:588
.58. 5...:
8. 82 a «85 3888
..8. e885 3 82 an: M8. 3.58
8.285382; «82882382883
«8. 283. «8. 8.5 a 82 in... 28. .55
m8. 82 82 88.8 a 82 2.3 82 82 s 82 585
82 in... 82 8.5 a 82 e83. 82 38.8

82 be 82 3.889 ...... aaaoBz 82 2.... o. 82 e885 5592

8. 88.8
83.55.88: «8.585982%
«87.8.. .88.. 82 38.2 s 82 8:5 82 82 a. «85 .888
an... 2.. ..e 8...? 3.»... ”so... «8. 358.8 a .8. 5...: s8. 550.. a n8. 3:8.
n8. ...... ...... 3.; «8. 568.3
8. 8.: 82 8.; s 82 8...: 82 e952 3 82 89.552
82 82 82 82 a 82 2.3.8.5 82 baa... a 82 £588
82 3832 82 8.2582 a 82 8.2582 82 255 s 82 5:85
5350...: case... , . . ... . ._ .
... ease .. . ...... e2 1. ..e.....§.....> 8.5.. .85..» 32032.... .8 2......

 

 

    

 

 

BEND $53.8“. .05 tea women 3....— 5.0.32 358:5 m5

 

 

 

:5 ECU .....V... ..m O—A—QH.

70

smoothed millet price does provide some information and could be used in combination
with other indicators in the construction of an alternative vulnerability index.

In the next chapter, a more sophisticated index of vulnerability to food insecurity
is developed and we compare the realms with the reference cycles and with the smoothed

millet price index by region.

71

Chapter 4

A Leading Indicator Model
There is a circularity problem in that until we solve modelling problems and
discover the processes that generate economic data, we cannot derive the

probabilities of events, and hence cannot know how to appropriately estimate
the unknown parameters in any given model. Consequeme we must proceed
iteratively, stopping at every stage to ascertain what was learnt ﬁom each
empirical model about the mechanism, and hence whether the original

probability model was adequate to explain the evidence.
David F. Hendry, Dynamic Econometrics

4.1 Introduction

This chapter provides an overview of the leading indicators approach to measuring
economic activity (Appendix Chapter 4 presents the single-index model developed by
Stock and Watson in the early 1990's). We extend this single-index framework and

develop an application to depict vulnerability to food insecurity in Mali.

4.1.1 What do leading indicators lead?

Early detection of business cycles (early indications of recession or recovery) and
business cycle turning points have been of great concern for economists, policy makers,
investors, and business people. In the 1930's and 1940's, the National Bureau of
Economic Research brought together an extraordinary team of economists to develop a
‘ system of leading, coincident and lagging economic indicators. Initially, the charge was to
classify economic time series as informative in describing the level of economic activity.
The eﬂ‘ort produced a listing of leading indicators but not an index The index composed
of these series was not developed until aﬁer further analysis in the 1950's. The selected

72

indicators are meant to capture movements related to production time, the ease of
adaptation, market expectations, as well as prime movers such as monetary and ﬁscal
policies. Over 400 economic time series have been identiﬁed. These include new orders
for consumer goods, housing starts, manufacturing labor inputs, money supply, and
unemployment insurance claims. This system has been adjusted somewhat over the last
ﬁve decades and is still part of American and European macroeconomic landscape.

As Lahiri and Moore (1991) note, the leading indicator approach emerges ﬁ'om the
idea that economic and business forecasting can beneﬁt from viewing market—oriented
economies as driven by business cycles. These business cycles are comprised of sequences
thatrepeatthemselves andthese sequences formtheunderpinnings ofthebusinesscycle
itself. The leading economic indicator (LEI) method is to search for these reoccurring
sequences, describetheirbehaviorandmeaning, andusetherntobetterunderstandand
forecast the business cycle. The methodology for leading indicators is not restrictive in
deﬁning the duration ofan expansion or contraction and other characteristics because the
“observed sequences -leads and lags- are quite variable over time and are often
systematically diﬁ'erent at peaks and troughs, and because cycle phases vary widely in
duration” (Lahiri and Moore, 1991). As de Leeuw (1991) notes, while the LEI continues
to out perform many theoretically based competitors, the system of leading indicators is
the least theoretical of forecasting tools. This solid performance of the LEI has led many
to answer critics of its lack in theoretical foundations with the query: [I the index works,

why notjust use it?

73

Koopmans (1947) responds to this in “Measurement without Theory” by
explaining that such atheoretical models cannot provide the means by which to develop
inferences about the outcomes of stabilization policies. Koopman also notes that the LEI
approach could not depict how structural changes in the economy might impact indicators.
While Koopmans did not protest the value of the index as a forecasting tool, he
questioned its ability to assist policy makers in their decision making. The focus on
statistical regularities as the basis of selection in this statistical context could not carry
over to any other phenomenon in Koopman’s view. While the leading indicator approach
has been found to hold up over time and space ( Lahiri and Moore, 1991), there has been
a tremendous amount of research to derive more theoretically based alternatives. The
National Bureau of Economic Research (NBER) continues to be a leader in the
development of composite indexes to serve in summarizing the state of national
macroeconomic activity and the analysis of business cycles. In the effort to construct
models relying on econometric and economic foundations, NBER has sponsored or
cosponsored many studies. In particular, the models developed by Sargent and Sims
(1976), Stock and Watson (1991), and Kim and Nelson (1998) addressed the
comovements in various macroeconomic indicators in their analysis of US. business
cycles. In this research, we follow closely the work of Stock and Watson (1991) in the
development of an explicit indicator model of famine early warning. In the appendix to
this chapter, we present an overview of the Stock and Watson approach to. modeling
leading economic indicators. The next section develops an application of this ﬁ'amework

to the modeling of an index to capture the state of vulnerability to food insecurity.

74

4.1.2 An Application of the Single-Index Model to Food Security Vulnerability

Some of the modeling issues facing business cycle analysts resemble the process
of developing methods to ascertain the state of vulnerability to food insecurity. As noted
in the introductory chapters, famine early warning systems follow a variety of socio-
economic, epiderrriologic and agroclimatic indicators to track food security conditions in
vulnerable regions in Aﬁica. Some of these indicators have been identiﬁed as coincident,
leading or lagging in their ability to depict times of food insecurity. This state of
vulnerability to food insecurity is an unobservable state in the same sense as the state of
the economy in the work by Stock and Watson (1991) and Kim and Nelson (1999).

In deﬁning the unobserved variable theoretically, Stock and Watson (1991) query:
what do leading indicators lead? In formulating FEWS, the indicators of focus were
selected as signals of the state of vulnerability for food insecurity hour a donor
perspective. They offer indicators at the late stages of vulnerability, famine or near famine
conditions, rather than the early indications of vulnerability of communities to diminished
access to food or the insecurity of livelihoods.

Households employ a variety of coping strategies when faced with food insecurity.
Watts (1983), Corbett (1988), and Davies (1995) found that households respond to
changes in their economic status through strategies that may be employed sequentially or
concurrently in an effort to conserve resources. FEWS (1999) notes that there is a
“general progression of types of activities that forms a broad pattern that can be applied to

virtually any household and region”. These types of activities include (listed in order of

75

action in response to increasing insecurity): adaptation, divestment of liquid assets,
divestment of productive assets, and outrnigration. Figure 4.1 illustrates these household
responses to food security crisis. Under adaptation, there is a shift in household
consumption ﬁ'om preferred foods to those more readily available. At this stage one
would expect the market to reﬂect the aggregate effect of households’ adaptive behavior
through an increase in cereal prices. This scenario might be generated by agroclimatic
factors or a general economic decline that decreases the demand for labor and depresses
household income. When adaptation is no longer eﬁ'ective, households may choose to
implement the strategy of divestment of liquid assets where a household begins to sell off
small animals, jewelry, or seek loans ﬁ'om informal sources. In this phase, one would
expect to see a decline in the prices for small animals and a further increase in cereal prices
with a continued decline in the terms of trade (cereal per animal). In the next stage, there
is the divestment of productive assets such as draﬁ animals, large animals, and farm
equipment. One would expect a decline in the price of draft and large animals, decrease in
land prices, and a continued increase in cereal prices. Guanigratr'on is a ﬁnal strategy
where households relocate in search of food and work. At this ﬁnal stage, there would be
dramatic increases in malnutrition, disease, and mortality.

FEW S incorporates this depiction of household strategies under food security
emergencies into its selection of indicators of vulnerability. The prices of cereals,
livestock, famine foods, and terms of trade between cereals and animals are aﬁ‘ected at

each stage. In addition, the agroclimatic indicators such as vegetative cover and rainfall

76

estimates reﬂect the conditions that can prompt households to invoke these strategies.

Table 4.1 presents the operating assmnptions used by FEWS in its vulnerability analysis.

77

 

Reversibility

 

Commitment of Low __

High

DONOR RESPONSES

 

 

Development ——-'

 

 

 

 

 

 

 

 

 

 

domestic resources

 

 

 

 

; Mitigation ‘ ,'
' 1.. Relief ':_r,
HOUSEHOLD VULNERABILITY
i Moderat ' ==E---—- I '
.
- ' ExEme L
HOUSEHOLD STRATEGIES
Adaptation ' Divestment 8
Diet change, borrowing, I Li‘l“id ‘ PM” '5
i seasonal labor migration ' Assets Assets 3
' Crop & Livestock Adjustments 3

Farmland sale
Outmigration

 

 

 

Time h

 

 

 

Figure 4.1 I .Source: FEWS,1998

 

V78

 

Table 4.1 Operating Assumptions used by FEWS
l. Households respond rationally to changes in their economic condition.

 

 

2. Households seek to conserve their resources.

 

3. Markets exist in some form virtually everywhere and respond to the forces of supply

and demand with varying degrees of eﬂiciency.
4. The ability of households to maintain acceptable levels of food consumption is

 

conditioned by (a) the depth, diversity and quality of their resource base, (b) the

breadth of their income portfolio, and (c) their relationship to economic, social, and

 

political hierarchies.
511mm food Wmscanbemfmd fromagsregatesecondwm
6. Vulnerability assessments contain information that can guide both relief and

 

 

development activities.

 

 

 

WWI”

Thisfocusonthelate stagesoffoodinsecurity(famine)hasbeenacriticismof
earlywarning systems fromtheironset (DavisandBuchanan, 1991, Campbell, 1981,
Walker, 1979, Sen ,1989, Glantz 1987, 1992, and Deuai,l991). However, one must
consider the context of the development of institutions such as FEWS. In the case of
FEWS, donorssoughtthe perceived“best” informationinordertobeeﬂ‘ectiveinthdr
response to crisis, famine, situations. So, the indicators outlined above serve their purpose
from a donor’s perspective. FEW S is more concerned with identifying groups or regions
ofvulnerabilityratherthanarrivingat speciﬁc ﬁgures. Further, asoutlined inTable4.l,

79

FEWS has worked to eliminate the schism between the development process (enhancing
or seeming livelihoods) and the ﬁrnctioning of early warning systems. As early warning
systems evolve, there seems a desire for a more comprehensive approach to early warning
which includes livelihood security indicators as an important component in assessments.
However, the indicators such as terms of trade and prices still dominate current
vulnerabilityassessmentsandwillbeusedinthisresearch.

So back to the question what do leading indicators lead? The indicators of cereal
prices, livestock prices, rainfall estimates, vegetative indexes and terms of trade can serve
to depict the vulnerability of households to food insecurity on an aggregate level. FEWS
uses the convergence of evidence approach to arrive at its vulnerability assessments. The
model presented here seeks to analyze in a more formal approach the existing methods of
famine early warning using information FEWS has relied upon since their inception. This
leading indicators model could be useful and complement existing modes of analysis in the
systems. Stock and Watson (1991) found that their alternative index to the Index of
Coincident Economic Indicators provided a mathematical underpinning to the existing
ﬁamework developed by business cycle pioneers. In this research the goal is to provide a
modelwhhappropﬁatemathemaﬁcalundapinningsmdaﬁameworkthatenbances
current famine early warning capacities.

The model developed here takes as a foundation that comovements in agroclimatic
and socioeconomic variables have a common element, a dynamic common factor, that can
be captured by a single underlying unobservable variable. The unobserved variable is
takento represent the “state ofvulnerability to food insecurity”. The problem is to

80

estimate the current state of vulnerability which is some common element of the
ﬂuctuations in key time-series variables. The unobserved variable must be identiﬁed and
estimated. '

As in the Stock and Watson approach, we develop a parametric “single-index”
modelinwhichthe stateofvulnerabilityto food insecurity, V; , isanunobserved
component ofmultipletimeseries. TheKalmanﬁlterisusedto constructtheGaussian
likelihood ﬁrnctionbecausethemodelislinearintheunobservedvariables. We estimate
unknownparametersofthemodelbymaximumlikelihood keepinginmindthatthe
Kahnanﬁlterautomaticallycomputestheminimummeansquareerrorof V, , usingall
information available through period t. The resulting scalar estimate, V“, , serves as the
indexfordepictingthestateofvulnerabilitytofoodinsecmity.

The single index is estimated using data on agro-climatic and socioeconomic
factors for eight cercles in Mali: Mopti, Gao,Timbuktu, Segou, DJenne, Douentza,
Tenenkou, and Youvarou (recall that Segou, Djenne, Douentza, Tenenkou, and Youvarou
arepresentedintheappendices). We usemonthlydatatoconstructthemodel foreach
region for the period ofOctober/November 1986 - January 1998. Our index is then
compared to the reference cycles developed in Chapter 3. Recall that these reference
cyclesarebasedonthevulnerabilityassessmentsissuedinthemonthly SAPreportswhich
are formulated by consolidating assessments by local, regional, and national oﬁcials in
Mali.

Identiﬁcation and estimation of the single index model creates a variety of

challenges. Firstwhilethereerdstreferencecyclesthatmwdldeﬁnedmtheanalysisof

81

economic downturns and recovery (the Department of Commerce’s index of coincident
economic indicators, for example), such well accepted and well- deﬁned concepts have not
been constructed or generally accepted as common to famine early warning operations.
This creates a problem in referencing or deﬁning periods of historical vulnerability to food
insecurity a priori. Second, the interpretation of the estimate for the unobserved index
presents numerous challenges related to the lack of any existing index or reference cycle
methodology. In contrast to the eﬁ'orts of Stock and Watson (1991), there is no basis for
comparison, no clear quantitative benchmarks for the evaluation of the performance of the
outputsofthismodel. TheseissueswereaddressedinChapter3 ofthisresearchwhere
we used the reports developed by the Systeme d’Alerte Précoce (SAP) to create a
reference cycle for each region under investigation in Mali. Qualitative assessments by
local and regional oﬁcials were used to develop the reference periods of vulnerability to
food insecurity.

We also comparetheresuhsforthesingleindexofvulnerabihtytofoodinseanity
with the simple index (exponentially smoothed millet prices) developed in Chapter 3 as a
benchmark

4.2 The Model

Stock and Watson (1991) and later Kim and Nelson (1999) formulate a probability
model that provides a mathematical deﬁnition ofthe state ofthe economy. While there

may be multiple indexes that depict aspects of the state of economy, they view their

82

construction of a single-index model as a useﬁrl ﬁrst approximation Koopmans oﬁ‘ers the
following insight in his review of Burns and Mitchell’s (1946) seminal work:

The notion of a reference cycle itself implies the assumption of an

essentially one-dimensional basic pattern of cyclical ﬂuctuation, a

background pattern around which the movements of the individual

variables arearrangedinamarmerdependentontheirspeciﬁcnatureas

well as on accidental circumstances...This “one-dimensio ” hypothesis

maybeagood approximation, inthe same senseinwhichtheassumption

of circular motion provides a good ﬁrst approximation to the orbits of the

planets. It must be regarded, however, as an assumption of the “Kelper

stage”, based on observation of many series without reference to the

underlying economic behavior of individuals.
Stock and Watson present a parametric “single-indat” model in which the state of the
economy, C,, is an unobserved component of multiple time series. The Kalman ﬁlter is
usedto constructtheGaussianﬁkelihoodﬁmctionbecausethemodelislinearinthe
unobserved variables. They estimate unknown parameters of the model by maximum
likelihood keeping in mind that the Kalman ﬁlter automatically computes the minimum
mean square error of C, using all information available through period t. The resulting
scalar estimate is the index for depicting the state of economic activity (see the appendix).

Following closely the Stock and Watson approach, the next section provides a
general overview of the development of a single index of vulnerability to food insecurity.
This endeavor aims to develop a ‘ﬁrst approximation’ to depicting the complex
phmomenon of vulnerability in the spirit of Koopmans assessmm as quoted above. The

Kalman Filter and State-Space Representation are presented in this chapter.

83

4.2.1 The single-index model for vulnerability to food insecurity

This section presents the single-index model as it is developed in Kim and Nelson
(1999) and Stock and Watson (1991) building on the approach presented earlier. In this
chapter, the dynamic structure of the single-index model of vulnerability to food insecurity
based on the characteristics of the Malian data is fully developed for each cercle and builds
from the model presented here. Let X, denote a k x 1 vector of agroclimatic and
socioeconomic time-series variables which are hypothesized to move contemporaneously
with general food security conditions. In this study these variables consist of millet prices
and an index ofvegetative cover. As developed in Stock and Watson (1991) and Kim and
Nelson (1998), the single-index model X ,, consists of two stochastic components: the
common unobserved scalar time-series variable, or “index”, V, , and a k-dimensional .
component that represents idiosyncratic movements in the series and measurement error,
v, . In keeping with the Stock and Watson (1991) formulation, both idiosyncratic
components, D, , and the unobserved index are modeled as having linear structures

and V, is assumed to appear contemporaneously in each variable.

Themodel is written as:

(4.1) Xe = D: + MFALW} + Vi. i=1.....k.j=o.1...q.
(4.2) 47(L)V, = 6 + a), w, ~i.i.d.N(o,af,)
(4.3) vit = V’ﬂvm-r + r/Iizvik2 + eit 3,, ~ i.i.d.N(O,a,’)

L denotes the lag operator;(L) is a scalar polynomial that is depicted as second order
which is common in the literature. This also seems reasonable for modeling the

vulnerability index since a two month lag in the monthly series is able to reﬂect cyclical

84

characteristics. 1“..(L) is a scalar polynomial that depicts the number oflags ofthe state
variable that appears in each indicator equation. This representation implies that the
equation for the ith indicator will be expressed as a function of j lags of the unobserved
state variable. Idiosyncratic components, D, , are assumed to be time-invariant
(intercepts), and the unobserved component enters each equation in (4.1) with diﬂ‘erent
weights, contemporaneously. To reﬂect these considerations, we rewrite equation (4.2) as
(4-4) 14-6:“(K-l-a)+¢2(V;-2—§)+wt

The next step in building this model is to develop suﬁcient identifying restrictions
on the model in order to estimate unknown parameters and to extract estimates of V, .
Sargent and Sims (1977) and Stock and Watson (1991) note that the main identifying
assumption in the model expresses the ﬁmdamental notion that the comovements ofthe
time series are driven by a single source, V, . This implies that (e,,,e,,,...,V,) are
mutually uncorrelated at all leads and lags. However, individual series may be
autocorrelated. This restriction essentially means that dependence on the single-index
accounts for all the observed cross-relations among the coincident indicators. In
addition, a normalization to scale V, is identiﬁed by setting var(a) , )=l (Stock and
Watson (1991), Sargent and Sims (1977), Kim and Nelson (1999) and Harvey (1989)). It
is important to note that the above speciﬁcation results in the parameters D, andd not
being separately identiﬁed in the ﬁrst sample moment of the ith indicator X ,-, . From
(4.1) we see that the ﬁrst population moment for the ith indicator, X , , consists of two
parameters:
(4.5) E(Xs = D. + 7,1“ ”(L)”. + err) = Di + 7.5

85

Stock and Watson (1991) suggest writing the model in the form of deviations from means
in order to solve the identiﬁcation problem in NILE. This concentrates D, + 7,6 out of

the likelihood ﬁlnction:

Model in Deviation from Means

(4.6) xi. = 7.1“,(L)V.+ ea i=l,....,k, j= o,1,2,...qi
(4-7) V. = ’lvt-l + ”Vi-2 + a):

(4-8) err = Vileu-l + Vlzem-z + 3r:

where x,, = X,, - E(X,,) and v, = V, - 6
After this reformulation, the model is written in state-space form after which the

Kalman ﬁlter is used for maximum likelihood estimation of the model based on prediction

error decomposition and for inferences on v, .

86

4.2.2 State-Space Representation of Single-Index Model of Vulnerability

In constructing the state-space representation for the vulnerability index, we will
allow for some dynamics in the system in the sense that the indicators will not necessarily
be coincident. We would expect some agroclimatic and socioeconomic indicators to lead
or lag others. Stock and Watson focused on coincident and contemporaneous indicators
so did not build in such relationships in their work. To illustrate this formulation, a state-
space representation of the single-index model from equations (4.6) through (4.8) where

we allow the number of indicator variables, to be four, k=4, is:

Measurement Equation (4. 9)

5:1,“ ”7,0 0 orooooooo’eu

xmeYZIYnOOOlOOOOOew,
x37300000001000e2,

 

 

 

 

at“, :y,,,y,,7n'y,300000010Je2,,_l

 

 

87

Transition Equation (4.10)

 

 

 

 

 

 

V, 3’le P W,
VH . , v,_2 0
v” o, e, o o o o o 0 pH, 0
v“ l o o o o o o o ..H o
,1, o 1 o o o o o o ,1.“ £1,
a,“ o o l o o o o o 8,“ 0
e = o o o 0 on an 0 0 e + an
2" o o o o l o o o ”H
re», 0 0 e”,2 0
“a: o o o o o 0 in, in, ear-l ‘2:
eat-l o o o o o o l o “31-2 0
e4, a,M 6,,
eU-l .eu'zl 0

 

 

The above representation treats indicators variables x2, and 1:4, as lagging the
otherindicatorsinthemodel. Asinthecaseofthe StockandWatsonmodel, ﬁomthis
state-space representation, estimates for the model parameters can be derived using
maximum likelihood estimation based on prediction error decomposition. With these
estimates, the Kalman ﬁlter is used to estimate the state vector conditional on information

uptotimet. Weﬁndthe(l,l) elementofthe statevectoris V... .

4.2.3 The construction of the single index of vulnerability to food insecurity

The vulnerability index derived ﬁ'om the single-index model is the minimum mean
square error estimator Of V, constructed using data on the coincident variables through
time t. We denote this as V”, . A further identifying assumption is that the estimate of the
mean for V, can be expressed as a weighted average of the observed indicators. The
weights are retrieved ﬁ'om the Kalman ﬁlter matrices (Stock and Watson (1991) and Kim
and Nelson (1999).
Given v,,, , t = 1, 2, ..., T, we need to estimated to construct the new index,

V,,, . This follows ﬁ'om the expression
(4.11) V

I

,, = v,|, + 6

If all the parameters of the model (4.6) to (4.8) are known and we apply the Kalman ﬁlter
we can

obtain V,|, .
The relationship between V,,, and the observed

variables, X, = [X],,X2,,....X,,,]' canbeexpressedas

(4.12) V

r|r

= W(L)Xt

where W(L) is a polynomial lag operator that relates the transformed vulnaability index to
current and past values of the observed variables. We ﬁnd:

(4.13) E[V,,,] = E[W(L)X,]

We retrieve the mean of the unobserved variable
sincea = W(l)E(X) and 5 = W(1)X

Once W0) is identiﬁed,6 is estimated given 2? . Kim and Nelson (1999) note that the

89

relationship between v,l, and x, can also be expressed as
(4.14) v,,, = W(L)x, ,

which suggests that we can retrieve W(1) from the model expressed as deviation from
means

(4.6) to (4.8). Stock and Watson (1991) identify W(1) from the last recursion of the
Kalman ﬁlter applied to the state-space model written as a deviation ﬁ'om means. A full
derivation of the Kalman Filter can be found in Section 4.3.

In Section 4.4, we investigate the univariate characteristics of the monthly
agroclimatic and socioeconomic data we will use to construct the vulnerability indexes for
eight regions in Mali. In Section 4.5, we present the empirical results of the models as
well as the interpretation Of the vulnerability indexes by region and discuss their

performance as the indexes are compared to the reference cycles developed ﬁ'om the

monthly SAP reports.

4.3 Estimation
4. 3. 1 The Kalman Filter Approach

The Kalman ﬁlter can be used to compute the Gaussian likelihood function for a
trial set of parameters. The ﬁlter recursively constructs the minimum mean square error
estimates of the unobserved state vector given data on observed variables (Kim and
Nelson (1999), Harvey (1989), Hamilton (1994)). The ﬁlter consists of two sets of
equations, the prediction and updating equations This ﬁ'amework also allows the
construction of retrospective estimates of the state vector, and in particular C", , the state
of the economy in Stock and Watson’s model. These estimates of the state vector based
on the entire sample are computed using the Kalman smoother (Kim and Nelson (1999),
Harvey (1989), Harvey (1981), Hamilton (1994)).

The Kalman ﬁlter is a recursive procedure for computing the optimal estimator of
the state vector at time t, based on information available at time t. This information
includes the entire history up to t. The Kahnan Filter provides a mechanism that
continually updates the state vector as new observations become available. An important
feahueoftheKalmmFﬂteristhatwhenthediszcesmdmeimﬁalstateveaorm
normally distrlbuted, the likelihood function can be calculated through prediction error
decomposition. As Harvey (1989) notes, a standard result on the multivariate normal
distribution is then used to show how it is possrble to calculate recursively the distribution
of the state vector, conditional on the information set at time t, for all t ﬁ'om 1 to T.
These conditional distributions are themselves normal and thus completely speciﬁed by

theirmeansandcovariancematrices. Itisthevaluesinthese matricesthattheKalman

91

Filter computes. A detailed overview of the Kalman Filter using a time varying paramter
model as an example is provided in the following section.
This section draws ﬁ'om the excellent presentation found in Kim and Nelson

(1999). Consider this example of a time varying parameter model to illustrate the Kalman

Filter algorithm:
(4.15) y, = x,B, + e, , t = l,2,3,....T e, ~ i.i.d.N(0, R)
(4.16) B, = p + FB,_l + v, , v, ~ i.i.d.N(0, Q)

Here, y, is IX]; X. is 1x 1: vector of exogenous predetermined variables; it is assumes the
innovations are independent. B, is kx 1; Fand Q are ltx It.

The Kalman ﬁlter provides the means to compute the optimal estimate of the
unobserved-state vector B,, t= 1, 2, ..., T, based on the appropriate information set,
assuming that 0., F, R, and Q (hyperparameters) are known. The basic ﬁlter refers to an
estimate of B, based on information available up to time t, and smoothing to an estimate
of B, based on all the available information in the sample through time T. If some of the
hyperparameters are not known, they need to be estimated before one can make inferences
concerning B,. Maximum-likelihood is usually used to estimate the model’s unknown
hyperparameters.

We use Kim and Nelson’s (1999) following notation and description of the Kalman

Filter and state space representation:

1|; the information set.

[3,... 1 = EIB. I We 1] expectation (estimate) of B, conditional on
information up to t - 1.

92

Pa“ = EKB. - Brandi. - 3...-1Y]
9..-... = Emit]

P... = Ellis. - will. - B...)’l
”..-, = EDl.|tr..,] = xﬁnet

"qr—t = yr ' yrlr-l
1.... = Ema]
Bar = Elﬁtl‘h']

PM = EKB; - swam: - ptITY]

covariance matrix of B, conditional on
information up to t - 1.

expectation (estimate) of B, conditional on
information up to t.

covariance matrix of B, conditional on
information up to t.

forecast of y, given information up to
time t - l.

prediction error.
conditional variance of the prediction error.

expectation (estimate) Of B, conditional on
information up to T (the whole sample).

covariance matrix of B, conditional on
information up to T (the whole sample).

Assuming that x, is available at the beginning oftime t and a new observation ofy, is

made at the end of time t, the Kalman ﬁlter (basic ﬁlter) consists of the following two

steps:

1. Prediction: At the beginning of time t, we may want to form an optimal predictor

ofy,basedonalltheavailableinformationuptotimet - 1:y,,,_,. Todothis,

we need to calculate B,,,_ ,.

Updating. Oncey,isrealizedattheendoftimet,thepredictionerrorcanbe

calculated: 1),,“ = y, - y,,,_,. This predictionerrorcontainsnewinformation

93

about B, beyond that contained in B,,,_ ,. Thus, after observing y, a more accurate
inference can be made of B,. B,,,, and inference of B, based on information up to
time t, may be of the following form: B,,, = B,,,_l + K,q.,,_ ,, where K, is the

weight assigned to new information about B, contained in the prediction error.

To be more speciﬁc, the base ﬁlter is described by the following six equations:

Prediction
(4-17) Btlf'l = p’ + Far-llr-l'

(4.18) P, = FP,_,,,,,F’+ Q,

Il-l
(4-19) 11",.1 = y: ' J’m-l = Y: ‘ xtBﬂr-l’
(420) j;|t-l = errlr-lxr, + R,

Updating
(421) Bar = Bell-r + Kinds-1’

(422) P”: = Prlr-l + KtxtPtIt-l’

where K, = P

,,,_ 112,93}. 1 is the Kalman gain, which determines the weight assigned to
new information about B, contained in the prediction error.

In equation (4.17), an inference on B,given information up to time H is a function
of an inference on B,_ 1 given information up to H by construction of the time-varying
parameter model. Thus uncertainty underlying B,_ , is a function of the uncertainty

underlying B,_ ,,,_ 1 and Q, the covariance of the shocks to B,. The prediction error of the

94

time-varying parameter model consists of: l) the prediction error due to error in making an
inference about B,, and 2) the prediction error due to the random shock e, . Thus in
equation (4.20) , the conditional variance of the prediction error is a function of the
uncertainty associated with B,,, 1 and of R, the variance of e, .

The updating equation in (4.21) depicts B,,, as a sort of weighted average of B,_ 1
and new information contained in the prediction error 1),,,_ 1, the weight assigned to new
information being the Kalman gain, K, . In this characterization, as the uncertainty
associated with B,,,_1 increases, relatively more weight is given to new information in the
prediction error, n,,,_ 1. Thus, 11.1,, 1 becomes a more valued source of information.

Figure 4.2 maps the ﬂow of the Kalman Filter procedure. Given the initial vales of
B0,, and Pope the six equations of the basic ﬁlter, (4.17) through (4.22) can be iterated for
t=l,2,...,T. This provides us with the minimum mean squared error estimate of B, , given
information up to time t or H. For the stationary B, in equation (4.16), the unconditional
mean and covariance matrix of B, may be employed as the initial values, B0,, and Pop.
The unconditional mean of stationary B, is derived as
(4.23) EIBJ = a + Fall. ,1 + Elv.‘ .

(4.24) B,,, (1 + F130,, (At Steady State),
(425) B... (I. - a" ‘- a.

The unconditional covariance matrix of stationary B, is derived as

(4.26) Cov(B,) = FCov(B,_1)F{f + Cov(v,),
(4.27) P0,, = FPOIOF’ + Q, (At Steady State),

(4.28) vec(Po,,1) = vec(FP,,oF’) + vec(Q),

95

(4.29) vec(Po,o) = (F c F)vec(Po,,1) + vec(Q)
(4.30) vec(Pow) = (1 - F o F)‘ lvec(Q)
as vec(ABC) = (C’ o A)vec(B) .

Smoothing (B,,) provides a more accurate inference on B,, because it uses more
information than the basic ﬁlter. Equations (4.31) and (4.32) can be iterated backwards for
t= T-l, T-2, l, to retrieve the smoothed estimates:

Smoothing

(4°31) Bur: B-t-,,,P,,,F’P,:1,,(B,+1,, FBrIr - l1),

_I
(4.32) P.I =P.. ..’P+P.P +1|T(:+1|1'PP:+1|:) .1.PP.1.

pole PoIov [(0) = 0
l
1 <

 

ptIt-l = p‘ + FBr-llr-l

= I;‘I,t-l|t-1}7l+ Q

tll—l

“ill-t = yr " ytIt-l = yr - HtptIt-l - A1:

I
fllr-l = HrPrIr-lHr + R

11

1(0) = 1(0) - %h((2ﬂ)"lf;l,.1l) ' é'fhllr-lftltl-lmlt-l

l

I -1
Bl]: = ptIt-l + Prlr-lHtjllt-lnrlr-l

P

r|t =

I l
Pr|t~l ' Prll- lHrjl‘lr- lHrPrlr-l

 

 

1(a) = - i": ln((21t)" li....l)- énnl... inﬁrm...

Figure 4.2 Flowchart for the Kalman Filter
(Kim and Nelson, 1999)

97

The above derivation assumes that the model’s parameters are known. When this is
not the case, most often the parameters are unknown, they must be estimated before any
inferences can be made on B,. Thus, B, is conditional on these estimated parameters. It is
possible to evaluate the likelihood function based on prediction error decomposition. This
means that when the observations are normally distributed, insofar that we have the
prediction error and its variance, the log likelihood can be calculated. For the given
parameters in the model, the Kalman ﬁlter provides us with prediction error , 11,“. 1, and
its variance, £1, 1 in equations (4.19) and (4.20) as by-products. In addition, if Bo and the
shocks in equations (4.15) and (4.16) are Gaussian, the distribution of y, conditional on the
information set, Ir,_ 1, is also Gaussian:

(433) Ml Va- 1 "' N0?“- laﬁle- r):

and the sample log likelihood is depicted as
1 T l T -

(4.34) lnL = " — 2 1119711}... 1) ' " 23 "Ill- rlril'Mr— 1’
2 (.1 2 I‘- l

which can be maximized with respect to the unknown parameters of the model.

4.3.2 State-Space Representation and the Kalman Filter

The aim of the state-space formulation is to clearly express dynamic systems that
involve unobserved state variables and construct the corresponding state vector in such a
waythatitcontainstherelevantinformationofthesystemattimetandthatitdoessoby
having as small a number of elements as possible. Such a state space form is known as a

minimal realization (Harvey, 1989).

98

State-Space form consists of two equations: a measurement equation and a
transition equation. The measurement equation depicts the relationship between the
observed variables with the unobserved state variables while the transition equation
describes the dynamics of the state variables.

A representative state-space model is as follows:

Measurement Equation

(4.35) y, = H,B, + A2, + e, ,

Transition Equation

(4.36) B, = a + FB,_1 + v, , v, ~ i'rd.N(0, Q)

s, ~ i.i.d.N(0, R)

v, ~ i.i.d.N(0, Q)

E(e.V.’) = 0.

where y, is an n x 1 vector of variables observed at time t; B, is a k x 1 vector of
unobserved state variables; H1 is an n x k matrix that links the observed variables and the
unobserved variables; 2, is an r x 1 vector of exogenous or predetermined variables; a is k x
l; vt is k x 1. Elements ofthe Ht matrix may be either data on exogenous variables or
constant parameters.

Once a dynamic time series model is written in this state-space formation, the
Kalman ﬁlter is the means by which to obtain inferences on the unobserved state vector B,,
conditional on the parameters of the model and the appropriate information set.

There can be several state-space forms to represent the same dynamic system. For

the Stock and Watson coincident economic indicators model there are at least three state-

99

space representations in the literature (Stock an Watson (1991), Hamilton (1994), Kim and
Nelson (1999)). These representations are used with the Kalman ﬁlter to produce identical

results for estimates of the trial parameters.

4.4 Data and Preliminary Analysis
4.4.1 Description of Data

There are several vulnerability indicators monitored by FEWS and SAP. The data
considered for a single-index model of vulnerability to food insecurity in Mali are regional
rainfall levels, river levels, remote sensing data on vegetative cover (NDVI), market prices
for staple cereals and grains, and market prices for livestock This research proposes a
parsimonious model involving NDVI and millet prices. Selecting just two variables
facilitate the detection of incremental improvements over the simple index, our benchmark
index. We are better able to note the contributions of the single index methodology and the
value of the content of the adjusted NDVI relative to an index based soley on millet prices.
Work to expand the number of indicators used in the construction of the single-index of
vulnerability is left for ﬁlrther research. The series are monthly observations for the period
October/November 1986 through January 1998 for Gao, Mopti, Timbuktu, Djenne,
Douentza, Tenenkou and Youvarou. SAP extended its monitoring reach to include cercles

such as Segou alter 1986. The time period under investigation for Segou is April 1988

through January 1998 (see appendix).

100

Prices

Monthly data were accessed ﬁ'om FEW S’ Priceman data management system.
FEWS gathers secondary data on prices from SAP and Mali’s Market Information System
(SIM). Retail prices used are for millet and goats (used in Chapter 5). Millet is a crop
closely monitored by early warning systems and is a prime indicator of vulnerability
(vulnerability is increasing as millet prices increase). The millet prices were not seasonally
adjusted because these seasonal components capture both the height and driving forces of
the hungry season. These feature provide strong explanatory power in terms of levels and
key turning points for the context of depicting vulnerability to food insecurity. As
described in the previous chapter, the livestock to grain terms of trade is also used as an
indicator related to food security and can serve as a conﬁrming indicator of food stress.
Missing values in the price series were ﬁlled in with the average of the two nearest points.
This method produced reasonable results and is easily reproduced should this research be
developed for ﬁeld use.
NDVI

The Normalized Diﬁ'erenced Vegetative Index (NDW) is a measure of vegetative
covergathcredfrom satelliteimagery. TheNDVIisthediﬂ'erenceofnear-inﬁ'aredand
visible reﬂectance values normalized to fall between the values of -1 and 1, where
increasing positive values indicate increasing green vegetation negative values represent
nonvegetative surface features such as water, ice, and clouds (Eidenshink and Faudeen
(1997)). The index is recorded every ten days as a composite. The decal ﬁgures supplied

by FEW S were transformed into monthly averages. The value of the NDVI is adjusted to

101

reﬂect the diﬁ‘erence ﬁ'om its seasonal mean. This adjustment is then scaled and is
increasing when the observation is below the seasonal mean thus reﬂecting vulnerable
conditions. There were no missing values in these data series.

4.4.2 Univariate Analysis

There are many ways to model economic time series. It is an important ﬁrst step
when considering model speciﬁcation to perform univariate analysis to ascertain whether
the series of interest are stationary or nonstationary as well as to determine other
characteristics such as the structure of the disturbance term. The distinction between
stationary and non-stationary time series has been addressed widely with much controversy
in the literature. Ifa time series is stationary, its mean and variance are well deﬁned and do
not vary over time. If on the other hand, a series is nonstationary, its mean and variance
are diﬁcult to interpret because they are not necessarily globally stable (they vary over
time). Ifa time series is stationary after differencing, the series is said to be integrated of
order one, 1(1) and a stationary series is 1(0).

Stationary time series are usually modeled using methods outlined in Box-Jenkins
(1976), Harvey (1990), and Hamilton (1994) to name a few, these include autoregressive,
moving average, combinations of autoregressive and moving average components (ARMA)
representations. These reduced form models have sometimes demonstrated improved
predictive capabilities over structural models (Harvey, 1991 and Hamilton, 1994).

Thepresenceofunitrootscanhave seriousconsequencesintheestimationand
inference of time-series models. The existence of unit roots can lead to results such as

spurious regressions as highlighted by Phillips (1989) and many others. The controversy

102

surrounding unit root tests centers on characteristics of series that can be treated in
deterministic terms rather than as stochastic. Maddala and 1m (1999) provide an excellent
overview of the literature on unit root testing. The benchmark in the debate is the work of
Nelson and Plosser (1989) who apply unit root tests to the most closely watched U.S.
macroeconomic time series. Nelson and Plosser found that most of these series possessed
at least one unit root. They concluded that any shocks over the period of study resulted in
permanent eﬂ‘ect on the evolution of the series and that these effects build on one another
over time. Structural shifts were ignored.

When considering structural shifts as applied in the Nelson and Plosser data, Perron
(1989) provides evidence that contradicted the previous work Perron evaluated the
impacts ofevents or shocks such as the Great Crash of 1929 and the Oil Shock of 1973 by
modeling them as trends breaks or shifts. He incorporated structural breaks into the
Augmented Dickey-Fuller ﬁamework. This innovation revealed that most of the
macroeconomic series should be modeled as consisting of transitory ﬂuctuations around a
trend or trend-break.

In Mali, the events of the implementation of a market information system (SIM) in
1989 and the devaluation of the CFA in January 1994 engendered signiﬁcant structural
shifts. Devaluation in particular caused prices of livestock and cereals to increase when
demand for these products increased through trade with surrormding countries (Y ade,
Teﬂt, Staatz, Kelly 1999). This work uses Augmented Dickey-Fuller (ADF) unit root
tests as well as those developed by Perron to investigate the univariate characteristics of
the time series.

103

The ADF test evaluate the null hypothesis of the existence of a unit root in a time
series. The estimated equation is as follows with critical values ﬁ'om Table 20.1 in

Davidson and MacKinnon (1993):

(4.37) Ay, = u + ay,_1 + Bt + 2:1c,Ay,_, + e,

The Perron tests add structural breaks (an intercept shift, a trend shill , and a combined
intercept and trend shiﬁ) to the ADF ﬁnmework, critical values for the estimate of a are
found in Perron in Tables IV.B, VB, and VI.B, respectively . The structural shifts are

explored here for the eﬁ’ect of devaluation in January 1994 for all three Perron forms:

(4.38A)

AP. = P4 + “PM + B! + 10”. + Finally... + e.

(4.38B)

Ay, = l‘ "' “yr-l + 31’ + (B: " “Bl )DT, + 2,1;101Ay,_1+ e,

(4.38C)

Ay, ' l‘ + “yr-r + 31’ + YD”: "' (Br " Bl )DT, + 2:itr‘hAJ’rsl + “a

104

Thetests are conductedwiththenullhypothesisofaunitroot, the estimate ofais
compared to critical values appropriate for each test. The null hypothesis is rejected for
estimates below the critical values, these values are provided in Table 4.1.0. Tables 4.4.1
through 4.4.3 present the results of the Augmented Dickey-Fuller and Perron tests for the
presence of unit roots in the millet price series. Tables 4.2 and 4.3 provide descriptive
statistics of the time series used in the construction of the single index of vulnerability to
food insecurity. A simple linear regression of the price data on a constant and two trend
variables to depict pre-devaluation and post-devaluation are presented here. Figures 4.1.1

through 4.3.2 present graphs of millet prices and the adjusted NDVI.

Table 4.2 Descriptive Statistics for Retail Millet Prices

 

Cercle T Min Max Mean 0’ Trend S.E.
t1= Predeval
t,=Postdeval

Gao 135 60 195 99.94 917.45 t1= -.0160 24.49
t,= 1.31‘”

Mopti 135 40 182 84.19 979.92 t1= .0650 26.25
t,= 1.220"

Timbuktu 136 so 200 114.89 962.88 t,= .0510 26.51
t,=1.052“'

 

 

 

*, ”, and ”* denote signiﬁcance at the 10%, 5% and 1% levels, respectively.
Predeval and Postdeval refer to pre-devaluation and post-devaluation, respectively
S.E: refers to standard error of the regression

105

Table 4.3 Descriptive Statistics for Adjusted NDVI

 

Cercle T Min Max Mean 0’

Gao 135 -40.80 40.80 -3. 1590 385.53
Mopti 135 -40.80 52.50 -0.3515 324.69
Timbuktu 136 -35.40 54.2 -0.3848 428.50

 

 

 

Table 4.1.0 Critical Values for Augmented Dickey-Fuller and Perron Unit Root Tests

 

 

Level of ADF Perron Perron Trend Perron

signiﬁcance Intercept Shift Shift Intercept and
Trend Shift

1% -3.96 -4.42 -4.51 -4.75

5% -3.41 -3.80 -3.85 -4.18

10% -3.13 -3.51 -3.57 -3.86

 

 

The results for the ADF unit root tests for the adjusted NDVI rejected the null
hypotheses of the existence of unit roots. However, the test results for millet prices were
ambiguous. There is evidence of stationarity for each price series (the null hypotheses of
unit roots were rejected) with both the ADF and Perron tests, except for the DJenne millet
price series. For DJ enne, the ADF provided strong evidence that leads to the rejection of

the null hypothesis of presence of a unit root in the series (see appendix). Since the single

106

index model accounts for idiosyncratic aspects of each indicator, the level of the millet

price series is selected as the indicator variable as well as the level of the adjusted NDVI.

107

 

Figure 4.1.1 Gao: Retail Millet Price

220.
20d
1 an
1 ea
1 4a
1 20
1 00

aol

BO.

 

Millet Price (CFA/kg)

4o

 

 

Figure 4.1.2 Gao: Adjusted NDVI

60.
40.

201

 

NDVI (adusled)
L

 

 

108

 

 

assesses use. .\._ one as see as s codename sease .... n5 .... ..

 

 

 

 

 

 

109

 

 

 

 

 

 

 

5.- tune. 93.. ...:.r as... .85- e:- aod- :e. 8."- 2
an- m:- 87 :34. «a... man. «2.. Sa- am.- am- 2
as.- oea- Sn- sam- aomr one «2.- 8."- ME.- S._- u
out- ema- oae- tn:- 3? .25- 3.. :.m- 8N.- Nma- a
oun- eux- «an- and. 8s..- :a- 87 8."- 8m.- ..em.m- a

82> on; on; 8:5 on; 83> . 33> on;
89m 8ch seem Beau coed :85 soon can, seem mes

Ago-no.6
33m nee-F Ace-cob
a noose: Gonna 83m noose:
seam 333m can ease :85 38:5 pas
3832 seam manage, seen
3.5” 3.5825 meme—32 3F— qﬂaosbm o: 883 05$ 33
ea: mean SE as: mass ea: mane some 8:2 mass :62 Bases, no Benz

 

 

 

 

 

 

accuses: .. 55 inseam .. some... ...... ems

eeueoeam-eez ..e 3852:: ....z o... co and. echo.— eea coin-ease 8.533, 92. seen one 3... use...

 

 

 

Figure 4.2.1 Mopti: Retail Millet Price

 

 

 

Millet Price (CFA/kg)

 

110

 

 

 

Figure 4.2.2 Mopti: Adjusted NDVI

 

 

 

 

 

iv
IIOMG- d l

 

401

60:

m a 4 m
Ase-.33 .32

Month

 

 

111

Seances «82 a: as as 3.2 23 3 nonsense 888 r... one .... ..

 

 

 

 

 

 

 

 

 

 

 

 

 

oce- 83. Re.- .83. 3:.- SN- «2.. eaa- an- 8.3- 2
ex.- oea- can- .83. 3? 32- 32.- 8a. 3:.- 83- 2
e8.- mﬁ- E..- .93- «NM.- Em- 32.- 3."- Set .23- a
eon.- ..2- 8.... .Sa- 23.- 8....- S- so."- 83- ...:e. a
mom.- Ra- «2... ..8a. N2.- .Ng on.- ..53- 8e.- ...:.n- N
83> 83> 83> 83> 83> 83> 83> 83>
one 385 one 826 83. 83.5 doom m9. doom m9,
Ace-8%
833 82s sease Ado-5e
nausea: «an 8o: £5 noose—v €93
32m 3588 32m 3.88m 32m 3.88m 38. Ensues.
33:32 9:83 mg on 88334 05$ was
some 8:5 .33 83a ea: .33 8am 332 ﬁne 83m ea: 33— Snz 833?, so 2832

 

 

 

 

 

 

 

8381:: ... 5.5 3.5.53 . 85.3 35. ﬁe. b53333
.82 as 38.32:: :5 o... ..e an“. 32.3.— 3... cease-him: 335.53. ea... £38: who:

ﬁnd 033—.

112

 

Figure 4.3.1 Timbuktu: Retail Millet Price

300

 

1O

Millet Price (CFA/kg)

 

O

 

 

 

113

 

 

 

Figure 4.3.2 Timbuktu: Adjusted NDVI

 

 

 

6G

a
2

m

new

383 .52

Month

 

 

114

 

 

Figure 4.3.2 Timbuktu: Adjusted NDVI

 

 

60a

20

O-

a.m

A383 .52

 

Month

 

 

114

.%>88§ .338. .x: 35 a.“ see as 3 88833 888 ...... 83 .t ..

 

 

 

 

 

 

 

 

 

 

 

 

 

am.- 22.? m2..- ..2 .1 9a.- 9&- x: .- on". E.- oua. 2
8m.- ..231 as... 3.3. naur 2 .a. on? 8."- E .- m2- 2
29. Sa- 8? and. men- 2 .u. we .- z."- 32. «3- 3
Sn.- .8.«- at- :55. 83- 3."- § .- 5.”. 5.- was. a
«a..- .83- 94.- :83.- aou- 3."- ma .- 8.3- we .- :2 .m- a

8:5 83> 83> 83> 83> 83> 83> 83>
one 335 boom :85 boom 335 boom "5.4 coo-m m9
Anatomy
new 85 scene 928.3
a. cabana—o e33 889 833 8885 Gas
32m 388.com seam Esteem 32m 388.53 «as 3588...
main—32 wage-e. mg 0: 8% 39¢ awe-H
3E 8:533. some ea: 38m 83m ea: mane 83m ea: :52 592 Bane-a an 882

 

 

 

 

 

 

 

8:818: ... 8.5 ...-.3253 a 823? .8. .33. stance-cm
.82 as seesaw ....z o... as any apnea ...... sin-moms: 388...»: see £38: .523...

—.n.v 03-8

115

The results of the ADF and Perron tests for millet prices point to stationarity of the
series or to the the signiﬁcance of the trend shift attributable to devaluation. The ADF
tests of the adjusted NDVI for each cercle tend to reject the null hypothesis of a unit root.
Gao and Timbuktu have adjusted NDVI series that demonstrate an upward trend point to a
deterioration of agricultural conditions in the Northeast of Mali. Segou also exhibits some
upward movement in the adjusted NDVI. From these results, the levels of the series rather
than the ﬁrst differences will be used to model the single index of vulnerability to food
insecurity (as in Stock and Watson (1991) and Kim and Nelson (1999)). The eﬂ‘ects of
devaluation, the increase in the prices of millet post-January 1994, as well as the
environmental decline as reﬂected in the adjusted NDVI will then be captured in the single-
index ﬁ'amework. A ﬁrller discussion of is presented in the next section.

4.5 Results ‘
4.5.1 Model Specification

Early warning systems such asFEWS use numerous indicatorsto tryto detect
turning points in food security levels. For the sample period under investigation, two of the
most consistently monitored and reported indicators are millet prices and the normalized
diﬂ‘erenced vegetative index (NDVI). In this study, the time-series for the adjusted NDVI
and the retail price of millet were selected as the key indicators in the construction of a
single index to depict vulnerability to food insecurity The model was kept parsimonious
in order to establish a base model for the single index framework in this context. This
model could be expanded to include other prices, agro-climatic factors and socioecononric
factors (such as goat prices, liver levels and rainfall levels) more explicitly in the future.

The adjusted NDVI is used to capture the extent of vegetative cover. The higher the

116

adjusted value the less “green” the area relative to the seasonal mean; vulnerability is
viewed as increasing with increases in this measure. The retail price of millet captures the
general market conditions in the cercle; vulnerability is assumed to be increasing in the
price of this staple. The effect of the 1994 devaluation is essentially treated as a pulse that
is averaged out in this model in deviation from means (Harvey, 1996). The single index is
constructed so that vulnerability to food insecurity is increasing as the value of the index
increases. As discussed in the previous section, the model is speciﬁed in levels of the time
series rather than in ﬁrst diﬂ'erences.

The dynamic structure of the vulnerability index is speciﬁed in the spirit ofHendry.
Because there does not exist another commonly used index of vulnerability to food
insecurity, the speciﬁcation and construction of this model has little previous research to
rely on. As Hendry notes, this process of discovering kernels of insight at various stages in
the development of a model has its endpoint in providing compelling empirical results. The
usefulness of an econometric model is its capacity to synthesize data such that the
outcomes ﬁ'om the modeling process convey or ﬁame a narrative. As discussed in earlier
chapters, the single-index constructed for each cercle is juxtaposed with qualitative
assessments from the monthly SAP reports. These reference periods provide other
qualitative information concerning the unobservable state of vulnerability to food

Intuitively, one would expect millet price to be a leading indicator, ahead of the
adjusted NDVI, of vulnerability to food insecurity. Poor rains are perceived by economic

actors and reﬂected in markets before the eﬂ’ects are manifest in terms of crop greenness.

117

A poor harvest of millet and sorghum captured as an upturn of the adjusted NDVI should
lag millet price increases pre-harvest and post-harvest, assuming no intervention in markets.
One would also expect millet price increases if contra-season crops and wild cereals failed
to reconstitute household reserves fully. This reasoning was incorporated into the
dynamics of the single-index model. In Section 4.2, we developed the econometric
ﬁamework for the single-index model. Here, the model is speciﬁed and estimated in levels
for each of the eight regions. The dynamics are captured in the equations for adjusted
NDVI and retail millet price through the lags of the unobserved state variable as entered in
each expression. Equation (4.6) is rewritten as (4.39) resulting in the state space
representation for the vulnerability index as follows:

xi! = 7th + 7:1 Vt-l + + YUVt-J + “it

(4.39) ’= 1.2 (1=millet price;2=adjustedNDm
j= 1.2-3.4

118

Measurement Equation

 

 

 

. V, ,
Vr-l
(44o X1, y, 711 n, 1 o o o e
. ) = It
X1, 71 111 711, 0 0 1 0 e
1,t-l
‘2:
far—r
Transition Equation
. . 1 .
V: ‘4», o, o o o o o Vr-n to,
Vet l o o o o o 0 VP: 0
(4141) Vt—j - 0 0 l 0 0 0 0 Vr-j-l + 0
“1t 0 0 0 ‘711 I'12 0 0 era-l e1,
e1,_1 0 0 0 0 0 1 0 e11,_1 0
er 0 o o o 0 $11 an a“, , e1,
,2,“ .o o o o o o 1, L3,”, _ o ,

 

 

 

 

 

 

 

The models for the eight regions are estimated as outlined in Section 4.3. The
results for Segou, Djenne, Douentza, Tenenkou, and Youvarou are presented in the

appendix The lags associated with the adjusted NDVI and retail millet price equations

119

 

were determined through an iterative process. The hypothesized relationship between the
two indicators was featured as coincident or with the adjusted NDVI as a lagging
indicator. Millet price will most likely respond to an expected poor harvest or outside
conditions more readily than the adjusted NDVI which depicts the greeness of vegetative
cover. The number of lags selected for each variable in the eight regions are presented in
Table 4.4. The parameter estimates are presented in Tables 4.5.1 through 4.5.3. Model
selection was based on the signiﬁcance and appropriateness of the parameter estimates, the
highest likelihood values, and upon whether the index was consistent with the information
represented by the individual series. These models with the highest likelihood function
among candidates are presented here. We ﬁnd that the vulnerability indexes for each
region are sinusodal reﬂecting the cyclical nature of both millet prices and NDVI. These
cycles also depict vulnerability conditions such that the hungry season and increasing millet
prices should tend to coincide temporally. Parameter estimates for millet prices tended to
be signiﬁcant whereas those for the adjusted NDVI varied. The parameter estimates for

the irmovations on NDVI tended to be signiﬁcant for all the models.

120

Table 4.4 Number or lags or v, in Adjusted NDVI and

 

Millet Price Equations
Cercle Retail Millet Price Adjusted NDVI
680 2 4
Mopti 1 3
Timbuktu l 3
Segou l 2
DJenne 1 3
Douentza 3 3
Tenenkou l 2
Youvarou 1 1

 

 

 

The scale of the estimated indexes, V, , varied from region to region. Because price
and NDVI indices were not constructed a priori to make regional information comparable,
the estimated vulnerability indexes are not directly comparable ﬁ'om region to region. To
address this problem, index estimates were standardized. The ﬁnal estimates of a single-
index of vulnerability to food insecurity were rescaled to run from 0 to 1, with 0 depicting
the least vulnerable state and 1 depicting the most vulnerable state in the time period
examined. These indices are presented in Figures 4.5.1 through 4.5.3.

4.5.2 The Single Index for each Region

This section provides an overview of vulnerability conditions for each region as
they relate to the vulnerability indexes. Each summary is followed by the vulnerability
mapping for the cercle (Figures 4.5.1 through 4.5.3) and the corresponding table of

parameter estimates. Table 4.5 provides a summary of the single index turning points for

121

 

 

 

 

 

 

 

 

 

 

 

A38. «I...
...... ...-...... ....- ...-......
a... 3.8.... 3.... «3.358.828.3338:
28.58.32.982 a... 853.83....
.... .....4. .... .388... 8 .... .3883 a... 5...... ...... 8.35..
m... 5...... .... 9...... ...... ...... .... 5...... ...... ......
a... 8.5.2 .... 8...... ...... .... bane... .... ......
8.. .388... .... 3.... .... 3...... 8 .... 5......
8.. .... 8.. ...... 8 ...... 38.... 8.. 3...... 8 8.. .83.... 53:35....
.... 83......
a... 38......
m... 5.8..
.... ...: 8 a... 3.88..
o... ...—.53: a... ...-...... 8 .... 5...... n... 88.3....
3.1.8.5... 9... 3.8.3.8.... .....< «8.38.3.388
n... 38...... a... ...... 8 a... ...... .... ......
.... ...... ...... .38... 8 .... 3.... .... 5. 8 .... 83......
8.. .8»... 8.. ...... 8 5.. 3...... .... .38...
5.. 3.... ...... ...... ...... .388... ...... .3825: ...... ...... 8 a... 5.8.... 8......
.... .38...
5.3.2.5.... .... 3.8.8.3533
3.3338333... 3.2388838...
m... 388.3 8 .... 3...... v... 88...... 8 n... ......s...
n... ...... a... ...... ...... 3.... a... 388.3
a... .3895: .... 3.... 8 .... 5...: .... 8.3.8.. 8 .... .3552
.... 5...... 8.. ...... 8 ...... .833. 8.. 83...... 8 ...... .3883
3.. .3895... ...... .359... 8 ...... .3582 5.. 3.... 8 ...... 83...... 2.0
, non... binge—...... ,_ w -. w . , e . , ‘ r .. My... , 1:... . . .
...... 8...... .88.... ...... ., 88.8.8.5. 8.28.... .. 83...... .89...

 

 

122

Gao, Mopti, and Timbuktu. If a turning point appeared in advance of moderate or highly
vulnerable periods, this was viewed as a valuable feature of the index. Whether the level of
the index distinguished among periods of low, moderate and high vulnerability was also a
key feature upon which to evaluate the efﬁcacy of the indexes.

Gao

We turn to Figure 4.5.1 which maps the single index of vulnerability to food
insecurity in Gao with the qualitative reference cycles constructed from the monthly SAP
reports.

The vulnerability index for Gao demonstrates a sharp drop at the beginning of the
period under study, October 1986. The initial drop in the index reﬂects the seasonal
decline in prices at harvest time which was further depressed with the release of food aid in
the cercle. There is a signiﬁcant upturn in the vulnerability index at December 1986 which
corresponds with the reports, this upturn continues through the harvest.

January 1998 is a period of increasing vulnerability with migration pressures and
markets poorly stocked in cereals. February through May 1988 are declared periods of
food insecurity in the cercle. Food aid deliveries are spaced out during this period and
appear to mitigate ﬁtrther deterioration in conditions. The vulnerability index captures this
as a continued increase from the turning point of October 1987.

The vulnerability index has another turning point in April 1989 which is conﬁrmed

as the onset of a vulnerable period in the qualitative reports. In May and June 1990, food

123

 

 

amo>>=co§

 

 

I91 I
m._.<mwn_O_2

X00:—
3.39053)

«Mo 900319138 pue xapUI hmuaum we

 

 

 

 

 

 

 

 

may Pas—Sn. 8 33 San—952
bin—93.: 68h 3
banshee—.5 no means: 296 nongouom ...—a 565 5:55.055» 30 ﬁnd 9.5»:—

 

 

124

aid distributions and wild cereals mitigate food security problems where a average harvest
isexpected.ThevulnerabilityindexreﬂectsthisasthelocalpeakocwrsinAugust199O
and the decline corresponds to the qualitative assessments.

Aﬂer August 1990, the index continues its decline. A food aid delivery in
November 1992 serves to assist the most vulnerable. This occurs at the next tuming point
in the Gao vulnerability index, this upturn corresponds to conditions reported in the SAP
assessments. In July, the United Nations High Commission on Refugees establishes a camp
inGao. Thismrtsideassistancemayaccount forthe slight downturninthevulnerabilityas
reﬂected in the index There is another turning point in the vulnerability index in August
1993 whichisconﬂrmedwhenanexpectedpoorharvestisrealizedand sharpincreasesin
millet prices around the cercle reported in September and October such that there is an
increase vulnerability to food insecurity throughout the rest of 1993.

The Gao vulnerability index shows a steep increase until January 1996. The index
captures the eﬂ‘ects ofdevaluation on millet prices and the increase in the adjusted NDVI
during this period. The decline in vegetative cover may depict the disruptions to farming
due to civil conﬂict and the deterioration of environmental conditions in the region While
there are continued downturns and turning points the relative level of the vulnerability
index is much higher than for the rest of the year. This whole period in the sample is a time
ofhigh vulnerability in Gao cercle primarily due to the disruptions caused by civil conﬂicts
and then with repatriation of displaced persons. The were high levels of disease in this
period as well. The relative downturn in August 1996, that still reﬂects a high level of

vulnerability could be related to food aid delivered the previous month which depressed

125

millet prices and to the harvest when prices typically drop quite sharply in October and
November.

Overall, we ﬁnd the experimental vulnerability index for Gao to appropriately
reﬂecttheperiodsofvulnerabilityto foodinsecurityintheregion Theindexis composed
of just two variable: millet price and an adjusted NDVI such that events, such as food aid
deliverieswithrespecttomilletprices, appeartobecapturedintheindex. Intermsof
providing a mechanism to detect early onset of threats to food security, the index performs
fairly with turning points generally one or two months in advance of problems as described

in the monthly SAP reports.

126

Table 4. 5.1 Parameter Estimates for the Vulnerability Index, Gao (2,4)

 

 

 

01 .1346 .0168
y; -.0679 .0153
721 .5922 .2128
7;, -.5950 .9550
7;, -. 1483 1.0097
724 .2393 .3224
in -1.1862 .615
#22 -0.3518 .3650
02 0.0088 .0309
Likelihood 69.49
Value

 

127

Mopti

The vulnerability index for Mopti Cercle (see Figure 4.5.2) consists of a larger
number ofdramatic turning points which indicate an increase in vulnerability at these
points. At the beginning of the sample period, October 1986, the index starts at a relatively
high level (0.6) and decreases at harvest reﬂecting the typical drop in millet prices at
harvest. Beginning in May 1987 there is a slight upturn in the index reﬂecting an increase
ondemandsforfoodswithanincreaseinmigrationtothearea,theimpactonpricesis
dampened with the food aid distributions. After the 1988 harvest, the vulnerability index
continues its decline from very high levels to its nadir at October 1989. There is a sharp
increase in prices in November and December but a good harvest replenishes household
reserves. Thereisaconespondingnnningpomtmthevulnerabﬂitymdexwithanmcrease.
The vulnerability conditions are moderate when compared to the period prior to August
1988 and this seems reﬂected in the index Food aid deliveries arrive in June and July
whichappearsasabriefplateauintherisingindex

InJanuary 1991 thereisasteepincreaseinpricesandmanyareasunder
surveillance: Korientza, Sendegue, Fatoma, Korma, Soufouroulaye and Soye. This is
reﬂectedinthevulnerabilityindex. AnAprilfood aiddeliveryappearsasadeclineinthe
vulnerabilityindexwhichturnsupwardagainright aﬂerthedelivery. Thevulnerability
index has a distinctive turning point in August 1991 which provides a two month lead into
a moderately vulnerable period that continues into August of 1992.

The situation stabilizes and improves until post-harvest of 1993 when poor harvests

cause migration and several areas grow increasingly more vulnerable. This situation is

128

 

 

unstrucos.

ll 96
20 96

9676
9066

H Z6
20 Z6
96%

' ”'69
9019

7068

'

so l6
~ .. 90'06

 

 

mum
I0. I I
w...<N.m.n.O.2_. ,
XOUF:
3:59.053)!

.33 baa—an 8 exam hex—E952

@238... 89... 8 rings...»

he wean“: 3:0 85.....qu use 56.: banshee—.5 mane:

”'99

 

0.0

91013 aouraraa pue xapul AmgqeraumA gdow

«.3. 95m...—

 

 

 

129

captured in the index with a turning point in August 1993, providing at least a month ahead
indication of a change in conditions relative to the SAP reports. March of 1995 is the
onset of a very vulnerable period with high disease and sharp price increases. This is
depicted in the index with a turning point at least two months prior to the SAP assessments
reporting a deterioration in food security conditions. As captured in the vulnerability index,
food security begins to deteriorate in 1997 and problems persist for the rest of the sample
period. Sharp price increases, disease, and weak household reserves characterize most of
1997 and January 1998.

Overall, the vulnerability index captured signiﬁcant turning points and the severity
ofvulnerabilityto food insecurityinMopti. AsinGao, the indexappearstobecapturing
more than mean seasonal ﬂuctuations but additional information that communicates the
progression of conditions over the sample period. The price response to food aid deliveries
is communicated through the index and when these deliveries are signiﬁcant and made at
regular intervals early enough in the hungry season, they appear to be effective in

improving conditions without dramatic price eﬂ‘ects.

130

Table 4.5.2 Petrarame Estimates for the Vulnerab ' ' Index, - ti (,3)

     
 
 

(I), 1.6364 .1336
(b, -.6694 .1091
y, -.1121 .07810
7" .1721 .08551
in 1.2850 .1263
1h, -.4019 .1219
al .0987 .0334
y, .1203 .1603
721 .0783 .0216
7,, .0640 .2110
y” -.2258 .1596
hr .2465 .3148
*2: .0309 .1499
02 .5152 .2067
Likelihood 28.06
Value

 

 

 

131

Timbuktu

The vulnerability index for Timbuktu in Figure 4.5.3 reveals several distinctive
turning points and numerous minor turning points. A distinctive turning point occurs in
February 1989 which is conﬁrmed as an one month leading indicator to the reports of onset
of poor conditions for food security as reported in March and persist through August of the
same year when conditions stabilize with harvest.

The index leads the problems of high disease and prices noted in April 1992 as a
cercle-wide phenomenon. A distinctive turning point in the index occurs in late 1992 as
Nomad populations are at risk and deterioration in conditions persist as civil conﬂict
persists into 1993 and 1994. Another turning point that foreshadows a sharp increase in
vulnerability to food insecurity occurs at May 1994. Ahard hungry season is expected for
1998, the vulnerability index has another turning point in December 1997 reﬂecting sharp
price increase.

Overall, the vulnerability index offers some early warning for Timbuktu giving a
month lead at several point in the sample period leading sharp increases in vulnerability by a
month or more. The mid-1994 through the January 1998 is a period of marked increase in
vulnerability relative to the rest of the sample period and is captured by the single-index.
The adjusted NDVI tended to increase over the sample period and signiﬁcantly contributes

to the increase in the upward trend in the index.

132

 

 

cocksucos.

 

%w%%%um%n7wmwmﬂmm
WWMMW.-MMWWMMW¢
10.1' A: i .. .H
. .... 3;“ ..
m....<w.m.00.2...1.. :W . ,
xouc. . _: a... m.
. . x... ..
3.5.205...) . . . _
ﬂ

 

 

 

 

m8. 5:5. s 32 .335
3.5.89: :8... 8 hangs...»
he means: 295 85.523” can .39: 5:320:15 .5.—ESE.

[0'19
0L'98

‘ 0.0

 

 

IN.

91vo were we xapul WWW/t mm

am... 253

 

 

133

Table 4. 5.3 Parameter Estimates for the Vulnerability Index, Timbuktu (1,3)

 

(I), 1 . 1613 .02781
(I); -.1854 .2699
y, .3637 .0946
y“ -.31 10 .0951
911 1.3756 .3278
.12 -.4564 .3019
o, .0780 .0729
7, .0476 .0524
711 -.0144 .05400
1,; .0687 .0558
1;, .0784 .0572
'21 .4186 . 1536
*2: -.0203 .1209
a, .1776 .0321
Likelihood 52.15
Value

 

 

 

134

4.6 Comparison of the Single Index with the Simple Index and Reference Cycles

Table 4.6 presents turning point summaries for the simple and single indexes as well
as reference cycle highlights for each region. For Gao, Mopti, and Timbuktu we do ﬁnd
that the simple index provides more appropriate turning points with some lead time to the
onset of vulnerability. In addition, the levels of the single indexes better correspond to the
severity of food insecurity conditions as depicted in the reference cycles. Adjusted NDVI
signiﬁcantly enhances the early warning capacity of the single index over the simple index
for most regions. This is not the case for Douentza, Tenenkou and Youvarou (all cercles in
Mopti Region) which are primarily livestock trading economies (see Appendix Chapter 4).
Detailed summaries follow.
Gao

Figure 4.6.1 presents a mapping of the single and simple indexes with the reference
cyclesforGao. The singleindex capturethekeyturningpointswithonetothreemonths
lead time. However the simple index coincides with vulnerability conditions post-1995
without early warning features. The single index ofvulnerability to food insecurity
provides desirable features such as timely turning points and levels that reﬂect the severity
of vulnerability conditions.
Mopti

The Mopti single index is less smooth than the single index (Figure 4.6.2). Both
indexes performbetter (inbothlevelsand turningpoints) atthebeginningand end ofthe
sample period. The simple index captures general conditions in 1990 and 1992 but the

levels do not match the relative severity of vulnerability conditions. OveralL the single

135

 

 

 

 

 

 

 

Aug 038V

82 2.3.6.— ...a 28......

«2285.52.83. n2.8§s§.8§8n

22 2.: ... a2 5...: 22 35.2 s 8....

R2 ......4. a2 .8588 s 82 .3338... 22 28...... ...... 288a

32 5...: 82 28...... a2 8.... ...... 2a.. 82 85.2 ...... 2....

82 8.8... 82 23 82 5...: ...... 82 2.3....— 32 ......<

82 .858: 82 28.3.. $2 8.... 82 ass-<8 82 2.52
822.2 8222...... 525~8§28§o 2333.83.22.82. ...—Zia.

. S2 23......—

82 8.82.»...

82 5...:

82 22 a 82 .838.—

82 .8362 82 28...» 8 82 sea: 82 28.5..

m2285... 82.333.33.213 825.823.2836

82 .8322 82 35.2 82 2.... ... 82 .13 a2 2....

R2 2.... 82 2.2 :22 .888 ... 82 8.... 82 22 s 82 28......

82 .32.... :2 =8... 82 2.... ... 52 33.2 82 82.8
82 8.... ...... 2.2 S2 2.... 82 .838: ...... .8628: 52 2.... s 82 28...... ...—:2

R2 .888

S2 8.... ...... 22 82 2.8.... a 82 8.8.3

2.3582288 822232.888

82 8.8.3 8 82 5.8.. 82 2.28.. 8 a2 2......»

82 2.... ...... 8.... «a2 .8683

82 8.... 82 8.... 3 82 22a: 82 28...?— s 82 .8587.

82 2.2 82 2.2 s 82 28.6.. 82 28...... 8 52 8.8.3
82 .852: 82 .8588 a 82 8.5.5... 52 8.... 8 E2 28...... 25

   
 

 

 

 

 

 

855 3:9.»qu m5 5...» 5e:— oagm o5 we... not:— .o in e5 5.... :30 e. .r 033—.

136

 

 

.mmkﬁcoi

 

 

 

 

 

 

10:..- _ , 0.0 m...
. _ _ 6., 0
mnr<mmoo<£ x .2 , i./, m.
. , m. a. W
X00... 0.0:...W A , A f H. Wm
III ..ﬁ. m 3
X002. 3 w ., .. ,2. MW
: ,W . . 7 .v. D.
3___na.oc_:> . . . no
I _ , 9
H ...c. m...
, ,, .0. u
m
m.
d a
.0.?

 

 

 

 

 

 

 

”a. 2.2.5... 5 on... 325962
5.382.. cook 5 552205...» ....
”5.532 29.0 02.9.9.9: ...... 23...: 9.9.25 .5....— 5=EE2==> can. .6... «Suﬁ

 

137

 

 

 

10.1 I
w...<w.m.00_2 .

XO—ur: 0_QE_W

X0UC.
D...n.w.oc_:>

LNMCIEEﬁLOéd

 

 

 

 

NWWWWWWWWWWWWHW
WWWUWWHOWWH!WWH
:____ . _____...Io.o M
,.,,_ 8 AW.
\_.H r ,_ x
,,,,__,, a. .2, MW
2 u g, D.
. ., V W
. a. m...
,., ,,.,, . m
:. ... W .0 W
i: . z: , “a. 0
,M ,5, .m. M
. , _ mﬂ
. _,,
.
f. _ .
0..

 

 
 

2.2 28...... .. 32 .852...
22.58... ..8... ... 25.28....» ...

95.9.2 «.96 «2.9.83. ...... .56... 0385 2.2.... .5.—3.22:...» ...—o:

«...... 9...»...

 

 

138

index possesses key turning points that are appropriate for the early warning of the onset
of vulnerable periods.
Timbuktu

The simple index for Timbuktu captures the high vulnerability to food insecurity in
1992 while the levels of the single index fails to do so (Figure 4.6.1). The single
vulnerability index performs better than the simple index in 1989 and post-1995. Adjusted
NDVI is especially important in depicting vulnerability conditions in the later part of the
sample period. The upward trend in adjusted NDVI reﬂects the disruption in planting due
to civil conﬂict as well as environmental degradation. Vulnerability to food insemrity is
high post-1995 due to these factors and the single index performs better than the simple

index over this period both predictively and descriptively.

From close study of these vulnerability mappings we ﬁnd a visual and intuitive
representation of the evolution of vulnerability in the eight regions under study. On the
whole, the single vulnerability indexes provide information concerning the onset of
vulnerable conditions with one to three month lead times. The characterization of
moderate and high periods of vulnerability generally correspond to the index levels over the
sample period.

Certainly, further research to include more indicator variables in the construction of
the single indexes could lead to measures that are smoother with more distinct turning

points. This parsimonious vulnerability index, incorporating just two variables, performed

139

 

 

...wochucoE

l0 96
l [6
_==10 96

6
9
”IV

”a: base. 3 £3 5:35
:58”: as: 3 5:35.95; .3 ”5232
295 853:: ...; 5...: 2:35 5...: £355; 5:332. no... 2:2,:

 

 

X012... 0_QE_w.

X002.

a.:nﬂ..0r._3>|

 

-d

____

IO_II
“53500.2

aplo aoualajau pue saxapul nmnqwu

 

 

 

 

 

 

reasonably well for the eight regions in Mali. The single indexes along with the qualitative
assessments provide tools to communicate the evolution of conditions related to food
security in these regions as well as in forecasting conditions. This capacity may be a useful
tool for analysts and decision makers. Policy implications for these vulnerability mappings

are explored in concluding chapter.

141

Time Series Analysis of Famine Early Warning Systems in Mali

Volume II

By

Cynthia A Phillips

A DISSERTATION
Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
Department of Agricultural Economics

2002

Chapter 5

Vulnerability to Food Insecurity and
Regional Goat Market Integration in Mali

5.1 Introduction
Market Integration and Food Security Vulnerability

Research in market integration relies on the correlations between regional prices as
indicating market integration. Such models have numerous shortcomings. For example,
country-wide macroeconomic shocks such as devaluation or inﬂation, might overshadow
more subtle price movements, possibly leading the analyst to strong yet spurious
correlations and inferences.

This study explores regional price information as well as regional measures of
vulnerability to create a structural model. This research incorporates vulnerability index
for Mopti, developed earlier, whereby the level of vulnerability to food insecurity
delineates the market integration regimes. A framework is developed for depicting the
movements from one regime of market integration, for example one of a high level of
integration, to the next. The regimes characterized in this study differentiate two differing
states market integration that are triggered by a change in the level of vulnerability to food
insecurity. We examine spatial market integration in goat markets in the region of Mopti
to determine whether the markets perform differently in vulnerable periods than in food
secure periods.

In this chapter we explore regime shifts in market integration as they relate to

periods of vulnerability to food insecurity in Mali. Goat prices are monitored by FEWS

142

and SAP because goats and other ruminants and larger animals are viewed as assets in
agro-pastoral households. Prices of these animals tend to ﬂuctuate with the climate of
food security vulnerability. In vulnerable times, households tend to sell oﬁ‘ animals even
though grain-to livestock terms of trade are less favorable in order to secure food so to
avert a short-term crisis. In addition, periods of food stress may foster the breakdown of
arbitrage activities and increase transactions costs such that trade relationships deviate
from their functioning during low stress periods. The innovation of this research is the use
of structural information, the state of vulnerability to food insecurity, as the trigger

variable for regime shiﬁs.

5.2 Literature Review
5.2.1 Cointegration

The evaluation of market integration in spatially separated markets has generated a
considerable amount of empirical research and debate. Most work has focused on the
examination of price correlations to determine the extent to which shocks are transmitted
among markets. As discussed caller, it is important to ﬁrst investigate the univariate
characteristics of the price series in order to determine whether the univariate series are
stationary or nonstationary. Once this is ascertained, appropriate econometric techniques
can be employed since the treatment of stationary and nonstationary data are distinctly
diﬂ‘erent. Many of the earlier works on market integration failed take this distinction into

account.

143

More recently, the cointegration models of Engle and Granger (1987) have been
widely used in studies of market integration to investigate long-run relationships between
variables. Goodwin (1992) uses cointegration tests of Johansen (1988) and Johansen and
Juselius (1990) to investigate the Law of One Price in international wheat markets. These
approaches test the null hypothesis of no cointegration in that the residuals are tested for
stationarity. In effect, the conclusions are based on the acceptance of an alternative
hypothesis.

Fackler (1993), like Alderman (1989), performs the proper univariate
investigations of the price series which leads them to use more appropriate time-series
techniques. He notes that it is only meaningful to discuss long-run equilibrium between
endogenous variables when the series are nonstationary. When a time-series is stationary,
long-run restrictions are not appropriate because the series move about a constant mean.
However, additional univariate investigations in the area of autoregressive conditional
heteroskedasticity might enhance the empirical and theoretical work.

Some literature has attempted to address the issue of the degree to which markets
are integrated. Tirnmer constructed an index of market connection, to be discussed
further below. Heytens also used this notion in his applied work. However, these studies
have been limited by their econometric approaches. In the Tirnmer and Heytens studies,
the univariate characteristics were not ﬁilly investigated. Timmer's index of market
connection relies on the assumption that prices follow AR(1) processes. Since it is most
likely that prices are integrated (nonstationary), this leads to bias in the standard error

estimates, and thus the index constructed with these estimates is of little value. The

144

manner in which previous studies, including those employing techniques to treat
nonstationary data, test for market integration does not allow for varying degrees of
integration. The hypothesis that markets are integrated is either rejected or accepted.
This dichotomy is very restrictive.
As many researchers have noted, transactions costs have been ignored in models of

L 1 market integration . As Goodwin and Piggott (2000) point out, the primary mechanism
ensuring integration is spatial trade and arbitrage. The structure and evolution of
transactions costs can affect the structure and stability of price linkages of spatially

‘- separate markets. The role of transactions costs in regime shifts is considered in work on
1 market integration by Baulch (1994), Spiller and Wood (1996), and others. Market

integration has also been examined in a nonlinear ﬁ'amework though the use of threshold

cointegration models (Obsfeld and Taylor, Goodwin and Grenes). Goodwin and Piggott
(2000) employ a model of threshold cointegration to explore the role of transactions costs
in inhibiting price adjustments which can affect tests of market integration. They evaluate
price linkages among several local North Carolinian corn and soybean markets. Goodwin
and Piggott’s analysis employs nonlinear modeling techniques to account for regimes
shifts within a neutral band to depict the eﬂ‘ects of transactions costs on long-run
equilibrium relationships. Threshold models characterize a regime switch as triggered by
a transition variable (or variables) crossing some threshold value. This threshold
parameter delineates alternative regimes. Hitherto, most tests for market integration
express the long-run price adjustment as a linear phenomenon while threshold

cointegration allows for alternative regimes to exist along with the long-run relationship.

145

Their results demonstrate that large shocks (shocks above some threshold) evoke a
diﬂ‘erent response than do smaller shocks.

We examine the ﬁmctioning of markets during times of food security vulnerability
or famine and draws from the research incorporating threshold cointegration models.
While most threshold models use the series under examination as the threshold variable,
this research uses the vulnerability index as the trigger variable for regime shifts. The
trigger variable here is taken to be exogenous. This may be a strong assumption but the
distribution theory for a nonexogenous has yet to be developed. It is assumed that periods
of high vulnerability to food insecurity could destabilize trading relationships by sharply
increasing transactions costs or causing other disturbances to equilibrium price
relationships. The fact that market A is a net seller of goats to market B in a non-stress
period does not imply that this linkage would prevail in times of vulnerability.

In part of this research, we examine the functioning of goat markets in Mopti.
Goats are an important asset to households in the region. As discussed in Chapter 4,
during times of food insecurity a sell oﬂ‘ of these assets is often employed as a strategy to
mitigate hardship. Mopti Cercle, the regional capital, is the central market in the region.
Djenne, Douentza, Tenenkou and Youvarou are peripheral markets for trade in the area
(see Figure 2.1). We incorporate additional structural information and allow differential
integration in times of stress using the vulnerability indicator developed in the ﬁrst part of
this study. We explore two regimes rather than developing a neutral band as in the work

of Goodwin and Piggott (2000). In this chapter we present the univariate analysis of goat

146

prices for Mopti. In the next section, the econometric framework for the threshold

cointegration model is developed and empirical results presented.

5. 2. 2 Threshold C ointegration

Cointegration techniques have been widely used to model long-run relationships
such as the relationship between consumption and income or the Permanent Income
i Hypothesis (Campbell, 1987), stock prices and dividends (Campbell and Shiller, 1987) as
‘ well as market integration (Alderman, 1989, Goodwin, 1990). The concept of
cointegration was developed to depict the long-run equilibrium relationships between
A nonstationary variables (Granger, 1986 and Engle and Granger, 1987 ).

Systems of cointegrated variables can be characterized by an error correction
model which captures the adjustment process under which the long-run equilibrium is
maintained. Typically, this cointegration is viewed as linear in that the system tends to
move toward this unique equilibrium state in every period. Models of threshold
cointegration address the case when the movement toward equilibrium does not take place
in every period (Balke and Fomby, 1997). Tong (1989) pioneered theoretical work in
threshold time series models. Balke and Fomby (1997) note that an example might be the
presence of ﬁxed costs of adjustment that prevent economic agents from adjusting
continuously. Only when the deviation from equilibrium exceeds some critical threshold
do the beneﬁts of adjustment exceed the cost and economic agents act to move the system
back toward equilibrium. This example relates to many economic phenomenon; especially

notable is the case of eﬂicient ﬁnancial markets in which the presence of transactions costs

147

may create a band in which asset returns are free to diverge and in which arbitrage
- activities exist. This idea also relates to policy interventions in exchange rate management
2’ and commodity price interventions which are often characterized as discrete interventions.
These discrete adjustments can be characterized using models of threshold cointegration.

There has been grth in the literature related to threshold cointegration and

: threshold autoregressive models in ﬁnance, economics and agricultural economics

.4 (Franses and van Dijk 2000, Hansen 1997 and 2000, Martens et al 1998, Potter 1995,

T Koop et al 1996, Goodwin and Piggott, 2000). Most research explores cases where the
cointegrating relationship is inactive inside a given range and then becomes active once the
system has gone too far out of equilibrium. For example, Goodwin and Piggott’s work on
market integration in corn and soybean markets characterize their model with a neutral
band depicting transactions costs. Further, the threshold variable selected in the literature
is the series, or residual series, under investigation. Threshold models where the series
under investigation is the threshold as well are known as Self-Exciting Threshold
Autoregression models, SETAR.

In this work, we examine market integration between goat markets in Mopti
region exploring the long-run equilibrium price relationships in Mopti goat markets.
Rather than viewing a neutral band, we model the process as deviations ﬁ'om an
equilibrium relationship (market integration) such that the associated error corrections
model responds to the deviations from the equilibrium relationship; the strength of the
relationship depends on how far the variable is away from the equilibrium relationship.

One could think of this as a controller trying to return the control variable to a certain

148

level as opposed to allowing deviations to ﬂuctuate within some range. The relationship is
modeled as one where the cointegrating relationship between variables turns on and off.

In addition, the threshold variable used in this investigation is the index of vulnerability to
food insecurity for Mopti Cercle, the central market, developed in Chapter 4. We use this
index of the central market as the benchmark for vulnerability to food insecurity in the
region. The SETAR approach is also investigated.

Balke and Fomby hold that the conventional approach to testing for cointegration,
the Engle-Granger approach developed for linear time series, is also capable of detecting
threshold cointegration. Hansen (2001) has developed a new test for threshold
cointegration but for this research we approach testing for cointegration with the
conventional method which is most common and accepted in the literature. The ﬁrst step
then is to test for threshold cointegration is to test for cointegration with the Engle-
Granger approach and then to determine whether there is threshold behavior present in the
time series.

Typically, cointegrating relationships (for the case of two variables, y, and x,) can
be depicted as
(5.1) y, - [3x.. = z, where z = pz_1+ 8

Such that

|p| < 1 implies cointegration

|p| near 1 implies nonstationary, no cointegration

|p| = 1 implies the existence of a unit root, no cointegration.

149

As Hansen (1992) notes, the case where |p| < 1 is the linear case of cointegration as
originally deﬁned by Granger (1986). In the above representation, the cointegrating
residuals are represented as 2, .

Next we consider nonlinear, threshold, behavior where various regimes may be
present. In threshold cointegration the linear autoregressive structure is altered to reﬂect

the delineation between regimes triggered by the threshold value c, such that

pm if |z_1|s c,

(5.2) p =
pm if le | > c.

This formulation depicts a two regime AR(1) model with diﬁ‘erent means in each regime.
Other representations allow for more complex state dependent dynamic behavior meaning
that certain properties of the time series such as mean , variance, and/or autocorrelation,
are diﬂ‘erent depending on the regime. We focus on this regime process as stochastic and
build on the current literature to expand the framework to capture autoregressive models
of higher orders. First we estimate the cointegration relationship and investigate the

characteristics of the residuals from this cointegrating relationship.

5.3 Model Speciﬁcation and Estimation
We begin by investigating the nature of the relationships between goat markets in
the Mopti region. Mopti Cercle is characterized as the central market and surrounding

markets of DJenne, Douentza, Tenenkou and Youvarou as peripheral markets. In the

150

Section 5.4.1 we explore the univariate characteristics retail goat price series for each
market region and determine the series to be I(l) time series. Recall from Chapter 3,
Douentza is a main livestock trading area whereas the other peripheral markets are more
diverse in their trading activities. Based on these ﬁndings, threshold cointegration is used
to explore relationships between goat prices between cercles in the Mopti region of Mali.
We proceed by building threshold autoregressive models (TAR) ﬁom the time
series of residuals from the cointegrating relations. We treat the vulnerability index for
Mopti as the threshold variable, q, , and we construct the SETAR versions for
comparison: the threshold variable may be a lagged value of the series itself or a lagged
value for the vulnerability index. The TAR model assumes that a regime is determined by
the value of the threshold variable relative to a threshold value. SETAR models treat the
regime switching behavior as discrete, other models such as the Smooth Transition
Autoregressive models (STAR) allow for a gradual transition from one regime to the next.
The SETAR model can be approximated by the STAR model by adjusting for a smoothing
parameter (Franses and van Dijk, 2000). In this research we focus on discrete regime
transitions and leave the smooth transition models for further research. Speciﬁcally, the
model we will explore considers two regimes, when the threshold variable is the Mopti
vulnerability index we refer to these regimes as one of low vulnerability (Regime l) and

one of high vulnerability (Regime 2). The model is expressed as:

(p +(py_+...+rpy_+e ifq_sc,
(5.3) y‘___ { 0.1 1.1 :r p.1tp r u

‘90.: 1’ ‘Pmyx-r "' + ¢p'2,y‘-p 4' 6, iqu > c.

151

where i= 1,2,3,4. We estimate AR(p) models for a variety of nonlinear cases and select
candidates based on an adjusted AIC criterion. One consideration in this research is that
higher order dynamics (p>1) may be appropriate for the models under investigation. We
assume, however, that the order of the autoregression is the same for each regime in a
region, but that the order can differ ﬁ'om region to region. Tong (1990) deﬁnes an
alternative AIC for a two- regime SETAR model as the sum of the two Ale for the
AR(p) models in the two regimes, we use a version that assumes the lag order is the same
for each region:
(5.4) AIC (p) = nlrnof + n21ne§ + 4(p + 1)
where nl and n2 are the number of observations in regime l and regime 2, respectively;
and
0: and a: are the variance of the residuals for regime] and regime 2, respectively.
Even though at is assumed to have the same variance across regimes, the estimates can
differ.

The estimation of the two regime SETAR model involves sequential conditional
least squares estimation of the parameters of interest which are
¢,,i=0,l, ..... ,p ,j=1,2;c,o’.Undertheadditional
assumption of normally distributed innovations, the resulting estimates are equivalent to
maximum likelihood estimates (F ranses and van Dijk 2000). Parameter estimates
$1.4 c ) are conditional on the threshold value. The corresponding residuals are denoted
as Ere) = y.- Memo)

where (bl = (4)”, <le, 4)” )’ j= 1,2 and (I) = (4),, 452 )’ with

152

2L1 £r(c)2'

x, = (1 . yt-p-m: J’,-P)’. The variance is expressedas 63 =

al.-

The least squares estimate of c can be obtained by minimizing this residual variance, that is

(5.5) é= argmincec &(c)

where C denotes the set of all possible threshold values. The ﬁnal estimates of the
autoregressive parameters are given by (j) = M 6 ) , while the residual variance is
estimated as O = O’( r? ) (Franses and van Dijk 2000).

Direct search is used to solve the minimization problem in (7.6). Hansen (1997)
derives a limiting distribution for threshold estimates that is free of nuisance parameters
apart ﬁom a scale parameter. The estimates of the autoregressive parameters are
consistent and asymptotically normal.

In constructing conﬁdence intervals for c, Hansen (1997) suggest an approach that
relies on the inversion of the likelihood ratio test-statistic to test the hypothesis that the

threshold is equal to some speciﬁc value c0 , given by

 

(02(60) ' 02(3))

5.6 LR =
( ) (co) n 02

where the 100 ... a percentile of the asymptotic distribution of the LR-statistic, see
Hansen (1997) for the percentiles for various values of a . The 100* a%
conﬁdence interval for the threshold is given by the set Ca consisting of those values of c

for which the null hypothesis is rejected at signiﬁcance level a. The set C“ provides a

153

valid conﬁdence region since the probability that the true threshold value is contained in
Ca approaches a as the sample size becomes large (F ranses and van Dijk 2000).

In the empirical analysis we also provide the asymptotic 95% conﬁdence interval
for the parameter. The estimates of the autoregressive parameters 4), and 432 are
asymptotically normally distributed. For example, the asympototic 95% conﬁdence
interval for each parameter in Regime 1 is constructed as
($1,, - 1.966”, , in, + 1.96 a”) where p is the lag order.

To account for proper asymptotic behavior a positive trimming percentage, the
percentage of the observations to trim for the beginning and the end of the sample, is
required (Hansen, 1992). The literature suggests a trimming percentage of 15%, this level
is a rule of thumb ﬁgure rather than driven by rigorous investigation. The sample sizes
used in this study are suﬂiciently small (n=135) such that a trimming percentage of 5% is
selected in order to retain the maximum number of observations while meeting the
appropriate restrictions.

An important question to answer in constructing regime switching models is
whether the additional regime adds to explaining the dynamic behavior of y, relative to the
linear AR model. We must construct the linear AR(p) model and compare it to our two
regime AR(p) models. Neglected heteroscedasticity may lead to spurious rejection of the
null hypothesis of nonlinearity. Granger and Terasvirta (1993) and F ranses and van Dijk
suggest using speciﬁcation tests developed by Wooldridge (1990, 1991) to create more
robust linearity tests. Hansen (1997) outlines procedures to compute a heteroskedasticity-

consistent (HCC) variant of the LM-type test-statistic against a SETAR alternative. After

154

estimating the SETAR models with the threshold variable as the series and the alternative
threshold of the Mopti vulnerability index, v, ,this HCC test of linearity against the SETAR
alternative is conducted for each of the candidate models.

In dealing with nonlinear models, traditional impulse response ﬁrnctions (TIRF) fail
to capture the complex dynamics developed with threshold models. TIRFs are history
independent, syrmnetric such that both negative and positive shocks provoke the similar
responses in magnitude in a system and the linear nature creates a proportional response to
an impulse. Nonlinear models require careful consideration in developing a ﬁamework for
investigating the persistence of shocks to the system. For nonlinear models, impulse
response functions are history dependent, asymmetric, and shocks are not proportional in
response to an impulse. Many researchers have used generalized impulse response
functions (GIRF) to explore the effect of shocks to SETAR models (Potter 1995, Koop et
a1 1996, Goodwin and Piggott 2001). The GIRF is a function of the shock and histories
which are realizations of random variables. In the GIRF, the expectations of Ym. are
conditioned on the history and/or the shock to the system. In this study, the exogenous
threshold variable, the Mopti vulnerability index, creates some additional and complex
challenges in constructing the generalized impulse response functions which are beyond
the scope of this research. Future research will explore the nonlinear impulse response

functions for the models developed in this chapter.

155

5.4 Data and Preliminary Results
5. 4. 1 Univariate Analysis of Goat Prices

The ﬁrst step in empirical analysis is the investigation of the univariate charactistics
of the retail goat prices for each cercle. Descriptive statistics are presented in Table 5.1.
Graphs of the series are presented in Figure 5.1 to Figure 5.5. Based on the ADF tests as
outlined in Chapter 4, the retail price of goats appear to be nonstationary in each cercle
(see Table .2). The null hypothesis of the presence of a unit root is not rejected. Similar
tests were conducted on the ﬁrst differences of the retail goat price series were found to
be stationary ruling out integration of an order greater than one. Having established these
characteristics, appropriate tests for cointegration between retail goat prices in Mopti and

each cercle are conducted with results presented in the next section.

156

Table 5.1 Descriptive Statistics for Retail Goat Prices

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Cercle T Min Max Mean 02 Trend S.E.
t1= Predeval
t2= Postdeval

Mopti 135 3,000 15,550 7,472.78 9,145,065 t1= 64.65.” 1711.61
t,= 76.97'”

DJenne 135 2,500 12,900 6,281.74 5,504,399 t1= -36.15m 1393.50
t,= 72.92‘”

Douentza 135 2,000 15,000 6,128.04 7,030,163 t1= 49.86.” 1654.42
t,= 59.06‘”

Tenenkou 135 3,375 12,500 6,731.11 5,056,069 t1= -31.87m 1483.79
t2= 62.84‘"

Youvarou 135 2,420 9,875 5,497.99 3,262,388 t1= -21.38m 1341.64
t,= 51.15‘“

 

 

*, ", and *** denote signiﬁcance at the 10%, 5% and 1% levels, respectively.
Predeval and Postdeval refer to pre-devaluation and post-devaluation, respectively
S.E: refers to standard error of the regression

157

202809.56.— .m_o>o_ .XL 98 o\em .o\oo_ 2: 3 cacaoQEwmm 395v ...: .28 ...... ....

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

on: .- 5 .m- mm: .- wed- $3.- 2.7 _N_ .- 2 .N- 539. N67 N—
w: .. med- no. .- Sum- 38.. 3.7 on: .- 3.7 mmaor 3.7 o—
Kﬁ- 3.7 E m- om..- 3.8.- 2.7 S: .- 3.7 2 _.- and- w
3: .- 3 .N- 5.- and- moor em. 7 mo. .- nod- whaor and- v
v3 .- owd- on: .. own- N2 .. and- N07 cod- mom- 3N- N
2...; 2...; 2.3, 33> 2:5»
83— hn< 33— ﬁne. 33— “:3: 83— an; 83— “:3:
was .3
3.328% ace—cosh. «5:28A— oeeonn use: Lon—:52
Eamom :32 E
83...— .aemv .8.— btgezﬁmreez ..e 3852.»! ==z 2: he 39—. 3.3.7.5835 tou=oEw=< :5... 3.39m N6 033—.

158

 

 

 

rice

Retail Goat P

.33
.32
.38
.33
.33
.33
.38
.33
.33
.3;
.38
was
:33
.38
.32
.3;

 

 

 

aazoee
1111

1

Figure 5.1 Mopti

l'i

4

2

Ansoczmomucmmzoé 8:; .80

3.8

MONTH

 

 

159

 

 

Figure 5.2 DJenne: Retail Goat Price

 

 

 

 

 

 

Z
1

m a 6 4 2 :8

A8228 3:832: 85 .80

MONTH

 

 

160

 

 

Retail Goat Prrce

Figure 5.3 Douentza

 

 

.33
.38

.38
.33
.33
.38
5.8
.38
:35
5.8
.38
.38
.38
.38
.33

 

 

l
1 0
Bl
6.
4.
2.
o

1
1
1

@223 .2883 83 as

2.8

MONTH

 

 

161

 

 

Figure 5.4 Tenenkou: Retail Goat Price

 

..L
L

 

 

 

 

 

 

’6

8

$10

LI.

0

.88}

C

(U

8

06.

5

.34

‘h- I

O.

(.332 .
$3833833333833833
85888888888338883

 

162

 

 

 

Figure 5.5 Youvarou: Retail Goat Price

 

 

 

 

 

 

A10

5

Es.

O

U)

13

C

36‘

3

O

5

.8 41

C

0.

$2
2543;8338'2325558T55
sssszsasdsaaaasss
MONTH

 

 

163

Empirical Analysis
5.4.2 Cointegration Tests of Retail Goat Prices in Mopti Region

Four goat market interactions are examined in Mopti: Mopti-DJenne, Mopti-
Douentza, Mopti-Tenenkou, and Mopti-Youvarou. We apply Engle and Granger’s two

step method for testing cointegration and ﬁrst perform OLS on:

(5.7) BMW“ = a + ﬂP,‘ + s,

where BMW” represents the retail goat price for Mopti Cercle, the central market and

the retail goat price for the peripheral markets is depicted by P' in each cointegrating

l

regression. The results are presented in Table 5.3.

We then conduct three procedures for testing cointegration: the Johansen
maximum eigenvalue and trace tests (Johansen ,1988), the Hansen Lc test (Hansen, 1992)
, and the Phillips-Ouliaris Pz test (Phillips and Oulian's, 1990). The results are reported in
Table 5.4. The maximum eigenvalue test has as its null hypothesis that ther are r+l
cointegrating vectors with an alternative hypothesis that there are r cointegrating vectors.

The trace test has as its null hypothesis that there are r cointegrating vectors. We
ﬁnd that the results of these Johansen tests provide evidence of one cointegrating vector
for all four regions under investigation. The trace tests for Mopti-Djenne and Mopti-
Tenenkou for the test of r=0 do not reject the null hypothesis of no cointegrating vectors.
Johansen and Juselius (1990) suggest that the maximum eigenvalue test may be more

reliable than the trace test. The maximum eigenvalues tests supports the existence of one

164

cointegrating vector for the two regions. Therefore, we ﬁnd support for the existence of
one cointegration vector in Mopti-DJenne and Mopti-Youvarou.

The Hansen Lc test, originally developed to test parameter stability, provides a test
of the null hypothesis of cointegration, a hypothesis which is not rejected for all regions.
The Lc is asymptotically robust to heteroskedasticity; its variance matrix is exactly the
central component in the heteroskedasticity-robust covariance matrix of White (White
1980, Hansen 1992). The Phillips-Ouliaris PZ test, tests the null hypothesis of no
cointegration, this null hypothesis is rejected for all regions.

To examine the Law of One Price we conduct an LR test where the null hypothesis
1 ‘ that a= 0 and l3=l. The results are reported in Table 5.4. The null hypothesis is rejected
for all regions. The null hypothesis implies that transactions cost are proportional and
invariant with respect to price. This rules out risk premia and other factors as Goodwin
and Piggott (2000) note. They further remark that the rejection of the null in these tests is
not surprising and should not be taken as evidence against integration. These results are
consistent with the results in the following section. In particular, threshold eﬂ‘ects would
not be consistent with the Law of One Price. With a threshold model, each regime has
characteristics distinct ﬁ'om other regimes. Thus, these regimes could depict diﬂ‘erences in

transactions costs and risk premia which are clearly in violation of the Law of One Price.

165

Table 5.3 Results from Cointegration Regression Goat Prices between Mopti
Markets

BMW = a + ﬂP,‘ + 8, (i=Peripheral Markets: DJenne, Douentza, Tenenkou, Youvarou)

 

 

 

 

 

 

Markets (1 B R2

Mopti-DJenne .960 (.443) 1.041 .650
(.066)

Mopti-Douentza 1.491 (.363) 1.040 .707
(.058)

Mopti-Tenenkou .007 (.486) 1.103 .660
(.069)

Mopti-Youvarou .502 (.517) 1.310 .604
(.092)

 

 

 

 

 

Standard errors for parameter estimates are in parentheses

166

 

 

 

 

 

Table 5.4 Cointegration TestingResults
Markets Test Test Statistic
Mopti- Max Eigen Value Test: r=0 12.17”
DJenne Trace Test: r=0 11.36
Max Eigen Value and Trace Test: r=l 0.0875
Hansen L, Statistic 0.2887
Pl Test 110.77”
LR Test of a= O and [i=1 31.22'”
Mopti- Max Eigen Value Test: r=0 23.26”
Douentza Trace Test: r=0 22.06”
Max Eigen Value and Trace Test: r=1 1.210
Hansen L, Statistic 0.2260
PZ Test 123.72“
LR Test of a= 0 and [i=1 74.08'”
Mopti- Max Eigen Value Test: r=0 24.62”
Tenenkou Trace Test: r=0 24.27"
Max Eigen Value and Trace Test: r=l 0.3562
Hansen L, Statistic 0.4549
P, Test 129.81“
LR Test of (F 0 and [i=1 13.84'”
Mopti— Max Eigen Value Test: r=0 14.48”
Youvarou Trace Test: r=0 13.89
Max Eigen Value and Trace Test: r=1 0.5931
Hansen L, Statistic 0.1452
Pz Test 131.63”
LR Test ofa= 0 and [i=1 92.11'”

 

 

 

 

 

*, **, and "* denote signiﬁcance at the 10%, 5% and 1% levels, respectively.

The maximum Eigenvalue and Trace tests are Johansen cointegration tests critical values are drawn from
Maddala and 1m (2000). The L, test is a test with the null of cointegration from Hansen (1992). The P, is
the Phillips-Ouliaris (1990) cointegration test with the the null hypothesis of no cointegration.

Based on these results, we go on to investigate whether or not there is evidence of
nonlinearities in the cointegrating relationships. Hansen’s approach is revisited in

constructing conﬁdence intervals for threshold parameters in the TAR ﬁ'amework.

167

Figures 5.6 through 5.9 provide a graphs of the residuals ﬁom the cointegrating

regressions along with the Mopti vulnerability index.

 

Figure 5.6 Mopti Vulnerability Index and residuals from Cointegrating
Regression, Mopti and DJenne Retail Goat Prices

1')

 

 

Vulnerability

Index

Moptileenne
nmmduams

 

Rescaled Values for Index and Residuals

 

 

 

 

 

168

 

 

Mopti Vulnerability Index and residuals from Cointegrating

Regression, Mopti and Douentza Retail Goat Prices

Figure 5.7

a

m

e
s w
w om
r lu
e .I
L3
u
vm Mm

 

 

 

 

 

£58m 93 62. 383m 3 83>

169

MONTH

 

 

 

 

Figure 5.8 Mopti Vulnerability Index and residuals from Cointegrating
Regression, Mopti and Tenenkou Retail Goat Prices

2-
181

 

  

O
‘n I‘ W n
-'\.l::. ... \".'::

I. ' t, ' o o
’54. :;‘._»“.,u.' ‘3' -‘,t‘.' g; I "1' Vulnerability
.

 

MoptilTenenkou

residuals

 

 

Value of Rescaled Index and Residuals
'3

 

 

170

 

 

 

Figure 5.8 Mopti Vulnerability Index and residuals from Cointegrating
Regression, Mopti and Tenenkou Retail Goat Prices

 

Vulnerability

MoptilTenenkou

 

 

 

Value of Rescaled Index and Residuals

 

-1 01 residuals
33833833833333833
83883888888338883

MONTH

 

170

 

 

 

 

 

 

Mopti Vulnerability Index and residuals from Cointegrating

Vulnerability

Index

Mopti|Youvarou

residuals

Figure 5.9
Regression, Mopti and Youvarou Retail Goat Prices
m 2
To 1 ‘
.3 1 -
8 1 ,
n: 1 ,
.8 1 s
to
a: 8
1: 6
E 4 . i
B 2 ‘\ 5" I 5,9535 it
8 o "1, g"; '1‘: :‘o, 'u .5“ , . $3: '15:}: “a:
3 - ; ‘h'h : f" g" ..‘l' "N’VE z’.: :c ' l 0:
m ‘ﬁ‘r ‘ ’ . l
“5 i ’
a, 3
2 .
g -1 ..
v-N JKMv-fﬂﬂéﬁdv:t~'
FQQﬁQQnOQvQQﬁQQﬁQ
88288 88$$8338883
MONTH

 

171

 

5.5 Empirical Results

In the ﬁrst part of this section we present the empirical results ﬁ'om estimation of
the threshold cointegration models for the four market relationships in the Region of
Mopti. The second part provides a broader discussion of this investigation into regimes
shifts in Mopti goat markets.
5. 5. 1 Empirical Results
Mopti-DJenne

Tables 5.5 through 5.7 present the results of model estimation of threshold
cointegration models for the goat markets in DJenne and Mopti. We found the AIC
criterion (Table 5.5) to suggest the best model as one where the threshold variable is the
vulnerability index lagged two periods, v,2 , and an AR(3) structure. The HCC test results
in Table 5.6 suggests that the null of no threshold effect, a linear alternative, is strongly
rejected. The estimates of the model parameters are found in Table 5.7. The value of the
threshold level was estimated as 4.87 such that a regime of low vulnerability is deﬁned as
occurring where v,2 < 4.87; and a regime of high vulnerability to food insecurity occurs
above this threshold value. As the threshold variable, the vulnerability index is lagged
two periods implying that the regime shift is triggered in time t by the vulnerability
conditions two periods prior. The AR(3) model is stationary in both regimes. The
constant terms in the regimes are of opposite sign. This suggests that possibly the
transactions costs or risk premiums in the two regimes differ. One would expect the

transportation costs between markets to increase in vulnerable periods. Further, this also

172

suggests a tendency of retail goat prices in the central market to be relatively higher in
periods of high vulnerability and lower in periods of low vulnerability. This result is
consistent with the value of the asset diminishing in periods of crisis as households sell oﬂ‘

their goats and other assets in order to mitigate food security crisis in the short run.

173

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 5.5 AIC value for SE TAR models for cointegrating relationship between

Mopti and DJenne Goat Prices
Threshold p (lag leugtlrforAR)
Variable

0 1 2 3 4 5
yH 117.99 110.56 110.14 113.84 111.34 116.06
y,2 121.33 98.76 109.48 113.84 111.84 110.58
y,,3 137.09 106.36 100.03 110.02 98.86 101.86
y,_4 119.21 97.04 103.80 107.50 104.41 102.12
v,l 127.10 104.96 105.61 98.25 100.22 104.48
v,2 128.19 107.99 108.26 .3335. 7 .1 97.23 103.11
v,_3 135.27 108.73 108.15 I 99.15 97.98 102.89
vM 131.33 108.96 110.60 I 102.82 103.37 107.86
Table 5.6 p-values for HCC test of linearity against a SETAR alternative for the

cointegrating relationship between Mopti and DJenne Goat Price
Threshold p (lag order)
Variable

0 1 2 3 4 5

yH 0.000 0.430 0.472 0.056 0.134 0.142
y,_2 0.000 0.021 0.155 0.291 0.650 0.459
y,3 0.002 0.022 0.013 0.024 0.014 0.014
y,_4 0.000 0.001 0.003 0.014 0.000 0.000
v” 0.000 0.004 0.053 0.023 0.035 0.048
v,, 0.000 0.006 0.098 i0.004 - ’ i 0.002 0.006
v,_3 0.000 0.132 0.505 0.005 0.004 0.009
v,_, 0.001 0.035 0.350 0.092 0.062 0.013

 

 

 

 

 

 

 

 

174

 

 

5. 7 SE T AR estimates for the cointegrating relationship between Mopti and

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

DJenne Goat Prices
Confidence Intervals (95%)

Asymptotic LR Statistic
Variable Estimate Low High Low High
Regimel
v,_2 s 6 ( 80 obs.)
Constant -0.294 -0.572 -0.016 -0.575 -0.007
yH 0.336 0.150 0.522 0.136 0.495
y,2 0.008 -O.213 0.229 -0. 189 0.229
y,3 0.342 0.121 0.563 0.115 0.599
Regime 2
v,_2> é ( 50 obs.)
Constant 0.449 0.004 0.894 -0.0179 0.944
yH 0.362 0.121 0.603 0.146 0.581
y,2 0.291 0.058 0.582 0.0769 0.547
y,3 -0.390 -0.645 -0.135 -0.678 -O.140
Threshold 4.87 4.57 5.74

 

 

 

 

 

 

 

 

175

Mopti-Douentza

Tables 5.8 through 5.10.1 to 5.10.3 present the results of model estimation of
threshold cointegration models for the goat markets in Douentza and Mopti. We found
the AIC criterion (Table 5.8) to suggest the best model as one where the threshold
variable is the residual series itself (with an AR(O) structure) along with two other models
where vulnerability index lagged three or four periods, v,3 and VM , and an AR( 1)
structure are compelling candidates as well. The HCC test results in Table 5.9 suggests
that the null of no threshold eﬁ‘ect, a linear alternative, is strongly rejected for all three of
these models. The estimates of the model parameters are found in Tables 5.10.1 to
5.10.3. For the ﬁrst candidate (Table 5.10.1) , the threshold variable was selected as yH
and the structure of the model in each regime was just a constant term, this is a pure
SETAR model: the threshold variable is the series itself. The sign of the constant term in
the model for the low vulnerability regime, Regime 1, is negative; while that in the high
vulnerability regime, Regime 2, is positive. The reasoning outlined for Mopti-DJenne is
consistent for the three models presented for Mopti-Douentza. It is easy to envision the
regime switching behavior in this ﬁamework. A discrete jump from one value (the
constant term in each regime) to the next is triggered by the value of the series in the
previous period.

The model candidates that present the threshold variable as the lagged vulnerability
index are now examined (Tables 5.10.2 and 5.10.3). The value of the threshold level was
estimated as 4.88 such that a regime of low vulnerability is deﬁned as occurring where

vM < 4.88; ,v,,3 < 4.88; and a regime of high vulnerability to food insecurity occurs above

176

this threshold value. The models for each regime are AR(l), stationary, with parameter
estimates for the autoregressive terms very similar among regimes. As in the case of
DJenne, the constant terms in the regimes are of opposite sign. This suggests that possibly
the transactions costs or risk premiums in the two regimes differ. One would expect the
transportation costs between markets to increase in vulnerable periods. Similarly, the sell
off of asserts as vulnerability to food insecurity could be taking place. Recall that
Douentza is the most active trading partner in livestock with the central market, Mopti, of
the regions studies in this research. The results ﬁ'om this investigation seem to suggest
that there are threshold effects but that the adjustment process is similar in each regime.
The level of the vulnerability index lagged three or four periods is providing the
information concerning the regime shift in time t. This may indicate that this information
could be useﬁrl in detecting opportunities for intervention that could stabilize this asset

market in order to deter household from selling off their wealth in times of crisis.

177

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 5.8 AIC values for SE TAR models for cointegrating relationship between
Mopti and Douentza Goat Prices
Threshold p (lag length for AR)
Variable
0 1 2 3 4 5
y,l 85.44 1 88.30 93.63 97.80 106.40 112.85
y,2 101.05 88.92 94.22 99.98 105.87 112.18
y,, 117.43 97.72 102.32 107.53 106.80 113.15
y” 116.83 97.05 92.56 100.04 104.84 103.49
vH 105.15 90.22 94.30 99.93 96.86 103.01
v,2 100.89 90.27 93.45 99.54 103.95 109.02
v,, 100.41 88.29 " 1 97.58 102.87 101.94 107.48
v,4 98.813 88.67 <1 94.13 99.96 105.56 110.48
Table 5.9 p—valua for HCC test of linearity against (1 SE TAR alternative' for the
cointegrating relationship between Mopti and Douentza Goat Prices
Threshold p (lag length for AR)
Variable
0 1 2 3 4 5
y,1 0.000 ‘1 0.014 0.084 0.087 0.178 0.238
y,, 0.000 0.012 0.131 0.242 0.483 0.639
y,, 0.016 0.713 0.991 0.975 0.500 0.399
y,4 0.283 0.771 0.619 0.107 0.181 0.135
v,1 0.000 0.007 0.045 0.086 0.177 0.247
v,, 0.001 0.013 0.042 0.142 0.277 0.339
v,3 0.000 0.059 '1 0.126 0.315 0.533 0.507
v,4 0.000 £0,001 ‘9 323 0.094 0.179 0.301

 

 

 

 

 

 

 

 

178

 

 

5. 10. 1 SE T AR estimates for the cointegrating relationship between Mopti and

 

 

 

 

 

 

 

 

 

 

Douentza Goat Prices
Conﬁdence Intervals (95%)

Asymptotic LR Statistic
Variable Estimate Low High Low High
Regimel
yH s 6 ( 83 obs.)
Constant -0.675 -0.942 -0.408 -0.900 -0.400
Regime 2
yH > 6 (47 obs.)
Constant 1.020 0.570 1.470 0.580 1.49
Threshold 0.305 0.238 0.437

 

 

 

 

 

 

 

179

 

5. 10.2 SE TAR estimates for the cointegrating relationship between Mopti and

 

 

 

 

 

 

 

 

 

 

 

Douentza Goat Prices
Confidence Intervals (95%)

Asymptotic LR Statistic
Variable Estimate Low High Low High
Regimel
v,3 s 6 (81 obs.)
Constant -0.374 -0.648 0.100 -1.99 0.178
yH 0.332 0.116 0.548 -0.469 0.690
Regime 2
v,_, > e (49 obs.)
Constant 0.449 0.002 0.896 -0.897 1.061
yH 0.391 0.175 0.607 -1.00 0.618
Threshold 4.88 l .05 8.24

 

 

 

 

 

 

 

180

 

5. 10.3 SE T AR estimates for the cointegrating relationship between Mopti and

 

 

 

 

 

 

 

 

 

 

 

Douentza Goat Prices
Confidence Intervals (95%)

Asymptotic LR Statistic
Variable Estimate Low High Low High
Regimel
VM s 6 (81 obs.)
Constant -0.438 -0.750 -0.126 -0.723 -0.0816
y,,, 0.329 0.098 0.560 0.131 0.598
Regime 2
VM > e ( 49 obs.)
Constant 0.592 0.228 0.956 0.176 0.899
yH 0.331 0.138 0.524 0.141 0.521
Threshold 4.88 4.30 5.98

 

 

 

 

 

 

 

181

 

Mopti-Tenenkou

Tables 5.11 through 5.13 present the results of model estimation of threshold
cointegration models for the goat markets in Tenenkou and Mopti. We found the AIC
criterion (Table 5.11) to suggest the best model as one where the threshold variable is the
vulnerability index lagged two periods, vH , and an AR(3) structure. The HCC test results
in Table 5.12 suggests that the null of no threshold eﬁ‘ect, a linear alternative, is strongly
rejected. The estimates of the model parameters are found in Table 5.13. The value of
the threshold level was estimated as 5.09 such that a regime of low vulnerability is deﬁned
as occurring where v,_l < 5.09; and a regime of high vulnerability to food insecurity occurs
above this threshold value. The constant terms in the regimes are of opposite sign. This
suggest that possibly the transactions costs or risk premiums in the two regimes differ.
One would expect the transportation costs between markets to increase in vulnerable
periods.

Recall that Tenenkou is a relatively wealthier area than Djenne, Douentza, and
Youvarou. There are other economic activities in the region beyond trade in goats and
other livestock. This may provide more resilence in vulnerable periods than in other
regions such that households are less prone to selling oﬁ‘ their assets until later in crisis
periods. Perhaps this is the reﬂected in the higher value of the level of the threshold

variable that triggers regime shifts and the shorter lag on the variable itself.

182

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 5.1 1 AIC values for SE TAR models for cointegrating relationship between

Mopti and Tenenkou Goat Price
Threshold p (lag length of AR)
Variable

0 1 2 3 4 5
yH 124.19 119.06 113.61 122.05 122.14 113.01
y,_2 127.14 119.61 120.86 127.11 123.80 106.93
y,,3 132.38 114.91 111.43 117.02 121.15 119.56
y” 128.05 113.91 114.38 112.79 121.89 128.19
v,l 129.07 111.38 106.69 107.30 113.92 125.75
v,2 129.46 121.26 112.43 118.18 119.32 107.98
v.._, 127.41 115.12 113.67 116.53 117.58 119.34
vM 130.60 114.51 111.42 119.66 122.41 118.59
Table 5.12 p-values for HCC test oflinearitjv against a SE TAR alternative for the

coinmrelationshjp between Mopti and Tenenkou Goat Prices
Threshold p (lag length of AR)
Variable

0 1 2 3 4 5

yH 0.000 0.842 0.982 0.689 0.860 0.850
y,2 0.000 0.029 0.726 0.925 0.994 0.483
y,_3 0.000 0.255 0.830 0.427 0.647 0.971
yH 0.000 0.037 0.572 0.140 0.275 0.411
v.., 0.000 0.141 0.060 0.001 0.008 0.011
v,2 0.002 0.163 0.426 0.024 0.086 0.051
v,, 0.000 0.087 0.093 0.026 0.111 0.023
vH 0.000 0.046 0.132 0.034 0.094 0.029

 

 

 

 

 

 

 

 

183

 

5. 13 SE T AR estimates for the cointegrating relationship between Mopti and
T enenkou Goat Prices

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Confidence Intervals (95%)

Asymptotic LR Statistic
Variable Estimate Low High Low High
Regimel
v,_l s 6 ( 83 obs.)
Constant -0.265 -0.532 0.002 -0.583 -0.0043
yH 0.299 0.166 0.432 0.156 0.441
y,,2 0.042 -0.087 0.171 -0.114 0.163
y,,3 0.174 0.035 0.313 -0.021 0.332
Regime 2
VM > c ( 47 obs.)
Constant 0.360 -0.132 0.852 -0.116 0.774
yH 0.316 -0.105 0.636 -0.0532 0.670
y,.2 0.557 0.296 0.818 0.310 0.776
y,3 -0.240 -0.491 0.011 -0.451 0.420
Threshold 5.09 4.41 8.52

 

 

 

 

 

 

 

Mopti-Youvarou

Tables 5.14 through 5.16 present the results of model estimation of threshold
cointegration models for the goat markets in Youvarou and Mopti. We found the AIC
criterion (Table 5.14) to suggest the best model as one where the threshold variable is the
vulnerability index lagged two periods, v,3 , and an AR(l) structure. The HCC test results
in Table 5.15 suggests that the null of no threshold effect, a linear alternative, is strongly
rejected. The estimates of the model parameters are found in Table 5.16. The value of
the threshold level was estimated as 5.25 such that a regime of low vulnerability is deﬁned
as occurring where v,_3 < 5.25; and a regime of high vulnerability to food insecurity occurs
above this threshold value. The constant terms in the regimes are of opposite Sign. This
suggests that possibly the transactions costs or risk premiums in the two regimes diﬁ'er.
One would expect the transportation costs between markets to increase in vulnerable
periods. However, Youvarou may experience especially low trading volume in highly
vulnerable periods relative to the other areas which may account for the larger diﬁ‘erential

between the estimates of the constant terms in each regime.

185

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 5.14 AIC values for SE TAR models for cointegrating relationship between

Mopti and Youvarou Goat Prices
Threshold p (tasters!!! 0! AR)
Variable

0 1 2 3 4 5
y,,l 136.24 136.49 120.60 124.76 127.66 131.35
y,2 147.52 124.16 125.27 129.45 130.05 134.73
y,,3 143.07 125.60 119.77 131.05 133.39 138.76
y“ 141.00 119.76 118.64 123.33 126.89 133.66
VM 133.50 123.93 115.24 123.68 127.41 131.65
v,,2 127.05 118.96 120.92 114.23 121.95 126.77
v,, 109.94 105.97 107.75 113.04 113.05 116.44
VM 122.57 116.19 118.65 123.14 119.25 120.42
Table 5.15 p-values for HCC test of linearity against a SETAR alternative for the

cointegrﬂrg relationship between Mopti and Youvarou Goat Prica
Threshold p (lag length of AR)
Variable

0 1 2 3 4 5

y,,l 0.000 0.354 0.152 0.034 0.080 0.016
y,2 0.000 0.011 0.194 0.431 0.521 0.335
y,3 0.000 0.002 0.028 0.326 0.877 0.699
y“ 0.000 0.041 0.185 0.317 0.333 0.346
v,_l 0.000 0.000 0.014 0.026 0.167 0.165
v,2 0.000 0.000 0.010 0.013 0.078 0.118
v,, 0.000 0.000 0.001 0.002 0.004 0.001
v,_, 0.000 0.000 0.012 0.008 0.002 0.011

 

 

 

 

 

 

 

 

186

 

5. 16 SE T AR estimates for the cointegrating relationship between Mopti and
Youvarou Goat Prices

 

Confidence Intervals (95%)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Asymptotic LR Statistic
Variable Estimate Low High Low High
Regimel
v,3 s 6 ( 86 obs.)
Constant -0.442 -0.754 -0. 130 -0.841 -0.158
yH 0.489 0.273 0.705 0.193 0.675
Regime 2
v,,,> c (44 obs.)
Constant 1.145 0.518 1.772 0.455 1.70
y,_, 0.089 -0. 160 0.338 -0. 160 0.403
Threshold 5.25 4.83 5.27

 

187

 

5.5.2 Implications of Threshold Cointegration Results and
Market Integration in Mopti Region

Overall, there is strong evidence of threshold effects in the cointegrating
relationships for goat prices in each Mopti goat market investigated in this study. The
level of the vulnerability index that delineates the two regimes is near 5 (on a scale ﬁ'om 0
to 10, which is scaled up by a factor of 10 from those developed in Chapter 4 ) or higher
which supports the intuition behind the vulnerability index. The consistent and signiﬁcant
difference between the two regimes appears to be the estimate of the constant term for
each region which is negative in the low vulnerability regime and positive in the high
vulnerability regime. This suggests that the difference may be capturing the disparity in
transactions costs in the two regimes. In addition, the negative constant term suggests
that the retail goat price in Mopti, the central market, tends to be lower in periods of
lower vulnerability to food insecurity than in periods of high vulnerability. This implies
that if goats are viewed as an asset, their value is diminished in periods of high
vulnerability relative to periods of low vulnerability, which is consistent with the behavior
of selling off assets to mitigate food insecurity. Further, the results may reﬂect low
trading volumes or in thin markets at times of high vulnerability. The structure of the
regime shift depicts a discrete jump. The sale of assets such as goats may unfold in such a
manner that desperate sellers are forced to sell off more than is desired at sharply lower
prices due to their low bargaining power. Once under a regime of high vulnerability to
food insecurity, the Mopti markets could be characterized as ‘lumpy asset markets’; this

appears to be supported by this empirical investigation.

188

The use of an external threshold variable, the vulnerability index, adds a structural
interpretation to this modeling of market integration. Traditional cointegration techniques
would suggest that the there is a single regime and that there is a long run equilibrium
relationship that prevails in each period. However, after viewing the relationship as
nonlinear we ﬁnd that a more compelling narrative unfolds that is consistent with
economic theory. The existence of threshold effects explains why the Law of One Price
fails to hold in light of evidence of cointegration. Cointegration required that transactions
costs are bounded. The evidence, in particular the stationary expressions for the AR(p)
models in each regime for each region, suggests that transactions costs are bounded but
differ in the each vulnerability regime. The transition from one level of market integration
to another is a jump triggered by the level of vulnerability. Future research might examine

a smoother transition from one regime to the next.

189

Chapter 6
Conclusions and Policy Recommendations

6.1 Conclusions

Information empowers consumers, farmers, businesses, traders, policy makers, and
investors when it is transformed into knowledge which is useful to constituents. This
investigation builds on the work done by researchers at SIM, SAP, and other information
systems as well as through cooperative efforts at universities and government agencies.
This dissertation contributes to the knowledge base by exploring the development and
application of measures of vulnerability to food insecurity, drawing from current and past
research of FEWS and SAP in Mali. This research creates a reference cycle and a single
index of vulnerability to food insecurity, incorporating both qualitative and quantitative
indicators of food insecurity. In addition, a ﬁ'amework is developed to incorporate these
indexes in the analysis of market integration. Speciﬁcally, the research outcomes include:
1) construction of a food security vulnerability index and evaluation of its effectiveness in
depicting the state of vulnerability to food insecurity for regions in Mali using NDVI and
price data; 2) employment of robust econometric tests for market integration which are
structural in nature and; 3) use of the vulnerability indicators in the construction of models
of market integration to assess the impact of vulnerability on the degree and stability of
market integration between regions.
6.1.1 The Single Index of Vulnerability to Food Insecurity

In the ﬁrst part of the dissertation, indexes that capture the unobservable state of

vulnerability to food insecurity were developed for eight regions in Mali: Gao, Mopti,

190

Timbuktu, Segou, Djenne, Douentza, Tenenkou and Youvarou. These indexes were
individually referenced to the qualitative information on food security conditions available
in monthly reports by SAP. These qualitative vulnerability conditions include sharp
increases in prices, low levels of food stuffs in the market, civil unrest, pest infestation,
poor harvests and high levels of disease. The estimated indexes were displayed graphically
along with these qualitative indicators to provide a visual representation of the pattern of
vulnerability to food insecurity by region. The resulting indexes were not comparable
across regions but provided an overview on a region by region basis. We found that the
estimated vulnerability indexes captured the key turning points of vulnerability to food
insecurity in the eight regions. These turning points, anchored in the index, typically
occurred two to three months in advance of reports of conditions of moderate
vulnerability in regions. In addition, the turning points were followed by moderate and
then highly vulnerable conditions, in many cases. When food aid delivered in periods of
moderate vulnerability mitigated worsening conditions, in general. The vulnerability
mappings developed in the ﬁrst part of this research provide a tool for detecting patterns
of food security conditions and holds some potential as a tool in policy analysis.

While the spread and ﬁmctioning of early warning systems in Aﬁica is considered
by many a success story in development, there persists a disconnect in early warning of
vulnerability to food insecurity and response measures (Von Braun et a1, 2000, Buchanan
and Davies 1995, FEWS, 1998). One issue is that the information gathered and
communicated to decision makers fails to evoke interventions or response to crisis

because it can be perceived as subjective and viewed as less than conclusive. This

191

research explores this aspect by providing a reproducible method for combining the early
warning indicators which are relied upon by early warning analysts. The vulnerability
indexes developed here provide a mechanism for assessing current vulnerability conditions
in a dynamic framework, one can compare the severity of conditions within historical
periods.

In this research, qualitative information from monthly SAP bulletins was used to
construct reference cycles of vulnerability to food insecurity for eight regions in Mali.
These assessments were used to characterize periods as: low, moderate, and high
vulnerability. Compiling information in this manner provided a means by which to ‘
compare the qualitative information with the quanitative information in the form of
vulnerability mappings. First, a key vulnerability indicator, millet prices, was transformed
into smoothed millet price indexes for each region. This simple index served as a
benchmark for evaluating the effectiveness of developing a more sophisticated index
(composed of NDVI and millet prices), the Single index of vuhrerability to food insecurity.

Turning points in the indexes were compared to the reference cycles. If a turning
point appeared in advance of moderate or highly vulnerable periods, this was viewed as a
valuable feature of the index. Whether the level of the index distinguished among periods
of low, moderate and high vulnerability was also a key feature upon which to evaluate the
efﬁcacy of the indexes.

Overall, we found the single index approach to capture the more dramatic shifts in
vulnerability to food insecurity in Gao, Mopti, and Timbuktu. These turning shifts were

conﬁrmed by the reference cycles. The single index proved superior to the simple

192

smoothed millet price index in the three regions providing early warning of vulnerability
with one to three months lead times.

NDVI appears to have added signiﬁcant value to the vulnerability index especially
in regions where the trend in NDVI depicted a decline in crop greenness, this was the case
in Gao, Timbuktu as well as Segou (see the appendix for Chapter 4). The simple index
out-performed the more sophisticated index in regions dominated by livestock trading
(Tenenkou and Youvarou).

6. 1. 2 Regime Shifts in Market Integration

The second part of the dissertation investigated the stability of markets with
respect to the level of vulnerability to food insecurity. The goat markets in Mopti and
surrounding cercles were investigated in terms of market integration. Departures from
integration as revealed through price relationships in the region are assumed to stem from
spatial arbitrage and transactions costs conditions. We examined the goat market in Mopti
with Mopti cercle as the central market and Douentza, Djenne, Tenenkou and Youvarou
(all cercles within the Mopti region) as peripheral markets. The goat markets were
selected in order to explore the behavior in an important asset market (goats) during food
secure and food insecure periods. As described in Chapter 4, a coping strategy to mitigate
insecurity in its later stages is to sell 08' assets to purchase food. One would suspect that
this behavior would be revealed in the price relationships between markets for goats in a
region.

The vulnerability index for the central market was used as a threshold or indicator

variable when exploring the stability of the spatial goat price relationships. This is an

193

important innovation because it provides a structural approach to explaining the regime
shifts. The vulnerability index serves as a structural trigger, depicting the state of
vulnerability to food insecurity, that derives from economic logic and intuition rather that
just being interpreted as a statistical phenomenon. We found that the cointegrating
relationship between the goat market (reﬂected through goat prices) in Mopti and its
surrounding regions exhibited nonlinearities that suggest the asset market performs
diﬁ‘erently under periods of vulnerability to food insecurity and more stable periods.
Further, the Mopti vulnerability index served as an appropriate threshold variable such that
regime shifts occurred at threshold values denoting a period of heightened vulnerability to
food insecurity in all cases. The difference in the behavior of markets is attributable to a
shift in transactions costs, a risk premium, spatial arbitrage and/or the implementation of
ceping strategies that includes the sell oﬂ‘ of goats. The desperate sellers are trading under
less that favorable conditions which may force them to sell more goats than desired at a
low value just to insure that trade takes place. This could explain the discrete regime

shift.

6.2 Policy Recommendations
the balance of short term and long term needs to be addressed as concern for people
ﬁrst is appropriate not only on ethical grounds, but also in terms of sound economics:
appropriate relief represents a basic investment in human development”
(Von Braun et al 2000)

There has been an increasing tendency in the 1980's and 1990's on the part of

international donors and Aﬁican governments to focus on short term solutions rather than

194

long term development. International organizations and the United States spent more on
short term relief and peacekeeping operations than on development programs in the early
1990's (von Braun et al, 2000). By diminishing the focus on development orientation, the
conditions that perpetuate vulnerability to food insecurity remain and are exacerbated.
The ability of individuals and households to endure severe conditions and rebound
afterward is diminished when faced with these circumstances without mechanisms to
preserve their asset base and livelihoods.

Together, the qualitative and quantitative indexes capture the evolution of food
security in the examined regions. This depiction may assist in policy analysis with regards
to the development and targeting of strategies to ensure food security. In this new
millennium, the challenge facing policy makers and donors is to develop an ‘optimal policy
mix that combines short and long term strategies (relief and development) in mutually
reinforcing ways ‘ (Von Braun et al 2000). While this inquiry does not aim to deﬁne the
‘optimal’ policy mix, thinking about this research in its context as a tool with potential to
assist decision makers in this quest may be helpﬁrl.

How does this research contribute to the development of such an optimal policy
mix? By adding a method for exploring the process of vulnerability, providing a mapping
of qualitative and quantitative measures to provide a dynamic presentation of vulnerability
conditions. The resulting representations can highlight patterns. This capacity could
contribute as a tool in developing a ﬁ'amework for public intervention that encompasses

two dimensions: the {we of policies and programs to implement and the timing of the

195

implementation of these actions. The vulnerability mappings developed here provide a
tool for exploring these dimensions on a region by region basis.

This research develops a ﬁ'amework for depicting the movements and adjustments
from one regime of market integration, for example one of a high level of integration, to
the next. The implications for early warning and response are clear with respect to
targeting vulnerable areas and building on existing linkages to effectively respond in
troubled locations. With the proper information ﬂows, times of crisis have the potential to
present entrepreneurial opportunities which could spill over to new economic activity in
the region long after the initial period has passed. Thus, SAP would expand its ability to
serve in a variety of capacities, including as a tool in regional planning and development.

The exploration of market integration in goat markets offers a tool to investigate
the role of asset transfers and credit programs in the preservation of livelihoods. The
vulnerability index for the central market serves as an indicator in turning points or regime
Shifts in the asset market, the market for goats. There is a point at which households
choose to sell off their livestock and this typically follows a persistent deterioration in their
most liquid assets. A sell off of livestock represents the relinquishing of their long term
ability to maintain food security. There is a need in the early stages of a decline in food
security of vulnerable households to intervene with short term and long term programs
such as employment generation and credit programs to preserve livelihoods or to expand
the choice set of vulnerable individuals. The methods developed in the second part of the

dissertation can assist analysts in assessing the level of market integration and information

196

ﬂows and structuring institutional initiatives that promote food security and poverty
alleviation.

Through augmenting analytical approaches, SAP and FEWS could also work more
effectively and creatively with other existing and emerging information systems. The goal
of developing a decentralized market information system is to “foster an efﬁcient, timely,
reliable, and donor-independent agricultural and food marketing system in Mali and
Strengthen the capacity of private and public sectors to use the resulting market
information eﬂ‘ectively to promote agribusiness growth and food security.” (Dembéle' and
Staatz, 1999) SAP and external information systems are key parts of this vision and
important partners in improving information ﬂows as well as coordination of early warning
activities and analysis in the region.

This research develops new methods for analyzing data currently archived and
collected by existing famine early warning systems. Exploring new methods for examining
this information and developing complementary tools for depicting vulnerability to food
insecurity could shed light on the evolution of the processes that detract from secure
livelihoods for individuals in many regions in Aﬁica. It is hoped that this research will
make such a contribution to early warning capacities and assist policy makers in
constructing effective policy portfolios that combine relief activities and development
oriented interventions.

6.3 Future Research
Future research could include the expansion of the number of indicators in the

construction of the vulnerability indexes. Prices of other commodities, indicators of civil

197

unrest, and other agroclimatic indicators could serve to create smoother vulnerability
indexes with more well deﬁned turning points. Other modeling approaches that address
seasonality and other periodicities in the single-index model could also be explored. In
addition, the development of techniques to model generalized impulse response functions
that explore the dynamics of threshold models where the threshold variable is exogenous.
This would contribute to better understanding of these models especially in the context of

regime shifts in market integration.

198

APPENDIX CHAPTER THREE

A3.l Regional Descriptions: Segou, DJenne, Douentza, Tenenkou, Youvarou
Segou

Segou is located along the Niger River about 240 km ﬁom Bamako. The Ofﬁce
du Niger, the organization promoting rice production in West Aﬁica, is headquartered in
Segou. Rainfall in the area averages 600 mm annually. Segou, along with Sikasso, is a

major surplus producing region in cereals. SAP began to monitor this cercle in 1988.

DJenne
‘ Located to the southwest of Mopti, DJenne is one of the oldest settled areas of
West Africa. DJenne connects to Mopti by the Bani River. Annual rainfall is
approximately 500 mm. DJenne was in its prime in the fourteenth and ﬁﬁeenth centuries
when it was a region proﬁting ﬁ'om trans-Saharan trade and inhabitants enjoyed high living
standards. The Grand Mosquee of DJenne is a landmark of this picturesque area.
Economic activity centers on trade in livestock and cereals.
Douentza

Douentza cercle lies east of Mopti Cercle and shares a borders with Timbuktu to
its north. Douentza’s average annual rainfall is about 450m. According to Davies
(1996), the importance of monitoring the region is that it is the frontier between
transhumant pastorialism and millet cultivation. The regions agro—pastoral producers are

some of the most vulnerable in agroecological terms. Recurrent drought have undermined

199

livelihood security and is representative of other marginal areas for this reason. The main
transport route, the paved road from Bamako to Gao, runs through Douentza. While
trade has expanded little economic growth is evident in the cercle. Douentza is a main
transit zone for Sahelian livestock moving to southern markets despite policy impediments
devaluation of currency. Douentza has historically served as the ﬁrst reﬁlge of people
from the northern areas in search of pasture and water.
T enenkou

Tenenkou lies southwest of Mopti Cercle, north of DJ enne and south of Youvarou
and receives an average annual rainfall of 450mm. Tenenkou is a comparatively wealthy
zone in the region, it attracts rural to rural migrants in search of paid work during the rice
harvest and in ﬁshing camps. Most transhumant pastoralists who rely on the Delta
pastures travel through this cercle.
Youvarou

Youvarou is a microcosm of the Inner Niger Delta and the surrounding drylands.
With average annual rainfall of 250 mm and the lakes areas of Debo and Walado,
Youvarou Cercle is home to millet cultivation, agro-pastoralism, agro-ﬁshing, transhumant
ﬁshing and transhumant pastoralism. Youvarou lies northwest of Mopti Cercle.
A3.2 Reference Cycle Mappings

In this section we present reference cycle summaries. Table A3.1 provides a
summary of moderate and vulnerable periods for each region. Figures A3.2.1 through

A3 .25 present a graphical representation of these reference cycles.

200

Table A3.1 Summary Classifications for Reference Cycles

 

 

 

 

 

 

 

 

 

Region Periods of Moderate Periods of High Vulnerability
Vulnerability .

Segou April 1988 to July 1988 August to December 1992
November and December 1989 May 1993 to October 1994
September and October 1990 May 1995 to October 1995
January 1991 to March 1991 April to December 1997
July and October 1991
January to July 1992
January to April 1993
March and April 1995

DJenne January 1987 to June 1987 November and December 1986
February and March 1989 July 1987 to August 1988
November and December 1989 February 1990 to June 1991
April 1992 to September 1992 November and December 1992
February 1993 September 1995 to July 1996
September 1993 to June 1994 October 1997 to January 1998
May 1995 to August 1995
March and April 1997

Douentza August 1989 November 1986 to September 1988
January and February 1990 March to August 1990
September to December 1990 January to September 1991
October 1991 to May 1992 October 1995 to January 1998
January 1993 to July 1993
March to June 1994
February 1995 to September 1995

Tenenkou February to July 1987 October 1986 to January 1987
January and February 1988 August 1987 to December 1987
January to April 1989 March to August 1988
January to July 1990 October and November 1989
February and March 1992 April 1990
November 1992 to February 1993 March 1991 to May 1992
August 1993 to October 1993 May 1995 to August 1996
February 1994 to July 1994 May 1997 to January 1998
February to April 1995
September 1996 to April 1997

Youvarou January 1987 to June 1987 October to December 1986
August 1989 to August 1990 July 1987 to September 1988
February to Airgun 1992 September 1990 to July1991
May to September 1993 September 1995 to September 1996
February to June 1994 May 1997 to September 1997
March to August 1995
October 1996 to April 1997
October 1997 to January 1998

 

201

 

Segou

Segou tends to be a far more food secure cercle relative to the other cercles in this
study. Overall, we ﬁnd the reference cycle mapping ( Figure A3 .2. 1) depicts a gradual
increase in vulnerability in Segou over the sample period, April 1988 through April 1998.
Through late 1989, the main concern in the cercle rests with the refugee populations,
otherwise food security is reported as good.

A sharp price increase in October and the problems for six villages which are
abandoned in search of food due to pest damage are reported. Food aid is delivered to the
most vulnerable areas in December 1989. Food security stabilizes or improves throughout
1990. By April 1991 there is an epidemic of measles and steep price increases until
harvest. There are pressures of migration from areas suﬂ‘ering from civil unrest which
causes a pressures on food security. There are problems reported in the SAP assessments
in 1992 where a continued increase in vulnerability conditions is noted: Migration
pressures, price increases, disruption in trading in livestock with producers in Mopti
Region, disease outbreaks and reports of weak household reserves. It is important to note
that this period shows the ﬁrst assessments of household reserves as “weak”in Segou for
this sample. Conditions of moderate vulnerability persist throughout 1994 with sharp
increases in prices and disease outbreaks as well as low quantities of cereals in the markets
in July 1994. Food security improves post-harvest as captured.

From March through May there are outbreaks of measles and meningitis. In June
there is an increase in prices reported as well as poor rains. Conditions deteriorate with

low levels of cereals in the markets as well as even higher prices and the spread of disease.

202

The harvest is fair to poor and there is a cholera outbreak in November and December
1995. In 1996, SAP reports household resources as weak for much of the year and
disease outbreaks persist. By August 1996 conditions appear at their worst for the year;
in household reconstitute reserves with fonio and harvest. Food aid is delivered in
February and July. Overall, conditions seem to improve post harvest.

While disease persists into 1997, food security conditions appear stable until July
when the SAP assessments report household reserves as weak in the cercle. There are
food aid deliveries in August and September which, along with a good harvest, appear to

improve food security overall.

203

 

 

10:..-

mbémooz _. .

mat—59m:— ceeh 3 3:39.95?» we manage: 0.96 35.8%;— gnaw

 

LﬂozﬁuCO—Z

iuuqeraulnn 10 310.13 aouarajaa nodes

83 age... 3 8.: .23.

3.3. 2am...“—

 

 

204

DJ enne

The reference cycle mapping for Djenne is presented in Figure A3.2.2. Signiﬁcant
levels of food aid are delivered from October through December 1986 that may contribute
to mitigating weak HHRting point. Moderate to high vulnerability persists through to
harvest of 1988. Conditions improve providing stability for food security after the
harvest.

Late 1989 is a period of high vulnerability due to a rise in prices and the poor
harvest. These conditions contribute to the decline in food security for households for
1990 and 1991. Over this period, there are severe measles outbreaks in the cercle,
dramatic price increases, low quantities of cereals marketed, and many areas that are
declared at risk. A good harvest is realized which serves to reconstitute household stocks.
However, migration pressures due to civil conﬂicts (ﬁshermen from the North are
relocation in Djenne, for example) and environmental challenges disrupts food security
somewhat. Moderate vulnerability occurs in the later part of 1992. In September 1993
there is an onset of more vulnerable conditions throughout the cercle. 1994 proves to be a
year with price increases and the spread of meningitis as well as deaths due to measles.
Food aid in May, June and July appear to mitigate ﬁrrther decline we ﬁnd conditions
improves post-harvest.

In late 1995 vulnerability increases in the Cercle due to a poor harvest and
increases in migration in search of food. Prices increase throughout the cercle with Konio
in the North particularly vulnerable. Food aid deliveries in May and June improve

conditions although major problems in Konio persist. There are moderate vulnerability

205

conditions in 1997: irregular migration, price increases and a poor harvest create

problems for households ﬁ'om October into January 1998, the end of the sample period.

206

 

 

habitats-2

% WW. mmolom
Ir sl'

10$-

“Calm—DOS.

 

 

 

inuqeraumn 10 epic eouarareu auuam

 

 

 

82 eggs. 3 83 .8882
€58:— eeeh 8 5:38.05?» me wanna: 296 85.53— 2393 «Add. «.5me

 

 

207

Douentza

The reference cycle mapping created for Douentza presented graphically in Figure
A3 .2.3. There are conditions of high vulnerability to food insecurity throughout 1987 and
1988 with a major jump at harvest time in 1987. The years poor harvest created a very
diﬁicult hungry season in 1988 with household reserves exhausted by April 1988.
Migration ﬁ'om Timbuktu and elsewhere along with high food prices and the consumption
of famine foods characterize this period. July and August 1988 are the height of the
severity of problems in the cercle according to the SAP summaries. Consistent food aid
throughout the period keeps a crisis ﬁ'om setting in. By September 1988 conditions
improve throughout the Douentza cercle with a good agricultural outlook and a
subsequent good harvest of millet.

In 1989, food security conditions are satisfactory in the cercle with Mondoro and a
few other areas having some problems in August. Vlfrth a poor agricultural outlook. The
SAP reports Show the cercle at risk ﬁ'om August through September 1989. This
vulnerability persists and increases over 1990 especially in the North with HHR weak in
the cercle and some areas declared as in high risk. Food aid deliveries proceed in March,
June and July which help to mitigate crisis in the most vulnerable regions. By August the
region shows some improvement in food security conditions which continues through
early 1991. Some areas are still at risk while others are under surveillance. In early
1991, conditions in the cercle are reported to include some migrations in search of food
and areas at risk. The food security conditions improve after harvest although some

arrondissments are under surveillance. The index continues to decline and this

208

corresponds to a satisfactory food security situation in the cercle as reported by SAP. A
poor harvest in 1992 sets the stage for conditions of vulnerability in the cercle. The North
is particularly vulnerable during 1993 with regions declared DA by mid-year. Food aid
delivered in April, June, September and October serves to assist the most vulnerable and
to keep prices from sharply increasing. There is some migration out of Douentza cercle
due to poor conditions. Household reserves are noted as weak as of October. An average
to poor harvest in the cercle ﬁrrther threatens food security. Throughout 1994, the
northern areas continue to be at risk and the entire cercle is reported as vulnerable to food
insecurity. There are some problems with disease outbreaks and an expected poor
harvest. Food aid is delivered in April and July.

In 1996 vulnerability persists with sharp price increases, disease outbreaks, weak
household reserves; these conditions worsen over the course of the year. Food aid
deliveries from April through September help the most vulnerable in the cercle.

Conditions deteriorate throughout 1997 and are exacerbated by an inﬂux of reﬁrgees.

209

 

 

LNOtSHCO—Z

wmmmolom
W.” W l.

 

.W
W

minimum/t 10 61010 900919198 enema

 

 

 

meg baa—3H 3 33 3:80.52
3.598:— eeeh 8 3:59.05?» he means: «.95 8:93qu «5:225 m.~.m< «new;

 

 

210

Tenenkou

The period begins with the regions in the North as at risk with the South under
surveillance. This situation persists into 1987 and worsens by year’s end. There is food
aid delivered in large quantity throughout the period. There is a sharp increase in prices
by September 1988 and weak household reserves for the rest of the year. Vulnerability
increases throughout 1988 with many areas with household reserves exhausted by May
and access and cost of cereals prohibitive by June. The agricultural outlook is excellent as
reported by SAP and the harvest reconstitutes household reserves such that 1989 is a year
of satisfactory food security with some price increases. The poor harvest of 1989 pushes
up prices and there is a noted decline in cereals in the market. These conditions of high
vulnerability are captured in the reference cycle mapping (Figure A3 .2.4).

Vulnerability persists in 1990 with out-migration in search of food in some areas.
Conditions stabilize in November and December but overall the cercle is still vulnerable. In
1991 there is heavy population movements due to insecurity and the entire cercle appears
to be vulnerable throughout the year. Dioura is a trouble area in 1992 but appears to be
an isolated problem. There is some irregular migration in November and December of
1992 as well as the spread of measles. Conditions of insecurity and irregular migration
persist into 1993 when by September hoarding of cereals is reported. By September 1994
conditions improve with a good harvest. In early 1995, there are high vulnerability
conditions which correspond to a high level of disease among livestock, a key income
source in the region. There is also a high level of cholera and measles reported as well an

inﬂux of refugees. These conditions along with a poor harvest create conditions of high

211

vulnerability in the cercle until harvest. However, by May of 1997 vulnerability to food
insecurity increases as SAP reports indicate weak household resource levels and high
disease. Poor rains and a poor harvest in 1997 cause continued vulnerability in the cercle
as well as irregular migration and price increases. Food aid is localized around Dioura

although needed throughout the cercle.

212

 

 

LNGkSuCO-Z

10.1 I
w._.<n_w00.2 8......

_
_,__
.0:
I;
u__.

 

 

 

 

 

 

 

 

 

 

 

 

m__ 5:

iuuqerauln/t 10 apio 00819193 norueuel

as: base. 3 82 .235
.3238:— eeeh 8 35:32:?» he means: 295 85.501 sea—5:3. Wade. «...—mm..—

 

 

213

Youvarou

The reference cycle mapping constructed for Youvarou (Figure A3 .2.5) has
classiﬁcations similar to the Tenenkou reference cycle with the exception of the end of the
sample period. Food aid distributions and general conditions in 1986 and 1987 are quite
similar. Conditions improve after August 1988 due to from the eﬁ‘ect of food aid
deliveries and a good harvest that serves to reconstitute household reserves. By August
1989 there is a shortage of staples in the market, price increases and the consumption of
famine foods to ﬁll gaps. Moderate vulnerability prevails until April of 1990 when a low
level of cereals in markets is reported as well as a low number of traders. The entire
cercle is under watch at year’s end. Vulnerability persists until September 1991 with a
good harvest. Conditions are more stable for food security in 1992 except for problems

that are localized to Ambiri and Sah. Regular food aid in 1992 assists the most vulnerable.

Beginning in May 1993, there are moderate vulnerability in the cercle with
progressively increasing prices, measles outbreaks, and low levels of millet in markets by
August. In February of 1994 signiﬁcant problems are observed with respect to price
increases and the availability of cereals in the market. Repatriation of refugees in 1995,
the spread of cholera, and nomads returning with weak resources create vulnerable
conditions in the cercle. A poor harvest perpetrates vulnerability into 1996. In
October/November 1996 there is an improvement in food security when a good harvest
ensues. By April 1997 conditions reﬂect moderate to high vulnerability for the rest of

1997, the end of the sample period.

214

 

 

boots—.0.).

we

I.

 

IO.II

wk<mm002 -,

_____
_.. ..F.
.J ,._

 

 

 

 

imrqerauln/t 10 epic eouarelau norennol

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

I:

as: 525. 3 can .835
£332: eeeh 3 3:395:15 no mines: «the 85.33— 2395...» m.~.n< enema

 

 

215

A33: Tables A3.4 through A38: Vulnerability Assessments by
Region
(Segou, Djenne, Douentza,Tenenkou, Youvarou)
Reference cycles, depicted as shaded areas in Figures A3.2.1 through A3.2.5 in
Chapter 3, were constructed from the monthly SAP reports and are reported in this

appendix Table A3.2 is reprinted here to facilitate interpretation of the summaries.

Table A3.2 Characteristics of Reference Periods for Vulnerability Assessments

 

 

1. General qualitative assessment of vulnerability in food security summary

2. DEL: Some economic difficulties (as presented in Table 2.6)
DES: Severe economic diﬁculties
DA: Food Deﬁcit
CA: Food Crkis
FA: Famine

Household reserves reported as “weak”

Food aid authorized

Sharp increase in staple prices reported

Low level of staples in market reported

Disease outbreaks classiﬁed as epidemics (meningitis, cholera and/or
measles)

Consumption of wild cereals reported

High level of migration into the cercle reported

0. Civil conﬂicts reported

eras-r»

."

~99

 

Note: HHR refers to 'household reserves ’ in Tables 113.4 through A3.8
FFW refers to ‘Food for Work Program ”

216

 

Table A3.4

Vulnerability Assessments for Segou based on SAP Reports

 

Year

VrrlnerabilityAssessrnerrtbasedonSAPReports ‘

 

1988

April and May: overall ok some potential problems ﬁom displaced
persons
June: Pockets of problems
May through August: Food aid
HHR improve with food aid
June: Some vulnerability for refugees
August: positive ag outlook, refugee population still problems
August through November: Situation for refugee population
“precarious”
Situation for cercle good overall

 

1989

January through December: Overall, HHR good except for refugee
population

September: some pest damage to crops

October: Sharp price increases in some areas, FOOD Aid for some
November: General price increases, 6 villages abandoned which were
hard hit by pest damage

December: Food aid to most vulnerable areas

 

1990

Food security good overall
September through November: some areas vulnerable due to

pest damage to crops

 

1991

April through June: Epidemic of measles under control by July
June and July: Price jumps for staples generally
Good rainfall
August: price increases for millet and 80th but good Ag outlook
September: Reports good overall with average harvest espected

 

1992

 

 

January: Some areas under surveillance, increase in migration (due to

crvrl conﬂict) and prices increase
February: In Tenenkou SAP report, note a decline in buyers from Segou
and Nonio due to insecurity

Price increases

March: Price increases but cereals in market to meet demand

June and July: increase in measles outbreak

August and September: Price increases and continued spread of measles
Food security improving overall

October: HHR weak

 

217

 

 

1993

January, June and September: Food aid and Food for Work
May through August: Marked price increases in some areas
October: High increase in rice prices

November: Measles outbreak, poor rice harvest in many areas

 

l 994

March through May: Meningitis outbreak during hungry season
July: High level of disease, Sharp price increases with low quantities in
markets

 

1995

March through May: Measles and meningitis outbreaks

June: Increase in Millet Prices, poor rains

August: Decline in cereals in market, increase in disease, sharp increase
in prices in general

September: HHR weak

Cholera outbreak in November and December

Ag outlook: Fair

 

l 996

January and Febnrary: Disease outbreaks

HHR: Weak May through September...improvements with harvests

June: HHR weak, price increases, low quantities in markets,, high disease
July: Continued spread of measles, price increases, low rainfall, HHR
weak

August: HHR weak, disease declines, outrnigration to Burkina Faso
October: HHR reconstituted with harvest, average ag outlook

February, July: Food aid

 

1997

April: high level of measles and meningitis

May: disease continues to spread, HHR weak for migrants

July: HHR weak, high level of disease

August: Disease declines, FOOD AID , HHR are weak to average
September: HHR weak, Food Aid to Boron

October: Ag outlook good, prices decline but nonhabitual migration in
some outlying areas

 

1998

 

 

January: HHR improve with harvests
Generally, positive reports

 

Source: SAP

218

 

Table A3.5 Vulnerability Assessments for DJenne based on SAP Reports

 

Year

VulnerabilityAssessmentbasedonSAPReports

 

1986

October through December: vulnerable
Food aid delivered =>improved HI-IR

 

1987

January and February: No major problems reported
March: Noted vulnerability in some areas
May: Disease outbreaks reported and problems with rats
June: Measles spread
July: Some price increases in cercle
August through October. Sharp price increases with poor rains
November: HHR poor in many areas
January through June: somewhat vulnerable=> food aid deliveries
July through December: vulnerable or food insecure
food aid deliveries

 

1988

January: Increasing vulnerability with increased migration
HHR weak, Rice harvest poor to average
February through May : Vulnerable, HHR weak, poor pastures,
migration
March: HHR weak, abnormal migration in search of water and pastues
June through August: Highly Vulnerable
June: Price of millet increases dramatically, absence of wild cereals
P0pulation movements in search of food
July: FOOD AID, creation of nutrition centers, low quantities in markets
HHR weak
August: still problems but more cereals in markets
September through October: Ag outlook good/decline in vulnerability
Improvements with folio and rice harvest

 

1989

 

 

February: increase in migration to the area
March: Slight increase in prices

November and December: sharp increase in prices
Overall, low vulnerability due to good harvest

 

219

 

 

l 990

February and March: Measles outbreak with prices variable
April: Sharp increase in measles and meningitis, slight increase in prices
in cercle
May: Price increases
June and July: Sharp price increases and pests throughout cercle
September and October...some vulnerable areas noted,

Low quantities in markets with sharp price
increases

Most of cercle under surveillance
November and December: areas under watch (poor rice harvest impedes

reconstitution of HHR)

 

1991

January through June: Increasing vulnerability with markets poorly
stocked with cereals
July through August: Ag outlook good decreasing vulnerability
October: Migration pressures due to civil conﬂicts elsewhere and those

displaced due to environmental challenges
November: Increase in migration of ﬁshers relocating due to conﬂicts in
North

Some bird and pest damage to rice crops

December: Price increases reported but full reports not submitted

 

1 992

April through September: Gradual increase in vulnerability with poor Ag
Outlook
HHR: fair to weak as year progresses
April: Measles outbreak
September through December: HHR weak

 

 

1993

 

February: ﬁshing outlook poor
September through December: Increasing vulnerability in isolated areas
HHR compromised due to poor harvest

 

220

 

 

1 994

January through May : Vulnerable. . .HHR weak
Meningitis outbreak, many villages at risk
Price increases
May and June: Food aid
June: Deaths due to measles, HHR weak in Djenne and Sofara
July: Improvement in HHR with food aid but Djenne and Sofara DA,
Price increases for millet and sorghum
August through December: decreasing vulnerability noted with
good ag outlook

 

1995

May and June: Sharp increase in cereal prices in Jenne and Konio
Disease outbreaks
July: Food aid and increase in disease
August: cholera, measles and Food aid
September: decline in disease but increase in millet prices
November and December: Konio at risk, measels,
low level of cereals in markets
Irregular migrations due to food insecurity

 

l 996

January through July: Konio at risk

Noted: cholera and measles outbreaks

Price increases throughout cercle, HHR weak
May and June: Food aid
August through December: Problems for Konio anticipated

 

1997

March: Food aid, Konio still vulnerable
July through December: HHR: fair with increasing vulnerability noted
October through December: DEL Konio
Irregular migration continues
poor Ag Outlook, overall HHR weak
Migrant workers with poor resources in
search of work

 

1998

 

 

January: Poor harvest => HHR not reconstituted ﬁrlly : vulnerable

 

Source: SAP

221

 

Table A3.6 Vulnerability Assessments for Douentza based on SAP Reports

 

Year

VulnerabilityAssessmentbasedonSAPReports

 

1986

October through December: vulnerable with HHR@ 3 months
October and November: Food aid

 

1987

January through June: Food aid

January through June: At risk to at high risk throughout cercle
February: Migration from Timbuktu

March NGO study ﬁnds 7.3% children malnourished

May: Consumption of famine foods noted,migration

May through October: increasing vulnerability, poor ag outlook
November and December: Food aid authorized due to poor harvest

 

1988

January through June: Increasing vulnerability. HHR notably weak
....entire cercle at risk

January and February: Increase in migration in search of food

February, April, June and July: Food aid

April: HHR exhausted

July and August: Height of severity of problems throughout region

(general assessment)

September: Improvement of HHR and general conditions
Good ag outlook and subsequent harvest
=>decreasing vulnerability

 

1989

January through July : stable

August: Some areas vulnerable...Mondoro reports decrease in number of
meals per day

September: Food aid

August through December: Poor ag outlook, at risk

 

1990

 

 

January through June: Increasing vulnerability especially in North, HHR
weak
March through July: Boni, Mondoro, Hombori and N’ Gouma high risk
March, June and July: Food aid distributions. HHR better with
Fonio consumption

August: some decrease in prices with food aid distributions
October through December: Improvement by October but some areas

still at risk and others under surveillance

 

222

 

 

1991

January through May: N’Gouma, Mondoro vulnerable with Mondoro
declared at risk.
Food aid and consumption of wild cereals serves
to mitigate severe problems
April through September: Food aid
October and November: HHR improve with secondary crop harvests
Ag outlook good with some areas under watch
Some village to village migration in search of
food
December: Douentza Central and N’Gouma under surveillance

 

1 992

March: Problems in ‘Gouma, Douentza Central, Hombori
(localized DES)
May: Measles outbreak
September: HHR improve with fonio sauvage
October through December: Poor harvest... HHR fair overall but some
areas vulnerable localized

 

1 993

January through June: increasing vulnerability in the North.

January through July: DA in the Northeast (April “precarious”)

April, June, September and October: Food aid

October through December: Mondoro DA
HHR weak in Mondoro and Hombori
Some migration to Burkina Faso and Cote

d’Ivoire
Ag outlook average to poor in some areas

 

1 994

January through April: Mondoro and Hombori at risk
January through June increasing vulnerability

January through April: Wild cereals/famine foods ﬁll gaps
April through July: Food aid distributions

Good harvest

 

 

1995

 

January through June: stable

February through April: Meningitis outbreak

April and July: Food aid

April through September: Problems in N’Gouma

July: poor harvest expected, recession ag outlook good

 

223

 

 

l 996

Oﬂ‘ season crops: good
April, June, July and September: Food aid

Sharp price increases
February through June: Meningitis and measles outbreaks
April through September: weak HHR
November and December: Prices increasing, meningitis spreads

 

1997

January and September: Food aid

February through December: HHR weak with poor harvest following
the previous season

July: Inﬂux of refugees

 

 

l 998

 

January: vulnerable

 

Source: SAP

224

 

Table A3.7

Vulnerability Assessments for Tenenkou based on SAP Reports

 

Year

VulnerabilityAssessmentbasedonSAPReports

 

1986

North at Risk
October through December: Food aid distributions
HHR: 6-9 months in South

3-5/1-2 months available in North

 

1987

January through June: Food aid

January through June: Vulnerable. . .rnigration ﬁ'om smaller villages

Ag outlook: good w/ prices decreasing in July

August through December: Several areas weak- Dioura and Diafarabe
Ag outlook poor with prices increasing
HHR weak

Increasing vulnerability through DecembermFood aid authorized

 

1988

Jamrary: Dioura and Diafarabe at risk

March through August: increasing vulnerability with HHR exhausted in
most areas by May

March, June and July: Food aid

June: Access and cost of cereals prohibitive

September: excellent Ag outlook

 

1989

Generally food secure throughout the year with steady price increases
Potential problem: poor harvest and poor outlook for recession ag
October: decline in quantities in market with sharp price increases
November: Prices volatile in region several areas under surveillance

 

1990

 

 

January and February: Dioura under surveillance
Outrnigration to increase HHR
April: Price increases
May and June: Food aid authorized
July: Food aid distributions end
August and September: Sharp price increases
June and July: Pest infestations
October: Low quantities in markets, very high prices, slight improvement
in HHR
November and December: Conditions stabilize

 

225

 

 

1991

January: HHR reconstituted with rice harvest
March and April: ﬂood zone areas at risk
Several areas with HHR weak
May through July: Price increases sharp
June and July: Some vulnerable areas: famine foods and secondary crops
ﬁll gaps

February through September: Dioura at risk (“in crisis”in July)
May, August and September: Food aid distributed
October: Good harvests expected
Vaccination program suspended due to civil unrest in region
November and December: Dioura under surveillance
Heavy population movement with insecurity

 

1992

January through June: Dioura is problem whole year (preharvest)
Millet price increases
Problems in Kotia

March: Ambiri( DEL) and Diafarabe

Measles

April: spread of measles, villages abandoned in Dioura

Ag outlook: fair....HHR improve with harvest...some measles cases

reported

November and December: irregular migration

 

1 993

February: Some problems with irregular migration

May: Food aid

September: uncertain with ag outlook due to inconsistent rainfall
Hoarding reported

August through October: HHR: fair to weak with some improvements

with harvests

 

 

1 994

 

February: Diafarbe problems

June and July: Price increases noted for Millet
Diondiori and Diafarabe (DE)
Chicken pox outbreak

September through December: HHR stable and subsequently
reconstituted from harvest

 

226

 

 

1995

February ,March and April: migrants return with poor resources
High disease levels among livestock

May through December: cholera and measles outbreaks

June: large number of refugees in Dioura

April/July/August: Food aid to Dioura and Tenenkou

September: inmigration continues

November and December: Increase in disease related deaths (cholera)
Sharp price increases
Irregular migration continues

Ag outlook: fair to poor...

 

1 996

February through July: HHR: weak ...... disease outbreaks throughout
cercle
February through June: Dioura at risk
June: Food for Work
Ag outlook: Good except Dioura with restocking of HHR
high level of disease cases throughout year

 

1997

January through April: HHR: fair....meningitis and measles outbreaks
May through September: HHR weak and disease persists
June, September and October: Food aid mostly distributed around
Dioura
Ag outlook fair to poor with poor rainfall
October: Price increases

Irregular migration

HHR weak

 

1998

 

 

January: At risk with weak HHR
rainfed crops and rice: poor harvests

 

Source: SAP

227

 

Table A3.8 Vulnerability Assessments for Youvarou based on SAP Reports

 

Year

VulnerabilityAssessmentbasedonSAPReports

 

1986

October through December: HHR weak
Sah and Ambiri especially vulnerable
Food aid distributions

 

1987

Northeast: High vulnerability for entire year

January through June: Food aid

Poor rains increase vulnerability

June: Consumption of famine foods reported

July thorugh December: “precarious situation in cercle”

 

1988

February: Increased migration to area

March through August: Food security crisis

January, February, June and September: Food aid

May: HHR exhausted.

August: HHR improvements as food aid distributed

Good rains ...good Ag outlook..HI-IR improve post harvest

 

1989

Stable

August: shortage of staple in markets, consumption of secondary crops
ﬁlls gaps

Ag outlook: Average to poor

HHR improve with harvest including rice

 

1990

Overall stable.

March through May: Problems in Sah

April and May: Decline in availability of staples in markets

Low number of traders noted

September through December: Poor rains=>poor harvest some areas
under watch

 

1991

 

 

South: stable local rice ﬁlls gaps

January through May: North- at risk... precarious by April

February and April through July: Food aid distributions which are noted

as slow to arrive...end in July

September through December: HHR Improvements with secondary
crops then harvest

 

228

 

 

1 992

January: rice more successful than other crops

February through August: vulnerable...”precarious”in Sah and Ambiri
May/June/July/August: food aid

August: Fonio sauvage

September: improvements in food security with secondary crops
HHR and food security improves with harvest (good)

HHR improve except in Sah

 

1993

Food security: “good”beginning of year
May-September: steady price increase throughout year
Measles outbreaks
By August and September low quantity of millet in
markets
Enough rice in market but price increases
August: Food aid in Ambiri
August: PPIV sells rice during hungry season
Ag outlook fair
October: increase HHR with fonio and millet

 

1994

February: Sharp increase in price of staple cereals in Ambiri and
Youvarou

May and June: Decline in availability of cereals in market

HHR stable overall

Good rains and good harvest improve HHR

PPIV sells stock and enhances food security

 

1995

March: Return of nomads with weak HHR

April and July: Food aid distribution

July through December: Cholera outbreak

September: Return of refugees from Mauritania to Gathi-Loumo
Poor rains=> poor ag outlook including for rice

 

1 996

January through September: Low levels of cereals in markets
disease outbreaks
May and June: Food aid to Sah
Ag outlook: good to poor in cercle with rice harvest expected to be fair

 

1997

HHR: weak and continue to decline throughout year

May through September: Markets poorly supplied with cereals
October: HHR insumcient

Poor ag outlook, rice outlook also poor

 

1998

 

 

January: Vulnerable with weak HHR
Sharp price increases

 

Source: SAP

229

 

A3.4 Comparing the Smoothed Millet Price Index to the Reference Cycles

A simple index (exponentially smooth millet prices) was constructed for each of
the regions. Table A3.5 summarizes turning points of the simple index and periods of
moderate and high vulnerability to food insecurity. Figures A3.4.1 through A3.4.5
provide a mapping of the reference cycles with the simple index for each region. For the
areas of Tenenkou and Youvarou we found fairly good matching of turning points to
moderate and high periods of vulnerability. For DJenne and Douentza, turning points
coincide with reference cycles, such that they are more descriptive rather than predictive.
The Segou simple index failed to cature the onset or the severity of vulnerability
conditions. These results, as well as the results for Gao, Mopti and Timbuktu simple
indexes, suggest that millet prices do tend to coincide with vulnerable periods. These
strong descriptive contributions are harnessed in the development of a more sophisticated

vulnerability index in Chapter 4.

230

 

m... .238... s n... 53......

 

 

 

 

a... 0...... ... .....2

n8. ...... ... 8... has...

a... ...—...... s a... .285 «a. ..E s ...... .238

a... ......E :8. .8683. s 5...... ...... .2582. ... .33....

...... .....< ...... 2...... s .....S ...... ......3. .... baa...
S... ...—.832 8... .83.... s 3... .3532 a... .3»... .85....—

...§ .....c .... 5....

ma. .2.»... ... ...... ....

...... $35.88.. ...—88 a... 0...... ... a... .253...

...... ...... ... m... ...—38.... a... 3.5....

...... .2....82. ...... .358: «a. .33.... s «a. ......

...... 8.... .... 8.... ... ...... ......3. a... .388... .... .2326...

...... .....E 8... as»... s a... ...... a... .2.: .... $8....
5... an»... ...... .2882. .... ...—808.. 5.. 0...... ... ...... has... 2.53

...... ......~ .... .....z

a... ...... s has...

«a... ...... ... 3......

...... .335 .... ......

a... .3582. ... ...... ...... .....2 s ...... ......»

ma. 8.... a... .385 s .8. ..2 ...... ......ao .... .....asaom

...... ...... ...... .238 s ...... .... ...... .2582. .... .2398:
...... $2.... «a. .358: ... 5...... 8... ...... s 8... ....Z .85

.2.... as: 2...... £2.55...»
... as... use... .8. bites; 5...... .2.... 3.5.3:. ... .2.... as»...

 

 

 

 

 

8.9.0 3.3.0.3— 0... ...... .0....— ootm .052 358.5 2.. 3......an m.n< 93:.

231

 

.... 95.8.3288

 

 

 

 

 

 

.... ....< 8 .... .28...

.... ...»... 8 ......

.... .288... 8 .... .... .... .5.. 8 .52....

.... ...... .... .288... 8 .... .288... .... .288... 8 ....

a... ...... 3...... 8 ...... .233... a... ...»... 8 9......

.... 9...... .... .25.... 8 .... ...... .... ...»... 8 .... ...»...
...... ...... ...... .268... 8.28... .... 28.8.... ......n 8......»

.... ....< 8 .... .258...

.... ....< 8 5......

.... ......n 8 .... .... ...... a... 8 .... 3......

.... 3...... 8 .... .... a... .28... 8 .... Ea...

.... .... 8 .... ...... ...... ...E... 8 .... .239...

.... 5...... .... ...... .... ...... .... ...E...

.... .... .... .2....sz .... .28... .... .... 8 9......

.... .... .... ...»... 8 ...... .... ....< 8 9......

.... ....E... ...... .238... 8 .... ...»... .... 5...... .... .5....
.... ...»... .... 3.5.8.... .28... ...... 2883...... 8.5....

...... 8...... ......

.. a.................. £22.... 533...... 35.22.... 8:22:33. .5....

 

 

232

 

 

.woxrxrﬁco

m...<m.w005. ﬁn.

 

 

 

XOUC. DEE—ml

IO_II

.... 8...... 8 .... ......
~.......u...... ......—
3 3.5.82.1. > ... 9.3%... 29.0 8......qu ...... 5...... ....EE ....wom

S.

'0.

Im-

am aoualapu pue xapU| aldwgs noﬁas

...... ......

 

 

233

 

 

.aotﬁcoi

M. ......
W W
I0.II :

H96

6
.03.
0
..c;
__

wkémDOS. : -

 

 

X00...- 0_QE_W

 

9|vo aouajam pue xapul qdwgs auuara

 

 

 

.... .83.... 8 .... .282...
5.38.... .5....
... ...—339......» .... wag—9.2 29.0 8.5.5.3. ...... .2.... o.......@ 2.5—.m— ~.v.m< 9...»..—

 

 

234

 

 

..ﬂOtrn—COE
.01..
.00
191'

mur<mm00<£

 

 

X00... 0_QE_m

 

«I
91050 acumen pue xaml 6|de muenoa

 

wma. has... ... 33 ...—5.2.7.
323...... 6...... ...
...—.2222...» ... ”.2.—...: 29.0 8:223. ...... 5....— 2......m «5:925 mid... 9...»...

 

 

235

 

 

Laotcucoi

 

IO.II
mk<mm00§§

X012... 0.QE_W

9pm acumen pue xapul alduus noxuaual

 

 

 

 

 

 

 

 

 

32 S33. 3 £3 3:35
3:585 25% 3
35:89:15 3 Mains: 0.95 85.53— 98 5c:— oE—Em sag—55H 33¢. 0.5»:—

 

 

236

 

 

Lmotcucos.

10.1- J

a... ...«v

mLI<mWDOE "w ....

 

XOUC. 0.QE.w

 

ed
apAo aouamaa pue xapul aIdwgs nmemoA

 

 

«a. 523.. 3 33 .235
53.582: 68..— 8
b23805?» we using—Z 9.9.0 «cabana .5.. BE.— o...E.m .5.—«25> m.v.m< 95m...—

 

 

237

APPENDIX HAPTER F UR

A4.l The singleindex model of the U.S. business cycle

This section presents the single-index model of the state of the economy as it is
developed in Kim and Nelson (1999) and Stock and Watson (1991), both of which were
based on the early work of Sargent and Sims (1977). In Chapters 4 and 5, the dynamic
structure of the single-index model of vulnerability to food insecurity based on the

characteristics of the Malian data is ﬁrlly developed for each cercle and builds from the

model presented here. Let X , denote a k x 1 vector of the log of macroeconomic time-

series which are hypothesized to move contemporaneously with economic conditions.
These variables consist of four coincident variables used in the construction of the
coincident index: industrial production, personal income less transfer payments,
manufacturing and trade sales, and employees on nonagricultural payrolls. As developed
in Stock and Watson (1991) and Kim and Nelson (1998), in the single-index model 1’“
consists of two stochastic components: the common unobserved scalar time-series
variable, or “index”, C, , and a k-dirnensional component that represents idiosyncratic
movements in the series and measurement error, e, ,. In keeping with the Stock and

Watson (1991) formulation, both idiosyncratic components, Dl , and the unobserved

index are modeled as having linear structures and C, is assumed to appear
contemporaneously in each variable. The model is written as:

(A4.l) Y,

15, + y,C, + e,, i= 1,2,3,4

(A42) C, 6 + «C.-. + M3.-. + W“

238

(A43) e“ = the,H urge _2 + e“,
e, ~i.i.d.N(o,a,’), i = 1, 2, 3, 4
w, ~i.i.d.N(o,a,’,)
Stock and Watson select a second order autoregressive structure for the index and the
measurement without any justiﬁcation. Idiosyncratic components, 0,, are assumed to be
time-invariant (intercepts), and the unobserved component enters each equation in (1)
with different weights, contemporaneously. Many macroeconomic are found to possess
stochastic trends and these stochastic trends could be captured by C, . Thus, by this
construction, each element of 1’, would have this trend in common. We would then need
to consider whether the series were cointegrated of order (1, 1) (Engle and Granger
(1987)). Stock and Watson (1991) found that they could not reject the null hypothesis
that these series, Y“ , possess a unit root. Further they note that without compelling
evidence of cointegration this model can be reformulated to look at comovements among
ﬁrst diﬂ‘erences of the coincident variables at frequencies other than zero in order to
extract estimates of C,. Harvey, Fernandez-Macho, and Stock (1987) explore unobserved
components models with cointegrated variables.

Thus, Stock and Watson reforrnulate the system (A4. 1) through (A43) in terms of

growth rates (ﬁrst diﬂ‘erences) of the variables:

(A44) AY,‘ = D, + LAC, + e“ , i=1,2,3,4
(A45) (AC, - 6) = ¢1(ACH - a) + (lr2(AC,_2 - 6) + wP
(A46) e“ = ‘Pueu-r "' “hem—2 * Err

239

e, ~i.i.d.N(o,a,’), i = 1, 2, 3, 4

w, ~i.i.d.N(o,o,’,)

The inclusion of a non-zero idiosyncratic term allows for different drifts for A If, . A C ,

Is the common component and a: is set equal to 1 in order to normalize the common
component. The polynorrrials, are assumed to have roots outside the unit circle. For
convenience in presentation of the model, henceforth, they will both be deﬁned as second
order (as in Stock and Watson (1991)).

The next step in building this model is to develop suﬁcient identifying restrictions

on the model in order to estimate unknown parameters and to extract estimates of A C, .

Sargent and Sims (1977) and Stock and Watson (1991) note that the main identifying

assumption in the model expresses the ﬁmdamental notion that the comovements of the

time series are driven by a single source, A C, . This implies that (eu,e2,,..., AC,) are

mutually uncorrelated at all leads and lags. However, individual series may be
autocorrelated. This restriction essentially means that dependence on the single-index

accounts for all the observed cross-relations among the coincident indicators. It is

important to note that the above speciﬁcation results in the parameters D, and 6 not
being separately identiﬁed in the ﬁrst sample moment of the ith indicator A Y” . From

(A44) we see that the ﬁrst population moment for the ith indicator, A X, , consists of

two parameters:

240

(A47) E(AY,,) = D, + 7,6

Stock and Watson (1991) suggest writing the model in the form of deviations from means

in order to solve the identiﬁcation problem in MLE. This concentrates D, + 7,6 out of

the likelihood function:
Model in Deviation from Means [Kim and Nelson (1999)]
(A48) Ay,, = y,Ac, + e,, , i=1,2,3,4
(A49) (AC, - 6) = <|>,(AC,_l - 6) + (I>,(AC,_2 - 6) + w,
(A410) e, = ¢,,e,,,, tyne _, + 6,,
whereAy,, = Alf, - E(AX,) and Ac, = AC, - 6.
e, ~i.i.d.N(0,o,2), i = 1, 2, 3, 4
w, ~i.i.d.N(o,a3,)
Aﬁer this reformulation, the model is written in state-space form aﬁer which the
Kalman ﬁlter is used for maximum likelihood estimation of the model based on prediction

error decomposition and for inferences on Ac, .

A4.1.2 State-Space Representation of Single-Index Model

A state-space representation of the single-index model ﬁom (A48) through (A410) is:

241

Measurement Equation

 

 

0’: = H,B, + er )’

yr

0 0 0
y2 0 0 0
y3 0 0 O

 

[ha 741 V42 743

100000001
00100000
00001000

 

00000010

242

 

 

Transition Equation

 

 

 

 

 

 

 

 

F 1 F "
Ac, Ac,_, w,
AC,-, 1 he” 0
Ac” 4;, 4., o 0 o o 0 0 A0,, 0
Act-3 l 0 0 0 0 0 O 0 Act-4 0
o 1 o 0 o o 0 0

e1; 31.1-1 61:
e o o 1 o o o o o e 0

Z“ = o 0 0 0 0,, 0,, o o e‘“ +

2‘ 0 o 0 o 1 0 o o ”’1 a”
e2,l a 0 O e,,_2 0

“a. o 0 o o 0 0 0,, 0,, ear-1 e3,
e3.t-1 o o 0 0 0 o 1 o 331-2 0
e4; ear-1 £4:
e‘J‘ 1 . .e4J-2 1. O

(B: = F p-l + V, )

From this representation, estimates for the model parameters can be derived using
maximum likelihood estimation based on prediction error decomposition. With these

estimates, the Kalman ﬁlter is used to estimate the state vector conditional on information

up to time t. We ﬁnd the (1,1) element of the state vector is A6,".

243

A42 The construction of the leading index

The index depicting the unobserved state is derived from the single-index model is

the minimum mean square error estimator of C, constructed using data on the coincident

variables through time t. We denote this as C,,, . A ﬁrrther identifying assumption is that

the estimate of the mean growth rate for C, can be expressed as a weighted average of the

observed indicators. The weights are retrieved from the Kalman ﬁlter matrices (Stock

and Watson (1991) and Kim and Nelson (1999).

Given Ac," , t = 1, 2, ..., T, we need to estimated to construct the new index,
Cm . This follows from the expression
(A411) C”, = C,,,_l + AC,, + 6

Ifall the parameters of the model (A48) through (A410) are known and we apply the

Kalman ﬁlter we can obtain C

tlt '

The relationship between C and the observed

tlt
variables, A1", = [AY,, A?” AY3, A13, ’ can be expressed as
(A412) AC," = W(L)AX

where W(L) is a polynomial lag operator that relates the transformed vulnerability index to

current and past values of the observed variables. We ﬁnd:

(A413) EIAC...1= Emu/31:1

We retrieve the mean of the unobserved variables since

244

(A414) 6 = W(1)E(AY) and

(A415) cf = W(1)AE .

Once W(l) is identiﬁed, 5 is estimated given A 1"— . Kim and Nelson (1999) note that the

relationship between Ac, and A y, can also be expressed as

It

(A416) Ac

.1. = W(L)Ay,,

which suggests that we can retrieve W(I) ﬁ'om the model expressed as deviation from
means (A48) through (A410). Stock and Watson (1991) identify W(I) from the last
recursion of the Kahnan ﬁlter applied to the state-space model written as a deviation ﬁom
means. Kim and Nelson (1999) present the following interpretation of this process.

We retrieve several products ﬁom the Kalman ﬁlter recursion applied to the state-

space model expressed in deviation ﬁom means, in particular we have,

(A4-17) pm = BrIr-l '1' Kranr- 1’

(A418) B,,, = [3,,H + K,(Ay. - @713...- 1),
(A419) B,., = FBt—Ht—l + KtA-Vr " K,H Btlr- 1'
(A420) p," = Fp-l|‘-1 + KgAy: - Kert-llt- 1’

(I - KrH)Fpr-r|r-l + KrAyr

(A421) [3,,

245

The Kalman gain, K, , approaches the steady state Kalman gain, K, as t-o no , given a
stationary transition equation (Harvey (1989) and Stock and Watson (1991)) . For a set
of parameter estimates of the model, one applies the Kalman ﬁlter to the model in
deviation ﬁ'om means. The Kalman gain at the last iteration, K1" is the steady state
Kalman gain. One will note that this convergence of K, for t = l, 2, ..., T to the
steady state is fairly quick. By this we have B,,, = B,_ ,,,_, and K, = K, and from

equation (A419) we have:

(A422) B... = (1 - (1 - KH)FL)"KAy,

where L is the lag operator. As noted previously, the (1,1) element of B,,, is Ac,|,, W(L)
is given by the (1,1) element of (I - (I KH)FL)’ lK. Therefore, W(I) in equation

(A415) is given by the (1,1) element of (I - (I KH)F)' lK.

A43 Empirical results of the Stock and Watson model

Stock and Watson (1991) compared their index to the existing index of coincident
indicators published by the U.S. Department of Commerce. They found a high level of
correlation between the two series. They also found that the standard deviation of the
existing index far exceed their version due to the construction of the weights for each
index: the commerce index weights sum to 1.8 but those forA C, It sum to l by
construction. In order to present the two indexes on one graph for a meaningful

comparison, Stock and Watson scaled their version to be comparable to the DOC version.

246

Kim and Nelson (1999) revisit this development of a single index by updating the sample
period to January 1960 through January 1995 and by incorporating the possibility that
employment is not coincident but lagging other variables. Kim and Nelson also adjust
their index to be comparable with the DOC index. They found that treating employment
as a lagging indicator did not enhance model performance. Both Stock and Watson and
Kim and Nelson found their indexes to reveal higher growth in the 1970's and lower
growth in the 1980's that the DOC index. Overall, both alternative indexes were found to
be strikingly similar to the DOC index especially at business cycle ﬁequencies.
Presumably, Koopmans would be pleased by these developments in business cycle
research as the theoretical foundations used in their development certainly address his

concerns as a pioneer in this work.

247

A 4.4 Univariate Analysis: Segou, DJenne, Douentza, Tenenkou and Youvarou

 

Table A4.1 Descriptive Statistics for Retail Millet Prices
Cercle T Min Max Mean 02 Trend S.E.
t,= Predeval
t1=Postdeval
Segou 118 25 175 72.98 911.45 t,= -.0059 27.4
t2= .7830'” 2
DJenne 135 28 193 73.52 1242.8 tl= .0374 32.1
4 t2= 1.051“ 8
Douentza 135 46 190 87.71 982.60 t,=.0420 27.9
t2= 1.028'” 9
Tenenkou 135 35 180 75.80 969.91 tl= .0692 28.4
t2= .9870'” 9
Youvarou 135 25 225 102.15 1557.3 t,=.0713 31.1
1 t2= 1.771'” 8

 

 

*, ", and *" denote signiﬁcance at the 10%, 5% and 1% levels, respectively.

Predeval and Postdeval refer to pre—devaluation and post-devaluation, respectively

SE: refers to standard error of the regression

248

 

Table A42 Descriptive Statistics for Adjusted NDVI

 

Cercle
Segou
DJ enne
Douentza
Tenenkou

Youvarou

 

T

118
135
135
135
135

Min

-47.00
-58.10
-48.40
-73.30
-54.70

Max

43.00
71.90
78.00
80.00
78.60

Mean

-0.3220
-1.1081
-1.3718
-2.0378
-2.8459

380.10
472.25
345.93
594.50
436.89

 

Table A 43 Critical Values for Augmented Dickey-Fuller and Perron Unit Root

 

Tests ‘
Level of ADF Perron Perron Trend Perron
significance Intercept Shift Intercept and
Shift Trend Shift
1% -3.96 4.42 -451 -475
5% -3.41 -3.80 -3.85 -418
10% -3.13 -3.51 -3.57 -3.86

 

 

The results for the ADF unit root tests for the adjusted NDVI rejected the null

hypotheses of the existence of unit roots. However, the test results for millet prices were

ambiguous. There is evidence of stationarity for each price series (the null hypotheses of

unit roots were rejected) with both the ADF and Perron tests, except for the Djenne millet

price series. For Djenne, the ADF provided strong evidence that leads to the rejection of

the null hypothesis of presence of a unit root in the series. Since the single index model

249

 

 

accounts for idiosyncratic aspects for each indicator, the level of the millet price series is

selected as the indicator variable as well as the level of the adjusted NDVI.

250

23888. .35. as. 23 9% ..xoo. 2.. a 853:3. 280.. ...: use .2. ..

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

n.5- ...o.m- ehmr he..- nevr vmd- Nvmr barm- emwr ..E..m- N.
3....- oeN- v3.- ohN- vmmr eed- m... .- mm .N- 23.- .:mm .1 o.
«3.. 2..m- 9.....- SN- m3“.- o...- on. .- mam- e.w.- ...N. .m- m
envr Nm.m- 2.? MN..- near .Nm.m- oom- ..Vm.m- 3».- ........v- v
99..- m...m- cow: .35.- .5? Led. oomr ..N...m- .2..- ...ow.m- N
33> 33 > 33 > 33 > 33 > 33 > 33 > 33>
83. 33.5 .03. 33:0 83. 33:0 83. .....< 83. ...Q<
Echo...
3w 2.5. .85.: .55.:
a. .8225. 3w 2.8.. 3.5 38.2.... £9...
32m. 3.5835 32m. 33625 32m 3.583% 30.5 33.3.5
3.833. 3.883 3.83%... o: 8882 $53 mm“...
8.... 3...... .38.. co... 8...... .33.. 8F. 6...... =83. 8.5. 8:3. =83. .>DZ v0.3.3. .0 .3837.
3.23.95: 3 ......w 33.35% a 32...? E... 5.3 Eta—8.23m
3:7. .3 £853.... :3. 2.. ..e 38... :83. ...—a 3:33.33... 333.344 Ea... 3.33. aewom .66 9.3...

251

 

 

Figure 4.4.1 Segou: Retail Millet Price

 

 

2O
1
1 a o
7%"
3:2.
§ 501
EL.
.512
3 0 - _ _ _ _ _ _ _ j _ _
‘9 ‘9 0.9 0
%q?03°e%o.*qfo;°’o%é?4r‘Of’e%e§’q?o,
Month

 

252

 

 

 

Figure 4.4.2 Segou: Adjusted NDVI
so.
40-
20-

O-

 

 

 

I'o
o

 

 

 

 

NDVI (adjusted)
L
D

c'»
o

 

 

 

 

253

 

29888. .232 .x... 23 gm £2 2: 3 850mg: 28% ...: as .: .-

 

 

 

 

 

 

 

 

 

 

whmr :36- :or :36- S

56.6- :mo6- 9.6.- .bm6- o—

uomr - :36- 68. ...vm.v- n

ma. .- ...2 .v- Rm .- Eam- v

E _ .- :6v6- 6 _ m .- :36- N

o:_a> 83— an: 33> 83. mg
33V
“.85 .8335 o: 3882 Gav mws
8.5 was =3; :52 833 Mo .0962

 

 

 

Eta-3:59:92 .... amoﬁoahm ...-Z 05 ..c amok. Sim-meta 63553 :3...— 3_=8~— 2:8:— ﬁmé 035—.

254

 

 

 

Figure 4.5.1 DJenne: Retail Millet Price

 

300

250

200

 

 

3.32 85 is

 

 

255

 

 

Figure 4.5.2 DJenne: Adjusted NDVI

80-

 

 

 

 

 

501

40:

20.

o
2"”
ﬁne
340i
'2? V
CB
t-sa
3
2-ao__v '__"_'___

 

 

256

 

202638. .225. X: as .\.m £2 2.. a scam-E... Sosa ...: Ea ....- ..

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Sb.- .6o6- 39- :36- 36,- 056- ommr .2 6- m g «T an .N- S
36- N66- «06.. ov6- «Sr 36- an..- and- ac?- .v~6- o—
wSVr 2 6- 53? _66- SN- woN- h: .- 2am- mwvr :36- w
wmvr .56- cm?- :36- 6o6.- 9.6- 8m.- :36- NET :3 .w- v
36.- .86- 2.6.- :am6- mmmr .2.. 6- v2 .- :3. 6- 94,- 38+- N
33> 33> 33> 0:3, on?» o:_a> 33> o=_a>
80M .855 83m 33th 89m 303:0 Hood mg 89m mQ<
Eaton—v
22m use; 55% Acoeomv
a. E835 5.5 22,—. £5 Essa: £9:
:35 .8325m v.85 Baogm 93on 35835 :35 35822
mgmvé. 9:832 magaﬁx o: 8883 £93 awn-H
3cm «2:2 :83— ootm 852 :83— ootm 852 .mﬁox 8th 852 36% 592 9335?. no h2.252
25.3.56: 3 £5 ..F—59:55 a :85? ...—a 5m? Eta-838m
-=cz he maﬁauaamm :52 2: .3 38H :23.— caa Sim-.5595 68:25.3: :3...— 8_=8~— «5:23: $3. 03:.

257

 

 

Figure 4.6.1 Douentza: Retail Millet Price

20

15

1O

 

 

‘8
E
9..
8 501
E
E
'3 o

 

 

 

258

 

 

 

Figure 4.6.2 Douentza: Adjusted NDVI

’3

4a-

 

 

O

 

-2

Ndvi (adjusted)

 

 

259

 

2282.8. .225. .x: as .\.m ..\.o_ as a 8:85.32“ 223 ...: Ea .... ..

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

oin- mm.m- 96.- .mn. m- 94.- eod- omar .2 .m- 86- 3.3 .v- 2
So.- .851 30- :XM- omvr inm- vumr .am.m- 9%.- ..S.m- 2
«Gr 3mm- NS..- 5.6- mcmr ood- >2 .- 86- Nmmr ..wwM- m
~57 mod- 53.. 3.6- ommr E .m- v: .- .om.m- arm.- ...No.m- v
.57 :34“- awmr ..wm.m- mmmr ood- m3 .- ..vad- wwwr 53.n- N
33> o:_a> 25> 33> o:_d> 33> “53> “53>
83— EomEU 83— Eowtu 83— .3ch 89m an: 83m an?
Acotomv
32m 32,—. Aston: 823.6
a. 383...: can BE 85 38.26 £9;
:85 3:88am :35 3.3085 x35 3.32:5 V305 3.308%
mama—83‘ mam—=33 9.383 0: $833. Ann—ac awn-—
3th 3:3 Exam 3.5m BEE =Som 8.5— 852 530% 3.5— 8:3 =83— ~>DZ “Baa“? mo .3892
acts—«>0: «a £5 .2.—8.5m a 32.3.: new 5.3 bra—.335
-=oz be 3859...?— ==z 2: no 58,—. 5:8.— u—ﬁ ..o__=-.—-.m8_u_a 68:25.3‘ EP: 3.36m 255:3. :3 033—.

260

 

 

Figure 4.7.1 Tenenkou: Retail Millet Price

200

15a

3

 

 

Millet Pn'ce (CFA/kg)

 

 

261

 

 

 

Figure 4.7.2 Tenenkou: Adjusted NDVI

10¢

ND VI (adjusted)

 

262

 

 

23883. .232 <1 23 «a £2 2: a 8.3%»... 22...... ...: 23 .... ..

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

$5.. and- 2%.. .86- weer mud- «2 .- RN. 2%.. 3&8- N—
EW. ohm- «8.. mad- com... 2 .N- air ﬁnd- SM. .36.- @—
avr cad- NIH- 2...“- 03.. and- um _ .- on.” ommr 86.. m
02..- m3”- mum..- 3am- 5a.- and- of .- .mm.m- mwmr .... :1 v
S“..- ”hm- m3? 335. emmr and- 82.- tend- 09.... .33.? N
255 0:3, 25$ 33> 33> 33> 33> o:_a>
89m .856 80M :82th 83— .335 83— ma 83— mg
32.3%
32m 38,—. Anatomy 9.26.:
a. 38.26 55 22." 8% E835 :9:
£35 33825 :35 E3895 “.35 358qu M35 3325.».
wEEBm< mugged. mg»? o: 88:34 39$ away—
REA 8:3 :89: 025 «2:2 38% 8E 852 mas”— ootm 652 aged 592 3633. we 63:52
Evans—«>09 ... aim 3.32:5 a 2.2.3.: was 5.3 53.35sz
.52 .... 3852:: ...—Z 2: ..o 38,—. coho.— eaa 3:573:95 $85893. =3...— 3_=8¢ =Ea>=c> ﬁn... 03:.

263

 

 

Figure 4.8.1 Youvarou: Retail Millet Price

30r

20v

 

264

 

 

 

 

Figure 4.8.2 Youvarou: Adjusted NDVI

1 0G
80 n
604

4G

 

 

 

 

 

ND w (adjusted)
L

 

 

265

 

 

A4.4 The single Index of Vulnerability to Food Insecurity

Table A4.5 provides a summary of turning points as they relate to the reference cycles
developed in Chapter 3.

Segou

Segou tends to be a far more food secure cercle relative to the other cercles in this
study. Overall, we ﬁnd the vulnerability index in Figure A4.41 depicting a gradual increase
in vulnerability in Segou over the sample period, April 1988 through April 1998. There
are some distinct turning points throughout the sample period which opens with a relatively
high level for the vulnerability index which falls toward the beginning of 1988's hungry
season. The index shows a general decline until it reaches its nadir in late 1989. The main
concern in the cercle rests with the refugee populations, otherwise food security is reported
as good.

The turning point in September 1989 foreshadows sharp price increase in October
and the problems for six villages which are abandoned in search of food due to pest
damage. The index shows an upturn in late 1991 which leads the problems reported in the
SAP assessments in 1992 where a continued increase in vulnerability conditions is noted.
Food security improves post-harvest as captured in Segou’s vulnerability index.

The next turning point occurs in February 1995, from March through May there are
outbreaks of measles and meningitis. The vulnerability index continues its upward climb
and appears to capture these conditions of vulnerability to food insecurity. Overall,
conditions seem to improve post-harvest 1996 and this is reﬂected in the index. The index

appears to level off in August 1997 which corresponds to a good harvest.

266

The vulnerability index constructed for Segou appears to capture signiﬁcant turning
points with a month or two lead time to when the SAP assessments indicate problems. The
single-index appears to capture the consistent increase in vulnerability in Segou after 1992
when household resources are noted as weakest in the period under investigation and when

the adjusted NDVI has relatively higher volatility.

267

 

 

 

 

LNOECHCO—Z

.mm. .%.»%.WW.
W. WWLW

IO. I I
m.._.<m.m.n_0.2 :4

XOUC.
b___nac0c_:>

_M

 

 

 

SIMS aouararau P“e XGPUI MINWUIM "0598

 

 

32 525. 3 «we E3.
52:82: tech 3 55:32:?»
he means: 29m0 353—9: can no“...— ﬁﬁnﬁoEP’ aeuom “6.3.. 0.5»:—

 

 

268

Table A4.4.1 Parameter Estimates for the Vulnerability Index, Segou (1,2)

 

 

 

Parameter Parameter Estimate Standard Errors
do, 1.7599 .2371
«b, -.7646 .2364
Yr -.4720 .0945
y" .4887 .0851
in 1.4716 .2703
in -.5414 .1989
01 .0235 .0509
y, .0884 .0801
7,, -.l683 .1317
71, . 1023 .0820
#2, .3586 .1043
$22 -.0321 .0187
a, .6774 .0918
Likelihood -22.68
Value

 

269

 

DJ enne

The vulnerability index for Djenne (see Figure A4.4.2) has as its nadir the beginning
of the sample period. We note that the series that underlie the index, millet prices and the
adjusted NDVI both have their lowest values in November 1986. Signiﬁcant levels of food
aid are delivered from October through December 1986 that may contribute to this low
starting point. There is a sharp increase in the index that persists through to harvest of
1988 which depict an progression in the level of vulnerability to food insecurity.

The turning point of March 1989 depicts the upcoming rise in prices and the poor
harvest which contributes to the decline in food security for households for 1990 and 1991.
The DJ enne vulnerability index shows a steep increase over this period during which there
are severe measles outbreaks in the cercle, dramatic price increases, low quantities of
cereals marketed, and many areas that are declared at risk. An index turning point occurs
in December 1991 foreshadowing moderate vulnerability in the later part of 1992. The
next turning point of July 1993 appears ahead of the September onset of more vulnerable
conditions throughout the cercle.

The next major turning point in the vulnerability index appears in late 1995 and is
an early indication of a poor harvest and increases in migration in search of food. Prices
increase throughout the cercle with Konio in the North particularly vulnerable. Food aid
deliveries in May and June improve conditions although major problems in Konio persist.
Here the index seems to overstate vulnerability conditions when compared to the SAP

assessments. The index captures the moderate vulnerability conditions in 1997 irregular

270

migration, price increases and a poor harvest create problems for households from October
into January 1998, the end of the sample period.

Overall, the DJenne vulnerability index captures the major turning points in threats
to food security over the sample period. It seems to overstate the severity of conditions in
1996 which may be a result of the entire Mopti region experiencing a poor harvest in

1995/96 thus creating very high prices for millet in the region.

271

 

 

 

...wozcucoﬁ

I0_I I
wb<mw00§l

X000:
>£=ﬂﬂhoc=a>

 

 

 

aplo aouarajaa pue xapuI luuqeraulnA auuala

 

 

$2 been. 8 £3 aunausoz
£2.82: 8.5 8 £328.;
he 33%: 0.95 35.5qu was money mam—5395.5 239—d N.v.v< 95»:—

 

 

272

Table A4.4.2 Parameter Estimates for the Vulnerability Index, Djenne (1,3)

 

 

 

Parameter Parameter Estimate Standard Errors
41, 1.2003 .4333
02 -.3418 .3742
‘Yr .3779 .1427
y“ -. 1741 .2106
*1: 1.5390 .1687
tn -.5921 .1298
a, .0295 .5190
y, .1235 .0845
7,, -.1295 .1028
7,, .0873 .0893
7,, -. 1297 .0862
‘in .6338 .0854
V22 -. 1004 .0271
a, .6282 .0756
Likelihood 4.60
Value

 

 

273

 

 

Douentza

As with DJ enne, the vulnerability index created for Douentza presented graphically
in Figure 4.4.3 begins at the nadir for for the same reasons. The index depicts high
vulnerability to food insecurity throughout 1987 and 1988 with a major jump at harvest
time in 1987. The vulnerability index captures the increase in vulnerability with a turning
point in July 1989 providing a one month advance indicator as depicted by the SAP
reports.

The Douentza index hovers around the August 1990 level until its next turning
point in April 1991. In early 1991, conditions in the cercle are reported to include some
migrations in search of food and areas at risk. The food security conditions improve after
harvest although some arrondissments are under surveillance. The index continues to
decline and this corresponds to a satisfactory food security situation in the cercle as
reported by SAP. There is some migration out of Douentza cercle due to poor conditions.
Household reserves are noted as weak as of October 1993. The index does not seem to
capture this situation perhaps because of the large food aid deliveries or that the problems
are localized to the North. The vulnerability index appears to capture this as there is a
turning point in February 1994 with a rise that continues until the harvest which was very
good in 1994.

Conditions deteriorate throughout 1997 and are exacerbated by an inﬂux of
reﬁrgees. The index continues to rise over this period . Overall, the vulnerability index
captures the vulnerability conditions with some lead time for most of the sample period.

However, it fails to capture the vulnerability situation in 1993 perhaps because of the

274

signiﬁcant food aid deliveries in the region depressing prices and thus a signal through

prices is absent or appropriately signaling the need for food aid.

27S

 

 

llllllli.

 

 

 

 

 

 

 

mmwwwmwmw
..mewwwmww
10:..- I 2 _ jod
m..r<¢m.00.2 will a. z w
. S .4 m
>u___ndnﬂc_3> T?) t F_ W
l _ . ,v. m
U M __ _ M. a
a w t _,, m
...... u w _ ._ I0. ”I
... _ _ a W
, m ,_ _ m
a. : é a. m
,4 W. m
m f _3 m“ m
: .2. ; 7 6.?

 

 

 

 

 

33 baa-.2. 3 33 3:89.52
bun—.82: cook 8 5:39.05?»
.3 means: 296 35.8qu ...—a 5?: ﬂanges—.5 «Bee—:5 m.v.v< 25E

 

 

276

Table A4.4.3 Parameter Estimates for the Vulnerability Index, Douentza (3,3)

 

 

 

Parameter Parameter Estimate Standard Errors
4:, 1.8604 .1174
4), -.8653 .1092
7, .1473 .0829
7,, -.4289 .1029
7,, .4924 .0900
713 -.1910 .1 140
*11 1.1359 .1034
in -.4329 .0680
a, .0831 .2460
7, -.0130 .0575
7,, .1289 .1050
722 -.0679 .1214
7,, -.0368 .0090
‘l'zr .6212 .0982
V22 -.0965 .0305
o, .5784 .0731
Likelihood -5.1249
Value

 

277

 

Tenenkou

The ﬁrst dramatic turning point for the Tenenkou vulnerability index (Figure
A4.4.4) appears in September 1988. Vulnerability increases throughout 1988 with many
areas with household reserves exhausted by May and access and cost of cereals prohibitive
by June. The poor harvest of 1989 pushes up prices and there is a noted decline in cereals
in the market. These conditions of high vulnerability appear to be captured by the index
with a sharp increase ﬁ'om the nadir of the index in September 1989.

The index is increasing during 1991 and 1992 with a turning point in October
1992, providing one month advance notice of the increase in vulnerability in November
1992. By September 1994 conditions improve as a good harvest ensues and is reﬂected as
a drop in the vulnerability index. There is a sharp increases in the index in early 1995
which corresponds to a high level of disease among livestock, a key income source in the
region. There is also a high level of cholera and measles reported as well an inﬂux of
refugees. These conditions along with a poor harvest create conditions of high
vulnerability in the cercle until harvest.

Overall, the index constructed for Tenenkou captures signiﬁcant turning points

accurately and in advance of assessments made by SAP.

278

 

 

Enid >95... Hanan—8.. «SE—2.93:5. was. 2:. ”2.2.2.8 Ova—o 393:5 on

Tenenkou Vulnerability Index and Reference Cycle

 

 

 

 

 

 

 

<=Ea3¢EQ 8 Exx— Ewen—.13.
Cage—.2. Ema 3 35.58. 53
.. .D .
.P .W
n
W
., (...—3010652
.Nr .DQOX
— “Em 2.00m.»>im
0.0 I 1.01
.II N h m .01
mm. N. % now. %

2.01.21.2me

 

 

279

Table A4.4.4 Parameter Estimates for the Vulnerability Index, Tenenkou (1,2)

 

f

 

 

Parameter Parameter Estrmate‘ Standard Errors

l
45, 1.6045 .1570
4), -.6436 .1259
7, -.3293 .0887
7,, .4453 .0555
in 1.3171 .3942
tn -.4337 .2596
a, .0290 .0410
7, .0455 .0841
7,, .1434 .0913
7,, -. 1292 .0677
*2: .0642 .0892
in -.0913 .0269
o, .5535 .0667
Likelihood 1.02
Value

 

 

280

Youvarou

The index constructed for Youvarou (Figure A4.4.5) has similar turning points to
the Tenenkou index with the exception of the end of the sample period. Increases in the
index persist until August 1988 when improvements are noted ﬁ'om the effect of food aid
deliveries and stable conditions return due to a good harvest that serves to reconstitute
household reserves.

The index shows May 1993 as a turning point for moderate vulnerability in the
cercle, this is conﬁrmed by the SAP assessments with progressively increasing prices,
measles outbreaks, and low levels of millet in markets by August. The index appears to
capture conditions throughout 1994,1995, and 1996; there is an improvement in food
security when a good harvest ensues. By April 1997 there is another turning point
indicating moderate vulnerability for the rest of 1997 which appears to understate the
severity of conditions as presented in the SAP assessments. However, the next turning
point in January 1998 is captured.

Overall, the Youvarou vulnerability index appears to capture the signiﬁcant turning
points in the food security conditions in the cercle. The index does fail to capture the

magnitude to problems occurring in 1997 but does report relatively moderate vulnerability.

281

 

 

anotcacos.

 

 

 

. m mm VD % mow W ”one. Mw
I W W N N W W N W:
10:..- _ 2 22 0.0 W
222 .
2 ,2 n
2,. .N.
XOHVC- 2 2 22. m
2,,
b_._nwtoc.3> 2 _2 W
l 22222 m
2 , 2. ... ma
2 2 222,2 W1
,2. 22 2 .e. 9W.
22 . 22, 2 222 a
2. 2222 .2 222, w
. 22 2 ,, 222 .
2 222 22 22, .m w
2222 22 22 M
22,2 22 22, m.
2, 2 .2 22
,_2, , O. r m.

 

m8. S.E.... 8 £3 3:85
mat—585 each 8 55:22:...»
no ”5.5a: 2&0 conﬁouem can 59.— bEaabE-S .5.—«28> m.v.v< 25E

 

 

282

Table A4.4.5 Parameter Estimates for the Vulnerability Index, Youvarou (1,1)

 

 

 

Parameter Parameter Estimate Standard Errors
4), 1.3037 .2831
4), -.4249 .1845
7, .3960 .0284
7,, -. 1509 .1039
m, 1.8704 . 1861
11,, -.8746 .1741
a, .0005 .00025
7, -.0238 .0724
7,, -.0072 .0750
1b,, .651 l .0812
7,, -.0362 .0815
a, .5972 .0682
Likelihood 22.73
Value

 

 

283

A.4.5 Comparing the Single Index of Vulnerability to Food Insecurity and
the Simple Index

Figure A4.5.1 through A4.5.5 provide a mapping of the reference cycles, the simple
index (exponentially smoothed millet prices), and the single index of vulnerability to food
insecurity. A brief discussion of the mappings follows.

Segou

The Segou simple index (Figure A45. 1) captures the increase in vulnerability post-
1995 with a coincident turning point. The highest values for the simple index appear in this
time frame appropriately. However, shiﬁs in vulnerability in earlier periods are not
depicted with timeliness. The single index increases appropriately with turning points that
capture the more dramatic shifts in vulnerability conditions. This rise reﬂects the upwrd
trend in NDVI in Segou. In Segou, the more complex index is superior to the simple
index.

DJ enne

Figure A4.5.2 reveals the single index for DJenne to provide key turning points
ahead of the simple index. The single index also capture the severity of conditions more
appropriately (higher index values correspond to higher vulnerability levels. However, the
results suggest that both indexes fail to capture the moderate vulnerability conditions in

mid-l 994.

284

Douentza

The simple and single indexes (Figure A453) mark similar performances post-
1995. However, the simple index performs better by appropriately capturing vulnerability
conditions in late 1990 and into early 1992.
Tenenkou

The simple index provides smoother and more distinctive turning points with
appropriate levels relative to the single index (Figure A454). Here the adjusted NDVI
doesn’t improve upon the simple index performance as an early warning indicator. This
may be due to the fact that Tenenkou is primarily a livestock trading region rather than a
agricultural area.
Youvarou

The results for Youvarou are similar to those of Tenenkou. The simple index
appears to outperform the single index. Households depend on the market for their grain
and cereal staples so the exponentially smoothed millet price, the simple index, might have
more use as an early warning indicator in these regions. Market forces depict conditions of

vulnerability to food insecurity through the simple index.

285

 

«8.
8.88.8 s 8... 8.3.8..

 

 

 

 

 

 

 

 

..8. 8.... s .8...

n8. ...... 8 .8. 8.8....

..8. 83...... ... m8. .288 «8. 8.. ... .8. .283

.8. 88...... m8. 8...... .8. 2.2.8888 s 888.... ..8. 82.882. ... 8.8888

..8. 8.82 ..8. ......< :8. 8.83. s 8.8... ..8. 8.3.8.. ...... 88......
8... .2582 ..8. .2898: 8... .2883 8 8... .8823. 88. 8.85. 388..

8. ......< ...... 8.8..

m8. .885. 8 m8. ...:

..8. .288 88. 88...... 8 .8. .288 ..8. 28.. ... «8. .8888...

m8. 88.. e8. ...... 8 8... 8.88.8 «8. 8.8.8..

.8. 8288... «8. .2882. ...... .8888. «8. 8.88.8. 8 .8. .....Z

8... 2.2.88 8. 88.. .8. 88.. s ..8. 882...... .8. .2888. ...... .8888...

88. 8.82 ..8. 8.8:. 8... 88...... 8 ..8. ...... 8... .98... ...... 882......
8.. 8.8952 .8. 8.8.... 8... 82.88.... ...... .2895: ..8. 28.. ... ..8. 8888.. 8.8....

m8. ...... ....- .98...

«8. ......< 8 88......

.8. ...... ... 8888...

.8. 83.8.. .8. 8.88.. ...... ....n

98. .8825... ..8. .8882. 8 ...... .8. 8.8:. s .8. 83......

«8. 8888.8 88. 8.... .8. 8.88.. 8 8... ...... e8. 8.88.. ...: 8.88.8

.8. .88... n8. ...... ..8. .888 8 n8. 82 8... .2888. ...... .8888:
.8. .88.... ..8. 888...... .8. 88808.. s 8.83. 88. ...... ... 88. ...... 8&8

8...... 8:89.83. 8.8. 8...... 2.8.... 888.88...»
888.88.88.55. 888.88.88.58: 88888.8..588888 888828.882. 8.8..

 

 

8.80 88888. 2.. ...... 8.8. 8.... 8...... 88888 8.. ”8.8.8.5 3.3. ......Q

286

 

SQ? ... 2503 .632

 

 

 

 

 

 

 

.8. 8...... 8 .8. .28...
..8. ...... ..8. ...... 8 .8. .28...
.8. .25.... 8.. 8.8.28 .8....
.8. 8...... 8. .288... 8 ..8. .... .8. 8.... 8 8......
n8. 8...... m8. .8... .8. .288... 8 m8. .288... .8. .288... 8 ....
.8. .288... .8. .8.... .81.... 8 .8. .288... .8. 8.8... 8 8......
.8.. 8...... .8. 8...... 8.. 8.88.8 8 8.. ...... .8. 8.8.... 8 .... 8.8...
8.. ...... 8.. o... 8.. .288... 8 .28... 8.. ...... 8 8.. 8...... .8.»...
.8. ...... 8 8.. .288...
8.. ...... 8 8......
.8. 8... 8 .8. 8......
8.. 8...... 8 .8. .... .8. .28... 8 8.. 8.8...
.8. 8.8... 8 m8. .... .8.
.8. .28... .8. .... 8 .8. .8.... 8...... 8 .8. .28.»...
m8. 8...... .8. 8...... .8. ...... .8. ...... .... 8......
.8. 8.8... m8. .8.. .... .288... .8. .28... .8. ...... 8 8......
.8. .288... .8. .... 8.. 8.8... 8 .8... .8.. ...... 8 8......
8.. .288... .8. 8...... 8.. .288... 8 8.. 8.8... 8.. 8...... .8. 8......
8.. 8... 8.. 8.8... 8.. 8...... 8 8.. .28... 8.. ...... 8 8...... 8...»...
8..... 88.82.... 8.8. 8..... 8..... 88.8.8... .
... 8...... 88...... 8.. ... 8...... 88...... 8.. 88.82.... 5...... .38.. 88.2.... 88.... 8.8..

 

 

287

 

 

Box/220.2

l96

6 6
Z I!
. .

90 [6
____W 96
:90. 96
:90 96

 

I

 

 

3*. e

 

X00...—

:5:

___ ____

, 9
1. :4 #_

32 525. s 83 E3
@589: each 8 £222.?» 3
main“: 9.95 8:283— uua .52: «ESE .52: mum—Sass?» aowom

%
‘WI;

~
_
.
_

‘-

>H=_DNLOC_J>l

<x-

 

IO_I I
WP<MNDO§ m
X09... 0_nF:w

 

 

 

 

 

 

 

 

along acumen pue saxapm noﬁas

3.3 2:»:

 

 

288

 

 

Lﬂﬂzr—ucos.

 

 

 

 

 

 

 

 

 

 

 

wanﬁmﬁumﬁmmwmmﬂmﬂm
WWHWWWHmmemmmM;
IO-II 7 77 77 m1; 7 ——___ . 7 0.0
m....<m_m.00_277 7 VA _ 7 7
XOUEOEEE \7JL7 7 .77 77 7 .N.
:2. 7 _7_ _ 7 777 77 7.77,
3:39.053) 7 7 7 7 7 7 7 .7 7 7 7 77 .V.
l 77 7 77.7 77 a 7 7 7 777
7 7 7 7 77 .
7 7 ’ 7_7 7 7 7
7 7 77 7 777 7 7 .0.
7 7 : 7 7777 7
7’ 77 _ v. 7 7 7
/7 7 7 .m.
7 a, 7 7 7 7 77.
7 717 _ 7 7 77 7 77
.7 7 7 77 7 .0?

 

wamﬁ ran—ﬂan. 3 cams hog—802.2
3:33.: 95,.— 3 $595.5 2. «5.522

2:0 ooaououom ES .52: 2:85 .53: 5:39.95?» 2302— N.m.v< PEER

quo aouajajau pue saxapm auuarq

 

 

289

 

 

Laotcucoi

 

 

 

 

 

Maw .W Po .W
0 .W .W .H
I0.Il 7777_j0 .0 W
m._.<m_m.n_0_27.r 777 m
X005 ﬂashm ..N. W
I I I x
X005 Wm
3:39.05...) .v. W
— m
77 .0. m
.7 .m
M1
In.
.0. r

 

 

 

 

82 0.2.5:. 3 32 .2032
52:82: 025 3 55:32—15 «a ”5.532
206 «2.280% ES .52: oEEmm .565 mum—5395.5 «5:225 m.m.v< «...—um,”—

 

290

 

 

 

Laotcucoﬁ

 

 

 

 

 

 

 

 

 

6 6 6 6 6 6 6 6 6 6 8 8 8 9 8
WWW—7mwwmmwwwmwwwm
ICE. 7 7 _77 7 777 7 7777 0.0 m
: 7 7 7 7777 77777 a
m._.<mmn_0_>_pi 7 7777 1777‘. 7 _ 777_ 7 7777.7 m...
7 7 7 777.7777 777 .777 7 77.. so: m
X005 0.00.:m 7 777777777 77 7 I
..... 7 7 7 7. 7 7 W
X00»... 7 7 7777 7 7 %
B___nm._oc_3> . 7 77 7777 77 :v m.
77 7777.7 7 777 w
7 777.. 777 .
77 7 777 77 7 7 a w.
777 7 7 77 m.
77 7 .0. 0|
.7 7 7 a
7 77 77 77
7 77 7 7
77 77 7 77 7
. d...

 

83 05:73. 3 83 5280
£058.: each 8 bag—«.555» no maﬁa—«E
9.96 8:29qu can .55:— oE—Em .52: 5:555:15 sou—55% v.m.v< «...—u:—

 

 

291

 

 

...wnCCrzcoE

 

 

 

 

 

 

 

 

 

 

 

 

. . . . . mmmmmm
I. m. m I...Ur W m rmlru
IO_II 77.77. x 0.0 NA
77.
. 77 :7 777 /
m.._.<m_m_n_05_né .777 77 W.
7777 77 7 I u . m
77 7 7 _ N I
XOUr: 0_QE_W 77777 7 7, Wu
III 777. 7 _ 7 a
7 .7 7 7 x
x00... 77 7 7 7777 W
7 1?.
3:59.053) 7 7 _ W
I 77 p
77 mm
777 I0. AH“...-
777 777 «MW
7 7 MW
7 . O
7 77 d M
7 77 q
7
7 d...

 

 

 

83 ESE: 3 33 .235
mat—80m:— ccom 8 5:26.055» .3 ”Emma—z
295 8:20.37: ES .5....— 295m .52: 5:39.05?» .5.—«25> m.m.v< «...—awn

 

292

 

References

Alamgir, M., 1981. “An Approach toward a theory of famine.” In J. Robson (ed.)
Famine: Its Causes, Eﬂects, and Management. Gordon and Breach, New York

Alderman, Harold, 1992. “Intercommodity Price Transmittal: Analysis of Food Markets
in Ghana.” Working Paper #884, World Bank, Washington, DC.

Balke, NS. and T. B. Fomby, 1997. “Threshold Cointegration.” International
Economic Review 38, 627-46

Baneljee, Anindya, Juan Dolado, John W. Galbraith, and David F. Hendry, 1993.
Cointegration, Error-Correction, and the Econometric Analysis of Non-stationary
Data. Oxford University Press, Oxford.

Becker, Jasper, 1996. Hungry Ghosts: Mao ’s Secret Famine. The Free Press, a Division
of Simon and Schuster Inc., New York, NY

Bingen, R. James, David Robinson and John M. Staatz, 2000. Democracy and
Development in Mali. Michigan State University Press, East Lansing

Brock, Karen, 1999. “Implementing a Sustainable Livelihoods Framework for Policy-
Directed Research: Reﬂections from Practice in Mali.” IDS Working Paper No
90, Brighton: IDS

Buchanan-Smith, M. and Susanna Davies, 1995. Famine Early Warning andResponse:
The Missing Link. Intermediate Technology Publications, London

Burns, AF . and WC. Mitchell, 1946. Measuring Business Cycles, NBER Studies in
Business Cycles No. 2. Columbia University Press, New York

Campbell, John Y., 1987. “ Does Saving Anticipate Declining Labor Income?: An
Alternate Test of the Permanent Income Hypothesis.” Econometrica 55: 124- 73

Campbell, John Y., and Robert J. Shiller, 1988. “Interpreting Cointergrated Models.”
Journal of Economic Dynamics and Control 122505-22

Chambers, R., Longhurst R. and Pacey, A (eds) 1981. Seasonal Dimensions to Rural
Poverty. Frances Pinter, London

Chen, R., 1995. “Threshold Variable Selection in open-loop threshold autoregressive
models.” Journal of Time Series Analysis 16, 461-81

293

Crow, B. 1986. “U.S. Policies in Bangladesh: The Making and Braking of Famine?”
Development Policy and Practice: Working Paper No. 4. The Open University,
Milton Keynes

Davidson, Russell and James G. Mackinnon, 1993. Estimation and Inference in
Econometrics. Oxford Univesity Press, Oxford

Davies, Susanna, 1996. Adaptable Livelihoods: Coping with Food Insecurity in the
Malian Sahel. St. Martin’s Press, Inc, New York

de Leeuw, Frank, 1991. “Toward a theory of leading indicators.” In K. Lahiri and G.
Moore (eds), Leading Economic Indicators: New approaches and forecasting
records. Cambridge University Press, Cambridge

Dembélé, Niama Nango and Staatz, John M., 1999. “The Impact of Market Reform on
Agricultural Transformation in Mali”, paper presented at the
Tegemeo/ECAPAPA/MSU/USAIDWorkshop on Agricultural Transformation,
Nairobi, Kenya June 27-30, 1999

Desai, Meghnad, 1988. “The Ecology of F amine”. In G. Harrison (ed.), Famine.
Oxford University Press, Oxford

Devereux, Stephen, 1993. Theories of Famine. Harvester Wheatsheaf, Hertfordshire,
Great Britain

de Waal, A., 1989. Famine that Kills: Darﬁir, Sudan, 1984-85. Claredon Press, Oxford

Downing, Thomas, 1991. A Framework for Vulnerability Assessment. FEWS Working
Paper. USAID, Washington, DC.

Dreze, Jean (ed.), 1999. “The Economics of F amine.” The International Library of
Critical Writings in Economics, Elgar Reference Collection, Cheltenham, UK

Dréze, J. and Sen, A, 1989. Hunger and Public Action”. Clarendon Press, Oxford

Dréze, J ., A. Sen, and A Hussain, 1995. The Political Economy of Hunger. Claredon
Press, Oxford

D’Souza, F. ‘Farnine: Social Security and an Analysis of Vulnerability’. In G. Harrison
(ed.), Famin”. Oxford University Press, Oxford

Engle, RF. and C.W.J. Granger, 1987. “ Co-integration and Error-correction:
Representation, Estimation and Testing.” Econometrica, 55, 251-276

294

F ackler, Paul L., 1994. “Spatial Equilibrium and Tests of Market Efﬁciency and
Integration. " Working Paper. Department of Agricultural and Resource
Economics, North Carolina State University

FEWS, 1999. FE WS Current Vulnerability Assessment Guidance Matual.
FEWS/U SAID Washington, DC.

F ranses, Philip Hans and Van Djik, Dick. Non-linear Time Series Models in Empirical
Finance. Cambridge University Press, Cambridge, 2000.

Glantz, M. (ed.) Drought and Hunger in A ﬁica: Denying Fanine a Future. Cambridge
University Press, Cambridge, 1987.

Goodwin, B. and N. Piggott, 2001. “Spatial Market Integration in the Presence
of Threshold Effects.” American Journal of Agricultural Economics

Granger, C.W.J., 1981. “ Some Properties of Time-series Data and their use in
Econometric Model Speciﬁcation.” Journal of Econometrics, 16, 121-130

Granger, C.W. J. and Terasvirta, T., 1993. “Modelling Nonlinear Economic
Relationships ” Oxford University Press, Oxford

Hamilton, James D., 1994. “Time Series Analysis.” Princeton University Press,
Princeton, New Jersey.

Hansen, B. E., 1997. “Inference in TAR Models.” Studies in Nonlinear Dynamics aid
Econometrics 2, 1-14.

-----, 2000. “Testing for Threshold Integration.” Working Paper, University of
Wisconsin, Madison, Wisconsin (online)

Harrison, G. (ed.), 1988. Fanine. Oxford University Press, Oxford

Harriss, Barbara, 1979. “Is There Method in my Madness: Or is it Vice Versa'?
Measuring Agricultural Market Performance.” Food Research Institute Studies,
Vol XVII, No 2: 197-218

Harvey, Andrew, 1990. The Econometric Analysis of Time Series. Wiley and Sons.
New Yorlg New York

------- 1989. Forecasting, Structural Time Series Models and the Kalmat Filter.
Cambridge University Press, Cambridge

Hendry, David F ., 1995. Dynamic Econometrics. Oxford University Press, Oxford.
295

 

1993. Econometrics: Alchemy or Science? Blackwell Publishers,
Oxford, UK and Cambridge, USA

Hendry, David F. and Mary S. Morgan, 1995. Foundations in Econometric Analysis.
Cambridge University Press, Cambridge

Hussein, Karim and John Nelson, 1998. “Sustainable Livelihoods and Livelihood
Diversiﬁcation.” IDS Working Paper No 69, Brighton: IDS

J ohansen, S., 1988. “Statistical Analysis of Cointegrated Vectors.” Journal of Economic
Dynanics ard Control, 12: 231-254

Johansen, S. and K. Juselius, 1989. “The Full Information Maximum Likelihood
Procedure for Inference on Cointegration—“nth Applications.” Preprint 1989 #4,
Institute of Mathematical Statistics, University of Copenhagen. 58 pp

Jordon, William Chester, 1996. The Great Famine: Northern Europe in the Fourteenth
Century. Princeton University Press, Princeton, New Jersey.

Kim, Chang-J in and Charles R Nelson, 1999. State-Space Models with Regime
Switching. MIT Press, Cambridge, Massachusetts

Koop, G., M.H. Pesaran and Potter, 1996. “ Impulse response analysis in nonlinear
multivariate models.” Journal of Econometrics 74, 119-47

Lahiri, Kajal and Moore, G.H., (eds), 1991. Leading Economic Indicators: New
Approaches and Forecasting Records. Cambridge University Press, Cambridge

Maddala, G. S. and In-Moo Kim, 1998. Unit Roots, Cointegration, and Structural
Charge. Cambridge University Press, Cambridge

Martens, M., P. Koﬁnan and ACE Vorst, 1998. “ A threshold error correction for
interday futures and index returns.” Journal of Applied Econometrics 13, 245-63

Newman, Lucile F. (ed), 1995. Hunger in History: Food Shortage, Poverty, and
Deprivation. World Hunger Program, Brown University Press, Massachusetts

Pierstrup-Andersen, 2000. Food Policy Research for Developing Countries: Emerging
Issues and Unﬁnished Business, Food Policy v25, no. 2: 125-41

Potter, S. M., 1995. “A Nonlinear Approach to US GNP.” Journal of Applied
Econometrics 10: 109-125

296

Poulton, Robin-Edward and Ibrahim ag Youssouf, 1998. A Peace of Timbuktu:
Democratic Governatce, Development, aid African Peacemaking. United
Nations, New York and Geneva

Rakodi, Carole, 1999. “A Capital Assets Framework for Analyzing Household Livelihood
Strategies: Implications for Policy.” Development Policy Review 17, 315-342

Ravaillion, M., 1987. Markets and Fanine. Claredon Press, Oxford

Riley, Frank, 1993. Vulnerability Assessment. FEWS Working Paper. USAID,
Washington, DC.

Rivers, J PW. 1988. “ The Economics of Famine”, In G. Harrison (ed.), Fanine.
Oxford University Press, Oxford

Sargent, T. J ., and C .A. Sims, 1977. “Business Cycle Modeling without Pretending to
have too much a-priori Economic Theory.” Iin C. Sims et al., New Methods in
Business Cycle Resea'ch. Minneapolis: Federal Reserve Bank of Minneapolis

Stock, James H. and Mark W. Watson, 1991. “A Probability Model of the Coincident
Economic Indicators.” In K. Lahiri and G. Moore (eds), Leading Economic
Indicators: New Approaches aid Forecasting Records. Cambridge University
Press, Cambridge

Sen, Amartya, 1981. Poverty and Fanines. Claredon Press, Oxford

1999. Development as Freedom. Alfred Knopf Publishing,
New York, NY

 

Steffen, Philip Nathan, 1995. “The Roles and Limits of the Grain Market in Assuring
Household Food Security in Northern Mali: Implications for Public Policy”. Ph.
D. Dissertation, Michigan State University, East Lansing

Tirnmer, Peter C., 1987. The Corn Economy of Indonesia. Cornell University Press,
Ithaca, New York.

Tong, H., 1990. Nonlinear Time Series: A Dynanical Systems Approach. Oxford
University Press, Oxford.

Tsay, R S., 1989. “Testing and Modeling Threshold Autoregressive Processes.”
Quarterly Journal of the American Statistical Association, 84:231-240.

297

Walker, R, 1989. Fanine Early Waning Systems: Victims and Destitution. Earthscan,
London

Woolridge, J .M., 1990. “A uniﬁed approach to robust, regression based speciﬁcation
tests.” Econometric Theory 6, 17-43

-—-----, 1991. “On the application of robust, regression-based diagnostics to models
of conditional means and conditional variances.” Journal of Econometrics 47, 5-46

Von Braun, Jochim, Tsfaye Teklu and Patrick Webb, 1999. Fanine In A ﬁ'ica. For
The International Center for Food Policy Resea'ch by John Hopkins University
Press, Baltimore and London

Yade, Mbaye, Chohin-Kuper, A, Kelly, V., Staatz, J ., Teﬁ’t, J ., 1999. “ The Role of
Regional Trade in Agricultural Transformation: The Case of West Aﬁica
Following the Devaluation of the CFA Franc”, Paper presented at the
Tegemeo/ECAPAPA/MSU/USAIDWorkshop on Agricultural Transformation,
Nairobi, Kenya June 27-30, 1999

298

 

7'

 

1293 024