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THE EFFECTS OF POLICY CHANGES ON SPATIAL GRAIN
MARKET EFFICIENCY IN ETHIOPIA

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AS FAW NEGAS SA MULETA

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THE EFFECTS OF POLICY CHANGES ON SPATIAL GRAIN MARKET
EFFICIENCY IN ETHIOPIA

By

Asfaw Negassa Muleta

A DISSERTATION

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

DOCTOR OF PHILOSOPHY
Department of Agricultural Economics

2004

ABSTRACT
THE EFFECTS OF POLICY CHANGES ON SPATIAL GRAIN MARKET
EFFICIENCY IN ETHIOPIA
By

Asfaw Negassa Muleta

Building on the standard parity bounds model (PBM), a stochastic gradual
switching model with three trade regimes is developed. The extended parity bounds
model (EPBM) improves the standard PBM in two ways. First, it traces the time path of
the effect of policy changes on spatial market efﬁciency and tests whether the effect of
the policy changes is instantaneous or gradual. Second, it allows for statistical tests of
structural change in spatial market efﬁciency due to the policy changes. A Monte Carlo
simulation experiment is conducted to assess the performance of the EPBM. Then the
EPBM is applied to analyze the effect of grain marketing policy changes on spatial
efﬁciency of maize and wheat markets in Ethiopia. The results show that prior to the
policy changes there is high probability of spatial inefﬁciency in maize and wheat
markets and the effect of policy changes on spatial market efficiency is not statistically
signiﬁcant in many cases. The cases where policy changes did inﬂuence spatial market
efﬁciency have some important implications for the conduct of grain marketing policies
in Ethiopia. Furthermore, the nature of observed spatial inefﬁciency for maize and wheat
markets is different implying that the two commodities might require different policy
responses in order to improve spatial market efﬁciency. Maize traders made losses most

of the time while wheat traders made excess proﬁts most of the time.

Copyright by
ASFAW NEGASSA MULETA
2004

To Aynalem, Naol, Robsuny and Biruktawit for their love, support and encouragement

iv

ACKNOWLEDGEMENTS

I would like to express my sincere thanks to those who helped and supported me
in completing my Ph.D. program. I am especially grateﬁil to Robert Myers, my major
professor and dissertation supervisor, for his excellent supervision and providing me with
unreserved support, encouragement and inspiration throughout my program. Bob, thank
you so much for your belief in me and leaving your door wide open for me; these were
the sources of my energy for my perseverance. I would also like to express my special
thanks to my guidance committee members: Thomas Jayne, John Staatz, John Strauss,
and Eleni Gabre-Madhin for their encouragement and invaluable comments and
suggestions on my dissertation.

I am very grateﬁJl for the generous ﬁnancial support I received from the
department of agricultural economics through the food Security 11 project and the College
of Agriculture and Natural Resources for my graduate study. The ﬁnancial support of
International Food Policy Research Institute (IFPRI) through the project on “Policies for
Sustainable Land Management in the Ethiopian Highlands” for my ﬁeld research in
Ethiopia is also gratefully acknowledged. I would also like to thank IFPRI and the
International Livestock Research Institute for allowing me access to the Ethiopian grain
traders’ database. Eleni also kindly provided me with her 1996 Ethiopian grain traders
survey database for which I am very grateﬁil. Various government institutions in Ethiopia
provided me with the data I needed in my research. Particularly, I would like to thank the
Ethiopian Grain Trade Enterprise for providing me with unique price data and the former
Ministry of Economic Development and Cooperation and the Ministry of Transport
Authority for providing me with monthly and annual time series truck shipment freight
rate data.

I would like to thank Eric Crawford who provided me with very kind advice,
support and encouragement during my entire study program. I would also like to thank
Allan Schmid, James Oehmke, Felix Nweke and Daniel Clay who provided me with
support and encouragement. My sincere gratitude to Steven F ranzel and Wilfred Mwangi
for their inspiration, support, encouragement, and for playing crucial roles in my earlier

professional development which helped me to be successful in my graduate school.

I am greatly indebted to my friends at Trinity Church, especially, Richard Roberts
and Gretchen Roberts for their spiritual, emotional and ﬁnancial support through some
rough times. I would also like to extend my sincere thanks to my sister-in-law Biruktawit
Yitbarek and my colleagues and friends in Ethiopia and the USA: Kebede Daka,
Yerusalem Nephtalem, Habtalem Kanea, Chaltu Nephtalem, Tesfaye Makonnen, Legesse
Dadi, Isac W/Mariam, Soressa Ergena, Getahun Mesﬁn, Genet Michael, Tibebe Eshete,
Azeb Tessema, Wolday Amha, Samson Dejene, Mulugeta Mekuria, Mulat Demeke and
Tesfaye Zegeye for their very kind support and encouragement. I owe special thanks to
them.

I would like to thank my fellow students in the department of agricultural.
economics for their friendship, support and encouragement. Particularly, I am very
indebted to Brady Deaton, Yoko Kijima, Eric Knepper, and Julie Stepanek. I would also
like to thank: Laila Racevskis, Julius Okello, Gerald Nyambane, Meeta Punjabi, Horacio
Gonzalez, Patricia Makepe, David Mather, Koﬁ Nouve, William Shields and Denise
Mainville. The wonderful supports and services of Sherri Rich, department secretary;
Linda Beck, Brian Hoort and Chris Wolf in the computer service are also very much
appreciated.

I am always greatly indebted to my father, Negassa Muleta and My mother, Fite
Ourgessa, who did not have the chance at modern education themselves beyond
elementary school, but have realized its beneﬁts to their children and worked so hard to
send all their children to school. Your sacriﬁce and hard work on the farm for our sake is
highly appreciated. Finally, I would like to express my deepest gratitude to my wife,
Aynalem Tadesse, for her constant love, full support and encouragement. This
dissertation is dedicated with the greatest love and affection to Aynalem and to our
dearest children Naol and Robsuny. I am always grateﬁrl to God for blessing me with

such loving family.

vi

TABLE OF CONTENTS

LIST OF TABLES ................................................................................. ix
LIST OF FIGURES ................................................................................. x
CHAPTER 1 INTRODUCTION ................................................................. 1
1. 1 Background ................................................................ 1
1.2 Problem Statement ........................................................ 3
1.3 Objectives of the Study .................................................. 6
CHAPTER 2 AN OVERVIEW OF THE WHOLESALE GRAIN TRADE IN
ETHIOPIA ....................................................................... 8
2. 1 Introduction ................................................................ 8
2.2 Regional Patterns in Grain Production 8
2.3 Characteristics and Performance ........................................ 9
2.4 Marketing Infrastructure ................................................ 12
2.4.1 Roads .............................................................. 12
2.4.2 Trucks ............................................................. 14
2.4.3 Telephone and Telecommunication Services ............... 15
244 Marketing and Pricing Information ........................... 16
2.5 Evolution of Grain Marketing Policies in Ethiopia. . . . . . 17
2.5.1 Imperial Regime ................................................. 18
2.5.2 Socialist Regime ................................................. 19
2.5.3 Recent Developments ........................................... 25
2.6 Conclusions ............................................................... 28
CHAPTER 3 LITERATURE REVIEW ......................................................... 36
3. 1 Introduction ............................................................... 36
3.2 Spatial Price Correlation Analysis ..................................... 36
3.3 Delgado Method .......................................................... 37
3 .4- Ravallion Method ......................................................... 37
3.5 Co-integration Methods .................................................. 38
3.6 Parity Bounds Model ..................................................... 41
3.7 Conclusions ................................................................ 42
CHAPTER 4 EMPIRICAL MODEL ............................................................. 44
4. 1 Introduction ................................................................ 44
4.2 Conceptual Framework .................................................. 44
4.3 Extensions to the Parity Bounds Model ................................ 48
4.4 Estimation Procedures ..................................................... 57
4.5 Conclusions ................................................................ 58

vii

CHAPTER 5 MONTE CARLO SIMULATIONS .............................................. 61

5. 1 Introduction ............................................................... 61
5.2 Simulation Model ........................................................... 61
5.3 Results of Simulation Experiments .................................... 65
5.4 Conclusions ............................................................... 67
CHAPTER 6 DATA SOURCES AND DESCRIPTION ................................... 72
6.1 Data Sources ............................................. 72
6.2 Construction of Interregional Grain Transfer Costs ................. 74
6.3 Descriptive Analysis ...................................................... 77
6. 31 Wholesale Grain Prices .................. 77
6 3 2 Truck Shipment Freight Rates. .. .......78
6. 3 3 Grain Transfer Costs and Spatial Price Differentials ....79
6.4 Conclusions ................................................................ 79
CHAPTER 7 EMPIRICAL RESULTS .......................................................... 94
7. 1 Introduction ................................................................ 94
7.2 Empirical Results for Maize ............................................. 95
7.2.1 Spatial Market Efﬁciency Prior to the Policy Changes... .96
7.2.2 The Effects of the Policy Changes .......................... 102
7.2.3 Conclusions for Maize......... 105
7.3 Empirical Results for Wheat ........................................ 105
7.3.1 Spatial Market Efﬁciency Prior to the Policy Changes .. 106
7.3.2 The Effects of the Policy Changes .......................... 108
7.3.3 Conclusions for Wheat......... 109
7 .4 Conclusions .............................................................. 1 10
CHAPTER 8 SUMMARY AND CONCLUSIONS ........................................ 123
REFERENCES ..................................................................................... 132

viii

Chapter 2
Table 2.1
Table 2.2

Table 2.3

Chapter 5

Table 5.1

Table 5.2

Chapter 6

Table 6.1

Table 6.2

Table 6.3

Table 6.4

Table 6.5

Chapter 7
Table 7.1

Table 7.2

Table 7.3

List of Tables

Productions of Major Food Crops by Regions in

Ethiopia (‘000 quintals) ...................................................

Productions of Major Food Crops in Ethiopia

...... 29

(‘000 quintals) ....................................................................... 30

Chronology of Government Grain Market Interventions in

Ethiopia

Results of Monte Carlo Simulation Experiments to Assess

the Performance of EPBM ...............................................

Results of Monte Carlo Simulation Experiments to Assess the

Performance of EPBM Estimates of the transition Period ............

Summary Statistics of Monthly Wholesale Prices
(Birr/ 100 kg) of Maize and Wheat for

31

....... 68

...... 69

Selected Markets (1996208 to 2002:08) ...................................... 81

Spatial Correlation of Monthly Wholesale Prices

(Birr/lOO kg) of Maize and Wheat by Different Time Periods ............ 82

Summary Statistics of Open Market Truck Shipment
Freight Rates (cents/ 100 kg/km) for Selected Routes

in Ethiopia (1994 to 2002) ................................................

Structure of Grain Transfer Costs (Birr/ 100 kg)

...... 83

Based on Cross-Sectional Surveys of Grain Traders ....................... 84

Summary Statistics of Grain Transfer Costs and Spatial

Price Differentials for Maize and Wheat .................................

Minimum Observed Months of Trade Flows for

..... 85

Selected Maize and Wheat Market Pairs .................................... 112

Conditional Maximum Likelihood Estimates of EPBM
Parameters for Selected Maize Market Pairs

(1996:08 to 2002:08) ............................................................ 113

Conditional Maximum Likelihood Estimates of EPBM
Parameters for Selected Wheat Market Pairs

(199608 to 2002:08) ........................................................... 114

ix

Chapter 2
Figure 2.1
Figure 2.2
Figure 2.3
Figure 2.4
Chapter 4
Figure 4.1

Figure 4.2

Chapter 5
Figure 5.1

Figure 5.2

Chapter 6
Figure 6.1
Figure 6.2
Figure 6.3

Figure 6.4

Chapter 7
Figure 7.1
Figure 7.2
Figure 7.3

Figure 7.4

List of Figures

Administrative Regions and Zones of Ethiopia ............................... 32
Road Lengths in Ethiopia for Different Classes of Roads ................... 33
The Number of Registered Small (7 tons capacity)

and Big (7.1 to 18 tons) Trucks ................................................. 34
Evolutions of the Number of Telephone Lines and Apparatuses ........... 35

Alternative Linear Time Paths of Structural Change in Trade

Regime Probabilities .............................................................. 60
Time Path of Structural Change in Trade Regime Probability
due to the Policy changes for a Hypothetical Case ........................... 61

Evolutions of Simulated Spatial Price Differentials (SPD) and Transfer

Costs (TC) .......................................................................... 70
Log Likelihood Function for Different
Lengths of Transition Period Using Simulated dataset ........................ 71

Co-movements in Maize Price Levels for

(1996208 to 2002:08) ............................................................ 86
Co-movements in Wheat Price Levels

(1996208 to 2002:08) .............................................................. 88
Maize Spatial Price Differentials (SPD)

and Transfer costs (TC) for (1996208 to 2002:08) ............................ 90
Wheat Spatial Price Differentials (SPD)

and Transfer Costs (TC) for (199608 to 2002:08) ......................... 92

Maize Log Likelihoods for Different Time

Lengths of Transition Period ................................................ 115
Wheat Log Likelihoods for Different Time

Lengths of Transition Period ................................................ 117
Magnitude of Losses and Gains from Inefﬁcient

Trade for Maize ................................................................ 119
Magnitude of Losses and Gains from Inefﬁcient

Trade for Wheat ................................................................ 121

CHAPTER 1
INTRODUCTION
1.1 Background

During the socialist Derg-regime, the Ethiopian government maintained a heavy
interventionist approach in its grain marketing policies. Through marketing parastatals
and cooperatives, the government controlled grain prices and restricted interregional
grain movements and private traders participation in the grain trade. The effects of these
policies on the development of grain markets, the agricultural sector, and the national
economy have been well studied (e.g. Lirenso, 1987; Franzel et al., 1989; Dadi et al.,
1992). In more recent years, however, the Ethiopian government has embarked on
various market reform measures to address the problem of poor grain market
performance. Many questions remain regarding the speed of adjustment in grain market
performance in response to policy changes, and how these policy changes are affecting
spatial grain marketing efﬁciency in Ethiopia.

It has been argued that the management of market reform requires an
understanding of the operation of local markets, the strategies and responses of private
traders, and how both relate to changes in the institutional and policy environment of
markets (Kherallah et al., 2002). Such an understanding is crucial to the design,
implementation, and evaluation of marketing policies, institutions, and marketing
infrastructure required for the development of grain markets. The key challenge now is to
move beyond market liberalization to the issue of how to design input and output markets

to catalyze smallholder productivity and income growth (Jayne et al., 2002).

In spatial price analysis, the terms “spatial market efﬁciency” and “spatial market
integration” are very widely used, sometimes interchangeably. However, there has been a
growing recognition that these terms are related but not equivalent, and that there is a
need to distinguish between them (Fackler, 1996; McNew, 1996; McNew and Fackler,
1997; Fackler and Goodwin, 2001; Barrett et al., 2000; Barrett and Li, 2002). Spatial
market efﬁciency is an equilibrium condition whereby all potential proﬁtable spatial
arbitrage opportunities are exploited. Spatial efﬁciency is concerned with whether the
optimal amount of trade is occurring. This optimality condition requires that spatial price
differentials be less than or equal to transfer costs, equal with trade. If there is no trade, a
spatial price differential less than transfer cost is also consistent with spatial market
efficiency. However, if the spatial price differential is greater than transfer cost the
market is inefﬁcient either with or without trade.

On the other hand, spatial market integration is deﬁned as the extent to which
demand and supply shocks arising in one location are transmitted to other locations
(Fackler, 1996; McNew, 1996; McNew and Fackler, 1997; Fackler and Goodwin, 2001).
Observing direct trade ﬂows between two spatially distinct markets is a sufﬁcient but not
necessary condition for some degree of spatial market integration (Barrett et al., 2000;
Barrett and Li, 2002). Direct trade linkages between regions are not necessary for spatial
integration because if regions belong to a common trading network then price shocks
may be transmitted indirectly through the network (F ackler and Goodwin, 2001). Markets
that are not well integrated may transmit inaccurate price information that distorts
marketing decisions and contributes to inefﬁcient product movements (Goodwin and

Schroeder, 1991).

Market integration has usually been conceived in terms of the co-movements or
long-run relationship between spatial prices (F ackler, 1996). However, spatial integration
is neither necessary nor sufﬁcient for spatial efficiency (and vice versa) so that tests for
integration do not always generate the appropriate inference regarding spatial market
efficiency (Fackler, 1996; McNew, 1997; McNew and Fackler, 1997; Fackler and
Goodwin, 2001; Barrett et al., 2000; Barrett and Li, 2002). The development of the parity
bounds model (PBM) represents one attempt to make the distinction between spatial
market integration and spatial market efﬁciency more clear, while overcoming most of
the weaknesses of the conventional methods of testing for market integration.l When data
on prices, transfer costs and trade flows are simultaneously available, the PBM allows a
clear distinction between spatial market efﬁciency and spatial market integration (Barrett

and Li, 2002).

1.2 Problem Statement

The effects of policy changes on spatial grain market efﬁciency can be either
instantaneous or gradual. The standard PBM has been used mostly to analyze spatial
grain market efﬁciency within a given (constant) marketing policy regime (e. g. Sexton et
al., 1991; Fafchamps and Gavian, 1996; Baulch, 1997; Barrett et al., 2000; Barrett and Li,
2002; Penzhorn and Arndt, 2002). In cases where it has been used to analyze the effects
of marketing policy changes on spatial market efﬁciency, the effect of policy changes is
assumed to be instantaneous (e.g. Park et al., 2002). This involves simply estimating a

different PBM for different sub-periods, with each sub-period corresponding to a

 

' The weaknesses of the conventional methods to testing market integration are discussed below in Chapter
3.

different policy regime. However, the PBM may be mis-speciﬁed and the results and
policy implications might be misleading if the actual effect of marketing policy changes
on spatial market efficiency is gradual and moves through a transition period, as might be
expected in many cases. It may take some time for the traders to learn and understand the
new marketing policy changes, assess its implications for reorganizing their businesses,
make new investment and disinvestment decisions, and to access resources required to
make the necessary adjustments in response to policy changes.

In general, the standard PBM does not allow for a test of a structural change in
spatial grain market efﬁciency due to policy changes. Knowledge of the time path of the
effects of market reform on spatial market efﬁciency would be very useful for properly
assessing the effects of marketing policy changes on spatial market efﬁciency, and for
designing marketing policies, institutions and marketing infrastructure. Thus, there is a
need to improve and extend the standard PBM so that it allows for gradual transition
between spatial market efﬁciency states as a result of changes in the policy environment,
and to develop a test of whether such structural changes in spatial market efﬁciency are
statistically signiﬁcant. This dissertation addresses both of these needs.

Another problem with implementing the PBM empirically is that time series data
on transfer costs are rarely available, particularly in developing countries like Ethiopia.
As a result, most empirical PBM studies have assumed transfer costs are equal to a
constant plus a serially uncorrelated error for a given marketing policy regime (e.g.
Sexton et al., 1991; Fafchamps and Gavian, 1996; Baulch, 1997; Barrett et al., 2000;
Barrett and Li, 2002; Penzhorn and Arndt, 2002). However, this assumption is very

restrictive, particularly when the PBM is used to analyze the effects of policy changes.

This is because if transfer costs are assumed to be equal to a constant plus a serially
uncorrelated error when they actually ﬂuctuate systematically over time, then the PBM
may misinterpret spatial price deviations as evidence of inefﬁciency when they are
actually just a rational response to changes in transfer costs. Thus, there is a need to go
beyond the conventional transfer cost assumptions and ﬁnd better ways of using data that
are available to construct more appropriate inferences about historical movements in
transfer costs. This issue is addressed in this dissertation as well.

In October 1999, in its continued market reform process, the Ethiopian
government amalgamated the Ethiopian Grain Trade Enterprise (EGTE) with the
Ethiopian Oil Seeds and Pulses Export Corporation (EOPEC) and re—established it as a
public enterprise. The amalgamated EGTE is not required to intervene directly to
stabilize grain prices, and its major objective is commercial proﬁtability by focusing on
exportable grains (Bekele, 2002). The effect of the changes in the EGTE’s organizational
structure and its reduced role in stabilizing grain prices, on spatial grain market efficiency
has not been studied so far. Such information should be useful to policy makers,
researchers, and donor communities interested in understanding the effects of grain price
stabilization policy changes on grain market development in Ethiopia. It would inform
the debate concerning the design and implementation of new grain marketing policies
that facilitate the emergence of a well developed and competitive grain marketing system
in Ethiopia, and may have implications for other developing countries involved in their

own market reform processes.

1.3 Objectives

There are two major objectives in this study: (1) to provide an improved modeling
approach for analyzing the adjustment path and the extent of structural change in spatial
grain market efﬁciency in response to policy changes; and (2) to provide empirical
evidence on the dynamic adjustment path of structural changes in spatial market
efﬁciency for maize and wheat in Ethiopia as a result of grain marketing policy changes
implemented in October 1999.

A stochastic gradual switching model is developed which builds on the standard
parity bounds model and extends it in two ways. First, the extended model traces the
adjustment path of spatial efﬁciency changes in response to policy changes and tests
whether the effect of a policy change is instantaneous or gradual. If it is gradual, the
model also allows determination of the length of time required for the transition from old
to new spatial efﬁciency regime. Second, the extended model allows for statistical tests
for structural change in spatial efﬁciency regimes due to the policy changes. In the
process of implementing the extended PBM model to study spatial market efﬁciency in
Ethiopian grain markets, it is shown how the standard transfer costs assumptions can be
generalized, even if a full time series of transfer cost data are not available, as long as one
has access to cross-sectional transfer cost data for particular periods that have been
collected via trader surveys and time series data on truck shipment freight rates.

The remaining sections of the dissertation are organized as follows. The following
chapter presents an overview of wholesale grain trade in Ethiopia. The third chapter
provides a brief literature review on empirical methods used to analyze spatial market

efﬁciency. The fourth chapter gives a detailed speciﬁcation of the parity bounds model

and extends it to enable analysis of the dynamic effects of marketing policy changes on
spatial grain market efﬁciency. The ﬁfth chapter presents a Monte Carlo simulation
experiment to assess the performance of the extended parity bounds model. The data
sources and descriptions are given in chapter six. The empirical results for maize and
wheat are presented in chapter seven. Finally, the summary and conclusions are provided

in chapter eight.

CHAPTER 2
AN OVERVIEW OF THE WHOLESALE GRAIN TRADE IN ETHIOPIA

2.1 Introduction

This chapter provides background information on the operation of wholesale grain
trade in Ethiopia.2 This is intended to provide a context in which to analyze and interpret
the spatial price relationships for selected regional maize and wheat markets in Ethiopia.
First, regional patterns in grain production and trade are discussed. Second, the
characteristics and performance of wholesale grain markets are discussed. Third,
discussion turns to how marketing infrastructure has changed since market reforms were
initiated in 1990. Fourth, the evolution of grain marketing policies in Ethiopia is

discussed. Finally, there are concluding comments.

2.2 Regional Patterns in Grain Production

Among eleven regions of Ethiopia (Figure 2.1), the production of grain is
concentrated in Oromiya and Amhara. In the 1995/96 production season Oromiya and
Amhara accounted for 48.6% and 32.2% of tota1 grain production, respectively (Table
2.1). Within the Oromiya and Amhara regions the production of grain is also
concentrated in certain zones. For example, maize production is concentrated in East
Wellega, East Shewa, West Shewa and Jimma zones of Oromiya, and the Gojam zone of

Amhara region. The production of wheat is concentrated in the Bale and Arsi zones of

 

2 This study is conducted at the wholesale level involving several regional grain markets. Thus, detailed
review of the vertical marketing channel is not provided here. However, for detailed discussions of vertical
marketing channels the interested readers can referee to Dessalegn et al., 1998; and Gabre-Madhin, 2001.

Oromiya. Thus, the production and consumption of grains are geographically dispersed
which gives opportunity for interregional grain trade.

Cereals are the most important grains produced in Ethiopia both in terms of total
production and quantity marketed. In the 1995/96 production season, cereals accounted
for 87% of total grain production. Furthermore, most of the grain produced is consumed
on farm. For example, in the 1995/1996 production season the marketed proportion of
grain production for all types of grain was 28%, while the proportion for Ieﬂ, wheat,
barley, maize and sorghum was 24%, 25%, 31%, 25%, and 12%, respectively (Negassa

and Jayne, 1997).

2.3 Characteristics and Performance

A detailed description of the characteristics of wholesale grain trading ﬁrms in
Ethiopia can be found in Dessalgen et al., 1998; and Gabre-Madhin, 2001. Some of the
important characteristics are highlighted here. Firms involved in wholesale grain trade are
small scale and, in most cases, the owner is the sole employee and manager of the
business. Grain trade is not specialized in that wholesale traders in a surplus area can be
engaged in assembling while wholesale traders in a deﬁcit region can engage in retailing.
Wholesale traders are also engaged in other non-grain trade activities. Grain trade
enterprises are characterized by a very low asset base. For example, only a few own their
transport capital and most of them rent storage space.

One of the most important institutional changes in the Ethiopian grain marketing
system following market liberalization has been an increased role of brokers in inter-

regional grain movement. Brokers have played a key role in the coordination of grain

buying, selling, and transporting by matching buyers and sellers, inspecting and
witnessing transactions, and providing guarantees to enforce contracts (Gabre-Madhin,
1999a; and Gabre-Madhin, 1999b). Coordination through brokers reduces transaction
costs of the marketing system (Gabre—Madhin, 1999b).

Major entry barriers in grain markets are lack of sufﬁcient start-up capital for
ﬁnancing grain trade operations, high cost of ﬁnding convenient locations in the market
place, and lack of access to appropriate and adequate storage (Dessalegn et al. 1998).
Recently, economies of scale are also becoming an important entry barrier because of the
emerging large private share and large companies owned by regional political parties,
which are also occasionally involved in grain trade.3 Both farmers and merchants also
lack access to high quality market information needed for making good marketing
decisions.

In the post-reform period, restrictions on grain movement have been one of the
most serious impediments to interregional grain trade. The introduction of ﬁscal
decentralization in 1992, which deﬁned the sharing of revenue between the central and
regional governments, created an opportunity for restrictions on grain trade at the local
and regional levels for the purpose of raising tax revenue.4 A number of studies have
showed that roadblock (“kella”) charges account for a signiﬁcant proportion of traders’
interregional margin, and argue that the amount of tax and the way of tax collection have
increased risk and uncertainty, and consequently increased transaction costs of
interregional grain trade (Diskin and Molla, 1994; Tirfe and Abraham, 1994; Negassa and

Jayne, 1997; Gabre-Madhin, 2001).

 

3 This is particularly apparent in the local purchase of food aid, where the capacity of small wholesale
traders is limited.
‘ Proclamation No. 33 of 1992.

10

These studies noted several weaknesses in the implementation of roadblock
charges: the lack of complete information on the exact amount of kella charges by
wholesalers and truck drivers; multiplicity of charges; and lack of clarity in the directives
used. These problems led to arbitrariness of the charges and misinterpretation by the tax
collectors at roadblocks to suit their own individual situations. While the taxes on grain
can serve ﬁscal objectives of the regional governments, they increase grain marketing
costs and work against government’s efforts to stimulate productivity enhancing
technologies.

Several studies have analyzed the effects of market liberalization on spatial
market performance in Ethiopia using different methods. For example, following
Ravallion (1986) and Dercon (1995) analyzed short-run and long-run price adjustment
and found that the integration of regional markets with Addis Ababa (the major urban
market in Ethiopia) increased for teff with market liberalization.

Using a pair-wise price correlation analysis, Negassa and Jayne (1998) ﬁnd that
the degree of spatial market integration increased with market liberalization. Similarly, in
the Bako area of Ethiopia, market integration tests based on pair-wise price correlation
analysis and Granger’s co-integration method indicate an improvement in market
integration as a result of market liberalization (Negassa, 1996). Negassa (1998) employs
spatial price transmission tests using weekly wholesale prices to show that grain markets
in Ethiopia exhibit a high degree of vertical and spatial integration following market
liberalization. Amha (1999) also found strong short-run and long run relationships
between Addis Ababa market and other regional markets. However, there are no studies

looking at spatial grain market efﬁciency in Ethiopia. The spatial grain market efﬁciency

11

is an area which need more work because spatial efﬁciency and spatial integration are not

the same thing.

2.4 Marketing Infrastructure

Spatial grain market efﬁciency depends on the existing marketing infrastructure,
institutions, and policies which inﬂuence both physical costs of moving, handling, and
storing grain as well as the transaction costs involved in searching for trading partners,
and negotiating, monitoring, and enforcing contracts. The objectives of this section are to
discuss the evolution of marketing infrastructure in Ethiopia and its implications for

spatial grain market efﬁciency.

2.4.1 Roads

Ethiopia is mostly a rural country and development of the road network is
important to integrate rural areas into the rest of the economy. In particular, given the
country’s wide dispersion of production and consumption centers, the development of
roads is critical for interregional grain trade and for regional and household food security.
An improvement in rural road quantity (length or density) and quality lowers travel time
and reduces vehicle running and maintenance costs, which therefore lowers the actual
costs of marketing agricultural produce and reduces the costs of delivering inputs to farm
households.

There are three classes of road quality used in Ethiopia: asphalt, gravel, and rural.
The asphalt and gravel roads are all-weather roads while the rural roads are seasonal and

not useable during the rainy season. Figure 2.2 shows the evolution of the road network

12

in Ethiopia over the period from 1989 to 2001. The quantity of asphalt road was 4,109
km in 1989 while it was 3,924 km in 2001. The reduction in asphalt road is due to lack of
maintenance for the existing asphalt roads and limited construction of new asphalt roads.

Gravel roads increased slightly from 8,966 km in 1989 to 12,467 km in 2001.
However, the only period when gravel roads showed a signiﬁcant upward trend was
during 1993 to 1995. Since then the quantity of gravel roads has remained almost
unchanged. On the other hand, there was a signiﬁcant upward trend in rural roads in that
they increased by 64 percent from 5,232 km in 1989 to 14,480 km in 2001. Thus, road
investment was mainly at the level of rural feeder roads, with less improvement in all-
weather roads.

The observed road development strategy, which focuses on rural feeder roads, has
several implications for grain marketing. First, with rural and gravel roads being the
major road types, the major grain transport from production areas to the consumption
centers has to be completed over a short time period during the dry season immediately
after harvest. This situation deprives producers and regional grain traders the opportunity
to store grain on farm or in the production areas to take advantage of higher prices later in
the season. Second, there is also pressure on a limited marketing infrastructure to
transport grain to consumption centers over a shorter time period, which might increase
the demand for marketing services and hence increase marketing costs. Third, the cost of
operating trucks on gravel and rural roads are also higher which might increase the
marketing cost. It is also more expensive to operate modern trucks with higher capacity
on feeder roads due to higher maintenance costs. The feeder roads encourage the use of

older and smaller trucks.

13

2.4.2 Trucks

The public sector dominated the provision of transport services during the
socialist regime, during which parastatal transport enterprises owned transport ﬂeets.
Grain traders in Ethiopia mostly used (owned or rented) trucks with less than 200 quintals
(20 tons) capacity for grain transport. Trucks and trailers of higher capacity were used to
transport fertilizer from the port to the distribution centers and to transport relief foods.

Since reforms in the early 19905, the total number of trucks has increased
signiﬁcantly (Figure 2.3). During the period from 1992/93 to 2000/01, the number of
trucks with a capacity up to 70 quintals increased by 155 percent (from 10,630 trucks to
27,069 trucks). On the other hand, trucks with higher capacity of 71 to 180 quintals have
increased by 88 percent (from 5,590 trucks to 10,518 trucks). However, the increase in
the big trucks was only between 1994 and 1996 and since then the number of big trucks
has remained almost the same.

Dessalegn et a1. (1998) indicate that only about 15% of grain wholesalers (mostly
big traders) have their own truck and non-owners depend on private and state-owned
freighters and NGO’s. Furthermore, their study shows that more than 55 percent of grain
traders reported that it takes a week to get a truck on rental and 12 percent reported that it
takes up to two weeks. They also report that truck shortages have tied up their limited
working capital in inventory. Thus, in view of the signiﬁcant number of wholesale grain
traders who do not own their own trucks, the availability of trucks, and the rates at which
they are rented, are very critical for the well-functioning of interregional grain trade.

There are several types of business ﬁrms operating in the transport sector. These

include: (1) private limited liability companies that own trucks and run their business

14

independently; (2) share companies which facilitate ﬁnding clients (truck users) for their
shareholders but do not own their own trucks; (3) safety net share companies which own
trucks and rent to others;5 (4) big transport companies which are owned and run by the
regional political parties (e. g. Black Lion, Dinsho, Trans, etc.,); and (5) public transport
enterprise (e. g. Bekelcha).

The transport sector is dominated by big companies linked to political parties
which have differential access to capital to buy and own modern ﬂeets. The independent
transporters are limited to old—fashioned trucks and operate in the remote areas where the
modern ﬂeets can’t operate. Most transport activities are related to food aid relief
operations in which independent transporters with traditional ﬂeets do not have a
competitive advantage to compete with party-owned companies, due to economies of
scale. The independent transporters also do not have the capacity to move all the relief

items within a short time, as required by relief organizations.

2.4.3 Telephone and Telecommunication Services

Telephone is one of the most important means of communication used by grain
traders in Ethiopia to obtain market information needed for trading grain. Thus, the
availability and quality of the telephone system affects marketing costs by inﬂuencing
grain producers and traders’ timely access to market information, and by enhancing the
ability to ﬁnd and negotiate transactions with trading partners.

During the socialist regime, access to telephone lines and apparatus was

extremely difﬁcult and the waiting time to own a telephone line and apparatus was very

 

5 These are former government employees who owned the government’s ﬂeets under the scheme of public
enterprise privatization.

15

long. Under the present regime, there has been a steady improvement in the number of
telephone lines and telephone apparatuses (Figure 2.4).6 The number of telephone lines
has increased by 168 percent from 105,985 in 1987/88 to 283,683 in 2000/2001.
Telephone apparatuses also increased by 132 percent from 135,413 in 1987/88 to 313,501
in 2000/01. The telephone density (total number of lines divided by total population) also
increased by 72 percent from 0.25 in 1991/92 to 0.43 in 2000/01. The improvement in
telephone and telecommunication services has undoubtedly reduced marketing costs
associated with obtaining market information. For example, it implies less personal travel
time to different markets in order to obtain information about other markets and trading

partners.

2.4.4 Marketing and Pricing Information

Grain trader access to market information is very important for the efﬁcient
operation of interregional grain trade. The availability of market information and the
ability of grain traders to utilize it efﬁciently affect the extent to which grain traders can
exploit proﬁtable spatial arbitrage opportunities. Likewise, producers also need market
information to make their production and marketing decisions and policy makers need it
to make effective policy decisions.

Traditionally, grain traders relied on informal sources of market information, such
as friends and neighbors who visited markets, calling friends or traders in different
markets, making a visit to the market, etc. Under the socialist regime, the way traders got
market information did not change. A few government organizations like the Agricultural

Marketing Corporation (AMC) collected agricultural prices around the country.

 

6 Recently, the cell-phone is also getting very popular in Ethiopia but we do not have statistics on its use.

16

However, AMC collected the prices for its own internal marketing and administrative
decision-making. The information collected was not adequately analyzed and
communicated to other economic agents.

In most cases, regional wholesale grain traders have relied on brokers in central
markets for information about prices in those central markets. Recently, a system of
collection and dissemination of price information through radio broadcast and bulletins
was started by the Grain Market Research Project (GMRP) in 1996. However, when the
project ended in 1998, the data collection continued but the analysis, radio broadcast, and
reporting of the market information either discontinued or continued on a very limited
scale.

In addition to price information, actors in the grain market need information on
food aid pledges and arrivals; planned and actual local grain purchases by donor
agencies; planned and actual commercial imports and exports of grains; the expected
production situation (surpluses and shortages); stock release from the food security
reserve or intended purchases for the food security reserve; changes in demand for grain.
Currently, there are no well coordinated channels through which this information is
communicated to the various participants. Thus, the information gap in the market
continues to be sizable and contributes to higher coordination costs and increased risk in

the market.

2.5 Evolution of Grain Marketing Policies in Ethiopia

Over the last several years the nature of government interventions in Ethiopia

grain marketing, either through direct participation in grain marketing activities (buying,

17

storing, selling, transporting, etc) or in designing and implementing grain marketing
policies, witnessed several changes. Different political regimes enacted different sets of
policies that inﬂuenced the current state of grain market development in Ethiopia. The
objective of this section is to document and analyze grain marketing policy changes
under three political regimes: the imperial regime (prior to 1974); the socialist regime
(1974 to 1990); and the present regime (1991 to the present) and to see how these
evolving grain marketing policies have affected grain market development. The
chronology of major government grain market interventions in Ethiopia is given in Table

2.3.

2.5.1 Imperial Regime

The ﬁrst attempt by the imperial government to intervene in grain markets was
the establishment of the Ethiopian Grain Board (EGB) in 1950.7 The EGB was involved
in grain export licensing; quality control; overseeing marketing intelligence; and the
regulation of domestic and export purchases and sales (Holmberg, 1977; Lirenso, 1987).
However, it has been argued that government intervention in grain market during the
imperial regime was geared exclusively toward providing services to private exporting
organizations and merchants, which were feudal landlords or those with close
relationships with feudal landlords (Gutema, 1988).

The EGB was able to control prices of exported grains but failed to stabilize
domestic prices because it did not hold stocks. As a result, the government established the

Ethiopian Grain Corporation (EGC) in 1960.8 The objectives of EGC were to hold

 

7 Ethiopian Grain Board Proclamation No. 113 of 1950.
8 General Notice No. 267 of 1960.

18

stocks, stabilize grain prices and improve grain production for export. However, the
EGC was also ineffective because of its very low market share in relation to a strong
private sector (Lirenso, 1987). The EGC also suffered from lack of sufficient capital and
an inability to generate sufficient proﬁt to cover its administrative and overhead costs
(Holmberg, 1977). There were strong intra—year and inter-year ﬂuctuations in grain prices
and the EGC was not able to stabilize grain prices to any great extent.

The literature on Ethiopian agriculture during the imperial period suggests the
following. First, government intervention in grain marketing and pricing was very
limited. The attempted interventions were also not effective due to lack of sufﬁcient
resources to implement the planned interventions. The major objectives of the
interventions were neither to improve the incentive structure for producers nor to improve
the food security of consumers, but to serve the interests of the landlords as the landlords
were the main sellers of grain. The interventions were urban biased in that the major
focus was the stabilization of the wholesale and retail prices in major cities. The
interventions also focused on certain production regions and urban areas while neglecting
other regions (particularly, remote areas). AS a result, the interventions did not contribute
much toward the development of interregional grain trade. Lack of well-developed
marketing infrastructure also characterizes the grain marketing system that existed during

this period.

2.5.2 Socialist Regime

In order to meet its objectives of socializing production, distribution and

marketing, the government designed and implemented various policy instruments (for

19

details, see: Lirenso, 1987; Lirenso, 1994; Franzel, 1989; and Lemma, 1996). The
government determined the annual quantity of grain purchased by the government-
marketing agency through compulsory delivery quotas; ﬁxed grain prices; restricted
private grain trade and interregional grain movement; determined the days on which the
local markets were held; and rationed grain to urban consumers.

The government’s desire to integrate the organizations engaged in the
procurement and distribution of inputs and grain marketing resulted in the establishment
of the Agricultural Marketing Corporation (AMC) in 1976. The AMC operated under the
Ministry of Agriculture as an autonomous public enterprise and obtained its support from
the World Bank.9 The principal objective of the corporation was to execute the
government’s policy in grain marketing, procurement and distribution of inputs, and
maintaining a national grain reserve.

Speciﬁcally, the AMC was involved in functions such as purchasing agricultural
products for export, or importing agricultural products and selling in the domestic market.
The AMC was also given the responsibility for purchasing and selling inputs in the
domestic and foreign markets; could purchase, process, mill, transport, sell or store
agricultural products or inputs; and could construct, equip and maintain buildings, silos,
storage facilities, grain elevators and other structures and machinery.

The AMC, which had its head ofﬁce in Addis Ababa and branch ofﬁces in other
parts of the country, was managed by a board of directors drawn from various
government ofﬁces. The functions of the board were to lay down policy guidelines;
approve the budget, annual reports and accounts; and approve the corporation’s strategy

at the opening of each season for implementation of the government’s decision on prices,

 

9 Agricultural Marketing Corporation Establishment Proclamation No. 105/1976.

20

imports, buffer-stock, exports of agricultural products and input supply and distributions.
The board also analyzed management’s recommendations with regard to the monthly
adjustment of the corporation’s buying price structure and supervised the operations of
the corporation.

The AMC was revamped in 1987.10 The board of directors stopped managing
AMC. Instead, the corporation had a general manager and was allowed to have deputy
mangers and other necessary staff. The supervising ministry was the Ministry of
Domestic Trade. The major objectives of the AMC remained to buy grain, at prices ﬁxed
by the Government, from suppliers and sell it in wholesale quantities with a view to
socializing distribution and creating an equitable market. The AMC was no longer
responsible for direct export of grains, import of agricultural products, and purchase and
sale of inputs. The major focus became domestic grain procurement, distribution and
marketing.

The AMC continued buying grain from suppliers (individual producers, producer
cooperatives, service cooperatives, state farms, and private traders) and sold to public
organizations and other organizations engaged in retail trade, public enterprises engaged
in export trade, and government ofﬁces. The AMC supplied grain to the government,
public organizations and private factories that used grain as raw material. The AMC was
also engaged in other activities, such as maintaining a national emergency grain reserve
and constructing, equipping and maintaining buildings, silos, storage facilities, grain
elevators and other structures.

The AMC had a nationwide marketing network and owned a huge amount of

resources. In 1987, AMC had 104 purchase and/or sales centers (Gutema, 1987). During

 

‘0 Legal Notice No. 103 of 1987.

21

the same year, AMC had 6.3 million quintals of storage capacity in 81 locations in the
country and owned a ﬂeet of 225 trucks that handled 25 to 30 percent of its annual
transport requirement. In subsequent years, the resources and the extent of activities of
the AMC increased. During the period from 1989 to 1990, the AMC had 8 regional
ofﬁces, 27 branch ofﬁces, 121 purchasing and/or selling centers and 2013 grain
collection points (Lirenso, 1994).

The AMC procured grain from the peasant sector, service cooperatives, and
private merchants (who served as collection agents to whom the corporation paid a
commission of 4 to 5 Birr/quintal) (Lirenso, 1987). AMC also received direct delivery
from producer cooperatives and state farms based on planned production targets. The
domestic grain procurement from the peasant sector was based on directives issued by the

government.

Grain procurement by AMC was concentrated in the major grain producing
regions. For example, more than 80 percent of AMC’s grain supplies came from three
regions: Shewa, Gojam and Arsi (Gutema, 1987; Lirenso, 1987). The role of private grain
traders shrank under the socialist regime. For example, over the period from 1982 to
1986, the licensed traders’ share in AMC’s annual purchase decreased from 70 percent to
10 percent (Gutema, 1987). In general, with complete socialization, it was expected that
the AMC would be the only public wholesale grain market organization in the country. In
the country’s Ten Year Development Plan, the AMC’s share of marketable surplus was to

increase to 80 percent in 1990 as compared to 59 percent in 1987 (Gutema, 1987).

Generally, private traders were banned from trading. In areas where the AMC was

not able to handle surplus grain, private traders were allowed to operate. But the traders

22

were required to sell a signiﬁcant proportion of their purchases to AMC at prices 15
percent to 20 percent higher than the prices received by the farmers for their crops
(Franzel et al., 1989). Franzel et al. (1989) indicate that individuals were also restricted
from transporting more than 100 kg of grain and this was strongly enforced during the

period before the area’s quota has been fulﬁlled.

Important critiques of the socialist government’s grain marketing and pricing
policies are provided by several researchers (e.g., Pausewang, 1986; Lirenso, 1987;
Franzel et al., 1989; Dadi et al., 1992; and Lemma, 1996). First, the ﬁxed prices did not
adequately account for the cost of production and cost of marketing. Second, the amount
of quota allocated did not take into account the production capacity and consumption
requirements of farm households. For example, farmers had to buy from the market to
meet quota requirement and buy grain for home consumption after making quota
delivery. Moreover, the quota system and ﬁxed price did not provide incentives to the
producers.

The forced quota delivery at a ﬁxed price had negative impacts on farmers. It
reduced farmer incomes, promoted the marketing of low-quality produce, increased
farmers’ dependence on local markets, decreased regional grain market integration, and
decreased the proﬁtability of fertilizer use in grain production (Franzel et al. 1989).
Quota delivery and tax payments came immediately after harvest and contributed to the
weak bargaining position of farmers (Kefyalew and Negassa, 1993).

The public grain marketing system also had negative impacts on the operational
efﬁciency of private sector grain trade. First, regional governments were a considerable

impediment to interregional grain trade. The participation of private traders varied from

23

region to region. In some regions the private sector was banned totally from participating.
Whenever they were allowed to operate, they were asked to meet several conditions to
stay in the grain marketing business. These conditions included delivering quality grain
to AMC, meeting the quota within the speciﬁed time limit, quota delivery to AMC
(accounting for at least 50 percent of traders’ purchases), respect ﬁxed producer price, no
hoarding, and avoiding the illegal movement of grain (Lirenso, 1987).

The Ministry of Domestic Trade issued licenses to private grain traders upon
recommendation from the GPTF in their respective regions, and proof that the applicants
were not engaged in any other non-grain business activity. The private traders
participated after meeting all these conditions. The government enforced the restrictions
on the private grain trade through roadblocks. Any trader attempting to move grain prior
to meeting the quota delivery and without getting permission from the GPTF lost all the
grain at the roadblock, as well as his/her trading license (Franzel et al., 1989).

The socialist government started introducing changes in grain marketing policies
in 1988 due to donor pressure for more reform, internal political pressure, worsening
economic conditions, and the ideological and economic policy changes in the former
USSR and Eastern European countries (Lirenso, 1994, Amha, 1995). In 1988, the
government allowed private permits to move grain as long as traders agreed to sell half of
their grain to AMC at AMC prices (Franzel et al., 1989). In March 1990, the government
undertook major grain marketing policy reform. The government removed restrictions on
private trade and interregional trade, abolished the ﬁxed price and forced quota delivery,

and eliminated the monopoly power of AMC.

24

2.5.3 Recent Developments

Under the present regime, direct government intervention in grain marketing
through marketing parastatals has decreased considerably compared to the socialist
regime. Following the overthrow of the socialist government in May 1991, various
economic reform programs were launched. The reorganization of government parastatals
began in 1992.11 As a result, the Agricultural Marketing Corporation (AMC) was
reorganized in 1992 as a public enterprise and allowed to operate in the open market in
competition with the private sector.12 Its name was also changed to the Ethiopian Grain
Trade Enterprise (EGTE). Its objectives included stabilization of markets and producer
prices to encourage increased output, stabilization of grain prices and markets to protect
consumers from unfair grain prices, earning foreign exchange through exporting grains to
the world market, and maintaining a grain buffer stock for market stabilization.

Since its re-organization in 1992, the EGTE has implemented the government’s
policies of grain price stabilization using a variety of instruments, such as producer ﬂoor
prices, pre-announced producer prices, holding of stocks, and export market development
(Bekele, 2002). However, it has been argued that the social objective of price
stabilization has negatively affected the enterprise’s commercial objectives of
competitiveness and proﬁtability (Bekele, 2002). Furthermore, EGTE was also not
effective in stabilizing grain prices due to its limited grain purchases and sales network
and shortage of working capital. The closure of branch ofﬁces and purchase and/or sales
centers in regions with less potential for grain production, and in remote areas, resulted in

shrinkage of EGTE’s grain-marketing network, and consequently reduced procurement

 

H Council of Ministers Regulation No. 25/1992
’2 Council of Ministers Regulations No. 104/1992.

25

and led to under utilization of EGTE’s resources (Lirenso, 1994). For example, since
1992, EGTE has accounted for less than 5 percent of cereal traded nationally (Jayne et
al,1998)

In October of 1999, the government amalgamated EGTE with the Ethiopian Oil
Seeds and Pulses Export Corporation (EOPEC) and re-established it as a public
enterprise.l3 As of now, the EGTE has three objectives - to purchase grain from farmers
and sell in local and export markets, to contribute towards stabilization of markets for
farmers to encourage them to increase outputs, and to engage in other related activities
conducive to the attainment of its purposes. The amalgamated EGTE is not required to
directly intervene in grain price stabilization and its major focus is on exportable grains
(Bekele, 2002). Clearly, the focus has changed to the commercial viability of the EGTE.

The effect of recent changes in the EGTE’s organizational structure on spatial
grain market efﬁciency has not been studied so far. It is hard to predict with certainty the
likely effect of these policy changes on spatial grain market efﬁciency. However, there
are three possible scenarios. First, the policy changes may have no effect on spatial grain
market efﬁciency. This is possible if the constraints faced by EGTE before the marketing
policy change such as reduced marketing network, shortage of working capital and
reduced market share, limited its effectiveness and performance. There are no indications
that these constraints have been alleviated.

Second, with its reduced role in price stabilization, the EGTE would be in a better
position to pursue its commercial objectives of proﬁtability more aggressively and
compete in the open market more effectively. This might induce more competition in the

grain market and improve spatial market efﬁciency. Even though the EGTE has not been

 

'3 Council of Ministers Regulations No. 58/1999.

26

effective in stabilizing prices, its mere presence in the market with a price stabilization
objective had inﬂuenced the psychology and operation of private sector grain traders.
This is because of the uncertainty involving when, where and how the EGTE was going
to intervene in the market. With this uncertainty reduced because EGTE has now dropped
its price stabilization objective, grain trader participation and the operation of the grain
market may improve.

Third, with the sole objective of commercial proﬁtability, it is also possible that
the EGTE might act as a monopsony and/or monopoly and reduce competition in the
grain market. When considered as an individual grain trading entity, the EGTE is in a
much better position than small scale wholesale traders in terms of resources (e.g., trucks,
storage facility, working capital, etc.,) needed to operate the grain business, and might
take advantage of this to inﬂuence prices in its favor. Thus, it might reduce the spatial
efﬁciency of grain markets. EGTE might also focus on more proﬁtable and accessible
markets resulting in lower market integration.

Additional information on the effects of the changes in the government’s price
stabilization policy on spatial grain market efﬁciency in Ethiopia should be useful to
policy makers, researchers, and donor communities interested in understanding the
effects of such marketing policy changes on grain market development in Ethiopia. It
would inform the debate on designing and implementing new grain marketing policies
that facilitate the emergence of a well developed and competitive grain marketing system.

The research would also broaden understanding of the on-going market reform process.

27

2.6 Conclusions

In Ethiopia there is geographic dispersion of grain production which provides an
opportunity for interregional grain trade. It is observed that ﬁrms involved in wholesale
grain trade are small- scale, owner managed, lack specialization, and have a very low
asset base. Major entry barriers in grain markets are found to be lack of sufﬁcient start-up
capital for ﬁnancing grain trade operations, high cost of ﬁnding convenient locations in
the market place, and lack of access to appropriate and adequate storage. In the post—
reform period, restrictions on regional grain movement have been one of the most serious
impediments to interregional grain trade. Such restrictions have increased costs and risks
associated with interregional grain trade. A review of grain marketing policies also shows
that the private grain marketing system has not been developed in Ethiopia because either

the government knowingly or unknowingly discouraged its development.

28

 

 

 

Table 2.1 Productions of Major Food Crops by Regions in Ethiopia
(‘000 quintals)
Production season Average

Region 1999/2000 2000/2001 Production Percentage
Tigray 6223.0 6579.5 6401.3 6.8
Afar 245.9 301.8 273.9 0.3
Amhara 28807.1 32304.2 30555.7 32.2
Oromiya 43719.8 48512.9 46116.4 48.6
Somalia 225.2 262.5 243.9 0.3
BeniShangul 1845.5 1770.6 1808.1 1.9
SNNPR 7387.6 10455.1 8921.4 9.4
Gambela 249.1 297.5 273.3 0.3
Harari 58.1 44.0 51.1 0.1
Addis Ababa 83.0 127.0 105.0 0.1
Dire Dawa 61.4 86.9 74.2 0.1
All regions 88910 100742 94826.0 100

 

Source: Based on Table lb in Amha 2002.

29

Table 2.2 Productions of Major Food Crops in Ethiopia (‘000 quintals)

 

 

 

Production season Average

Type of grain 1999/2000 2000/2001 Production Percentage
Cereals 77412.6 88108.0 82760.3 87.3
Teff 17175.3 19187.9 18181.6 19.2
Barley 7419.3 7583.5 7501.4 7.9
Wheat 12126.2 13689.6 1290790 13.6
Maize 25254.7 28858.8 27056.8 28.5
Sorghum 11811.4 15232.5 13522.0 14.3
Millet 3195.1 3042.8 1597.6 3.3
Oats 430.3 512.9 471.6 0.5
Pulses 9594.5 10067.9 9831.2 10.4
Oilseeds 1902.9 2566.1 2234.5 2.3
All crops 88910.0 1007420 94826 100.0

 

Source: Based on Table la in Amha 2002.

30

Table 2.3 Chronology of Government Grain Market Interventions in Ethiopia

 

 

 

 

 

 

 

Year Intervention Stated objectives
1950 Ethiopian c To license grain export and control quality
Grain c To oversee marketing intelligence
Board (EGB) 0 To regulate domestic and export purchases and export sales prices
1960 Ethiopian a To purchase and sell grain in the local and foreign markets
Grain 0 To establish grain purchase and sales outlets throughout the country
Corporation 0 To hold stocks to stabilize prices
(EGC)
1976 Agricultural . To purchase agricultural products for export or sell in the domestic
Marketing market
Corporation To import agricultural products
(AMC) To purchase and sell inputs within Ethiopia or abroad
To purchase, process, mill, transport, sell or store, agricultural
products and inputs for proﬁt or otherwise
0 To construct, equip and maintain buildings, silos, storage facilities,
grain elevators and other structures and machinery
To maintain a national grain reserve
1987 Agricultural To buy grain from supplies and sell to: a) mass organizations and
Marketing other organs engaged in retail trade, b) public enterprises engaged
Corporation in export trade, and c) government ofﬁces
(AMC) c To supply grain to government, mass organization and private
factories that use same as raw material
To maintain a national emergency grain reserve
To construct, equip and maintain, for its own use, buildings, silos,
storage facilities, grain elevators and other structures and machinery
c To sell or otherwise dispose of, in accordance with directives from
the Minister, any grain prone to deterioration or unﬁt for human
consumption
1992 Ethiopian a To stabilize markets and prices in order to encourage producers to
Grain increase their output and protect consumers from unfair grain prices
Trade To export grains to earn foreign exchange
Enterprise To maintain grain buffer stock for market stabilization
(EGTE) To engage in any other related activity for the attainment of its
objectives
1999 Ethiopian 0 To purchase grain from farmers and sell in local and mainly in
Grain Trade export markets
Enterprise . To contribute towards stabilization of markets for farmers’ produce
(EGTE) to encourage them to increase their outputs

To engage in other related activities conducive to the attainment of
its purposes

Source: Various issues of Negarite Gazetta.

31

Figure 2.1 Administrative regions and zones of Ethiopia

 

 

Source: United Nations Emergency Unit for Ethiopia, March 2000

32

Figure 2.2 Road Lengths in Ethiopia for Different Classes of Roads

 

 

 

 

 

 

 

—Asphalt
- - 'Gravel
- Rural
5
a:
5
.1
u
8
n:
C) O V— N (0 L0 N ‘-
aeeeeieiaiiés
Year

 

 

 

Source: Ethiopian Road Authority (2002)

33

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 2.3 The Number of Registered Small (7 tons capacity) and Big
(7.1 to 18 tons) Trucks
30000
. O
3 25000 Big ,
§ 20000 - - -Smal| , '
E 15000 ‘ . ’ .. 1 A,
E 10000 ‘4 - " "' T T — ——=-— ﬁ—
= i
Z 5000 ‘
0 I I I I l l T T1 l
m V LO (.0 l\ co 0) C) ‘-
0) O) O) O) O) O) O) O O
C) O) O) 0') O) O) O) O O
r- v— ‘- s- 1— !- s— N N
Year

 

 

Source: Central Statistical Authority (2002).

34

 

Figure 2.4 The Number of Telephone Lines and Apparatuses

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

350 _ .................................................................................................................... . .........................................
300 L' f
a 250 .. .. . . /
o Apparatuses ./
¢
8 200 ,, .
5'1 - " " '
.. a " " It 1- " "' “I
is: 150 _, .. — I
50
0 l l l 1 I l l l I l l l T
00 O) O ‘- N 0') V‘ to (D N co 0) o ‘-
co co 0) O) O) O) O) O) O) O) O) O) O O
O) 03 O) O O) O) O) O) O) O) O) O) O O
1- ‘— v- ‘- v- ‘- ‘- ‘- ‘- 1- 1- !- N N
Year

 

 

 

Source: Source unknown (obtained from Dr. Mulat Demeke, 2002).

35

CHAPTER3
LITERATURE REVIEW

3.1 Introduction

There have been continuous improvements in empirical methods used in
evaluating the performance of agricultural markets in terms of spatial efﬁciency. This
chapter provides a brief review of methods used in the analysis of spatial market
efﬁciency. The strengths and weaknesses of the various methods are discussed and some
of the weaknesses in existing empirical methods for analyzing spatial grain market

efﬁciency are identiﬁed.

3.2 Spatial Price Correlation Analysis

Spatial price analysis methods typically require time series data either for prices
only, for prices and transfer costs, or for prices, transfer costs and trade ﬂows. ‘4 In early
studies the degree of spatial market integration was measured using price correlations
between two different price series (e.g. Jones, 1968; Blyn, 1973; Timmer, 1974). Price
correlation is a relatively simple way to measure market integration, but suffers ﬁom
various weaknesses.

First, price correlation assumes instantaneous price adjustment and can’t capture
the dynamic nature of a marketing system (Heytens, 1986; Ravallion, 1986; Sexton et al.,
1991). Second, it is possible that price correlation might suggest spurious market
integration because the prices may tend to move together for reasons other than market
integration; like common trends, common seasonality, monopoly price ﬁxing, etc.

(Harriss, 1979; Delgado, 1986; Heytens, 1986). Third, price correlation tests may also

 

1‘ A detailed review of methods of spatial price analysis can be found in F ackler and Goodwin (2001).

36

overestimate lack of market integration if a lag in market information produces a lag in
the price response between markets (Barrett, 1996). Fourth, price correlation treats only a
pair of markets at a time and can’t be used for evaluating the marketing system as a
whole (Delgado, 1986). In order to overcome the weaknesses of price correlation tests,
various alternative methods have been developed (e. g. Delgado, 1986; Ravallion, 1986;

Engle and Granger, 1987; Johansen, 1988).

3.3 Delgado Method

Delgado’s variance decomposition approach tests market integration for the
marketing system as a whole instead of using pair-wise tests. The method purges out the
common trends and seasonality present in the price series before testing for market
integration. It implicitly assumes constant transport and transaction costs for any two
markets within a system for a given season. The spatial integration between pairs of
markets for a given season is indicated by the equality between the spatial price spread
and the constant transport and transaction costs during that season, subject to random
noise. However, this approach is based on a test of contemporaneous price relationships

and does not allow for dynamic relationships between prices in different markets.

3.4 Ravallion Method

The Ravallion (1986) method assumes a radial spatial market structure between
a group of local markets and a single central market, and price formation in the local
markets is mainly inﬂuenced by trade with the central market. This method allows testing

of several hypotheses regarding spatial market integration (market segmentation, short-

37

run market integration, and long-run market integration) between local and central
markets, after controlling for seasonality, common trend, and autocorrelation. However,
this method also has weaknesses. First, the assumption of radial market structure does not
always hold due to inter-seasonal ﬂow reversals and direct trade links between regions
(Barrett, 1996). Second, the method assumes constant inter-market transfer costs, and if
transfer costs are time—varying, the test of market integration is biased against market
integration (Barrett, 1996). Third, the method also does not distinguish market integration

due to non-competitive behavior such as collusion (F aminow and Benson, 1990).

3.5 Co-integration Methods

Commodity prices exhibit several common stochastic properties such as high
volatility, stochastic trends (unit roots), comovements in commodity prices, time varying
volatility, and excess kurtosis. These properties have implications for the econometric
methods used in spatial price analysis (Myers, 1994). For example, Ravallion (1986) uses
OLS regression to test various hypotheses of spatial market integration. However, in the
presence of stochastic trends (unit roots) in the price series the classical assumptions of
OLS regression are violated and hypothesis testing may be problematic. This led to the
application of co-integration analysis which tests for market integration by taking the
presence of stochastic trends in the price series into account. The idea behind
cointegration analysis is that a set of economic variables move together in the long-run,
even if they driﬁ apart in the short-run, because of common economic forces such as the

market mechanism and government intervention (Engle and Granger, 1987).

38

Cointegration analysis involves several steps (for details, see: Engle and Granger,
1987). The ﬁrst, step is to determine the order of integration of the univariate price series
using appropriate unit root tests.” Second, if both prices series are integrated of the same
order, run a cointegrating regression of one series on the other. Third, apply unit root tests
to the residuals from the cointegration regression. The absence of a stochastic trend in the
residual from the cointegration regression indicates that there is a cointegrating (long-run
equilibrium) relationship between the two price series. Fourth, if cointegration is
accepted, error correction models can be developed to study the short-run price
relationships (Engle and Granger, 1987).

The Engle and Granger cointegration approach assumes a stationary spatial
marketing margin for markets to be integrated. However, it has been argued that if
transaction costs are non-stationary, lack of cointegration can also be consistent with
market integration (Barrett, 1996). Also, ﬁnding cointegration does not always mean
market integration. If the markets are subject to cointegrated supply and/or demand
shocks, macroeconomic shocks (for example, money supply or interest rates), speculation
or overreaction, then prices can be cointegrated without market integration or market
efﬁciency (Pindyck and Rotemberg, 1990).

Furthermore, the Engle and Granger cointegration approach does not allow for the
investigation of all possible cointegrating vectors in a multivariate system (Myers, 1994;
Fackler, 1996). Johansen (1988) developed a multivariate method of cointegration
analysis which uses a maximum likelihood test of the hypothesis of cointegrating

relationships among several economic time series. Following Johansen, a multivariate

 

‘5 The order of integration is the number of differences required before the series becomes stationary (Engle
and Granger, 1987).

39

test for market integration under nonstationary prices has also been developed and
applied (see, for example, Gonzalez-Rivera and Helfand, 2001; Asche et al., 1999).

In general, the different methods of testing for market integration discussed above
depend on an assessment of the comovement of price series, or the long-run relationship
between prices, and have been found to have several weaknesses (Barrett, 1996; Fackler,
1996; and Baulch, 1997). These methods assume stationary spatial marketing margins,
stationary transaction costs, and/or that markets are linked by a constant trade pattern
(uni-directional and continuous). However, these assumptions are often violated and so
the resulting test of market integration may be misleading (Fackler, 1996). The key
argument here is that cointegration measures a linear spatial price relationship and when
there are discontinuities in trade and trade reversals, the spatial price relationships are no
longer linear and cointegration tests are invalid (Baulch, 1997).

Recently, there have been two major developments in methods used to account
for transaction costs in spatial price analysis - threshold cointegration tests and the parity
bounds model (PBM).16 Threshold cointegration is based on the idea that the presence of
transactions cost creates a “neutral band” within which prices in different markets are not
linked (Blake and Fomby, 1997; Mainardi, 2001; Abdulai, 2000; Goodwin and Piggott,
2001; Goodwin and Harper, 2000). It is argued that equilibrium is restored only when
localized shocks result in price differences which exceed the “neutral band”. The major

advantage of threshold cointegration is that it does not require observations on transaction

 

‘6 The PBM refers to the situation where there are statistically determined upper and lower bounds of
transfer costs, and is discussed in section 3.6. Spatial price efﬁciency requires that the spatial price
differentials be within these bounds. Early studies on tests of spatial market efﬁciency based on the
comparison of spatial price differentials and transfer costs are found in Hay and McCoy (1977); and Gupta
and Mueller (1982).

40

costs. However, it is highly parameterized and still assumes ﬁxed transaction costs

(Fackler and Goodwin, 2001).

3.6 Parity Bounds Model

Early developments and applications of the PBM are found in Spiller and Haung
(1986) and Spiller and Wood (1988). The PBM has been further developed and applied
by several researchers (e.g. Sexton et al., 1991; Fafchamps and Gavian, 1996; Baulch,
1997; Barrett et al., 2000; Barrett and Li, 2002; Park et al., 2002; Penzhorn and Arndt,
2002). The PBM allows for transfer costs, trade reversals, and autarky. It measures the
probabilities of being in different spatial market efﬁciency regimes over the sample
period. The PBM can indicate not only whether the markets are efﬁcient but also the
extent to which the markets are inefﬁcient.

Furthermore, when data on prices, transfer costs and trade ﬂow are
simultaneously available, the PBM allows a clear distinction between spatial market
efﬁciency and spatial market integration (Barrett and Li, 2002). Barrett and Li (2002)
identify six market conditions based on the relationship between spatial price differentials
and transfer costs, and whether there is trade. This distinction is lacking in other studies
that use only prices or only prices and transfer costs.

However, the PBM has also been criticized on many grounds. Fackler (1996)
provides three major critiques with regard to the PBM. First, he argues that there is no
link between economic theory and the distributional assumptions used in the PBM. From
this, it is argued that the appropriateness of the interpretation of regime probabilities

depends on the validity of distributional assumptions made. In response, Monte Carlo

41

experiments have been used to test for the sensitivity of the results to different
distributional assumptions (Baulch, 1997; Barrett and Li, 2002). Second, this model
handles only a pair of markets at a time. Third, the result may be misleading because the
approach considers short-run deviations from equilibrium as inefﬁciency whereas it may
actually represent traders’ rational responses to lags in information and shipment ﬂows.
In the context of on-going market reform and policy changes in developing
countries, the standard PBM also needs ﬁirther improvements in order to properly assess
the effect of policy changes on spatial market efﬁciency. This is because the standard
PBM has been used mostly to analyze spatial market efficiency within a given, constant
marketing policy regime (e. g. Sexton et al., 1991; Fafchamps and Gavian, 1996; Baulch,
1997; Barrett et al., 2000; Barrett and Li, 2002; Penzhorn and Arndt, 2002). In cases
where it has been used to analyze the effects of marketing policy changes on spatial
market efﬁciency, the effect of policy changes is assumed to be instantaneous (e. g. Park
et al., 2002). However, the PBM is mis-speciﬁed and the results and policy implications
might be misleading if the actual effect of marketing policy changes on spatial market
efﬁciency is gradual and moves through a transition period, as might expected in many

cases.

3.7 Conclusions

The standard PBM overcomes many weaknesses of the traditional methods of
spatial price analysis discussed above. However, in the context of ongoing market reform
and policy changes in developing countries, it does not allow for a test of a structural

change in spatial grain market efﬁciency conditions due to marketing policy changes.

42

Thus, there is a need to improve and extend the standard PBM so that it allows for
gradual transition between spatial market efﬁciency states as a result of changes in the
policy environment, and to develop a test of whether such structural change in spatial

market efﬁciency is statistically signiﬁcant.

43

CHAPTER 4
EMPIRICAL MODEL

4.1 Introduction

In Ethiopia, there is a great diversity among different regions in terms of their
agricultural production conditions such as soils, climate, and rainfall. As a result, it has
been observed that some regions have excess supply while other regions have excess
demand which gives rise to the possibility of interregional grain trade. This chapter
discusses the conceptual framework for analyzing the performance of interregional grain
trade in terms of efﬁciently allocating grain over space. Building on the standard parity
bounds model, an empirical model that allows for an adjustment path and a test of
structural change in spatial market efﬁciency due to policy changes is developed and

outlined.

4.2 Conceptual Framework

A recent review of models used in spatial price analysis can be found in Fackler
and Goodwin (2001). In general, empirical tests of the performance of spatially separated
markets are conducted within the framework of spatial price equilibrium (SPE) theory
developed by Enke (1951), Samuelson (1964) and Takayama and Judge (1964). The key
prediction of this theory is that price relationships between spatially separated
competitive markets depend on the size of transfer costs. In particular, in spatially
efﬁcient markets the price difference between regions engaged in trade should be less

than or equal to transfer costs.

44

Consider two markets located in different regions (i and j) that may engage in
trade for a given homogenous commodity. For the two regional markets, the autarky
prices (prices which equalize the supply and demand in respective regional markets

without trade) at time t for market 1 and j can be represented as:

(41) P1.” =0” +4.1

(4.2) P; =a,. +4..

where at, and a], are time varying mean autarky prices which depend on supply and

demand shifters in the local markets, and C ,1 and C 1., are stochastic disturbance terms

affecting the autarky prices in the respective regional markets. The transfer costs, T C)”,

for conducting interregional trade between the two regional markets at time t is modeled

as a random variable with time varying mean transfer costs, 7 1,, and random component

(4-3) TCﬁr : 711‘: +811:

where e,” is normally distributed with mean zero and variance 0.2 for all trade regime
probabilities. Given the above formulation of autarky prices and transfer costs, three

mutually exclusive and exhaustive spatial arbitrage conditions or trade regimes could be

45

identiﬁed based on the relative sizes of contemporaneous spatial price differentials and
transfer costs.'7
In regime one, trade may or may not be occurring and the spatial price differential

is equal to transfer cost:

(44) Pit _Pjt : TC)“

where P“ and P), are contemporaneous prices in the 1"” and j” regional markets,
respectively. This is a condition for a spatially efficient market either with or without
trade. In this regime, due to competitive pressure in the marketing system, the traders are
not making excessive or economic proﬁts from regional trade. With trade between the
two regional markets, the actual prices P" and P), may differ from the autarky prices and
the price movements in different markets are related due to changes in either market’s
supply and demand conditions or the stochastic disturbance terms.

In regime two, the spatial price differential is less than transfer cost and is given

as:

(4.5) R, —PJ., < TC,“ .

This regime represents a market condition where no proﬁtable arbitrage opportunities

exist between the two markets. The two markets may be in autarky but prices are

 

‘8 The assumption of contemporaneous relationship between spatial prices can also be relaxed. Thus,
trading regimes that take into account the lag/lead relationships between the spatial market prices can also
be formulated.

46

efﬁcient. However, if there is trade it is inefﬁcient because traders are making losses.
This indicates that efﬁcient allocation does not necessarily require physical trade ﬂows
between markets. In this regime the autarky prices and the actual prices are identical in
the respective regional markets. The prices in the two regions are independent due to very
high transfer costs, and shocks are not transmitted across the markets.

Finally, regime three is given as a condition where trade may or may not be

occurring and the spatial price differential is greater than the transfer cost:

(4.6) R, -Pﬂ > TC”,

In this regime, the spatial arbitrage condition is violated and the markets are not efficient
but may be integrated to some extent if some trade is occurring. In this regime, there are
opportunities for proﬁtable spatial arbitrage that are not being exploited. If the markets
are efﬁcient, competition is expected to equalize the spatial price differentials and
transfer costs, and the transfer costs are the largest price difference that can exist between
two markets engaged in trade. It is argued that violation of the spatial arbitrage condition
is an indication of the existence of impediments to trade between markets and should be
considered as evidence supporting the lack of perfect market integration (Baulch, 1997).
Among several conditions that may lead to regime three is the existence of transportation
bottlenecks, non—competitive pricing practices, government controls on product ﬂows
between regions, government price support activities, licensing requirements, and quotas

(Tomek and Robinson, 1990; Baulch, 1997). The empirical model is discussed next.

47

4.3 The Extended Parity Bounds Model

The empirical model developed here to analyze the effects of the policy changes
on spatial grain market efﬁciency is a stochastic gradual switching model. Building on
the earlier work of Baulch (1997), Sexton et a1. (1991) and Spiller and Wood (1988), this
model extends the standard PBM in two ways. First, it traces the time path of the effects
of the policy changes on the spatial efﬁciency regime probabilities. This allows
determination of whether the effect of the policy changes is instantaneous or gradual and
if it is gradual the approach also allows the determination of the time period required for
the full effects of the policy changes to be realized. Thus, the extended model provides a
better understanding of the nature of transition from old to new policy regime. Second,
the extended PBM also allows for statistical tests of structural change in the probabilities
of spatial efﬁciency regimes due to the policy changes.

Let the probability of regimes one, two, and three deﬁned as before be A1, A2, and
A3, respectively. Suppose that transfer costs are unobservable but known to be related to

an (possibly biased) observable transfer cost estimate yj’n. Then, the unobservable

transfer costs can be modeled as:

(4-7) TC)" : 180 + £1721 +ejit

where y)?" is the observable transfer cost estimate, [30 and [31 are unknown parameters and

0

.. . g . .
ejrt IS a random shock.1 The 7],, rs also given as:

 

‘8 The detailed discussion of the procedures used in the construction of grain transfer costs from cross-
sectional surveys of grain traders and time series data on truck shipment freight rates is given in section 3.

48

+a,FR,)

(4.8) 7;, = alFR, +a2(Pﬂ,
where 011 is the proportion of transport cost in the interregional grain trade computed
from cross-sectional surveys of grain traders, FRi is the freight rate at time t and or; is
traders normal proﬁt assumed to be 7% of the sum of grain purchase price (Pm) plus
a,FR,. Then, assuming that spatial prices and transfer costs are stochastic and the
transfer cost between the two markets is independent of the direction of trade ﬂows, we

can redeﬁne the conditions for regimes one, two and three given in equations (4), (5) and

(6), respectively, as follows:

(4.9)

 

P. —P,.|— A. — 5.77.. = e...

(4.10)

 

o —
P. —P,-,|— A. — A7... — e... —u,..

(4-11)|P11_PJ:I_IB0 _1817711:ejrr+v111

where u," and v,” are non-negatively valued random variables that measure the deviation
(if any) between price differentials and transfer costs. The error terms e,,-,, u,-,,, and v,” are
assumed to be normal, half-normal, and half-normal independently distributed random
variables with standard deviation equal to cc, 0“, and 0., respectively. The e,” is an error

term which applies to the transfer costs. The u,” and v,” are composite error terms of the

49

disturbance terms in the demand and supply ﬁinctions for the pair of markets considered,
and their magnitude depends on the relative imbalances between demand and supply in
individual markets.

In regime one, the markets are spatially efﬁcient and the variance of the spatial
price differentials is given by the variance of transfer costs between the two markets, of.
In other words, the variability in the spatial price differentials is explained fully by the
variability in the transfer costs between the two markets. Then, the parity bounds (or
conﬁdence interval) for the spatial price differentials can be constructed using the
variance of the disturbance term for regime one and the exogenously given or

endogenously estimated transfer costs. Thus, the parity bounds for spatial price
differentials can be given as ,60 +317?" i 20., where Z is a critical value for normal
distribution at a given statistical signiﬁcance level. On the other hand, the variance of the
spatial price differentials under the autarky condition is given as of + of while the

variance of spatial price differentials for regime three is given as 032 + of.

Let the contemporaneous difference between spatial price differentials and

transfer costs be given as a random variable In: IP11- jtl - ,60 — Ari“, where m can be

considered as expected “economic” proﬁt made from regional trade.” Then, the joint

probability density function for It, over the entire trading regime is given as:

(4.12)f.(7r.16)=4ﬁ.(7r.16) + 42f..(7r.l6) + (1—4—4.)/..(7r.16)

 

'9 The spatial price differential is also corrected for losses during storage and transporting grain and the
procedure used is discussed in chapter 6.

50

where A), A2 and A3 =(1 - A) - A2) are deﬁned as before; the fit’s are mixture normal
distributions which are given for regime one, two, and three, respectively; and 0 is a
parameter vector (A), A2, Ag, [30, [31, of, of, and of) to be estimated. The probability
density function for regime one is the ordinary normal density function while for regime

two and regime three the density functions are truncated half-normal density ﬁrnctions

and are given as follows:

 

 

0'

e C

Pr—Pj‘r_ 0— 11011
(4.13)fl.=-Oj—¢[ ' I '6 ﬂy ]

(4.14)

— _—1

GP —P,. wow/2.1%

 

it

R.-P).|-ﬂo-ﬂlrf.. 1-0 ,

f2! _[(0',2 +auz)mj¢[ (0,2 +03),2 _ (0,2 +032)!”

 

 

 

 

 

 

 

 

— —I

_ _ _ _ _ 0 ﬂ
Pit—Pﬂ'_ﬂo-/617;lr l—(D GP“ Pﬂl 160 ﬂlyﬂ!)0'¢

f3! :[ 2 2 21/2]¢[ 2 2)1/2 2 21/2
(0, +crv) (0, +03) - (0', +0,)

 

 

 

 

 

 

 

 

where (p (.) and (I) (.) denote the standard normal probability density and cumulative
distribution functions, respectively.
The likelihood function for it, based on the joint probability density functions

deﬁned above for the different trade regimes over the entire study period is given as:

51

(4.16)L=I][4f.. + 4m. + (FA-MA]-

The parameters can be obtained by maximizing the logarithm of the above likelihood
ﬁinction using numerical optimization. However, this is the standard PBM that does not
allow us to see the adjustment paths and the effects of the policy changes on the
probabilities of different trade regimes.

Park et a1. (2002) were the ﬁrst to apply the PBM to analyze the effects of market
reform on spatial market efﬁciency. Park et al. (2002) estimated the relative frequencies
of realized spatial arbitrage opportunities for Chinese grain markets over four sub-periods
under the implicit assumption that the effects of policy changes on the regime
probabilities are instantaneous. Here, however, we allow both for instantaneous and
gradual change in regime probabilities due to the policy changes. In other words, our
model allows us to estimate the length of adjustment period required for the ﬁrll effects of
policy changes to be realized.

Our proposed PBM extension changes the standard PBM from a stochastic
switching model to a stochastic switching model with gradual probability changes.
Hereafter we call this the extended parity bounds model (EPBM). The model allows the
identiﬁcation of time paths characterizing the stmctural changes in regime probabilities
as a result of the policy changes. It is possible that there may be immediate adjustment
from the old to the new policy regime, which implies that the full effects of the policy
changes are instantaneous or abrupt. However, the assumption of instantaneous
adjustment in market conditions in response to policy changes may be unrealistic. It

might take some time for the traders to learn and understand the new policy changes,

52

assess the implications for reorganizing their business, make investment and
disinvestment decisions, and to obtain resources required to make necessary adjustments.
The EPBM allows determining the path of structural changes in regime probabilities as a
result of the policy changes.20

To accomplish the above objectives we modify the joint probability density

function and likelihood function for standard PBM given in (12) and (13) as follows:

(4.17)
A(z.16)=A.f..(rr.16) + 40.14.0416) + 4.12.0416) + 520.f2.(7r.|9) +

(1_2’l —’12 _61D! —62Dr)f3r(7[r '8)

(4.18)
L:ﬁ[11flr + althlr + ’12er + 62Drf21 + (1_A'I_’17_61Dt—52Dt)f3l]

where 5;. measures the structural change in the probability of being in regime k due to the
policy changes and D, is a transition variable which characterizes the alternative time
path of structural change in regime probabilities and is constructed following Ohtani and
Katayama (1986) as described below.

Let the end date of the old marketing policy regime and the beginning date for

realization of the full effect of the new policy on regime probabilities be denoted by 151

and 12, respectively. Then, Dt takes the value of 0 for t1 and earlier dates, between 0 and 1

 

2° The information on the nature of the adjustment path across several markets is useful to see if there are
differential responses to policy changes among different markets and to determine what policy changes are
required in order to speed up the response.

53

for the period between 121 and t2, and 1 for I; and later dates. The length of period
between I) and T: represents the length of transition period required for the adjustment in
the grain marketing system before the full effects of the policy changes on trade regime
probabilities are realized.

The pattern of transition from r) to 12 can be represented using different functional
forms (linear or non-linear). Figure 2.1 shows alternative linear time paths for the
transition from 1:1 to t; as represented by different D(’s. For example, if the length of
transition period is 10 months then 1/ 10 (10%) of the adjustment occurs every month and
by the 5th month half of the adjustment is complete. Thus, the linear functional form for
the transition period assumes constant speed of adjustment over the whole transition
period.21 In our model, 131 is known but 12 is treated as a parameter to be estimated. The
log likelihood function is maximized for different possible ‘tz values and the 1'2 value that
has the maximum log likelihood ﬁinction is selected. The different lengths of transition
period are captured by using N-Tl different transition variables corresponding to each
time period since the introduction of the new policy regime, where N is the total number
of observations. In our case, N-Tl is equal to 35 and thus the number of maximized log
likelihood values is 35.

The approach followed here is similar to that of Moschini and Meilke (1989),
which is used in the estimation of the time path of structural change in US. meat
demand. However, there is one basic difference between our approach and Moschini and

Meilke (1989). In the case of Moschini and Meilke (1989), both the starting date (11) and

 

2' In other studies of structural changes, functional forms which allow for different speed of
adjustment during the different times of the transition period are also used (for example, see:
Goodwin and Brester, 1995).

54

the end date (12) are to be estimated from the model. But in our case the starting date is
known and only the end date is to be estimated from the model. The optimum length of
transition period is given by the length of time elapsed between 121 and ‘tz. The case where
I; is equal to n+1 (a period immediately aﬁer the policy changes) represents abrupt or
instantaneous change in policy regime which implies no transition period.

On the other hand, ‘tz greater than t1+l represents a gradual transition from old to
the new policy regime. The length of transition period depends on the ﬂexibility that
grain traders have to make investment or disinvestment decisions as deemed necessary in
response to the new marketing policy changes. It also depends on the extent of awareness
of grain traders about the new marketing policy changes and how they perceive the
effects of policy regime changes on their grain business operation. It can be hypothesized
that different grain traders in different regions have different capacity and ability to
assess and respond to changes in the marketing policy environment.

The case where the effect of the policy changes is instantaneous is a special case
of EPBM which is equivalent to separately estimating the PBM parameters for different
sub-periods. This corresponds to the Park et al. (2002) speciﬁcation. The joint test of
structural changes in all regime probabilities is conducted using the likelihood ratio test
based on the restricted (no structural change) and unrestricted EPBM parameter
estimations. The restricted EPBM is estimated by setting all 5’s to zero which means
under the null hypothesis of no structural change the LR test statistic is x2 distributed
with three degrees of freedom. In addition, where the LR test shows signiﬁcant structural

change, individual t-tests are used to test signiﬁcance of EPBM parameters. For example,

55

statistically signiﬁcant values for 5;, indicate that there has been structural change in the
probability of trade regime k as a result of a given policy change.

Thus, the probabilities for the different trade regimes are determined
simultaneously for the three periods: (1) period before the policy changes, (2) during the
transitional period, (3) the period during the full effect of the policy changes. For
example, a time path of structural change in a regime probability where the probability
has increased as a result of policy change is given for a hypothetical case in Figure 2.2.
For the period before the policy changes, the probability estimates for the different trade
regimes are given by A). On the other hand, the probability estimates for the transition

period and after the firll effect of the policy changes is realized are given as:

(4.19) 1, +51), .

Since the parameter estimates are probabilities, the probabilities for a given time period
should add up to one over the entire trade regimes, which requires the impositions of the

following restrictions during the estimation procedure:

(4.20) 0 s ,1, s1

(4.21) 031,45, 3]

(4.22) 2 2,. =1

56

(4.23) 2 5,. = 0

In general, the EPBM represents an improvement over the standard PBM in that it
allows tracing of the time path and a statistical test of structural change in spatial market
efficiency due to the policy changes. However, the EPBM also has several weaknesses
similar to that of the standard PBM which are discussed by Fackler (1996). First, the
results are often sensitive to the distributional assumptions made. Second, the difﬁculty
in accurately estimating the transfer costs might also bias the results. Third, there is also
the identiﬁcation problem that any estimated effects may be due to other changes that

occurred around the time of the policy change.

4.4 Estimation Procedures

There are four basic stages in EPBM estimation. The ﬁrst stage is to collect grain
prices and transfer cost data. The second stage is to specify the appropriate log likelihood
function to be maximized using a maximization algorithm. The third stage is to determine
the optimum time length required for the transition from old to new policy regime. The
optimum time length is determined by maximizing the value of log likelihood function
for all possible time lengths of transition period. Finally, the EPBM parameters estimates
are obtained conditional on the optimum length of time required for the transition from
the old to the new policy regime.

The log likelihood ﬁinction being maximized to obtain EPBM parameters

estimates is highly non-linear. As a result, there are two major problems that might be

57

encountered in numerical maximization: (1) the existence of multiple local maxima, and
(2) lack of convergence. There are several strategies that can be used to tackle these
problems, as discussed in the TSP users guide (Hall and Cummins, 1999). These
strategies include: (1) the choice of appropriate maximum likelihood estimation
algorithm, (2) the choice of appropriate starting values, and (3) grid search on certain
difficult parameters or full grid search on all parameters. In addition, graphical analysis
of the relationship between spatial price differentials and the transfer costs series is also
useﬁrl in assessing the EPBM estimates.

There are several algorithms provided in TSP to maximize the log likelihood
function. In our case, we used the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm,
which is found to perform best in our situation as compared to other algorithms available.
The BFGS uses analytic ﬁrst derivatives and a rank one update approximation to the
Hessian (Hall and Cummins, 1999). During the estimation procedure, the values of
regime probabilities are restricted to the range between 0 and 1 and the standard

deviations are also restricted to be positive using implicit functional forms.

4.5 Conclusions

In the context of on-going market reform in developing countries, there is a need
for an improvement in the existing methods of spatial market efﬁciency analysis in order
to better inform the debate toward designing and implementing new grain marketing
policies that facilitate the emergence of a well developed and competitive grain
marketing system. The standard parity bounds model, while it addresses most of the

weaknesses in the conventional methods, does not allow for a test of structural change in

58

spatial market efﬁciency as a result of policy changes. This dissertation, building on the
standard parity bounds model, develops an empirical model that allows for tracing the
time path of structural change in spatial market efﬁciency conditions due to the effect of
policy changes. However, the EPBM also has several weaknesses similar to that of the
standard PBM such as the sensitivity of the results to the distributional assumptions

made.

59

Figure 4.1 Alternative Linear Time Paths of Structural Change in Trade Regime
Probabilities

 

 

 

 

 

 

 

8

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

88888

 

 

 

 

—1
.(
.(

 

 

I ITIIITIITITTT YYTITTTIT TIYTYTTTT

Year

 

 

 

Figure 4.2 Time Path of Structural Change in Trade Regime Probability due to the
Policy Changes for a Hypothetical Case

Regime probability

 

Adjustment path

 

 

A

Ari Xi + OiDt

 

 

 

 

 

131 T2
Time Period

60

CHAPTER 5
MONTE CARLO SIMULATION EXPERIMENTS
5.1 Introduction

A Monte Carlo experiment is conducted to evaluate the performance of EPBM
and improve understanding of how the PBM works. This is useful in the consequent
implementation of the model using a real dataset. The simulated prices and transfer cost
data are randomly generated by making assumptions regarding the distributional
properties of the data series and using a well-deﬁned economic model.

By construction, the simulated prices are spatially efﬁcient (at equilibrium) each
period. Then, in order to allow for inefﬁciencies in the marketing system, the equilibrium
condition is shocked to create a wedge between spatial price differentials and transfer
costs. In other words, the simulated data allow us to mimic the different trade regimes
with known probabilities. The following sections present the details of the procedures

used and the results of the Monte Carlo experiments.

5.2 Simulation Model

The simulation model used in conducting the Monte Carlo experiment is a two-
location version of a three-location spatial equilibrium model developed by McNew and
Fackler (1997). For a given homogenous commodity, each location is assumed to have a

linear excess demand ﬁrnction:

(51) qr! ZIP(0” _pr()’ 1:122

61

where q), is the quantity of excess demand, b, is the slope of the excess demand curve, a),
is the autarky price and p), is the price in the market 1' when there is trade between the two
locations. The necessary and sufﬁcient conditions for equilibrium in a two-location
model are that the excess demand for the two locations sum to zero and the
complementary slackness condition between proﬁts to spatial arbitrage and the quantity

of trade is satisﬁed:

(5.2) Zq,=o, i=l,2

(53) p21 ”p11 —tCm S 0: S12: 2 0, (p2: ‘pu _tcrzr)s121 Z 0

where 1c);I is a positive transfer cost required to move one unit of homogeneous
commodity from location 1 to location 2 at time t; and Sm is the quantity of
homogeneous product shipped from location 1 to location 2 at time t.22 There are three
possible trade regimes based on the observed relative sizes of the spatial price
differentials and transfer costs: (1) the efﬁcient trade regime where the spatial price
differentials and transfer costs are equal; (2) the autarkic trade regime where the spatial
price differential is less than the transfer costs; and (3) the inefﬁcient trade regime where
the spatial price differential is greater than the transfer costs.

The price series used in the Monte Carlo experiments are constructed based on the
spatial equilibrium conditions given in (5.2) and (5.3). Assuming that location 2 has a
higher autarky price than location 1 and trade is taking place, the equilibrium prices for

location 1 (pm) and location 2 (p21) are given as:

 

22 There can also be trade from 2 to 1, this is handled by assuming that there is symmetric transfer costs
between 2 and 1 and taking the absolute value of spatial price differentials.

62

1
(5-4) p11 : TEE—(bran +bzazr —b2tcm),

l 2

(5-5) p2: 2 p1: +1012:

The autarky prices in each market are assumed to have a ﬁrst-order autoregressive

structure:

(5-6) a1: = #10 +lullaIt—l +31:

(5-7) a2: = #20 +IUZIa21—l +82:

where the ,u’s are parameters which characterize the time series properties of autarky
prices, a), and 32, are disturbance terms for the respective autarky prices. In principle,
there are several possible time series representations of autarky prices depending on the
values of the us chosen. Here, to make the experiment simple, the constant terms 040,8)
in the autarky prices equations are set to 0 and the coefﬁcients on lagged autarky prices
(,u,1’s) are set to 1. Thus, the autarky prices are non-stationary unit-root processes with
zero drift. The mean and variance of the disturbance terms for autarky prices can also
vary, but here the time series observations for the disturbance terms are drawn from
independent normal distributions with mean 0 and standard deviations of 2 using a
random number generator. Then, given the initial autarky prices and the time series

observations of disturbance terms, the autarky price series are created using equation

63

(5.6) and (5.7). The initial autarky prices for market 1 and 2 are set at 95 and 102,
respectively.

The transfer cost between the two locations is assumed to be stationary and drawn
from a normal distribution with mean 2 and standard deviation of 0.25. The initial
transfer cost is set to 4. However, to create a wedge between the spatial price differentials
and transfer costs, the mean value of transfer costs was either increased by 2 or decreased
by 1 for some randomly selected observations. The observations on a given set of transfer
cost and price data are randomly assigned to a given regime for each period using a
uniform normal distribution. The choice of the number by how much to increase or
decrease the transfer costs is arbitrary. However, the increase (decrease) should be
sufﬁcient for the spatial price differentials to lie below (above) the parity bounds, thus
creating three trade regimes with known probabilities for a given policy regime.

After the prices and transfer costs are generated with known trade regime
probabilities a Monte Carlo experiment is conducted to investigate the accuracy of EPBM
in measuring the levels and changes in the actual trade regime probabilities for the
simulated dataset. For this purpose, the time series observations of transfer costs and
price data are divided into two periods (before and after policy change) with known but
different trade regime probabilities under each policy regime. The actual probabilities of
regime 1, 2, and 3 are 50%, 25%, and 25%, respectively before the policy change and
25%, 10%, and 65%, respectively, after the policy change. In the experiment, the above
underlying true regime probabilities for the period before and after policy change are
estimated conditional on zero time length required for adjustment. In order to assess the

sensitivity of EPBM parameter estimates to sample size, the experiment is conducted for

64

three different sample sizes (N = 76, 120, and 200) which are assumed to be
representative of the lengths of available price series in developing countries. The
simulatiOn is repeated 1000 times for each sample size.

The EPBM is also tested for its accuracy in identifying the optimum length of
time required for the full effect of policy change to be realized on regime probabilities.
For this purpose, the data is simulated under 6 different time lengths required for
adjustment: 0, 2, 5, 10, 15, and 20 periods. A sample size of 76 involving 500 replications

is used for this simulation.

5.3 Results of the Simulation Experiments

The EPBM parameter estimates for each replication are obtained by maximizing
the conditional log likelihood ﬁrnction given in equation (4.18).23 The estimation results
using the simulated data set are presented in Table 5.1. The summary statistics reported
include sample means, bias, standard error, root mean square error, and minimum and
maximum values for each parameter estimate. The sample means of EPBM parameter
estimates is the average of the 1000 estimates from the simulation experiments. The
experiments indicate that the sample mean is equal to the true EPBM parameter values to
the nearest two decimal points.

The bias of the EPBM parameter estimate is obtained by subtracting the actual
EPBM parameter value used in generating the simulated data set from the mean value of
the corresponding EPBM parameter estimate obtained from the simulated dataset. The
bias of EPBM parameter estimate indicates, on average, how much the estimator will

over-estimate (positive bias) or under-estimate (negative bias) the actual parameter value.

 

23 Conditional on zero time length for transition period

65

Both negative and positive biases are observed for the estimated parameters from the
simulation experiment. However, the size of bias is very close to zero in all cases
indicating the unbiasedness of EPBM in predicting the regime probabilities and capturing
the structural changes in regime probabilities.

The precision of EPBM parameters estimators are also assessed using the
standard error (SE) and the root mean square error (RMSE) of the estimates. The SE of
the parameter estimate measures the dispersion of the parameter estimate around the
sample mean. On the other hand, the RMSE measures the dispersion of the parameter
estimate around the true parameter value. Both the SE and the RMSE measures are very
close to zero indicating the EPBM parameter estimates are precise. The minimum and
maximum values for the parameter estimates also indicate narrow ranges over which the
EPBM parameter estimates are obtained.

Figure 5.2 shows the values of the log likelihood ﬁrnction under different time
lengths for transition period. The estimated optimal time path for the simulated data set is
also presented in Table 5.2. The results show that when the true lengths of the adjustment
period in the data generating mechanism get higher the EPBM estimates of the length of
time required for transition is biased downward in all cases, and the size of bias increases
as the actual length of transition period underlying the data generating process increases.
For example, the actual transition length of 0, 2, 5, 10, 15 and 20 periods are used in the
simulation, the average estimated time lengths are 0, 1, 3, 7, 10, and 12 months,
respectively. This indicates some caution should be exercised in using and interpreting

results from estimation of the length of the adjustment period using the EPBM.

66

5.4 Conclusions

The results of Monte Carlo experiments indicate that the EPBM estimates the
levels and the changes in trade regime probabilities with very high accuracy regardless of
the sample size used, conditional on a zero time length for the transition period. The fact
that the EPBM also performed very well in the small sample case is very encouraging
given limited sample sizes often available for spatial price analysis. The EPBM provides
unbiased prediction of the optimum length of transition period when the actual time
length required for transition period is zero. However, the EPBM estimate of the length
of the transition period is biased downward when the actual length required for the
transition period is greater than zero. The size of bias also increases as the actual length
of transition period increases. Thus caution must be exercised in using EPBM when the

transition period is very long, for example, more than a year.

67

 

 

 

 

 

Table 5.1 Results of Monte Carlo Simulation Experiments to Assess the Performance of
EPBM
Summary PBM parameters Structural changes
Statistics 14:050. A2=0.25 A3=0.25 81=-0.25 62:0 15 550.40
N=76
Mean 0.49973 0.25062 0.24965 -0.25036 -0.l4885 0.39921
Bias -0.00027 0.00062 -0.00035 -0.00036 0.001 15 -0.00079
SE 0.00176 0.00360 0.00417 0.00198 0.00748 0.00788
RMSE 0.00178 0.00365 0.00419 0.00201 0.00756 0.00791
Min 0.49908 0.24982 0.19495 -0.27815 -0. 17799 0.35032
Max 0.52751 0.27802 0.25044 -0.22643 -0.10012 0.45569
N=120
Mean 0.49966 0.25086 0.24949 -0.25020 -0. 14959 0.39980
Bias 0.00034 0.00086 -0.00051 ~0.00020 0.00041 0.00020
SE 0.00132 0.00333 0.00358 0.00166 0.00566 0.00587
RMSE 0.00136 0.00344 0.00362 0.00167 0.00568 0.00587
Min 0.49926 0.24995 0.23351 -0.26694 -0. 16699 0.36701
Max 0.51626 0.26695 0.25045 -0.23379 -0.11673 0.41721
N=200
Mean 0.49968 0.25054 0.24978 -0.25024 -0. 14908 0.39933
Bias -0.00032 0.00054 -0.00022 -0.00024 0.00092 -0.00067
SE 0.00058 0.00206 0.00214 0.00118 0.00421 0.00438
RMSE 0.00066 0.00212 0.00215 0.00121 0.00431 0.00443
Min 0.49941 0.24993 0.23023 -0.26032 -0. 17008 0.37037
Max 0.50965 0.27013 0.25039 -0.24025 -0.12007 0.42042

 

' These are actual probabilities used in the simulation and convergence is achieved

in all replications. The estimates are also conditional on instant transition from old

to new policy regime.

68

Table 5.2

Results of Monte Carlo Simulation Experiments to Assess the Performance of

EPBM Estimates of the Transition Period

 

Length of Adjustment in months (I).

 

 

SW” 9 =0 (=2 (=5 4:10 4:15 =20
Statistics

Mean 0.038 1.188 3.006 6.998 10.014 12.004
Bias 0.038 -0.812 -1.994 -3.002 -4.986 -7.996
SF. 0.381 1.494 0.118 0.265 0.161 0.245
RMSE 0.382 1.699 1.998 3.014 4.989 8.00
Min 0 l 2 6 10 11
Max 4 14 5 10 12 14
Mode 0(99.2%) 1(98.4%) 3(99.2%) 7(96.8%) 10(99.2%) 12(96.4%)
Note: ' l is the time length between t) and 12.

69

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 5.1 Simulated Spatial Price Differentials (SPD) and
Transfer Costs (TC)
N=76
5
4
'5 3
E
a 2
>
1
0
15 913172125293337414549535761656973
Timeperiod
N=120
5, ................................................... I “SPD 1'0 I .....................................
4 E A I A
‘g 3 l 1 ll 1
g 2 - A L Any-tn
E ’ 1 V
>
1
1815222936435057647178859299106113120
Tlme perlod
N=200
5 i
—SPD -—TC
4 2L2”, T 1 ﬁ* " T "’4" :7## # # ##I
3 2 l‘ ' I lift/)1 ;
g V :l ‘ h ii'J ‘ '11
1 77,,i ‘ ‘7 .22, ll v ' ’V‘Ui" E
1 12 23 34 45 56 67 78 89 100111122133144155166177188199
Tlme perlod

 

7O

 

 

 

Figure 5.2 Log Likelihood Function for Different Lengths of Transition Period Using
Simulated Dataset

 

 

 

   

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

   

c==o "2
51
§ § 50
5 5
g g 49
3 s ‘8
'6 '3 47
3 3 46
9' '>'
1471013161922252831343740 15913172125293337
Length of adjustment (perlods) Length of adjustment (periods)
[=5 1:10
§ 50. .................................................................................... § 58 .......................
= 5
- 55
g 49 A g
_ _—j
'5 48 ‘v— '5 52
0 ‘1
g 47 T'IITIVIIVIIIFYT'IVF1IIITIIIIIr]rrvlrrr g
14 71013161922252831343740 1 5 913172125293337
Length of adjustment (perlods) Length of adjustment (perlods)
0:15 (:20
u g 55
8 54
5 5
3 g 53
' ' 52
3 3 5.
'6 '6
8 3 50
S S 49
14 71013161922252831343740 1 6111621263136
Length of adjustment (perlods) Length of adjustment (perlods)

 

 

 

 

 

71

CHAPTER 6
DATA SOURCES AND DESCRIPTION
6.1 Data Sources

There are two cereal crops, white maize and white wheat (from now on, simply
referred to as maize and wheat), which are considered in this study based on the
completeness of the dataset available, importance in interregional grain trade, and degree
of homogeneity of consumer preferences. T eﬁ: which is a very important staple crop in
Ethiopia, is not included in this study due to the difﬁculties involved in examining spatial
price relationships among regional teff markets. This is because teff varieties grown in
different locations are heterogeneous and consumer preferences for these varieties are
variable, but the available teﬂ price data for Addis Ababa and other regional markets are
based only on the color of teﬁ’. The more appropriate teﬂ price data needed for spatial
price analysis would be collected by color and origin of teﬂ.

The main data required for estimating the parity bounds model are wholesale
grain prices for different markets, interregional grain transfer costs and the start date for
the new policy regime. For this purpose, weekly wholesale maize and wheat price data
are obtained from the Ethiopian Grain Trade Enterprise (EGTE) for the period from
August 1996 to August 2002.

Since August of 1996, the EGTE has collected weekly price data for different
varieties of ﬁve major cereal crops at different stages of the vertical marketing channels
(producer, wholesale and retail) in 26 markets. The cereal crops consisted of maize

(white and yellow), teﬂ (white, mixed, and red), wheat (white, red, mixed, and food aid

72

wheat), sorghum (white, yellow, and red), and barley (white, black and mixed).24 The
price data are collected by EGTE ﬁeld staff who transmit weekly price data to the
EGTE’s headquarters in Addis Ababa by telephone. Then, the price data are entered into
computer spreadsheets and compiled for ﬁirther analysis or for distribution of raw data to
various users.

The weekly price series are converted into monthly series by taking the
unweighted mean of weekly price observations for a given month. The weekly price
series is converted into monthly price series for two main reasons. First, the frequencies
of transfer costs were monthly or annual, so monthly aggregation is needed to have
comparable levels of aggregation for both wholesale prices and transfer costs. Second,
the use of low frequency (monthly or annual) price data is recommended in order to
allow sufﬁcient time for the realization of inter-market arbitrage (Baulch, 1997).

The EGTE has also collected qualitative weekly grain ﬂow data for the same
markets and this data is available for the periods from August 1997 to June 1998 and
from January 1999 to August 2002. The grain ﬂow data collection was interrupted for six
months, from July 1998 to December 1998. This period coincides with the last phase of
the Grain Market Research Project. After GMRP was phased out in 1998, the grain price
and ﬂow data collection has continued with the ﬁnancial support from the European
Union (EU). For the selected commodities, the EGTE grain ﬂow dataset consists of
market level weekly data on total quantity purchased in the market, percentage purchased

outside the market, the ﬁrst and second most important sources of grain inﬂows to the

 

2‘ A well-organized and systematic grain price and ﬂow data collection was started by Grain Market
Research Project (GMRP) in August of 1996 having EGTE as an institutional home. The Grain Market
Research Project was a collaborative research project among Ministry of Economic Development and
Cooperation (MEDaC) of Ethiopia, Michigan State University (MSU) and USAID/Ethiopia.

73

market, total quantity sold in the market, percentage sold outside the market and the ﬁrst
and second most important destinations of grain outﬂows from the market.25

Interregional grain transfer costs are estimated using cross—sectional surveys on
marketing costs of interregional grain trade and time series truck shipment freight rates
data. The marketing costs of interregional grain trade are calculated based on two cross-
sectional surveys of grain traders in Ethiopia. The ﬁrst survey was conducted by Gabre-
Madhin in 1996 while the second one was conducted in 2002 by International Food
Policy Research Institute (IFPRI) and International Livestock Research Institute (ILRI).
These surveys document detailed marketing costs on the latest transaction involving
either intraregional or interregional grain trade.

Monthly and annual time series freight rates data are collected from MEDaC and
the Ministry of Transport Authority (MTA) for the period from 1993 to 2002. The
portion of the freight rate dataset series which is available only on an annual basis is
converted into a monthly series using a monthly freight rate index constructed from the
monthly freight rate series. Next, the construction of estimates of total grain transfer costs

using these two sources of data are discussed.

6.2 Construction of Interregional Grain Transfer Costs

A complete time series data on interregional grain transfer cost is rarely available,
particularly in developing countries like Ethiopia. Given this problem, several approaches
have been used in measuring the transfer costs data needed for the implementation of the

PBM. If time series transfer cost data is readily available, it can be considered exogenous

 

2’ The important sources and destinations markets are determined based on subjective assessment of EGTE
ﬁled staff and no actual grain ﬂows are recorded by sources and destinations.

74

in the PBM analysis (e.g., Barrett et al., 2000; Barrett and Li, 2002). However, if time
series transfer cost data is not available, there are two alternatives. The ﬁrst alternative is
to estimate the transfer costs using the PBM based on the observed spatial price
differentials (e.g., Park et al., 2002). However, this implicitly assumes a time invariant
transfer cost. The second alternative is to estimate transfer cost data either using the
marketing cost computed ﬁ'om grain trader surveys and adjusting for inﬂation (e.g.,
Baulch, 1997) or inﬂating the time series transport cost data by a certain percentage to
account for the unmeasured components of transfer costs (e.g., Penzhorn and Arndt,
2002)

In our case, the speciﬁc procedures used in calculating interregional grain transfer
costs data for the implementation of the EPBM are as follows. The ﬁrst step is to
calculate variable marketing costs for recently completed interregional grain trade ﬁ'om
cross-sectional surveys of grain traders. Following Gabre-Madhin (1996), the marketing
cost is classiﬁed into eight broad categories: sacking, handling, storage, transport,
roadblocks, broker’s service, travel, and tips and others. The average variable marketing
costs estimated for both 1996 and 2002 are roughly the same, about 26 Birr/ 100 kg
(Table 6.4). An examination of the structure of variable marketing costs indicates that the
transport cost is one of the most important components of the cost. For example, in 1996
about 61% of variable marketing cost is attributed to transport while in 2002 this
percentage is 72%. The unweighted average percentage of transport cost in the variable

marketing cost for the two sample grain traders’ surveys is found to be 68.16%.26

 

2" The percentage of transport cost in the variable marketing cost is computed for the aggregate overall
surveyed markets instead of computing it for individual markets or speciﬁc trade routes. This is because of
limited number of observations for individual markets and trade routes in the grain trader surveys. The
assumption of constant percentage of transport costs in marketing costs may is very strong and implies that

75

In the second step, the computed unweighted average percentage of transport cost
is applied to time series freight rate data in order to obtain time series data on variable
marketing costs. For example, if transport cost accounts for 50% of the variable
marketing cost, the time series variable marketing cost data is generated by multiplying
the time series freight rates by two.27

The opportunity cost of the wholesale grain trader as a manager of a grain
business is also included in the computation of the variable marketing cost of regional
grain trade. Thus, the computed value of interregional grain transfer cost is given as the
sum of the variable marketing costs and regional grain traders’ ‘normal’ proﬁt margin. In
the context of regional grain trade, the ‘normal’ proﬁt margin could be the minimum
proﬁt the regional wholesale trader would be willing to accept to engage in interregional
grain trade. In other words, the normal proﬁt is what the regional trader would earn from
the second best alternative employment. There is no readily available estimate of traders’
normal proﬁt in Ethiopia. In this study, following Dessalegn et al. (1998), the regional
grain traders’ normal proﬁt is assumed to be 7% of the sum of wholesale grain price in

the exporting market and variable grain marketing costs.28 Finally, the computed

 

the only source of temporal variation in the transfer cost data is the freight rate. However, here the transfer
cost computed from the trader’s survey is used only as a starting point in the EPBM estimation. Hence the
assumption of constant percentage might not be as restrictive as is it initially appears.

27 The ﬁxed/ operating costs like vehicle maintenance, storage and pest control, taxes and fees, wages, loses
and costs of capital are difﬁcult to obtain and are not included in the computation of marketing cost.

28Conceptually, the opportunity cost of those engaged in grain trade must be included in the computation of
grain transfer cost. However, there is difﬁculty in obtaining accurate opportunity cost for managers of grain
trade business and as a result very rough assumptions are made regarding trader’s normal proﬁt. For
example, Baulch (1997) adds certain ﬁxed margins to the freight rates in order to derive the transfer costs.
In our case, the normal proﬁt is given as 7% of marketing costs and grain purchase price in the export
market The actual normal proﬁt margin could be lower or higher than 7%. However, this assumption may
not have a very signiﬁcant impact on the EPBM results as the transfer costs computed from trader surveys
are used only as starting points in the parametric estimation of transfer costs using the EPBM.

76

interregional grain transfer costs is used as a starting point in the subsequent estimation of
interregional grain transfer costs and trade regime probabilities using the EPBM.

The spatial price differentials are obtained by taking the differences between the
wholesale grain prices in the importing and exporting markets after adjusting the
wholesale prices in the importing markets for grain losses (due to, for example, weight
losses, pests, spillages, etc., ) in the process of exporting grain. In this study, an average
of2. 18% grain loss in transporting grain from one regional market to another is assumed
based on the estimate from grain trader survey by Dessalegn et al. (1998). They indicated
that 83% of the surveyed merchants experience weight loss ranging from 0.1% to 16%.
Thus, the importing market wholesale prices are multiplied by 0.9782 (1-0.0218) to

obtain the spatial price differentials used in the EPBM estimation.

6.3 Descriptive Analysis
6.3.1 Wholesale Grain Prices

Descriptive statistics for the price data used for this study are given in Table 6.1.
One important observation is that both maize and wheat wholesale price levels have
declined in all markets after the grain marketing policy changes in October 1999. The
average wholesale prices for maize decreased by 3 to 28 Birr/ 100 kg, depending on the
markets while for wheat the wholesale prices have decreased by 6 to 36 Birr/ 100 kg. On
the other hand, the levels of price variability as measured by the coefficient of variation
have increased after the policy change for all maize and wheat markets considered. The
coefﬁcient of variation for maize markets increased from 1% to 14% while for wheat

markets it increased from 5 to 17%. The observation of minimum and maximum values

77

also shows that prices ﬂuctuated widely for both maize and wheat. The other notable
feature of the price series is that the level of variability is higher for the markets in the
surplus producing areas than the markets in the deﬁcit regions.

From the plots of maize and wheat wholesale prices, a high level of co-movement
is observed in the selected maize and wheat market pairs (Figures 6.1 and 6.2). This is
also reﬂected in large and statistically signiﬁcant spatial correlation coefficients
computed for the periods before and after policy changes and the entire study period
(Table 6.2). For example, for the price series covering the entire study period, the price
correlation coefﬁcients are found to be greater than 0.80 for all maize market pairs and
for 6 of 8 wheat market pairs. These high levels of co-movement might seem to indicate
spatial grain market efﬁciency. However, a careﬁil examination of the relationships
between observed inter-market price differences and costs of transferring grain is
required for a full investigation of this issue, which is the subject of this dissertation

research.

6.3.2 Truck Shipment Rates

The summary statistics of open market truck shipment rates covering the period
from 1994 to 2002 for inbound and outbound shipments in reference to Addis Ababa
market are also presented in Table 6.3. The average freight rates have showed very little
change during the period from 1994 to 2002. For outbound shipments, the average freight
rates varied from 3.50 cents/ 100 kg/km to 4.5 cents/ 100 kg/km. The variability in freight
rates on both outbound and inbound shipments is relatively low. For example, the

coefﬁcient of variation is less than 15% for 8 of 9 routes for outbound shipments and 8 of

78

9 routes on inbound shipments. In general, the freight rates were already high before the
initiation of reforms and did not show a downward trend with the improvement in truck
availability, which might be an indication of lack of competitive pressure in the transport
sector. The high freight rate could also be due to high cost of operation on poor roads and

increases in fuel prices.

6.3.3 Grain Transfer Costs and Spatial Price Differentials

The evolution of grain transfer costs and spatial grain price differentials for
selected maize and wheat market pairs are given in Figure 6.3 and 6.4, respectively. The
summary statistics are also given in Table 6.5. For the entire study period, the lowest
level of maize spatial price differential, about 12 Birr/ 100 kg, is observed between Addis
Ababa and J imma markets. On the other hand, the highest maize spatial price differential,
34 Birr/ 100 kg, is observed between Addis Ababa and Mekele. The level of variability in
maize spatial price differentials varied from 41% to 61% while it varied from 30% to
80% in the case of wheat. The spatial price differential variability is larger than the level
of variability observed for grain transfer costs estimated from the survey and from the
EPBM. For the selected market pairs where both maize and wheat are considered, the

grain transfer costs estimated by EPBM are higher for maize than wheat in 5 of 6 cases.

6.4 Conclusions
The EGTE price and grain ﬂow database provides an opportunity for the analysis
of the effect of the policy changes on the spatial market efﬁciency in Ethiopia. However,

a time series grain transfer cost data on interregional grain trade is not available. In order

79

to overcome this problem, the grain transfer cost data is constructed using cross-sectional
surveys of grain traders and truck shipment ﬁ'eight rate data available from secondary

sources.

80

 

 

 

 

 

Table 6.1 Summary Statistics of Monthly Wholesale Prices (Birr/ 100 kg) of Maize
and Wheat for Selected Markets (1996:08 to 2002:08)
Maize Wheat

Markets Mean Min Max Mean Min Max

1996:08 to 1999:09
Addis Ababa 103.41(26)' 63.00 169.50 174.26(16) 125.20 247.00
Bale Robe -- -- -- 126.34(18) 83.25 169.25
Dessie 112.50(22) 79.50 170.80 188.01(15) 142.50 264.25
Dire Dawa 126.24(22) 87.20 196.80 217.66(11) 174.50 268.00
Hosanna 101 .21(27) -- -- 136.27(18) 93.75 188.00
Jimma 91.15(34) 47.50 164.75 -- —- --
Mekele l42.64(15) 115.25 189.00 243.69(7) 205.00 272.50
Nazareth 104.02(29) 58.25 175.19 165.01(l7) 111.44 225.25
Nekempte 88.11(33) 45.00 151.56 -- -- ~-
Shashamane 92.68(33) 50.60 168.00 153.65(18) 102.80 209.50

1999: 10 to 2002208
Addis Ababa 85.97(36) 48.50 133.75 160.74(25) 106.25 243.25
Bale Robe -- -- -- 108.08(32) 56.40 161.40
Dessie 98.98(30) 61.00 145.75 170.15(20) 108.50 223.60
Dire Dawa 123.67(23) 83.75 183.25 211.74(16) 147.50 267.00
Hosanna -- -- -- 1 18.22(35) 66.25 189.80
Jimma 71.3 1(43) 37.50 119.50 -- - -
Mekele 120.88(28) 72.50 199.25 207 .86(17) 135.94 252.00
Nazareth 9008(33) 53.75 140.00 l48.68(25) 97.63 222.50
Nekempte 60.12(47) 27.00 122.50 - -- —-
Shashamane 85.72(39) 45 .25 145.40 138.13(3 1) 80.00 206.40

1996:08 to 2002:08
Addis Ababa 9505(32) 48.50 169.50 167.78(21) 106.25 247.00
Bale Robe -- -- -- ll7.59(26) 56.40 169.25
Dessie 106.02(26) 61.00 170.00 179.45(18) 108.50 264.25
Dire Dawa 125.01(22) 83.75 196.80 214.82(14) 147.50 268.00
Hosanna -- -- -- 127.62(27) 66.25 189.80
J imma 81 .64(40) 37.50 164.75 -- -- --
Mekele 132.21(23) 72.50 199.25 226.51(14) 135.94 272.50
Nazareth 97.33(32) 53.75 175.19 157.18(22) 97.63 225.25
Nekempte 74.69(43) 27.00 15 l .56 -- -- --
Shashamane 89.34(35) 45.25 168.00 146.21(25) 80.00 209.00

 

Note:

0 . . . . . . .
F rgures 1n parenthesrs are coefﬁcrent of variation expressed 1n percentages and (--)

indicates that statistics not computed because either data is not available or not relevant in
that case.

81

 

 

 

Table 6.2 Spatial Correlation of Monthly Wholesale Prices (Birr/ 100 kg) of Maize and
Wheat by Different Time Periods
1996:08 to 1999: 10 to 1996208

Distance 1999:09 2002:08 to 2002308
Market pairs (km) Maize Wheat Maize Wheat Maize Wheat
Addis & Bale Robe 442 -- 0.874 - 0.934 - 0.913
Addis & Dessie 402 0.936 0.873 0.985 0.937 0.964 0.911
Addis & Dire Dawa 515 0.890 0.779 0.827 0.884 0.832 0.847
Addis & Hosanna 232 -- 0.944 - 0.924 -- 0.930

Addis & Jimma 346 0.976 -- 0.984 -- 0.980 --
Addis & Mekele 783 0.855 0.570 0.907 0.766 0.885 0.681

Addis & Nekempte 327 0.929 -- 0.947 -- 0.931 -
Dire Dawa & Nazareth 417 0.908 0.683 0.778 0.849 0.832 0.785
Dire Dawa & Shashamane 572 0.911 0.749 0.704 0.850 0.804 0.814

 

82

Table 6.3

Summary Statistics of Open Market Truck Shipment Freight Rates (cents/ 100
kg/km) for Selected Routes in Ethiopia (1994 to 2002)

 

 

 

Outbound Inbound
Transport Route Mean CV (%) Min Max Mean CV (%) Min Max
Addis <—-> Awassa 3.624 5 3.321 4.121 3.529 3 3.335 3.767
Addis <—>Dessie 3.474 2.755 4.007 3.494 3 3.212 3.679
Addis <—> Debre Markos 4.527 19 2.963 5.585 4.363 21 2.841 5.585
Addis H Dire Dawa 3.980 3 2.877 5.863 2.872 16 1.941 3.845
Addis <—> Gonder 3.701 6 3.233 4.137 3.371 6 2.886 3.733
Addis <—> Jimma 3.577 7 3.072 4.631 3.538 6 3.340 4.137
Addis <~> Mekele 3.950 10 2.873 4.797 3.487 3 3.124 3.651
Addis (—> Metu 3.568 5 3.205 4.082 3.627 6 3.300 4.254
Addis <—> Nekempte 3.576 6 2.540 4.111 3.516 4 3.277 4.035

 

Source: Computed based on data from Ministry of Economic Development and Cooperation (MEDaC),
1998; and Ministry of Transport Authority (MTA), 2002.

83

Table 6.4 Structure of Grain Transfer Costs (Birr/ 100 kg) Based on
Cross-Sectional Surveys of Grain Traders

 

 

 

 

1996 2002

Cost category Mean Min Max Mean Min Max
Sacking 1.31( 118)' 0.00 6.00 1.55(62) 0.00 5.60
Handling 1.96(49) 0.00 6. 50 l.54(53) 0.00 4.00
Storage 0.08(350) 0.00 1.50 005(520) 0.00 2.00
Transport 15.38(73) 1.00 70.00 l9.30(81) 2.00 93.00
Road blocks 2.43(113) 0.00 13.00 0.31(335) 0.00 5.25
Brokers 1.01(92) 0.00 5.50 0.57(l 18) 0.00 3.00
Travel 0.38(226) 0.00 6.00 0.85(227) 0.00 12.00
Tips/ others 3.37(264) 0.00 60.00 l.08(l95) 0.00 10.05
Total cost 25.93(69) 4.00 113.00 25.24(65) 4.45 98.77
Transport (%) 60.72(26) 1.32 89.80 71.71(24) 8.85 97.22
Observations 150 129

 

Source: Computed based on Grain trader surveys by Gabre:Madhin (1996) and IFPRI and ILRI (2002) for
grain traders involved in interregional grain trade. Figures in parenthesis are coefficient of
variation expressed in percentages.

84

 

 

 

 

 

Table 6.5 Summary Statistics of Grain Transfer Costs and Spatial Price Differentials
for Maize and Wheat
Market pairs Estimated Absolute spatial price
Transfer costs (Birr/ 100 kg) differentials (Birr/ 100 kg)
Maize Wheat Maize wheat
1996208 to 1999:09
Addis & Bale -- 31.37(2) - 44.12(31)
Addis & Dessie 1331(9) 12.57(13) 9.67(68) l3.08(76)
Addis & Dire Dawa 30.54(15) 39.26(20) 20.82(55) 38.66(46)
Addis & Hosanna - 21.71(12) - 34. 19(27)
Addis & Jimma 12.29(12) -- 10.6l(65) -
Addis & Mekele 3226(9) 41.39(28) 36.13(40) 64.12(36)
Addis & Nekempte 20. 19(1) -- 15.27(47) -
Dire Dawa & Nazareth 25.40(l7) 53.62(15) 20.26(56) 47.91(44)
Dire Dawa & Shashamane 38.45418) 52.16(18) 30.56(41) 58.83(32)
1999:10 to 2002:08
Addis & Bale -- 2381(4) -- 49.15(30)
Addis & Dessie 14.37(12) l3.88(20) 11.00(46) ll.87(90)
Addis & Dire Dawa 32.35(17) 4202(23) 35.23(48) 4638(42)
Addis & Hosanna -- 23.22(19) -- 3902(41)
Addis & Jimma 12.64(19) - 12.79(43) -
Addis & Mekele 3294(9) 43.67(29) 32.27(43) 42.58(62)
Addis & Nekempte 2027(1) -- 23.98(40) --
Dire Dawa & Nazareth 27.08(19) 5709(18) 31.90(55) 58.45(34)
Dire Dawa & Shashamane 40.27(19) 55.84(21) 36.89(57) 6600(35)
1996:08 to 2002:08
Addis & Bale -- 3 l . 16(3) -- 4654(30)
Addis & Dessie 13.82(1l) l3.02(18) 10.3 1(57) 12.88(80)
Addis & Dire Dawa 31.4l(16) 40.59(22) 27.73(58) 42.36(44)
Addis & Hosanna -- 22.43(16) -- 36.51(36)
Addis & Jimma 12.46(16) -- 11.66(54) -
Addis & Mekele 3294(9) 42.48(28) 3428(41) 5380(50)
Addis & Nekempte 2023(1) -— l9.45(49) -
Dire Dawa & Nazareth 2620(19) 5528(17) 25.84(6l) 52.96(40)
Dire Dawa & Shashamane 39.34(19) 53.95(20) 33.64(52) 63.78(34)

 

Note: ' Figures in parenthesis are coefficient of variation expressed in percentages and (-) indicates that
the statistics not computed.

85

Figure 6.1

Co-movements in Maize Price Levels (1996:08 to 2002:08)

 

a) Addls Ababa and Mkempte

 

 

 

 

 

 

 

 

 

 

 

MID. ....................................................................
160.00 NE—
? - A
§ 120.00 A
g 50.00-
m
40.00 ’V’ /
.00 n n I in . m 1
N .- N
is. a i 55. E a a

 

 

 

 

 

 

 

 

 

b) Addis Ababa and Jimma
200.00
“AA

160.00 ......JA #—
.2
C, 120.00
0
v-
E 8000 1
E

40.00 .7 -5
.(X) .......................................................................

 

 

 

c) Addls Ababa and are We

 

250.00

 

AA

"00

 

200.00

 

 

 

 

150.00 -

100.00 -

Blrrl100 kg

 

 

 

.8
88

 

1996
1997
19913=
19995
2W1
2002

 

 

31m 100 kg

 

 

 

300.00 -
250.00

 

 

 

 

5
8

150.00
100.00
50.00

 

 

 

 

 

 

86

 

 

Figure 6.1 (Continued)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

e) Addis Ababa and Dessle f) Shashamane and om Dawa
200.00 250.00
160.00 200.00
3 120 00 a?
o ' a 150.00 -
O O
1- 1-
E 80.00 1:: 103.00 <
in m
40.00 50.00
N v- N
8 a 9 8 .8. 8 8 §
1- 1- 1- v- N N N ,-
Year
9) Nazareth and [Ire mwa
250,00 .......... ........ ........ ..... ......
.......... DD .
200.00
3' FUR
3 150.00 \P ,.
~ \ ,./
,5 100.00 WW
I:
50.00 p
.00 IIIIIIIIIII IIIIIFIIIIII‘IIIHIIIII I I1 I] I I If

 

 

 

 

 

87

 

 

 

 

 

 

 

   

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 6.2 Co-movements in Wheat Price Levels (1996:08 to 2002:08)
a) Addls Ababa and Bale lbbe b) Addis Ababa and Hosanna
28000 300.00
240.00 A\ "—AA‘ _M
25000 AA
3 200.00 V“ BR “—W‘
h
3 160.00 [‘- 3 200.00 M V\
O
'- 8 150.00 ...- IL
'E 120.“) \ I ~ V
a .... V v’ \ J i / W \
- Wt] 100.00 .4
40.00
5000
.m nmunrnrrmnmnnrnuvnnmmun"munnnllunrn"
" ‘- 00 -----m-
8 a 8 8 g g .. .. .. ..
F F ‘- 1- g a a a a 3 i a
Year Y."
c) Addls Ababa and ﬁre Ma 11) Addis Ababa and Mekele
300.00 300.00 —AA
250.00 - 250.00
2 200.00 3 200.00
8 O
,. 150.00 3 150.00
'E E
a 100.00 a 100.00
50.00 50.00
N 1-
i 5 i i 2 a E
Year

 

 

 

 

 

88

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 6.2 (Continued)
e) Shashamane and Dire Dawa
300.00 ......
“$5
250.00 « 00 ~—
g 200.00 ‘W fr, /m"A "
‘- . U ‘1, K i’.
5 100.00 \Vx ,/
50.00
.00 .....................................................................
[s 1- N
8 8 8 8 8 8 a
e- v- v- v- N N
Year
g) Addls Ababa and Dessie
280.00
240.00
200.00
I?
8 160.00
E 120.00
“9 80.00
40.00
.00
[x e- N
8 8 8 8 8 8 8
e- e- s- v- N N N
Year

 

 

 

89

 

8er 100 kg

1) Nazareth and cm Dawa

300.00
250.00
200.00
150.00
100.00

50.00

 

 

 

Maize Spatial Price Differentials (SPD) and Transfer Costs (TC)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

b) Addis Ababa and Dire Dawa

Blrri100kg
0888888388

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 6.3
(1996208 to 2002:08)
a) Addls Ababa and Dessie
35
30
25 —TC
33 «mm-SPO
8 20
E ,5. l. h
... , ' W-
mnl'!.8\ WW.)
J 111.11 a u 8 .1
1 I ’W’
0 ”mm" mm"... n mn nvrm mmmmmmmu
8 8 8 8 8 8 8
Year
c) Addls Ababa and Jlmma
30 _
25 i ---«-SPD
1 ﬁ 1‘
M. . . n
x i 1 i
g 15 L; E‘ 11 L!
E 10 i. 2. N 3
J ' =
, M \q. v JV
. ......... f ,,,,,,,,, . .-.“, .
8 '5 8 a 8 8 8
a 2 a 2 a a a
Year

 

 

 

 

 

d) Addls Ababa and Mekele

 

3 a: 3 2 3 g g
2 2 22 23 8 N 8
Year

 

 

 

9O

 

I) Shashamane and lire Dawa

 

 

m Noam
.‘ W Foo~

m ooow

 

 

i: 82

Year

 

m mam.

«u~~spo

 

 

 

m 58m.

cIV

 

 

 

 

 

 

 

..mome
mmwmwmo

9. 2: EB

 

 

91

 

 

(Continued)
e) Addls Ababa and Nekempte

 

Figure 6.3

NOON

Doom

noo—

Year

000—

p
u —

.
.
v
.
u
.
.

g) Nazareth and [Ire mwa

hoo—

 

oom—
MW :. mw.a nu :0 nu my nu nu nu mw MW mw mw MW mw mw mw mm nu

3322

9. 2: E5 8. 8' EB

 

 

 

 

 

Figure 6.4

Wheat Spatial Price Differentials (SPD) and Transfer Costs (TC)
(1996108 to 2002:08)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

a) Addls Ababa and Bale lbbe b) Addls Ababa and Hosanna
120 80
...—3530 50+- mspo {if I
0
gm g“ 8818'
‘E A
a 40 E30 ‘A . MW
20 20'
10
o 0 W "I IHHIIIWIHI l IIIIIIHHIH‘UVFH'" VUVUHWHIIHI
N e-
8 8 8 8 8 8 8
Year
c) Addls Ababa and Dessie
50 ................................................... T C. ...................... a)
— 80
4o 5 ........ SP0 ,0
:30 a 1 i e 60
8 l n ‘ 8 50
e- 1-
s 20 8 L. . . s :2
a a
10- l a “M :3
o mmr'.” .82?) o
h 0 1- N N '-
8888888 8888-888
Year Year

 

 

92

 

 

 

Figure 6.4

(Continued)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Blrd 100 Kg

 

f) Nazareth and are mwa

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

e) Addls Ababa and Mekele
120
no 1".
a 80 I A " """" SPD { L
g .‘
§
E
In
0 .m w w "n "m...“ ...... w w
0 '- 0 0 " N
a a a a 8 8 8
Year
140 ...............................
120
a 100
x
8 80
E 60*
a 40_
20
1996 1997 1998 1999 2000 2001 2002
Year

 

 

93

 

CHAPTER 7
EMPIRICAL RESULTS
7.1 Introduction

Without information on the actual grain trade ﬂow data, it is generally not
possible to estimate the exact probabilities of being in spatially efficient and spatially
ineﬁicient regimes. This is because regime one (with or without trade) represents
spatially efficient arbitrage and regime three (with or without trade) represents spatially
inefficient arbitrage, but regime two could be either spatially efﬁcient (without trade) or
spatially inefﬁcient (with trade). In the presence of a signiﬁcant probability of regime
two, actual trade flow data are required to separate the probability of regime two into
spatially efficient and inefﬁcient outcomes. The EGTE grain ﬂow data is used in the
interpretation of trade regime probabilities estimated by EPBM. Thus, in order to
facilitate the presentation of empirical results we ﬁrst provide a brief description of
EGTE grain ﬂow data involving the markets included in this study.

There are ten important markets which are considered in this study which are
either from grain surplus areas or grain deﬁcit areas. The markets selected from the
surplus producing regions include Addis Ababa, Bale Robe, Hosanna, Jimma, Nazareth,
Nekempte, and Shashemene, while the markets selected from the deﬁcit regions include
Dessie, Dire Dawa, and Mekele. Most of these markets are considered in the spatial price
analysis of both maize and wheat, while a few are considered only for either maize (e. g.,
J imma and Nekempte) or wheat (e. g., Bale Robe and Hosanna).

The minimum observed frequencies of maize and wheat trade ﬂows for selected

market pairs are given in Table 7.1. The frequency of ﬂow data for a given market pair is

94

determined based on the weekly observations of ﬁrst and second most important sources
and destinations markets for a given commodity and a given market.29 The ﬂow data is
observed on a weekly basis and aggregated to monthly ﬂow observations and the
frequencies reported here are based on the number of months for which trade ﬂow was
observed out of the possible 26 months before the policy changes and 35 months after the
policy changes.

The minimum frequency of maize ﬂows between selected markets varied from
15% to 100%. The lowest frequency of maize flow is observed between Dire Dawa and
Nazareth prior to the policy changes. After the policy changes, the frequency of maize
flow decreased for 3 of 7 market pairs, increased for 3 of 7 market pairs and remained the
same for one market pair. In the case of wheat, the frequency of trade flow varied from
39% to 100% for the period before the policy changes, and after the policy changes the
frequency of trade ﬂow increased in two cases, decreased in two cases and remained the
same in three cases. In general, even with limited grain flow data, it is observed that most
of the selected market pairs are linked by continuous trade ﬂows for most of the time

during the study period.

7.2 Empirical Results for Maize
Empirical results from the EPBM are given in Table 7.2 for selected maize market
pairs. The conditional maximum likelihood estimates of trade regime probabilities (it’s),

the change in trade regime probabilities (5’s) due to the policy changes, the standard

 

29 Furthermore, the frequencies are minimum observations because the information on trade flows when the
market is less important (e. g. third, fourth, ﬁfth, etc.,) as source or destination market is not collected. Thus
the actual frequencies of trade ﬂows could be equal or higher than the frequencies reported here.

95

deviations of proﬁt for different trade regimes (0’s), and the parameter estimates of
transfer costs (B’s) are shown at the top of Table 7.2. The estimated lengths of transition
periods, the values of the log likelihood for restricted (no structural change) and
unrestricted estimations, the chi-square (x2) statistics for likelihood ratio (LR) tests of the
joint hypothesis of no structural change in regime probabilities, and the number of
observations used in the analysis are shown at the bottom of Table 7.2. The plots of the
value of log likelihood function for different lengths of transition period are given in
Figure 7.1. The plots of the sizes of losses or gains from inefﬁcient trade for selected

maize market pairs are given in Figure 7.3.

7.2.1 Spatial Market Efﬁciency Prior to the Policy Changes

For the period before the policy changes, the probability of regime one (M),
where the spatial price differential is equal to transfer cost, is less than 1% and
statistically signiﬁcant at the 1% level for 3 of 7 selected maize market pairs. It varied
from 20% to 34% for the other 4 of 7 selected market pairs. Thus, prior to the policy
changes, the probability of the spatial price differential being equal to transfer cost, which
is consistent with spatial market efﬁciency whether or not trade is actually occurring, is
very low for most market pairs and, less than 35% for all market pairs.

On the other hand, the probability of regime two (M), where the spatial price
differential is less than transfer cost, is found to be large and statistically signiﬁcant at the
10% level for all maize market pairs. For example, the probability of being in regime two
prior to the policy changes are greater than 65% and statistically signiﬁcant at the 1%

level for 6 of 7 selected maize market pairs. Regime two can also be consistent with

96

spatial market efﬁciency if no trade is occurring between the markets. If trade does occur
in regime two, then it is presumably conducted at a loss, which would be inconsistent
with spatial market efficiency.

During the same pre-policy change period, the probabilities of regime three (Ag),
where the spatial price differential is greater than transfer cost, is found to be small but
statistically signiﬁcant in most cases. The only large and statistically signiﬁcant
probability of regime three is observed between Addis Ababa and Mekele, which has a
68% probability of regime three, which is statistically signiﬁcant at the 1% level. Of
course, regime three is spatially inefﬁcient whether there is trade or not because there are
arbitrage proﬁts from potential trade.

In general, the period before the policy changes is characterized by large and
statistically signiﬁcant probabilities of the spatial price differential being less than
transfer cost, while the probability of the spatial price differential being greater than or
equal to transfer cost is generally small. This indicates that the probability of proﬁtable
spatial arbitrage opportunities (probability of regime one plus probability of regime three)
for maize prior to the policy changes is very low for the selected maize market pairs. The
fact that regime two dominates also indicates that there is a high probability that maize
traders made losses during this period, if they engaged in actual trade.

The one exception to the above conclusion is Addis Ababa — Mekele, which was
estimated to have a 68% probability of spatial price differential greater than transfer cost,
indicating spatial inefﬁciency and potential gains from additional trade. This result is
consistent with the observation of strict and persistent control on grain ﬂows ﬁ'om Addis

Ababa to the Tigray region, which might have created maize shortages in Tigray and

97

increased prices there. The purpose of the grain movement control was to raise tax
revenue. The grain movement control was enforced through a roadblock raised at
Alamata, a small town which is strategically situated on a major grain route connecting
Addis Ababa to Mekele. It is a strategic location because grain traders who want to ship
grain to Mekele from or via Addis Ababa do not have any better alternative route by
which they can avoid this roadblock. Grain can also enter Tigray via Gonder in the North.
However, this route involves longer distance and its costs may have exceeded the
roadblock charge at Alamata. Thus, the ability of regional maize traders to take
advantage of proﬁtable spatial maize trade opportunities between Addis Ababa and the
Tigray region is limited by this regional grain trade block.30

With very large and statistically signiﬁcant estimated probability of spatial price
differential less than grain transfer costs, one would generally expect very low maize
ﬂow among these markets during this period, because spatial arbitrage would be
unproﬁtable. In other words, the probability of market segmentation is very high.
However, a close examination of maize flow data between these markets during this
period shows that there have indeed been frequent maize ﬂows between these markets.
This would suggest maize traders were engaged in maize trade but were making losses
which indicate spatial inefﬁciency.31

For example, based on the EGTE’s grain ﬂow data, maize trade ﬂow between

Jimma and Addis Ababa and Wellega and Addis Ababa occurred at least for 95% of the

 

3° The roadblock charges are included in the computation of grain transfer costs. However, it is diﬁicult to
capture the whole magnitude of the roadblock charge from a few cross-section surveys. For example, the
time wasted at the roadblock, the spoilage and quality deterioration, missed market opportunities can’t be
easily quantiﬁed from cross-section surveys.

3‘ This result might also be due to aggregation error in the prices and transfer costs which masks periods
when trade was proﬁtable.

98

months prior to the policy changes (Table 7.1). At the same time the probabilities of
spatial price differential less than transfer cost is at least 75%. These results indicate there
is high probability of spatial maize market inefﬁciency prior to the policy changes.
Generally, Western maize producing regions like Jimma and Wellega have a limited
export outlet for surplus maize production, and it is commonly observed that, even when
prices are relatively low in Addis Ababa, maize exports to Addis continue from these
regions. Hence, prices continue to fall in Addis Ababa. Grain traders in surplus producing
regions have the option to sell their grain in their local markets when the price in Addis
Ababa or other regional markets is not favorable. However, the surplus absorption
capacities of local markets are limited.

There are several factors which might cause spatial inefﬁciency of maize markets
in which there is high probability of making losses by maize traders. First, the lack of
well-developed storage facilities in maize supply markets might force the continuous
ﬂow of grain to central or other deﬁcit markets, even if maize prices are not favorable in
these markets. The rational for this might be to reduce ﬁrrther revenue losses because of
waiting for better price which might lead to spoilage, quality deterioration, and maize
prices in the maize destination markets might also ﬁrrther decrease while waiting.
Second, liquidity constraints and shortage of working capital due to missing or imperfect
credit markets for grain traders can also force maize traders to liquidate grain, even if it
means a loss. It has been observed that grain traders in Ethiopia have poor access to
formal credit and other forms of ﬁnancial services. Author’s personal observation of
grain markets indicate that proceeds from current grain (e.g., maize) sales are used by

grain traders for reﬁnancing future grain purchases and settling other debts which

99

indicate that the opportunity costs of capital tied up in grain stock is very high when the
grain traders have limited access to credit.

Third, regional maize wholesale traders might have difﬁculty matching proﬁtable
purchase and sale decisions due to inadequacy or unavailability of market information
regarding future price movements and changes in supply and demand conditions in the
source and destinations markets. Fourth, there may be too many maize traders but these
traders might lack economies of scale in their operation contributing to higher cost of
marketing. Fifth, maize traders might also be limited by their grain trading skills to adjust
to the very dynamic grain marketing situations.

If inefﬁcient (unproﬁtable) trades are taking place a natural question to ask is:
how do maize grain traders survive in the long-run in the face of high probability of
making losses? It is observed that the wholesale grain trade is not a specialized business
in Ethiopia. Regional grain traders usually keep a diversiﬁed portfolio of business
activities (grain and non-grain) and that might help to spread the losses. Regional grain
traders also combine interregional grain trade activities with local grain trade activities. A
lot of grain traders are also observed to operate without a license, while those with a
license complain about the unfair competition from unlicensed grain traders (Dessalegn
et al., 1998). Operating without a license might allow grain traders (experienced or new)
to enter and exit out of the grain trade sporadically, depending on market conditions, and
still avoid government tax payments, hence reducing their marketing costs.

The other possible reason why the grain traders might survive could be due to the
offsetting or compensating effects of fewer but larger gains for many but smaller losses.

In order to investigate this issue we have computed the size of losses or gains from trade

lOO

and plotted these for selected maize market pairs in Figure 7.3. The sizes of losses or
gains from trade are computed as a proportion of the difference between spatial price
differential and transfer costs to the cost of grain plus the transfer cost. The plots show a
few episodes of unusually very high gains for most maize market pairs and there are also
episodes of very high losses. However, in order to exactly determine the compensating
effects of larger gains we need data on the total volume of grain transacted.

There are also indications that it might still be proﬁtable for large scale wholesale
grain traders to engage in spatially proﬁtable arbitrage even when smaller wholesale
grain traders ﬁnd it unproﬁtable. Osborne (1997) argues that large and small wholesale
grain traders in Ethiopia have different cost structures because of economies of scale.
This means that large wholesale traders can sell at the same price as the smaller traders
and still make a proﬁt because of lower cost.

The standard deviations of “economic” proﬁt from spatial arbitrage estimated for
different trade regimes are statistically signiﬁcant at the 5% level for 19 of 21 cases. For
each market pair, the standard deviation estimated for regime three (CV) is found to be the
largest. As regime three is unambiguously inefﬁcient, this indicates that the variability in
the “economic” proﬁt from spatial arbitrage is higher when the market is inefﬁcient. It is
also observed that the standard deviations of regime two are higher than that of regime
one in 5 of 7 cases. The other important observation regarding variance estimates is that
the standard deviations for market pairs involving Addis Ababa and deﬁcit markets are
larger than the standard deviations involving Addis Ababa and surplus markets. This
indicates that the degree of risk in trading maize is relatively higher between Addis

Ababa and grain deﬁcit markets than Addis Ababa and grain surplus markets.

101

7.2.2 The Effects of the Policy Changes

Likelihood ratio (LR) statistics are used to test the joint hypothesis of no
structural change in trade regime probabilities due to the policy changes for selected
maize market pairs, after having estimated the optimal adjustment path to the policy
changes.32 The chi-square statistics for the LR tests are presented at the bottom of Table
7.2. The results show that there is no statistically signiﬁcant joint structural change in
trade regime probabilities for 4 of 7 maize market pairs (Jimma and Addis Ababa, Addis
Ababa and Dessie, Nazareth and Dire Dawa, and Shashamane and Dire Dawa) at the 10%
level. On the other hand, the joint structural change in trade regime probabilities is
statistically signiﬁcant at the 5% level in 3 of 7 maize market-pairs, which include Addis
Ababa and Nekempte, Addis Ababa and Dire Dawa and Addis Ababa and Mekele.

To some extent, the variation in the responses of regional maize markets to the
recent policy changes can be explained by the history of government market interventions
which have varied from region to region and may have different effects on the levels of
private sector grain development and grain traders’ perceptions of risk and uncertainty.
Generally, the markets where the policy change appears to have had little effect appear to
be where the private sector grain trade already had been relatively more tolerated by the
government marketing agencies during socialist regime (e. g., Nazareth and Shashamane).

During the socialist regime, it was observed that private grain trade in Southern
Ethiopia was much more tolerated by government marketing agencies than in other

regions of Ethiopia (Osborne, 1997). So the degree of risk and uncertainty perceived due

 

32 Optimal adjustment paths were chosen based on a likelihood maximization procedure, as discussed
earlier. The optimal adjustment path estimates will be explained in more detail below.

102

to the presence of EGTE in these markets might have already been low and the recent
policy changes might not bring signiﬁcant change in the attitude and operations of private
grain traders. On the other hand, the joint structural change in regime probabilities is
statistically signiﬁcant for trade between Nekempte and Addis Ababa. Nekempte is
located in a maize surplus producing region and has historically been one of the major
focuses of government marketing activities (private grain trade sector was highly
suppressed). So in this case the changes in policy appear to have had an effect. Structural
change is also signiﬁcant for trade between Addis Ababa and Dire Dawa and Addis
Ababa and Mekele markets. Dire Dawa and Mekele markets are also grain deﬁcit areas
where there had been heavy government intervention.

Of three maize market pairs with statistically signiﬁcant joint structural change in
trade regime probabilities, Addis Ababa and Nekempte and Addis Ababa and Mekele
adjusted to the new policy changes gradually over a period of less than or equal to six
months while the trade between Addis Ababa and Dire Dawa adjusted instantaneously
(Table 7.2 and Figure 7.1). The variation in the length of transition period among market
pairs indicates that the speed by with which grain traders adjust to new policy regimes
may depend on their location. The market pairs where the speed of adjustment is gradual
appear to be where the marketing infrastructure, like road network and grain storage, is
relatively less developed (e. g., Nekempte) and the destination market is far from surplus
producing areas and drought affected (e.g., Mekele). On the other hand, where the
adjustment is instantaneous (Dire Dawa) infrastructure is more developed with grain

traders engaging in relatively larger purchases having more storage capacity, longer

103

experience in the grain trade, and better road networks connecting the markets with other
regional markets.

For markets where there is statistically signiﬁcant structural change as a result of
policy changes, individual t-tests on the structural change parameters (5’s) are evaluated
to investigate the effect of the policy changes on trade regimes probabilities. With the
policy changes, there is a large shift to regime three for Addis Ababa and Nekempte and
Addis Ababa and Dire Dawa, which suggests unexploited spatial arbitrage opportunities
have increased and spatial market efficiency has therefore declined. The probability of
spatial price differential less than transfer cost also decreased for both market pairs but
Addis Ababa and Nekempte experienced large a decrease which is statistically signiﬁcant
at the 5% level. However, the change in the probability of spatial price differential equal
to transfer cost is not statistically signiﬁcant at the 5% level for both market pairs.

For Addis Ababa and Mekele the probability of spatial price differential equal to
transfer cost increased and the change is statistically signiﬁcant at the 5% level. The
probability of spatial price differential less than transfer cost also increased slightly but is
not statistically signiﬁcant at the 10% level. The probability of spatial price differential
greater than transfer cost decreased considerably and this is statistically signiﬁcant at the
5% level. The large decrease in the probability of spatial price differential greater than
transfer cost, and corresponding large increase in the probability of spatial price

differential equal to transfer cost, suggests an increase in spatial market efﬁciency.

104

7.2.3 Conclusions for Maize

In general, prior to the policy changes all the maize market pairs considered are
spatially inefﬁcient with high probability. It is observed that the probability of spatial
price differential less than transfer cost is greater than 65% for 6 of 7 maize market pairs,
while the frequency of trade ﬂow observed for these market pairs appears to be
signiﬁcant. Together, these results indicate that grain traders were active but made loses
during this period. In other words, too much trade was taking place relative to that which
we would expect in a spatially efﬁcient market. Policy changes had statistically
signiﬁcant effect on regime probabilities at the 5% level in 3 of 7 maize market pairs.
However, as a result of the policy changes the spatial maize market efficiency has
improved only for trade between Addis Ababa and Mekele, while for the other market
pairs spatial efficiency either deteriorated (Addis Ababa and Dire Dawa) or was not

affected (the rest of market pairs).

7.3 Empirical Results for Wheat

The empirical results for selected wheat market pairs are given in Table 7.3. The
conditional maximum likelihood estimates of trade regime probabilities (A’s), the change
in trade regime probabilities (6’s) due to the policy changes, and the standard deviations
of proﬁt for different trade regimes (o’s) are shown at the top of Table 7.3. The estimated
lengths of transition period, the values of the log likelihood for restricted (no structural
change) and unrestricted estimations, the chi-square ()8) statistics for LR tests of the joint
hypothesis of no structural change in regime probabilities, and the number of

observations used are shown at the bottom of Table 7.3. The plots of the value of log

105

likelihood function for different lengths of transition period are given in Figure 7.2. The
plots of the sizes of losses or gains from inefficient trade for selected wheat market pairs

are given in Figure 7.4.

7.3.1 Spatial Market Efﬁciency Prior to the Policy Changes

For the period before the policy changes, the probability of spatial price
differential equal to transfer cost is less than 1 % and statistically signiﬁcant at the 1%
level for all wheat market pairs. Thus, the probability of the spatial price differential
being equal to transfer cost, which is consistent with spatial market efﬁciency whether or
not trade is actually occurring, is almost zero in all wheat market pairs.

The probabilities of spatial price differential less than transfer cost are also found
to be less than 1% and statistically signiﬁcant at the 1% level for 5 of 7 wheat market
pairs. The probability of spatial price differential less than transfer cost is greater than
80% and statistically signiﬁcant at the 1% level only for Addis Ababa and Dessie, and the
Dire Dawa and Nazareth market pairs. From EGTE ﬂow data (Table 7 .1), it is observed
that the frequencies of wheat ﬂow for the same market pairs are 100% which indicate
strong trade ﬂows even when the price differential does not cover transfer cost. This is
inconsistent with spatial market efficiency.

However, prior to policy changes, the probabilities of spatial price differential
greater than transfer cost are found to be very large and statistically signiﬁcant at the 5%
level in most of the cases. For example, in 5 of 7 selected wheat market pairs (Bale Robe
and Addis Ababa, Hosanna and Addis Ababa, Addis Ababa and Dire Dawa, Addis Ababa

and Mekele and Shashamane and Dire Dawa), the probability of spatial price differential

106

greater than transfer cost is found to be greater than 99% and statistically signiﬁcant at
the 1% level. For the period before the policy changes, a small probability of spatial price
differential greater than transfer cost is observed only between the Addis Ababa and
Dessie, and Nazareth and Dire Dawa wheat market pairs.

Thus, in the case of wheat, the period before the policy changes is characterized
by large and statistically signiﬁcant probability of spatial price differential greater than
transfer cost, while the probability of spatial price differential less than or equal to
transfer cost are, with few exceptions, very small and mostly not statistically signiﬁcant.
The very large probabilities of spatial price differential greater than transfer cost indicate
that the wheat markets are spatially inefficient. This could be due to the lack of
competition in wheat wholesale trade either in the production areas or consumption areas.
This could also be due to shortages of wheat supply in these markets resulting from
restrictions on grain movement such as through roadblocks at Alamata. The high
probability estimates of regime three are consistent with the observations of high
frequency of wheat ﬂow between pairs of markets considered but the quantities supplied
might not be sufﬁcient to meet the local demand.

Prior to the policy changes, the frequency of wheat trade ﬂow between Addis
Ababa and Bale Robe is 89% while it is 100% between Addis Ababa and Hosanna (Table
7.1). However, given the normal or bumper harvests for most of the time before the
policy changes, observing a high probability of spatial price differential greater than
transfer cost is more consistent with lack of competition or due to restrictions in
wholesale wheat trade than the shortages of wheat to be supplied to these markets. In this

regard, a high concentration ratio of wheat wholesale trade is also observed for some

107

markets like Shashamane and Nazareth (Dessalegn et al., 1998). A high concentration
ratio is one of the conditions for anti-competitive behavior in the market. Even though
wheat grain traders made proﬁt most of the time during the study period, there are also
periods when wheat traders made very high losses (Figure 7.4). For example, for Addis
Ababa and Mekele wheat market pairs a loss which is greater than 20% was observed.
The standard deviations of “economic” proﬁt from spatial arbitrage estimated for
different trade regimes are statistically signiﬁcant at the 5% level for 16 of 21 cases. For
each wheat market pair, the standard deviation estimated for regime three (CV) is found to
be the largest in 5 of 6 cases. As regime three is unambiguously inefﬁcient, this also
indicates that the variability in the “economic” proﬁt from spatial arbitrage is higher

when the market is spatially inefﬁcient.

7.3.2 The Effects of the Policy Changes

Likelihood ratio (LR) statistics are used to test the joint hypothesis of no
structural change in trade regime probabilities due to the policy changes for selected
wheat market pairs. The chi-square statistics for the LR tests are presented at the bottom
of Table 7.3. The results show that there is no statistically signiﬁcant joint structural
change in trade regime probabilities for 6 of 7 wheat market pairs, at the 10% level. On
the other hand, the joint structural change in trade regime probabilities is statistically
signiﬁcant at the 5% level for just 1 of 7 wheat market pairs.

Structural change due to the policy effect is signiﬁcant only for Addis Ababa and
Mekele, which also shows instantaneous adjustment to the policy changes. For this

market pair, with the policy changes there is no change in the probability of spatial price

108

differential equal to transfer cost. However, the probability of spatial price differential
less than transfer cost increased and is statistically signiﬁcant at the 5% level. The
probability of spatial price differential greater than transfer cost also decreased and this
decrease is statistically signiﬁcant at the 5% level. This result is consistent with the
decrease in trade ﬂow between Addis Ababa and Mekele which changed from 92% prior
to the policy changes to 66% after the policy changes (Table 7.1). In general, as result of
policy changes, the trade between Addis Ababa and Mekele changed from a situation of
too little trade (high probability of regime 3) to too much trade (high probability of
regime 2). Under these conditions it seems that traders made losses while the consumers
in Mekele market might have gained from the wheat price decrease. In most of the cases,
the Addis Ababa and Mekele market is observed to behave differently from other market
pairs, which might be because of the roadblock charges and control on grain going to

Tigray.

7.3.3 Conclusions for Wheat

Prior to the policy changes, all the wheat market pairs considered are spatially
inefﬁcient most of the time. In 5 of 7 market pairs, the probability of spatial price
differential greater than transfer cost is statistically signiﬁcant at the 5% level. This is
inconsistent with spatial market efficiency. On the other hand, the probability of spatial
price differential less than transfer cost is greater than 80% for 2 of 7 wheat market pairs,
where high frequency of wheat trade ﬂow was also observed for these market pairs. This
is also consistent with spatial market inefficiency, as grain traders would have lost money

if they actually traded during this period. The structural change is signiﬁcant only for

109

Addis Ababa and Mekele market pair, where the nature of spatial inefficiency changed
from high probability of making excessive proﬁt to high probability of making losses.

Thus, following the policy changes wheat markets are still spatially inefficient.

7.4 Conclusions

Prior to the policy changes, both maize and wheat markets appear to be spatially
inefficient most of the time. The likelihood ratio test shows that there is statistically
signiﬁcant joint structural change in trade regime probabilities in 3 of 7 maize market
pairs and in 1 of 7 wheat market pairs as a result of the policy changes. However, the
policy changes did not bring any signiﬁcant improvement the spatial efficiency of maize
and wheat markets except in the case of Addis Ababa and Mekele where the spatial
efﬁciency of the maize market improved after the policy changes, and in the case of
Addis Ababa and Dire Dawa where the spatial market efficiency deteriorated for the
maize market following the policy changes. Thus, maize and wheat markets are also
spatially inefficient for most of the time after the policy changes.

However, it is observed that the nature of spatial inefﬁciency is different for
maize and wheat markets. In the case of maize, spatial inefﬁciency is mostly due to the
fact that there is high frequency of grain ﬂow while there is high probability of spatial
price differential less than grain transfer cost. In this case, if the grain traders are actually
trading they are making losses. In the case of wheat the spatial market inefficiency is
mostly due to high probability of spatial price differential greater than transfer cost. This
is consistent with spatial market inefﬁciency whether or not there is trade, but indicates

too little trade is occurring rather than too much.

110

The fact that the nature of spatial market inefﬁciency observed for maize and
wheat is different implies that the two commodities probably require a different policy
response in order to improve spatial market efficiency. One of the possible reasons for
the observed differences in the nature of spatial inefficiency between maize and wheat
might be due to the difference in their market structures. The geographic locations of
surplus maize and surplus wheat producing regions are different. Maize is produced
mainly in the Western regions of Ethiopia while wheat is grown in central regions of the
country. The marketing infrastructure, particularly the road network, is relatively more
developed in the central regions. Among other things, this might have attracted
investment in storage and other marketing facilities in the wheat areas, which encouraged
the development of relatively larger wholesale grain traders which can inﬂuence wheat
prices. The analysis of the structure and conduct of wholesale grain trade in Ethiopia by
Dessalegn et al. (1998) also indicates that the wheat markets are more concentrated. On
the other hand, the marketing infrastructure in the Western region is less developed and
the grain traders are expected to be smaller sized and maybe numerous compared to the
central regions.

Finally, as with empirical studies of spatial market efficiency, it is
important to keep in mind that data and estimation methods have inherent weaknesses.
For example, the results are sensitive to the accuracy of transfer cost estimated from the
survey and the distributional assumptions made. Therefore it is always important to
interpret the empirical results with caution and think critically about the implications of

the results for the design and implementation of public policy.

111

 

 

 

 

Table 7.1 Minimum Observed Months of Trade Flows for Selected Maize and
Wheat Market Pairs
1996:08 to 1999210 to 1996:08
1999:09 2002:08 to 2002:08
Market pairs Maize Wheat Maize Wheat Maize Wheat
Addis & Bale Robe -- 23(89) -- 34(97) -- 57(93)
Addis & Dessie 22(85)’ 26(100) 35( 100) 31(89) 57(93) 57(93)
Addis & Dire Dawa 15(58) 26(100) 13(37) 35(100) 28(46) 61(100)
Addis & Hosanna -- 26(100) -- 35(100) -- 61(100)
Addis & Jimma 26(100) -- 35(100) -- 61( 100) --
Addis & Mekele 18(69) 24(92) 4(11) 23(66) 22(36) 47(77)
Addis & Wollega 25(96) -- 35(100) -- 60(98) --
Dire Dawa & Nazareth 4(15) 26(100) 0(0) 35(100) 4(7) 61(100)
Dire Dawa & Shashamane 23(89) 10(39) 35(100) 34(97) 58(95) 44(72)
Note: *The maximum possible number of monthly observations for the period before and

after the policy change is 26 and 35, respectively and ﬁgures in parenthesis are
percentages of months with trade ﬂows.

112

 

 

 

 

Table 7.2 Conditional Maximum Likelihood Estimates of EPBM Parameters for
Selected Maize Market Pairs (1996:08 to 2002:08)
Market pairs

EPBM J inuna & Nekempte Addis & Addis & Addis & Nazareth & Shashamane &
Parameters Addis & Addis Dessie Dire Dawa Mekele Dire Dawa Dire Dawa
Regime probabilities
l, 0.001a 0.236 0001‘I 0.326c 0001’ 0.201 0.339
12 0.872 ‘ 0.763 ‘ 0.889 ' 0.673 ' 0.315 ' 0.798 ' 0.660 c
i; 0.127 0001' 0.110 0.001ll 0684‘ 0001‘ 0.001
Structural changes
5, +0997a +0133 0.000 .0325 ° +0662" +0588" 0196
62 -0.871 " -0.762 ' +0110 0233 +0022 0796‘ +0009
63 -0. 126 +0629 ' -0110 +0558 ' -0.684 ‘ +0208 +0187°
Standard deviations
(3C 5.181 ' 4.854a 2.498a 4.456' 6.475 ' 10.100' 7.265II
0,, 3.159c 8.945' 5.706a 15.764' 10.658'| 8.572b 15.112‘
oV 6.512 10.540‘ 10.007b 16.961“ 18.621“ 30.651' 31.534'
Transfer costs
[30 29.774 ‘ 21.240 b 25.502 ' 64.368 ‘ 56.603 ' 58.452 ' 85.934 ‘
[31 —0.686 “ -0.042 -0.390 a -0.840 ' -0.414 b -0.970 ' -1.094 '
Transition period (t) 0 6 35 0 5 0 0
Log likelihood

Restricted -23 1 .612 -259.456 -224.630 -297.220 -293.538 -292.277 -296.697

Unrestricted -230150 -251.988 -223.589 -289.548 -285.519 -289.469 -292.484
LR Test

x2(3) Statistics 2.90 14.936 ' 2.08 15.344 ' 16.038 ' 5.618 1.74
Observations 73 73 73 73 73 73 72
Note: Trade is more than 99% uni-directional, the ﬁrst and second market in the list of market pairs

being the source and destination market, respectively. Note also that ‘, b and ° indicate statistical
signiﬁcance at 1%, 5%, 10%, respectively. The possible values of 0 range from 0 to 35.

113

Table 7 .3 Conditional Maximum Likelihood Estimates of EPBM Parameters for

Selected Wheat Market Pairs (1996:08 to 2002:08)

 

 

 

 

Market pairs

EPBM Bale & Hosanna Addis & Addis & Addis & Nazareth & Shashamane
Parameters Addis &. Addis Dessie Dire Dawa Mekele Dire Dawa & Dire Dawa
Regime probabilities
l. 0001' 0001‘ 0.001' 0001' 0.001‘ 0001' 0.001‘I
1.2 0.001a 0001' 0998' 0001' 0001' 0.811 ' 0001'
1.3 0.998 a 0.998 ' 0001' 0.998 “ 0.998 ‘ 0.188 0.998 '
Structural changes
81 0.000 0.000 0.000 0.000 0.000 +0998 ' 0.000
82 0.000 +0109 0.000 0.000 +0.711' -0.811‘I 0.000
83 0.000 -0.109 0.000 0.000 -0.711' -0.187 0.000
Standard deviations
o, 6.234‘ 2.583b 9.886' 16.465' 11.212‘ 15.017” 17.583‘
6,, 0.001a 2.275 0.452 0.001a 12.620“ 11.917b 0.001'
0,, 19.843’1 19.214' 23.491 2.231 27.393“ 23.096b 10.321
Transfer costs
[30 16.445 ' 52.707‘ 32.040 b 105.399 ' 147.938 " 123.428 ' 127.981 ‘
B, 0438’ -1.362 ‘ -0.542 ° -1 .462 " -1.672 ' -1.822 a -1.557'
Transition period (8) 0 0 3 2 0 4 0
Log likelihood

Restricted -289. 931 -279.268 -71.106 -308.244 -33 .041 -315.703 -312.793

Unrestricted -289.931 -278.385 -71.106 -308.244 -20.799 -313.480 -312.753
LR Test

18(3) Statistics 0.000 1.766 0.000 0.000 24.484 ' 4.446 7.818
Observations 73 73 73 73 73 73 72
Note: Trade is more than 99% uni-directional, the ﬁrst and second market in the list of market pairs

being the source and destination market, respectively. Note also that ', b and ° indicate statistical
signiﬁcance at 1%, 5%, 10%, respectively. The possible values of 8 range from 0 to 35.

114

Figure 7.1 Maize Log Likelihoods for Different Time Lengths of Transition Period

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

a) Addis Ababa and Jimma b) Addls Ababa and thkempte
-22900 rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr 448.00 ..................................
13 5 7 91113 15 17 19 212325 2729 313335
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1’ -229.50 1: -250.oo
° 8
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115

Figure 7.1 (Continued)

 

 

 

 

 

  

 

 

 

 

 

 

 

 

 

 

 

e) Addls Ababa and Mekele 1) cm Dawa and Nazareth
-282.00
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Figure 7 .2 (Continued)
e) Addis Ababa and Mekele f) Nazareth and are Dawa
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118

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 7.3 Magnitudes of Loses and Gains from Inefficient Trade for Maize
a) Addls Ababa and Dessle b) Addis Ababa and are mwa
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119

 

 

(Continued)

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Figure 7.4 Magnitudes of Losses and Gains from Inefficient Trade for Wheat
a)Addls Ababa and Bale lbbe b) Addls Ababa and Hosanna
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c a 40
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121

 

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122

CHAPTER 8
SUMMARY AND CONCLUSIONS

In recent years, the Ethiopian government has embarked on various market reform
measures aimed at improving grain market performance. Research is needed to improve
understanding of the operation of grain markets and the effects of policy changes on grain
market development. As discussed in Chapter 3, the various conventional methods which
have been used to study spatial market efﬁciency and/or spatial market integration
depend on an assessment of the co-movement of prices, or the long-run relationship
between prices. These methods assume stationary spatial marketing margins, stationary
transfer costs, and/or that markets are linked by a constant trade pattern (uni-directional
and continuous). However, these assumptions are often violated and so the resulting test
of market integration may be misleading and have adverse consequences on policy
decisions.

The standard parity bounds model (PBM) represents one of the recent
developments which attempt to overcome some of the weaknesses of the conventional
methods discussed in Chapter 3. The PBM allows for transfer costs and explicitly
incorporates spatial arbitrage conditions in a test for spatial market efﬁciency. However,
in the context of on-going market reform and policy changes in developing countries, the
standard PBM needs ﬁirther improvements in order to properly assess the effect of policy
changes on spatial market efficiency. This is because the standard PBM has been used
mostly to analyze spatial market efﬁciency within a given constant policy regime. In
cases where it has been used to analyze the effects of policy changes on spatial market

efﬁciency, the effect of policy changes is assumed to be instantaneous. However, the

123

PBM is mis—speciﬁed and the results and policy implications might be misleading if the
actual effects of policy changes on spatial market efﬁciency are gradual and move
through a transition period, as might be expected in many cases.

Therefore, there are two major objectives for this study: (1) to provide an
improved modeling approach for analyzing the adjustment paths and the extent of
structural change in spatial grain market efﬁciency in response to policy changes; and (2)
to provide empirical evidence on the adjustment path and extent of structural changes in
spatial market efficiency for maize and wheat in Ethiopia as a result of recent grain
marketing policy changes implemented in October 1999.

In Chapter 4, building on the standard parity bounds model, a stochastic gradual
switching model with three trade regimes was developed to analyze the effects of policy
changes on spatial market efﬁciency. The extended parity bounds model (EPBM)
improves the standard parity bounds model in two ways. First, it traces the time path of
the effects of policy changes on spatial efﬁciency regime probabilities. Hence, the model
allows the effects of policy changes to be instantaneous or gradual and, if they are
gradual, the model also allows estimation of the length of time required for the ﬁill
effects of policy changes to be realized. Thus, the EPBM allows a better understanding of
the nature of transition from old to new policy regimes. Second, it allows formal
statistical tests to be undertaken for structural change in the probabilities of different
trade regimes due to policy changes.

The EPBM is estimated using maximum likelihood and utilizes data on observed
grain transfer costs and wholesale grain prices for several regional markets in Ethiopia.

One of the problems with implementing the PBM empirically is that time series data on

124

transfer costs are rarely available, particularly in developing countries like Ethiopia. As a
result, most empirical PBM studies have assumed transfer costs are constant over time for
a given policy regime. However, this assumption is very restrictive, particularly when the
PBM is used to analyze the effects of policy changes. This is because if transfer costs are
assumed to be constant when they actually ﬂuctuate considerably over time, then the
PBM may misinterpret spatial price deviations as evidence of inefﬁciency when they are
actually just a rational response to changes in transfer costs. Thus, there is a need to go
beyond the constant transfer cost assumption and ﬁnd better ways of using data that are
available to construct more appropriate inferences about historical movements in transfer
costs. Chapter 6 discusses the data sources and the steps followed in the construction of
grain transfer costs based on two cross-sectional surveys of grain traders in Ethiopia and
time series truck shipment freight rate data. The spatial efﬁciency interpretations of trade
regime probabilities estimated by EPBM are also guided by the EGTE grain flow data.
Monte Carlo experiments were conducted to assess the performance of the
EPBM. The design and the results of Monte Carlo experiments are discussed in Chapter
5. The results show that the EPBM estimates the level and the changes in trade regime
probabilities with high accuracy, even in relatively small samples, conditional on zero
time length required for transition between policy regimes. However, the EPBM
estimation of the optimal time length is biased downward and the size of the bias
increases with the actual time length required for transition from old to new policy
regime. Thus, caution must be used in interpreting results when the transition period is

very long, for example, more than a year.

125

The EPBM is applied to examine the effects of Ethiopian grain marketing policy
changes implemented in October 1999 on spatial efﬁciency of maize and wheat markets
and the results are presented in Chapter 7. Prior to the policy changes, all the maize
market pairs considered are spatially inefﬁcient with high probability. However, the
nature of observed spatial inefﬁciencies varies among maize market pairs. On one hand,
it is observed that the probability of spatial price differential greater than transfer cost is
about 68% for Addis Ababa and Mekele. This is inconsistent with spatial market
efﬁciency whether or not there is actual trade. The result indicates that too little maize
trade was taking place relative to that which we would expect in a spatially efﬁcient
market. One of the reasons for this spatial inefﬁciency may be due to the restriction of
grain movement between Addis Ababa and Mekele which is enforced through a
roadblock at the town of Alamata. This might restrict trade ﬂow into Mekele market and
thus increase maize prices in that region.

On the other hand, it is observed that the probability of spatial price differential
less than transfer cost is greater than 65% for 6 of 7 maize market pairs (Addis Ababa
and J imma, Addis Ababa and Nekempte, Addis Ababa and Dessie, Addis Ababa and Dire
Dawa, Nazareth and Dire Dawa, and Shashamane and Dire Dawa), while at the same
time the frequency of maize trade ﬂow observed for these market pairs appears to be
signiﬁcant. Together, these results indicate that maize traders were active but made losses
during this period. In other words, there was too much maize trade relative to that which
we would expect in a spatially efﬁcient market.

There are several factors which might cause spatial inefﬁciency of maize markets

in which there is high probability of making losses by maize traders. First, the lack of

126

well-developed storage facilities in maize supply markets might force the continuous
ﬂow of grain to central or other deﬁcit markets, even if maize prices are not favorable in
these markets. The rationale for this might be to reduce ﬁirther revenue losses from
waiting for a better price, which might lead to spoilage, quality deterioration, and lower
maize prices in destination markets. Second, liquidity constraints and shortage of
working capital due to missing or imperfect credit markets for grain traders can also force
maize traders to liquidate grain, even if it means a loss. It has been observed that grain
traders in Ethiopia have poor access to formal credit and other forms of ﬁnancial
services. As a result, proceeds from current grain (e.g., maize) sales are used by grain
traders for reﬁnancing next grain purchases and settling other debts which indicate that
the opportunity costs of capital tied up in grain stock is very high when the grain traders
have limited access to credit.

Third, regional maize wholesale traders might have difﬁculty matching proﬁtable
purchase and sale decisions due to inadequacy or unavailability of market information
regarding future price movements and changes in supply and demand conditions in the
source and destinations markets. Fourth, there may be too many maize traders but these
traders might lack economies of scale in their operation contributing to higher cost of
marketing. Fifth, maize traders might also be limited by their trading skills to adjust to
the very dynamic grain marketing situations following market reform.

If inefﬁcient (unproﬁtable) maize trades are taking place a natural question to ask
is: how do maize grain traders survive in the face of high probability of making losses? It
is observed that the wholesale grain trade is not a specialized business in Ethiopia.

Regional grain traders usually keep a diversiﬁed portfolio of business activities (grain

127

and non-grain) and that might help to spread the risks. Regional grain traders also
combine interregional grain trade activities with local grain trade activities. A lot of grain
traders are also observed to operate without a license, while those with a license complain
about the unfair competition from unlicensed grain traders (Dessalegn et al., 1998).
Operating without a license might allow grain traders (experienced or new) to enter and
exit out of the grain trade sporadically, depending on market conditions, and still avoid
government tax payments and hence reduce their marketing costs.

Grain marketing policy changes had statistically signiﬁcant effects on maize trade
regime probabilities at the 5% level only in 3 of 7 market pairs (Addis Ababa and
Nekempte, Addis Ababa and Dire Dawa and Addis Ababa and Mekele). Of those 3
market pairs, 2 of them (Addis Ababa and Nekempte and Addis Ababa and Mekele)
adjusted gradually over six months or less while Addis Ababa and Dire Dawa adjusted
instantaneously. However, as a result of the policy changes spatial efﬁciency of maize
markets appears to have improved only for trade between Addis Ababa and Mekele. For
all the other market pairs, spatial efﬁciency either was not signiﬁcantly affected (Addis
Ababa and Jimma, Addis Ababa and Dessie, Addis Ababa and Nazareth and Dire Dawa
and Shashamane and Dire Dawa) or deteriorated (Addis Ababa and Dire Dawa).

In the case of wheat, prior to the policy changes, all the wheat market pairs
considered are spatially inefﬁcient most of the time. Similar to maize markets, the nature
of spatial inefﬁciency of wheat markets also varied among wheat market pairs. In 5 of 7
wheat market pairs, the probability of spatial price differential greater than transfer cost is
statistically signiﬁcant at the 5% level; this is inconsistent with spatial market efﬁciency

irrespective of actual trade ﬂows. On the other hand, the probability of spatial price

128

differential less than transfer cost is greater than 80% for 2 of 7 wheat market pairs. In
these cases, high frequency of wheat trade ﬂow was also observed. This is also
inconsistent with spatial market efﬁciency as grain traders would have lost money if they
actually traded during this period. There is statistically signiﬁcant joint structural change
in wheat trade regime probabilities only for Addis Ababa and Mekele with instantaneous
adjustment. For this market pair, the probability of spatial price differential greater than
transfer cost decreased by about 71% while the probability of spatial differential less than
transfer cost increased by the same magnitude.

There are several possible reasons for the high frequency of spatial price
differential being greater than transfer cost in the case of wheat. First, it could be an
indication of lack of competition in the wheat marketing system, lack of information on
proﬁtable spatial arbitrage opportunities, and wheat traders’ inability to take advantage of
arbitrage opportunities for several reasons (e. g. weak institutions and infrastructure
supporting spatial grain markets and barriers to interregional trade). Second, the high
frequency of spatial inefﬁciency could also be an indication of high risk inherent in the
wheat marketing system. Generally, when risks are high traders require higher margins to
compensate for the risk in the marketing system. In the Ethiopian context, some of the
risks faced by grain traders could be due to unforeseen changes in policies affecting grain
markets, food aid and commercial imports of grains and changes in supply and demand
for grain.

It is also important to note that the nature of spatial market inefﬁciency observed
for maize and wheat is different and this might require a different policy response for the

two commodities. In the case of maize, in most of the cases there is high probability of

129

spatial price differential less than transfer cost while there is also high probability of trade
ﬂow between these markets. So the maize market is more often characterized by too
much trade rather than too little. In the case of wheat, in most of the cases there is high
probability of spatial price differential greater than transfer cost. In general, the wheat
market is more often characterized by too little trade rather than too much.

One of the possible reasons for the observed differences in the nature of spatial
inefﬁciency between maize and wheat might be differences in maize and wheat market
structures. The geographic locations of surplus maize and surplus wheat producing
regions are different. Maize is produced mainly in the Western regions of Ethiopia while
wheat is grown in the central regions of the country. The level of marketing infrastructure
is relatively more developed in the central regions. Among other things, this might have
attracted investment in storage and other marketing facilities in the wheat areas which
encouraged the development of relatively larger wholesale grain traders which can
inﬂuence wheat prices. The analysis of the structure and conduct of wholesale grain trade
in Ethiopia by Dessalegn et al. (1998) indicates that the wheat markets are more
concentrated. On the other hand, the marketing infrastructure in the Western region is
less developed and grain traders are expected to be smaller sized and more numerous
compared to the central regions.

The results indicate that there are spatial inefﬁciencies in maize and wheat
markets in Ethiopia both before and after the policy changes. This shows that resources
are being misallocated in transferring maize and wheat from surplus producing regions to
grain deﬁcit regions. The implication of these results is that maize and wheat markets are

characterized by periodic gluts and shortages which can undermine the welfare of

130

producers, grain traders and consumers. In most of the cases, the effect of past policy
changes on spatial grain market efﬁciency is not signiﬁcant. However, in cases where
signiﬁcant structural change occurred the markets adjusted to the policy changes either
gradually or instantaneously. Thus, an instantaneous response to the policy changes
cannot be taken for granted but should be tested empirically.

Finally, as with all empirical studies of spatial market efﬁciency, it is important to
keep in mind that the data and estimation methods have inherent weaknesses. For
example, the results are sensitive to the accuracy of transfer costs estimated from the
cross-sectional survey of grain traders and time series truck shipment freight rate data.
Results are also sensitive to the distributional assumptions made in implementing the
EPBM. Therefore it is important to interpret the empirical results with caution and think
critically about the implications of the results for the design and implementation of public
policy. The other limitation of this study is also that it only assesses the degree of spatial
efﬁciency of grain markets and does not provide actual evidence on why the markets are

not spatially efﬁcient.

131

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