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Bell & Howell InformatiOn and Leaming300 North Z8eb Road, Ann Arbor, MI 48108-1346 USA
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Anthony Q.Q. Aboagye
. Finaneial Flows, Macroeconomie Poliey andThe Agricultural sector in Sub-Saharan Africa
Faeulty of Management
Ph.D
National Ubraryof Canada
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ACKNOWLEDGEMENTS
1am extremely grateful to all the members of my thesis committee for their willingness tobe on my committee even though 1had not worked closely with sorne of them previously.The committee comprises F. Grimard, K. Gunjal, (co-supervisor), J. Jorgensen, J-O.Simonato and G. A. Whitmore (co-supervisor). Members ofmy committee went beyondthe cali ofduty in providing helpful suggestions and advice, in being generous with theirtime and for their words of encouragement and challenging me to do even better. 1amvery thankful for that. 1am particularly grateful to Professor Whitmore for providing ashoulùer to lean on dwing the later half ofmy studies here.
[ also wish to express my appreciation to the University of Ghana, the Govenunent ofGhana, MeGill University and Prof. Whitmore for providing me financial and other forrosof support at various stages ofmy studies.
[ have as weIl henefited from my association with several people, both staffand studentshere in McGiIl and elsewhere including B. Campbell, M. Dagenais and J-C. Duan. andwish to thank themall.Ialso wish to thank my external examiner, Fidel Ezeala-Harrisonfor his useful comments.
Final1y, 1wish to dedicate this thesis to my parents, Yao and Victoria Aboagye, Foanor.Lynette, and the rest of the family for unwavering support and ail the lime we spent apart.
Somelhing has broken down in Africa - throwing billions ofdol/ars will nof changeanything. Africa must learn ta became wha/e again ... We are not just wulking digestivetracts. Africa ;s more than a client jôr international soup ki/chens. We must build ourself.l'om the ground up ... infrastructure, logistics and superstructure, including the bra;n.
Kizerbo (1989)
ii
ABSTRACT
This thesis focuses on the effects ofdevelopment assistance (ODA), private foreigncommercial capital (PFX), domestic savings (SAV), the openness of the economy andproducer priees on agrieultural output, and on export and domestic shares ofagrieulturaloutput in sub-Saharan Afiiea (SSA). This study uses panel data spanning 27 eountriesand the period 1970 ta 1993.
The production function is a Cobb-Douglas type. Statie export and domestic shareequations are derived from a specification of the agricultural gross domestie productfunclion. Transformed auto-regressive distributed-Iag versions of the slatie share modelsare used to investigate long-run dynamics, persistence and implementation lags in theshare response mode!.
Agrieultural output is affected as follows. OOA, PFX and SAV have small positive ornegative impact depending on agrieultural region or economic poliey environment. Theimpact ofopenness of the economy is negative in ail agricultural regions, however, thereis evidence of positive effect ofopenness within improved policy environment. None ofthese effects are statistically significant.
Export share is affeeted as follows. OOA, PFX and SAV have small positive impact insorne agrieultural regions and poliey environments, both in the short-run and in the longrun. PFX is not signifieant anywhere. ODA is significant only when eountries aregrouped by poliey environment in the short-run. SAV is significant in the short-run onlyin sorne regions, and significant in the long-run only in others. Openness has positiveimpact in the short-run. This is significant in many regions. hs long-run impact is mostlypositive but not significant anywhere. The impact ofprodueer price is mostly positive butnot significant.
Efforts to encowage economic activities in rural communities such as improvements indomestic terms of trade in favor of agriculture, together with the provision ofinfrastructure are likely to stimuJate output. Strategies to diversify and processagricultural exports in the face of falling agricultural commodity prices should bepursued.
iii
RÉsUMÉ
Cette thèse s'intéresse aux effets de l'aide au développement (A1D), à ceux du capitalprivé, étranger et commercial (CPE), à ceux de l'épargne des ménages (EM), à ceux del'ouverture de l'économie et à ceux des "prix de producteurs" sur le produit agricole ainsique sur les parts nationales et à l'exportation du produit agricole en Afriquesubsaharienne. Cette étude utilise des données en panel allant de 1970 à 1993 et portantsur vingt sept pays.
Une fonction de production du type Cobb..Douglas est utilisée. Les équations statiquesd~s parts nationales et à l'exportation sont dérivées à panir d'une spécification de tafonction du produit agricole national brut. Des versions transformées autorégressives àretards distribués des "modèles statistiques pour les parts" sont utilisées. Cela permetd'étudier les dynamiques à long terme, les effets de persistance ainsi que l'implementationdes retards dans les réponses des "modèles pour les parts".
Le produit agricole est affectée comme suit. L'(A1D), le (CPE) et 1'(EM) ont un faibleeffet, positif ou négatif. Cela dépend de la région agricole ou de l'économiqueenvironnementale. L'impact de l'ouverture de l'économie est negatif dans les régionsagricoles. Pourtant, un effet positif de l'ouverture de l'économie en moins del'amélioration de l'économique environnementale a identifié. Aucun de ces effets n'eststatistiquement significatif.
La part à l'exportation est affectée comme suit. L'(AID), le (CPE) et 1'(EM) ont un faibleeffet positif dans quelques-uns régions agricoles et dans quelques-uns économiquesenvironnementales, à la fois à court et à long terme. Le (CPE) n'est pas significatif.L'(A1D) est significatifquand les pays sont régroupés par l'économiqueenvironnementale. L'effet de l'épargne des ménages est significatifde court terme danscertaines regions et de long tenne dans d'autres. L'ouverture de l'économie a un effetpositif à court terme. Cet effet est significatif dans plusieurs régions. Son effet à longterme est surtout positif sans être partout significatif. L'effet des "prix de producteurs" estpresque toujours positif mais non significatif.
Les efforts à encourager les activités économiques dans les communautés rural sont denature à favoriser la croissance du produit agricole. Il s'agirait notamment d'améliorer lesechanges commerciaux intérieurs en faveur de l'agriculture ainsi qu'un meilleurapprovisionnement en infrastructure. Par ailleurs et face à la baisse des cours mondiauxdes produits agricoles, des stratégies visaut à diversifier et à transformer les exportationde produits agricoles doiveut être poursuivies.
TABLE OF CONTENTS
Chapter 1: INTRODUCTION
Chapter 2: LITERATURE REVIEW 12Production Function Studies 12Short..Run supply Response Studies 13Long-Run Supply Response Studies 15Other Related Studies 15
Chapter 3: ECONOMIC THEORY AND METHODOLOGY 17Panel Data 17Economie Growth Theory 18Rational for Decomposing Capital 23Econometrie Specification .. Production function 25Econometrie Specification .. Analysis ofagricultural shareresponse in the short..run 28Econometrie Specification .. Dynamics ofagricultural shareresponse 34Econometrie and Other Related Issues 44
CHAPTER 4: DATA 48Data Description and Sources 48
CHAPTER 5: EMPIRICAL RESULTS .. PRODUCTION FUNCnON 54Regression Results 55Human Capital Variable 56Diagnostic Tests of Regression Adequacy 58Agro-Climatic Regions 59Policy Groups 72Swrumary 82
CHAPTER 6: EMPIRICAL RESULTS - ANALYSIS OF AGRICULTURALSHARE RESPONSE IN THE SHORT-RUN 85Regression Results 86Diagnostic Tests of Regression Adequacy 86Agro-Climatic Regions 87Policy Groups 98Summary 106
iv
CHAPTER 7: EMPIRICAL RESULTS - DYNAMICS OF AGRICULTURALSHARE RESPONSE 110Estimation 110Results and Discussion 112Diagnostic Tests of Regression Adequacy 112Parameter Estimates 116Summary ofDynamic Share Response 130Comparison of Long.Run and Short·Run Estimates 133
CHAPTER 8: CONCLUSIONS AND POLICY IMPLICATIONS 137Policy Implications 137Increasing Agricultural Output 138Increasing Export Earnings 142Limitations of this Study 145
REFERENCES 147
APPENDIX 155
v
Chapter 1
Introduction
This thesis examines the impact of foreign financial fiows, domestic savings, labor
as well as the role of the macroeconomic environment, external terms of trade and
variability of the weather on the agricultural sector of sub-Saharan Africa (SSA).
Importance of Agriculture in SSA. In SSA, agriculture is practised mostly in
the nrraI areas, where it is the mainstay of most of the rllral people. Agriculture has
been described as the "lifebIood" and the "engine of growth" of sub-Saharan Africa.
In a nutshell, this refers to its importance as a source food, income, foreign exchange
earnings, employment, etc. for many Africans. The importance of agriclùture in SSA
can be seen from the fact that agriclÙtural share of overall gross domestic product
(GDP) in 1993 was about one-third (World Bank, 1995). Further, The Food and
Agricultl1ral Organization of the United Nations (1996) estimates that about two
thircls of the labor force in SSA was engaged in agric\.Ùture in 1995. Foreign exchange
earnings froID 8gricultural exports account for very high proportions of total foreign
1
2
exchange earnings of many COlmtrïes. The following table illustrates the proportions
for four countries.
Table 1.1: Agriclùtural exports as a percentage of total merehandise exports
Year 1980 1985 1990 1993
Burkina Faso 50 37 -13 32
Cote d'Ivoire 67 78 53 50
Ghana 68 64 46 32
Central Mrican Republic 42 50 30 24
l\Iali 94 90 75 75
Kenya 55 73 68 51
l\Ialawi 89 93 94 89
Rwanda 49 61 92 88
Tanzania 70 69 59 89
Source: Computed from World Bank (1996a).
Unfortunately, this sector has not received sufficient attention. Helleiner (1992),
among others, has argued that ''probably the single most important poliey mistake
in the 1960s and 1970s was the neglect of agriclÙture," in SSA. Furthermore, the
World Bank (1989) reports that in spite of the contribution of agriculture to these
economies, the proportion of government expenditure devoted to agriclùture during
the early 1980s was less than 10 percent on average. By and large, investment in
research and development, extension services, or infrastructure has been inadequate.
3
Indeed, agriclùture in SSA is characterized by the following.
Agricultural Systems and Rural Communities: Agriculture in SSA is dom
inated hy peasant farmers using ooly basic fanning implements. There is heavy
relianee on nature (for rain and sail fertility). Further, only minimal use is made of
inputs snch as high yiclding sccds and fcrtilizcr. Storage facilities are poor, leaJiug
ta waste during harvest seasons and shortages during leau seasons. AIso, the terms
of trade between agriclùtllral output and the rest of the economy are largely biased
against agriclùture due mostly to lùgh direct and indirect taxes on agrÏclùtnral out
put. In addition, agriclùtural output faces clifficlÙt access ta markets due ta poor
infrastructure snch as roOOs. Institutional support in the form of access to extension
services or financiaI credit are aIso very weak. 1
As a consequence, agriclùtural output has suffered. and with it the welfare of t.he
people. most of whom live in the nlfaI areas where agriclùture has been and still is the
mainstay. AEriea Recovery (1997) reported that up to 40 percent of the people of SSA
are technically lmder-nourished. This is due in part to the fact that food production
has Dot kept up with poptùation growth. It adds that over the period 1965 to 1993
agricultural output grew at an average rate of 1.8 percent while population grew at
about 2.9 percent. Rodrik (1997) adds that, per capita real gross domestic prodnct
(GDP) in most of SSA is currently below the 1970 levelon average. It is people who
live in nlral areas who hear the brunt of this reduetion.
Rural non-agrictùtural activities in SSA are aIso at a very low level due to the
1 Base<! on authorls own knowledge, but see aIso UNCTAD (1997).
4
direct link between them and agriclÙture. Haggblade, Hazell and Brown (1987) have
documented that the link between agricultural and non-agricultural activities in SSA
is lower than it is in Asia or Latin America. This weak linkage is attributed to low nse
of inputs, low output and low growth in agriculture. Typically, the linkage between
agriclùture and nrraI non-agricultlrral activities takes three forros. Increased use of
inputs in agrictùturc crcatcs a buch."\vard linkage bctween agrictùture and the suppliers
of these inputs, Second, increased output (due at least in part to more inputs) trans
lates into higher spending on agriclÙtural and nOll-agriclùtural activities by farmers
(constunption linkages). Consumption linkages are closely tied to the third fonn of
linkages, the forward linkages. These are the links between agrictùtural output and
non-agriclùtural activities that process agrictùtnral ontput (for local consumption),
Consequences of strong linkages will include a halt to rural-tO-urban migration, as
non-land owners and others find non-agrictùtlrral employment. Also, most of these
rural non-agricultural activities are labor intensive, they thus have the potential to
rednce twemployment. Being labor intensive, nrral industries generally require little
investrnent in capital or foreign exchange, thus, being potentially easier and cheaper
to establish. Processing of agricultural output also help improve the terms of trade
between the nrral areas and the urban centers. The importance of the potentiallink
ages that agriclùture can provide may he obtained frOID the following statement in
Africa Recovery (1998) in reference to SSA economies.
"One stnlctural concem is that higher growth achieved in sorne cOlwtries
in recent years is to sorne extent driven byenergy and mining sectors.
•5
These are relatively isolated from the rest of the economy and thus have
little direct impact on living standards of the majority of the poptùation."
Indeed, the World Bank has recognized the importance of rural economies in de-
veloping cOlmtries and has developed a nrraI sector strategy.2 It is based on the
realization that L'Rlrral welfare cannot improve without a thriving nrraI economy of
which a prosperons agriCtÙtlrre is a necessary condition." Indeed, Haggblade et al.
(1987) present sorne evidence showing that expenditure elasticity for locally produeed
non-farm goods is high. The World Bank's strategy calls for L'public investment to
complement the private sector." This author agrees with the Bank's analysis regard-
ing the importance of agriculture in nrraI economies. Further, this thesis takes the
view that the weak linkages that are cllrrently suggested to exist in SSA provide the
potential for grawth. The extent of these linkages shall be econametrically established
in this study by focusing on the role of capital in agriclùture in SSA.
l'vIany govermnents that did not pay much attention ta agricultlue in the past
reasoned that industrializatian was the way forward. They went about their indus-
trialization efforts by transferring resoluces away from agriclùture. In the process.
they practically killed the LLgeese that laid the golden eggs". The faet of the matter is
that, in addition to its raIe in providing employment, incarne and foreign exchange,
attention ta agriclùture will advance industrialization efforts by providing the raw
materials for industry. Farmers and their dependents will be ready markets for in-
dustrial output, while savings made out of agriclùtural incorne will he available for
2 14Revitalizing the World Bank's Approach to Rural Development." Talle presented at the McGillEconomie Policy ~Ianagement Seminar Series by A. l'tIcCalla, (1998).
6
investment in industry. (See for example, Ezeala-Harrison, 1996).
Financial Flows and Macroeconomie Policy. A major objective of this study
is to investigate the impact of the factors of production on agricultural output to
understand how agricultural output may he increased. In particular, tms study fo
cuses on the role of capital and macroeconomic policy. For SSA: inv~tmflnt. rapit.al
may come from domestic savings, or foreign sources. Foreign finandal flows to SSA
take two forros, official development assistance (ODA) and private foreign commercial
flows (PFX).
The term official development assistance generally refers to resources that are
provided on concessional terms. That is, terms less stringent than will be obtained
in capital markets. The Organization for Economk Co-operatioll and Development
(OECD, 1994) prescrihes three criteria (at least one of which mnst be satisfied) for
a flow of resonrces to qualify as ünA. These are, (i) resollrces are provided by offi
cial agencies, induding state and local governments or their execlltive agencies. (ii)
resources are provided with the promotion of economic development and welfare as
the main objective, or, (iii) resources are provided on concessional terms, and as well
convey a grant element of at least twenty-five per cent.
In recent years, the importance of development assistance in providing funds to
SSA has increased, as domestic savings and private foreign flows have fallen. OECn
(1994) reports that in 1992, 31.1%, of all ODA funds went to SSA, a slight decline from
the 1990 level. Unfortunately, while the proportion of total ODA to SSA has remained
high, total ODA to developing countries has been declining in real terms since 1990,
7
(World Bank, 1997). For example, total aDA in 1995 declined by 10 percent in real
terms over the level in 1994 (World Bank, 1997). Another important aspect of aDA
is described by the same source as "... the composition of ODA has shifted, with a
significant portion being used to fund emergency relief and peacekeeping activities
and less going toward long-term development needs."
PFX arc dcfincd to include foreign direct investnlellt, ~Onllllel'dalloalls fl'uru for
eign banks and other sources, as weIl as portfolio eqlùty investments in financial and
eqlùty nlarkets. The amount of PFX going to the region has fallen sllbstantially
from the peak of an annnai average of 8.9% of all total private flows to developing
c01mtries dllring 1977 ta 1982, to an annual average of 1.6% between 1990 and 1995,
(Bhattacharya, ~Iontiel and Sharma, 1997).
Over the period 1970 to 1993, domestic savings for a sampIe of 27 countries (llsed in
this stndy) averaged jllSt Imder Il % of GDP. Wide variations exist among the cOlln
tries of SSA. Table 1.2 presents a SllInmary of actual üDA, PFX and SAY amolmts
in millions of constant 1987 United States dollars (US$).
Table 1.2. Average amotmts of ODA, PFX and SAY in 1987 US$ in SSA.
Period aDA PFX SAY
1975-79 5,616 1,860 38,283
1980-84 6,801 1,734 20,032
1985-89 8,184 1,048 17,783
1990-93 10,147 1,520 14,208
Source: Computed from data on hand. See chapter 4.
8
As can be seen from the table, domestic savings are indeed the largest source of
investment nmds, but these amounts have been falling steadily. aDA funds have in
creased substantial1y to a sizeable proportion. But as noted, sinee 1990 these amounts
have been falling tao. PFX amolmts are small eomparatively.
[t is argued in this thesis that the three eomponents of capital will have different
cffccts on agrictùturc. A dollar of ODA flaw is providcd on humanitarian and ethical
grollnds and is development oriented. On the other hand, the decision for a dollar
of PFX flow is made on a risk and retum hasis, and is clearly trade oriented (World
Bank, 1997). Thus, ODA flows are unlikely to flow into the same projects as PFX.
Domestic savings are likely to he invested in areas of the eeonomy that are different
from those into whieh foreign flows are invested. ~Iore about this in chapter three.
Another issue investigated in this thesis is the relationship between the agrkultural
seetor and macroeconomie poliey. ~Iacroeconomie poliey issues include, tight control
of fiscal budgets, tight monetary poliey and liberalization of international trade and
foreign exchange markets. These measures are snpposed to lead ta more efficient nse
of resourees, henee inereased economic growth. This thesis investigates the impact
of poliey at two levels. First, by including a poliey variable that changes with time.
Second, by considering groups of cOlmtries classified by changes in macroeconomic
policy.
A further objective of this thesis is to investigate the response of the domestic and
export shares of agriclùture to producer priee changes, factor inputs, economic poliey
and external shocks. This is done for severa! reasoDS. For one thing, the value of
agricwtural exports as a share of total agricwtural output has been falling. In 1993,
9
the average export share was 25 percent, having been as high as 50 per cent in the
late 1970s.
Falling export share of agrictùturaI output has been happening in the face of
anecdotal evidence that foreign finandaI flows, recent economic recovery programs
and government polides favor export agriclùture relatively more than agriclùture for
domcstic constunption. The apparent bias in favor of exports takes the form of higher
producer priees for exports, availability and distribution of inputs and infrastructure
in areas that grow export crops (or rear animaIs for export). As well, research and
development efforts favor agriclùturaI exports more. Indeed, falling export share of
agriclùture is happening in the face of indications of food security problems in sorne
countries.
Generally, crops grown for export are called "cash crops", enlphasizing the fact
that these are the crops that have been grown traditionally for incorne. Support
services sucb as marketing boards that existed in the past catered principal1y to export
produce. By providing ready markets for these produce, the boards created huge
incentives to farmers of snch prodnce who no longer had to contend with marketing
their produce in the face of poor raad netwarks, etc.
Objectives of This Study. In sum, the objectives of this thesis are the following.
In the first place, to investigate the impact of domestic savings, development assis
tance, private foreign commercial capital, macroeconomic policy and extemal shocks
on aggregate agricultural output using panel data for SSA countries spanning the pe
riad 1970 to 1993. Then the response of the export and domestic shares of aggregate
10
agricultural output to these factors as weil as producer priees are investigated, first in
a static model, and then in a dynamic model for short-nm and long-nm implications.
The analysis is performed when data from (i) countries from aIl parts of the continent
are pooled together, (ii) COtmtries are grouped together according ta agra-climatic re
gions, and (Hi) in the case of agrictùtural output and static share investigations, when
~olmtries are grouped by improvements in economic policy.
Contribution of This Study. This study makes contribution to the literatllre
in severa! respects. By disaggregating investments into three eomponents: official
development assistance, private foreign cornmerdal flows and domestic savings, the
impact of each fonn of capital on total agriclùtllral output., and on export a.nd da
mestic shares of total agriclùtural output in the short-run and in the long-nm can be
investigated.
This study also contributes to lmderstanding the impact of macroecononlÎc policy
and extemal shocks on agriclùtural output as weIl as on export and domestk shares
of agriclùtural output in SSA.
Another contribution of tms study is adoption of an approach that allows equaI
study of bath agriclùture for exports and agriclùture for domestic cOIlSlunption.
Studying agriculture for domestic consumption has posed sorne challenge in the past,
(see Jaeger (1992) for example). In studying farmers' response ta agriclùtural ex
ports and agriculture for domestic consumption, focus is not on prices only, but also
on factors of production and economic policy.
Yet a further contribution of this study is to go beyond individual cotmtry case
Il
studies (sucb as Bautista and Valdes (1993) and Krueger, Schifr and Valdes, 1991)
to provide econometric evidence that holds across regions and cOlmtries over time by
pooling cross-colmtry and time series data. More on this in chapter three.
Organization of the Study. This thesis is organized as follows. Chapter two
reviews pertinent literature on agrictùtural production nUlctions, short-run and long
nm agrictùtural supply response, and other related studies. Chapter three develops
the econornic and econometric basis for the study, while chapter four discusses the
data used in the study and their sources. In chapters five, six and seven, empirical
estimates of the parameters of the meta-production flUlction, the static and dynamic
share respanse models respectively, are presented and discussed. Chapter eight con
clncles by painting out policy implications of this stlldy as weB as its liuùtations.
Chapter 2
Literature Review
The introduction to this thesis has discussed the importance of agriculture as well
as agriclùtural systems in SSA. This chapter reviews selected previons studies that
have pertinent bearing on the CUITent one. It starts with past studies on production
flIDctions, and then discusses studies on agriclùtural supply responses.
2.1 Production Functions Studies
Lan and Yotopowos (1989) formalized the concept of a common agricll1tural produc
tion flIDction for severa! COlIDtries. This is called a meta-production nmction. They
explained that the lmderlying assumption is that these countries have access to the
same production techn01ogy. Different cOlIDtries may however operate on clifferent
parts of this production flIDction dne to the nature of each C01IDtry'S endowment of
natura! resources, relative prices of inputs, macroeconomic environment, etc. They
used panel data for 43 cOlIDtries ta estimate Cobb-Douglas and transcendentalloga
rithmic production functions. Their sample included both developed and developing
cOlmtries.
Norton, Ortz and Pardey (1992) investigated the impact of aDA on agricultural
12
13
output in 98 developing countries of the world. They postulated. and estimated a
polynomiallag structure for annual ODA flows. They fOlmd the coefficient of ODA
to he positive in sorne specifications of their production flUlctions, and negative in
others (depending on the variables in the model).
Kherallah, Begrun, Peterson and Ruppel (1994) also investigated the impact of
ODA on agricultural growth in a simlùtancous cquations model for 56 developing
COIUltries. They also considered the impact of PFX and SAY. However, while t.hey
specified a polynomial lag structure for GDA, they only considered cnrrent annnal
flows of PFX and SAY. They fOlUld the impact of ODA and SAVon growth of
agriclùtural output to he positive, but that of PFX to be negative.
Gllnjal and Gichenje (1997) investigated the short-nUl and long-niD impacts of
ODA on agrictùtural out.put in SSA. They controlled for the effect. of economic policy
environment by llsing a dummy variable to identify those eOllntries that were lUlder
going economic reforms and those that were not. They fOlmd the impact of OOA
in the aggregate SSA sample ta be significant and positive. Depending on t.he Sllb
sample of cotmtries used, they found the impact of ODA to be positive or negative.
They did Dot, however, recognize other forms of capital snch as PFX or SAY in their
models.
2.2 Short-Run Supply Response Studies
Binswanger, Yang, Bowers and l\'Iundlak (1987) investigated aggregate short-run sup
ply response of agriculture to priee and various public inputs using panel data for 58
countries. Their study eovered the period 1969 to 1978. They found that the main
14
determinants of output are variables representing infrastructure and that the efIect
of priee was relatively weak.
Jaeger (1992) studied the short-run response of total agrieultural exports and
individual export crops to real producer priees, real effective exchange rates, weather
and disaster variables. He used panel data for 21 cOtmtries of SSA covering the period
1970 to 1987. He aIso examined the possibility that export agrieulture may crowd
out food production. He fOlmd that in the short-nm, elasticities of tree crop exports
are only moderately responsive ta price incentives. Annnal crops exports are more
elastic. He also fOl.md that "growth in export agrietùture does Ilot appear to come
at the expense of food production". He added that there was evidence to suggest
that poor policies have had a major role in the decline of African agriculture, but
cOlnmented that lack of price data for food crops prevented him from studying the
impact of poliey on food production the way he did for agricultural exports.
Elmi (1994) estimated short-nm and long-run agrictùtural export and food crop
output to real producer priees, weather, fertilizer and a lagged dependent variable.
His data consisted of 20 SSA cOlmtries, and covered the period 1974 ta 1989. He round
that "aggregate agrictùtural export and food sllpply responses ta priees in tropical
Africa are both positive and significant but inelastic".
SchifI and ~Iontenegro (1997) surveyed the literature on sllpply response and
provide sorne evidenee in support of the complementarity between producer priees and
public goods (infrastructure, supporting services, legal and institutional framework)
using data for 18 countries of the world of whieh three are in sub-Saharan Africa.
15
2.3 Long-Run Supply Response Studies
The literatllre on long-run response is limited. Bond (1983) estimated aggregate and
individual crop long-nm and short-run supply response to producer prices and lagged
dependent variables for individual SSA cOlmtries. The average value of her long-nID
estimates is sometimes quoted as the long-run value for SSA.
As noted above, Elmi (1994) too has estimated long-nm supply response for SSA.
He found that long-nm response to producer priee increases exceeds short-nm re-
sponse.
2.4 Other Related Studies
Other relate<! studies include Leie (1990). who undertook a \Vorld Bank sponsored
individual country case studies on the impact of aOA on agriclùtl1ral development
in 6 sl1b-Saharan African cOlmtries. She fOlmd the impact to he "surprisingly sInall" .
Knleger, Schiff and Valdes (1991) edited a World Bank sponsored comparative
study (individl1al cOlmtry case studies) that assesses the impact of direct and indirect
intervention in agriclùtural priees in 18 cOlmtries. Titree of these are SSA countries.
They found the effect of direct intervention (direct taxation) on all selected products
(inc1uding 'exportables') to he negative. This effect was however dominated by the
effect of indirect intervention (industrial protection poliey and over-valuation of real
exchange rate).
Similarly, Bautista and Valdes (1993) also edited a volume that investigated the
effect of trade and macroeconomic policies on agriclùture in 18 eountries (not identical
16
to the Knleger et al. sample}. Two ofthese cOlUltries are in sub-Saharan Africa. They
also conclude that restrictions on trade, foreign exchange rates, direct and indirect
taxes constitute bias against agric\ùture.
One realizes from this review that, none of these studies has explicitly investigated
the impacts of the factors of production. This study attempts to fill this gap in the
literaturc. In particular, physical capitul is disaggrcgatcd iuto aOA, PFX and SAV.
In addition. tlùs study investigates the effect of the macroeconomic environment at
two levels, (i) by using a variable that uses the openness of the economy as a ffieasllfe
of the macroeconoic environment, a variable that has received empirical support in
recent years (Sachs and \Varner, 1995 and Edwards~ 1998), as weil as pooling cOlmtry
data using on classifications based on improvements in economie policy. Further, the
methodology adopted here enables equal investigation ofhoth agriclùtllrc for domestic
consumption, as weIl as agrÎClùture for export.
Chapter 3
Economie Theory andMethodology
This chapter discusses the methodology and economic theory underlying this stlldy.
It discusses panel data, economic growth theory. disaggregation of capital. and the
three models that are investigated in this study.
3.1 Panel Data
This study pools crosS-ColUltry and time series data for severa! SSA COlmtrïes. AlI
cOlmtries with total poplùation of at least one million and for which pertinent data
are available are included in the study. The study covers the period 1970 to 1993.
The meta-production nmction is estimated llsing data for 27 countries. The studies
on export and domestic shares of agriculture eonsist of data for 23 eOlmtries. The
eonstraint was produeer priee data.
The advantages of using panel data are many and are discussed in Baltagi (1995),
Hsiao (1986) and Lau and Yotopoulos (1989) among others. They include increased
number of observations, inereased ranges of variation of the variables in the model,
thereby allowing for more precise estimates, and reduced multicollinearity among
17
18
explanatory variables. In addition, use of panel data makes it possible to differentiate
between economies of scale and technical change, as weIl as the ability to study
dynamic effects (which cannot he done using cross-sectional data ooly). Thus, use
of panel data in this thesis will enable one to make valid inferences about the state
of affairs in the region beyond what can be done using only individual cOlmtry case
study dat.a.
However, when one pools data across many cOlmtries, one must recognize t.he
potential of differences in definition, measurements and even qualities of input.s across
COIUltries. There is also the question of differences in economic environment across
COlUltries. In a.ddition, one should be careflù not to carry the assumption of a commOll
production technology too far. The latter issue may be addressed by statistical tests
however. This is discussed later in sub-section 3.7.3.
3.2 Economie Growth Theory
The models employed in this study use ideas from both neoclassical and endogenous
growth theories.
3.2.1 Neoclassical and Endogenous Growth theories
Starting from the theoretical production function stated in terms of stocks of physical
capital and labor, the neoclassical growth theory of Solow (1956), for example, is
derived by considering steady state values of capital per effective unit of labor. Neo
classical growth theory emphasizes the role of factor accUIDlùation in the production
process. However, with land fixed for example, increases in variable factors such as
19
Labor eventually Lead to decline in marginal productivity of Labor. In Solow's version of
the neo-classical growth model, per capita incorne increases with the rate of saving in
the economy, but decreases with the rate of poplùation growth. This modeL envisages
a steady state Level of incorne per capita growth which depends on an exogenousLy
given rate of technical progress.
Endogenous growth modeLs build upon the ideas of neoclassical growth theory by
allowing intangible inputs such as knowledge acquisition, human capital (e.g. skills
acquired by Labor) as weIl as factors that enhance the efficiency of inputs ta affect
the production process. These models are thus able to explain non-decreasing retarns
to reprodllcible factor inputs (Romer. 1986, 1990). Within this context, there is no
steady state LeveL of incorne. Incomes can continue growing withollt bOlmds. Poor
economic growth in SSA has been blamed especially on inadeqllate investment and
more recently on bad macroeconomic policy environment which has hampered the
efficiency with which inputs are nsed.
There exists another strand of the literature (Boserup, 1981, and Kelly and
Schmidt, 1994, for example) which argues that poplùation increase may induce changes
or innovdtion in production technology, in social organization, in economies of scale or
agglomeration of economies (density of economic activity) , leading ta greater prodac
tivity. This view is certainly not universally held. rvIany authors (e.g., Binswanger and
Deininger, 1997) have argued that not all societies experiencing poplÙation growth
have shown increased productivity.
20
3.2.2 More on Endogenous Growth Theory
This sub-section highlights selected issues from endogenous growth theory.
Ruman Capital
Endogenous growth theorists have argued strongly for inclusion of human capital in
production functions cithcr bccause it is a direct input into research (RoIller, 198G,
1990), or because of its positive externalities (Lucas, 1988). While the intuition for
this inclusion is easy to fol1ow, appropriate measures of human capital are not easy
to find. School em'ollment rates, literacy rates, years of schooling! even wage rates of
the labor force have been used as proxies in many studies. On the whole! estimat.es
of the coefficient of these variables have yielded mixed results in empirical stndies.
In agricultural production functions, use of these proxies has sometimes yielded
Imacceptable restùts. Lan and YotOPOlÙOS (1989) report l' ... the estinlated coeffi
cients for general education still appear unreasonably large" and subseqnently drop
this variable from their mode!. These authors also qllote similar finding by Kawagoe!
Hayami and Ruttan (1985). Binswanger et al. (1987) aIso obtained similar (lmac
ceptable) filldings. In a widely cited study, Levine and Renelt (1992) fOlmd that
common measures of hllman capital do not have robust influence on GDP growth.
Rather, the statistical relationship is sllbject to the COtUltries included in the sample,
the time period tmder consideration, as well as other variables included in the mode!.
~Iore recently, Levin and Raut (1998) have provided evidence that suggests that it is
the manufactllrîng export sector, not primary commodity exports, that benefits froID
education. Indeed, sorne formulations (such as Loayza, 1994) of endogenolls growth
21
models treat human capital as an efficiency factor (see the following section) rather
than as a factor input.
This study is formtùated in terms of stocks of factor inputs and uses the average
years of schooling of the labor force as a proxy for the stock of human capital.
EfBciency Factors and Policy Issues
As mentioned above, endogenous growth models posit a linkage between public poli
cies and long-nID growth. In partictùar, outward oriented trade policies are said to
promote competition and thereby raise the efficiency with which factor inputs are
used. l'rade ë:ÙSO enhances externalities resulting from access to improved technology
(Grossman and Helpman, 1991), KhéUl (1987), Lucas (1988) and Ramer (1986, 1990).
Enlpirical findings in favor of the impact of trade have been provided by Sachs and
Warner (1995) and Edwards (1998), among others. Other efficiency factors that have
been fOlmd ta affect growth worldwide (and aIso within SSA) indude social capital
(institutions. associations, etc.) and public services (infrastrncture, etc.). See for
example Collier and Glmrung (1997) and Haque and Aziz (1997). In particlùar, the
raIe of infrastructure in promoting agriewtural output has been emphasized by Antle
(1983) and Binswanger et al. (1987).
Bad poliey environment in SSA has been blamed partly for SSA's poor economie
performance, (e.g., World Bank, 1989). Prodded by the Intemationall\tlonetary Fl.lnd
(I~[F) and the World Bank, most sub-Saharan African eountries have embarked on
sorne fonn of poliey reforme These are generally referred to as Structural Adjustment
Programs (SAP). The object of these reforms is threefold: (i) to eliminate or reduce
22
fiscal deficits, (ü) to rein in monetary poliey 50 as to reduee inflation, and (ili) to
remove domestic cnrreney over-valnation by domestic currency liberalizing foreign
exchange markets. The idea is that these policies will make SSA economies more
efficient and competitive with the rest of world. The region will then benefit from the
advantages associated with international trade. These policies are having a profound
impact on SSA economies. Given the extensive theol'etit:al al.lù eillpirical support,
this study uses a trade variable as a proxy for the macroeconomic environment.
External Shocks
For a typical SSA co\mtry, changes in its external terms of trade affect her foreign
currency receipts from agric\ùtural €",,<ports. This in turn affects the resources (priee
and non-priee) that these governments devote to agriculture (e.g., proportion of in
ternational prices paid to producers, provision of infrastnlctnre, etc.). For most of
SSA, agric\ùtural exports are a major sonrce of their foreign exchange earnings (over
which they have direct control), yet these cOlmtries have virtually no control over
international commodity priees. From their empirical study of growth rates over
time, Easterly, Kremer, Pritchett and Summers (1993) conclude that shocks, espe
cially terms of trade, are important in determining long-nm growth. Naturally for
agric\ùture, weather is also an important exogenous shock.
This study uses these ideas from neo-classical and endogenous growth models to
investigate the impact that the stocks of the variables (whose growth rates enter
growth theory models) have on the level of aggregate agricultural incarne as weIl as
on the export and domestic shares of agrictùtural output in SSA.
23
3.3 Rationale for Decomposing Capital
It is the position of this thesis that the three eomponents of capital have different
impact on agriclùture. A dollar of aOA flow is provided on humanitarian and ethical
grounds and is development oriented. On the other hand, the decision far P FX flow
is made on a risk and return basis, and is clearly trade oriented (World Bank, 1997).
In faet, a major rationale for OOA is imperfections in international capital markets
(Le., faihITe of foreign private capital to flaw to certain regions). It wO\ùd appear that
countries that carmot attract PFX are the ones most in need of OOA. Further. while
sections of the anA literature argue that such flmds are fungible and may actuéùly
help dissuade cornlpt and rent-seeking gavernments from pursuing rational economic
policies, PFX, especially foreign direct investments, are Imlikely to be diverted or
miSllSed.
The rationale for disaggregating domestic savings (SAV) from foreign financial
flows is essentially ta emphasize the ideas of the "two-gap planning mode!" (Chenery
and Strout, 1966), and the Harrod-Oomar model, (Domar. 1946, and Harrad, 1948).
These suggest that, foreign financial flows to a developing COlmtry play two roles,
(i) providing investment flmds, and (ii) making possible investments in areas t.hat
require foreign capital, that would otherwise not have been possible with domestic
savings only.
In addition, in the case of private domestic savings (by individuals or corpora
tians), who are assumed ta he individual utility maximizers, these savings are in rel
atively small amounts, and decision making regarding where to invest snch amounts
24
is potentially different than decisions involving much larger aOA and PFX amalmts.
Government investments using domestic savings (taxes) may he influenced. by political
and other considerations that may Dot influence aOA or PFX.
The essence of the argument is that, different sources of capital target different
types of investments. In the empiricalliterature, Papanek (1973) first disaggregated
forcign financiai fio\vs iuto forcign aid, forcign privatc invcstment and other flows in
a study of the impact of investment ou economic growth.
3.3.1 Stocks of Capital Versus Polynomial Lags
Since investments take time to come ta fnlition, analysis of the effects of these flows
must recognize this time lag. A time lag of 6 years has been used in a nnrnber of
analyses that have investigated the effects of GOA. These stlldies indllde, Ivlosley,
Hudson, and Horrel (1987), Norton, Ortiz and Pardey (1992), Kherallah. Beghin.
Peterson and Rllppel (1994), Gunjal and Gichenje (1997). The empirieal basis for
this is the World Bank (1984) report which sllggests that. on average, "Vorld Bank
flmded projects require a total of 7 years for cumulative cash flows to equal the original
investments. Assllming that the six-year lag is appropriate, one wOlùd then have ta
estimate the parameters of the polynomial as well as the degree of the polynoDÙal.
The appropriate equivalent stnlctllres for PFX and SAV will then have ta he estimated
as well.
What is more, this lag structure approach is somewhat ad hoc. It amounts ta
"payback period" appraach to capital budgeting, where only the length of time re
quired for the sum of dîSColmted cash flows frOID a project to equal the original amount
25
invested is being counted. One would prefer an approach that is equivalent to the
"net present value method" , which considers all cash flow accrtùng to an investment
irrespective of when they occur. To this end, flow of investments, and of poplùation
and human capital used in growth models are accuInlùated ioto stocks. F\rrther, the
use of stocks rather than current flow variables has the potential to help reduce the
problem of simlùtaneity between inpnts and out.pnt.s.
Above then is the economic basis Imderlying the analysis performed in this study.
The discussion now shifts to economic specifications: first the meta-production flmc-
tion, then the static and dynamic share response models.
3.4 Econometrie Specification: Production Function
This section specifies and comments on the production function modelled here. The
meta-production function has been described by Lau and Yotopoulos (1989) as .4 a
common nnderlying production function that cao be used to represent the input-
output relationship of a given indllstry, e.g. agriclùture, in all countries, ..." The
first objective of this study is to estimate such a flillction for SSA. The Imderlying
asslunption is that these countries have access to the same production technology.
The model is a two-way error component "Cobb-Dollglas type" production tlmction
within the context of utility maximization with competition, where output depends
on factor inputs, efliciency factors and external shocks.
26
3.4.1 The Model
Following the discussion in the preceding sections, the production function to be
estimated is posited as follows. The transcendentallogarithmic flmction was originally
proposed. Tests that the cross-terms in that specification were zero were accepted,
leading to the following specification.
where,
AGDP is total agriclùtural output per hectare of agriclùtural land;
LAER is agriclùturallabor force per hectare of agriculturalland;
KODA is the stock of official development capital per hectare of agriclùturalland;
I(P FX is the stock of private foreign commercial capital per hectare of agriclù-
tl1ral land;
K S AV is the stock of domestic savings per hectare of agricultural land;
HeAP is the measure of the stock of human capital.
In this specification, i stands for a cOlmtry within the panel of data (i = 1. 2, ... , 27),
and t stands for time, (t = 1970,1971, ..., 1993). The stocks of physical capital are
lagged one year to allow time for investment flow to come ioto the production process. 1
Binswanger, Yang, Bowers and ~hmdlak (1987) used a lag of two years.
The production nmction looks like the typical Cobb-Douglas production nmction.
However, no assumptions are made with respect to the magnitudes or sum of the
elasticities 13t, (32, f33' (34, and 135, That is, decreasing returns to scale has not been
1 Also, in constructing the stock of physical capital, endogeneity between output and investment iscontrolled for by assuming that all investments were made at the beginning of the year.
27
imposed. The variable Ait represents the efficiency and external shock factors im
pacting agrictÙtllral production (diSCllSSed above). Linearizing by taking logarithms
yields, the full statistical model
In(AGDP)it - /3tln(LABR)it + ,B2ln(KODA)~t-l + 1331n(KP FX)it-l
+,84 In(KSAV)it-1 + f3sln(HCAP)it + 136(OPEN)it
/37 (EX01) it + 138 (EX02) it + € it • (3.1 )
where,
OPEN is a measure of the openness of the economy;
EX01 measures the terms of trade:
E ..~02 measures variations in weather and other related shocks that impact pr~
dllction. The error tenn, éd, is decomposed into an lIDobservable COlilltry-specific
effect, Qi , (fixed over time), and an tmobservable time-specific effect, Àt • (fixed across
cOlmtries), and a random term. eit. That is,
(3.2)
The country and time specifie components of the error term may he fixed or
random. In this conte.xt, a fixed efIects model is posited since the sample of COlmtrïes
is not random. Rather, these are the SSA cOlmtries for which complete data for this
study are available, and the sample practically exhausts the countries of interest to
us. Inference made in this study may therefore he considered to apply to this group
of countries. In addition, statistical tests for random effect specification (l\tlundlak,
28
1978) yield evidence against sncb specification.
Country and time effects account for differenees between countries and time that
affect inputs and outputs, and hence help control the potential for simultaneous equa-
tians bias. Country dummy variables capture differences in soil fertility, differences in
government policies such as infrastnlcture, the impact of marketing boards, pricing,2
external markets for exports, clùtllral/institutional differences, clifferences in techni-
cal efficiency in production across countries, etc. They are also assllmed ta capture
inter-conntry differences in definitions, measllrements and qualities of outputs and
inputs. Time dummy variables captnre effects that are specifie to time, for example,
changes in total factor produetivity over time for aU cOlmtries.
While the two-way error component model allows one to capture differences be-
tween cOlmtries and over time, this approach uses np many degrees of freedom. Having
established that the fixed-effect specification is Dot reject.ed, t.he time trend is llsed
ta capture alltonomous growth in productivity over time, (Hicks neutral technical
change3 ), and the time dummy variables were dropped.
Static domestic and export share equations are discussed next.
3.5 Econometrie Specification: Analysis of Agricultural Share Response in the Short-Run
This section explains the econometric approach adopted in investigating the supply
response of farmers in the export and domestic sub-sectors of sub-Saharan African
agriculture to priees, factor inputs, macroeconomic poliey, and exogenous shoeks in
2 Produeer priees are investigated in the next two papers.3 It will aIso capture the effects of omitted trending variables.
29
the short-run.4 Static supply responses are widely estimated in the literature.
The gross domestic product (GDP) funetion (also ealled profit ftmction) approach
is used to obtain domestic and export shares. Export share is defined as the value of
total agriclùtural exports divided by total agrietÙtural output. Domestic share is one
minus the export share.
Use of the profit funetion approach enables one ta analyze the domestic sub-sector
just as weIl as the export sub-sector. Thus, one cireumvents sorne methodological
and data problerns alluded to by Jaeger (1992) among others. This approach also
makes it possible for one to investigate the response of these sub-sectors not orùy ta
price, but also ta changes in factor inputs, external shocks, and the effect of policy
environment. Duality between profit and cost functions allows specification of a
flexible profit funetion without worrying about the specifie form of the production
ftmction.
3.5.1 Profit Function
Consider lltility maximizing economie agents making decisions with respect to factor
inputs, output priees and other variables that affect their profits. Given perfect com-
petition in input and output markets, the decision they face is stated as maximizing
the value of output quantity y, subject to available production technology, T, fac-
tor endowment vector, x, (capital, land, labor and human capital), and a vector of
prevailing positive output priees, p. Priees of agriclùtural exports are exogenously de-
termined by governments or in the world market, while domestic agrielÙtural priees
.. Often, this study shall refer ta the agricultural sector as being made up of two sub-sectors: theexport sub-sector and the domestic sub-sector.
30
may be determined by the demand and supply situation in the home country or
sometimes by governments.
By definition, the profit nmetion ?r, in terms of output priees and input vector
may he written as the solution to a maximization problem5
rr(p, x) = max{p'y: (x, y) f T,p» O},y
(3.3)
Restrictions are then imposed to ensure that the flllction is weil behaved, (i.e.,
existence of a solution and a ma.ximtun, giving rise ta the concept of a restricted
profit function). The restrieted profit funetion is homogenous of degree one, convex,
increasing in output priees and non-increasing in input prices.6 Under the assllmp-
tion of constant returns to seale, it is aIso homogenalls of degree OOP, concave and
increasing in qllantities of fixed inputs. The asslunption of constant retllrns ta scale
is used often. In this context, it is justified on the grolmds that. agrieultllral land,
the only factor input that is nat repraducible is far now, at least. not a canstraint in
SSA.
~IcFadden (1971) has shown the existence of a one-to-one correspondence between
the set of concave production flmctions and the set of profit fllnctions. This observa-
tion allows one not ta worry about the specifie fonn of the production hmction.
3.5.2 Specializing the Model
The agrictÙtural profit nmction (GDP function) is represented here as a transcenden-
tallogarithmic nmction. ft is a second order Taylor's approximation to an lmknown
5 The GDP function has been studied by ~IacFadden (1971)t Lau and Yotopoulos (1971}t Samuelson(195~)t Diewert (1974) among others.6 See for example, Chambers (1988)t Diewert (1974)t and Varian (1992).
31
nmctional form (Kmenta, 1967, Christensen, Jorgenson and Lau, 1973). This special-
ization of the GDP function enables one to obtain value shares of each sub-sector of
agrictùture (export and domestie) in terms of factor inputs, producer priees, external
priees, a policy variable, and the weather, by difierentiating with respect to producer
priee in each sub-sector. The two sub-sectors span total agrieultural production.
The translog function also allows the elasticity of substitution between inputs to
be flexible, and does not impose input-output separability. It has been used by Kohli
(1978) and Lawrence (1989) to study substitution possibilities between Canadian im-
ports, exports and domestic inputs or outputs. ~Iore recently ~Iartin and Warr (1993.
1994) llsed it to study the decline of agriclùtlrre vis-a-vis Inanufacturing and service
sectors of Indonesia and Thailand. As indicated above. the agriclùtnral production
fllDction equation (3.1) is obtained as a restricted translog flmction. Here~ we restate
it as a translog flIDction with additional priee terms. These extra terniS l'anse little
trouble since the impact only the precision of estimates. Even then, this is not the
model that is eventually estimated.
The GDP flmction is then written as
lnAGDP
+
2 1 2 2 6
= 80 + L 8i lnPi + ? L L T'ij ln pdn Pj + L Bk ln Fki=l .. i j k=l
1 6 6 2 6
22: 2: 6km ln Fk ln Fm + 2: L'lik ln pdn Fk,k=l m=l i=l k=l
(3.4)
where, AGDP is the agriclùtlrral GDP; the Pi are the producer priees prevailing in
the domestic (i = 1) and export (i = 2) sub-sectors; and the Fk are the six factor
inputs, namely, the stock of anA (KI), the stock of PFX (K2), the stock of SAY
32
(K3), agriculturallabor force, (LABOR), agrieulturalland (LAND), and the stock of
hlunan, (HCAP).
Export and domestic share eqllations are derived as follows. By Yotmg's theorem
(symmetry of the mixed partial second derivatives), "Yij = "Yji, Dkm, =8mk , in the GDP
ftmction equation (3.4). In addition, homogeneity of degree 1 in priees requires that
E;=l (Ji = 1; E;=l rij = 0; 2:;=1 'Yij = 0; E:=t L;=1 rij = 0; L1 T/ik = o.
The share of each sub-sector in agrÏctùtural output is then obtained by logarithmic
differentiation with respect to the respeetive priees as
and
SI = (JI + 1'11 ln Pl + 1'12 10 P2 + 1111 ln KI + 1/l21n K2 + '113 ln K3
+1/14 ln LABOR + TJ15 ln LAI'!D + TJ16 ln HeAP,
S2 - (J2 + 1'21 ln Pl + "Y22 ln P2 +'121 1nKl + TJ22 1n K2 +'1231nK3
+1]24ln LABOR + '1]25 10 LAND + 1]261n HCAP,
(3.5)
(3.6)
where, SI is the share of the domestic sub-sector in agriclùtural GDP and 82 is the
share of the export sub-sector. Factors of produetion are assluned to be mobile be
tween the two sub-sectors with the rental price of each given by its marginal praduct.
Share equations are then augmented with a measure of the macroeconamic pal
iey variable, and external shocks variables. Allowanee is made for the influence of
country-specifie and time-specific effects as done for the production function above.
Priees are then expressed relative to each other, since the share equations being
profit functions must be homoge~ousof degree one in priees. Given the assumption
33
of constant returns to seale in factor inputs, factor inputs are deflated by the LAND
variable. This foeuses the discussion on yield (per hectare). The normalization aIso
helps in eontrolling for heteroskedasticity among the eOllntries (of different sizes).
Thus, a representative share equation is DOW written as
s = a~ + a~ ln Pl + 0; ln KODA + a~ ln K PFX + Q~ ln KSAVP2
+o~ InLABR + a~ ln HCAP +~. (3.7)
where, [<ODA, KPF..:'(, KSAV,and LABR, are as defined for the production flmc-
tion. The share equations to be estimated are then written as
+04 1n(KSAV)it-1 + Qsln(LABR)it + Q6 1n(HCAP)it
ADON/it - ,8t ln(PRICE)it + {32 ln(!(ODA)it-1 + f33 ln(KPF"Y)it-1
+{34 ln(KSAV)it-1 + 13s ln(LABR)it + ,86 In(HCAP)it
for the export and domestic shares AEXP and ADOAt/ respectively. PRICE is the
ratio of the index of real produeer priee of agrieultural exports to the index of the
real producer priee of agriculture for domestic constunption. The variables 0 PEN,
EXOl, and EX02 may he viewed as components of o~ in equation (3.7). They have
34
the same meaning as in the production function above. AIso, ri and 6i are the country-
specific effects, and (t and Vt are the time-specific effects as in the production fUDction.
The two shares must add up ta one (adding-llp condition). Hence the relationship
between the slope coefficients in eqnations (3.8) and (3.9) are,
Here too, having established that the fixed-etfect specification is not rejected, the
time dummy variables are dropped and a time trend is used to capture autonomO\lS
growth in prodnctivity over time (as weIl as effects of omitted trending variables).
The trend coefficients in the two equations are also the negatives of each other, while
the cOtmtry dtunmy variables equal one minus the other.
3.6 Econometrie Specification: Dynamics of Agricultural 8hare Response
This section models the dynamics of aggregate export and domestic shares of snb-
Saharan African (SSA) agriclùt.ure. In spite of interest in static share response in its
own right, one notes that short-run response obtained from static models does not
take into account the fact that the empirical relationslùps on the ground may not be in
long-run equilibrhun. Indeed, agriclùtural snpply may not respond immediately and
ft.ùly to changes in the explanatory variables. This may be due to habits, persistence,
implementation lags, misinterpreted real priee changes, and other factors.
This section modifies the static specifications of the previons section to allow for
deviations from steady state. An autoregressive distributed-lag model is adopted
35
(Hendry, Pagan and Sargan, 1984). The specification is general enough to allow
estimation of both long-run equilibrium and short-nm dynamic parameters, both
being of interest in this study.
To illustrate. Tree crops naturally take time to come into production after being
planted. Farmers and others engaged in one sub-sector of agrÏCtùture will require tirne
to leam new skills shatùd they wish to move from one sub-sector ta another. But they
will probably take even longer to put aside previously acquired habits (Le., ways of
making a living). Land dedicated to one crop may require substantial time lag ta be
ready for another crap. A piece of land previously nsed as a cocoa, rubber or coffee
farm for example, takes long ta be rid of the fibrous roots (and taproots tao), as
well as changes in acidity of the soil caused by these trees. Further, physkal capital
nlay not he readily withdrawn from one application to another since retooling may he
called for. Expenditnre of additional S1.uns of money to acqtùre new capital eqllipment
may require severa! years of planning and reallocation of profits from one sub-seetar
to the other. Yet, it is known that farmers being utility maximizers respond t.a priee
changes, technical change and other factors that enhance their welfare. Long-nln
aggregate supply responses will include the effect of reallocation of resOllrces from
one snb-sector of agriclùtl1re ta another, or from non-agrictùtllral activities ta the
agricultural sector (or vice-versa), changes in government expenditures, etc. The
approach adopted in this paper precisely addresses these concerns by building upon
the findings of the static share eqllations above.
Jaeger (1992), Binswanger (1989), and Binswanger, Yang, Bowers and l\JItmdlak
(1987) among others, who have themselves estimated agriclÙtural supply elasticities
•36
from static equations have argued that supply elasticities obtained from static equa
tions, though common, are best viewed as short-nm responses. Short-nm responses
may however be of interest in their own right: for short term impact evalnation of
poliey and priee changes for example. Or where it is believed that the data generation
proeess has changed rendering long-nm estimates diflielùt or impossible to compute.
Starting \Vith the static equations. a general autoregressive distribllted-Iag modpl
(ADL) of the dynamics of each share equation involving static parameters (from wruch
long-nm parameters may be inferred) is derived. ~[aking inferences indirectly from
the ADL specification has been shawn to be problematic. For one thing, additional
computations are ealled for. For another, since these computations involve ratios
of regression coefficients, problerns arise in regard to estimating standard errors of
the long-nln coefficients from finite sample estimates. These have been discllssed
by Kmenta (1984) and Bewley and Fiebeg (1993) and Kesavan. Hassan. Jensen and
Johnson (1993).
In this study, the ADL is transformed 50 as ta be able to estimate directly long
nm parameters and their standard errors. The transformation also allows one to
investigate the extent of persistenee and of implementation lags. Thus, no additional
computations shall be reqnired. The transformation used was proposed by Bewley
(1979) and has been diseussed by Bewley and Fiebeg (1993), Banerjee, Dolado. Gal
braith and Hendry (1993), and Wickens and Breusch (1988). I<esavan et al. (1993)
for example, used such an approach ta study the demand for meat (beef, chicken
and pork) in the United States. ~Iartin and Warr (1993, 1994) also llsed a similar
specification to study the dynamics of the decline in the agriclùtural sector vis-a-vis
37
manufacturing and services sectors in Indonesia and Thailand. Anderson and Blun-
deU ( 1983) used a sirnilar approach that combined both long-nm relationships and
short-nm behavior ta test consmners' expenditure patterns in Canada.
3.6.1 Distributed Lag Specification
Consider a static two-way error component model
P
Yit = L {3kx ikt + ê it ,
k=1
(3.10)
where Xik is the data pertaining ta variable k, (k = 1, .,., p) in COlmtry i, at time t, and
êit has both cotmtry-specific and time-specific cOlnponents. Naw allow lagged depen-
dent and explanatory variables ta enter the model ta account for habits. persistence.
adjustment costs, etc.. that cause agents nat to adjust their activities immediately.
This resnlts in an ADL which may be written as
m p n
Yit = L CtjYit-j + L L ,BkjXikt-j + êlt,j=1 k=l j=O
(3.11 )
where the Qj are the coefficients of the lagged dependent variables. the Bkj are the
coefficients of the jth lag ( j = 0, 1, ... , n) of the kth explanatory variable of eqnatian
(3.10), k = l, ... ,p.
Ta make inference about long-run parameters withant tlSing a transfonnation snch
as Bewley's, one wOlùd first have to estimate an the Ctj and Pkj and then proceed
to compute the equilibrium parameters of the relatiansmp between the dependent
variable and the explanatory variables. The long-nm impact 8k of the kth explanatory
variable is then derived as,n
8k = À L{3kj,j=O
(3.12)
38
where
For long-rtm equilibrium relationship to exist, it is req\ùred that
(3.13)
The Bewley transformation is achieved by the following algebraic manip\ùation.
One, for each exogenous variable Xk, k = l, ...~p~ subtract 13kjXkt-J from each ,BkjXkt-j
(j = 1,2, ... , n) term on the right of the ADL, equation (3.11), and add the same to
the PkOXkt term (Le., levels of the variable) also on the right. Second, for each QjYit-j
on the right, subtract GjYit from both sides of the equation. Then re-arrange and
collect terms. Thus the Bewley transfonnation is obtained as
Yi' = -,\ fOj(Yil - Yil-j) - t [,\ t,3k] (X'k' - X'k.-i )])=1 k=l )=1
+~ [,\ ~8k]] X,k' + ,'lé", (3.14)
where, À r:.;=o 13kj is the long-nUl effect on y due ta a change in variable Ik. This
transformed model is linear in parameters which can thus be estimated directly (co-
efficients and their standard errors). Note that the first two terms on the right capture
short-nm dynamics (Le., persistence and implementation lags).
The autoregressive model, the partial adjustment model and the static model are
all nested in equation (3.14). See Kesavan et al. (1993) or Hendry et al. (1984) for
discussions on appropriate restrictions that will yield each of these reduced models.
If required, these restrictions can he tested to determine which of the restricted forms
is more appropriate (depending on the situation one is modelling).
•39
Next, the dynamic domestic and expart share equations are derived.
3.6.2 Dynamics of Agricultural Share Equations
Consider the static export share equation analyzed in the preceding section with time
dummy variables replaced by a time trend variable,
AEXPit - Ài + P1ln(KODAitJ + 132 ln(l{ P FXid + .L13 ln(K.sA \lid
+t14 ln(LABRïd + I3s0PENit + (36 EXOl it
(37E~~02it+ /3sln(PRIC Eit ) + ,8gT REND + eit, (3.15)
where 'i refers to country (i - 1, ... , 23). in the panel data, and t is time (t =
1970,1971, ... ,1993).
The ..\i are the cOlmtry-specific dummy variables. t.he 8j , j - l. 2..... 9 are the
coefficients of the varions variables and eit is a random errar term.
Up ta five annuallags of the dependent and explanatory variables are hypothesized
ta impact enrrent share. The rationale for five years rests on the grolmds that tree
crops may take up to five years ta come on stream, land previously used for one crop
may t.ake that long to be ready for another. Farmers may take this long to adapt
their habits, plan and realloeate resources from one sub-sector to another.
A general-to-specific modelling approach that does not reqlùre one to specify a
priori that the sequence of lag coefficients progresses in any particlùar manner is used
to achieve a parsimonious specification of the ADL. Tests for significance of lags were
conducted by considering difIerences among successive lags (that is, each additional
lag must provide new information), to obtain a parsimonious specification for the
40
ADL. The ADL is analyzed to ensure that the residuals are stationary as weil as
for the existence of long-mu relationship between the dependent variable and the
explanatory variables.
Once an ADL is obtained, the steps outlined above are followed to achieve the
corresponding Bewley transformation. Four Bewley transformations were obtained.
One for the overall data (23 countries frem 1970 te 1993 callcd 'Overall'), one for the
Western agro-climatic region (West), one for the Eastem-Southern agro-dimatie re
gion (East) and one for the Sudano-Sahel agro-climatic region (Sudano). This section
does not model the stnlctural adjustments period (1981 ta 1993), because the use of
lagged values leaves too few observations for meaningflù estimation. Policy implica
tions will be made with respect to the macropolicy variable. OPEN. Stability of the
data generating process was analyzed for the statie model, as weil due consideration
was given ta the presence of lagged dependent variables on the right.
By way of illustration, the ADL for the Overall specification is
+1PlOln(KODAitl + 'rlhsln(KODAit - S )
+l/J20 ln(KP F X it ) + 'W21 ln(KP FXit -d + 'W22ln(KP F )(it-2)
+1/130 ln(K S AVit) + 1/;34 ln(K S AVit-4)
+1/140 In(LAB14t) + 'l/J43In(LAB~t-3) + 'w4s ln(LAB14t-s)
+WsoOPENit + t/J6oEX01it + 1/;6sEX0 1t-S
+1/J70E ~"(02it + 1/J71EX02it- 1
+t/Jsoln(PRICEit ) + 'l/J84,ln(PRICEit - 4 )
+1/JgT REND + éit.
41
(3.16)
The following are the Bewley transformations obtained for the export shares:
(i) Overall
AE...Y:Pit = Gi + (3t(AEXPit - AE...Y:Pit-d + f33(AE ...Y:Pit - AE...Y: Pit-3)
+1'10 In(KODAit ) + 1'20 m(KP F...Y,t ) + 1'30 In(KSAV':d
+1'40 In(LAB~t) + 1s00PENit + 160EX'Ol it
+1'70E)(02it + 1'so In(PRICEit )
+"'(ls(ln(KODAit ) -ln(KODAit - s))
+1':u(ln(KPF)(ie) -m(KPF...Y:1t-d)
+1'22(ln(KPFXit ) -ln(KPFX1t- 2 ))
+1'34(ln(KSAVid -ln(KSAVit-4))
+1'43(ln(LAB~t)-ln(LAB~t-3))
+1'45(ln(LAB~t)-ln(LAB~t-s))
+1'6s(EXOlt - EXOl t - s )
+1'71(EX02it - E.~02it-l)
+Î84(ln(PRICEit ) -ln(PRICEit - 4 ))
+i9TREND+éit, (3.17)
(ii) East
AEXFit - Qi + {31(AEX~t - AEXFit-d + /32(AEX~t - AEXPit- 2 )
+{3s(AEXPit - AEXPit- S )
+'10 ln(KODAit ) + 1'20 In(KPFXit) + 1'30 ln KSAVit)
+'40 In(LAB~t) + '"YsoOPENit + '"Y6oEXOlit
+,iOEX02it + 1'soln(PRICEid
+,14(ln(KODAit ) - ln(!(ODAit - 4))
+,ls(ln(KODAid - In(KODAit_s))
+'"Y22(ln(KPFXid -ln(KPF~'(it-2))
+'"Y31(ln(KSAVit) -ln(KSA\lat-d)
+'"Y3s(ln(KSAVid -ln(!(SAVit-s))
+'43(ln(LAB~t)-ln(LAB~t-3))
+'"Y45(ln(LAB~d-ln(LAB~t_5))
+'"Y6s(EXOlt - EXOlt- s)
+'"Yn (EX02 it - EX02it- 1)
+784(ln(PRICEit ) -ln(PRICEit- 4))
+79TREND + êit,
(iü) West,
42
(3.18)
AEXPit - Qi + (3t(AEXPït - AEX~t-l)
+"'(10 In(KOD~t) + 120 In(KPFX it ) + "'(30 ln(KSAlItt)
+"'(40 In(LAB~t) + 1500PENit + 1'60E..-YOl it
+Î70E ..-Y02it + 180 ln(PRIC Eit )
+Î15(ln(KOD~d -ln(KODAit - s))
+r21(ln(KPFXid -ln(KPF.Xtt-d)
+"'(34(ln(KSAVjt) -ln(KSAVit-4))
+143(ln(LAB~d-ln(LAB~t-3))
+r45(ln(LAB~d- In(LABRit_ s ))
+184(ln(PRICEit ) -ln(PRICEit-,d}
+ÎgTREND + ê1tl
(iv) Sudano,
AEXPit - Qi + f3t(AEXPit - AEXPit-d
+1'10 In(KODAit ) + 1'20 In(KPF Xit) + 130 ln(KSAlItt)
+"'(40 In(LABRït) + 1500PENit + 160EX01it
+170EX0 2it + rsoln(PRICEit )
+112(ln(KOD~t) -1n(KODAit_2 ))
43
(3.19)
44
+1'2s(ln(KPF Xid - ln(KPF X it- s))
+1'34 (ln(K S AVit) - ln(K S AVit-4))
+1'4s(ln(LABR;t} - ln(LAB~t-5))
+1'84 (ln(PRIC Eit ) - ln(PRICEit - 4 ))
+1'gTREND + eit·
Note that the specifications vary somewhat from one region to another. This must
be due to regional differences in persistence and implementation lags.
ln the literature, a typical panel has a short time series (about 5 observations),
thus the effect of presample observations (Le., observations prior to the start of the
study) on the stlldy Imder consideration have been discllssed in sorne sections of the
literature, e.g., Pakes and Griliches (1984). Given a time series of as many as 23
obsenrations per COlmtry in this study, the impact of tmohserved presample data will
have minimal impact.
3.7 Econometrie and Other Related Issues
This section discusses relevant econometric issues.
3.7.1 Single Equation Specification
It is weIl known that most macroeconomic variables are simtùtaneously determined.
Thus, simtùtaneous equations specification wotùd probably be most appropriate in
general. However, use of single equation specification in economic studies is far more
common. In the current context, one rationalizes the single equation specification on
the following grounds. Inclusion of country-specifie and time-specific dummy variables
45
in the production function mitigates the effects of unobserved components that may
enter both output and inpnts. Second, measuring inputs as stock variables rather
than actual CUIrent flow variables goes sorne way towards avoiding the problem of
simultaneous determination of output and inputs. (The random distllrbance term is
of course assllmed ta he lillcorrelated with ather right-hand side qnantities).
3.7.2 Heteroskedasticity, Autocorrelation and Errors in Variables
Paoling data for different countries immediately raises the question of whether residual
variances vary from one COlultry to another. In general they do. Use of inclividual
country dummy variables goes sorne way in addressing this problem. In addition.
standardizing ontput and factor inputs by dividing these qllantities by the agricultural
land area also helps reduce the problem of residual heteroskedastidty due to large
differences in cOlmtry sizes. Norton et al. (1992) and Binswanger et al. (1987) for
example used tms approach.
The third step in addressing the heteroskedasticity problem wotùd be to estimate
the variance-covariance matrLx by a robust estimator. If autocorrelation is also fOlwd
to exist, then a modification to the robust estimator that addresses autocorrelation
shaH be used.
One heeds the advice of Dagenais (1994) and lVlankiw (1995) among others and
hesitate to transform the model to remove seriai correlation because of concern that
such transformation may introduce more bias in coefficient estimates than otherwise.
This concem stems froID the very real chance that measurement errors are present
46
in the data. Economists who work with SSA data generally believe that the data
qllality sholùd he improved further7•
3.7.3 Stability of the Data Generating Process and Pooling
To pool time series data for severa! countries, it is important to check whether there
exists statistical justification. Testing of restrictions (Le., pooling) may proceed along
many tines. This study tested for poolability in the context of parameter stability
for bath the production flmction and the supply response models as follows. First,
the cOlmtries were divided into three agro-climatic regions (Eastem-Sollthern region,
Sudano-Sahel region and Western region).8 Then model pararneters were estimated
for each region over the entire period (1970 - 1993). Tests for structural breaks in the
slope parameters between the following time periods were then performed for each
region:
(i) 1970 - 1980 and 1981 - 1993, (ii) 1970 - 1981 and 1982 - 1993, (Hi) 1970 - 1982
and 1983 - 1993, (iv) 1970 - 1983 and 1984 - 1993, (v) 1970 - 1984 and 1985 - 1993,
and (vi) 1970 - 1985 and 1986 - 1993.
Next, tests were performed for difIerences in slope parameters hetween pairs of
agriclùtlU"al regions over the suh-periods 1970 - 1980 and 1981 - 1993 and over the
entire periode (Many (e.g., the World Bank) consider the 1980s to have been a par-
tictùarly difficlùt time for many African countries. It is also dnring this decade that
structtU"al adjustment programs took hold. A Wald test that recognized heteroskedas-
7 The problem of errors in variables is pervasive however. Duncan and Hill (1985), Altonji and Siow(1987) for example, have reported these problems even with the popular Panel Study of IncorneDynaimcs Data (Psm).8 Countries belonging to each region are indicated in subsequent chapters.
47
ticity and first order seriai correlation was used).
Then pooling data for all countries, tests for structural break over the following
periods were aIso performed: (i) 1970 - 1980 and 1981 - 1993, (H) 1970 - 1981 and
1982 - 1993, (iii) 1970 - 1982 and 1983 - 1993, (iv) 1970 - 1983 and 1984 - 1993, (v)
1970 - 1984 and 1985 - 1993, and (vi) 1970 - 1985 and 1986 - 1993.
Finally, a group of ninctccn S5A cOtUltrics classificd into thrcc subgroups were
considered. The first SUbgrOllp is classified as having lmdergone LARGE positive
changes in macroeconomic poliey (fiscal, monetary and exchange rate policies) be
tween 1981 - 1986 on one hand, and 1987 - 1992 on the other. The second as having
lmdergone S~IALL positive changes in macroeconomic policy over the same period.
The third as having lmdergone POOR (negative) changes in macroeconomic poLicy
aiso over the same period. This classification was performed by Bouton. Jones and
Kiguei (1994). Production nmctions and static share equations were estimated for
each sllbgroup as classified. Tests were then perfornled for differences in parameters
between pairs of subgroups. Reslùts are reported in chapters 5 and 6.
•Chapter 4
Data
This section discusses data sources and data set constrnction. The data are drawn
from various sources, mostly publications of the World Bank and the Food and Agri-
clùtllral Organization of the United Nations. The data cover the period 1970 to
1993.
4.1 Data Description and Sources
AGDP, Agricultural Gross Do'mestic Product ai factor cast:
The source of this data is World Bank (1995) and earlier issues. Agriclliturai
output includes agriclùtural and livestock production, fishery and forestry output.
Data are published in current values of domestic enrreney and aiso 1987 values of
domestic curreney. Agricultllral gross domestic produet in constant 1987 domestic
curreney values are eonverted to 1987 United States dollars (US$), using the World
Bank's conversion factor for each country. (The World Bank refers to it as Atlas
method. The method is a blend of both official exchange rates and paraliei market
exchange rates). 1
1 1987 values are used because many publications and data sources do 50, and aIso by this, dollarvalues of agricultural output show increases that generally agree with quantity outputs.
48
49
LAND, Agricultural land:
This is obtained from FAO Production Yearbook (1996) and earlier issues. Agri
clùtnrai land is computed as the sum of land used for (i) Arable and Permanent
Crops, (ii) Permanent Pasture, and (Hi) Forest and Woodland
KOD.4. physical stock of ODA:
Net annual Hows from 1960 ta 1995 in CIment United States dollars were supplied
by William Easterly of the World Bank. He obtained the data from üECD sources.
Stock computation: Flow data were first converted to constant 1987 US$ llsing US
consumer priee index (Deaton and l\tIiller, 1995). Next, stock data were const.nlcted
by the perpetuai inventory method: Kt = Kt- t (1 - 8) + /t, where Kt is the stock at
time t. and It is the time t flow. The initial stock of aOA flow is then eomputed as
Ka = /0/ (8+9 ). where 9 is the estimated average growth rate of real aDA investment.
and /0 is the flow at time O. {) is the depreciation rate of capital (discIlssed below). /0
and 9 are bath obtained by regressing the log of real aOA flow during the period 1960
ta 1969 against time and taking anti-Iogs. (Harberger, (1978). Nehru and Dhareshwar,
1993).
Rate of decay, {) : Brown, (1972) computed physical capital stade for Ghana using
survey and investment data obtained from the local statistical authorities. The de
predation rate implied in his computations is 6 ~ 7%. Nehnl and Dhareshwar (1993)
used 8 = 4% in camputing the physical capital stock for a group of 92 develaping
countries. l\Jlankiw, Romer and Weil (1992) used (8 + a) = 5%; a is the exogenous
rate of growth of technical progress. For both ODA and PFX, it is noted that sorne of
50
the infiows are Dot physical capital but consumables sucll as fertilizer and teclmical
assistance in the fonn of expartriate salaries. These are consumed within a relatively
short tirne. In this constnlctioD 8 was set to 10%.
For KODA, KPFX, SAY and HCAP (below) total stocks in each ecoDomy are used.
Sectorial data are Dot available. Thus one implicitly assumes that the components
relevant ta agriculture are a constant share of each total, or if not, that dcviations do
not bias restùts in any significant manner. See for example Binswanger et al., 1987).
K P F X, stock of p7'ivate commercial foreign flows:
Net capital armual flow data was also obtained from William Easterly of the
World Bank, and supplemented by data from World Bank (1996b) and earlier issues.
Easterly obtained his data from 11tIF sources. By definition, net private foreign
capital ('omprises foreign direct. investment, debt from foreign banks and other private
creditors, as well as portfolio equity investments. Stocks of PFX were constnlcted
as done for aDA from 1970 ta 1993. Whenever net flow for a particular year was
negative, previous stock was only depreciated, Le.. (~urrent negative flows were not
added to depreciated previous stock. The impact of these negative amollnts is not
10st however, since net negative flows of PFX come out of SAY and/or üDA.
K S AV, stock of domestic savings:
AnnuaI data on gross domestic savings were obtained from World Bank (1995)
and earlier issues. For post 1973 period, tms is available, as percent of GDP, (and
in domestic currency amounts). GDP for this period is published in US$ in World
Bank (1995). Stocks were constructed as done for üDA. Again, whenever savings
were negative, they were set ta zero.
•51
OPENJ openness of an economy:
l'vIeasured as foreign trade share of GDP, i.e., OPEN = (E=por~br;portS). Exports
are exports of goods and non-factor services, (free on board values). Imports are
imports of goods and non-factor services, (cost insurance and freight values). GDP
is obtained from World Bank (1995).
IICAP, hU/nan Capüal :;tvc;k:
The stoek of hUIDan capital is the average nnmber of years of schooling in the total
poplùation over age 15. These are obtained from Barro and Lee (1996) and Nehru,
Swanson and Dubey (1995). The Barro and Lee data set contains quinqllennial
stock data for 23 SSA countries from 1960 ta 1990. The Nehnl data set has annual
observations for 21 SSA cOlmtries from 1960 ta 1987. Starting with the Barro and Lee
data, schooling years for the intervening years were estimated and then extrapolated
for 1991 ta 1993. For the few COllntrîes not common to bath data sets but for wruch
Nehru et al. (1995) have data, these annua! data (1970 - 1987) values were llsed and
then extrapolated for the years 1988 ta 1993.2
LABR, the stock of agriculturallabor force in each economy:
This was abtained from World Bank (1995) and earlier issues and FAO Production
Yearbook (1996) and earlier issues.
E)(OI, Terms of trade:
This is defined as index of export priees divided by index of import priees. Ob-
tained from World Bank (1995) and earlier issues.
2 AIso, the process was reversed, i.e., started with Nehru et al. and complemented with Barro andLee. Both methods produced similar statistical estimates. This variable is discussed further in thenext chapter.
52
EXD2, Measure of weather variability:
This was constructed as the deviation of cereal yield from trend. Jaeger (1992)
has data from 1970 to 1987. This was extended to 1993. Indices of cereal production
per cotmtry are given in FAO Yearbook, varions issues. It is argued that this variable
also has the potential to measure the impact of unforeseen disnlption to cereal yield
snch é.lS the cffccts of political and social nnrest.
GDP, Gross Domestic Product:
Obtained from World Bank (1995) and earlier issues.
PRIeE, Ratio of the -index of real producer prices for exports to the index of real
producer prices of agriculture for domestie consumption:
Jaeger (1992) supplies Average Real Producer Priee for klajor Expo1t Commodities
(Index, 1980 = 100), and Real Producer Price for Major Food Crops (Index, 1980 =
100) both for the period 1970 to 1987. These are computed as Laspeyres~ indices with
1980 as base and extended to 1992 using representative export and food crop prodllcer
priees in local ctlITencies. Producer priees were obtained from \Vorld Bank (1996a)
and earlier issues. 1980 production quantities were obtained from FAO Produetion
Yearhook (1984). Consumer priee indices were obtained from World Bank (1995).
In addition ta extending the Jaeger price indices to 1992, computations were also
done for cases where the Jaeger data has missing values. Complete price data were
available for 23 countries.
AEXP, share of agricultural exports in total agricultuml output:
Computed as the ratio of the value of agricmtural exports over total agrictùtural
gross domestic product (AGDP).
53
Agricultural exports are obtained froID FAü Trade Yearbook (1996) and earlier
issues. (AGDP is described above).
ADOJ\;/, domestic consumption of total agricultural gross domestic product:
This equals one minus AEXP.
Chapter 5
Empirical Results - ProductionFunction
This chapter presents and discllsses estimation resnlts for the meta-production fllnc-
tion. First, the simple correlation coefficients between pairs of pooled variables in
the production function are presented in Table 5.1. It is observed that factor inputs
are most higlùy correlated with agricultllral ontput. Among explanatory variables.
pairwise correlation is highest between the stock of domestic savings and private for-
eign capital, and between the stock of development assistance capital and the labor
force. OPEN is negatively correlated with AGDP, so is the correlation between the
terms of trade (EXOl) and private foreign capital stock (KPFX) and between terms
of trade and development assistance. Variations in the weather appear not to be
correlated with any variable. High simple correlation coefficients do not necessarily
imply multicollinearity problems, neither do Low coefficients indicate that there are
no multicollinearity problems. Investigation of multicollinearity is reported below.
54
55
Table 5.1. rvlatrix of correlation coefficients (aIl countries)
AGDP KODA KPFX KSAV OPEN EXOI EX02
AGDP 1
KODA 0.691 1
KPFX 0.511 0.511 1
KSAV 0.618 0.428 0.767 1
OPEN -0.190 0.066 0.283 0.127 1
EXOI 0.011 -0.145 -0.11 0.047 0.105 1
EX02 0.006 0.005 0.019 0.018 0.013 -0.009 1
LABR 0.893 0.722 0.397 0.544 -0.262 0.029 -0.015
Noting that simple correlations may not be very illuminating in a multiple regres
sion context, attention now shifts ta regression for further insight.
5.1 Regression Results
Prelinùnary tests of ordinary least squares residllals of the meta-prodllction flmction
equation (3.1) indicated that the residuals were heteroskedastic and autocorrelated.
For example, for the panel consisting of aIl cOlmtries, the likelihood ratio test yielded a
chi-square statistic of 1,124 with 26 degrees of freedom for the nlùl of no heteroskedas
ticity. The Durbin-Watson statistic was 0.67.
The model was then re-estimated by a form of the generalized least squares (GLS)
method that corrects for heterosked.asticity and autocorrelation in estimating a con-
56
sistent variance-covariance matrix. The model was estimated on a persona! computer
numing Winrats - 32 version 4.3. The variable OPEN is lagged one year, out of
concern for possible simultaneity between OPEI'! and ACDP sinee the numerator of
OPEN involves agriclùtural exports. These exports are included in AGD P, the de
pendent variable. A consistent estimate of the variance-covarianee matrix is obtained
in the presence of heteroskedasticity and autocorrclation of rcsiduals by specifying the
L'ROBUSTERROR8" and L'LACS" options in ~Vinrats. The ROBUSTERRORS
option is important in situations where sorne forms of the GLS may be 'Linconsistent
bet~ause the regressors (or instnunents) are correlated with past residllals". (Doan.
1992).1 Snch cases have been discussed by Brown éUld ~[aital (1981) and Hayashi
and Sims (1983) among others. COlmtry-efIects and coefficients of other variables of
the meta-production ftmction equation (3.1) are estimated in one step.
Estimates are reported for the panel of all cOllntries (called LOverall'). as weIl as
for each of Western (West), Eastern-Southern (East). and Sndano-Sahel (Sndano)
agrictùtural regians. Anather set of estimates is reported far eotmtries grollped by
macroeconomic policy environment.
5.1.1 Human Capital Variable
The original specification. equation (3.1), included HCAP. Upon estimation however.
the coefficient of this variable was relatively large and significant, while the LABR
variable was insignificant in sorne cases. This was troubling given what is common
knowledge (as weIl as what this author knows about the situation on the grotmd in
l Rats Users ~Ianual Version 4.
57
SSA). Forward stepwise regression approach was then used ta build the model froID
ground up, starting with LABR and adding one variable at a time. Inclusion of HCAP
in the model at any stage caused many variables to either lose their significance or
even reverse the sign of their coefficients.
Clearly, the assumption that the average years of schooling among farmers equals
the national average is tmtenable, (again based on the author's knowledge of SSA).
Indeed, the majority of African farmers have little or no formai schooling. A typical
Mrican worker who has attended school is uIÙikely to be a full time farmer. Certainly,
these farmers have acquired human capital but years of schooling does not capture this
capital. Also, at a conceptuallevel one finds it difficlùt to justify the position that a
cOlmtry with twice as many years of schooling per average worker as another, has twice
as much farming human capital as this other country (given equal pop\ùation). HCAP
was then dropped from all subsequent estimations. Thus, parameters estimated here
should be viewed as being contingent on human capital. As discllSSed in chapter
three, empirical reslùts in respect of proxies for hllman capital have been troublesome
in sorne cases.
Unfortlmately, in a multiple regression such as this, one cannot easily determine
the direction of bias, if any, on estimated coefficients when HCAP is omitted. Simple
correlation coefficients will not be enough. See Greene (1997) for further discussion.
Further, Hsiao (1986) has ar~led that use of country-specifie effects helps reduce or
even avoid omitted variable bias. The effect of the omitted variable will be absorbed
by the country-specifie dummy variable.
•
58
5.1.2 Diagnostic Tests of Regression Adequacy
1'[any standard diagnostic tests were perforrned to assure validity of estimation results.2
They include the following.
Stationarity test
Autocorrelation functions (ACF) of all variables in the regression were computed
and plotted. For all variables, the ACF plots died down quickly. Indeed, less than
1 percent of the elements of any of the ACfs were significantly difIerent from zero.
This leads ta the conclusion that each pooled variable was stationary.
Nfulticollinearity
On an individual country basis, the labor and physkal capital variables for sorne
countries are fairly higWy correlated. But upon pooling, the correlations die clown
substantially. See table 3 for pairwise correlation coefficients of variables of pooled
data. In addition, computed variance inflation factors for the pooled variables in
the model are well below 10. One concludes therefore that mlùticollinearity is not a
problem.
Outliers
Three tests ta isolate olltlîers and inflllential observations were performed. They
are, tests for (i) observations that are olltlying with respect to the explanatory vari
ables (leverage values), (ii) observations that are outlying with respect to the depen
dent variable (studentized deleted residuals) and (Hi) observations that have signifi
cant impact on parameter estimates (Cook's distance).
Leverage values were computed for individual observations. None of the computed
2 See for example Neter, Wasserman and Kutner (1996).
59
values exceeded 0.12 (which is 2k/n, where k is the nlunber of coefficients estimated
and n is the nlUllber of observations). Bence, none was considered to be outlying
with respect ta the explanatory variables. Tests for outlying cases with respect to the
dependent variable were performed by computing studentized deleted residuals. This
time just under 2 percent of the observations fell within 2.5 percent of either tail area
of thp r'orresponding t distribution. Howevcr, no furthcr test, Cook's distance, sug-
gested that none of these cornes close to having had significant influence on regression
coefficients.
5.1.3 Agro-Climatic Regions
Prior to estimating the model, statistical tests for poolability of the data aver time
and across regions were performed. There was statistical support for pooling the
data as discussed below. Regression reslùts are presented in t.wo parts. First, for
the Overall and the three agro-climatic regions covering the entire period of study
in table 5.2. then for countries grouped by macroeconomic policy improvements in
table 5.3. A test for the eqllality of the capital coefficients in the West. was rejected.
Disaggregated capital coefficients are thus reported in all cases.3
3 Given the large number of parameters being estimated, the risk of type 1 error is controlled at 0.01in ail of the discussions below.
Table 5.2: Estimates of meta-production function parameters1 for
agricultural regions, (1970 - 1993). See legend on next page.
Variahlps2 Ovprall3 West4 East5 Sudano6
ln(KODA) 0.054 -0.006 0.026 0.077
(1.584) (-0.064) (0.483) (1.007)
ln(KPF)() 0.021 0.221** -0.039 0.090*
(0.086) (5.226) (-1.349) (2.299)
ln(KSAv') 0.030 -0.114 0.067 -0.008
(1.060) (-1.662) (1.726) (-0.168)
ln(LAER) 0.278 0.908* 0.782** -0.249
(1.827) (1.951) (4.411) (-1.036)
OPEN -0.033 -0.186 -0.358* -0.065
(-0.394) (-1.721) (-2.102) (-0.478)
EXOI 0.017 0.043 0.066 -0.028
(0.371) (0.946) (1.321) (-0.180)
EX02 0.108* 0.075 0.0104 0.095
(2.263) (0.783) (1.711) (1.069)
TREND 0.003 0.008 -0.004 -0.006
(0.782) (1.195) (-0.582) (-0.790)
D. F. 560 139 224 181
60
1 t-statistics in parenthesis below coefficient estimates.
* and ** indicate statistical significance at 0.05 and 0.01
levels respectively. D. F. is degrees of freedom.
2 Variables are defined in chapter 3.
30verall: West, East and Sudano together.
.tWest: Benin, Cameroon, Cote d'Ivoire, Ghana. Nigeria, Sierra Leone,
and Togo.
5East: Bostwana, Burundi, Ethiopia, Kenya. l\Iadagasca, rvlalawi,
~Iauritius, Rwanda, Tanzania, Zamhia and Zimbabwe.
6Sudano: Burkina Faso, Central Africa Republic, Chad, Cambia,
rvlali, lVlallritania, Niger, Senegal and Slldan.
61
62
Table 5.2 (continued): Estimates of fixed country-specifie
effects for agricultural regions. Continues next page.
Countries Overall1 West2 East3 Sudano4
Benin 4.215** 5.528**
Cameroon 4.278** 6.510**
Cote d" Ivoire 4.879** 6.752**
Ghana 4.924** 6.600**
Nigeria 4.607** 5.888**
Sierra Leone 3.545** 5.000**
Togo 4.379** 5.753**
Bostwana 1.952* 5.142**
Bllflmdi 5.180** 5.370**
Ethiopia 3.367** 4.271 **
Kenya 3.981** 5.578**
~Iadagascar 3.117** 4.627**
rvlalawi 3.602** 4.585**
rvlauritius 6.500** 7.560**
Rwanda 5.344** 5.432**
Tanzania 3.121** 4.374**
Zambia 2.105** 4.289**
Zimbabwe 3.454** 4.951**
Table 5.2 (continued): Fixed country-specifie effects for"
agricultural regions.
Countries Overall1 West2 East3 Sudano4
Burkina Faso 3.949** 3.131**
Central Afriea Rep. 2.993** 1.074
Chad 2.311 ** 0.289
Gambia 4.355** 3.972**
1vlali 3.616** 2.232**
1vlauritania 2.544** 0.180
Niger 4.173** 3.295**
Senegal 4.300 2.450**
Sudan 4.097** 2.450**
* and ** indicate significance at 0.05 and 0.01 respeetively.
1,2,3,'1 Overall, West, East and Sudano are defined above.
63
64
Parameter Estimates
Wald tests for stnlctural change were performed as discussed in chapter three. There
was not mnch evidence against parameter stability. For example, Wald tests for
changes in slope pararnet.ers hetween 19iO - 1980 and 1981 - 1993 yielded f'hi-sqnared
statistics of 12.52 (p-value 0.09), 7.49 (p-value 0.3S), and 20.12 (p-value 0.01) for the
West, East and Sudano regions respectively, for the nlùl hypothesis of no change in
the slope parameters.
Similarly, tests for differences in the slope parameters during the period 1970
1980 between the West and the East is 19.42 (p-vall1e 0.01); between the West. and
Sndano it is 17.07 (p-value 0.02); and between the East and Slldano 11.47 (p-value
0.12). Thirdly, when data for all three regions were pooled and the test for stnlctural
change between 1970 - 1980 and 1981 - 1993 was performed, the chi-squared statistic
for the nnll of no stnlctural change was 12.89 (p-valne O.OS), lending support to
the nlùl. This would imply that stnlctural adjl1stment programs which have mostly
taken hold since the beginning of the 19S0s, have not significantly impacted regional
agric\ùtural production.
There was sorne evidence for change in model parameters between 1970 - 1985 and
1986 - 1993 when all cOlmtries were pooled. But given the rednced size of the sample
for the second period, rednced emphasis was placed on this result and parameters
were estimated for the entire period 1970 - 1993.
Indeed, in discussing data pooling, Baltagi (1995) argues that sometimes the ques-
65
tion is "to choose on 'pragmatic grolmds' between two sets of estimators ... and hence
achieve in a sense, one of the main motivations behind pooling". Continuing, he
quotes Toro-Vizcarrondo and Wallace (1968, p.56D) who write, "... if one is willing
to accept sorne bias in trade for a reduction in variance, then even if the restriction
[Le., pooling} is not trlle one might still prefer the restricted [Le., pooled] estimator" .
Thc finding of insnfficicnt c\idcncc in favar of parameter change suggests that
the structure of agriclùtural production has not changed much in any of the three
regions over this period. This goes to buttress the argument that agriclùtnre has
been receiving little attention. Farming methods and the other difficnlt situations
that farmers have faced in the past apparently persist. Indeed. the coefficient of the
trend variable, the measure of total factor prodnctivity growth. is not signifieant in
any region (presented in table 5.2). The findings with respect to the policy gronps
indicate a somewhat different picture and are reported Iater in table 5.3.
Overseas Development Assistance, KODA: KODA has positive impact on
agriclùtural output in the Overall, East and Sudano specifications. However. the
magnitude of its coefficient is only a small fraction of that of the labor variable in
each region. The p-values for the t-statistics of its coefficients are 0.11, 0.95. 0.63.
0.31 in the Overall, West (where it is negative), East and Sudano respectively. This
leads ta the conclusion that this variable is not significant in determining agrictùtural
output in any region. The finding for the Overall data is probably stronger than is
implied in LeIe (1990) and Burnside and Dollar (1997), but weaker than Glmjal and
Gichenje (1997). LeIe concludes from six SSA country case studies that donors (Le.,
ODA) have "surprisingly small impact on agricultural development ...." Burnside
66
and Dollar (1997) conclude from their study of aOA and economic growth in 56
developing countries (which included 21 SSA cOlmtries) that ODA has no positive
effect on growth in the presence of poor policies. While Gunjal and Gichenje (1997)
report a coefficient of 0.045 Overall, which is close in magnitude to the finding of this
study, but with 0.01 significance level. Thus one may conclude that the impact of
rlpvplopment. ~",~istan('f' on agrirnltnral output. is small and within reasonable range
of previons estimates.
Foreign private capital flows, KPFX: The impact of this variable on agriclùtllre
varies across the regions. It is small and positive but clearly not significant in the
Overall. It is however, positive and significant in the West (p-value < 0.01) and
practically so in the Sudano (p-value 0.02). In the West, its magnitude (0.22) is large
(compared to other physical capital variables) but only about one-qu~U'ter the Bize of
the labor variable. In the Sudano, it is the factor input with the largest coefficient.
KPFX is negative but not significant (p-value 0.18) in the East. The significant impact
of private foreign commercial flows in the West may be due ta the more prominent
role of agrictùtural exports in sorne of these economies. This region grows permanent
tree crops with well established history of exports earnings which are required ta
service PFX. (Agricultural output grew at 2.5% annually in this region over the
period). Cocoa and coffee in Cote d'Ivoire, Ghana, Cameroon, Togo, and Nigeria4 ,
virtually guarantee export earnings. Also, one may be picking up the tendency for
sorne PFX to go directly into agriclùture in Cote d'Ivoire and Cameroon. Agriclùture
4 Large oil exports complicate the issue for Nigeria.
67
in Ghana too, may have henefited indirectly from PFX flows into the COlmtry during
the earlier and later years of tlùs study. (There is no substantial foreign investment
in agriculture in Ghana). While there is a positive relationship between KPFX and
agrÏClùtural output in the Sudano, it is unlikely that PFX was invested directly in
agriclùture. Rather agriclùture may have benefited fron1 PFX investments in mining
in t.his rpginn. Th~ negat.ive impact of this variable in the East suggcsts allocation
or re-allocation of PFX out of agriclùture (or agrictùture related activities) due ta
declining international primary agrictùtural cornmodity priees. It wotùd appear that
by pooling all regions, one dilutes the positive effect of K PFJ'y seen in the West (and
Sudano).
Domestic savings, KSAV: K S AV is not significant in the Overall or in any re
gion. The p-values of the t-statistics of t.he coefficients are 0.29. 0.10, 0.08, 0.87 in the
Overall, West, East and Slldano respectively. The coefficient is positive in the Overall
cmd in the East ooly. It is negative in the West and Sudano. As a factor input, one
would have expected its coefficient ta be positive in ail cases. The positive coeffi
cient in the East may be explained by the faet that, in the East. producer priees of
agriclùtural exports reflect international priees more than in any other region. (Note
the apparent contradiction with PFX. However, this is consistent with the argument
that domestic savings and private foreign capital are likely to have different impact
on agriclûture). This would impact positively on priees of domestic agriculture. since
otherwise a11 farmers will grow exports. By reflecting international priees, the effect
is ta recluce the bias of domestic terms of trade against the agricultural sectar in
68
this region. Thus local investors in the East find investments in agriclùture relatively
more profitable than in the West for example. In most countries in the West, the
damestic terms of trade is biased. substantially against agriculture. Thus domestic
investors in the West find agriclÙtural iDvestments lmprofitable and they allocate or
re-allocate resources ta other sectors of the economy at the detriment of agriclùture.
Indccd. Knleger ct al. (19!H) report that direct and indirect taxes on agricultural
exports averaged 51.6 percent in Ghana, Cote d'Ivoire and Zambia over the period
1960 - 84.5 The bias against agriclùture has not improved much in recent years
in spite of liberalization policies in a nllmber of cotmtries. Ghana for example has
recently been described as I4A nation of traders". (Centre for Policy Analysis. 1997),
referring to the buoyant retail trade in imported merchandise. Another example is
Nigeria, where the availability of ail contriblltes to the neglect of agriclùtl1re.
K S AV is very small and not significant in the Sudano regression. Here tao.
peasant agriclùture is not profitable being in the Sahel (near desert) region. On the
whole therefore, it wo\ùd appear that domestic savings tao have rniIÙmal or negative
impact on total agriclùtural output in SSA.
Labor, LABR: The labor variable is positive and almost significant in the Overall
(coefficient 0.28, p-value 0.06). In the West tao, the coefficient is positive and large
(0.91) and almost significant (p-value 0.05). The figures for the East are, coefficient
0.78, p-value less than 0.01. In all these cases, labor is the factor of production
5 Cote d'Ivoire and Ghana are in the West. Zamhia is in the East. However, agriculture is onlya small part of the Zamhian economy, currently about 15% of GDP. The equivalent percentage forGhana and Cote d'Ivoire is about 35.
69
with the largest coefficient except in the Sudano where it is negative. Positive and
significant labor coefficients are in accord with the observed situation on the grolmd,
and are also predicted by economic theory. The non-significance of the coefficient of
labor in the Sudano may he due to peculiarities of this region (Sahara desert belt
of Africa little snited for agriclùture, sparse poplùation, civil strife, etc.). It is also
possible that poor data qnality (for this region) is contributing ta this finding. 6
Openness, OPEN: The variable OPEN is negative in ail regians. It is not signifi-
cant in the Overall, West or Sudano, but is practically significant in the East ( p-vallle
0.03). Given that enrrent theory Sllggests that opeIUless is conducive to g;rowth, one
may wonder what cOlùd be happening here. It is noted that the benefits ta apenness
arise when competition induees increased efficiency in the nse of factor inputs. ete.
By losing export market share, in the face of increased openness, one snnnises that
the efficiency with which factor inputs are use<! must be lower in SSA than it is in
competing regions. (Ng and Yeats, 1997, disCllSS this and present evidence showing
that between 1962 - 1964 and 1991 - 1993, SSA lost a reasonable portion of its share
of üECD agriclùtural imports. By losing export market share in the face of increased
openness, one surmises that it is the relative inefficiency in the use of factor inputs,
in agriclùture in SSA that is driving the negative coefficient of OPEN. What is more,
to an endogenous growth theorist, the negative coefficient of this variable may be
capturing the fact that these economies have either stagnated or retrogressed.
Here is another view of what is happening in SSA. Recall the definition of OPEN
6 Dropping Sudan because of long ranging civil war from the Sudano sample did not change mattersvery much.
70
as trade (imports and exports) share of GD? Thus since gross GD? has in general
either increased. or remained steady, a higher value of OPEN means higher value
for imports and exports. However, most countries in SSA continue ta suffer negative
trade balances, most of the imported items being consnmables for retail trade. For
many, increases in total exports have been due to higher minerai exports. Bence as
npenn~s has reslllted in more imports, agric1.ùtural output has suffered.7 This ties
in with allocation of resonrces away from agrictùtllre as discussed above.
To obtain another view of this variable, its interactions with both KODA and
K P FX were considered. The new variables were called K 0 _0PEN and K P_0PE IV
respectively. One finds that the coefficient of KO_OPEN is positive and significant
in the Overall regression. ft is positive and almost significant in the West, but c1early
insignificant in both the East and Sudano. Realizing that 0 D.4 is a rnL'Ced bag of
grants (possibly to relieve hardship) and concessional loans (these days as a response
ta improvements in policy)! it is not clear how to argue on economic grotmds one way
or the other for a positive (or negative) value of the coefficient of this variable. The
argument is more straight Corward when K P _0PEN is considered.
KP_OPEN is negative but not significant in the Overall. 1t is negative and
significant in the West. The variable is not significant in the East, while significant
and positive in the Sudano. Thus, on the whole, one might say that private foreign
capital elasticity of agriclûtural output falls or does not increase the more open the
econoroy. This would not have been expected.8 Things may yet change however.
1 Food imports are small but on the încrease.8 One notes however that in the specification with the interaction terms, private foreign capitale1asticity of agricultural output defined as (:t: :~~k) is the SUIn of two tenns. For the West, (theonly place with significant values) the actual value of this elasticity over the period however, is
71
These restùts are essentially capturing what has happened in the pasto With the
adoption of new policies, the impact of private foreign capital on agriculture might
change if this sector becomes as competitive as the other sectors, (mining for exampie).
For another insight, this variable is further investigated below when cotmtries are
grouped by macroeconomic policy improvement.
External Shocks, EX01: The external terms of trade variable. EXOI. is weak
(p-values more than 0.18 in all cases). The data show that SSA's external terms of
trade have been falling. The stnlcture of African economies however has been snch
that farmers in most co\mtries are not iInpaeted directly by changes in the terms of
trade, sinee it is domestic governments who set producer priees of agricultllral exports
especially. AlI t.he same, these governments must no donbt consider this variable in
setting domestic producer priees. Deaton and ~Iiller (1995) have suggested that
governments try to avoid having to lower domestic priees in response to falling terms
of trade. The argument is that, governments expecting the terms of trade to falI
wotùd no donbt set today's produeer priees in anticipation of lower t.erms of trade in
the future. This is not the full picture however. Governments may not have lowered
domestic nominal priees in response to falling terms of trade, but falling terms of trade
mean lower revenues from external trade with whieh to make public investments (sueh
as provision of infrastnlctnre), mostly based on imported inputs. Even in cotmtries
where domestic priees track international prices more c1osely, falling external terms
of trade for primary agrieultural exports make the agricultural sector less attractive.
+0.08, showing a positive impact on average.
•72
Weather Variability, EX02: The EX02 variable is almost significant in the
Overall (p-value 0.02) and fairly strong (p-value 0.08) in the East. It is weak in the
West and Sudano. That this variable is positive and fairly strong emphasizes the
dependence on nature in SSA agriclùture. By and large, irrigation (or use of high
yielding seeds) is Ilot at ail significant in the overall picture. Reducing the current
state of complete cxposnrc to the clements is onc fcature of 5ub-Saharall Afrkall
agriclùture that caIls for long-term plans.
Trend and Country Dummy Variables: The coeffident of the time trend is
not significant in any regression. This wotùd sllggest that Hicks nelltral technkal
progress has not been significant in SSA agrictùture.9 Thtls~ increases in AGDP are
coming from increases in inputs only (mostly labor). Trus, theory suggests, restùts in
diminishing returns. COtUltry dummy variables are all significant in the Overall, East
and West. Confirming the hypothesis of significant tUlobserved cotmtry effects. Three
out of nine COtUltry dummy variables in the SUdéUlO region are not. Non-significance
of sorne cOlmtry dtunmy variables (or similarity of estimates), may suggest that these
cotmtries have enough cornmon characteristics to assign them a single dummy, but
trus is not purslled.
5.1.4 Policy Groups
The countries of LARGE, SlVIALL and POOR poliey groups are listed below. To
compare how things stood during the period of interest 1981 - 1993 against the pre-
9 One recognizes the argument of embodied technical progress. Howevert in this same context, onefinds in the next two chapters that Hicks neutral technical chaIlge is significant in export agricultureand domestic agriculture share equations.
73
ceding period 1970 - 1980, Wald test fol' stability of slope coefficients over the two
periods were performed. For the LARGE group, the clù-squared statistic for the ntùl
of no change in slope coefficients was 38.14 with a p-vallle less than 0.001. Thus, the
nu11 is rejected. Two variables are identified as having slope coefficients that are sig
nificantly different between the two periods. KODA is sigrùficantly larger during the
second period than the first, while KPFX is significantly smaller during the second
period than the earlier period.
For the SrvIALL group, the chi-squared statistic is 11.43 with a p-value of 0.13,
hence one concllldes there has been no change in the slope parameters over the two
periods. For the POOR group, the chi-squared statistic was 31.40 with p-vallle far
less than 0.001. Hence here tao, one conc1udes that slope parameters have changed.
This time too KODA is identified as being significantly larger during 1981 - 1993
than dllring 1970 - 1980, while KSAV is almost significantly lower during 1981
1993 than 1970 - 1980. For LARGE and POOR, the findings contradict that for
agricultural regions where one could not. reject the nnU of no change in parameters.
This is hardly surprising however. In grouping by agriclùtural regions, one is taking
advantage of a common characteristic of the countries, their c1imate which wo\ùd not
have changed over an eleven year period (1970 - 1980). In grouping into LARGE,
S~IALL and POOR, during the period 1981 - 1993 another characteristic is used.
ehanges in maeroeconornic poliey. However one has no information about any cornmon
eharacteristic that they countries may have shared during 1970 - 1980. Certainly,
eaeh grouping eomprised cOlmtries from different agric\ùtural zones. The rest of this
discussion focuses on the period 1981 - 1993, the period of economic reforms.
74
Next, one tested for poolability across policy groups over the period 1981 - 1993.
The test suggests that one cotùd ooly pool countries belonging to the LARGE and
SlVIALL groups. Significantly higher positive slope coefficients of KODA, KSAV and
EX01 in the POOR group are identified as the reason for this finding. (These are dis
cussed below). This finding suggests convergence of sorts of those groups of cOlmtries
t.hat. are responding to poliC'y rflforms. Based on these findings, data for LARGE and
Sl\tIALL cOlmtries are pooled. POOR is presented on its own. A statistical test for
the equality of the coefficients of ln(KODA) and ln(KPF)() for the POOR group
rejects equality.
Table 5.3: Parameter estimates for meta-production function 1
for Pol-icy G'roups, 1981 - 1993.
Variables2• POOR3 4SrvIALL/5 LARGE
ln(/<ODA) 0.514** 0.120
(3.945) (1.342)
ln(KPFX) 0.040 -0.0351
(0.576) (-0.543)
ln(KSAV) 0.111 -0.191*
(1.508) (-1.961)
ln(LABR) 0.623 1.041
(1.316) (1.639)
OPEN -0.182 0.146
(-1.310) (1.186)
EXOI 0.298** -0.447**
(4.318) (-4.145)
EX02 0.085 0.222**
(0.680) (4.058)
TREND -0.012 -0.030
(-1.343) (-1.854)
D. F. 58 135
See legend on next page.
75
76
l t-statistics in parenthesis below coefficient estimates.
* and ** indicate statistical significance at 0.05 and 0.01
levels respectively. D. F. is degrees of freedom.
2 Variables are defined in chapter 3.
3 POOR: Benin, Cameroon, Cote d'Ivoire, Rwanda, Sierra Leone and Zambia.
4SMALL: Central Africa Republic, Kenya, rvlalawi, l\Iali, Niger, Nigeria,
Senegal and Togo.
5LARGE: Gambia, Ghana, l\tladagasca, Tanzania, and Zimbabwe.
77
1 Table 5.3 (continued): Fixed country-specifie effeets for Policy groups.
Country POOR S~IALL/LARGE
Gambia 6.901**
Ghana 6.704**
~Iadagascar 8.158**
Tanzarua 7.926**
Zimbabwe 6.457**
Central Afriea Republic 6.319**
Kenya 6.904**
!vralawi 6.954**
~Iali 8.264**
Niger 7.094**
Nigeria 6.541**
Senegal 7.565**
Togo 7.332**
Benin 2.283
Carneroon 3.090
Cote d '1voire 3.209
Rwanda 1.438
Sierra leone 1.592
Zamhia 1.310
* and ** indicate significance at 0.05 and 0.01 respectively.
78
Regression Parameters
Parameter estimates for the combined LARGE and S~IALL and the POOR groups
are reported in table 5.3 and discussed next.
Group: Combined LARGE & SMALL. These group of COlUltries are dassified
as having shawn positive improvenlent.~ in thf'ir ffiRf'rOPConomir poliC"'y f>nvironment
between the years 1981 - 1986 and 1987 - 1992. The coefficient of the stock of aOA
is positive, small, (about one-tenth the labor coefficient), but not signifieant (p-vallle
0.16). The coefficient of the stock of PFX is negative, small and not signifieant (p
value 0.59). The stock of domestic savings has a negative coefficient that is one-fifth
the magnitude of the labor coefficient. It is practically significant (p-value 0.05).
Thus. the three forms of capital have different impact on agrîcultllral outpnt. The
negative coefficient of domestic savings is partictùarly t.ronblesoIne. That increases
in domestic savings are having detrirnental effect.s on agrictùture speaks t.o (i) no
investments of domestic savings in agrictùture, émd/or (ii) investments of domestic
savings in other sectors and in the process having negative impact on agrictùture.
The second cornes about for example, when the flow of investments to other sectors
(retail trade, say) restùt in farmers having reduced access to credit (which is already
very low).
Positive policy environment in this context means the economy has become more
liberalized in many respects. One may see this as rednction in indirect taxation
(Le., reduction in domestic currency over-valuation) of agriclùtural output. However,
direct taxes (government price fixing) for the major items - coffee, cocoa, etc. - still
79
existed over this period. Unfortunately, the supposed reduction in indirect taxation
wOlùd not benefit farmers that much as long as governments continue to set prices
low. To this, one adds that devaluation was generally inflationary and as a farmer,
being on fi.xed prices is no way to keep up with inflation.
Substantial amolmts of development assistance (concessionalloans) may have been
rPrpivpd hy t.hpse governments during this period. This nnalysis shows that thcsc did
nat impact the agricultural sectar significantly. The story with respect ta PFX fiows is
that, the period under consideration coincided with private foreign capital flight (both
foreign direct investment and bank loans) out of SSA. Over the period, agriclùtural
output did grow at a modest 1.5%, hence the negative relation with (generally) KPFX.
The labor coefficient (LAER) is once again the largest factor input c.ontributing
positively to agrÎcnltllral outpnt. It is however not significallt (p-value 0.10). lndeed.
as discussed in the introduction, agriclùture as practised in SSA has traditionally
had a high labor component. But more activities in other sectors of the economy
engendered by liberalization, have been attracting YOlmger and more energetic people
away from farming.
OPEN is positive but not significant (p-value 0.23). Openness has meant. reduced
direct taxation on agriclùture as a whole, but more sa for domestic agriclùtllre, (gov
ernments not fixing priees for this sub-sector) and may thus he encouraging output.
However, by the coefficient not being significant, the impact of openness has not been
as strong as is implied by theory.
In the specification involving the interactions between openness and foreign capital
flows, it turns out that KP_OPEN is positive (0.12) but not significant (~value
80
0.20), while KO_OPEN is negative (-0.05) and not significant (p-value 0.45). This
suggests that the elasticity of agriclùtural output with respect to private foreign
capital increases with openness in good policy environment. Opening up economies
ta the rest of the world is a major pIank of the policy reform package by which these
cOlmtries have been judged as having done weIl. This is what growth theory wOlùd
prcdict. It has bccn argucd that it is not obvious how ta explaill t.he KO_OPE1\/
variable, given the composition of ODA.
Back to table 5. The coefficient of the ternIS of trade (E..Y01) is negative, large
(-0.44) and significant (p-value < 0.01). The finding here must be picking up the
positive trend in agriclùtural output (annual growth rate of about 1.5%), against
falling terms of trade of primary agriclùturaI exports over the period. (This may aIso
explain why domestic savings will be invested in those sectors of the economy with
more favorable international (in addition to domestic) terms of trade). The weather
variable (E..Y02) impacts agriclùtural ontput significantly. The inlpact is positive.
This finding speaks ta the extent of reliance on nature. The trend variable is negative
but it is not significant. This suggests deterioration in total factor prodllctivity in
agrÏclùture. This may not be surprising when viewed in the context of insignificant or
negative physical capital coefficients. AlI COlmtry dummy variables for the combined
LARGE and Sl\IALL regression are significant.
Group: PODR. Peculiar things happen here. While labor is pùsitive and not sig
nificant, this is the only group in which KDDA is positive, large (0.51) and significant
(p-value < 0.01). KPFX is positive, small (0.04) but not significant (p-value 0.56).
81
KSAV is alse positive (0.11) but net significant (p-value 0.13). The cOlmtries in this
group include Benin, Cameroon, Cote d'Ivoire, Rwanda, Sierra Leone and Zambia.
Thus, growth in agricultural output in these cOlmtries increases with OOA, KPFX
and SAV. Their policy environment may be judged to he bad, but they seem to have
made the crucial connection between agriclùtural output and capital. Indeed, the
annual growth rate in agrictùtural output per hectare for this group is 2.1% (over
the period 1981 to 1993), larger than for the cOlmtries judged to have positive policy.
Of the three, KPFX is smallest and weakest probably for the same reason that this
variable is not significant in most other specifications. The general tendency is for
private foreign flows to go into trade oriented sectors. The unusual positive impact
of KODA on agriclùtural output is not easy ta explain. We can aoly guess that it is
being targeted at agriculture specifically.
The labor coefficient is positive, large (0.62) but not significant. The variable
OPEN is negative but not significant. This follows from being classified as poor pol
ieyenvironment. Here, the external terms of trade are significant and positive llnlike
our finding for the other group. Poor policy environrnent implies poor performance
in respect of fiscal, monetary and exchange rate policies. Three of the six cOlilltries,
(and practically a fOluth, Rwanda), are members of the CFA currency zone, where
the CFA was considered to have been over-valued for most of the period tmder consid
eration (UNCTAD, 1997). Given this environment, the exc.hange rate for conversion
of agricultural output to United States dollars will influence the outcome more than
in other regions or groups. One thinks this is probably what EX01 is reflecting. lO
10Also, included in this group is Cote d'Ivoire which has tried in the past ta hold off cocoa exports
1
82
The weather variable has no explanatory power this time, suggesting other variables
have stronger explanatory power. The trend coefficient once again suggests nega-
tive productivity growth. The coefficient is however not significant. This suggests
higher total factor productivity than in the LARGE and SlVIALL group of countries.
The finding is however consistent with the positive impact of the capital variables.
Conntry effects are significant, but the test that they are jointly zero is rcjectcd..
Next, the preceding reslùts are summarized.
5.1.5 Summary
This chapter has reported and discllssed estimation of parameters for the meta-
production nmctions of agriclùtural output in 27 sub-Saharan African eOllntries.
Estimates were obtained for (i) aIl cOlmtries pooled together, (ii) cOlmtries pooled
together by agro-climatic region, and (iii) cOllntries pooled together on the basis of
improvement in macroeconomic policy environment. The production ftmction is cast
within the framework of neoclassical and endogenous growth models. Physical capital
is disaggregated into the stock of official development assistance, the stock of private
foreign commercial fiows, and the stock of domestic savings in order to focus on the
impact of each component. Labor, openness of the economy, external terms of trade
and weather variahility are the other regressors. The proxy for human capital, the
average years of schooling within the labor force was judged to be lUlsatisfactory in
captluing human capital of farmers and was therefore excluded.
While statistical tests suggest that the slope parameters are unchanged over the
during periods that they consider the world priee of the commodity to be too Iow. In such a situation,measured output will tum to vary positively with terms of trade.
1
83
two periods 1970 - 80 and 1981 - 93, (periods without and with structural reforms
respectively), for each agricultural region, the evidence is diffenmt for policy groups.
For these (policy groups), significant differences exist between the slope coefficients
of the LARGE group over the two periods. The same holds for the POOR group. For
the 1981- 93 period, however, significant differences in slope coefficients exist between
C'ornhinpo LARGE and SIvIALL on one hand, and POOR on the other. Differences
in the slope coefficients of the capital variables and openness between the two are
identified as being statistically significant.
One finds that the impaet of development assistance is positive everywhere except
in the West. This variable has significant impact on agricllitlual output only in t.he
POOR group. In ail the regions as weil as in the combined LARGE and S~IALL
group. the magnit.udes are only a small fraction of the impact of the labor variable.
The magnitude is mllch doser to the labor coefficient in the POOR group. Private
foreign capital has positive impact on output in aIl regressions except those for the
East and combined LARGE and SrvIALL. The impact is signifieant ol1ly in the West
and Sudano. The magnitudes are about the same as for development assistance.
Domestic savings are positive in the Overall, East, and POOR groups, but not signif
icant anywhere, though almost so in the combined LARGE and S~IALL where the
coefficient is negative. Here too, the magnitude of the impact of this variable is about
the same as for the other capital variables.
Labor is the variable with the highest impact in all cases. Its impact is positive
and significant in all regions but the Sudano-Sahel region where it is negative. It is
positive but not significant in the policy groups. On the whole, the openness measure
84
bas negative or insignificant impact on agricultural output. With respect to the agro
climatic groups, the terms of trade have no significant impact on agricultural output.
Its impact is significant for the poliey groups: large and positive for POOR, and
large and negative for combined LARGE and SMALL. On the whole, variations in
the weather have practically significant explanatory power for changes in agrictùtural
output. Technical change is virtually non-existent within the agricultural regiuus
(bath in magnitude and significance). It is negative in both policy groups, (and
alrIlost. significant in the combined LARGE and Sl\IALL).
The next two chapters present and discuss estimation restùts for the static and
dynamic export and domestic shares of agriclùtural output.
Chapter 6
Empirical Results - Analysis ofAgricultural Share Response in theShort-Run
This chapter discusses estimation of the static export share eqnation (3.8) with the
time-specific dlunmy variables replaced by a Ume trend. The response of the ex-
port (and domestic) shares of agriclùtllre ta prodllcer priee changes, factor inputs.
economic policy and external shoeks is investigated in order to nnderstand why agri-
cultural exports as a share of total agricultural output has been falling over the years.
This has been so in spite of anecdotal evidence that Foreign financial flows, recent eco-
nantie recovery programs and government polides favor export agrictùtllre relatively
more than agriclùture for domestic consnmption. Short-nln responses are cornmon in
the literature and are of interest in short term impact evaluation of policy and priee
changes. As weil, short term impact of changes in the capital and other variables are
of interest here.
The complete panel consists of 23 out of the 27 co\wtries previously used. in the
production function investigation. Producer priee data ww) not available for 4 of
those countries. Pairwise correlation coefficients between the variables are similar to
85
1
86
those presented in table 3. In addition, export share (AEXP) is mildly negatively
correlated with AGDP, KODA, EX02 and LABR. It is positively correlated with
OPEN, KSAV, PRIeE, KPFX, and EXOI. Again, for a more complete understanding
of the relationship between the dependent and independent variables in the model
one turns to regression analysis.
6.1 Regression Results
As done previously, iIÙtial estimation and testing of the model by ordinary least
squares yielded heteroskedastic and alltocorrelated residuals. A generalized least
squares method was then llsed to estimate cOlmtry-effects and coefficients of other
variables of the export share eqllation in one step in ~Vinrats - 32 versian 4.3. 1
6.1.1 Diagnostic Tests of Regression Adequacy
Following the steps taken in the previons chapter for the meta-production fllnction,
standard diagnostic tests were performed to assure validity of estimation reslùts. Au-
tocorrelation ftmction tests snggested that the variables in the model were stationary
in levels, while variance inflation factors compllted for ail explanatory variables were
all weil below 10.
Outliers
Once again, tests for, outlying cases with respect to the regressors (leverage val-
lles of observations) and dependent variables (studentized deleted residl1als) were
performed. No significant leverage values were determined. Just about one percent
1 Again, for a consistent estimate of the varianc~covariancematrix in the presence of heteroskedasticity and autocorrelation ofresiduals t the uROBUSTERRORS" and "LAGS" options in Winratsare specified as previously described.
•87
of the studentized deleted residuals feU within 2.5% of either tail of the corresponding
t distribution. Finally, computation of the Cook's distance statistics did not suggest
that any observation had significant influence on parameter estimates.
6.1.2 Agro-Climatic Regions
Regression results are presented in two parts. First, for the Overall and the three
agro..climatic regions covering the entire period of study, then for countries grouped
by macroeconomic policy environment. One year lagged OPEN and PRIeE were
used as instrumental variables for CUITent values of the two variables.
Parameter Stability
For agriclùtural regions, Wald tests for stability of slope coefficients were performed as
before for stability over time within each region. Sample results are the following chi
sqllared statistics: 18.09 (p-value 0.02), 20.57 (p-value 0.008), 22.10 (p-value 0.006)
and 16.44 (p-value 0.03) for the East, WP.St., Sndano and Overall respectively. for
stability of slope coefficients between 1970 - 80 and 1981 - 93. The chi-squared statistic
for poolability of data between pairs of regions are 17.11 (p-value 0.02) for poolability
between West and Sudano, 13.74 (p-value 0.09) for poolability between West and
East, and 15.55 (p-vall1e 0.05) for poolability between East and Sudano. Thollgh the
values of sorne of these tests throw sorne doubt on the validity of poolability of the
data on statistical grolwds, one goes ahead and pools the data on pragmatic grolmds
as previously discussed. As before, parameter estimates are reported for the Overall,
West, East, and Slldano agriclÙtural regions over the entire period (1970 to 1993) in
table 6.1. The findings with respect to the three poliey groups are different. These
88
are reported later in table 6.2.2
Parameter Estimates
Estimated parameters for this section are presented in table 6.1 for the export share
equations. For the Overall data, estimation with 23 cOlmtry dtunmy variables yields
t statistics for 17 cOlmtry dllmmy variables were not significantly different from zero.
The values of five of these dllmmy variables were clearly close ta each other. Upon
testing far eqnality of these five, one cOlùd not reject the nlùl hypothesis that they
were equal. The test that they were jointly zero was rejected however. These five
were then pooled to form one dununy variable, and the equation re-estimated. The
impact of this was to change the coefficient and standard errar estimates of sorne
variables somewhat. One reasons that the variances of the conntry dl1mmy variables
were not efficiently estimated initially, possibly due to collinearity between couutry
dummies and sorne variables. By combining the dummy variables. one opts for a
more efficient variance. The priee of this however, is possible bias in coefficient
estimates. As discussed earlier, the rationale for doing so has been discussed by
Baltagi (1995) and others. In the Sudano estimation, two pairs of COlmtries were
assigned a common dummy. While for the West, three sets of cauntries (made np af
four and twa cOlmtries) were assigned one dummy per set. In the East, ail countries
retained their individual dummy variables as all were statistically significant and fairly
different from each other. Equality of the coefficients of In(KPFX) and ln(KSAV)
was rejected. for Sudana.
2 Given the large number of parameters being estimated, the risk of type 1 error is again controlledat 0.01 in all of the following discussion.
89
1 Table 6.1. Parameter estimates for export share regression1 -
Agricultural regions (1970 - 1993).
Variable2 OveralP West4 East5 Sudano6
ln(KODA) 0.026 0.003 0.075 0.041
(1.354) (0.097) (1.888) (1.417)
ln(KPF.X) 0.019 -0.010 0.047 0.024
(1.377) (-0.340) (1.612) (1.777)
ln(KSAV) 0.013 0.010 0.114** -0.042**
(0.681) (0.362) (3.198) (-2.506)
ln(LAER) 0.035* -0.153** -0.065 0.019
(1.959) (-5.737) (-0.458) (0.288)
OPEN 0.380** 0.367** 0.624 0.722**
(5.006) (3.104) (1.575) (3.648)
EXOI 0.076** 0.062* 0.065* 0.103*
(3.420) (2.180) (2.009) (1.986)
EX02 -0.045 -0.072 -0.046 -0.063
(-1.260) (-0.0976) (-.878) (-1.794)
ln(PRICE) 0.019 0.062* 0.099 -0.020
(1.023) (2.131) (1.476) (-0.364)
TREND -0.012** -0.101** -0.017** -0.009**
(-6.250) (-4.705) (-3.904) (-2.528)
D. F. 478 142 180 140
See legend on next page.
1 t-statistics in parenthesis below coefficient estimates.
* and ** indicate statistical significance at 0.05 and 0.01
leve1s respectively. D. F. is degrees of freedom.
2 Variables are defined in chapter 3.
30verall: West, East and Sudano together.
-lWest: Benin, Cameroon, Cote d'Ivoire, Ghana. Nigeria, Sierra Leone.
and Togo.
5East: Burundi, Ethiopia, Kenya, rvladagasca, ~Ialawi,
Rwanda. Tanzania, Zambia and Zimbabwe.
6Sudano: Burkina Faso, Central Africa Republic. Gambia.
rvlali, Niger, Senegal and Sudan.
90
91
Table 6.1 (continued): Estimates of fixed country-specifie
effects for agricultural regions. Continues next page.
COlwtries Overall1 West2 East3 Sudand1
Beninw1 -0.167 -0.226
Cameroonw1 0.110 -0.226
Cote dl! Ivoire O.302lt' 0.127
Ghana@,tu2 0.080 -0.034
NigeriaWl -0.211 -0.226
Sierra Leonew2 0.042 -0.034
Togotu 1 -0.258 -0.226
Bltrllndi -0.070 -0.816
EthiopiaC» 0.080 -0.632*
Kenya 0.218 -0.946*
rvladagascar 0.434** -0.305
ivIalawi 0.427** -0.591
Rwanda -0.250* -1.162*
Tanzania 0.241 -0.651
Zamhia -0.070 -1.262*
Zimbabwe 0.514** -0.568
Table 6.1 (continued): Fixed cOlmtry-specific effects for
agricultural regions.
COlmtries Overall1 West2 East3 Sudano't
Burkina Faso 0.002 0.009
Central Afriea Rep. s2 0.214* -0.130
Gambias1 -0.192 -0.150
~[ali''' 0.080 0.060
Nigers1 -0.197 -0.150
Senegarti 0.080 0.141
Sudan,,*,s2 0.080 -0.130
* and ** indicate significance at 0.05 and 0.01 respectively.
1,2,3,4 Overall, West, East and Sudano are defined above.
s1Cmmtries assigned common dummy variable in the Sudano.
s2Colultries assigned common dummy variable in the Slldano.
wlColmtries assigned common dnmmy variable in the West.
w2Countries assigned common dlunmy variable in the West.
gColmtries assigned common dummy variable in the Overall.
92
1
93
Stocks of capital KODA, KPFX, and KSAV : The coefficients of the stock
of the development assistance variable in the export share equations are positive in
al! regions. The magnitudes are all small however, (less than a tenth of the coefficient
of OPEN, the variable with the largest coefficient). The coefficient is largest in the
East (0.075) where it is strongest (p-value D.OG). KPFX is positive ill ail regiouti
but the West, and more or less of the same magnitude as KODA. It aIso has its
largest magnitude in the East (0.047). It is nat significant in any region. Thus, the
suggestion that both forms of foreign finanda! flows are likely ta impact export share
of agriclùture positively has sorne support in the Overall, East and Sudano. Recall
that in the production function, KPFX had a very significant and positive coefficient
in the West. By not being significant in the export share equation, one conc1udes
that in relative terms private foreign capital does not promote one sub-sector over
the other in this region.
The stock of domestic savings is significant in both the East and Sudano, (p
values < 0.01 in both cases). The coefficient is positive in the East, but negative in
the Sudano. This variable is positive but not significant in the Overall and West.
The magnitude of the coefficients of this variable in the various regions are about the
same as they are for KODA and KPFX. The coefficient is largest in the East with a
value of 0.11. Thus one concludes that domestic savings have a tendency to impact
export share positively in the West, Overall and in the East. Significantly 50 in the
latter case. On the other hand, domestic savings have a significantly negative impact
on exports in the Sudano. For this region, the unusual impact of the labor variable
1
94
in the production nmction has aIready been noted.. This negative impact of domestic
savings on export share is another. One hastens ta add that sorne countries included
in this latter region are landlocked. Exports must go by air, or travel by land and
then rely on shipping facilities of other cOtmtries. Either scenario will raise the cast of
exports, rendering investments in this sub-sector of agrictùture less attractive. This
e.xplanation is in addition ta the f'let th'lt many of thcsc countries are in the Sahel
zone as previously noted. (Note that negative coefficients in an export share equation
implies positive coefficient in the domestic share equation).
One notes that all forms of capital have greatest positive impact in the East. The
explanation must be that, by allowing producer priees to reflect international priees
more, t.he bias in domestic terms of trade against agrictùture in SSA is smallest in
this region. Hence on a relative basis, more investments are made in agrkulture in
the East than in othee regions.
Labor force, LABR: The labor coefficient is small but positive in the Overall
(0.04) and in the Sudano (0.02) export share equations, (p-values 0.05 and 0.77 re
spectively). The impact of the variable is larger but negative in the East and West
(coefficients -0.062 and -0.15 respectively). It is significant only in the West. Thus,
one wotÙd conclude that increases in the labor variable lead to practically significant
increases in expart share of agriculture Overall. However, in the West, labor force
increases actually lead to a reduction in export share to the advantage of domestic
share. For the other two regions, the impact of labor force increase does not appear
to favor the share of any particlùar sub-sector significantly. The explanation for the
95
finding for the West (for example Ghana) may be found in the increasing attrac
tiveness of the domestic sub-sector due to deteriorating food secllrity situation, little
government involvement in setting prices in the domestic sub-sector, as weil as low
(sometimes falling) real producer prices for exports. (Governments fix nominal prices
orny).
Openness, OPEN: This variable is positive in an regions. It is aIso significant
everywhere but the East where it has a p-value of 0.11. The positivity of this variable
is probably due to the significant role agriclùtural earnings play in SSA trade earnings.
That is, trade share of GDP tends to rise and faH with agriclùtllral exports. Hence
the advice from sorne quarters that agriclùtural export earnings must be given serions
attention. As export shares move in the sarne direction as OPEN, domestk shares
must be moving in the opposite direction, hence openness has strong and negative
impact on domestic share in all regions. Its impact is weaker in the East. OPEN,
the policy variable which also proxies the extent of liberalization of the economy
including reduction in direct and indirect taxes, may not be significant in the East
because lower gains will he realized from further 'opening up' in this region than has
already happened because of the argument that this region more than others already
enjoys sorne of the gains from liberalization, (lower direct taxation of agrictùtural
exports).
Terms of trade, EXOl: That the external terms of trade variable is positive and
practically significant in all regions in the export equation is not surprising. Export
•96
share values are obtained at international priees. And agric1.ùtural exports constitute
a significant portion of the exports of most eountries. Thus, international prices for
agricultnral commodities influence the determination of the terms of trade of most of
these countries. Hence, the export share tend to rise and faIl with the terms of trade.
\Veather VariabHity, E)(02: The coefficient of this variable is not significant in
the any specification of the export share equation. This is what one would expect.
On the whole, one w01.ùd expect variability in weather ta affect bath domestic and
export sllb-sectors almost equally.
Ratio of export-to-domestie produeer priees, PRIeE: The role of price in
sllpply response has been studied extensively. Here, it is positive in all regions but
the Sudano. It is practieally significant (p-value of 0.03) in the West only. Thus
increases in the PRIeE variable lead to higher export share of agriculture except in
the Slldano. This is to be expected sinee an increase in this variable will result from
either an increase in the real producer priees of exports (numerator), or a decrease in
the real domestic agrieultural producer priee (denominator). In the Slldano, increases
in the variable PRIeE have a tendency to lead to a falI in the share of exports.
This appears unusual, but negative elasticities have been noted in the literatnre in
the eontext of subsistence agriclùture. In snch econornies, higher prodncer priee for
agricultural exports means the need to sell fewer quantities to meet onets needs, (e.g.,
Ezea1a-Harrisan, 1996). This is one more unusual finding with respect ta the Sudano
region.
1
97
Jaeger (1992), Elmi (1994) and Bond (1983) have reported priee elasticities for
agricultural exports, food crops and aggregate agrictùture. Conceptually, their figures
are not directly comparable ta the ones obtained here since they were obtained from
different models. Be that as it may, the implication of the findings of this study
is that, the role of producer priee increases by themselves though positive in most
countries, is limited (Dinswanger, 1989, allJ Clùùbuel', 1988).
Time TREND: In all Ca':ies, the coefficient of the trend variable is negative,
small and significant in the export share equations, hence positive and significant in
the domestic share specifications. Interpreting this coefficient as productivity growth,
one wo\ùd conclude that in aggregate, productivity changes are having significantly
negative impact on export shares.
98
6.1.3 Policy Groups
Separate tests were performed for stability of slope parameters with respect ta the
three policy groups, LARGE, S~IALL and POOR as was described in chapters three
and five. First, for stability over time for each poliey group betwccn the pcriods 1!J70
- 1980 (period prior ta being classified) and 1981 - 1993 for example, and then for
poolability across regions. The chi-squared statistic for constancy of slope coefficients
for the LARGE group was 25.24 with a p-value of 0.001. Thus, one cannot accept
the hypothesis of no change over the two periods. The slope coefficients of KODA
and KPFX are identified as being sigIÙficantly different over the two periods. The
test shows that the slope coefficient for KODA was significantly smaller in the 1981
93 period than in the period 1970 - 80, while that for KPFX was significantly larger
during the 1981- 93 period than during 1970 - 80. This wOllld suggest that KODA did
not promote export share as much as it did in the earlier period (probably benefiting
other sectors of the economy in the later period), while KPFX on the other hand,
promoted export share more.
For the SMALL group, the corresponding chi-squared statistic was 18.69 with p
value 0.02. This gives borderline indication of no change in slope coefficients. (This
time KPFX is identified as the only slope coefficient that is almost significantly dif
ferent between the two periods). It is lower during 1981 - 93 than 1970 - 80. For the
POOR group, the chi-square statistic was 10.80. Thus, one is unable to reject the
null hypothesis of no change in slope parameters.
99
Poolability of data across all three groups, or, for any pair during the period 1981
93 is strongly rejected. The pairwise chi-squared statistics are 36.27, 42.94, and 25.26
for LARGE and S~lALL, LARGE and POOR and SlVIALL and POOR. AlI p-vaiues
are less than 0.001. Equality of the coefficients of ln(KODA) and ln(KSAV) was
rejected for bath LARGE and PODR.
100
1 Table 6.2. Parameter estimates for export share regression1
for Policy groups, 1981 - 1993.
Variable2 LARGE3 Sl\JIALL4 POORs
ln(KODA) 0.129** 0.057** -0.303*
(2.839) (3.530) (-2.259)
ln(KPFX) -0.032 0.033 -0.079
(-0.464) (0.613) (-0.997)
ln(KSAV) -0.023 0.040 0.051
(-0.258) (1.412) (1.135)
ln(LAER) -0.398 -0.068 1.692**
(-1.135) (-0.232) (2.618)
OPEN 0.105 0.153 -0.329
(1.547) (1.674) (-1.338)
EX01 0.257** 0.084 0.158**
(3.697) (1.529) (2.509)
EX02 -0.091* -0.114** -0.078
(-1.908) (-2.574) (-1.384)
ln(PRIeE) 0.061 0.138** -0.016
(1.819) (3.936) (-0.469)
TREND -0.016 -0.006 -0.027**
(-1.608) (-1.139) (-2.497)
D. F. 46 80 57
See Iegend on next page.
1
101
1 t-statistics in parenthesis below coefficient estimates.
* and ** indicate statistical significance at 0.05 and 0.01
levels respectively. D. F. is degrees of freedom.
2 Variables are defined in chapter 3.
3LARGE: Gambia, Ghana, ~Iadagasca, Tanzania, and Zimbabwe.
4SMALL: Central Africa Republic, Kenya, rvlalawi, rvlali, Niger, Nigeria,
Senegal and Togo.
5 POOR: Benin, Cameroon, Cote d'Ivoire, Rwanda, Sierra Leone and Zarnbia.
102
1 Table 6.2 (continned): Fixed cOlUltry-specific effects.
Export share equations - Policy groups.
Country LARGE SlVIALL POOR
Gambia -0.740
Ghana -0.931
lVladagascar -1.056
Tanzania -1.139
Zimbabwe -0.583
Central Africa Republic -0.392
Kenya -0.466
~Ialawi -0.017
lVlali -0.339
Niger -0.627
Nigeriasl -0.697
Senegal -0.385
Togos! -0.697
Benin 2.756
Cameroon 3.865
Cote d'Ivoire 3.924*
Rwanda 0.779
Sierra leone 2.817
Zamhia 4.621
* indicates significance at 0.05 level.
1
103
Parameter Estimates
Estimates of parameters for the policy groups are presented in table 6.2.
Capital, KODA, KPFX, KSAV:
In both the LARGE and S~IALL subgroups, the KODA coefficient is positive and
significant. The magnit.lUip of t.hp ('oeffident in LARGE (0.13) is twice n.s big as that
for SivIALL (0.06). The KODA variable is negative (-0.30) and significant for the
POOR group. The stock of private foreign capital is not significant for any group.
Its coefficients are negative in the LARGE and POOR groups, but positive in the
SlVIALL group. AlI coefficients are smaller in magnitude théUl corresponding ones for
KODA. Domestic savings have virtually no expléUlatory power for the LARGE group,
but have substantially more explanatory power and positive in Sl\·IALL and POOR,
though not significant for either group.
[t wo\ùd appear that linking flow of concessionalloans (a high proportion of OOA
in recent times) to improved policy environment is having heneficial influence on
the export share (over domestic share) of those conntries that. are able to attraet
such loans. In the POOR countries, aDA appears to be benefiting domestic share
of agriclùture instead. If the objective of aDA is to help improve foreign exchange
earnings, in the LARGE and S~IALL groups of cOlmtries, but on the other hand
ta help improve the domestic sub-sector in the POOR group, then aDA is serving
its purposes. These apparently different objectives of ODA are not contradictory
however. For, sorne countries may require aOA to improve the level of agriculture for
domestic consumption, while for others whose agriculture for domestic consumption
104
is adequate, OOA may be geared towards increasing export earnings.
On the other band, it wOlùd appear that private foreign capital does not signifi
cantly impact either the export or domestic sub-seetor in any policy group. Domestic
savings too do not significantly impact the share of one sub-sector over the other in
any group either.
Labor, LAER:
The labor coefficient is positive, large and significant only in the POOR export
share equation. This is the only region where labor input positively impacts export
share over domestic share. In the LARGE group, the LABR coefficient is big but
negative, while in S~IALL, it is negative but not big. Both are not significant. Seing
negative implies that they are benefiting the domestk share more. Liberalization in
the face of high direct taxes on agricultllral exports must be driving lltili ty maximizing
farmers to the lower taxed domestic sub-sector.
Openness, OPEN:
Openness is positive and fairly big in the export share equations of LARGE and
SIvIALL countries. This is not surprising sinee policy eompliance includes liberaliza
tion of the agricultural sector (as weIl as others). This is in addition to the faet that
agriclùtural exports are still a good portion of each country's exports. The variable
is not significant however. This may be due to the faet that there are limits to the
benefits of openness if other steps such as investments, training of people, (Rodrik,
1997) are not taken in addition. The coefficient is negative in the POOR group.
This follows from the classification of these countries as being in a negative policy
environment. That the coefficient is not significantly negative here too follows from
105
the fact that agriCtÙtural exports are substantial in spite of being classified as poor
poliey environment.
Terms of trade, EXOI :
The extemal terrns of trade variable is signifieant and positive for all groups in the
export share equation. This is not surprising sinee export share values are obtained
at internatiùuéÙ prit2es, and a.') explaiuoo pl'eViOlltily, agl'iClùtlUa! exports coustitute
a significant portion of the exports of most countries and hence they influence the
determination of the terms of trade of most of these countries. Hence, the export
shares tend ta rise and faU with the terms of trade.
Weather variability, EX02 :
The coefficient of this variable in the export share equation is small and negative
aeross ail three groups. The magnitudes are almost equaI aeross the groups. The
coefficient is significant only in the S~IALL group. One is Imable to explain this
later observation except ta note that, there are fewer observations in these policy
regressions, thus pectùiar events may stand out more here.
PRIeE:
This variable is positive in both LARGE and Sl\tIALL gronps. It. is significant
in Sl\tIALL, and has a p"value of 0.06 in LARGE. The coefficient. of this variable
is negative and not significant in POOR. Reduced government intervention in priee
determination is a major plank of poliey reform. Reforming countries are the ones
responding to calls ta liberalize their economies, including giving farmers higher pro
ducer priees. Thus, it appears that as governments respond ta the calI 50 do farmers
(Knleger et al., 1991, and Bautista and Valdes, 1993). The same argument explains
106
why in poor poliey environment, (which translates ta lower level of liberalization,
hence higher government control over priees), producer priees have no significant im
pact on export share. There is a slight tendency for priee increases to favor domestic
share in the POOR countries.
TREND:
The trend coefficient is negative across all groups. In addition, it is significant
in the POOR group. Interpreting tms coefficient as productivity growth. one wOlùd
conclude that aggregate productivity is falling in the export sub-sector across the
groups. The drop is however larger and stronger in the POOR policy group. The
other side of the argument is that productivity appears to be improving more in the
domestic sub-sector in the POOR group, than in t.he other two groups.
6.1.4 Summary
This chapter reports the reslùts of estimating the static response of export shaxe of
agriclùture in 23 sub-8aharan African cOlmtries over the period 1970 to 1993. Share
equations are deduced using the profit nmction (GDP function) approach. Physical
capital is disaggregated into the stock of official development assistance, the stock of
private foreign commercial flows and the stock of domestic savings as before. The
analysis also focuses on the openness of each economy as a measure of economic
poliey, labor, terms of trade, and the role of the weather. The data are pooled
together in several ways: (i) aU countries in one panel, (ii) COlmtries within one agr~
elimatic region, (three regions), and (iü) cotmtries grouped by improvements in their
macroeconomic poliey.
•107
Statistical tests suggest that slope parameters are unchanged between the two
periods 1970 - 80 and 1981 - 93 far agricultural regions. The evidence is somewhat
different for poliey groups. For these, a statistical test suggests a change in slope
coefficients for the LARGE group over the two periods. The two foreign capital
variables stand out as having changed significantly during the second period for the
LARGE group. The iJllpact of ODA wa.s lower in the second period, while that of PFX
was larger. Slope coefficients were lUlchanged for the SJ\.IALL and POOR groups.
The reslùts show that the impact of the stock of development assistance is positive,
small but not significant either Overall or in any of the regional groups. On the other
hand, the stock of development assist.ance impacts export share of agriculture in the
policy groups significantly. The impact of tms variable on the share of agricultllral
exports is highest (positive and significant.) in the LARGE group, followed by the
S~IALL group. Its impact is negative, fairly large and significant in the POOR
group.
Private foreign capital fiows too impact the export share of agricultnre positively
in all the regions but the West, where the impact in negative, small and insignificant.
The impact is small in the other regions tao, and of about the same magnitude as
development assistance. Within the policy groups, the impact of tms variable is not
significant any where. It is negative in bath LARGE and POOR, but positive in
Sl\JIALL.
Domestic savings have positive impact in aIl the regjans but the Sudano (where
they are negative). They are significant only in the East and Sudana. The magnitude
of the impact of this variable is about the same as the other two capital variables.
108
Within the policy groups, the impact of this variable is not significant, and its magni
tude is about the saIne as that of private foreign capital. It is negative in the LARGE
group, but positive in Sl\tIALL and POOR.
Labor is positive Overall and in the Sudano. It is negative in the West and East.
It is significant in the West, and almost 50 in the Overall. Among the policy groups,
the impact of labor is negative in the LARGE and S:tvIALL groups, but positive in
the POOR group, the ooly group in which it is significant.
Openness has positive impact on export share everywhere exeept in the POOR
poliey group. However, it is significant only in the Overall, West and Sudano. The ex
ternal terms of trade are aIso positive and signific~mt or aImost significant everywhere.
The weather variable is negative everywhere, but not significant anywhere.
Domestic priees are positive everywhere except in the Sudano and in the POOR
eountries. They are significant in the SrvIALL cotmtries, and almost significant in the
West and LARGE. Finally, there is ample evidence that changes in productivity are
having negative impact on export share in aIl regions and policy groups.
The findings suggest that once again. the stnlcture of the response of export and
domestic shares have not changed significantly aver the period across the agrictùtural
regions. However, when looked at fram economic policy point of view sorne changes
have oecurred. Development assistance for the cotmtries grouped lmder LARGE
poliey used to have a larger impact in the earlier period (1970 - 80) on export share
than during the second period (1981 - 93). On the other hand, private foreign capital
used to have a sma1ler impact on export share during the earlier period than it did
during the second period.
109
The findings further suggest that, the raIe of the capital variables is not significant
in determining export shares though the tendency is for capital ta impact export
shares positively. Labor is no longer dorninating other factor inputs in determining
export share the way it did in determining of agricultural output. The impact of
producer price increases though in general positive is not significant in determining
sllb-~ector shares. There is weak evidellce of suLsistellce agriculture in the Sudano.
Opermess and terms of trade are in general significant in determining export shares.
The next chapter investigates share response in adynamie framework.
Chapter 7
Estimation and Empirical Results Dynamics of Agricultural ShareResponse
This chapter discusses the techniques used to estimate the parameters of the dynamic
share response mode1, and then presents estimation reslùts. In spite of interest in
static share response in the literature. one notes that short-rnn response obtained
from static models do not take into aceount the faet that the empirical relationships
on the grolmd may not be in long-run eqnilibrium. Indeed. agricultnral supply may
not respond immediately ta changes in the explanatory variables. This may be dne
ta habits, persistenee, implementation 1ags, misinterpreted real priee changes. and
ather factors. This ehapter modifies the statie specifieatians of the previons chapter
ta alIow for delayed effects of changes in variables.
7.1 Estimation
The export and domestic share equations eonstitute a set of seemingly unrelated re-
gressions, hence, to avoid singularity of the variance-covariance matrix, one equation
is deleted. In ail cases, only the export share (AEXP) specification is estimated. The
110
111
slope coefficients in the corresponding domestic share (ADûM) equation are obtained
by reversing the signs of the corresponding coefficients in the export share equations.
The cOtmtry dummy variables of the ADû~I equations equal one minus the corre
sponding values in the export share equation. Standard errors remain unchanged.
Observe that the transformation from the ADL to the Bewley specification leaves
intact cOlmtry-specific dummy variables. IndeeJ, this tl'ausforIllatioll preserves the
error process. Equations (3.17), (3.18), (3.19), and (3.20) are estimated by instnl
mental variable (IV) regressions. This is necessary for two reasons: (i) for consistent
estimation due to the presence of contemporaneous terms in the export share vari
able on the right-hand side, and (ii) to obtain identical estimates as wOlùd have been
obtained by estimating the ADL and then computing long-nm coefficients. This is
shawn in Bewley (1979, 1986), and Wickens and Breusch (1988). The IVs that will
insure these are the regressors of the ADL.
Specifical1y, they are: (i) each regressor that appears in levels in the Bewley
transformation, Xkt, serves as its own IV, except OPEN and PR/CE. These two
variables are possibly simtùtaneollsly determined and one year lags are used as IVs
for these. Thus, the IV for KODAt is [<ODAt , LABRt is the IV for LABRt etc.,
whereas the IV for OPENt is OPENt- 1 and for PR/CEt it is PR/CEt- l • (ii) For
each regressor in the Bewley transformation that is a lagged deviation from levels,
the lagged value is used as the IV. That is, Xkt-j is the IV for (Xkt - Xkt-j). for aIl j
and k. In particular, AEXPt- 1 is IV for (AEXPt - AEXPt-d, ln(KODAt-d is the
IV for [ln(KODA t ) -ln(KODAt_d}, and ln(LABRt _ a) is the IV for [ln(LABRt}
m(LABRt- a)}, etc. Further, in a panel data context, Arellano(1989) recommends
1
112
these IVs over other candidate IVs.
Each specification is estimated by pooled generalized least squares method using a
robust estimator for the variance-covariance matrix to obtain estimates of all dummy
variables and other variables in the models in one step as in the previous cases.
Estimation is done in Winrats-32 version 4.3 numing on a persona! computer. In all
cases, one cOlùd not reject the nlùl hypothesis of no autocorrelation in the residuals
as the following illustrates. Thus coefficient estimates obtained here are consistent
in spite of the presence of lagged dependent variables on the right-hand side. The
regression of the residual et on its first lag, et-l, (et = pet-l + Ut, where Ut is a
random error term) yields the following estimates for p : 0.02, p-value 0.78; 0.08,
p-value 0.28; -0.01, p-value 0.88; -0.01. p-value 0.97; for the Overall. West, East. and
Sudano respectively. The ntùl hypothesis is that p = 0. 1
7.2 Results and Discussion
First, an analysis of the adeqnacy of the regressions are discnssed, then the parameter
estimates are presented and discussed.
1.2.1 Diagnostic Tests of Regression Adequacy
Estimation reslùts are presented in table 7.1 for the export share equation. The nota-
tian used for deviations of lagged dependent and independent variables from CUITent
level in the tables are given below. On the whole, many cotmtry dummy variables
1 Keane and Runkle (1992) proposed a method for estimating dynamic panel-data modeIs withseriai correlation. This W8S considered but not pursued here, partiy because seriai correlation is nota problem, but aIso because their method is principally aimed at random efFects modeIs (though itmay serve fixed effects models just as weIl).
113
have t statistics that are not significantly different from zero (or from one another).
For sorne of those not significantly different from zero, statistical tests could not reject
the null hypothesis that these cotmtry-specific dummy variables are equal. Tests that
these cOtmtry effects are jointly zero are however rejected. Four of these countries
were then assigned a common dummy variable and the model re-estimated as done
pl'eviou::ily. By a.':i::iiguiug cOUUllOH JlUillllY variable;, ulle upts for a more efficient
variance at the risk of biasing coefficients as discussed in chapter 6.
Residuals and other Diagnostics
Ordinary least squares residual errors were tested and found ta be heteroskedas
tic. The chi-squared statistics for the likelihood ratio tests of the ntùl of no het
eroskedasticity are 1,023.12 with 22 degrees of freedom for the Overall, 123.45 with
6 degrees of freedom for the West, 247.11 with 8 degrees of freedorn for the East,
and 242.01 with 6 degrees of freeclom for Slldano). Ta correct for heteroskedastidty
the ROBUSTERRORS option in Winrats is specified in compllting the variance
covariance matrLx. Tests for stationarity of the residuals of bath the ADL and the Be
wley transformation were aIso performed using their alltocorrelation ftmctions (ACFs)
as discussed earller. AlI ACFs were fOlmd to be stationary.
lv/odel stability
The question of the stability of the data generating process of the static share
response models has been addressed in chapter six where support for stability of the
process was found. However, there is an additional complication introduced here
by the presence of lagged dependent variables on the right-hand side. The issue is
that of stationarity of regressors (which now include lagged dependent variables).
114
First, the existence of long-run relationship between the dependent variable and the
explanatory variables is determined by checking that the condition of inequality (3.13)
in chapter three is satisfied. This is indeed found to be the case in all specifications.
For the ADL, it was confirmed that the absolute value of the sum of the coefficients
of the lagged dependent variables on the right is less than one. For illustration, the
coefficients of the ADL for the Overa1l are given in the appeuŒx. For this ADL, the
SUffi of the coefficients on the lagged AEXP terms is 0.64, which is less than 1.
Second, stationarity of regressors in the presence of lagged dependent variables is
assured if the roots of the polynomial equations formed using the coefficients of the
lagged dependent variables in the ADL as coefficients of the polynomial term alllie
olltside the unit circle, (Kesavan et al. (1993), Bewley (1986, p.61) and Greene, 1997).
This is aIso fotmd to be the case. (For the Overall case, \Ising coefficients of the ADL
given in the appendix, the equation of interest is 1-0.547z - 0.088z3 = O. Its roots lie
outside the unit circle. Other checks for stability that were performed include ordinary
least squares as an altemate estimation method. As expected standard errors of this
approach were smaller than the GLS errors. Also, the data were pertnrbed slightly
by using lags of variables instead of levels. lags as instrumental variables for variables
in levels, and moving averages of variables. AIl yielded reslùts that are not vastly
diHerent from each other.
The following notation is adopted in reporting parameter estimates.
K02 =In(KODAit )-ln(KODA;t-2);
K04 =ln(KODA;t)-ln(KODA;t-4);
K05 =ln(KODA;t) -ln(KODA;t_s);
X015 == EXOl t -EXOl t _ s;
X021 =EX02it-EX02it_l;
Dl =AEXPt - AEXPt - 1 ;
KP1 =ln(KPFXit ) -ln(KPFXit- 1);
KP2 == ln(KPF...Yit ) -ln(KPFXit- 2);
KSI == In(!(SAVit) -ln(KSAVit-4);
KS4 == In(KSAVid -ln(KSAVit-4);
KS5 == In(KSAVit) -ln(KSAVit-s);
LBI =: In(LABR,t) - ln(LAB~t-d;
LB3 == In(LAB~t) - Ln(LAB~t_3);
LB5 = In(LABRit ) -ln(LABltït-s);
P4 =In(PRICEit ) -ln(PRICEit_td;
115
D2 =AEXPt - AEXPt-2;
D3 == AEXPt - AEXPt - 3 ;
D5 = AEXPt - AEXPt - 5 ;
116
7.2.2 Parameter Estimates
This sub-section discusses parameter estimates reported in table 8.2
Lagged dependent variables.
In the specifications for the Overall, East and West, coefficients of the deviations
of the lagged dependent variables from current levels are all significant. indkat.ing
strong evidence of persistence (or slow adjustments). In the Overall regrcssion, two
deviations of the dependent variables from CIment levels are significant. In the West,
whereas all deviations (of lags) of the dependent variable (1 ta 5) on their own are
significant, theyall become insignificant in the presence of the first deviation. ResIùts
are reported for this latter case only. In the East, the first, second and fifth deviations
remain significant. In the Sudano however, the t statistic of this coefficient is not
significant (p-value 0.23), suggesting no persistence. This is another unusual finding
for this region. A possible explanation may he the practice of shifting clùtivation
and migration (more prevalent here than in other parts of SSA). This likely helps to
moderate persistence.
2 In aIl of the following discussion, parameters are considered significant when their marginal significance level is 1% or less.
117
1 Table 7.1: Dynamic share response estimation.
Coefficients of deviations of lagged export share (1970 - 1993). 1
Variable2 Overall6 West3 East4 Sudano5
Dl -1.450** -1.534** -0.805** -0.384
(-5.038) (-3.063) -3.248) (-1.200)
D2 -0.383**
(-2.443)
03 -0.236*
(-2.110)
D5 0.293**
(2.993)
O. F. 376 106 132 104
1t statistics in parentheses below coefficient estimates.
* and ** indicate significance at 0.05 and 0.01 levels respectively.
2 D. F. is degrees of freedom. AlI variables are defined above.
3West~ Benin, Cameroon, Cote d'Ivoire, Ghana, Nigeria, Sierra Leone and Togo.
4East: Ethiopia, Kenya,IvIadagasca, lVlalawi, Bunmdi, Rwanda,
Tanzania, Zambia and Zimbabwe.
5Sudano: Central Africa Republic, Gambia, Ivlali, Niger, Senegal, Sudan
and Burkina Faso.
60verall: West, East and Sudano together.
118
• Table 7.1 (continued): Dynamic share response estimation.
Estimates of long-run coefficients of explanatory variables l
(export share equations, 1970 - 1993). See legend on next page.
Variable2 Overa1l3 West4 East5 Sudan06
ln(KODA) -0.002 0.024 0.045 -0.111**
(-0.058) (0.360) (1.196) (-2.903)
ln(KPF~~) 0.022 0.063 0.060 0.012
(0.956) (0.641) (1.418) (0.635)
ln(KSA1/) 0.045** 0.032 0.078 0.094**
(2.602) (0.954) (1.569) (3.212)
ln(LAER) -0.116** -0.251 ** -0.049 0.265*
(-4.0189) (-4.912) (-0.548) (2.302)
OPEN 0.248 -0.012 0.242 0.120
(1.687) (-0.041) (0.638) (0.673)
E~~Ol -0.043 0.055 -0.058 0.444**
(-0.853) (0.933) (-0.776) (4.215)
E)(02 -0.057 -0.014 0.108 -0.056
(-0.656) (-0.107) (1.352) (-1.013)
ln(PRICE) 0.055 0.101 0.274* -0.077
(0.911 ) (1.545) (1.983) (-1.426)
TREND -0.014** -0.013** -0.023** 0.034
(-3.978) (-2.313) (-4.147) (1.157)
O.F. 376 106 132 104
1
119
1 t-statistics in parentheses below coefficient estimates. * and ** indicate statis
tical significance at 0.05 and 0.01 levels respectively. 2 D. F. is degrees of freedom.
Other variables are defined in chapter 3. 3,4,5,6 West, East, Sudano and Overall are
defined above.
120
Table 7.1 (continued): Dynamic share response estimation.
Coefficients of deviations of lagged variables 1
(export share equations, 1970 - 1993). See legend on next page
Variable2 Overall3 West4 East5 Sudano6
K02 -0.093
(-1.098)
K04 -0.265*
(-2.184)
K05 -0.048 -0.202* 0.220*
(-1.213) (-1.998) (1.986)
KPI -0.087 0.330
(-1.465) (1.236)
KP2 0.093* -0.045 0.011
(2.113) (-0.330) (0.351)
KSI 0.289*
(2.081)
KS4 -0.066* 0.006 -0.077**
(-2.266) (0.061) (-2.744)
KS5 -0.205**
(-3.576)
D. F. 376 106 132 104
1 t-statistics in parentheses below coefficient estimates. * and ** indicate
statistical significance at 0.05 and 0.01 levels respectively. 2Variables are
defined above. D. F. is degrees of freedom. 3,4,5,6 West, East, Sudano and
Overall are defined above.
121
122
Table 7.1 (continued): Dynamic share response estimation.
Estimates of coefficients of deviations of lagged variables
from Cllrrent level1 (export share equations, 1970 - 1993).
Variable2 Overa1l3 West4 East5 Sudano6
LB1 -0.281
(-0.816)
LB3 0.427 1.781
(1.525) (0.943)
LB5 -0.348* -2.494 -0.095 0.010
(-1.895) (-1.721 ) (-0.528) (0.099)
X015 0.176** 0.156**
(4.853) (3.498)
X021 -0.097 -0.286**
(-1.310) (-3.587)
P4 -0.017 0.044 -0.073 0.034
(-0.5563) (0.787) (-1.286) (1.157)
D. F. 376 106 132 104
1 t-statistics in parentheses below coefficient estimates.
* and ** indicate statistical significance at 0.05 and 0.01 levels respectively.
2 Variables are defined above. D. F. is degrees of freedom.
3,4,5,6 West, East, Sudano and Overall defined above.
123
Table 7.1 (continued): Dynamic share response estimation.
Fixed cOlmtry-specific effects in export share equations
(1970 - 1993). Continues next page.
Countries Overall West East Sudano
Benin@,wl -0.180* -0.376
Cameroon<c, w2 -0.180* -0.647**
Cot d'Ivoirew3 0.120 -0.216
Ghanaw1 -0.043 -0.376
Nigeriaw2 -0.379** -0.647**
Sierra LeonewJ -0.029 -0.216
Togowl -0.164 -0.376
Burundi 0.208* -0.080
EthiopiaE1 0.080 -0.007
Kenya -0.008 -0.238
rvladagasca 0.206* 0.314
~lalawiE2 0.527** 0.176
Rwanda 0.115 -0.389
TanzaniaE1 0.048 -0.007
Zamnbia -0.428** -0.474
ZimbabweE2 0.390** 0.176
Table 7.1 (eontinue<!): Dynamie share response estimation.
Fixed country-specifie effects in export share equations
(1970 - 1993). Continues next page.
Countries Overall West East Sudano
Burkina Faso -0.039 0.237
Central Afriea Rep.@ -0.180* 0941
Gambias1 -0.025 0.025
Niger~·sl -0.180* 0.025
Senegal 0.044 0.453
SudanQ -0.180* 0.566
* and ** indicate signifieanee at 0.05 and 0.01 respectively.
slColmtries assigned common dummy variable in the Slldano.
52Colmtries assigned eommon dummy variable in the Sndano.
u:1Countries assigned eommon d\unmy variable in the \Vest.
w2Countries assigned common dnmmy variable in the \Vest.
w3Conntries assigned common d\unmy variable in the West.
EIColmtries assigned eommon dummy variable in the East.
E2Countries assigned eommon dummy variable in the East.
'f}Countries assigned common dummy variable in the Overall.
124
125
Development assistance, KODA: The coefficient of ln(KODA) suggests that
the stock of official development assistance does not have significant long-nm (LR)
effect on AEXP in either the Overall, East or West. In fact, its long-nln impact in the
Overall is almost nil (magnitude -0.002 with a large p-value). It does have significantly
negative LR impact in the Sudano (implying positive impact on ADOlVI). In all cases,
the magnitude of the coefficients are relatively small (being largest in the Sudano).
The deviation of the fifth lag from current level is negative but not significant in
the Overall, negative and almost significant in the West, and positive and almost
significant in the East. Discussion of these reslùts follow after presenting reslùts of
KPFX and KSAV.
Private foreign ftows, KPFX: The coefficient of ln(KPF ~y) suggests that the
stock of private foreign commercial flows does not have significant LR impact on
AEXP in the Overall or any region. The coefficients are all positive (though still
smali) and bigger than corresponding ones for KODA except in the Sudano. The
deviation of the second lag from ctment level has positive and almost significant
impact in the Overall specification, while that of the first lag is negative but not
significant in the Overall. The deviation of the first lag is positive but not significant
in the West.
Domestic savings, KSAV: The coefficient of In(KSAV) suggests that the stock
of domestic capital has significant positive long-run (LR) effect on AEXP in the
•126
Overall and Sudano specifications. The LR impact of this variable is positive but
not significant in the East (p-value 0.12) or in the West (p-value 0.34). Further,
deviations of the fourth lag of this variable from current level (which measures delayed
impact) is significant or almost significant and negative Overall and in the Sudano.
The deviations of the first and fifth lags of this variable from the current level are
signilkaut (or è:ÙJuost so) in the Ea::it, the first beillg positive, the second negative.
Thus, the impact of the three components of physical capital vary ac.cording to
region. The stock of development assistance appears not to have large or significant
LR impact on the export share of agriculture in any region, or Overall. It does
have positive LR impact. on domestic share in the Slldano however. The stock of
private foreign commercial flows too does not appear ta have large or significant LR
effect on AEXP, (hence not on ADO~[ either). Domestic savings have significant
positive LR effect on export share Overal1. Further, a11 capital stock variables aIso
have substantial delayed effect.s. KPFX has the shortest delayed effect.s of up ta two
years only, while it is up to five years for bath KODA and KSAV.
Labor, LABR: The coefficient of ln(LABR) suggests that the LR effect of the
labor variable on export share is negative and significant in the Overall and in the
West. It is positive and significant in the Slldano region. Labor has small, negative
but non-significant LR impact on the export share in the East. It has no significant
delayed eflects in this region either. In the Overall specification, the third and fifth lag
deviations from current levels have substantial (magnitude and marginal significance)
impact on export share. In the specification for the West, ooly the fifth lagged devia-
127
tion of this variable has substantial negative delayed impact on AEXP. No deviation
of LABR has significant impact on AEXP in Sudano.
The negative long-run relationship in ail but the Sudano may be explained by the
fact that the deteriorating food security situation as weIl as government control over
producer priees of exports make it increasingly attractive for farmers to turn to food
production aver time. Thus, the long-nUl effects of changes in LABR are positive
for ADO~,t That, in the Overall, lagged deviations are substantial is not surprising.
Pensant fanners (easily the backbone of Afriean agrÎClùture) hardly have other skills
with which to make a living. They are thus likely to continue to earn their living
doing what they know how to do best, unless externuating cïrCllIIlstances force them
ta do otherwise. In fact, unlike conventional wisdam, which suggests ease of labor
movement relative ta other factors of production, in this context, labor may weIl he
the most immobile factor input.
Priee, PRIeE: The coefficient of ln(PRICE) suggests that the LR impact of
the ratio of the index of the real producer priee for the export sub-sector ta that of
the domestic sub-sector on AEXP is positive, small and not significant in the Overall
specification (coefficient 0.06, p-value 0.36). It is positive, larger but not significant in
the West, (coefficient 0.10, p-value 0.12). ft is positive, and largest in the East where
it is almost significant (coefficient 0.27, p-value 0.04), and negative, somewhat small
and not significant in Sudano (coefficient -0.08, p-value 0.15). The higher estimate for
the East is probably due ta the higher tendency for countries in this region ta allow
domestic producer prices to track international prices more closely. The impact of
128
this would likely be that price increases more likely maintain (if not improve) the level
of domestic terms of trade between agriculture and the rest of the ecouomy. Thus
sucb priee changes are likely to be seen by farmers to be worth their while, and so
they respond by producing relatively more exports. The deviations of lagged prices
are not significant in any region or overall.
Estimates obtained here may not be directly comparable to those of .Jaeger (1992),
Elmi (1994) or Bond(1983) because all three models are different than that estimated
here. In particlùar, Elmi and Bond in tlSing the Nerlove technique focus on sup
ply response to priee, the model here considers other factors that determine output.
Further, Binswanger (1989) and Schiff and ~[ontenegro (1997) have expressed reserva
tions about the Bond estimates. Indeed, Chhibber (1988) has found that byaccOlmt
ing for other variables (i.e., other than priee) t.hat. affect sllpply response in models
that emphasize priee only, he obtains lower estimates of priee elast.icities. However,
the general conclusion that aggregate response to prodllcer priees are low is confirmed
here.
Openness (OPEN) and Terms of Trade, EXOl: The coefficient of OPEN
suggests that the LR effect of the extemal trade share of gross domestic product on
AEXP is positive but not significant in the East, Sudano, or Overall. The impact is
relatively large in the Overall and in the East. OPEN is very small and negative in
the West. That the long-run impact of OPEN is positive (except for the West) but
not significant anywhere may be due to the fact that openness (liberalization) which
translates to higher producer prices for farmers will by itself not raise the export
•
•
129
share of agriculture significantly in the long-run. In fact, its impact in the West is
almost non-existent. NIore is called for (this is discussed later). Indeed, export share
has been dropping Overall, and in all regions.
In regard to the terms of trade, the coefficient of EXOI suggests that the LR
effect of this variable on AEXP is positive in the West (where it is small, coefficient
0.06) and Slldano (where it is rather large, coefficient 0.44). It is significant ooly in
the Sudano region. The value of the coefficients are negative in the Overall and in
the East. The deviation of the fifth lag from current level however, has positive and
significant impact on AEXP in the Overall and the East only.
The importance of the terms of trade shock to growth has been enlphasized by
Easterly, Kremer. Pritchett and Summers (1993). That the long-nin EXOI V"dIiable is
not significant in the East, West and Overall may be due to falling share of agricllltural
exports in total exports over time. When agricultural exports are a larger proportion
of total exports, international priees of agrictùtural exports influence a country's
overall terms of trade (the variable being tlsed here) more.
Weather, EX02: This variable does not appear ta have significant LR effect on
either AEXP or ADOrvl as suggested by the coefficient of the EX02 variable. The
deviations of the first lagged values from CUITent level have significant negative impact
on AEXP in the East ooly. No doubt, the weather must impact both sub-sectors
of agriculture. Aboagye (1998a) finds that this variable has sigrùficant impact on
agrictùtural output. One rationalizes that the apparent non-significance of the LR
impact of this variable on export share is because of the faet that the variable very
130
likely affects both sub-sectors almost equally. It is Dot immediately obvious why the
variable is significant in the East.
TREND: This variable is expected to measure technical change (as weIl as other
omitted trending variables). It is negative and significant in the Overall, West, and
East. It is not significant in the Sudano. One interpretation is that, total factor
praductivity in the export sub-sector is falling, but less than snggested in the static
specification (chapter six), once long-nm effects of changes in factor inputs and other
explanatory variables have worked their way iota the system. If one accepts this
however, one must then also accept that significant productivity gains are taking
place in the domestic sub-sector. The first is easy to accept sinee even in the face
of illcreasing stocks of inputs, SSA's export share has been falling. The second may
also be true, with the explanation that any food security concerns may not be due to
lack of productivity growth on per capita basis.
7.2.3 Summary - Dynamic Share Response
This chapter has estimated a dynamic specification of the export share of agrictùture
in sub-Saharan Africa in arder to investigate the long-ntn relationslùp as well as per
sistence between the shares of export and domestic sub-sectors of agrictùtural output
and the stocks of development assistance, private foreign commercial capital, dames
tic savings, producer prices, openness of the economy, tenns of trade and variations
in the weather. Panel data far 23 su~Saharan African countries covering the periad
1970 to 1993 were used.
Estimated coefficients of deviations of lagged dependent variables from CIment
131
values are significantly different from zero in all regions but the Sudano. These give
indications of slow adjustments in all regions but the Sudano. Estimation restÙts
further suggest that in the long-run, the stock of development assistance appears to
he small and not to have more impact on the share of one suh-sector over the other in
the Overall specification. In the Western or Eastem-Southem agricllltural regions the
long-nm impact of this variable appears ta favar the export 5hare, though ils Ï.lllpact
is not sigIÙficant. In the Sudano-Sahel region however, it appears that the stock
of development assistance has significant negative long-nm impact on export share.
The stock of private foreign commercial flows has small, positive long-nm impact
on the export share in all regions. This impact is however not significant anywhere.
Domestic savings significantly affect export share positively in the Overall and in the
Sudano-Sahel region in the long-nm. In the other two regions, the long-nm ilnpact
of tohis variable is aIso positive but not significant. Of the capital variables, it is the
stock of domestic savings that has the largest positive impact in all the regions. The
stock of private foreign capital is next.
These variables also have sllbstantial delayed effects. Private foreign capital has
the shortest delayed effect of IIp to two years oruy. Delayed effects are up to five years
for bath development assistance and domestic savings. These findings are consistent
with (i) Bhattacharya, ~[ontiel and Shanna (1997), who have documented evidence
that suggests that foreign commercial investments yield high annllal returns on in
vested capital, (Le., quick payback of invested capital), (ii) a study by the World Bank
(1984) which suggests that payback of invested capital on the bank's funded projects
(development assistance) are realized more slowly (than indicated by Bhattacharya
132
et al.), and (iü) investment of domestic capital generally taking a longer term view.
The reslùts suggest that, Overall and in the Western region, the long-nm effects of
increases in the labor force impact export share significantly negatively. In the East
tao, the impact is negative but not significant. The impact of this variable in the
Sudano on export share is positive. Further, this variable exercises significant delayed
effects. Oth~r thau iu the East, the magnitude of the impact of this variable on export
share is larger than any of the capital variables. The Labor variable also exercises
significant delayed effects (up ta five years). This wOllld be the consequence of farro
labor not moving easily between agricultllral sub-sectors, or between agriclùtl1re and
other sectors of the economy.
For producer priees, one finds that the long-nm impact of priee increases favor
the export share of agriclùture everywhere except in the Sndano-Sahel region. This
impact is not significant anywhere. In the East, it is almost significant. This is the
region in wmch it has the largest magnitude. The priee variable has no significant
lags.
The long-nm effect of the openness of these economies on the export share is
positive in all regions but the West. It is however, not significant anywhere. In
regard to the extemal terms of trade, the long-nm effect of this variable on export
share is positive in the Western and Sudano-Sahel regions. It is only significant in
the Iater. The impact is negative and Dot significant in the Eastem-Southern region,
or Overall. Changes in the terms of trade affect the shares of the suh-sectors for up
ta five years.
The long-run impact of the weather affects both sub-sectors almost equally. The
133
measure of technical change suggests that total factor productivity in the export sub
sector has been falling in all the regions except in the Sudano-Sahel region. In this
region, there is indication of rising productivity in the export sub-sector.
Thus, this exercise has provided evidence of slow adjustments in response ta
changes in factor inputs and other factors that affect profitability of the agriclùtural
sector in SSA. One wOlùd look ta these slow adjustments as the basis for apparent
differences that mayexist between the short-nm and long-nm impact of the variables
in the models. TItis is done next.
1.2.4 Comparison of Long-Run and Short-Run Estimates
This section compares the short-nm (static) and long-nm (dynanùc) effects of the
stocks of development assistance, private foreign capital, domestic savings, the open
ness of each economy, the terms of trade and variability of the weather on the export
and domestic shan~ of agriclùtural output. Comparison is for regional findings ooly.
sinee no policy gronp estimates are available for the dynamic mode!.
Long-nin price coefficients exceed short-nm coefficients in the Overall, as weIl as in
the East and West. This is consistent with theory, where the arglunent is that over the
short-nUl, total resources allocated ta agriclùture are generally fixed. Hence changes
in aggregate response will be small, compared to the long-nm when reallocation of
resources within the two sub-sectors of agriculture, and to and from agriclùture wOlùd
have taken place, if price signais were perceived to be permanent. Even in the Sudano
where the price coefficients are negative, the magnitude of the coefficient is larger in
the long-run than in the short-mn. This would still be consistent with the subsistence
134
agriculture hypothesis advanced. (Here too, in the long-nm more resources would be
allocated away from the export sub-sector, in the case of a positive priee signal).
Unfortlmately, the relationships between the long-run and static coefficients iu
respect of the other variables have not been weil established in the literature. This
study finds that the coefficients of the development assistance variable are not signif
icant in the export share equation either in the short-nul 01' iu the long-nul, iu the
East, West or Overall. Thus, the impact of development assistance does not henefit
one sub-sector relatively more than the other. [n the production function tao, one
finds that tms variable does not have significant impact on total agricllitural output.
Similarly, tms study finds that private foreign commercial flows are not significant
in the export share equation either in the short-nm or in the long-nm in all the
regions. This variable however, has the tendency to favor the export share both in
the long-nID and in the short-nln in all the regions (except possibly in the West).
The short-nm and long-nm estimates with respect ta domestic savings are dif
ferent. In the short-nm domestic savings do Ilot sigIÙficantly favor any sllb-sector
overall, but they favor (significantly) export share relatively more in the long-nm.
Short-nln estimates measure only one period impact (in this case one period later),
whereas the long-run estimates measltre as weIl the impact of past changes in this
variable. Ta llnderstand what lS happening better, consider the ADL from which
the Bewley transformation was obtained. (Recall that the specific instnunents used
in the estimation ensme that long-run estimates obtained here would be identical
ta those that would have been obtained if one had deduced them from the ADL).
From the ADL, the long-nm estimates would be obtained as a function of the sum of
135
coefficients of lagged. domestic savings (refer to equation 3.12). Thus, one concludes
that the efIects of the sum of lagged domestic savings are significant and positively
impact the export share relatively more than they impact domestic share. These
lagged effects are not captured in the static specification. The explanation for the
different behavior of this variable in the short-nm and in the long-nm in the vari
ous regions follows along similar lines. Indccd, this cÀ~lanation for the behaviol' uf
domestic savings is valid for aIl variables in the mode!.
The impact of the labor force in the Overall is different between the short-run and
long-nm specifications. {ts impact is positive in the static equation, but negative in
the long-nm equation. The explanation is similar to that for domestic savings. In
this case however, increases in the labor force are associated with falling export share
in the Overall in the long-nm. Again one looks to the ADL for interpretation. The
long-nm coefficients are a flmction of the SlUll of lagged coefficients. Unfortunately,
even t.hough the labor force has been increasing, export share has been falling. Thus
the long-nm impact of increases in the labor variable on export share is negative.
(The impact of labor in the EWit, West and Sudano have the same signs bath in the
short-nm and in the long-nm. The magnitudes differ however, but the explanation
for these differences are along the same Hnes as provided far the Overall).
The apenness variable is positive in all regions in the short-nm. It is positive in
the long-nm too for the Overall, East and Sudano. The only discrepancy is in the
West, where it is negative (and very small ) in the long-run but not at all significant.
The extemal terms of trade variable is positive and essentially significant in ail speci
fications in the short-run, negative and non-significant in the Overall and East in the
136
long-run, but positive in both West and Sudano and significant only in the later. It
wOlùd appear that given the situation faced by fanners the impact of openness would
not be long lasting. Considering that the impact of openness is to reduce direct and
indirect taxation of agriculture, the benefits of this to farmers is higher producer
priee. But farmers face other constraints beyond priee. Infrastnlcture is a major one.
Thus without addl'essing these otIler l.:OllstraïllLs, the efIecL of opeWless on the export
share will not be strong in the long-omo The tenDS of trade too is not significant
in the long-nm because with falling agricultural commodity priees, the proportion of
other items in this index increases, thus Limiting the role of this variable in explaining
export share (of agriclùtllre) variation in the long-nm.
The weather variable is not significant in either specification. The coefficient of
the trend variable is negative and significant both in the short-run and in the long-run
in all but Sudano. In the Sndano, while significant in t.he short-nm it is not in the
long-nm suggesting sorne improvement in prodllctivity in the export sub-sector in the
log-nm.
Chapter 8
Conclusions and PolicyImplications
This chapter presents the policy implications arising from this study. Attention is
also drawn ta possible short comings of the findings of dûs study.
8.1 Policy Implications
The policy recommendations of this study have two objectives: ensllring higher agri-
clùtural output, thereby generating more economic activity in the rural areas, hence
leading to higher overall p.conomic growth, and secondly, earning more from SSA
agriclùtural exports to help indllstrialization efforts by helping pay for capital equip-
ment imports, etc. Taking the position that the existing situation in the agriclùtural
sector is unsatisfactory, the recommendations aim to achieve these two objectives by
avoiding the drawbacks of the impact of economic reforms on agriclùture, (as being
currently irnplemented in sorne SSA countries). These drawbacks became apparent
in the investigation of LARGE, SlVlALL and POOR policy groups. (This thesis ac-
cepts that over the next several years the worldwide momentum for economic poliey
reforms is unstoppable). The recommendations faIl into two categories: those that
137
•138
can he implemented immediately and will have quick impact, and those that wotùd
he implemented over time and will have long-term impact. They vary byagric,ùtural
region.
8.1.1 Increasing Agricultural Output
The findings of this study hclp isolate the areas that need attentioll. The fiuwug
of no struct.ural change in the production functions over time across agric,ùtural re
gions must be addressed sa as ta improve the situation. The structure of agriclùtllral
production must change, in order that agricultural output may increase significantly.
Agric,ùtural output may be increased by either increasing quantities of inputs, or by
ensuring higher productivity, or both. This study recommends a conlbined approach.
Of particular interest to this stlldy are the l'ales of capital and economic poliey. In
the first place, the impact of the three components of capital is unacceptably low
(sometimes even negative). Secondly, the finding with respect to the policy vari
able suggests that economic policy in most areas is negatively related to agric,ùtlual
output. AlI these show up in the negative total factor productivity growth. The rec
ommendations of this stndy faIl into two categories: those that can be implemented
immediatelyand are expected to have quick impact, and those that wo,ùd he impIe
mented over time and will have long-term impact. They vary by agric,ùtural region
and poliey group.
The basic recommendations are summarized as reduction in the bias in domestic
terms of trade against agriculture, and the need for capital investments in infras
trnctural projects. Improved domestic terms of trade are urgently required in those
139
places in which the domestic savings have negative coefficients. This is the case in
the countries of LARGE and S~IALL group and those of the West and Sudano agri
cultural regions. Economie reform policies as being currently implemented result in
domestic terms of trade being biased against agriclùture because of relatively higher
direct taxation of agricwture (producer priee fixing hy governments, especially for
exports). Indeed, in Ghana and UganJa the leaJiug refofllliug cotwtries in SSA,
Africa Recovery (1997) reports that average annual growth rates of earnings from
agrictùtural exports were -4.3% between 1990 and 1995 for Ghana, a deterioration
over the 1985 - 89 average of 3.2%, and -11.3% between 1990 and 1993 for Uganda,
also a deterioration over the 1985 - 90 average of -7.4%. One thinks this reflects at
least in part, the effect of bia.sed damestic tenns af trade against agriclùture.
The recomendation is far reduction in direct ta.xation of agric\ùture. The conse
quence of this can he felt within a short-period of time as this sectar beeomes more
competitive and farmers respond immediately by taking better care of existing farms
by being able to afford seed and fertilizer inputs as weIl as fanning implements such
as machetes, cutlasses and hoes and improved seeds. Farm implements are probably
best fashioned damestically in factories or hy local blacksmiths and lathe machine
operatars who know domestie fanning techniques very well. (In most cases, steel
for this may have to he imported). Simîlarly, higher quality seeds that have already
been engineered and/or tested loeally and found to be suitable wOlùd naw became
more affordable. Studies on rnrallinkages are seant, but two studies cited in Hagg
blade et al. (1987) suggest high expenditure elasticity far loeally produced non-farro
gaods. For example, a 10% increase in rural household incorne 100 to 13% increase
140
in expenditure on non-agricultural goods and services in Sierra Leone and Nigeria.If
this policy is perceived to be credible and long lasting, it will aIso have long term
impact. To balance the expected shortfall in government revenues due to rednction
in taxes on agriculture, governments will have to impose higher taxes on other sectors
of their economies. One has reservations about the use of the value-added tax being
prOu10ted by the \Vorlù Dallk, ou the gl'UlUlds that the rllechanisms by which taxes
collected will make their way iuto government coffers are plagued with loopholes.
The negative impact of development assistance in the cOlUltries of the West caUs
for a review of those areas of the €Conomy iuto which 0 DA is targeted. A modification
or redirection of development assistance wOlùd be reqllired. It nlay not be possible
to take action on this front immediately however.
Private foreign capital has negative impact on agriclùtllral ontput in the conntries
of the LARGE/SrvIALL group as weIl as those of the Eastern region. To reverse this
may require taking actions t.hat will halt the fiight of foreign capital ont of these
countries, as weIl as hait the re-allocation of private foreign capital (within eotmtry)
away from agriclùture. This will happen when cOlmtries adopt credible palicies that
lower bath economic and political risks that foreign capital faces. This too may not
be achievable in a short-time. As part of a long-t.erm plan, governments sholùd create
incentives that will attract private foreign capital into processing agriclùtllral output
for exporte It is the position of these authors that private foreign capital is luilikely
to be attracted to primary agriculture in a significant way.
In all cases, the long-term objective must be to raise the impacts of aIl capital vari
ables on agricultural output. Improvements in basic infrastructure especially roads
•
141
would be a major step forward. These have been shown in the literature (e.g. Bin-
swanger et al., 1987, and Antle 1983) to have significant impact on agrictùtural output.
Development assistance as weIl as government portion of domestic saving shotùd be
directed to tms end. However, constraints on the provision of public goods resulting
from balaneed budgets, required of reforming eOlmtries (good poliey), severely curb
governments' ability to provide infrastructure. Glven that the state of infrastructure
in Illost SSA econornies is woefully inadequate, this constraint is a serions develop-
mental bottleneck. So is the debt burden. Reforming conntries have been granted
snbstantial loans. Debt servicing conditions attached ta these leave little ftmds for
governments ta make investments. For example, Africa Recovery (1996) reports that
in 1994, the ratio of the amotmt of debt service to total exports for Ghana was 24.6%
and 44.2% for Uganda. l
Investment. of domestic savings in research and development as well as extension
services has aIso not received much attention. Thirtle, Atkins, Bottomley, Gonese,
Govereh and Khatri (1993) have shawn that expenditllres on research and extension
services significantly explain total factor productivity in Zimbabwe for both the com-
mercial and commtmal sectors. In Ghana, government spending on agriclùtnre which
was only 3.9% of recurrent expenditure in 1990, declined to 1.4% in 1996. This paper
takes the view that agriculture must be aceorded a higher priority.
Given the long lead times required for public investments to come on stream, and
the generallag in private capital response to public investments, it is necessary that
1 Many international bankers consider 25% to be dangerously high. Uganda has since (1998) benelited from the "Highly Indebted Poor Countries" program. This has lowered her debt burden.
142
attention be paid ta the provision of public goods early in the economic refonn process
so that valllable time is not lost. It would appear from this study that valuable time is
heing lost. One way to complement domestic government resources in providing public
goods wotùd be a return ta the original vision of development assistance: creating
an enabling environment for private sector development. This is a calI for reduction
in the proportion of development assistance flmds alloèateJ for elUel'geucy rdlef and
peacekeeping. (Based on OECD data, UNCTAD (1997) reports that in 1995,24 % of
bilateral aOA commitments were in the form of food aid and emergency assistance).
This calI wOlùd be realistic if the need for such efforts were rednced by addressing
the canses of civil strife and lUUest, (or if tlmds for snch activities came from other
allocations). It may he argued that depressed economic performance is an important
cause of civil strife and lUlrest. Using development assistance for emergency relief and
peacekeeping only amolUlts ta putting out one tire aftel' another withont addressing
the root canse, wlùch is economic to a good extent.
8.1.2 Increasing Agricultural Export Earnings
Increasing agriclùtural export earnings is important for sllb-Saharan African conn
tries. Ta this end, reduction in indirect taxation through liberalization (openness)
of the economies (devaluation is one component) is being pursued. However, excess
direct taxation of agriclùture (more than in other sectors) remains. From this study,
one finds that the impact of the three capital variables on agrictùtural exports is
small, as was the case for agricultural output. In the short-term, the magnitudes of
the coefficients are least in the West, followed by Sudano. They are highest in the
143
East where direct taxes are lower. The deduction from this finding is that eOlmtries
in the West and Slldano have more to do by way of improving domestic tenus of trade
than those in the East. This should he an immediate term poliey. The long-term
impact of all capital variables on export share are positive (except development assis
tance in Sudano) and small, suggesting that in ail regions, efforts aimed. at increasing
the impact of these variables must he pursued as a long-tenu stl'ategy.
The recommendation for increasing export earnings are two fold, just as those for
increasing agriclùtural output. In the short-term, the focus sholùd be to rehabilitate
(increased inputs) existing farms, while the long-term plan wOlùd be for cOlmtries ta
move away from exporting few primarily raw agriclùtural prodllce to exporting di
versified and processed agrÎClùtural produce. The arguments posed above for the raIe
of domestic savings, development assistance and private foreign capital in increasing
agriclùtural output will naturally contribute to increasing the volume of agriclùtnral
exports. But in addition, if efforts to process agriclùtural produce prior ta exparting
them are successflù, agriclùtural export earnings wOlùd further increase.
One strong reason for cautioning lmdue emphasis on promoting agriclùture for
external trade across SSA is that on the world stage, SSA countries have compar
ative advantage in producing sorne agriclùtural primary products. The situation in
SSA is that many COlmtries have common agricmtural exports. For example, co
coa is common to Cameroon, Cote d'Ivoire, Ghana, Nigeria, Sierra Leone and Togo;
coffee is common to Bunmdi, Cameroon, Cote d'Ivoire, Ethiopia, Kenya, Rwanda,
Tanzania, Uganda and Zaïre; groundnuts are common to Gambia, Mali and Senegal.
Together (even individually in sorne cases) they are important suppliers to the world,
144
(Gersovitz and Paxson, 1990). Increasing individual country output will surely lower
international priees further. This will restùt in what is sometimes ealled "immeseriz-
ing growth", (that is, growth in output that leads to poverty due to lower prices).2
This situation has been deseribed by sorne authors as static comparative advantage,
which is not in the long-nm interest of SSA. (We live in a dynamic world). This twist
may not have ueell cOllsiJereJ by Roùdk (1997) when he caUs for efforts that ~~ill
sharply incl'ease traditional and non-traditional exports." He is not alone.
The idea of encouraging so called non-traditional exports to diversify the export
base is laudable. As the phrase suggests, these wOlùd be new export products. How-
ever, with common climate, groups of cOlmtries are likely to diversify into similar
activities, most probably agriclùture related. (Global competition in the face of weak
industrial base are other reasons). Diversification of exports ta include many prod-
ncts common to many COlmtries will however, be an improvement over the eurrent
situation. This thesis suggests a broadening of the concept of non-traditional exports
to include vallle-added. items. That is, processing of traditional primary agriclùtural
products. To encourage snch investments, govemments will have to provide incentives
that are at least as attractive as those they currently provide ta the rnining sectar.
In this regard, more and more cocoa beans could be processed into cocoa butter, for
example. In Ghana, a major cocoa producing cotmtry, most of the talk on reforming
the export sub-sector centers on diversifying exports (non-traditional exports), rather
than processing traditional exports.
2 International commodity agreements and cartels are notoriously unstable and are not realisticalternatives.
•
145
In discussing trade reform, Rodrik (1997) calls for managing the distribution of
the consequences of trade reform so that winners can emerge early. This study rather
suggests that managing these issues sa that consequences are broad-based (benefit a
large proportion of the population) should be the target. A far reaching way to assure
this wOlùd be for benefits of trade reform to the accnle to the agricultural sector, the
s~tor that illlpacts the lives of the Iuajurit.y of the people.
8.2 Limitations of this Study
Four aspects of this study come to mind as areas that cOlùd use sorne improvernent.
The first is the question of possible bias of parameter est.imates due to the faet that
data are pooled across cOlmtries of sub-Saharan Afriea, a large continent. As noted in
the text, the basis of this is the assllmption that all countries have access to the same
production technology. The point here is the realization that it is possible that. aIl
COlIDtries do not have aceess ta the same production technology. This study addresses
this concem in two ways: (i) by performing statistical tests for poolability, and (ii)
by providing regional estimates. Statistical tests provide support for pooling, while
regional estimates are less likely to be biased since countries in a region are more
likely to have more in common, inc1uding production teclmology.
Another area of sorne concern is the quality of data. Gersovitz and Paxson (1990)
express the point c1early when they wrote,
"... it is useful to keep in mind the diffiC1Ùties with respect to data that
confront any inquiry into economic conditions and prospects in Africa.
These countries ... lack sufficient resources to devote to the collection of
146
statistics. As a result, information on their economies is limited in scope
and quality and is not avaiJable in a timely fashion ...."
This study address this concem by using data from credible sources, mostly from
the World Bank and The Food and Agriclùtural organization. It is recommended
that strong emphasis be placed on data collection and management issues as part of
structural adjustment programs.
A related issue is the question of conversion of amotmts in domestic currencies
to United States dollars (US$). This study has followed the World Bank's approach
by tlsing the Atlas method to convert domestie currencies to U8$. This method
incorporates so called "official" and 44parallel" market rates. An alternative approach
that is common in the literature is the Purchasing Power Parity approach. This
latter approach addresses the question by trying to establish the qnantities of clifferent
currencies that are required ta buy equivalent quantities of goods and services in their
respective cOllntries. 80th ainl to achieve the same objective. Tms study has tlsed
the Atlas approach because its conversion factors are available for a11 cotmtries of
interest. As well, use of the Atlas approach shotùd be more consistent with the rest
of the data, since most of it is coming from the same source (World Bank).
Finally, the question of an appropriate measure of the human capital of farmers
also needs to be addressed for a more complete understanding of the state of affairs.
147
REFERENCES
Africa Recovery, vol. 10, No. 2, October 1996, United Nations Office of Communications and Public Information.
Airiea Recovery, vol. Il, No. 2, October 1997, United Nations Office of Communications and Public Information.
Afriea Recovery, vol. Il, No. 3, February 1998, United Nations Office of Communications and Public Information.
Altonji. J. G.. and A. Siow~ 1987~ "Testing the R.flsponsp of Consnmpt.înn t·n
Incorne Changes with (noisy) Panel Data," Quarterly Journal of Economies,102, 293 - 328.
Anderson, G., and R. Bhmdell, 1983, " Testing Restrictions in a FlexibleDynarnic Demand System: An Application to Consumers' Expenditure inCanada," Review of Economie Stud'ies, L, 397 - 410.
Antle, J. rvL, 1983, "Infrastnlctllre and Aggregate Agricultural Productivity:International Evidence," Economie Development and Cultural Change, 31, 609- 620.
Arellano, rvL,1989, ~~A Note on the Anderson-Hsiao Estinlator for Panel Dat.a."Economies Letters 59, 87 - 97.
Bagachwa, rvL S. D., and F. Stewart, "Rural Industries and Linkages in SubSaharan Africa: A survey," in Stewart, LaU and Wangwe (eds) AlternativeDevelopment Strategies in Sub-Saharan Africa. St.. rvIartin's Press.
Baltagi, B. H., 1995, Econometrie Analysis of Panel Data, John Wiley & Sons.
Banerjee, A.t J. Dolado, J. W. Galbraith, D. F. Hendry, 1993, Co-'integration,Error-Correction, and the Econometrie A.nalysis of Non-Stationary Data, Oxford University Press.
Bardsen, G., 1989, "The Estimation of Long-run Coefficients from ErrorCorrection tvlodels," Oxford Bulletin of Economies and Statistics, 51, 345 350.
Barro, R. and J. W. Lee, 1996, " International tvleasures of Schooling Years andSchooling Quality," American Economie Review Papers and Proceedings, 82(2),218 - 223.
Bautista, R. ~1. and A. Valdes, 1993, "The Relevance of Trade and Macroec~
nomic Policies for AgrictÙture," in R. rvr. Bautista and A. Valdes (00), The BiasAgainst Agriculture, Institute of Contemporary Studies.
Bewley, R., 1979, 'The Direct Estimation of Equilibrium Response in a Linearhtlodel," Economies Letters, 3, 357 - 361.
148
Bewley, R., 1986, Allocation Models, Harper & Row, Inc.
Bewley, R. and D. G. Fiebeg, 1993, "Why are Long-Run Parameter Estimatesso Disparate," The Review of Economies and Statistics, 345 - 349.
Bhattacharya A., P. J..~Iontiel, and S. Sharma, 1997, " Private Capital Flowsto Sub-Saharan Africa: An Overview of Trends and Determinants," Financeand Development, 3 - 6.
Binswanger, H., 1989, "The Poliey Response of Agriclùture," Proceedings of theWorld Bank Annual Conference on Development Economies.
Binswanger, H. and K. Deininger. 1997, "Explaining Agricultural and AgrarianPolicies in Developing Countries," Journal of Economie Literature, x....XXV. 1958- 2005.
Binswanger, H., ~I. Yang, A. Bowers, and Y. ~hmdlak, 1987, "On the Determinants of Cross-cOlmtry Aggregate Agrieultnral Supply," J Durnal of Econometries, III - 131.
Bond, rvr. E., 1983, "Agriclùtural Responses to Priees in Sllb-Saharan AfricanCOlmtrîes," [MF Staff Papers 30, no. 4: 703-26.
Boone, P., 1996, " Politics and Effectiveness of Aid." European Economie Re'mew, 40, 289 - 329.
Bosenlp, E., 1981, Population and Teehnological Change: A Study of Long- TermTrends, University of Chicago Press.
Bonton, L., C. Jones and ~I. Kiguel, 1994, "rvlacroeconomic Reforrn and Growthin Mriea," Policy Research Working Paper 1394, ~[acroeconomic Growth Division, The world Bank.
Brown, T.~I., 1972, "l'vlacroeconomic Data on Ghana (Part 1)," Economie Bulletin of Ghana, Vol 2, No.1. 25-53.
Brown, B., and S. l\tlaital, 1981, "What Do Economists Know? An EmpiricalStudy of Experts' Expectations," Econometrica, vol 49, 491 - 504.
Burnside, C., and D. Dollar, 1997, "Aid, Policies and Growth," Policy ResearchWorking Paper 1777, fvlacroeconomic Growth Division, The World Bank.
Centre for Poliey Analysis, 1997, Quarterly Report on Ghana, No. 3, Accra.
Chambers, R. G., 1988, Applied Produ.ction Analysis: A Dual Approach, Cambridge University Press.
Chenery, H. B., and A. M. Strout, 1966, "Foreign Assistance and EconomieDevelopment," The American Economie Review, vol LVI, No. 4, Part I.
•
•
149
Chhibber, A., 1988, "Raising Agricultural Output: Price and Non-priee Factors," Finance and Development, 44 - 47.
Christensen, L., D. Jorgenson, and L. Lau, 1973, "Transcendental LogarithmicProduction Frontiers," Review of Economies and Statistics, 55, 28 - 45.
Collier, P., and J. W. Gunning, 1997, Explaining African Economic GrowthPerformance. Paper PresentOO at Tenth Anniversary of CSAE Oxford.
Dagenais, Iv!., 1994, L'Parameter Estimation in Regression 1\IIodei with Errars inthe Variables and Autocorrelated Disturbances," Journal of Econometrics, 64,145 - 163.
Deaton, A. S., and R. I. 1\IIiller, 1995, International Commodity Priees, Macroeconomie Performance and Politics in Sub-Saharan Africa, International FinanceSection, Department of Economics, Princeton University.
Diewert, W. E., 1974, " Applications of Duality Theory," in IvI. D. Intriligatorand D. A. Kendrick (00), Prontiers of Quantitative Economies II.
Doa:n. T. A., 1992, RATS User's Man'ual, Version 4.
Domar, E., 1946, "Capital Expansion, Rate of Growth and Employment,"Econometriea, 14, 137 - 147.
Duncan, G. J., and L4 An Investigation of the Extent and Consequences of rvIeasurement Error in Labor Economic survey data," Journal of LaboT Economies,3,508 - 532.
Easterly, W., 1\11. Kremer, L. Pritchett, and L. H. Summers, 1993, H Good Polieyor Cood LllCk? C01Ultry Growth Performance and Temporary Shocks," Journalof Nlonetary Economies, 32.
Edwards, S., 1998, "Openness, Productivity and Growth: What Do We ReallyKnow?," The Economic Journal 108, 383 -398.
Elmi, O. S., 1994, Agricultural Priees and Supply Response, 1\II.Sc. Thesis, Department of Agriclùtural Economies, rvIcGill University.
Ezeala-Harrison, F., 1996, Economie Development: Theory and Policy Applications, Praeger.
Food and Agriclùtural Organization, 1996, FAO Production Yearbook, Rome.
Food and Agricllitural Organization, 1996, F:40 Trade Yearbook, Rome.
Gersovitz, l\tI., and C. Paxson, 1990, The Economies of Africa and the Prieesof Their Exports, International Finance Section, Department of Economics,Princeton University.
•
150
Ghura, D., and !vI. T. Hadjimichael, 1996, " Growth in Sub-Saharan Afriea,"[MF Staff Papers, vol 4, no. 3, 605 - 634.
Gichenje, H., The Impact of Official Development Assistance to African Agriculture, M. Sc Thesis, Department of Agriclùtural Economies, McGill University.
Greene, W. H., 1993, Econometrie Analysis, Second Edition, !vlacmillan Publishing company.
Greene, W. H. 1997, Econometrie Analysis, Third Edition, tvlacmillan Publishing company.
Grossman, G. Iv!., E. Helpman, 1991, "Trade, KnowlOOge Spillovers, andGrowth," European Economie Review, 35, 517 - 526.
Gunjal K., and H. Gichenje, 1997, Economie Impact of International Development Assistance on African Agriculture. Paper Presented at the Joint Meetingsof Canadian Agrictùtural Economies and Farm Ivlanagement Society and t.heAmerican Agriclùtural Economies Society at Toronto.
Haggblade, S., P. Hazell, and J. Brown, 1987, " FarmjNon-farm Linkages inRural Sub-Saharan Mrica: Empirical Evidence and Poliey Implications," Discussion Paper, Agrictùture and Rural Development Department, The WorldBank, Washington, D. C.
Haque, U. N., and J. Aziz, 1997, "The Quality of Governance: ~Second Generation' Civil Service Refonn in Africa," HvlF Working Paper.
Harberger, 1978, " Perspectives on Capital and Technology in Less DevelopedCountries." in IvL J. Artis and A. R. Nobay (00), Conte'mporary EconomieAnalysis, London.
Harrod, 1948, Towards a Dynamic Economies, ~Iacmillan.
Hayashi, F., and C. A. Siros, 1983, "Nearly Efficient Estimation of Time SeriesIvIodels with Precietermined, But Not Exogenolls, Instnunents," Econometrica,vol 51, 783-798.
Helleiner, G. K., 1992," Structural Adjustments and Long-Term Developmentin Sub-Saharan Africa," in Stewart, Lall and Wangwe (OOs) Alternative Development Strategies in sub-Saharan Afriea, St. l'vlartin's Press.
Hendry, Pagan and Sargan, 1984, "Dynamic Specification," in Z. Griliches and1\1. Intriligator (eds), Handbook of Econometries, 2, North-Holland.
Hoch, 1., 1962, "Estimation of Production Function Parameters CombiningTime-series and Cross-Section Data," Econometrica, vol 30, no. 1, 34 - 53.
Holtz-Eakin, D., W. Newey, and H. S. Rosen, 1988, " Estimating Vector Autoregressions with Panel Data," Econometrica, vol 56, no. 6, 1371 - 1395.
151
Hsiao, C., 1986, Analysis of Panel Data, Cambridge University Press.
Jaeger, W., 1992, "Effects of Economie Policy on Africau AgrictùtuIe," WorLdBank Discussion Paper, Africa Technical Department, series No. 147.
Johnston, J., 1984, Econometrie Methods, third edition, l\tIcgraw-Hill.
Kawagoe, T., Y. Hayami, and V. W. Ruttan, 1985, " The Intercountry Agricultural Production Function and Productivity Differences Among COtmtries,"Journal of Development Econornics 19, 113 - 132.
Kcanc, ~'L P, and D. E. , Rllnklc, 1D92, "On the Estimation of Panel-Datal\tlodels with Seriai Correlation When Instnunents Are Not Strictly Exogenous,"Journal of Business & Economie Statisties, vol 10, no. 1, 1 - 9.
Kesavan, T., Z. A. Hassan, H. H. Jensen, and S. R Johnson, 1993, "Dynamicsand Long-Rlm Structure in U.S. lVIeat Demand," Canadian Jo'urnal of Agricultural Economies 41, 139 - 153.
Kelley, A., and R. Schmidt, 1994, "Poplùation and Incorne Change: RecentEvidence," World Bank Discussion Paper 249, \Vorld Bank. Washington. D.C.
Khan, l\tL S., 1987, " lVlacroeconomic Adjustment in Developing COlmtries: APolicy Perspective," World Bank Research Observer, 2, 23 - 42.
Kherallah, lVI. W., J. C. Beghin, E. W. Peterson and F. J. Ruppel, 1994, "Impacts of Official Development Assistance on Agriclùtllral Growth, Savings andAgriclùtural Imports." AgrieuLtural Economies Il, 99 - 110.
Killick, T., 1985, l'Economic Environment and Agricultllral Development - TheImportance of ~IacroeconornicPolicy," Food Poliey, 29 -40.
Kizerbo, J., 1989, "Discussant's Contribution," in L. Enunerrij, One World orSeveral'l, 257 - 261.
Kmenta, J., 1967, "On Estimation of the CES Production F\mction," International Eeonornie Review, 8, 180 - 189.
Kmenta, J., 1986, Elements of Econometries, rvlacmillan.
Kohli, U. R., 1978, "A Gross National Prodllct Flmction and Derived Demandfor Imports and Sllpply of Exports," Canadian Journal of Economies, XI, no.2, 170 - 182.
Krueger, A., ~1. Schiff, and A. Valdes, 1991, The Political Economy of Agrieulturai Pricing Poliey, Johns Hopkins, Baltimore.
Lau, L. J., and P. A. Yotopoulos, 1971, "A Test of Relative Efficiency andApplication to Indian Agriculture," Ameriean Economie Review, 69, 94 - 109.
•
152
Lau, L. J., and P. A. Yotopoulos, 1989, "The Meta-Produetion Function Approach to Technological Change in World Agriculture," Journal of DevelopmentEcono'mies 31, 241 - 269.
Lawrence, D., 1989, "An Aggregator ~'lodel of Canadian Export Supply andImport Demand Responsiveness," Canadian Journal of Economies, 22, 503 521.
LeIe, U., 1990, Agrieultural Growth and Assistance to Africa: Lessons of aQuarter Century, les Press.
Lcvin, A., and L. K. Raut, 1!J97, "Complementarities betw€ell Expol'ts àHU
Ruman Capital in Economie Growth: Evidence from the Semî-IndustrializedCOlmtries," Economie Development and Cultural Change, 155 - 174.
Levine, R., and D. Renelt, 1992, " A Sensitivity Analysis of Cross-CountryGrowth Regressions," American Economie Review 32 (9), 1777 - 1795.
Lucas, R. E., 1988, "On the l\lechanies of Economie Development," Journal offlI/onetary Economies, 22, 3 - 42.
Levy, V., 1988, "Aid and Growth in Snb-Saharan Africa: The recent Experience," European Economie Review 32, 1777 - 1795.
Loayza, N. V., 1994, "A Test of International Convergence Hypothesis UsingPanel Data," Policy Research Working Paper 1333, l\Iaeroeconornic GrowthDivision, The World Bank.
l\IacFadden, D. L., 1971, ~, Cost, Revenue and Profit F\metions," in D. L. l\IacFadden (00), The Econometrie Approaeh to Production TheonJ. Amsterdam.
l\IcCalla, A., 1998, "Revitalizing the World Bank's Approach to Rural Development." Talk presentOO at the l\rcGill Econonùc Policy l\Ianagement SeminarSeries.
l\Iankiw, G. N., D. Romer, D. N. Weil, 1992, "A Contribution to the Empiricsof Economie Growth," Quarterly Journal of Economies, vol 107, 407 - 37.
l\Iankiw, G. N, 1995, The Growth of Nations, Brookings Papers on EconomieAetivity l, 275 - 326.
~Iartin, W. J., and P. G. Warr, 1993, "Explaining the Relative Decline of Agriclùture: A supply-Side Analysis for Indonesia," The World Bank EconomieReview, vol. 7, No. 3, 383 -401.
lVlartin, W. J., and P. G. Warr, 1994, "Determinants of Agrieulture's RelativeDecline: Thailand," Agricultural Economies 11,219 - 235.
~Iosley, P., J. Hudson, and S. Horrel, 1987, " Aïd, Public Sector and the l\Iarketin Less Developecl Countries," Economie Journal 97, 616 - 641.
•
•
153
Mundlak, Y., 1978, "On the Pooling of Time Series and Cross-Sectional Data,"Econometrica 46,69 - 86.
Nehru, V., and A. Dhareshwar, 1993, "A New Database on Physical CapitalStock: Sources, Methodology, and Results," Revista Analisis de Eeonomico, vol8, no. 1, 37 - 59.
Nehru, V., E. Swanson, and A. Dubey, 1995 "A New Database on HumanCapital Stock: Sources, ~Iethodology, and Results," Journal of DevelopmentEconomies, 379 - 401.
Neter, J., \V. \Vasserman, and hi!. H. Kutner, 1996, Appl~ed L1.near Stat'lstzcaLNJodels, Fourth Edition, Irwin.
Newey, W., and K, West, 1987, " A Simple Positive 8emi-Definite, Heteroscedasticity and Autocorrelation Consistent Covariance fvlatrix," Econometrica, 55,703 - 708.
Ng, F., Md A. Yeats, 1997, " Open Economies Work Better! Did Africa'sProtectionist Polides Cause Its rvlarginalization in World Trade?" World Development, Vol 26, no. 6. 889 - 904.
Norton, G. W., J. Ortiz and P. G. Pardey, 1992, L'The Impact of Foreign Assistance on Agrictùtural Growth," Economie Development and Cultural Change40, 775 - 786.
ûECD, 1994, Development Co-operation: 1993 Report. Paris.
Pakes, A. and Z. Griliches, 1984, "Estimating Distribnted Lags in Short Panelswith an Application ta the Specification of Depreciation Patterns and CapitalStock Constnlcts," Review of Economie Studies 51, 243 - 262.
Papanek, G. F., 1973, "Aid, Foreign Private Investment, Savings and Growthin Less Developed COlmtries," JO'urnal of Political Economy, 120 - 130.
Pritchett, L., 1994, "Poplùation, Factor Accumtùation and Productivity,"Working Paper, ~Iacroeconomic Growth Division, The World Bank.
Pritchett, L., '~Where's all the Education Gone," Working Paper, ~[acroeco
nomic Growth Division, The World Bank.
Rodrik, D., 1997, T'rade Policy and Economie Performance in Sub-SaharanAfrica. Paper Prepared for the Swedish ~Iinistry of Foreign Affairs.
Romer, P. rvI., 1986, " Increasing Returns and Long-nm Growth," Journal ofPolitical Economy, 94, 1002 • 37.
Romer, P. rvI., 1990, " Endogenous Technical Change," Journal of PoliticalEconomy, 98, 871 - 103.
154
Ruttan, V. W., 1989, "Why Foreign Assistance," Economie Development andCultural Change 37, 411 - 24.
Sachs, J., and A. Warner, 1995, Brookings Papers on Economic Activity l, 1 118.
Samuelson, P. A., 1953..4, "Priees of Factors and Goods in General equilibrium,"Review of Economic Studies, 21, 1 - 20.
Schiff. 1\11., and C. ~Iontenegro, 1997, "Aggregate Agricultural Supply Responsein Developing COlmtries," Economic Development and Cultural Change, vol 45,No. 2, 393 - 410.
Solow, R., 1956, " A Contribution to the Theory of Economie Growth," QuarterLy Journal of Economics, LXXI, 312 - 320.
Thirtle, C., J. Atkins, P. Bottomley, N. Gonese, J. Govereh and Y. Khatri,1993, " Agricultural Prodnctivity in Zimbabwe," The Economic Journal. 103,474 - 480.
Toro-Vizcarrondo, C., and T. D. Wallace, 1968, " A Test of the lVlean SquareCriterion for Restrictions in Linear Regression," Journal of the American Statistical Association 63, 558 - 572.
UNCTAD, 1997, " The Least Developed COlmtries 1997 Report," New York.
Varian, V.,1992, Microeconomie Analysis. Third Edition, W. W. Norton andCompany.
White, H.~ 1980, "A Heteroscedasticity-Consistent Covariance ~Iatrix Estimator and a Direct Test for Heteroscedasticity," Econometrica, 48, 817 - 838
World Bank, 1984, Tenth Annual Review of Project Performance Audit Results.Washington, o. C.
World Bank, 1989, Africa: From Crisis ta Sustainable Development. Washington D. C.
World Bank, 1994, Adjustment in Africa: Refonns, Results and the Raad Ahead,Washington, O. C.
World Bank, 1995, World Tables, Baltimore: Johns Hopkins University for theWorld Bank.
World Bank, 1996a, African Development indicators, Baltimore: Johns HopkinsUniversity for the World Bank.
World Bank, 1996b, World Debt Tables, Washington, D.C.
World Bank, 1997, Global Development Finance, Washington, D. C.
APPENDIXA
Coefficients of the Overall export share ADL1• Legend on next page.
Variable2 Coefficient Variable2 Coefficient
AEXP{1} 0.547** ln(LABR) -0.013
(12.458) (-0.328)
AE.YP{3} 0.088** ln(LABR){3 } -0.161**
(2.271) (-2.745)
ln(KODA) -0.016 ln(LABR){5} 0.130**
(-1.200) (2.475)
ln(KODA){5} 0.019 OPEN{1} 0.054
(1.414) (1.706)
ln(KPFX) 0.017 EXOI 0.050*
(1.031 ) (2.495)
ln(KPFX){l} 0.026 EX02 -0.058*
(1.171) (-2.564)
ln(KPFX){2} -0.033* ln(PRICE){l} 0.009
(-1.957) (0.702)
ln(KSAV) -0.008 ln(PRICE) {4} 0.004
(-0.768) (0.328)
ln(KSAV){4} 0.025** TREND -0.005**
(2.383) (-4.056)
155
1 t-statistics in parentheses below coefficient estimates.
* and ** indicate statistical significance at 0.05 and O.011evels respectively.
2 Variables are defined in chapter 3. Nwnbers in {.} represent lag nwnber.
156