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JAD
Journal of African Development
2002 | Volume 5, #2
Table of Contents
1. Capital Mobility, Saving and Investment Link: Evidence from Sub-Saharan Africa
Douglas K. Agbetsiafa
2.
Real Exchange Rate Distortions and External Balance Position of Nigeria: Issues and
Policy Options
Chukwuma Agu
3. Institutional Reform and Economic Growth in Africa
Sylvain H. Boko
4. Fertility, Education, and Market Failures
Sylvain Dessy and Stephane Pallage
5. Trade Liberalization and Customs Revenues: Does trade liberalization lead to lower
customs revenues? The case of Kenya
Graham Glenday
6. Impact of the Structural Adjustment Program on the Agricultural Sector and Economy of
Nigeria Nii O. Tackie and Odiase S. Abhulimen
7. Why do Resource Abundant Countries Have Authoritarian Governments?
Leonard Wantchekon
1
Impact of the Structural Adjustment Program on the Agricultural
Sector and Economy of Nigeria
by
Nii O. Tackie and Odiase S. Abhulimen
104 Morrison-Mayberry Hall
Tuskegee University
Tuskegee, AL 36088
Phone: (334) 727-8813
Fax: (334) 727-8812
[email protected]
July 2001
2
Introduction
Nigeria is the most populous nation in Africa with about 103 million people. The
land area is almost 360,859 square miles and has potential for development with her
enormous natural and human resources (Burren, 1998). However, despite these blessings
Nigeria is still characterized by inequality in income distribution, poor health and
education standards, high unemployment rate, high debt, and relatively low agricultural
productivity. These shortcomings can be attributed to mismanagement and a gross
misconception of the ideals of economic growth and development by key political
leaders. Economic growth is a measure of the increase in economic indicators such as
per capita income and gross domestic product (GDP). Economic development, on the
other hand, is perceived as "a multidimensional process involving major changes in social
structures, popular attitudes, and national institutions, as well as the acceleration of
economic growth, the reduction of inequality, and the eradication of poverty" (Todaro,
1997, p.16). Economic development, therefore, necessarily entails economic growth.
The Nigerian post independence economy can be viewed in three distinct phases,
namely, first phase from 1960 to 1973; second phase, 1974 to 1982; and third phase,
1983 to present (Oshikoya, 1990). During the first phase, the economy was largely
sustained by agriculture. Substantial expansion in infrastructure, public utilities, and the
construction sectors was supported by the agricultural sector. Economic growth in the
second phase was largely propelled by increased oil exports. This induced huge public
investments and over importation of foreign-made goods. Increases in oil prices in
1973/74 and 1979/80 further precipitated huge transfer of wealth to the country.
Policymakers perceived this windfall to be the opening to extensive development plans.
3
The government embarked on expansion of urban-based construction, transportation,
communication networks, as well as an ambitious construction of a new national capital
in Abuja. Increases in public sector investment were also accompanied by expansion of
general government consumption. Aggregate expenditure thus exceeded domestic output
by a large margin. In fact, in the second phase, agriculture took a back seat to the oil
industry.
As a result of the massive squandering of resources and mismanagement in the
second phase, the third phase witnessed serious economic deterioration, external debt
crisis, financial fragility, and rising inflation. Buren (1998) attributed the decline in
economic growth to falling and unstable world oil prices after 1981. Thus, the
government became increasingly over-extended financially, with insufficient revenue
from petroleum to pay the rising cost of imports, finance major development projects,
and service external debt payments.
To deal with the deteriorating economic conditions, the government with the
assistance of the World Bank designed a structural adjustment program (SAP) which was
to be implemented beginning June 1986. The SAP aimed at facilitating economic
growth as a means of jump-starting the economy towards sustainable economic growth
and development. The objectives of the program included the following:
(1) reconstructing and diversifying the productive base of the economy, by reducing the
dependence on oil and imports, (2) laying a basis for sustaining non-inflationary growth,
(3) making substantial progress towards fiscal and balance of payment viability,
(4) improving efficiency of the private sector’s contribution to economic growth, through
liberalized trade and privatization of public sector enterprises, (5) devaluing the naira,
4
and (6) reducing government deficits (Buren, 1998; Oshikoya ,1990). Also included in
the SAP, were policies geared towards reversing the trend of Nigeria being a heavily
indebted food-importing economy to it becoming a strong domestic food-producing and
exporting economy. Thus, the following policy measures were implemented: (1) removal
of all government subsidies on essential consumer goods such as petroleum products and
food, (2) active export promotion of all items including food staples, other agricultural
products, and raw materials, and (3) general import restrictive measures for all items
including food, medicine, and raw materials (Harrison, 1993).
Generally, structural adjustment entail policies, designed by world financial
institutions such as the World Bank and the International Monetary Fund (IMF), aimed at
improving the socioeconomic conditions of implementing nations. Adoption and
implementation of such policies (e.g., currency devaluation, trade liberalization,
privatization, and removal of subsidies) in the 1980s and 1990s was perceived as a means
of reversing the pervasive social and economic problems of developing nations. There
has been considerable debate about the effects of such SAP measures. A host of
researchers (e.g., Reed, 1996; Nwosu, 1992; Olomola, 1994) believe that structural
adjustment policies are essential prerequisites for economic recovery, adjustment to, and
development in the new global market place. On the contrary, many other economists
and social scientists such as Igbedioh and Aderiye (1994), Awoyomi (1989), and
Momoh, (1995) argue that SAP measures have led to recessions and poor standards of
living in developing countries. There is limited empirical work on the impact of the SAP
on the Nigerian agricultural sector and economy as a whole. This study is intended to
help fill the gap in the literature. The goal of this study is to analyze the effects of SAP
5
on the agricultural sector and economy of Nigeria. Specifically, it assesses the influence
of structural adjustment on selected indicators – agricultural production, net agricultural
exports, contribution of agriculture to GDP, and real GDP growth rate -- and the relative
impact of these indicators on the agricultural sector. The study is largely limited to the
agricultural sector because of its relative importance in the Nigerian economy. On the
average agriculture has accounted for 32 percent of GDP and more than 70 percent of
employment. As such, deceleration in the growth of the agricultural sector would
influence the rate of growth of the whole economy if it is not accompanied by greater
increases in other sectors of the economy.
Specific SAP Policy Measures in the Agricultural Sector
SAP policy measures in agriculture included the following items:
(1) the removal of all government subsidies on food and other agricultural products,
(2) promotion of the production and export of nontraditional agricultural products,
(3) import restrictive measures on food and other locally produced agriculturally based
raw materials, (4) the establishment of the Directorate of Food, Road, and Rural
Infrastructure as a major instrument for fostering rural and agricultural development, and
(5) increase of the budgetary allocation to the system of agricultural development projects
as a major instrument for agricultural development (Harrison, 1993; Kajisa et al. 1997;
Nwosu 1992).
The overall objective of implementing structural adjustment in the agricultural
sector was to increase agricultural production and export of agricultural products.
Because of the relative importance of agriculture to the economy, this was supposed to
contribute to improvement in the growth of the economy.
6
Methodology
Measuring Impact of Programs
Several methods have been used to measure the impact of programs on whole
economies or sectors of economies. First, there is the before-after approach that
compares the values of variables in the period before a program is implemented to those
in the period after implementation. The major shortcoming of this approach is it assumes
that all program outcomes are the result of program variables. Although the before-after
approach has some degree of bias as an estimation procedure, it nevertheless, has
inherent objectivity (Moshin and Knight, 1985). The shortcoming of the before-after
approach can be greatly reduced if further statistical tests are conducted on the issue in
question. Studies, which have used the before-after approach, include those by
Reichmann and Stillson (1978), Connors (1979), Kelly (1982), Zulu and Nsouli (1985),
Pastor (1987), and Gylfason (1987).
Second, there is a modified form of the before-after approach called reference
(control) group approach or with-without approach. This method assumes that the
outcome of subjecting program and control group countries to non-program determinants
would be similar for both groups had not the program countries received the program.
Any differences between the two groups, therefore, are attributed to the program
determinants. The bias here, compared to the previous approach, is lower, yet other
errors may be present because program countries may differ from control group countries
in terms of characteristics (Moshin and Knight, 1985; Goldstein and Montriel, 1986).
7
Examples of Studies that have used this approach are Donovan (1981, 1982), Loxley
(1984), Pastor (1987), and Gylfason (1987).
Third, there is the actual-versus-target approach. This approach compares actual
program performance for key macroeconomic factors to targets for these factors set by
the host country and multilateral agency. The success of a program can be gauged by the
extent to which program targets are achieved, but knowing this requires access to
confidential information on country. This information is less likely to be released by the
multilateral agency or host country. In addition, program variables may be affected by
other nonprogram variables which may cause targets to be underachieved or
overachieved (Khan, 1990). Reichmann (1978), Beveridge and Kelly (1980), and Zulu
and Nsouli (1985) used this approach in their studies.
Fourth is the counterfactual approach. This approach compares the actual
behavior of key macroeconomic variables in the program country with the outcomes that
would have been observed in the absence of the program (Moshin and Knight , 1985).
The downside of this approach is it is very subjective.
The fifth and final approach to be alluded to here is the comparison-ofsimulations approach. This method uses simulations of economic models to determine
the hypothetical performance of Fund-type policies or policy packages and alternative
policy packages (Khan, 1990). The drawbacks to this method are two fold: one, the real
world effects of program performance may be different from simulated results, and two,
program performance may be different when supported and implemented by a
multilateral agency because of credibility attached to agency. Of course, when program
is implemented outside multilateral agency authority another set of outcomes may be
8
obtained. As Khan (1990, p. 209) puts it, “such credibility effects are automatically
captured by the outcome-based approaches, but not necessarily by the model-based
approach like the comparison-of-simulations method.” Khan and Knight
(1985, 1981) used this approach.
Choosing the Approach
Since a mere descriptive analysis is narrow in its focus and may be biased, and
also for reasons indicated above, an explanatory analysis delving into the relationships
among variables is provided. This explanatory analysis relies on a model.
The model reflects the effects of SAP on the agricultural sector, and thus, the
general economy. Figure 1 highlights the model under investigation and shows the
expected relationships among the variables under consideration; the signs are indicative
of the combined influence of the SAP variable on a target variable. Starting from the
left-hand side, SAP is presumed to have an improvement in overall agricultural
production. The overall improvement in agricultural production is expected to increase
net agricultural exports (agricultural exports minus agricultural imports), this in turn is
supposed to improve the contribution of agriculture to GDP, translating ultimately to a
positive growth in real GDP growth rate, ceteris paribus.
The study, therefore, did not follow the mathematical model used by other studies
cited previously in this study (e.g., Zulu and Nsouli, 1985; Pastor, 1987; Khan, 1990).
Rather, it adopts the methodology of path analysis, used by Rajaonarivony (1996) in
analyzing effects of IMF programs on Madagascar’s economy. In path analysis,
9
predictors change depending on the variables being analyzed at a particular time (Kim
and Kohout, 1975).
**** Put Figure 1 Here ****
Data Collection
Data for the variables are from the Food and Agriculture Organization (FAO) data
files. The data are as follows: agricultural production, measured as production index with
1989-91 as base year; agricultural exports, measured in dollar value; agricultural imports,
measured in dollar value; contribution of agriculture to GDP, measured in percent; real
GDP growth rate measured in percent; and SAP measured as a dummy. The data covered
the period from 1970 to 1997.
Data Analysis
The set of relationships shown in Figure 1 is called a multi-stage path model or
path analysis. That is, a dependent variable at a particular stage in the model becomes an
independent variable for a subsequent stage. For instance, agricultural production is
dependent on SAP, while SAP and agricultural production are assumed to influence net
agricultural exports. Similarly, SAP, agricultural production, and net agricultural exports
are supposed to influence contribution of agriculture to GDP. An interconnected series of
multiple regressions is commonly used to evaluate the model.
The beta coefficient was used to evaluate the model. The beta coefficient
measures the relative impact or importance of an independent variable on the dependent
variable (e.g., when there are two beta values of say .02 and .34, the .34 value has more
impact than the .02 value). When there is one independent variable, the beta coefficient
10
is the bivariate correlation coefficient r. The larger the beta coefficient, the stronger a
variable’s relationship to the dependent variable.
The SAP variable is expected to have both direct and indirect effects on target
variables. As stated before, SAP should increase agricultural production; thus there
should be a strong direct impact. Similarly, agricultural production is presumed to
influence net agricultural exports directly. The SAP variable also has an indirect effect
on net agricultural exports. It has an indirect effect on net agricultural exports through its
influence on agricultural production, in addition to whatever direct impact it has on net
agricultural exports.
The major advantage of path analysis is that it allows estimation of both direct
and indirect effects of a variable on another in the causal model. The direct influence is
the beta for each independent variable in a particular multiple regression, or the bivariate
r if there exists only one independent variable. Indirect effects can only occur if a two or
more-stage linkages exist between a dependent variable and an independent variable.
The indirect effect is then estimated by the product of the betas along the indirect path.
The sum of the direct and indirect effects yields the combined effect at any level. For
example, in the causal model, Figure 1, the SAP variable is hypothesized to influence net
agricultural exports both directly and indirectly. Indirectly, SAP influences net
agricultural exports through agricultural production as an intervening variable. The direct
influence of SAP on net agricultural exports is its beta in multiple regression with
agricultural production. That is, SAP and agricultural production are independent
variables and net agricultural export is the dependent variable. The indirect effect of the
SAP variable is calculated by multiplying the bivariate r of SAP and agricultural
11
production with the beta of agriculture production. Similarly, all direct and indirect
influences are calculated. See Appendix A for mathematical details.
In general, the following combined effects should result: implementation of SAP
will cause an increase in agricultural production (positive effect); a rise in net agricultural
exports (positive effect); an increase in contribution of agriculture to GDP (positive
effect); and an increase in real GDP growth rate (positive effect). In short, the total
causal effect of implementation of SAP on real GDP growth rate should be positive.
Findings
Table 1 shows the results of the bivariate analysis between the SAP variable and
the other variables. The bivariate results reflect the direct effect of SAP if there were
only one independent variable. The program’s effects on agricultural production, net
agricultural exports, and real GDP growth rate are positive as expected. The program’s
effect on contribution of agriculture to GDP is negative, contrary to expectation. The
direct effect of SAP on agricultural production was .85.
**** Put Table 1 Here ****
Tables 2 through 5 show the results of the multi-stage analysis. The multi-stage
analysis shows the path progression of the effects of SAP in the agricultural sector (see
Appendix A for mathematical details). Table 2 shows the results of the multiple
regression analysis on net agricultural exports. The beta for SAP is .45 and for
agricultural production -.37. The direct effect of SAP on net agricultural exports is .45
12
and the indirect effect -.31. The combined effect of SAP on net agricultural exports is .14
(Appendix A). As expected the influence of SAP on net agricultural exports is positive.
**** Put Table 2 Here ****
The results of the multiple regression analysis on contribution of agriculture to
GDP are in Table 3. The betas are .25, -.47, and .48 for SAP, agricultural production,
and net agricultural exports, respectively. The direct effect of SAP on contribution of
agriculture to GDP is .25 and the indirect effect -.19. The combined effect of SAP on
contribution of agriculture to GDP is .06 (Appendix A), a positive expected sign.
**** Put Table 3 Here ****
Table 4 shows the multiple regression analysis on real GDP growth rate.
Agricultural production, net agricultural exports, and contribution of agriculture to GDP
had a positive influence on real GDP growth rate. The respective betas are .36, .60, and
.12. However, the SAP variable had a negative direct impact of -.36 (Appendix A) on the
real GDP growth rate. This unexpected sign may be explained by the inflation factor.
That is, with inflation accounted for in real GDP the “true” direct effect of SAP reflects a
negative or decreased value.
**** Put Table 4 Here ****
It was hypothesized that the SAP will improve agricultural production which will
positively influence net agricultural exports, which will in turn positively influence
13
contribution of agriculture to GDP, and this will ultimately improve real GDP growth rate
in total. The results indicate that the hypotheses are not contradicted.
The total causal impact of SAP in the agricultural sector on real GDP growth rate
is in Table 5. The SAP had an overall expected positive causal impact of .69 on real
GDP growth rate. This comprises a direct and an indirect effect. The direct effect is -.36.
The indirect effect passes through three intervening variables, agricultural production
(.85), net agricultural exports (.14) and contribution of agriculture to GDP (.06). This
indirect effect sums up to 1.05. It is the sum of the direct and indirect effect at each stage
that yields the total causal impact. The indirect effect offset the negative value of the
direct effect. It is likely that some aspects of SAP, incentives, such as favorable producer
prices may have influenced the positive effect. The statistical analysis shows that
overall, SAP in the agricultural sector resulted in an improvement in real GDP growth
rate.
**** Put Table 5 Here ****
Summary and Conclusion
The focus of the study is to analyze the effects of SAP on the agricultural sector
and economy of Nigeria. The data for study are from the files of the FAO covering the
period 1970 to 1997. Multiple regression and path analysis are used to analyze the data.
The results reveal that the hypotheses, in terms of the path analysis, are not
contradicted. SAP had a positive impact on agricultural production, which in turn, had a
positive impact on net agricultural exports, which in turn, had a positive impact on
contribution of agriculture to GDP, which ultimately led to a positive impact on real GDP
growth rate.
14
This indicates that overall SAP is beneficial to the Nigerian agricultural sector and
economy. Therefore, the Nigerian authorities should keep the SAP policies in the
agricultural sector in place. Since agriculture is very important to the Nigerian economy,
an improvement or growth in this sector ultimately influences growth of the overall
economy.
The contribution of this study to the body of literature on this topic
notwithstanding, it has some limitations. First, all improvements in the agricultural
sector and the Nigerian economy cannot be attributed to SAP. The results presented here
can be viewed as suggestive. Second, one cannot exhaust in any one analysis the many
variables that might account for performance of the agricultural sector and economy of
Nigeria. Third, no agreement has been reached as yet as to the most appropriate way of
evaluating impact of programs or policies. Indeed, none of the approaches is totally
satisfactory.
Notes
1. Note that levels of significance are not stated in the findings section. The reason is
that in multiple regression analysis, the assumption normally is significance tests are
performed on data set that is a representative random sample of a population. This study
does not have a random sample from a population. The data, then, are the population.
When data include all observations on a population, coefficient estimates are best viewed
as population parameters. Hence, tests of statistical significance become inappropriate.
The most appropriate method for evaluating the significance of coefficients is by
evaluating the relative impacts of beta coefficients (Ringquist, 1994; McClosky, 1985;
Henkel, 1976).
2. For interested readers, coefficient for AGP r in Table 1 was significant at .01 level
(2-tailed); coefficient for NAGEX in Table 3 was significant at .05 level (2-tailed);
and coefficient for NAGEX in Table 4 was significant at .05 level (2-tailed).
15
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18
Appendix A
Stages of Relationships
1. Influence of SAP on agricultural production
AGP = f (SAP)
Direct effect: bivariate r
.85
.85
2. Influence of SAP on net agricultural exports
NAGEX = f (SAP, AGP)
Direct effect: beta of SAP
.45
Indirect effect: bivariate r of SAP-AGP x beta of AGP ) .45 + (-.31)
.85
-.37
= .14
3. Influence of SAP on contribution of agriculture to GDP
CAGGDP= f (SA, AGP, NAGEX)
Direct effect: beta of SAP
.25
Indirect effect: bivaraite r of SAP-AGP x beta of AGP x beta of NAGEX
.85
-.47
.48
.25 + (-.19)
= .06
4. Influence of SAP on real GDP growth rate
RGDPGR = f (SAP, AGP, NAGEX, CAGGDP)
Direct effect: beta of SAP
-.36
Indirect effect: bivariate r of SAP-AGP x beta of AGP x beta of NAGEX
.85
.36
.60
x beta of CAGGD
.12
19
5. Total Causal Impact of SAP on RGDPGR equal to:
Direct causal: beta of SAP in 4, -.36 plus
Indirect causal: through AGP in 1, .85, through NAGEX in 2, .14, and
through CAGGDP in 3, .06 (i.e., -.36 + 1.05 = .69
20
Figure 1. Diagram of the Research Model
Agricultural Production
+
+
Net Agricultural
Exports
Structural
Adjustment Program
+
Contribution of
Agricultural to GDP
Real GDP
Growth
Rate
+
+
21
Table 1
Bivariate Correlation of SAP Dummy with Other Variables
Variables
Agricultural Production
Net Agricultural Exports
Contribution of Agriculture to GDP
Real GDP Growth Rate
r
.85
.14
-.08
.02
22
Table 2
Results of the Multiple Regression Analysis on Net Agricultural Exports
Independent Variables
beta
SAP (Dummy)
.45
Agricultural Production
- .37
23
Table 3
Results of the Multiple Regression Analysis on Contribution of Agriculture to GDP
Independent Variables
beta
SAP (Dummy)
.25
Agricultural Production
- .47
Net Agricultural Exports
.48
24
Table 4
Results of the Multiple Regression Analysis on Real GDP Growth Rate
Independent Variables
beta
SAP (Dummy)
- .36
Agricultural Production
.36
Net Agricultural Exports
.60
Contribution of Agriculture to GDP
.12
25
Table 5
Causal Impact of SAP in the Agricultural Sector on Real GDP Growth Rate
Relationships
Partial
Total
Direct Causal
-.36
Indirect Causal
1.05
Through Agricultural Production
.85
Through Net Agricultural Exports
.14
Through Contribution of Agriculture to GDP
.06
Total Causal
.69