AGRICULTURAL TRADE LIBERALIZATION AND ECONOMIC GROWTH: IS THERE A CONNECTION IN SUB-SAHARAN AFRICA?
CHIKHURI, Krishna* Abstract
This paper investigates the agricultural sector role into the African economic development process. These last years, several studies examined the contribution of agriculture to the economic growth of the less developed countries or the developing countries. We build on these empirical works to undertake a comparative analysis of the linkage between agricultural trade liberalization and economic growth in SSA. We use Panel data analysis to examine the long-run structural relationships between agricultural trade liberalization and economic growth. The results support the claim that agricultural growth through trade liberalization is a major determinant to economic growth in Sub- Saharan Africa.
JEL Codes:
Keywords: Agriculture, Sub-Saharan Africa, Trade and Growth 1. Introduction
No country has been able to sustain a rapid transition out of poverty without raising productivity in its agricultural sector. Despite this historical role of agriculture in economic development, both the academic and donor communities lost interest in the sector, starting in the mid-1980s. This was mostly because of low prices in world markets for basic agricultural commodities, caused largely by the success of the Green Revolution in Asia. After two decades of neglect, interest in agriculture is returning. One of the main reasons for this renewed interest is that economic growth is the main vehicle for reducing poverty and that growth in the agricultural sector plays a major role in that overall growth as well as in connecting the poor to growth.
For most African countries, agriculture and trade are often seen as important catalysts for economic growth. Agriculture is an important vehicle for technology transfer from developed countries to African countries and Agriculture could also promote growth by providing additional employment in a labor surplus economy. International trade is also recognized as an instrument of economic growth, since trade facilitates more efficient production of goods and services by shifting production to more competitive. Given the importance of agriculture in most Africa countries GDP, removal of trade distortions would raise their earnings from agricultural exports. Besides, expanded opportunities for investment and trade would generate multiplier effects that would further enhance economic growth and poverty alleviation.
The developing countries have witnessed a profound advancement of economic policy, particularly in case of trade strategies during the last five decades. Both domestic and global factors have impressed upon the need for more outward-oriented (or liberalized) trade policy regimes. The foundation of General Agreement on Tariffs and Trade (GATT) in 1947 and World Trade Organization (WTO) in 1995 have been the key forces for free trade. The major quantitative barriers to trade, i.e. tariff and non-tariff barriers (quotas,
* Krishna Chikhuri, Senior Agricultural Planning Officer - Ministry of Agro-Industry & Food Security, Mauritius. E-mail: [email protected]
licenses and technical specifications, among other restrictions) have substantially been reduced or dismantled after the enforcement of agreements under auspices of GATT and WTO.
Historically, Africa has followed an IS strategy based on a socialist economic model with close state control of the economy. The government's economic policies had limited success during the early years of independence. However, in the 1990s, many countries implemented structural adjustment programs based on an export-led growth strategy.
Government took number of steps towards freer economy, and gradually introduced comprehensive macroeconomic and structural reforms in the country e.g. shifting from fixed exchange rate to a policy of flexible exchange rate, privatization policy, removal of subsidies, tariff reduction, etc.
It is straightforward to tell a story of why agricultural growth is a necessary condition for a country's economic development in the poorest areas of the world. There the share of the population in agriculture, as well as the share of food in consumption are so high that income generation, or new income streams in the terminology of Schultz (1964), have to come from agriculture if they are to make any substantial national impact.
An understanding of the determinants of poverty and the mechanisms for reducing it in a sustainable fashion has also undergone a quiet revolution in the past decade. Part of this understanding is recognition that economic growth is the main vehicle for reducing poverty, but for this to work the distribution of income must not deteriorate too sharply.
In many circumstances, growth in the agricultural sector has been an important ingredient in the formula that connects economic growth to the poor
While the nature of relationship between international trade and growth - the hypothesis of export led growth or growth led exports - has been widely examined using the total export of various economies, developing or developed, there have not been many attempts to examine the role of agricultural trade liberalization on economic growth in Africa. This could be one of the reasons as to why the nature of the relationship between agricultural trade policy and the growth still remains unclear.
Accordingly, the aim of this empirical paper is to investigate the agricultural sector role into the African economic development process. These last years, several studies were interested, to the examination of the agriculture contribution to the economic growth of the less developed countries or the developing countries. We build on these empirical works to undertake a comparative analysis of the linkage between agricultural trade liberalization and economic growth inn SSA. We use Panel data analysis to examine the long-run structural relationships between agricultural trade liberalization and economic growth.
The remainder of the paper is organized as follows. Section 1 provides a brief review of literature on the role of agricultural sector in economic growth. Section 2 gives an overview of SSA trade policy in Agriculture while Section 3 depicts the Agricultural Trade Performance in Africa. Section 4 presents the econometric framework of our study.
The next Section (Section 5) discusses empirical results with distinction between long-run relationship and short-run dynamics. Some Concluding remarks and findings are given in Section 6.
1.1 Brief review of literature
There can be little doubt that, historically, trade has acted as an important engine of growth for countries at different stages of development, not only by contributing to a
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more efficient allocation of resources within countries, but also by transmitting growth from one part of the world to another. The doctrine that trade enhances welfare and growth has a long and distinguished ancestory dating back at least to Adam Smith (1723- 90). Smith, in his famous book, An Inquiry into the Nature and Causes of the Wealth of Nations (1776), stressed the importance of trade as a vent for surplus production and as a means of widening the market thereby improving the division of labour and the level of productivity.
In the 19th century, Smith’s productivity doctrine of the benefits of trade developed into an export-drive argument, particularly in the African colonies, which explains why classical trade theory is often associated with colonialism.
Following Smith, David Ricardo (1772-1823) developed the theory of comparative advantage and showed rigorously in his Principles of Political Economy and Taxation (1817) that on the assumptions of perfect competition and the full employment of resources (although not made explicit), countries can reap welfare gains by specializing in the production of those goods with the lowest opportunity cost and trading the surplus of production over domestic demand, provided that the international rate of exchange between commodities lies between the domestic opportunity cost ratios (see later). These are essentially static gains that arise from the reallocation of resources from one sector to another as increased specialization, based on comparative advantage takes place. These are the trade-creation gains that arise within Customs Unions or Free Trade Areas as the barriers to trade are removed between members, but the gains are once-for-all. Once the tariff barriers have been removed, and no further reallocation takes place, the static gains are exhausted.
This is in contrast to the dynamic gains from trade which continually shift outwards the whole production possibility frontier of countries if trade is associated with more investment and faster growth based on scale economies, learning by doing and the acquisition of new knowledge from abroad, particularly through foreign direct investment. It is the dynamic gains from trade that are focused on in modern trade theory (see Helpman and Krugman, 1985) and in “new” growth theory (see Grossman and Helpman 1991), and which constitute a vital link in the casual chain between exports and growth.
There can be little doubt that, historically, trade has acted as an important engine of growth for countries at different stages of development not only by contributing to a more efficient allocation of resources within countries, but also by transmitting growth from one part of the world to another. Not all countries, however, necessarily share equally in the growth of trade or its benefits. This will depend on: the production and demand characteristics of the goods that a country produces and trades; the domestic economic policies pursued, and the trading regime it adopts. For example, taking the developing countries as a whole, the volume of exports has grown slower than for developed countries since 1950-5 percent per annum compared to 8 percent- because developing countries still largely produce and export primary commodities and low value-added manufactured goods with a relatively low income elasticity of demand in world markets.
The discrepancy in rates of growth of exports has been even wider in value terms because the terms of trade of developing countries has deteriorated vis à vis developed countries causing the developing countries’ share of the total value of world trade to have fallen from 30 percent in 1965 to approximately 20 percent today.
Given the predictions of trade theory and the facts, the important point to make in this introduction is that the issue for developing countries in general, and Africa in particular, is not so much whether to trade but in what to trade, and the terms on which trade should take place with. In this context, the natural starting point for Sub-Saharan African countries is the agricultural tradable sector given the resource endowment of the majority of these countries.
The macroeconomic linkage between agricultural sector and economic growth has been debated virtually from two broad points of view. The first concerns the direct contribution of agriculture to economic growth arises via Lewis linkages. The “Lewis Linkages”
between agriculture and economic growth provide the non-agricultural sector with labor and capital freed up by higher productivity in the agricultural sector. These linkages work primarily through factor markets, but there is no suggestion that these markets work perfectly in the dualistic setting analyzed by Lewis (1954). Chenery and Syrquin (1975) argue that a major source of economic growth is the transfer of low-productivity labor from the rural to the urban sector. If labor markets worked perfectly, there would be few productivity gains from this structural transfer.
This point of view suggests that agriculture provides input materials, capital and labour for the rest of economy in order to raise the total national output since the industrial sector is more productive than agriculture and the modernization of the economy and, therefore, the growth of the global output passes by a certain taxation of agriculture as means to develop the industrial sector and to transfer resources from agriculture toward the other sectors (forward linkage effects).
The indirect contributions to economic growth can be explained through the Johnston- Mellor linkages. The “Johnston-Mellor Linkages” allow market-mediated, input-output interactions between the two sectors so that agriculture can contribute to economic development. These linkages are based on the agricultural sector supplying raw materials to industry, food for industrial workers, markets for industrial output, and the exports to earn foreign exchange needed to import capital goods (Johnston and Mellor, 1961).
Again, for the Johnston-Mellor linkages as with the Lewis linkages, it is difficult to see any significance for policy or economic growth unless some of the markets that serve these linkages are operating imperfectly (or, as with many risk markets, are missing altogether). That is, resource allocations must be out of equilibrium and face constraints and bottlenecks not immediately reflected in market prices if increases in agricultural output are to stimulate the rest of the economy at a rate that causes the “contribution”
from agriculture to be greater than the market value of the output, i.e. the agricultural income multiplier is greater than one (Timmer, 1995).
These last years, several studies were interested, always according to various methodological approaches, to the exam of the agriculture contribution to the economic growth of the less developed countries or the developing countries.
With respect to agriculture in relation to overall Gross Domestic Product (GDP) growth, in a cross-sectional panel of 52 developing countries, Gardner (2005) found no significant evidence of agriculture leading overall economic growth. But in a more sophisticated analysis using Granger-causality tests on very similar data, Tiffin and Irz (2006) found
"overwhelming evidence that supports the conclusion that agricultural value-added is the causal variable”. Based on econometric work by Sumarto and Suryadi (2003) on the topic of agricultural growth as related to poverty for Indonesia, Timmer (2005) concludes,
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"Roughly two-thirds of the reduction in poverty observed during the period of fastest growth in manufactured exports was due to growth in agricultural output at the provincial level." Yet Fane and Warr (2003), in a general equilibrium model of the same economy, conclude, "Contrary to the assumptions of many commentators, the poor do much better if a given amount of GDP growth is produced by technical progress in services or in manufacturing than if it is owing to technical progress in agriculture." As is apparent from broader assessments of this issue, such as those conducted by Valdes and Foster (2005) and Timmer (2005), the results of econometric analyses are inconclusive and even contradictory with one another
Kanwar (2000) and Chaudhuri and Rao (2004) suggest that in estimating the relation between agricultural and non-agriculture sectors, the former should not be assumed to be exogenous, rather, this should first be established. Kanwar (2000) criticize also the
“neglect” of agricultural sector role in the development process of the less developed economies. In his study, the author studies the co-integration of the different sectors of the Indian economy in a multivariate vector autoregression framework to circumvent problems of spurious regressions given the presence of non-stationarity data.
Yao (2000) demonstrates how agriculture has contributed to China's economic development using both empirical data and a co-integration analysis. Two important conclusions are drawn. First, although agriculture's share in GDP declined sharply over time, it is still an important force for the growth of other sectors. Second, the growth of nonagricultural sectors had little effect on agricultural growth. This was largely due to government policies biased against agriculture and restriction on rural-urban migration.
Katircioglu (2006) analyze the relationship between agricultural output and economic growth in North Cyprus, a small island which has a closed economy using co-integration.
This author use annual data covering 1975-2002 period, to find the direction of causality in Granger sense between agricultural growth and economic growth. His Empirical results suggest that agricultural output growth and economic growth as measured by real gross domestic product growth are in long-run equilibrium relationship and there is feedback relationship between these variables that indicates bidirectional causation among them in the long-run period. This study concluded that agriculture sector still has an impact on the economy although North Cyprus suffers from political problems and drought.
Tiffin and Irz (2006) using the Granger causality test and co-integration in the panel data for 85 countries, find evidence that supports the conclusion that agricultural value added is the causal variable in developing countries, while the direction of causality in developed countries is unclear.
All these studies and reflections have made useful contribution to understand the link between agricultural sector and economic growth. While a number of linkages can be envisaged, the general idea seems to be one where the contribution of agricultural growth to economic development varies markedly from country to country and from one time period to another within the same economy. However and up to our knowledge, for the Sub-Saharan African region, studies and models related with this topic are generally
“limited”.
2. Sub-Saharan Africa Trade Policy in Agriculture
Agriculture is at the heart of the most African economies especially those in the Sub- Saharan Region and is critical for food self- sufficiency. The agricultural sector generates
around 20 per cent of GDP and, at best, 40 per cent of total export earnings through the export of tobacco, cotton and horticultural produce, among others; employs 66 per cent of the country’s labour force (the majority being women); and accounts for about 60 per cent of all raw materials for industry. The main agricultural products are maize, cotton, tobacco, wheat, coffee, sugarcane, peanuts, millet, soybeans, sheep, pigs and goats.
Agriculture provides the opportunities to address extreme poverty in Africa, where the proportion of people living below the poverty line, of less than US$1 a day, increased from 47.6 percent in 1985 to 59 percent in 2000. As a result, more and more people in Africa have limited access to food and other basic amenities such as potable water, minimum health care and education, effectively limiting the opportunities available to them. Poverty and nutritional status are closely linked. About 26 percent of the people in Africa – more than 200 million people, particularly women and children – are undernourished; this is a reflection of poverty. It deepens other aspects of poverty such as incapacity to work and resistance to disease. It also affects children’s mental development and educational achievements. Figure 1, in the Annex, shows top production in SSA countries for year 2008.
Agriculture is not limited to subsistence food crops and livestock production but includes crops grown for sale, such as tobacco, cotton and flowers. Most agricultural households rely to some extent on sale of agricultural products. Thus, access to markets, finance and supporting infrastructure are crucial.
Agricultural trade is undoubtedly the single most important link between trade and poverty in Sub Saharan Africa, where farming accounts for more than two thirds of total employment and constitutes the main income source for the vast majority of the poor.
Agriculture is, directly and indirectly, the mainstay of nearly two-thirds of Africans. In addition to being an important source of individual and household incomes, it also constitutes the bedrock of most national economies. Agricultural production and the domestic trade in agricultural products are central to the functioning of local markets, the fight against poverty, the provision of employment, and the quest for greater national food security.
Trade policies in Africa underwent major changes within the framework of the SAPs.
There was greater liberalization in foreign trade through the reduction of non-tariff barriers and decreases in customs levies applied to imports in a large number of countries.
African countries stopped fixing exchange rates and overvaluing their currencies, and instead applied a series of devaluations in order to promote exports and help businesses become more competitive.
African agricultural exports enjoy a dominant position in the international trade relations of the continent, including formal and informal intra-African cross-border exchanges.
Furthermore, the agricultural sector serves as a key source of raw materials for the production of a variety of semi- and fully-processed commodities. Services connected to the promotion of agricultural production and productivity also occupy a significant position in most African economies. In sum, agriculture continues to offer one of the best potential bases for promoting overall economic development in Africa, including opportunities for the growth and expansion of the industrial sector.
There is a broadly shared consensus that if African countries succeed in mastering their agricultural policies in a manner that not only diversifies output and boosts productivity but also promotes strong linkages with other economic sectors and serves broad social
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policy objectives, the continent will be well on its way to turning the table of underdevelopment. Building and renewing a critical mass of domestic capacity for the design and implementation of sound agricultural policy in rapidly changing contexts is, therefore, absolutely necessary for the acceleration of Africa’s development. This is all the more so as the African continent remains an important and growing target for the export of subsidised agricultural commodities, including food products, that threaten to displace local producers from national and regional markets, and which carry implications for national and regional food security. At the same time, many new producers have emerged in the world market that compete vigorously with African cash crop exports, eroding the continent’s share of the global trade in a number of important primary commodities.
While old concerns such as the terms of trade for African agricultural exports and the massive subsidisation of less efficient developed country agricultural producers by their governments remain outstanding, new issues centring around oligopolistic controls exercised by major corporations in the global seed market, the introduction of genetically- modified crops, the sustainability of the environment, and the increased interest of international financial speculators in international agricultural markets have posed new policy challenges to African countries.
These challenges come against the backdrop of new pressures on African small holders who constitute the bulwark of the agricultural sector in most of the continent and a massive scramble for - and grabbing of - arable land across the continent by a range of international commercial interests, including multinational agri-business firms. An agricultural policy which is fit for the challenges faced by African countries must capture the complex inter-connections between domestic and global processes if durable national development is to be delivered through the mobilisation of the opportunities offered by the agricultural sector.
Several African countries have also become assertive on agricultural issues in international trade debates. South Africa played a lead role in the recent WTO negotiations, with Uganda, Botswana, and Kenya also becoming vocal players. Four West African countries-Burkina Faso, Mali, Chad, and Benin-have called on the United States to cut the $1-3 billion it spends each year subsidizing American cotton growers. More broadly, African politicians have criticized unfair trade policies and their impact on Africa's long-term development.
Horticulture, which includes vegetables, fruits and cut flowers, has become a major activity. It has grown to be the single largest category in world agricultural trade, accounting for over 20 percent of such trade in recent years. While in sub-Saharan Africa (SSA), horticultural exports now exceed US$2,000 million, this is only 4 percent of the global total. Significant opportunities for expansion, therefore, exist in Africa to boost employment as well as foreign currency earnings. The challenges would be to adequately deal with environmental problems, which include pollution from chemicals.
An opportunity which is yet to be fully exploited is irrigated agriculture. Only six percent of the total cultivated land is under irrigation in Africa, compared to 33 percent in Asia.
In a region where droughts are prevalent, often destroying crops and exacerbating food insecurity, irrigation could be a key factor in enhancing food security. Irrigation increases yields of most crops by between 100 and 400 percent, and it has been projected that in the next three decades, 70 percent of gains in cereal production globally would be from
irrigated land. According to the Economic Commission for Africa (ECA), little progress towards sustainable development can be achieved until Africa reaches a minimum level of developing and managing water resources for secure food and agricultural production.
In order to maximize the potential of the agricultural sector, institutional and governance reforms which increase opportunities for rural people, such as better access to finance, and support the development of small and microenterprises is essential. Agricultural opportunities are closely linked to global trade policies and practices.
The emphasis on trade policies adopted by African countries from the early-1980s was part of a new development framework which sought to promote greater openness in order to boost growth and encourage their competitive integration into the globalization process.
An important stylized facts, at odds with orthodox economic analysis, is that Sub-Saharan African countries are surprisingly open to international trade. Measured by the standard index of “openness” of African economies (i.e. exports plus imports as a percentage of GDP). Sub-Saharan African Countries typically display an openness ratio in the order of 50-60 percent, comparable to the average of the European Union Countries, and about three times higher than that displayed by the world’s biggest importer, the United States.
3. Agricultural Trade Performance in Africa
Agricultural exports also represent the bulk of total merchandise exports in most African countries. Most African countries have experienced substantial increased in export volumes when they were forced to undertake trade liberalization strategies with Structural Adjustment Programmes during the 1980s and 1990s. As can be appreciated from Table 1, in the Annex, estimates by FAO of the volume of exports shows that in aggregate African exports increased by more than 150 percent over the 18 year period from 1990- 2008.
African agricultural exports have mainly been proportionate to the increase in GDP.
However, the agricultural sector was not spared by the global economic slowdown in the late 1970s, which negatively affected sub-Saharan African economies. Against a background of improved macroeconomic conditions, the sector recovered from this downturn in the mid-1990s. Subsequently, agricultural growth accelerated from 2.3 per cent per annum in the 1980s to 3.8 per cent between 2001 and 2005 (World Bank, 2008b). However, this was hardly reflected in several indicators of the agricultural sector's performance. Figure 2, in the Annex, show top Imports in SSA.
This could be explained, in part, by the fact that global trade in agriculture is no longer dominated by the traditional bulk commodities. African exporters have also been facing a sharp decline in the price of most of their commodities. World prices for many of the commodities that Africa exports declined between 1990 and 2010: cocoa, cotton, sugar and copper by over 25%, coffee by 9% and minerals overall declined by 14% (WTO).
This does not mean that agricultural trade is unimportant for Africa: compared to other developing country regions, sub-Saharan Africa (SSA) tends to have high export/GDP and import/GDP ratios. In simple terms, exports are very important to African countries even if African exports are not very important in the world market.
However, the net trade performance in agriculture has been decreasing over the years and this trend has been reflected in the economic performance of these countries, as seen in Figure 3 in the Annex.The value of sub-Saharan African agricultural production remained stable between 1995 and 2008, while the nominal value of its agricultural exports
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increased noticeably from about $2.4 billion to $2.8 billion between 1995 and 2000, before rising to $6.1 billion in 2008 (FAOSTAT). However, as compared with the significant increases in the value of agricultural exports from Latin America and from East and South-East Asia, the increase in the value of sub-Saharan African agricultural exports following liberalization appears rather modest. Figure 4 highlights the Top exports in SSA which consists of Meat, Maize, and Oranges etc.
Net Trade Performance in agriculture =
I m I m
a g r i c u l t u r e a g r i c u l t u r e
a g r i c u l t u r e a g r i c u l t u r e
E x p o r t p o r t
E x p o r t p o r t
There are several trends in the destination of African exports. However, these appear to have been generally unaffected by the process of trade liberalization. European countries continue to represent the largest market for African exports, although their share has been decreasing steadily over time as the influence of historical ties on African trade patterns diminishes.
Another important aspect of SSA agricultural trade is their continuing dependence on traditional commodity exports which reflects the region's inability to tap fully into the international trade in "market-dynamic" (non-traditional) commodities, such as horticulture and processed foods. These products are highly income-elastic, with lower rates of protection in industrialized and large developing countries.
Looking at individual country experiences, it appears that the countries that have been most successful in exporting agricultural products are those in which a deliberate export orientation of agriculture and product diversification was pursued by Governments.
Overall, however, most sub-Saharan African countries continue to export traditional bulk agricultural commodities. Only a few countries have started to export new market- dynamic horticultural products and value-added agricultural products.
Before concluding this section, it is worth noting that the gdp per capita of the SSA group has been following an upward trend since the 1980’s.
4. Conceptual Framework & Econometrics Model Specification
The conventional neo-classical production function was used to test the impact of agricultural trade liberalization on Economic Growth and Openness indicators on agricultural trade liberalization were included in the basic model for that purpose. Most of the times empirical studies on economic growth begin with the neoclassical model, originally proposed by Solow (1956):
1 1
t t t t t
Y K L
(1)Where Yt = Aggregate production function of the economy at time t, tis Total factor productivity at time t,
K
tis Real capital stock at time t,L
tis Employed labour force at time t and
1t is the Usual error term and independent from all the explanatory variables.Because this study aims to investigate if and how agricultural trade liberalization affects economic growth via increases in productivity, we assume that total factor productivity
can be expressed as a function of agricultural trade openness and other exogenous factors
C
t :, ,
2)
t t t t
A T C
(2), ,
2)
t t t t
A T
C
(3)Where
T
t = Trade openness at time t and
2t = usual error term and independent from all the explanatory variables. We combine equation (3) with equation (1) and obtain:, , , ,
3t t t t t t
Y C K
L T
(4)Where
3t = usual error term and independent from all the explanatory variables,
=Elasticity of production with respect to capital
K
t ,
= Elasticity of production with respect to labour force and
= Elasticity of production with respect to trade openness.Taking natural logs (ln) on both sides of equation (4) gives an estimable linear function:
ln Y
t C
t ln K
t ln L
t ln T
t
4t (5)Where all coefficients are constant elasticities, C1t = lnCt is a constant parameter,
4t = usual error term and independent from all the explanatory variables, which reflects the influence of all other factors.5. Data & Empirical Results
The study is based on secondary source of time series data covering the time period 1980- 2007 for Sub-Saharan African Countries. The countries used in the panel are selected entirely on the basis of data availability1. An increase in trade openness is often considered the increase in the size of the country’s trade. Generally it is considered a proxy for trade liberalization. The higher level of trade openness reveals the success of trade liberalization policies. Therefore we use the net trade performance in agriculture as a measure of trade liberalization.
In this study, the panel data econometric approach was applied. Therefore, the model needed to deal with cross-section (N) and time series (T) data. Therefore, the growth function to be estimated used in the study is based on Equation (6) and can be illustrated as follows:
1 2 3
it it it it it it
y k l t
(6)i = 1,2,…,N t = 1,2,…,T
1 Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Central African Rep, Chad, Comoros, Congo, Cote D’Ivoire, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Rwanda, Senegal, Sierra Leone, South Africa, Sudan, Swaziland, Togo, Zambia & Zimbabwe.
171 Where
i refers to the number of individual countries in the panel, t refers to the number of observations over time.
y
i t is log of gdp per capitak
it is log of Capital (Gross investment)l
it is log of labour forcet
it is the agricultural trade performance indexTable 2, in the Annex, presents the Fixed effects (FE) regression results of individual variables on the growth function
Generally, a panel data set can be estimated in one of three ways, depending on whether the individual cross-section effects are considered to be constant, fixed or random. The corresponding statistical models are the ordinary least squares (OLS) model, the random effects (RE) model, and the fixed effects (FE) model. OLS simply assumes that the unobservable individual-specific effects do not differ i.e., they are homogenous effects, whereas RE and FE consider these effects into the model.
In order to determine whether the estimation of model (6) should be an OLS model or a Fixed effect (FE) model, the Homogeneity F-test was executed to assess whether the countries share a common intercept and slope coefficient. The results of the homogeneity F-test indicated that there was no common slope and intercept for all countries and regions implying that unobservable country-specific effects existed in all estimations.
Therefore the FE model was the most appropriate to apply in our estimation.
The results from the FE regression indicated that all external factors had a positive impact on growth of gdp per capita (Table 2). The contribution of net trade performance in agricultural trade (t) to growth of gdp per capita also showed a positive impact; however, the impact of labour was found to be negative. This outcome may have been due to the fact that the as gdp per capita increases, there was a shift of labour force from agricultural sector.
5.1. Panel unit root tests source. This section is included in the Annex.
5.2. Panel Cointegration tests. This section is included in the Annex.
5.3. Panel Cointegration estimation
Although Pedroni’s methodology allows us to test the presence of cointegration, it could not provide estimation of long-run relationship for panel framework, in presence of cointegration, several estimators are proposed: OLS, Fully Modified OLS (FMOLS), dynamic OLS (DOLS), and Pooled Mean Group (PMG). Chen, McCoskey and Kao (1999) analysed the proprieties of the OLS estimator and found that the bias-corrected OLS estimator does not improve over the OLS estimator in general.
Kao and Chiang (2000) study the asymptotic distributions for ordinary least squares (OLS), fully-modified OLS (FMOLS), and dynamic OLS (DOLS) estimators in cointegrated regression models of panel data. Their Monte Carlo simulation results show that the OLS estimator has a non- negligible bias infinite sample, the FMOLS estimator does not improve over the OLS estimator in general, and the DOLS outperforms both the OLS and FMOLS estimators.
Table 7.5 reports the estimation of the Cointegrating Regression results based on the DOLS.
6. Concluding Remarks
Most African governments initiated programmes of agricultural market liberalization in the 1980s as part of economic structural adjustment programmes. Yet many remain unconvinced of the most fundamental elements of the process. Some governments openly contend that agricultural market liberalization has contributed to the crisis facing small farm households across the continent, that private sector response and international trade has been too slow and too weak to spur development, and that the state should get back into direct distribution of strategic inputs and/or commodities and restrict regional and international trade to achieve food security.
In recent decades, the potential contribution of agriculture to economic growth has been a subject of much controversy among development economists. While some contend that agricultural development is a precondition to industrialization, others strongly disagree and argue for a different path.
Our hypothesis that the agricultural sector would have a positive impact on economic growth and the trade openness of agriculture would be a major contributing factor is confirmed. Despite much debate and qualitative analyses of the trade liberalization to economic growth and development, few empirical investigations on the specific linkage between agricultural liberalization and economic growth issue exist. This paper examines the role of the net trade performance in agriculture as an “engine of growth” by analyzing data for 36 economies in Sub-Saharan Africa with the panel data analysis.
Furthermore, the results also suggest that agricultural trade openness has a positive effect on GDP per capita. This study provides evidence in support of increasing public and private resources allocated to agricultural research and infrastructure development. This is particularly needed in many African countries where the agricultural sector has been marginalized. In many cases, developing countries that were net food exporters (e.g., Zimbabwe) have become net food importers and have become dependent on international food aid. Of course, this change in fortune could be attributed to natural disasters and Table 5: Panel Cointegrating
Regression results -DOLS Explanatory
variables
R2 = 0.47
k
0.78l
0.25t
.015Source: Computed
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changes in climatic conditions such as drought (e.g., Niger). However, in many other cases, the demise of the agricultural sector has been driven by domestic policies that intentionally promoted industrialization-led development while marginalizing the agricultural sector.
Agricultural Trade liberalisation and growth seem to be positively correlated, and exports act as an engine of growth. How powerful is the engine, however, depends on the production and demand characteristics of the goods produced and exported. Countries specialising in the production and export of primary products do not perform as well as countries specialising in the production and export of value-added goods.
However, there are several issues of concern to African countries.
One of such concern to policymakers is about the consequence of trade liberalization on State revenue. It should be recalled that the budgetary income of these countries is closely tied to customs revenue.
Another concern is the limited reforms that have been implemented and the history of trade policy reversal in Africa, the credibility of an open African trading policy remains low. Few African countries have taken advantage of WTO agreements to lock-in their commitments to open trade particularly with trading partners from industrial countries.
Like South Africa, other African countries could seek reciprocal free trade agreements with advanced industrial country partners in order to secure market access for their exports, which is currently conditional, partial and unilateral. Reciprocal free trade agreements may have the additional benefit of eliciting higher levels of FDI and technology flows.
In the light of the above, SSA countries should consider agricultural trade liberalization as a means to poverty reduction and improving the lives of their citizens. The appropriate regulatory framework need to be put in place though to ensure that the distributional effects of trade liberalization are well spread among the rural poor.
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Annex on line at the journal Website: http://www.usc.es/economet/aeid.htm
175 Annex
Table 1: Volume Indexes of Exports, (1990-2008) 1990 1995 2000 2008
World 100 136.1 126.2 325.5
Africa 100 125 113.7 259.1
- Sub-Saharan Africa 100 120.8 112.7 256.2
Oceania 100 115 129.1 223.7
Latin America 100 148.5 150.1 529.6
U. states 100 137.9 132.6 285.4
Asia 100 164.1 139.1 405.4
Source: Calculated from FAOSTAT
Table 2: The Fixed effects (FE) regression results of individual variables on the growth function Explanatory
variables
Coefficients
c
0.47k
0.39l
-0.14t
0.14R-Square 0.92 F-statistic 311.4 No. of observations:
1036
Source: Computed
Table 3: Panel unit root Tests, 1980-2007
IPS LLC PP ADF
stat p-value stat p-value stat p-value stat p-value
y
0.47074 0.6811 2.30640 0.9895 68.0168 0.6738 70.6803 0.5879k
4.14926 1.0000 3.13571 0.9991 47.3789 0.9932 37.3246 0.9999l
6.00109 1.0000 -3.60989 0.0002 107.605 0.0065 57.2641 0.9250t
-0.29591 0.3837 -1.31841 0.0937 95.2475 0.0488 77.7677 0.3597 Source: ComputedTable 4: Pedroni Residual Cointegration Test Series: LOG(GDPPC) LOG(K) LOG(L) NTPA Sample: 1980 - 2007
Within Dimension Between Dimension
Statistic Prob Statistic Prob
Panel v-statistic 1.839218
0.0329
Group rho- statistic
2.161517
0.9847 Panel rho-statistic 0.751046
0.7737
Group pp- statistic
-
1.637215 0.0508
Panel pp-statistic -
1.436438 0.0754
Group adf- statistic
-
0.336853 0.3681
Panel adf-statistic -
2.000828 0.0227 Source: Computed
177 Figure 1: Top Production in SSA (2008)
Source: FAO
Figure 2 : Top Imports from SSA
Source: FAO
Figure 3: Trend in Net Trade Performance in Agricultural Trade (SSA)
Source: Computed.
Figure 4 Top Exports from SSA
Source : FAO
179 1.5.1 Panel unit root tests source
Before proceeding to cointegration techniques, we need to verify that all variables are integrated to the same order. As with standard cointegration tests, it is important to know the stationarity properties of the data to ensure that incorrect inferences are not made.
Testing for stationarity in panel data differs somewhat from conducting unit root tests in standard individual time series; these differences will be discussed in what follows.
We begin by classifying our unit root tests on the basis of whether there are restrictions on the autoregressive process across cross-sections or series. Consider a following AR(1) process for panel data:
, 1
i t i i t i t i it
y y x
Where i = 1, 2,…..N Cross-section units or series, that are observed over periods, t=1,2,….,T
The
x
i t represent the exogenous variables in the model, including any fixed effects or individual trends,
i are the autoregressive coefficients, and the errors
i t areassumed to be mutually independent idiosyncratic disturbance. If
i
1, yi , is said to be weakly (trend-) stationary. On the other hand, if
i
1 theny
i contains a unit root.For purposes of testing, there are two natural assumptions that we can make about the i . First, one can assume that the persistence parameters are common across cross-
sections so that i for all i . The Levin, Lin, and Chu (LLC), test employ this assumption. Alternatively, one can allow
i to vary freely across cross-sections. The Im, Pesaran, and Shin (IPS), and Fisher-ADF and Fisher-PP tests are of this form.Results are shown in Table 3.
The results of the most unit root tests performed above indicated that most of variables were not stationary at I(0) (Table 3). After performing the first difference operation, however, all data were stationary at I(1). These results confirmed the necessary conditions for performing panel cointegration, thus we continued with the identification long-run growth relationships by applying the Pedroni panel cointegration test.
1.5.2 Panel Cointegration tests
Once the order of stationary has been defined, we would apply Predroni’s cointegration test methodology. Indeed, like the IPS panel unit root, the panel cointegration tests proposed by Pedroni (1999) also take in account heterogeneity by using specific
parameters which are allowed to vary across individual members of the sample. Taking into account such heterogeneity constitutes an advantage because it is unrealistic to assume that the vectors of cointegration are identical from an individual to another for the panel.
The implementation of Pedroni’s cointegration test requires estimating first the following long run relationship:
1 2 3
it it it it it it
y k l t (4)
i = 1,2,…,N t = 1,2,…,T
where i refers to the number of individual countries in the panel ;N refers to the numbers of individual members in the panel; T refers to the number of observation over time. The structure of estimated residuals is as follows:
^ ^ ^ ^
it
it1
itPedroni has proposed seven different statistics to test panel data cointegration. Out of these seven statistics, four are based on pooling, what is referred to as the “Within”
dimension and the last three are based on the “Between” dimension. Both kinds of tests focus on the null hypothesis of no cointegration. However the distinction comes from the specification of the alternative hypothesis. For the tests based on “Within”, the alternative hypothesis is
i 1
for all i, while concerning the last three test statistics which are based on the “Between” dimension, the alternative hypothesis is 1
, for all i.The finite sample distribution for the seven statistics has been tabulated by Pedroni via Monte Carlo simulations. The calculated statistic tests must be smaller than the tabulated critical value to reject the null hypothesis of absence of cointegration.
Applying the Pedroni Panel Cointegration Test, the results reported in Table 4 above. The null of no cointegration was rejected by four of the panel tests, namely the Panel v- statistic, Panel pp-statistic, Panel adf-statistic and Group pp-statistic. These four rejections of the null strongly indicate the existence of cointegration relationships in our growth relation function.
Results are shown in table 4.
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