European Journal of Government and Economics 10(1), June 2021, 80-104 European Journal of Government and Economics ISSN: 2254-7088 Governance and net-import dependency on food and agricultural products in Sub-Saharan Africa: does any causality exist? Esther N. Mwangia,b*, Fuzhong Chena, Daniel M. Njorogec a School of International Trade and Economics, University of International Business and Economics, Beijing, China b Department of Business and Economics, Karatina University, Karatina, Kenya c Institute of Food Bioresources Technology, Dedan Kimathi University of Technology, Nyeri, Kenya * Corresponding author at: esthermwangi2013@gmail.com Abstract. Though most countries in Sub-Saharan Africa (SSA) are agricultural-based, the region is a net importer of food and agricultural products and experiences the highest level of food insecurity globally. The government have a joint goal of achieving a favourable balance of trade and food security; hence this study examines the causal relationship between quality of governance and net-import dependency on food and agricultural products for 25 SSA countries during the period 1995-2015. Principal component analysis is employed to develop a governance index based on the six worldwide governance indicators and a multivariate panel vector error correction framework applied to infer causality in the short and long run. The results reveal that a higher governance index is correlated with a lower net-import dependency ratio and the relationship is statistically significant. Evidence of unidirectional causality running from governance to net-import dependency is reported in 14 SSA countries, mainly in the long run. In conclusion, improving governance quality could support reduced food and agricultural net-import dependency through promoting agriculture production, exports and consequently reduced trade deficits in the long run. Hence, governance reforms in the region should be placed at the heart of the agricultural development agenda. Keywords. agriculture; causality; governance; net-import dependency; Sub-Saharan JEL Codes. C59; H11; Q17 DOI. https://doi.org/10.17979/ejge.2021.10.1.5947 1. Introduction Global food demand is expected to increase with the increasing world population which is projected to be over 9.1 billion by 2050 with much of the population increase expected to be seen in SSA (Parker, 2011; OECD and FAO, 2016; EIU, 2018). Governments and international organizations are working aggressively by employing policies and innovating ways tailored towards eliminating hunger and malnutrition for all in the context of the Sustainable Development Goal (SDG) agenda 2030. Most countries rely on food and agricultural imports to cover their demand deficits and feed their growing population. The number of countries depending on food imports and the rate of import dependency have been growing gradually as population and national income increases. However, most developing countries listed as Net Food-Importing Developing Countries (NFIDCs) by World Trade Organization (WTO) suffer from food trade deficit (WTO, 2016) and more often than not find difficulty in covering their food import bills. Less Developed Countries (LDCs) and NFIDCs receive WTO Agriculture mailto:esthermwangi2013@gmail.com https://doi.org/10.17979/ejge.2021.10.1.5947 Esther N. Mwangi et al. / European Journal of Government and Economics 10(1), June 2021, 80-104 81 Agreement special treatment1, which may be further driving them to fall into the net-importer trap. The 2007-2008 global financial crisis revealed that international trade on food is not always efficient in delivering food security (Christiaensen, 2009; Gilbert, 2011; Murphy, 2015). This view led most economies to re-examine their food supply agenda with much consideration on food self-sufficiency2 and sustainability. Food self-sufficiency is now open to debate where some authors hold the view that international agricultural trade promotes food security by stabilizing food supply and food prices (Clapp, 2015; FAO and Ishrat, 2016; Clapp, 2016; EIU,2018), while others advocate domestic production to meet local food demand (Christiaensen, 2009; Murphy, 2015). More importantly, advocates of international agricultural trade are cognizant of the fact that imports dependency makes a country vulnerable to external shocks such as food price spikes, volatility, food riots and export bans imposed by exporting countries (Clapp, 2016; Martin, 2017; EIU, 2018). Moreover, trade deficits have undesirable implication to the overall economic performance of a country. Despite SSA being well endowed with fresh water, labour and arable land for agriculture production, the region is a net importer of food and agricultural products, having the highest percentage cereal import dependency of 18.59 percent on average over the years from 2001 to 2013. Moreover, it is the most food-insecure region globally and has the highest prevalence of undernourishment which has been increasing from 2014-2017. The percentage of the population suffering from chronic food deprivation was 23.2 in 2017 (FAO et al., 2018). Apparently, the underlying problem in SSA and Africa, in general, is beyond a resource endowment one. Besides, the underdevelopment problem and food insecurity in the region is partly due to failures in local policy, institutions, local governance and international governance regimes (World Bank, 1989; Chhotray and Stoker, 2008; IFPRI, 2018). Moreover, the frequent food and agriculture scandals in African economies pertaining to food supply, food safety and health standards, and embezzlement of funds meant for agriculture and rural development is clear evidence of deficiency in governance. Governance failure portrayed by political and economic instability, limited voice and accountability, low government effectiveness, poor regulatory quality, corruption, and the poor rule of law, is among the major challenges facing the implementation of agriculture policies for development agenda (World Bank, 2008). According to IFPRI (2018), a few African countries have improved their food and nutrition status significantly as a result of their governments’ commitment and reforms for agriculture development and food security. The big question, therefore, is how governance in SSA positions itself in reference to a country’s food and agriculture net-import dependency amidst high levels of food and nutrition insecurity. Basically, there is limited literature on the relationship between governance and agricultural trade despite the critical role of government in regulating trade and ensuring food and nutrition security. As a matter of fact, much of the existing literature is qualitative and is characterized by policy responses to major global food crises such as the world food crisis of the early 1970s, the 1 LDCs and NFIDCs are eligible to request financial assistant under aid programme to facilitate their food imports see WTO (2016). 2 For a broader discussion on food self sufficiency, see Clapp J. (2016). Esther N. Mwangi et al. / European Journal of Government and Economics 10(1), June 2021, 80-104 82 2007-08 food crisis and 2010-11 price shocks (Margulis, 2017). This study examines the causal effect of the quality of governance on food and agricultural products import dependency in SSA. The study is quantitative and focuses on the disaggregated dimensions of governance. Moreover, we develop a composite governance index based on the six worldwide governance indicators using the Principal Component Analysis (PCA). We compare the effect of each governance indicator and the overall governance index on net-import dependency. Consequently, the causality between the composite governance index and import dependency on food and agricultural products is examined. The study contributes to the existing agricultural international trade literature by showing that governance plays a significant role in the food and agricultural import dependency status of a country. The results reveal that a higher governance index is correlated with lower net-import dependency. As such, strengthening corruption control measures, government effectiveness, voice and accountability, and the rule of law significantly reduces net-import dependency on food and agricultural products. Hence, increasing governance quality in SSA could support reduced net-import dependency in the long run. These results provide a basis for formulating policies designed to promote international agricultural trade for economic development and food security. The rest of this paper is organized as follows; section two provides a review of literature on agricultural international trade and governance. Empirical application and procedure of analysis are explained in section three, while section four presents the results and further discussions on the findings. Finally, section five provides the conclusion and recommendations of the study. 2. Literature review Governance and agricultural trade Governance and agricultural trade interact in different pathways that promote the four pillars of food security, namely, food accessibility, availability, utilization and stability. Agriculture production facilitates food supply and availability while agricultural trade ensures the distribution of food and global food stability. Government has a role in solving market failure and promoting competition which in turn lowers commodity prices making food affordable and accessible to consumers. In addition, through the labor market, households earn income which increases their purchasing power which enables them to diversify their dietary intake and improve their nutritional status. Furthermore, trade policies affect government services which in turn impact either positively or negatively on the various food security dimensions and the general performance of the national economy (FAO and Ishrat, 2016). Furthermore, stable governance reduces the uncertainty that hinders investment and pollutes the business environment, thereby promoting international trade. According to Martin (2017), trade liberalization reduces poverty rates and improves nutritional outcomes. However, export subsidies in developed countries lead to dumping in developing countries. Dumping gradually destroy local agricultural industries which cannot Esther N. Mwangi et al. / European Journal of Government and Economics 10(1), June 2021, 80-104 83 compete in the international market, causing more harm to the importing countries. Logically, if the money used to cover imports bills were spent within the domestic economy, it would have a multiplier effect, thereby boosting agriculture and economic growth. Sadler and Magnan (2011) provide an overview of strategies which if adopted by importing governments, could lead to a reduction of risks associated with imports dependency. The government ought to and can create an enabling environment for the smooth functioning of the different pathways and agencies actively involved in food policy to support agriculture for development (Gupte and Longhurst, 2018). In addition, some studies (Fanzo et al., 2014; Nisbett and Barnett, 2017; Kohli et al., 2017) found that a high level of political leadership is an important driver and creates an enabling environment for improved food security and nutrition in a country. However, most national governments in developing countries face difficulty in providing public goods such as the rule of law, civil peace, infrastructure and public research necessary for promoting agricultural productivity (Paarlberg, 2002). While on the one hand, governance affects international trade, on the other, a feedback effect exists whereby a country’s governance is shaped by international trade (Eichengreen and Leblang, 2008). Governance at the national level should be redesigned to effectively provide public goods and services in order to promote agricultural productivity, international agricultural trade and accelerate the realization of Sustainable Development Goal number 2 (SDG2). Trends on food and agricultural import dependency About 16 percent of the world population depends on international trade to meet their food and agricultural products demand. It is expected that most low-income countries will continue to depend on external land and water resources (Fader et al., 2013). Trade, whether domestic or international, increases household income and government revenue. By this, it increases diet diversity and promotes the stability of food supply (Burnett and Murphy, 2014; Brooks and Matthews, 2015). In 1993, Latin America and the Caribbean were the leading grain importer in the world with an import dependency of 36.5 percent followed by SSA with 13.6 percent (Paarlberg, 2002). Africa, Asia and the Caribbean have been net importers of food and agricultural products on average and have been experiencing trade deficit for all periods between 1990 and 2016. Moreover, they are net importers of cereals and pulses, which are the major foods that they depend on for their daily meals. The trade deficits have been increasing over the years, and the figures more than doubled for the period 2010-2016. The increase in trade deficits could partly be explained by the rising import dependency coupled with the rising international food prices. For all these periods, most developed countries had a food and agricultural trade surplus. Interestingly, Europe has a trade deficit on food and agricultural products for all the periods but, the deficits decreased significantly for the period 2010-2016, contrary to what was happening in many developing countries. These world trade balances on agricultural products, food and selected crops are illustrated in Appendix A, Table A.1. Esther N. Mwangi et al. / European Journal of Government and Economics 10(1), June 2021, 80-104 84 According to Valdés and Foster (2012), the number of developing countries who are both net-agricultural importers and net-food importers increased from 74 in 1999 to 89 in 2009, with an increase from 25 to 31 out of 51 SSA countries. Most developing countries are net-importers of staple food grains, and their dependency on imports has been increasing over the recent years (Murphy, 2015; EIU, 2017). All African countries depend on food and agricultural products imports though the degree of dependency varies across countries. FAO (2012), using data for the period 1960-2007, reported that Africa, despite its agricultural potential, has been a net importer of food and of agricultural products. During 2007-2011, 37 African countries were net importers of food while 22 countries were net importers of agricultural raw materials (Blein et al., 2013). Due to arable land and water constraints, the Middle East and North Africa (MENA) mostly depend on imported food and is the largest grain importer in the world (Sadler and Magnan, 2011). Conceptual framework For the purpose of discussions in this paper, the study adopts the United Nations Development Programme (UNDP) (1997) definition of governance as; “the exercise of political and administrative authority at all levels to manage a country’s affairs. It comprises the mechanisms, processes and institutions, through which citizens and groups articulate their interests, exercise their legal rights, meet their obligations and mediate their differences”3. This definition consolidates three World Bank definitions of governance. That is, “the exercise of political power to manage a nation’s affairs” (World Bank, 1989), “the manner in which power is exercised in the management of a country’s economic and social resources” (World Bank, 1994), and “the manner in which public officials and institutions acquire and exercise the authority to shape public policy and provide public goods and services” (World Bank, 2007). World Bank uses six indicators, namely, control of corruption, government effectiveness, political stability and absence of violence, regulatory quality, voice and accountability, and the rule of law to assess the quality of governance (Kaufmann et al., 2004). Good governance is built on three basic principles, namely, Participation and Inclusion, Accountability and Rule of Law, and Non-Discrimination and Equality (UNDP, 2011). Based on these principles, good governance is participatory, consensus-oriented, accountable, transparent, responsive, effective and efficient, equitable and inclusive, and respects the rule of law. It takes into account the interests and the most vulnerable in society in decision-making and minimizes corruption. In addition to these components of good governance, Organization for Economic Cooperation and Development (OECD) states that good governance is also forward-looking in the sense that it is able to make predictions about future trends and develop policies to deal with anticipated future changes. Any compromise on the stated principles and components of good governance results in poor governance and its consequences. Good governance is claimed to promote international trade for sustainable economic development (UN, 1998; Kaufmann and Kraay, 3 See more discussion on governance in UNDP Strategy Note on Governance for Human Development, 2000. Esther N. Mwangi et al. / European Journal of Government and Economics 10(1), June 2021, 80-104 85 2002; Resnick and Birner, 2006). According to Knack and Keefer (1995), the quality of governance significantly affects the investment rate in an economy. Moreover, donor agencies tag their aid disbursement on the quality of a country’s governance (Gisselquist, 2012). 3. Data and methodology Data The study used Panel data for 25 SSA countries for the period 1995-2015. The countries were selected based on data availability. Since countries are heterogeneous in reference to their levels of development, environmental aspects, and consequently imports demand, they were classified into middle income, low-income countries, oil producers, and non-oil producers based on World Bank classification. The countries included in the sample are shown in Table A.2. Data were mainly collected from World Bank, Food and Agriculture Organization (FAO), The Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) and International Monetary Fund (IMF). Net Imports Dependency Ratio (NIDR) was computed based on FAO definition of Import Dependency Ratio (IDR)4. NIDR on food and agricultural products was computed using data on total agriculture, which includes cultivation of crops for food and feed, cash crops, livestock production, plus forestry, hunting, and fishing as described by FAO. It was calculated as follows: NIDR= (Imports-Exports)*100/(Production+Imports-Exports) (1) The quality of governance was measured using the Worldwide Governance Indicator (WGI) scores on the six governance dimensions, namely voice and accountability, corruption control, government effectiveness, political stability and absence of violence, regulatory quality, and the rule of law (Kaufmann et al., 2004; 2010). We developed a composite governance index that comprehensively captures the six dimensions of governance using PCA. Table 1 presents the description of the variables and the corresponding data sources. The dependent variable is NIDR, while the predictor variables of interest in this study are the six governance dimensions and the composite governance index. Control variables added in our analysis include agriculture productivity indicators such as agriculture value added per worker and total factor productivity (TFP). Other control variables are the exchange rate, inflation, foreign reserves, population, economic growth and natural resource endowment indicators, including fresh water and agricultural land. 4IDR is computed as; imports * 100/ (production + imports - exports). Esther N. Mwangi et al. / European Journal of Government and Economics 10(1), June 2021, 80-104 86 Table 1. Variable description and data sources. Variable Code Definition Data source Net-import dependency ratio NIDR Ratio of net imports to domestic food utilization Computed Governance Index Govindex Composite governance indicator Computed Control of corruption Corrpcont Governance indicator 1 World bank Government effectiveness Goveff Governance indicator 2 World bank Political stability and absence of violence Polstab Governance indicator 3 World bank Regulatory quality Regqlty Governance indicator 4 World bank Rule of law Ruoflaw Governance indicator 5 World bank Voice and accountability Voiceacc Governance indicator 6 World bank Agriculture value added per worker Agrivapw Productivity indicator 1 FAOSTAT Total factor productivity TFP Productivity indicator 2 FAO Exchange rate Exrate Real exchange rate IMF Inflation CPI Consumer Price Index IMF Foreign currency reserves Foretodebt Foreign reserves to debt ratio World bank Population lnpopu Log (population) World bank Economic growth GDPpc Real GDP per capita World bank Fresh water endowment lnfreshh20 Log(fresh water) FAOSTAT Agricultural land lnland Log(agricultural land) FAOSTAT Methodology Model identification To examine the impact of the quality of governance on food and agricultural import dependency, panel data framework was used due to the flexibility it allows in modelling differences in behaviour across entities and its ability to solve sample selection bias (Greene, 2012). Panel regression models, namely, Pooled Ordinary Least Squares (OLS), which assumes homogeneity, Fixed Effect (FE) and Random Effects (RE), were estimated in order to select the appropriate model for the data. The general modelling framework is expressed as: itiititiitit cxzxy εβεαβ ++=++= ''' (2) where ic capture the heterogeneity or individual effect and itε is the idiosyncratic errors. For the case of pooled regression, iz contains only a constant term. However, if iz is unobserved but correlated with itx then fixed effect would be the appropriate model. Finally, if ic is uncorrelated with itx , then random effect would be evident. First, the pooled OLS and RE were estimated using STATA software. Breusch-Pagan Lagrange Multiplier (LM) test developed by Breusch and Pagan (1980) was done to make a decision between a random effects regression and a Pooled OLS regression. The null hypothesis of variances across entities is zero, H0: σ²µ=0; was tested against the alternative hypothesis that variances across entities is not equal to zero; H1:σ²µ≠0. The LM test statistic is calculated by: Esther N. Mwangi et al. / European Journal of Government and Economics 10(1), June 2021, 80-104 87 2 1 1 2 1 2 1 )( )1(2           − − = ∑ ∑ ∑ = = = − n i T t it n i i e eT T nTLM (3) The estimated pooled OLS and RE equations are given in model 4 and 5, respectively. itititit XGNIDR εβλα +++= '' (4) where NIDR is the net import dependency ratio, G is a vector of governance indicators and X is a vector of control variables. In the case of pooled OLS, λ and β include constant terms. itiititit XGNIDR εµβλα ++++= '' , (5) where, µi is a group specific random element. Consequently, RE model and FE model were estimated and Hausman specification test devised by Hausman (1978) was done in order to make a choice between RE model and FE model. Under the null hypothesis, the preferred model is RE, while for the alternative hypothesis, FE is the preferred model. The Hausman test statistic is computed as: )( ∧ −= REFEbH β ′ )(][ 1 ∧ − −− REFEREFE bVV β (6) where b and ^ β are the coefficient vectors, V is the covariance matrix, FE is the consistent estimator and RE is the efficient estimator. The FE models which account for entity fixed effect only and for both entity and time fixed effects are as follows: itiititit XGNIDR εγβλ +++= '' (7) ittiititit XGNIDR εσγβλ ++++= '' (8) where γi is the country fixed effect and σt is the year fixed effect. Cross-section dependence and Panel unit root test Pesaran (2006) show substantial bias and size distortions in estimates when cross-section dependence in panel data is overlooked. Hence we test the data for cross-section dependence using LM test devised by Breusch and Pagan (1980). The test has a null hypothesis of no Esther N. Mwangi et al. / European Journal of Government and Economics 10(1), June 2021, 80-104 88 cross-section dependence 0)cov(:0 =jtitH εε for all t and ji ≠ and an alternative hypothesis of cross-section dependence 0)cov(:0 ≠jtitH εε . To compute the LM statistic, fixed effect model (7) is first estimated then the LM is calculated as: ∑∑ − = += = 1 1 1 2ˆ N i N ij ijCD TLM ρ 0 2/)1(2 −NNχ (9) where ijρ̂ is the estimate of the pair-wise correlation of the residuals from the estimation of the model (7). Given that causality tests require the variables to be stationary, we tested our data for non- stationary using panel unit root tests proposed by Maddala and Wu (1999), and Pesaran (2007). The Fisher-type test by Maddala and Wu (1999) denoted as MW hereafter is a first-generation panel unit root test and assumes cross-section independence across panels. The assumption of cross-section independence is very restrictive; hence we also apply the Cross-sectional augmented Im, Pesaran, and Shin (CIPS) test proposed by Pesaran (2007). The CIPS test is a second-generation panel unit root test that allows for cross-sectional dependency. Both tests can be applied in unbalanced panels and series having different lags. The tests are based on the following model: it pi z ztizitiiiti yyy εβρα +∆++=∆ ∑ = −− 1 ,,1., (10) where ∆ is the first difference operator, iρ and iβ are the autoregressive coefficients, T is the time span of the panel, and N is the number of cross-sections. The null hypothesis, 0:0 =iH ρ for all I, all panels contain unit root is tested against the alternative hypothesis; 0:1