Agriculture in Developing countries and the Role of Government: From an Economic Perspective Agriculture has been a critical driver of well-being for centuries, ensuring food security and catalyzing productivity needed for economic prosperity (Robin, 2011). According to the International Food Policy Research Institute, sixty-five percent of African relies on agriculture as a primary source of livelihood, where small-scale famers are responsible for ninety percent of agricultural production (IFPRI, 2009). Across the African continent, there has been a renewed commitment from governments, non-governmental organizations and the private sector to move agriculture from a development challenge to a business opportunity. As a result, countries such as Nigeria are moving to once again become a net exporter rather than importer of agricultural commodities (Robin, 2011). However, despite these developments, many smallholder farmers, who form the backbone of Africa’s agriculture sector, remain trapped in poverty (Robin, 2011). Indeed, African spending on agriculture represented 6 to 7 per cent of the total national budget for 1980-05, while in Asia the corresponding number was 6-15 per cent (IFPRI, 2009). The objective of this research is to determine the manner in which government assistance to African farmers varies taking into account factors such as rural population share, real GDP per capita, arable land share etc. The data for this research will be based on a new World Bank dataset of indicators of distortions to domestic price of agriculture and non-agriculture commodities drawn from a sample of 40 countries of which 20 are from Sub-Saharan Africa, 12 from Asian developing and 8 from Latin American developing countries. Those indicators compiled by Anderson and Valenzuela (2008) contain the nominal rates of assistance to agricultural tradables relative to non-agricultural tradables and the nominal rates of assistance to agricultural importables and agricultural exportables (Bates and Block, 2009). In addition, another indicator “cash-food bias index” shows how producers of cash crops are treated relative to producers of food crops will also be incorporated. We will examine how factors such as rural population share, real GDP per capita, arable land share, natural resource endowment, and geographical location affect the assistance farmers receive from government. We are hypothesizing that rural population share will have a negative impact on the level of assistance farmers receive. Indeed, Bates and Block (2009) argue that government policies toward agriculture will tend to be detrimental to farmers the greater “the rural dwellers share of population” depending upon the nature of the party system. When taking into account geographical location, we argue that farmers living in coastal countries will experience less support from government compared to those living in landlocked countries. In fact, Ndulu et al. (2007) conclude that landlocked countries are more likely to show least bias against agriculture trade than coastal states which tend to display the greatest bias. The evidence of natural resource endowment on government agricultural policies has been mixed. Bourguignon and Verdier (2000) suggest that governments of resource rich countries will tend to exhibit less support for agriculture since the existence of natural resources may prevent redistribution of political power towards the middle classes and thus prevent adoption of growth-promoting policies; and Isham et al (2003) to add that resource wealth worsens quality of institution because it allows governments to avoid accountability and resist modernization. However, Bates and Block (2009) contend that governments of resource rich countries have a tendency to enact policies that favor producers of both food and cash crops. They argue that Governments of resource rich countries specifically in Africa have tended to protect food crops, raising the level of domestic prices above those prevailing in world markets, while taxing cash crops (Bates and Block, 2009). When using arable land share as a proxy for the overall importance of agriculture, Bates and Block (2009) find that it is positively related to policy orientation of governments towards agriculture. This study takes a new approach by providing a cross-regional analysis of government level of assistance to farmers in Asian and Latin American developing countries, which will enable us to compare government support for agriculture in the three regions while focusing primarily on Africa. For this purpose three regressions will be used for each region. In the first regression government level of assistance to farmers will be measured by the relative rate of assistance which captures the relative support given to agriculture versus non-agricultural tradables, and it is found as follows: (Anderson et al. 2008, Bates and Block 2009) RRA=(1+〖NRA〗〖ag〗^t ))/(1+〖NRA〗_(〖nonag〗^t ) )-1, where 〖NRA〗_(〖ag〗^t ) is the nominal rate of assistance to agricultural tradables, and 〖NRA〗_(〖nonag〗^t ) is the nominal rate of assistance to non-agricultural tradables. The second regression will have the trade bias index as a measure of government level of assistance to farmers, it determines the relative assistance of government to exportables versus importable. It is found as follows: (Anderson et al. 2008, Bates and Block 2009) TBI=(1+〖NRAag〗_x)/(1+〖NRAag〗_m )-1, where 〖NRAag〗_x is the nominal rate of assistance to agricultural exportables and 〖NRAag〗_m is the nominal rate of assistance to agricultural importable. The third regression seeks to determine whether producers of cash crops compared to producers of food crops benefit the most from government policies. We use the “cash-food bias index as a measure of government level of assistance to farmers. It can be found as follows: (Anderson et al. 2008, Bates and Block 2009) CFBI=(1+NRAcashcrops)/(1+NRAfoodcrops)-1, where NRAcashcrops refers to the nominal rate of assistance to cash crops and NRAfoodcrops is the nominal rate of assistance to food crops. Our generic model is: y_it=α+δ_1 Resource rich+δ_2 Landlocked +δ_3 Rural population share+δ_4 (Resource rich*Rural population shareit+X_it β+U_i+ε_it , where y_it is our dependent variable depicting government level of assistance to farmers for country i in year t through the policy indicators defined above, Resource rich is a dummy variable for resource rich-countries, Landlocked is a dummy variable for landlocked contries, Rural population share is the share of a country’s population living in rural areas, X stands for the control variables such as real GDP per capita, arable land share in country i in year t, V_i the random disturbance that captures unobserved time invariant country-specific effects, and ε_it is the error term associated with country i in year t.The parameters of our models will be estimated using the fixed effects and random effects models following Greene (2010). The fixed effect is specified as: y_it=X_it^' β+α_i+ε_it , where α_i=Z_i^' α, embodies all the observable effects and specifies an estimable conditional mean. It implies that Z_i is unobserved, but correlated withX_it. The random effects model is specified as: y_it=X_it^' β+α+U_i+ε_it , where U_i is a group-specific random element, similar to ε_it except that for each group, there is but a single draw that enters the regression identically in each period. Then, following Hausman (1978) we will perform the Hausman’s specification test. Using these outcomes as the background of their decision making process policy makers in developing countries particularly in Africa may advocate for a transformation of the agriculture sector with an emphasis on improving farmers’ wellbeing. If for instance, it is provided that farmers are worse off in regions with a high rate of rural population share, policy makers may consider taking actions that encourage farm modernization or the use of productive inputs in agriculture. References Anderson, K., and E. Valenzuela (2008), Estimates of Distortions to Agricultural Incentives, 1955 to 2007, core database at Anderson, K., M. Kurzweil, W. Martin, D. Sandri and E. Valenzuela (2008), “Measuring Distortions to Agricultural Incentives, Revisited”, World Trade Review 7(4):1-30. Bates, R., S. Block (2010), “Political Institutions and Agricultural Trade Interventions in Africa”, American Journal of Agricultural Economics 93, 317-323. Bourguignon, F., and T. Verdier (2000a), “Oligarchy, Democracy, Inequality, and Growth”, Journal of Development Economics 62: 285-313. Diao, X., P. Hazell, D. Resnick, and J. Thurlow (2007), “The Role of Agriculture in Development: Implications for Sub-Saharan Africa”, Research Report 153, Washington DC: IFPRI. Greene, W.H. (2012), Econometric Analysis, Upper Saddle River, NJ: Prentice Hall, seventh edition, 2012. Hausman, J.A. (1978), “Specification Tests in Econometrics”, Econometrica 46(6):1251-1271. International Food Policy Research Institute (2009), Media briefing on GM Crops for African Farmers, May 19, 2019, Washington DC: IFPRI. Retrieved from Isham, J., L. Pritchett, M. Woolock, and G. Bushy (2003), “The Varieties of the Resource Experience: How Natural Resource Export Structures Affect the Political Economy of Economic Growth”, World Bank, Washington DC. Ndulu, B., P. Collier, R. Bates and S. O’Connell (2007), The Political Economy of Economic Growth in Africa, 1960-2000, 2 volumes, Cambridge: Cambridge University Press. Robin, J. (2011), “Bringing Finance and Innovation to Advance a Green Revolution in Africa”, This is Africa published by the Financial Times Limited, London, United Kingdom. World Bank (2007), World Development Indicators 2007, Washington DC: World Bank.


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