Distortion of agricultural incentives in East Africa: effects on agricultural value added

Dube, Biru Gelgo; Gobena, Adeba Gemechu; Beyene, Amsalu Bedemo Article Distortion of agricultural incentives in East Africa: effects on agricultural value added Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Dube, Biru Gelgo; Gobena, Adeba Gemechu; Beyene, Amsalu Bedemo (2024) : Distortion of agricultural incentives in East Africa: effects on agricultural value added, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-17, https://doi.org/10.1080/23322039.2023.2285068 This Version is available at: https://hdl.handle.net/10419/321381 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: 2332-2039 (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Distortion of agricultural incentives in East Africa: effects on agricultural value added Biru Gelgo Dube, Adeba Gemechu Gobena & Amsalu Bedemo Beyene To cite this article: Biru Gelgo Dube, Adeba Gemechu Gobena & Amsalu Bedemo Beyene (2024) Distortion of agricultural incentives in East Africa: effects on agricultural value added, Cogent Economics & Finance, 12:1, 2285068, DOI: 10.1080/23322039.2023.2285068 To link to this article: https://doi.org/10.1080/23322039.2023.2285068 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Published online: 19 Feb 2024. Submit your article to this journal Article views: 1092 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 DEVELOPMENT ECONOMICS | RESEARCH ARTICLE Distortion of agricultural incentives in East Africa: effects on agricultural value added Biru Gelgo Dube a , Adeba Gemechu Gobena a and Amsalu Bedemo Beyene b a College of Agriculture and Veterinary Medicine, Jimma University, Jimma, Ethiopia; b School of Policy Studies, Ethiopian Civil Service University, Addis Ababa, Ethiopia ABSTRACT This study examines the effects of distortion of agricultural price incentives on agricultural value added in East Africa. The World Bank, IFPRI, FAO, and CSP are the sources of data. The dataset ranges from 1981 to 2018, and the error-corrected LSDV model is used to analyze the data. The results indicate that agricultural price incentives have positive and significant effects on agricultural value-added. Aggregate nominal assistance coefficient, exportable agricultural products nominal assistance coefficient, and nominal rate of protection have increased agricultural value-added significantly. Agricultural price incentives targeting different levels of value addition have larger effects than those targeting aggregate outputs. This implies that agricultural incentive policies and market conditions in support of local producers are vital to enhancing AVA in East Africa. Besides, larger areas of arable land, lower agricultural employment, a smaller population size, a larger GDP, less spending on education, and a better-performing polity contribute to a significant increase in the regional agricultural value added. The results generally imply that agricultural price incentives are vital to accelerating agricultural value addition in East Africa. Governments in this region should thus consider revising agricultural policies in a pro-agricultural way to further accelerate regional growth in agricultural value-added. Enhancing agricultural price support needs to be a crucial element of policy revisions in the region. IMPACT STATEMENT In East Africa, agriculture is the main source of employment for a large section of the population. However, agricultural incentives have been reportedly distorted against agriculture, and sectoral income has been low. Consequently, farmers’income from agricultural value addition has been low. This study reports the effects of the distortion of agricultural incentives on agricultural value added in East Africa. The study shows that favorable agricultural incentives enhance agricultural value-added. The findings have strong implications for the region’s smallholders, who are the subject of heavy taxation, either directly or indirectly. It will have far-reaching consequences for the poor, who rely on agriculture for a living. In particular, the findings influence regional anti-agricultural policy design, which is vital for the regional goal of achieving inclusive growth and structural transformation. ARTICLE HISTORY Received 27 September 2023 Revised 27 October 2023 Accepted 13 November 2023 KEYWORDS Agricultural protection; price incentives; price distortion; NAC; NRP;LSDVC REVIEWING EDITOR Aye Goodness, University of Agriculture, Benue, Nigeria SUBJECTS Development Studies; Development Policy; Politics & Development 1. Introduction In Africa, agriculture has continued to play a strategic role in the process of economic development. Despite its dominant role, public spending in this sector appears inadequate on this continent when compared to the wealthiest countries (World Bank, 2022). The Anderson & Masters (2009) report shows that during the 1960s and 1970s, many African countries implemented pro-urban, anti-agricultural, and antitrade policies, while many high-income countries restricted agricultural imports and subsidized their farmers. The economic policy landscape of developing countries has long included distorted trade incentives. CONTACT Biru Gelgo Dube [email protected] College of Agriculture and Veterinary Medicine, Jimma University, Jimma, Ethiopia ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2285068 https://doi.org/10.1080/23322039.2023.2285068 In most of these countries, the provision of agricultural incentives has continued to be unfavorable (Bali~ no et al., 2019; Mukasa et al., 2020), though progress has been observed in recent years (Mukasa et al., 2020). Theories note that government intervention distorts agricultural markets (Jetha, 1982). Effective agricultural protection policies are based on what is politically acceptable (Masters, 1993), while their agricultural growth implications deviate from the predetermined outcomes. Kydland & Prescott (1982) argue that output growth variation is an effective reaction to external changes in the actual economic environment. They stress that intervention through fiscal or monetary policy to smooth growth fluctuations would be less effective, particularly in the short run. An integrated model of fiscal policy instruments notes that raising taxes is beneficial only if public spending is effectively directed to productive sectors (B enabou, 1996). Otherwise, it would hamper growth. This is well explained in the works of Grossman & Kim (1997); Alesina & Perotti (1996); Fay (1993). According to Stiglitz (1987), agricultural taxes and subsidies occur when governments try to control the sector. However, while subsidies would enhance agricultural production (Seitov, 2023), taxing the sector would be economically unprofitable and destructive, particularly in low-income countries. In Africa, to handle the expanding continental food demand, several continental frameworks and declarations have been initiated in the past ten years. Some of the key areas include removing unfair trade obstacles to encourage sustainable agricultural growth, ensuring food security, and eliminating poverty and hunger (AU, 2015;UN,2023). Nevertheless, the majority of the nations’policies remained anti-agricultural, especially the agricultural policies of the East African countries (Mukasa et al., 2020;WorldBank,2023). For most of the impoverished in this region, agriculture is the primary source of employment. Nevertheless, the distortion of agricultural incentives persisted until recently (Mukasa et al., 2020), and it is still unclear how such distorted policies would affect regional agricultural value added (AVA). Related studies note that agricultural protection influences crop production, livestock production, and household incomes positively (Dastagiri & Vajrala, 2018;Hemmingetal.,2018;Petreski,2014; Swinnen, 2021). Bollman & Ferguson (2018) argue that subsidies increase farm income. They note that the reductions in subsidies result in lower farm value-added and farm asset values. Seitov (2023) similarly argues that subsidies safeguard food security. Nevertheless, according to Omeje et al. (2019), a high level of agricultural protection has a negative effect on the growth of agricultural outputs. In this line, Chandel et al. (2019) argue that subsidies only serve short-term objectives and would have adverse effects on economic efficiency in the long run. Some studies generally advocate for agricultural protection (Bollman & Ferguson, 2018; Seitov, 2023; Ye et al., 2023), and others oppose such incentives for increased agricultural growth (Chandel et al., 2019; Heyl et al., 2022; Omeje et al., 2019; Swinnen, 2021). Numerous studies that employ subsidies (Bollman & Ferguson, 2018; Seitov, 2023; Ye et al., 2023) paid little attention to how complementary tax and subsidy policies affect agricultural growth. For those studies that utilize complementing tax and subsidy programs, the outcomes are incongruous (Chandel et al., 2019; Heyl et al., 2022; Omeje et al., 2019; Swinnen, 2021). None of them, however, attempt to examine the effect of distorted agricultural price incentives on AVA. To contribute to filling this gap, the current study explores the effects of distortion of agricultural incentives on AVA in East Africa. Using alternative cross-sectional time series of distorted regional agricultural policy datasets, namely the nominal assistance coefficient (NAC) and the nominal rate of protection (NRP), the study provides novel insights into the role that distorted agricultural price incentives play in agricultural value addition. These measures, of course, account for distortions of sectoral price incentives, which provide unique insights in this area, unlike many previous related studies that rely exclusively on either subsidies or tax measures (Bollman & Ferguson, 2018; Seitov, 2023; Ye et al., 2023). The findings thus extend the existing knowledge in the political economy literature by providing region-specific, robust findings that use alternative agricultural incentive measures. Furthermore, the positive response of agricultural value addition to rising food prices would have useful implications for food production. In this line, the findings provide insight into Malthusian fears about the future prospects of the food supply. 2. Literature review Growth theories presume that advances in productivity and technology are what drive output expansion (Harris, 2017). Nevertheless, mainstream growth models are widely criticized for their failure to include 2 B. GELGODUBE ET AL. political economy perspectives in economic analysis. Stiglitz (1987) notes the importance of understanding the political economy in the process of output growth. Politicians determine the best level of intervention based on their political interests (Jetha, 1982). Effective agricultural protection policies are based on what is politically acceptable (Masters, 1993), while their growth implications would deviate from the predetermined outcomes. For instance, in developing countries, government intervention in the agricultural sector through subsidies or taxes influences the choice of production resources, including the uptake of farm technologies (Hemming et al., 2018). In this sector, as the process of value addition becomes complex or as the sector loses comparative advantage, the need for protection increases (Olper et al., 2013). Countries with lower agricultural protection earn low incomes from this sector. Farm incentives facilitate farmers’assimilation of novel technologies, a crucial factor in advancing agricultural production and value addition. It augments or impedes value-added pursuits therein. Empirically, studies note that distortionary agricultural policies influence agricultural growth either in a negative or positive way. Using a quasi-natural experiment of China’s maize purchasing and storage policy reform, Ye et al. (2023) examine the impact of agricultural subsidy market-oriented reform on agricultural green development using the difference-in-difference model. The study showed that maize purchasing and storage policy reform promoted the improvement of the green total factor productivity of maize with a hysteresis effect. Similarly, a study by Seitov (2023) indicates that in India, subsidies safeguard agricultural food security, although a substantial portion of them support farmers in wealthier regions, distorting interstate agricultural growth. Targeted subsidies are noted to be important for longterm sustainable development. In this study, the direct effect of subsidies on agriculture is positive, although it was argued to be non-sustainable. Aiming at examining agricultural subsidies in the EU, a study by Heyl et al. (2022) notes that agricultural subsidies need to be substantially downscaled and implemented as complementary instruments along with other policy instruments such as quantity control. A review of livestock production systems, subsidies, and their implications by Chandel et al. (2019) indicates that subsidies serve short-term objectives, stating that they would have adverse effects on economic efficiency in the long run. Bollman & Ferguson (2018) estimate the impact of removing export subsidies on the local economies of Alberta, Saskatchewan, and Manitoba using a generalized difference-in-difference analytical method. In this study, the loss of the subsidy resulted in significantly lower farm value-added and farm asset values. Hemming et al. (2018) argue that fertilizer and seed subsidies have positive effects on consumer welfare and overall economic growth. Agricultural input subsidies increase the use of farm inputs and thus enhance agricultural yields. However, subsidy schemes are prone to inefficiency, bias, and corruption. According to Omeje et al. (2019), protection policy instruments such as subsidies and direct transfers have been widely used to enhance agricultural production. However, such distortionary policies would reduce a society’s welfare by causing inflation (Swinnen, 2021). According to Asano & Kosaka (2017), partial tariff reduction policies tend to make production and consumption activities inefficient. The study notes that a complete removal of the production subsidy has no effect on changing the negative welfare effects of tariff policy in Japan. According to Dastagiri & Vajrala (2018), whenever agricultural and food policies are in place, redistributive effects are always expected. Redistribution through supporting producers would raise inflation, particularly when it increases aggregate demand. Controlling for such types of adverse effects, Dastagiri & Vajrala (2018) argue that in developing countries, stimulus packages are vital policy options for agricultural development. Malan (2015) examines the determinants and effects of agricultural price distortions in Africa. By applying a linear panel model to agricultural distortion data obtained from 22 African countries, the study notes that the nominal rate of assistance (NRA)for agriculture does not have a significant effect on total agricultural production. It affects cocoa and cotton yields significantly. The study further argues that a more democratic country tends to have a more effective policy, and thus the effect of the NRA on yield tends to be stronger in more democratic countries. When democracy is controlled, the NRA has a significant and negative effect on wheat. The NRAs are positive for the traditional cash crops that are exportable and negative for the import-competing food crops. According to Riesgo et al. (2016), in sub-Saharan Africa (SSA), fertilizer subsidies are among the most common and politically sensitive policies. Mukasa et al. (2020) note that the amount of taxes paid by African farmers has significantly decreased during the past 40 years. Yet, this has not been buoyant in some countries. For COGENT ECONOMICS & FINANCE 3 instance, in Ethiopia, Hassen (2016) argues that agricultural income tax and land use fees are not buoyant. He underscores that the growth of the Ethiopian agricultural sector has no significant relationship with agricultural income tax resilience, highlighting the importance of further study on agricultural protection dynamics in relation to agricultural growth. Examining the impacts of different subsidy programs on rice production in Ghana, Badu & Lee (2020) note that fertilizer is more effective in rice production after subsidies. Examining the impacts of removing fertilizer subsidies on production, prices, income, and consumption, as well as fertilizer demand in Lesotho, Ratii (2016) notes that decreasing fertilizer subsidies reduces crop production and increases livestock production. The decrease in subsidies increases urban households’income while reducing rural households’income. This policy also increases the prices of internationally non-tradable products, while it does not influence the prices of internationally tradable goods. Consumption demand for tradable products increased, while consumption of non-tradable products decreased. The study used a multi-market model based on secondary data obtained in 2012/13. A study by Omeje et al. (2019) notes that agricultural protection has a negative effect on agricultural growth in Nigeria. The study investigates how macroeconomic factors, including agricultural protection, affect agricultural growth in Nigeria. It employs a multiple regression model and Granger causality tests on a dataset spanning between 1980 and 2016, and the authors argue against protected agriculture in favor of liberalized agriculture for sustained output growth. This clearly contradicts the evidence in much political economy literature. For instance, Vincent & Lee (1997) note that farm incomes of cereal producers increase by more than 40% due to protection rendered to agriculture in Korea. The other strands of literature considered for this study deal with factors influencing AVA other than agricultural price incentives. Nugroho et al. (2022) explore the impacts of exchange rates, foreign direct investment (FDI) inflows, total agricultural export values, agricultural import duties, and fertilizer imports on AVA in developing countries. Using a fixed effect (FE) model on panel data obtained from 17 developing countries during 2006–2018, the study showed that FDI inflows and agricultural export values increase AVA in developing countries. Onoja et al. (2017) examined the effects of trade openness, electricity consumption, education, and technology on agricultural value addition growth in Africa. Using an autoregressive distributed lag model on a dataset spanning from 1971 to 2011, the existence of a steady-state long-run relationship between agricultural value addition and education, trade openness, electricity consumption, and technology was additionally investigated. The study showed that technology and electricity consumption are the long-run determinants of the growth of agricultural value addition. In the short run, education, technology, and electricity consumption explained the variation of agricultural value addition in Africa. Muyanga & Jayne (2012) investigated the implications of increasing population density in Kenya’s rural areas on smallholder production and commercialization. Employing a correlated random effects (RE) estimator on a dataset obtained from surveys of panel data spanning from 1997 to 2010, they show that a rising population density is associated with a decline in farm productivity. They also note that smaller farm sizes reduce the potential to produce surpluses, limiting demand for purchased inputs and new technologies, which would further discourage AVA. Ben Jebli & Ben Youssef (2017) examined shortand long-run relationships between per capita carbon dioxide emissions, real gross domestic product (GDP), renewable and non-renewable energy consumption, trade openness ratio, and AVA in Tunisia. The study applies the vector error correction model and Granger causality tests to a dataset spanning from 1980 to 2011. Cointegration was observed between GDP and AVA, where short-run unidirectional causalities were running from GDP to AVA. In the long run, bi-directional causalities were observed. Badri et al. (2017) explore the effects of human development on the value added of the agriculture sector in selected developing countries. Applying the OLS model to a panel dataset spanning from 2006 to 2014, they show that human development has a positive and significant effect on AVA. They further note that education improves labor productivity by allowing one to understand, predict, recognize, and address business needs, which enhances participation in agricultural value addition. The study also shows that increased investment in physical capital leads to a higher capital stock in agriculture, which supports long-term growth in AVA. Allcott et al. (2006) regarded GDP as a crucial component of agricultural income. Using a dataset of rural public expenditures in a panel of Latin American economies, the study notes that non-social 4 B. GELGODUBE ET AL. subsidies reduce agricultural GDP. They further note that political and institutional factors are important in dictating the size and structure of rural public expenditures, through which they have large effects on agricultural GDP. According to Hendricks et al. (2023), policy changes in Sub-Saharan Africa are driven in part by external political shocks. Agricultural policies subjected to such external shocks are likely to influence agricultural production, which in turn would influence value addition in this sector. In general, although a majority of the reviewed studies present a number of factors that have potential effects on the growth of AVA, none of them attempt to quantify the implications of agricultural price distortion for AVA. This study thus primarily attempts to contribute towards filling this gap. 3. Methods 3.1. Data sources and description This study was conducted in East Africa. Based on the availability of a number of aggregate agricultural market distortion statistics, seven countries, namely Burundi, Ethiopia, Kenya, Rwanda, Sudan, Tanzania, and Uganda, were purposefully selected for the study. The economy of the region is largely dominated by agriculture and has been flourishing to support continental free trade to make mutual trade cheaper and quicker (UNCTAD, 2022), motivating the choice of this region as a unit of analysis. The study uses secondary data spanning from 1981 to 2018. The choice of this timeframe is guided by the availability of the time series of the distorted agricultural price incentive dataset. We use the NAC and NRP as measures of distorted agricultural price incentives. The NAC’s data covers the years 1981 to 2010, whereas the NRP’s data covers the years 2005 to 2018. The AVA data for each of the covered years is then obtained. The NAC is the ratio of domestic prices that producers effectively receive given the distortions in country iat year t(Pe it) and the undistorted border price (Pit) (Hendricks et al., 2023). That is NAC ¼ Pe it=Pit:A NAC less than one represents an anti-agricultural bias, and a NAC greater than one represents a pro-agricultural bias. We compute the NAC from the NRA. According to Hendricks et al. (2023), the NAC is simply a NRA þ1. The NRA data is extracted from the World Bank’s updated estimates of distortions to agricultural incentives by Anderson & Nelgen (2013). Limited by the availability of non-tradable and importable products in NRA data for some of the considered countries, we rely on tradable (specifically, exportable) products NAC 1 along with the overall NAC in our model estimation. Of course, the recent findings of Hendricks et al. (2023) note the importance of exportable products NAC for agricultural productivity growth in SSA. Another measure of distorted agricultural incentives in this study is the NRP. The NRP is a proportional difference between producer prices and border prices adjusted for distribution, storage, transport, and other marketing costs (Tokgoz et al., 2016)(Equation 1). It compares the producer price of a locally produced good with a similar internationally traded good. That is, NRP ¼ðDomestic price −Reference priceÞ Refernce price 100% (1) The price gap between the domestic and international prices is considered at two points on the commodity value chain. The initial point of competition for importable goods is at the wholesale level, while for exportable goods, it is at the exit border. A positive price gap results when the domestic price exceeds the reference price, indicating that domestic policies and market conditions support local producers. If a negative price gap emerges, it is an indication that domestic policies and market conditions penalize local producers. In the econometric specification, we also include additional control variables that are likely to affect AVA, as suggested by many prior studies (Badri et al., 2017; Barbero & Rodr ıguez-Crespo, 2020; Ben Jebli & Ben Youssef, 2017; Hendricks et al., 2023; Muyanga & Jayne, 2012; Nugroho et al., 2022). Arable land, agricultural employment, education expenditure, gross fixed capital formation (GFCF), GDP, population size, polity, and year dummies of major global economic shocks are considered. Land has been widely expounded as an output growth attribute since the inception of classical growth models (Arrow, 1962; Lucas, 1988; Romer, 1986; Solow, 1956). Larger farms encourage the use of modern technologies, which in turn influences AVA. The area of cultivable land may reflect the country’s potential to produce agricultural outputs that would have a direct influence on AVA through the supply COGENT ECONOMICS & FINANCE 5 of outputs that serve as inputs in the process of value addition. Larger farm sizes may be associated with economies of scale in input acquisition, which encourages farmers to participate in AVA. According to Muyanga & Jayne (2012), smaller farm sizes reduce the potential to produce surpluses, which may in turn cause capital constraints that impede the demand for purchased inputs and new technologies. Ben Jebli & Ben Youssef (2017) note that larger agricultural land increases agricultural production, allowing AVA to rise. This also supports the findings of Singariya & Sinha (2015), who claim that arable land has a long-term beneficial and consistent impact on AVA growth. In this study, land is referred to as the percentage of land that is arable. Population size is proxied by the population aged 15–64 years, as presented in Beyene (2022) and Garedow (2022). A larger population provides a larger market base, which encourages competition and induces innovations as well as technological advancements (Furuoka, 2009) that influence AVA. The rural population is controlled, following Muyanga & Jayne (2012). Populations in rural areas primarily provide labor for agricultural production and value-adding processes. Human capital has been widely acknowledged as a key output growth component, approximated by different indicators bounded by data availability. For instance, Beyene (2022)usesschoolenrollmentasa measureofhumancapital,whileCole&Chawdhry(2002) use human capital investment. In the current study, this variable is represented by education spending as a percentage of gross national income (GNI). According to Badri et al. (2017), education improves labor productivity by allowing one to understand, predict, recognize, and address business needs, which enhances participation in agricultural value addition. Educated labor takes advantage of various opportunities in the process of AVA (Nugroho et al., 2022). Given the growing number of agro-industries engaged in agricultural value addition in developing countries, the importance of educated labor is crucial to meeting the already rising food demands. Education, in this case, plays a vital role in creating new capacities, both quantitatively and qualitatively. The more decent and efficient the education is, the more it directs the labor employed in the AVA to be more successful in undertaking value addition. According to Onoja et al. (2017), education is important in agribusiness as it enables one to apply efficient technologies and skills to bring about a quantum leap in the level of AVA. GDP is considered a potential attribute of AVA in this study, measured in the per capita constant of 2015 USD. According to Gelgo et al. (2023), per capita GDP has a positive effect on AVA. An increase in GDP increases a country’s nominal expenditure in agriculture. Expenditure in agriculture enhances AVA by providing better opportunities for farmers to use farm technologies and other inputs. Allcott et al. (2006) and Singariya & Sinha (2015) regarded GDP as a crucial component of agricultural income. Barbero & Rodr ıguez-Crespo (2020) also note that countries with higher levels of economic development participate more in AVA. Following Badri et al. (2017), GFCF is considered a potential AVA attribute. Increased investment in physical capital leads to a higher capital stock in agriculture, which would support long-term growth in AVA. This is the net addition to the stock of fixed capital assets, including machinery and infrastructure, in the economy. A larger GFCF would thus improve farm infrastructure, encourage the use of machines and equipment, and support the process of value addition in agriculture. According to Hendricks et al. (2023), policy changes in Sub-Saharan Africa are driven in part by external political shocks. In a highly complex, interlinked, and dynamic global economic arena, policies at the country level are usually motivated by political interests. Agricultural policies subjected to such external shocks are likely to influence agricultural products, which in turn would influence value addition in this sector. We use the polity variable to account for such political dynamics. As highlighted by Kose et al. (2020), year dummies (the year 1982, 1991, 2007, 2008, and 2009), that indicate major global economic shocks are finally taken into consideration to forecast AVA in this study. Table 1 provides a summary of the variables used in this study, their sources, and the anticipated effects. 3.2. Model specification Some studies use simple linear relationships to model AVA (Ben Jebli & Ben Youssef, 2017; Melembe, 2021; Nugroho et al., 2022), while others, for instance, Epaphra & Mwakalasya (2017), use a log-log function to reduce the severity of the regressors’heterogeneity. For the present study, considering the merits of accounting both for log-linear and log-log relationships, we rely on the works of Rebelo (1992) 6 B. GELGODUBE ET AL. while considering Badri et al. (2017) and Epaphra & Mwakalasya (2017) to build an empirical model of the following form (Equation 2). lnyit ¼aþuGit þX k bklnZk it þeit (2) Where Pis a summation operator, lnyit is log of AVA in country iat year t,Git is a measure of Barro (1990)’s public intervention (measures of distorted agricultural price incentives in our case) in country i at year twith its coefficient, and lnZk it is a vector of log-transformed kcontrol variables in country iat year t. Vectors a,uand bkare parameters to be estimated. eit is the error term with two orthogonal components: the fixed effect denoted by liand the idiosyncratic shocks (mitÞ(Equation 3): eit ¼liþmit, where Eli ½ ¼Emit ½ ¼Elimit ½ ¼0 (3) Singariya & Sinha (2015) note that AVA in the current year is a function of the previous year’s AVA. With this concept, the dynamic expression of the response variable in the above specification (Equation 1) takes the following form (Equation 4): lnyit ¼aþhlnyi,t−1þuGit þX k bklnZk it þeit (4) Where lnyi,t−1is the lagged response variable in country iat year t-1 along its coefficient hand all others are as defined earlier. Building on the works of Hendricks et al. (2023) and Singariya & Sinha (2015), the following reduced econometric model is estimated in this study (Equation 5). lnAVAit ¼b0þb1lnAVAi,t−1þb2Distit þb3lnLandit þb4lnAgEmpit þb5lnEducit þb6lnGFCFit þb7lnPopit þb8lnGDPit þb9lnPolityit þb10Dy82 þb11Dy91 þb12Dy07 þb13Dy08 þb14Dy09 þeit (5) Where, ln is log operator and Dist is NRP and NAC (aggregated as well as only for exportable products). Others, namely AVA,Land,AgEmp,Educ,GFCF,Pop,GDP, and Polity 2 , are as explained earlier (Table 1). Dy82, Dy91, Dy07, Dy08, and Dy09, are year dummies showing the year 1982, 1991, 2007, 2008, and 2009, respectively. t, i, and eretain the earlier explanations. b 0 is constant, and b 1 -b 14 are coefficients of explanatory variables. 3.3. Estimation method Equation (5)’s error structure would follow the characteristics of most panel data models. The model would be impacted by statistical issues such as heteroscedasticity, autocorrelation, and cross-sectional dependency (Ramoutar, 2017). Numerous dynamic panel data estimators, namely the Arellano and Bond (1991) GMM-DIF estimator, the Blundell & Bond (1998) GMM-SYS estimator, and the Bruno (2005) biascorrected least squares dummy variable (LSDVC) estimator, can be considered in this case. However, panel data with a small number of cross-sectional units can cause the GMM estimators to be significantly skewed (Bruno, 2005). Despite the fact that employing small samples in macro-panels would help to reduce heterogeneity, employing GMM in this situation would result in inconsistent estimates. The Table 1. Data description, data sources, and hypotheses. Variables Description Hypothesis Sources Dependent variable AVA Agricultural value added (Million USD) –World Bank Independent variables NAC Nominal assistance coefficient; aggregated and for exportable þve World Bank NRP Total NRP (%) þve IFPRI Land Arable land (1000 hectares and % of total area of land) þve FAO Pop Agric % of people depending on farming −ve World Bank AgEmp % of people employed in agriculture −ve FAO Educ Education expenditure (% of GNI) þve World Bank GFCF Gross fixed capital formation (% of GDP) þve World Bank Pop Total Total population aged 15-64 years −ve World Bank GDP Per capita real GDP (in USD) þve FAO Polity Institutionalized democracy and autocracy index ranging from 0 (worst) to 1 (best) þve CSP COGENT ECONOMICS & FINANCE 7 databank.worldbank.org/. The Land, AgEmp, and GDP, are obtained from the FAO datasheet at: www.fao.org/faostat/en/. Polity data is retrieved from the CSP datasheet at: www.systemicpeace.org/inscrdata.html. 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Descriptive statistics (mean) Variable Burundi Ethiopia Kenya Sudan Rwanda Tanzania Uganda Overall NAC Total –0.84 1.00 0.78 –0.74 0.96 0.87 –[0.16] [0.12] [0.26] –[0.21] [0.07] [0.20] NAC Exportable –0.66 0.90 0.63 –0.49 0.75 0.69 –[0.14] [0.13] [0.27] –[0.22] [0.28] [0.25] NRP (%) −23.63 −32.11 5.90 –−0.09 −3.60 −3.48 −9.50 [40.78] [14.77] [20.55] –[26.87] [28.74] [26.64] [30.02] AVA (mil.) 995.98 11400.50 12806.09 13973.65 1736.87 7473.86 3949.14 9920.65 [47.18] [6259.05] [3902.51] [8100.45] [365.58] [3477.15] [1409.82] [6327.94] Land (1000hec) –12342.31 5102.05 16408.97 –10008.73 5643.95 9901.20 –[2183.05] [595.36] [2966.63] –[1948.87] [873.97] [4648.50] Land (%) 41.67 13.15 9.87 –46.01 13.32 33.26 26.21 [4.81] [0.97] [0.43] –[1.05] [1.17] [1.64] [14.87] AgEmp (%) 87.77 72.62 58.98 –75.09 69.51 69.41 72.23 [1.51] [3.65] [2.13] –[7.86] [2.53] [2.70] [9.45] Pop Agric –49359467 21158100 18478767 –24558033 17049067 26140000 –[9135483] [4277590.20] [2311021.10] –[4938955.10] [4060319.50] [13100000] Educ (%) 5.06 2.68 5.63 2.05 3.70 2.98 2.76 3.22 [0.73] [0.68] [0.58] [0.73] [0.43] [0.61] [0.86] [1.43] Pop Total 4784413.40 67552574 32526313 27327661 5880865.40 34671691 24777938 37400000 [665751.63] [22069213] [10257135] [7981421.10] [697885.61] [10910341] [8701477] [20200000] GDP (mil.) 2519.94 23672.54 37074.08 44811.29 6852.37 24132.98 11811.68 28300.51 [399.08] [22150.60] [15802.33] [24353.42] [1958.93] [14525.11] [8012.99] [21158.15] GFCF (%) 12.40 21.75 17.56 14.425 19.80 29.58 20.83 20.83 [1.45] [9.34] [2.17] [4.437] [3.50] [8.22] [6.27] [8.29] Polity index 0.70 0.37 0.48 0.29 0.34 0.34 0.35 0.36 [0.16] [0.19] [0.33] [0.21] [0.02] [0.12] [0.16] [0.22] Note. [ ] standard deviations. 16 B. GELGODUBE ET AL. Appendix B. Test results for the presence of FE and RE in the models F–test B –P LM test Models F–value p>Fv2–value p>v2 Model 1 113.00 0.000 0.00 1.000 Model 2 106.11 0.000 0.00 1.000 Model 3 23.96 0.000 0.00 1.000 Note.  shows significance level at 1%. COGENT ECONOMICS & FINANCE 17