Does agricultural productivity really matter for food security in a landlocked sub-saharan African country? The case of Burkina Faso

Transcription

1 Does agricultural productivity really matter for food security in a landlocked sub-saharan African country? The case of Burkina Faso Patrice Zidouemba Université Paris-Est Créteil (UPEC) Val-de-Marne, 61 Avenue du Général de Gaulle, Créteil France patrice.zidouemba@gmail.com Françoise Gérard Cirad UPR GREEN Jardin Tropical de la ville de Paris 45 bis, avenue de la Belle-Gabrielle Nogent-sur-Marne cedex France francoise.gerard@cirad.fr Abstract This article uses a CGE model to analyze the impacts on households' food security of the poor of possible trends of agricultural productivity in Burkina Faso. Negative trends are the result of land degradation while public investment in agriculture, through the provision of public goods, is expected to induce the positive trend. The results show not only the high sensitivity of consumption levels of the poor to the level of agricultural productivity but also stronger impacts on the urban poor. While the current food situation is already characterized by severe food insecurity, a decline in agricultural productivity is likely to plunge the urban poor into a food crisis. By contrast, positive trend on agricultural productivity is effective in the fight against poverty and food insecurity. Changes of agricultural productivity influence food consumption of the poor mainly through a strong variation in agricultural prices and real incomes. Key Words: Food security, Poverty, Computable General Equilibrium, Burkina-Faso 1

2 1. Context and Issue Burkina Faso is an agricultural-based country. Over 80% of the active population derives their income from agriculture, and the primary sector accounts for over 30% of the GDP. Agriculture plays a major role in household food security by providing them with incomes and by supplying markets. Domestically produced millet, sorghum, and corn account for 80% of households grain consumption, and 40% of the country s rice consumption is domestically produced. Since 1995, Burkina Faso has recorded over 5% of annual GDP growth on average. This growth rate is greater than that of the previous two decades as well as the average growth rate of the other countries of West African Economic and Monetary Union (WAEMU). Despite this growth rate, food and nutrition insecurity remains a major concern. The food and nutrition security has not significantly improved over the past two decades. Between 1990 and 2014, Burkina Faso has recorded a decline of the percentage of undernourished people (from 26% to 20.7%). The prevalence of food inadequacy declined from 34.2% to 25.6% over the same period, but the number of people suffering from undernourishment and the number of people in situation of food inadequacy have increased due to the strong growth of the population (3.1% per year). Anthropometric indicators show disappointing outcomes. In 2010, the percentage of emaciated children was at the same level than in 1993 (15.4%); the percentage of children suffering from delayed growth fell only by 5 points of percentage over the last two decades while those underweight still accounted for 26% in 2010 against 29% in The prevalence of anemia in the same category of population is very high (86% in 2011), marginally lower than in 1990 (89%). The great majority of the population is therefore vulnerable and exposed to high food and nutrition insecurity. Several studies have focused on the causes of the persistence of food and nutrition insecurity in Burkina Faso and in sub Saharan Africa (SSA) in general (Smith et al., 2000; Destombes, 2003; Poussart-Vanier, 2006; Zidouemba, 2014). The low living standards of the populations, their precarious livelihoods, and their poor dietary risk minimization capabilities lead to a structural poverty. The use of harmful agricultural practices for natural resources in a fragile agro-ecological environment, the persistence of high population growth rates increasing the number of dependents and the marginal ability of food producers to invest in their farms are some of the factors that result from this structural poverty. These factors also cause low labor productivity behind the high level of poverty, especially in a context of off-farm jobs opportunities scarcity. Burkina Faso s cereal productivity is among the lowest in the world and has experienced a relative stagnation over the past two decades (FAO and FIDA, 2013; Tittonell and Giller, 2013). There is a legitimate fear of a future decline in agricultural productivity in Burkina Faso. Agriculture faces major constraints that hamper its productivity. Scientific studies on nutrients balance evaluation and surveys among peasants support the idea that natural resources depletion and land degradation are important concerns (Lindqvist and Tengberg, 1993; Taonda et al., 1995; Gray, 1999; Visser et al., 2003). High population growth translates into growing pressure on natural resources. Depletion of arable fertile land for 2030 is a further difficulty (OCDE, 2012). Moreover, climate change may deteriorate agricultural production conditions (IPCC, 2007; Somé et al., 2012). However, the assessment of the loss of agricultural productivity due to these factors remains a difficult task as pointed out by the literature. On the one hand, detailed data on each of these factors, especially natural factors as the amount of nutrients and organic matter lost following the erosion, are limited. On the other hand, the impacts are very sensitive to adaptive choices made by farmers (by applying more labor or fertilizers or extending their use of land). Most studies that 2

3 have attempted to quantify these effects have focused on the impact of land degradation and climate change on agricultural productivity. There is, to our knowledge, no study assessing the impact of land degradation on the productivity of the agricultural sector in Burkina Faso. However, several studies have been interested in the issue at regional and country level. Bojö (1996) reviewed 12 studies on the cost of land degradation in seven SSA countries and concludes that annual productivity losses are generally small (1% or less in most studies, with bigger estimates in Malawi (4 to 11% per year) and Mali (2 to 10% per year)). Using a model of crop growth, Pagiola (1994, 1996) found that soil erosion only reduces agricultural productivity on steeper slopes of Morocco by about 20 to 30% over 50 years, implying annual losses of %, while in Kenya these reductions are about 20% over 10 years (be an annual loss of 2.2%). Lal (1995) shows that losses of grain, legumes, roots and tubers yields over the period have been estimated at 6.2% (0.3% per year) for SSA and 9% (0.5% annually) for Africa as a whole. It is very difficult to assess precisely the loss of productivity induced by soil degradation. The change in productivity requires substantial information regarding production and degradation processes, and how the specific nature of land degradation affects production. Moreover, the methodologies sometimes applied have inherent problems. First, Crop production is highly variable and depends, especially in rainfed production, on the reliability of the onset of the rain. Thus, yield decline is not always ascribable to land degradation. Second, Physical soil loss is only a rough proxy for soil fertility decline (Bishop, 1995) and it is difficult to attribute crop yields to differences in past erosion (Olson et al., 1994). Finally, Soil erosion can take many years to affect crop productivity and there is rarely a one-to-one relationship between the amount of soil lost and the effects on yields. The effects of erosion on productive capacity depend on the depth and quality of soil remaining and not on soil lost (Scherr and Yadav, 1996). Regarding climate change, there is growing consensus that it will likely reduce natural resources productivity particularly in SSA (IPCC, 2007). The quantitative assessment of this productivity decline has generally been realized using crop growth simulation models. Forecasts from different models indicate that agricultural productivity decline for SSA as a whole could be about 28% on average without carbon fertilisation by the 2080s, and about 18% with carbon fertilisation. However, these figures mask the extreme nature of the losses for some countries. For example, for countries that are dependent on dryland agriculture, productivity losses could be as much as 100% (Cline, 2007; Asafu-Adjaye, 2010). In the specific case of Burkina Faso, simulations of various climate change scenarios using General Circulation Models (GCMs), by Somé et al. (2012) show that this country, in its great majority is likely to experience a decline in agricultural productivity up to over 25% by 2050 compared to its 2000 level; this represents an annual decrease of 0.57% per year. Some areas in the north might even know a productivity drop of 100%, meaning land abandonment. Besides these natural factors, the weakness of public infrastructure and services in rural areas may be an additional cause of a probable decline in agricultural productivity. Indeed, it makes private activity not very profitable, leading then private investment to be set at a very low level. The low number of scientists involved in agricultural research (240) as well as low budgets allocated to research underline the weakness of pubic capital in research and development (Stads and Kaboré, 2010). The agricultural extension services are almost inexistent. According to some experts in this field, only one extension officer is responsible for on average producers spread over a 3

4 very large area, with a little travel budget 1. Road infrastructure is very poor (56 km/1000 km 2 ) with a linear of rural roads estimated at 46,095 km (FAD, 2004), inaccessible when it rains. Only 15% of the rural population have access to electricity (MMCE, 2007). The literacy rate is low at the national scale (29%) due to a largely illiterate rural population (World Bank, 2012). The potential for irrigation remains largely unexploited. The Organization for Economic Cooperation and Development (OECD) estimates that irrigated agricultural areas represent only 0.81% of the total areas cultivated and 14.9% of irrigable potential (OCDE, 2012). Public investment in rural sector may partly change the situation given the current situation just described and the importance of yields gap in Burkina Faso as in SSA in general. Yields of the main grains crops are estimated only at 25% of potentially attainable yields (Mutegi and Zingore, 2013). Public policies that can improve the supply of infrastructure and agricultural services can play a vital role in achieving food and nutrition security. Indeed, investment in rural areas is seen as an essential element in the fight against poverty and food insecurity (World Bank, 2008; Barrett et al., 2010; De Janvry, 2010; De Janvry and Sadoulet, 2010). It is expected from growth of investment some positive impacts on food security, not only in rural areas but also in urban areas, declining prices due to the increase in production enabling to satisfy both rural and urban populations (Timmer, 2000; FAO, 2012). In order to generate this growth of investment, many authors highlight the need for public infrastructure to create a more favorable environment for private activities (Barro and Sala-I-Martin, 1995; Aghion and Howitt, 1998; Anderson et al., 2006). The review of the literature identifies six complementary areas of public investment able to boost agricultural productivity (Zidouemba and Gérard, 2015): agricultural research and development, extension, irrigation, rural roads, rural electrification and rural education. Agricultural research leads to the development of improved techniques and adapted to local realities for increasing agricultural productivity and promoting a more sustainable use of natural resources. However, to take full advantage of technology from agricultural research, farmers must be able to acquire and use them appropriately. Agricultural extension thus develops systems through which these technologies are taught to farmers. Public investment in irrigation helps to increase agricultural productivity beyond its function of input by increasing productivity of other inputs, including improved seeds and chemical fertilizers. Rural road density has been shown to be among the most important contributors to productivity growth in agriculture. This is due to the impact that better roads have in reducing the transport component of input costs and transaction costs of marketing products. In addition, roads improve the flow of information on market conditions, new technologies, and reduce the potential risks to their enterprises. Rural electrification opens non-agricultural employment opportunities through the development of the food industry that is at the same time an additional market for agricultural products, thus increasing the producer price and rural incomes. Finally, Public investment in rural education, by increasing the stock of human capital can increase labor productivity and thus off-farm incomes. The investment in education is also complementary to the investment in research and extension, since better-educated farmers are likely to adopt (and encourage their colleagues to adopt) improved production techniques and better management of natural resources. This paper seeks to answer three fundamental questions: What will be the consequences of a loss in agricultural productivity on economic growth and food and nutrition security in Burkina Faso? Are urban households likely to experience the consequences of agricultural productivity 1 Public extension agents have the responsibility to provide farmers with information on farming techniques and to promote innovations. 4

5 degradation as well? By contrast, which consequences have to be expected in case of an agricultural productivity growth, assuming that such growth may be generated by public investment in agriculture? In order to deal with these questions, a dynamic Computable General Equilibrium (CGE) model is used because it allows to consider simultaneously the households economic behavior and the impacts of prices and incomes on their food security and the intersectoral, factor-market, budgetary, and macroeconomic implications of productivity changes. Moreover, several previous studies, including Weyerbrock (2001) and Wiig et al. (2001) have used this tool to address the issue of agricultural productivity changes. It will then, be used for analyzing the current situation and testing alternative agricultural productivity levels in Burkina Faso. The remainder of the article is organized as follows. Section 2 describes the model structure and database for the simulations. Section 3 presents and discusses the simulation results with special reference to the agricultural sector. The final section summarizes the results and provides some comments. 2. Model structure and database 2.1. The CGE Model To study the impact of agricultural productivity change, a recursive dynamic model is developed 2. It reproduces the main characteristics of a classical CGE model (Dervis et al., 1982; De Melo, 1989), but has been slightly modified to represent some characteristics of Burkina Faso s economy. First, we distinguish households according to income level (poor and non-poor) and place of residence (rural and poor). Second, we take into account the existence of an important urban unemployment. Third, we model the difficulty for workers to move from one sector to another. Finally, we convert households' food consumptions into volume (kilogram per capita per year) and compare them to some standards. Producers are assumed to maximize profits in perfectly competitive markets with production prices equal to marginal costs. The production function is a two-layer nested Leontief-CES function. At the bottom level, a CES function is used to combine primary factors (capital and labor) while a Leontief function describes the demand for intermediate input. At the top level, the composite primary factors and intermediate inputs are combined by a CES function to create the sectoral output. Domestic production is valued at basic prices net of taxes and inclusive of production subsidies from government. Households receive income from primary factors and transfers from other institutions. After paying income taxes, a fixed share is set aside as private savings and the rest is spent on consumption. With this constraint on total consumption expenditure, consumption is allocated to different goods and services according to a linear expenditure system (LES) demand functions. Total government revenues consist of all taxes and transfers from the rest of the world. These revenues are allocated to current public consumption according to a Leontief function and spent as subsidies on production and transfers to households and to the rest of the world. The difference between government revenues and expenditures makes up government savings. The 2 The starting point of this CGE model is a series of models developed at CIRAD (Gérard et al., 2002; Boussard et al., 2005; Gérard et al., 2012; Gérard et al., 2013). 5

6 investment demand in each sector is a fixed share of total savings and is added to the equipment from previous periods to determine the capital available for this sector for the next period. Standard assumptions have been adopted for international trade: a small open economy assumption that assumes exports and imports are elastic at given world prices. Domestic output is allocated between exports and domestic sales subject to a constant elasticity of transformation (CET) function, while domestic market demands are derived from the Armington function (Armington, 1969). These functions allow limited substitution possibilities to be introduced between domestic sales and exports for producers and between domestically produced goods and imports for consumers. An exogenous price for salaried workers allows to account for wage rigidities and the existence of unemployment. Initial unemployment is set at 18% for non-agricultural salaried labor and at 1.1% for agricultural salaried labor. These rates correspond respectively to the 2005 urban and rural unemployment rates (INSD, 2008). This may have important consequences for the results as any increase in activity will result in an increase in the volume of employment rather than an increase in labor payments as would be the case with full employment. However, commodity prices are assumed to balance commodity markets. In order to reproduce the scarcity of jobs opportunities outside the agricultural sector as well as the skills required and the time necessary for training (Gérard et al., 2012), we explicitly modeled the difficulty for workers to move from one activity to another. Four aggregate sectors have been defined: agriculture, agro-industries, other industries, and services. Labor is then assumed to be perfectly mobile within the 10 years simulation horizon only among sectors belonging to the same aggregate sector (e.g. agricultural labor can move from rice sector to corn sector but never to sector of education). This implies differentiating labor wages between aggregate sectors. Capital is assumed to be sector-specific. Three macroeconomic balances are included in CGE models: the current government balance, the external balance, and the savings-investment balance. For the government balance, government savings and all tax rates are fixed while government consumption is flexible to balance government accounts. For the external balance, in order to reflect the situation of the CFA franc that is pegged to the euro, the nominal exchange rate is set at its initial level. Both foreign savings and real exchange rate thus clear the external balance (as consumer price index is flexible). For the savings-investment balance, closure is savings-driven (or neoclassical closure) and investment is determined by the sum of private, government and foreign savings. The dynamic of the model is based on population growth, capital accumulation, and productivity trend. The population is expected to increase at an exogenous rate of 3.1% (INSD, 2006). Population growth increases both labor supply and the demand for goods and services. To take into account the flow of migration of rural population to the cities, we assume that the agricultural labor supply increases at a rate below the average national rate (be 2.5% against 5.3% for non-agricultural labor) 3. Productivity changes are included in CGE models by varying the scale parameter of the production function commonly considered as the productivity parameter or total factor productivity (TFP) (Pauw and Thurlow, 2011; Löfgren et al., 2013). We introduce a trend 3 Rural population growth rates differ from one decade to another, but is around 2.5% against about 5.3% for the urban population (INSD, 2006) 6

7 parameter prodtrend asec in the production function (equation 2) in order to modify it according to the scenario. The production function is of the following form: XD 1 i i i CI VA (1) i, t i, t i i, t 1 i i, t where is the production of sector i at period t ; the level of intermediate consumption of sector i at period t ; value added of sector i at period t ; et are parameters of production function ; is the productivity of sector i at t. Agricultural productivity is defined by: prodtrend (2) asec, t 1 asec, t asec 2.2. Data Data used include the Social Accounting Matrix (SAM) and various behavioural parameters. The SAM was constructed for the reference year 2005 by Burkina Faso s Ministry of Agriculture, Fisheries, and Hydraulic Resources. There are 24 production sectors (and 22 commodities) in the SAM. Since agriculture is the focus of the study, 11 agricultural sectors are included as part of these 22 sectors. Utilizing the household expenditure survey data by INSD (2003), the household income and consumption data were disaggregated into 4 household groups according to income level and area of residence (rural versus urban). These groups are representative of the situation of about 6 million of poor in rural areas, 600,000 poor in urban areas, 5 million of non-poor in rural areas, and 2 million of non-poor in urban areas. Annual per capita incomes are about 62,100 CFA ( USD) francs for the rural poor, 56,000 CFA (96.03 USD) francs for the urban poor, 201,000 CFA ( USD) francs for the rural non-poor and 291,000 CFA ( USD) francs for the urban non-poor. The per capita income for rural poor is 25% below the poverty line (83000 CFA francs) and 32% for the urban poor. Labour supply has been split into three groups (agricultural salaried labor, agricultural family labor, and non-agricultural salaried labor); capital is split into two categories: agricultural capital and non-agricultural capital. As shown in table 1, most of the rural poor income comes from the remuneration of agricultural primary factors while urban poor income is essentially derived from the remuneration of non-agricultural factors. The weakness of incomes of the poor is the result of their low factors endowment (Table 2). They have little access to capital (20% of agricultural capital and 13% of non-agricultural capital) and are largely affected by underemployment in urban areas. Then, the urban poor are almost deprived of any access to primary factors. They essentially hold their labor force but they meet difficulties to find jobs as underlined by the fact that they only represent 1% of non-agricultural employment. Table 1 Sources of households primary incomes (percentage) Agricultural primary factor Non-agricultural primary factor Salaried agricultural labor agricultural family labor agricultural capital non-agricultural salaried labor non-agricultural capital Total Rural Poor Urban Poor Rural Non-Poor Urban Non-Poor Source: Computed from the Social Accounting Matrix 7

8 Table 2 Allocation of factors of Production (percentage) Agricultural primary factor Non-agricultural primary factor Salaried agricultural labor agricultural family labor agricultural capital non-agricultural salaried labor non-agricultural capital Rural Poor Urban Poor Rural Non-Poor Urban Non-Poor Total Source: Computed from the Social Accounting Matrix The food security analysis is based on two groups of products: grains (millet, sorghum, fonio, rice, and corn) and animal products (meat and fish). Indeed these products play a major role in Burkina Faso s food security. According to the ministry of agriculture and food security (MASA, 2010), grains account for more than 42% of households food expenditure and constitute the main source of energy intake, while meat and fish provide the essential of animal proteins. Moreover, grains and animal products provide, on average, more than 80% of carbohydrate intakes, 70% of energy intake, 42% of lipids intakes, and 32% of protein intakes. Households consumption levels, compared to the levels recommended for Burkina Faso by the Permanent Interstate Committee for Drought Control in the Sahel (CILSS, 2004) that is 203 kg/capita/year for grains and 14 kg/capita/year for animal products show a high food insecurity in 2005 (table 3). The grains deficit is particularly important for urban poor (33%). Deficit for rural poor is more limited (14%). The deviation from the standard is also pronounced for animal products whose deficit reaches 31% and 22% for the urban poor and the rural poor respectively, pointing beyond the deficit in quantity, low quality of food. Table 3 Annual Per Capita Income and Grain Consumption in kilogram 4 corn rice other grain * grain (total) meat/fish (total) Rural Poor 40 (23%) 15 (8%) 120 (69%) Urban Poor 68 (50%) 25 (18%) 43 (32%) Rural Non-Poor 74 (28%) 41 (16%) 145 (56%) Urban Non-Poor 90 (41%) 80 (37%) 47 (22%) * Other grains consist of millet, sorghum, and fonio Source: Computed from the Social Accounting Matrix At macroeconomic level, the contribution of agricultural sector to national GDP as provided by the SAM was 35%, compared to 22% for industry and 43% for services. Traditional grains millet, sorghum, and fonio are the largest contributors to agricultural GDP (33%), followed by livestock (27%), cotton (12%), and other agricultural products (10%). Export revenues mainly come from agricultural products (57%). These exports concentrate on cotton, which alone accounts for 45% of total export value. Most of the country s imports are non-agricultural products (93%) and 80% of agricultural products imports consist of rice, followed by other agricultural products (10%) and fruits (7%). 4 Food consumption is converted into kilogram capita -1 year -1 for this study in order to have a more realistic vision of the food situation 8

9 Behavioural parameters included in the model are the trade elasticities (Armington and CET), income elasticities, the primary factors substitution elasticities, and the elasticities between aggregate primary factor and intermediate demand. Since Burkina Faso is a small trading country and thus a price taker in most of the global commodity markets, export demand curves should be fairly flat, reflecting that export demands are very elastic. As a result, the export demand and armington elasticities for most of goods are given large values (around 12). For the agricultural sectors, the elasticity values are even bigger (around 17) in considering the relatively small size of agriculture exports. Income elasticities are based on a study by the Burkina Faso's Ministry of Agriculture and Food Security (MASA, 2010) (see table in Appendix). Primary factors substitution elasticities as well as elasticities between aggregate primary factor and intermediate demand have been assigned low values (0.75 and 0.3 respectively) following Breisinger et al. (2009). Sensitivity tests have been performed to check the robustness of the modeling results. 3. Simulations analysis The model is used to perform four simulations. The baseline simulation which has been parameterized to reproduce the main trends of the economy over the period is used to reproduce the evolution of the system over the period and to analyze the dynamics at work. The key stylized fact in the baseline is that the food situation is improving only slowly. The system comes to cope with the population growth of 3.1% but growth in per capita consumption of grains as well as animal products does not exceed 2% per year (table 4). Progress is thus very slow and a 10-year horizon does not enable reaching the CILSS standards for food security. The slow growth in per capita income of the poor explains the weakness of progress on food security. In rural areas, the annual growth of per capita income is 1.1% for the poor, against 1.6% for the non-poor. In urban areas, the growth of per capita income is higher: 2.3% per year for the poor against 2.6% for non-poor thereby confirming a non-pro-poor growth in Burkina Faso. Table 4 Initial and baseline evolution of food consumption and per capita income Grain Meat/Fish (kg) Per capita income (CFA francs) consumption (kg) 9 Growth rate of per capita income (%) Rural Poor ,100 69, Urban Poor ,000 68, Rural Non-poor , , Urban Non-poor , , Source: Social Accounting Matrix and CGE simulation As emphasized in section 1, there is a legitimate fear of a future decline in agricultural productivity in Burkina Faso, likely to make the situation even more difficult for the poorest (land degradation, climate change, population pressure on natural resources, the lack of public infrastructure ). Given our focus on the impact rather than the cause of productivity decrease, we model productivity decline as an exogenous trend. Because of the uncertainty about future productivity decreases, we consider productivity decrease from -0.5% yearly (optimistic scenario) to -1.5% (pessimistic scenario). Fortunately, it is possible to avoid this scenario and even to observe an opposite trend of this productivity with, for example, public investment in the agricultural sector (Anderson et al., 2006; World Bank, 2008; De Janvry and Sadoulet, 2010). In a forthcoming paper, Zidouemba and Gérard (2015) estimate the impact on agricultural productivity of an annual investment of 100 billion CFA francs (171 millions USD) in six types of capital (agricultural research and

10 Kg/person/year development, extension, irrigation, rural roads, rural electrification and rural education). While the impact decreases over time, this investment allows an average annual productivity growth of 2.38% over a period of five years. We then rely on these results to simulate a productivity growth that is supposed coming from a public investment in agriculture. Accordingly, this scenario is assumed to increase agricultural productivity by 2.38% per year from 2010 to The counterpart of this productivity gain is a cost of 100 billion CFA francs (171 millions USD) that are assumed to be provided by foreign aid. However, because of the adopted closure (foreign savings is endogenous and investment fits savings available), foreign savings is reduced by the amount of foreign aid. Therefore, there is a diversion of a part of foreign savings to finance the public investment program and fewer savings available for private investment Impacts of agricultural productivity degradation While the situation is already worrying for the poor in the baseline scenario, the results of agricultural productivity degradation scenario indicate a sharp deterioration in food consumption both for rural and urban population (Fig. 1 to 4). The drop of consumption is 8.7 kilograms and 7.7 kilograms for rural poor and urban poor respectively in the low degradation scenario. This drop is 24.8 kilograms and 22.1 kilograms for rural poor and urban poor respectively in the severe degradation scenario. Impacts are less important for the rural poor whose consumption drops by -12% than for urban poor (-13%). Declines for animal products are relatively small over a period of 10 years: about 0.4 kilogram (-3.2%) per capita for the rural poor and 1.3 kilogram (- 10.5%) per capita for the urban poor. However, these impacts become noticeable in the longer term. For example, when the simulation is extended to , one realizes that the loss is about 2 kg (-11%) for the rural poor and 3.5 kg (-15%) for the urban poor. These changes reflect a gradual deterioration in the food security for the poor households particularly in urban areas _BAU 2015_OPT. 2015_PES. Maize Rice Other grains Nom of CILSS Note: 2015_BAU, 2015_OPT., and 2015_PES. mean the baseline as usual for 2015, the optimistic scenario for 2015, and the pessimistic scenario for 2015 respectively. Fig. 1. Impact on grain Consumption of rural poor 5 Results not reported in this paper 10

11 Kg/person/year Kg/person/year _BAU 2015_OPT. 2015_PES. Maize Rice Other grains Nom of CILSS Note: 2015_BAU, 2015_OPT., and 2015_PES. mean the baseline as usual for 2015, the optimistic scenario for 2015, and the pessimistic scenario for 2015 respectively. Fig. 2. Impact on grain Consumption of urban poor _BAU 2015_OPT. 2015_PES. Meat_Fish Nom of CILSS Note: 2015_BAU, 2015_OPT., and 2015_PES. mean the baseline as usual for 2015, the optimistic scenario for 2015, and the pessimistic scenario for 2015 respectively. Fig. 3. Impact on Meat and Fish Consumption of rural poor 11

12 Kg/person/year _BAU 2015_OPT. 2015_PES. Meat_Fish Nom of CILSS Note: 2015_BAU, 2015_OPT., and 2015_PES. mean the baseline as usual for 2015, the optimistic scenario for 2015, and the pessimistic scenario for 2015 respectively. Fig. 4. Impact on Meat and Fish Consumption of urban poor While this result may at first seem counter-intuitive as one would expect that rural population, who are the most involved in agricultural activities, be the most affected it is explained by two mechanisms: a sharp increase of agricultural prices and a contraction of overall economic growth that implies an increase in urban unemployment. The decline of agricultural productivity makes agricultural productions slightly more difficult and results in a downward trend of these productions that generates prices increase (Table 5). Prices increases are larger than productions decreases for most of agricultural sectors, leading to value added growth. Unlike agricultural sectors, industry and services sectors experience a slight decrease in activity not compensated by the evolution of prices. A modest increase in prices is observed in industry sectors but this increase is less than the decrease in the volume of production leading to a decrease in value added. For aggregated service sector, the evolutions are rather unfavorable, both in terms of volume of production and prices. Three main factors combine in fact to adversely affect the productions of non-agricultural sectors: falling agricultural production (used as intermediate inputs in industry and services), rising agricultural prices (which increases production costs) and declining real incomes of households (which reduces the demand for goods and services produced by these sectors) (Table 6). 12

13 In spite of international trade and high armington elasticities, there is a significant impact on agricultural prices, due to the low share of imports in the domestic supply 6. In Burkina Faso, domestic production ensures indeed the essential of food intake except for rice, whose imports account for substantial share in the supply of the domestic market (57% Table 7). Then, rice price increases only by 3.5% compared to the baseline in the pessimistic scenario, while corn whose share of imports in domestic demand represents only 0.36%, has a price that increases by 23%. These impacts on prices are quite realistic because of the country's landlockedness linked to both its geographical location and its poor transport infrastructure. This result had already been highlighted by Montaud (2003) who showed using a CGE model that a 20% reduction of tariff rate has limited impacts on the households consumption in Burkina Faso because of the difficulty to substitute imports with local products. The growth of agricultural prices results in a decline of real per capita income (Table 6). These declines range from 1.4% to 4.3% in rural area and from 2.3% to 7.6% in urban area. One can observe that the loss of income due to the agricultural productivity degradation is more important for the urban poor. The growth of agricultural prices and the decline in real income which is the direct consequence of prices growth affect the whole economy (Table 8). Indeed, the sectors that use agricultural goods as intermediate consumption see their production costs being expanding while demand for final goods contracts due to the decline in per capita real incomes. Consequently, there is a contraction of economic activity ranging from -2.1% to -7.3%. All of three aggregate sectors experience a contraction of activity, but this contraction is less marked for agricultural sector due to the rise in agricultural prices. Agricultural activity only contracts by 1.3% to 2.8%, against 1.5% to 6.8% for industry and 3% to 10.5% for the service sector. The decline in overall economic activity, especially the industry and service translates into higher urban unemployment, which rose from 17.7% in the baseline scenario to 18.7% in the optimistic scenario and to 21.9% in the pessimistic scenario. Accordingly, the urban poor are doubly penalized: the growth of food prices and the contraction of economic activity. The contraction of economic activity results in a decline in nominal wages due to both the decrease in the remuneration of non-agricultural capital and the rise in urban unemployment. The growth of agricultural prices increases the cost of their consumption basket and thus reduces real incomes. By contrast, rural poor partially benefit from higher agricultural prices resulting in an increase of the remuneration of agricultural primary factors. Thus, the growth of nominal income (due to the rising price of labor) partly offset the rise in food prices so that the decline in real income is less marked than in urban areas. 6 The more the imports of a product represent an important share in the domestic demand, the less the increase in the domestic price of this product is significant, as a stable international prices (the small country hypothesis) controls the upward pressure of the domestic price. 13

14 Table 5 Impacts of agricultural productivity decline on production and prices (percentage) Productions deviation from baseline in 2015 Prices deviation from baseline in 2015 Optimistic Pessimistic Optimistic Pessimistic Corn Rice Other grains Vegetables Groundnuts Cotton Fruits Livestock Other agricultural prod Industries Services Table 6 Impacts of agricultural productivity degradation on real per capita income (CFA francs) Initial income Income in 2015 Loss of per capita income (2005) Reference Optimistic Pessimistic Optimistic Pessimistic Rural poor 62,101 69,282 68,299 66, ,995 Urban poor 56,073 68,787 67,207 63,552-1,580-5,235 s Table 7 Share of exports/imports in total production/absorption (percentage) Imports Exports Corn Rice Other Grains (millet, sorghum, fonio) Vegetables Groundnuts Cotton Fruit Livestock Other agricultural products Minerals Meat and Fish Textile Fertilizer Other industrial products Restoration Transport Other market services Education Health Other non-market services Trade Source: Computed from the Social Accounting Matrix 14

15 Kg/person/year Table 8 Impacts of agricultural productivity degradation on the values added in 2015 (percentage) Optimistic Pessimistic Agriculture Industry Services global GDP Impact of agricultural productivity improvement By contrast, the results of the scenario testing an agricultural productivity improvement show a significant and fast improvement of the food situation of the poor. Indeed the CILSS standard for grains is reached in 2012 for the rural poor, while the grain deficit for the urban poor is only about 9% against 23% in the baseline. The CILSS standard is reached in 2014 (Fig. 5 and 6). Similarly, for animal products, the scenario with agricultural productivity improvement enables a sufficient consumption growth to reach the standard of CILSS despite of the large initial gap. This standard is reached by 2015 for the rural poor and by 2013 for the urban poor. The results of the agricultural productivity improvement are thus particularly positive for the food and nutrition security of poor households (Fig. 7 and 8) Note: 2015_BAU, 2015_OPT., 2015_PES., and 2015_PROD. mean the baseline as usual for 2015, the optimistic scenario for 2015, the pessimistic scenario for 2015, the agricultural productivity improvement scenario for 2015 respectively. Fig. 5. Evolution of grains consumption of the rural poor (kg capita -1 year -1 ) _BAU 2015_OPT. 2015_PES. 2015_PROD. Maize Rice Other grains Nom of CILSS 15

16 Kg/person/year Kg/person/year _BAU 2015_OPT. 2015_PES. 2015_PROD. Note: 2015_BAU, 2015_OPT., 2015_PES., and 2015_PROD. mean the baseline as usual for 2015, the optimistic scenario for 2015, the pessimistic scenario for 2015, the agricultural productivity improvement scenario for 2015 respectively. Fig. 6. Evolution of grains consumption of the urban poor (kg capita -1 year -1 ) Maize Rice Other grains Nom of CILSS Note: 2015_BAU, 2015_OPT., 2015_PES., and 2015_PROD. mean the baseline as usual for 2015, the optimistic scenario for 2015, the pessimistic scenario for 2015, the agricultural productivity improvement scenario for 2015 respectively. Fig. 7. Evolution of Meat and Fish consumption of the rural poor (kg capita -1 year -1 ) _BAU 2015_OPT. 2015_PES. 2015_PROD. Meat_Fish Nom of CILSS 16

17 Kg/person/year _BAU 2015_OPT. 2015_PES. 2015_PROD. Meat_Fish Nom of CILSS Note: 2015_BAU, 2015_OPT., 2015_PES., and 2015_PROD. mean the baseline as usual for 2015, the optimistic scenario for 2015, the pessimistic scenario for 2015, the agricultural productivity improvement scenario for 2015 respectively. Fig. 8. Evolution of Meat and Fish consumption of the rural poor (kg capita -1 year -1 ) These positive results are obtained by two main mechanisms: a decrease in agricultural prices (Table 9) and a strong growth in real incomes. The improvement of agricultural productivity enables strong growth of agricultural production that generates a significant decrease in agricultural prices. For instance, grains production is 9% greater than its baseline level while grains price is 12% below its baseline level. One can also observe a growth in activity for non-agricultural sectors followed by a marginal decline in prices for industries and even an increase in prices for services. These trends are rather favorable to non-agricultural sectors and are explained by two factors: a decline in agricultural intermediate consumption expenditures enabled by the growth of agricultural production and the decline in agricultural prices and a rise of households demand for non-agricultural products. For the latter factor, the decline in agricultural prices results in a lower living cost and a rise in real incomes especially for poor households who spend a larger share of their income on food consumption (Table 10). Consumer prices indexes decrease indeed by -3.8% for the rural poor, -2.7% for the urban poor, -1.4% for rural non-poor. One observes, by contrast, a slight increase in the consumer prices index for urban non-poor (+ 0.5%) reflecting the fact that this category of population consumes more services whose prices increased on average by 4.5%. As in the case of agricultural productivity degradation scenario, the strong impacts on consumer prices are explained by the weakness of the shares of imports in domestic supply, so that the influence of international prices on domestic prices is very small. A high percentage variation of imports (Table 11) only generates little impacts on food prices as this implies a very small change in volume. The momentum given to the agriculture thereby affects positively the non-agricultural sectors because of the close links between agricultural and non-agricultural sectors and leads consequently to a higher economic growth. GDP annual growth is 6.5% in agricultural productivity improvement scenario while it was 5.2% in the baseline. Impacts on growth of 17

18 aggregate values added are stronger for non-agricultural sectors as a whole than for agriculture due to the strong decrease in agricultural prices. The annual growth rates are 4.6%, 6.8%, and 7.1% for agriculture, industry, and services respectively while they were 3.8%, 6.4%, and 5.7% in the baseline. The consequence of the strong growth in non-agricultural sectors, especially in services, is a significant decline in urban unemployment. While the baseline scenario shows a slight decline in urban unemployment over the period of 10 years (18% to 17.7%), the agricultural productivity improvement scenario enables its reduction by nearly half (9.5%) (Fig. 9). The decline of urban unemployment, in conjunction with the decline in agricultural prices, translates into an important increase in households real incomes in a much more pronounced for the urban poor whose average per capita income in the reference situation is very far from the poverty line of 83,000 CFA francs. Indeed, the per capita real income for urban poor is 20.7% higher than in the baseline, against 10.3% for the rural poor. This scenario is particularly pro-poor as the non-poor per capita incomes are only 8.9% and 9.2% higher than the baseline, respectively for the rural and urban non-poor. The growth of real per capita incomes is such that urban poor reach the poverty line in 2015 (83,019 CFA francs) while the rural poor approach it (76,386 CFA francs) (Fig. 10 and 11). For the latter, the depth of poverty in the agricultural productivity scenario is only 8% against 17% in the baseline in They reach the poverty line by in the scenario of agricultural productivity improvement while even by 2025 it was not reached in the baseline, highlighting the ability of an agricultural productivity improvement to accelerate the growth of the incomes of the poorest. The strong positive impacts on urban poor are justified by the fact that they benefit both from the drop in agricultural prices and the acceleration of economic growth involving a decline in urban unemployment. By contrast, the sharp decline in agricultural prices partially mitigates improving rural incomes. This result is fully in line with the work of Timmer (2000) on pro-poor growth enabled by improving productivity in agriculture. Table 9 Impacts of agricultural productivity improvement on productions and prices (percentage) Growth rate of production Deviation from the baseline in 2015 Reference Investment Productions Prices Corn Rice Other grains grains (total) Vegetables Groundnuts Cotton Fruits Livestock Other agricultural prod Industries Services Extended simulations to 2025 are not included in this paper 18

19 Table 10 Change in the index of consumer prices by type of household Consumer Prices Index in 2015 (base 100 in 2005) Reference Investment Deviation from baseline (%) Rural Poor Urban Poor Rural Non-Poor Urban Non-Poor Table 11 Changes in imports and exports volume compared to the reference (percentage). imports Exports Corn Rice Other grains Vegetables Groundnuts Cotton Fruits Livestock Other agricultural prod Industries Services _BAU 2015_OPT. 2015_PES. 2015_PROD. Note: 2015_BAU, 2015_OPT., 2015_PES., and 2015_PROD. mean the baseline as usual for 2015, the optimistic scenario for 2015, the pessimistic scenario for 2015, the agricultural productivity improvement scenario for 2015 respectively. Fig. 9. Impact on Unemployment (percentage) 19

20 Fcfa/person/year Fcfa/person/year _BAU 2015_OPT. 2015_PES. 2015_PROD. Note: 2015_BAU, 2015_OPT., 2015_PES., and 2015_PROD. mean the baseline as usual for 2015, the optimistic scenario for 2015, the pessimistic scenario for 2015, the agricultural productivity improvement scenario for 2015 respectively. Fig. 10. Agricultural productivity improvement and real incomes of the rural poor Per capita income Poverty line _BAU 2015_OPT. 2015_PES. 2015_PROD. Per capita income Poverty line Note: 2015_BAU, 2015_OPT., 2015_PES., and 2015_PROD. mean the baseline as usual for 2015, the optimistic scenario for 2015, the pessimistic scenario for 2015, the agricultural productivity improvement scenario for 2015 respectively. Fig. 11. Agricultural productivity improvement and real incomes of the urban poor Beyond the positive impacts on the food security and the living standards of poor, agricultural productivity growth is also accompanied by a significant improvement in the trade balance. The trade deficit is reduced from 563 billion of CFA francs (963 millions USD) in the baseline to 371 billion of CFA francs (634 millions USD) in the agricultural productivity improvement scenario, be a decrease of about 34%. Moreover, the growth of the activity of the various sectors of production, as well as rising incomes and consumption allow more tax revenues. The public 20