Peanut research and poverty reduction: impacts of variety improvement to control peanut viruses in Uganda.

Scientists, research administrators, and policy makers face increased pressure to justify public investments in agricultural research. As demands grow for scarce funds, evidence is needed to demonstrate that agricultural research generates attractive returns compared to alternative investments. The result has been an increase in studies projecting benefits of current and proposed research and estimating benefits of previous research (Smith and Pardey 1997; Morris and Heisey 2003). Research managers also feel increased pressure to direct publicly funded agricultural research toward the needs of small-scale farmers and the poor. Policy makers call on research managers to explicitly consider poverty reduction objectives in resource allocations (Byerlee 2000; Alwang and Siegel 2003).

Agricultural research can significantly influence the level and the distribution of income and can reduce poverty in several ways. Technology adoption can lower per-unit cost of production, increase the supply of food, and raise incomes of adopting producers. Outward supply shifts can lower food prices to the benefit of consumers, while producers, particularly late-adopters, may lose. Depending on the input bias of the technical change, input demands may change. Increased labor demand may raise wages, including earnings of poor laborers. The poor also gain disproportionately as consumers from lower food prices, as they spend a high proportion of their income on food (Thirtle, Lin, and Piesse 2003). Technological changes may bring new cropping patterns whose characteristics are difficult to predict. Higher productivity could also create broad-based multiplier effects within the rural community, inducing employment creation in industries related to agricultural production, such as value-added processing, and roadside marketing. These distributional effects are theoretical, and net impacts on the poor of agricultural research are case-specific and require empirical quantification.

This article develops and applies a procedure for predicting the impacts of agricultural research on poverty levels, examining the poverty-reducing impact of peanut-disease-resistance research as an example. Our procedure combines market-level information on economic surplus changes with a procedure to allocate income changes to individual households. We examine the characteristics of farmers affecting their likelihood of technology adoption and use this information to create a technology adoption profile. Associated changes in poverty resulting from adoption are computed measures of the Foster--Greer--Thorbecke (FGT) type (Foster, Greer, and Thorbecke 1984). Calculations of predicted income changes at the household level are aggregated to the market level and reconciled with market-level calculations of surplus changes. This new technique that uses additional economic analysis to exploit the link between economic surplus changes and aggregate poverty rates, will be of interest to policy makers and others interested in understanding the impact of agricultural research alternatives on poverty reduction.

Despite increased interest in understanding poverty impacts of agricultural research, few ex ante studies of impacts of agricultural research on aggregate poverty have been conducted. Widely used ex ante assessment tools, such as economic surplus analysis, can be disaggregated by region and for population subgroups to examine the distribution of research impacts on groups such as households in lower expenditure quintiles, by region, etc.

Findings from such efforts support the notion that research benefits are unevenly distributed. Impacts on household-level and aggregate poverty are considerably more difficult to measure or predict, primarily because poverty status is household-specific while most surplus measurement is conducted at the market level. In an ex ante setting, research-induced shifts in the aggregate supply function lead to market-level surplus changes; such shifts are caused by decisions made by individual farmers to adopt and the subsequent impact on their marginal cost of production. The market-level economic surplus approach requires, among other things, estimates of technology adoption, which may vary by region, by household conditions and other factors. Thus, the market-level approach cannot yield measures of poverty changes without a system for allocating market surplus changes to individual households.

Agricultural Research and Poverty in Uganda

Rural households in sub-Saharan Africa depend heavily on agriculture, with peanuts the principal source of digestible protein, cooking oil, and vitamins in many African countries. Peanut productivity has a significant impact on the economic and nutritional well-being of a large segment of the population. Unfortunately, peanut production is affected by several viruses and diseases, the most common being Groundnut Rosette, a viral infection first reported in Tanzania in 1907 (Gibbons 1977). Groundnut Rosette has caused devastating losses to peanut production in Africa. The Rosette epidemic in 1994-1995 in central Malawi and eastern Zambia destroyed the crop; groundnut area in Malawi fell from 92,000 ha in 1994-1995 to 65,000 ha in 1995-1996 and losses in Zambia were estimated at US$5 million in 1995-1996. Overall losses due to Rosette in Africa were estimated at about US$156 million per annum (ICRISAT 2005).

Peanut varieties with (partial) resistance to Rosette virus have been developed for Uganda, (1) a country where most people earn less than US$1.00 per day and rural poverty is pervasive (World Bank 2006). While peanuts are not as important to diets in Uganda as they are in West African countries, they are important in certain sub-regions, particularly in Eastern Province, the focus of this study. (2) Research leading to a virus-resistant variety in Uganda may have significant economic benefits, and importantly, reduce poverty.

The distribution of benefits from Rosette-resistant peanut varieties may be biased toward the poor for several reasons. First, peanuts are mainly produced by small-scale farmers in sub-Saharan Africa, most of who are poor. Productivity gains may raise incomes among adopters, possibly lifting poor families above the poverty line. Second, peanut seeds are regularly purchased even by poor farmers since stored seeds lose their productive potential over time. The need to purchase virus-resistant seeds may not represent a significant barrier to adoption if production costs per unit of output for resistant varieties are lower than for traditional varieties. Finally, groundnuts represent an important consumption item in poorer households, allowing them to capture benefits of price reductions that may occur as research induces supply shifts.

Methods

Economic surplus analysis is combined with household-level data analysis to construct ex ante estimates of changes in poverty resulting from adopting virus-resistant peanut varieties. The surplus analysis provides estimates of changes in prices and economic surplus under various assumptions about technology adoption. The household-level analysis uses consistent information about changes in production costs associated with adoption and consumption patterns to infer household-specific changes in income; it allocates economic surplus to individual producers and consumers. With appropriate survey weights, household income changes can be used to estimate changes in aggregate poverty and changes in aggregate income, which, in the context of the model, should be consistent with findings from the market-based surplus analysis.

Economic Surplus Analysis

Standard approaches to ex ante estimation of research impacts involve several steps: (a) calculating a k-shift, representing the unit-cost reduction associated with use of a new technology; (b) gathering information on expected adoption rates and their evolution over time; (c) combining (a) and (b) with market-related information on supply and demand elasticities and equilibrium prices and quantities (Alston, Norton, and Pardey 1995). These steps allow estimation of price, quantity and corresponding economic surplus changes associated with technology adoption. Modifications to the techniques include efforts to distinguish among producer groups, who may vary in propensity to adopt different technologies (Mutangadura and Norton 1999), regional variation to reflect spatial differences in cost, shipping, prices, and markets (Mills 1997), and regional differences in productivity (Karanja, Renkow, and Crawford 2003). The challenge then is to allocate the economic surplus to specific households.