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

The probit parameter estimates are used to create a household-specific index of likelihood of adoption of new technologies, and peanut-producing households are ordered according to this index. We simulate three different adoption levels--15, 30, and 50%. Income changes implied by adoption of the new technology are applied to the first 15, 30, and 50% of the peanut-producing sample according to each individual household's adoption probability. This simulation ignores time dynamics associated with adoption; as noted above, our interviews with agents and scientists indicated that the 50% level of adoption would only be achieved after many years. As adoption grows over time from 15 to 50%, the distribution of income gains and losses changes among producers and consumers. Early-adopting producers will gain at low levels of adoption, while nonadopters will see their prices fall. The surplus captured by the 15% of adopters may be reinvested in productive capital that might lead to higher incomes (and less likelihood of poverty) in future years. Our simulation ignores this outcome.

Open Economy Case

In the open economy case, all income gains accrue to adopting producers through their changes in producer surplus. Using equation (3) and the K-shift from the surplus analysis, the change in income for adopting households can be approximated as:

(4) d[[pi].sub.i](t) = KP [Q.sub.i](1 + 0.5K [epsilon]) = 0.371 P [Q.sub.i](1 + (0.5(0.371))) = 0.44 P [Q.sub.i]

or a 44% increase over the base value of peanut production. Postadoption household income for adopters is [y.sub.i.sup.0] = [y.sub.i.sup.0] + d[[pi].sub.i] ([tau]), where [y.sub.i.sup.0] is initial (total household) income. This post-adoption income for adopters is compared to the poverty line (5) and the change in the FGT poverty index resulting from technology adoption is computed.

As the assumed adoption rate increases, more low-income producers fall into the category of adopting households, pulling down the mean household income of adopters (table 4). However, adoption of the Rosette-resistant peanut varieties leads to a modest increase (5 to 6%) in household income. This modest impact occurs because among peanut-producing households, peanut income is about 20% of total income.

In the closed economy case, the income gains to adopting households (producer plus consumer surplus) range from 2.3 to 2.5% of preadoption income, depending on the assumed rate of adoption (table 4). Nonadopting producers see minor drops in total income (the loss in producer surplus is not quite offset by gains in consumer surplus). Nonproducing consumers of peanuts also gain from lower prices; at the 50% level of adoption, the price decline is associated with a 1.7% rise in total annual income.

All three poverty indices fall modestly as a result of technology adoption (table 5). If all peanut producers in the region were to adopt the new varieties, under our assumptions about yield and cost changes, the poverty headcount among adopting households would fall about 4% in the open economy case. In the closed economy case, because the income gains are spread over many producers and consumers, the decline in poverty resulting from adoption is negligible.

The poverty gap and severity indices also fall following spread of the new peanut variety.

In the case of the open economy model, the poverty severity index falls by 2% with 100% adoption (from 0.1896 to 0.1716), representing a 10.5% decline in poverty severity. Since the poverty gap and severity indices fall as adoption increases, a number of households move closer to the poverty line and there is less inequality among poor households. Both these factors further highlight the poverty-reduction benefits of the new Rosette-resistant peanut seed.

The different assumptions about adoption rates have subtle effects on the distribution of household income. These differences are illustrated in figure 3, which shows the base density of income for peanut farmers in the open economy case subtracted from the density of income at different levels of adoption (a negative density difference implies that the postshift distribution has relatively fewer households in that range). At the 15% level of adoption, the postadoption income distribution is shifted slightly to the right of the actual (preadoption) distribution, but the shift occurs very close to the $0.75 per day poverty line (the left-hand vertical line) and to its right. Few households at the very low end (left-hand tail) of the income distribution see their incomes grow as a result of adoption. At higher rates of adoption, the increase in income at very low levels of income becomes more pronounced. Higher adoption rates imply bigger rightward density shifts and more income increases for low-income farmers.

[FIGURE 3 OMITTED]

Our analysis of household-specific adoption rates hints that there will be a difference in the aggregate surplus change derived from the household analysis and that predicted by the usual market model (Table 6). The reason for this discrepancy is that the market model assumes that adoption is independent of income and farm size. The aggregate surplus change at the 15% adoption rate assumes that 15% of the total base quantity of output is subject to the yield increase, while the household analysis shows that the first 15% of adopters are likely to be wealthier and have more land available than others; thus 15% adoption is likely to be associated with more than 15% of the base quantity.

While the impact on aggregate poverty reduction is rather modest, the analysis examines a single agricultural technology and does not account for dynamic effects, such as increased acreage devoted to peanuts and labor market effects. The impact of the new Rosette-resistant variety on demand for labor is likely to be minimal and, given a situation with high levels of seasonal underemployment, the labor market effects will be small at best. Over time, modest increases in incomes may lead to increased investments in household assets, leading to poverty-reducing growth effects.

Finally, additional sensitivity analysis was conducted around the key parameter of the per unit cost reduction due to either a different change in yield or input cost as a result of the new variety. For example, the projected increased cost of inputs as a result of adopting the new technology was fairly high at 50%. We reduced that increase to 25% and recalculated economic surpluses, net present values, and poverty rates. Details are available from the authors, but basically, the NPVs of benefits for the open economy case increased to $62.0 million and $51.3 million at 3 and 5% discount rates and for the closed economy case to $58.3 million and $48.2 million, increases of 42-44%. Poverty rates declined about 5% (0.7084 to 0.6548) for the headcount index for peanut producers in the open economy case, and a half percent (0.7084 to 0.7025) in the closed economy case. The severity index declined from 0.1896 to 0.1642 in the open economy case and from 0.1896 to 0.1833 in the closed economy case.

Conclusion

Results indicate that sizable research benefits are generated by adopting Rosette-resistant varieties. When we assume an open economy, these benefits accrue to adopting farmers, and are estimated to be from US$35.6 to $62.0 million over fifteen years. The poverty indices show modest reductions in poverty, reflecting the fact that these surplus changes are distributed among a large number of peanut-producing households, many of whom are not poor. As assumed adoption rates increase, more poverty is reduced, because the poor are, in general less likely to adopt new technologies than the nonpoor. The depth and severity indices also fall with adoption, indicating that more households are drawn closer to the poverty lines (and hence escaping poverty) as a result of adoption.

In the closed economy cases, price declines due to research-induced supply shifts lead to lower aggregate benefits of research (US$34.0 to $58.3 million). As these benefits are spread over more people (both producers and consumers), benefits per household decline and poverty reduction is small. This example indicates the importance of understanding a country's market conditions when estimating research impacts.

The main contribution of this article is in illustrating a simple but important point. We have presented a method of allocating economic surplus changes to individual households; the method can be used to estimate other distributional impacts such as inequality, poverty impacts by subgroups etc. The method can easily be adapted to other cases where policy makers wish to have ex ante information on agricultural research's impact on poverty reduction.

The authors would like to thank Charlene Brewster, two anonymous reviewers, and the Journal editor for comments and Charles M. Busolo-Bulafu for supplying data and information and helping with logistics during information collection in Uganda. The financial support of the USAID through the Peanut CRSP and IPM CRSP (LAG-G-00-93-0053-00) is gratefully acknowledged.

[Received April 2005; accepted June 2006.]

References

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Alwang, J., and RB. Siegel. 2003. "Measuring the Impacts of Agricultural Research on Poverty Reduction." Agricultural Economics 29:1-14.

Byerlee, D. 2000. "Targeting Poverty Alleviation in Priority Setting for Agricultural Research." Food Policy 25:429-45.