Week 7 Targeting and Food Aid Development Issues in Africa Spring 2006

Contents –Targeting Public Transfer –Self-targeting –Causes of emergencies –Research Example: Jayne, Strauss, Yamano, and Molla, “Targeting of food aid in rural Ethiopia: chronic need or inertia?” Journal of Development Economics, vol. 68: –Research Example: Yamano, Alderman, and Christiaensen, “Child Growth, Shock, and Food Aid in rural Ethiopia” American Journal of Agricultural Economics

Targeting Public Transfers J.S. Mill characterized the design of effective public transfers as “one of giving the greatest amount of needful help with the smallest amount of undue reliance on it.” Targeting has become important because governments are pressured to reduce poverty Three issues: administrative costs, incentive effects, and political economy considerations

Final income y The ideal solution z Aid (z - y) Tax aid tax Original income (y) Net Transfer -z 0 z Poverty line The marginal tax rate Slope is 1, suggesting an additional income completely replaces aid. Thus, poor households (y<z) have no incentives to earn income, while y <z.

Final income (y+z) y A uniform Transfer z aid tax Original income (y) Net Transfer (z) -z 0 z zSlope is 0, suggesting an additional income is an additional unit increase. Thus, poor households (y<z) have incentives to earn income.

Administrative costs Targeting is not Free! To target well, the provider needs to have good information about potential recipients. It is, however, costly to obtain good information about who should be eligible and how much they should receive. R: the revenue required to implement a program A: administrative costs NP: leakages to the non-poor P: the effective transfer to the poor R = A + NP + P Targeting: F = P / (P + NP) The Administrative costs as a proportion of the revenues are C = A / (A + NP + P) C F 1 0

Incentive problems: notice that under the ideal targeting, the poor do not have any incentive to work. Targeting using indicators: assessing income is extremely difficult in developing worlds. Thus, programs use indicators, such as household size, land size, or assets. One of the indicators could be region.

Regional Targeting To reduce the targeting costs, while maintaining a certain level of targeting, regional targeting is used: Example: Suppose that there are two regions: Region A: 80 percent poor, population size N Region B: 20 percent poor, population size N Scenario 1: Uniform distributions of aid in both regions A and B achieve 50 % targeting Scenario 2: Uniform distributions of aid in region A only achieve 80 % targeting Scenario 2: Uniform distributions of aid in region B only achieve 20 % targeting Concentrating the distributions only in region A reduces the administrative costs also.

Self-targeting Workfare: a screening argument and a deterrent argument A screening argument: a work requirement can be used to discourage the non-needy who have high opportunity costs A deterrent argument: imposing a work requirement may encourage certain kinds of behavior. Participants may invest in skill formation. Transfers in kind: by providing poor quality goods, the programs can select the poor who can not afford better quality goods.

Food-for-Work Program in Northern Ethiopia

Conflicts and Famine Most of Africa’s recent famines occurred within the context of armed conflicts (see handouts). Without conflicts, relief systems can prevent famines by using food aid and other means (e.g., food crisis in Southern Africa in 2002; flood in Mozambique in 2001). Thus, conflicts preventions and resolutions are among effective famine preventions and mitigation efforts.

Food Aid: An Example of Food Crisis in Southern Africa –USAID estimated that up to 14.4 million people in six southern African countries (Lesotho, Malawi, Mozambique, Swaziland, Zambia, and Zimbabwe) would need food aid between 2002 and 2003 because of droughts. –This food crisis in Southern Africa was considered as worsened because farming system has been weakened by the HIV/AIDS epidemics. –See handouts. –Some evidence in next slides.

Cereal Production per capita (Mt / person) in Southern Africa in

Food Aid per capita (Mt / person) in Southern Africa in

Sum of crop production and Food Aid per capita in Southern Africa in

Research Example “Targeting of food aid in rural Ethiopia: chronic need or inertia?” Journal of Development Economics, vol. 68: How well is food aid targeted to the poor? Regional vs. Household targeting: Simulation Results

Food Aid Targeting Food aid is only weakly targeted to poor regions and households. Free Distribution is better targeted to the poor than Food For Work. Past food-aid reception matters: Chronic need or Inertia? Regional or Household Targeting?

Simulation: Various Targeting Scenarios Targeting Scenarios Total # of Recipient Household s Per Capita Income Quartile PoorLower Middle Upper Middle Least Poor Actual Targeting Regional Two stage A Two stage B Household Total2,

Regional vs. Household Targeting Regional Targeting: –Cost effective (information and implementations) –But high degrees of errors A combination of two with better targeting at both levels is recommended

Food Aid Distribution

Table 1 Food Aid Distribution in Ethiopia Descriptive Analysis - The EA-level Analysis EAs with Food Aid EAs w/o Food Aid Number of EAs Annual Expenditure852 Birr1,111 Birr * Food Aid Reception22.5 Birr0 Birr ** Damaged Plot Areas30.2 %16.7 % ** Need Assessment %8.9% ** Long-run Rainfall866.3mm1017mm ** C.V. of Rainfall **

Results on Food Aid Allocation (Tobit) –Need Assessment Positive –C.V. of Long-run RainfallPositive –EA-level ExpenditureNegative –Peri-UrbanNegative –Damaged Plot AreasPositive Shocks account for only 13 percent of allocation Inertia and long-run variables account for 87 percent

Child Growth in Height “Child Growth, Shock, and Food Aid in rural Ethiopia” American Journal of Agricultural Economics, by Yamano, Alderman, and Christiaensen

Figure 1. Child Growth in a Six-Month Period and Food Aid

Table 5 Child Growth in Height: Children aged 6-24 months OLS IV EA-level variables P.C. Food Aid (Birr) A (2.24)* (2.23)* Damaged Plot Areas (ratio) (1.84) (2.16)* (2.50)* Child-level variables Initial Height (9.11)** (8.92)** (8.47)** Female Education0.166 (2.36)* (2.34)* (2.29)* Number of children1,083

Implications Plot damage has a negative impact on child growth –The average proportion of damaged plot areas in 1995/96 is 21 percent, indicating a 0.18 cm reduction (2.7%) –50 percent >> 0.88 cm reduction (13%) Food aid has a positive impact on child growth –A 1.6 cm increase in a typical food-aid-receiving community A Simulation: 1% plot damage –0.38 Birr more food aid, which increases growth by 0.027cm –0.018 cm slower growth –Thus, food aid should be able to off-set the potential negative impact of plot damage

Conclusions Shocks reduce child growth, especially among children aged 6 to 24 months Food aid has a positive impact on child growth and appears to be able to off-set the shocks Yet, the child stunning remains high Further studies are needed to understand why this is the case –Chronic factors (poor nutrition and health services) –Transitory factors (shocks and responses)