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Week 7 Targeting and Food Aid

Development Issues in Africa

Spring 2006

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Contents– Targeting Public Transfer– Self-targeting– Causes of emergencies– Research Example: Jayne, Strauss, Yamano, and Moll

a, “Targeting of food aid in rural Ethiopia: chronic need or inertia?” Journal of Development Economics, vol. 68:247-288

– Research Example: Yamano, Alderman, and Christiaensen, “Child Growth, Shock, and Food Aid in rural Ethiopia” American Journal of Agricultural Economics

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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

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Final income

y

The ideal solution

z

Aid (z - y)

Tax

aid

tax

Original income (y)

Original income (y)

Net Transfer

-z

0z

Poverty lineThe marginal

tax rate

Slope is 1, suggestingan additional income completely replaces aid. Thus, poor households (y<z) have no incentives to earn income, while y <z.

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Final income (y+z)

y

A uniform Transfer

zaid

tax

Original income (y)

Original income (y)

Net Transfer (z)

-z

0z

z Slope is 0, suggestingan additional income is an additional unit increase. Thus, poor households (y<z) have incentives to earn income.

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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

10

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• 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.

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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.

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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.

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Food-for-Work Program in Northern Ethiopia

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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.

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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.

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0.0

50.0

100.0

150.0

200.0

250.0

1997 1998 1999 2000 2001 2002 2003

Angola Lesotho Malawi MozambiqueNamibia Swaziland Zambia Zimbabwe

Cereal Production per capita (Mt / person) in Southern Africa in 1997-2003

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Food Aid per capita (Mt / person) in Southern Africa in 2000-2003

0.0

5.0

10.0

15.0

20.0

25.0

2000 2001 2002 2003

Angola Lesotho Malawi MozambiqueNamibia Swaziland Zambia Zimbabwe

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0.0

50.0

100.0

150.0

200.0

250.0

2000 2001 2002 2003

Angola Lesotho Malawi MozambiqueNamibia Sqaziland Zambia Zimbabwe

Sum of crop production and Food Aid per capita in Southern Africa in 2000-2003

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Research Example

“Targeting of food aid in rural Ethiopia: chronic need or inertia?” Journal of Development

Economics, vol. 68:247-288

• How well is food aid targeted to the poor?• Regional vs. Household targeting:

Simulation Results

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05

00

10

00

Fo

od

Aid

40

00

60

00

80

00

10

00

01

20

00

Gra

in P

rod

uct

ion

1975 1980 1985 1990 1995 2000 2005Year

Grain Production Food Aid

Grain Production and Food Aid in Ethiopia

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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?

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Simulation: Various Targeting Scenarios

Targeting Scenarios

Total # of

Recipient Househol

ds

Per Capita Income Quartile

Poor Lower Middle

Upper Middle

Least Poor

Actual Targeting

570 177 180 134 79

Regional 581 245 155 116 65

Two stage A

590 259 163 114 54

Two stage B

610 279 167 113 51

Household 571 325 185 51 10

Total 2,867 717 717 717 716

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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

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Food Aid Distribution

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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 116 415

Annual Expenditure 852 Birr 1,111 Birr *

Food Aid Reception 22.5 Birr 0 Birr **

Damaged Plot Areas 30.2 % 16.7 % **

Need Assessment 1984-88 28.8 % 8.9% **

Long-run Rainfall 866.3mm 1017mm **

C.V. of Rainfall 0.324 0.267 **

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Results on Food Aid Allocation (Tobit)

– Need Assessment 1984-88 Positive– C.V. of Long-run Rainfall Positive– EA-level Expenditure Negative– Peri-Urban Negative

– Damaged Plot Areas Positive

• Shocks account for only 13 percent of allocation• Inertia and long-run variables account for 87 percent

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Child Growth in Height

“Child Growth, Shock, and Food Aid in rural Ethiopia” American Journal of Agricultural Economic

s,

by Yamano, Alderman, and Chri

stiaensen

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Gro

wth

in H

eig

ht

(cm

)

Initial Age in Month12 24 36 48 60

4

5

6

in a

six

-mo

nth

pe

rio

d7

Without Food Aid

With Food Aid

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

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Table 5 Child Growth in Height: Children aged 6-24 months

OLS OLS IV

EA-level variables

P.C. Food Aid (Birr)A 0.028

(2.24)*

0.070

(2.23)*

Damaged Plot Areas (ratio)

-1.213

(1.84)

-1.433

(2.16)*

-1.763

(2.50)*

Child-level variables

Initial Height -0.261

(9.11)**

-0.255

(8.92)**

-0.248

(8.47)**

Female Education 0.166

(2.36)*

0.164

(2.34)*

0.161

(2.29)*

Number of children 1,083 1,083 1,083

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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

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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)