Targeting and Public Targeting and Public ExpenditureExpenditure

Margaret Grosh

ThemesThemes

General Issues– Goals– Measurement– Stylized facts

Applications to social safety nets– Comparison of instruments

TargetingTargeting

Goal -- to concentrate benefits among the neediest

Implication– some people benefit and others do not

AND/OR– needier get bigger benefit than less

needy

Benefits of targetingBenefits of targeting Assumptions

– 15 million population– 3 million poor– $150 million budget

No targeting– everyone gets $10– 80% of funds go to

the non-poor

Benefits of targetingBenefits of targeting Assumptions

– 15 million population– 3 million poor– $150 million budget

No targeting– everyone gets $10– 80% of funds go to

the non-poor

Targeting - Option I– only poor receive

$50– same budget

Benefits of targetingBenefits of targeting Assumptions

– 15 million population– 3 million poor– $150 million budget

No targeting– everyone gets $10– 80% of funds go to

the non-poor

Targeting - Option I– only poor receive $50– same budget

Targeting Option II– only poor receive $10– budget reduced to

$30 million

Stepping backStepping back

What is the role of broad-based vs targeted programs in poverty reduction?

Where is the distributional instrument placed?

How private is the good? Is goal (only) poverty reduction? What is the concept of poverty –

utility, income, capabilities?

Measurement Measurement (the usual morass of detail)(the usual morass of detail)

The counterfactual: pre-intervention welfare– Usual measurement problems

• Recording and valuing consumption• Comparing across time and space• Equivalence scales

– Behavioral change in response to provision• Labor supply• Consumption of goods/services• Private transfers

Measurement Measurement (the usual morass of detail)(the usual morass of detail)

The value of the benefit– Cost is not value (vaccines)– Costs hard to measure (data problem)– Values not same across hh (schools)– Quality differences (data problem)

Conventional measuresConventional measures

Errors of inclusion/exclusion– Simple– Discrete– Weighting issue

TARGETING ERRORS AND TARGETING ERRORS AND ACCURACYACCURACY

ACTUAL STATUS

POOR NON-POOR

CLASSIFIED

AS

POOR

NON-POOR

Error of Exclusion

Type I

Error of Inclusion

Type II

CORRECTLY

DENIED BENEFITS

GOOD TARGETING

INCORRECTLY DENIED BENEFITS

INCORRECTLY GIVEN BENEFITS

Conventional measuresConventional measures Errors of inclusion/exclusion

– Simple– Discrete– Weighting issue

Full distributional analysis of incidence and coverage / concentration coefficients and curves

Extended Ginis (Clert and Wodon, 2000)

Average vs marginal incidence

Stylized factsStylized facts Health, education as whole sectors usually mildly

progressive– Progressive as % of welfare– Less so absolutely

Primary > secondary > tertiary– Demographics of measure– Pyramid effect– Self-selection into private market

Food price subsidies absolutely regressive, relatively progressive

Transfers > health, education

0

10

20

30

40

50

60

70

80

PER

CEN

T

CHILE JAMAICA COSTA RICA PERU BOLIVIA

PRIMARY HEALTH PRIMARY EDUCATION TARGETED PROGRAMS

Share of Benefits Accruing to the Poorest 40 Percent, by Country and Sector

Applications to social safety netsApplications to social safety nets

What are reasonable expectations?

What do we know about options?

Targeting is a tool, not goalTargeting is a tool, not goal(I.e. must balance tradeoffs)(I.e. must balance tradeoffs) Benefits

– lower costs– greater impact

Errors of exclusion (undercoverage)

Costs– administrative– political economy– incentive

Errors of inclusion (leakage)

Administrative costsAdministrative costs

Targeting costs only a portion of total administrative costs

Usually more exact targeting imposes higher administrative costs

Just because costs exist doesn’t mean they aren’t worth paying

Incentive EffectsIncentive Effects OECD literature worries about work

disincentives from means tests, measures them

May be less important in some of our programs because:– not based on means test

• eligibility• benefit level

– incentive more to conceal income than reduce it– low level benefit, so incentives remain

Political EconomyPolitical Economy

Can affect:– support and budget for safety net– mix of programs– details of each

Reasons to support program– own present benefits– future benefits– benefits for others you care about– altruism, externalities– suppliers– Coalitions

Quantifying the TradeoffQuantifying the TradeoffStudy of 30 Latin American programs, late

1980s early 1990s (not contradicted to date)Tried to measure

– errors of inclusion– errors of exclusion– administrative costs

• total• of targeting

– qualitative information on requirements, options

Table 4.2 Types of Subsidized Social Programsin Grosh's Sample

TYPE OF GOVERNMENTSUBSIDIZED PROGRAM

NUMBER OF PROGRAMSIN THE SAMPLE

Delivery of food commoditiesor subsidies

8

Delivery of school lunches 3Delivery of food stamps 5Delivery of free or reduced-

cost health services or healthinsurance

3

Delivery of student loans orfee waivers

3

Delivery of cash 3Provision of jobs 2Delivery of day care 2Delivery of mortgages 1Total 30Source: Grosh 1995.

0

25

50

PER

CEN

T

HIGH MID

75

LOW

100

GENERAL FOOD

SUBSIDIES,N = 7

TARGETEDPROGRAMS,

N = 18

PRIMARYHEALTH CARE,

N = 11

PRIMARYEDUCATION,

N = 11

Share of Benefits Accruing to the Poorest 40 Percent, by Sector

INDIVIDUAL ASSESSMENT (15)INDIVIDUAL ASSESSMENT (15)

MEETS CRITERION

DO NOT MEET CRITERION

TARGETING

GROUP CHARACTERISTICS (9)GROUP CHARACTERISTICS (9)

TARGET GROUP

SELF-TARGETING (6)SELF-TARGETING (6)LONG WAITING LINES

WORK REQUIREMENT

STIGMA

USE OTHERPRODUCTS

0

25

50

PER

CEN

T

HIGH MID

75

LOW

100

INDIVIDUALASSESSMENT,

N = 9

GEOGRAPHICASSESSMENT,

N = 5

SELF-ASSESSMENT

N = 4

Share of Benefits Accruing to Poorest 40 Percent, by Targeting Mechanism

Errors of exclusionErrors of exclusion

Lacked data on participation ratesUnclear interpretation

– self-targeting (good) – errors of exclusion (bad)

budget, outreach, communications, logistics, etc. appear more important than mis-identification due to screening

GEOGRAPHICASSESSMENT,

N = 5

SELF-ASSESSMENT

N = 4

0

10

20

PER

CEN

T

HIGH MID

30

LOW

5

15

25

INDIVIDUALASSESSMENT,

N = 9

Total Administrative Costs as a Share of Total Costs, by Targeting Mechanism

GEOGRAPHICASSESSMENT,

N = 6

SELF-ASSESSMENT

0

10

20

PER

CEN

T

HIGH MID

30

LOW

5

15

25

INDIVIDUALASSESSMENT,

N = 7

Targeting Costs as a Share of Total Costs, by Targeting Mechanism

Figure 9: Targeting Cost Share and Benefits Accruing to Poorest 40 Percent

Share of Targeting Costs (%)

20

40

60

80

100

01 2 3 4

ConclusionsConclusions

progressivity of incidenceadministrative costs not prohibitiveno a priori ranking by mechanism

Self-TargetingSelf-Targeting Good or service available to all, but only

the poor choose to use Examples

– hard physical labor for low wages– broken rice, coarse bread, etc.– waiting times– stigma

May be difficult to find vehicle suitable for large transfers

Costs to beneficiaries reduce net benefits

Categorical targetingCategorical targetingAge (child allowances, non-contributory

pensions)Disability, unemploymentEthnicity (scheduled castes in India, Natives in

Canada)

Easy to medium administrativelyMay not be very precise

GeographicGeographic

More accurate the smaller the unit used

But a limit based on data, service delivery system, politics

More viable for services used daily than yearly

New tool merging census and survey data may make more accurate

Proxy means testProxy means test

Increasingly popular A synthetic score calculated based

on easily observed characteristics (household structure, location and quality of housing, ownership of durable goods)

At the complex end of requirementsIndicators tend to be static

Community-Based TargetingCommunity-Based TargetingUse existing local actor (teacher, nurse,

clergyman) or new civic committee to decide who gets what– local actor may have best information, but– structure may impinge on actors’

performance in their original local roles,– may generate conflict– capture by local elites still possible– little empirical evidence to date