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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