Equality of Opportunities from a Fiscal Perspective:

Education in Liberia

January 26, 2010

Jose Cuesta and Ana AbrasPRM PR

April 25, 2011

We measure EqOpp, so what?

Diagnostics serve several purposes: Understand distribution of opportunities Monitoring over time, across regions,

internationally Understand key obstacles to universal

access

BUT how to go beyond diagnostics? Fiscal side of policy interventions Pilot: education spending in Liberia

Education in Liberia: Spending

Liberia Education CSR2010 & PEMFAR’09:

– At 3.2% of GDP public education spending in Liberia is low compared to SSA averages

– But has doubled from 2004 to 2008 – International aid (2007): US$ 38 million– Household spending: US$ 27 million– Public spending on education: US$ 12.2 million– In 2008: US$ 15 million (aid) and US$ 23.6 million

Education in Liberia: Policies

WB CSR 2010 Policy Recommendations: • Increase enrolment at age 6: abolish entry exams• Narrow regional disparities: Prioritize spending into poor

areas; school lunches• Reduce gender disparities: provide scholarships to needy

girls in targeted areas• Increase budget in 2ry as enrolment increases• Improve quality of education: training & certification• Improve management: HHRR database for teachers linked

to payroll

Public Education in Liberia: Interventions

Sectoral interventions (FTI-Catalytic Fund 2010):– School construction– Text books for grades 4-9– School grants– School health (de-worming)– Management: community involvement and

payroll management– Revise teachers’ salaries and incentives to rural

areas

How can HOI fiscal analysis help?

• Supplementing the distributive dimension of sector diagnostics– From BIA to Opp BIA, focusing on opportunities

rather incomes/expenditures/wealth

• Linking (the fiscal side of) policy proposals with improvements in opportunities – Simulate the effect of budgetary changes and orcomposition on opportunities

What can and cannot do?

– The analysis focuses on fiscal dimension:• Amount and • Distribution of resources (by level, region, gender)

– Most useful to analyze increases in spending (increasing teachers’ salaries), targeting to poor areas or poor girls, elimination of fees, free text books, changes in international aid

– Cannot say much on issues such as de-worming, quality training, management reforms

MODELLING PROBABILITY

– Estimate a logit model, the dependent variable is the opportunity (attending school age 6-15) and independent variables are the circumstances (child gender, hh head gender, education and age, region, u/r, number of children in hh, single parent, mother alive, father alive. Use CWIQ 2007

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Type ID

Description Estimated Probability of access

1 Rural, female child, head with no primary 53.80%

2 Rural, male child, head with no primary 55.40% 3 Urban, female child, head with no primary 54.80% 4 Urban, male child, head with no primary 55.40% 5 Rural, female child, head with primary 67.50% 6 Rural, male child, head with primary 67.50% 7 Urban, female child, head with primary 74.80% 8 Urban, male child, head with primary 72.90%

Linking HOI with Fiscal PoliciesFiscal Simulations: Cost & efficiency implications of improving Educational Opportunities

• TRADITIONAL BIA:– What is the distribution of

public spending across income/asset levels?

• “OPP” BIA – What is the distribution of

public spending across opportunity groups?

SIMULATION:• How much would an

additional dollar spent on education affect the distribution of educational opportunities among children?

• How much would it cost to close access gaps across children?

Benefit Incidence Analysis and Opportunities

Benefit Incidence Analysis

STEPSFISCAL / SECTOR DATA• Public spending on education by

level (not possible by region)• Total beneficiaries • Unitary gross benefit (per

beneficiary)HH DATA• Private out of pocket contributions

per beneficiaryFINALLY• Unitary Net benefit per beneficiary

PEMFAR 2009

 Q1

(Poorest Q2 Q3 Q4

Q5(Richest)

Unit costhousehold

spending on education

Unit costgovernmentspending on education

Primary education 8.3 12.5 17.9 25.8 35.8 19.9 6.9Secondary education 20.1 33.6 45.6 54.9 74.8 48.2 84.5

Higher education 307.9 381.6 210.3 171.1 180.4 199.2 279.7

Total 13.8 20.9 29.7 41.6 57.6 33.4 21.8

Per Student Expenditure on education, by Quintile and Level of Education, 2007 (US$)

Distribution of unitary cost of primary school

Quintiles of wealth

-40

-30

-20

-10

0

1 2 3 4 5Quintiles of wealth

Unitary private spending Net benefitUnitary government transfer

Spending by quintile of wealth

Quintiles of opportunities

-60

-40

-20

020

1 2 3 4 5Quintiles of probability

Unitary private spending Net benefitUnitary government transfer

Spending by quintile of probability

Quintiles of wealth: Q1: -1.80 -.60; Q2 -.60 -.06; Q3 -.06 .38; Q4 .38 1.09; Q5 1.10 9.78. Quintiles of probability: Q1: 0-0.45; Q2: 0.45-0.52, Q3: 0.52-0.71; Q4: 0.71-0.85; Q5: 0.85-1.

Public spending in education is progressive but for the wrong reasons

Who is getting how much?Share of public spending on education captured by group and probability of access

0.0

5.1

.15

.2.2

5.3

1 2 3 4 5 6 7 8Type

Fraction of Total Benefit Fraction in the Population

Share of total benefit by type and share in the population

0.0

5.1

.15

.2.2

5.3

1 2 3 4 5Wealth Index

Fraction of Total Benefit Fraction in the Population

Share of total benefit by wealth quintile

Progressive but clearly not pro-poor: Opp BIA shows it more clearly

FISCAL POLICY SIMULATION

Sim- STEP 1: MODELLING PROBABILITY– Estimate a logit model, the dependent variable is the opportunity (attending

school age 6-15) and independent variables are the circumstances (child gender, hh head gender, education and age, region, u/r, number of children in hh, single parent, mother alive, father alive

Sim – STEP 2: SPENDING AS CIRCUMSTANCE – Logit model expanded to include gross unitary benefit as circumstance

– A new probability is estimated for each household

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FISCAL POLICY SIMULATION

Sim – STEP 3: POLICY SHOCK – Assuming each parameter constant in Step 2, new distributions of gross

unitary benefits are allocated to each household (i.e., higher $, new beneficiaries) and its probability re-estimated

Sim – STEP 4: ATTRIBUTION – The difference in probabilities between Step 3 and Step 2 is the attributed

effect on the policy simulation

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A few considerationsTechnical Issues

• ENDOGENEITY: Expected at aggregated level but not at hh level

• POLICY INSTRUMENT AS CIRCUMSTANCELevel of public spending exogenous to individual

Interpretation

• WHAT DOES THIS SIMULATION TELL US?From a hh level, the extent to which personal circumstances act as obstacles of an opportunityFrom a fiscal point of view, how spending (S) may counteract personal circumstances (D)

4 simulations

SIMULATION 1: MORE RESOURCES FOR ALL An additional 70% of public spending on education–thought perhaps as increasing teachers’ salaries- is transferred as it is right now

SIMULATION 2: NO FEES PAID BY HOUSEHOLDSAn additional subsidy to households that report paying fees

SIMULATION 3: NO NON-FEE COSTS An additional subsidy to households that report paying for non-feeitems

SIMULATION 4: REDISTRIBUTIONpublic spending on primary education from children in household inurban areas is redistributed among all children in household in ruralareas

Simulation Results

Simulation Results Baseline (pre shock) Sim 1 Sim 2 Sim 3 Sim 4

Probability: 63.00% 65.00% 65.00% 67.00% 63.50% HOI 57.00% 61.00% 59.60% 61.40% 56.50% Cost $11.45* $8** $5.5** $10.54** Urban:$-11.4 ** % increase of Rural:$3 ** public spending*** - 70.00% 48.03% 92.05% 0.00% Group Probability:

Urban**** 69.00% 70.00% 70.00% 70.00% 67.20% Rural**** 60.00% 63.00% 63.00% 65.00% 61.70% Type 1 53.89% 55.85% 56.61% 58.86% 55.37% Type 2 55.41% 57.74% 58.26% 60.59% 56.97% Type 3 54.84% 55.63% 56.03% 57.10% 53.64% Type 4 55.43% 56.66% 56.86% 58.10% 53.91% Type 5 67.53% 70.17% 70.07% 72.06% 68.94% Type 6 67.52% 70.23% 70.34% 72.56% 69.09% Type 7 74.89% 75.85% 75.74% 76.42% 73.94% Type 8 72.96% 75.08% 73.98% 74.81% 71.29% Note: * Total cost per capita for kids in public school. ** Extra per capita spending for kids in public school. *** Total public expenditure on primary and secondary education of US$12.2 million ****Numbers for rural and urban population

SIMULATION 2: NO FEES PAID BY HOUSEHOLDSAn additional subsidy to households that report paying fees

.5.6

.7.8

.9

1 2 3 4 5 6 7 8 Type

Pre-Intervention Post-Intervention

Probability of access by type - subsidy of school fees Smallest win: Urban female child with educated head

Largest win: Rural male child with educated head and Rural male child with non educated head

SIMULATION 3: NO NON-FEE COSTS An additional subsidy to households that report paying for non-fee items

Smallest win: Urban male child with educated head

Largest win: Rural male child with non educated head

.5.6

.7.8

1 2 3 4 5 6 7 8Type

Pre-Intervention Post-Intervention

Probability of access by type - increase in aid

Wrapping up1. Opp BIA provides a supplemental darker picture than the

traditional BIA

2. Circumstances act as serious obstacles to improve opportunities

3. “Policy matters”: simply increasing resources for all or taking away from some to give to others may not do the job

4. How policy is done will have determine the pattern of winners and losers, although simulations show that effects are not large.

Thank you!

jcuesta@worldbank.org

http://www.worldbank.org/poverty

HOI for Education OpportunitiesCWIQ 2007

HOI significantly different from coverage for 3 of 4 opportunities Much better picture for enrollment than starting or finishing

primary on time late entry into schools

020

4060

8010

0H

OI (

%)

Opportunity

HOI Attending School (6-11 yrs) HOI Attending School(12-15 yrs)HOI Start 1rt grade on time HOI Finish 6 gradeConfidence Interval Coverage

Source : Authors ' calculation with CWIQ 2007

Educational Opportunities

HH wealth, location and HH head characteristics (education. Gender) or child characteristics (age, gender) are most important for access to education opportunities

In 2010, preliminary results suggest ethnicity and religion seem to matter as well

Child characteristics

Head characteristics

HH characteristics

Locale

Orphan

Wealth

0

0.2

0.4

Which circumstances are most important for inequality of opportunity in education ?

Attend 6-11Attend 12-15Start 1 on timeFinish 6

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4060

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HOI Coverage

Attendance 6-11

Comparison with a selection of African countries -I(late 2000’s)

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8010

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HOI Coverage

Attendance 12-15

Liberia

Liberia lags behind most countries, but exhibits common patterns Attendance improves with age late entry into school in all countries Significant gap between HOI and coverage, esp. for lower age group

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HOI Coverage

Finish 6 grade

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HOI Coverage

Start primary on time

Comparison with a selection of African countries -II(late 2000’s)

Liberia

HOI and coverage low for all countries, and Liberia does better on both than some of the other countries

Evidence of late entry into school for all countries