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Equality of Opportunities from a Fiscal Perspective: Education in Liberia . Jose Cuesta and Ana Abras PRM PR April 25, 2011 . January 26, 2010. We measure EqOpp , so what?. Diagnostics serve several purposes : Understand distribution of opportunities - PowerPoint PPT Presentation
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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
i
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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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ii
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^)1( iTP
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
simi
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iTP
iTP
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^
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^^)1()1( i
sim
i TPTP
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.
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
020
4060
8010
0%
moz
ambi
que
liber
ia
tanz
ania
zam
bia
rwan
da
mad
agas
car
mal
awi
ugan
da
ghan
a
nam
ibia
HOI Coverage
Attendance 6-11
Comparison with a selection of African countries -I(late 2000’s)
020
4060
8010
0%
liber
ia
mad
agas
car
moz
ambi
que
rwan
da
tanz
ania
ghan
a
mal
awi
zam
bia
ugan
da
nam
ibia
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
020
4060
8010
0%
rwan
da
moz
ambi
que
tanz
ania
liber
ia
ugan
da
mad
agas
car
mal
awi
zam
bia
ghan
a
nam
ibia
HOI Coverage
Finish 6 grade
020
4060
8010
0%
tanz
ania
rwan
da
liber
ia
moz
ambi
que
zam
bia
ghan
a
nam
ibia
ugan
da
mad
agas
car
mal
awi
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