An Exploratory General-Equilibrium Analysis of Time, Gender, and Education In Ethiopia
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Transcript of An Exploratory General-Equilibrium Analysis of Time, Gender, and Education In Ethiopia
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An Exploratory General-Equilibrium Analysis of Time, Gender, and
Education In Ethiopia
Hans LofgrenDevelopment Economics Prospects Group
World Bank
Presentation for the DfID – World Bank Seminar “Integrating Gender into Country-Level Growth Analysis: Practical Tools and Analytical
Approaches,” London, June 2-3, 2008
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INTRODUCTION
• Purposes: – Method: develop MAMS (Maquette for MDG
Simulations) for gender– Empirical: explore gender policy in Ethiopia
• Outline1. MAMS
2. Ethiopia application
3. Conclusions
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1. MAMS
• Developed for MDG analysis; turned into general framework for country-level, medium-to-long-run development policy analysis.
• First application to gender.
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1. MAMS
Model Structure
• MAMS is an extended, dynamic-recursive computable general equilibrium (CGE) model designed for MDG analysis.
• MAMS is complementary to and draws extensively on sector and econometric research on MDGs.
• Motivation behind the design of MAMS: – An economywide, flexible-price model is required for
development strategy analysis.
– Standard CGE models provide a good starting point.
– But Standard CGE approach must be complemented by a satisfactory representation of 'social sectors'.
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1. MAMS
General Features
• Many features are familiar from other CGE models:– Computable solvable numerically– General economy-wide– Equilibrium
• optimizing agents have found their best solutions subject to their budget constraints
• quantities demanded = quantities supplied in factor and commodity markets
• macroeconomic balance
– Dynamic-recursive the solution in any time period depends on current and past periods, not the future.
– A “real” model: only relative prices matter; no modeling of inflation or the monetary sector.
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1. MAMS
MDGs
• Extended to capture the generation of MDG outcomes.• MAMS covers MDGs 1 (poverty), 2 (primary school
completion), 4 (under-five mortality rate), 5 (maternal mortality rate), 7a (water access), and 7b (sanitation access).
• The main originality of MAMS compared to standard CGE models is the inclusion of (MDG-related) social services and their impact on the rest of the economy.
• Social services (education at different levels, health, and water-sanitation) may be produced by the government and the private sector.
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1. MAMS
The engendered version of MAMS … (1)
• covers full time use (net of personal care time) of population in labor-force age, disaggregated by gender, education, activity (different GDP activities, home services, leisure).
• disaggregates the different education levels and their links to the labor market by gender
• nests the demand for labor – see figure. Rationale: Need to consider responses in employment by gender to changes in relative wages.
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1. MAMS
Labor nesting
Male Female Male Female Male Female
Less than completed secondary
Completed secondary
Completed tertiary
Aggregate
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1. MAMS
The engendered version of MAMS … (2)
• has special treatment of leisure and home services:– commodities disaggregated by gender and education– only demanded by the household– each commodity produced with one kind of labor as input (no
consideration of substitutability in production)– per-capita quantities from different labor types are rigid (limited
responses to changes in incomes and wages)
• has fixed total per-capita demand for home service outputs; labor time responds to productivity changes.
• Non-neoclassical treatment justified by the special nature of leisure and home services: – norms important in time allocation by gender and education– leisure produced and consumed by the same person.
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1. MAMS
The engendered version of MAMS … (3)
• Across all GDP activities, wage discrimination against females: – wage paid < marginal value product (MVP). – surplus (the gap) paid to male labor.
• Treatment justified by need to consider:– the fact that economic benefits of increasing female
employment > financial benefits reaped by female workers;
– impact or reduced discrimination (direct on earnings; indirect on broader indicators, considering differences in male and female spending patterns)
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2. Ethiopia application
• Development of database matching model characteristics: – disaggregating payments and accounts related to
labor and leisure in the SAM;– creating separate time accounts that match SAM
payments; and – disaggregating education-related data by gender
(accounting for the situation in the base-year and gender-specific responses to changes in the determinants of educational outcomes)
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2. Ethiopia application
Disaggregation for Ethiopia (1)
• Sectors (activities and commodities):– Government: education (four cycles); health,
water-sanitation; other infrastructure; other– Non-government GDP: agriculture, industry,
private health services, other private services– Non-government non-GDP: home services,
leisure (by gender and education)
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2. Ethiopia application
Disaggregation for Ethiopia (2)
• Factors– Labor (by gender and education)– Government capital (by government sector)– Private capital– Agricultural land
• Institutions– Household– NGO– Government– Rest of World
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2. Ethiopia application
Simulations: period and description
• Period: 2005-2030.
• Description: see table below.
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2. Ethiopia application
Description of simulationsName Description
basebusiness-as-usual scenario with 6% annual growth in real GDP at factor cost
edtxtax-financed expansion (increased quality) in education after 1st primary cycle
edsame as edtx except for that financing is provided by foreign grants
ed+eled + high male-female labor substitution elasticities in GDP activities
ed+el+hped+el + increased productivity growth in home service production
ed+el+hp+pped+el+hp + increased productivity growth in private GDP production
ed+el+hp+pp+rded+el+hp+pp + removal of wage discrimination against females
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2. Ethiopia application
Results: BASE
• Macro: – aggregates grow at rates in the range of 5-7%; – increased share of domestic taxes in GDP.
• Education: – enrollment grows more rapidly the higher the cycle and for
females; female/male GERs increase;• Labor:
– employment: female (in GDP) grows more rapidly than male; the higher the level of education, the more rapid growth.
– wages: female grow less rapidly than male at all education levels• Time use:
– for all groups, time share for GDP activities increase at the expense of home services; the reduction is larger, the higher the level of education, and much larger for females than males
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2. Ethiopia application
Results: EDTX (tax-financed education expansion)
• Macro: – dramatic increase in GDP share of domestic taxes (from 11% to
20%);– real GDP growth increases by 0.2 %-age points per year. – increased growth for government demand (1.4-1.8 %-age
points), decreased growth for private (by 0.2-0.4 %-age points)• Education: for secondary and tertiary, strong increases
in enrollment growth and GERs (by 8-11 %-age points)• Labor:
– employment: slight growth decline at the lowest education; more rapid growth at higher levels (esp. tertiary level and esp. for females)
– wages: inverse relation between changes in employment and wage growth
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2. Ethiopia application
Results: ED (aid-financed education expansion)
• Macro – compared to BASE:– no change in GDP share of domestic taxes; foreign aid GDP
share increases by 7.5 %-age points. – real GDP growth increases by 0.6 %-age points per year. – increased growth for government demand (1.7-2.1 %-age
points), increased growth for private (by 0.4-0.8 %-age points)
• Education – compared to EDTX: outcomes similar to but slightly stronger;
• Labor – compared to EDTX: – employment: only small changes in growth– wages: stronger growth across the board
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2. Ethiopia application
Results: ED+EL (less gender bias)
• Compared to ED, minimal changes except for relative male/female wages – see figure below.
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2. Ethiopia application
Results: ED+EL (less gender bias)
Wage growth (%) and gender bias
0
2
4
6
ed
ed+el
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2. Ethiopia application
Results: ED+EL+HP (increased home service productivity)
• Macro – compared to ED+EL: growth increases for GDP and all parts of domestic final demand (by 0.3-0.7 %-age points);
• Labor – compared to ED+EL: – Employment: increased supply of market labor, especially for
females with the least education– Wages: wages for most labor types grow more rapidly as a
result of the acceleration of over-all growth; downward pressure on wages for females with the least education;
• Time use – compared to ED+EL: home service shares decline (by 4-15 %-age points) in proportion to original shares of each labor type; most of the saved time moves into GDP production;
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2. Ethiopia application
Results: ED+EL+HP+PP (increased private GDP productivity)
• Macro – compared to ED+EL+HP: – real GDP growth reaches 7.9% (+0.7 %-age
points – growth gains for domestic final demand 0.2-
0.6 %-age points
• Labor – compared to ED+EL+HP: strong wage gains (0.5-0.6 %-age points for all labor types)
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Results: GDP
GDP at factor cost (% growth per year)
5
6
7
8
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Results: Secondary enrollment
GER, secondary (%)
0
10
20
30
40
50
60
2005 base edtx ed ed+el+hp+pp
male
female
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Results: Secondary wages
Wage growth, secondary (%)
012345
base edtx eded+el
ed+el+hp
ed+el+hp+pp
ed+el+hp+pp+rd
Male
Female
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Results: Secondary wage income
Wage income growth, secondary (%)
4
6
8
10
12
base edtx eded+el
ed+el+hp
ed+el+hp+pp
ed+el+hp+pp+rd
Male
Female
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3. Conclusions (1)
• Main results: – Growth in female higher education
accelerates GDP growth (esp. if financed by aid) and improves over-all welfare, including most MDG indicators;
– Rates of female wage growth depend on growth in educated labor demand and the removal of discrimination against women in wage and employment decisions.
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3. Conclusions
3. Conclusions (2)
• Future work (drawing on emerging micro evidence):– incorporate links between incomes under female
control and the allocation of spending across different types of consumption and savings;
– add female education indicators to the determinants of health and education outcomes.
• Such extensions make it possible to consider additional channels through which improved female education contributes to human development.