An Exploratory General-Equilibrium Analysis of Time, Gender, and Education In Ethiopia Hans Lofgren Development Economics Prospects Group World Bank Presentation.

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

INTRODUCTION

• Purposes: – Method: develop MAMS (Maquette for MDG

Simulations) for gender– Empirical: explore gender policy in Ethiopia

• Outline1. MAMS

2. Ethiopia application

3. Conclusions

1. MAMS

• Developed for MDG analysis; turned into general framework for country-level, medium-to-long-run development policy analysis.

• First application to gender.

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'.

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.

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.

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.

1. MAMS

Labor nesting

Male Female Male Female Male Female

Less than completed secondary

Completed secondary

Completed tertiary

Aggregate

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.

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)

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)

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)

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

2. Ethiopia application

Simulations: period and description

• Period: 2005-2030.

• Description: see table below.

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

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

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

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

2. Ethiopia application

Results: ED+EL (less gender bias)

• Compared to ED, minimal changes except for relative male/female wages – see figure below.

2. Ethiopia application

Results: ED+EL (less gender bias)

Wage growth (%) and gender bias

0

2

4

6

ed

ed+el

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;

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)

Results: GDP

GDP at factor cost (% growth per year)

5

6

7

8

Results: Secondary enrollment

GER, secondary (%)

0

10

20

30

40

50

60

2005 base edtx ed ed+el+hp+pp

male

female

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

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

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.

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.