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An Exploratory General-Equilibrium
Analysis of Time, Gender, and
Education In Ethiopia
Hans Lofgren
Development 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
•
Outline
1. 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.
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
Aggregate
Less than
completed
secondary
Male
Female
Completed
secondary
Male
1. MAMS
Female
Completed
tertiary
Male
Female
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)
1. MAMS
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)
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 simulations
Name
base
edtx
ed
ed+el
ed+el+hp
ed+el+hp+pp
ed+el+hp+pp+rd
Description
business-as-usual scenario with 6% annual growth in real
GDP at factor cost
tax-financed expansion (increased quality) in education
after 1st primary cycle
same as edtx except for that financing is provided by
foreign grants
ed + high male-female labor substitution elasticities in
GDP activities
ed+el + increased productivity growth in home service
production
ed+el+hp + increased productivity growth in private GDP
production
ed+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
6
4
ed
2
ed+el
0
ry
ry
ry
ry
ry
ry
a
a
a
a
a
a
i
i
t
d
t
d
d
d
r
n
n
n
e
er
t
on
o
o
t
o
,
c
c
c
c
,
e
e
le
s
se
se
se
al
a
,
,
<
<
M
e
e
m
l
,
l
e
a
a
e,
le
l
F
a
m
M
a
M
Fe
Fem
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 0.20.6 %-age points
• Labor – compared to ED+EL+HP: strong
wage gains (0.5-0.6 %-age points for all
labor types)
2. Ethiopia application
Results: GDP
GDP at factor cost (% growth per year)
8
7
6
5
e
bas
edt
x
ed
el
+
d
e
+hp
l
e
ed+
+pp
p
h
l+
e
+
ed
Results: Secondary enrollment
GER, secondary (% )
60
50
40
male
30
female
20
10
0
2005
base
edtx
ed
ed+el+hp+pp
Results: Secondary wages
Wage growth, secondary (% )
5
4
3
2
1
0
Male
Female
e
ba s
ed tx
ed
p
p
el
rd
ed + d +el+h l+hp+p p+pp+
e
e
l+ h
e
ed +
+
ed
Results: Secondary wage income
Wage income growth, secondary (% )
12
10
Male
8
Female
6
4
e
ba s
ed tx
ed
p
p
el
rd
ed + d +el+h l+hp+p p+pp+
e
e
h
ed + d +el+
e
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 (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.
3. Conclusions