Transcript No Slide Title

Feminist Development
Economics
Irene van Staveren
(Institute of Social Studies, Erasmus University Rotterdam).
Seminar Once Upon a Time …. For Master of Arts Development Economics,
University of Warsaw, April 2010.
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Gender in the economy
 Gender = the culturally and socially constructed
differences between men and women.
 Gender inequality in: Incomes (Yf/Ym = 60%) and wages
(gap = 16% in formal sector), Human Development,
Decision Making, Division of labour
 Gender asymmetries in Institutional context: property
rights (inheritance, collateral for credit), marriage and
family law (individual contracting, child custody and child
tax benefits), fiscal and pension policies (OECD GID
online database)
Gender is often ignored in
economic analyses
 Gender is generally regarded as exogenous: f.e. female
labour force participation rate as a constraint on
sustainability of pension systems in EU
 Gender is sometimes regarded as a social impact variable:
women may benefit less than men from a particular policy:
f.e. breadwinner benefits in fiscal policy
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Invisibility of women due to lack of gender
disaggregation of variables
 Outcome variables
– Income inequality, time-use, poverty, etc.
 Market process variables
– Access and distortions (prices, segmentation)
 Public services variables
– Access, user fees, inequality in spending
 Institutional variables
– Property rights, market management, decision
making power
Gender is an endogenous
economic variable
 Influencing access to and control over resources, such as
land, education, wage income by formal and informal
institutions
 Shaping agency and choices, for example in segmented
labour markets between ‘blue’ and ‘pink’ jobs
 Driving macroeconomic trends such as through the female
labour force participation rate affecting wage levels, tax
base (aging population), exports
 Underlying a female intensive substantial unpaid economy
(often more work hours than paid labour).
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Disregard of the care economy: the
economy of unpaid work
 Care economy = part of the economy where scarce
resources are allocated through gifts of unpaid labour
time
 Gender biases in economic analysis due to ignoring care:
– missing 70% of world output
– female labour is assumed to be infinitely elastic
– disregarding the economic functions of the care economy:
• stabilising market volatility by providing a basic social
safety net
• (re)producing the labour force in short run and long run
• generating social capital (trust, responsibility, cooperation)
Inefficiency of gender
inequality in markets
hiring men
efficiency
discrimination
High
product
ivity
Low
product
ivity
hiring women
Inefficiencies from gender
inequality in markets
 Inefficiencies in the allocation of resources, for example in financial
markets: micro level
– Women have higher pay back rate in microfinance
– Loans to women generate higher wellbeing effects in households
– Women generate higher marginal returns on loan investments than men
 Inefficiencies in access to public services such as education: macro level
– If girls in sub-Saharan Africa would have had the same school enrolment
rates as in Asia, African economic growth rates could have been doubled
over the period 1960-1992
– Missing the MDG on gender equality by 2015 will result in GDP losses
between 0.1 and 0.3 percentage points
Mechanisms: combination of asymmetric institutions reflecting power, and
the law of diminishing marginal returns
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Inefficiency of gender
inequality
 Kenya: providing female farmers with equal
inputs (seeds, fertilisers, pesticides) and
education as male farmers raises yields at
household level over 20%
 Globally: when female/male rate of
education is less than 0.75, GNP is 25%
lower than in countries with less gender
inequality in education
 Latin America: elimination of gender inequality
in labour markets (discrimination in jobs and
wages) rises women’s wages by 50% and GDP
by 5%
 Tanzania: reducing time burdens of female
coffee and banana farmers increases household
income by 10%, labour productivity by 15% and
capital productivity by 44%
Gendered institutions and
access to resources
 Gendered institutions limit women’s:
– access to resources like education and jobs
– women’s agency even when they have access to
resources
 Resource model:
– RESi = C + β1FGIj + β2IFGIk + ε
 Achievement model:
– ACHl = C + β3FGIj + β4IFGIk + β5GDPln +
β6GDPlnSQ + β7RESi + ε
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Institutional variables
 Formal gendered institutions:
– Laws on parental authority (PA)
– Laws on violence against women (VIO)
– Women’s land rights (LR)
 Informal gendered institutions:
– Share of women marrying under 20 years old
(EM)
– Prevalence of female genital mutilation (FGM)
– Demographically missing women (MW)
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Estimation results resource model:
Independent
FMeducation
variables:
FGI
IFGI
Constant
Adjusted R2
N
Fnonagricultural LFP
-0.30***
-0.41***
(-3.63)
(-5.44)
-0.38***
-0.32***
(-4.50)
(-4.17)
***
***
(64.09)
(32.10)
0.36***
0.42***
(40.88)
(55.04)
142
153
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Estimation
results
achievement
model:
FMlife expectancy
Fdecision making power
GDPln
3.12** (2.56)
-0.60 (-0.53)
GDPlnSQ
-3.02** (-2.50)
0.74 (0.66)
FGI
-0.10 (-0.95)
-0.31*** (-3.09)
IFGI
-0.18* (-1.68)
-0.02) (-0.20)
Fnalf
0.35*** (3.63)
0.26*** (2.90)
FMedu
-0.16 (-1.63)
0.06 (0.60)
Constant
*** (4.07)
(0.74)
Adjusted R2
0.30*** (10.05)
0.42*** (14.75)
N
128
127
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Conclusions on endogenous role of
gendered institutions:
 Level of GDP is insufficient to explain women’s
empowerment
 For some achievements formal gendered
institutions are a constraint, whereas for other
achievements informal gendered institutions are a
constraint
 Women’s access to resources is insufficient for
women’s empowerment
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Macro economic example 1: Gender-biased
competitiveness
 Asian GDP growth is strongly export-driven,
which in turn can be explained by the combination
of a high female share in export employment
(75%) and the high gender-wage gap in Asia
(women’s wages as 50-65% of men’s wages)
 Two explanatory factors behind the correlation (R2
> 0.85) of gender wage gap and GDP growth
(Stephanie Seguino):
– Cost price reduction, increasing competitiveness
– Increase in profit share, increasing the resources
available for technological upgrading
Gendered growth model (Seguino):
 Growth equation: Y = A f(K, Lf, Lm, HCf, HCm)
Y = GDP; A = technology; f = function; K = capital; Lf and
Lm are female and male labour supply; HCf and HCm are
female and male human capital
=> Testing this equation shows that a large unexplained part
can be attributed to wage discrimination
 Technical change: A = C(1 + φt)eσWGAP
A = technical change; C = time-invariant effect; φ (phi)=
external effects; e = nominal exchange rate; σ (sigma)=
effect of gender wage differentials on growth
=> Testing this equation shows the important explanatory
power of the gender wage gap
Macro economic example 2: The unpaid
economy in macroeconomic analysis
 Nonlinear dynamic Keynesian growth cycle model
(Korkut Erturk and Nilufer Cagatay): substitution
of women’s paid work for unpaid work is pictured
as savings:
• propensity to save varies with the level of activity
• feminization of the labour force is countercyclical:
negative relation in investment function, through
lower female wages. which increases the
profit/wages ratio.
• savings function consists of the rate of capacity
utilization and unpaid production.
Gendered savings- and investment model
(Erturk and Cagatay)
 Capacity utilization as function of excess demand
(Investment minus savings):
Δu = α [i(u, Lf) – s(u, UW)]
u = rate of capacity utilization; α = constant; I = I/K; s =
S/K; Lf = female share in the labour force; UW =
women’s unpaid work
 Rate of feminization of the paid economy:
Δ Lf = β(u* – u)
u* = normal rate of capacity utilization; β = constant
Model outcomes:
 During a contraction of the economy, female labour time
expands, both in paid work (feminization of the labour
force) and in unpaid work (unpaid work to substitute for
consumption of market goods)
 If the feminization impact on investment is smaller than
the feminization impact on savings, feminization worsens
the contraction of the economy, whereas if the
feminization impact on investment is bigger than on
savings, this may stimulate economic recovery
Conclusion
 There is a two-way relationship between the economy and gender:
gender is often an endogenous variable; and gender inequality is not
only a possible effect of macroeconomic policy but may also limit the
effectiveness of macro economic policies
 Taking gender into account will help to:
– Improve economic theory and models, through:
•
•
•
•
gender disaggregation
gender variables
unpaid work as an economic sector
asymmetric institutions
– Improve policy effectiveness and gender equality (win-win), by:
• eliminating gender distortions in markets
• preventing moral hazard shifting risks to women’s unpaid work
• reaping returns on investment through redistribution of resources from males
to females at macro level (I, G, c versus s), meso level (institutions, f.e. labour
laws or property rights) and micro level (agricultural resources, credit,
education)