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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. 1 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 3 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). 5 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 8 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 11 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) 12 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 13 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 14 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 15 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)