Gender and Intra-household Bargaining

  Stockholm Doctoral Course Program in Economics Development Economics II: Lecture 8

  Gender and Intra-household

Bargaining

  Masayuki Kudamatsu

  IIES, Stockholm University

  

Big questions in this lecture

  LDCs?

• Mortality (“missing women”)

  cf. Anderson and Ray (2010) in class

• Educational attainment
• 79 girls for every 100 men in

  secondary/tertiary schools

• Labor market opportunities
>Political representation
• 15.9% of MPs: women

LDCs

  Following Duflo (2005), we ask 3 questions: Does poverty reduction reduce

• gender gap? Does empowerment of women
>cause development? How can empowerment of w

  1-1 Poverty causes gender gap?

• Poor HH may allocate less resources to girls than to boys
• This is very hard to verify, however.
• Cannot observe what each individual in a HH eats
• If observed, parents may change behavior

• Girls may need less than boys

• Deaton (1989) proposes a way to

  1-1a Deaton (1989)

  Estimate the cost of an additional kid in terms of adult goods by π

  ij

  = ∂q i /∂n j

  ∂q

  i

  /∂x · n x

  i

  : consumption of adult good i

• q

  j

  : # of kids of gender-age group j

• n
• x: total expenditures

  1-1a Deaton (1989) (cont.)

  ∂q /∂n

  i j n

  π = ·

  ij

  ∂q /∂x x

  i

• ij

  Does π differ between boys and

  girls of same age group? No in Cote I’voire (Deaton 1989) or

• in Pakistan (Deaton 1997)

  ⇒ In everyday life, no discrimination

  1-1b Evidence in times of crisis

  Rose (1999): mortality during

• droughts higher for girls in India

  Except for those HHs w/ assets to sell

• ⇒

  Insurance against risk / escape from poverty will help

  Miguel (2004): Old women

• murdered (“witch killing”) after crop failure in Tanzania Bjorkman (2008): Girls drop ou

  1-1c Lack of employment opportunities

• Rise of software industry in India in

  Munshi and Rosenzweig (2006):

• the ’90s

  ⇒ Return to education in English ↑

  ⇒ Enrollment for low-caste girls ↑ (more than low-caste boys) Boys: need to maintain the caste

• network for job search Girls: no such institutional constraints

  1-1c Lack of employment opportunities (cont.)

  Why matters?

  (1) Return to education for girls ↑ Jensen (2010)

• (2) Wife’s bargaining power ↑

  Woman’s preference for girl’s education and health, together with (2), may lead to less discrimination

  1-1d Summary

  Poverty reduction helps narrowing gender gap by making HHs less vulnerable to

• income shocks offering employment opportunities
• to women

  1-2 Does female empowerment cause development?

  Poverty reduction, however, seems

• not enough

  Missing women in South Korea &

• Taiwan today

  Female empowerment may be

• needed, but it’s costly (men suffer) If it brings about development,

  1-2a Mother’s education

  Literature finds its robust correlation

• w/ child health

  Strauss and Thomas (1995) for a

• survey

  Breierova and Duflo (2004) exploit

• Indonesia’s school expansion as exogenous change in education

  ⇒

  Result: Father’s education equally important for infant mortality in

  1-2b Income in the hands of women

  Literature repeatedly finds its

• correlation with child health

  

cf. Microfinance / CCT often target

women

  Duflo (2003) & Edmond (2006): use

• a rapid increase in 1990-93 of the pension benefits for black men aged 65+ & black women aged 60+

  1-2b Income in the hands of women (cont.)

  Duflo (2003): Girls with

• grandmother: taller than those without; no effect of living with grandfather or on boys Edmonds (2006): Boys with
• grandfather: more likely to attend secondary school

  1-2c Political representation

• Chattopadhyay & Duflo (2004)
• For randomly chosen rural villages in India since 1992, only women can

  become village chief (Pradhan)

• βR _j
• γD _ij

  R _j

• ε _ij
• Estimate Y _ij =

  α

  _i
• Y _ij

  : policy i in municipality j

• R _j

  

: reservation dummy

• D _ij

  : extent to which women care policy i more than men in municipality j (e.g. drinking water)

• Result: ˆ γ > 0

  1-2c Political representation (cont.)

• ij is not necessarily large for

  But D

  pro-development policies _ij

  For education / road, D is small (⇐

• Men travel more)
• Exploit close-elections for state

  Clots-Figueras (2010)

• legislature in India where woman barely wins against man Result: Female legislators invest in

  1-2d Summary

• Female empowerment may or may not promote development
• In each dimension, evidence is mixed once exogenous variation is exploited

  1-3 How to achieve female empowerment

• Perception bias against women may not go away with development e.g. “Stereotype threat” (Spencer et al.

  1999)

• Literature finds two effective interventions (both from India):

  (1) Cable TV (Jensen and Oster 2009) (2) Political reservation (Beaman et al. decision-making

  We saw gender gap appears to be

• related to wife’s bargaining power w/i HH via better employment opportunities We also saw income in the hands of
>women sometimes matters How do we think about t

  2-1 Unitary HH model

  Although a HH consists of multiple

• individuals, it’s often assumed that a HH maximizes a unique utility function One implication of such HH models:
• income earned by different members will be pooled

  

⇒ Who earns income should not affect

  As we saw some examples above,

• who earns income does matter empirically

  ⇒

  Appropriate to model HH decision-making as bargaining by HH members But it’s hard to observe

• intra-household bargaining process

  2-2 Collective HH model

  Chiappori (1992) proposes

• imposing only one restriction on the intra-HH bargaining process: Pareto optimality This can be modelled by assuming
• that HH solves the following problem:

  A B

  max u (x) + λu (x) _x

  A B

  max u (x) + λu (x) _x

  

�

x ≤ Y

  s.t. p λ: B’s relative bargaining power

• Relative income, local sex ratio (how

• easy to get re-married), etc.

  ⇒

  Who earns income does affect λ consumption via changes in

  2-3 Application of collective HH model

  Anderson and Baland (2002) use

• this model to show that married women in Kenyan slum participate in ROSCA because otherwise their husband consumes too much today

  2-4 Testing Pareto efficiency

  But is intra-HH resource allocation

• really Pareto-efficient? Udry (1996): No in Burkina Faso
• Plots owned by women yield less
• (and use less inputs such as fertilizer) than those owned by men, conditional on plot characteristics and HH-crop-year FEs

  2-4 Testing Pareto efficiency (cont.)

• Rangel and Thomas (2005) show

  Follow-up to Udry (1996):

• counter-evidence in Senegal & Ghana Akresh (2008): less inefficient if
• negative rainfall shocks Goldstein and Udry (2008): because
• women are less likely to hold office in charge of land allocation

  2-4 Testing Pareto efficiency (cont.)

  Is intra-HH resource allocation

• really Pareto-efficient? Bobonis (2009): Yes in Mexico
• By combining Bourguignon et al.
• (2009)’s method with exogenous variation in factors affecting λ (familiar

  to development economists):

  Progresa and rainfall

  2-4 Testing Pareto efficiency (cont.)

  Bourguignon et al. (2009): Denote HH demand function for

• good i by C i = ξ i (x, p, a, z)

  x: total HH income / expenditure

  p: price vector

• a: preference factors (age etc. )
• z: distribution factors (those affecting

  demand directly, not via preference/constraints) Bourguignon et al. (2009) (cont.): Assume: ∃i , k , ξ (x, p, a, z) is strictly

• i

  monotone in z k Denote one of such z k ’s by z

• 1

  ⇒ = ζ(x, p, a, z , C )

  z

  1 − 1 i

• j i

  Plug this into ξ (·) for j �= i

  ⇒ C = θ (x, p, a, z , C ) j − 1 i j

  (z-conditional demand) Bourguignon et al. (2009) (cont.): A necessary & sufficient condition

• for Pareto efficient allocation is

  i

  ∂θ (x, p, a, z

  1 , C i ) − j

  = 0, ∀j �= i, ∀k �= 1 ∂z k _i

  In other words, C summarizes all the

• information from z

  ⇐ z affects the location on the Pareto Bobonis (2009): Check this condition with Mexican

• HH data by estimating, for good j,

  j j j j � j

  C = α C + β z + γ x + a δ + ε

  j i

  2 j

• _i

  

C : child clothing consumption

₂ z : rainfall (income earned jointly)

• x: HH total expenditure
• _i
₁ C & x: instrumented by z (Pro
• treatment indicator: income earned by mother) & HH income

Bobonis (2009) (cont.): Findings Child clothing goes up with

• Progresa treatment (Table 4 Row 3)

  ⇒

  Assumption ( ξ(·): strictly monotonic in z

  1 ) satisfied j

  System OLS ( ε : allowed to be

• correlated across j’s)

  j ⇒

  Fail to reject the null that β = 0, ∀j

Other topics on gender/marriage

  Bride price / dowry

• See Anderson (2007) for a literature review

  _{Beaman, Lori et al. 2009. “Powerful Women: Does Exposure Reduce Bias?.” Quarterly Journal of Economics 124(4): 1497-1540. ! Anderson, Siwan, and Debraj Ray. 2010. “Missing Women: Age and Disease.” Review of Economic Studies 77: 1262-1300. !} Anderson, Siwan, and Jean-Marie Baland. 2002. “The Economics of Roscas and Intrahousehold Resource Allocation.” Quarterly Journal of Economics 117: 963-995. ! _{Identification..” Review of Economic Studies 76(2): 503-528. !}

_{BOURGUIGNON, FRANÇOIS, MARTIN BROWNING, and PIERRE-ANDR CHIAPPORI. 2009. “Efficient Intra-Household Allocations and Distribution Factors: Implications and}

_{453-503. !} Bobonis, Gustavo J. 2009. “Is the Allocation of Resources within the Household Efficient? New Evidence from a Randomized Experiment.” Journal of Political Economy 117(3):

Breierova, Lucia, and Esther Duflo. 2004. “The Impact of Education on Fertility and Child Mortality: Do Fathers Really Matter Less Than Mothers?.” NBER Working Paper 10513. !

  

_{Spencer, Steven J., Claude M. Steele, and Diane M. Quinn. 1999. “Stereotype Threat and Women's Math Performance, ,.” Journal of Experimental Social Psychology 35(1): 4-28. !}

_{Strauss, John, and Duncan Thomas. 1995. “Human Resources: Empirical Modeling of Household and Family Decisions.” In Handbook of Development Economics, Amsterdam:}

Rose, Elaina. 2010. “Consumption Smoothing and Excess Female Mortality in Rural India.” Review of Economics and Statistics 81(1): 41-49. ! _{Udry, Christopher. 1996. “Gender, Agricultural Production, and the Theory of the Household.” The Journal of Political Economy 104(5): 1010-1046. !} Elsevier Science B.V., p. 1883-2023. !