Gender, Intra-household Decisionmaking, and the Demand for Children:

Evidence from a Field Experiment in Lusaka, Zambia

Nava Ashraf Erica Field Jean Lee(Harvard University)

Motivation

• What explains “excess fertility” and unmet need for contraception? – Access to Contraception– Intra-Household bargaining

• In many settings, well-documented spousal differences in demand for children

Highly relevant for family planning policy

• What kind of economic and social impact does increasing a woman’s control over fertility through contraceptive choice have?

This Project

• Field experiment with public health clinic in Lusaka, Zambia designed to lower access barriers (mainly cost) to modern contraceptive methods, conducted in July-August 2007

• Offered random subset of women a voucher that facilitated access:– Appointment with dedicated family planning nurse at

government clinic – Guaranteed access to all forms of hormonal contraception,

including two forms of long-term methods previously unavailable (one stocked out for years, one never made widely available)

– No cost, no waiting time

• Within treatment group, randomized whether voucher was given to woman alone or in the presence of her husband

Voucher

Setting• Peri-urban low- and middle-income neighborhoods in Lusaka,

Zambia

• High total fertility, high maternal mortality

• Contraceptive methods available in public clinics, private clinics, pharmacies

• In practice, rationed by wait times, stock-outs

• Official policy that husband consent is not required to obtain contraceptives; in rural areas husband must consent

Field experiment details• Study carried out in cooperation with the Society for Family

Health and Chipata Clinic

• Women in study recruited from neighborhoods in catchment area of Chipata Clinic by community health workers– Sampled from birth records– Randomly selected house numbers

• Women eligible to participate in the study if they– Were between the ages of 18 and 45– Had last given birth between January 2004 and

December 2006– Not currently pregnant or sterile/infertile– Not known to have health conditions for which hormonal

contraceptives contraindicated

Experimental Design

• Women randomized into treatment arms (stratified using data from recruitment visit)

– Control arm took baseline survey, received info about HIV and condom use

– Treatment arm did above, and additionally received info about family planning methods and voucher

• Treatment women randomized into individual vs. couples arms (stratified using data from baseline survey)

– Individual arm received voucher alone

– Couples arm received voucher together with husbands

Introduces random variation in degree of asymmetric information – i.e. wife’s ability to hide contraceptive choice

Potential Selection Issue

• Approximate 25% of women in both treatment arms dropped from experiment after baseline survey because husbands could not be found

• These women necessarily excluded from experiment, but after randomization occurred

• Administered a “wife-only” treatment in order to stay in larger contraceptive experiment , but had to be dropped from couples vs. individuals experiment

Approach to Selection• Check for differential attrition across treatment and control

– no apparent differences

• Run ITT (compare outcomes according to initial assignment), but effects are very diluted

• Can exclude women whose husbands could not be found since no reason to anticipate differential attrition across treatment:– In protocol, women not told whether they would receive

individual or couples treatment– In theory, enumerators/CHW didn’t know assignment

Data

• In-depth background survey of wife

• Short background survey of husband

• Administrative data from clinic on use of voucher and method chosen during voucher appointment

• Coming up: Follow-up survey on methods used, fertility outcomes, economic/social impacts.

The 2008 Injectable Scare in Zambia

Main regression:

• Outcomes of interest:

– Use of voucher– Choice of “concealable” method

UsedVoucher i 0 1 Icouplesi X i i

Hus. Wants no

more

Hus. Wants more

Variable (i) (II) (III) (IV)

Assigned to Couples Treatment -0.089** -0.092*** -0.019 -0.121***(0.035) (0.035) (0.065) (0.043)

Age 0.003 0.001 0.007(0.006) (0.010) (0.008)

Husband's Age -0.001 0.007 -0.006(0.004) (0.006) (0.005)

Highest schooling completed -0.001 -0.008 0.001(0.007) (0.011) (0.001)

Husband's highest schooling completed -0.008 0.001 -0.013(0.007) (0.019) (0.008)

Average monthly income (USD) -0.000* -0.000 -0.000(0.000) (0.000) (0.000)

Husband's average monthly income (USD) -0.000 -0.000 0.000(0.000) (0.000) (0.000)

Number of living children 0.011 -0.023 0.022(0.020) (0.041) (0.027)

Ideal number of children -0.007 -0.014 0.001(0.014) (0.026) (0.018)

Husband's ideal number of children 0.001 0.009 0.003(0.007) (0.031) (0.007)

Currently using pill, IUD, injectable or implant 0.028 0.052 0.029(0.036) (0.066) (0.044)

Constant 0.507*** 0.498*** 0.390*** 0.533***(0.024) (0.129) (0.233) (0.159)

Observations 828 796 257 537R-squared 0.008 0.018 0.026 0.032

TABLE VEffect of Assignment to Couples Treatment on Voucher Use

Full Sample

* significant at 10%; ** significant at 5%; *** significant at 1%

ITT Results

• 6 percentage points, significant at 5% level

Full SampleVariable (i)

Assigned to Couples Treatment -0.060**(0.030)

Constant 0.435***(0.021)

Observations 1082R-squared 0.004

TABLE IVEffect of Assignment to Couples Treatment on Voucher Use

* significant at 10%; ** significant at 5%; *** significant at 1%

Results on contraceptive use

• 6.4 percentage point diff in choice of any long-term method and 6.2 percentage point diff in choice of implants or injectables (highlighted study methods)

Full SampleVariable (i)

Assigned to Couples Treatment -0.064**(0.031)

Constant 0.341***(0.045)

Observations 828R-squared 0.004

Effect of Assignment to Couples Treatment on Long-term method

* significant at 10%; ** significant at 5%; *** significant at 1%

Interpretation of first estimate1. Effect of access lower when harder for wife to hide actions

• Other possible interpretations:2. Lack of information/more negative prior beliefs about contraceptives

among men3. Distrust of surveyor/community health worker teams or of clinic4. General non-cooperative decision-making about consumption, unrelated

to fertility

• Distinguish between stories by looking at heterogeneity in treatment effects by individual and household characteristics

• In particular, divide sample according to whether husband wants more children than wife– Anticipate no negative effect among couples in which husband wants no

more children

Hus. Wants no

more

Hus. Wants more

Variable (i) (II) (III) (IV)

Assigned to Couples Treatment -0.089** -0.092*** -0.019 -0.121***(0.035) (0.035) (0.065) (0.043)

Age 0.003 0.001 0.007(0.006) (0.010) (0.008)

Husband's Age -0.001 0.007 -0.006(0.004) (0.006) (0.005)

Highest schooling completed -0.001 -0.008 0.001(0.007) (0.011) (0.001)

Husband's highest schooling completed -0.008 0.001 -0.013(0.007) (0.019) (0.008)

Average monthly income (USD) -0.000* -0.000 -0.000(0.000) (0.000) (0.000)

Husband's average monthly income (USD) -0.000 -0.000 0.000(0.000) (0.000) (0.000)

Number of living children 0.011 -0.023 0.022(0.020) (0.041) (0.027)

Ideal number of children -0.007 -0.014 0.001(0.014) (0.026) (0.018)

Husband's ideal number of children 0.001 0.009 0.003(0.007) (0.031) (0.007)

Currently using pill, IUD, injectable or implant 0.028 0.052 0.029(0.036) (0.066) (0.044)

Constant 0.507*** 0.498*** 0.390*** 0.533***(0.024) (0.129) (0.233) (0.159)

Observations 828 796 257 537R-squared 0.008 0.018 0.026 0.032

TABLE VEffect of Assignment to Couples Treatment on Voucher Use

Full Sample

* significant at 10%; ** significant at 5%; *** significant at 1%

Summary of preliminary findings

• Evidence for importance of differences in fertility preferences • Effect only for women whose husbands want more

children• Some evidence that effect is increasing in degree of

disparity between husband’s and wife’s preferences• Effect only for younger women, not for older women with

completed fertility

• No evidence that this reflects differences in information, priors (if anything, effect is not increasing in education)

• No evidence that this reflects general conflict over consumption

Implications• More evidence against unitary model of household

• Possible evidence of inefficiency in household decisions over fertility when asymmetric information regarding family planning choices

• Spousal disagreement may account for significant portion of unmet need for contraception

Policy:• Technologies or policies that shift control of fertility from men to

women may lead to reductions in fertility • Male Involvement programs in Family Planning that only involves

informing men may undermine family planning objectives.

Further Work• One-year follow-up survey:

– Retrospective history of contraceptive use– Fertility outcomes

Very important to be able to describe what happened after injectable scare on access and contraceptive use, as well as measuring the impact of intervention.

Will also look at range of short-run and long-run individual and household outcomes (comparing treatment and control women), including:

– Attitudes/beliefs about future, mental and physical health– Female labor market outcomes– Children’s outcomes

• Policy Dialogue in Zambia: June 2009

Other data:

• Measures of degree of cooperation in decisions about other consumption

1. Answer to hypothetical question: “If you were given 5000 kwacha, how much of it would you keep hidden from your husband?”

2. Transfers between partners in last week relative to total household income (future)