Gendered Spheres of Learning and Household Decision-Making over Fertility

This paper investigates whether information asymmetries within households about maternal health risk can explain persistent spousal disagreement over fertility in a high-fertility, high-maternal-mortality setting. The authors develop a theoretical model and conduct a randomized field experiment among approximately 500 couples in peri-urban Lusaka, Zambia, where the lifetime risk of maternal death is 1 in 59 women and the maternal mortality ratio is 398 deaths per 100,000 live births.

The central mechanism is a communication barrier that arises from conflicting fertility preferences between spouses. When husbands have higher desired fertility than wives (4.43 vs. 4.19 children on average in the study sample), wives who are better informed about maternal health risk lack the incentive to credibly transmit that information to their husbands. Strategic communication concerns — not a generically lower propensity of men to learn from women — drive this asymmetry. The model predicts a pooling equilibrium in which no informative communication flows from wives to husbands when preference divergence is sufficiently large.

The experiment randomized whether the maternal mortality information curriculum was delivered to the husband or the wife in each couple, with both spouses in all arms also receiving a family planning curriculum. This design isolates the incremental effect of the maternal mortality information and permits identification of direct versus spillover effects within the household.

Consistent with the model, treated husbands significantly update their beliefs about maternal health risk factors, and their wives also update — information flows from husbands to wives. By contrast, treated wives update their own beliefs, but their husbands do not update at all. The test that spillover effects are symmetric is rejected (p-value = 0.097 for risk factors index; p-value < 0.001 for direct vs. indirect effects on men). The communication asymmetry is most pronounced among husbands who, at baseline, want a child as soon as possible — precisely the households with the greatest preference conflict.

Both treatment arms reduce fertility. Households in which the husband is treated experience a 43% reduction in the probability of having a child or being pregnant in the year following the intervention. The fertility reduction is strongest when the wife faces higher ex ante risk based on her birth history, consistent with the model’s prediction that treatment effects are concentrated among households with high maternal health costs.

The transfers evidence is the key differentiator between the two arms. When the wife is treated, fertility declines but is accompanied by a significant reduction in transfers from husband to wife, consistent with the wife updating her own beliefs without being able to convey them to her husband, who then reduces compensation. When the husband is treated, fertility declines without the same reduction in transfers — and treated husbands report higher communication with their spouse about family planning and higher relationship satisfaction. This combination is consistent with the husband treatment resolving the information gap directly, enabling efficient contracting, whereas the wife treatment leaves the information asymmetry in place.

The study is conducted in informal settlements of Lusaka, a prime-age urban sample in which the average woman is 28 years old with 2.6 children at baseline. Scope conditions: results apply to a setting with very high maternal mortality, large baseline spousal fertility gaps, and strong traditional beliefs (55.5% of men cite marital infidelity as a leading cause of maternal complications). Generalizability to lower-risk or lower-preference-gap settings is explicitly circumscribed by the model’s comparative statics.

Q: What is the baseline gender gap in knowledge of maternal health risk? A: Men are less likely than women to identify high parity (72.0% vs. 77.7%) and advanced maternal age (74.3% vs. 84.6%) as risk factors. In seven hypothetical scenarios rating complication likelihood on a 0–10 scale, men report lower scores than women in six out of seven cases. Despite Zambia’s 1-in-59 lifetime maternal mortality risk, only 27.6% of men (vs. 53.4% of women) report having attempted to discuss maternal health risk with their spouse.

Q: What drives the gender gap in knowledge? A: The authors argue the gap stems from “gendered spheres of direct and indirect knowledge accumulation of maternal labor and delivery outcomes.” Women are embedded in social networks where maternal mortality episodes are more salient: 11.0% of women report knowing a close friend who died giving birth, vs. 6.8% of men knowing a close friend whose wife died. The gap widens with social distance to the victim, suggesting women’s networks give them systematically more exposure to maternal mortality events.

Q: How does the model explain the failure of within-household communication? A: The model places husband and wife preferences as minimizing the distance between realized fertility and their respective net fertility optima (ideal fertility minus weighted maternal health cost). When the husband’s ideal fertility is high enough, he makes transfers to induce the wife to bear more children than her private optimum. Given these incentives, a wife who is informed about high health costs has an interest in exaggerating the cost to extract larger transfers. Because the husband anticipates this, no informative communication occurs in equilibrium — the only equilibrium is a pooling equilibrium where the wife’s message is uninformative regardless of her true cost realization.

Q: What is the specific asymmetry in belief updating observed in the experiment? A: Among treated husbands, both husbands and their wives update beliefs about maternal risk factors — information flows from husband to wife. Among treated wives, only the wife updates; her husband does not. The Wald test rejects equal direct and indirect effects on men at p < 0.001 and rejects symmetric spillovers at p = 0.097 for the risk factors index. There is no symmetric restriction binding for women’s updating across arms.

Q: How large is the fertility effect and which arm drives it? A: Households in which the husband is treated experience a 43% reduction in the probability of having a child or being pregnant in the year following the intervention. This effect is described as of the same order of magnitude as other household-level interventions shown to reduce pregnancy (citing Ashraf, Field, and Lee 2014). The fertility reduction is strongest among households where the woman faces higher ex ante risk based on birth history, consistent with the model’s Prediction 5 that effects are concentrated where theta_j is high.

Q: How do transfers differ between the wife-treated and husband-treated arms? A: When the wife is treated, the fertility decline is accompanied by a significant reduction in transfers from husband to wife. When the husband is treated, the fertility decline is not accompanied by a similar reduction in transfers. The authors interpret this pattern as: wife treatment leaves the husband uninformed, so he reduces transfers when he observes her reducing fertility without understanding why; husband treatment resolves the information gap, allowing efficient renegotiation without penalizing the wife.

Q: Which husbands fail to update beliefs even when their wife is treated? A: Husbands who at baseline want a child “as soon as possible” do not update their beliefs in response to their wife’s treatment status. These men also reduce transfers to their wife more than other groups when she is treated. In the model, these are precisely the households with the highest conflict of interest (high alpha_H), where the pooling equilibrium prediction is sharpest.

Q: What is the role of traditional beliefs about maternal mortality? A: 55.5% of men and 42.0% of women report (without prompting) marital infidelity as a leading cause of maternal labor and delivery complications — greater weight than assigned to lack of healthcare and poor health status combined. This stigma directly reduces women’s willingness to raise concerns about birth complications with their spouse, reinforcing the communication barrier the model formalizes.

Q: What are the welfare implications of targeting men vs. women with information? A: The fertility reduction from husband treatment is not inferior to that from wife treatment, but husband treatment also produces improvements in marital surplus — treated husbands report higher communication with spouse about family planning, higher relationship satisfaction, and greater closeness — whereas wife treatment reduces transfers to the wife, indicating she bears a financial cost. The authors argue male-targeted information can reduce unmet need for family planning while enhancing rather than exacerbating household conflict.

Q: Does this paper provide field experimental evidence on strategic communication models? A: The authors claim this is the first field experimental evidence directly testing models of strategic communication (Crawford and Sobel 1982; Mailath 1987; Crawford 1998, 2019), wherein persistent preference differences and conflict of interest impede communication and beliefs updating. Prior tests of these models were conducted in the lab; this paper provides the first real-world behavioral test with consequential decisions (fertility) in a high-stakes setting.

Q: What is the unmet need for family planning in the study sample? A: Overall, 32% of women in the sample report not using modern contraceptives at baseline. Of the 33% of women who want no more children, 27% are not using any modern contraceptive (8% of the overall sample). Of the 52% of women who wish to delay giving birth by at least one year, 23% are not using any modern contraceptive (12% of the overall sample).

Q: How does the model characterize the husband’s partial internalization of maternal health costs? A: The husband’s utility function includes the maternal health cost theta_j scaled by delta (0 ≤ delta ≤ 1), capturing how much weight he places on his wife’s risk. When delta is sufficiently high and the husband’s ideal fertility (alpha_H) is sufficiently low, or when his disutility of transfers (gamma) is sufficiently low, informative communication can occur after the husband is treated. When delta is low, the husband discounts his wife’s risk and communication barriers are more severe regardless of treatment.

Maternal health cost (theta): A random variable representing the welfare cost borne by the wife from childbearing, including mortality risk and morbidity. In Zambia, distributed with a higher mean than the worldwide distribution. Enters the wife’s utility directly and the husband’s utility only scaled by delta, his degree of internalization of her cost.

Gendered spheres of learning: The paper’s term for the systematic differential in experiential exposure to maternal mortality outcomes between men and women, arising from gender-segregated social networks. Women witness maternal mortality events more directly through closer social ties, while men’s networks provide systematically less exposure.

Communication barrier (pooling equilibrium): The equilibrium outcome in the model where no informative signal is transmitted from an informed wife to her uninformed husband about the true realization of maternal health cost. Arises because the wife’s incentives to misreport are independent of the true cost realization, making any message uninformative when preference conflict is sufficiently large.

Intra-household information spillover: The transmission of information learned by one spouse to the other as a consequence of the treated spouse’s belief update. The paper documents asymmetric spillovers: information flows from treated husbands to their wives, but not from treated wives to their husbands.

Husband’s demand for children (alpha_H): The husband’s ideal fertility level, which governs the degree of preference conflict within the household. Baseline husband desire for a child as soon as possible serves as the empirical proxy for high alpha_H and is the key moderator of spillover and transfer effects.

Degree of internalization (delta): The parameter in the husband’s utility function (0 ≤ delta ≤ 1) capturing how much weight he places on his wife’s maternal health cost. When delta is high and gamma (disutility of transfers) is low, communication can occur in equilibrium after the husband is treated.

Unmet need for family planning: Women who wish to space or limit births but are not using modern contraception. In the study sample, 32% of women report not using modern contraceptives at baseline, with substantial shares among both those wanting no more children and those wishing to delay.