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This paper estimates the causal effects of family size on girls' education in Mexico, exploiting prenatal son preference as a source of random variation in the propensity to have more children within an Instrumental Variables framework. It finds no evidence of family size having an adverse effect on education. The paper then weakens the identification assumption and allows for the possibility that the instrument is invalid. It finds that the effects of family size on girls' schooling remain extremely modest at most. Families that are relatively large compensate for reduced per child resources by increasing maternal labour supply.

ObjectiveParents may rely on information provided by extended family members when making decisions concerning the health of their children. We evaluate whether extended family members affected the success of an information intervention promoting infant health.MethodsThis is a secondary, sequential mixed-methods study based on a cluster randomised controlled trial of a peer-led home-education intervention conducted in Mchinji District, Malawi. We used linear multivariate regression to test whether the intervention impact on child height-for-age z-scores (HAZ) was influenced by extended family members. 12 of 24 clusters were assigned to the intervention, in which all pregnant women and new mothers were eligible to receive 5 home visits from a trained peer counsellor to discuss infant care and nutrition. We conducted focus group discussions with mothers, grandmothers and peer counsellors, and key-informant interviews with husbands, chiefs and community health workers to better understand the roles of extended family members in infant feeding.ResultsExposure to the intervention increased child HAZ scores by 0.296 SD (95% CI 0.116 to 0.484). However, this effect is smaller in the presence of paternal grandmothers. Compared with an effect size of 0.441 to 0.467 SD (95% CI −0.344 to 1.050) if neither grandmother is alive, the effect size was 0.235 (95% CI −0.493 to 0.039) to 0.253 (95% CI −0.529 to 0.029) SD lower if the paternal grandmother was alive. There was no evidence of an effect of parents’ siblings. Maternal grandmothers did not affect intervention impact, but were associated with a lower HAZ score in the control group. Qualitative analysis suggested that grandmothers, who act as secondary caregivers and provide resources for infants, were slower to dismiss traditionally held practices and adopt intervention messages.ConclusionThe results indicate that the intervention impacts are diminished by paternal grandmothers. Intervention success could be increased by integrating senior women.

Understanding whether and how connections between agents (networks) such as declared friendships in classrooms, transactions between firms, and extended family connections, influence their socio-economic outcomes has been a growing area of research within economics. Early methods developed to identify these social effects assumed that networks had formed exogenously, and were perfectly observed, both of which are unlikely to hold in practice. A more recent literature, both within economics and in other disciplines, develops methods that relax these assumptions. This paper reviews that literature. It starts by providing a general econometric framework for linear models of social effects, and illustrates how network endogeneity and missing data on the network complicate identification of social effects. Thereafter, it discusses methods for overcoming the problems caused by endogenous formation of networks. Finally, it outlines the stark consequences of missing data on measures of the network, and regression parameters, before describing potential solutions.CREDIBLY IDENTIFYING SOCIAL EFFECTS 1017 unobserved dimensions could influence within-group interactions, and through this the actual outcome. Ignoring variation in interactions within such groups can lead to misleading conclusions and policy design, as shown in recent work by Carrell et al. (2013).More recently, a growing body of research within empirical economics uses data which directly measure interactions between pairs of agents (network data hereon) to sidestep these issues. This growth has been spurred by the increasing availability of such data, as well as the development of methods to identify and estimate social effects with such data. Starting with Bramoullé et al. (2009) andDe Giorgi et al. (2010), methods have been developed to overcome the reflection problem. They show how information on network structure can be used to break the simultaneity, and obtain the necessary exclusion restrictions for parameter identification. These methods, reviewed in detail by Advani and Malde (2014), Topa and Zenou (2015) and Boucher and Fortin (2015), impose strong restrictions on the network formation process and the quality of the data.In particular, the network is assumed to be exogenous conditional on observed agent-and network-level characteristics, and to be fully and perfectly observed by the researcher. Both assumptions are unlikely to hold in practice. In a schooling context, for example, personality traits which are rarely observed by a researcher might influence both a child's choice of friends and her schooling performance. Estimates of the influence of a child's friends' outcomes on her outcomes will be biased if her choice of friends is not accounted for. Similarly, accurately collecting fine-grained information on all connections is very costly and logistically challenging, making it rare to observe a complete, perfectly measured network. This has important implications for identification of social effects using restrictions based on the network struct...

In many contexts we may be interested in understanding whether direct connections between agents, such as declared friendships in a classroom or family links in a rural village, affect their outcomes. In this paper, we review the literature studying econometric methods for the analysis of linear models of social effects, a class that includes the ‘linear-in-means’ local average model, the local aggregate model, and models where network statistics affect outcomes. We provide an overview of the underlying theoretical models, before discussing conditions for identification using observational and experimental/quasi-experimental data.

Credit constraints are considered to be an important barrier hindering adoption of preventive health investments among low-income households in developing countries. However, it is not obvious whether, and the extent to which, the provision of labelled micro-credit-where the loan is linked to the investment only through its label-will boost human capital investments, particularly when it is characterised by other attractive attributes, such as a lower interest rate. This paper studies a cluster randomised controlled trial of a sanitation micro-credit program in rural India, which made available lower interest loans for sanitation. The loans were linked with sanitation through their name only. The loans were not bundled with any toilet, and loan use was weakly monitored, but not enforced. Hence it is not directly obvious that the loan should boost sanitation investments. A simple theoretical framework indicates that the intervention could increase sanitation ownership through three channels-relaxation of credit constraints, salience of the loan label, or the lower interest rate. The presented empirical evidence, combined with model predictions, allow to conclude that the loan label-which to date has not received much attention in the literature-significantly impacts households borrowing and investment behaviour. Labelling loans is thus a viable strategy to improve uptake of lumpy preventive health investments.

This paper studies the relationship between group size and informal risk sharing. It shows that under limited commitment with coalitional deviations, this relationship is theoretically ambiguous. It investigates this question empirically using data on sibship size of household heads and spouses from rural Malawi, exploiting a social norm among the main sample ethnic group to define the potential risk-sharing group. We uncover evidence of worse risk sharing of crop losses in larger potential risksharing groups, and rule out alternative explanations for the findings. A simple calibration exercise indicates that our empirical findings are consistent with the theory.

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