Áureo de Paula

We develop a framework for identifying the relative influence of joint decision-makers’ beliefs without assuming Pareto optimality when beliefs differ

We study how the diffusion of online dating platforms has shaped intermarriage patterns by race and education in the United States.

One-quarter of married, fertile-age women in Sub-Saharan Africa report not wanting a pregnancy and yet do not use contraceptives. To study this issue, we collect detailed data on women’s subjective probabilistic beliefs and estimate a structural model of contraceptive choices. Our results indicate that costly interventions like eliminating supply constraints would only modestly increase contraceptive use. Alternatively, increasing partners’ approval of methods, aligning partners’ fertility preferences with women’s, and correcting women’s beliefs about pregnancy risk absent contraception have the potential to increase use considerably. Results from a within-subject experiment testing this last finding are highly consistent with the structural estimates.

Nearly one-quarter of married, fertile-age women in Sub-Saharan Africa say that they want to avoid pregnancy but are not using contraceptives. To the best of our knowledge, this paper is the first to study this puzzle in a developing country using detailed data on women’s subjective probabilistic beliefs about contraception and contraceptive attributes. Simulations based on a structural model suggest that costly interventions such as eliminating supply constraints would only have modest effects on contraceptive use. Alternatively, increasing partners’ approval of methods, aligning partners’ fertility preferences with women’s, and correcting women’s expectations about pregnancy risk absent contraception have the potential to increase use considerably. We provide additional empirical support for this last result through an experiment in which we find that simply (and effectively) informing women about underlying pregnancy risk increases stated intentions to use contraception substantially.

This paper introduces measures for how each moment contributes to the precision of parameter estimates in GMM settings. For example, one of the measures asks what would happen to the variance of the parameter estimates if a particular moment was dropped from the estimation. The measures are all easy to compute. We illustrate the usefulness of the measures through two simple examples as well as an application to a model of joint retirement planning of couples. We estimate the model using the UK-BHPS, and we find evidence of complementarities in leisure. Our sensitivity measures illustrate that the estimate of the complementarity is primarily informed by the distribution of differences in planned retirement dates. The estimated econometric model can be interpreted as a bivariate ordered choice model that allows for simultaneity. This makes the model potentially useful in other applications.

We present results on the identification of social networks from observational panel data that contains no information on social ties between agents. In the context of a canonical social interactions model, we provide sufficient conditions under which the social interactions matrix, endogenous and exogenous social effect parameters are all globally identified. While this result is relevant across different estimation strategies, we then describe how high-dimensional estimation techniques can be used to estimate the interactions model based on the Adaptive Elastic Net GMM method. We employ the method to study tax competition across US states. We find the identified social interactions matrix implies tax competition differs markedly from the common assumption of competition between geographically neighboring states, providing further insights for the long-standing debate on the relative roles of factor mobility and yardstick competition in driving tax setting behavior across states. Most broadly, our identification and application show the analysis of social interactions can be extended to economic realms where no network data exists.