The Effect of Parental Rural-to-Urban Migration on Children’s Cognitive Skill Formation (Job Market Paper)
Large-scale rural-to-urban economic migration in developing countries leaves millions of rural-origin children growing up separated from their migrant parents. Due to the limited parent-child interaction, parental migration poses developmental challenges for left-behind children. This paper develops a structural model of household migration to evaluate the effects of parental migration decisions on the dynamics of children’s cognitive skill formation from birth until the end of the developmental stage at age 14. I estimate the model using micro-level data from the Indonesia Family Life Survey via simulated maximum likelihood. I find that children’s cognitive skill formation is sensitive to the duration of parental migration: being left behind one year during childhood reduces cognitive skill rank by 0.53 percentiles per year. I also find that there is a 0.30 standard deviations increase in left-behind children’s skills at the end of the developmental stage if their parents had not left them. Using the estimated model, I simulate a series of counterfactual migration policies. I show that migration policies that incentivize family migration with their children to urban destinations are effective in fostering children’s cognitive development: an annual migration subsidy of $150 lifts children’s cognitive skill by 0.14 standard deviations.
Ex Ante Evaluation of the Effect of Migration Policy on Children’s Cognitive Achievements
China’s large-scale rural-to-urban migration has affected more than 30 million left-behind children. This paper proposes several migration policies that are aimed at improving left-behind children’s cognitive achievements. To quantify the impacts of these policies prior to implementation, I specify a model of household migration that embeds a child’s cognitive skill production function. I use the model solution to construct a nonparametric matching estimator to directly evaluate the impact of counterfactual migration policies. The proposed estimation strategy is computationally inexpensive and does not require the estimation of the full structural model. By exploiting income variation using data from the China Family Panel Studies, I find that a non-migration subsidy is most effective in improving children’s cognitive achievements when it targets low-income families and younger children. When associated with middle school graduation, the policy-induced change in cognitive achievements translates into an 8.6 percentage points increase in graduation rate for children from low-income households.
Estimating Peer Effects on Career Choice: A Spatial Multinomial Logit Approach (with Robin Sickles & Jenny Williams. Forthcoming, Advances in Econometrics)
Peers and friends are among the most influential social forces affecting adolescent behavior. In this paper, we investigate peer effects on post-high school career decisions and on school choice. We define peers as students who are in the same classes and social clubs and measure peer effects as spatial dependence among them. Utilizing recent development in spatial econometrics, we formalize a spatial multinomial choice model in which individuals are spatially dependent in their preferences. We estimate the model via Pseudo Maximum Likelihood using data from the Texas Higher Education Opportunity Project. We do find that individuals are positively correlated in their career and college preferences and examine how such dependencies impact decisions directly and indirectly as peer effects are allowed to reverberate through the social network in which students reside.
Work in Progress
Estimating Demand for Early Childcare and Education Arrangements
This paper develops and estimates a model of household demand for early childcare in the United States. The National Survey of Early Care and Education reveals that parents often choose multiple types of childcare, and the duration for each chosen type varies across households. These features depart from the classical multinomial demand models that consider a unique choice from mutually exclusive alternatives. To account for multiple discreteness, I develop a model in which parents with heterogeneous tastes regarding childcare type are allowed to choose multiple childcare arrangements. The econometric specification, a mixed multiple discrete-continuous logit model, arises naturally from a household utility maximization problem. The model is estimated using simulated maximum likelihood. The rich information on childcare choices from the dataset allows me to construct the potential choice set for each household. I show that utility parameters are inconsistently estimated when the choice set of childcare arrangements is unknown or misspecified.
Material & Time Investments, Migration, and Child Outcome (with Ajinkya Keskar)
This paper studies the effect of parental rural-to-urban migration on the cognitive skills and health outcome of left-behind children in China. We use factor analysis to decompose the impact of parental rural-to-urban migration into (increased) material investments and (reduced) parent-child time interaction using data from the China Family Panel Studies. By exploiting exogenous variations in distance and labor market demand shocks, we estimate the causal impact of parental migration using a fixed effect instrumental variable strategy. We aim to disentangle the substitution effect (less parental time) from the income effect (higher earnings) on child development.