Do parents have preferences over the gender of their children, and if so, does this have negative consequences for daughters versus sons? In this paper, we show that child gender affects the marital status, family structure, and fertility of a significant number of American families. Overall, a first-born daughter is significantly less likely to be living with her father compared to a first-born son. Three factors are important in explaining this gap. First, women with first-born daughters are less likely to marry. Strikingly, we also find evidence that the gender of a child "in utero "affects shotgun marriages. Among women who have taken an ultrasound test during pregnancy, mothers who have a girl are less likely to be married at delivery than those who have a boy. Second, parents who have first-born girls are significantly more likely to be divorced. Third, after a divorce, fathers are much more likely to obtain custody of sons compared to daughters. These three factors have serious negative income and educational consequences for affected children. What explains these findings? In the last part of the paper, we turn to the relationship between child gender and fertility to help sort out parental gender bias from competing explanations for our findings. We show that the number of children is significantly higher in families with a first-born girl. Our estimates indicate that first-born daughters caused approximately 5500 more births per year, for a total of 220,000 more births over the past 40 years. Taken individually, each piece of empirical evidence is not sufficient to establish the existence of parental gender bias. But taken together, the weight of the evidence supports the notion that parents in the U.S. favour boys over girls. Copyright © 2008 The Review of Economic Studies Limited.

Past estimates of the effect of family income on child development have often been plagued by endogeneity and measurement error. In this paper, we use two simulated instrumental variables strategies to estimate the causal effect of income on children's math and reading achievement. Our identification derives from the large, non-linear changes in the Earned Income Tax Credit (EITC) over the last two decades. The largest of these changes increased family income by as much as 20 percent, or approximately $2,100. Using a panel of almost 5,000 children matched to their mothers from National Longitudinal Survey of Youth datasets allows us to address problems associated with unobserved heterogeneity, endogenous transitory income shocks, and measurement error in income. Our baseline estimates imply that a $1,000 increase in income raises combined math and reading test scores by 6 percent of a standard deviation in the short run. The gains are larger for children from disadvantaged families and are robust to a variety of alternative specifications. We find little evidence of long-run income effects, with most of the effects disappearing after one year.

Self-selected migration presents one potential explanation for why observed returns to a college education in local labor markets vary widely even though U.S. workers are highly mobile. To assess the impact of self-selection on estimated returns, this paper first develops a Roy model of mobility and earnings where workers choose in which of the 50 states (plus the District of Columbia) to live and work.Available estimation methods are either infeasible for a selection model with so many alternatives or place potentially severe restrictions on earnings and the selection process. This paper develops an alternative econometric methodology which combines Lee's (1983) parametric maximum order statistic approach to reduce the dimensionality of the error terms with more recent work on semiparametric estimation of selection models (e.g., Ahn and Powell, 1993). The resulting semiparametric correction is easy to implement and can be adapted to a variety of other polychotomous choice problems. The empirical work, which uses 1990 U.S. Census data, confirms the role of comparative advantage in mobility decisions. The results suggest that self-selection of higher educated individuals to states with higher returns to education generally leads to upward biases in OLS estimates of the returns to education in state-specific labor markets. While the estimated returns to a college education are significantly biased, correcting for the bias does not narrow the range of returns across states. Consistent with the finding that the corrected return to a college education differs across the U.S., the relative state-to-state migration flows of college-versus high school-educated individuals respond strongly to differences in the return to education and amenities across states.

Family violence is a pervasive and costly problem, yet there is no consensus on how to interpret the phenomenon of violence by

Past estimates of the effect of family income on child development have often been plagued by endogeneity and measurement error. In this paper, we use two simulated instrumental variables strategies to estimate the causal effect of income on children's math and reading achievement. Our identification derives from the large, non-linear changes in the Earned Income Tax Credit (EITC) over the last two decades. The largest of these changes increased family income by as much as 20 percent, or approximately $2,100. Using a panel of almost 5,000 children matched to their mothers from National Longitudinal Survey of Youth datasets allows us to address problems associated with unobserved heterogeneity, endogenous transitory income shocks, and measurement error in income. Our baseline estimates imply that a $1,000 increase in income raises combined math and reading test scores by 6 percent of a standard deviation in the short run. The gains are larger for children from disadvantaged families and are robust to a variety of alternative specifications. We find little evidence of long-run income effects, with most of the effects disappearing after one year.