Economists have long been ambivalent about whether the discipline should focus on the analysis of markets or should be concerned with social interactions more generally. Recently the discipline has sought to broaden its scope while maintaining the rigor of modern economic analysis. Major theoretical developments in game theory, the economics of the family, and endogenous growth theory have taken place. Economists have also performed new empirical research on social interactions, but the empirical literature does not show progress comparable to that achieved in economic theory. This paper examines why and discusses how economists might make sustained contributions to the empirical analysis of social interactions.

This essay discusses the effect of technical change on wage inequality. I argue that the behavior of wages and returns to schooling indicates that technical change has been skill-biased during the past sixty years. Furthermore, the recent increase in inequality is most likely due to an acceleration in skill bias. In contrast to twentiethcentury developments, much of thr technical change during the early nineteenth century appears to be skill-replacing. I suggest that this is because the increased supply of unskilled workers in the English cities made the introduction of these technologies profitable. On the other hand, the twentieth century has been characterized by skillbiased technical change because the rapid increase in the supply of skilled workers has induced the development of skill-complementary technologies. The recent acceleration in skill bias is in turn likely to have been a response to the acceleration in the supply of skills during the past several decades.

We are grateful to Yongsung Chang and three referees, particularly the final referee, for useful comments. Saasha Celestial-One provided excellent research assistance. Does Schooling Cause Growth? Barro (1991) and others find that growth and schooling are highly correlated across countries. A model is examined in which the ability to build on the human capital of one's elders plays an important role in linking growth to schooling. The model is calibrated to quantify the strength of the effect of schooling on growth by using evidence from the labor literature on Mincerian (1974) returns to education. The upshot is that the impact of schooling on growth explains less than one third of the empirical cross-country relationship. The model is extended to address the choice of schooling, showing that faster growth can induce more schooling by raising its effective return. Calibrating schooling choices suggests that this reverse channel can potentially explain one half or more of the observed relationship between schooling and

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This article discusses the history underlying the new literature, describes some of what has been learned thus far, and looks ahead towards making further progress

This chapter provides a survey and exposition of the development of the earnings function as an empirical tool for the analysis of the determinants of wage rates. Generically, the term "earnings function " has come to mean any regression of individual wage rates or earnings on a vector of personal, market, and environ-

This paper examines the family income–college enrolment relationship and the evidence on credit constraints in post-secondary schooling. We distinguish short run liquidity constraints from the long term factors that promote cognitive and noncognitive ability. Long run factors crystallised in ability are the major determinants of the family income- schooling relationship, although there is some evidence that up to 8 % of the total US population is credit constrained in a short run sense. Evidence that IV estimates of the returns to schooling exceed OLS estimates is sometimes claimed to support the existence of substantial credit constraints. This argument is critically examined. This paper interprets the evidence on the relationship between family income and college attendance. Fig. 1 displays aggregate time series college participation rates for 18–24 year old American males classified by their parental income. Parental income is measured in the child’s late adolescent years. There are substantial differences in college participation rates across family income classes in each year. This pattern is found in many other countries; see the essays in Blossfeld and Shavit (1993). In the late 1970s or early 1980s, college participation rates start to

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.

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