Knowledge that Transforms

Some of the most important but difficult issues in modern societies revolve around a simple question: What factors ensure that a young person will have a good start when she or he first enters the labor market? The importance of this question has been driven home by three sets of research findings. First, there is substantial persistence in labor market outcomes over the life cycle. Good or bad outcomes early in a career are strong indicators of long-term success or failure. Second, although immutable factors like parents’ education exert a powerful and lasting influence on children’s outcomes, there is an important causal role for potentially malleable factors like schools, neighborhoods, and local institutions. Third, some groups of youth—particularly those from disadvantaged family backgrounds—appear to be especially vulnerable to both temporary shocks and permanent features of the environment in which they were raised. Traditionally, economists have thought of employment as a key metric for assessing the initial success of young people. By this standard, youth are much worse off today than in earlier decades. As shown in figure 1, the average fraction of 16–24-year-olds working in any week fell from around 60% in the late 1970s to around 50% today. The decline for teenagers was even steeper. A closer examination of the relative employment rate of youth (plotted in the bottom line in fig. 1) suggests that it has been trending downward over the past 40 years, with discrete declines after each of the last four recessions (in 1982–83, 1991, 2001, and 2007–9). Viewed from this perspective, the Great Recession is just the latest in a long series of setbacks for young workers. Of course, employment is only part of the story. Given smaller family sizes and higher incomes, an increasing fraction of US families may decide

In the last three decades, Canada and the United States showed different paths in per capita gross domestic product growth, skill premiums, and inequality. Worker quality and price differences both play a role but are difficult to distinguish. Human capital prices and quantities are estimated using methods we developed previously. In the United States, there was faster growth and a much more rapid rise in skill premia and inequality. This was primarily due to different paths for the relative price paid to rent high-skilled human capital in the two countries, rather than differences in relative quantities.

The United States and Canada are as close economically and socially as any pair of countries in the world. They share similar cultural traditions and economic institutions. They are also closely linked by trade and multinational firms that operate on both sides of the border. Nevertheless, the two countries differ inmany small but important ways that ultimately affect individual outcomes and overall labor market performance. Canada has a more comprehensive set of social programs that tend to be more redistributive than those in the United States. Canada also has a higher rate of immigration, with nearly twice as many immigrants per capita. The Canadian economy is more reliant on the natural resource sector, while the United States has a larger tech sector. The United States has a wider distribution of income, with higher poverty rates and a higher share of people with earnings far above themedian salary. It also experienced a far deeper and longer-lasting recession in 2007–8, the consequences of which are still being analyzed and debated. There is a long tradition in social science of using comparisons between the United States and Canada to uncover the impacts of different institutions and policies, including work in political science (e.g., Lipset 1990), criminology (e.g., Sloan et al. 1988), medicine (e.g., Gorey et al. 2009), demography (e.g., Boyd 1976), and labor relations (e.g., Meltz 1985). Building on this tra-

We show that, on the basis of the initial-screening point system used in Canada, immigrants who subsequently move to the United States are more highly educated than their counterparts from the same source countries in the United States and have much better outcomes. High-skill immigrants who transit through Canada before moving to the United States do so fairly early after arrival, and they represent a substantial share of the population of young, highly educated immigrants in Canada. Thus, Canada is best seen as a transitory destination rather than as a training ground for later emigration to the United States.

We extend Altonji and Mansfield’s control function approach to allow for multiple group levels and complementarities. Our analysis provides a foundation for a causal interpretation of multilevel mixed effects models in the presence of sorting. In our empirical application, we obtain lower-bound estimates of the importance of school and commuting zone inputs for education and wages. A school/location combination at the 90th versus 10th percentile of the school/location quality distribution increases high school graduation and college enrollment probability by at least .06 and .17, respectively. Treatment effects are heterogeneous across subgroups, primarily due to nonlinearity in the educational attainment model.

Referrals can improve screening and self-selection of applicants during the hiring process. We model and estimate how referral information affects the selection of employees through job offers, acceptances, and turnover. Using data from a call center company, we show that referrals help employers attract applicants of superior performance. Yet performance differences between referred and nonreferred workers diminish with tenure through selective turnover. Our estimates reveal that referrals allow employers to screen on hard-to-observe but performance-relevant attributes for employees of high performance and high propensity to stay. Thus, referred applicants complete much of the sorting during the hiring process.

A displaced worker might rationally prefer to wait through a long spell of unemployment instead of seeking employment at a lower wage in a job he is not trained for. I evaluate this trade-off using micro data on displaced workers. To achieve identification, I exploit the fact that the more a worker has invested in occupation-specific human capital, the more costly it is for him to switch occupations and therefore the higher is his incentive to wait. I find that between 9% and 17% of total unemployment in the United States can be attributed to wait unemployment.

Conventional estimates of Frisch labor supply elasticities are biased in the presence of borrowing constraints. We develop an incomplete-markets model with two-earner households and derive a new estimation approach for the Frisch elasticity that yields unbiased estimates even in samples that include borrowing-constrained households. Our approach exploits that the strength of the estimation bias depends on individuals’ relative contribution to household earnings. It takes the form of a simple interaction term model with minimum data requirements. Using Panel Study of Income Dynamics data, we estimate Frisch elasticities of about 0.7 for men and rather homogeneous Frisch elasticities across the population.

We compare patterns of unemployment between Canada and the United States during the Great Recession. We document a rise in long-term unemployment in Canada, similar to findings in earlier work. We consider an extended matching model using restricted-access data from the Canadian Labour Force Survey, which contains information on time since last job for both unemployed and nonparticipants. We create a new historical vacancy series for Canada based on relative employment in “recruiting industries” to construct a monthly Beveridge curve for Canada. Allowing for duration dependence in flows between unemployment and nonparticipation is crucial for explaining long-term joblessness.