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«American Economic Association In Honor of David Card: Winner of the John Bates Clark Medal Author(s): Richard B. Freeman Source: The Journal of ...»

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When David gave his first paper on the minimum wage [18] and reported that the employment rates of teenage workers in California rose in the wake of theJuly 1988 increase in the state's minimum wage, I was probably not alone in being highly skeptical of the results. After all, California is weird; teenagers put beans in their ears or worse; and state CPS files are subject to sampling problems. Rather than regarding this result as a fluke and rejecting the monopsony interpretation out of hand, David, ever the empiricist, began to look for other evidence on the response of employment in low-wage labor markets to changes in the minimum wage.

Expanding on the California result, he examined the effect of the 1990 increase in the federal minimum on teenage wages and employment across the country by comparing states in which the federal minimum had considerable bite on teenage labor with states in which it did not [24]. The rise in the minimum raised teenage wages markedly in low-wage states but had no apparent effect on employment or school enrollment patterns. California might be odd, but all of America?

Perhaps the most convincing work in the minimum area-and certainly the easiest to explain to your relatives-focuses on particular sectors likely to be greatly affected by the minimum, such as the fast-food industry. Building on an earlier literature that explored the effects of the minimum by comparing employment in sectors more and less affected by the minimum (southern sawmills, for instance), David and Alan Krueger developed new data on employment in fast-food stores before and after the 1992 increase in the NewJersey minimum [34] and contrasted employment changes in those stores with employment changes in stores in neighboring Pennsylvania, which did not increase its minimum. They found that employment in comparable minimum wage jobs did not fall in New Jersey relative to Pennsylvania despite the increased wage cost. This is one of the best-known empirical studies in labor economics today. I do not exaggerate by reporting that in any country that considers changing its minimum wage, someone is sure to ask about those NewJersey hamburger flippers.

One reason for the attention given to area studies of changes in minimum wages is that they use a transparent scientific methodology. First, you show that the wages of low-paid workers subject to the new minimum in fact increased. Then you examine employment of affected groups before and after the increase. Next, you find a "control group": workers in that area unaffected by the minimum or workers in a neighboring area where the minimum did not increase and compare changes in employment. The difference in differences in employment-the change in employment for the affected group minus the change in employment for the nonaffected group-measures the potential effect of the change in the minimum.

As most readers undoubtedly know, this work has been criticized in various ways. David Card and Alan Krueger's book Myth and Measurement: The New Economics In Honor of David Card:Winnerof theJohn Bates ClarkMedal of theMinimumWage[B2], which brought their results together, caused considerable controversy not only in the profession but outside, as well. Some economists, myself included, lauded the book for its careful empiricism, while others criticized it in a special symposium published in the July 1995 Industrialand LaborRelationsReview.

What has come from this debate? My assessment is that the Card-Kruegerwork is essentially correct: the minimum wage at levels observed in the United States has little or no adverse effect on employment. At the minimum, the book changed the burden of proof in debates over the minimum, from those who stressed the potential distributional benefits of the minimum to those who stress the potential employment losses.

The Effect of School Resources Schools matter in educational outcomes-test scores vary considerably by school even after accounting for differences in student backgrounds-but it is difficult to determine which inputs make schools matter, at least in the range of variation in resources in the United States today. Some studies find that more spending per pupil, say due to cuts in class sizes, improve student test scores. But others find that conditional on family background, neighborhood characteristics and the like, spending does not matter in test scores. As economists, we are largely interested in the effects of school resources on the earnings of students later in life rather than on test scores per se. But given the long time lag between the resources spent on, say, elementary schooling and labor market earnings, the weak link between school resources and test scores, and the weak link between test scores and earnings, determining whether additional school resources pay off in increased earnings is a daunting task.

In an exciting set of papers, David and Alan Krueger have sought to crack this difficult empirical problem by linking educational resources in a state when a worker was in school to earnings in later life. Consider the earnings of workers educated in a state that spends a lot on schooling and the earnings of workers in a state that spends little. All else the same, if school resources raise earnings in later life, the wages of a more-educated worker ought to exceed those of a less-educated worker in the high-spending state than in the low-spending state. The problem is to parse the data so that all else is the same. Since differences in earnings by education will vary with labor market conditions, one problem is to differentiate the effect of school resources in a state from state labor market conditions. Another problem is to differentiate between the effects of school resources and the effects of simply being brought up in a state: it isn't the New York city schools that make New York youngsters so worldly wise.





David and Alan's solution is to compare earnings differentials by education among persons working in the same labor market but educated in states with different resources to schooling. Assume a world where the residents of Iowa and Montana migrate to New York,and where Iowa spends more resources on education than does Montana. Now compare the ratio of the wages of high school to grade school graduates from Iowa and Montana working in New York. If the additional Journal of Economic Perspectives Iowa school resources have a positive payoff on the return to schooling, the high school/grade school wage ratio will be higher among Iowans than among Montanans. This procedure controls for pure state-of-birth effects on earnings and stateof-residence labor market effects on earnings. It is not a perfect estimate. One problem is a potential selectivity bias in cross-state migration: migrants to New York are unlikely to be randomly chosen from the relevant high school and grade school populations. If, for example, only Montanans who obtain Iowa-level skills in high school migrate to New York, estimates of the effect of schooling on earnings using this procedure will understate the school resource effect.

To undertake such an analysis requires large data sets: with just a few thousand observations, there would be far too few Montanans and Iowans working in New York to yield reliable conclusions. In [23], David and Alan use 1,018,477 individual observations on white men in the 1980 Census to estimate the effect of school resources and find a substantial difference in earnings by education in response to differences in pupil-teacher ratios at the state level. In [22], they use observations on 728,284 individuals in the 1960, 1970 and 1980 Censuses to examine differences in the earnings of blacks and whites educated in the South with very different resources. They find that about 20 percent of the narrowing of the black-white earnings gap between 1960 and 1980 was due to improvements in the quality of black schools in earlier years. In [47] they show that gaps in earnings and education by race mirror gaps in school resources in the Carolinas: there is something to learn from southern states as well as from New Jersey.

Potential problems remain with the use of state data on schooling and Census of Population data on migrant earnings-the measures of school quality, their effect on the level of schooling, and so on-which David and Alan recognize [40].

These problems have generated a host of further studies, some supportive and some critical. That the massive differences in the educational resources given to black and white children in the old South substantially affected the future earnings of the two groups is the strongest empirical finding in this work. The more controversial findings on the effects of the moderate (though still noticeable) differences in school spending on earnings provide a strong antidote to the claims that school spending simply doesn't matter.

Social Programs and Outcomes No one seems to obtain a Ph.D. from Princeton in labor economics without examining training or social programs using administrative or experimental data.

David's first paper on social prograins (written jointly with Orley Ashenfelter) [4] contrasted the longitudinal structure of the earnings of Comprehensive Employment and Training Act trainees using administrative data with the earnings of a control group using Current Population Survey files. This paper showed that analysts must consider the permanent, transitory and trend changes in earnings of the two groups to make sensible inferences, and it concluded that there was strong evidence that CETA training benefited women but not men. In [11], David (with Dan Sullivan) reported positive effects of CETA participation on employment, RichardB. Freeman 175 which were larger for classroom than on-the-job training. In both of the CETA papers, David and his coauthors use several overidentified models to fit the data and to assess the effects of the programs. This approach is probably about the best that one can do with nonexperimental data, and it may also be needed in the imperfect experimental data that even our best training experiments generate.

David has also examined the economic effects of four other social programs:

unemployment insurance taxes (with Philip Levine) [31 ], workers' compensation (with Brian McCall) [44], the 1964 Civil Rights Act (with Alan Krueger) [27] and an earnings subsidy program, the Self-Sufficiency Project, in Canada (with Philip Robins) [46]. In the unemployment insurance paper [31 ], data on unemployment insurance tax costs for firms in five major industries in 36 states is combined with CPS data (a characteristic of a Card analysis) on temporary layoffs to discover that experience rating has a substantial effect on the probability of layoffs. Persons who worry about the unintended consequences of incomplete experience rating on layoffs will find this paper to their liking. In the workers' compensation paper [44], Card and McCall use a 10 percent random sample of first reports of injuries filed with the Minnesota Department of Labor and Industry to test the popular claim that workers fraudulently report injuries incurred at home as occurring on the job on Mondays. Card and McCall show that the proportion of injuries disputed by employers are no different on Mondays than on other days, and the proportion does not differ between workers with and without medical coverage.

Persons who believe that fraud is rampant in social programs will not like this paper. In [27], Card and Krueger use Social Security earnings data to follow cohorts before and after 1964 to see if the 1964 Civil Rights Act affected racial differences in earnings. They contrasted median black workers with 25th percentile white workers as a crude adjustment for the possibility that any improvement in relative black earnings might simply be reflecting compression of the overall wage structure that would benefit the average black more than the average white.

Persons who believe that antidiscrimination programs are unimportant in improving the black economic position will not like their conclusion that in fact the legislation mattered mightily. Finally, the analysis of the Canadian Self-Sufficiency Project [46] uses the program's randomized design experimental features to examine its effect on labor force attachment and welfare participation. The finding that supplementing the earnings of long-term welfare recipients in a way that gives a big bonus for getting a full-time (30 hours per week) job greatly increases work effort and reduces welfare rolls provides evidence that labor supply incentives are significant for this population. Persons who favor welfare reform that provides a high safety net for traditional recipients who get jobs will find this result to their liking.

Overall, these papers show that artful empirical analysis can readily yield results all over the political spectrum. The point of David's work is to get the best evidence on the effects-be it CPS data, information from state Labor Departments, Social Security earnings files, or experimental data-to analyze that evidence with care and report the results, however they come out. I found it especially stimulating to Journal of Economic Perspectives consider these papers as a group, given their different data, issues and findings.

Wage Structures and Differentials The structure of U.S. wages has widened substantially in the past 20 or so years.

The pay of the more-educated, older and higher skilled increased relative to the less-educated, younger and lesser skilled. In addition, the dispersion of earnings within virtually every skill group also widened. In [28], David and Rebecca Blank examined how the dispersion of wages, unemployment and the level of hourly wages affect the distribution of family incomes using a regional panel data set constructed from CPS files. They find that family income and poverty are closely related to the widening wage inequality and slow growth of average wages, and that cyclical decreases in unemployment did not benefit the low-income families relative to other families.



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