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Unlike other data sets, recent interviews from the National Longitudinal Survey of Youth obtain information on who bears the explicit costs of training. The data indicate that the employer almost always pays the explicit cost of training that the worker receives on the employer's premises and often pays for the explicit costs of what appears to be off site general training. Furthermore, our wage regressions indicate that completed spells of general training paid for by previous employers have a larger effect on the wage than completed spells of general training paid for by the current employer. While these results are contrary to the conventional human capital model, we present a model that demonstrates how contract enforcement considerations can lead to employers paying for purely general training. An employer in our model offers a future wage guarantee in order to provide an assurance that he will not extract excessive rents from workers who demonstrate by not quitting that they place a relatively high valuation on the employer's job. When this wage guarantee is binding, a small increase in a worker's productivity caused by an increase in his stock of human capital will not cause the employer to pay a higher wage. This sharing of the returns to general training makes the worker less willling to pay for the training by himeself but provides the employer with an incentive to share the cost. Standard errors in parentheses. Sample Size = 14801. ** implies statistically significant at the 5% level (two tailed test). * implies statistically significant at the 10% level (two tailed test). Mean (standard deviation) of dependent variable is 1.8851 (0.4945). All equations control for an intercept, non-employer paid training spells, multiple training spells in the current year and in previous years, indicators for whether no training is observed in previous years at the same or other jobs because of left censored data, tenure, year, AFQT, race, gender, age, marital status, number of children, residence in an urban city, SMSA size, local labor market unemployment rate, union status, firm size, experience, education, school attendance in the previous year, multiple-site firm, number of previous jobs ever held, part-time work, government employment, and industry and occupation.

The rise of the "gig economy" has attracted wide attention from both scholars and the popular media. Much of this attention has been devoted to jobs mediated through various online platforms. While non-traditional work arrangements have been a perennial subject of debate and study, the perception that new technology is producing an accelerated pace of change in the organization of work has fueled a resurgence of interest in how such changes may be affecting both workers and firms. This paper provides a typology of work arrangements and reviews how different arrangements, and especially gig activity, are captured in existing data. A challenge for understanding recent trends is that household survey and administrative data paint a different picture, with the former showing little evidence of the growth in self-employment that would be implied by a surge in gig activity and the latter providing evidence of considerable recent growth. An examination of matched individual-level survey and administrative records shows that a large and growing fraction of those with self-employment activity in administrative data have no such activity recorded in household survey data. The share of those with self-employment activity in household survey data but not administrative data is smaller and has not grown. Promising avenues for improving the measurement of self-employment activity include the addition of more probing questions to household survey questionnaires and the development of integrated data sets that combine survey, administrative and, potentially, private data.