Based on hourly wage rates from nearly all McDonald’s restaurants, and prices of the Big Mac sandwich, we find an elasticity of the wage with respect to the minimum wage of 0.7. This elasticity does not differ between affected and unaffected restaurants because many restaurants maintain a constant wage ‘premium’ above the minimum wage. Higher minimum wages are not associated with faster adoption of touch-screen ordering, and there is near-full price pass-through of minimum wages. Minimum wages lead to higher real wages (expressed in Big Macs per hour) that are one fifth lower than the corresponding increases in nominal wages.

The available estimates of the wage elasticity of male labor supply in the literature have varied between -0.2 and 0.2, implying that permanent wage increases have relatively small, poorly determined effects on labor supplied. The variation in existing estimates calls for a simple, natural experiment in which men can change their hours of work, and in which wages have been exogenously and permanently changed. We introduce a panel data set of taxi drivers who choose their own hours, and who experienced two exogenous permanent fare increases instituted by the New York City Taxi and Limousine Commission. Our preferred estimate suggests that their elasticity of labor supply is about -0.2.

In 1987 the federal government permitted states to raise the speed limit on their rural interstate roads, but not on their urban interstate roads, from 55 mph to 65 mph for the first time in over a decade. Since the states that adopted the higher speed limit must have valued the travel hours they saved more than the fatalities incurred, this experiment provides a way to estimate an upper bound on the public’s willingness to trade off wealth for a change in the probability of death. We find that the 65 mph limit increased speeds by approximately 3.5% (i.e., 2 mph), and increased fatality rates by roughly 35%. In the 21 states that raised the speed limit and for whom we have complete data, the estimates suggest that about 125,000 hours were saved per lost life. Valuing the time saved at the average hourly wage implies that adopting states were willing to accept risks that resulted in a savings of $1.54 million (1997$) per fatality, with a sampling error that might be around one-third this value. Since this estimate is an upper bound of the value of a statistical life (VSL), we set out a simple structural model that is identified by variability across the states in the probability of the adoption of increased speed limits to recover the VSL. The empirical implementation of this model produces estimates of the VSL that are generally smaller than $1.54 million, but these estimates are very imprecise.

In this paper we use data on brothers, and fathers and sons, to estimate the economic
returns to schooling. Our goal is to determine whether the correlation between earnings and
schooling is due, in part, to the correlation between family backgrounds and schooling. The
basic idea is to contrast the differences between the schooling of brothers, and fathers and sons,
with the differences in their respective earnings. Since individuals linked by family affiliation
are more likely to have similar innate ability and family backgrounds than randomly selected
individuals our procedure provides a straightforward control for unobserved family attributes.
Our empirical results indicate that in the sample of brothers the ordinary least squares
estimates of the return to schooling may be biased upward by some 25% by the omission of
family background factors. Adjustments for measurement error, however, imply that the
intrafamily estimate of the returns to schooling is biased downward by about 25% also, so that
the ordinary least squares estimate suffers from very little overall bias. Using data on fathers
and sons introduces some ambiguity into these findings, as commonly used specification tests
reject our simplest models of the role of family background in the determination of earnings.

This brief paper presents the reasons that I have come to conclude
that the evaluation of the economic benefits of training programs will
be greatly enhanced by the use of classical experimental methods. In
particular, I am convinced that some of these training programs should
be operated so that control and experimental groups are selected by ran-
dom assignment (randomized trials). It follows that a simple comparison
of earnings, employment, and other outcomes as between control and
experimental groups subsequent to participation in the experimental
program will provide a simple and credible estimate of program success
(or failure).
The principal reason why randomized trials should be used in this
field is that too much of the non-experimental estimation of the effects
of training programs seems dependent on elements of model specification
that cannot be subjected to powerful statistical tests. Moreover, these
specification tests are merely necessary and not sufficient for the
acceptability of a particular non—experimental estimation method, as an
extensive example due to LaLonde demonstrates.

This paper uses a new survey to contrast the wages of genetically
identical twins with different schooling levels. Multiple measurements of
schooling levels were also collected to assess the effect of reporting
error on the estimated economic returns to schooling. The data indicate
that omitted ability variables do not bias the estimated return to
schooling upward, but that measurement error does bias it downward.
Adjustment for measurement error indicates that an additional year of
schooling increases wages by 12-l6t, a higher estimate of the economic
returns to schooling than has been previously found.