Michael Greenstone

First name
Michael
Last name
Greenstone
Abstract

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.

Year of Publication
2002
Number
463
Date Published
04/2002
Publication Language
eng
Citation Key
Journal of Political Economy, vol. 112, no. 1, pt. 2, 2004
Ashenfelter, O., & Greenstone, M. (2002). Using Mandated Speed Limits to Measure the Value of a Statistical Life. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01sq87bt61q (Original work published April 2002)
Working Papers
Abstract

Previous empirical studies have uncovered little evidence that environmental regulations reduce
industrial activity. This paper presents new evidence on the effects of these regulatory interventions by
using the 1970 and 1977 Clean Air Act Amendments’ division of counties into “high” and “low”
regulation categories. Polluting manufacturers in the high regulation counties were subject to
substantially stricter regulations than those in low regulation counties. The evaluation strategy is to
assign the more than 1.75 million plant observations from the microdata underlying the 1967-1987
Census of Manufacturers to precisely defined “regulated” and “unregulated” groups, based on their
county’s regulatory status, their emissions of the regulated pollutants, and the year. After controlling for
a wide variety of factors including plant level characteristics, unrestricted industry shocks, and
unrestricted county shocks, the estimates suggest that in the first 15 years after the Amendments became
law (1972-1987), high regulation counties (relative to low regulation ones) lost approximately 100,000
jobs, $50 billion in capital stock, and $30 billion (l987$) of output in pollution intensive industries.
Although substantial in affected counties, the “lost” manufacturing activity associated with these
regulations was relatively modest when compared to the size of the entire manufacturing sector. These
estimated “losses” are larger than those found in the previous literature. This difference can be explained
by the previous literature’s inability to control for unobservable factors that are correlated with
regulation and the Amendments’ simultaneous regulation of multiple pollutants.

Year of Publication
1998
Number
408
Date Published
11/1998
Publication Language
eng
Citation Key
7990
Greenstone, M. (1998). The Impacts of Environmental Regulations on Industrial Activity: Evidence from the 1970 and 1977 Clean Air Act Amendments and the Census of Manufacturers. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01qr46r081p (Original work published November 1998)
Working Papers
Abstract

In this paper we show that omitted variables and publication bias lead to severely biased
estimates of the value of a statistical life. Although our empirical results are obtained in
the context of a study of choices about road safety, we suspect that the same issues
plague the estimation of monetary trade-offs regarding safety in other contexts.

Year of Publication
2004
Number
479
Date Published
01/2004
Publication Language
eng
Citation Key
8102
Ashenfelter, O., & Greenstone, M. (2004). Estimating the Value of a Statistical Life: The Importance of Omitted Variables and Publication Bias. Retrieved from http://arks.princeton.edu/ark:/88435/dsp018336h1898 (Original work published January 2004)
Working Papers
Abstract

We estimate the effect of the reduction in credit supply that followed the 2008 financial crisis on the real economy. We predict county lending shocks using variation in pre-crisis bank market shares and estimated bank supply-shifts. Counties with negative predicted shocks experienced declines in small business loan originations, indicating that it is costly for these businesses to find new lenders. Using confidential microdata from the Longitudinal Business Database, we find that the 2007-2009 lending shocks accounted for statistically significant, but economically small, declines in both small firm and overall employment. Predicted lending shocks affected lending but not employment from 1997-2007.

Year of Publication
2014
Number
584
Date Published
11/2014
Publication Language
eng
Citation Key
9105
Greenstone, M., Mas, A., & Nguyen, H.-L. (2014). Do Credit Market Shocks affect the Real Economy? Quasi-Experimental Evidence from the Great Recession and ‘Normal’ Economic Times. Retrieved from http://arks.princeton.edu/ark:/88435/dsp016395w932w (Original work published November 2014)
Working Papers