No abstract

Empirical research about tax evasion and the informal economy has exploded in the past few decades, seeking to shed light on the magnitude and (especially policy) determinants of these phenomena. Quantitative information informs the analysis of policy choices, enables the testing of hypotheses about determinants of this phenomenon, and can help with the accurate construction of national income accounts. Even as empirical analysis has burgeoned, some have expressed doubts about the quality and usefulness of some prominent measures. The fact that high-quality data is elusive is neither surprising nor a coincidence. The defining characteristic of tax evasion and informal economic activity-that they are generally illegal-often renders unreliable standard data collection methods such as surveys. Unlike invisible phenomena in the natural sciences, these invisible social science phenomena are hard to measure because of choices made by individuals. Analysis of tax evasion and the informal economy must proceed even in the absence of the direct observability of key variables, and theory should guide the construction and interpretation of evidence of the "invisible." In this paper, we address what can be learned using micro or macro data regarding tax evasion and the informal economy under given conditions and assumptions, and critically review some of the most common empirical methods in light of our conclusions. We conclude with an entreaty for researchers in this field to enlist in the "credibility revolution" (Angrist and Pischke in J. Econ. Perspect. 4(2):3-30, 2010) in applied econometrics.

Over the last few years, marijuana has become legally available for recreational use to roughly a quarter of Americans. Policy makers have long expressed concerns about the substantial external costs of alcohol, and similar costs could come with the liberalization of marijuana policy. Indeed, the fraction of fatal accidents in which at least one driver tested positive for tetrahydrocannabinol has increased nationwide by an average of 10% from 2013 to 2016. For Colorado and Washington, both of which legalized marijuana in 2014, these increases were 92% and 28%, respectively. However, identifying a causal effect is difficult due to the presence of significant confounding factors. We test for a causal effect of marijuana legalization on traffic fatalities in Colorado and Washington with a synthetic control approach using records on fatal traffic accidents from 2000 to 2016. We find the synthetic control groups saw similar changes in marijuana‐related, alcohol‐related, and overall traffic fatality rates despite not legalizing recreational marijuana. (JEL K42, I12, I18)

We thank Michael Kuhn, Simeon Minard, and Glen Waddell for helpful comments. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

No abstract