By Osborne Jackson and Thu Tran

The “hidden tax” resulting from larceny crime refers to the higher prices paid by consumers to producers who raise prices in order to pass on some of the associated cost of such theft. In the same vein, consumers who are victimized by larceny theft could pass along some of the associated cost that they bear by spending less. This study analyzes larceny crime as a hidden tax in order to examine its welfare implications. Using traditional tax theory, the authors first characterize how larceny crime might create distortions in a given product market. Employing a sample covering 17 US states during the 2000–2015 period, they then use the enactment of higher felony larceny thresholds to generate exogenous variation in larceny crime by product market. A felony larceny threshold is the dollar value of stolen property at or above which a larceny offense may be charged in court as a felony rather than a misdemeanor. Focusing on the subset of larceny crime that is likely most affected by raising the larceny threshold, the authors calculate baseline hidden tax rates and then examine how changes in larceny rates related to higher thresholds affect this tax. They use these estimated changes in the hidden tax rates to compare the associated welfare costs of larceny crime across product markets.''

Boston: Federal Reserve Bank of Boston, Research Department. 2020, 33

By The Pelican Institute

This year, disturbing reports of increased crime have dominated the news, both in Louisiana and nationally.

Crime is a serious issue that demands thoughtful solutions to deter criminal behavior and promote public safety. They should be guided by data and evidence, not anecdotes. That’s why Pelican set out to review and better understand the underlying data from the Federal Bureau of Investigation’s Uniform Crime Report and Bureau of Justice Statistics.

Comparing Louisiana’s crime data with other states paints a complete picture of the scale of the problem and its potential causes.

New Orleans LA: Pelican Institute. 2022, 12pg

By Richard Rosenfeld, James Austin

Employing a small number of predictive variables, the authors of this report created statistical models to forecast violent and property crime rates in New York City. The models estimated yearly changes in New York City’s crime rates from the early 1960s through 2021, estimates that corresponded very closely to the actual rates. The authors then used these models to forecast annual changes in crime rates through 2026. The forecast for violent crime is a slight decrease each year through 2026, while the forecast for property crime shows slight yearly increases. Finally, the projected impact on New York City’s violent crime rate of reducing the state imprisonment rate by 25% would be minimal. No association was found between imprisonment rates and property crime.

New York: Harry Frank Guggenheim Foundation. 2023, 18pg

By Richard Rosenfeld, James Austin

In this report, the authors devised statistical models to “predict” past yearly changes in Los Angeles’s rates of violent and property crime from the early 1960s through 2021, employing a very small set of predictive variables known to be associated with levels of crime. The yearly changes projected for those years corresponded quite closely to the actual changes. The authors then used the models to forecast crime trends through 2026. Violent crime is forecast to decline through 2026, while property crime is expected to rise modestly in the same period. The analysis also finds that if California imprisonment rates were reduced by 20%, the effect on crime in Los Angeles would be minimal.

New York: Harry Frank Guggenheim Foundation. 2023, 21pg

By James Austin, Richard Rosenfeld and Todd Clear

Florida has benefited from the national drop in crime that began in the early 1990s. Its growth in incarceration also paralleled the steady national imprisonment rise of the last forty-five years. Florida’s rate peaked around 2010 and has been declining ever since. Policy makers would benefit from defensible projections of future trends in crime, and especially from estimates of the effect that further reductions in the number of people in jail and prison might have on those trends. The authors of this study developed quantitative models—explained here in non-technical language—of the effects of various demographic and economic factors, as well as the imprisonment rate, on Florida’s past crime rates. They then used these models to project crime trends into the 2020s, both with and without the assumption of a substantial reduction in imprisonment.

New York: Harry Frank Guggenheim Foundation. 2021, 28pg