Prisons and crime: Backwards in high heels

pObjectives: Prisons reduce crime rates, but crime increases prison populations. OLSbr / estimates of the effects of prisons on crime combine the two effects and are biased towardbr / zero. The standard solutionmdash;to identify the crime equation by finding instruments forbr / prisonmdash;is suspect, because most variables that predict prison populations can be expectedbr / to affect crime, as well. An alternative is to identify the prison equation by findingbr / instruments for crime, allowing an unbiased estimate of the effect of crime on prisons.br / Because the two coefficients in a simultaneous system are related through simple algebra,br / we can then work backward to obtain an unbiased estimate of the effect of prisons onbr / crime.br / Methods: Potential instruments for crime are tested and used to identify the prisonbr / equation for the 50 U.S. states for the period 1978ndash;2009. The effect of prisons on crimebr / consistent with this relationship is obtained through algebra; standard errors are obtainedbr / through Monte Carlo simulation.br / Results: Resulting estimates of the effect of prisons on crime are around -0.25 plusmn; 0.15.br / This is larger than biased OLS estimates, but similar in size to previous estimates based onbr / standard instruments.br / Conclusions: When estimating the effect of a public policy response on a public problem,br / it may be more productive to find instruments for the problem and work backwardbr / than to find instruments for the response and work forward./p