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...on value completely characterizes criminal behavior. In particular, the probability of committing a crime for legal immigrants simply equals the probability of receiving a crime opportunity worth more than z∗L, cL = 1− F (z∗L). (5) For illegal immigrants, we must first condition the probability of committing a crime on the risk of deportation, cI = (1− δ) [1− F (z∗I )] . (6) The log probability of committing a crime for each individual, conditional on legal 8See also Ehrlich (1973), Grogger (1998), and Machin and Meghir (2004) for later developments; Burdett et al. (2003), Lochner (2004), and Lee and McCrary (2009) provide extensions in dynamic settings that are most similar to ours. 9Note the analogy with the notion of “reservation wage” commonly adopted in equilibrium search models of labor (see Rogerson et al., 2005, for a survey). 9 status, can be written compactly as ln c(h) = ln cI(h) + β(h)L, where L = 1 if the immigrant is legal and L = 0 otherwise, and β(h) ≡ ∆ ln c(h) is the causal effect of legal status conditional on h. Using equations (5) and (6), we obtain β(h) ≈ δ − [F (z∗L)− F (z∗I )] . (7) The sign of (7) depends on two effects. On the one hand, holding constant the propensity to engage...

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...e sense that a criminal faces a certain probability of being caught and sent to prison. The probability of prison depends on the skill of the criminal as measured by criminal human capital and the amount of time spent in the criminal sector as measured by experience in the sector. While legal human capital may decline in prison in addition to depreciation effects, for example due to reputation effects, criminal human capital may increase if criminals in prison learn from each other. Among dynamic models 14 Other dynamic models of criminal participation include Flinn (1986), Lochner (2004) and Lee and McCrary (2009). 17 of crime, this model is useful because it can accommodate a broad range of determinants for criminal careers, from labor demand shocks to neighborhood effects. Recent work by Bell et al. (2014) investigates the strength of these types of dynamic effects with reference to recessions. Specifically, they test whether recessionary conditions at the point of school exit influences participation in crime by comparing outcomes across cohorts. US data on incarceration shows that local experience of a recession (defined as the unemployment rate being 5 percentage points higher than normal) results...

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...ith the tenor of previous and recent surveys such as Eide et al. (1994), Durlauf and Nagin (2010, 2011) or Ritchie 11 See Caplan (2006) for the relevance of rational-choice models for terrorism. (2011). However, Durlauf and Nagin (2010) correctly state that most effects are measured as marginal effects in addition to the already existing long sentence. Thus, more reliable evidence would be based on discontinuous jumps of severity. Levitt (1998) has found a significant drop in the offending of young adults when they reach the age of 18, i.e. the age of jurisdiction for adult courts in Florida. Lee and McCrary (2009), who were able to measure the discontinuous change of majority in weeks and days instead of years (as in Levitt 1998), confirmed the negative sign but results were insignificant. Other abrupt and unexpected changes of deterrence come from natural experiments. Maurin and Ouss (2009) and Drago et al. (2009) study the effect of external variations arising in response to collective pardons in France and Italy, respectively. Maurin and Ouss (2009) show that five years after release those who have received a reduced sentence as a consequence of the pardon had a 12% higher rate of recidivism than th...