The logic of incapacitation is the prevention of crime via the forced removal of known offenders from the community. The challenge is to provide a plausible estimate of how many crimes an incarcerated individual would have committed, were s/he free in the community rather than confined in prison. The objective of this study is to provide estimates of the incapacitation effect of first-time imprisonment from a sample of convicted offenders.
The data are official criminal records of all individuals convicted in The Netherlands in 1997. Two different analytical strategies are used to estimate an incapacitation effect. First, the offending rate of the imprisoned individuals prior to their confinement in 1997 provides a “within-person counterfactual”. Second, imprisoned offenders are paired with comparable non-imprisoned offenders using the method of propensity score matching in order to estimate a “between-person counterfactual”. Incapacitation estimates are provided separately for juvenile imprisonment (ages 12–17) as well as adult imprisonment (ages 18–50), and for male and female offenders.
The best estimate is that 1 year of incarceration prevents between 0.17 and 0.21 convictions per year. The use of additional data sources indicates that this corresponds to between roughly 2.0 and 2.5 criminal offenses recorded by the police.
The current results suggest that, insofar as imprisonment is used with the primary goal of reducing crime through incapacitation, a general increase in the use of incarceration as the sanction of choice is not likely to yield major crime control benefits.
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This contrasts with “top-down” studies of prison effectiveness (Spelman 2000), which provide aggregate estimates of the total effect of imprisonment yet are agnostic about whether the effects operate through deterrence or incapacitation (Gibbs 1968; Kuziemko and Levitt 2004; Levitt 1996; Marvell and Moody 1994). Some researchers advocate a purely top-down approach to the study of incarceration (see Miles and Ludwig 2007).
While the obvious measure of central tendency in incapacitation studies is the mean (Piquero and Blumstein 2007), this estimate is sensitive for outliers in the sample. In a sample, such as the Rand sample, in which half of the population committed very few offenses and a much smaller percentage commit many crimes annually the mean of the group will increase sharply. In such highly skewed distributions it could be problematic to interpret the mean offending rate as the number of crimes averted by incarceration. Given that the current focus is on the effects of first imprisonment, the criminal histories of offenders in our sample are more homogeneous, rendering estimating an overall mean incapacitation effect less problematic.
Research efforts using risk assessment tools to identify high-rate offenders and target them for longer prison sentences have been notoriously unsuccessful (Auerhahn 1999, 2003; Gottfredson and Gottfredson 1994; Greenwood and Turner 1987), calling into question the wisdom of a selective incapacitation policy.
When drug offenses were included in the latter two studies, median offense rates were doubled.
The assumption of constant offending is also problematic because with age offenders tend to decrease their offending rate (Farrington 1986). Especially when estimating the effect of long-term prison sentences, disregarding intermittency and the effect of age on offending would result in grossly overestimating the incapacitation effect. In contrast to within-person incapacitation estimates, the between-person counterfactual approach takes into account that offending rates are not stable over the life course—especially if offenders in the control group are matched to offenders in the experimental group directly on their age.
The studies by Sweeten and Apel (2007) and Owens (2009) represent only two of a new generation of incapacitation research. One strand of this research employs simulation techniques (Bhati 2007; Blokland and Nieuwbeerta 2007), while another strand takes advantage of a variety of aggregate “natural experiments” (Barbarino and Mastrobuoni 2008; Johnson and Raphael 2009; Kessler and Levitt 1999; Ramirez and Crano 2003).
In the Dutch criminal justice system, the public prosecutor has the discretionary power not to prosecute every case forwarded by the police. For instance, he or she may decide to drop the case if prosecution would probably not lead to conviction due to lack of evidence or for technical considerations (e.g., procedural or technical waiver). The public prosecutor is also authorized to waive prosecution ‘‘for reasons of public interest’’ (i.e., waiver for policy considerations). The Board of Prosecutors-General has issued national prosecution guidelines under which a public prosecutor may decide to waive a case for policy reasons, for example, if measures other than penal sanctions are preferable or more effective, prosecution would be disproportionately unjust or ineffective in relation to the nature of the offense or the offender, or prosecution would be contrary to the interest of the state or the victim (Tak 2003).
Fencing, property, theft, violent sexual, fornication (younger than 16 years), violent theft, threatening/assault, aggravated assault, aggressive severe injury, public violence, trespass, offense against authority, public order, insulting, arson endangering human life, other/cruelty to animals, traffic, opium act, weapons act, other crimes.
We did not calculate imprisonment effect for the exact duration of the prison term the offender was sentenced to, which was in most cases less than a full year, because we lack information on the exact timing of the actual prison term, as well as on the exact data of commission of the offense. Estimates were based on dates of registration of the offense and dates of sentencing.
Offenders from the experimental group for which we were unable to find a match differ from offenders in the experimental group that we could match. The propensity scores of the unmatched offenders in the experimental group were high compared to those of offenders we were able to find a match for; these offenders were too different in terms of observed variables from offenders in the control group. Additional analyses showed that the persons we were not able to match were relatively more criminally active in the year prior to their incarceration. Furthermore, female offenders are slightly underrepresented in the matched sample and regarding age the matched sample is on average younger (mean 25.61) compared to the unmatched sample (mean 27.95).
Although age is included in the propensity score model, two matched offenders can have the same propensity score but might not actually be of the same age. Therefore, we additionally match directly on age, sex and offense type. The strategy of estimation within subpopulations defined by the covariates has precedence in the literature on the propensity score methodology (see Rosenbaum and Rubin 1984). We distinguish eight age categories: (1) 12-17; (2) 18-21; (3) 22-25; (4) 26-30; (5) 31-35; (6) 36-40; (7) 41-45; (8) 46-50.
Note that for the incarcerated persons 14.5 percent was correctly assigned in the full sample by the propensity score model compared to 44.2 percent in the matched sample.
Note that, consistent with prior research, our current aim is to provide a single estimate of the average incapacitation effect for the entire population of those imprisoned for the first time—stratified by age period and sex. As one anonymous reviewer noted from a (selective) policy perspective it is also important to gain insight in the extent to which incapacitation effects are concentrated in the most active offenders. While we value this suggestion, it is to be noted that the current focus is on the incapacitation effects of first-time imprisonment and that variation in the number of prior offenses in our sample is thereby limited as those most active are also more likely to have been previously sentenced to prison. Effects of various selective scenarios using Dutch data were estimated in Blokland and Nieuwbeerta (2007) and Vollaard (2011). In the current study, we control for heterogeneity in the between-person counterfactual approach, because offenders are matched on pre-existing rates of offending. Pre-existing rates of offending at the time of conviction is said to be a perfect control for population heterogeneity (Bushway et al. 2004). For future research that focuses on selective incapacitation it would also be interesting to study the incapacitation effect stratified by propensity score. Such stratification gives insight in whether the highest (say) 10 percent of propensity scores can account for a disproportionate share of the total incapacitation effect.
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Wermink, H., Apel, R., Nieuwbeerta, P. et al. The Incapacitation Effect of First-Time Imprisonment: A Matched Samples Comparison. J Quant Criminol 29, 579–600 (2013). https://doi.org/10.1007/s10940-012-9189-3
- Incapacitation effect
- Propensity score matching