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Marriage and Desistance from Crime in the Netherlands: Do Gender and Socio-Historical Context Matter?

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Abstract

Over the last two decades, research examining desistance from crime in adulthood has steadily increased. The evidence from this body of research consistently demonstrates that salient life events—in particular, marriage—are associated with a reduction of offending across the life course. However, previous studies have been largely limited to male samples in the United States. As a result, questions regarding the universal effect of these relationships remain. Specifically, research is needed to assess whether the desistance effect of life events like marriage varies by gender and/or socio-historical context in countries other than the U.S. The present research addresses these gaps by examining the relationship between marriage and criminal offending using data from the Criminal Career and Life Course Study (CCLS). The CCLS includes criminal conviction histories spanning a large portion of the life course for nearly 5,000 men and women convicted in the Netherlands in 1977. Because we assess change over multiple observations within and between individuals, we utilize hierarchical models to estimate gender and contextual effects of marriage on criminal offending (i.e., any, violent, and property convictions). Overall, we find consistent support for the idea that marriage reduces offending across gender and socio-historical context. Notably, we find that the reduction in the odds of offending due to marriage is significantly greater for individuals in the most contemporary context. The implications of these findings are discussed.

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Notes

  1. Based on identifying information from 1977, researchers at the NSCR were able to trace 89.4% of the original sample (n = 5,164), leaving a total of 4,615 individuals in the sample to be analyzed. The characteristics of these 4,615 individuals are similar to the total sample consisting of 5,164 persons, and therefore can be regarded as representative of all offenders in 1977. For more information on the full CCLS sample we refer readers to the CCLS codebook (Nieuwbeerta and Blokland 2003) and previous publications based on this dataset (Blokland and Nieuwbeerta 2005; Blokland et al. 2005).

  2. Given the decreasing sample sizes after age 55, we conducted all of our analyses twice: first using data for the full age range (12 thru 79) and second for ages 12–55. Importantly, the substantive results do not differ across the full versus the age restricted sample. However, it should be recognized that using the restricted age means that for 2% of the cases the offense by which they were sampled in 1977 is not included in the analysis. This may potentially bias the estimate of the effect of marriage for that group (but the direction is unclear since it depends on the number of persons married and divorced at that age). Since, the substantive results of the analyses on the full and the age restricted sample are nearly identical and most notably in the analyses on the full age range we find that the good marriage effect is strongest in the most contemporary context/group, we are confident that our results are robust. Thus, to limit the length of this paper we did not present the results of both analyses, but only for the age restricted sample (12 thru 55).

  3. While the GDF contain information on all offenses that have lead to any type of judicial interference, here we use only information on those offenses that were either followed by a conviction or a prosecutorial disposition due to policy reasons, thereby excluding cases that resulted in an acquittal or a prosecutorial disposition due to insufficient evidence.

  4. Unlike birth-cohort studies, the age range in the sample is broad and skewed, ranging from 12 to 79 with a peak at age 18. This feature has two implications. First, the convictions recorded for the sample cover a long period—from 1924 to 2002 (when the data collection period concluded). Second, individuals were not randomly sampled from the entire population; they were all criminally active in 1977.

  5. Given its prevalence in 1977, the sample for driving under the influence was confined to 2%. Less common, serious offenses were over-sampled including: 25% of all robbery, public violence, and battery cases; 100% of all cases involving murder (including attempts), offenses against decency, rape, child molesting, and other sexual assaults; and 17% of all drug offenses. Additionally, because the sample was one of cases, not people, offenders who had two or more convictions in 1977 were more likely to be included in the study. In analyzing the data a weighting factor is included so that the weighted sample represents the distribution of offense types and individuals as they were convicted in 1977.

  6. A reviewer raised a concern that the non-random sampling strategy employed to create the CCLS data may result in substantively different samples across the three groups. Specifically, older individuals in the sample, because they were convicted of a crime in later adulthood (after age 32), are by definition long-term offenders. Therefore, the older group may capture more criminally active individuals while the younger group may be more representative of the general population in regards to their level of criminal activity. We agree that this concern is an important one and may influence the findings from our analysis. However, it is crucial to keep in mind two points. First, the inclusion criterion for this sample was a conviction for any offense. Thus, individuals may be in the sample because of a violent or property offense or they may be in the sample due to a drunk driving or a public order offense. Seriousness of the offense in 1977 was not used for inclusion in the sample. In fact, examining a variety of descriptive factors, we find that the youngest group has a significantly earlier age of onset and they are more likely to be convicted of a serious offense. Second, previous analyses with these data (see Blokland et al. 2005:940) indicate that the youngest group remains as active—if not more so—than the older group, across the life course.

  7. Although the traditional estimation strategy for hierarchical non-linear models has been the use of penalized quasi-likelihood (PQL), we utilize Laplace estimation as it has been shown to provide more precise estimates (Raudenbush et al. 2000; Snijders and Bosker 1999). Additionally, the Laplace estimation method produces a model fit statistic (deviance) which is not available with PQL. We also conducted analyses using the frequency of criminal convictions. Because of the relative rarity of a criminal conviction in each age-period, the rarity of multiple convictions in a given year, and because the substantive results did not differ, we chose to report the estimates from the logit model.

  8. Age, age2, and age3 are divided by 10 (for ease in estimation) and are grand mean centered.

  9. We include a random age linear slope in the model for any conviction. The model indicated that a random age linear slope was not significant for both the violent conviction and property conviction analyses. Therefore, we assume a fixed effect for age in these models. The inclusion of error terms for the age2 and age3 parameters did not allow the model to converge.

  10. The descriptive patterns presented for the full sample maintain for gender and socio-historical context specific samples. These tables are available upon request from the lead author.

  11. Tables for the sensitivity analyses are available from the lead author upon request.

  12. The equation for this model is: \( \pi _{4} = \beta _{{40}} + \beta _{{41}} (female) + \beta _{{42}} (group2) + \beta _{{43}} (group3) + \beta _{{44}} (female) \times (group2) + \beta _{{45}} (female) \times (group3) \)

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Acknowledgments

The authors would like to thank Wayne Osgood, David Kirk, and Terceira Berdahl for their comments on an earlier version of this paper. In addition, the authors thank the editor of JQC and the three reviewers for their helpful comments and assistance in improving the paper.

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Authors and Affiliations

  1. Department of Criminology and Criminal Justice, University of Maryland, 2220 LeFrak Hall, College Park, MD, 20742, USA

    Bianca E. Bersani & John H. Laub

  2. Netherlands Institute for the Study of Crime and Law Enforcement (NSCR), Wassenaarseweg 72, P.O. Box 792, 2300 AT, Leiden, The Netherlands

    Paul Nieuwbeerta

  3. Department of Sociology, Utrecht University, Heidelberglaan 2, 3584 CS, Utrecht, The Netherlands

    Paul Nieuwbeerta

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  1. Bianca E. Bersani
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  2. John H. Laub
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Correspondence to Bianca E. Bersani.

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Bersani, B.E., Laub, J.H. & Nieuwbeerta, P. Marriage and Desistance from Crime in the Netherlands: Do Gender and Socio-Historical Context Matter?. J Quant Criminol 25, 3–24 (2009). https://doi.org/10.1007/s10940-008-9056-4

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