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The Economic Maturity Gap Encourages Continuity in Offending

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Abstract

Purpose

In the study of criminal careers, factors that predict continuity in offending are of importance to both theory and policy. One recently advanced hypothesis is Moffitt’s “economic maturity gap,” which argues that some adolescence-limited offenders may be mired in a poor economic situation. As only one study to date has examined this hypothesis, the current study seeks to extend this line of research by assessing the relationship of the economic maturity gap on later offending.

Methods

Using data from the Cambridge Study in Delinquent Development, three distinct operationalizations of the economic maturity gap are used to predict continued offending into mid-adulthood.

Results

Findings support the hypothesis that adolescence-limited males who experience this gap in late adolescence are more likely to continue offending into adulthood.

Conclusions

Experiencing poor economic circumstances helps to maintain offending into mid-adulthood.

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Notes

  1. While Moffitt [30] has discussed the possibility of another distinct offending group, childhood-limited offenders, the current study relies upon the original conceptualization of the taxonomy as this new group is of less direct relevance to our focus.

  2. Although Moffitt has extended [29], clarified [39], and revised [30] her original theory, the specific hypothesis we test emanates from her 1993 and 1994 works and has not been altered by these more recent publications.

  3. While our study also utilizes data from the same project, we extend Hagan’s research by considering multiple factors of fiscal well-being such as job quality and satisfaction. Further, we limit our analyses to a subsample of the males who are hypothesized to be on the adolescent-limited offending path.

  4. Based on Moffitt’s [28] original taxonomy, Craig and her colleagues [7] utilized Latent Class Growth Curve analysis to identify a three-class model. This form of modeling assumes a fixed slope and functional form for each trajectory class [32]. Various models were assessed and a quadratic form was selected due to overall model fit and parsimony. Additionally, as the posterior probabilities were greater than the threshold of 0.80, the models were successful in classifying a substantial majority of the individual trajectories into one of the three classes [33]. This method identified 7.2% of the sample as LCP offenders, 69.8% as abstainers, and 23% as AL offenders. While prior research by Nagin et al. [36] also used the CSDD to identify distinct offending trajectories, the Craig et al. [7] strategy sought to identify 3 offending groups while the Nagin et al. study furthered the work undertaken by Nagin and Land [34] who identified 4 groups (high-level chronic—13.4%, low-level chronic—9.9%, adolescent-limited—12.7%, and never convicted—64%).

  5. We recognize that there is no precise, empirically based method for identifying AL offenders, and as such, we use this label for heuristic purposes only. We return to this point later in the manuscript.

  6. The respondents were between the ages of 18 and 26 when asked these questions.

  7. It could be argued that those males who were unemployed should be included in the low-status job category. However, as our index already includes a measure of unemployment, we did not want to double-count this particular category.

  8. Readers may note that based on these coding decisions, it would be possible for a male that was incarcerated at age 18 to have a score of 0 or 1 on this index, based upon if he was in debt or not. It should be noted that only one respondent in our sample was incarcerated at this time.

  9. Two additional operationalizations were also utilized in supplemental analyses. The first measure combined both essential and non-essential expenditure scales and then subtracted the respondents’ income from this sum. The second measure of the economic maturity gap represented our attempt to differentiate those experiencing the economic maturity gap from those that are not. We first assessed the distribution of the first measure in order to identify potential cut-points in the sample. Based on the distribution, we split the sample at approximately half. Those who scored 75 or more on the first measure were coded as 1, or experiencing the economic maturity gap to a greater degree than those who scored below this. Those who scored below a 75 were coded as 0. The results were substantially the same when compared to those presented in the manuscript and are available upon request.

  10. The cut points for the essential/non-essential expenditures and income measures were original to the CSDD data.

  11. We recognize the initial coding of these scales were not equal to one another pound-to-pound, an artifact of the original data collection. However, by recoding them, we retained the relative distribution and variability of the spending and income measures.

  12. Note that this is similar to arrests in the USA.

  13. It is also worth noting the offending scores for the full sample. Among the full CSDD, the adult offending score mean was 0.73 (standard deviation = 1.92) and ranged from 0 to 15.

  14. Supplemental logistic regressions were also estimated using a dichotomous offending measure where 1 indicated that the individual had been convicted between the ages of 22 and 40. The results were substantially similar and are available upon request.

  15. Supplemental chi-square and logistic regressions were also estimated using dichotomous measures of these main variables. The results indicated there was a significant difference in adult offending measures between those who experienced the economic maturity gap and those who did not.

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Author information

Authors and Affiliations

  1. College of Health and Public Service, University of North Texas, 1155 Union Circle #305130, Denton, TX, 76203-5017, USA

    Jessica M. Craig

  2. School of Economic, Political and Policy Sciences, The University of Texas at Dallas, 800 W. Campbell Rd. Mail Station 31, Richardson, TX, 75080, USA

    Alex R. Piquero

  3. Institute of Criminology, University of Cambridge, Sidgwick Avenue, Cambridge, CB3 9DA, UK

    David P. Farrington

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  1. Jessica M. Craig
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  2. Alex R. Piquero
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Correspondence to Jessica M. Craig.

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Craig, J.M., Piquero, A.R. & Farrington, D.P. The Economic Maturity Gap Encourages Continuity in Offending. J Dev Life Course Criminology 3, 380–396 (2017). https://doi.org/10.1007/s40865-017-0065-6

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  • DOI: https://doi.org/10.1007/s40865-017-0065-6

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