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Impulsivity or Discounting? Evaluating the Influence of Individual Differences in Temporal Orientation on Offending

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Abstract

Objectives

Few studies have evaluated Nagin and Pogarsky’s (2004) proposed distinction between impulsivity and discounting. This study evaluates and expands upon their framework to consider distinct discounting rationales, perceived age-at-death (PAAD) and expected social value (ESV), and whether impulsivity conditions the effects of these discounting rationales on offending; both in the short-term and over time.

Methods

Negative binomial and group-based trajectory modeling strategies are used in conjunction with a sample of high-risk males from the Pathways to Desistance Study to assess Nagin and Pogarsky’s framework.

Results

In the negative binomial models, impulsivity and PAAD influence offending, while ESV does not. Impulsivity level also does not condition the effects of discounting. In the trajectory models, PAAD and impulsivity influence offending, while ESV does not. Here, impulsivity level moderates the effect of ESV, but not PAAD.

Conclusions

Findings support Nagin and Pogarsky’s general framework of inter-temporal choice but also encourage scholars to focus more closely on the diverse rationales for temporal-orientation. Further, consideration of these mechanisms within a developmental perspective is necessary. Implications for theory and future research are discussed.

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Notes

  1. An inter-temporal choice is a decision in which the costs and/or benefits occur at two different points in time.

  2. Nagin and Pogarsky (2001) provided an example of how discounting could affect the perceived cost of crime from a deterrence perspective. Imagine an individual is faced with a fine. If the individual devalues paying the fine in the future (e.g., paying the fine 30 days from now relative to paying today), the fine may be perceived as less severe as a result of the individual’s tendency to discount future costs. The same logic can be applied to delayed rewards, such as perceived future benefits, which will be our focus in this manuscript.

  3. Impulsivity is often considered one dimension of self-control. The low self-control-offending relationship also provides general support for the impulsivity-offending relationship (e.g., Arneklev et al. 1993; Cochran et al. 1998; Grasmick et al. 1993; Hay 2001; Nagin and Paternoster 1993; Piquero and Tibbetts 1996; Pratt and Cullen 2000).

  4. A discount factor \({\delta }_{t}={\left(\frac{1}{\left(1+r\right)}\right)}^{t}\) allows a weight to be added to future costs or benefits. The weight is dependent on the number of time periods a cost/benefit is delayed (t) and an individual’s discount rate (r). The discount rate is the extent to which a person decreases future values. Those with a high discount rate are more present oriented and devalue the future (see Nagin and Pogarsky 2001).

  5. Missing cases are the result of missing values on the following variables (frequency and percentage in parentheses): impulsivity (3, < 1%), PAAD (72, 6%), ESV (9, < 1%), certainty (2, < 1%), personal rewards (1, < 1%), neighborhood conditions (2, < 1%), family arrest (3, < 1%).

  6. To create annual recall measures, 6-month consecutive waves were combined, where any indication of a crime in either of the 6-month recall periods was considered an indication of that crime for the year. This is often done with the Pathways data to create uniform recall periods (Monahan et al. 2009, 2013; Piquero et al. 2012).

  7. To create annual follow-ups, again, consecutive 6-month waves were combined for years 1, 2, and 3 by using a summated average of non-missing values. This measure was included as a control variable in the Year 1 analysis.

  8. Additional post-estimation procedures were used to assess the accuracy of the trajectory model. First, the average posterior probability of group assignment (AvePP) is reported. Nagin (2005, p. 88) suggests all groups reach a minimum AvePP threshold of .7, which all groups exceed (ranging from .85 to .96). Second, the odds of correct classification (OCC) was evaluated. The OCC is the ratio of the odds of a correct classification into group j based on the AvePP value, to the odds of correctly classifying individuals into group j based on the estimated proportion of the sample belonging to group j. The OCC values in our model are well above 5 (group values range from 11 to 226), which Nagin (2005, p. 89) suggests is the threshold required for a model to demonstrate high assignment accuracy.

  9. Our PAAD measure is open-ended. Respondents indicated their PAAD without an age threshold referenced in the question. This operationalization differs from measures available in the Add Health data (Brezina et al. 2009; Borowsky et al. 2009; Nguyen et al. 2012), which asks respondents to indicate: (1) their “probability of being killed by 21”; or their “chances of living to age 35.” While there are various ways to operationalize and conceptualize PAAD, the measures and studies that have implemented them all similarly focused on temporal decision-making and individuals’ views of their future.

  10. The forthcoming results do not change substantively when evaluating the expected number of years left (PAAD – current age) rather than the PAAD.

  11. Analyses evaluating the relationships between baseline covariates and Year 1 offending have a smaller sample size than the trajectory analyses. This is because inclusion in the negative binomial models requires respondents to be a part of the analytic sample required for the trajectory analyses and offending information at the first annual follow-up. Descriptive statistics for this subsample are available upon request.

  12. We also considered analyzing only the top 25% of the impulsivity scale as “high impulsivity” and the bottom 25% of the impulsivity scale as “low impulsivity.” When doing this, results are substantively similar. However, we decided against this because restricting the sample causes at least one of our SRO trajectory groups to fall below 5% of the sub-sample and meaningful comparisons can no longer be made between groups (Nagin 2005).

  13. The maximum changes per wave (9 = (|6–1| +|1–6| +|5–2| +|2–5| +|4–3| +|3–4|) / 2) times the number of wave transitions (7).

  14. A total of 6 maximum rank changes per wave, times 7 transitions.

  15. To put these values into context, a score of 9 indicates an individual’s perceived likelihood that the events would happen to them was “good = 3” and he perceived these events as at least “somewhat important = 3,” on average. A score of 25 suggests all events were perceived as “very important” and having “excellent” likelihoods of occurring.

  16. This moderation finding is consistent if impulsivity is dichotomized at different thresholds or if impulsivity is measured continuously.

  17. There is no evidence that these findings are the result of multicollinearity given the weak-to-moderate correlations among temporal orientation measures: (Impulsivity-PAAD r = −0.14; Impulsivity-ESV r = −0.14; PAAD-ESV r = 0.34).

  18. All controls are have been omitted from output for parsimony. Full results are available upon request.

  19. Predicted probabilities were computed by hard classifying individuals into trajectories and conducting multinomial logistic estimation.

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Acknowledgements

We are grateful to Kate Doubler and Bryanna Fox for their valuable feedback on the manuscript.

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Authors and Affiliations

  1. Department of Criminology, University of South Florida, 4202 E. Fowler Ave, SOC 332, Tampa, FL, 33620, USA

    Chae M. Jaynes, Richard K. Moule Jr. & Mateus Rennó Santos

  2. School of Criminal Justice, University At Albany, SUNY, Albany, NY, USA

    James T. Hubbell

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Appendix

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See Figs.

Fig. 4
figure 4

Mean value of impulsivity over time

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Fig. 5
figure 5

Trajectories of impulsivity. Note After determining a 6-group solution was optimal, the order of the trajectories were selected (BIC = −9305.73): Group 1 (quadratic, AvePP = 0. 90); Group 2 (quadratic, AvePP = 0.82); Group 3 (intercept-only, AvePP = 0.77); Group 4 (quadratic, AvePP = 0.77); Group 5 (linear, AvePP = 0.87); Group 6 (quadratic, AvePP = 0.86)

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Fig. 6
figure 6

Mean value of expected social value over time

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Fig. 7
figure 7

Trajectories of expected social value (ESV). Note After determining a 5-group solution was optimal, the order of the trajectories were selected (BIC = −22,402.35): Group 1 (intercept-only, AvePP = 0. 89); Group 2 (intercept-only, AvePP =0 .81); Group 3 (quadratic, AvePP = 0.77); Group 4 (quadratic, AvePP = 0.77); Group 5 (quadratic, AvePP = 0.91)

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7, Tables

Table 7 Descriptive statistics of self-reported offending (SRO)
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Table 8 Descriptive statistics of independent variables by level of impulsivity (N = 1035)
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Table 9 Impulsivity correlations over time
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Table 10 Expected social value (ESV) correlations over time
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10.

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Jaynes, C.M., Moule, R.K., Hubbell, J.T. et al. Impulsivity or Discounting? Evaluating the Influence of Individual Differences in Temporal Orientation on Offending. J Quant Criminol 38, 831–859 (2022). https://doi.org/10.1007/s10940-021-09518-5

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  • DOI: https://doi.org/10.1007/s10940-021-09518-5

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