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Minimum Dropout Age and Juvenile Crime in the USA

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Abstract

This study examines the effects of a minimum dropout age (MDA) on juvenile crime in the community. The findings are that increasing the MDA from 16 to 18 decreases the crime rate by 9%. The results imply that an MDA greater than 16 reduces crime in the community for individuals aged 16-to-18. Although a higher MDA is primarily intended to increase educational attainment, this policy has a secondary impact on reducing community crime.

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Notes

  1. See Diffey and Steffes (2017).

  2. For detail definitions of each category of crime, please visit https://www.fbi.gov/services/cjis/ucr.

  3. The African-American ratio is assumed to proxy for community or cultural factors.

  4. Considering a policy intervention in states' MDA, this study first examines whether an increase from 16 to either 17 or 18 affects the crime rate. The control group is states with an MDA of 16 because every state has a minimum dropout age of at least 16, the treatment groups are states that increase their MDAs from 16 to 17 or from 16 to 18.

    To examine the effect of increasing the MDA as a policy intervention, this study employs following empirical strategy:

    $$ Y_{ist} = \alpha_{i} + \lambda_{s} + \tau treatment + X_{ist} + \sigma T_{s} + u_{ist} $$
    (3)

    where Yist is the juvenile crime rate of county i in state s at time t, treatment = 1 for states that increases their MDA from 16 to 17 time t, otherwise 0. In other words, treatment is the binary indicators for states that increase their MDAs from 16 to 17. This empirical strategy allows to compare the crime rate between the post- and pre-intervention (first difference) and crime rate between the treatment and control groups (second difference). As increasing the MDA reduces juvenile crime rate, the treatment effects τ are expected to be negative and statistically significant. In other words, an estimated τ implies how increasing the MDA from 16 to 17 decreases crime rate in the community. To examine the parallel pre-policy trends, using Eq. 1, we construct an event study as follows. First, we consider a binary indicator for MDA of 17 as a single treatment. Second, we separate this indicator out into two leads and two lags. Finally, considering total crime rate as dependent variable, the model is estimated. The coefficients of estimate (with p-values for t -statistic) are as follows: lag 1 = − 1.23 (p value = 0.05), lag 2 = − 1.59 (p value = 0.04), lead 1 = 0.45 (p value = 0.12), and lead 2 = 0.46 (p value = 0.19). For example, the estimated coefficient for lag 2 indicates that total crime rate is fewer by 1.59 per 1,000 individuals after 2 years of increasing the MDA.

    Secondly, following the similar approach, this study considers increasing an MDA from 16 to 18 a policy intervention. In such case, states that increased their MDA from 16 to 18 are the treatment group, and states with an MDA of 16 are control group. Third, states that increase their MDA from 16 to either 17 or 18 are considered as treatment group, and states with an MDA of 16 are considered as the control group. Finally, states that increase their MDA from 17 to 18 are considered as treatment group, and states that have an unchanged MDA of 17 are considered as control group.

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Acknowledgements

I would like to sincerely thank Virginia Wilcox, Jeremy Groves, and Maria Ponomareva for their helpful comments and suggestions. I would also like to thank Philip Oreopoulos for sharing data on minimum dropout age laws. I am thankful to Anna A. Klis, D. Mark Anderson, Brantly Callaway, Cl´ement de Chaisemartin, Md. Rafi Hossain, Syed Abu Hasnath, Md. Alauddin Majumder, Md. Rafayet Alam, Isaac M. Mbiti, Paolo Buonanno, and other participants in the seminars of the Economics Department at Northern Illinois University, Illinois Economics Association Meeting, Midwest Economics Association Meeting, and Missouri Valley Economic Association Meeting. I am also indebted to Gail Jacky, Jeff Gard, Ashley Bartelt, Maxwell Hoover, Manahari Adhikari, Carson Shoupe, and Justin Ness at Northern Illinois University for their valuable time to discuss the education system in the USA. I also owe special thanks to the editor, Cynthia A. Bansak, and anonymous referees for their useful comments. All remaining errors are mine.

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  1. Economic and Public Policy Research Group, Department of Humanities and Social Sciences, Dhaka University of Engineering and Technology (DUET), Gazipur, 1707, Bangladesh

    Md. Abdur Rahman Forhad

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Appendix

Appendix

See Tables 10, 11, and 12.

Table 10 Numbers of states by MDA, 1950 to 2019.
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Table 11 Summary statistics for community crime, individual category
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Table 12 Interaction terms with African-Americans and crime in the community, individual category
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Forhad, M.A.R. Minimum Dropout Age and Juvenile Crime in the USA. Eastern Econ J 47, 378–405 (2021). https://doi.org/10.1057/s41302-020-00184-2

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