Predicting and Preventing Shootings among At-Risk Youth

Chandler, Dana R.; Levitt, Steven D.; List, John A.

doi:10.1257/aer.101.3.288

Cited by 57 publications

(55 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Unruly individuals were 16 percentage points more likely to be dead at the end of the two-year period. This result is similar to that obtained by Chandler et al (2011). The authors analyzed data from…”

Section: Mortality After Two Yearssupporting

confidence: 89%

Living on the edge: Youth entry, career and exit in drug-selling gangs

Carvalho

Soares²

2016

Journal of Economic Behavior & Organization

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractWe use data from a unique survey of members of drug-trafficking gangs in favelas (slums) of Rio de Janeiro, Brazil, to characterize drug-trafficking jobs and study the selection into gangs, analyzing what distinguishes gang-members from other youth living in favelas. We also estimate wage regressions for gang-members and examine their career path: age at entry, progression within the gangs' hierarchy, and short-to medium-term outcomes. Individuals from lower socioeconomic background and with no religious affiliation have higher probability of joining a gang, while those with problems at school and early use of drugs join the gang at younger ages. Wages within the gang do not depend on education, but are increasing with experience and involvement in gang-related violence. The two-year mortality rate in the sample of gang-members reaches 20%, with the probability of death increasing with initial involvement in gang violence and with personality traits associated with unruliness.

show abstract

Section: Mortality After Two Yearssupporting

confidence: 89%

Living on the edge: Youth entry, career and exit in drug-selling gangs

Carvalho

Soares²

2016

Journal of Economic Behavior & Organization

View full text Add to dashboard Cite

show abstract

“…Chalfin et al (2016) provide some preliminary evidence of how machine learning may improve predictive accuracy in these and other personnel decisions. Chandler, Levitt, and List (2011) predict highest-risk youth so that mentoring interventions can be appropriately targeted. Abelson, Varshney, and Sun (2014), McBride and Nichols (2016), and Engstrom, Hersh, and Newhouse (2016) use machine learning to improve poverty targeting relative to existing poverty scorecards.…”

Section: Prediction In Policymentioning

confidence: 99%

Machine Learning: An Applied Econometric Approach

Mullainathan

Spiess

2017

Journal of Economic Perspectives

1,366

803

View full text Add to dashboard Cite

Machines are increasingly doing “intelligent” things. Face recognition algorithms use a large dataset of photos labeled as having a face or not to estimate a function that predicts the presence y of a face from pixels x. This similarity to econometrics raises questions: How do these new empirical tools fit with what we know? As empirical economists, how can we use them? We present a way of thinking about machine learning that gives it its own place in the econometric toolbox. Machine learning not only provides new tools, it solves a different problem. Specifically, machine learning revolves around the problem of prediction, while many economic applications revolve around parameter estimation. So applying machine learning to economics requires finding relevant tasks. Machine learning algorithms are now technically easy to use: you can download convenient packages in R or Python. This also raises the risk that the algorithms are applied naively or their output is misinterpreted. We hope to make them conceptually easier to use by providing a crisper understanding of how these algorithms work, where they excel, and where they can stumble—and thus where they can be most usefully applied.

show abstract

“…Other illustrative examples include: (i) in education, predicting which teacher will have the greatest value add (Rockoff et al, 2011); (ii) in labor market policy, predicting unemployment spell length to help workers decide on savings rates and job search strategies; (iii) in regulation, targeting health inspections (Kang et al 2013); (iv) in social policy, predicting highest risk youth for targeting interventions (Chandler et al, 2011); and (v) in the finance sector, lenders identifying the underlying creditworthiness of potential borrowers.…”

Section: Prediction Problems Are Common and Importantmentioning

confidence: 99%

Prediction Policy Problems

Kleinberg

Ludwig

Mullainathan

et al. 2015

American Economic Review

466

252

View full text Add to dashboard Cite

Most empirical policy work focuses on causal inference. We argue an important class of policy problems does not require causal inference but instead requires predictive inference. Solving these “prediction policy problems” requires more than simple regression techniques, since these are tuned to generating unbiased estimates of coefficients rather than minimizing prediction error. We argue that new developments in the field of “machine learning” are particularly useful for addressing these prediction problems. We use an example from health policy to illustrate the large potential social welfare gains from improved prediction.

show abstract

Predicting and Preventing Shootings among At-Risk Youth

Cited by 57 publications

References 1 publication

Living on the edge: Youth entry, career and exit in drug-selling gangs

Living on the edge: Youth entry, career and exit in drug-selling gangs

Machine Learning: An Applied Econometric Approach

Prediction Policy Problems

Contact Info

Product

Resources

About