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Benefit-Cost Analysis for Crime Policy

Benefit-Cost Analysis for Crime Policy. Roseanna Ander Executive Director, University of Chicago Crime Lab & Jens Ludwig McCormick Foundation Professor, University of Chicago. Why benefit-cost analysis (BCA)?. Crime control is costly…

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Benefit-Cost Analysis for Crime Policy

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  1. Benefit-Cost Analysis for Crime Policy Roseanna Ander Executive Director, University of Chicago Crime Lab & Jens Ludwig McCormick Foundation Professor, University of Chicago

  2. Why benefit-cost analysis (BCA)? • Crime control is costly… • With >2 million people behind bars, U.S. has highest incarceration rate in world by far • 2006 expenditures on criminal justice = $214 billion • Ignores all the (surely massive) non-monetary social costs of crime control, such as impacts on distressed communities • With massive budget deficits at every level of government, growing interest in scaling back

  3. Why benefit-cost analysis (BCA)? • But crime itself is also costly… • $1 to $2 trillion per year • Disproportionately affects most disadvantaged among us (ex: leading cause of death, blacks 15-24) • How much crime control should we do? • This is one important use of BCA for crime policy: • Helps us weigh benefits of scaling back criminal justice system, vs. costs of having more crime • Good BCA counts non-monetary impacts as well

  4. Second important use of BCA for crime policy • Figure out how to get maximum amount of crime control for given costs of crime control • (ex) 2006 we spent $98.9b on police, $46.9b on judicial / legal, $68.7b on corrections • Is this remotely close to optimal mix? Could we get more crime control w/ same or lower cost?

  5. An example from the MIT Poverty Lab (2008) • What are the costs of obtaining an additional child-year of education in developing countries? • Conditional cash transfer program • Hiring extra teachers, class size 80→40 • De-worming treatment

  6. An example from the MIT Poverty Lab (2008) • What are the costs of obtaining an additional child-year of education in developing countries? • Conditional cash transfer program ($6,000) • Hiring extra teachers, class size 80→40 • De-worming treatment

  7. An example from the MIT Poverty Lab (2008) • What are the costs of obtaining an additional child-year of education in developing countries? • Conditional cash transfer program ($6,000) • Hiring extra teachers, class size 80→40 ($200) • De-worming treatment

  8. An example from the MIT Poverty Lab (2008) • What are the costs of obtaining an additional child-year of education in developing countries? • Conditional cash transfer program ($6,000) • Hiring extra teachers, class size 80→40 ($200) • De-worming treatment ($3.25)

  9. An example from the MIT Poverty Lab (2008) • What are the costs of obtaining an additional child-year of education in developing countries? • Conditional cash transfer program ($6,000) • Hiring extra teachers, class size 80→40 ($200) • De-worming treatment ($3.25) • Lessons: • Each intervention ‘works,’ but social good from given $ can be increased thousand-fold by allocating resources to most efficient uses • In resource-limited world, crucial need to know which interventions are most cost-effective

  10. How to obtain benefit-cost estimates? • Two steps: • Measure the causal impact of a policy intervention (measured in natural units like arrests, etc.) • Convert impacts in natural units to common dollar metric, to facilitate comparison to costs • Each of these steps is much more challenging than they might initially appear…

  11. How to measure the causal impacts of policies? • Selection bias concern (e.g., effects of Boy Scouts) • Randomized clinical trials solve this for sure: • Treatment and control groups randomly assigned • B/c of random assignment, any difference in average outcomes for two groups unambiguously due to the “treatment” (program or policy) • Are clinical trials only way to measure impacts? • “Randomistas” (e.g., Larry Sherman) vs. “Research pluralists” (e.g., Rob Sampson)

  12. Why this matters hugely for crime policy • No guarantee bias need be small • (Ex) Hormone Replacement Therapy (HRT) • For many years, encouraged for post-menopausal survivors of breast cancer • Based on observational (epidemiological) studies that controlled for rich set of covariates • OK if we have good theory, but I think our theories quite bad (so this design is like “regression-adjust and pray”) • Finally a randomized trial of HRT was carried out…

  13. Why this matters hugely for crime policy • No guarantee bias need be small • (Ex) Hormone Replacement Therapy (HRT) • For many years, encouraged for post-menopausal survivors of breast cancer • Based on observational (epidemiological) studies that controlled for rich set of covariates • OK if we have good theory, but I think our theories quite bad (so this design is like “regression-adjust and pray”) • Finally a randomized trial of HRT was carried out… • Had to be stopped early (HRT increased risk of cancer 3x) • BCA of biased impact estimate leads to bad decisions

  14. Middle ground? • Not “RCT or bust,” but rather, aspiration for as much “design-based” research as possible • Institutional knowledge about how / why some groups but not others receive “treatment” • Plausible that assignments unrelated to potential outcomes (conditional on observable variables) • Feasible “natural experiment” crime examples: • Lotteries • Discretion creating nearly random assignment • Assignment through prioritization • Policy changes over time

  15. Lotteries (Already common in education, housing) • Often excess demand for a government service • Allocate limited resources among eligibles by lottery • Note randomization not inconsistent with prioritization, b/c there is eligibility screen (key is, do # eligibles > # slots?) • Compare eligible persons who did vs. did not win lottery (groups similar aside from lottery outcome) • Example from University of Chicago Crime Lab: • Cook County Juvenile Temporary Detention Center, convert 500 bed facility to run like 10 50-bed facilities • Half of residential units currently therapeutic • Randomize youth for whom JTDC no strong priors

  16. Discretion • Institutional structure of criminal justice system creates quasi-lottery • Decision-makers possess discretion, and vary in their propensity to assign “treatments” • Defendants randomly assigned to decision-makers • Examples from Chicago: • Judge payola scandal, so now randomization to judges • Charles Loeffler (2010), judges vary in prison sentence lengths (judge as instrumental variable for estimating effects of prison time on labor market outcomes) • Judges also vary in assigning alternatives to detention

  17. Assignment through prioritization • Often occurs when reducing decision-maker discretion is a goal • Keys are ranking of people / places, and fixed thresholds in treatment decision • People or places prioritized for some intervention on basis of some scalar score (like predicted risk) • Those just above threshold receive one treatment, those just below receive a different treatment • Person just above & below threshold nearly identical, difference in ranking in narrow range close to random • Regression discontinuity – compare above / below threshold

  18. (Ex) Chen and Shapiro American Law & Economics Review, 2007

  19. Policy changes over time(Ex) Ludwig & Cook, 2000 JAMA

  20. Second crucial step for BCA • Convert impacts in natural units (arrests, dropouts, etc.) to dollar metric • Without this, no way to compare benefits to costs (which are already in dollars) • Dollar metric does not imply that only tangible “economic” costs matter! • Indeed, intangible costs drive social costs of crime • (Ex) Cook and Ludwig 2000, social costs of gun violence each year ~$100b • Medical costs & lost earnings tiny share of that

  21. 3 methods for measuring intangible costs • Jury trial awards • Not a representative sample of crimes • “What would it take to make victim whole?” not the right question for crime policy • Labor & housing market risk / price tradeoffs • Omitted variables concerns • Plus wrong target (miss altruistic concern for others) • Contingent valuation (CV) – hypothetical market Qs • People have incentives to think about this already (labor & housing markets), but hypothetical behavior is hypothetical • High priority: Learn more about CV in crime application (compare to environmental area)

  22. Why BCA is so important • Each extra $1 spent on police, ~$4 to $8 worth of ↓ crime (B/C ratios of 4:1 to 8:1) • From study of COPS program, Evans & Owens (2006) • BCA elaboration from Donohue & Ludwig (2007) • Now consider secondary prevention experiment Crime Lab carried out in Chicago • Social-cognitive skill program for at-risk middle school and 9th grade boys in Chicago Public Schools • $ value of crime impacts imply B/C ratio of 1.4:1 to 9:1, but imprecisely estimated, not significant • Considering impact on high school graduation as well suggests B/C ratio of 8:1 (upper end of police range)

  23. Conclusions • Benefit-cost analysis only makes sense if we are sure we have good estimate for policy impact • Since intangible costs drive social costs of crime, crucial that we learn more about how to measure those (and intangible costs of crime control also) • Without better crime policy analysis (causal inference and benefit-cost analysis) we are blinding policymakers to the difficult tradeoffs they face in this area

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