Part G-I Some Examples of Empirical Work on The Economics of Crime and Punishment

1. Part G-I Some Examples of Empirical Work on The Economics of Crime and Punishment Crime_G

2. Objectives - understand the importance of empirical work in crime and punishment - understand the nature of empirical work in crime and punishment - understand why the ‘crime and deterrence’ debate will continue for some time Crime_G

3. Implications of measurement problems: Example: questions such as how to decrease youth crime become difficult to answer: More police, more convictions, longer sentences – yes our model of rational crime would conclude that this approach should have a negative impact on crime. More youth community programs, better education, better employment opportunities - yes our model of rational crime would conclude that this approach should have a negative impact on crime. But from the perspective of efficient policy the question is: which approach yields the greatest deterrence per dollar ? Crime_G

4. Which approach yields the greatest deterrence per dollar ? Recall there are two components to the Social Cost of Crime: 1. Direct harm to victim and harm to society 2. Cost of deterrence We need to measure both in comparing alternative approaches to decreasing crime. Crime_G

5. Measurement problems Measuring the social harm of crime: 1. Direct harm to victim and harm to society (perceived safety) (security/insecurity) real vs. imagined - difficult to quantify and aggregate (much of this harm is subjective in nature) - the number of reported crimes provides only a rough gauge to whether or not harm costs are rising or falling - very difficult to determine the marginal benefits associated with changes in policy Crime_G

6. Measuring the cost of deterrence 2. Cost of deterrence - not too difficult to quantify what we currently spend (police, courts, prisons, private security expenditures) - more difficult to determine costs such as ‘erosion of the rights of all citizens’ - very difficult to determine the marginal deterrence associated with changes in specific expenditures (how much deterrence per additional dollar spent?) Crime_G

7. What is the deterrence effect of an increase in the policing budget (more apprehensions)? Court budget (more convictions)? Prison budgets (more convictions/longer sentences)? What is the deterrence effect of an increase in preventative measures (more community programs, more street lighting, anti-theft devices, etc)? What is the deterrence effect of a decrease in personal freedoms (right to privacy, search and seizure, gun ownership, etc)? Crime_G

8. We often discuss crime on a very general (aggregate level) but there are many different types of crime: Homicide, Assault, Robbery, Theft, Drunk driving, Prostitution, Gambling, Pot smoking, Use of hard drugs, Non-Criminal Code violations Note that the Motor Vehicle Act, Income Tax Act, by-laws, etc. share prevention and enforcement resources with crime prevention and enforcement). In order to really discuss efficient deterrence, we would need to consider the problem at a disaggregate level. Consider some specific crime and the cost/benefit of alternative deterrence strategies for that specific type of crime. The ‘efficient’ composition and amount of deterrence for homicide is likely to be different than for shoplifting or tax evasion. Crime_G

9. Much (most?) empirical work focuses on relatively narrow questions. What impact did the change in a given law have on a given level of crime activity? What is the cost of imprisoning a person? What determines the clearance rate for ‘break and enters’? Such narrow question are generally more tractable. Crime_G

10. Testing for ‘Rational Cheating’ The Economics of Illegitimate Activities: Further Evidence – Mixon and Mixon, Journal of Socio-Economics, Vol. 25, No. 3, PP 373-381, 1996 Question: Can the benefits and costs of cheating (to the individual) be identified and do these factors affect the likelihood of an individual cheating as predicted by the model of rational crime? Crime_G

11. The Survey How to get data on cheating? 157 students economics and accounting students were surveyed (at a larger Southern university). This is really the only way that you could generate a data set suitable for standard statistical analysis of this type of phenomenon. Crime_G

12. The Results of the Survey 1. Have you ever observed another student cheating on an exam or written assignment at SLU? OBCHEAT + a. Yes (98) 62% b. No (59) NR (0) 2. Have you ever seen another student get caught cheating at SLU? a. Yes (15) 9% CCAUGHT - b. No (142) NR (0) 3. Based on your experience in the classroom at SLU, what percentage of students do you think cheat on a typical exam? a. No more than 1% (37) 24%PERCHT + d. Between 30% and 30% (16) b. Between 1% and 10% (71) e. More than 30% (4) c. Between 10% and 20% (26) NR (3) Crime_G

13. 4. Which response accurately describes your behavior at SLU? a. Have never cheated on a test or written work (99) 63% b. Have cheated once on a test or written work (22) c. Have cheated more than once, but less than five times on a test or written work (30) d. Have cheated five times or more on a test or written work (6) NR (0) CHEHAB dependant variable 5. If you answered “b,” ” c,” or “d” on question 4, have you ever been caught? a. Yes (3) 2% b. No (55) NR (0) 6. If you answered “c” or “d” on question 4, and you answered “a” on question 5, did you cheat again after being caught? a. Yes (0) b. No (3) NR (0) Crime_G

14. 7. Do you know anyone who routinely cheats on exams? a. Yes (39) 25%KNOCHT + b. No (117) NR (1) 8. If you were caught copying another student’s answers on an exam, what would you expect to happen to you? a. Nothing more than a reprimand (2) b. Be forced to retake the exam (33) c. Have my course grade lowered by a letter or more (31) d. Receive an F for the course (70) 45% e. Be suspended from SLU for at least one semester (20) NR (1) PENAL - 9. In your opinion, cheating at Southeastern Louisiana University is: a. Not a problem (52) 33% b. A trivial problem (65) c. A problem deserving some concern (35) d. A serious problem (4) NR (1) Crime_G

15. 10. My current classification is: a. Freshman (8) d. Senior (65) b. Sophomore (40) e. Grad. Student (7) c. Junior (37) NR (0) 11. My current grade point average is: GPA - a. 3.50-4.00 (15) b. 3.00-3.49 (44) c. 2.50-2.99 (54) d. 2.00-2.49 (36) e. less than 2.00 (8) NR (0) Crime_G

16. A regression model Y is the dependant variable (what you want to explain or understand) The X’s are the explanatory variables ε is the error term (what cannot be explained by the model) A Linear regression model looks like this: Y = b0 + b1*X1 + b2*X2 + ... + bk*Xk + ε Crime_G

17. A regression model How to interpret the coefficients (b’s)? Y = b0 + b1*X1 + b2*X2 + ... + bk*Xk + ε b1 measures the change in Y caused by a one unit change in X1holding all other X’s constant A linear regression model is a statistical technique for implementing the ceteris paribus assumption In some cases the b’s can be interpreted as an elasticity or they can be used to calculate the elasticity of the X. This allows use to answer questions such as: How responsive is the crime rate to ‘more police on the street’? Crime_G

18. A regression model Generally we are interested in two aspects of the regression results: Y = b0 + b1*X1 + b2*X2 + ... + bk*Xk + ε 1. Do the individual coefficients (b’s) contribute to the explanation of the variation in the dependent variable? For example if b1 is found to be ‘statistically significant’ then we can concluded that variation in the value of the explanatory variable X1 have a ‘statistically significant’ effect on the dependent variable Y, holding all other X’s constant 2. How well does the overall model explain the variation in Y? To answer this question we consider the error term (ε) and functions of the error term (i.e. R2). Crime_G

19. Mixon and Mixon regression results They used a LOGIT regression model since the dependent variable was an index variable. This complicates the interpretation of the coefficients (b’s) somewhat? Ideally, Mixon and Mixon could have expressed the coefficients as probabilities but they did not and we do not have enough information to do it. Unfortunately LOGIT does not allow use to easily assess the explanatory power of the model Crime_G

20. Table 2. Ordered Logit Results (1) (2) (3) (4) CPA 0.3650* 0.422* 0.4181* 0.4125* (2.28) (2.40) (2.38) (2.32) OBCHEAT 1.3467* 1.4040* 1.2968* (3.20) (3.23) (2.94) CCAUCHT 0.9261 1.0586 (0.75) (0.86) SEECC 0.1659 -0.1976 (0.12) (0.14) PERCHT 0.2064 0.3353* 0.2195 (1.15) (1.94) (1.21) Crime_G

21. Table 2. Ordered Logit Results (1) (2) (3) (4) KNOCHT 1.0305* 0.9304* (2.61) (2.31) PENAL -0.0409 -0.1323 -0.1155 -0.1449 (0.25) (0.76) (0.67) (0.82) INTERCEPT -1.4701 -2.9948* -3.1889* -.9428* (1.80) (2.97) (3.25) (2.96) CHI-SQUARE 11.80* 29.50* 80.46’ 81.53* Crime_G

22. Deterring Drunk Driving Deterring Drunk Driving Fatalities: An Economics of Crime Perspective, Benson, Rasmussen and Mast, International Review of Law and Economics, Vol. 19, pp 205-225, 1999 Question What is the effectiveness of alternative policy tools used in the control of DUI (driving under the influence of alcohol)? Crime_G

23. They consider a sample of 48 US state over 9 years The differ with respect to drink laws/alcohol laws, definition of drunk driving, penalties, enforcement and many other non-criminological factors. The authors are interested in the deterrence effect of specific laws BUT there experiment must control for all other factors that might affect the dependent variable (the number of ‘drunk’ drivers in a state involved in a fatal car accident divided by the total number of drivers in the state). The number of explanatory variables in such regression models grows quickly. Crime_G

24. TABLE 3. Driver involvement Equation (2) (BAC >0.01) Independent Variables Coefficient t statistic Attitudes towards alcohol and driving Legal drinking age 0.0901867** 2.208 Dram-shop laws -0.07977** -2.31 (tort liability against bars) Enforcement rules (Pr. of being caught) Open-container laws -0.10299** -2.51 (in cars) Anti-consumption laws -199e-02 -0.48 (in cars) Police per capita -0.00154 -1.50 Crime_G

25. Independent Variables Coefficient t statistic Enforcement rules (Pr. of being caught) Preliminary breath-test laws -0.00343 -0.10 Illegal per se laws -0.01612 -0.21 Implied-consent laws -0.00015 -0.77 (for breadth tests) No-plea-bargaining laws 0.008445 0.153 Administrative per se laws -0.00015 -0.51 (automatic suspension at time of arrest) Crime_G

26. Independent Variables Coefficient t statistic Punishment Jail for 1st conviction -0.04167 -1.53 Jail for 2nd conviction 0.000617 0.264 Fines for 1st conviction 0.000055 0.349 Fines for 2nd conviction -0.00002 -0.52 Suspension for 1st conviction 0.000551 0.871 Suspension for 2nd conviction -0.00002 -0.52 Crime_G

27. Independent Variables Coefficient t statistic Various control variables Seat-belt laws -0.03042 -1.17 Vehicle miles per driver 6.52e-05* 5.962 Ethanol per capita 0.38952* 3.338 (alcohol consumption) Metropolitan population -0.00874 -1.60 Males 16–44 per capita 6.6694 1.593 Per capita disposable income 0.00004** 1.961 Unemployment rate -0.0198** -2.38 Crime_G

28. Independent Variables Coefficient t statistic Control variables – Taste (community values) Dry-county population 0.014147 0.101 Catholics 0.41875 0.32 Mormons -5.6694 -1.23 Southern Baptists 9.0812* 3.306 Other Protestants 2.8491* 2.893 Crime_G

29. Summary Statistics Adjusted R2 0.906 F-statistic 50.7 Note: Dependent variable = ln[R/(1 2 R)]. N = 432. Intercepts, year, and state dummy variables not shown *Significant at the 0.01 level. **Significant at the 0.05 level. ***Significant at the 0.10 level (in two-tailed tests). Crime_G

30. Some additional tests in the presence of multicollinearity TABLE 4. F tests for deterrence variables Equation 2 Deterrence Variables Tested All deterrence variables F[16,348] = 2.96* Alcohol control (legal drinking age, dram-shop laws) F[2,348] = 5.36* Probability of arrest (police per capital, open-container laws, anti-consumption laws, illegal per se laws, preliminary breath-test laws, implied-consent laws) F[3,648] = 2.08*** Probability of being stopped (police per capita, open-container laws, anti-consumption laws) F[3,348] = 3.20** Crime_G

31. Some additional tests in the presence of multicollinearity Probability of arrest given being stopped (illegal per se laws, preliminary-breath-test laws, implied-consent laws) F[3,348] = 0.27 Expected punishment for 1st and 2nd offenses (administrative per se laws, no-plea-bargaining laws, fines, jail, suspensions) F[8,348] = 1.21 Expected punishment given conviction for 1st and 2nd offenses (jail, fines, suspensions) F[6,348] = 1.59 *Significant at the 0.01 level. **Significant at the 0.05 level. ***Significant at the 0.10 level. Crime_G

32. Capital Punishment as a Deterrent Does Capital Punishment Have a Deterrent Effect? New Evidence from Postmoratorium Panel Data – Dezhbahsh, Rubin and Sheperd, Americam Law and Economics Review, Vol. 5, No. 2, 2003 Question Does capital punishment deter murder? Crime_G

33. Table 1. Executions and Executing States Year No. of Executions No. of States with Death Penalty 1977 1 31 1978 0 32 1979 2 34 1980 0 34 1981 1 34 1982 2 35 1983 5 35 1984 21 35 1985 18 35 1986 18 35 1987 25 35 1988 11 35 1989 16 35 1990 23 35 1991 14 36 1992 31 36 1993 38 36 1994 31 34 1995 56 38 1996 45 38 1997 74 38 1998 68 38 1999 98 38 2000 85 38 Source: Snell, Tracy L. 2001. Capital Punishment 2000. Washington, D.C.: U.S. Bureau of Justice Statistics (NCJ 190598). Crime_G

34. Table 2. Status of the Death Penalty Jurisdictions without a Death Jurisdictions with a Death Penalty on Penalty on December 31, 2000 (No. of Executions 1977-2000) Alaska Texas (239) Virginia (81) District of Columbia Florida (50) Missouri (46) Hawaii Oklahoma (30) Louisiana (26) Iowa South Carolina (25) Alabama (23) Minnesota Arkansas (23) Georgia (23) Maine Arizona (22) North Carolina (16) Michigan Illinois (12) Delaware (11) Massachusetts California (8) Nevada (8) North Dakota Indiana (7) Utah (6) Rhode Island Mississippi (4) Maryland (3) Vermont Pennsylvania (3) Washington (3) Wisconsin Nebraska (3) Oregon (2) West Virginia Kentucky (2) Montana (2) Colorado (1) Wyoming (1) Idaho (1) Ohio (1) Tennessee (1) South Dakota (0) Connecticut (0) Kansas (0) New Hampshire (0) New Jersey (0) New Mexico (0) New York (0) Source: Snell, Tracy L. 2001. Capital Punishment 2000. Washington, D.C.: U.S. Bureau of Justice Statistics (NCJ 190598). Crime_G

35. Crime_G

36. Data and Estimation Panel data for 3,054 counties from 1977 to 1996 (61,080 observations) Three equation simultaneous system model Must estimate subjective probabilities - the econometrics is a bit tricky but we can interpret the results in a more or less straightforward manner. Crime_G

37. Table 3. Two-Stage Least Squares Regression Results for Murder Rate Estimated Coefficients Regressor Model 1 Model 2 Model 3 Deterrent Variable Probability of arrest -4.037 -10.096 -3.334 (6.941)** (17.331)** (6.418)** Conditional probability of death sentence -21.841 -42.411 -32.115 (1.167) (3.022)** (1.974)** Conditional probability of execution -5.170 -2.888 -7.396 (6.324)** (6.094)** (10.285)** Crime_G

38. Control Variables Regressor Model 1 Model 2 Model 3 Other Crimes Aggravated assault rate 0.0040 0.0059 0.0049 (18.038)** (23.665)** (22.571)** Robbery rate 0.0170 0.0202 0.0188 (39.099)** (51.712)** (49.506)** Crime_G

39. Control Variables Regressor Model 1 Model 2 Model 3 Economic Variables Real per capita personal income 0.0005 0.0007 0.0006 (14.686)** (17.134)** (16.276)** Real per capita unemployment insurance payments -0.0064 -0.0077 -0.0033 (6.798)** (8.513)** (3.736)** Real per capita Income maintenance Payments 0.0011 -0.0020 0.0024 (1.042) (1.689)* (2.330)** Crime_G

40. Control Variables Regressor Model 1 Model 2 Model 3 Demographic Variable African American (%) 0.0854 0.1114 0.1852 (2.996)** (4.085)** (6.081)** Minority other than African American (%) -0.0382 0.0255 -0.0224 (7.356)** (0.7627) (4.609)** Male (%) 0.3929 0.2971 0.2934 (7.195)** (3.463)** (5.328)** Crime_G

41. Control Variables Regressor Model 1 Model 2 Model 3 Age 10±19 (%) -0.2717 -0.4849 0.0259 (4.841)** (8.021)** (0.4451) Age 20±29 (%) -0.1549 -0.6045 -0.0489 (3.280)** (12.315)** (0.9958) Population density -0.0048 -0.0066 -0.0036 (22.036)** (24.382)** (17.543)** NRA membership rate, (% state pop. in NRA) 0.0003 0.0004 -0.0002 (1.052) (1.326) (0.6955) Crime_G

42. Intercept 6.393 23.639 -12.564 (0.4919) (6.933)** (0.9944) F-statistic 217.90 496.29 276.46 Adjusted R2 0.8476 0.8428 0.8624 Notes: Dependent variable is the murder rate (murders/100,000 population). In Model 1 the execution probability is (number of executions at t)/(number of death row sentences at t-6). In Model 2 the execution probability is (number of executions at t+6)/(number of death row sentences at t). In Model 3 the execution probability is (sum of executions at t+2+t+1+t+t-1+t-2+t-3)/(sum of death row sentences at t-4+t-5+t-6+t-7+t-8+t-9). Sentencing probabilities are computed accordingly, but with a two-year displacement lag and a two-year averaging rule. The estimated coefficients for year and county dummies are not shown. *Significant at the 90% confidence level, two-tailed test. **Significant at the 95% confidence level, two-tailed test. Crime_G

43. Does Crime Pay? Wilson and Abrahamse, Does Crime Pay?9 JUSTICE QUARTERLY 359, 367 (1992). Question Why do some criminals become ‘career criminals’? Can the expected financial gain explain their behaviour? Crime_G

44. Wilson and Abrahamse compared the gains from crime and from legitimate work for a group of career criminals in state prisons Prisoners were divided prisoners into two groups: mid-rate offenders and high-rate offenders Income from crime: data from the National Crime Survey’s report of the average losses by victims in different sorts of crimes were used to estimate the annual income for criminals Income from legitimate sources: the prisoners’ estimates of their income from legitimate sources Two-thirds of the prisoners had reasonably stable jobs when they were not in prison and, on average, the prisoners believed that they made $5.78 per hour at those legitimate jobs Crime_G

45. Table 12.1 Cooter and Ulen Criminal and Legitimate Earnings per Year (1988 Dollars) HIGH-RATE MID-RATE Crime Crime Work Crime Work type Burglary/theft $ 5,711 $5,540 $ 2,368 $7,931 Robbery $ 6,541 $3,766 $ 2,814 $5,816 Swindling $14,801 $6,245 $ 6,816 $8,113 Auto theft $26,043 $2,308 $15,008 $5,457 Mixed $ 6,915 $5,086 $ 5,626 $6,956 Source: Wilson and Abrahamse, Does Crime Pay? 9 JUSTICE QUARTERLY 359, 367 (1992). Crime_G

46. Findings: For mid-rate criminals, working pays more than crime for every type of crime except auto theft For high-rate offenders, crime paid more than legitimate work for all crimes except burglary The major cost of crime to the criminals, time in prison is not accounted for in Table 12.1 (specialist in crime) When the cost of time in prison is included the net income from crime fell below the income from legitimate work for both mid-rate and high-rate offenders Crime_G

47. Why, then, do career criminals commit crime? Wilson and Abrahamse reject two explanations. First, prisoners felt they had no meaningful opportunity for legitimate work BUT two-thirds of the prisoners were employed for some length of time during the period examined Second, the prisoners had such serious problems with alcohol and drugs that they could not hold legitimate jobs - two-thirds of the offenders had drinking or drug problems BUT evidence from other studies indicates that these problems do not normally preclude legitimate employment Wilson and Abrahamse conclude that career criminals are “temperamentally disposed to overvalue the benefits of crime and to undervalue its costs” because they are “inordinately impulsive or present-oriented.” (they discount punishments for uncertainty and futurity more highly than other people) Crime_G