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

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Part G-I Part G-I

Some Examples of Empirical Some Examples of Empirical Work on The Economics of Crime Work on The Economics of Crime

and Punishmentand Punishment

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ObjectivesObjectives

- understand the importance of empirical work - understand the importance of empirical work in crime and punishmentin crime and punishment

- understand the nature of empirical work in - understand the nature of empirical work in crime and punishmentcrime and punishment

- understand why the ‘crime and deterrence’ - understand why the ‘crime and deterrence’ debate will continue for some time debate will continue for some time

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Implications of measurement problems:Implications of measurement problems:

Example: questions such as how to decrease youth crime Example: questions such as how to decrease youth crime become difficult to answer:become difficult to answer:

More police, more convictions, longer sentences – More police, more convictions, longer sentences – yesyes our our model of rational crime would conclude that this approach model of rational crime would conclude that this approach should have a negative impact on crime.should have a negative impact on crime.

More youth community programs, better education, better More youth community programs, better education, better employment opportunities - employment opportunities - yesyes our model of rational crime our model of rational crime would conclude that this approach should have a negative would conclude that this approach should have a negative impact on crime.impact on crime.

But from the perspective of efficient policy the question is: But from the perspective of efficient policy the question is: which approach yields the greatest deterrence per dollar ?which approach yields the greatest deterrence per dollar ?

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Which approach yields the greatest deterrence per dollar ?Which approach yields the greatest deterrence per dollar ?

Recall there are two components to the Social Cost of Crime:Recall there are two components to the Social Cost of Crime:

1.1. Direct harm to victim and harm to Direct harm to victim and harm to society society

2.2. Cost of deterrenceCost of deterrence

We need to measure both in comparing alternative We need to measure both in comparing alternative approaches to decreasing crime.approaches to decreasing crime.

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Measurement problemsMeasurement problems

Measuring the social harm of crime:Measuring the social harm of crime:

1.1. Direct harm to victim and harm to society Direct harm to victim and harm to society ((perceivedperceived safety) (security/insecurity) real safety) (security/insecurity) real vs. imaginedvs. imagined

- difficult to quantify and aggregate (much of this harm is - difficult to quantify and aggregate (much of this harm is subjective in nature)subjective in nature)

- the number of reported crimes provides only a rough - the number of reported crimes provides only a rough gauge to whether or not harm costs are rising or falling gauge to whether or not harm costs are rising or falling

- very difficult to determine the marginal benefits associated - very difficult to determine the marginal benefits associated with changes in policy with changes in policy

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Measuring the cost of deterrenceMeasuring the cost of deterrence

2.2. Cost of deterrenceCost of deterrence

- not too difficult to quantify what we currently spend - not too difficult to quantify what we currently spend (police, courts, prisons, private security expenditures)(police, courts, prisons, private security expenditures)

- more difficult to determine costs such as ‘erosion of the - more difficult to determine costs such as ‘erosion of the rights of all citizens’ rights of all citizens’

- very difficult to determine the - very difficult to determine the marginal deterrencemarginal deterrence associated with changes in specific expenditures (how associated with changes in specific expenditures (how much deterrence per additional dollar spent?) much deterrence per additional dollar spent?)

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What is the deterrence effect of an increase in the policing What is the deterrence effect of an increase in the policing budget (more apprehensions)? Court budget (more budget (more apprehensions)? Court budget (more convictions)? Prison budgets (more convictions/longer convictions)? Prison budgets (more convictions/longer sentences)? sentences)?

What is the deterrence effect of an increase in preventative What is the deterrence effect of an increase in preventative measures (more community programs, more street lighting, measures (more community programs, more street lighting, anti-theft devices, etc)? anti-theft devices, etc)?

What is the deterrence effect of a decrease in personal What is the deterrence effect of a decrease in personal freedoms (right to privacy, search and seizure, gun freedoms (right to privacy, search and seizure, gun ownership, etc)? ownership, etc)?

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We often discuss crime on a very general (aggregate level) but We often discuss crime on a very general (aggregate level) but there are many different types of crime:there are many different types of crime: Homicide, Assault, Homicide, Assault, Robbery, Theft, Drunk driving, Prostitution, Gambling, Pot Robbery, Theft, Drunk driving, Prostitution, Gambling, Pot smoking, Use of hard drugs, Non-Criminal Code violations smoking, Use of hard drugs, Non-Criminal Code violations

Note that the Motor Vehicle Act, Income Tax Act, by-laws, etc. Note that the Motor Vehicle Act, Income Tax Act, by-laws, etc. share prevention and enforcement resources with crime share prevention and enforcement resources with crime prevention and enforcement).prevention and enforcement).

In order to really discuss efficient deterrence, we would need to In order to really discuss efficient deterrence, we would need to consider the problem at a disaggregate level. consider the problem at a disaggregate level.

Consider some specific crime and the cost/benefit of alternative Consider some specific crime and the cost/benefit of alternative deterrence strategies for that specific type of crime.deterrence strategies for that specific type of crime.

The ‘efficient’ composition and amount of deterrence for homicide The ‘efficient’ composition and amount of deterrence for homicide is likely to be different than for shoplifting or tax evasion.is likely to be different than for shoplifting or tax evasion.

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Much (most?) empirical work focuses on relatively narrow Much (most?) empirical work focuses on relatively narrow questions.questions.

What impact did the change in a given law have on a given What impact did the change in a given law have on a given level of crime activity?level of crime activity?

What is the cost of imprisoning a person?What is the cost of imprisoning a person?

What determines the clearance rate for ‘break and enters’?What determines the clearance rate for ‘break and enters’?

Such narrow question are generally more tractable. Such narrow question are generally more tractable.

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Testing for ‘Rational Cheating’ Testing for ‘Rational Cheating’

The Economics of Illegitimate Activities: Further EvidenceThe Economics of Illegitimate Activities: Further Evidence – – Mixon and Mixon, Journal of Socio-Economics, Vol. 25, No. 3, Mixon and Mixon, Journal of Socio-Economics, Vol. 25, No. 3, PP 373-381, 1996 PP 373-381, 1996

Question:Question:

Can the benefits and costs of cheating (to the individual) be Can the benefits and costs of cheating (to the individual) be identified and do these factors affect the likelihood of an identified and do these factors affect the likelihood of an individual cheating as predicted by the model of rational individual cheating as predicted by the model of rational crime? crime?

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The SurveyThe Survey

How to get data on cheating?How to get data on cheating?

157 students economics and accounting students were 157 students economics and accounting students were surveyed (at a larger Southern university).surveyed (at a larger Southern university).

This is really the only way that you could generate a data set This is really the only way that you could generate a data set suitable for standard statistical analysis of this type of suitable for standard statistical analysis of this type of phenomenon. phenomenon.

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The Results of the SurveyThe Results of the Survey1. Have you ever observed another student cheating on an exam or 1. Have you ever observed another student cheating on an exam or

written assignment at SLU? written assignment at SLU? OBCHEAT +OBCHEAT +a. Yes (98) a. Yes (98) 62%62%b. No (59)b. No (59)NR (0)NR (0)

2. Have you ever seen another student get caught cheating at SLU?2. Have you ever seen another student get caught cheating at SLU?a. Yes (15) a. Yes (15) 9%9% CCAUGHT -CCAUGHT -b. No (142)b. No (142)NR (0)NR (0)

3. Based on your experience in the classroom at SLU, what 3. Based on your experience in the classroom at SLU, what percentage of students do you think cheat on a typical exam?percentage of students do you think cheat on a typical exam?a. No more than 1% (37) a. No more than 1% (37) 24%24% PERCHT PERCHT ++d. Between 30% and 30% (16)d. Between 30% and 30% (16)b. Between 1% and 10% (71) b. Between 1% and 10% (71) e. More than 30% (4)e. More than 30% (4)c. Between 10% and 20% (26) NR (3)c. Between 10% and 20% (26) NR (3)

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4. Which response accurately describes your behavior at SLU?4. Which response accurately describes your behavior at SLU?a. Have never cheated on a test or written work (99) a. Have never cheated on a test or written work (99) 63%63%b. Have cheated once on a test or written work (22)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 c. Have cheated more than once, but less than five times on a test or written work (30)or written work (30)d. Have cheated five times or more on a test or written work (6)d. Have cheated five times or more on a test or written work (6)NR (0)NR (0) CHEHAB dependant variableCHEHAB dependant variable

5. If you answered “b,” ” c,” or “d” on question 4, have you ever 5. If you answered “b,” ” c,” or “d” on question 4, have you ever been caught?been caught?a. Yes (3)a. Yes (3) 2%2%b. No (55)b. No (55)NR (0)NR (0)

6. If you answered “c” or “d” on question 4, and you answered “a” on 6. If you answered “c” or “d” on question 4, and you answered “a” on question 5, did you cheat again after being caught?question 5, did you cheat again after being caught?a. Yes (0)a. Yes (0)b. No (3)b. No (3)NR (0)NR (0)

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7. Do you know anyone who routinely cheats on exams?7. Do you know anyone who routinely cheats on exams?a. Yes (39) a. Yes (39) 25%25% KNOCHTKNOCHT + +b. No (117)b. No (117)NR (1)NR (1)

8. If you were caught copying another student’s answers on an exam, 8. If you were caught copying another student’s answers on an exam, what would you expect to happen to you?what would you expect to happen to you?a. Nothing more than a reprimand (2)a. Nothing more than a reprimand (2)b. Be forced to retake the exam (33)b. Be forced to retake the exam (33)c. Have my course grade lowered by a letter or more (31)c. Have my course grade lowered by a letter or more (31)d. Receive an F for the course (70)d. Receive an F for the course (70) 45%45%e. Be suspended from SLU for at least one semester (20)e. Be suspended from SLU for at least one semester (20)NR (1)NR (1) PENAL PENAL - -

9. In your opinion, cheating at Southeastern Louisiana University is:9. In your opinion, cheating at Southeastern Louisiana University is:a. Not a problem (52) a. Not a problem (52) 33%33%b. A trivial problem (65)b. A trivial problem (65)c. A problem deserving some concern (35)c. A problem deserving some concern (35)d. A serious problem (4)d. A serious problem (4)NR (1)NR (1)

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10. My current classification is:10. My current classification is:

a. Freshman (8) a. Freshman (8)

d. Senior (65)d. Senior (65)

b. Sophomore (40) b. Sophomore (40)

e. Grad. Student (7)e. Grad. Student (7)

c. Junior (37) NR (0)c. Junior (37) NR (0)

11. My current grade point average is:11. My current grade point average is: GPAGPA - -

a. 3.50-4.00 (15) a. 3.50-4.00 (15)

b. 3.00-3.49 (44) b. 3.00-3.49 (44)

c. 2.50-2.99 (54) c. 2.50-2.99 (54)

d. 2.00-2.49 (36)d. 2.00-2.49 (36)

e. less than 2.00 (8)e. less than 2.00 (8)

NR (0)NR (0)

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A regression modelA regression model

Y is the dependant variable (what you want to explain or Y is the dependant variable (what you want to explain or understand)understand)

The X’s are the explanatory variablesThe X’s are the explanatory variables

εε is the error term (what cannot be explained by the model) is the error term (what cannot be explained by the model)

A Linear regression model looks like this:A Linear regression model looks like this:

Y = bY = b00 + b + b11*X*X1 1 + b+ b22*X*X22 + ... + b + ... + bkk*X*Xkk + + εε

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How to interpret the coefficients (b’s)?How to interpret the coefficients (b’s)?


bb11 measures the change in Y caused by a one unit change in measures the change in Y caused by a one unit change in XX11 holding all other X’s constantholding all other X’s constant

A linear regression model is a statistical technique for A linear regression model is a statistical technique for implementing the implementing the ceteris paribusceteris paribus assumption assumption

In some cases the b’s can be interpreted as an elasticity or 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 they can be used to calculate the elasticity of the X. This allows use to answer questions such as: How responsive is allows use to answer questions such as: How responsive is the crime rate to ‘more police on the street’? the crime rate to ‘more police on the street’?

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Generally we are interested in two aspects of the regression Generally we are interested in two aspects of the regression results:results:


1.1. Do the individual coefficients (b’s) contribute to the explanation Do the individual coefficients (b’s) contribute to the explanation of the variation in the dependent variable? of the variation in the dependent variable?

For example if bFor example if b1 1 is found to be ‘statistically significant’ then we is found to be ‘statistically significant’ then we can concluded that variation in the value of the explanatory can concluded that variation in the value of the explanatory variable Xvariable X11 have a ‘statistically significant’ effect on the have a ‘statistically significant’ effect on the dependent variable Y, dependent variable Y, holding all other X’s constantholding all other X’s constant

2. How well does the overall model explain the variation in Y? 2. How well does the overall model explain the variation in Y?

To answer this question we consider the error term (To answer this question we consider the error term (εε) and ) and functions of the error term (i.e. Rfunctions of the error term (i.e. R22). ).

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Mixon and Mixon regression resultsMixon and Mixon regression results

They used a LOGIT regression model since the dependent variable They used a LOGIT regression model since the dependent variable was an index variable.was an index variable.

This complicates the interpretation of the coefficients (b’s) This complicates the interpretation of the coefficients (b’s) somewhat?somewhat?

Ideally, Mixon and Mixon could have expressed the coefficients as Ideally, Mixon and Mixon could have expressed the coefficients as probabilities but they did not and we do not have enough probabilities but they did not and we do not have enough information to do it. information to do it.

Unfortunately LOGIT does not allow use to easily assess the Unfortunately LOGIT does not allow use to easily assess the

explanatory power of the modelexplanatory power of the model

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Table 2. Ordered Logit ResultsTable 2. Ordered Logit Results

(1) (1) (2) (2) (3) (3) (4) (4)

CPA CPA 0.3650* 0.3650* 0.422* 0.422* 0.4181* 0.4181* 0.4125* 0.4125* (2.28) (2.28) (2.40)(2.40) (2.38) (2.38)

(2.32) (2.32)

OBCHEAT OBCHEAT 1.3467* 1.3467* 1.4040* 1.4040* 1.2968* 1.2968*

(3.20)(3.20) (3.23) (3.23) (2.94) (2.94)

CCAUCHT CCAUCHT 0.9261 0.9261 1.05861.0586

(0.75) (0.75) (0.86) (0.86)

SEECC SEECC 0.1659 0.1659 -0.1976 -0.1976 (0.12)(0.12) (0.14) (0.14)

PERCHT PERCHT 0.2064 0.2064 0.3353* 0.3353* 0.2195 0.2195 (1.15) (1.15) (1.94) (1.94) (1.21) (1.21)

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Table 2. Ordered Logit ResultsTable 2. Ordered Logit Results

(1) (1) (2) (2) (3) (3) (4) (4)

KNOCHT KNOCHT 1.0305* 1.0305* 0.9304* 0.9304* (2.61) (2.61) (2.31) (2.31)

PENALPENAL -0.0409 -0.0409 -0.1323 -0.1323 -0.1155 -0.1155 -0.1449 -0.1449 (0.25) (0.25) (0.76) (0.76) (0.67)(0.67) (0.82) (0.82)

INTERCEPT INTERCEPT -1.4701 -1.4701 -2.9948* -2.9948* -3.1889* -3.1889* -.9428* -.9428* (1.80) (1.80) (2.97)(2.97) (3.25)(3.25) (2.96) (2.96)

CHI-SQUARE 11.80*CHI-SQUARE 11.80* 29.50* 29.50* 80.46’80.46’ 81.53* 81.53*

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Deterring Drunk DrivingDeterring Drunk Driving

Deterring Drunk Driving Fatalities: An Economics of Deterring Drunk Driving Fatalities: An Economics of Crime PerspectiveCrime Perspective, Benson, Rasmussen and Mast, , Benson, Rasmussen and Mast, International Review of Law and Economics, Vol. International Review of Law and Economics, Vol. 19, pp 205-225, 199919, pp 205-225, 1999

QuestionQuestion

What is the effectiveness of alternative policy tools What is the effectiveness of alternative policy tools used in the control of DUI (driving under the used in the control of DUI (driving under the influence of alcohol)? influence of alcohol)?

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They consider a sample of 48 US state over 9 yearsThey consider a sample of 48 US state over 9 years

The differ with respect to drink laws/alcohol laws, definition of The differ with respect to drink laws/alcohol laws, definition of drunk driving, penalties, enforcement and many other non-drunk driving, penalties, enforcement and many other non-criminological factors.criminological factors.

The authors are interested in the deterrence effect of specific The authors are interested in the deterrence effect of specific laws BUT there experiment must control for all other factors laws BUT there experiment must control for all other factors that might affect the dependent variable (the number of that might affect the dependent variable (the number of ‘drunk’ drivers in a state involved in a fatal car accident ‘drunk’ drivers in a state involved in a fatal car accident divided by the total number of drivers in the state).divided by the total number of drivers in the state).

The number of explanatory variables in such regression The number of explanatory variables in such regression models grows quickly.models grows quickly.

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TABLE 3. Driver involvement TABLE 3. Driver involvement Equation (2) (BAC >Equation (2) (BAC > 0.01) 0.01) Independent VariablesIndependent Variables CoefficientCoefficient t t statistic statistic

Attitudes towards alcohol and drivingAttitudes towards alcohol and driving

Legal drinking age Legal drinking age 0.0901867**0.0901867** 2.208 2.208

Dram-shop lawsDram-shop laws -0.07977**-0.07977** -2.31 -2.31 (tort liability against bars)(tort liability against bars)

Enforcement rules Enforcement rules (Pr. of being caught)(Pr. of being caught)

Open-container lawsOpen-container laws -0.10299**-0.10299** -2.51-2.51(in cars)(in cars)

Anti-consumption lawsAnti-consumption laws -199e-02-199e-02 -0.48 -0.48 (in cars)(in cars)

Police Police per capitaper capita -0.00154-0.00154 -1.50 -1.50

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Independent VariablesIndependent Variables CoefficientCoefficient t t statistic statistic

Enforcement rules Enforcement rules (Pr. of being caught)(Pr. of being caught)

Preliminary breath-test lawsPreliminary breath-test laws -0.00343-0.00343 -0.10 -0.10

Illegal Illegal per se per se laws laws -0.01612-0.01612 -0.21-0.21

Implied-consent lawsImplied-consent laws -0.00015-0.00015 -0.77 -0.77 (for breadth tests)(for breadth tests)

No-plea-bargaining laws No-plea-bargaining laws 0.008445 0.008445 0.153 0.153

Administrative Administrative per se per se lawslaws -0.00015-0.00015 -0.51 -0.51 (automatic suspension at time of arrest)(automatic suspension at time of arrest)

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PunishmentPunishment

Jail for 1st convictionJail for 1st conviction -0.04167-0.04167 -1.53 -1.53

Jail for 2nd conviction Jail for 2nd conviction 0.000617 0.000617 0.264 0.264

Fines for 1st convictionFines for 1st conviction 0.000055 0.000055 0.349 0.349

Fines for 2nd convictionFines for 2nd conviction -0.00002-0.00002 -0.52-0.52

Suspension for 1st conviction Suspension for 1st conviction 0.000551 0.000551 0.871 0.871

Suspension for 2nd convictionSuspension for 2nd conviction -0.00002-0.00002 -0.52 -0.52

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Various control variablesVarious control variables

Seat-belt lawsSeat-belt laws -0.03042-0.03042 -1.17 -1.17

Vehicle miles per driver Vehicle miles per driver 6.52e-05* 6.52e-05* 5.962 5.962

Ethanol Ethanol per capitaper capita 0.38952* 0.38952* 3.338 3.338(alcohol consumption)(alcohol consumption)

Metropolitan populationMetropolitan population -0.00874-0.00874 -1.60 -1.60

Males 16–44 per capita Males 16–44 per capita 6.6694 6.6694 1.593 1.593

Per capita Per capita disposable income disposable income 0.00004** 0.00004** 1.961 1.961

Unemployment rateUnemployment rate -0.0198**-0.0198** -2.38 -2.38

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Control variables – Taste (community values)Control variables – Taste (community values)

Dry-county population Dry-county population 0.014147 0.014147 0.101 0.101

Catholics Catholics 0.41875 0.41875 0.32 0.32

MormonsMormons -5.6694-5.6694 -1.23 -1.23

Southern Baptists Southern Baptists 9.0812* 9.0812* 3.306 3.306

Other Protestants Other Protestants 2.8491* 2.8491* 2.893 2.893

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Summary StatisticsSummary Statistics

Adjusted R2Adjusted R2 0.906 0.906

F-statistic F-statistic 50.7 50.7

Note: Dependent variable = ln[Note: Dependent variable = ln[RR/(1 2 /(1 2 RR)]. N = 432. )]. N = 432.

Intercepts, year, and state dummy variables not shownIntercepts, year, and state dummy variables not shown

*Significant at the 0.01 level.*Significant at the 0.01 level.

**Significant at the 0.05 level.**Significant at the 0.05 level.

***Significant at the 0.10 level (in two-tailed tests).***Significant at the 0.10 level (in two-tailed tests).

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Some additional tests in the presence of Some additional tests in the presence of

multicollinearitymulticollinearity TABLE 4. F tests for deterrence variables TABLE 4. F tests for deterrence variables Equation 2 Equation 2

Deterrence Variables TestedDeterrence Variables Tested

All deterrence variables All deterrence variables F[16,348] = 2.96* F[16,348] = 2.96*

Alcohol control Alcohol control ((legal drinking age, dram-shop lawslegal drinking age, dram-shop laws)) F[2,348] = 5.36* F[2,348] = 5.36*

Probability of arrest (Probability of arrest (police per capital, open-container laws, anti-police per capital, open-container laws, anti-consumption laws, illegal per se laws, preliminary breath-test consumption laws, illegal per se laws, preliminary breath-test laws, implied-consent lawslaws, implied-consent laws) ) F[3,648] = 2.08*** F[3,648] = 2.08***

Probability of being stopped (Probability of being stopped (police per capita, open-container police per capita, open-container laws, anti-consumption lawslaws, anti-consumption laws)) F[3,348] = 3.20** F[3,348] = 3.20**

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Some additional tests in the presence of Some additional tests in the presence of

multicollinearitymulticollinearity Probability of arrest given being stopped (Probability of arrest given being stopped (illegal per se laws, illegal per se laws,

preliminary-breath-test laws, implied-consent lawspreliminary-breath-test laws, implied-consent laws) ) F[3,348] = 0.27 F[3,348] = 0.27

Expected punishment for 1st and 2nd offensesExpected punishment for 1st and 2nd offenses ((administrative per se laws, no-plea-bargaining laws, fines, jail, administrative per se laws, no-plea-bargaining laws, fines, jail, suspensionssuspensions) ) F[8,348] = 1.21 F[8,348] = 1.21

Expected punishment given conviction for 1Expected punishment given conviction for 1st st and 2nd offenses and 2nd offenses ((jail, fines, suspensionsjail, fines, suspensions) ) F[6,348] = 1.59 F[6,348] = 1.59

*Significant at the 0.01 level. *Significant at the 0.01 level. **Significant at the 0.05 level. **Significant at the 0.05 level. ***Significant at the 0.10 level.***Significant at the 0.10 level.

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Capital Punishment as a DeterrentCapital Punishment as a Deterrent

Does Capital Punishment Have a Deterrent Effect? Does Capital Punishment Have a Deterrent Effect?

New Evidence from Postmoratorium Panel Data New Evidence from Postmoratorium Panel Data – – Dezhbahsh, Rubin and Sheperd, Americam Law Dezhbahsh, Rubin and Sheperd, Americam Law and Economics Review, Vol. 5, No. 2, 2003and Economics Review, Vol. 5, No. 2, 2003

QuestionQuestion

Does capital punishment deter murder?Does capital punishment deter murder?

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Table 1.Table 1. Executions and Executing StatesExecutions and Executing StatesYearYear No. of ExecutionsNo. of Executions No. of States with Death PenaltyNo. of States with Death Penalty

19771977 11 313119781978 00 323219791979 22 343419801980 00 343419811981 11 343419821982 22 353519831983 55 353519841984 2121 353519851985 1818 353519861986 1818 353519871987 2525 353519881988 1111 353519891989 1616 353519901990 2323 353519911991 1414 363619921992 3131 363619931993 3838 363619941994 3131 343419951995 5656 383819961996 4545 383819971997 7474 383819981998 6868 383819991999 9898 383820002000 8585 3838

Source: Snell, Tracy L. 2001. Capital Punishment 2000. Washington, D.C.: U.S. Bureau of Source: Snell, Tracy L. 2001. Capital Punishment 2000. Washington, D.C.: U.S. Bureau of Justice Statistics (NCJ 190598).Justice Statistics (NCJ 190598).

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Table 2. Status of the Death PenaltyTable 2. Status of the Death PenaltyJurisdictions without a DeathJurisdictions without a Death Jurisdictions with a Death Penalty on Jurisdictions with a Death Penalty on Penalty on December 31, 2000Penalty on December 31, 2000 (No. of Executions 1977-2000)(No. of Executions 1977-2000)

AlaskaAlaska Texas (239)Texas (239) Virginia (81) Virginia (81) District of ColumbiaDistrict of Columbia Florida (50)Florida (50) Missouri (46) Missouri (46) HawaiiHawaii Oklahoma (30)Oklahoma (30) Louisiana (26) Louisiana (26) IowaIowa South Carolina (25)South Carolina (25) Alabama (23)Alabama (23)MinnesotaMinnesota Arkansas (23)Arkansas (23) Georgia (23)Georgia (23)MaineMaine Arizona (22)Arizona (22) North Carolina (16)North Carolina (16)MichiganMichigan Illinois (12)Illinois (12) Delaware (11)Delaware (11)MassachusettsMassachusetts California (8) California (8) Nevada (8)Nevada (8)North DakotaNorth Dakota Indiana (7)Indiana (7) Utah (6)Utah (6)Rhode IslandRhode Island Mississippi (4) Mississippi (4) Maryland (3)Maryland (3)VermontVermont Pennsylvania (3)Pennsylvania (3) Washington (3) Washington (3) WisconsinWisconsin Nebraska (3)Nebraska (3) Oregon (2)Oregon (2)West VirginiaWest Virginia Kentucky (2)Kentucky (2) Montana (2)Montana (2) Colorado (1)Colorado (1) Wyoming (1)Wyoming (1)

Idaho (1)Idaho (1) Ohio (1)Ohio (1)Tennessee (1) Tennessee (1) South Dakota (0)South Dakota (0)Connecticut (0)Connecticut (0) Kansas (0)Kansas (0)New Hampshire (0)New Hampshire (0) New Jersey (0)New Jersey (0)New Mexico (0)New Mexico (0) New York (0)New York (0)

Source: Snell, Tracy L. 2001. Capital Punishment 2000. Washington, D.C.: U.S. Source: Snell, Tracy L. 2001. Capital Punishment 2000. Washington, D.C.: U.S. Bureau of Justice StatisticsBureau of Justice Statistics (NCJ 190598). (NCJ 190598).

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Data and EstimationData and Estimation

Panel data for 3,054 counties from 1977 to 1996Panel data for 3,054 counties from 1977 to 1996

(61,080 observations)(61,080 observations)

Three equation simultaneous system modelThree equation simultaneous system model

Must estimate subjective probabilities - the Must estimate subjective probabilities - the econometrics is a bit tricky but we can interpret econometrics is a bit tricky but we can interpret the results in a more or less straightforward the results in a more or less straightforward manner. manner.

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Table 3. Two-Stage Least Squares Regression Results for Table 3. Two-Stage Least Squares Regression Results for Murder RateMurder Rate

Estimated CoefficientsEstimated Coefficients

RegressorRegressor Model 1 Model 1 Model 2 Model 2 Model 3Model 3Deterrent VariableDeterrent Variable

Probability of arrestProbability of arrest -4.037 -4.037-10.096-10.096 -3.334-3.334(6.941)** (6.941)** (17.331)** (17.331)** (6.418)**(6.418)**

Conditional probability Conditional probability of death sentence of death sentence -21.841-21.841 -42.411-42.411 -32.115-32.115

(1.167)(1.167) (3.022)** (3.022)** (1.974)**(1.974)**Conditional probabilityConditional probability of execution of execution -5.170-5.170 -2.888-2.888 -7.396 -7.396

(6.324)** (6.324)** (6.094)**(6.094)**(10.285)**(10.285)**

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Control VariablesControl Variables

RegressorRegressor Model 1 Model 1 Model 2 Model 2 Model 3Model 3

Other CrimesOther Crimes

Aggravated assault Aggravated assault rate rate 0.00400.0040 0.00590.0059 0.00490.0049

(18.038)**(18.038)** (23.665)**(23.665)**(22.571)**(22.571)**

Robbery rate Robbery rate 0.01700.0170 0.02020.0202 0.01880.0188(39.099)**(39.099)** (51.712)**(51.712)**

(49.506)**(49.506)**

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Economic VariablesEconomic Variables

Real per capita Real per capita personal income personal income 0.00050.0005 0.00070.0007 0.00060.0006

(14.686)**(14.686)** (17.134)**(17.134)**(16.276)**(16.276)**

Real per capita Real per capita unemploymentunemploymentinsurance paymentsinsurance payments -0.0064-0.0064 -0.0077-0.0077 -0.0033 -0.0033

(6.798)**(6.798)** (8.513)** (8.513)** (3.736)**(3.736)**Real per capita Real per capita Income maintenance Income maintenance PaymentsPayments 0.0011 0.0011 -0.0020-0.0020 0.00240.0024

(1.042)(1.042) (1.689)*(1.689)* (2.330)**(2.330)**

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Demographic VariableDemographic Variable

African American (%) 0.0854African American (%) 0.0854 0.1114 0.1114 0.18520.1852 (2.996)**(2.996)** (4.085)**(4.085)** (6.081)**(6.081)**

Minority other thanMinority other than African American (%) -0.0382African American (%) -0.0382 0.02550.0255 -0.0224-0.0224

(7.356)**(7.356)** (0.7627)(0.7627) (4.609)**(4.609)**

Male (%) Male (%) 0.3929 0.3929 0.29710.2971 0.29340.2934(7.195)**(7.195)** (3.463)**(3.463)** (5.328)**(5.328)**

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Age 10±19 (%) Age 10±19 (%) -0.2717-0.2717 -0.4849-0.4849 0.02590.0259(4.841)** (4.841)** (8.021)**(8.021)** (0.4451)(0.4451)

Age 20±29 (%) Age 20±29 (%) -0.1549-0.1549 -0.6045-0.6045 -0.0489-0.0489(3.280)** (3.280)** (12.315)** (12.315)** (0.9958)(0.9958)

Population density Population density -0.0048-0.0048 -0.0066-0.0066 -0.0036-0.0036(22.036)** (22.036)** (24.382)** (24.382)**

(17.543)**(17.543)**

NRA membership rate,NRA membership rate, (% state pop. in NRA) 0.0003(% state pop. in NRA) 0.0003 0.00040.0004 -0.0002-0.0002

(1.052)(1.052) (1.326)(1.326) (0.6955)(0.6955)

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Intercept Intercept 6.3936.393 23.63923.639 -12.564-12.564(0.4919)(0.4919) (6.933)** (6.933)** (0.9944)(0.9944)

F-statisticF-statistic 217.90 217.90 496.29 496.29 276.46276.46Adjusted RAdjusted R22 0.8476 0.8476 0.8428 0.8428 0.86240.8624

Notes: Dependent variable is the murder rate (murders/100,000 Notes: Dependent variable is the murder rate (murders/100,000 population). In Model 1 the execution probability is (number of population). In Model 1 the execution probability is (number of executions at t)/(number of death row sentences at t-6). In executions at t)/(number of death row sentences at t-6). In Model 2 the execution probability is (number of executions at Model 2 the execution probability is (number of executions at t+6)/(number of death row sentences at t). In Model 3 the 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-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-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 8+t-9). Sentencing probabilities are computed accordingly, but with a two-year displacement lag and a two-year averaging rule.with a two-year displacement lag and a two-year averaging rule.

The estimated coefficients for year and county dummies are not The estimated coefficients for year and county dummies are not shown.shown.

*Significant at the 90% confidence level, two-tailed test.*Significant at the 90% confidence level, two-tailed test.**Significant at the 95% confidence level, two-tailed test.**Significant at the 95% confidence level, two-tailed test.

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Does Crime Pay?Does Crime Pay?

Wilson and Abrahamse, Wilson and Abrahamse, Does Crime Pay?Does Crime Pay? 9 JUSTICE 9 JUSTICE QUARTERLY 359, 367 (1992).QUARTERLY 359, 367 (1992).

QuestionQuestion

Why do some criminals become ‘career criminals’?Why do some criminals become ‘career criminals’?

Can the expected financial gain explain their Can the expected financial gain explain their

behaviour? behaviour?

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Wilson and Abrahamse compared the gains from crime and Wilson and Abrahamse compared the gains from crime and from legitimate work for a group of career criminals in state from legitimate work for a group of career criminals in state prisons prisons

Prisoners were divided prisoners into two groups: mid-rate Prisoners were divided prisoners into two groups: mid-rate offenders and high-rate offendersoffenders and high-rate offenders

Income from crime: data from the National Crime Survey’s Income from crime: data from the National Crime Survey’s report of the average losses by victims in different sorts of report of the average losses by victims in different sorts of crimes were used to estimate the annual income for crimes were used to estimate the annual income for criminals criminals

Income from legitimate sources: the prisoners’ estimates of Income from legitimate sources: the prisoners’ estimates of their income from legitimate sourcestheir income from legitimate sources

Two-thirds of the prisoners had reasonably stable jobs when Two-thirds of the prisoners had reasonably stable jobs when they were not in prison and, on average, the prisoners they were not in prison and, on average, the prisoners believed that they made $5.78 per hour at those legitimate believed that they made $5.78 per hour at those legitimate jobsjobs

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Table 12.1 Cooter and UlenTable 12.1 Cooter and UlenCriminal and Legitimate Earnings per Year (1988 Dollars)Criminal and Legitimate Earnings per Year (1988 Dollars)

HIGH-RATE HIGH-RATE MID-RATEMID-RATE

Crime Crime CrimeCrime WorkWork CrimeCrime WorkWorktypetype

Burglary/theft $ 5,711 Burglary/theft $ 5,711 $5,540 $5,540 $ 2,368 $ 2,368 $7,931 $7,931 Robbery Robbery $ 6,541 $ 6,541 $3,766 $3,766 $ 2,814 $ 2,814 $5,816$5,816Swindling Swindling $14,801 $14,801 $6,245 $6,245 $ 6,816 $ 6,816 $8,113$8,113Auto theftAuto theft $26,043 $26,043 $2,308$2,308 $15,008$15,008 $5,457$5,457MixedMixed $ 6,915 $ 6,915 $5,086$5,086 $ 5,626$ 5,626 $6,956$6,956

Source: Source: Wilson and Abrahamse, Wilson and Abrahamse, Does Crime Pay? Does Crime Pay? 9 JUSTICE 9 JUSTICE QUARTERLY 359, 367 (1992).QUARTERLY 359, 367 (1992).

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Findings: Findings:

For mid-rate criminals, working pays more than crime for every For mid-rate criminals, working pays more than crime for every type of crime except auto theft type of crime except auto theft

For high-rate offenders, crime paid more than legitimate work For high-rate offenders, crime paid more than legitimate work for for all all crimes except burglary crimes except burglary

The major cost of crime to the criminals, time in prison is not The major cost of crime to the criminals, time in prison is not accounted for in Table 12.1 (specialist in crime)accounted for in Table 12.1 (specialist in crime)

When the cost of time in prison is included the net income When the cost of time in prison is included the net income from crime fell below the income from legitimate work for from crime fell below the income from legitimate work for both mid-rate and high-rate offendersboth mid-rate and high-rate offenders

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Why, then, do career criminals commit crime? Why, then, do career criminals commit crime?

Wilson and Abrahamse reject two explanations. Wilson and Abrahamse reject two explanations.

First, prisoners felt they had no meaningful opportunity for First, prisoners felt they had no meaningful opportunity for legitimate work BUT two-thirds of the prisoners were legitimate work BUT two-thirds of the prisoners were employed for some length of time during the period employed for some length of time during the period examinedexamined

Second, the prisoners had such serious problems with alcohol Second, the prisoners had such serious problems with alcohol and drugs that they could not hold legitimate jobs - two-and drugs that they could not hold legitimate jobs - two-thirds of the offenders had drinking or drug problems BUT thirds of the offenders had drinking or drug problems BUT evidence from other studies indicates that these problems evidence from other studies indicates that these problems do not normally preclude legitimate employmentdo not normally preclude legitimate employment

Wilson and Abrahamse conclude that career criminals are Wilson and Abrahamse conclude that career criminals are “temperamentally disposed to overvalue the benefits of “temperamentally disposed to overvalue the benefits of crime and to undervalue its costs” because they are crime and to undervalue its costs” because they are “inordinately impulsive or present-oriented.” (they “inordinately impulsive or present-oriented.” (they discount punishments for uncertainty and futurity more discount punishments for uncertainty and futurity more highly than other people)highly than other people)

Documents

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