Dry Law and Homicide: Evidence from the São Paulo Metropolitan Area.

Dry Law and Homicide: Evidence from the São Paulo Metropolitan Area

FGV-EESPBiderman Ciro

Rio-PUCMello De P M João

PMSP - Edudação de SecretariaSchneider Alexandre

The question

Between March-01 and August-04, 16 out of 39 municipalities in the São Paulo Metropolitan Area (SPMA) passed laws restricting the sales of recreational alcohol

Is it an effective policy to fight the ultimate form of violent crime, homicide?

Why is it interesting?

Policy: Large number of cities adopted/plan to adopt such laws

Bogotá, Colombia first example. Many other Brazilian cities followed the example in the SPMA

They are costly in terms of welfare As Mr. Franklin would probably argue

Surprisingly little evidence that this type of intervention works

Not even benefit side clear so far Hard to underestimate the costs of violence, though

A simple policy intervention


Economics of Crime: Not clear at all that outright prohibition works

US in the 1920 (Miron and Zweibel, AER 1991, JEL 1995)

Not clear whether consumption reduced Violent crime due to absence of legal contract resolution

probably increases. Applies to drugs in general in present days

Outright prohibition may be radical enough to induce a substitution effect [Thorton, 1998]

Price oriented interventions (taxation) do not work (Miron 1998, JDI)


Economics of Crime SPMA type interventions

Focused on recreational alcohol consumption Not radical enough to trigger substitution effects? Not radical enough to produce illegal activities to

circumvent prohibition? Alcohol and social interaction:

Complements in the production of nasty behavior? May well be economical from a welfare perspective:

high crime environments


Criminology literature Large ongoing debate on the alcohol abuse – violent crime

link Literature 1: direct individual evidence from felons

McCLelland et alli’s classic The Drinking Man Psychological experiment comparing fantasies of sober and

intoxicated young men Direct police report data on rates of intoxication among

arrestees Hutchinson et alli, 1998 BJOMS: British report data city-center

crimes Gawryszewski et alli [2005]: toxicological data from murder

victims’ corpses US: Estimation of Blood Alcohol Concentration levels

amongst murder convicts


Literature 1 Problem:

Omitted factors Common factors determining alcohol (ab)use and

criminal behavior Child abuse Psychological disturbances

Selection Booking and inmate data: substance abusers more likely

to get caught Then drinking good for enforcement?

Why is it interesting? Literature 2: is the alcohol-crime nexus amplified by social

interaction? Direct individual evidence

British Crime Survey 2001/2002: 21% of all night-time violent incidents in or around pubs

Stockwell et alli [2003] with Australian data: bars preferred venue of alcohol purchase for felons prior to committing violent crimes

Cross-sectional local variation in the presence of bars and crime rates

Roncek and Maier [1991], cross-sectional data on Cleveland residential blocks: recreational licensed establishment associated with higher crime

Scribner et alli [1995], LA counties: assaults associated with presence of bars even after controlling for country demographics

Gorman et alli [1998], New Jersey counties: no effect after controlling for demgraphics


Literature 2: is the alcohol-crime nexus amplified by social interaction?

Problems Direct individual evidence

Same as above: drinking in bars makes it easier to get caught. Would be better with occurrence not booking or inmate data

Cross-sectional local variation in the presence of bars and crime rates

Crime, alcohol consumption, and bars occur concurrently with factors such as

Poverty? Low education? No other forms of entertainment? If result arises: not convincing → hard to control If results does no arises → standard errors should be large

SPMA dry laws: (almost) perfect empirical opportunity High crime environment: (almost) any policy

worth trying 2002 monthly murder rate: 3.64 per 100thd 2002 US rank: 2nd. Slightly below DC with 3.81

per 100thd. NYC in its peak: 3.56 per 100thd Cross-sectional and time-series variation in

legislation on operations of bars Bogotá had uniform adoption

Pure time-series severely inferior Chance to beat the pure cross-sectional results

SPMA dry laws: (almost) perfect empirical opportunity Same metropolitan area, over a “short” period of time

We start with a minimum level of homogeneity Subject to approximately the same aggregate economic and

social shocks Or at least as close as one probably gets

Whether policy would “work” not obvious Literature not clear Weak law-and-order environment Beggar-thy-neighbor effects

The name here for “general equilibrium effects”

One little problem though: Adoption is a choice of the city

Self-selection Case and Besley unnatural experiments Lack of external validity

Counterfactual not crystal clear Few observations on the cross-section dimension

Propensity score matching procedure to correct for selection suffer from micronumerosity (N = 16)

SPMA dry laws: (almost) perfect empirical opportunity

Chronology of events

March-01: Barueri imposes a 11PM-6AM (weekdays), 2AM-6AM mandatory closing hours for bars Few exceptions: not located near schools, outside

“crime zones” Most likely to exclude upper-middle class

establishments But in practice almost all were “included”: as of Sep-

05, only 50 out of


Several cities followed suit with very similar lawsCity Date- Dry Law

Barueri Mar-01Jandira Aug-01Itapevi Jan-02

Diadema Mar-02Juquitiba May-02

São Lourenço da Serra Jun-02Suzano Jun-02

Itapecerica Jul-02Mauá Jul-02

Ferraz de Vasconcelos Sep-02Embu Dec-02

Osasco Dec-02

Embu – Guaçu Apr-03

Vargem Grande Paulista Dec-03

São Caetano Jul-04Poá Aug-04

Table I Source:Kanh and Zanetic (2005), months alchool laws passed in city council

Data

Monthly homicides from Secretaria Estadual de Segurança de SP, Jan-2000/Dec-2004 Includes murders and non-negligent manslaughter

in the American classification No manslaughter (no car accidents) Homidices suffer little from under-reporting

But some from taxonomy Cross-reference with hospital (SUS) data confirms

very little problem with data (De Mello and Zilberman [2006])

Data

Demographic data from PNADs (1999, 2000 and 2003), census (2000)

City characteristics such as establishment of municipal secretary of justice, municipal police force from Kahn and Zanetic [2005]

Political data from TRE-SP

General strategy

Use the cross-section and time series variation to estimate the effect of implementing the law:

Compare the dynamics of homicide in adopting and non-adopting cities Also use knowledge of the institutions

Argue that counter-factual is not absurd

despite adoption being a choice Argue with empirics and institutions

Evidence: summary statistics

Summary statistics: adopting and non-adopting citiesMean adopting (16 cities)** Mean non-adopting (23 cities)

Homicides per thd inhabitants4.22 3.42

(0.92) (0.61)4.62† 3.67‡*(1.31) (0.39)*3.71 3.43‡

(1.79) (0.18)2.30 1.44

(3.46) (2.68)3.42 2.78

(2.69) (2.20)4.21 3.51

(2.30) (1.62)Demographics

Population (in thd) 186 6521997-2004 (164) (2100)

Population (in thd)* 2081997-2004 (269)

Population 210 21412 months before adoption† (177) (275)

Population 215 21912 months after adoption† (179) (281)

%Male Population, age 15-30 14.5 13.71997-2004 (0.7) (0.6)

%Male Population, age 15-30 14.6 13.712 months before adoption† (0.4) (0.5)

%Male Population, age 15-30 14.5 13.512 months after adoption† (0.5) (0.6)

Educational Attainment9.43 9.29

(2.99) (2.54)8.28 7.52

(2.66) (2.44)6.97 7.00

(1.08) (0.79)Income

Income per capita 8847 82931999-2004 (7178) (4623)

Income per capita 8484 864112 months after adoption† (6651) (5086)

Income per capita 9535 975712 months after adoption† (6990) (6107)

Jan-1997/Dec-2004

12 months after adoption†

12 months before adoption†

Table II Source: Secretaria de Segurança do Estado de São Paulo, Fundação SEADE, and Kahn and Zanetic [2005].Standard Errors in parentheses. †: for each city, average over the period (12 months before or after the adoption), thenaveraged over adopting cities. ‡ average homicides for non-adopting cities; period of reference is the average adoptionperiod (July 2002). * = excludes São Paulo. * * = São Caetano and Poá excluded for late adoption

Number of years of Schooling

Population > 150,000

100,000<Population< 150,000

Population < 50,000

High school drop-out rate (2002)

High school drop-out rate (1999)

Adopting cities more

violent

But were not abnormally violent

before adoption

Not sharply distinguishable in

terms of populationNo significant

diff in trends of population

Diff not surprising given adopting more

violent

Even then, diff not thrilling

No diff in trends

Diff expected but undistinghuishable

in practice

Trend if anything

goes againstNo diffAgain

undistinguishable in levels and trends

Evidence: trends2

34

5H

omic

ides

per

100

thd

80 100 120 140 160Date

Source: Secretaria de Segurança do Estado de São Paulo

Evolution of Homicides - Scatterplot and Fractional Polynomial

Jan-1997 Aug-2002

Average adoption period

23

45

6H

omic

ides

per

thd

inha

b (A

dopt

ing)

100 120 140 160 180date

23

45

6H

omic

ides

per

thd

inha

b (N

on-a

dopt

ing)

100 120 140 160 180date

Figure II Source: Secretaria de Segurança Pública do Estado de São Paulo

Red/Blue = Adopting/Non-adopting Dash/Solid = Before/AfterEvolution of Homicides - Spline Regressions

Exogenous break

See what the standard errors are like in the figure above

Imposes a candidate for structural break in Jul-2002 (average adoption period)

Dependent Variable: Homicides per 100thd inhabitants(1) (2) (3) (4)

OLS/Newey-West FGLS/Cochrane-Orcutt OLS/Newey-West FGLS/Cochrane-OrcuttNon-adopting Non-adopting Adopting Adopting

3.801 3.507 8.708 8.796(1.409)*** (1.690)** (2.484)*** (2.538)***

-0.025 -0.023 -0.065 -0.066(0.010)** (0.012)* (0.018)*** (0.018)***

-0.250 -0.243 -0.001 -0.001(0.007)*** (0.007)*** (0.013) (0.012)

Durbin-Watson 1.123 1.998 2.007 1.648R-squared 0.823 0.746 0.555 0.627

No Observations 60 60 60 60

Structural Break §

Structural Break*Time

Time

Table III Source: Secretaria de Segurança do Estado de São Paulo, Kahn and Zanetic [2005]. §: Exogenous structural break assumed to be atthe average period of adoption (July 2002). Standard Errors in parentheses robust heteroskedasticity and first-order serial correlation (Prais-Winsten). * = significant at the 10% level. ** = significant at the 5% level. ***: significant at the 1% level. Sample restricted to lateNovember 1999 onwards. Columns (1) and (3): Newey-West standard errors corrected for an AR(2) process on the error term. Columns (2)and (4): FGLS (Cochrane-Orcutt) assuming AR(1) process on the error term.

AdoptingNon-adopting

Endogenous break

Let the data choose whether there was a structural break and when If chooses break after 140 for non-adopting, suspicious If it chooses break too far for adopting too far from 140,

especially if before, suspicious Break can occur in any period τ (starting Jan-2001)

estimate:

Break is at:

tt timetimebreakbreakHomicide

3210 *

2max minarg R

Dependent Variable: Homicides per 100thd inhabitants(1)† (2)† (3)‡ (4)‡

OLS/Newey-West FGLS/Cochrane-Orcutt OLS/Newey-West FGLS/Cochrane-OrcuttNon-adopting Non-adopting Adopting Adopting

2.270 2.102 9.832 9.782(1.440) (1.708) (1.975)*** (2.300)***-0.014 -0.013 -0.075 -0.075(0.012) (0.013) (0.015)*** (0.018)***-0.029 -0.032 0.010 0.010

(0.011)*** (0.011)*** (0.013) (0.015)Durbin-Watson 1.075 2.020 1.704 2.000

R-squared 0.829 0.755 0.642 0.582No Observations 60 60 60 60

Structural Break

Structural Break*Time

Time

Table IV Source: Secretaria de Segurança do Estado de São Paulo, Kahn and Zanetic [2005]. †: Endogenous structural break for non-adopting cities: November-2001. ‡: Endogenous Structural Break for adopting cities: May-2002. * = significant at the 10% level. ** =significant at the 5% level. ***: significant at the 1% level. Structural break assumed to be at the average period of adoption (July 2002);Sample restricted to late November 1999 onwards. Columns (1) and (3): Newey-West standard errors corrected for an AR(2) process on theerror term. Columns (2) and (4): FGLS (Cochrane-Orcutt) assuming AR(1) process on the error term.

Non-adopting: break at Nov-2001, not significant

Adopting: break at May-2002, significant

Controlling for covariates

Difference-in-differences approach Allows us to control for:

Concurrent events such as the establishment of a municipal secretary of justice, and a municipal police force (guarda municipal)

Recent dynamics of homicide Important since adoption is a choice. Dynamics of homicide

can affect both adoption and future crime: High crime → adoption High crime today → lower crime tomorrow (mean-reversing

process, for instance) City fixed effects Period (month) specific effects


Estimated model:

ititit

tiit

ControlsAdoptLaw

dAdoptPerioLawHomicide

2

210

Per 100thd inhabitants Identifies adopting citiesIdentifies “adoption period”Like the interaction term in a

normal diff-in-diffs model

Includes: 1) Lags of homicide, 2) Municipal force, 3) municipal secretary of justice; 4) income; 5) population; 6) city specific dummies; 7) month specific dummies


Model for the variance:

Homicide is a relatively rare occurrence. City level data → observations from small cities are very noisy

For more common types of crime, this would not be such a problem

it

it populationVar

2


What is in εit that can be dangerous? Other policy reactions to crime, such as police

If police indeed responds to crime at this speed then inclusion of lagged homicide will “proxy” for police

POLICE DOES NOT RESPOND TO CRIME AT THIS SPEED

City policing is defined by law (no specific periodicity) based to maintain an uniform number of policemen per capita

This defines battalions size in the short-medium run. There is some flexibility between battalions when they cover more than one city, which is the exception

São Paulo has ?? battallions A city like ?? has one battalion

Number does not tell all the story though Increase in repression, more street policing less back office?

Probably do not have a short-term impact

Controlling for Covariates

Results with all firms included

Dependent Variable: Homicides per 100thd inhabitantsJan-1999 - Dec-2004

Apr-1999 - Dec-2004

Jul-1999 - Dec-2004

Jul-1999 - Dec-2004

Jul-1999 - Dec-2004

(1) (2) (3) (4) (5)†-0.622 -0.415 -0.346 -0.381 -0.421

(0.135)*** (0.131)*** (0.136)*** (0.150)*** (0.148)***

No Observations 2808 2691 2574 2574 2508

R-squared 0.521 0.542 0.546 0.546 0.489Homicides t- 1/Homicides t- 3 No Yes Yes Yes YesHomicides t-4 /Homicides t-6 No No Yes Yes Yes

Municipal Force? No No No Yes YesSecreatry of Justice? No No No Yes Yes

Demographic controls? No No No Yes YesCity Dummies? Yes Yes Yes Yes Yes

Period Dummies? Yes Yes Yes Yes YesTABLE IV: Source: Secretaria de Secretaria Estadual de Segurança Pública de São Paulo, Fundação SEADE, andKahn and Zanetic [2005]. Robust standard Errors in parentheses. *** = significant at the 1% level, ** = significant5% level. FGLS procedure using variance model for population. Standard errors robust to heteroskedasticity. † =Excludes São Paulo

AdoptLaw

= 17% of homicides in non-adopting citiesOr 746 homicides annually in the city of São Paulo

Controlling for the dynamics of homicides indeed dampens the effect...

But it is still there, statistically and practically: 9.5% of homicides in non-adopting citiesIncluding demographic controls

Excludes the city of São Paulo, 56% of the population

Controlling for CovariatesDependent Variable: Homicides per 100thd inhabitants

Jul-1999 - Dec-2004

Jul-1999 - Dec-2004

Jul-1999 - Dec-2004

Jan-2001 - Dec-2003

Jan-2001 - Dec-2003

Jan-2001 - Dec-2003

(1)† (2)‡ (3)§ (4) (5)‡ (6)§-0.341 -0.351 -0.353 -0.605 -0.634 -0.696

(0.174)** (0.137)*** (0.105)*** (0.237)*** (0.241)*** (0.158)***

No Observations 2574 2574 2574 1404 1404 1365R-squared 0.390 0.555 0.481 0.488 0.484 0.147

Homicides t- 1/Homicides t- 3 Yes Yes Yes Yes Yes YesHomicides t-4 /Homicides t-6 Yes Yes Yes Yes Yes Yes

Municipal Force? Yes Yes Yes Yes Yes YesSecretary of Justice? Yes Yes Yes Yes Yes Yes

Demographic controls? Yes Yes Yes Yes Yes YesCity Dummies? Yes Yes No Yes Yes No

Period Dummies? Yes Yes Yes Yes Yes Yes

AdoptLaw

TABLE V: Source: Secretaria de Secretaria Estadual de Segurança Pública de São Paulo, Fundação SEADE, and Kahn andZanetic [2005]. Robust standard Errors in parentheses. *** = significant at the 1% level, ** = significant 5% level. FGLSprocedure using variance model for population. Standard error robust to heteroskedasticity. †: No model for the variance: all citieswith the same weight. ‡: robust to contemporaneous error correlation. §: Fixed -effect modelo with AR(1) model for the errorterm.

No model for the Variance, more impact on standard deviation

Accounts for contemporenous between panel auto-correlation

Fixed-effects mdoel with AR(1) model for within-panel autocorrelation (Betrand, Duflo and Mullainathan [2004])

Sample restricted to Jan-2001, Dec-2003. Late adopters become controls

A placebo Experiment

Move “adoption date” to 18 moths before

Jul-1999 - Dec-2004

Jul-1999 - Dec-2004

Jul-2001-Dec-2003

Jul-1999 - Dec-2004

(1) (2) (3) (4)†-0.089 -0.092 -0.013 -0.098(0.104) (0.105) (0.183) (0.116)

No Observations 2802 2805 1404 1368R-squared 0.551 0.547 0.483 0.478

Homicides t- 1/Homicides t- 3 Yes Yes Yes YesHomicides t-4 /Homicides t-6 Yes No No No

Municipal Force? Yes Yes Yes YesSecreatry of Justice? Yes Yes Yes Yes

Demographic controls? Yes Yes Yes YesCity Dummies? Yes Yes Yes Yes

Period Dummies? Yes Yes Yes Yes

AdoptLaw

TABLE IV: Source: Secretaria de Secretaria Estadual de Segurança Pública de São Paulo, Fundação SEADE,and Kahn and Zanetic [2005]. Robust standard Errors in parentheses. *** = significant at the 1% level, ** =significant 5% level. FGLS procedure using variance model for population. Standard errors robust toheteroskedasticity. †: Excludes São Paulo.

Dependent Variable: Homicides per 100thd inhabitants


“Homogenizing” adopting and non-adopting cities: similar to “matching”

Procedure proposed by Crump, Hotz, Imbens and Mitnik [WP, 2006] Calculate propensity score (probability of adopting the law)

based on observables This included political variables such as whether there was

a change in mayor, mayor’s party proportion of city representatives, whether the election was close, etc.

Exclude all cities that are “two dissimilar”: only scores above the median

Little different fro Crump et alli because we wanted to maintain cities more homogeneous on adoption time


Jan-2001 - Dec-2003

Apr-1999 - Dec-2004

Jan-2001 - Dec-2003

(1) (2) (3) (3)-0.890 -0.936 -0.501 -0.763

(0.177)*** (0.286)*** (0.189)*** (0.293)***No Observations 1440 740 1320 740

R-squared 0.482 0.440 0.506 0.546Homicides t- 1/Homicides t- 3 No No Yes YesHomicides t-4 /Homicides t-6 No No Yes Yes

Demographic controls? No No Yes YesCity Dummies? Yes Yes Yes Yes


AdoptLaw

TABLE IV: Source: Secretaria de Secretaria Estadual de Segurança Pública de São Paulo, Fundação SEADE,and Kahn and Zanetic [2005]. Robust standard Errors in parentheses. *** = significant at the 1% level, ** =significant 5% level. FGLS procedure using variance model for population. Standard errors robust toheteroskedasticity. Same controls as in table IV, except for municipal secretary and municipal guard interactedwith period of adoption for there is no variation in this variable within the sub-sample. City and perioddummies included. Sample selection based on propensity score, only scores above the median included (Aruja,Barueri, Biritiba Mirim, Caieiras, Diadema, Embu, Embu-Guaçu, Franco da Rocha, Guarulhos, Itapecerica daSerra, Itapevi, Jandira, Juquitiba, Mairiporã,Osasco, Santana do Parnaíba, São Caetano do Sul, São Lourençoda Serra, Suzano, Taboão da Serra).

Beggar-thy-neighbor?

Prohibition in one city could just shift crime to the neighbor

The effect arises but overall effect is zero Important for policy

It is analogous to the so-called “general equilibrium effects” of policy intervention

Beggar-thy-neighbor

Ideally one would like to: Restrict the attention to adopting cities with not bordering

non-adopting cities And non-adopting cities with no bordering adopting city This leave us with too few observations (1 adopting city,

Juquitiba) So we exclude, from the sub-sample of “homogenized”

cities, all adopting cities whose border within ten miles of a non-adopting one. Three adopting ones: Osasco (ten miles Guarulhos), Itapevi and Barueri (border with Santana do Parnaíba)


Jan-2001 - Dec-2003

Apr-1999 - Dec-2004

Jan-2001 - Dec-2003

(1) (2) (3) (3)-1.202 -1.160 -0.633 -1.061

(0.236)*** (0.429)*** (0.247)*** (0.461)**No Observations 1224 629 1122 629

R-squared 0.515 0.463 0.524 0.478Homicides t- 1/Homicides t- 3 No No Yes YesHomicides t-4 /Homicides t-6 No No Yes Yes

Demographic controls? No No Yes YesCity Dummies? Yes Yes Yes Yes


AdoptLaw

TABLE IV: Source: Secretaria de Secretaria Estadual de Segurança Pública de São Paulo, Fundação SEADE,and Kahn and Zanetic [2005]. Robust standard Errors in parentheses. *** = significant at the 1% level, ** =significant 5% level. FGLS procedure using variance model for population. Standard errors robust toheteroskedasticity. Same controls as in table IV, except for municipal secretary and municipal guard interactedwith period of adoption for there is no variation in this variable within the sub-sample. City and perioddummies included. Sample selection based on propensity score, only scores above the median included (Aruja,Barueri, Biritiba Mirim, Caieiras, Diadema, Embu, Embu-Guaçu, Franco da Rocha, Guarulhos, Itapecerica daSerra, Itapevi, Jandira, Juquitiba, Mairiporã,Osasco, Santana do Parnaíba, São Caetano do Sul, São Lourençoda Serra, Suzano, Taboão da Serra). Excludes Barueri, Itapevi and Osasco.

Conclusion

Results suggest that punctual, focused restriction of recreational sales of alcohol does have a beneficial impact on violent crime (homicides) Computed with our lower estimate (Crump et alli

estimate excluding Diadema) the effect is 360 homicides for the city of São Paulo annually

This is some 8% homicides

Evidence: trends2

34

56

Ho

mic

ides

per

thd

inha

b

80 100 120 140 160Date

Figure I Source: Secretaria de Segurança do Estado de São Paulo

Red = Adopting Cities Blue = Non-adopting CitiesEvolution of Homicides - Scatterplot and Fractional Polynomial Fit

Average Dry Law Adoption

Dry Law and Homicide: Evidence from the São Paulo Metropolitan Area.

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Transcript of Dry Law and Homicide: Evidence from the São Paulo Metropolitan Area.