Alchool and Misbehavior: Evidence from Sales Restrictions

in the São Paulo Metropolitan Area

João De Mello (PUC-RIO) and Alexandre Schneider (EAESP-FGV and São Paulo Mayorship)

Research Question

Is there evidence that restricting alcohol sales in bars has an impact on violent crime?

Is it relevant for policy?

• Several Latin-American cities have adopted similar restrictions– Bogotá 1994 is the most prominent example– Diadema often cited as successful crime fight

• The Economist, 10/20/2005

• Several historical examples of similar legislation– American Prohibition

Is it relevant for policy?

• On the other hand ...– Sales restrictions also entail welfare losses

• Therefore:

– Is there an effect on violent crime?– Is it first-order?– Are there less intrusive means of offsetting (alleged)

adverse effects of alcohol consumption?

Is it relevant for policy?

• Mr. Churchill on the later point:

“In our country, just as in ours, an enormous problem of misery, poverty, and crime ... resulted from alcohol. We, however, used different weapons. We used the weapons of regulation and taxation," Winston Churchill, referring to the prohibition, on a US tour speech in 1931

Overview• The Law

• The Chronology of Events

• Data

• The Empirical Strategy

• Results

• Conclusion

The Law

• Diadema, for example:

The Law

• Other cities that adopted have very similar laws

• It varies slightly on

– Specific times

– Specific days of the week

The Chronology of EventsMês/Ano da Aprovação da Lei Seca - Municípios da Região Metropolitana de São Paulo

MunicípioData da Lei

Barueri Mar-01

Jandira Aug-01

Itapevi Jan-02

Diadema Mar-02

Juquitiba Maio-02

São Lourenço da Serra Jun-02

Suzano Jun-02

Itapecerica Jul-02

Mauá Jul-02

Poá Ago-02

Ferraz de Vasconcelos Set-02

Embu Dez-02

Osasco Dez-02

Embu – Guaçu Abr-03

Vargem Grande Paulista Dec-03

São Caetano Jul-04

Kahn, Túlio e Zanetic, André. Municípios na Segurança Pública – Em Estudos Criminológicos 4, Julho 2005.

There is variation in adoption timing

16 cities out of 39 in the SPMA

The Chronology

• Cities that have not adopted– Franco da Rocha, Guararema, Guarulhos,

Biritiba Mirim, São Bernardo do Campo, Santa Isabel, Arujá, Itaquaquecetuba, Mairiporã, Mogi da Cruzes, Rio Grande da Serra, São Paulo, Pirapora do Bom Jesus, Ribeirão Pires, Taboão da Serra, Santana do Parnaíba, Santo André, Salesópolis, Cotia, Francisco Morato, Cajamar, Caieiras, Carapicuíba

– 23 out of 39 cities in the SPMA

Data

• City level data on Homicides from Jan-1999 to Dec 2004– Secretaria de Segurança Pública do Estado de São

Paulo

• City population – IBGE

• Presence of Alcohol Law (and timing), Municipal Justice Secretary, Municipal Policing– Kahn and Zanetic [2005]

The Empirical Strategy

• Difference-in-Difference Model

– Compare the evolution of homicides, before and after the adoption, between cities that adopted and cities that have not adopted the Law

– Cities that have not adopted the law are the “control group”

• It decreases significantly the possibility of capturing spurious effects due to

– Time trends

– Concurrent events

The Empirical Strategy

ititti

itiit

CONTROLSMONTHCity

CLAWLAWHOMICIDES

ε

γγγ

++Ω+Σ

+++= 210

• i = City

• t = MonthHomicides per thousand inhabitants

Dummy for the cities that adopted the law

Dummy for the cities that adopted the law and for the periods after adoption

City specific DummyMonth specific Dummy

Municipal police and Municipal Justice Secretary

The Empirical Strategy

• The Hypothesis:

ititti

itiit

CONTROLSMONTHCity

CLAWLAWHOMICIDES

ε

γγγ

++Ω+Σ

+++= 210

γ2 < 0 if the law has an effect on homicides

The Empirical Strategy

• Our model:

• Traditional

ititti

itiit

CONTROLSMONTHCity

CLAWLAWHOMICIDES

ε

γγγ

++Ω+Σ

+++= 210

itititi

tiit

CONTROLSCityTREATMENTLAW

TREATMENTLAWHOMICIDES

εγ

γγγ

++Σ+×+

++=

3

210

? No uniform treatment period

Monthly dummies do the job

The Empirical Strategy

• Model for the Variance– Huge heterogeneity in city-size– Homicide is not such a common occurrence– Observations from small cities much noisier than large

cities– We weight the data to “turn the model heteroskedastic”

( )it

it populationVAR

2σε =

The Empirical Strategy: Caveats

• Unobserved heterogeneity between control (non-adoption) and treatment (adoption) groups

• Endogeneity of adoption

• Spillover effects

Caveats: Unobserved Heterogeneity

• Police reaction to increases in crime– Adopting cities adopt in high crime periods. – Policing also react to high crime periods

• Why doesn’t it hurt us too bad?– Decision level: state government– Difference in the timing of reaction– Rigidity of allocation of police force– Size of police force does not respond to crime:

• By law, city-level police force is determined by population• Any major changes have implies changes in the law

Caveats: Endogeneity of Adoption

• We want to estimate:

• But

Law Crime

Crime Law

Caveats: Endogeneity of Adoption

• Sign of the OLS bias: Hard to determine

– Crime before affects adoption

– Adoption affects crime after

– Sign of bias would depend on the dynamics of homicide

Caveats: Endogeneity of Adoption

• The scenario that would hurt us:

– Cities adopt in periods of historically high homicide

– Homicide is a mean-reversing period

Caveats: Endogeneity of Adoption

• Does homicide look mean-reversing?

Caveats

• Law induces more drinking in neighboring cities

– Solution: compare only far away cities

ResultsDependent Variable: Homicides per 100thd inhabitants

Date < December 2003

Whole sampleDate < December 2003,

excluding São Paulo(1) (2) (3)

-0.227 0.173 -0.253(0.581) (0.348) (0.409)

-0.585*** -0.434*** -0.593***(0.144) (0.145) (0.180)

0.072*** 0.089*** 0.063**(0.021) (0.026) (0.028)

0.064*** 0.076*** 0.058**(0.021) (0.024) (0.026)

0.074*** 0.099*** 0.083***(0.021) (0.023) (0.023)

0.081*** 0.099*** 0.086***(0.021) (0.019) (0.026)

0.068*** 0.080*** 0.074**(0.021) (0.025) (0.029)0.625 0.488 0.738*

(0.983) (0.354) (0.417)-0.480*** -0.176 -0.516**

(0.178) (0.215) (0.216)-0.920 -0.346 -0.646(0.717) (0.344) (0.407)-0.165* 0.065 -0.234(0.098) (0.103) (0.155)

-8.48e-07* -5.45e-07 -1.64e-06(5.02e-07) ( 3.87e-07) (2.57e-07)

Number of Observations 2301 2808 2242

Municipal Force*Time of Adoption

Municipal Secretary Justice

Municipal Secretary*Time of Adption

Law

AdoptLaw

Homicides per 100thd inhabitants t- 1

Homicides per 100thd inhabitants t-2

Homicides per 100thd inhabitants t-3

Homicides per 100thd inhabitants t-4

Homicides per 100thd inhabitants t-5

Population

Municipal Police Force

Persistence in homicide

ResultsDependent Variable: Homicides per 100thd inhabitants, Only Diadema

Only Sub-Sample of Controls‡

All cities without law

(1) (2)0.886 -0.136

(3.169) (4.731)0.907 -0.793

(0.793) (0.279)-1.988*** -1.597***

(0.511) (0.024)0.086* 0.071***(0.048) (0.024)0.047 0.067***

(0.048) (0.024)0.056 0.091***

(0.048) (0.024)0.129*** 0.093***(0.048) (0.024)0.046 0.071***

(0.048) (0.025)-1.824** 3.982(0.818) (4.976)0.263 -0.221

(0.365) (0.165)0.528 2.951

(0.933) (4.832)-0.171 -0.039(0.313) (0.102)

-1.52e-06 -1.84e-07(4.15e-06) (4.63e-07)

Number of Observations 504 1800

Dummy Diadema

Dummy Diadema x Dummy Treatment Period

Homicides per 100thd inhabitants t- 1

Homicides per 100thd inhabitants t-2

Dummy Treatment Period†

Municipal Police Force

Municipal Force*Time of Adoption

Municipal Secretary Justice

Municipal Secretary*Time of Adption

Population

Homicides per 100thd inhabitants t-3

Homicides per 100thd inhabitants t-4

Homicides per 100thd inhabitants t-5

Control group: similar size and not contiguous to Diadema

Results

• Economic significance and contrafactuals– Is it first-order?

• 0.585 (all treated) = 77% of a standard deviation of homicides

• If São Paulo had adopted (Back-of-envelope calculations based on estimate):– 0.585x100x12=702 annual homicides