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.
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
Why is it interesting?
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)
Why is it interesting?
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
Why is it interesting?
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
Why is it interesting?
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
Why is it interesting?
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
Chronology of events
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
Chronology of events
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
Controlling for covariates
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
Controlling for covariates
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
Controlling for covariates
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
Controlling for covariates
“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
Dependent Variable: Homicides per 100thd inhabitantsJan-1999 - Dec-2004
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
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. 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)
Dependent Variable: Homicides per 100thd inhabitantsJan-1999 - Dec-2004
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
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. 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