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

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: 2 nd. 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 laws

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 Adopting cities more violent But were not abnormally violent before adoption Not sharply distinguishable in terms of population No 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 against No diff Again undistinguishable in levels and trends

Evidence: trends Jan-1997 Aug-2002 Average adoption period

Evidence: trends

Exogenous break See what the standard errors are like in the figure above Imposes a candidate for structural break in Jul (average adoption period) Adopting Non-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: 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: Per 100thd inhabitants Identifies adopting cities Identifies “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

Controlling for covariates What is in ε it 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 CITES INCLUDED ≈ 23% of the average crime rate and half a standard deviation In lifes for city of São Paulo, annually: 0.842*100*12 ≈ 1818, or 20% of the annual homicides in São Paulo over the period City fixed effects take some of the reuslt but not much Another little piece when period spefic effects are included São Paulo is 56% of the sample. Weighted procedure could be unduely driven by SP. Results stronger Sample closer to adoption periods. São Caetano and Poá are controls now Including lags of homicide indeed decrease effect but it still arises. Lowest estimate: 495 annual homicies in São Paulo, or some 10%

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 Exludes Diadema, that could be driving all results

Controlling for covariates High crime cities Only adopting and non-adopting with crime rates above the median

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 subsample of “homogeneized” cities, all adopting cities whose border within ten miles of a non-adoting three adopting ones: Osasco (ten miles Guarulhos), Itapevi and Barueri (border with Santana do Parnaíba)

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