Crime and Savings in Brazil: an Empirical Investigation

João Manoel P. de Mello

Eduardo Zilberman

LACEA, 2005

Motivation

• Main focus of the crime literature is on the determinants of crime

• Another branch of this literature tries to estimate the cost associated with crimes– Material cost– Welfare cost

Motivation

• The effects of crime on economic variables have not been studied extensively

• If crime distorts agents’ decisions, it represents another source of cost to the society, generally unaccounted for by the literature.

Research Question

• How do crime and savings relate empirically?

• Results:– Property crime seems to increase savings;– Violent crime as a whole is not significant;– Savings appear to affect negatively property crimes,

but it does not affect violent crimes.

Theoretical Reasons

• Through probability of death:– Crime raises probability of death increases

consumption increases savings reduce– This channel should be stronger for violent crimes

• Through the precautionary motive:– Crime raises future flows of income are more

volatile savings raise– How important is the precautionary motive?

• Lusardi (1998) – It exists but is not very large• Gourinchas e Parker (2001) – It is important at low wealth

levels

Theoretical Reasons

• Through the marginal utility:– If marginal utility of consumption is decreasing on

crime, an increase in crime will reduce consumption Savings will be higher

– A caveat:• Instead of substitute consumption intertemporally, people

could substitute one type of good, that is “taxed” by crime, for another, that is not “taxed” by crime. No effect on savings

• However, if crime rate is expected to fall, people will postpone consumption of “taxed” goods

Theoretical Reasons

Figure 2 - Crimes per 100.000 Habitants for São Paulo

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Property Crime Rate Violent Crime Rate

Descriptive Statistics (São Paulo 2000)(reported crimes)

Fraud 4.66% Manslaughter 0.98%Extortion via kidnapping 0.03% Felony murder 2.44%Other extortions 0.10% Involuntary assault 22.37%Achieved theft 30.40% Felony assault 33.23%Attempted theft 1.17% Attempted murder 1.94%Achieved theft of vehicles 11.65% Other violent crimes 39.03%Attempted theft of vehicles 0.17%Achieved qualified theft 9.10%Attempted qualified theft 0.47%Achieved robbery 20.95%Attempted robbery 0.83%Achieved robbery of vehicles 12.16%Attempted robbery of vehicles 0.13%Robbery followed by murder 0.07%Other property crimes 8.12%

Reports: 966,788 Reports: 522,831

Table 2

Property crimes Violent crimes

Dados

• Unit of observation: city in São Paulo state, 2000.

• Savings measure: total amount of deposits in savings accounts and long-term CDs (Certificates of Deposit).

• Crime measure: total number of crimes reported.

OLS Regressions

Violent PropertyIncomePC 8.747 8.937

(2.172)*** (2.111)***

IncomePC2-0.668 -0.689

(0.184)*** (0.179)***Crime100 0.031 0.122

(0.054) (0.043)***Gini 1.825 1.672

(0.584)*** (0.564)***Rural -0.160 -0.203

(0.226) (0.223)Adults -1.649 -1.130

(1.847) (1.851)Divorce 1.125 0.792

(2.239) (2.214)WorkHours 1.353 1.267

(0.560)** (0.570)**Education -0.212 -0.320

(0.326) (0.330)DummyPoup 0.137 0.142

(0.086) (0.085)*DeficitPC 0.000 0.000

(0.000) (0.000)Banks100 0.479 0.509

(0.078)*** (0.080)***Density 0.242 0.233

(0.031)*** (0.030)***WealthPC 11.831 9.417

(8.864) (8.654)

WealthPC2-2.480 -1.821(2.348) (2.292)

Constant -45.340 -43.817(9.858)*** (9.451)***

Observations 566 566R-squared 0.60 0.60

Robust standard errors in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%

OLS Regressions: Interpretations

1. For violent crime, effects could be offsetting themselves;

2. Channels are more relevant for property crime;

3. Expectations is relevant only for property crime;

4. In Brazil, people can protect themselves better from violent crimes (example: avoiding pass in isolated areas at night).

OLS regressions: property crimes

Robbery and Theft Vehicles Fraud Extortion

Crime100 0.149 0.091 0.035 0.042(0.041)*** (0.018)*** (0.020)* (0.023)*

Observations 566 566 566 566R-squared 0.61 0.62 0.60 0.60

Robust standard errors in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%

Dealing with the reverse causality

• Focus: property crime

• Assumption: savings decrease crime

Coefficient is unambiguously positive

• Strategies for identification:– Look for exogenous variation in crime– Estimate the reverse effect

Exogenous variation in property crimes

• Instruments used:– Number of pay phones per 100.000 habitants– Drug trafficking apprehensions per 100.000

habitants– Drug consumption apprehension per 100.000

habitants– Others instruments used (updated version):

• Maternal mortality in 1984• Victims of car accidents per 100.000 habitants

Instrument: drug trafficking and drug consumption

• Inclusion condition: does the drug market affect crime?

– Robberies and thefts are means used by gangs to finance themselves;

– Gangs need young poor people and guns to protect their “market”;

– Addicted people could commit crimes to sustain their addiction.

Instrument: drug trafficking and drug consumption

• Exclusion condition: does not the drug market affect savings above and beyond demographics and income?

– Most of the drug dealers are poor and young people that probably would not save if they were not linked to this activity

– Most of the drug consumers are young people that probably would not save if they were not addicted

First Stage

IncomePC -1.743 -1.955 -2.054 -2.041 -2.242(2.629) (2.437) (2.558) (2.402) (2.249)

IncomePC20.182 0.192 0.209 0.205 0.214

(0.231) (0.213) (0.224) (0.210) (0.196)Gini 1.631 1.379 1.261 1.260 0.978

(0.568)*** (0.563)** (0.557)** (0.537)** (0.539)*Rural 0.246 0.337 0.358 0.340 0.425

(0.204) (0.203)* (0.209)* (0.198)* (0.200)**Adultos -4.438 -4.800 -4.415 -3.308 -3.742

(1.579)*** (1.554)*** (1.544)*** (1.595)** (1.581)**Divorce 4.473 3.756 3.108 1.942 0.997

(2.461)* (2.485) (2.460) (2.574) (2.567)WorkHours 0.498 0.686 0.576 0.432 0.566

(0.700) (0.703) (0.705) (0.693) (0.703)Education 1.021 0.801 0.950 0.783 0.591

(0.354)*** (0.354)** (0.343)*** (0.343)** (0.343)*DummyPoup -0.051 -0.054 -0.034 0.006 0.003

(0.088) (0.087) (0.086) (0.085) (0.084)DeficitPC 0.000 0.000 0.000 0.000 0.000

(0.000) (0.000) (0.000) (0.000) (0.000)Banks100 -0.243 -0.283 -0.218 -0.187 -0.215

(0.070)*** (0.070)*** (0.069)*** (0.067)*** (0.067)*Density 0.066 0.040 0.065 0.066 0.044

(0.039)* (0.037) (0.038)* (0.036)* (0.035)WealthPC 21.502 20.970 21.163 18.840 18.933

(8.802)** (8.233)** (8.757)** (8.512)** (8.013)**

WealthPC2-5.822 -5.758 -5.747 -5.156 -5.257

(2.349)** (2.208)*** (2.337)** (2.275)** (2.153)**Phones100 0.294 0.2709

(0.066)*** (0.063)***DrugTraffic100 0.052 0.0079

(0.016)*** (0.020)DrugCons100 0.083 0.080

(0.016)*** (0.019)***Constant -11.190 -10.050 -10.050 -7.474 -6.764

(12.236) (11.152) (11.991) (11.090) (10.090)

Observations 566 542 566 566 542R-squared 0.32 0.34 0.33 0.36 0.38

Instrument: pay phones

• Inclusion condition: does the pay phone affect the perception of crime?

– Pay phones: easier to report crime; – Disque Denúncia program, which encourages people to

report potential and actual crimes; – One caveat:

• This instrument seems to alter the number of reported crimes, but not crimes de facto

• However, crime perception is also important to determine savings decision

– Pay phones may capture the confidence on public services, that makes people more prone to report crimes to the police.

Instrument: pay phones (first stage)

Phone100 Sewage Garabage Water HospBeds100 Bus100

0.294 -0.002 -0.007 -0.003 -0.043 0.031

(0.066)*** (0.001) (0.010) (0.003) (0.044) (0.033)

Table 7

*Controls used in these regressions are not reported here.

Sewage - % of households with sewage

Garbage - % of household with garbage collection

Water - % of household with water provision

HospBeds100 - logarithm of hospital beds per 100000 habitants

Bus100 - logarithm of buses per 100000 habitants

Effects of these variables on property crime (OLS regressions)*

2SLS regressions: second stage

Instrument: Phones100 DrugTraf100 DrugCons100 All

Crime100 0.603 1.588 0.640 0.659

(0.262)** (0.615)** (0.222)*** (0.188)***

2SLS regressions: second stage

Instrument: Phones100 DrugTraf100 DrugCons100 All

Crime100 0.603 1.588 0.640 0.659

(0.262)** (0.615)** (0.222)*** (0.188)***

Why do we believe that DrugCons100 is a better instrument than DrugTraf100?

• Some cities may have a disproportionate amount of documented trafficking inasmuch as they could be distribution centers

• Drug trafficking is a more infrequent occurrence, and produces more outliers

Other stuff on the paper

• Savings, when instrumented for by the number of banks per capita, decrease property crime, while has no effect on violent crime

• We check robustness by– Controlling for regional fixed effects– Changing the saving measure for residential

capital per capita