The Determinants of Criminal Victimization in São Paulo State-Brazil

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The Determinants of Criminal Victimization in São Paulo State-Brazil


Fábio Augusto Reis Gomesa,∗∗∗∗, Lourenço Senne Pazb

aJoão Pinheiro Foundation, School of Governments and Center for Research in International


bUniversity of Maryland, Department of Economics


In this paper we investigate the determinants of criminal victimization by burglary/larceny in São Paulo - the most populous Brazilian state with 37 million inhabitants, responding for more than one third of the Brazilian GDP. In general, empirical works on victimization find a monotonically increasing relationship between city size and the likelihood of being a victim of crime, supported, theoretically, by both the probability of being arrested is lower in large cities and larger cities harbor a greater proportion of crime-prone individuals. However, once people respond to incentives, we advance saying that, in larger cities, potential victim have a greater incentive to invest more in private protection, and so the city size effects, theoretically, becomes ambiguous. In fact, we do not identify a monotonically increasing relationship between city size and victimization path. Furthermore, our findings suggest that people in the same income ranges are differently affected according to city-size.

Keywords: Victimization; burglary/larceny; city size; São Paulo state, Brazil. JEL classification: K40, K42, O17.

Área: Microeconomia Aplicada

We are grateful to Luiz Renato Lima, Luis Braido and Cleomar Gomes for their comments. The usual disclaims apply.



Crime has become a topic of increasing relevance among economists since Becker (1968) introduced the idea that criminals are rational and self-interested agents whose behavior can be best understood as an optimal response to incentives. Based on this approach, many authors tried to shed more light on this topic as Ehrlich, I. (1973), Sah (1991), Ehrlich, I. (1996), Charumilind, C. and Thorbecke, E. (2002). However, as they focus on criminal decision, they offer few clues as to which individuals are most likely to be victims of crime, which is an important topic to policy-makers especially in violent countries like the Latin American ones.

Seminal papers from Hindelang et al. (1978) and Cohen et al. (1981) discussed victimization issues from the point of view of sociology and criminology, and presented an effort to build theoretical framework to map what individuals are more likely to be victim of a crime. The models developed were the life-style and opportunity models. Although these models are essentially descriptive, their terminology could be used to understand two important papers on victimization, from the economics point of view, which are Glaeser and Sacerdote (1996), hereafter GS, and Gaviria and Páges (2002), hereafter GP.

Using a victimization survey done in several US cities, GS reported some stylized facts about victimization likelihood. The most important was that there is more crime in cities, especially large cities, in relation to rural areas, even after controlling for individual characteristics. Recently, GP analyzed the determinants of property crime victimization in Latin American cities, focusing mainly on the socioeconomic status of families as well as how city population sizes and recent growth affected the probability of being a victim of crime. Also, they presented a model to explain when wealthy individuals are more likely to be victim of property crime, based on investment in private protection. They concluded that the typical victim of crime in Latin America comes from rich and middle class households and tends to live in larger cities. They conjecture that this effect comes from the following possibilities: i) lower arrest probability in large cities, and ii) larger cities harbor a greater proportion of crime-prone individuals.

Thus, our purpose is to assess the determinants of the individual risks of being a victim of violence in the most populous Brazilian state, São Paulo - which has about 37 million inhabitants spread into 643 cities, and it produces more than one third of the Brazilian GDP. So, we employ the data from Seade’s 1998 Life Condition Survey (Pesquisa de Condição de Vida), hereafter PCV, a survey that contain information about burglary/larceny victimization and detailed people's profile.


Our theoretical approach is based on GS and GP, which bear many similarities with the life-style and opportunity models, once they also focus on socioeconomic variables. About the city effect, we advance taking into account that if larger cities are more violent, its households have an extra incentive to investment in private protection. This is a non-trivial extension of the model, once it means that the relationship between city size and the likelihood of being a victim of crime is not necessarily monotonically increasing. Therefore, beyond to investigate the role of many individuals’ characteristics, we focus on the following questions. Larger cities (or cities that grow mare quickly) are more violent? Who is more affected by crime: rich or poor people? This effects change with city size?

Our results indicate that although city size is an important factor in the likelihood of being a victim of burglary/larceny it is not the most important. The potential target life-style (income, education, etc) is by far a more important factor. Another important finding is that the likelihood is not monotonically increasing in the agent’s income, and the main reason for that is the investment in self-protection.

This paper is organized as follows. Section 2 addresses the models and the determinants of the victimization. The data set, the estimations and the results are presented in section 3. Finally, the conclusions are drawn in section 4.

2. The Determinants of the victimization by properties crime

Hindelang et al. (1978) e Cohen et al. (1981) were the first to discuss victimization issues and mapping what individuals are more likely to be victim of a crime. They developed the life-style and opportunity models, pointing out that the main determinants of victimization are: i) exposure, the physical visibility and accessibility of persons or objects to potential offenders at any time or place; ii) proximity, the physical distance between areas where potential crime targets reside and areas where relatively large populations of potential offenders are found; iii) guardianship, the effectiveness of persons' private security guards, law enforcement, objects such as alarms, in preventing violations from occurring; iv) target attractiveness, the material or symbolic desirability of persons or property targets to potential offenders; v) definitional properties of specific crimes, the features of specific crime that act to contain strictly instrumental actions by potential offenders.


there are many citizens and each one of them has an exogenous wealth, which could be viewed as the type of individual, In the first stage, being aware of the criminal characteristics, citizens decide how much to invest in private protection. In the second stage, citizens are matched with criminal, who in turn decide whether or not to commit a crime, taking for granted the victim’s wealth.

To incorporate another features from the life-style and opportunity models, we extended GP model. The extension consists in modifying both stages of the game. In the first one, we consider many types of individuals (citizens or potential targets) and criminals. In the second stage, we discuss more accurately the matching process between citizens and criminals, since there are many criminals now.

We suppose that there is 1,2,…,N risk neutral individuals in the society, where n are c

criminals and nv = Nnc are potential victims. The target (i) attractiveness is proxied by her income, wi, and she can invest ei in her self-security. The offender’s (j) benefit of a crime is given by

δjwi, where δjU[δL;δH] is an offender (j) specific parameter. Thus, for each criminal this parameter

is drawn from a uniform distribution and it may reflect either the offender’s ability or her preference for certain types of crime. The offender has a 1-p(ei) likelihood of having a successful offense

against victim (i), which is decreasing in ei . In case of failure, she will face a punishment, Fj, where

Fj=F(δj) with F’(δj)>0. So, Fj U[FL; FH] with FL =F(δL) and FH=F(δH).

When the offender meets the potential victim, he will attack when the expected benefit surpasses the expected punishment:

( )


1− pei


δjwi > p

( )

ei Fj (1)

( )

i j j i j i F w w e p δ δ + < (2)

It is assumed that the offender is able to observe ei. The investment ei is endogenous and it is

chosen by the potential victim taking into account distribution functions of δj andFj. To be precise,

potential victims take in account the mean criminal:

( )

H2 L j i E δ δ δ δ ≡ = + ∆ (3)

( )

H 2 L

( ) ( )

H 2 L j i F F F F F F E = + = δ + δ ≡ ∆ (4)

Therefore, the chosen ei level will be, on average, the one that makes the offender indifferent

about committing or not the crime and it is given by:

( )


( )

i F i i w w e p δ δ ∆ + ∆ ∆ = * (6) ( ) ∆ + ∆ ∆ = − i F i i w w p e δ δ 1 * (7)

Note that ei*=ei*

( )

wi . Thus, it is worth investing in security (guardianship) as long as such

investment does not exceed the crime expected loss. Once potential victim consider the mean criminal, the expected loss when she meets a criminal is E

( )

δjwi =∆δwi. However, by taking into account that the victim meets the criminal with probability θ =nc




, the expected loss for one meeting is given by θ∆δwi. Then, suppose that victims meet a fraction σ from society individuals,

with reposition, and then the total expected loss becomes σNθ∆δwi. Thus, potential victims invest in

private protection if ei* <σNθ∆δwi. It is important to note that the expected loss is increasing in N

(the number of city’s inhabitants), σ (a measure of exposure), θ (which is, approximately, the criminal’s fraction), victim’s income and mean criminal ability.

Therefore, if the probability of being arrested is lower in large cities and larger cities harbor a greater proportion of crime-prone individuals, the expected loss due to crime is higher than in smaller cities, which creates an incentive to people living in large cities to invest in private protection. Thus, the city size effects on victimization become ambiguous

It is worth noting that there are two possible cases according to ei*

( )

wi concavity.

Case 1) ei*

( )

wi is concave, i. e., the security investment cost is decreasing.


σNθ∆δw e*

( )



w* Graph(1)

As a result, only those agents who have *

i i w

w < will not invest in security. Thus the concave cost of investment in self-security diminishes the attractiveness of rich people.


Case 2) ei*

( )

wi is convex, i. e., the security investment cost is increasing. e e*

( )

w σNθ∆δw w w* Graph(2)

Hence, only those agents whose *

i i w

w > will not invest in self-security. Thus, the convex shape of the investment cost in self-security strengths the attractiveness of rich people.

According to these two cases, the agent chooses either * =0 i e or * ( )1 >0 ∆ + ∆ ∆ = − i F i i w w p e δ δ ,

according to her wealth level.

As we said, the potential targets meet criminals with probability θ, which is considered exogenous in the model. Thus, as a result, the likelihood of an agent (i) being attacked by a criminal (j) is given by

( )

< + × = i j j i j i ij F w w e p V δ δ θ Pr * (8)

It has been shown that * =0 i e or * ( )1 >0 ∆ + ∆ ∆ = − i F i i w w p e δ δ . So,

( )

< + + < ∆ + ∆ ∆ × = i j j i j i j j i j i F i ij w F w p w F w w w V δ δ δ δ θ δ δ 0 Pr Pr (9)

After some algebraic manipulation, it is possible to prove that,

( )



( )


( )

j j


i j j i j j j i j j i j i F i gF w F w p kF w F w w w δ δ δ δ δ δ δ δ δ δ = < + < < < = + < ∆ + ∆ ∆ Pr 0 Pr Pr Pr (10)


where k =



) (



and g= p

( )




( )



wi . The inequality comes from the fact that

( )

0 '• >

p and it implies if one invests in self-security, it is possible to reduce the chance of being a

victim, as predicted by the above models. Remember that, ceteris paribus, we expect that in larger cities a greater number of populations invest in private protection, since the total expected loss

increase with city size. Thus, if ei*

( )

wi is concave, it is possible that rich people in larger cities invest

more in private security then rich people in small cities.

Assuming that kF

( )

δLL and kF

( )

δHH, which occur when FH FLH δL then

( )



( ) +

( )

+ − − = − = < j L H L H H L H kF j L H j j d F F F kF H j δ δ δ δ δ δ δ δ δ δ δ δ δ 1 1 Pr (11)

This probability would be zero only if kF

( )

δLH, but this inequality implies that

( )


( )

H LF δ δ F δ

δ > , which is impossible to hold because F'

( )

• >0. In other words, even if one

invests in private security, there is always a chance of her being a victim, which means that there is

at least one δJ that makes (11) larger than zero.

Notice that Pr



( )



is not of a function of income once the investment in security is

exactly the one that counterbalances the attractiveness generated by income. In case of a zero

investment in security, because p

( )

0 is not a function of income and δjwi




is an

increasing function of income, the larger the income the larger will be Pr



( )

0 <δjwi






fact, assuming that gF

( )

δLL and gF

( )



H H i

) (

L L i



H δ F δ w F δ w

δ < + + and we can see that

( )


( )


( ) H L



( )



gF j L H j j i j j i j gF d gF w F w p H j δ δ δ δ δ δ δ δ δ δ δ δ δ − − = − = < = + < Pr 1 1 0 Pr (12)

Thus, as long as g is decreasing in income, the probability (12) increases in income. This means that attractiveness, proxied by income, increases the chance of who do not invest in self-security being victims of crimes.

3. Data, Regressions and Results

A major empirical issue that delayed research about victimization is the lack of reliability of official data, in the sense that not all crimes committed are reported to the police. This results in a severe underreporting bias of official statistics, as explained in Soares(2004). However, GS and GP


works were immune to this critique, since they use victimization surveys. These surveys have been done in US since the seventies and since the nineties in some countries of Latin America.

3.1 The data

The PCV - Seade’s 1998 Life Condition Survey (Pesquisa de Condição de Vida), is a household survey which allows the identification of the household and its members, at the individual, family and domicile level, and also provides information about housing, employment, income, and exposure to violence. The latter topic is assessed by asking if the individual were a victim of burglary/larceny in the twelve preceding months from the interview. Notice that these data does not provide the number of crime suffered per individual, but only if she were or not a victim of burglary/larceny.

In the PCV burglary and larceny were considered equivalent even though they are distinct social interactions. Burglary is a crime without physical violence that, in general, is not reported to the police unless the stolen property is expensive or it is insured. Larceny involves physical violence threat and, therefore, is considered more serious.

The survey was conducted between June and November of 1998 in seven regions of São Paulo state: São Paulo metropolitan area, Santos metropolitan area, and five other regions covering the remaining of the state, making a total of 15,000 domiciles visited in 104 cities, all of those with more than 20,000 inhabitants. The data about population and population growth were obtained at Seade (2004). Notice that the same find of growth rate for 1, 2, 5 and 10 years presented a positive correlation of 0.95. We chose to use the 2-years growth, although the results are robust to using any other growth rates.

An important feature of the law enforcement system in Brazil is that the laws are the same for all states, i.e. they are national laws, and the police and criminal justice system are established at state-level. Since our data encompass only São Paulo state, we don’t need to control for differences in police and judiciary systems and procedures.

In table 1 the main variables are presented with their respective means and standar deviations. We can see that around six percent of the whole São Paulo State inhabitants were victims of burglary/larceny. Another fact is that 40% of inhabitants live in cities with more than 1 million inhabitants and less than 10% lives in cities with less than 100,000 inhabitants.


The figures turn to be more dramatic if we consider crime incidence per family. Table 2 shows the percentage of individuals and families that were victims of burglary/larceny by regions of São Paulo State. Regardless the region, a large share of families had at least one member victimized by burglary/larceny, at least each region 12.2% of families were victimized.

<<Table 2 enters here>>

Table 3 reports the percentage of population that were victim of burglary/larceny according to city size ranges. The four ranges were chosen in a similar fashion to what GS and GP did. The first range is for cities with more than 20,000 and les than 100,000 inhabitants. The next range is for cities with less than 500,000, but more than 100,000 inhabitants. The third range encompasses cities with more than 500,000 and less than one million inhabitants. The last range includes cities with more than one million inhabitants.

<<Table 3 enters here>>

Table 4 shows the percentage of population in each per capita income range according to the city size ranges. There are five per capita income ranges namely: poor (less than one minimum wage), low middle-class (between one and three minimum wages), middle-class (between three and five minimum wages), high middle-class (between five and eight minimum wages), and rich (more than eight minimum wages).

<<Table 4 enters here>>

3.2 The Estimated Models

As GS first noted for US data that crime incidence tend to be larger in metropolitan areas (MA) and in large cities, being increasing in the city size. From table 2 we can see that the victimization rates are higher in metropolitan areas than in the other regions. Besides city size, GP presented evidence towards a positive correlation between population growth in cities and crime. To assess such stylized facts and other theories such as life-style, we will employ a Probit model using the survey sample weights and the following specification.

Yihr= c + Xiβ1 + Zhβ2+ γr + εihr (13)

where Yihr is a dummy variable indicating if the individual i who lives in domicile h at region r was a

burglary/larceny or assault victim. Xi is a vector of the individual characteristics (age, race,

schooling), Zh is a vector of the domicile characteristics (number of dwellers, marginal dwelling). γr


The first set of regressions was used to verify the impact of city-size, metro-area, and city growth in the likelihood of becoming a victim. Table 5 shows the regressions output. The basis (omitted category) is cities with less than 100,000 inhabitants. City growth is the population growth in percentage terms from 1996 to 1998.

<< Table 5 enters here >>>

The estimated coefficients are significant and imply a relationship between city size and victimization. From those regressions we can see that contrary to GP findings the relationship between city size and likelihood of being a victim of burglary/larceny is not monotonically increasing, i. e. the likelihood of being a victim is larger for the 500,000 to 1,000,000 range than for cities with more than 1 million, although such likelihood for both ranges are larger in relation to smaller city-size ranges. And the fact of living on the Santos Metropolitan Area increases this likelihood even more, but the same is not true for São Paulo MA which showed a positive but not statistically significant effect. It is worth noting that both MA contain cities of several sizes, having at least one city in each range, so the fact of being MA has a distinct effect. GP found that the estimated city size growth coefficient was positive and statistically significant. In fact, it was a corner stone of their paper. However, in our estimations the coefficient was negative and statistically significant.

In order to establish a benchmark, in United States, according to GS, the likelihood of being victim of burglary/larceny for a household living in a city with more than 1 million inhabitants is 28% larger than if she lived in a city with 50,000 to 100,000 inhabitants. For GP, the same comparison generated a figure of 78% and for us 82%.

Obviously one may argue that city size and city size growth could be capturing individuals characteristics, i. e. the individuals that are more likely to be victims choose to live in certain types of cities. To verify this hypothesis, we ran new regressions trying to control for such individual characteristics, which are reported in table (6).

The first regression included socio-demographic control variables such as age, gender race, origin, employment, family characteristics and income ranges. The second regression included variables related to education, in order to account for both different life-styles related to education and to control for understatement of income. And the third regression includes housing related variables such as number of dwellers, public utilities and if the family lives in an apartment.

<< Table 6 here >>

The city-size stills an important determinant of for burglary/larceny victimization since in both set of regressions all coefficients not only kept the same signal and magnitude but also were


statistically significant. Interestingly, at this time the São Paulo MA variable was statistically significant and positive, and its marginal effect was larger than the one for Santos MA. The city growth variable remained negative but not statistically significant.

The life-style related variables also seem to have an important role in victimization. The age range variables, whose omitted range was 0-15 years, were positive and tended to be declining as age increases, which is also reported in the literature. Gender was also important, being men more likely to be victims. The variables related to race, origin and employment coefficients were not statistically significant, except for black and/or multi-racial background which was significant in the first regression but not in the second regression. The family status, which omitted category was single, has a significant marginal effect, being negative for married persons (couples) having or not children, although for single parent families the effect was not statistically significant.

In the first regression income range variables, which omitted range was the poor, were positive and statistically significant. Its marginal effects were increasing up to the high middle class range, being the effect for rich people smaller than the one for high middle class. Another variable included that could capture both income and life-style was the family ownership of a car. In the first regression this variable coefficient was positive and statistically significant.

The inclusion in the second regression of the education range variables, whose omitted category was 0 to four years of education, generated statistically significant estimated coefficients and the variables black, car and low mid income were no more statistically significant.

The inclusion of housing related variables in the third regression did not change the statistical significance of the previous variables. The apartment home dummy variable was not statistically significant. But the number of persons living in the same home was statistically significant and had a negative sign, i. e. the larger the number of dwellers the smaller the likelihood of becoming a victim. Maybe this variable could be capturing for poverty because poor people not only tend to have more children but also several generations of the same family live in the same house. The last dummy variable included was marginal dwelling, which is “1” if the home street is located in a street without public illuminating, garbage disposal service, electricity, water or sewage service, or the street is unpaved, and “zero” otherwise. Its coefficient was negative and statistically significant, which means that poor people living in marginal dwellings are less likely to be victim of burglary/larceny.

The results from table (6) indicated that the implied elasticity between city size and crime drops from 0.16 (from the last model of table 5) to 0.041 (from the last model of table(6)) a fall of 75%, in contrast to the 33% found by GS and 8% found by GP. So, contrary to both previous papers,


the city size is not the main determinant of victimization likelihood, in fact, in our case the life-style of the potential target is more important.

An interesting fact is this decline in relation to high middle class, of the likelihood of rich people being victim of burglary/larceny. Three possible reasons behind such result are that the police protect more rich people, rich people adopt a middle-class life-style to disguise the offenders, and the last one is that rich people can afford to invest in personal safety by hiring bodyguards, using shielded cars, etc. If the first reason happens, it would be the case that rich people would be protected by police in smaller cities too, because if rich people can influence the police in large cities, there’s no reason to not influence police in small cities. But it is not the case according to our regressions. The second reason does not appear to be corroborated by our results because if the disguise strtategy were successful, the high middle-class would adopt it. As far as we know, there is not a victimization survey that asks about private investment in security. So, we are led to believe that this drop in the likelihood for rich people is due to investment in self-protection.

The regression results pose some safety policy issues. The first is since middle class and rich people are more likely to be victims of burglary/larceny, they will need more police protection than poor people. In terms of public policy it would be interesting to know if the effect of income depends on the city size were the possible victim lives. Although the survey does not mention in the question in which city the crime happened, we assume that the crime happened in the city where the victim lives. Therefore we ran regressions using the interaction between income range and city size. The results for burglary/larceny are reported in table 7.

<< Table 7 here >>

Contrary to GP, the regression output for burglary/larceny shows that people in different income ranges are differently affected according to city-size. For instance, rich people are more likely to become victims in cities with 100,000-500,000 and 500,000-1,000,000 inhabitants, and in the last range the likelihood is more than five times larger than if they lived in cities with more than one million people, which effect although positive was not statistically significant.

On the other hand, high middle class people have more chance to become victims in cities with more than one million inhabitants, which was the only city size range statistically significant. The middle class people is more likely to be victimized in cities with more than 500,000 inhabitants, being the largest effect for cities in the 500,000-1,000,000 range, and the effect for cities with less than 500,000 was positive but marginally statistically non significant. All of the interactions effects for low middle-class income range were not statistically significant as also happened with the low middle income dummy variable.


GP proposed an interesting hypothesis that large cities would have a larger share of crime prone individuals than smaller cities. We can’t reject this hypothesis because from table 4 we can see that the larger the city the larger is the proportion of rich and high middle-class people in the overall population. This fact constitutes an attractive for crime prone individuals to go to large cities, and the crime rate for rich people should be higher than in smaller cities, given they invest the same amount in self-protection.

In such case, we could try to measure the effect of the investment in self-protection by the difference between the coefficient of the interaction dummy variable for a rich person living in a 500,000-1,000,000 city and the interaction for a rich person living in a city with more than 1,000,000 inhabitants, that would be: 14.39% - 3.48% = 10.91%.

Since we attributed this reduction of the likelihood to the investment in self-protection, one might ask why the other people do not invest? The answer should be that the self-protection investment is so expensive that only rich people that live in cities with more than 500,000 inhabitants may be able to invest, i.e. the investment cost is smaller than the expected loss due to burglary/larceny.

4. Conclusions

Our results indicate that although city size is an important factor in the likelihood of being a victim of burglary/larceny it is not the most important. The potential target life-style (income, education, etc) is by far a more important factor. Contrary to GP’s finding, the recent city-size growth was not statistically significant.

Another important finding is that the likelihood is not monotonically increasing in the agent’s income, and we believe that the main reason for that is the investment in self-protection.


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Table 1 – Summary Statistics

Variable Mean Std. deviation Min Max

burglary/larceny 5.81% Age in years 29.73 18.7153 7 99 Men 48.13% Black/Multiracial 27.77% Asian 1.33% Migrant 48.17%

Years living in current home 18.91 16.01213 0 99

Years of Education 6.49 4.235891 0 17

Employed, retired or studying 74.39%

Living in cities with less than 100,000

inhabitants 9.689%

Living in cities with 100,000 to 500,000

inhabitants 37.59%

Living in cities with 500,000 to 1,000,000

inhabitants 11.44%

Living in cities with more than 1,000,000

inhabitants 41.28%

Living in Metropolitan Areas 65.99%

Household total income (local currency) 1522.55 1531.051 9.98 22292

Household per capita income (local currency) 426.83 475.7183 1.99 7500

Number of persons per home 4.23 1.766363 1 15

Household that owns a car 48.89%

Marginal Household 21.71%

Population Growth (in the last two years) 4.92% 1.991969 -1.70 14.246

Table 2 -Victimization by Region of São Paulo State

Region Sao Paulo MA Central Leste Santos MA Norte Oeste Vale do Paraíba Burglary/Larceny

Individuals 6.6 4.1 4.7 6.8 4.5 4.2 5.6 Family 19.9 12.2 13.9 19.3 14.4 12.3 17.1

Note: Family means at least one person of the family. MA refers to metropolitan area.

Table 3 – Victimization per city size

City Size (thousand) (%) of population

victim 20 to


Table 4 – % of population per Income range per city size

City size (thousand) Income 20 to 100 100 to 500 500 to 1,000 More than 1,000 poor 25.7263 23.95712 21.9432 20.36537 lowmid 59.0958 57.35569 51.19739 47.90777 middle 11.668 13.15424 16.82887 16.59734 himid 1.6235 3.28833 5.82249 7.86335 rich 1.8865 2.24462 4.20804 7.26617

Table 5 –Marginal Effect of city size and growth on victimization

(1) (2) (3)

Variable Burglary

larceny Burglary Larceny Burglary larceny

100,000 – 500,000 0.0304* (0.0065) 0.0268* (0.0064) 0.02540* (0.0064) 500,000 – 1,000,000 0.0659* (0.0115) 0.0642* (0.0117) 0.0546* (0.0110) > 1,000,000 0.0554* (0.0073) 0.0515* (0.0089) 0.0403* (0.0091) Santos MA 0.0371* (0.0067) 0.0342* (0.0067) São Paulo MA 0.0062 (0.0054) 0.0093 (0.0055) City growth -0.2773* (0.1072) Number of observations 28583 28583 28583 Pseudo R2 0.0083 0.0098 0.0105

Note: Robust standard errors reported in parenthesis, evaluated at the means. * means statistically significant at 5% level.


Table 6 – Burglary/Larceny Victimization Probit Estimations Output – Life style controls

Model (1) (2) (3)

Independent Variables Coefficient (dF/dx) P>|z| Coefficient (dF/dx) P>|z| Coefficient (dF/dx) P>|z|

100,000 – 500,000 0.0222 0.000 0.0214 0.000 0.0226 0.000 500,000 – 1,000,000 0.0437 0.000 0.0412 0.000 0.0435 0.000 > 1,000,000 0.0285 0.000 0.0265 0.001 0.0286 0.000 São Paulo MA 0.0363 0.000 0.0332 0.000 0.0381 0.000 Santos MA 0.0130 0.013 0.0134 0.010 0.0139 0.008 City growth -0.1324 0.185 -0.1116 0.259 -0.0835 0.413 Age 16 to 24 0.0618 0.000 0.0390 0.000 0.0400 0.000 Age 25 to 34 0.0719 0.000 0.0509 0.000 0.0481 0.000 Age 35 to 44 0.0674 0.000 0.0499 0.000 0.0476 0.000 Age 45 to 59 0.0541 0.000 0.0475 0.000 0.0444 0.000

Age more than 60 0.0394 0.000 0.0417 0.000 0.0366 0.000

Male 0.0284 0.000 0.0284 0.000 0.0288 0.000 Black\multi-racial -0.0091 0.032 -0.0065 0.126 -0.0052 0.225 Asian 0.0019 0.907 0.0001 0.997 0.0007 0.964 Foreigner 0.0044 0.794 -0.0019 0.907 -0.0046 0.773 Migrant -0.0039 0.310 -0.0002 0.953 0.0001 0.972 Employed or Occupied 0.0083 0.132 0.0070 0.200 0.0082 0.136 Couple only -0.0305 0.000 -0.0304 0.000 -0.0294 0.000

Couple with children -0.0352 0.000 -0.0351 0.000 -0.0233 0.002

Single parent family -0.0083 0.252 -0.0092 0.198 -0.0025 0.746

Car 0.0096 0.020 0.0066 0.111 0.0055 0.192

Low middle class 0.0107 0.026 0.0065 0.178 0.0021 0.679

Middle class 0.0250 0.000 0.0132 0.060 0.0063 0.366

High middle class 0.0642 0.000 0.0442 0.000 0.0341 0.002

Rich 0.0495 0.000 0.0284 0.015 0.0188 0.111

Schooling 5 to 8 0.0255 0.000 0.0241 0.000

Schooling 9 to 12 0.0381 0.000 0.0365 0.000

Schooling more than 12 0.0422 0.000 0.0407 0.000

Marginal dwelling -0.0097 0.041

Number of dwellers -0.0045 0.001

Apartment 0.0003 0.958

Pseudo R-squared 0.0488 0.0540 0.0565

Observations 28583 28583 28583

Note: Marginal effects reported, Robust standard errors were used. All models included a constant, (*) dF/dx is for discrete change of dummy variable from 0 to 1; z and P>|z| are the test of the underlying coefficient being 0


Table 7- Burglary/Larceny Interactions

Income Range: Rich High Middle Middle Low Middle

Independent Variables Coefficient (dF/dx) P>|z| Coefficient (dF/dx) P>|z| Coefficient (dF/dx) P>|z| Coefficient (dF/dx) P>|z|

100,000 – 500,000 0.0201 0.001 0.0220 0.000 0.0185 0.002 0.0251 0.001 500,000 – 1,000,000 0.0355 0.000 0.0425 0.000 0.0360 0.000 0.0411 0.000 > 1,000,000 0.0261 0.001 0.0241 0.001 0.0239 0.003 0.0220 0.012 São Paulo MA 0.0328 0.000 0.0338 0.000 0.0323 0.000 0.0326 0.000 Santos MA 0.0136 0.009 0.0131 0.008 0.0129 0.013 0.0135 0.009 City growth -0.0947 0.346 -0.1309 0.094 -0.0679 0.511 -0.1724 0.182 Age 16 to 24 0.0390 0.000 0.0390 0.000 0.0389 0.000 0.0391 0.000 Age 25 to 34 0.0505 0.000 0.0510 0.000 0.0507 0.000 0.0510 0.000 Age 35 to 44 0.0500 0.000 0.0495 0.000 0.0497 0.000 0.0501 0.000 Age 45 to 59 0.0475 0.000 0.0475 0.000 0.0473 0.000 0.0477 0.000

Age more than 60 0.0421 0.000 0.0417 0.000 0.0414 0.000 0.0418 0.000

Schooling 5 to 8 0.0257 0.000 0.0257 0.000 0.0257 0.000 0.0255 0.000

Schooling 9 to 12 0.0383 0.000 0.0383 0.000 0.0385 0.000 0.0382 0.000

Schooling more than 12 0.0427 0.000 0.0428 0.000 0.0424 0.000 0.0423 0.000

Low middle class 0.0062 0.191 0.0064 0.179 0.0062 0.186 - -

Middle class 0.0128 0.066 0.0132 0.058 - - 0.0119 0.075

High middle class 0.0435 0.000 - - 0.0445 0.000 0.0435 0.000

Rich - - 0.0288 0.013 0.0287 0.013 0.0281 0.014

income * 100-500,000 0.0526 0.032 -0.0078 0.670 0.0244 0.055 -0.0060 0.465

income*500,000-1,000,000 0.1439 0.000 -0.0015 0.949 0.0332 0.048 -0.0001 0.989

income * >1,000,000 0.0348 0.061 0.0491 0.003 0.0247 0.028 0.0086 0.253

income * City growth -0.6016 0.163 0.4862 0.202 -0.3312 0.154 0.1111 0.488

Pseudo R-squared 0.0549 0.0549 0.0549 0.0542

Observations 28583 28583 28583 28583

Note: Marginal effects reported, Robust standard errors were used. All models included a constant, dF/dx evaluated at the means, for dummy variables dF/dx is a discrete change from 0 to 1; z and P>|z| are the test of the underlying coefficient

being 0

Omitted controls: male, Black/multi-racial, Asian, Foreigner, migrant, Employed or Occupied, Couple only, couple with children, Single parent family, car