For this assignment, all you have to do is to select a topic, write an introduction, formulate a research question (see examples above) and a hypothesis. Keep it simple! Then, you will also review thr

For this assignment, all you have to do is to select a topic, write an introduction, formulate a research question (see examples above) and a hypothesis. Keep it simple! Then, you will also review thr
Journal of Development Economics Ž. Vol. 67 2002 181 – 203 www.elsevier.comrlocatereconbase Patterns of crime victimization in Latin American cities Alejandro Gaviria, Carmen Pages ) ´ Inter-American DeÕelopment Bank, 1300 New York AÕenue, Washington, DC 20577, USA Received 1 November 1999; accepted 1 March 2001 Abstract In this paper, we draw a profile of the victims of crime in Latin America. We show that the typical victims of property crime in Latin America come from rich and middle class households and tend to live in larger and faster growing cities. On the whole, our results indicate that urban crime in Latin America is, to an important extent, a reflection of the inability of many cities in the region to keep up with the increasing demands for public safety brought about by a hasty and disorderly urbanization process.q2002 Elsevier Science B.V. All rights reserved. JEL classification:K40; K42; O54 Keywords:Crime; Victims; Urbanization; Latin America 1. Introduction Crime has become a staple feature of many cities in Latin America. As any casual observer would immediately notice, muggings, burglaries, carjackings and even homicides occur with alarming frequency in many urban centers throughout this region. But despite the sense of urgency brought about by rising crime levels, few studies have attempted to explore the magnitude and causes of urban crime in Latin America. 1 )Corresponding author. Ž. Ž. E-mail addresses:[email protected] A. Gaviria , [email protected] C. Pages . ´ 1 Ž. Ž. Ž . See Bourguignon 1999 , Fajnzylber et al. 1998 and Londono et al. in press for previous ˜ attempts to uncover the causes in Latin America. 0304-3878r02r$ – see front matterq2002 Elsevier Science B.V. All rights reserved. Ž. PII: S0304-3878 01 00183-3 () A. GaÕiria, C. PagesrJournal of DeÕelopment Economics 67 2002 181 – 203 ´ 182 Lack of reliable data has been the most important impediment to research on the evolution and nature of crime in developing countries in general and Latin American countries in particular. Official crime statistics are often incomplete and suffer from serious problems of under-reporting. Victimization surveys, the alter- native to official records, are either unavailable or incomplete. Comparable cross-country data are even more difficult to come by; which is why most cross-country studies on the determinants of crime and violence have focused exclusively on homicide rates. In this paper, we use an unusual data set to study the patterns of crime victimization in Latin America. Our main source of data is the Latinobarometer, a public opinion survey covering more than 50,000 households in 17 Latin Ameri- can countries. This survey, although not specifically designed to study crime, provides a uniquely data set of comparable cross-country information on criminal victimization. Our approach is more descriptive than analytical. We first lay out some empirical regularities and only then offer some interpretations, which reflects not so much our methodological preferences as the constraints imposed by our data. Our analysis focuses mainly on how the relative socioeconomic status of an individual, the population size of her city of residence, and the city’s recent population growth affects the probability of being a victim of crime. We find that this probability increases with socioeconomic status, city size, and urban growth. We argue that the positive connection between socioeconomic status and the probability of victimization may be driven by both the difficulties of the relatively wealthy in protecting themselves against street crime and the tendency of burglars and kidnappers to target wealthy victims. Little can be said, however, about the distribution of the crime burden across social classes because we do not observe household investments in crime avoidance. We cannot provide a definitive interpretation of the positive connection be- tween city size and crime. Two different hypotheses are consistent with our Ž. empirical results: i the probability of arresting a criminal is lower in larger cities Ž either because there are diseconomies of scale in the production of arrests or .Ž. because larger cities invest relatively less in law enforcement , and ii larger cities harbor a greater proportion of crime-prone individuals. We are able to reject the also plausible hypothesis that larger cities have more crime because they present wealthier victims. We also reject the hypothesis that the association between city growth and crime is driven by the characteristics of the victims. Thus, we do not find evidence that the higher levels of crime present in faster growing cities are due to an excess Ž of rich or poor people in these areas perhaps attracted by higher opportunities in . the city or lower opportunities in rural areas . Instead, we find that the levels of confidence in the police and the judiciary are lower in faster growing cities and that lower levels of confidence in the police are in turn associated with higher victimization rates. These results suggest that urban growth increases victimization () A. GaÕiria, C. PagesrJournal of DeÕelopment Economics 67 2002 181 – 203 ´ 183 Ž. by lowering the effectiveness and hence the reputation of law enforcement institutions. On the whole, our results indicate that urban crime in Latin America is, to an important extent, a consequence of the inability of many cities in the region to keep up with the increasing demands for public safety brought about by a hasty and disorderly urbanization process. The rest of this paper is organized as follows. Section 2 describes the data. Section 3 presents the empirical methodology. Sections 4, 5 and 6 examine the link between crime and socioeconomic status, city size and urban growth, respec- tively. Finally, Section 7 draws some general conclusions. 2. Victimization data for Latin America In this paper, we use the Latinobarometer to study the patterns of crime victimization in Latin America. The Latinobarometer is a public opinion survey covering 17 Latin American countries. 2This survey has been regularly conducted every year since 1996. Each year, 1500 individuals have been interviewed in each country. Although there have been some adjustments to the survey questions and answer formats, many questions have remained the same and are comparable over time. The sampling method varies slightly from country to country as implementa- tion is contracted out to national polling firms. Quotas were included in most cases to ensure representation across gender, socioeconomic status, and age. Here, we combine the data sets of 1996, 1997 and 1998 in order to get larger samples. The survey is restricted to urban populations. We use sample weights to correct the oversampling of richer households. 3The weights were created so that that the distribution of individuals across education groups in the sample matched the actual distribution of the urban population in the country in question. All results below are weighted, but they do not differ substantially from the unweighted ones. Ž Although the Latinobarometer is not a victimization survey its emphasis is on . political attitudes and social values , all rounds of the survey have included a question about crime victimization at the household level. 4The Latinobarometer also contains detailed information about the demographic characteristics of both the respondent and the head of the household, as well as information about trust in the police, the judicial system and other public institutions. Fig. 1 shows the average victimization rates for all the countries included in the Ž survey. The levels of victimization are staggering. In five countries Ecuador, 2In 1996, the Latinobarometer also include Spain. Unless otherwise mentioned, Spain was not included in our analysis. 3This is a common problem in large opinion surveys. For example, both the World Values Survey and Eurobarometer also oversample individuals form higher socioeconomic groups. 4The exact wording of the question is,AHave you or any member of your family been assaulted, robbed of victimized in any way during the past twelve months?B () A. GaÕiria, C. PagesrJournal of DeÕelopment Economics 67 2002 181 – 203 ´ 184 Fig. 1. Victimization rates by country. . Mexico Venezuela, El Salvador and Guatemala more than 40% of the urban households experienced at least one episode of victimization during the year previous to the survey. In Guatemala, at least one person in every two households was victimized. Spain, the only industrialized country included in the survey, has the lowest victimization rates in the sample, and Uruguay, Panama and Chile exhibit the lowest victimization rates in Latin America. A serious shortcoming of the Latinobarometer is the absence of information about the type of victimization. We will assume here that the victimization data obtained from this survey correspond mainly to property crimes; an assumption justified by the fact that property crimes usually represent the bulk of all criminal offenses. 5Another shortcoming of the Latinobarometer is the absence of data on household income. All rounds of the survey have included, however, two sets of questions related to the socioeconomic status of the households. The first set Ž includes questions about ownership of durable goods respondents were asked if any member of the household owns a car, a computer, a television, a washing . machine, and so on . The second set includes questions about housing character- Ž istics respondents were asked if their place of residence has access to potable . water, sewage, electricity and so on . In this paper, we use this information to rank households according to their socioeconomic status. The procedure entails three main steps. First, we use Principal Components to compute a weighted average of the relevant household 5 Ž. See, for example, Londono et al. in press . ˜ () A. GaÕiria, C. PagesrJournal of DeÕelopment Economics 67 2002 181 – 203 ´ 185 Fig. 2. Material wealth and education of the head. attributes, then we rank all households on the basis of this average and, finally, we use the corresponding ranking to compute quintiles of socioeconomic status. 6Fig. 2 shows the average years of education of the household head by quintiles of socioeconomic status. As expected, this variable increases steadily across quin- tiles, lending credence to our use of household possessions to approximate socioeconomic status. In order to more carefully examine the positive link between crime and city-size mentioned in Section 1, we also use a victimization module included in theEncuesta Nacional de Calidad de Vidaof Colombia. This survey is representa- tive of urban areas of the country and includes 5623 households. All questions in the survey were administered only to the heads of household, and thus refer to incidents affecting either the respondent or any member of his household. Unlike the Latinobarometer, this survey contains information on property crimes as well Ž. as other crimes such as homicides, assaults including rape and kidnappings. Only serious incidents were reported, which explain the low victimization rates obtained Ž. the national average is below 13% . 6Principal Components are often used to approximate socioeconomic status in the absence of Ž. reliable income data. Filmer and Pritchett 1998 show that durable goods and housing attributes are observed with much more precision than consumption expenditures, and that indicators of socioeco- nomic status based on these variables are much less sensitive to temporary disturbance on household welfare that similar indicator based consumption data. () A. GaÕiria, C. PagesrJournal of DeÕelopment Economics 67 2002 181 – 203 ´ 186 3. Empirical methodology Most economic models of crime focus on the incentives faced by prospective criminals. The main conclusions of these models are well known: the higher the return of criminal activities and the lower the probabilities of arrest and incarcera- Ž tion, the higher the individual propensity to commit crimes see Becker, 1968; . Ehrilch, 1973 . These models, however, offer few clues as to which individuals are most likely to be the victims of crime. Most crime models, for example, do not offer any prediction as to whether crime affects mainly individuals from disadvan- taged social groups — an important question, not only from a fairness viewpoint but also because it may yield some insights about the root causes of crime. In this paper, we use the following specification to study the patterns of crime victimization in Latin America YscqX bqZ uq l q zq ´ ,1 Ž. ijct ijct jc c t ijct whereYis a dummy variable showing whether a member of familyiwho lives ijct in cityjof countrycwas a victim of crime in yeart,Xis a vector of ijct Ž household characteristics including education of the household head, relative . socioeconomic status, and house ownership ,Zis a vector of city characteristics jc Ž. including population size and population growth , l is a country effect, z is a ct year effect, and ´ is an individual error term. ijct Country effects are included to control for unobserved country attributes that do Ž. 7 not change drastically over time e.g., social capita and other cultural aspects . Year effects are included to control for unobserved factors that vary uniformly Ž. over time e.g., common macroeconomic shocks and changes in the questionnaire . Unless otherwise mentioned, the analysis below is robust to the exclusion of both country and year effects. Ž. We use a Probit model to estimate Eq. 1 . Linear probability models yield almost identical results, suggesting that our findings are robust to the choice of estimation method. In the second to last section of the paper, where we need to deal with some problems of simultaneous causation, we use a Maximum Likeli- Ž hood Method to estimate a two-step Probit model with endogenous variables see Newey and Whitney, 1987 for a theoretical discussion and Evans et al., 1992 for . an application of this model . Descriptive statistics of the most important covariates are shown in Table 1. The mean victimization rate of the sample is 38.6%, 72.7% of the households own Ž a house, 30% own a car, and 6.8% live inAmarginalBdwellings i.e., those . without access to water or sewage connections . More than 20% of the households 7One may argue that individuals living in safe countries may have a different perception of what it means to be victimized vis-a-vis individuals living in crime-ridden countries. Country-fixed effects control for these differences. () A. GaÕiria, C. PagesrJournal of DeÕelopment Economics 67 2002 181 – 203 ´ 187 Table 1 Summary statistics Variable Observations Mean Std. dev. Crime 27,134 0.386 – Household size 27,474 4.871 2.13 Education of head 27,474 9.042 4.45 Head of house employed 27,474 0.821 – Own a house 27,474 0.727 – Own a car 27,474 0.299 – Marginal household 27,474 0.068 – -20,000 27,474 0.088 – 20,001 – 50,000 27,474 0.090 – 50,001 – 100,000 27,474 0.085 – 100,001 – 300,000 27,474 0.154 – 300,001 – 700,000 27,474 0.134 – 700,001 – 1,000,000 27,474 0.075 – 1,000,001 – 2,000,000 27,474 0.151 – )2,000,000 27,474 0.223 – Trust in police 26,783 0.342 – Population growth 13,281 2.382 1.69 This table summarizes the data set that we actually used in our estimates. Although the original pooled Latinobarometro contains data for about 50,000 households, missing data for some variables reduces the subset that we can actually use to around 27,500 observations. in the sample live in cities of more than 2 million inhabitants and only 8.8% live in cities of less than 20,000 inhabitants. In the next sections, we focus on the effects of socioeconomic status, city size and city growth on the probability of being a victim of crime. We examine not only the independent contribution of these factors, but also how they interact with each other and with some country-level indicators. We show below that something can be learned about the root causes of crime by studying the patterns of crime victimization along these three variables. 4. Victimization and socioeconomic status In this section, we study the effect of socioeconomic status on the probability of Ž. being a victim of crime. Our main results are summarized in Table 2. Column 1 shows that the probability of being victimized is substantially higher for the fifth Ž and fourth quintiles and noticeable higher for the third quintile the baseline group . 8 is the first quintile . On average, an individual from the top quintile is 8 8Table 4 shows victimization rates by socioeconomic status for all the countries in the sample. With exception of Bolivia, Ecuador and Panama, crime rates at the fourth of fifth quintile. () A. GaÕiria, C. PagesrJournal of DeÕelopment Economics 67 2002 181 – 203 ´ 188 Table 2 Relative socioeconomic status and probability of victimization, Probit estimation: marginal effects Ž. Ž. Ž. 12 3 Ž . Ž. Ž. Second quintile 0.0006 0.009 0.002 0.01y0.003 0.01 Ž. Ž. Ž. Third quintile 0.034 0.009 0.035 0.010 0.020 0.01 Ž. Ž. Ž. Fourth quintile 0.066 0.009 0.065 0.011 0.044 0.01 Ž. Ž . Ž . Fifth quintile 0.081 0.01 0.075 0.012 0.054 0.012 Ž. Ž. Household size 0.006 0.001 0.006 0.001 Ž. Ž. Education of head 0.003 0.0007 0.003 0.0007 Ž. Ž. Employment of head 0.005 0.008 0.009 0.008 Ž. Ž. Own a housey0.038 0.006y0.035 0.006 Ž. Ž. Own a car 0.004 0.008 0.006 0.008 Ž. Ž. Marginal household 0.022 0.013 0.016 0.013 City size dummies No No Yes Number of observations 27,127 27,127 27,127 Obs.P0.386 0.386 0.386 2 PseudoR0.016 0.018 0.035 All regressions include year and country dummies. Standard errors are reported in parenthesis. Baseline probability refers to the Normal Cumulative Distribution evaluated at the constant of the Probit estimation. percentage points more likely to be a victim of crime than an individual from the bottom quintile. 9 Ž. Ž In column 2 , we control for some key household attributes education and employment of the head of the household, home and car ownership, and a few . others in order to investigate some obvious channels through which relative socioeconomic status could affect the probability of victimization. The effects of the different attributes are, for the most part, relevant, but cannot completely explain away the effect of socioeconomic status on the probability of victimiza- tion. Owning a house reduces the probability of victimization by 3.8 percentage points. Living inAmarginalBhouseholds increases the probability of victimization by more than 2.0 percentage points, though this effect is not statistically signifi- cant at conventional levels. Finally, both employment of the household head and car ownership do not have an independents effect on the probability of victimiza- tion. Ž. Column 3 shows the effects of socioeconomic status after controlling for the size of the city of residence. Wealth effects are smaller in this specification, suggesting that city size is an important channel through which socioeconomic status raises the probability of victimization. That is, richer people tend to live in 9 Ž. Ž In a recent study, Cruz 1999 finds that victimization rates weighted by the inverse of the . frequency increase with socioeconomic status in Cali, Rio de Janeiro and Jose. ´ () A. GaÕiria, C. PagesrJournal of DeÕelopment Economics 67 2002 181 – 203 ´ 189 Ž larger cities and larger cities tend in turn to have higher victimization rates the . 10 latter effect is thoroughly examined in the next section . To investigate whether the relationship between socioeconomic status and the probability of victimization is affected by country-wide inequality, we add the urban Gini coefficient and its interactions with the quintile dummies to the Ž previous specification country fixed-effects are excluded in this case for obvious . reasons . The results, not shown here, indicate that income inequality has a small effect on the distribution of crime across rich and poor households. As inequality increases, the rich bear a smaller share of all incidents of victimization. This may be due to several factors. First, more unequal societies often devote more public Ž. resources to protect the rich Bourguignon, 1999 . Second, more unequal societies often pursue with greater vehemence crimes against rich and middle income families. 11And last, rich and middle-income families may find it easier to insulate Ž themselves from crime in more unequal societies spatial segregation, for example, . tends to be higher in more unequal societies . The relationship between relative socioeconomic status and crime is robust to changes in the sample of countries considered. In particular, we examine whether the reported coefficients change when we progressively exclude countries from Ž. 12 our sample according to the alphabetic order of their names . We find that neither the size nor significance of the coefficients change substantially from one sample to the next. Why do the wealthy bear a disproportionate share of all property crimes? This result is consistent with two general models. In the first model, criminals and victims are matched randomly and investments in private protection exhibit sharp diminishing returns, whereas in the second model criminals are matched dispropor- tionately to wealthy victims and private investments in protection exhibit constant returns. In the first model, wealthy households invest very little in private protection, as they decide to bear some victimization risk instead of paying the Ž high price of completely insulating themselves against crime see Appendix A for . a formal treatment of this idea . In the second model, wealthy households invest more in private protection but their investments are not enough to offset the 10The effect of socioeconomic status on criminal victimization appears to be large in Latin America than in the United States. The raw data for the United States shows that there is a small positive Ž. correlation between properly crime and household income Bureau of Justicce Statistics, 1998 . This correlation is negative, however, after controlling for demographic characteristics and city size. There is, in particular, a clear negative association between household income and the incidence of burglaries, Ž. assaults and common thefts Glaeser and Sacerdote, 1999 . 11The Latinobarometer offers indirect support to this idea. The correlation between the proportion of respondents in a country who state that all citizens in their country of residence are equal before law and the Gini coefficient isy0.45, indicating that more unequal societies tend to be more suspicious about the fairness of the justice system. 12The results are available from the authors upon request. We are grateful to an anonymous referee for suggesting this robustness check. () A. GaÕiria, C. PagesrJournal of DeÕelopment Economics 67 2002 181 – 203 ´ 190 greater victimization risk associated with the tendency of criminals to go after them. Presumably, the first model applies to those crimes where the matching between criminals and citizens is mainly a matter of chance. Examples may Ž. include street crimes including muggings and armed robberies and common thefts. The second model applies to those crimes in which criminals carefully select their victims so as to maximize expected gains. Examples may include burglaries and kidnappings. The empirical results presented above are likely to be driven by a combination of these two forces. Although we lack the information to discriminate between these two alternative models, the evidence does not support the common view that the rich are usually more sought out by criminals in more unequal societies. 5. Victimization and city size Crime has become a preeminently urban problem in developed and developing countries alike. In the United States, for example, there is a well-documented connection between city size and criminal rates. Anecdotal evidence suggests that such connection also holds in Latin America, which is particularly worrisome given this region’s high levels of urbanization and urban concentration. 13 Table 3 shows that, in Latin America, the probability of being a victim of crime is substantially higher in larger cities. A household living in a city of more than one million inhabitants is 20 percentage points more likely to be victimized than a Ž household living in a city of less than 20,000 inhabitants the baseline group in the . regression . Surprisingly, the probability of victimization does not change much when the one-million-inhabitants threshold is surpassed. The evidence suggests, indeed, a natural division of cities in three groups: a first group composed of cities of less than 100,000 inhabitants that exhibit relatively low crime rates, an intermediate group composed of cities between 100,000 and one million inhabi- tants, and a high-crime group composed of cities of more than one million inhabitants. Interestingly, our results indicate that the city size effect is much larger in Latin America than in the United States. Thus, while in the United States, a household living in a city of one million inhabitants or more is 28% more likely to be victimized than a household living in a city between 50,000 and 100,000 inhabitants, the corresponding figure for Latin America is 71%. 14 Table 4 shows that the positive connection between city size and crime holds not only for the region as a whole, but also for most of the countries taken 13AMegacitiesBare much more common and are growing faster in Latin America that anywhere else Ž. in the world Gaviria and Stein, 2000 . 14 Ž. See Glaeser and Sacerdote 1996 for the US figure. () A. GaÕiria, C. PagesrJournal of DeÕelopment Economics 67 2002 181 – 203 ´ 191 Table 3 City size and probability of victimization, Probit estimation: marginal effects Ž. Ž. Ž. 12 3 Ž. Ž. 20,001 – 50,000 0.036 0.015 0.030 0.015 Ž. Ž. 50,001 – 100,000 0.023 0.016 0.014 0.016 Ž. Ž. 100,001 – 300,000 0.116 0.015 0.103 0.015 Ž. Ž. 300,001 – 700,000 0.134 0.015 0.118 0.015 Ž. Ž. 700,001 – 1,000,000 0.136 0.017 0.114 0.017 Ž. Ž. 1,000,001 – 2,000,000 0.199 0.016 0.182 0.016 Ž. Ž. )2,000,000 0.216 0.015 0.194 0.016 Ž. Ž. Second quintiley0.003 0.010 0.001 0.017 Ž. Ž. Third quintile 0.021 0.010 0.053 0.018 Ž. Ž. Fourth quintile 0.045 0.011 0.067 0.019 Ž. Ž. Fifth quintile 0.054 0.013 0.092 0.022 Ž. Size2 0.136 0.019 Ž. Size3 0.178 0.019 ) Ž. Second quintile Size2y0.019 0.027 ) Ž. Third quintile Size2y0.063 0.024 ) Ž. Fourth quintile Size2y0.058 0.024 ) Ž. Fifth quintile Size2y0.072 0.027 ) Ž. Second quintile Size3 0.009 0.024 ) Ž. Third quintile Size3y0.019 0.024 ) Ž. Fourth quintile Size3y0.001 0.027 ) Ž. Fifth Quintile Size3y0.021 0.026 Number of observations 27,127 27,127 27,127 Obs.P0.386 0.386 0.386 2 PseudoR0.023 0.027 0.027 Standard errors are reported in parenthesis. All regressions include country and year fixed effects. In Ž. Ž. columns 2 and 3 , we also control for household characteristics. Baseline probability refers to the Normal Cumulative Distribution evaluated at the constant of the Probit estimation. separately. As shown, for 14 of the 17 countries under analysis, victimization rates are the highest in the largest city. 15Table 4 also shows that while most South American countries are represented in all city-size groups, only few of the Central American countries are represented in the larger city-size groups. Given this, it is fair to say that all results pertaining to cities with populations over one million apply exclusively to South American countries. To explore the channels through which city size affects crime rates, we add Ž. household and city characteristics to the specification shown in column 1 of Ž. Table 3. The results of column 2 indicate that the implied elasticity between city size and crime drops by about 8% in this case, suggesting that in Latin America only a small fraction of the effect of city size on crime can be accounted for by 15 Ž The results are also very robust to the progressive exclusion of countries according to their names’ . alphabetic order from the sample. () A. GaÕiria, C. PagesrJournal of DeÕelopment Economics 67 2002 181 – 203 ´ 192 Table 4 Percent of crime victimization across city size a -20 20 – 50 50 – 100 100 – 300 300 – 700 700 – 1000 1000 – 2000 2000)))) Argentina 11.06 31.5 17.99 39.62 28.54 38.02 41.01 Ž. Ž. Ž. Ž. Ž . Ž . Ž . Ž . 0 91 65 82 137 374 671 1882 ))) ))) )))))) Bolivia 37.94 38.0431.46 36.78 Ž. Ž. Ž. Ž . Ž . Ž . Ž . Ž. 0 0 0 46 561 486 673 0 ))) ))) ))) ))) ))) Brazil 46.0143.28 36.35 Ž. Ž. Ž. Ž. Ž. Ž . Ž . Ž . 0 6 16 16 27 118 161 428 ))) ))) )))))) Colombia 31.61 33.54 30.37 44.62 Ž. Ž. Ž. Ž . Ž . Ž . Ž. Ž . 0 0 0 589 64 107 0 447 ))) ))) ))) Costa Rica 30.15 38.14 46.91 43.01 54.54 Ž. Ž. Ž. Ž. Ž. Ž. Ž. Ž. 593 311 180 195 104 0 0 0 ))) )))))) ))) Chile 11.98 29.02 25.02 33.73 Ž. Ž. Ž . Ž . Ž . Ž. Ž. Ž . 0 0 148 46 173 0 0 1066 )))))) Ecuador 50.34 38.27 38.06 42.66 47.25 62.41 Ž. Ž. Ž. Ž. Ž. Ž. Ž. Ž. 140 424 334 301 0 283 496 0 ))) ))) ))) El Salvador 47.46 44.11 34.19 47.14 53.82 Ž. Ž. Ž. Ž. Ž. Ž. Ž. Ž. 267 176 136 114 47 0 0 0 ))) ))) ))) ))) ))) Guatemala 46.23 60.54 60.19 Ž . Ž . Ž. Ž . Ž. Ž. Ž. Ž. 206 187 0 206 0 0 0 0 ))) ))) Honduras 36.32 39.02 44.11 40.60 46.72 66.60 Ž. Ž. Ž. Ž. Ž . Ž. Ž. Ž. 284 209 127 105 61.2 130 0 0 Mexico 32.69 29.55 27.54 33.80 43.52 50.90 39.4958.22 Ž. Ž. Ž. Ž. Ž. Ž. Ž. Ž. 107 176 342 193 528 191 118 466 ))) ))) ))) Nicaragua 33.35 38.36 34.87 37.97 48.01 Ž. Ž. Ž. Ž. Ž. Ž. Ž. Ž. 213 204 172 92 0 210 0 0 ))) ))) ))) Panama 22.45 28.35 31.36 56.9038.34 Ž. Ž. Ž. Ž. Ž. Ž. Ž. Ž. 484 500 224 43 469 0 0 0 ))) )))))) ))) ))) Paraguay 31.57 29.78 38.16 Ž. Ž. Ž . Ž . Ž. Ž. Ž. Ž . 0 0 195 166 0 0 0 215 )))))) Peru 26.73 25.64 43.99 34.34 35.52 42.01 Ž. Ž. Ž. Ž. Ž. Ž. Ž. Ž . 0 30 44 412 672 122 0 1366 ))) )))))) ))) ))) Uruguay 20.19 29.97 36.68 Ž. Ž. Ž . Ž . Ž. Ž. Ž . Ž. 0 0 237 370 0 0 1506 0 ))) Venezuela 30.00 42.64 45.82 48.44 47.93 35.96 54.99 Ž. Ž. Ž. Ž. Ž. Ž. Ž. Ž. 110 134 69 152 252 57 528 0 Ž. Source: Latinobarometer 1996 – 1998 . Weighted data. Number of observations per country and city size is in parentheses. aCity size in thousands of inhabitants () A. GaÕiria, C. PagesrJournal of DeÕelopment Economics 67 2002 181 – 203 ´ 193 differences among cities in household characteristics. 16 By contrast, Glaeser and Ž. Sacerdote 1999 find that in the United States a much larger fraction of the effect Ž. of city size on crime 33% can be accounted for by household characteristics. This comparison suggests that the driving forces underlying this effect may be quite different in both places. Ž. Column 3 explores the interaction between socioeconomic status and city size. We reduce the number of city-size brackets to the three groups mentioned above in order to facilitate the interpretation of the results. The interactions are negative for the most part, and statistically significant only for intermediate cities. All in all, the patterns of victimization do not vary consistently with city size — that is, neither rich nor poor households fare comparatively worse in large cities vis-a-vis small ones. Fig. 3 shows the effects of city size on various types of victimization for the case of Colombia. Robberies grow monotonically with city size, as does the fraction of households reporting that crime is their main problem in their commu- Ž. nities perceived criminality . In contrast, homicides and assaults are much more Ž common in medium-size cities especially Cali and Medell n, two well-known ´ . drug-trafficking strongholds than in Bogota, which has a population of well over ´ 4,000,000 inhabitants. These results hold up after controlling for household and Ž city characteristics e.g., the percentage of families in the city with unsatisfied basic needs, the percentage of individuals in the city with primary and secondary . education, and so on . What explains the connection between city size and crime? Three factors can be mentioned. 17 First, the returns to crime can be higher in larger cities, perhaps because larger cities usually present wealthier victims and more developed mar- kets for second-hand goods. Second, the probability of arresting a criminal may be lower in larger cities, because either larger cities spend less in law enforcement or have lower levels of community cooperation with the police or require more officers per inhabitant to produce an arrest. And third, larger cities may have a Ž disproportionate share of crime-prone individuals e.g., idled males, distressed . migrants, street children or drug abusers . Can we discriminate among the different causes mentioned above? Although not completely, some clues emerge from the previous analysis. First, the effect of city size on crime cannot be explained by the presence of wealthier victims in larger cities. If that were the case, one would expect that, contrary to the evidence, this effect would decline substantially once we control for socioeconomic status and other household attributes. Second, the city-size effect cannot be explained by Ž. the fact that the rich arguably the best victims are easier targets in larger cities. If that was the case, one would expect that, again contrary to the evidence, the rich 16 Ž. See Glaeser and Sacerdote 1996 for a detailed explanation of how to compute these elasticities. 17 Ž. See Glaeser and Sacerdote 1996 for a formal treatment of this topic. () A. GaÕiria, C. PagesrJournal of DeÕelopment Economics 67 2002 181 – 203 ´ 194 Ž. Ž . Fig. 3. a Robberies and city size in Colombia. b Homicides and assults and city size in Colombia. Ž. c Perceived criminality and city size in Colombia. would be relatively more victimized as city size grows. So we are left, by elimination, with two possible explanations: larger cities have either lower proba- bilities of arrest or a higher proportion of individuals with a greater inclination Ž. toward criminal activities or both . Table 5 casts some doubts on the latter hypothesis. This table shows, for the case of Colombia, the relationship between city size and a few variables often deemed as strong predictors of high criminal incidence. These variables are the () A. GaÕiria, C. PagesrJournal of DeÕelopment Economics 67 2002 181 – 203 ´ 195 Table 5 Ž. Crime risk factors and city-size in Colombia percentage of households per city-size category City size Broken Idleness Percent of Communities with Ž. Ž. Ž. Ž. families % rates % migrants % drug problems % -20 21.3 30.1 14.2 14.9 20 – 50 22.0 33.6 8.5 8.1 5 – 200 25.3 30.3 11.3 14.2 200 – 500 25.1 33.7 10.6 22.8 )500 25.4 33.6 6.3 21.1 Bogota 20.1 26.8 5.2 18.5 Ž. Bogota is the largest city in the country with a population of 1994 est. 5,131,582 inhabitants. Source Ž. of data presented in table: Encuesta Nacional de Calidad de Vida Colombia, 1997 . fraction of households in which at least one parent is absent, the fraction of idled men, the fraction of households that migrated to the city in question during the previous five years, and the fraction of community leaders reporting that drug consumption is a serious problem in their communities. With perhaps the excep- tion of the latter variable, nothing in this table appears to suggest that larger cities in Colombia contain disproportionate fractions of crime-prone individuals. If anything, the opposite is true. Thus, at least in the case of Colombia, the greater criminal prevalence in larger cities may have more to do with law enforcement and less to do with the presence of either individuals at risk or better victims. Of course, additional evidence is needed to generalize this conclusion. 6. Victimization and city growth In this section, we study whether the probability of victimization is higher in more rapidly growing cities. In theory, rapid urban growth may raise crime for Ž many reasons, including a higher concentration of richer individuals attracted by . rising opportunities in cities , congestion of law enforcement and social services, massive unemployment, and increasing poverty. In practice, however, few studies have explored the connection between these two variables. We measure city growth as the annual rate of population growth from 1985 to 1995. Our main source of data is the United Nations data set on urban agglomera- tions. For cities that are not included in this data set, we use different sources, mainly country-specific statistical abstracts. For a few other cities, however, we could not get reliable estimates of population growth. As a result, adding popula- tion growth to our specification entails the loss of a few thousand observations, meaning that we cannot readily compare the results of this section to our earlier results. () A. GaÕiria, C. PagesrJournal of DeÕelopment Economics 67 2002 181 – 203 ´ 196 Table 6 City growth and probability of victimization, Probit estimation: marginal effects Ž. Ž. Ž. 12 3 Coefficient on city growth 0.0147 0.0196 0.020 Ž. Ž.Ž. Ž. Standard error 0.0055 0.0062 0.0074 Other variables – Wealth and household Wealth, city size characteristics and household characteristics Number of observations 17088 14961 12950 Number of cities 69 69 67 2 PseudoR0.016 0.022 0.023 All regressions include country and year fixed effects. Ž. Table 6 presents the main results of this section. Column 1 reports the effects of population growth on victimization rates after controlling for country and year effects. As shown, city growth has a positive and statistically significant effect on crime rates. On average, an increase of one percentage point in the rate of population growth will increase the probability of victimization by almost 1.5 percentage points. Ž. Column 2 reports the effects of city growth on the probability of victimization after we add the quintile dummies and other household characteristics. As shown, city-growth effects are even larger in this case. Similarly, city growth effects remain practically unchanged after controlling for the size of the city. On the whole, the estimates presented above suggest that rapid urbanization is associated with a substantial increase in crime. Moreover, because higher criminal Ž rates may actually reduce population growth they often curtail migration rates by . causing people to flee cities , these estimates are likely to underestimate the effect of city growth on crime. 18 What explains the connection between city growth and crime? Our results indicate that the relationship between city growth and crime cannot be attributed to the characteristics of the victims. Thus, the higher levels of crime in faster growing cities are not driven by the concentration of high-income households in these cities. We are left with two possible explanations: rapid urban growth Ž. increases crime by attracting or nurturing a larger share of crime-prone individu- als or urban growth diminishes the effectiveness— and hence the reputation— of law enforcement institutions. Below, we offer some preliminary evidence consistent with the latter idea. We first show that confidence in the police is lower in rapidly growing cities, we then show that victimization rates are higher in cities with lower levels of confidence in 18 Ž. Cullen and Levitt 1999 show that in the United States, each additional reported crimes leads, on average, to one fewer resident. () A. GaÕiria, C. PagesrJournal of DeÕelopment Economics 67 2002 181 – 203 ´ 197 Table 7 Ž. Correlations between crime, population growth and confidence in institutions correlations are at the city level Crime Population Confidence Confidence Confidence Confidence in growth in police in judiciary in president political parties )) ) ) Crime 1.000 0.227y0.467y0.276y0.192y0.274 )) ) Population growth 1.000y0.398y0.347y0.086y0.307 )) ) Crime in police 1.000 0.589 0.517 0.535 )) Crime in judiciary 1.000 0.481 0.570 ) Crime in president1.000 0.592 Crime in political parties1.000 Ž). Correlations marked with are significant at the 5% level. () A. GaÕiria, C. PagesrJournal of DeÕelopment Economics 67 2002 181 – 203 ´ 198 Table 8 Ž Crime victimization and confidence in the police and judiciary dependent variable: probability of . being victimized , marginal effects PROBIT IV PROBIT IV PROBIT IV PROBIT Ž.Ž .Ž . President Political Parties President, PP Confidence in policey0.189y0.1504y0.270y0.135 Ž . Ž. Ž. Ž. Ž. Standard error 0.055 0.185 0.205 0.151 Number of 17,097 12,737 16,870 12,737 observations Number of cities 151 149 151 149 2 PseudoR0.026 – – – All regressions include country and year fixed effects as well as controls for household attributes and city size. Cities with fewer than 30 observations were excluded from the sample. the police, and finally, we show that there appears to be a causal link going from low confidence in the police to higher victimization rates. Table 7 shows, among other things, that the levels of confidence in the police and the judiciary tend to be lower in faster growing cities. The correlation coefficient is in both cases close to 0.4 and statistically significant. Table 8 shows, for its part, that lower levels of confidence in the police are correlated with higher probabilities of victimization, even after controlling for household attributes and city size. On average, an increase of 20 percentage points in the level of confidence in the police will be associated with a decline in the probability of victimization of almost 4 percentage points. 19 Needless to say, the confidence people bestow on the police is likely to be greatly affected by the incidence of crime, meaning that a causal connection between the incidence of crime and confidence in the police remains an issue. We attempt to solve this problem by instrumenting the level of confidence in the police using confidence in the president and political parties. Our choice of instruments is based on two facts. First, the correlation between confidence in the police, on the one hand, and confidence in the president and political parties, on the other, is very high. As shown in the Table 7, the correlation coefficient is greater than 0.5 in both cases, meaning that both variables are good predictors of the level of confidence in the police in a city Ž perhaps because the confidence of all government institutions are determined by a . common factor . And second, the instruments have little explanatory power when Ž. added directly to Eq. 1 . The estimated coefficients are very small and always smaller than their estimated standard errors, and the variables did not add to the 19We assign to each person the average confidence on the police for his city of residence after excluding his own answer. We eliminated all households who live in cities that contain fewer than 30 observations in the sample. () A. GaÕiria, C. PagesrJournal of DeÕelopment Economics 67 2002 181 – 203 ´ 199 explanatory power of the equation, meaning that confidence in the president and political parties are unlikely to have an independent effect on the probability of victimization. In our estimation, we assume that the level of confidence in the police in a city can be written as the following linear model csx g qx g q ´ ,2 Ž. yij11 2 2yij wherecis the mean confidence in the police in cityjafter excluding individual yij i,xare the determinants of the probability of victimization andxare the 12 exogenous variables, in this case, the levels of confidence in the president and political parties computed at the city level. We assume that the error terms of Eqs. Ž. Ž. 1 and 2 are distributed according to a bivariate normal distribution with Ž. Ž. correlation coefficient s. We simultaneously estimate Eqs. 1 and 2 under the previous assumptions using a Maximum Likelihood procedure. 20 Ž. Ž. Columns 2 to 4 of Table 8 present the Probit IV estimation results. As shown, the coefficient on confidence in the police remains negative and does not vary substantially, though is measured with smaller precision. Indeed, this coeffi- cient is not significant in any of the IV estimations, reflecting, perhaps, a problem Ž. with our sample size our confidence variables are measured at the city level . All Ž. in all, these results present suggestive but no conclusive evidence to the effect that there exists a causal link between lower levels of confidence in the police and higher probabilities of victimization. In our view, the former results provide some support to our claim to the effect that the link between population growth and crime is partly driven by the overload of law enforcement institutions and the subsequent deterioration in their effective- ness and reputation. 7. Concluding remarks In this paper, we draw a profile of the victims of crime in Latin America. We show that — at least in the case of property crime — the typical victims of crime in Latin America come from rich and middle class households and tend to live in larger cities. We also show that households living in cities experiencing high population growth are more likely to be victimized than household living in cities with more stable populations. We offer various explanations to these facts, and while we cannot yet provide definite answers to some of the questions raised by this paper, we are at least able to reject some plausible hypotheses. 20Specifically, we use a program written by Deon Filmer at the World Bank, available in the internet at http:rrglue.umd.edur;gelbachrado. () A. GaÕiria, C. PagesrJournal of DeÕelopment Economics 67 2002 181 – 203 ´ 200 We have not attempted here to explain why crime rates are higher in Latin America than in other areas of world. However, our analysis suggests that the higher levels of urban concentration and the faster rates of population growth that are typical of many Latin American countries are partly to blame for the higher criminal rates. Of course, several other factors not mentioned in this paper are also very important. Drug trafficking, for example, is conspicuously absent throughout, as are social capital and other cultural elements. 21 Acknowledgements We thank Ricardo Fuentes for valuable research assistance and F. Bourguignon for excellent comments. This paper reflects the opinions of the authors and not necessarily those of the Inter-American Development Bank. Appendix A. A simple model of crime victimization Are wealthy individuals more likely to be victims of property crime? The answer to this question depends on the relative strength of two opposing forces. On the one hand, the wealthy are more desirable targets for criminals and, on the other, they have more reasons to invest in private protection against crime. Here we investigate the circumstances under which the first force dominates the second, thus making wealthier individuals more likely to fall prey to criminals. Ž The structure of the model is simple. There are two actors citizens and .Ž criminals and two stages. In the first stage, citizens who differ only in their . wealth holdings decide how much to spend in private protection. In the second stage, citizens are matched with criminals who in turn decide whether or not to Ž. commit a crime upon observing the wealthwof their prospective victims and Ž. their investments in private protectione. Criminals make their decisions on the basis of mere pecuniary factors. They weigh in two factors: a successful criminal Ž. attempt will mean enjoying a bounty of a timesw aF1 , and a failed criminal attempt — that occurs with probabilityp— will mean incurring a fine ofF. Three additional assumptions are made. First, the probability of apprehension is Ž assumed to increase monotonically with the expenses in private protection i.e., wx X . pspe, wherep)0 . Second, victims and criminals are assumed to be risk neutral. And last, criminals are assumed to have complete information in that they 21 Ž. Lal 1998 argues, for example, that the reason why urbanization has not been so disruptive in Muslim countries is that in these countries people rely mainly on informal communities mechanism to Ž. control crime e.g., Teheran, a city of 10 million inhabitants, has very low crime rates . This argument can explain, among other things, why congestion of law enforcement institutions has not had the same consequences in the Middle East as in Latin America. () A. GaÕiria, C. PagesrJournal of DeÕelopment Economics 67 2002 181 – 203 ´ 201 observe their victim’s wealth and are able to correctly infer their chances of being apprehended. Thus, a criminal will attempt to victimize citizeniwho possess a wealth ofw i and have investedein private protection as long as the following inequality holds i wx wx 1ype awypeF)0. A1 Ž. Ž. ii i Because all citizens are paired with criminals, citizenican avoid being victimized only by investing at leasthin private protection, wherehcorre- ii sponds to the expenses in private protection that would make a criminal indifferent between attempting to steal fromior not doing it because poses too high of a risk. In short, awi Žy1. hspA2 Ž. i awqFi wherep Žy1. is the inverse of the functionpthat links private expenses in protection to the probabilities of punishing a criminal. Ž. Eq. A2 gives, for each level of wealth, the minimal expenses on private protection required to scare criminals away: any amount belowhis insufficient i and any amount above it is superfluous. Citizens face thus a binary decision; they either investhin private protection or do not invest at all. Obviously, they will i investhonly if it does not exceed the prospective losses of being victimized. i That is, if hF aw.A3 Ž. ii Wealthier individuals would need, all else being equal, greater investments in private protection to avoid victimization. This is immediately apparent from the Ž. first derivative of expression A2 with respect tow, dh aF s)0. A4 Ž. 2 X dw wx Fy awph Ž. i But are wealthier individuals willing to incur in the higher costs of private protection? Or will they instead prefer to bear some crime? As we shall see below, the answer to this question depends on the second derivative ofhwith respect to w. Fig. 4 depicts the two types of relevant solutions of the model. 22 In the first Ž. case, Eq. A2 is concave,his below awfor higher values ofw, the wealthy invest in private protection, and the poor are victimized. In the second case, Eq. Ž. A2 is convex and the conclusion switches: the poor are the ones who invest in 22Fig. 4 summarizes all cases of economic interest. For same parameter values, the two curves in the graph will never intersect, meaning that all citizens are victims of crime or on citizens are victims of crime. () A. GaÕiria, C. PagesrJournal of DeÕelopment Economics 67 2002 181 – 203 ´ 202 Fig. 4. Private investments in security versus criminal losses. protection and the wealthy the ones who bear the brunt of crime. In short, all Ž. depends on the concavity of Eq. A2 . Ž. What determines the concavity of Eq. A2 ? The answer is evident by looking at the second derivative ofhwith respect tow, 2 XY 22 wx wx dh a F2Fq awphqFph Ž.Ž. sy.A5 Ž. 234 X dw wx Fq awph Ž. Ž. Clearly, Eq. A5 will be negative unless the second derivative ofpis both negative and large in absolute value. So the wealthy will routinely invest in private protection in order to avoid victimization unlesspexhibits sharp diminishing returns to scale. The intuition is straightforward; if the marginal returns of an extra peso spent in private protection against crime areÕerylow, the wealthy will find it extremely expensive to reach the level of protection needed to avoid victimization and will rationally decide to bear some crime. Otherwise, they will invest the necessary amount to escape victimization. References Becker, G., 1968. Crime and punishment: an economic approach. Journal of Political Economy 76, 169 – 217. Ž. Bourguignon, F., 1999 .ACrime, Violence, and Inequitable Development.BPaper prepared for the Annual Bank Conference on Development Economics. The World Bank. Bureau of Justice Statistics, 1998. Criminal Victimization 1997. US Department of Justice, Washing- ton, DC. Cruz, J.M., 1999. La victimizacion por violencia urbana: niveles y factores asociados en ciudades de ´ Ž. America Latina y Espana. Pan American Journal of Public Health 5 4r5. ˜ Cullen, J., Levitt, S., 1999. Crime, urban flight, and the consequences for cities. Review of Economics and Statistics 81, 159 – 169. Ehrilch, I., 1973. Participation in illegitimate activities: a theoretical and empirical investigation. Journal of Political Economy 81, 521 – 565. () A. GaÕiria, C. PagesrJournal of DeÕelopment Economics 67 2002 181 – 203 ´ 203 Evans, W., Oats, W., Schwab, R., 1992. Measuring peer group effects: a study of teenage behavior. Journal of Political Economy 100, 966 – 991. Fajnzylber, Lederman, Loayza, N., 1998. Determinants of Crime Rates in Latin America and the World: An Empirical Assessment. World Bank Latin American and Caribbean Studies, November, Washington, DC. Ž. Filmer, D., Pritchett, L., 1998 . Estimating Wealth effects Without Income or Expenditure Data: Educational Enrollment in India. Mimeo, DECRG, the World Bank. Washington, DC. Ž. Gaviria, A., Stein, E. 2000 .AUrban Concentration around the World: a Panel Approach.BUnpub- lished Manuscript. Inter-American Development Bank. Glaeser, E., Sacerdote, B., 1999. Why is there more crime in cities? Journal of Political Economy 107, S225 – S259. Lal, D., 1998. Unintended Consequences: The Impact of Factor Endowments, Culture, and Politics on Long-Run Economic Performance. MIT Press, Cambridge, MA. Londono, J.L, Gaviria, A., Guerrero, R., 2000. Epidemolog a y costos de la violencia en America ˜´Latina. Inter-American Development Bank, In press. Newey, Whitney, K., 1987. Efficient estimation of limited dependent variable models with endogenous explanatory variables. Journal of Econometrics 36, 231 – 250.
For this assignment, all you have to do is to select a topic, write an introduction, formulate a research question (see examples above) and a hypothesis. Keep it simple! Then, you will also review thr
Race, Ethnicity, Gender, and Violent Victimization Toya Z. Like-Haislip 1and Karin Tusinski Miofsky2 Abstract The victimization literature has clearly established race, ethnicity, and gender disparity in victimization risks whereas contemporary work has demonstrated that the inter- section of these characteristics produces complex patterns in victimization risks. However, explanations for these differences within and across gender and race and ethnicity must continue to be explored and to that end the purpose of this research is to examine whether the well-established risk factors for victimization such as daily or routine activities and neighborhood conditions similarly influence risks for violent vic- timization among varying gender, racial, and ethnic groups. Using data from the National Crime Victimization Survey (NCVS) 12 Cities study, the authors find that risks do vary both across and within gender. While routine activities are significant predictors of females’ risks, neighborhood conditions seem to be better indicators of males’ risks. Also, routine activities and neighborhood conditions have disparate effects on males’ and females’ risks across race and ethnicity. For example, while fre- quent use of public transportation has a null effect on White females’ risks for violent victimization, it increases such for Black and Latina females. Likewise, although resi- dential stability decreased the risks of violent victimization for White and Latino males, it increased the likelihood of victimization for Black males. Keywords African/Black Americans, Latino/Hispanic Americans, National Crime Victimization Survey (NCVS) 120, victimization, White Americans 1Department of Criminal Justice and Criminology, University of Missouri, Kansas City, MO, USA2Department of Sociology and Criminal Justice, University of Hartford, Hartford, CT, USA Corresponding Author: Toya Z. Like-Haislip, Department of Criminal Justice, University of Missouri, 5215 Rockhill Road, Kansas City, MO 64110, USA Email: [email protected] Race and Justice 1(3) 254-276 ªThe Author(s) 2011 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/2153368711409059 http://raj.sagepub.com at FLORIDA STATE UNIV LIBRARY on September 4, 2016 raj.sagepub.com Downloaded from The complexities of the gender, race, ethnicity, and violence relationship are evident in the victimization literature. Research has consistently demonstrated that with the exception of rape/sexual assault males are more likely than females to be victims of violent crimes while minorities (and particularly Blacks and Latinos) are more likely to be violently victimized than are Whites (Catalano, 2005, 2006; Rand, 2009). These dichotomous comparisons, however, mask the importance of intersections between gender, race, and ethnicity and their influence on individuals’ experience with vio- lence. Interestingly, recent victimization research has shown that rates of violent victimization are notably high among Black males and females relative to their White and Latino counterparts. Furthermore, the gender gap in Black rates of overall victimization is much more narrow than that found between Whites and Latinos (Lauritsen & White, 2001). For instance, Lauritsen and White (2001) report nearly identical rates of violent victimization among Black males and females (26.9 and 26.3, respectively). The disparities, however, are larger for the other groups, with rates for White males (23.2 per 1000) exceeding that of White females (15.5 per 1000) and similarly Latino males (31.3 per 1000) possess higher rates than their female coun- terpart (18.9 per 1000; Lauritsen & White, 2001).While this race/ethnicity and gender pattern has clearly been demonstrated in victimization research, explanations for these differences remain unclear. Scholars have routinely considered males and females experiences with violence separately. Traditional criminology has focused almost exclusively on the plight of males (and minority males in particular) and explanations for their criminal behavior (e.g., Anderson, 1999; Cohen & Felson, 1979; Messerschmidt, 1993; Sampson & Laub, 1993; Sullivan, 1989). Feminist criminology, on the other hand, has focused primarily on specific forms of violence against women such as intimate partner violence and rape and sexual assault—to the detriment of a comprehensive understanding of females’ risks for violence in general (Heimer & Kruttshcnitt, 2006, p. 2). More scarce are the extant literatures on gender, race, and ethnic variations in victimization risk and causes for these disparities (but see Lauritsen & White, 2001). It is important to examine whether the intersection of these characteristics condition risks for violent victimization. Although some suggest that demographic characteristics are indicators of structural (and possibly cultural) factors that increase risks for violent victimiza- tion, few have systematically studied this possibility. It remains unclear whether well-established risk factors for victimization such as daily or routine activities and neighborhood conditions similarly influence risks for violent victimization among varying gender, racial, and ethnic groups. To that end, the goal of the present study is to explore whether ‘‘known’’ risks factors for violent victimization—indicators of individual lifestyle and community characteristics—are common across gender, race, and ethnicity. In assessing these relationships, we contribute broadly to the study of victimization and specifically to the body of work on gender, race, and ethnic variations in violent victimization. As Lauritsen and Carbone-Lopez (2011) note, studies along these lines present important theoretical implications and essentially help to determine whether general approaches in the study of violence are warranted or whether the development of ‘‘specialized Like-Haislip and Miofsky 255 at FLORIDA STATE UNIV LIBRARY on September 4, 2016 raj.sagepub.com Downloaded from theories’’ that are gender- and/or race and ethnic-specific are necessary (p. 1). Furthermore, in our examination of the proximate causes of victimization, we also consider both within and across gender differences insuch. Conventional wisdom and empirical research lead us to believe that there are both similarities and differences in males’ and females’ experiences with violence; how ever, we also posit that there are within gender differences (especially across race and ethnicity) in violent victimization. In doing so, we assess common and unique feat ures of victimization risks within and across gender. Violent Victimization at the Intersection of Gender, Race, and Ethnicity At the forefront of the study of victimization and its relation to demographic char- acteristics such as gender, race, and ethnicity is the lifestyle model (Hindelang, Gottfredson, & Garafalo, 1978; also see Garofalo, 1987) and routine activities theory (Cohen & Felson, 1979). The lifestyle model posits that our lifestyles, which consist of daily activities centered on vocation and leisure, are largely predicted by individual characteristics and structural constraints based on these characteristics. The lifestyle pattern (or daily activities), in turn, influences the amount of exposure to places and individuals that present victimization risks (Hindelang et al., 1978; also see Garofalo, 1987). In a similar vein, routine activities theory suggests that daily or recurrent activities related to work, family, and leisure are important. The structure of these activities can impact personal victimization as well as the overall crime rate by increasing the likelihood of convergence in time and space between motivated offenders and suitable targets in the absence of capable guardians (Cohen & Felson, 1979). Overall, these theories propose that differences in victimization risks are explained by variations in lifestyles and routine activities. These theories account for gender and racial/ethnic differences in victimization using this premise. Considering gender differences, lifestyle, and routine activities approaches suggest that lifestyle differences between males and females account for disparities in their victimization risks. In other words, once routine and daily activities are considered gender is no longer an explanatory factor (Woodward & Fergusson, 2000; but see Mustaine, 1997). Females’ lower risks for violent victimization relative to males were attributed to their fewer interactions away from home and thus their diminished exposure to dangerous places such as night clubs and bars, for example, where assaults are prevalent. These differences in male/female interactions away from home might also explain contextual differences in victimization risk across gender. Males are commonly victimized by strangers or those whom they had not previously had contact with. This is not the case for females, however, who are far more likely to be victi- mized by persons they know such as their spouses, intimate partners, acquaintances, and friends (Rand, 2008; Rennison & Welchans, 2000). Feminist scholars have long critiqued traditional lifestyle/routine activities approaches for their inattention to the violence females experience in their homes or familiar settings (Heimer & Kruttshcnitt, 2006, p. 2). For example, in 2008 females were nearly four times more 256 Race and Justice 1(3) at FLORIDA STATE UNIV LIBRARY on September 4, 2016 raj.sagepub.com Downloaded from likely to be violently victimized by an intimate partner than were males, with females’ rates totaling 4.3 per 1,000 compared to a rate of 0.8 per 1,000 for males (Catalano, Smith, Snyder, & Rand, 2009).Despite these trends, the gap in male and female interactions away from home may have narrowed in recent decades, especially following World War II when female involvement in the work force increased noticeably. In 1960, 97 %of males in the United States were involved in the labor force compared to only 42.9 %of females. However, female participation skyrocketed to 75.3 %by 2005 while the rate for males dropped to 90 %(Mosisa & Hipple, 2006). Undoubtedly, female participation in the workforce has increased the amount of time that they spend away from home and consequently may have impacted the aforementioned victimization trends in notable ways. In their analysis of gender differences in violent victimization from 1973 to 2004, Lauritsen and Heimer (2008) find that the gender gap in simple and aggravated assault has closed dramatically especially due to the substantial drops for males yet relatively slower declines for females. They postulate that female movement into the public sphere may have lead to fewer acts of interpersonal violence in public settings for which males were at greater risks. But this shift may have also inadvertently led to greater opportunities for female victimization as female interactions in public settings increased and thus their greater likelihood of exposure to motivated offenders increased (Lauritsen & Heimer, 2008). In addition to lifestyle or daily activities such as workforce participation, structural constraints may also influence risks for victimization. Garofalo (1987) argues that these constraints are of particular importance because they can have a direct effect on exposures and associations conducive to victimization via place of residence. Simply put, the contextual features of communities not only determine individual and group adaptations (and thus the lifestyles they adopt) but also influences victimization risks ‘‘by sheer proximity—and hence exposure—to potential offenders’’ (Garofalo, 1987, p. 38). From this perspective, race and ethnicity are not directly connected to victi- mization but rather are indicative of macrostructural factors that shape life, including the conditions under which people live. Though ignored relative to demographic characteristics, research on lifestyle/routine activities theory that considers neigh- borhood conditions have indeed found that neighborhood disadvantage is a significant predictor of victimization risk (Mustaine & Tewksbury, 1998; Sampson, 1987). That being said, while lifestyle/routine activities theory notes the importance of structural conditions (see Garofalo, 1987, p. 38), neighborhood theorists have long argued the significance of community context and its impact on individuals’ experience with violence (Bursik & Grasmick, 1993a; Sampson, Raudenbush, & Earls, 1997; Shaw & McKay, 1942). Importantly, research in these areas suggests that racial and ethnic differences in victimization risks subside once community disadvantage is considered (Krivo & Peterson, 1996; Lauritsen, 2003; Lauritsen & White, 2001; McNulty & Bellair, 2003; Peterson & Krivo, 1993). The disproportionate rates of victimization among Blacks and Latinos compared to Whites is not surprising then given that they are less likely to live in ecological equality with Whites (Massey & Denton, 1993; Sampson & Wilson, 1995). Sampson and Wilson (1995) in their study of the largest Like-Haislip and Miofsky 257 at FLORIDA STATE UNIV LIBRARY on September 4, 2016 raj.sagepub.com Downloaded from cities in the United States were unable to find one city in which Whites and Blacks live in ecological equality. Similarly, Massey and Denton (1993) document the historical context of these trends in residential segregation equating it to a system of apartheid that cannot be simply explained by economic differences between these groups but instead is rooted in deep-seated racism and prejudice in America 1(also see Fischer, Gretchen, Jon, & Michael, 2004; Lewis Mumford Center for Comparative Urban and Regional Research, 2001; Logan, Stults, & Farley, 2004). Consequently, Blacks in particular are more likely to be concentrated in neighborhoods suffering from extreme levels of social and economic disadvantage that are characterized by extremely high levels of disorder and crime (Massey & Denton, 1993; Peterson, Krivo, & Browning, 2006; Skogan, 1990; Wilson, 1987). As Garofalo (1987) had proposed a decade ear- lier, Sampson and Wilson (1995) theorize that these structural disadvantages make residents (and in this case Blacks) particularly vulnerable to violence since these struc- tural inequalities inadvertently curtail mainstream goals and values thereby exacerbat- ing risks for violent offending and victimization in their communities. Taken together, the extant research on violence—and particularly that on violent victimization—has emphasized the importance of both situational and contextual factors including individuals’ lifestyles as well as the environments in which they live. While scholars agree that these factors predict victimization risks, it remains unclear whether they are similarly related to victimization risks across gender- and race/ethnic-specific groups. The problem is that past works have considered ethnicity, race, and gender independently, largely disregarding their interaction and likely complex effect on risks for nonfatal forms of violent victimization (see DeCoster & Heimer, 2006; Simpson & Gibbs, 2006 for full discussion on the complexity of inter- sectionalities). Feminist scholars, in particular, have critiqued criminological research and theory for ignoring the importance of gender (and its intersection with race, eth- nicity, class, etc.) and ‘‘assuming that theories designed to explain male behavior are equally applicable to females’’ (Heimer & Kruttshcnitt, 2006, p. 2). Furthermore, Lauritsen and Carbone-Lopez (2011) point out, relatively few works have considered the extent to which gender conditions the impact of ‘‘known’’ correlates of victimiza- tion (p. 1). In consideration of these assessments, we further assert that it is important to examine whether gender and race/ethnicity moderates the relationship between victimization risks and theoretical variables consistently linked to victimization. We agree with feminist works that primacy must be given to gender and its intersection with other demographic characteristics to explain variations in victimization risks. However, we also critique feminist works in that they have focused almost exclusively on those forms of violence particular to females such as domestic violence and sexual assault, ignoring how these intersections are important to male and females’ risks alike. These factions in the study of violence have limited our understanding of violence and the development of a comprehensive theoretical explanation of variations in victimization risks. As aforementioned, routine activities/lifestyle theories are commonly used to explain gender differences while neighborhood conditions are often purported to explain racial/ethnic differences in victimization risks. Yet, it remains unclear how these theoretical variables impact victimization risks across intersections of gender, 258 Race and Justice 1(3) at FLORIDA STATE UNIV LIBRARY on September 4, 2016 raj.sagepub.com Downloaded from race, and ethnicity. Therefore, we examine the importance of these factors and explore whether they present unique or common effects on victimization risks among non-Latino White males and females, non-Latino Black males and females, and Latino males and females. Method Data The data for the present study are derived from the National Crime Victimization Survey (NCVS) 12 Cities study. The Bureau of Justice Statistics (BJS) and the Office of Community-Oriented Policing Services (COPS) collaborated to collect data on vio- lent and property victimizations, perceptions of local policing and community safety across 12 cities utilizing community-oriented policing strategies. The sampled cities are Chicago, Illinois; Kansas City, Missouri; Knoxville, Tennessee; Los Angeles, California; Madison, Wisconsin; New York, New York; San Diego, California; Savannah, Georgia; Spokane, Washington; Springfield, Massachusetts; Tucson, Ari- zona; and Washington, District of Columbia. A random sample of households within each city was identified by the Demographic Statistical Methods Division (DSMD) using the GENESYS Random-Digit Dialing (RDD) Sampling System to gather house- hold telephone numbers which corresponded with city zip codes. 2It is important to note this sampling strategy differs from the multistage, multiclustered sampling tech- nique used for the traditional NCVS. Data collection for the NCVS 12 Cities study occurred over a 4-month period, starting in February 1998. Interviews were conducted via telephone using computer assisted telephone interviewing (CATI). The CATI method, used by the NCVS between 1992 and 2007, was implemented to reduce pos- sible errors by interviewers and to improve the quality of the data collected. 3 Sample The NCVS 12 Cities survey includes measures of individuals’ demographic charac- teristics, routine activities, and perceived neighborhood conditions in addition to measures of violent victimization. As noted above, non-Latino Whites and Blacks and Latinos are the focus of this research. These groups constitute the majority (or 95 %)of the 13,918 original participants in the NCVS 12 Cities study. This classification of race, which considers ethnic origin, is important because race and ethnicity are not mutually exclusive categories in any NCVS data including the NCVS 12 Cities data. 4 Latinos can be of any racial background with approximately 40 %of those in the present study self-identifying as White, while only 7 %report their race as Black. This distinction of ethnic and racial (or ethnoracial) background is especially critical in assessing risks for victimization since ‘‘‘White’ versus ‘Black’ comparisons result in an overestimation of risks among Whites because ‘Hispanics’ [Latinos] are included with Whites, whereas ‘Hispanic’ versus ‘non-Hispanic’ comparisons under- estimate group differences because Blacks and Whites are combined in the ‘non- Hispanic’ category’’ (Lauritsen & White, 2001, p. 43). Therefore, the present study Like-Haislip and Miofsky 259 at FLORIDA STATE UNIV LIBRARY on September 4, 2016 raj.sagepub.com Downloaded from employs a designation of race that takes into account individuals’ ethnic origin also. While consideration of other ethnoracial groups is needed, such comparisons present an analytical challenge due to their small sample size; therefore, these groups are not considered in the present study. 5 The sample of non-Latino Whites, non-Latino Blacks, and Latinos was reduced for two essential reasons. First, one of the key variables (perceived disorder) was asked only of respondents aged 16 and older so the sample is restricted to those fitting this age criteria. 6Second, missing data on other key variables (indicators of routine activ- ities) led to further reductions in the sample size. It is important to point out, however, the percentage missing for each routine activity measure is 2 %or less. Subsequently, the total sample of non-Latino Whites, non-Latino Blacks, and Latinos aged 16 and older comprised of 11,971 was further reduced to 11,512 due to missing values on the routine activities measures. Because the intersection between ethnicity, race, and gen- der as it relates to risks of nonfatal violent victimization is of importance, the sample is disaggregated across these dimensions and is a comparison of the following ethnic/ race-specific gender groups: non-Latino White and Black males and females, and Latinos and Latinas. 7 Dependent Variable The dependent variable is nonfatal violent victimization. 8Each respondent was asked to report whether they were a victim of an attempted or completed rape/sexual assault, robbery, or simple or aggravated assault in the past 12 months (0 ¼no and 1 ¼yes ). A scale of violent victimization was created in which a value of ‘‘1’’ was assigned to those who reported experiencing one or more of these victimizations in the past year. A summary indicator of violent victimization is used as opposed to separate analyses for each victimization type since certain incidents of violence are rare. For example, less than 1 %of the respondents from each race/ethnic-specific gender group reported being a victim of rape or sexual assault. Independent Variables Routine activities. Involvement in the workforce is believed to be an important pre- dictor of victimization, especially given the recent shifts for females, and so we included a measure of employment history which considers whether or not the participants worked a job in the past year. The NCVS 12 Cities participants were also asked three questions that serve as additional indicators of routine activities: (a) How often do you spend the evening away from home? (b) How often are you gone shopping? and (c) How often do you ride public transportation? In the survey, participants were given the option to answer almost every night or day (coded 1), at least once a week (coded 2), at lease once a month (coded 3), less often (coded 4), or never (coded 5). These codes were reverse-ordered to account for an increase in the value for these items representing greater frequency or involvement in the respective activities. The results of principal component analyses reveal little commonality between these items. 9Supplemental tests 260 Race and Justice 1(3) at FLORIDA STATE UNIV LIBRARY on September 4, 2016 raj.sagepub.com Downloaded from also suggest the same. For example, the reliability coefficient or Cronbach’safor the three items is very low (.15). Therefore, these survey items are not combined but con- sidered separately in the analyses and are labeled Evening Away,Shopping ,andPublic Transit in the descriptive tables. Though some scholars have noted the lack of speci- ficity in using vague measures like frequen cy at which individuals spend the evening away from home as an indicator of a routine activity that places one at risks for vic- timization (Mustaine & Tewksbury, 1998), it is a common measure and directly assesses the theoretical claims of the rou tine activities approach (Cohen & Felson, 1979). The other measures, ShoppingandPublic Transit , are indicative of specific activities one may engage in outside of ho me, hence placing them in proximity to motivated offenders. Neighborhood factors. The length of years the individual has lived at his/her address is used as an indicator of residential stability and is considered here given that previous studies have associated residential stability with violent victimization (i.e., Lauritsen, 2003). Although the NCVS 12 Cities survey does not include direct measures of neighborhood conditions, it does provide questions on perceptions of neighborhood conditions. As noted above, those 16 years of age and older were asked to report whether physical and/or social disorder exists in their neighborhood. 10 Principal com- ponents analyses using a varimax rotation were performed to determine the extent of commonality among the 14 survey questions measuring signs of physical and social disorder in the participants’ neighborhoods. 11 Two components were retained— Disorder and Homeless/Transient—and composite scales were created using the aver- age of the z-scores of the items included in each component. The Disorder Scale consists of the following items: ‘‘abandoned buildings/cars,’’ ‘‘rundown/neglected buildings,’’ ‘‘public drinking/drug use,’’ ‘‘public drug sales,’’ ‘‘loitering/hanging out,’’ and/or ‘‘truancy/youth skipping school’’ in the neighborhood. ‘‘Panhandling/begging’’ in the neighborhood and ‘‘transient/homeless populations sleeping on the streets of the neighborhood’’ are included in the Homeless/Transients Scale. The reliability coeffi- cients (Cronbach’s a) for these scales are .82 and .71, respectively. Finally, controls for demographic characteristics associated with violent victimi- zation are included in the study. Victimization surveys have consistently linked age (Klaus & Rennison, 2002) and marital status (Catalano, 2005) to violent victimization. Age ranges from 16 to 90 across the sample. Marital status is divided into the fol- lowing categories: married (reference group), divorced/separated, widowed, and never married individuals. Though annual household income is also a correlate of violent victimization (Catalano, 2005), it is not included in the final analyses because of the extent of missing data for this measure. 12 Findings Bivariate Results Descriptive statistics across the groups are provided in Tables 1 (males) and 2 (females). As shown in Table 1, there are both similarities and differences across Like-Haislip and Miofsky 261 at FLORIDA STATE UNIV LIBRARY on September 4, 2016 raj.sagepub.com Downloaded from Table 1.Descriptive Statistics on the Restricted NCVS 12 Cities Sample, 1998 a(Males) Non-Latino White Non-Latino Black Latinos Mean Median Dev. % Mean Median Dev. % Mean Median Dev. % Age 45.1 42.0 18.3 43.6 38.0 18.8 33.7 29.0 13.3 Income b 11.0 12.0 2.9 9.6 10.0 3.410.1 11.0 3.5 Current address (yrs) 11.2 6.0 12.6 9.4 4.0 10.66.4 3.0 8.0 Disorder .2 0.0 .3 .3 0.0 .3.3 0.2 .3 Homeless/transient .2 0.0 .3 .2 0.0 .3.2 0.0 .4 Shopping 4.1 4.0 .6 3.9 4.0 .7 4.1 4.0 .8 Evening away 3.8 4.0 1.0 3.5 4.0 1.4 3.8 4.0 1.1 Public transportation 1.7 1.0 1.2 2.0 1.0 1.42.0 1.0 1.5 Employed in past year 74.367.674.8 Never married 34.737.344.0 Violence victims 6.76.7 7.1 Note. NCVS 12 Cities study (ICPSR 2743).aThe descriptive statistics are based on the unweighted samples of non-Latino White males ( n¼ 3,938), non-Latino Black males ( n¼ 748), and Latinos ( n¼ 557).bBased on 14 unequal categories of income: 1 ¼<$5,000; 2 ¼$5,000–7,499; 3 ¼$7,500–9,999; 4 ¼$10,000–12,499; 5 ¼$12,500–14,999; 6 ¼$15,000–17,499; 7 ¼ $17,500–19,999; 8 ¼$20,000–24,999; 9 ¼$25,000–29,999; 10 ¼$30,000–34,999; 11 ¼$35,000–39,999; 12 ¼$40,000–49,999; 13 ¼$50,000–75,000; 14 ¼$75,000 or more. Due to the extent of missing cases for this variable, it is not included in the final analytic models. 262 at FLORIDA STATE UNIV LIBRARY on September 4, 2016 raj.sagepub.com Downloaded from Table 2.Descriptive Statistics on the Restricted NCVS 12 Cities Sample, 1998 a(Females) Non-Latina White Non-Latina Black Latina Mean Median Dev. % Mean Median Dev. % Mean Median Dev. % Age 45.9 44.0 17.9 47.1 44.0 18.735.0 33.0 12.6 Income b 10.9 12.0 3.2 8.1 8.0 4.27.7 9.0 4.3 Current address (yrs) 12.8 7.0 13.1 11.9 8.0 11.66.7 4.0 7.2 Disorder .2 0.0 .3 .3 0.2 .3.3 0.4 .3 Homeless/transient .1 0.0 .3 .2 0.0 .3.2 0.0 .3 Shopping 4.2 4.0 .7 3.9 4.0 .74.0 4.0 .7 Evening away 3.6 4.0 1.0 3.3 4.0 1.4 3.6 4.0 1.3 Public transportation 1.7 1.0 1.2 2.4 2.0 1.62.4 2.0 1.6 Employed in past year 63.062.058.0 Never married 25.243.337.9 Violence victims c 4.3 5.18.0 Note. NCVS 12 Cities study (ICPSR 2743).aDescriptive statistics are based on the unweighted samples of non-Latino White females ( n¼ 4496), non-Latino Black females ( n¼ 1164), and Latinas ( n¼ 609).bBased on 14 unequal categories of income: 1 ¼<$5,000; 2 ¼$5,000–7,499; 3 ¼$7,500–9,999; 4 ¼$10,000–12,499; 5 ¼$12,500–14,999; 6 ¼$15,000–17,499; 7 ¼ $17,500–19,999; 8 ¼$20,000–24,999; 9 ¼$25,000–29,999; 10 ¼$30,000–34,999; 11 ¼$35,000–39,999; 12 ¼$40,000–49,999; 13 ¼$50,000–75,000; 14 ¼$75,000 or more. Due to the extent of missing cases for this variable, it is not included in the final analytic models. 263 at FLORIDA STATE UNIV LIBRARY on September 4, 2016 raj.sagepub.com Downloaded from males in the study. Males in each racial and ethnic group report similar levels of homeless and transient populations in their neighborhoods and frequency in shopping, spending evenings away from home, and riding public transportation. While they report the exact same level of homeless/transient populations in their communities, White males and Latinos, on average, spend slightly more time shopping and out in the evenings than do Black males. Black males and Latinos are slightly more likely to ride public transportation than are White males. Also, the percentage of those who experience violent victimization are comparable across race and ethnicity among the males, with the percentage for Latinos (7.1%) being slightly higher than that for White and Black males (6.7 %). The differences, however, are more notable among the sampled males in terms of their age, marital status, income levels, perceptions of neighborhood disorder, and residential stability. The average age among White males is 45.9 years and is higher than the average age among Black males (43.6 years) and Latinos (33.7 years). Likewise, White males report notably higher annual household income levels than did males in the other racial and ethnic categories. Their mean income range between $35,000 and $39,999, while the average income among Black males and Latinos range between $25,000 and $29,999 and $30,000 and $34,999, respectively. Latinos and Whites also fare better in terms of employment over the past year with their rates nearly equal at 74 %compared to 67 %for Black males. The proportion never married is higher among Black males (37.3 %) and Latinos (44 %) than White males (34.7 %). Black males and Latinos were slightly more likely to report disorder in their communities than were White males, while White males, on average, were more likely to have lived at their current address (11.2 years) longer than Black males (9.4 years) and Latinos (6.4 years). There are more marked differences than similarities among the female participants. Considering their commonalities, White and Black females and Latinas report roughly equal levels of time spent shopping with the average for White females (4.2) being minimally higher than Black females and Latinas (3.9 and 4.0, respectively). The mean scores for evenings spent away from home are also similar as they equal 3.6 for White females and Latinas, while the mean is only slightly lower at 3.3 for Black females. Though the difference is small, Black females and Latinas are more likely to report disorder and homeless/transient populations in their neighborhoods than are White females. In all other respects, the sampled females vary considerably. The average age is higher for Black females (47.1 years) than it is for White females (45.9 years) and Latinas (35 years). There is also a noticeable gap between the average annual household income of White females and minority females considered in this study. White females, whose household incomes are between $30,000 and $34,999, make considerably more than Latinas (between $17,500 and $19,999). The income level among Black females is above that of Latinas—ranging between $20,000 and $24,999—but still less than that of White females. These income disparities are likely tied to differences in the women’s work histories. White and Latina females are more likely to be employed (63 %and 62 %, respectively) than are Black females (58 %). Black females and Latinas are more likely to ride public transportation and to have never been married than are White females. White females, on the other hand, are 264 Race and Justice 1(3) at FLORIDA STATE UNIV LIBRARY on September 4, 2016 raj.sagepub.com Downloaded from more likely to have lived at their current address (12.8 years) longer than are Black females (11.9 years) and Latinas (7.7 years). Lastly, the percentage violently victi- mized among Latinas is nearly double that of White females (8%vs. 4.3 %, respec- tively), while the percentage among Black females (5.1 %) lies between that of Latinas and White females. Multivariate Results Logit models are used to explore the relationship between routine activities, neigh- borhood conditions, and risks for violent victimization among non-Latino White and Black males and females and Latinos and Latinas. These analyses were performed using Stata software because of the clustered sample design of the NCVS 12 Cities data. As previously stated, a random sample of households for each city was obtained for the data to consequently cluster across the 12 cities. Stata is able to calculate robust estimates of the standard error based on this sampling strategy (StataCorp, 2005). Fur- ther, logistic regression analyses are especially appropriate when the independent and/ or control variables range in type (i.e., categorical or continuous) yet, the dependent variable is dichotomous (Mertler & Vannatta, 2002). With this analytic technique, it is not necessary to assume the independent or control variables are ‘‘normally distribu- ted, linearly related, or have equal variances within each group’’ (Mertler & Vannatta, 2002, p. 314). 13 Table 3 presents the results of the nonfatal violent victimization regression model for males, while Table 4 presents the results for females. As shown in Table 3, indicators of routine activities did not have a significant impact on White or Black males’ risks for violent victimization after controlling for age and marital status. And only one indicator is related to Latinos’ risks for violent victimization at significant levels—time spent shopping ( b¼ 0.839). Specifically, Latinos who spent more time out shopping are 2.31 times more likely to be victims of violent crime than those who do not. Results show evenings spent away from home, time spent riding public transportation, and employment are not significant predictors of victimization for any of the male groups. Results also show neighborhood conditions affect minority males’ risk for violent victimization dissimilarly from White males. Neighborhood conditions, when mea- sured as residential stability and perceived disorder, are significant predictors of violent victimization for Black males and Latinos. However, residential stability decreases risks for violent victimization for Latinos ( b¼ 0.051), but increases the risk for Black males ( b¼ 0.032). It is important to point out that perceived disorder is the greatest predictor of violent victimization for Black males (b ¼2.15) and Latinos ( b ¼ 1.69); indicating that for each unit increase in perceived disorder, the odds of violent victimization increase by 8.67 times for Black males and by 5.47 times for Latinos. Perceived disorder influences Black males and Latinos risk for victimization in the expected direction—disorder increases the risk for violent victimization among Black males and Latinos. While the relationships are not statistically significant for any group of males, it is interesting to note the presence of homeless/transient persons Like-Haislip and Miofsky 265 at FLORIDA STATE UNIV LIBRARY on September 4, 2016 raj.sagepub.com Downloaded from Table 3.Logistic Regression Models Predicting Risk for Nonfatal Violent Victimization Across Race and Ethnicity (Males) Non-Latino White Non-Latino Black LatinosbSEbSEbSE Demographic characteristics Age 0.019 .014 0.006 .017 0.001 .018 Never married 0.351*** .090 0.033 .429 0.393 .382 Lifestyle/routine activities Worked past year 0.085 .134 0.661 .374 0.465 .315 Time spent shopping 0.177 .167 0.168 .474 0.839*** .164 Evenings spent away from home 0.046 .126 0.162 .347 0.081 .161 Time spent riding public transportation 0.021 .098 0.051 .143 0.146 .094 Neighborhood conditions Residential stability 0.027 .017 0.032** .011 0.051* .028 Disorder 0.597 .411 2.159*** .649 1.699* .783 Homeless/transients 0.342 .267 0.243 .839 0.773 .492 Pseudo R 2 0.063 0.1020.116 *p < .05. ** p< .01. *** p< .001. Table 4. Logistic Regression Models Predicting Risk for Nonfatal Violent Victimization Across Race and Ethnicity (Females) Non-Latina White Non-Latina Black LatinasbSE bSEbSE Demographic characteristics Age 0.033** .017 0.059*** .013 0.065*** .019 Never married 0.223 .249 0.224 .486 0.441 .384 Lifestyle/routine activities Worked past year 0.544 .230 0.004 .150 0.616 .589 Time spent shopping 0.694** .168 0.342** .125 0.307 .342 Evenings spent away from home 0.230 .370 0.145* .091 0.012 .300 Time spent riding public transportation 0.169 .117 0.376* .163 0.226*** .038 Neighborhood conditions Residential stability 0.004 .035 0.0595*** .009 0.012 .032 Disorder 1.153* .608 0.272 .297 0.316 1.054 Homeless/transients 0.0715 .366 0.324 .355 0.539* .308 Pseudo R 2 0.091 0.158 0.088 *p < .05. ** p< .01. *** p< .001. 266 Race and Justice 1(3) at FLORIDA STATE UNIV LIBRARY on September 4, 2016 raj.sagepub.com Downloaded from in the neighborhood have a negative effect on Black and Latino males violent victimization but is positively related to White males violent victimization. And finally, none of the neighborhood conditions have a significant effect on White males’ risk for violent victimization.While observing the results among females, as shown in Table 4, predicting risk for nonfatal violent victimization across race and ethnicity are disparate. Overall, indi- cators of routine activities have a significant impact on Black females’ risks for violent victimization after controlling for age and marital status. However, the find- ings for White females and Latinas vary. Time spent shopping has a significant impact for White and Black females’ risks for violent victimization; however, time spent shopping significantly increases White females’ risk ( b¼ 0.694), while decreasing the risk among Black females ( b¼ 0.342). ‘‘Evenings spent away from home’’ is only a significant predictor of violent victimization for Black females. Specifically, Black females who spend evenings away from home are more likely to be victims of violent crime. Riding public transportation is associated with a significant increase in risk for violent victimization among Black females ( b¼ 0.376) and Latinas ( b¼ 0.226). Minority females who ride public transportation are more likely to experience violent victimization than are minority females who did not use public transportation. And lastly, employment is not significantly related to violent victimization for females of any racial or ethnic background. Neighborhood factors also had a dissimilar impact on females’ risks across race and ethnicity. Specifically, residential stability exerts a significant positive effect on Black females’ risks for violent victimization. While the relationships are not statis- tically significant for White females and Latinas, it is interesting to note that resi- dential stability has a negative effect on their risk for violent victimization but a positive association with Black females’ r isk. Perceived disorder is positively related to White females’ risks for violen t victimization after demographic char- acteristics and routine activ ities are considered. It is important to note that perceived disorder is the greatest predictor of violent victimization for White females ( b¼ 1.153); indicating that for each unit increase in perceived disorder, the odds of violent victimization incre ase by 3.17 times for White fe males. Finally, perception of homeless/transient persons in the neighborhood is only significantly related to Latinas’ risks for violent victimization. In particular, the presence of homeless/ transients in their neighborhood increases Latinas’ risk for victimization and this is the greatest predictor of their risk ( b¼ 0.539). Summary Using the NCVS 12 Cities study, the findings demonstrate there are both withinand across gender differences among these racial and ethnic groups. Considering the former, there are variations in violent victimization between females and between males. In general, measures of routine activities and neighborhood conditions dif- ferentially impact females’ risks for violent victimization. Time spent shopping is significantly related to violent victimization for White and Black females but not Like-Haislip and Miofsky 267 at FLORIDA STATE UNIV LIBRARY on September 4, 2016 raj.sagepub.com Downloaded from Latinas. However, this indicator influences White and Black females’ victimization in opposing directions; the relationship is positive for White females, but negative for Black females. Conversely, riding public transportation increases minority females’ risks for violent victimization but no significant relationship appears for White females. Furthermore, evenings spent away from home increase the likelihood of violent victimization for Black females but not for White females or Latinas. And finally, employment did not significantly affect any of the female groups.The data also indicate that when examining neighborhood factors, incongruent results occur within the female population’s risk for violent victimization. Residential stability increases the likelihood of violent victimization for Black females but not White females or Latinas. In contrast, the presence of homeless/transient populations in the neighborhood significantly increases Latinas’ risks but not White or Black females’ risks. Lastly, perceived disorder only has a significant influence on White females’ risk for victimization. In fact, perceived disorder increases the odds of violent victimization by 3.17 times for White females. There is only one notable difference within the male groups with respect to mea- sures of routine activities. Time spent shopping significantly increases the risk for violent victimization among Latinos but is not statistically significant for White and Black males. Yet, regardless of race/ethnicity, male’s risk for violent victimization is not influenced by employment, evenings spent away from home, or time spent riding public transportation. Overall, the results indicate neighborhood conditions influence minority males’ risk for violent victimization varyingly from White males. Specifically, residential stability is a significant predictor of Black males’ and Latinos victimization, but in contradictory directions; the relationship is positive for Black males, but negative for Latinos. Additionally, perceived disorder has a significant influence on Black males and Latino’s risk for victimization. In fact, perceived disorder increases the odds of violent victimization by 8.67 times for Black males and 5.47 times for Latinos. In contrast, the presence of homeless/transient populations in the neighborhood is not a significant predictor of violent victimization for males regardless of the racial/ethnic group considered. There are also differences in victimization risks across gender. Overall, measures of routine activities are significant predictors of females’ risks for violent victimization but not males’. Specifically, none of the routine activities measures produce similar sig- nificant effects on violent victimization for males and females of the same racial/ethnic background. Neighborhood conditions also present dissimilar results across gender. In fact, only one indicator of neighborhood conditions is a significant predictor of male and females’ risk for violent victimization within the same race/ethnic group; residential stability increases Black males’ and females’ risk for violent victimization. Discussion Recent research on violent victimization has underscored the need for data to examine how the intersections between gender, race, and ethnicity influence an individual’s 268 Race and Justice 1(3) at FLORIDA STATE UNIV LIBRARY on September 4, 2016 raj.sagepub.com Downloaded from experience with violence. It is sometimes difficult to disentangle concepts embedded in the intersections of gender, race, and ethnicity; and while few studies can capture the complexities of these factors, we believe the NCVS 12 Cities data can be used to construct a reliable picture of the disparities of gender, race, and ethnic variations in risk for violent victimization.The goal of this article has been to explore whether risks for violent victimization are common across gender, race, and ethnicity. The findings we discovered may be regarded as the next step in the evolution of intersectional approaches toward a better understanding of well-established routine activities and neighborhood conditions that influence risks for violent victimization among varying gender and racial/ethnic groups. An intersectional approach to studying variations in violent victimization is a natural progression for the extant literature through acknowledging gender, race, and ethnicity as dynamic relationships which simultaneously succeed at both microstruc- tural and macrostructural levels. These findings are imperative because they not only contribute broadly to the study of victimization but specifically to gender and racial/ ethnic variations in victimization. The findings also suggest criminologists must recognize the importance of developing integrated criminological theories—lifestyle and neighborhood conditions that frame inequalities of victimization, gender, and race/ethnicity—as the guide for future research. Our results showed that common criminological risk factors measured by routine activities and neighborhood conditions affect White, Black, and Latin males and females inconsistently. Beginning with routine activities, generally our findings suggested that routine activities were better predictors of females’ victimization risks than they were of males’ risks. These predictions, however, varied across race and ethnicity. Only one indicator of routine activities influenced males’ risks for violent victimization. Latinos who spent more time shopping were more likely to be victims of violent crime. Neither White nor Black males’ victimization risk was significantly impacted by measures of routine activities. A possible explanation for this complex finding is that the structure of daily activities differentially influenced victimization risk across race and ethnicity through the amount of exposure to motivated offenders who are unknown to the victim. Lauritsen and White (2001), for instance, found that Latinos were more likely than White and Black males to be victimized by strangers. It is plausible then that activities such as shopping are more often associated with stranger victimizations, which may be a more common occurrence among Latinos compared to White and Black males. The relationship between routine activities and violent victimization was also disparate throughout the female composition of the current sample. Black females’ risk for violent victimization was significantly related to three of the four measures of routine activities. Black females who frequently spent evenings away from home and frequently rode public transportation were at an increased risk for violent victimization. Latinas who frequently rode public trans- portation were also more likely to be victims of violent crimes. However, this was the only congruent finding among the female populations for the routine activities mea- sures. Time spent shopping was significantly related to White females’ risk for violent victimization, but notice this was contrary to the finding for Black females. White Like-Haislip and Miofsky 269 at FLORIDA STATE UNIV LIBRARY on September 4, 2016 raj.sagepub.com Downloaded from females who frequently shopped were at an increased risk but such risk was reduced for Black females who reported frequent shopping. We believe the differences found among females are the product of lifestyle differences rooted in structural inequalities across race, ethnicity, and class. For example, our finding that time spent riding public transportation significantly increased minority females’ risks for violent victimization but not White females’ risk may be tied to variations in microstructural and macro- structural constraints. Economic and residential inequalities, in particular, are central. Black females and Latinas in this study were more likely than White females to report using public transportation and their reliance on such may be linked to disparities in their reported income levels. Moreover, given the disproportionate concentration of Black females and Latinas in disadvantaged communities (Lauritsen & Rennison, 2006), the areas in which they are utilizing public transportation may be distinct and thus present greater opportunities for exposure to motivated offenders. Over the years, the lifestyle model/routine activities approach has gained significant popularity in explaining crime rates across gender. It may be that the explanatory power of this notion differs across race and ethnicity within the female population. Our results suggested that variations in females’ interactions in public settings provide important insight into racial and ethnic variations in risks for victimization.Furthermore, our findings suggested neighborhood conditions generally produced dissimilar affects on violent victimization across race, ethnicity, and gender. Our analyses indicated that these conditions significantly influenced risks among the minority but not White male population. Specifically, Latino and Black males who reported more disorder in their communities were at an increased risk for violent victimization. Residential stability, however, was differentially related to violent victimization across these groups of males. Black males who lived in their commu- nities longer were more likely to be victims of violent crimes; conversely, residential stability decreased risks for violent victimization for Latinos. None of the neighbor- hood conditions considered was significantly related to White males’ violent victi- mization risk. Moreover, our results proposed no consistent pattern for the relationship between neighborhood conditions and victimization risk among the female popula- tion. The three measures of neighborhood conditions—residential stability, disorder, and homeless/transients—impacted White and Black females and Latinas in dis- concert. Specifically, residential stability significantly increased Black females’ risk for violent victimization but not White females or Latinas. Disorder significantly increased White females’ risk for violent victimization but not Black females or Latinas. And, the presence of homeless/transients in the neighborhood only affected Latinas’ risk for violent victimization. Again, these intriguing findings are likely grounded in the complexities of intersecting inequalities across race, ethnicity, gen- der, and class. Our finding that neighborhood conditions are better predictors of Latino and Black males’ victimization risk is not surprising given the structural conditions often faced by minority communities. Minority, and especially pre- dominantly Black, neighborhoods are often plagued by various forms of extreme disadvantage including notably higher rates of residential instability, poverty, unemployment, and family disruption which can increase the odds of criminal 270 Race and Justice 1(3) at FLORIDA STATE UNIV LIBRARY on September 4, 2016 raj.sagepub.com Downloaded from behavior and victimization in these areas (Anderson, 1999; Bursik & Grasmick, 1993b; Krivo & Peterson, 1996; Peterson et al.,2006; Sampson, 1987; Sampson & Wilson, 1995; Wilson, 1987). Black communities mor e often suffer from these inequalities than do Latino communities (Massey & Denton, 1993; Velez, 2006). This potentially explains why residential stability significan tly increased Blacks’ (males and females) risk for violent victimization as well as the disparate findings across Blacks and Latinos/Latinas. The incongruent findi ngs among females may also be tied to these inequalities in neighborhood structure. It is possible that Black females’ reports of disorder in their communities had no significant bearing on their victimization risks once residential stability was con sidered given their greater exposure to disadvantaged communities that may be characterized by various types of disorder. However, we found that these neighborhood in dicators—perceptions of disorder and homeless/transient populations in th e neighborhood—mattered more to White females’ and Latinas’ risks, respectively. Future research to account for gender and race/ethnicity moderating the relation- ship between routine activities/lifestyle, neighborhood conditions, and victimization risk face considerable obstacles because the findings shown here were complex. Some of the difficulties relate to the limitations of the present study. In particular, the data for the present study derive from only 12 cities; to demonstrate the generalizability of the findings, future studies should be replicated in different settings and with related data. Furthermore, the NCVS 12 Cities study lacked precise measures of suggested theoretical mechanisms that would permit direct testing of various hypotheses. For example, both the routine activities and lifestyle perspective hold that exposure to motivated offenders predict victimization risks. We have only considered a limited range of activities that might be relevant without directly testing other components of the theory such as proximity to motivated offenders as the current dataset does not allow for such an examination. Moreover, a need for more measures of specific routine activities and lifestyle perspective are desired especially since the NCVS 12 Cities data were collected in 1998 and may not be as reflective of contemporary daily activities and lifestyles that place individuals at risk for violent victimization. Lastly, we relied on perceptional measures of neighborhood conditions since the NCVS 12 Cities data were collected at the city level and, therefore, preclude examination of direct measures of neighborhood characteristics (i.e., poverty) that are also associated with victimization risks. Subsequent studies then would benefit from further examination of neighborhood conditions and more specification of lifestyle/routine activity measures. Despite these limitations, our analyses revealed the importance of intersections between gender, race, and ethnicity and their influence on individuals’ experience with violence. By examining routine activities/lifestyle and neighborhood conditions on victimization risks among White, Black, and Latino males and females, we pro- vided evidence that past works have considered these factors exclusively, largely disregarding their interactional and complex effects on risks for nonfatal forms of violent victimization. We anticipate the current results will develop further under- standing of the complexities of the gender, race/ethnicity, and violence relationship by promoting researchers to investigate more intensely the patterns of victimization. Like-Haislip and Miofsky 271 at FLORIDA STATE UNIV LIBRARY on September 4, 2016 raj.sagepub.com Downloaded from Declaration of Conflicting Interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The authors received no financial support for the research, authorship, and/or publication of this article. Notes 1. Massey and Denton (1993) note that Blacks are far more likely to be segregated fromWhites compared to other racial/ethnic groups except Puerto Ricans. They propose that their integration into White communities relative to other Latino groups may be limited simply because of their darker skin tone. 2. See Smith, Steadman, Minton, and Townsend (1998) for a complete description of the NCVS 12 Cities study methodology. 3. See Cantor and Lynch (2000) for a full discussion of the effects of CATI on NCVS data collection. Because of budgetary concerns, the BJS discontinued the use of the CATI in 2007 (see Rand, 2008). 4. This classification of race which takes into account ethnic origin is important because race and ethnicity are not mutually exclusive categories and are not considered as such in any NCVS data including the NCVS 12 Cities data. The NCVS 12 Cities study uses the U.S. Census Bureau’s classification of race and ethnicity. Race is comprised of six categories: ‘‘White,’’ ‘‘Black,’’ ‘‘American Indian,’’ ‘‘Eskimo,’’ ‘‘Asian or Pacific Islander,’’ and ‘‘Other.’’ Ethnicity is comprised of two categories: ‘‘non-Hispanic’’ and ‘‘Hispanic.’’ Consequently, Hispanics or Latinos can be of any racial background. In the NCVS 12 Cities study, nearly 50 %of this group reported their race as ‘‘White,’’ 40 %reported being ‘‘Other,’’ 7 %reported being ‘‘Black,’’ and the remaining 3 %reported their race as ‘‘American Indian,’’ or ‘‘Asian or Pacific Islander’’ (also see Lauritsen, 2003; Lauritsen & White, 2001 for similar results). 5. Observation of other ethnic and racial groups is important but difficult using the NCVS 12 Cities data. About 3 %of the original sample (435 of 13,918) is non-Latino Asian, while the remaining group, non-Latino Others, only constitute about 2 %of the original sample (262 of 13,918). Note that these figures do not reflect possible further reductions in these groups’ sample size due to missing data on key variables (e.g., perceptions of neighborhood disorder). 6. Like the traditional NCVS, the NCVS 12 Cities study includes all household members aged 12 and older. However, the questions related to neighborhood conditions are only asked to those 16 and older. 7. Hereafter, non-Latino White and non-Latino Black are referred to as White and Black, respectively. 8. Hereafter, nonfatal or nonlethal violent victimization is referred to as violent victimization. 9. The results yielded two components (both with an eigenvalue slightly above 1). The third component had an eigenvalue close to 1 (0.82). Therefore, all two-way combinations of these three survey items were considered and the results were similar; the first component had an eigenvalue slightly above 1, while the eigenvalue for the second component neared a value of 1. The second component eigenvalue equaled 0.83 for the ‘‘evening away’’ and 272 Race and Justice 1(3) at FLORIDA STATE UNIV LIBRARY on September 4, 2016 raj.sagepub.com Downloaded from ‘‘shopping’’ combination, 0.99 for the ‘‘shopping’’ and ‘‘public transit’’ combination, and 0.95 for the ‘‘evening away’’ and ‘‘public transit’’ combination. 10. These are dichotomous measures which were coded 0 for no responses and 1 for yes responses. 11. Despite numerous analyses, six of the disorder items did not load well with the others and therefore were omitted. These items were poor lighting, overgrown shrubs/trees, trash, empty lots, vandalism/graffiti, and prostitution. 12. The sample size reduces from 11,512 to 9,771 when income is included in the analyses. Note, however, the findings are largely unchanged when income is considered. The same lifestyle/routine activities and neighborhood variables are significant predictors of victimi- zation for Black males and Latinos with or without the income measure in the analytical models. And these variables are unrelated to White males’ risks. The only caveat is that one indicator of lifestyle/routine activity—‘‘worked past year’’—that is not significantly related to males’ risk when income is not included is indeed a significant predictor of their risk once income is considered. Likewise, there is consistency in the predictors for violent victimization among the sampled females regardless of the inclusion of income in the ana- lytical models, with one exception. For Black females, the number of evenings spent away from home is not a significant predictor of their risk for violent victimization once income is considered. For White females an additional measure of routine activities—‘‘time spent riding public transportation’’—is a significant predictor of their victimization risk once income is considered. The predictors are the same for Latinas across models with and with- out the inclusion of income. 13. Note that there are also critiques of using traditional logit models when both the occurrence of the event of the dependent variable (e.g., being a victim of a nonfatal violent crime) is rare and the sample size is small as this can result in extremely biased estimates of the odds ratio (King & Zeng, 2001a, 2001b). In simulations, King and Zeng (2001a, 2001b) demonstrate that this bias is especially pronounced in studies where the event is very rare (e.g., less than 1 %) and the sample size is small (e.g., n< 1,000). They suggest the use of an ‘‘approximate Bayesian’’ method over the traditional logit model in these cases (King & Zeng, 2001a, p. 709). However, the present study is less subjected to such bias since it utilizes a sizable sample ( n> 10,000), of which nearly 5 %have been victims of a nonfatal violent crime. For example, in their demonstrations where the sample size is 10,000 and the event occurred for approximately 5 %of the sample, the percentage difference in the odds ratio derived from a traditional logit mode l versus their alternative method was less than 5 %. References Anderson, E. (1999). Code of the street: Decency, violence and the moral life of the inner city. New York, NY: W.W. Norton. Bursik, R. J., & Grasmick, H. (1993a). Neighborhoods and crime: The dimensions of effective community control. Lanham, MD: Lexington Books. Bursik, R. J., & Grasmick, H. (1993b). Economic deprivation and neighborhood crime rates, 1960–1980. Law & Society Review ,27 , 263–284. Like-Haislip and Miofsky 273 at FLORIDA STATE UNIV LIBRARY on September 4, 2016 raj.sagepub.com Downloaded from Cantor, D., & Lynch, J. P. (2000). 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S., & Gibbs, C. (2006). Making sense of intersections. In K. Heimer & C. Kruttshcnitt (Eds.), Gender and crime: Patterns in victimization and offending (pp. 269–302). New York, NY: New York University Press. Skogan, W. (1990). Disorder and decline: Crime and the spiral of decay in American neighborhoods. New York, NY: The Free Press. Smith, S. K., Steadman, G. W., Minton, T. D., & Townsend, M. (1998). Criminal victimization and perceptions of community safety in 12 united states cities, 1998. Washington, DC: United States Department of Justice. StataCorp. (2005). Stata statistical software: Release 9.2. College Station, TX: Author. Like-Haislip and Miofsky 275 at FLORIDA STATE UNIV LIBRARY on September 4, 2016 raj.sagepub.com Downloaded from Sullivan, M. L. (1989).Getting paid: Youth crime and work in the inner city. Ithaca, NY: Cornell University Press. Velez, M. (2006). Toward and understanding of the lower homicide rates in Latino versus Black neighborhoods: A look at Chicago. In R. D. Peterson, L. J. Krivo, & J. Hagan (Eds.), The many colors of crime: Inequalities of race, ethnicity, and crime in America (pp. 91–107). New York, NY: NYU Press. Wilson, W. J. (1987). The truly disadvantaged: The inner city, the underclass, and public policy. Chicago, IL: University of Chicago Press. Woodward, L. J., & Fergusson, D. M. (2000). Childhood and adolescent predictors of physical assault: A prospective longitudinal study. Criminology,38 , 233–262. Bios Toya Z. Like-Haislip is an assistant professor of Criminal Justice and Criminology at the University of Missouri, Kansas City. Her current research interests include mul- tilevel assessments of risks for violent victimization, racial and ethnic variations in victimization, and the intersection between gender, race, and class as it relates to victimization risks. Karin Tusinski Miofsky is an assistant professor of Sociology and Criminal Justice at the University of Hartford. Her research is concerned primarily with juvenile delin- quency, particularly with patterns of bullying and victimizations within schools. 276 Race and Justice 1(3) at FLORIDA STATE UNIV LIBRARY on September 4, 2016 raj.sagepub.com Downloaded from
For this assignment, all you have to do is to select a topic, write an introduction, formulate a research question (see examples above) and a hypothesis. Keep it simple! Then, you will also review thr
Social Differentiation in Criminal Victimization: A Test of Routine Activities/Lifestyle Theories Author(syf 7 H U D Q F H ‘ 0 L H W K H 0 D U N & 6 W D I I R U G D Q G – 6 F R W W / R Q g Source: American Sociological Review , Vol. 52, No. 2 (Apr., 1987yf S S 4 Published by: American Sociological Association Stable URL: http://www.jstor.org/stable/2095447 Accessed: 05-09-2016 16:53 UTC JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected]. Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at http://about.jstor.org/terms American Sociological Association, Sage Publications, Inc. are collaborating with JSTOR to digitize, preserve and extend access to American Sociological Review This content downloaded from 146.201.32.11 on Mon, 05 Sep 2016 16:53:16 UTC All use subject to http://about.jstor.org/terms SOCIAL DIFFERENTIATION IN CRIMINAL VICTIMIZATION: A TEST OF ROUTINE ACTIVITIES/LIFESTYLE THEORIES* TERANCE D. MIETHE MARK C. STAFFORD Virginia Polytechnic Institute J. SCOTT LONG and State University Washington State University Recent theories posit that social differentiation in the risks of criminal victimization is due to variation in routine activities/lifestyles which place some persons or their property in proximity to motivated offenders. For a sample of 107,678 residents in thirteen U.S. cities, measures of the nature and quantity of routine activities outside the home (major daytime activity, frequency of nighttime activityyf D U H L Q W U R G X F H G W R D V V H V s the mediational effects of these variables on the demographic correlates of victimization. Routine activities/lifestyle variables have relatively strong direct and mediational effects on individuals’ risks of property victimization but not for violent victimization. These findings are discussed in terms of their implications for further research on the relationship between demographic variables, routine activities! lifestyles, and criminal victimization. Major changes in work and leisure activities, lifestyles, and mobility patterns have occurred in the United States in the last few decades. Since the early 1960s, rates of out-of-home travel, college attendance and labor force participation of women (especially married womenyf D Q G V L Q J O H S H U V R Q K R X V H K R O G V K D Y e increased considerably (Cohen and Felson 1979, p. 598yf 7 K R X J K P R V W R I W K H V H F K D Q J H V K D Y e improved the quality of social life, Cohen and Felson (1979yf D U J X H W K D W W K H G L V S H U V L R Q R f activity away from the household and new manufacturing technologies (e.g., decreases in the size and weight of durable consumer goodsyf correspond to temporal changes in rates of criminal victimization. The basic premise of this “routine activity” approach is that structural changes in activity patterns influence crime rates by affecting the convergence in time and space of three necessary elements for criminal victim- ization: (1yf P R W L Y D W H G R I I H Q G H U V f suitable targets, and (3yf D E V H Q F H R I F D S D E O H J X D U G L D Q V . Although all three elements are important for direct-contact predatory violations, changes in * Direct all correspondence to Terance D. Miethe, Department of Sociology, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061. The data for this study were made available by the Inter-University Consortium for Political and Social Research. The data were originally collected by the National Institute of Justice. Neither the original collectors of the data nor the consortium bear any responsibility for the analyses or interpretations presented here. The authors would like to thank Michael Hughes, Paul Whiteley, and two anonymous ASR reviewers for helpful comments on an earlier draft of this manuscript. We also appreciate the support provided by the Center for Advanced Study in the Behavioral Sciences and by the John D. and Catherine T. MacArthur Foundation. target suitability and guardianship can affect crime rates even if offender motivation remains constant (Cohen and Felson, 1979, p. 589yf . Hindelang, Gottfredson, and Garofalo (1978yf propose a similar theory about the interrelation- ship between activity patterns and criminal victimization. According to their “lifestyle/ex- posure” approach, demographic characteristics (e.g., age, gender, income, marital statusyf D U e associated with various role expectations, which, in turn, lead to differences in lifestyles, exposure to risk, and subsequently to differ- ences in the likelihood of victimization (Hin- delang et al. 1978, p. 243yf + R Z H Y H U V L Q F e both theories presume that differences in routine activities or lifestyles mediate the demographic correlates of victimization, they are treated here as complementary approaches (see also Messner and Tardiff 1985yf . These theories have been used in recent years to explain both the temporal and social distribu- tion of victimization. For instance, Cohen and Felson (1979yf V K R Z W K D W W K H G L V S H U V L R Q R f activities away from the home is positively related to temporal variation in official rates of nonnegligent homicide, forcible rape, aggra- vated assault, robbery, and burglary. Similarly, Hindelang and his associates (Hindelang et al. 1978; Hindelang 1976yf U H S R U W W K D W K L J K H r victimization rates for males, the young, the unmarried, low-income persons, and racial/ ethnic minorities are consistent with the life- style/exposure theory because these groups have higher exposure to the risks of victimization. Routine activities/lifestyle approaches have also been used to explain the social ecology of criminal homicide (Messner and Tardiff 1985yf and differences in individuals’ risks of residen- tial burglary, assault, and personal larceny 184 American Sociological Review, 1987, Vol. 52 (April:184-194yf This content downloaded from 146.201.32.11 on Mon, 05 Sep 2016 16:53:16 UTC All use subject to http://about.jstor.org/terms ROUTINE ACTIVITIES/LIFESTYLE AND VICTIMIZATION 185 victimization (Clarke et al. 1985; Cohen and Cantor 1981, 1980; Cohen, Kluegel, and Land 1981yf . PROBLEMS WITH PREVIOUS TESTS While variation in individual and aggregate rates of victimization is commonly attributed to differences in routine activities/lifestyles, previ- ous applications of these theories are limited in several respects. The major problems with previous tests of the theories involve the operationalization of key concepts, the lack of independent measures of lifestyle characteris- tics, and the failure to address whether variation in routine activities/lifestyles can mediate and explain the level of social differentiation in the likelihood of criminal victimization. Routine activities and lifestyles have been defined as “recurrent and prevalent activities (especially formalized work, provision of food and shelter, and leisure activitiesyf Z K L F K S U R Y L G e for basic population and individual needs” (Cohen and Felson 1979, p. 593yf + H Q F H , routine activities may occur at home or away from home, although the primary activity examined in most previous studies is the amount of time spent outside the home with nonhouse- hold members (see Cohen and Felson 1979yf . Given this definition of routine activities/ lifestyles, individuals who spend more time away from home should have higher risks of victimization because of their greater suitability as a target (i.e., greater visibility and accessibil- ityyf D Q G G H F U H D V H G J X D U G L D Q V K L S + R Z H Y H U a major problem with previous indicators of lifestyle and nonhousehold activities (see Cohen et al. 1981; Cohen and Felson 1979yf L V W K D W V X F h measures fail to consider that many activities outside the home decrease target suitability and increase guardianship for some types of crime. For example, in the case of violent crime against persons, employment outside the home and school activities typically involve a physical environment that reduces a potential victim’s visibility and accessibility and enhances guard- ianship through coworkers and classmates. Similarly, standard measures of nonhousehold activity usually do not consider whether activi- ties outside the home occur during the day or night (cf. Clarke et al. 1985yf < H W I R U E R W h violent and property victimization, nighttime activity outside the home should be far riskier than similar daytime activity because of fewer potential guardians at night (see also, Hindelang et al. 1978yf 7 K H J H Q H U D O S R L Q W L V W K D W P H D V X U L Q g routine activities only in terms of the total amount of time persons spend away from home is problematic unless adjustments are made for persons “exposure to risk” by considering the nature and temporal patterning of these activities (see also Clarke et al. 1985; Stafford and Galle 1984yf . A related problem with previous tests is the lack of independent measures of routine activi- ties/lifestyles. In fact, most tests of the theories have examined only the demographic correlates of victimization and its temporal/spatial loca- tion, presuming that variation in routine activi- ties or lifestyles must be the underlying cause of differential rates of victimization (Messner and Tardiff 1985; Cohen and Cantor 1980; Hin- delang et al. 1978; Hindelang 1976yf + R Z H Y H U , while various demographic characteristics should be associated with target suitability, lack of guardianship, proximity to crime, role expecta- tions and routine activities/lifestyles, an ade- quate test requires the development of indepen- dent measures of lifestyles and nonhousehold activities (e.g., amount of time spent outside the home, frequency of nighttime activity outside the homeyf : L W K R X W V H S D U D W H P H D V X U H V R f lifestyle and nonhousehold activity, it is impos- sible to determine whether some individuals have higher victimization rates because of their lifestyles, their physical proximity to a high crime neighborhood, or some combination of factors. In addition to addressing these issues, the present study also examines several fundamental questions that have been neglected in previous applications. First, to what extent do differences in routine activities/lifestyles mediate the effects of demographic variables on individuals’ risk of criminal victimization? Second, is variation in the risk of victimization due to differences in routine activities/lifestyles uniform across vari- ous demographic groups? Third, do differences in routine activities/lifestyles and demographic differences among individuals provide a satisfac- tory explanation for the likelihood of criminal victimization? Fourth, are routine activity/life- style theories equally applicable to violent and property victimization? HYPOTHESES If social differentiation in the risk of victimiza- tion is explained by differences in routine activities/lifestyles, the introduction of measures of routine activities outside the home should mediate the impact of the demographic corre- lates of victimization. However, there are two distinct kinds of mediational or conditional effects. First, the mediational hypothesis under- lying our interpretation of routine activity/life- style theories would be supported if the direct effects of the various demographic variables are substantially reduced after controls are intro- duced for measures of activities outside the home. Second, the mediational hypothesis would be supported if there are substantial This content downloaded from 146.201.32.11 on Mon, 05 Sep 2016 16:53:16 UTC All use subject to http://about.jstor.org/terms 186 AMERICAN SOCIOLOGICAL REVIEW interactive effects between the demographic variables and measures of routine activities/ lifestyles. Either of these outcomes would suggest that differences in the nature and quantity of nonhousehold activity contribute to the level of social differentiation in individuals’ risk of criminal victimization. Though previously applied to both types of crime, there are several reasons why routine activity/lifestyle variables might exhibit stronger direct and mediational effects on the risk of property victimization than violent victimiza- tion. First, violent crimes against persons are often expressive (i.e., spontaneous, impulsiveyf rather than instrumental acts (e.g., directed toward an economic endyf + H Q F H L I P R W L Y D W H d offenders engage in a conscious selection of suitable targets who lack guardianship, the spontaneous nature of most violent crimes is incongruent with the strictly rational character- ization of human behavior underlying routine activity/lifestyle theories. Second, in contrast to most property offenses, violent crimes involve a direct confrontation between victims and offend- ers (see Luckenbill 1975yf ‘ L I I H U H Q F H V L Q U R X W L Q e activities/lifestyles may predispose some individ- uals to riskier places, but violent victimization is probably more dependent upon the specific interpersonal and situational dynamics in a particular social setting than on simple physical exposure to a risky situation. Given these differences, general measures of routine activi- ties/lifestyle should be more predictive of the differential risk of property victimization than violent victimization. METHODS The data used in this study were collected as part of the 1975 National Crime Survey (NCSyf for thirteen major U.S. cities.2 All individuals in roughly ten thousand households in each city were interviewed about their victimization experiences. The present analysis is restricted to those households (about 50 percent in each cityyf in which an attitude survey was also adminis- tered to each member of the household. The attitude survey elicited information about the characteristics of the household, opinions about crime, and how often respondents went out for entertainment at night and their major activity during the previous week. Excluding respon- dents who had missing information on any of the variables, the data include the victimization experiences, demographic characteristics, and major activities of 107,678 persons in 56,789 households. While more recent NCS data are available, none of the other surveys include measures of the nature and quantity of activities outside the home. Moreover, the selection of the 1975 city subsample is advantageous because supplemental data are available for a compara- ble time period on the amount of time individuals spend away from home during an average week in one of the thirteen cities (Chicagoyf 3 Variables The dependent (endogenousyf Y D U L D E O H V D U e whether or not a respondent was a victim of a violent crime and whether or not a property victimization was reported by the head of the household during 1974, that is, the year preceding the survey. Violent crimes included attempted and completed acts of assault, rob- bery, and personal larceny. Property crimes included attempted and completed burglary, household larceny, and motor vehicle theft.4 Only about 5 percent of the respondents were 1 The tendency for violent crimes to be mainly expressive acts and property crimes to be more instrumental is well documented. For further discussions of the differences between violent and property victim- ization, see Cohen et al. (1981yf + L Q G H O D Q J f, and Repetto (1974yf . 2 The data are actually a combination of two separate NCS samples: (1yf D V D P S O H R I K R X V H K R O G V L Q W K H I L Y e largest U.S. cities and (2yf D V D P S O H R I K R X V H K R O G V L Q H L J K t impact cities. The five largest cities are New York, Philadelphia, Chicago, Detroit, and Los Angeles. The eight impact cities are Newark (N.J.yf 6 W / R X L V , Cleveland, Dallas, Atlanta, Baltimore, Denver, and Portland (Oregonyf 7 R J H W K H U W K H V H W K L U W H H Q F L W L H V F R Y H r all major geographical regions in the United States. 3These supplemental data were originally collected by Walter R. Gove and Omer R. Galle. For an extensive discussion of this sample, see Gove and Hughes (1983yf . 4 Several additional comments about the coding of the dependent variables are necessary. First, separate analy- ses were also performed on each major violent and property crime-i.e., assaults, robberies, and personal larcenies (street theftsyf Y H U V X V U H V L G H Q W L D O O D U F H Q L H s (including burglary and household theftyf D Q G P R W R r vehicle thefts, respectively. While several of the demographic correlates varied somewhat by type of violent or property crime, the crime-specific results were consistent with those based on the aggregate analysis. Specifically, no mediational effects were observed for any of the violent crimes, whereas our activity/lifestyle measures mediated the demographic correlates of each type of property victimization. Although the aggregate models are presented here without loss of generality, the crime-specific analyses are available from Terance D. Miethe upon request. Second, the units of analysis are individuals and heads of households for the analysis of the risks of violent and property victimization, respec- tively. Since most of the property crimes included here are offenses against the household but each member of the household may have reported being victimized, only data on the head of the household are examined in the case of property victimization to reduce the possibility of multiple reporting. This content downloaded from 146.201.32.11 on Mon, 05 Sep 2016 16:53:16 UTC All use subject to http://about.jstor.org/terms ROUTINE ACTIVITIES/LIFESTYLE AND VICTIMIZATION 187 victims of a violent crime, but 23 percent of the households experienced a property victimization (see Table 1yf . The demographic variables include family income, gender, race, marital status, and age of each respondent or the head of the household in the case of property victimization. Each of these variables is dummy coded. Income was parti- tioned near the median family income of the sample (greater or less than $10,000yf 5 D F H Z D s coded to compare whites and blacks, with other racial groups excluded. Marital status was collapsed to compare married and unmarried persons. This dummy variable for marital status is also highly related to household density since few married persons live alone. Consequently, marital status serves as a proxy measure for the availability of capable guardians (see Hindelang et al. 1978yf ) L Q D O O D J H R I W K H Y L F W L P Z D s dichotomized to compare persons greater or less than thirty years old.5 The variables posited to mediate the impact of demographic attributes on the likelihood of victimization are measures of the quantity and nature of routine activities outside the home. Given that victimization occurs disproportion- ately at night (see Clarke et al. 1985; Messner and Tardiff 1985; Hindelang et al. 1978yf W K e frequency of nighttime entertainment (NIGHT ACTIVITYyf Z D V W K H E H V W D Y D L O D E O H P H D V X U H R I a nonhousehold activity that should increase exposure to risk. As shown in Table 1, this variable was dichotomized to compare persons who go out for entertainment at night less or more than once a week. The second routine activity/lifestyle variable (MAJOR ACTIVITYyf compares persons whose major daily activity is performed outside the home (i.e., work, schoolyf with persons whose primary activity occurs within or near the home (i.e., unemployed, retired, homemaker, unable to workyf . While other measures of routine activities/ lifestyles have been used in previous studies (see Cohen et al. 1981; Cohen and Felson 1979yf R X r measures of the nature and quantity of activity outside the home are justifiable on several grounds. First, both work and school activity (in contrast to other activity statusesyf D U H S D W W H U Q H d and recurrent activities. Persons leave for and return from work and school at approximately the same time every weekday, making these activities quite predictable from the perspective of motivated offenders. Second, if exposure to risk is determined by the amount of time spent outside the home, our measure of the type of major daily activity is a strong predictor of the quantity of nonhousehold activity. Specifically, analysis of the supplemental data from a sample of Chicago residents (N= 1,680yf V K R Z H G W K D W 3 percent of the variation in the total amount of time persons spend outside the home in an average week was explained solely by our dummy variable, which compares employed persons and students with all other daily activity statuses. These findings suggest that our mea- sure of daily activity status is a reasonable indicator of the relative exposure to risk due to the amount of nonhousehold activity. Third, since only 16 percent of the variation in major activity status in the NCS sample was explained by demographic characteristics, our measure of major daily activity is also fairly distinct from the individual’s demographic profile. Finally, given that nighttime activity is generally consid- ered riskier than comparable daytime activity, our measure of the frequency of nighttime activity taps another dimension of relative exposure to risk and is only moderately correlated with major activity status (r=.251yf . Thus, although a definitive test of routine activities/lifestyle theories may require detailed data on the precise nature of time utilization (data that are not presently availableyf R X r measures of routine activities/lifestyles nonethe- less reflect the quantity of activity outside the home, the nature of that activity, and its relative risk. ANALYSIS AND RESULTS Given dichotomous dependent variables, a series of logit models was estimated to predict the likelihood of violent and property victimiza- tion. Specifically, three hierarchical loglinear models were estimated for each type of victimization in order to assess the mediational effects of activity/lifestyle variables: (1yf D P R G H l including only the direct effects of the demo- graphic variables (Mlyf f a model in which the direct effects of both routine activity/lifestyle variables are added (M2yf D Q G f an extension of model 2 that includes all two-way interactions among each activity/lifestyle variable and each 5 Alternative coding schemes were also examined for several of the demographic variables. For example, three categories of age (<30, 30-64, 65 and olderyf D Q G I D P L O y income (<$10,000, $10,000-$25,000, >$25,000yf Z H U e used to asseses the degree of nonlinearity and problems with dichotomizing these variables. Consistent with other research (e.g., Cohen et al. 1981; Cohen and Cantor 1981; 1980yf W K H U H Z D V D W H Q G H Q F I R U W K H R O G H U D J H J U R X S D Q d the highest income group to have slightly lower risks of violent victimization than the respective middle catego- ries, whereas the highest-income group had slightly greater risks of property victimization than the middle-income group. Thus, while collapsing the medium and high cat- egories slightly attenuates the direct effects of these vari- ables, the magnitude of bias and its impact on the overall results are minimal. The use of the dichotomous coding of these variables preserved a sufficient number of cases in each cell and, consequently, minimized problems with small (i.e., zeroyf F H O O I U H T X H Q F L H V . This content downloaded from 146.201.32.11 on Mon, 05 Sep 2016 16:53:16 UTC All use subject to http://about.jstor.org/terms 188 AMERICAN SOCIOLOGICAL REVIEW Table 1. Variables, Coding, and Summary Measures Proportion Variable Code VVICT PVICT Endogenous variables Victimization 0 no .947 .770 1 yes .053 .230 Exogenous variables Family income of victim 0 >$10,000 .504 .430 (INCOMEyf 0 Gender of victim 0 female .556 .353 (GENDERyf P D O H 7 Race of victim 0 white .684 .686 (RACEyf E O D F N 4 Marital status of victim 0 married .541 .510 (MARITAL STATUSyf X Q P D U U L H G 0 Age of victim 0 >30 years .642 .762 (AGEyf H D U V 8 Frequency of nighttime activity 0 <1 night/week .605 .638 (NIGHT ACTIVITYyf Q L J K W Z H H N 2 Major daily activity 0 other .404 .356 (MAJOR ACTIVITYyf H P S O R H G V F K R R O 4 VVICT denotes violent victimization and PVICT property victimization. The sample proportions reported in the table are based on 107,678 individuals and 56,789 heads of households for the analysis of violent and property victimization, respectively. bThe category unmarried includes persons who are single, separated, divorced, or widowed. c Other major daily activities include homemaking, unemployed, unable to work, and retired. demographic variable (M3yf 7 K H S D U D P H W H r estimates and the likelihood ratio chi-square test of the fit of the models (LRX2yf I R U H D F K W S H R f victimization are presented in Tables 2 and 3. Risk of Violent Victimization Consistent with other studies on the correlates of violent victimization (e.g., Cohen et al. 1981; Cohen and Felson 1979; Hindelang et al. 1978; Hindelang 1976yf 0 R G H O U H Y H D O V W K D W W K H R G G s of violent victimization are higher among males, low-income persons, the unmarried, and the young (Table 2yf 7 K H U H Z D V Q R V L J Q L I L F D Q W G L U H F t effect of race on the odds of violent victimiza- tion. However, this model, which contains only the main effects of demographic variables, does not adequately represent the observed data. Model 2, which includes routine activity/life- style variables, improved the fit over Model 1 (LRX2MI-M2=28.98, d.f.=2, p < .001yf E X t did not change the effects of the demographic variables. In fact, the odds of violent victimiza- tion for each social group remained quite stable across each model. However, persons who spend relatively more time engaged in nighttime activity, ceteris paribus, have higher odds of violent victimization than less active persons, but there was no significant difference based on the type of major daily activity. Several significant interactions were observed when all two-way interactions among each demographic and routine activity/lifestyle vari- able were included in the model of violent victimization (Model 3yf ) L U V W W K H H I I H F W R f nighttime activity was greater among whites than blacks and greater among males than females. Second, differential risk by the type of major daily activities was greater among blacks, the unmarried, and young persons than among their counterparts. However, a comparison of Models 2 and 3 reveals that the inclusion of the joint effects of demographic and lifestyle variables did not significantly improve the overall fit of the model (LRX2M3-M2 = 9.00, d.f. = 10, p > .10yf 7 K X V E R W K L Q W H U P V R f reducing the direct effect of the demographic variables and the lack of strong interactive effects, there is little support for the mediational role of routine activity/lifestyle variables on the 6 The general practice in logliner analysis of searching for the best fitting model (through successively testing modifications of the saturated model by imposing one or more constraintsyf Z D V Q R W I R O O R Z H G K H U H E H F D X V H R I W K e nature of the research questions. Specifically, our primary concern was to evaluate the extent to which measures of activities/lifestyles mediate the demographic correlates of victimization. This research question requires only an estimation of three models which successively introduce controls for the direct effects of activity/lifestyle variables and two-way interactions among the activity and demographic variables. Nonethe- less, alternative models including higher-order interaction terms were also investigated. However, while this search revealed several significant higher-order interactions, the best fitting and most parsimonious model is the final model (M3yf U H S R U W H G L Q 7 D E O H V D Q G 7 K H J H Q H U D l inability to find a model that statistically fits the observed data is primarily due to the large sample sizes in this study. Under identical relative frequencies, a significant fit would be observed in the final models of violent victimization and property victimization if the sample was reduced to ten thousand observations. This content downloaded from 146.201.32.11 on Mon, 05 Sep 2016 16:53:16 UTC All use subject to http://about.jstor.org/terms ROUTINE ACTIVITIES/LIFESTYLE AND VICTIMIZATION 189 Table 2. Effects of Demographic and Activity/Lifestyle Variables on the Odds of Violent Victimization MI: Demographic M2: Activity M3: Interaction Effects Effects Added Effects Added Independent Variable Effecta (Ain x2yf ( I I H F W $ L Q [ f Effect (Ain x2yf RACE .94 NSb .97 NS _C GENDER 2.17*** 2.14*** – INCOME 1.19*** 1.22*** – MARITAL STATUS 1.57*** 1.51*** – AGE 2.32*** 2.21*** – NIGHT ACTIVITY 1.26*** – MAJOR ACTIVITY .95 NS – Interactions with NIGHT ACTIVITY RACE 95*** GENDER 1.04*** INCOME 1.01 NS MARITAL STATUS 1.02 NS AGE 1.02 NS Interactions with MAJOR ACTIVITY RACE .95** GENDER .99 NS INCOME .99 NS MARITAL STATUS .94*** AGE 94*** LRX2 356.87 298.92 208.89 d.f. 122 120 110 Prob 0.00 0.00 0.00 a Effects are from dependent variable model estimated with ANOVA-like constraints. The coefficients given for Models 1 and 2 can be interpreted as how that characteristic (as opposed to not having that characteristicyf F K D Q J H V W K H R G G V R I Y L F W L P L ] D W L R Q ) R U H [ D P S O H X Q G H U 0 R G H O E H L Q J E O D F N D s opposed to white changes the odds of victimization by a factor of .94. The interaction effects in Model 3 can be interpreted similarly. For example, having high nighttime activity (as opposed to low nighttime activityyf G H F U H D V H V W K H H I I H F W R I E H L Q J E O D F N D V R S S R V H G W R Z K L W H E D I D F W R U R I L H , the value of NIGHT ACTIVITY with RACEyf . b”NS” means that change in x2 value is nonsignificant. c The parameter estimates and significance tests for the “main effects” are excluded in Model 3 because these effects are nonestimable functions (see Long 1984yf . *** = p<.OOl; ** = p<.Ol; * = p<.O5. demographic correlates of violent victimization. Moreover, the direct effects of the activity/life- style variables are also relatively small in comparison to the demographic attributes (see Model 2yf . Risk of Property Victimization Consistent with previous research on the corre- lates of property victimization (e.g., Cohen et al. 1981; Cohen and Cantor 1981; Hindelang 1976yf 0 R G H O U H Y H D O V W K D W W K H R G G V R I S U R S H U W y victimization are higher, ceteris paribus, in households headed by persons who are male, black, unmarried, young, and have relatively high income (Table 3yf : K L O H D O O G H P R J U D S K L c variables had significant effects on the risk of property victimization, this model fits poorly with the observed data. Inclusion of measures of routine activities/ lifestyle in the estimated model yielded several results consistent with expectations (see Model 2yf ) L U V W E R W K D F W L Y L W Y D U L D E O H V K D G V L J Q L I L F D Q t effects on the odds of property victimization, with those who had higher nighttime activity and those whose major activity was performed outside the home having relatively greater risks. Second, the overall fit of the model of property victimization was substantially improved when the activity variables were included (LRX2M2- M1= 169.9, d.f.=2, p < .0001yf 7 K L U G W K e net effects of several demographic variables were reduced in magnitude and significance when the routine activity/lifestyle variables were included. For example, after controlling for the higher rates of nighttime and nonhousehold major activity among males, gender differences in the likelihood of property victimization are eliminated. Similarly, income and age differ- ences dissipate somewhat once controls are introduced for higher rates of nighttime and nonhousehold activity among younger persons and lower rates of participation in these activities among lower-income persons. While not significantly improving the fit over Model 2, Model 3 shows several significant interactions among the demographic and activ- ity/lifestyle variables. Specifically, differential This content downloaded from 146.201.32.11 on Mon, 05 Sep 2016 16:53:16 UTC All use subject to http://about.jstor.org/terms 190 AMERICAN SOCIOLOGICAL REVIEW Table 3. Effects of Demographic and Activity/Lifestyle Variables on the Odds of Property Victimization MI: Demographic M2: Activity M3: Interaction Effects Effects Added Effects Added Independent Variable Effecta (Ain x2yf ( I I H F W $ L Q [ f Effect (Ain x2yf RACE 1.08***b 1.12*** _C GENDER 1.12*** 1.03 NS – INCOME .71*** .83*** – MARITAL STATUS 1.64*** 1.56*** – AGE 2.11*** 1.87*** – NIGHT ACTIVITY 1.29*** – MAJOR ACTIVITY 1.37*** – Interactions with NIGHT ACTIVITY RACE .96** GENDER 1.01 NS INCOME 1.02 NS MARITAL STATUS 1.02 NS AGE .99 NS Interactions with MAJOR ACTIVITY RACE .99 NS GENDER .99 NS INCOME 1.00 NS MARITAL STATUS 1.08*** AGE .89*** LRX2 704.80 365.03 227.25 d.f. 122 120 110 Prob 0.00 0.00 0.00 Note: See notes to Table 2. risk of property victimization on the basis of the frequency of nighttime entertainment was greater among whites than blacks, whereas the effect of the location of daily activities was greater among the unmarried and older persons than among married and younger persons, respec- tively. Thus, coupled with the results of Model 2, differences in routine activities/lifestyle have notable direct effects and condition the impact of several demographic variables on the likeli- hood of property victimization. Activity-Specific Risk of Victimization To further examine the conditional effects of routine activity/lifestyle differences, activity- specific odds of victimization were computed for each demographic group. These activity- specific odds were computed from the table of expected values under Model 3 (our best-fitting modelyf 6 S H F L I L F D O O I R U H D F K L Q G H S H Q G H Q t variable, the table of expected values was collapsed over all other variables except the activity variables and the demographic variable in question. For example, the activity-specific odds of victimization for each racial group were found by collapsing the expected frequencies over all other demographic variables except race and computing the odds of victimization within each activity configuration (i.e., all combina- tions of MAJOR ACTIVITY and NIGHT ACTIVITYyf $ V L P L O D U S U R F H G X U H Z D V X V H G W o compute the odds of victimization for all other demographic groups. These predicted activity- specific odds for each type of victimization are presented in Table 4.7 As shown in the top panel of Table 4, there are several trends when activity-specific odds of violent victimization are compared within and 7 These activity-specific odds are averaged over the independent variables that have been collapsed. For example, the odds of victimization are higher for black males than black females, but these within-race differ- ences are lost when the odds of victimization for blacks are reported. Though collapsing over cells results in a loss of information, these average odds are used in Table 4 to simplify the discussion. However, the activity- specific odds of victimization were also examined separately for each of the thirty-two combinations of demographic variables (25yf : K L O H W K H D F W L Y L W V S H F L I L c odds differed in magnitude for each demographic configuration, the relative patterning of the results (i.e., in terms of identifying the activity configurations with the greatest and least riskyf Z H U H O D U J H O F R Q V L V W H Q W Z L W K W K R V e presented here. The expected odds of victimization by activity status for each of the thirty-two demographic configurations are available from Terance D. Miethe upon request. This content downloaded from 146.201.32.11 on Mon, 05 Sep 2016 16:53:16 UTC All use subject to http://about.jstor.org/terms ROUTINE ACTIVITIES/LIFESTYLE AND VICTIMIZATION 191 Table 4. Predicted Odds of Violent and Property Victimization for Each Demographic Group by Activity Status IN/LOWa IN/HIGH OUT/LOW OUT/HIGH Demographic Group Odds (Ratioyf E 2 G G V 5 D W L R f Odds (Ratioyf 2 G G V 5 D W L R f Violent victimization BLACK .050 .099 .048 .075 (1.52yf f (1.00yf f WHITE .033 .061 .048 .083 MALE .058 .125 .059 .104 (1.81yf f (1.69yf f FEMALE .032 .042 .035 .051 LOW INCOME .043 .087 .054 .100 (1.48yf f (1.26yf f HIGH INCOME .029 .054 .043 .071 UNMARRIED .053 .113 .058 .100 (1.89yf f (1.41yf f MARRIED .028 .035 .041 .056 YOUNG .077 .123 .074 .108 (2.57yf f (2.06yf f OLD .030 .034 .036 .046 Property victimization BLACK .249 .397 .330 .444 (1.37yf f (1.20yf f WHITE .182 .305 .274 .446 MALE .187 .315 .267 .410 (.84yf f (.72yf f FEMALE .223 .354 .370 .553 LOW INCOME .205 .335 .303 .494 (.92yf f (1.06yf f HIGH INCOME .224 .321 .287 .416 UNMARRIED .222 .369 .385 .583 (1.23yf f (1.57yf f MARRIED .180 .270 .246 .321 YOUNG .481 .607 .436 .604 (2.78yf f (1.70yf f OLD .173 .230 .257 .344 IN and OUT refer to major daily activities that are performed in or near the home and outside the home, respectively. The coding for this variable is identical to MAJOR ACTIVITY (see Table 1yf / 2 : D Q G + , * + U H I H U W R W K H I U H T X H Q F R I Q L J K W W L P H D F W L Y L W D Q G W K H F R G L Q J I R U W K H V H F D W H J R U L H V L s identical to the variable NIGHT ACTIVITY. bThe odds and the ratio of the odds were derived from the expected cell frequencies under Model 3 for both violent and property victimization. Odds refers to the odds of victimization for each group (see text for discussion of how these odds were computedyf 5 D W L R U H I H U V W R W K H U D W L R R I W K e odds of victimization for each demographic pair. For example, the value of 1.52 in the first column is found by taking the average odds of victimization for blacks in the IN/LOW category (.050yf D Q G G L Y L G L Q J W K L V E W K H D Y H U D J H R G G V I R U Z K L W H V L Q W K L V D F W L Y L W F D W H J R U f. across categories of demographic variables. First, for nearly all demographic groups (except blacks and younger personsyf S H U V R Q V Z K R K D d relatively low nighttime activity and whose major activity occurred within the home had the lowest risk of violent victimization. However, among the highest-risk category, there was far more variation due to the type of daily activity across demographic groups. Specifically, in combination with high nighttime activity, major daily activity in or near the home is associated with the greatest risk of violent victimization among blacks, males, the unmarried, and the young, whereas daily activity outside the home combined with high nighttime activity has the greatest risk for other groups. Second, the odds of violent victimization are relatively constant across activity statuses for females and both age groups but vary considerably by activity catego- ries for the other demographic groups. Finally, for all demographic pairs (e.g., blacks versus whites, males versus femalesyf V R F L D O G L I I H U H Q W L – ation in the risk of victimization is greatest among persons whose major daytime activity occurs near the home and who have relatively high nighttime activity. For instance, within this This content downloaded from 146.201.32.11 on Mon, 05 Sep 2016 16:53:16 UTC All use subject to http://about.jstor.org/terms 192 AMERICAN SOCIOLOGICAL REVIEW activity configuration, the predicted odds of violent victimization are at least three times higher among males, the unmarried, and the young than among their counterparts. There are also several trends in the activity- specific odds of property victimization for each demographic group. (Table 4yf ) L U V W Z L W K R Q e exception (young personsyf S H U V R Q V Z L W K O R w daytime and nighttime activity outside the home have the lowest risk of property victimization, whereas persons whose daytime and nighttime activity places them outside the home are predicted to have the greatest risks. Second, there is less variation in the expected likelihood of property victimization by activity status in households headed by persons who are either black, married, young, older, or have higher income than in other social groups. Third, the activity configurations that exhibit the most social differentiation in the risk of property victimization vary considerably across demo- graphic groups. For example, blacks have greater risks than whites within most activity categories, but there are no racial differences among persons with high levels of both daytime and nighttime activity outside the home. In contrast, households headed by women or unmarried persons have higher risks than their counterparts across activity categories, but differences by gender and marital status are most pronounced in households in which the head’s major daily activity occurs outside the home regardless of the frequency of nighttime activity. DISCUSSION Social differentiation in the likelihood of victimization is commonly attributed to differ- ences in routine activities/lifestyles that place suitable targets who lack guardianship in proximity to motivated offenders. In this study, routine activities/lifestyles are conceptualized as daytime and nighttime activities which contrib- ute to greater exposure to the risk of victimiza- tion. A person’s exposure to risk is assumed to be a function of the nature and quantity of activities outside the home. Consequently, persons who are employed or in school should have greater exposure to risk because these activities involve significantly more time outside the home, more physical exposure to other persons, and more patterned and predictable behavior than other daily activities. Given that nighttime activity is usually considered riskier than comparable daytime activity (see Messner and Tardiff 1985; Hindelang et al. 1978yf W K e frequency of nighttime activity outside the home is considered a lifestyle characteristic that is also associated with greater exposure to risk. Our results are consistent with predictions about the direct and mediational effects of routine activi- ty/lifestyle variables for the risk of property victimization but not for violent victimization. There are several possible explanations for the poorer performance of routine activity/lifestyle variables in explaining violent victimization. First, given that acts of interpersonal violence commonly occur near the home and are committed by persons who are related or other primary group members (e.g., friends, cowork- ersyf R X U D J J U H J D W H P H D V X U H R I Y L R O H Q W Y L F W L P L ] D – tion may have suppressed the impact of activity/lifestyle variables. However, separate analyses for persons victimized by strangers and nonstrangers, as well as the location of victimization (near home or away from homeyf , yielded results similar to those presented above. Alternatively, problems with the validity of our measures of activities/lifestyles can also be dismissed because these measures exhibited the expected direct and mediational effects on the likelihood of property victimization. A more reasonable interpretation of the results has to do with the nature of most violent victimizations. Specifically, as mentioned ear- lier, many violent crimes are expressive acts (spontaneous, impulsiveyf Z K L F K G H I W K H U D – tional characterization of criminal motivation underlying routine activity/lifestyle approaches. Similarly, violent crimes are relatively infre- quent acts and involve a direct confrontation between victims and offenders. It is this spontaneous and situational nature of violent crimes that may account for the poor perfor- mance of routine activity/lifestyle theories. If so, it is also unlikely that more refined measures of routine activities and situational data on the context of victimization would improve our ability to explain this type of victimization. While more predictive of property than violent victimization, current versions of routine activity/lifestyle theories are also incapable of explaining why the risk of victimization is stable across activity categories for some social groups but varied for other (Table 4yf ) R U L Q V W D Q F H Z K y are the predicted odds of violent victimization fairly uniform across activity statuses for females, younger and older persons, but vary considerbly across levels of activity outside the home for whites, low-income persons, and high-income persons? Similarly, how would a routine activity/lifestyle approach explain the relative stability of property victimization across activity statuses for households in which the head is black, high-income, married, young or old, whereas there is notable variation across levels of activity outside the home among other groups of persons? As these findings suggest, routine activity/lifestyle approaches may only be appropriate for explaining the likelihood of property victimization, and, even for these This content downloaded from 146.201.32.11 on Mon, 05 Sep 2016 16:53:16 UTC All use subject to http://about.jstor.org/terms ROUTINE ACTIVITIES/LIFESTYLE AND VICTIMIZATION 193 crimes, differences in routine activities/lifestyles that affect target suitability, guardianship, and exposure to motivated offenders may not be able to account for the relative risk of victimization for all subsets of persons. CONCLUSIONS Several changes in the opportunity structure and activity patterns in the United States have taken place in recent years. As noted by Cohen and Felson (1979, p. 598yf W K H U H K D Y H E H H Q P D M R r increases in out-of-home travel, single-person households, college attendance and labor force participation among women (especially married womenyf D Q G W K H S H U F H Q W R I K R X V H K R O G V O H I t unattended during the day. The market for durable consumer goods (e.g., televisions, stereos/radios, tape recordersyf K D V L Q F U H D V H d because of changes in the age structure and the rise in single-person households. The portability of these products (through decreasing size and weightyf K D V D O V R L Q F U H D V H G V L Q F H W K H H D U O s (Cohen and Felson 1979, p. 599yf $ E D V L c premise underlying routine activities/lifestyle theories is that these changes in activity patterns increase target suitability and decrease guardian- ship, and therefore affect the social and temporal distribution of victimization. How- ever, four major issues surrounding routine activities/lifestyle theories of victimization war- rant further investigation. First, greater theoretical attention needs to be devoted to the relative weight and importance of the three major components of routine activity/ lifestyle theories (target suitability, capable guardianship, motivated offendersyf $ V R X r results indicate, persons who may be more suitable as targets and generally lack guardian- ship are not necessarily those who are more likely to be victimized by property or violent crimes. For instance, based on target suitability alone, some demographic groups should be at high risk because they are more physically visible and accessible due to greater activity outside the home (males, unmarried or young personsyf R U D U H P R U H Y D O X D E O H D V W D U J H W V H J , older and high-income persons who have more valuable propertyyf < H W V R P H R I W K H V H J U R X S s (e.g. males, young personsyf Z R X O G E H O H V s suitable targets even if they were more visible and frequented riskier places because of their presumed greater physical ability to resist an attack and serve as their own guardians. If capable guardianship is the major determi- nant of victimization, persons who are currently unmarried should be especially vulnerable. Older persons and those with higher income should also be at greater risk because of the inability to serve as their own guardian (due either to less physical strength or greater activity outside the home, which should make their household property more vulnerableyf , Q F R Q – trast, if the principle of homogamy (Cohen et al. 1981yf D Q G H [ S R V X U H W R P R W L Y D W H G R I I H Q G H U V D U e the more important predictors of victimization, persons who are young, male, lower-income, or racial/ethnic minorities should be more prone to violent and property victimization. Yet, if these factors are the major determinants of victimiza- tion, how does one explain why the risk of victimization for some groups (e.g., young personsyf L V U H O D W L Y H O V W D E O H D F U R V V D F W L Y L W y categories, whereas for others who are in residential proximity to motivated offenders (e.g., low-income personsyf W K H U H L V J U H D W H r variability in risk across activity categories? As illustrated by these predictions and our results, an a priori determination of high-risk social groups requires a more systematic treatment of the interrelationships among the necessary conditions of victimization, as well as an assessment of the relative importance of each component. In their present form, routine activities/lifestyle theories are basically unfalsifi- able since the social distribution of victimization can easily be construed as consistent with at least one component of the theories. Second, as the proximate cause of victimiza- tion in both lifestyle and routine activity theories, the notion of “exposure to risk” requires further exploration. Cohen et al. (1981, p. 507yf G H I L Q H H [ S R V X U H D V W K H S K V L F D l visibility and accessibility of persons or objects to potential offenders.” However, several earlier studies treat exposure to risk as a two-step process. Specifically, Cavan and Ranck (1938yf note the difference between “predisposing” (i.e., structuralyf D Q G S U H F L S L W D W L Q J L H V L W X D – tionalyf I D F W R U V 7 K H Q R W L R Q R I D O H D W R U U L V N ” (Strodtbeck and Short 1964yf D O V R L P S O L H V a similar relationship between structural features and situational aspects of human behavior. When applied to theories of victimization, physical visibility, accessibility, and residential proximity to motivated offenders would be considered predisposing factors, whereas the absence of guardianship, target suitability, and the various situational dynamics involving victim and offender transactions would be considered precipitating factors. The advantage of this alternative conception of exposure to risk is that it may explain why some persons who are in close residential proximity to motivated offenders and who spend relatively more time outside the household do not have a greater likelihood of victimization. Third, although offender motivation is largely neglected or assumed to be constant, routine activities/lifestyle theories adopt a rational conception of criminal behavior (e.g., Clarke et al. 1985; Cohen et al. 1981; Cohen et al. 1980yf . This content downloaded from 146.201.32.11 on Mon, 05 Sep 2016 16:53:16 UTC All use subject to http://about.jstor.org/terms 194 AMERICAN SOCIOLOGICAL REVIEW Specifically, criminal victimization occurs when motivated offenders in close proximity to potential victims make a rational selection of suitable targets who lack guardianship. How- ever, while this image of criminal behavior may be consistent with the etiology of instrumental crimes (e.g., property offensesyf W K H H [ S U H V V L Y e and spontaneous nature of many violent crimes is largely inconsistent with the “rational behav- ior” postulate underlying routine activity/life- style theories. In fact, the increase in rates of interpersonal violence among strangers since the mid-1960s (see USDJ 1984yf D Q G W K H V X E V H T X H Q t increases in “random” violence suggest that the fundamental premise underlying these theories of victimization may be less applicable over time. As our models of violent victimization imply, alternative theories of criminal victimiza- tion (e.g., structural strain, status threats, frustration/aggression approachesyf P D E H P R U e appropriate than routine activities/lifestyle theo- ries in explaining the extent and social distribu- tion of violent victimization (see also Luckenbill 1975; Strodtbeck and Short 1964yf . Finally, our findings on the direct and mediational effects of routine activity/lifestyle variables on the likelihood of property victim- ization suggest a parallel between aggregate and individual rates of this type of victimization. Coupled with studies of temporal changes in rates of property victimization (Cohen et al. 1981; 1980; Cohen 1981; Cohen and Felson 1979yf G L I I H U H Q F H V L Q U R X W L Q H D F W L Y L W L H V O L I H V W O H s are also associated with greater risks of property victimization at the individual level of analysis. However, it is unclear whether aggregate changes in routine activities over time translate into comparable changes in individuals’ risk of victimization. Consequently, whether social trends that improve the quality of life for some groups (e.g., increased educational attainment and labor force participation among womenyf also produce a greater risk of victimization among these individuals remains a question for further research. REFERENCES Cavan, Ruth S. and K.H. Ranck. 1938. The Family and the Depression: A Study of One Hundred Chicago Families. Chicago: University of Chicago Press. 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