American crime drops: Investigating the breaks,dips and drops in temporal homicide

ObjectivesWhile a great deal of attention has been given to the 1990s crime drop, less is known about the more recent decline in homicide rates that occurred in several large U.S. cities. This paper aims to explore whether these represent two distinct drops via statistical evidence of structural breaks in longitudinal homicide trends and explore potentially differing explanations for the two declines. Methods: Using homicide data on a large sample of U.S. cities from 1990 to 2011, we test for structural breaks in temporal homicide rates. Combining census data and a time series approach, we also examine the role structural features, demographic shifts, and crime control strategies played in the changes in homicide rates over time. Results: Statistical evidence demonstrates two structural breaks in homicide trends, with one trend reflecting the 1990s crime drop (1994–2002) and another trend capturing a second decline (2007–2011). Time series analysis confirms previous research findings about the contributions of structural conditions (e.g., disadvantage) and crime control strategies (e.g., police force size) to the crime drop of the 1990s, but these factors cannot account for the more recent drop with the exception of police presence. Conclusions: Although both structural conditions and crime control strategies are critical to the longitudinal trends in homicide rates over the entire span from 1990 to 2011, different factors account for these two distinct temporal trends.     

Variations in age-specific homicide death rates: a cohort explanation for charges in the age distribution of homicide deaths

An age-period-cohort characteristic model previously used to explain age-period-specific rates of homicide arrests for those 15 to 49 from 1960 to 1995 is applied to measures of age-period-specific homicide deaths. The extension of this model to the examination of homicide victimization is significant because we are able to test the utility of the model across a longer time span (1930 to 1995) and a wider range of ages (10 to 79) and disaggregated by sex and race (Whites and non-Whites). Although the results indicate that past and recent shifts in age-period-specific rates of homicide deaths are associated with specific characteristics of cohorts, there are some important differences across race and sex groupings in the effects of these characteristics. The effects of the cohort variables examined in our model are independent of age and period, often substantively large, and last throughout the life course. The results are consistent with Durkheimian explanations of lethal violence, hypotheses from victimization theory, and basic tenets of cohort theory.     

Conflicting views on elder care responsibility in Japan

I examine the attitudinal ambivalence created by conflicting social expectations regarding parent-child devotion, filial obligation and family membership, and gender norms in a national population of Japanese adults. I ask: in a context of rapidly changing family and elder care norms, how do different beliefs and attitudes overlap and conflict and how are they related to elder care preferences? I analyze data from the 2006 Japanese General Social Survey and use Latent Class Analysis to identify latent groups in the population defined by their beliefs and examine the relationship between class membership and elder care preferences. I found variation in the population with respect to the measured beliefs as well as a relationship between patterns of beliefs and choice of elder caregiver. I found conflicting expectations regarding elder care responsibility in one latent class and this class also expressed elder care preferences that conflict with at least some of their strongly held beliefs.     

Social structure and homicide in post-Soviet Russia

The Russian homicide rate is among the highest in the world. Local rates are not uniform, however, but instead show tremendous variation throughout this large nation. As a result of varying levels of development, differential access to natural resources, past Soviet economic and social policies, and the varying pace of the social, political, and economic transition, there are also large differences across Russia in structural factors thought to be related to interpersonal violence. In this study, newly available vital statistics and socioeconomic data were employed to estimate the effects of social structural characteristics on homicide rates in the Russian regions (n = 78). Results revealed that commonly tested covariates such as poverty and single-parent households were positively associated with regional homicide victimization rates, as was a proxy for heavy drinking. Further, homicide rates in the Northern Caucasus and the regions east of the Ural mountains are significantly lower and higher, respectively, than in the rest of the country. These results are discussed in terms of the current Russian situation and integrated into the broader literature on social structure and homicide.     

Social welfare support and homicide: Longitudinal analyses of European countries from 1994 to 2010

The purpose of this research is to explore the extent to which retrenchment in welfare support is related to homicide trends across European countries between 1994 and 2010. Using a longitudinal decomposition design that allows for stronger causal inferences compared to typical cross-sectional designs, we examine these potential linkages between social support spending and homicide with data collected from a heterogeneous sample of European nations, including twenty Western nations and nine less frequently analyzed East-Central nations, during recent years in which European nations generally witnessed substantial changes in homicide rates as well as both economic prosperity and fiscal crisis. Results suggest that even incremental, short-term changes in welfare support spending are associated with short-term reductions in homicide—specifically, impacting homicide rates within two to three years for this sample of European nations.     

Civic communities and urban violence

Civic communities have a spirit of entrepreneurialism, a locally invested population and an institutional structure fostering civic engagement. Prior research, mainly confined to studying rural communities and fairly large geographic areas, has demonstrated that civic communities have lower rates of violence. The current study analyzes the associations between the components of civic communities and homicide rates for New Orleans neighborhoods (census tracts) in the years following Hurricane Katrina. Results from negative binomial regression models adjusting for spatial autocorrelation reveal that community homicide rates are lower where an entrepreneurial business climate is more pronounced and where there is more local investment. Additionally, an interaction between the availability of civic institutions and resource disadvantage reveals that the protective effects of civic institutions are only evident in disadvantaged communities.     

Studying neighborhood crime across different macro spatial scales: The case of robbery in 4 cities

Whereas there is a burgeoning literature focusing on the spatial distribution of crime events across neighborhoods or micro-geographic units in a specific city, the present study expands this line of research by selecting four cities that vary across two macro-spatial dimensions: population in the micro-environment, and population in the broader macro-environment. We assess the relationship between measures constructed at different spatial scales and robbery rates in blocks in four cities: 1) San Francisco (high in micro- and macro-environment population); 2) Honolulu (high in micro- but low in macro-environment population); 3) Los Angeles (low in micro- but high in macro-environment population); 4) Sacramento (low in micro- and macro-environment population). Whereas the socio-demographic characteristics of residents further than ½ mile away do not impact robbery rates, the number of people up to 2.5 miles away are related to robbery rates, especially in the two cities with smaller micro-environment population, implying a larger spatial scale than is often considered. The results show that coefficient estimates differ somewhat more between cities differing in micro-environment population compared to those differing based on macro-environment population. It is therefore necessary to consider the broader macro-environment even when focusing on the level of crime across neighborhoods or micro-geographic units within an area.     

Exploring the spatial wage penalty for women: Does it matter where you live?

Inequality between men and women has decreased over the past four decades in the US, but wage inequality among groups of women has increased. As metropolitan women’s earnings grew by 25% over the past four decades, nonmetropolitan women’s earnings only grew by 15%. In the current study we draw on data from the Current Population Survey to analyze the spatial wage gap among women. We explore differences in the spatial wage gap by education, occupation, and industry. Regression models that control for marriage, motherhood, race, education, region, age, and work hours indicate that metropolitan women earn 17% more per hour than nonmetropolitan women. Nonmetropolitan women earn less than metropolitan women who live in central cities and outside central cities. The gap in metropolitan-nonmetropolitan wages is higher for more educated women than for less educated women. The wage gap is only 5% for women without a high school degree, but it is 15% for women with a college degree and 26% for women with an advanced degree. Nonmetropolitan college graduates are overrepresented in lower-paying occupations and industries. Metropolitan college graduates, however, are overrepresented in higher-paying occupations and industries, such as professional services and finance.     

Predicting bias homicide across victim groups: A county-level analysis

Outside several notable exceptions, few studies have examined variations in bias crime occurrences across American communities, and how community-level factors may differentially shape violent and non-violent bias crimes across victim groups. Drawing from ecological theories of crime, this study asks, (1) what are the structural predictors of the likelihood of bias homicide occurrences? and (2) how do structural predictors differ across bias victim groups? To answer these questions, data on bias homicide are derived from the United States Extremist Crime Database (ECDB) for the years 1990 through 2014 and paired with socio- structural variables from the United States Census Bureau. Results are discussed relative to the goals of understanding where fatal bias crimes are more likely to occur as a means of informing law enforcement and policymakers interested in preventing and responding to this specific form of crime.     

White,black, and Hispanic male youths in central city labor markets

Data from the 1% 1980 Census Public Use Sample are used to estimate the determinants of employment and wage rates for out-of-school male youths residing in central cities. Separate calculations are undertaken for white, black, and Hispanic youths. Independent variables include individual, family background, and local labor market characteristics. Three basic findings emerge. First, racial inequality persists, with whites showing the best outcomes, Hispanics second, and blacks at the bottom. Second, underlying these unequal outcomes are rather different patterns of effect for personal and family characteristics by race. Note-worthy here is the Hispanic pattern of low reliance on schooling and high reliance on family. Finally, net of these effects, intercity differences are interesting and important. These include negative effects of city size and race composition effects which show white gains where blacks and Hispanics are a larger share of the population.     

Diverse trajectories of cocaine use through early adulthood among rebellious and socially conforming youth

This paper tests predictions of continuity and change in antisocial behavior over time as derived from population heterogeneity and life-course perspectives. These predictions are assessed with respect to a rarely studied form of delinquent/criminal behavior, cocaine use during the late-teenage and young adult years. We first examine the extent to which differential propensities toward antisocial behavior can be detected in a nationally representative sample of youth aged 14-16 in 1979. Based on self-reported delinquent and criminal activities in late adolescense, traditional cross-sectional latent-class analysis identifies three groups of antisocial/rebellious respondents and a group of non-offenders. We then follow these groups into early adulthood, examining age trajectories of cocaine usage between 1984 and 1998. Latent-class trajectory models identify clusters of respondents who show similar age trajectories of cocaine use over time and provide parameter estimates that predict membership in those clusters. In support of the population heterogeneity perspective, we find that antisocial/rebellious youth have higher probabilities of cocaine use throughout early adulthood than non-of-fending youth. There is, however, much variation in drug use patterns among the groups as they aged. In support of a life-course perspective, we find that social ties to schools, families, religion, and the labor market help differentiate youth who refrain from, maintain, or desist from using cocaine through early adulthood.     

Foreclosures and crime: A city-level analysis in Southern California of a dynamic process

Although a growing body of research has examined and found a positive relationship between neighborhood crime and home foreclosures, some research suggests this relationship may not hold in all cities. This study uses city-level data to assess the relationship between foreclosures and crime by estimating longitudinal models with lags for monthly foreclosure and crime data in 128 cities from 1996 to 2011 in Southern California. We test whether these effects are stronger in cities with a combination of high economic inequality and high economic segregation; and whether they are stronger in cities with high racial/ethnic heterogeneity and high racial segregation. One month, and cumulative three month, six month, and 12-month lags of foreclosures are found to increase city level crime for all crimes except motor vehicle theft. The effect of foreclosures on these crime types is stronger in cities with simultaneously high levels of inequality but low levels of economic segregation. The effect of foreclosures on aggravated assault, robbery, and burglary is stronger in cities with simultaneously high levels of racial heterogeneity and low levels of racial segregation. On the other hand, foreclosures had a stronger effect on larceny and motor vehicle theft when they occurred in a city with simultaneously high levels of racial heterogeneity and high levels of racial segregation. There is evidence that the foreclosure crisis had large scale impacts on cities, leading to higher crime rates in cities hit harder by foreclosures. Nonetheless, the economic and racial characteristics of the city altered this effect.     

Business structure,ethnic shifts in labor markets,and violence: The link between company size,local labor markets,and non-Latino homicide

Combining several schools of thought, including the civic engagement thesis, we extend current research by linking three things at the county level; firm size, the ethnic composition of labor markets, and violent crime. Our results suggest that larger businesses (based on the average number of persons employed) are more likely to have an external orientation and long recruitment reach, and this is linked to ethnic shifts in labor markets toward Latino workers. Such shifts are in turn associated with high rates of homicide among non-Latinos. Through indirect effects modeling, we find that increases in Black homicide are linked to rises in concentrated poverty, while increases in White homicide are linked to changes in unemployment. We discuss the implications of our findings.     

The social organization of criminal justice processing: An event history analysis

Crimes are social events that involve citizens and control agents interacting over time. Prior work neglects the dynamic and interactive qualities of these criminal events. Drawing from the work of Hawley and others, it is suggested that the processing of criminal events is a routine activity socially organized in time and space. Dynamic modeling techniques developed by N. Tuma are applied to longitudinal data collected on over 10,000 criminal events in California cities and used to model rates of transition from arrest to case disposition resulting from police release, prosecutor denial of complaint, or going to court. As the work of Hawley predicts, city size has much to do with the way criminal events are processed. For example, in larger cities it is demonstrated that crime specialists are processed more slowly than nonspecialists, and that each successive police processing of crime specialists results in slower rates of transition relative to nonspecialists; in smaller cities, it is demonstrated that black suspects are processed more quickly than whites, and that each successive police processing of black suspects results in faster rates of transition relative to whites. The former findings are explained in terms of rationalized intelligence gathering, the latter in terms of stereotyping and the harassment of minorities. The systematic form of the observed temporal changes, notwithstanding a large number of legal and extralegal variables taken into account, leads us to believe that we have identified important patterns of police activity. These and other findings convince us that the social organization of criminal justice processing deserves further study.     

Churning races in Canada: Visible minority response change between 2006 and 2011

Why do most people have stable responses to census race questions, while some do not? Using linked Canadian data, we examine personal, social, and economic characteristics that predict response stability as White or as one of six large visible minority groups, versus a change in response to/from White or to/from another visible minority group. Response change rates in Canada are generally comparable to those in the US, UK, and New Zealand. Likely reflecting the centuries-old hegemony of Whites in these countries, White is the most stable response group in Canada as well as the US, UK, and New Zealand. Multiple-race response groups are among the newest and least stable response groups. Social statuses and experiences (mixed ethnic heritage, immigration status, and exposure to own-group members) are generally more predictive of race response stability and change than economic (income level and change in income) or personal statuses (education, age). This highlights the socially-constructed nature of race group boundaries. Joining and leaving a group are often predicted by the same status/characteristic and in the same direction, hinting that the status/characteristic adds complexity to the race-related experiences of constituents.     

Latent class analysis of response inconsistencies across modes of data collection

Latent class analysis (LCA) has been hailed as a promising technique for studying measurement errors in surveys, because the models produce estimates of the error rates associated with a given question. Still, the issue arises as to how accurate these error estimates are and under what circumstances they can be relied on. Skeptics argue that latent class models can understate the true error rates and at least one paper (Kreuter et al., 2008) demonstrates such underestimation empirically. We applied latent class models to data from two waves of the National Survey of Family Growth (NSFG), focusing on a pair of similar items about abortion that are administered under different modes of data collection. The first item is administered by computer-assisted personal interviewing (CAPI); the second, by audio computer-assisted self-interviewing (ACASI). Evidence shows that abortions are underreported in the NSFG and the conventional wisdom is that ACASI item yields fewer false negatives than the CAPI item. To evaluate these items, we made assumptions about the error rates within various subgroups of the population; these assumptions were needed to achieve an identifiable LCA model. Because there are external data available on the actual prevalence of abortion (by subgroup), we were able to form subgroups for which the identifying restrictions were likely to be (approximately) met and other subgroups for which the assumptions were likely to be violated. We also ran more complex models that took potential heterogeneity within subgroups into account. Most of the models yielded implausibly low error rates, supporting the argument that, under specific conditions, LCA models underestimate the error rates.     

Determinants of fertility levels and change among developed countries: 1958–1978

This article provides a multivariate cross-national test of the hypothesis that national population/family planning policies have effected levels, and changes in fertility in developed nations over the past two decades. Variation is assessed in the total fertility rates (TFR) in 1978, and in the change in these rates between 1958 and 1978, among thirty developed countries. Measures include socioeconomic development, divorce, percent in consensual unions, female labor force participation, abortion policy, and level of contraceptive use by married couples and, government population/family planning policy. Seventy percent of the variation in 1978 TFR is related to the percent contracepting, female labor force, and the population/family planning policy measures. These are the only measures with significant direct effects. A longitudinal analysis of 1958 to 1978 change in TFRs is also conducted. This model increases R² to 75%, and the three independent variables remain significant. Implications of these findings for policy makers interested in increasing or decreasing fertility rates are noted.     

Rush to the border? Market liberalization and urban- and rural-origin internal migration in Mexico

In this study we examine the social and economic factors driving internal migration flows in Mexico. We pay particular attention to the effect that economic liberalization has had in encouraging migration to border cities. Our analysis of the origin and destination of migrants is carried out at a finer level of geographical detail than ever before. Microdata files from the 2000 population census allow us to distinguish urban- and rural-origin migrants to the largest 115 cities and metropolitan areas in the country. Our results indicate that economic liberalization, measured by the level of foreign investment and employment in the maquiladora export industry, strongly influences migrants’ choice of destinations. However, economic liberalization fails to fully account for the attraction of the border, as do the higher emigration rates to the United States from border cities. Our analysis also reveals that migrants to the border region and to cities with high levels of foreign investment are younger, less educated and more likely to be men than migrants to other parts of Mexico. Rural migrants are significantly more likely to move to the border and to cities with high levels of foreign investment than urban migrants. The results of our study have important implication for other countries opening their economies to foreign investment and international trade.     

Using latent class analysis to produce a typology of environmental concern in the UK

Factor analysis is often used to study environmental concern. This choice of methodology is driven by predominant theories that tie environmental attitudes to the multidimensional construct of environmental concern. This paper demonstrates that using a clustering method such as latent class analysis can be a valuable tool for studying environmental attitudes as they exist within a given population. In making the case for the value of latent class analysis in this context, we examine UK public concern for the environment and how this concern is associated with pro-environmental behaviours. To do this we use responses to DEFRA's 2009 Survey of Public Attitudes and Behaviours towards the Environment, which is still the most nationally representative survey of its type in the UK. Grouping respondents according to homogenous response patterns, we identify four classes of people, defined by their concern for the environment: Pro-environment, Neutral Majority, Disengaged and Paradoxical. To understand how these attitude classes are associated with behaviour and socio-economic status, class membership probability is regressed onto education, income and social grade, as well as 16 measures of environmental behaviour related to transport, food, recycling and home energy conservation. The results contradict most previous research with the environmental attitude classes by being highly predictive of environmental behaviour.     

Time dynamics of elder victimization: Evidence from the NCVS, 1992 to 2005

Persons who are relatively younger have a comparably higher incidence of crime victimization than those who are older. Comparative studies of crime rates among those 65 and older with those for younger persons across time are virtually nonexistent. This study provides these analyses. Data from the National Crime Victimization Survey are used to study the relative victimization rate among US elders. Macroeconomic indicators and environmental interventions were included to examine whether structural forces influence elder crime victimization rates. Such influences are fractionally cointegrated with the long-term elder property and personal crime rates, with each having a distinct data-generating process. The results of these analyses suggest that personal, but not property, crime rates have declined since the passage of the Crime Bill of 1994. As well, the summer months are associated with elevated rates of property crime committed against elders but not personal crime.