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Genetic risk factors correlate with county-level violent crime rates and collective disadvantage
Journal of Criminal Justice 41 (2013) 350–356
Contents lists available at ScienceDirect
Journal of Criminal Justice
Genetic risk factors correlate with county-level violent crime rates and collective disadvantage☆ J.C. Barnes a,⁎, Brian B. Boutwell b, Kevin M. Beaver c,d a
School of Economic, Political & Policy Sciences, The University of Texas at Dallas, 800 W. Campbell Rd., Richardson, TX 75080, United States College of Criminal Justice, Sam Houston State University, P.O. Box 2296, Huntsville, TX 77341, United States College of Criminology and Criminal Justice, Florida State University, 634 W. Call St., Tallahassee, FL 32306, United States d Center for Social and Humanities Research, King Abdulaziz University, Jeddah, Saudi Arabia b c
a r t i c l e
i n f o
Available online 12 July 2013
a b s t r a c t Purpose: Social scientists have a rich tradition of uncovering the neighborhood, structural, and ecological correlates of human behavior. Results from this body of evidence have revealed that living in disadvantaged communities portends myriad negative outcomes, including antisocial behaviors. Though it has long been argued that associations between neighborhood factors and individual-level outcomes may, at least partially, reflect genetic selection, a paucity of research has empirically investigated this possibility. Methods: The current study examined whether known genetic risk factors for antisocial behavior were predictive of exposure to disadvantage and violent crime measured at the county level. Drawing on genotypic data from the National Longitudinal Study of Adolescent Health, a dopamine risk scale was created based on respondents’ genotypes for DAT1, DRD2, and DRD4. County-level disadvantage was measured via Census data and violent crime rates were measured via the FBI’s Uniform Crime Reports. Results: Findings revealed that individuals with a greater number of dopamine risk alleles were more likely to live in a disadvantaged county and were more likely to live in a county with higher violent crime rates. © 2013 Elsevier Ltd. All rights reserved.
Introduction Do individuals select the environments to which they are exposed? Do people migrate toward certain neighborhoods? These questions have occupied a prominent position in the minds of social scientists for decades. Consider the following observation from Taft nearly 80 years ago (1933:700): Are criminals attracted into delinquency areas more frequently than into other parts of the city? Are the children of criminals settling in such areas delinquent entirely because they live in criminogenic neighborhoods, or largely because they had delinquent parents? Do people with other abnormal personality traits migrate to such areas rather than elsewhere, and if so are these traits factors in their criminal behavior? In all these cases the question would be, of course, whether ☆ This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). No direct support was received from grant P01-HD31921 for this analysis. ⁎ Corresponding author. E-mail address: [email protected] (J.C. Barnes). 0047-2352/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jcrimjus.2013.06.013
these personality types were attracted or forced into the areas; or whether they were such on arrival rather than produced by life in the region after arrival. In short, are delinquency areas selective, and if so are they centers of delinquency partly because they are selective? The study of the possible selective influence of areas of delinquency seems to the writer to have been somewhat neglected. Scholars have examined a litany of factors that might be important for understanding the clustering of individuals within neighborhoods. However, these studies have generally focused on social or environmental forces while excluding biological and genetic factors. Early sociological research was motivated to study the factors that underlie the clustering of individuals within neighborhood settings (Shaw & McKay, 1942). Preliminary observations led many sociologists to conclude that social factors must be predicted by preceding social factors (Durkheim, 1982). In other words, sociology left no room for biological explanations of behavior and decision-making (Udry, 1995), suggesting instead that the clustering of individuals in certain areas was the result of social influences. Contemporary genetics research, however, indicates that this early sociological explanation may be untenable (Udry, 1995; van den Berghe, 1990). It is now well established that genetic and biological factors play a role in personality development (Bouchard et al., 1990; Harris, 1998) and perhaps even in individuals’ selection into certain environments and neighborhood conditions (Rowe & Rodgers, 1997; Scarr
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& McCartney, 1983). Along these lines, the current study builds on recent genetics research in order to better understand the factors that underpin peoples’ decisions to live in certain areas and not others. In order to frame the current focus, we turn first to a discussion of geneenvironment correlation (DiLalla, 2002; Kendler & Baker, 2007; Scarr & McCartney, 1983). Gene-environment correlation (rGE) The phenomenon of gene-environment correlation (rGE) has been a topic of much discussion for at least the past 35 years (Plomin et al., 1977; Scarr & McCartney, 1983). Researchers have reported that many environmental variables are partially influenced by genetics (i.e., some portion of the variance is attributable to heritable factors; Kendler & Baker, 2007). In general, two types of rGEs might underlie the correlation between a person’s genotype and their exposure to certain neighborhood environments: active rGE and passive rGE (Scarr, 1992; Scarr & McCartney, 1983). The first type of rGE that is applicable to the current focus is active rGE. Active rGE occurs when a person seeks out environments to suit their genetic proclivities, a phenomenon more commonly referred to as niche-picking or self-selection. Research has revealed, for example, that individuals are likely to select into environments that are compatible with their genetic tendencies (e.g., Beaver et al., 2008). Active rGEs offer a framework for understanding how genetic factors can influence the nonrandom selection of people into particular environments, perhaps even large-scale environments such as neighborhoods, cities, and counties. The second type of rGE is referred to as passive rGE. Passive rGE recognizes that parents pass along both an environment and genes to their offspring. Since the child’s environment and the child’s genes both originate from the same source (i.e., their parents) the two are likely to be correlated. Recall that active rGE allows for the non-random sorting of people into certain environments based on their genotype. To the extent that this occurs at the parent-level (i.e., parents choose their own environments), we would expect the child’s genotype (which is passed from parent to child) to be correlated with the child’s environment (which was selected by the parents).
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Sampson (2008) was commenting on the results from a recent set of experiments, the MTO, which sought to determine whether moving out of disadvantaged neighborhoods positively impacts the lifestyles, the health, and the behavior of individuals exposed to those conditions. The MTO proceeded by offering moving vouchers (Section 8 vouchers) to selected families, but to the researchers surprise, many families passed on the opportunity to move out of their neighborhood and into a less disadvantaged one. As reported by Kling, Liebman, and Katz (2007), the compliance rate (i.e., the percentage of families offered vouchers who used the voucher) was between 47 and 60 percent. Moreover, many of the families that did accept the voucher moved into a neighborhood nearby or one that mirrored their original location. To the extent that “opting out” of the MTO is related to self-selection into a neighborhood, these results offer indirect support for the active rGE hypothesis. Namely, findings from the MTO suggest that neighborhood living conditions are, at least for some, the result of self-selection and not completely the result of environmental pressures. By extension, these findings may also support the passive rGE hypothesis. To the extent that parents select into certain neighborhoods (active rGE), their children’s genotypes will also tend to be correlated with neighborhood conditions (passive rGE). Current study Juxtaposing the findings from the MTO with the concepts of active rGE and passive rGE, a conceptual framework begins to emerge. As the diagrams in Fig. 1 display, it may turn out that individuals selfselect into certain neighborhoods based on their genetic propensities (i.e., active rGE; Panel A). It may also turn out that parents select into certain environments and, therefore, those environments are correlated with their children’s genotypes (i.e., passive rGE; Panel B). This is not to
A: Active rGE
rGE and residential selection Keeping the discussion of rGE in mind, sociologists have extensively noted that residential sorting is not the result of a random process nor does it appear to be strictly the result of environmental forces. Consider the following comment from Zorbaugh (1929:134-135) more than eight decades ago: “It is often remarked how difficult it is to get a family to consent to move out of the slum no matter how advantageous the move may seem from the material point of view, and how much more difficult it is to keep them from moving back into the slum.” More recently, Sampson (2008:213) extended this statement by noting: Humans are agents with the decision-making power to accept or reject treatments (Heckman & Smith, 1995). Statistics on the “takeup” rate show that a majority of MTO [Moving to Opportunity] families who were offered a voucher did not actually use it. Families who did use the voucher experienced less neighborhood poverty in comparison with the noncompliers, but the vast majority remained within a relatively short distance of their origin neighborhood. Moreover, many families moved back into poor neighborhoods that were very similar to the ones in which they started, surprising many observers. Yet no one should be surprised at these facts. Back in the 1920s, Zorbaugh (1929) noted the “pull” of the slum and how the strong nature of its social ties kept people returning. It is only from a middleclass point of view, or what Zorbaugh called the “budgetminded social agency” (1929, p. 134), that the behavior of those who have grown up in poverty seems “incalculable.”
B: Passive rGE
Fig. 1. Conceptual diagram of two rGEs for explaining the correlation between genotype and environmental conditions.
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say that genetic factors are the only influence on neighborhood selection. Rather, our hypothesis is that genetic factors that have been linked with antisocial behavior will explain a portion of the variance in different measures of structural conditions due to active rGE and/or passive rGE. No research, of which we are aware, has empirically considered this hypothesis. Methods Data Data for this analysis were gleaned from the National Longitudinal Study of Adolescent Health (Add Health; Harris, 2009). The Add Health data have been described at length elsewhere (Harris et al., 2003, 2006; Resnick et al., 1997). Briefly, the Add Health is a nationally representative, longitudinal survey of American youth who were enrolled in middle and high school during 1995. The study began with a school-level survey that included all students enrolled in more than 130 schools across the United States (N ≈ 90,000). From this sample of respondents, a subsample of roughly 20,000 was drawn and utilized in the longitudinal portion of the study. Shortly after the in-school surveys were completed, the longitudinal subsample was administered a more lengthy follow-up survey. These interviews were conducted in the respondent’s home (i.e., wave 1). The surveys addressed a range of topics germane to adolescence such as the respondent’s behavior, their relationships with peers, and their personality characteristics. Respondents ranged in age between 11 and 21 years. A second wave of data were collected a year after wave 1 (i.e., wave 2). Approximately six years after wave 1 interviews a third wave of data were collected. During wave 3 interviews, all respondents had reached young adulthood (age range was between 18 and 26 years). Two unique features of the Add Health design are capitalized upon by the current research. First, a host of county-level measures that can be linked with the individual respondents are available to Add Health researchers. These measures are available for wave 1, wave 2, and wave 3. Second, genotypic information was gathered for a subsample of the wave 3 respondents. Twins and full siblings (where both siblings were participating in the study) were asked to provide buccal cell samples so that they could be genotyped. Genotypic information was available for 2,574 respondents (Cohen, Feng, Florey, et al., n.d.). After eliminating one twin from each monozygotic twin pair (to avoid artificially decreasing standard errors; Haberstick et al., 2005) and after eliminating cases with missing data, final analytic sample sizes ranged between 2,212 and 2,268. Measures Genetic risk variable Dopamine risk. Scholars have shown that certain dopaminergic genes may be related to criminal and antisocial behavior (Beaver et al., 2007; Craig & Halton, 2009). These genetic risk factors, via rGE processes, may be related to the individual’s social environment. Included in the Add Health was genotypic information for three dopamine polymorphisms: DAT1, DRD2, and DRD4. Based on information gleaned from these three polymorphisms, a dopamine risk scale was generated. DAT1 is a dopamine transporter gene that has two common alleles; the 9-repeat allele and the 10-repeat allele. The 10-repeat allele has been identified as the risk allele (Guo et al., 2010; Rowe et al., 2001) and, as such, DAT1 alleles were coded so that 0 = 9-repeat allele and 1 = 10-repeat allele. Respondents with any other allele were assigned a missing value and were omitted from the analyses (Hopfer et al., 2005). DRD2 is a dopamine receptor polymorphism that has one allele known as the A1 allele and another known as the A2 allele. The A1 allele has been identified as the risk allele (Guo et al., 2007). Thus, respondents with two A2 alleles were coded as 0, those with one A1
allele were coded as 1, and respondents with two A1 alleles were coded as 2. The DRD4 polymorphism is a dopamine receptor gene that has been tied to antisocial and criminal behavior. The 7-repeat allele is regarded as the risk allele (Faraone et al., 2001; Rowe et al., 2001). Building on previous research, DRD4 alleles were coded so that the 7-repeat allele (along with the 8-, 9-, and 10-repeat alleles) = 1 and the 4-repeat allele (along with the 2-, 3-, 5-, and 6-repeat alleles) = 0. In order to generate the dopamine risk scale, respondents’ scores on the three dopamine polymorphisms (DAT1, DRD2, and DRD4) were summed together. The polymorphisms were coded co-dominantly, meaning that the value for each polymorphism reflected the number of risk alleles carried by the respondent. The dopamine risk scale ranged from a minimum of 0 (i.e., no dopamine risk alleles) to a maximum of 6 (i.e., six dopamine risk alleles). County-level variables Violent crime rate. Each year, the Federal Bureau of Investigation collects crime rate data from across the U.S. These data are published annually in the Uniform Crime Reports (UCR) and the county-level measures were included as part of the Add Health data collection at wave 1, wave 2, and wave 3. The violent crime rate variable was a composite index reflecting the number of robberies, aggravated assaults, rapes, and homicides per 100,000 residents in the respondent’s county. Violent crime rate data from wave 1 and wave 3 were analyzed in the present study. Collective disadvantage. Two county-level indicators of collective disadvantage were calculated based on prior research from Sampson et al., (1997). The first was generated using data from wave 1 by drawing on the 1990 Census data (the decennial census closest to wave 1 data collection). The second county-level measure of collective disadvantage was drawn from data available at wave 3, which reflected data drawn from the Census of Population and Housing in 2000. To create the collective disadvantage scale, the following measures were factor analyzed: the percentage of Black residents, the percentage of female headed households, the percentage of residents with an income under $15,000, the percentage of residents on public assistance, and the unemployment rate. Factor analysis revealed that the correlation structure of the five items was best explained with a single latent construct for both the wave 1 measure and the wave 3 measure. All factor loadings (except percent on public assistance at wave 3 which had a loading of .67) were greater than or equal to .75 and the reliability coefficient was .92 for the wave 1 measure and .89 for the wave 3 measure. Both scales were created using regression scoring based on the factor analysis results. Higher values reflected more collective disadvantage for both measures. Controls The respondent’s sex was included as a dichotomous variable (0 = female, 1 = male) as was the respondent’s race (0 = non-Black, 1 = Black). Information on respondent race was gleaned from wave 3 interviewer reports of the respondent’s race. Analysis plan In order to examine the relationship between the dopamine risk scale and the county-level measures of violent crime and collective disadvantage, the analysis proceeded in four steps. First, observed mean levels of violent crime and observed mean levels of collective disadvantage were calculated for respondents who had different levels of dopamine risk. Second, zero-order correlation coefficients were calculated between the dopamine risk scale, the violent crime rate, and the collective disadvantage scale. Third, OLS regression models were estimated. In these models, the violent crime rate variables and the collective
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disadvantage scales were each (separately) regressed on the dopamine risk scale. The estimates gleaned from these regression models were used to plot predicted violent crime rates and predicted collective disadvantage scores based on each respondent’s level of dopamine risk. Fourth, the relationship between dopamine risk and the county measures was assessed after statistically controlling for the respondent’s sex and race. One final point is worth noting before proceeding to the results. Recall that wave 1 data collection occurred when all of the Add Health respondents were in middle or high school and the age range was 11-21 years old. Due to this feature of the data collection, any correlation between the respondent’s genotype and the county-level measures (violent crime rate and collective disadvantage) is likely to be the result of passive rGE processes. Wave 3 interviews, however, were conducted when the respondents had reached young adulthood and the age range was 18-26. Although it is possible that some respondents remained living with their parents, it is likely that many had moved into homes of their own. Thus, any correlation between respondent genotype and the wave 3 county-level variables is likely to be the result of active rGE processes. Preliminary evidence of this point is found in the number of unique counties represented in the analyses. At wave 1, respondents hailed from 117 different counties. At wave 3, however, respondents lived in 387 different counties.
Findings Before proceeding to the analysis, it was important to determine whether the dopamine risk scale varied significantly across counties. If not, the remainder of the analysis would be futile in that we would be trying to explain a variable with a constant. In order to determine whether dopamine risk and total genetic risk varied at the county level, a two-pronged approach was followed. First, a one-way ANOVA was estimated where the dopamine risk scale was the response variable and the respondent’s county identifier was the factor variable. The results revealed that the dopamine risk scale (F = 2.03, df = 116, p b .0001) varied significantly across the different counties. The second approach was to aggregate the dopamine risk scale to the county level (by calculating the mean dopamine risk score for respondents in each county) and observe the distributional properties of the aggregated scale. This approach revealed that when aggregated to the countylevel the dopamine risk scale had a distribution that approximated normality (see Appendix A). Thus, a lack of variation on the dopamine risk scale should not hinder the analyses presented below. Mean scores on the violent crime rate and on the collective disadvantage scale are presented in Table 1. Notice that the mean scores are presented separately for respondents with different levels of dopamine risk. The top portion of the table presents the mean scores of violent crime rate and neighborhood disadvantage at wave 1. The bottom portion presents the mean scores on the violent crime rate and neighborhood collective disadvantage at wave 3. Presented in the first two columns of the top portion of Table 1 are the sample size (N) and the mean violent crime rate at wave 1. As can be seen, there were 330 respondents who had either a 0 or a 1 on the dopamine risk scale (because only 30 respondents scored a 0, the 0 and 1 categories were collapsed together. The substantive findings for all of the analyses were identical when the categories were not collapsed). For these respondents, the mean violent crime rate in their county of residence during wave 1 was 656.17 per 100,000 residents. As we move down the column into higher scores on the dopamine risk scale, a pattern begins to emerge. Specifically, mean violent crime rates at wave 1 appear to increase as scores on the dopamine risk scale increase. This suggests that dopamine risk may be tied to county-level violent crime rates. Presented in the last two columns of the top portion of Table 1 are sample sizes and the mean collective disadvantage scores for respondents with different levels of dopamine risk. In broad strokes,
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Table 1 Mean county violent crime rate and mean county collective disadvantage by dopamine risk Mean
Mean
Violent
Collective
Dopamine Risk
N
Crime Rate
N
Disadvantage
Wave 1 0–1 2 3 4 5–6
330 787 729 309 68
656.17 685.92 755.75 807.73 882.81
345 804 737 314 68
-.09 -.11 .03 .16 .28
Wave 3 0–1 2 3 4 5–6
346 786 712 307 63
508.13 498.36 572.54 581.14 665.84
346 786 712 307 63
-.07 -.10 .02 .15 .25
respondents who carried more dopamine risk tended to live in counties with a higher level of collective disadvantage. Moving to the bottom portion of Table 1, we see that respondents who scored lower on the dopamine risk scale tended to live in counties with lower (relatively) rates of violent crime and in counties with lower (relatively) levels of collective disadvantage. This pattern was not, however, consistent across all levels of dopamine risk. For instance, respondents with 2 dopamine risk alleles, on average, lived in counties with less violent crime than respondents who had 0 or 1 dopamine risk alleles. A similar finding emerged for the collective disadvantage measure. Presented in Table 2 are the zero-order correlation coefficients between dopamine risk, the county violent crime rate, and the county collective disadvantage scale. As can be seen, the dopamine risk scale was positively and significantly (p b .05, two-tailed) correlated with the wave 1 violent crime rate (r = .09), with the wave 3 violent crime rate (r = .09), with the wave 1 collective disadvantage scale (r = .10), and with the wave 3 collective disadvantage scale (r = .09). The next step of the analysis was to generate predicted rates of violent crime as a function of dopamine risk. In order to do so, an OLS regression model utilizing the wave 1 violent crime rate as the dependent variable and the dopamine risk scale as the independent variable was estimated and the coefficient estimates were used to generate predicted values (i.e., predicted violent crime rates for individuals with different levels of dopamine risk). The predicted wave 1 violent crime rates are presented graphically in Fig. 2, along with the coefficient estimates gleaned from the regression models in the figure caption. Consistent with the above findings, the dopamine risk scale was positively associated with wave 1 violent crime rates. At wave 1, respondents with the lowest level of dopamine risk (those with a 0 or a 1 on the dopamine risk scale) were predicted to live in a county with a violent crime rate of 640.37 per 100,000 residents. Respondents
Table 2 Correlations between individual-level dopamine risk and county-level violent crime rate and collective disadvantage W1 W3 Dopamine W1 Collective Violent Risk Violent Crime Rate Crime Rate Disadvantage Dopamine Risk W1 Violent Crime Rate W3 Violent Crime Rate W1 Collective Disadvantage W3 Collective Disadvantage ⁎ p b .05 (two-tailed).
.09⁎ .09⁎ .10⁎ .09⁎
.72⁎ .53⁎ .58⁎
.59⁎ .73⁎
.78⁎
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1000
Predicted Violent Crime Rate
900 800 700 600 500 400 300
Wave 1 Wave 3
200 100 0 0-1
2
3
4
5-6
Dopamine Risk Fig. 2. Predicted violent crime rate as a function of dopamine risk. Note: Wave 1: bdopamine risk = 56.15, SE = 22.08, p b .05 (two-tailed); Wave 3 bdopamine risk = 37.24, SE = 14.59, p b .05 (two-tailed); Standard errors were corrected for the clustering of cases within counties.
with the highest level of dopamine risk (those with a 5 or a 6) were predicted to live in a county with a violent crime rate of 864.97 per 100,000 residents; an increase of more than 35% over those with the lowest dopamine risk. A second OLS regression model predicted wave 3 violent crime rates with the dopamine risk scale. The results from this analysis are also presented in Fig. 2. As with the wave 1 violent crime rate, dopamine risk was positively and significantly associated with wave 3 violent crime rates. Respondents with the lowest dopamine risk were predicted to live in a county with 483.09 violent crimes per 100,000 residents at wave 3. Respondent with the highest level of dopamine risk were predicted to live in counties with 632.04 violent crimes per 100,000 residents at wave 3. The results from the next two OLS regression analyses are presented in Fig. 3. In the first analysis, the wave 1 collective disadvantage scale is utilized as the dependent variable and the dopamine risk scale is utilized as the predictor variable. Predicted scores on the collective disadvantage scale are plotted as a function of dopamine risk. A positive
0.3
Discussion
Predicted Collective Disadvantage
0.25 0.2 0.15 0.1 0.05 0 0-1
2
3
4
5-6
-0.05 -0.1
Wave 1 Wave 3
-0.15 -0.2
relationship was identified such that those with the lowest dopamine risk were predicted to live in counties with a disadvantage score of -.17 and those with the greatest dopamine risk were predicted to live in counties with a disadvantage score of .23. Recall that the collective disadvantage scale was generated as a factor score. Thus, the values roughly correspond to z-scores (i.e., standard deviation units). The fourth OLS regression model utilized the wave 3 collective disadvantage measure as the outcome variable. The same familiar pattern emerged. In short, respondents with fewer risk alleles on the three dopamine variables tended to live in relatively less disadvantaged counties at wave 3. Respondents carrying zero or one risk allele were predicted to live in counties with a disadvantage score of -.14 while those with five or six risk alleles lived in counties with a predicted disadvantage score of .20. The final analysis re-estimated each of the regression models presented in the figures, but this time the respondent’s sex and the respondent’s race were entered into the regression models as control variables. The results of these models are presented in Table 3, which is split into two panels. Panel A presents the results from the regression models where the violent crime rate variables served as the dependent variables. Model 1 reports the baseline estimates for the effect of the dopamine risk scale on the wave 1 violent crime rate (which are identical to those presented in Fig. 2). Model 2 entered the sex variable into the regression equation. Respondent sex does not significantly predict wave 1 violent crime rate and the effect of dopamine risk was unchanged when this variable was included. Model 3 enters the respondent’s race. As can be seen, when the respondent’s race is accounted for, the dopamine risk scale no longer exerts a statistically significant influence on wave 1 violent crime rates. Models 4 through 6 perform the same series of analyses with the wave 3 violent crime rate variable serving as the dependent variable. A substantively identical pattern emerged where the effect of the dopamine risk scale was rendered statistically insignificant when the respondent’s race was controlled. Finally, Panel B presents the results from six OLS regression models where the collective disadvantage variables were utilized as the dependent variables. As with the violent crime rate measures, models 7 and 10 indicated that dopamine risk significantly predicted wave 1 collective disadvantage (model 7) and wave 3 collective disadvantage (model 10). Models 8 and 11 revealed that controlling for the respondent’s sex did not affect the dopamine risk scale estimate. Models 9 and 12, however, indicated that the dopamine risk scale no longer significantly predicted either outcome once the respondent’s race was entered into the regression model.1
Dopamine Risk
Fig. 3. Predicted collective disadvantage as a function of dopamine risk. Note: Wave 1 bdopamine risk = .10, SE = .04, p b .05 (two-tailed); Wave 3 bdopamine risk = 09, SE = .03, p b .05 (two-tailed); Standard errors were corrected for the clustering of cases within counties.
Social scientists have long noted the impact of structural/ contextual factors (e.g., county-level variables) on individual-level behavior (Durkheim, 1982). Despite mounds of research into these influences, statistical significance can often be fleeting (Harden et al., 2009) and the theoretical links between macro-level factors and individual-level behavior often leaves much to be desired. This is not to say that neighborhood/contextual factors have no impact on individuals’ behavior; a series of ingenious experiments by Keizer et al., (2008) provide strong evidence that context influences normviolating behavior. Instead, available research indicates that the association between contextual factors and individual-level behavior is more complex than has traditionally been recognized. Findings from the current analysis offer a new perspective on the influence of structural and contextual influences. Specifically, the question of whether individuals self-select into certain environments was directly addressed by analyzing the association between molecular genetic data and exposure to county-level crime rates and county-level rates of collective disadvantage. Two important conclusions were gleaned from the analysis. First, the results of a series of statistical tests indicated that individual-level genetic risk predicted
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Table 3 OLS regression estimates before and after including control variables Panel A: Violent Crime Rate Wave 1
Dopamine Risk
Wave 3
Model 1
Model 2
Model 3
Model 4
Model 5
b(SE)
b(SE)
b(SE)
b(SE)
b(SE)
b(SE)
56.15⁎ (22.08)
56.15⁎ (22.13) -.32 (22.98)
29.90 (24.27)
.37.24⁎ (14.59)
37.18⁎ (14.59) -6.09 (14.28)
16.08 (10.57)
Male (=1) Black (=1)
458.51⁎ (145.86)
Model 6
418.60⁎ (160.09)
Panel B: Collective Disadvantage Wave 1
Dopamine Risk Male (=1)
Wave 3
Model 7
Model 8
Model 9
Model 10
Model 11
b(SE)
b(SE)
b(SE)
b(SE)
b(SE)
b(SE)
.10⁎ (.04)
.10⁎ (.04) .02 (.04)
.03 (.03)
.09⁎ (.03)
.09⁎ (.03) .04 (.04)
.03 (.02)
Black (=1)
1.16⁎ (.31)
Model 12
1.02⁎ (.26)
Note: All standard errors were corrected for the clustering of cases within counties. ⁎ p b .05 (two-tailed).
county-level measures of violent crime rates and collective disadvantage. These findings are important in that they suggest the sorting of individuals into certain areas (i.e., counties) is not random and may not be completely due to social/environmental forces. Instead, individuals who carry more “risk” alleles for antisocial behavior are more likely to live in areas where violent crime is more prevalent and where collective disadvantage is greater. Taken together, these findings provide support for gene-environment correlation (rGE) hypotheses. The findings from the wave 1 analysis lend support for the passive rGE hypothesis because all respondents were still in middle or high school at the time and, therefore, were most likely to be living with their parents. The findings from the wave 3 analysis lend support for the active rGE hypothesis because all respondents had reached young adulthood by this time and, therefore, were more likely to be living in areas of their own choosing. The second important finding to emerge from the analysis was that the correlation between genetic risk and the county-level variables was completely explained away by the respondent’s race. There are two possible explanations for this finding. The first explanation highlights the possibility that dopamine risk has an indirect influence on neighborhood selection, but dopamine risk is correlated with race, thereby masking the genetic effect. In short, the first explanation states that the genetic effect is not a statistical artifact. The second explanation states that dopamine risk is a proxy for race and, due to population stratification, blacks are more likely to live in high crime/disadvantaged areas meaning that the genetic effect is a statistical artifact. Unfortunately, identifying which of these two processes accurately explains the current findings is empirically impossible with the Add Health data. Thus, we are left only with theoretical considerations to ponder. There is certainly evidence to support both explanations and, realistically, it is likely that both processes can be credited. To be sure, genomic data, while clearly evincing the point that genetic variation is greatest within race rather than across race, has also shown that racial characteristics correlate with genotype at an impressive rate. Tang, Quertermous, Rodriguez, et al. (2005) revealed a nearly perfect overlap in self-reported race and groups identified with a genetic cluster analysis. At the same time, U.S. history is fraught with examples of racial discrimination (both historical discrimination and contemporary discrimination (Wilson, 1987)) and
segregation and we would be extremely negligent if we did not consider the lingering effects that these past policies, attitudes, and perceptions might have on today’s society (Massey & Denton, 1993; Wilson, 1987). In that respect, it is important to note a recent analysis by Warren et al. (2012) which demonstrated that Whites were less willing to reside in communities with Black residents if they also viewed Blacks as being criminal and representing an economic liability. Wilson (1987) appropriately summarized this position when he noted that “…long periods of racial oppression can result in a system of inequality that may persist for indefinite periods of time even after racial barriers are removed” (p. 147). We encourage future scholars to revisit these issues and we hope, at a minimum, that these findings will spark a discussion about the interplay between genetic factors, race, and exposure to certain structural/contextual factors such as county-level crime rates and collective disadvantage. We are sensitive to the fact that these issues are controversial and, by some, are considered taboo. Indeed, Kemper (1994) noted nearly 20 years ago that, “…if sociology and biology have not been on speaking terms in general, sociological disdain for the biological reaches its apogee when it comes to social stratification” (p. 48). We hope that analyses such as the present one will dissolve some of this disdain and will allow criminology and her scholars to contribute progressive, ethical, and moral research to 21st century scientific discourse. Limitations of the current study must be noted. First, data were gleaned from the Add Health study, meaning that we were limited to the genetic information included by the Add Health researchers. Extant evidence clearly indicates that dopamine genes are not the only genetic factors that correlate with antisocial behavior and, therefore, it is unlikely that the three dopamine genes examined here are the only factors that correlate with county crime rates and county collective disadvantage. Second, and related to the first, is that this analysis was restricted to two county-level measures (violent crime rates and collective disadvantage). The choice of these two county variables was obvious; a large literature has discussed the potential role that exposure to crime and disadvantage has on an individual’s behavior (e.g., Sampson et al., 1997). However, this does not mean that other county-level variables are unimportant and that rGEs do not exist between other genes and other environmental variables. Future work
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should expand on the current study by including different genetic markers and different environmental measures (preferably at different levels of aggregation such as the block-group level). Scholars have long questioned whether residential selection is the result of social forces, individual self-selection, or some combination of both. The logical answer is that both social and individual-level factors are at play and researchers have wrestled with trying to account for both types of influences when analyzing the impact of structural factors on individual-level behavior. The current study suggests that self-selection, perhaps even at the molecular genetic level, is important to consider when traversing these issues. Appendix A. Histogram of county-level dopamine risk
Note 1. A Bonferroni correction was carried out to adjust for multiple testing bias. We analyzed the impact of the dopamine risk scale on two different outcomes collected at two time periods. Correcting the critical value for the four tests suggests a p-value of .05/4 = .013 be used for hypothesis testing. Nearly all of the significant findings highlighted in the paper held up when this more conservative critical value was utilized. Specifically, the exact p-value for model 1 and model 2 was .014, the p-value for model 4 and model 5 was .011, the p-value for model 7 and model 8 was .011, and the p-value for model 10 and model 11 was .002.
References Beaver, K. M., Wright, J. P., & DeLisi, M. (2008). Delinquent peer group formation: Evidence of a gene X environment correlation. The Journal of Genetic Psychology, 169, 227–244. Beaver, K. M., Wright, J. P., DeLisi, M., Walsh, A., Vaughn, M. G., Boisvert, D., et al. (2007). A gene X gene interaction between DRD2 and DRD4 is associated with conduct disorder and antisocial behavior in males. Behavioral and Brain Functions, 3, 30. Bouchard, T. J., Lykken, D. T., McGue, M., Segal, N. L., & Tellegen, A. (1990). Sources of human psychological differences: The Minnesota Study of Twins Reared Apart. Science, 250, 223–228. Cohen, M. S., Feng, Q., Florey, F. A., Ford, C. A., Harris, K. M., Hewitt, J. K., et al. (n.d.). Biomarkers in Wave III of the Add Health Study. The Add Health Biomarker Team. Craig, I. W., & Halton, K. E. (2009). Genetics of human aggressive behavior. Human Genetics, 126, 101–113. DiLalla, L. F. (2002). Behavior genetics of aggression in children: Review and future directions. Developmental Review, 22, 593–622. Durkheim, E. (1982). The rules of sociological method (8th ed.). Chicago: University of Chicago Press. Faraone, S. V., Doyle, A. E., Mick, E., & Biederman, J. (2001). Meta-analysis of the association between the 7-repeat allele of the dopamine D4 receptor gene and attention deficit hyperactivity disorder. The American Journal of Psychiatry, 158, 1052–1057.
Guo, G., Cai, T., Guo, R., Wang, H., & Harris, K. M. (2010). The dopamine transporter gene, a spectrum of most common risky behaviors, and the legal status of the behaviors. PLoS One, 5, e9352. Guo, G., Roettger, M. E., & Shih, J. C. (2007). Contributions of the DAT1 and DRD2 Genes to Serious and Violent Delinquency among Adolescents and Young Adults. Human Genetics, 121, 125–136. Haberstick, B. C., Lessem, J. M., Hopfer, C. J., Smolen, A., Ehringer, M. A., Timberlake, D., et al. (2005). Monoamine Oxidase A (MAOA) and antisocial behaviors in the presence of childhood and adolescent maltreatment. American Journal of Medical Genetics. Part B, Neuropsychiatric Genetics, 135, 59–64. Harden, K. P., D’Onofrio, B., Van Hulle, C., Turkheimer, E., Rodgers, J. L., Waldman, I. D., et al. (2009). Population density and youth antisocial behavior. Journal of Child Psychology and Psychiatry, 50, 999–1008. Harris, J. R. (1998). The nurture assumption: Why children turn out the way they do. New York: Free Press. Harris, K. M. (2009). The National Longitudinal Study of Adolescent Health (Add Health), Waves I & II, 1994–1996; Wave III, 2001–2002; Wave IV, 2007-2009. [machinereadable data file and documentation]. Chapel Hill, NC: Carolina Population Center, University of North Carolina at Chapel Hill. Harris, K. M., Florey, F., Tabor, J., Bearman, P. S., Jones, J., & Udry, J. R. (2003). The National Longitudinal Study of Adolescent Health: Research Design. [www document]. URL: http://www.cpc.unc.edu/projects/addhealth/design Harris, K. M., Halpern, C. T., Smolen, A., & Haberstick, B. C. (2006). The National Longitudinal Study of Adolescent Health (Add Health) Twin Data. Twin Research and Human Genetics, 9, 988–997. Heckman, J. J., & Smith, J. A. (1995). Assessing the case for social experiments. Journal of Economic Perspectives, 9, 85–110. Hopfer, C. J., Timberlake, D., Haberstick, B., Lessem, J. M., Ehringer, M. A., Smolen, A., et al. (2005). Genetic influences on quantity of alcohol consumed by adolescents and young adults. Drug and Alcohol Dependence, 78, 187–193. Keizer, K., Lindenberg, S., & Steg, L. (2008). The spreading of disorder. Science, 322, 1681–1685. Kemper, T. D. (1994). Social stratification, testosterone, and male sexuality. In L. Ellis (Ed.), Social Stratification and Socioeconomic Inequality, volume 2, Reproductive and Interpersonal Aspects of Dominance and Status (pp. 47–61). Westport, CT: Praeger. Kendler, K. S., & Baker, J. H. (2007). Genetic influences on measures of the environment: A systematic review. Psychological Medicine, 37, 615–626. Kling, J. R., Liebman, J. B., & Katz, L. F. (2007). Experimental analysis of neighborhood effects. Econometrica, 75, 83–119. Massey, D. S., & Denton, N. A. (1993). American apartheid: Segregation and the making of the underclass. Cambridge, MA: Harvard University Press. Plomin, R., DeFries, J. C., & Loehlin, J. C. (1977). Genotype-environment interaction and correlation in the analysis of human behavior. Psychological Bulletin, 84, 309–322. Resnick, M. D., Bearman, P. S., Blum, R. W., Bauman, K. E., Harris, K. M., Jones, J., et al. (1997). Protecting adolescents from harm: Findings from the National Longitudinal Study of Adolescent Health. Journal of the American Medical Association, 278, 823–832. Rowe, D. C., & Rodgers, J. L. (1997). Poverty and behavior: Are environmental measures nature and nurture? Developmental Review, 17, 358–375. Rowe, D. C., Stever, C., Chase, D., Sherman, S., Abramowitz, A., & Waldman, I. D. (2001). Two dopamine genes related to reports of childhood retrospective inattention and conduct disorder symptoms. Molecular Psychiatry, 6, 429–433. Sampson, R. J. (2008). Moving to Inequality: Neighborhood effects and experiments meet social structure. The American Journal of Sociology, 114, 189–231. Sampson, R. J., Raudenbush, S., & Earls, F. (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science, 277, 918–924. Scarr, S. (1992). Developmental theories for the 1990s: Developmental and individual differences. Child Development, 63, 1–19. Scarr, S., & McCartney, K. (1983). How people make their own environments: A theory of genotype➔environment effects. Child Development, 54, 424–435. Shaw, C. R., & McKay, H. D. (1942). Juvenile delinquency and urban areas. Chicago: University of Chicago Press. Taft, D. R. (1933). Testing the selective influence of areas of delinquency. The American Journal of Sociology, 38, 699–712. Tang, H., Quertermous, T., Rodriguez, B., Kardia, S. L. R., Zhu, X., Brown, A., et al. (2005). Genetic structure, self-identified race/ethnicity, and confounding in case-control association studies. American Journal of Human Genetics, 76, 268–275. Udry, J. R. (1995). Sociology and biology: What biology do sociologists need to know? Social Forces, 73, 1267–1278. van den Berghe, P. L. (1990). Why most sociologists don’t (and won’t) think evolutionarily. Sociological Forum, 5, 173–185. Warren, P. Y., Stewart, E. A., Tomaskovic-Devey, D., & Gertz, M. (2012). White’s residential preferences: Reassessing the relevance of criminal and economic stereotypes. Race and Justice, 2, 231–249. Wilson, W. J. (1987). The truly disadvantaged: The inner city, the underclass, and public policy. Chicago: University of Chicago Press. Zorbaugh, H. (1929). The gold coast and the slum: A sociological study of Chicago’s near north side. Chicago: University of Chicago Press.
Contents lists available at ScienceDirect
Journal of Criminal Justice
Genetic risk factors correlate with county-level violent crime rates and collective disadvantage☆ J.C. Barnes a,⁎, Brian B. Boutwell b, Kevin M. Beaver c,d a
School of Economic, Political & Policy Sciences, The University of Texas at Dallas, 800 W. Campbell Rd., Richardson, TX 75080, United States College of Criminal Justice, Sam Houston State University, P.O. Box 2296, Huntsville, TX 77341, United States College of Criminology and Criminal Justice, Florida State University, 634 W. Call St., Tallahassee, FL 32306, United States d Center for Social and Humanities Research, King Abdulaziz University, Jeddah, Saudi Arabia b c
a r t i c l e
i n f o
Available online 12 July 2013
a b s t r a c t Purpose: Social scientists have a rich tradition of uncovering the neighborhood, structural, and ecological correlates of human behavior. Results from this body of evidence have revealed that living in disadvantaged communities portends myriad negative outcomes, including antisocial behaviors. Though it has long been argued that associations between neighborhood factors and individual-level outcomes may, at least partially, reflect genetic selection, a paucity of research has empirically investigated this possibility. Methods: The current study examined whether known genetic risk factors for antisocial behavior were predictive of exposure to disadvantage and violent crime measured at the county level. Drawing on genotypic data from the National Longitudinal Study of Adolescent Health, a dopamine risk scale was created based on respondents’ genotypes for DAT1, DRD2, and DRD4. County-level disadvantage was measured via Census data and violent crime rates were measured via the FBI’s Uniform Crime Reports. Results: Findings revealed that individuals with a greater number of dopamine risk alleles were more likely to live in a disadvantaged county and were more likely to live in a county with higher violent crime rates. © 2013 Elsevier Ltd. All rights reserved.
Introduction Do individuals select the environments to which they are exposed? Do people migrate toward certain neighborhoods? These questions have occupied a prominent position in the minds of social scientists for decades. Consider the following observation from Taft nearly 80 years ago (1933:700): Are criminals attracted into delinquency areas more frequently than into other parts of the city? Are the children of criminals settling in such areas delinquent entirely because they live in criminogenic neighborhoods, or largely because they had delinquent parents? Do people with other abnormal personality traits migrate to such areas rather than elsewhere, and if so are these traits factors in their criminal behavior? In all these cases the question would be, of course, whether ☆ This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). No direct support was received from grant P01-HD31921 for this analysis. ⁎ Corresponding author. E-mail address: [email protected] (J.C. Barnes). 0047-2352/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jcrimjus.2013.06.013
these personality types were attracted or forced into the areas; or whether they were such on arrival rather than produced by life in the region after arrival. In short, are delinquency areas selective, and if so are they centers of delinquency partly because they are selective? The study of the possible selective influence of areas of delinquency seems to the writer to have been somewhat neglected. Scholars have examined a litany of factors that might be important for understanding the clustering of individuals within neighborhoods. However, these studies have generally focused on social or environmental forces while excluding biological and genetic factors. Early sociological research was motivated to study the factors that underlie the clustering of individuals within neighborhood settings (Shaw & McKay, 1942). Preliminary observations led many sociologists to conclude that social factors must be predicted by preceding social factors (Durkheim, 1982). In other words, sociology left no room for biological explanations of behavior and decision-making (Udry, 1995), suggesting instead that the clustering of individuals in certain areas was the result of social influences. Contemporary genetics research, however, indicates that this early sociological explanation may be untenable (Udry, 1995; van den Berghe, 1990). It is now well established that genetic and biological factors play a role in personality development (Bouchard et al., 1990; Harris, 1998) and perhaps even in individuals’ selection into certain environments and neighborhood conditions (Rowe & Rodgers, 1997; Scarr
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& McCartney, 1983). Along these lines, the current study builds on recent genetics research in order to better understand the factors that underpin peoples’ decisions to live in certain areas and not others. In order to frame the current focus, we turn first to a discussion of geneenvironment correlation (DiLalla, 2002; Kendler & Baker, 2007; Scarr & McCartney, 1983). Gene-environment correlation (rGE) The phenomenon of gene-environment correlation (rGE) has been a topic of much discussion for at least the past 35 years (Plomin et al., 1977; Scarr & McCartney, 1983). Researchers have reported that many environmental variables are partially influenced by genetics (i.e., some portion of the variance is attributable to heritable factors; Kendler & Baker, 2007). In general, two types of rGEs might underlie the correlation between a person’s genotype and their exposure to certain neighborhood environments: active rGE and passive rGE (Scarr, 1992; Scarr & McCartney, 1983). The first type of rGE that is applicable to the current focus is active rGE. Active rGE occurs when a person seeks out environments to suit their genetic proclivities, a phenomenon more commonly referred to as niche-picking or self-selection. Research has revealed, for example, that individuals are likely to select into environments that are compatible with their genetic tendencies (e.g., Beaver et al., 2008). Active rGEs offer a framework for understanding how genetic factors can influence the nonrandom selection of people into particular environments, perhaps even large-scale environments such as neighborhoods, cities, and counties. The second type of rGE is referred to as passive rGE. Passive rGE recognizes that parents pass along both an environment and genes to their offspring. Since the child’s environment and the child’s genes both originate from the same source (i.e., their parents) the two are likely to be correlated. Recall that active rGE allows for the non-random sorting of people into certain environments based on their genotype. To the extent that this occurs at the parent-level (i.e., parents choose their own environments), we would expect the child’s genotype (which is passed from parent to child) to be correlated with the child’s environment (which was selected by the parents).
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Sampson (2008) was commenting on the results from a recent set of experiments, the MTO, which sought to determine whether moving out of disadvantaged neighborhoods positively impacts the lifestyles, the health, and the behavior of individuals exposed to those conditions. The MTO proceeded by offering moving vouchers (Section 8 vouchers) to selected families, but to the researchers surprise, many families passed on the opportunity to move out of their neighborhood and into a less disadvantaged one. As reported by Kling, Liebman, and Katz (2007), the compliance rate (i.e., the percentage of families offered vouchers who used the voucher) was between 47 and 60 percent. Moreover, many of the families that did accept the voucher moved into a neighborhood nearby or one that mirrored their original location. To the extent that “opting out” of the MTO is related to self-selection into a neighborhood, these results offer indirect support for the active rGE hypothesis. Namely, findings from the MTO suggest that neighborhood living conditions are, at least for some, the result of self-selection and not completely the result of environmental pressures. By extension, these findings may also support the passive rGE hypothesis. To the extent that parents select into certain neighborhoods (active rGE), their children’s genotypes will also tend to be correlated with neighborhood conditions (passive rGE). Current study Juxtaposing the findings from the MTO with the concepts of active rGE and passive rGE, a conceptual framework begins to emerge. As the diagrams in Fig. 1 display, it may turn out that individuals selfselect into certain neighborhoods based on their genetic propensities (i.e., active rGE; Panel A). It may also turn out that parents select into certain environments and, therefore, those environments are correlated with their children’s genotypes (i.e., passive rGE; Panel B). This is not to
A: Active rGE
rGE and residential selection Keeping the discussion of rGE in mind, sociologists have extensively noted that residential sorting is not the result of a random process nor does it appear to be strictly the result of environmental forces. Consider the following comment from Zorbaugh (1929:134-135) more than eight decades ago: “It is often remarked how difficult it is to get a family to consent to move out of the slum no matter how advantageous the move may seem from the material point of view, and how much more difficult it is to keep them from moving back into the slum.” More recently, Sampson (2008:213) extended this statement by noting: Humans are agents with the decision-making power to accept or reject treatments (Heckman & Smith, 1995). Statistics on the “takeup” rate show that a majority of MTO [Moving to Opportunity] families who were offered a voucher did not actually use it. Families who did use the voucher experienced less neighborhood poverty in comparison with the noncompliers, but the vast majority remained within a relatively short distance of their origin neighborhood. Moreover, many families moved back into poor neighborhoods that were very similar to the ones in which they started, surprising many observers. Yet no one should be surprised at these facts. Back in the 1920s, Zorbaugh (1929) noted the “pull” of the slum and how the strong nature of its social ties kept people returning. It is only from a middleclass point of view, or what Zorbaugh called the “budgetminded social agency” (1929, p. 134), that the behavior of those who have grown up in poverty seems “incalculable.”
B: Passive rGE
Fig. 1. Conceptual diagram of two rGEs for explaining the correlation between genotype and environmental conditions.
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say that genetic factors are the only influence on neighborhood selection. Rather, our hypothesis is that genetic factors that have been linked with antisocial behavior will explain a portion of the variance in different measures of structural conditions due to active rGE and/or passive rGE. No research, of which we are aware, has empirically considered this hypothesis. Methods Data Data for this analysis were gleaned from the National Longitudinal Study of Adolescent Health (Add Health; Harris, 2009). The Add Health data have been described at length elsewhere (Harris et al., 2003, 2006; Resnick et al., 1997). Briefly, the Add Health is a nationally representative, longitudinal survey of American youth who were enrolled in middle and high school during 1995. The study began with a school-level survey that included all students enrolled in more than 130 schools across the United States (N ≈ 90,000). From this sample of respondents, a subsample of roughly 20,000 was drawn and utilized in the longitudinal portion of the study. Shortly after the in-school surveys were completed, the longitudinal subsample was administered a more lengthy follow-up survey. These interviews were conducted in the respondent’s home (i.e., wave 1). The surveys addressed a range of topics germane to adolescence such as the respondent’s behavior, their relationships with peers, and their personality characteristics. Respondents ranged in age between 11 and 21 years. A second wave of data were collected a year after wave 1 (i.e., wave 2). Approximately six years after wave 1 interviews a third wave of data were collected. During wave 3 interviews, all respondents had reached young adulthood (age range was between 18 and 26 years). Two unique features of the Add Health design are capitalized upon by the current research. First, a host of county-level measures that can be linked with the individual respondents are available to Add Health researchers. These measures are available for wave 1, wave 2, and wave 3. Second, genotypic information was gathered for a subsample of the wave 3 respondents. Twins and full siblings (where both siblings were participating in the study) were asked to provide buccal cell samples so that they could be genotyped. Genotypic information was available for 2,574 respondents (Cohen, Feng, Florey, et al., n.d.). After eliminating one twin from each monozygotic twin pair (to avoid artificially decreasing standard errors; Haberstick et al., 2005) and after eliminating cases with missing data, final analytic sample sizes ranged between 2,212 and 2,268. Measures Genetic risk variable Dopamine risk. Scholars have shown that certain dopaminergic genes may be related to criminal and antisocial behavior (Beaver et al., 2007; Craig & Halton, 2009). These genetic risk factors, via rGE processes, may be related to the individual’s social environment. Included in the Add Health was genotypic information for three dopamine polymorphisms: DAT1, DRD2, and DRD4. Based on information gleaned from these three polymorphisms, a dopamine risk scale was generated. DAT1 is a dopamine transporter gene that has two common alleles; the 9-repeat allele and the 10-repeat allele. The 10-repeat allele has been identified as the risk allele (Guo et al., 2010; Rowe et al., 2001) and, as such, DAT1 alleles were coded so that 0 = 9-repeat allele and 1 = 10-repeat allele. Respondents with any other allele were assigned a missing value and were omitted from the analyses (Hopfer et al., 2005). DRD2 is a dopamine receptor polymorphism that has one allele known as the A1 allele and another known as the A2 allele. The A1 allele has been identified as the risk allele (Guo et al., 2007). Thus, respondents with two A2 alleles were coded as 0, those with one A1
allele were coded as 1, and respondents with two A1 alleles were coded as 2. The DRD4 polymorphism is a dopamine receptor gene that has been tied to antisocial and criminal behavior. The 7-repeat allele is regarded as the risk allele (Faraone et al., 2001; Rowe et al., 2001). Building on previous research, DRD4 alleles were coded so that the 7-repeat allele (along with the 8-, 9-, and 10-repeat alleles) = 1 and the 4-repeat allele (along with the 2-, 3-, 5-, and 6-repeat alleles) = 0. In order to generate the dopamine risk scale, respondents’ scores on the three dopamine polymorphisms (DAT1, DRD2, and DRD4) were summed together. The polymorphisms were coded co-dominantly, meaning that the value for each polymorphism reflected the number of risk alleles carried by the respondent. The dopamine risk scale ranged from a minimum of 0 (i.e., no dopamine risk alleles) to a maximum of 6 (i.e., six dopamine risk alleles). County-level variables Violent crime rate. Each year, the Federal Bureau of Investigation collects crime rate data from across the U.S. These data are published annually in the Uniform Crime Reports (UCR) and the county-level measures were included as part of the Add Health data collection at wave 1, wave 2, and wave 3. The violent crime rate variable was a composite index reflecting the number of robberies, aggravated assaults, rapes, and homicides per 100,000 residents in the respondent’s county. Violent crime rate data from wave 1 and wave 3 were analyzed in the present study. Collective disadvantage. Two county-level indicators of collective disadvantage were calculated based on prior research from Sampson et al., (1997). The first was generated using data from wave 1 by drawing on the 1990 Census data (the decennial census closest to wave 1 data collection). The second county-level measure of collective disadvantage was drawn from data available at wave 3, which reflected data drawn from the Census of Population and Housing in 2000. To create the collective disadvantage scale, the following measures were factor analyzed: the percentage of Black residents, the percentage of female headed households, the percentage of residents with an income under $15,000, the percentage of residents on public assistance, and the unemployment rate. Factor analysis revealed that the correlation structure of the five items was best explained with a single latent construct for both the wave 1 measure and the wave 3 measure. All factor loadings (except percent on public assistance at wave 3 which had a loading of .67) were greater than or equal to .75 and the reliability coefficient was .92 for the wave 1 measure and .89 for the wave 3 measure. Both scales were created using regression scoring based on the factor analysis results. Higher values reflected more collective disadvantage for both measures. Controls The respondent’s sex was included as a dichotomous variable (0 = female, 1 = male) as was the respondent’s race (0 = non-Black, 1 = Black). Information on respondent race was gleaned from wave 3 interviewer reports of the respondent’s race. Analysis plan In order to examine the relationship between the dopamine risk scale and the county-level measures of violent crime and collective disadvantage, the analysis proceeded in four steps. First, observed mean levels of violent crime and observed mean levels of collective disadvantage were calculated for respondents who had different levels of dopamine risk. Second, zero-order correlation coefficients were calculated between the dopamine risk scale, the violent crime rate, and the collective disadvantage scale. Third, OLS regression models were estimated. In these models, the violent crime rate variables and the collective
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disadvantage scales were each (separately) regressed on the dopamine risk scale. The estimates gleaned from these regression models were used to plot predicted violent crime rates and predicted collective disadvantage scores based on each respondent’s level of dopamine risk. Fourth, the relationship between dopamine risk and the county measures was assessed after statistically controlling for the respondent’s sex and race. One final point is worth noting before proceeding to the results. Recall that wave 1 data collection occurred when all of the Add Health respondents were in middle or high school and the age range was 11-21 years old. Due to this feature of the data collection, any correlation between the respondent’s genotype and the county-level measures (violent crime rate and collective disadvantage) is likely to be the result of passive rGE processes. Wave 3 interviews, however, were conducted when the respondents had reached young adulthood and the age range was 18-26. Although it is possible that some respondents remained living with their parents, it is likely that many had moved into homes of their own. Thus, any correlation between respondent genotype and the wave 3 county-level variables is likely to be the result of active rGE processes. Preliminary evidence of this point is found in the number of unique counties represented in the analyses. At wave 1, respondents hailed from 117 different counties. At wave 3, however, respondents lived in 387 different counties.
Findings Before proceeding to the analysis, it was important to determine whether the dopamine risk scale varied significantly across counties. If not, the remainder of the analysis would be futile in that we would be trying to explain a variable with a constant. In order to determine whether dopamine risk and total genetic risk varied at the county level, a two-pronged approach was followed. First, a one-way ANOVA was estimated where the dopamine risk scale was the response variable and the respondent’s county identifier was the factor variable. The results revealed that the dopamine risk scale (F = 2.03, df = 116, p b .0001) varied significantly across the different counties. The second approach was to aggregate the dopamine risk scale to the county level (by calculating the mean dopamine risk score for respondents in each county) and observe the distributional properties of the aggregated scale. This approach revealed that when aggregated to the countylevel the dopamine risk scale had a distribution that approximated normality (see Appendix A). Thus, a lack of variation on the dopamine risk scale should not hinder the analyses presented below. Mean scores on the violent crime rate and on the collective disadvantage scale are presented in Table 1. Notice that the mean scores are presented separately for respondents with different levels of dopamine risk. The top portion of the table presents the mean scores of violent crime rate and neighborhood disadvantage at wave 1. The bottom portion presents the mean scores on the violent crime rate and neighborhood collective disadvantage at wave 3. Presented in the first two columns of the top portion of Table 1 are the sample size (N) and the mean violent crime rate at wave 1. As can be seen, there were 330 respondents who had either a 0 or a 1 on the dopamine risk scale (because only 30 respondents scored a 0, the 0 and 1 categories were collapsed together. The substantive findings for all of the analyses were identical when the categories were not collapsed). For these respondents, the mean violent crime rate in their county of residence during wave 1 was 656.17 per 100,000 residents. As we move down the column into higher scores on the dopamine risk scale, a pattern begins to emerge. Specifically, mean violent crime rates at wave 1 appear to increase as scores on the dopamine risk scale increase. This suggests that dopamine risk may be tied to county-level violent crime rates. Presented in the last two columns of the top portion of Table 1 are sample sizes and the mean collective disadvantage scores for respondents with different levels of dopamine risk. In broad strokes,
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Table 1 Mean county violent crime rate and mean county collective disadvantage by dopamine risk Mean
Mean
Violent
Collective
Dopamine Risk
N
Crime Rate
N
Disadvantage
Wave 1 0–1 2 3 4 5–6
330 787 729 309 68
656.17 685.92 755.75 807.73 882.81
345 804 737 314 68
-.09 -.11 .03 .16 .28
Wave 3 0–1 2 3 4 5–6
346 786 712 307 63
508.13 498.36 572.54 581.14 665.84
346 786 712 307 63
-.07 -.10 .02 .15 .25
respondents who carried more dopamine risk tended to live in counties with a higher level of collective disadvantage. Moving to the bottom portion of Table 1, we see that respondents who scored lower on the dopamine risk scale tended to live in counties with lower (relatively) rates of violent crime and in counties with lower (relatively) levels of collective disadvantage. This pattern was not, however, consistent across all levels of dopamine risk. For instance, respondents with 2 dopamine risk alleles, on average, lived in counties with less violent crime than respondents who had 0 or 1 dopamine risk alleles. A similar finding emerged for the collective disadvantage measure. Presented in Table 2 are the zero-order correlation coefficients between dopamine risk, the county violent crime rate, and the county collective disadvantage scale. As can be seen, the dopamine risk scale was positively and significantly (p b .05, two-tailed) correlated with the wave 1 violent crime rate (r = .09), with the wave 3 violent crime rate (r = .09), with the wave 1 collective disadvantage scale (r = .10), and with the wave 3 collective disadvantage scale (r = .09). The next step of the analysis was to generate predicted rates of violent crime as a function of dopamine risk. In order to do so, an OLS regression model utilizing the wave 1 violent crime rate as the dependent variable and the dopamine risk scale as the independent variable was estimated and the coefficient estimates were used to generate predicted values (i.e., predicted violent crime rates for individuals with different levels of dopamine risk). The predicted wave 1 violent crime rates are presented graphically in Fig. 2, along with the coefficient estimates gleaned from the regression models in the figure caption. Consistent with the above findings, the dopamine risk scale was positively associated with wave 1 violent crime rates. At wave 1, respondents with the lowest level of dopamine risk (those with a 0 or a 1 on the dopamine risk scale) were predicted to live in a county with a violent crime rate of 640.37 per 100,000 residents. Respondents
Table 2 Correlations between individual-level dopamine risk and county-level violent crime rate and collective disadvantage W1 W3 Dopamine W1 Collective Violent Risk Violent Crime Rate Crime Rate Disadvantage Dopamine Risk W1 Violent Crime Rate W3 Violent Crime Rate W1 Collective Disadvantage W3 Collective Disadvantage ⁎ p b .05 (two-tailed).
.09⁎ .09⁎ .10⁎ .09⁎
.72⁎ .53⁎ .58⁎
.59⁎ .73⁎
.78⁎
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1000
Predicted Violent Crime Rate
900 800 700 600 500 400 300
Wave 1 Wave 3
200 100 0 0-1
2
3
4
5-6
Dopamine Risk Fig. 2. Predicted violent crime rate as a function of dopamine risk. Note: Wave 1: bdopamine risk = 56.15, SE = 22.08, p b .05 (two-tailed); Wave 3 bdopamine risk = 37.24, SE = 14.59, p b .05 (two-tailed); Standard errors were corrected for the clustering of cases within counties.
with the highest level of dopamine risk (those with a 5 or a 6) were predicted to live in a county with a violent crime rate of 864.97 per 100,000 residents; an increase of more than 35% over those with the lowest dopamine risk. A second OLS regression model predicted wave 3 violent crime rates with the dopamine risk scale. The results from this analysis are also presented in Fig. 2. As with the wave 1 violent crime rate, dopamine risk was positively and significantly associated with wave 3 violent crime rates. Respondents with the lowest dopamine risk were predicted to live in a county with 483.09 violent crimes per 100,000 residents at wave 3. Respondent with the highest level of dopamine risk were predicted to live in counties with 632.04 violent crimes per 100,000 residents at wave 3. The results from the next two OLS regression analyses are presented in Fig. 3. In the first analysis, the wave 1 collective disadvantage scale is utilized as the dependent variable and the dopamine risk scale is utilized as the predictor variable. Predicted scores on the collective disadvantage scale are plotted as a function of dopamine risk. A positive
0.3
Discussion
Predicted Collective Disadvantage
0.25 0.2 0.15 0.1 0.05 0 0-1
2
3
4
5-6
-0.05 -0.1
Wave 1 Wave 3
-0.15 -0.2
relationship was identified such that those with the lowest dopamine risk were predicted to live in counties with a disadvantage score of -.17 and those with the greatest dopamine risk were predicted to live in counties with a disadvantage score of .23. Recall that the collective disadvantage scale was generated as a factor score. Thus, the values roughly correspond to z-scores (i.e., standard deviation units). The fourth OLS regression model utilized the wave 3 collective disadvantage measure as the outcome variable. The same familiar pattern emerged. In short, respondents with fewer risk alleles on the three dopamine variables tended to live in relatively less disadvantaged counties at wave 3. Respondents carrying zero or one risk allele were predicted to live in counties with a disadvantage score of -.14 while those with five or six risk alleles lived in counties with a predicted disadvantage score of .20. The final analysis re-estimated each of the regression models presented in the figures, but this time the respondent’s sex and the respondent’s race were entered into the regression models as control variables. The results of these models are presented in Table 3, which is split into two panels. Panel A presents the results from the regression models where the violent crime rate variables served as the dependent variables. Model 1 reports the baseline estimates for the effect of the dopamine risk scale on the wave 1 violent crime rate (which are identical to those presented in Fig. 2). Model 2 entered the sex variable into the regression equation. Respondent sex does not significantly predict wave 1 violent crime rate and the effect of dopamine risk was unchanged when this variable was included. Model 3 enters the respondent’s race. As can be seen, when the respondent’s race is accounted for, the dopamine risk scale no longer exerts a statistically significant influence on wave 1 violent crime rates. Models 4 through 6 perform the same series of analyses with the wave 3 violent crime rate variable serving as the dependent variable. A substantively identical pattern emerged where the effect of the dopamine risk scale was rendered statistically insignificant when the respondent’s race was controlled. Finally, Panel B presents the results from six OLS regression models where the collective disadvantage variables were utilized as the dependent variables. As with the violent crime rate measures, models 7 and 10 indicated that dopamine risk significantly predicted wave 1 collective disadvantage (model 7) and wave 3 collective disadvantage (model 10). Models 8 and 11 revealed that controlling for the respondent’s sex did not affect the dopamine risk scale estimate. Models 9 and 12, however, indicated that the dopamine risk scale no longer significantly predicted either outcome once the respondent’s race was entered into the regression model.1
Dopamine Risk
Fig. 3. Predicted collective disadvantage as a function of dopamine risk. Note: Wave 1 bdopamine risk = .10, SE = .04, p b .05 (two-tailed); Wave 3 bdopamine risk = 09, SE = .03, p b .05 (two-tailed); Standard errors were corrected for the clustering of cases within counties.
Social scientists have long noted the impact of structural/ contextual factors (e.g., county-level variables) on individual-level behavior (Durkheim, 1982). Despite mounds of research into these influences, statistical significance can often be fleeting (Harden et al., 2009) and the theoretical links between macro-level factors and individual-level behavior often leaves much to be desired. This is not to say that neighborhood/contextual factors have no impact on individuals’ behavior; a series of ingenious experiments by Keizer et al., (2008) provide strong evidence that context influences normviolating behavior. Instead, available research indicates that the association between contextual factors and individual-level behavior is more complex than has traditionally been recognized. Findings from the current analysis offer a new perspective on the influence of structural and contextual influences. Specifically, the question of whether individuals self-select into certain environments was directly addressed by analyzing the association between molecular genetic data and exposure to county-level crime rates and county-level rates of collective disadvantage. Two important conclusions were gleaned from the analysis. First, the results of a series of statistical tests indicated that individual-level genetic risk predicted
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Table 3 OLS regression estimates before and after including control variables Panel A: Violent Crime Rate Wave 1
Dopamine Risk
Wave 3
Model 1
Model 2
Model 3
Model 4
Model 5
b(SE)
b(SE)
b(SE)
b(SE)
b(SE)
b(SE)
56.15⁎ (22.08)
56.15⁎ (22.13) -.32 (22.98)
29.90 (24.27)
.37.24⁎ (14.59)
37.18⁎ (14.59) -6.09 (14.28)
16.08 (10.57)
Male (=1) Black (=1)
458.51⁎ (145.86)
Model 6
418.60⁎ (160.09)
Panel B: Collective Disadvantage Wave 1
Dopamine Risk Male (=1)
Wave 3
Model 7
Model 8
Model 9
Model 10
Model 11
b(SE)
b(SE)
b(SE)
b(SE)
b(SE)
b(SE)
.10⁎ (.04)
.10⁎ (.04) .02 (.04)
.03 (.03)
.09⁎ (.03)
.09⁎ (.03) .04 (.04)
.03 (.02)
Black (=1)
1.16⁎ (.31)
Model 12
1.02⁎ (.26)
Note: All standard errors were corrected for the clustering of cases within counties. ⁎ p b .05 (two-tailed).
county-level measures of violent crime rates and collective disadvantage. These findings are important in that they suggest the sorting of individuals into certain areas (i.e., counties) is not random and may not be completely due to social/environmental forces. Instead, individuals who carry more “risk” alleles for antisocial behavior are more likely to live in areas where violent crime is more prevalent and where collective disadvantage is greater. Taken together, these findings provide support for gene-environment correlation (rGE) hypotheses. The findings from the wave 1 analysis lend support for the passive rGE hypothesis because all respondents were still in middle or high school at the time and, therefore, were most likely to be living with their parents. The findings from the wave 3 analysis lend support for the active rGE hypothesis because all respondents had reached young adulthood by this time and, therefore, were more likely to be living in areas of their own choosing. The second important finding to emerge from the analysis was that the correlation between genetic risk and the county-level variables was completely explained away by the respondent’s race. There are two possible explanations for this finding. The first explanation highlights the possibility that dopamine risk has an indirect influence on neighborhood selection, but dopamine risk is correlated with race, thereby masking the genetic effect. In short, the first explanation states that the genetic effect is not a statistical artifact. The second explanation states that dopamine risk is a proxy for race and, due to population stratification, blacks are more likely to live in high crime/disadvantaged areas meaning that the genetic effect is a statistical artifact. Unfortunately, identifying which of these two processes accurately explains the current findings is empirically impossible with the Add Health data. Thus, we are left only with theoretical considerations to ponder. There is certainly evidence to support both explanations and, realistically, it is likely that both processes can be credited. To be sure, genomic data, while clearly evincing the point that genetic variation is greatest within race rather than across race, has also shown that racial characteristics correlate with genotype at an impressive rate. Tang, Quertermous, Rodriguez, et al. (2005) revealed a nearly perfect overlap in self-reported race and groups identified with a genetic cluster analysis. At the same time, U.S. history is fraught with examples of racial discrimination (both historical discrimination and contemporary discrimination (Wilson, 1987)) and
segregation and we would be extremely negligent if we did not consider the lingering effects that these past policies, attitudes, and perceptions might have on today’s society (Massey & Denton, 1993; Wilson, 1987). In that respect, it is important to note a recent analysis by Warren et al. (2012) which demonstrated that Whites were less willing to reside in communities with Black residents if they also viewed Blacks as being criminal and representing an economic liability. Wilson (1987) appropriately summarized this position when he noted that “…long periods of racial oppression can result in a system of inequality that may persist for indefinite periods of time even after racial barriers are removed” (p. 147). We encourage future scholars to revisit these issues and we hope, at a minimum, that these findings will spark a discussion about the interplay between genetic factors, race, and exposure to certain structural/contextual factors such as county-level crime rates and collective disadvantage. We are sensitive to the fact that these issues are controversial and, by some, are considered taboo. Indeed, Kemper (1994) noted nearly 20 years ago that, “…if sociology and biology have not been on speaking terms in general, sociological disdain for the biological reaches its apogee when it comes to social stratification” (p. 48). We hope that analyses such as the present one will dissolve some of this disdain and will allow criminology and her scholars to contribute progressive, ethical, and moral research to 21st century scientific discourse. Limitations of the current study must be noted. First, data were gleaned from the Add Health study, meaning that we were limited to the genetic information included by the Add Health researchers. Extant evidence clearly indicates that dopamine genes are not the only genetic factors that correlate with antisocial behavior and, therefore, it is unlikely that the three dopamine genes examined here are the only factors that correlate with county crime rates and county collective disadvantage. Second, and related to the first, is that this analysis was restricted to two county-level measures (violent crime rates and collective disadvantage). The choice of these two county variables was obvious; a large literature has discussed the potential role that exposure to crime and disadvantage has on an individual’s behavior (e.g., Sampson et al., 1997). However, this does not mean that other county-level variables are unimportant and that rGEs do not exist between other genes and other environmental variables. Future work
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should expand on the current study by including different genetic markers and different environmental measures (preferably at different levels of aggregation such as the block-group level). Scholars have long questioned whether residential selection is the result of social forces, individual self-selection, or some combination of both. The logical answer is that both social and individual-level factors are at play and researchers have wrestled with trying to account for both types of influences when analyzing the impact of structural factors on individual-level behavior. The current study suggests that self-selection, perhaps even at the molecular genetic level, is important to consider when traversing these issues. Appendix A. Histogram of county-level dopamine risk
Note 1. A Bonferroni correction was carried out to adjust for multiple testing bias. We analyzed the impact of the dopamine risk scale on two different outcomes collected at two time periods. Correcting the critical value for the four tests suggests a p-value of .05/4 = .013 be used for hypothesis testing. Nearly all of the significant findings highlighted in the paper held up when this more conservative critical value was utilized. Specifically, the exact p-value for model 1 and model 2 was .014, the p-value for model 4 and model 5 was .011, the p-value for model 7 and model 8 was .011, and the p-value for model 10 and model 11 was .002.
References Beaver, K. M., Wright, J. P., & DeLisi, M. (2008). Delinquent peer group formation: Evidence of a gene X environment correlation. The Journal of Genetic Psychology, 169, 227–244. Beaver, K. M., Wright, J. P., DeLisi, M., Walsh, A., Vaughn, M. G., Boisvert, D., et al. (2007). A gene X gene interaction between DRD2 and DRD4 is associated with conduct disorder and antisocial behavior in males. Behavioral and Brain Functions, 3, 30. Bouchard, T. J., Lykken, D. T., McGue, M., Segal, N. L., & Tellegen, A. (1990). Sources of human psychological differences: The Minnesota Study of Twins Reared Apart. Science, 250, 223–228. Cohen, M. S., Feng, Q., Florey, F. A., Ford, C. A., Harris, K. M., Hewitt, J. K., et al. (n.d.). Biomarkers in Wave III of the Add Health Study. The Add Health Biomarker Team. Craig, I. W., & Halton, K. E. (2009). Genetics of human aggressive behavior. Human Genetics, 126, 101–113. DiLalla, L. F. (2002). Behavior genetics of aggression in children: Review and future directions. Developmental Review, 22, 593–622. Durkheim, E. (1982). The rules of sociological method (8th ed.). Chicago: University of Chicago Press. Faraone, S. V., Doyle, A. E., Mick, E., & Biederman, J. (2001). Meta-analysis of the association between the 7-repeat allele of the dopamine D4 receptor gene and attention deficit hyperactivity disorder. The American Journal of Psychiatry, 158, 1052–1057.
Guo, G., Cai, T., Guo, R., Wang, H., & Harris, K. M. (2010). The dopamine transporter gene, a spectrum of most common risky behaviors, and the legal status of the behaviors. PLoS One, 5, e9352. Guo, G., Roettger, M. E., & Shih, J. C. (2007). Contributions of the DAT1 and DRD2 Genes to Serious and Violent Delinquency among Adolescents and Young Adults. Human Genetics, 121, 125–136. Haberstick, B. C., Lessem, J. M., Hopfer, C. J., Smolen, A., Ehringer, M. A., Timberlake, D., et al. (2005). Monoamine Oxidase A (MAOA) and antisocial behaviors in the presence of childhood and adolescent maltreatment. American Journal of Medical Genetics. Part B, Neuropsychiatric Genetics, 135, 59–64. Harden, K. P., D’Onofrio, B., Van Hulle, C., Turkheimer, E., Rodgers, J. L., Waldman, I. D., et al. (2009). Population density and youth antisocial behavior. Journal of Child Psychology and Psychiatry, 50, 999–1008. Harris, J. R. (1998). The nurture assumption: Why children turn out the way they do. New York: Free Press. Harris, K. M. (2009). The National Longitudinal Study of Adolescent Health (Add Health), Waves I & II, 1994–1996; Wave III, 2001–2002; Wave IV, 2007-2009. [machinereadable data file and documentation]. Chapel Hill, NC: Carolina Population Center, University of North Carolina at Chapel Hill. Harris, K. M., Florey, F., Tabor, J., Bearman, P. S., Jones, J., & Udry, J. R. (2003). The National Longitudinal Study of Adolescent Health: Research Design. [www document]. URL: http://www.cpc.unc.edu/projects/addhealth/design Harris, K. M., Halpern, C. T., Smolen, A., & Haberstick, B. C. (2006). The National Longitudinal Study of Adolescent Health (Add Health) Twin Data. Twin Research and Human Genetics, 9, 988–997. Heckman, J. J., & Smith, J. A. (1995). Assessing the case for social experiments. Journal of Economic Perspectives, 9, 85–110. Hopfer, C. J., Timberlake, D., Haberstick, B., Lessem, J. M., Ehringer, M. A., Smolen, A., et al. (2005). Genetic influences on quantity of alcohol consumed by adolescents and young adults. Drug and Alcohol Dependence, 78, 187–193. Keizer, K., Lindenberg, S., & Steg, L. (2008). The spreading of disorder. Science, 322, 1681–1685. Kemper, T. D. (1994). Social stratification, testosterone, and male sexuality. In L. Ellis (Ed.), Social Stratification and Socioeconomic Inequality, volume 2, Reproductive and Interpersonal Aspects of Dominance and Status (pp. 47–61). Westport, CT: Praeger. Kendler, K. S., & Baker, J. H. (2007). Genetic influences on measures of the environment: A systematic review. Psychological Medicine, 37, 615–626. Kling, J. R., Liebman, J. B., & Katz, L. F. (2007). Experimental analysis of neighborhood effects. Econometrica, 75, 83–119. Massey, D. S., & Denton, N. A. (1993). American apartheid: Segregation and the making of the underclass. Cambridge, MA: Harvard University Press. Plomin, R., DeFries, J. C., & Loehlin, J. C. (1977). Genotype-environment interaction and correlation in the analysis of human behavior. Psychological Bulletin, 84, 309–322. Resnick, M. D., Bearman, P. S., Blum, R. W., Bauman, K. E., Harris, K. M., Jones, J., et al. (1997). Protecting adolescents from harm: Findings from the National Longitudinal Study of Adolescent Health. Journal of the American Medical Association, 278, 823–832. Rowe, D. C., & Rodgers, J. L. (1997). Poverty and behavior: Are environmental measures nature and nurture? Developmental Review, 17, 358–375. Rowe, D. C., Stever, C., Chase, D., Sherman, S., Abramowitz, A., & Waldman, I. D. (2001). Two dopamine genes related to reports of childhood retrospective inattention and conduct disorder symptoms. Molecular Psychiatry, 6, 429–433. Sampson, R. J. (2008). Moving to Inequality: Neighborhood effects and experiments meet social structure. The American Journal of Sociology, 114, 189–231. Sampson, R. J., Raudenbush, S., & Earls, F. (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science, 277, 918–924. Scarr, S. (1992). Developmental theories for the 1990s: Developmental and individual differences. Child Development, 63, 1–19. Scarr, S., & McCartney, K. (1983). How people make their own environments: A theory of genotype➔environment effects. Child Development, 54, 424–435. Shaw, C. R., & McKay, H. D. (1942). Juvenile delinquency and urban areas. Chicago: University of Chicago Press. Taft, D. R. (1933). Testing the selective influence of areas of delinquency. The American Journal of Sociology, 38, 699–712. Tang, H., Quertermous, T., Rodriguez, B., Kardia, S. L. R., Zhu, X., Brown, A., et al. (2005). Genetic structure, self-identified race/ethnicity, and confounding in case-control association studies. American Journal of Human Genetics, 76, 268–275. Udry, J. R. (1995). Sociology and biology: What biology do sociologists need to know? Social Forces, 73, 1267–1278. van den Berghe, P. L. (1990). Why most sociologists don’t (and won’t) think evolutionarily. Sociological Forum, 5, 173–185. Warren, P. Y., Stewart, E. A., Tomaskovic-Devey, D., & Gertz, M. (2012). White’s residential preferences: Reassessing the relevance of criminal and economic stereotypes. Race and Justice, 2, 231–249. Wilson, W. J. (1987). The truly disadvantaged: The inner city, the underclass, and public policy. Chicago: University of Chicago Press. Zorbaugh, H. (1929). The gold coast and the slum: A sociological study of Chicago’s near north side. Chicago: University of Chicago Press.