Neighborhood Disadvantage and Parenting: Behavioral Genetics Evidence of Child Effects.

581451

research-article2015

IJOXXX10.1177/0306624X15581451International Journal of Offender Therapy and Comparative CriminologyYun and Lee

Article

Neighborhood Disadvantage and Parenting: Behavioral Genetics Evidence of Child Effects

International Journal of Offender Therapy and Comparative Criminology 1–20 © The Author(s) 2015 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0306624X15581451 ijo.sagepub.com

Ilhong Yun1 and Julak Lee2

Abstract The criminological literature has a long tradition of emphasizing the socialization effects that parents have on children. By contrast, evidence from behavioral genetics research gives precedence to child effects on parental management techniques over parental effects on children’s outcomes. Considering these diverging lines of scholarship and literature, the current study explores a novel hypothesis that child effects on parenting may be conditioned by the level of the disadvantage of the neighborhood in which the child’s family resides. By using measures of perceived parenting as dependent variables, the researchers analyze data on 733 same-sex sibling pairs derived from the Add Health study by taking advantage of the DeFries–Fulker analytical technique. The results show that in adequate neighborhoods, between 43% and 55% of the variance in the measures of perceived parenting is due to genetic factors, whereas shared environmental effects are negligible. In disadvantaged neighborhoods, genetic effects are negligible, whereas shared environmental influences account for between 34% and 57% of the variance in perceived parenting. These results offer partial support for the contextualized gene–environment correlation, which provides initial evidence that although both parental socialization effects and child effects exist, these effects can be modified by the context. Keywords neighborhood disadvantage, parenting, gene–environment correlation

1Chosun 2Kyonggi

University, Gwangju, South Korea University, Suwon-si, Gyonggi-do, South Korea

Corresponding Author: Julak Lee, PhD, Department of Protection and Security Management, Kyonggi University, 154-42 Gwanggyosan-ro Yongtong-gu, Suwon-si, Gyonggi-do, 443-760, South Korea. Email: [email protected]

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International Journal of Offender Therapy and Comparative Criminology

Evidence abounds in the criminological literature that testifies to the salience of the socialization effects that parents have on their offspring’s delinquent and criminal involvement. For instance, after reanalyzing the data from the Gluecks’ classic study of 500 delinquents and 500 nondelinquents reared in low-income neighborhoods in Boston, Sampson and Laub (1994) concluded that the fundamental causes of delinquency are family processes centering on the parental supervision, attachment, and discipline of their young. Similar arguments and evidence have been consistently presented by a number of prominent criminologists (Gottfredson & Hirschi, 1990; Hirschi, 1969; Patterson & Stouthamer-Loeber, 1984; Wilson & Herrnstein, 1985; Wright & Cullen, 2001). The importance of parenting practices acknowledged by many criminologists stems from the “blank slate” view of children. This view is exemplified by the idea that “as the twig is bent, so grows the tree” and, therefore, underscores the role of parents as the main actors shouldering the responsibility of “bending” children into desirable shapes (Maccoby, 2000). This view considers parental socialization as purely an environmental process not affected by the child’s behavioral or personality traits. Such a perspective, however, has been called into question by scholars from other disciplines. Critics argue that the unidirectional interpretation of parental effects on child outcomes is overly simplistic and probably misspecified (Beaver & Wright, 2007; Harris, 1998; Huh, Tristan, Wade, & Stice, 2006; Lytton, 1990). Instead, they maintain that the correlation between parenting measures and child outcomes could be interpreted in many different ways. For instance, although a child’s maladaptive behaviors could occur owing to poor parenting, an equally plausible possibility is that the child’s maladaptive behaviors cause poor parenting. Also possible is that the two could influence each other in reciprocal ways. Relatedly, Huh et al. (2006) located four prospective studies on parenting deemed to be methodologically rigorous that accounted for the temporal ordering of parental and child behaviors. Collectively, 13 statistical models were tested in these four studies. Among these 13 models, 6 models found parent-to-child effects and 5 found reciprocal effects. However, 10 models revealed significant child-to-parent effects, a clear indication that not only do child effects exist but also that they are more prevalent and potentially stronger than parental socialization effects. Support for the influence of child effects can also be elicited from Stattin and Kerr’s (2000) seminal work on parental monitoring. According to this study’s findings, the degree of parental monitoring—a measure of parenting typically used in the criminological literature—is in fact rarely determined by what parents do or do not. Instead, it is largely determined by how much children are willing to disclose their activities to their parents. Thus, the measure of parental monitoring reflects a child’s activity rather than those of his or her parents. Findings in support of child effects have also been garnered from a wealth of behavioral genetics research (Beaver, 2011; Beaver & Wright, 2007; Kendler & Baker, 2007; Lytton, 1990). These studies have consistently shown that most measures of parenting are partially influenced by a child’s genetics. Highlighting child effects on parenting, Beaver and Wright (2007) argued,


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Yun and Lee To pretend that children are passive receptors of their environment and do not actively sculpt their families is wrong. A more complete and accurate explanation of behavior— one that recognizes the importance of child effects—is needed to understand more fully the cause of crime and delinquency. (p. 657)

In sum, the confluence of evidence from multiple disciplines shows that once child effects are held constant, the influence of parental socialization on the child is reduced substantially and that parenting practices themselves are largely reflective of the child’s genetic predispositions (Beaver & Wright, 2007; Lytton, 1990; Rowe, 1994; Wright & Beaver, 2005). By drawing on these two lines of thinking and research results—one emphasizing parental effects and the other highlighting child effects—the current study explores the relatively novel hypothesis that child effects may be conditioned by the degree of the structural disadvantage of the neighborhood in which the households are located. Specifically, we postulate that child effects become stronger, thus more visible, in adequate neighborhoods. By contrast, child effects become weaker, whereas parental effects become stronger in disadvantaged neighborhoods. We develop this hypothesis by drawing on the behavioral genetics concept of gene–environment correlation, which posits that virtually, all environmental variables including parenting are influenced, to at least some degree, by genetic factors (Kendler & Baker, 2007; Scarr & McCartney, 1983). At the same time, the hypothesis is also based on a series of empirical studies substantiating that genetic effects on human phenotypes (i.e., measurable characteristics) are modified by the environmental context (Cleveland, 2003; Dunne et al., 1997; Rowe, Jacobson, & Van den Oord, 1999). To test the hypothesis, we analyze National Longitudinal Study of Adolescents (Add Health) data on 733 same-sex monozygotic (MZ) twin, dizygotic (DZ) twin, and full-sibling pairs classified into structurally adequate and disadvantaged neighborhoods. The hypothesis is tested for three parenting measures as perceived by children, namely, maternal attachment, parental monitoring, and maternal involvement.1

The Importance of Parenting in Criminology A wealth of criminological research has empirically tested whether variations in parenting tactics affect the variation in an offspring’s behavioral problems and antisocial traits. By using a wide variety of samples and methodologies, research findings have consistently supported the proposition that parenting contributes meaningfully and significantly to shaping and molding child outcomes, including conduct problems, delinquency, crime, and antisocial behavior (Glueck & Glueck, 1950; Gottfredson & Hirschi, 1990; Patterson & Stouthamer-Loeber, 1984; Sampson & Laub, 1994; Wilson & Herrnstein, 1985). For instance, the aforementioned Sampson and Laub’s (1994) study of 500 delinquents and 500 nondelinquents in low-income neighborhoods showed that children raised in households characterized by structural disadvantages were more likely to become delinquents than those raised in better households. However, despite these


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structural disadvantages, children were significantly less likely to become delinquents if they were reared by parents who bonded with their children through attachment, who supervised them consistently, and who adopted reintegrative rather than punitive disciplinary measures. Cognizant of potential “child effects,” Sampson and Laub further used a control measure that consisted of a child’s irritability, aggressiveness, and the onset age of delinquency. Notably, parental effects held true even when child effects were statistically controlled for. In line with this research outcome, Loeber and Stouthamer-Loeber (1986) concluded in their meta-analytical review that parental socialization variables such as the lack of parental supervision, attachment, and involvement are among the most powerful predictors, more powerful than parents’ criminality or absence, of children’s behavioral problems and delinquency.

Parental Effects, Child Effects, and Behavioral Genetics Studies A recent stream of research calls into question the validity of the seemingly unquestionable impact of parental effects on children. Challenging evidence has been put forth by scholars from different disciplines who nonetheless share commonalities by dint of examining the complex interplay between biological and sociological factors in the etiology of behavioral outcomes (Beaver & Wright, 2007; Cleveland, 2003; Kendler & Baker, 2007; Lytton, 1990). According to them, the common practice of standard sociological methodologies’ (SSMs) interpretation of the correlations between parents and children as evidence of parental effects is at fault because it is rarely capable of separating the effects provided by the parents from the effects of the genes parents and children share (Beaver & Wright, 2007; Harris, 1998; Lytton, 1990; Wright & Beaver, 2005). For instance, on a research finding that abused children are more likely to become aggressive delinquents than children who were not abused, a conventional criminologist anchored in the SSM would conclude that the parents’ abuse was the cause of the children’s aggressive delinquency. Biosocial scholars, however, emphasize the more plausible possibility that the same genes that predisposed the parents to be abusive may have been passed onto the children, thereby causing them to be aggressive, too. In this case, the cause of the children’s delinquency is not abusive parenting, but the genes the children inherited from their parents. In addition, children play an active role in constructing and modifying parenting styles based on their genetic characteristics. Empirical evidence that gives strong credence to this notion comes from studies of infants, which collectively show that the difficult temperament of an infant is a significant precursor of reduced maternal responsiveness (Crockenberg & Acredelo, 1983; van den Boom, 1994). Because the targets of these studies are infants, it is highly unlikely that the environment (i.e., parenting) has caused the infants to have a difficult temperament to begin with. Similarly, adoption studies have also found statistically significant associations between the antisocial behaviors of the adoptee’s biological parents and the parenting styles of the


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adoptive parents (Ge et al., 1997; O’Connor, Hetherington, Reiss, & Plomin, 1995). Along the same line, Beaver’s (2011) analysis of Add Health twins data by using the DeFries–Fulker (DF) method revealed that at least 25% of the variance in perceived parenting (e.g., maternal attachment, maternal involvement, parental supervision) was explained by genetic factors. Collectively, these studies offer convincing and credible evidence that children’s genetic influences are implicated in generating different parenting practices.

Gene–Environment Correlation and Parenting An understanding of gene–environment correlation helps elucidate genetic influences on parenting. Three types of gene–environment correlations are usually discussed: passive, active, and evocative (Plomin, DeFries, & Loehlin, 1977; Scarr & McCartney, 1983). Passive gene–environment correlation arises because parents pass along both the environment and the genes to their children. In the case of parenting, children born to abusive and violent parents are at risk of being subjected to a familial environment characterized by violent and abusive parenting practices, which is largely a reflection of the parents’ genotypes. In addition to inheriting such an abusive environment, the children are simultaneously at risk of inheriting the parents’ abusive and violent genotypes, thereby generating the correlation between the children’s genotype and their abusive environment. Because these children do not have an active voice in choosing their genotype or the environment, this type of correlation is called passive gene– environment correlation. Active gene–environment correlation is somewhat akin to “niche-picking” where people play an active role in selecting or seeking out environments that are compatible with or conducive to their genetic propensities. An example of active gene–environment correlation is the phenomenon of homophily, in which adolescents seek out and befriend peers who are like them because of genetic influences. In terms of parenting, of course children are not at liberty to select their own parents. Nonetheless, they can select particular aspects of parental inputs and attend to them more than to other aspects. Based on their genetic predispositions, children also interpret the same parental input differently and react differently to it (Maccoby, 2000). Insofar as children’s selective attention, interpretations, and responses to parental inputs are influenced by their genetic proclivities, this phenomenon can be construed as a dimension of active gene–environment correlation. The third type of correlation—evocative gene–environment correlation—is clearly implicated in parenting. It reflects the fact that people elicit or evoke certain responses from others in their environment based, in part, on their genetically influenced characteristics. For instance, parents react to children with difficult temperaments by using negative and harsh parenting practices, while responding to affable children with warm and positive parenting (Crockenberg & Acredelo, 1983; van den Boom, 1994). The child effects reported in the parenting literature thus serve as an example of evocative gene–environment correlation. Although active and evocative gene–environment correlations are conceptually distinct, these two types are almost indistinguishable outside the laboratory setting.


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Thus, combined together, they are often described as nonpassive gene–environment correlation as opposed to passive gene–environment correlation (see Neiderhiser et al., 2004). In parenting, nonpassive gene–environment correlation is evidence of the role of the child influencing parenting practices. In other words, finding nonpassive gene–environment correlation indicates that parenting is partially a response to the child’s genetic influences, and this can be best reflected by heritability (h2) in behavioral genetics research. Interpreting heritability as being indicative of nonpassive correlation between children’s genes and parenting practices is plausible, especially when measures of parenting are created based on children’s self-reports (Neiderhiser et al., 2004). Interested readers are referred to Carey (2002) for an introduction to modelfitting techniques involving the concepts of heritability and shared/nonshared environments in behavioral genetics research.

Moderation of Genetic Influences on Parenting by the Neighborhood Context A wealth of behavioral genetics evidence indicates that genetic influences on a wide variety of human phenotypes are modified by contextual factors (Harden, Turkheimer, & Loehlin, 2007; Miles & Carey, 1997). This modification hypothesis was first tested with regard to cognitive abilities. The results of these studies have consistently pointed to an identical conclusion: That is, the heritability of IQ is greater among individuals in advantageous environments characterized by higher parental socio-economic status (SES) and education levels, whereas shared environmental influences are greater in disadvantaged environments (Fischbein, 1980; Harden et al., 2007; Rowe, Jacobson, et al., 1999; Turkheimer, Haley, Waldron, Onofrio, & Gottesman, 2003). Outside cognitive ability, Dunne et al. (1997) examined contextual effects on the age of first intercourse between two age cohorts of twins. This study showed that genetic influences were significantly greater in the younger cohort than in the older cohort. The researchers concluded that the relaxed social climate surrounding sexual intercourse among younger generations allowed the fuller expression of their genetic predispositions in determining the age of first intercourse. In addition, Rowe, Almeida, and Jacobson (1999) examined genetic and environmental influences on aggression. Their analysis also revealed that genetic influences on aggression increased in benign school environments characterized by high levels of aggregate family warmth, whereas shared environmental influences increased in adverse school environments with low levels of aggregate family warmth. The fact that genetic influences on a range of phenotypes are conditioned by the environmental context can be best elucidated in view of the theorization of Bronfenbrenner and Ceci (1994) and Scarr and McCartney (1983). According to these scholars, the wider range of opportunities available in adequate or advantageous environments serves as a facilitator of fuller genetic expressions. In such environments, nonpassive gene–environment correlation is enhanced, whereby individuals further seek out environments or evoke environmental responses conducive to the realization


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of their genetic potential. Because nonpassive gene–environment correlation is manifested in behavioral genetics models as a genetic influence (Neiderhiser et al., 2004), heritability should be higher in adequate environments than it is in disadvantaged environments. Conversely, genetic potential is less likely to be realized in extremely adverse environments because opportunities to express genetic potential will be fewer and, further, genetic expressions can be overwhelmed by adverse environmental factors (Bronfenbrenner & Ceci, 1994; Scarr & McCartney, 1983). A confluence of behavioral genetic studies thus leads to the conclusion that in adverse environments, environmental influences on a phenotype are stronger and heritability is weaker. In comparison with such behavioral genetics studies, conventional criminological studies based on SSMs have focused mostly on socio-environmental factors to explain an outcome measure of interest. Given the more prominent influence of environmental factors in adverse environments, one can surmise that SSMs can be particularly useful in ascertaining the effects of socio-environmental factors on an outcome measure in adverse environments. The same SSMs, however, may not be equally effective in adequate environments because they are not particularly capable of probing genetic effects, which are prominent in such environments. Along this line of reasoning, biosocial criminologist Anthony Walsh (1992) argued that sociological theories of crime may be adequate to explain dependent variables in low SES environments, whereas they may not be equally well generalizable to favorable environmental contexts. Drawing on this theorization and the empirical findings derived from behavioral genetics research, we similarly hypothesize that an offspring’s genetic influences on parenting styles are modified by the level of the structural disadvantage of neighborhoods. Just as children’s gene-based cognitive potential is more fully expressed in advantageous environments, we suspect that the offspring’s genetic potential to evoke particular parenting tactics, either positive or negative, should be better activated in adequate neighborhoods. At the same time, parents in these neighborhoods are likely to have enough energy and resources to accommodate each child’s particular needs and preferences. Thus, child effects on parenting should be stronger and more visible in adequate neighborhoods than in disadvantaged neighborhoods. Parents living in disadvantaged neighborhoods are more likely to be faced with rampant crime, violence, and poverty, thus experiencing chronic tension and stress (McLoyd, 1990; Wilson, 1987). Owing to such chronic tension, stress, and daily hassles to just get by, parents in highly disadvantaged neighborhoods will be less likely to have enough resources or energy left to attend to each child’s special needs and predilections (Furstenberg, 1993; Leventhal & Brooks-Gunn, 2000). In addition, residence in a disadvantaged neighborhood is tantamount to having a low social class. The construct of social class indexes the latent traits that lead to one’s SES. Those traits, partially influenced by genes, that make people successful in life in general (e.g., intelligence, conscientiousness, agreeableness) also make them successful parents. Walsh (2011) argued that in modern egalitarian societies, many lower-class parents residing in highly disadvantaged neighborhoods lack such traits. In support of Walsh, a recent molecular genetics study revealed that people who possess dopamine-related


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gene variants that are associated with impulsivity and aggressiveness are more likely to live in disadvantaged neighborhoods (Barnes, Boutwell, & Beaver, 2013). Consequently, parents living disadvantaged neighborhoods will be less sensitive to and less accommodating of each child’s unique gene-based characteristics. In other words, these parents are more likely to apply similar parenting styles based on their own genetic propensities to their children, irrespective of the children’s genetic relatedness or predispositions. Insofar as these parenting tactics are perceived similarly by the children and contribute to making them more similar, they constitute a component of shared environmental influences. Therefore, we can reasonably assume that when it comes to parenting, shared environmental influences will be stronger and heritability will be lower in adverse environments. To summarize, the hypothesis of the current study is that child effects on parenting are stronger in adequate neighborhoods than they are in disadvantaged neighborhoods, whereas shared environmental influences will display the opposite pattern across these two neighborhood types. To test this hypothesis, we analyze sibling pairs data taken from the Add Health study, using three parenting measures as dependent variables. Our dependent variables are not parenting practices as directly reported by parents themselves but rather those based on perceived parenting as obtained from each sibling. Measures of perception can be less reliable and valid, thereby constituting a weakness of this study. However, we have reason to believe that this may not be necessarily so. First, if parenting is to bring about a certain child outcome, the immediate cause of the outcome is not parental behavior itself, but the child’s interpretation of the parental behavior (Beaver, 2011; Rowe, 1983). Second, even if a child misinterprets parental behaviors, thus creating a less valid measure of parenting, such misinterpretation itself partially reflects the child’s personalities or other characteristics largely influenced by their own genetic predispositions. In other words, using parenting measures based on children’s perception is capable of partially taking into account active gene–environment correlation (Plomin et al., 1988). Third, a vast majority of criminological research has also used parenting measures based on perception. Thus, a behavioral genetics study using similar measures can offer a yardstick against which existing criminological literature can be reevaluated (Beaver, 2011). Against this backdrop, we explore a hypothesis of contextualized gene–environment correlation involving perceived parenting utilizing a behavioral genetics method. To date, no published study has examined how the neighborhood context may moderate parenting practices as perceived by children.

Method Data Data for the present study were drawn from the Add Health study, a longitudinal and nationally representative sample of American youths (Udry, 2003). Initial data collection began in 1994-1995, when respondents were enrolled in the 7th to 12th grades. Altogether, 132 schools in the nation were sampled by using stratified cluster sampling techniques. Students attending these schools were asked to complete a self-report


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questionnaire. More than 90,000 students completed the survey instrument. To obtain more detailed information from respondents, a stratified subsample was selected and reinterviewed at home. In all, 20,745 adolescents were interviewed in the in-home survey. Adolescents were queried about their familial relationships, delinquent involvement, and other issues related to adolescent development. The second wave of data was collected from 14,738 of these respondents in 1996. The third wave of data was collected in 2001-2002, when most participants had reached between 18 and 26 years of age. Overall, 15,197 participants were interviewed successfully. Included in the Add Health full sample is a subsample of sibling pairs. During the Wave 1 interviews, respondents were asked to indicate whether they currently lived in the same household with a twin-sibling. For those who answered affirmatively, the co-twin was added to the sample. Adolescents also indicated whether they lived with a half-sibling, a cousin, or a step-sibling. If their co-residing sibling was aged between 11 and 20 years, then they were also added to the sample. A probability sample of full siblings was additionally included in the sibling pairs sample. More than 3,000 sibling pairs were included in the sibling pairs sample. Among these pairs were the MZ, DZ, and full-sibling pairs used in the present analysis. Other sibling pairs, including halfsiblings, cousins, and step-siblings, were excluded because they were less likely to have resided in the same household in 1990, when the Census data were collected (5 years before the Wave 1 data collection), which was used in this study to construct the neighborhood disadvantage measure. Because developmental processes and the effects of the neighborhood context may differ for boys and girls (Cleveland, 2003), analyses were conducted only for same-sex sibling pairs. To reach conservative estimates, pairs with complete data for neighborhood disadvantage and parenting measures were included. The final analytical sample included 733 same-sex sibling pairs (177 MZ-twin, 151 DZ-twin, and 405 full-sibling pairs). The average age of the analytical sample was 16.2 (SD = 1.7), and gender was almost evenly distributed (48.5% boys).

Measures Dependent variables. Three parenting measures were used as dependent variables, all of which were garnered from the interviews in Wave 1. First, maternal attachment was measured by two items. Adolescents reported on a 5-point answer category how much they thought their mother cares about them and how close they feel to their mother. Responses to these two items were added to create the maternal attachment scale (α = .64). Higher scores on this scale indicated a higher level of maternal attachment as perceived by adolescents. Second, parental monitoring was measured by seven items that indexed for the degree of parental supervision and monitoring. Specifically, adolescents were asked on a binary answer category whether their parents let them make their own decisions on issues including curfews, friends, clothes, television viewing, or what they eat. Responses to the seven items were coded and summed to create the parental monitoring scale (α = .63), in a manner that higher scores indicated a higher level of supervision. Finally, maternal involvement was tapped by asking adolescents whether they had performed 10 different activities, such as shopping,


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playing sport, going to church, watching movies, or talking about personal problems, with their mother in the past 4 weeks. All these items were coded dichotomously. The responses to these 10 items were summed to create the maternal involvement scale, where higher scores reflected a higher level of maternal involvement. Because there is no convincing reason to believe that participating in a certain activity with their mother would necessarily lead respondents to participating in other disparate activities with mother, the internal consistency of this scale was relatively low (α = .44). Neighborhood disadvantage. A neighborhood disadvantage scale was constructed by using items drawn from Add Health’s Wave I contextual data file. This file included information on neighborhood structural disadvantages at the block level from the 1990 Census. The five items used for the neighborhood disadvantage scale were the proportion of single-parent households, proportion of households with an income less than USD$15,000, proportion of households that receive public assistance, proportion of African Americans, and unemployment rate. Because of potential collinearity, these items were factor analyzed, all of which loaded on a single factor (eigenvalue = 2.93, factor loadings > .66). Factor scores were used to construct the neighborhood disadvantage scale. This scale is similar to the ones used by other researchers analyzing neighborhood-level conditions (Cleveland, 2003; Sampson, Raudenbush, & Earls, 1997). Low self-control. To examine whether parenting measures covary with children’s level of self-control (Gottfredson & Hirschi, 1990), we included the five-item low selfcontrol scale used previously by researchers who have analyzed Add Health data (Perrone, Sullivan, Pratt, & Margaryan, 2004). Adolescents were asked on a 5-point scale whether they struggled to get along with teachers, finish their homework, keep their mind focused, and pay attention. According to Perrone et al. (2004), these items tap into the simple tasks, physical activities, and impulsivity components of low selfcontrol, as explained by Gottfredson and Hirschi (1990). An additional item asked whether they felt they did everything just right. This item captures variation in the self-centeredness dimension of low self-control (Perrone et al., 2004). However, because dropping the last item increased the alpha level from .66 to .70, we constructed the low self-control scale by summing the responses to only the first four items. Higher scores on this scale indicate lower levels of self-control. Delinquency. Research has shown that children’s delinquent involvement affects their parents’ parenting practices (Beaver & Wright, 2007). To examine how this association unfolds in relation to the neighborhood context, a 15-item delinquency scale was created. During the interviews in Wave 1, adolescents were asked to report how often, over the past 12 months, they had engaged in delinquent acts such as painting graffiti, damaging property, burglary, drug sale, theft, or gang fighting. The responses were coded and summed in a way that higher scores meant greater involvement in delinquent acts (α = .85).


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Plan of Analysis To explore the moderating effects of neighborhood disadvantage on parenting as perceived by children, a DF analysis was used to estimate the genetic, shared environmental, and nonshared environmental effects on the three parenting measures. The DF equation is an unstandardized regression-based equation originally designed for samples of twins in which one twin is selected for a certain extreme trait (DeFries & Fulker, 1985). Over time, the DF equation has undergone a series of modifications to be used for samples from the general population (Rodgers & Kohler, 2005; Rodgers, Rowe, & Li, 1994). The baseline DF equation predicts Sibling 1’s phenotype score (K1) from Sibling 2’s phenotype score (K2), the coefficient of genetic relatedness (R = 1.0 for MZ twins and R = .5 for DZ twins and full siblings), and the interaction term (R × K2) between R and K2. In this form, the unstandardized regression coefficient on K2 is the estimate of the proportion of variance due to shared environmental effects (c2) and the regression coefficient on the interaction term (R × K2) is the heritability coefficient (h2). The effects of the nonshared environment, plus the measurement error, are captured by the error term. The modification to the DF equation by Rodgers and Kohler (2005) advances the baseline equation and produces unbiased and stable estimates. The modified equation is as follows: K1 = b0 + b1 ( K 2 - K m ) + b2  R × ( K 2 - K m )  + e,

(1)

where Km is the mean of Sibling 2’s phenotype score. The modified equation simply uses mean-centered K2 in lieu of K2 in the baseline equation. Consistent with the baseline equation, b1 reflects shared the environmental effects, b2 heritability, and e variance accounted for by the nonshared environmental effects plus the measurement error. The coefficients in Equation 1 are similar to latent factors because they reflect only the proportion of variance explained by genetic, shared environmental, and nonshared environmental factors. Hence, the equation does not explain which particular nonshared environmental factors account for the explained variance. In this regard, an expansion of the DF equation by Rodgers et al. (1994) can be useful for uncovering the effects of specific nonshared environmental factors: K1 = b0 + b1 ( K 2 - K m ) + b2  R × ( K 2 - K m )  + b3ENVDIF + e.

(2)

This equation is identical to Equation 1 except that it contains the additional term ENVDIF, which refers to the absolute difference score between siblings on a particular phenotype. This term can be used to identify the sources of nonshared variance. For instance, Sibling 2’s score on the delinquency scale can be subtracted from Sibling 1’s delinquency score. The resulting difference score can be entered as ENVDIF to examine whether, for instance, nonshared variance in maternal attachment is explained by the difference scores in delinquency across siblings. More than one ENVDIF can be


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entered into Equation 2 without biasing the results. All other coefficients are interpreted the same as in Equation 1. One important issue pertaining to DF analysis is the ambiguity as to which member of the kinship pair should be assigned as the dependent variable (K1) and which as the independent variable (K2). This issue is typically resolved by using a double-entry approach (Kohler & Rodgers, 2001): Each sibling is entered once as K1 and then again as K2. Although this double-entry method produces unbiased coefficient estimates, the standard errors need to be adjusted because, by double entering each sibling, observations are no longer independent of each other, thereby violating the basic assumption of ordinary least squares (OLS) regressions. By using Huber–White robust standard errors (White, 1980), the violation of this assumption can be accounted for. To examine the hypothesis that child effects on parenting are moderated by neighborhood disadvantage, we estimated split sample models, an approach adopted by previous scholars that analyzed Add Health siblings data (Cleveland, 2003). Specifically, the neighborhood disadvantage scale was split at the 75th percentile. Respondents who had values above the 75th percentile were assumed to be living in disadvantaged neighborhoods, with the rest of the respondents living in adequate ones. Although using a higher cutoff point might have been beneficial in terms of detecting the effects of the disadvantaged neighborhood context, a corresponding reduction in sample size would have resulted in a considerable reduction of statistical power with regard to disadvantaged neighborhoods. Thus, the 75th percentile cutoff point, although appearing arbitrary to a degree, represents a compromise between setting the highest possible cutoff point and securing a sufficient sample size for disadvantaged neighborhoods. Using this cutoff, 55% of the households in disadvantaged neighborhoods were classified as single parent–headed, and 48% earned less than $15,000 in annual income.

Results The statistical analyses for the present study begin by examining the mean differences in perceived parenting practices between different types of neighborhoods. Although parental practices in highly disadvantaged communities are likely to be more detached and harsher than those in adequate communities (Furstenberg, 1993; Leventhal & Brooks-Gunn, 2000), differences in parenting across these neighborhoods may not be so distinct because the parenting measures of this study reflect children’s perception of parenting practices. The comparison of the mean values revealed that the values for the adequate neighborhood group were consistently higher than those for the disadvantaged neighborhood group, although the magnitude of the difference was not large: 9.45 versus 9.42 for maternal attachment, 5.03 versus 4.89 for parental monitoring, and 3.12 versus 2.84 for maternal involvement. Independent t tests showed that a statistically significant difference existed only with respect to maternal involvement (t = 2.87, p < .01). Next, we examined cross-sibling correlations for the three parental measures. Zeroorder cross-sibling correlations provide an initial estimation of whether these parenting measures are at least partially under the influence of genetic factors. If the MZ


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Yun and Lee Table 1. Cross-Siblings Correlations for Attachment, Monitoring, and Involvement. MZ twins (177 pairs)

DZ twins (151 pairs)

Full siblings (405 pairs)

.38* .40* .46*

.36* .34* .32*

.24* .22* .31*

Attachment Monitoring Involvement

Note. MZ = monozygotic; DZ = dizygotic. *p < .05, two-tailed test.

Table 2. Baseline DeFries–Fulker (DF) Models of Three Parenting Measures. Attachment Heritability Shared environment

Monitoring

Involvement

b

SE

b

SE

b

SE

.23 .16

.17 .11

.29* .10

.09 .06

.28* .17*

.09 .06

Note. Robust standard errors are presented. *p < .05, two-tailed test.

cross-twin correlations are larger than the DZ cross-twin and cross-full-sibling correlations, genetic influences on these measures can be inferred. Table 1 presents the cross-sibling correlations for the full sample of siblings. As can be seen, all the correlations were statistically significant. Yet, all the MZ cross-twin correlations were larger than those for DZ twins and full siblings. These results provide initial evidence that maternal attachment, parental supervision, and maternal involvement are partially influenced by the child’s genetic factors. Next, we calculated a series of DF models to estimate the relative effects of environmental and genetic influences on the dependent variables. Table 2 presents the results from fitting the baseline DF model to the data for the full sample. Two of the dependent variables—parental monitoring and maternal involvement—displayed significant genetic influences, whereas the shared environmental component was significant with respect to maternal involvement. This finding partially suggests that parental monitoring and maternal involvement are, at least in part, evoked by children’s genetic characteristics. In Table 3, the main hypothesis that children’s genetic effects on parenting may be contingent on the neighborhood context was examined. Two separate DF analyses were conducted: one for the disadvantaged neighborhood sample and the other for the adequate neighborhood sample. By looking at the column for attachment in the disadvantaged neighborhood model, we can see that shared environmental influences explained 57% of the variance in maternal attachment, whereas the heritability estimate showed a negative value of −.29. This negative value is the by-product of the DF analysis being essentially a regression-based function. OLS takes the linear function, which prevents the slope from tapering off at extreme scores, thereby potentially


14


Table 3. DeFries–Fulker (DF) Models of Three Parenting Measures by Neighborhood Disadvantage. Disadvantaged neighborhood

Attachment b

Heritability −.29 Shared .57* environment

Monitoring

Adequate neighborhood

Involvement

Attachment

Monitoring

Involvement

SE

b

SE

b

SE

b

SE

b

SE

b

SE

.37 .28

.06 .34*

.24 .16

.18 .37*

.19 .12

.43* −.05

.15 .09

0.44* .01

.13 .09

0.55* −.07

.13 .09


resulting in a regression line that stretches below the 0 point. For this reason, the negative estimate for heritability does not necessarily mean that heritability explained a negative amount of variance in maternal attachment. Instead, negative estimates are simply interpreted as being 0 in a DF analysis. Therefore, an appropriate interpretation is that in disadvantaged neighborhoods, 57% of maternal attachment is influenced by shared environmental factors, and the remaining variance is explained by nonshared environmental factors and the measurement error, whereas genetic factors do not add to the amount of variance explained. In adequate neighborhoods, the opposite pattern was found. Maternal attachment was explained by genetic factors, whereas none of the variance was accounted for by shared environmental factors. The exact same pattern applies to the two other outcome variables. Both parental monitoring and maternal involvement had relatively high shared environmental influences (c2 = .34 and .37, respectively) but weak, nonsignificant genetic influences in disadvantaged neighborhoods. In adequate neighborhoods, the parental measures had relatively high heritability (h2 = .44 and .55, respectively) and essentially zero shared environmental influences. This diametrically opposite pattern of nonshared and genetic influences contingent on the neighborhood context offers empirical support for the hypothesis of the present study. Although the current analysis supports our hypothesis, on average more than 50% of the variance in each dependent variable remains to be explained by nonshared environmental factors and the measurement error. Thus, in the next step, we explored whether two specific nonshared factors—low self-control and delinquency—were able to explain a proportion of the remaining variance. To do this, first, difference scores for the low self-control and delinquency measures were calculated between two siblings from each sibling pair. The two difference scores were then entered into Equation 2 simultaneously as ENVDIF variables. The results, presented in Table 4, point to two broad findings. First, the opposite pattern of heritability and shared environmental influences across neighborhood types remained stable despite the addition of the two nonshared features. Indeed, the pattern of the estimates shown in Table 3 is almost identical to that in Table 4 except for minor differences. Second, none of the


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Yun and Lee Table 4. DeFries–Fulker (DF) Models of Three Parenting Measures With Sources of Nonshared Environment. Disadvantaged neighborhood

Attachment B

Heritability −.29 Shared environment .57* Low self-control .01 Delinquency −.03

Adequate neighborhood

Monitoring Involvement

Attachment

Monitoring

Involvement

SE

b

SE

b

SE

B

SE

b

SE

b

SE

.38 .28 .02 .02

−.06 .34* .02 −.02

.24 .16 .02 .02

.17 .40* .02 .01

.19 .02 .02 .01

.39 −.01 −.01 −.01*

.29 .16 .01 .00

.38* .06 −.01 −.01

.14 .09 .01 .01

0.53* −.04 −.01 −.01

.14 .09 .01 .01


nonshared environmental features was significant, except for the effect of delinquency on maternal attachment in the adequate neighborhood model. The significant and negative coefficient for delinquency, although minimal, indicates that the sibling engaged in delinquency more often was treated with less attachment by his or her mother in adequate neighborhoods. As post hoc analyses, difference-in-coefficients Z tests were conducted to examine whether the estimates of heritability and shared environmental influences presented in Table 4 were statistically different between the neighborhoods (Paternoster, Brame, Mazerolle, & Piquero, 1998). In total, six Z tests (three dependent variables × two estimates) were performed. We found that the heritability estimate was statistically different between the neighborhoods with respect to parental monitoring at the .05 level, whereas the shared environment estimates were different with respect to maternal attachment and maternal involvement at the .05 level. The remaining three estimates were, at least, marginally significant (p < .10).

Discussion A firmly established line of criminological thinking emphasizes the importance of parenting in molding and shaping children’s behavioral outcomes (Gottfredson & Hirschi, 1990; Hirschi, 1969; Sampson & Laub, 1994). An equally well-established academic paradigm focusing on the interplay between biological and sociological factors avers that once children’s genetic characteristics are controlled for, parental effects on children are only minimal and, contrarily, child effects on parenting become more potent (Beaver & Wright, 2007; Harris, 1998; Lytton, 1990). By drawing on behavioral genetics’ theorizing and research on contextualized gene–environment correlation (Beaver, 2011; Bronfenbrenner & Ceci, 1994; Cleveland, 2003; Harden et al., 2007; Scarr & McCartney, 1983), we formulated a nascent hypothesis that may bridge the gap between these two lines of research findings, namely, child effects on parenting are contingent on the neighborhood context. The behavioral genetics literature indicates that genetic potentials for a phenotype are


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more fully expressed in adequate environments, whereas shared environmental effects become stronger in disadvantaged environments (Bronfenbrenner & Ceci, 1994; Scarr & McCartney, 1983). Our DF analysis of 733 sibling pairs data, including MZ-twin, DZ-twin, and fullsibling pairs, largely lends credence to the proposed hypothesis. When DF models were estimated for three parental measures—maternal attachment, parental monitoring, and maternal involvement—for the full sample, the heritability estimates exerted greater influences than shared environmental influences. Of greater interest, however, were the results of the neighborhood-specific models. For disadvantaged neighborhoods, shared environmental factors explained 57%, 34%, and 37% of the variance in maternal attachment, parental monitoring, and maternal involvement, respectively. Genetic influences, however, failed to explain any significant amount of the remaining variance in the dependent variables. Strikingly different results emerged for adequate neighborhoods. Here, genetic influences explained 43%, 44%, and 55% of the variance in each of the parenting measures, whereas shared environmental factors failed to explain the remaining variance. These results are in line with other behavioral genetics research that has shown that the heritability estimates of other phenotypes (i.e., IQ and aggression) are greater in advantageous environments, whereas shared environmental influences are greater in disadvantaged environments (Fischbein, 1980; Harden et al., 2007; Turkheimer et al., 2003). Importantly, the present findings can shed some light on the diverging empirical conclusions drawn by criminological and behavioral genetics researchers on the effects of parenting. First, criminological research that has found the existence of significant parenting effects on child behavioral outcomes (Sampson & Laub, 1994; Wright & Cullen, 2001) has failed to fully control for the child’s genetic influences, as is the case for most SSM-based research. Second, criminological research that has found considerable parenting effects may have targeted participants living in disadvantaged neighborhoods, where child effects on parenting are relatively weak. For instance, the participants for Sampson and Laub’s (1994) and Wright and Cullen’s (2001) research were both drawn from at-risk youths residing in rather disadvantaged communities. By contrast, behavioral genetics research is more likely to use large community samples primarily because of the need to obtain a large sample of twins for the sake of sufficient statistical power. Then, child effects that actively operate in the larger proportion of the sample from both average and advantaged neighborhoods may have subsumed the nonshared parental effects particularly visible in the smaller proportion of the disadvantaged sample. In that case, child effects would naturally appear to be significant and parental effects negligible. The present study demonstrates to criminologists the benefits that can be accrued by using a behavioral genetics framework. A fuller explanation of a behavioral outcome of interest can be obtained when both environmental and genetic factors are simultaneously taken into account. SSMs have limitations in this regard. It is important to comment on the statistical method used in this study. Most biometrical modeling in the behavioral genetics literature has been conducted by using structural equation modeling approaches (e.g., the ACE model). Compared with the unfamiliar ACE


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model, DF analysis is based on the traditional least squares estimation approach, with which most criminologists are accustomed. Although ACE modeling is more versatile in terms of the types of models that can be evaluated, DF analysis is simpler and more easily available; moreover, in most cases, the results of DF and ACE analyses are similar (Rodgers, Kohler, Kyvik, & Christensen, 2001). Thus, a DF analytical framework has the potential to be a useful tool for criminologists interested in examining both the nature and the nurture aspects of a phenotype of interest. The current findings illustrate the phenomenon of gene–environment correlation, namely, that human beings are not passive recipients of environmental influences but rather actively create and evoke important aspects of their social environment and interpersonal relationships (Plomin et al., 1977; Walsh, 2002). It should be noted that approximately 50% of the variance in each parenting measure remained unexplained in our models, which is attributable to nonshared environmental factors and the measurement error. Furthermore, we found that a child’s low self-control and delinquency resulted in null or only minimal effects. This rather unexpected finding may be partially related to the split-sample method used in the current study. By dividing the sample into two smaller groups, the variances of the covariates may have been reduced, thereby making it harder to locate significant effects.2 It may also reflect the fact that once genetic and environmental factors are associated with both parenting and the covariates are partialled out as in our analysis, the explanatory power of the covariates may reduce substantially. It is also necessary to note that the current study is different from Beaver’s (2011), although both used Add Health sibling pairs data, similar dependent variables, and DF analysis. Whereas Beaver examined the overall genetic effects on perceived parenting, our study expanded it by investigating the conditioning of such genetic effects by neighborhood type. Furthermore, the former used only twins data, whereas the latter incorporated full-sibling data as well, thereby potentially increasing the generalizability of the findings to the general population. The present findings should be viewed with caution in light of the following issues. First, the parenting measures used herein are based on children’s perception rather than parents’ reports of their own behaviors. Thus, the current findings should be interpreted as concerning genetic and environmental influences on the perception of parenting, and not on parenting itself. As we stated earlier, a behavioral genetics study can still contribute to the literature even when the outcome variable is based on children’s perception of parenting. Unbiased parameter estimates for actual parenting, not a perception of it, can be obtained by using a matched child-report and parent-report design under a behavioral genetics framework (see Neiderhiser et al., 2004). Second, the Census data used to classify neighborhoods were collected in 1990, whereas the Add Health Wave 1 data were collected approximately 5 years later. Measurement errors may have thus been introduced for those respondents who moved to different neighborhoods after 1990. Third, although the entire Add Health sample is nationally representative, the sibling pairs sample analyzed herein is not necessarily so. Last, although the measures used in this study were similar to those adopted in prior research, some of them had low alpha values. Readers are reminded that low alpha values could translate into more errors and thus inefficient parameter estimates.


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Despite these limitations, this study’s primary strength lies in its integration of sociological and behavioral genetics frameworks in conjunction with the use of contextual data. This integration allowed us to examine an interesting and potentially promising hypothesis that the neighborhood context modifies genetic and shared environmental influences on perceived parenting. Given that most human phenotypes, including crime and delinquency, are products of both environment and genes (Walsh, 2002), criminologists can gain enormously by integrating both perspectives. The current study is a step toward such an integrated approach. Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes 1. A sizable volume of missing data on paternal attachment and paternal involvement in the Add Health data precluded using these measures as additional dependent variables. 2. In support of this supposition, an auxiliary analysis of the full sample showed that both of the covariates were significant in the attachment model. Low self-control was significant in the monitoring model.

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