The Role of Neighborhood Disadvantage and Family Structure During Adolescence in Young Adults’ Experiences of Multiple Partner Fertility (MPF)

Abstract

Families in the United States have become more complex, with an increasing number of individuals having children with multiple partners, called multiple partner fertility (MPF). MPF has significant negative consequences for the well-being of adults and children. Understanding the correlates of MPF, particularly how familial and community constructs affect the fertility outcomes of youth, has important implications for prevention and intervention. However, while many studies have examined these constructs, few have looked at them together. Using data from the National Longitudinal Study of Adolescent to Adult Health (Add Health; N = 8,678), this study uses a prospective design to examine how family structure and level of community disadvantage experienced by youth predict MPF in young adulthood. Using multilevel, mixed effects modeling, we find that family structure appears to play a role in influencing the fertility outcomes of youth, more so than community poverty. Implications for policy and practice are discussed.

Keywords

multiple partner fertility family complexity community poverty youth young adulthood

Introduction

Greater complexity in children’s living arrangements has become increasingly common. Parental relationships are often unstable, involving relationship dissolution, repartnering, and increasingly, new partner fertility. In addition, marriages are happening at lower rates and later in people’s lives. This trend, coupled with high levels of nonmarital childbearing and unintended fertility, opens the door and increases the risk for adults to have children with multiple partners, or multiple partner fertility (MPF). Moreover, most of the aforementioned behaviors have been increasingly common among the most disadvantaged (Guzzo, 2014), resulting in more complex and fluid families in more disadvantaged communities (Cherlin, 2010). In 2014, it was estimated that 10% of all adults, 11% of all women, and 9% of all men aged 15 years or older have had children with more than one partner in the United States (Monte, 2017). And another study estimated that at least one in eight children lives in a household with half or stepsiblings (Manning, Brown, & Stykes, 2014).

MPF is not a new behavior, but it has historically been difficult to trace trends in MPF due to the complexity of data needed to measure it (Guzzo, 2014). However, data available in recent years shows that behaviors associated with MPF have been on the rise. Before, MPF used to occur after widowhood or divorce, while more recently, a series of other family behaviors have been the leading cause for MPF, including the broken link between sexual activity and marriage (Guzzo, 2014). MPF occurs when individuals have children with more than one partner, while single partner fertility (SPF) occurs when a parent has all his or her children with the same person. MPF is not defined by living arrangements, custody, or marital status, which means that children do not have to live with their biological parent for the parent to be counted as having MPF (Monte, 2017).

A series of negative consequences for adults and children experiencing MPF have been described in the literature, paying particular attention to disadvantaged individuals more likely to experience MPF. Given the rise of MPF and its known negative consequences, it is important to understand the predictors of MPF in order to target interventions and services at prevention efforts. Currently, numerous studies have documented the trends and correlates of MPF, but few studies have been able to utilize a prospective design to examine which correlates of MPF may have greater predictive utility in the MPF outcomes of young adults.

Within the MPF literature, family structure and socioeconomic disadvantage are focused on as important contexts for individuals when studying MPF (Monte, 2011; Smeeding, Garfinkel, & Mincy, 2011; Tach, Mincy, & Edin, 2010). Additionally, community context is often focused on as an important factor for fertility outcomes. Specifically, one study found MPF to be more common among socioeconomically disadvantaged individuals, mostly men. Those experiencing MPF were more likely to have lower levels of education and to live in poverty (incomes 150% or below the poverty line) compared with men who did not have children with more than one partner. In addition, MPF was more common among members of racial and ethnic minorities and there was some indication that men who lived in an “Other” family structure rather than with both biological parents or in a two-parent stepfamily, were more likely to experience MPF (Guzzo & Furstenberg, 2007a). Another study found that children growing up with both biological parents are less likely to experience teen pregnancy, which is a common pathway to MPF (McLanahan & Sandefur, 1994). While many studies have examined family structure (Carlson & Furstenberg, 2006; Guzzo & Furstenberg, 2007a, 2007b; Manlove, Logan, Ikramullah, & Holcombe, 2008) and community context separately (Harding, 2003) in direct or indirect association with MPF, to our knowledge, none of them have looked at the two constructs jointly to discern which one seems more important for determining the MPF outcomes of youth. It is essential to gain a better understanding of how these two contextual factors work simultaneously to impact the fertility outcomes of youth (aged 11 to 22 years) when they become young adults (aged 24 to 35 years) in order to better inform interventions and services working with children and families in disadvantaged communities.

The present study uses a prospective design to examine how family structure and level of community disadvantage experienced by youth (aged 11 to 22 years) predict MPF in young adulthood (aged 24 to 35 years). We focus our attention on youth’s early experiences, given the importance of this developmental stage on individuals (Bronfenbrenner & Morris, 2006). This study is novel in its ability to account for both community disadvantage and an array of family structures during adolescence in a large, nationally representative sample of youth. Most important, these participants are followed into young adulthood where self-reported MPF is assessed. Thus, using multilevel, mixed effects modeling, we are able to examine how family structure and community context in adolescence predict MPF in young adulthood. The findings will provide a better understanding of how these important familial and community constructs affect the fertility outcomes of youth, offering implications for prevention and intervention.

Background

Research about MPF has increased considerably in the past 10 years, not only due to more availability of data but also due to the increasing awareness and knowledge of the consequences of MPF for children and adult’s well-being. Increased MPF is worrisome because it is associated both directly and indirectly with poor outcomes for children and youth. Studies examining the association between MPF and well-being have made an effort to isolate the effect of MPF while controlling for factors such as socioeconomic context and other individual and household characteristics. In that sense, studies have found that even after controlling for a comprehensive set of variables, MPF is still found to have indirect (e.g., through the disruptions that MPF might exacerbate including paternal depression, parent’s involvement, etc.) and direct associations with the well-being of adults and children. Father’s MPF was found to be associated with aggressive behavior in children at age 3 (Bronte-Tinkew, Horowitz, & Scott, 2009), and MPF has been associated with child-reported delinquency and teacher-reported externalizing behavior for children with unmarried parents at time of birth (Fomby & Osborne, 2017). Moreover, Halpern-Meekin and Tach (2008) find that youth in families with half-siblings fare worse on GPA, delinquency, depression, and school detachment than children in two-parent families without half-siblings. Children with half-siblings are also more likely to use drugs and have sex sooner (Dorius & Guzzo, 2013), which may have consequences for the intergenerational transmission of MPF.

In addition, once one or both parents have had children with more than one partner, children might experience a decrease in the resources they receive from parents including financial contributions (e.g., formal and informal child support) as well as time, given that such resources would need to be distributed and prioritized across households (Carlson & Furstenberg, 2007; Manning & Smock, 1999, 2000; Meyer, Cancian, & Cook, 2005). Such circumstances become even more challenging for disadvantaged parents, who are more likely to have limited financial resources. Taken together, these studies offer evidence for the unique, negative impact of MPF on children and youth.

Additionally, MPF has important consequences for parents. MPF is correlated with higher rates of depression among young parents, and fathers are particularly unsatisfied with their parenting when experiencing MPF (Guzzo, 2014). Parents with MPF also report lower levels of social support (Harknett & Knab, 2007) and parental engagement with the focal child (Bronte-Tinkew et al., 2009). Notably, Monte (2011) finds that MPF is related to poor work outcomes and an increased reliance on welfare programs for a sample of low-income Black mothers. The author indicates that once women have a child to a second partner, they are less likely to work and more likely to rely on welfare. Some of the reasons the author puts forward for an increased reliance on welfare include the potential decrease in the paternal investment and the difficulties mothers experience in receiving reliable child support payments from nonresident fathers. Implications of this study suggest that within disadvantaged communities, MPF may lead to an even worse economic standing.

Given recent increases of MPF and rising concerns about its consequences for adults’ and children’s well-being, several studies have explored the potential correlates and risk factors for MPF. Even though early studies focus on describing the correlates of the behaviors that were thought of as pathways to MPF (e.g., union formation and dissolution, childbearing behaviors, and teen pregnancy), more recent studies shed light on direct correlates of MPF. We summarize the correlates of MPF from multiple studies in three groups: family factors, community and economic factors, and individual factors. In addition, as many of the cited studies have explored these factors later in life within individuals already experiencing MPF, we will discuss the importance of studying the associations of family structure and community context in adolescence with MPF in young adulthood to gain a better understanding of how these early life factors may play an important role in future fertility outcomes.

Family Factors

In terms of family structure, evidence suggests that living with one or both biological parents in a stable household reduces the likelihood of experiencing MPF, as well as reduces the likelihood of experiencing teen pregnancy (Carlson & Furstenberg, 2006; Guzzo & Furstenberg, 2007a; Logan, Manlove, Ikramullah, & Cottingham, 2006; Manlove et al., 2008). In addition to family structure, relationship volatility and resultant family structure changes and instability can create added stress for the parents and challenges for children’s development and well-being (Mitchell et al., 2015). Given the evidence supporting the importance of family structure in determining MPF outcomes, our study takes this a step further by examining the longitudinal impacts of family structure when community disadvantage and community context are also taken into account. In future work, we also plan to explore the role of family instability on young adults’ experiences of MPF beyond the role of family structure alone.

Moreover, while static family structure is important, indicators of family structure do not shed light on the qualitative nature of that family (e.g., parenting behaviors and parental attitudes). Parenting behaviors and attitudes have been associated with early sexual behavior and teen pregnancy, which are pathways to MPF (Miller & Moore, 1990). Specifically, Killoren and Deutsch (2014) found greater strictness and parental monitoring to be associated with lower sexual risk for Latino youth, and a family intervention that focused on reducing risky sexual behavior found improved quality of family relationships to be associated with reduced risky sexual behavior in adolescence (Caruthers, Ryzin, & Dishion, 2014). Similarly, parents’ disapproval of sex and pregnancy has been associated with both a decrease and moderation of risky sexual behavior in adolescence (Jaccard, Dittus, & Gordon, 1996; Khurana & Cooksey, 2012). As such, we include measures of parental relationships (i.e., engagement with parents and positive parenting experiences) and the youth’s perception of parental embarrassment over a pregnancy to account for the influence of parenting styles and attitudes on MPF.

Community and Economic Factors

A series of variables related to economic capacities have linked greater economic capacities to a reduced likelihood of early childbearing, an increased likelihood of marriage, and more stable relationships. These behaviors are negatively correlated with MPF (Goldstein & Kenney, 2001; Manning & Smock, 1995; Raley, 2000). In general, those who are economically disadvantaged are more likely to experience MPF (Guzzo & Furstenberg, 2007a). Moreover, those who are more disadvantaged are less likely to have stable relationships, likely due to financial constraints (Lewin, 2005). Evidence suggests that family instability and MPF are likely to co-occur (Fomby & Osborne, 2017), thus disadvantaged families with a propensity for unstable relationships may consequently be more likely to experience MPF. Thus, measuring the economic context of an individual seems particularly important for understanding the unique effects of family structure and neighborhood advantage on future fertility outcomes of youth.

In terms of the association between education (a proxy for economic advantage) and MPF, mixed results have been found when controlling for additional characteristics. Educational achievement and aspirations reduce the likelihood of early childbearing (Plotnick, 1992) which predicts MPF, and having low levels of education increases the likelihood of MPF, before controlling for other characteristics (Carlson & Furstenberg, 2006). In light of these findings, our study measures youth’s educational aspirations to assess if these beliefs have a longitudinal impact on fertility outcomes. Also, because we are interested in how experience and context during adolescence affects future MPF, we measure the highest level of education obtained by youth’s parents to estimate if this proxy of disadvantage has a lasting effect on their fertility. Overall, previous literature shows important associations of family, community, and economic factors with MPF, which motivates the goals of this study to understand how neighborhood advantage and family structure as a youth longitudinally affect MPF as a young adult.

Individual Factors

In addition to familial, community, and economic context variables, other individual-level characteristics have been associated with MPF. Race and ethnicity are correlated with experiences of MPF. Being a member of a disadvantaged group, including racial or ethnic minorities (non-Hispanic Black), increases the likelihood of having children to more than one partner (Carlson & Furstenberg, 2006; Manlove et al., 2008). However, the effect of race and ethnicity has been found to go away when other contextual characteristics are taken into account (Guzzo & Furstenberg, 2007a). In a similar way, fathers born outside the United States are less likely to experience MPF (Carlson & Furstenberg, 2006; Manlove et al., 2008), although this effect goes away when controlling for additional characteristics related to first sexual encounter and first birth. In general, when discussing the significance of race, ethnicity, and nativity—as is common when discussing patterns of family formation—some authors argue that the main difference among members of racial and ethnic minorities and disadvantaged individuals is the path through which they enter parenthood. And this path may be largely explained by the economic and structural difficulties they face in the United States. Thus, the present study includes measures of race, ethnicity, and nativity—in addition to measures of economic and community differences—to better understand the effect of demographic factors on fertility outcomes when other social and economic factors are accounted for.

Individuals’ sexual experiences, such as first sexual encounter, age at first birth, and use of contraception, have also been found as important correlates of MPF. Early childbearing and early sexual activity significantly increase the likelihood of experiencing MPF for both men and women because it allows for a longer fertility window and longer time to be at risk (Carlson & Furstenberg, 2006; Evenhouse & Reilly, 2010; Guzzo, 2014; Guzzo & Furstenberg, 2007a, 2007b; Logan et al., 2006; Manlove et al., 2008). It is important to highlight that even though rates of teen pregnancy have declined significantly in the United States, they remain above those of other developed countries. In 2016, the adolescent fertility rate, which is the number of births per 1,000 women aged 15 to 19 years reached 21% in the United States, while the average for high-income countries was 16%, or a low of 14% in the United Kingdom (United Nations Population Division, World Population Prospects, 2016). The study of teen pregnancy has revealed that most pregnancies during early ages are unintended, occur out of marriage, and that individuals experiencing early pregnancies are more likely to have repeated subsequent births (Mosher, Jones, & Abma, 2012), which we have aforementioned as potential pathways to MPF.

Theoretical Importance of Family Structure and Community Context

The bioecological model of human development posits that individuals grow up in context. Youth are nested within families, and families are nested within communities (Bronfenbrenner & Morris, 2006). The theory focuses on the importance of the developmental stage of an individual as well as the influence of proximal and distal processes in the person’s life. Proximal processes are the interactions between the person and their environment, and the influences of these processes can vary based on characteristics of the individual, the immediate and distal contexts of the individual (e.g., neighborhood, state, and country), and the time or developmental stage during which the proximal and distal processes are occurring. As this theory suggests, the proximal processes between adolescents and their families have a large role in the development and outcomes of youth. However, the context in which the family and adolescent is placed, as well as the other community members and institutions the families are interacting with, can all shape the way in which processes affect youth development. As such, for adolescents, the family structure and neighborhood are important factors in the proximal and distal processes affecting youth development and outcomes. Additionally, the level of community engagement from the adolescent is likely an important determinant of how salient the distal community processes are for development. And given that both family structure and level of neighborhood advantage are associated with experiences of MPF, it seems particularly important to understand how both of these factors affect youth’s future fertility outcomes.

In summary, past research has studied familial, and interpersonal correlates of MPF, as well as community-level factors associated with risky sexual behaviors and teen pregnancy, both of which have been identified as pathways to MPF. However, no study to date has made a clear effort to identify whether individual-level factors (e.g., family structure), community-level factors (e.g., neighborhood poverty), or an interaction of both have a stronger association with MPF. In order to fill the aforementioned research gap, the present study aims to answer the following questions: (a) Is family structure during adolescence associated with the likelihood of experiencing MPF in early adulthood? (b) Do neighborhood effects (through community poverty) during adolescence have an effect on MPF in early adulthood? and (c) Do the effects of community poverty on MPF differ by family structure and level of youth’s connection to their community?

Informed by the bioecological model of human development, we hypothesize that (a) family structure and neighborhood disadvantage will have unique, long-term impacts on the MPF outcomes of young adults and (b) the impact of neighborhood disadvantage will vary by family structure of the youth and the level of community engagement from youth. The current study has the potential to inform interventions and services aimed at supporting children and families in disadvantaged communities by providing a better understanding of how these two contextual factors (i.e., family structure and neighborhood disadvantage) interact to impact the fertility outcomes of youth as young adults.

Data and Sample

This study uses data from the National Longitudinal Study of Adolescent to Adult Health (Add Health), a longitudinal study of a nationally representative sample of youth in the United States in Grades 7 to 12 during the 1994-1995 school year. The cohort has been followed for four rounds of data collection, with the most recent occurring in 2008 when participants were aged 24 to 35 years. For the purposes of our study, we used information from the first, second, and fourth waves of data collection. The Add Health data set is particularly useful for our questions of interest because it allows us to investigate multiple different family structures and control for numerous important factors that influence fertility outcomes. Add Health also offers a longitudinal design for associating Wave 1 youth constructs with Wave 4 young adult fertility outcomes. Additionally, this study links to census data, so proxies of neighborhood-level factors can be measured.

Add Health data is also limited. Because we are only following our sample into early adulthood, we recognize that by the time we examine fertility outcomes, many participants are still in their childbearing years. Thus, their fertility history is likely not over yet. In this context, our estimates will be more representative of those individuals who have started their fertility history earlier in life, and early childbearing is often associated with greater disadvantage. As such, we are likely underestimating the prevalence of MPF, with more individuals likely reporting MPF if we followed their fertility into middle to late adulthood. Additionally, the influence of poverty and neighborhood context may be biased upward, as the more disadvantaged youth may have increased chances of experiencing MPF in their early childbearing years. Regardless of this limitation, Add Health still offers a uniquely rich, longitudinal data source that allows us to estimate MPF in young adulthood and its associations with individual and contextual characteristics during adolescence.

We measure survey responses as individual-level data and census measures of community poverty as community-level data. Additionally, census tract information is used from Wave 1 and Wave 2 to restrict the analysis to only youth who did not move between Wave 1 and Wave 2. One year elapsed between Wave 1 and Wave 2, and approximately 400 youth moved during this time. We also restricted the analysis to individuals who remained in a stable family structure between Wave 1 and Wave 2. In this way, at least 1 year of exposure to a particular neighborhood and family structure is ensured. As mentioned in our literature review, changes in family structure have been identified as having an impact on child and youth development. Therefore, since this article is focused on the effects of experiencing a certain family structure and neighborhood disadvantage, we restrict our sample to participants who remained in the same family structure for at least 1 year in an effort to isolate the effect of family structure from that of family instability.

Our analysis sample began with 20,473 individuals, of whom 5,885 were dropped from the analysis due to attrition between Wave 1 and Wave 2, and 2,849 were dropped due to attrition between Wave 2 and Wave 4. Next, 1,096 participants were excluded from the analysis because they were missing information on family structure, which is a key variable in our analysis, and an additional 650 were dropped because they were missing information on sample weights. Finally, we excluded participants who changed family structure or moved between Wave 1 and Wave 2 (n = 1,315), for a final analysis sample of 8,678 individuals. As part of our study, we used survey weights to adjust for attrition bias, and we used mean imputation to address the small amount of missing information on certain covariates and predictors (e.g., parent education and likelihood of attending college). Variables measured through youth response were missing a very small amount of data (<1%). Variables measured through parent responses had significantly more missing information (11.5%) since not all youth had parents respond to the parent survey. To adjust for measurement error due to this missing data, an indicator for missing information on any variable is included in all models to account for any bias that may be due to nonrandom missing information. Communities are defined by census tract (n = 1,316).

Measures

Multiple Partner Fertility

MPF is measured using a self-report variable in Wave 4. Participants were asked, “With how many persons have you ever had a romantic relationship or sexual encounter that resulted in a pregnancy?” A count of the number of partners that resulted in a pregnancy was then compared with the number of live births that resulted from those pregnancies. Using these two constructs, an indicator of MPF was created, with a value of 1 being given if a participant had two or more live births with two different partners. Those with a value of zero on the MPF indicator include adults with SPF and adults with no children.

Community Poverty

While the census data provides a breadth of information about group-level variables, we focused on the items that provide a proxy for community poverty to gauge the level of economic disadvantage in a neighborhood. Similar to the measurement strategy of Wickrama and Bryant (2003), our study measures community poverty using three indicators: the proportion unemployed, proportion below poverty, and proportion using public assistance. Wickrama and Bryant (2003) also included the proportion of single mothers with children and the proportion of individuals employed in the service sector in their measure of poverty. However, due to previous work that has included the use of public assistance as a proxy for poverty, we settled on the current three proportions for our indicator of community poverty because they speak most directly to the working and income status of a community (e.g., De Marco & Berzin, 2008; Zolotor & Runyan, 2006). And while other community-level variables (e.g., number of teenage pregnancies and racial composition) are also indicators of community disadvantage (Gaskin et al., 2014; Harding, 2003), we believe our measure of community poverty is an adequate representation of the economic well-being of the community without adding additional proxies. The scores of each census tract on these three items are summed and divided by three to obtain the average score of community poverty in each census tract. This measure was standardized for multilevel analyses.

Family Structure

The family structure for each youth is constructed with adolescent responses in which they list all of the people living in their home and what their relationship is to that individual. While youth listed living with grandparents, cousins, and aunts/uncles in a few cases, the current study focuses only on family structures that contained at least one biological or adoptive parent. Six categories of family structure were initially included in the analyses: single mother, single father, two biological (or adoptive), married parents, biological (or adoptive) mother and stepfather, biological (or adoptive) father and stepmother, biological (or adoptive) mother and male partner, and biological (or adoptive) father and female partner. There was such a small amount of youth who listed living in a biological father/stepmother and biological father/partner family structure, so we collapsed the separate mother/father categories into two categories of stepfamilies and cohabiting families. Therefore, the final analyses include five family structures: single mother, single father, married biological, stepfamily, and cohabiting. Throughout the rest of the article, we refer to “married biological” and “stepfamily” structures, but it is important to remember that stepfamily structures are also married. These same family structures were identified again in Wave 2, and our sample was restricted to youth whose family structure remained stable between Wave 1 and Wave 2.

Community Involvement

Community involvement of the adolescent is measured at Wave 1 with questions about the adolescent’s neighborhood: “You know most of the people in your neighborhood,” “In the past month, you have stopped on the street to talk with someone who lives in your neighborhood,” “People in this neighborhood look out for each other,” “Do you use a physical fitness or recreation center in your neighborhood,” and “Do you usually feel safe in your neighborhood.” Respondents indicated true or false to each question. False responses were given a value of 0 and true responses a value of 1. Items were then summed into a count variable ranging from 0 to 5 and divided by five to obtain the average. This variable was standardized for multilevel modeling.

Covariates

Numerous variables were included as covariates to account for any construct that may confound the relationships between community poverty, family structure, community involvement, and MPF. Measures of sexual behavior (e.g., use of birth control and age of first sexual activity), an indicator for having a first birth younger than 20 years old, an indicator of past sexual abuse, demographic characteristics (e.g., age, nativity, gender, race, and ethnicity), and educational measures (e.g., ever repeated a grade, desire and believed likelihood to attend college) were included in the model. In addition, we construct an indicator to identify those participants who had a birth before the first wave, and we use this information to conduct a sensitivity test in which we exclude them from our sample to identify if this biases our results in any way (see the appendix).

Learning about pregnancy in school and total household income at Wave 1 were all included in the model. Household income at Wave 1 was measured using a separate survey conducted with parents of the youth. There was a significant amount of missing data on this measure because not all youth had a parent who was able to complete the survey. Thus, we created an indicator for missing information on income and replaced the categorical income indicators with a value of zero if income information was missing. The missing income indicator is included in all analyses. Family characteristics, including positive parenting experiences, engagement with parents, parents’ education level, and a measure of the youth’s perception of parents’ embarrassment of pregnancy were included in the model to control for the effect of these family characteristics on fertility outcomes.

Method

Descriptive statistics were obtained to examine significant mean differences of predictor variables and covariates for young adults with MPF (7.8%) compared with young adults with SPF (36.5%), and no fertility (55.8%). The prevalence of MPF is lower in our sample than the national average, but this is likely due to the young age of the participants at Wave 4 data collection. Multilevel modeling was used to examine the associations of community poverty, family structure, and community involvement with MPF, while accounting for random, community-level effects. Sample weights were applied to accurately represent the participants and address attrition and sampling bias. The results of a likelihood ratio test showed a logistic, mixed effects model as the best fit for the analyses when compared with a model with only fixed effects. The final equation for the multilevel analysis is as follows:

\begin{array}{l} Y_{ij} (mpf) = (γ_{00} + γ_{01} [community poverty] + u_{0 j}) + \\ {(γ_{10} + γ_{11} [community poverty] + u_{1 j})}^{'} \\ (family structure, community involvement, covariates) + r_{ij} \end{array}

The mixed effects model has both a fixed and random part. The fixed part tells us information similar to an ordinary least squares regression model, in that the coefficients are interpreted as the change in Y_ij with a one-unit increase in the predictor variable. The random effects parameters tell us how much variance in the outcome is accounted for by community-level effects after controlling for explanatory variables. In this way, we can see how much of MPF is accounted for by community context. We can also see the role community poverty, family structure, and community involvement of youth play in fertility outcomes when community-level random effects are taken into account. Coefficients are reported as odds ratios (ORs).

Results

Descriptive Results

Simple mean differences between those with MPF compared with those with SPF and no fertility in Table 1 are consistent with previous literature in that those with MPF have a significantly higher prevalence of single mother and cohabiting families, and a significantly lower prevalence of married, biological families. Moreover, those with MPF lived as youth in communities with significantly higher community poverty, are more likely to be Black, and were more likely to have a total household income of $30,000 or less as an adolescent. This supports previous findings that show how individuals from disadvantaged and marginalized communities are more likely to experience MPF (Guzzo & Furstenberg, 2007a). Also consistent with prior literature, those with MPF were more likely to begin sexual activity at a young age, more likely to have a teenage pregnancy, and less likely to use birth control (Carlson & Furstenberg, 2006; Guzzo, 2014). We find that individuals with MPF are much more likely to have experienced sexual abuse. Thus, risky sexual behaviors and sexual trauma are more common among individuals with MPF. In terms of parenting, the perception of positive parenting experiences for young adults with MPF is slightly lower compared with those with SPF and no fertility. Moreover, young adults with MPF report significantly lower engagement with their parents as youth. And finally, those with MPF were less likely to have parents who received greater than a high school education and significantly more likely to have repeated a grade. In sum, these differences in family structure, income, demographic characteristics, parenting, sexual behavior, and education all suggest that those experiencing MPF are more disadvantaged across various indicators. As such, it seems even more pertinent to investigate how community and familial context influence the fertility outcomes of youth.

Table 1.

Mean Differences.

Covariates	MPF	SPF	No F^a	MPF vs. SPF	MPF vs. No F	SPF vs. No F
Participant age at baseline	15.7 (1.51)	15.6 (1.58)	15.3 (1.58)		***	***
Use of birth control	2.0 (1.11)	2.4 (0.91)	2.7 (0.75)	***	***	***
Pregnancy would embarrass family	3.1 (1.03)	3.5 (1.04)	3.5 (0.97)	***	***	**
Perceived college likelihood	3.7 (1.31)	4.1 (1.14)	4.3 (1.06)	***	***	***
Desire to attend college	4.1 (1.26)	4.4 (1.04)	4.5 (0.95)	***	***	***
Positive parenting	4.0 (0.78)	4.2 (0.75)	4.2 (0.70)	**	***	**
Engagement with parents (standardized)	−0.01 (0.88)	0.3 (1.00)	0.4 (0.99)	***	***	**
Ever sexually abused	7.9	3.3	1.9	***	***	***
Age at first birth	19.9 (2.58)	23.3 (3.32)	—	***
First pregnancy younger than 20 years	55.1	16.9		***
Had first sex at or before age 15	33.1	17.3	10.4	***	***	***
Had first sex between age 16 and 20	52.8	31.8	19.5	***	***	***
U.S. born	99.1	98.1	98.2	^†	^†
Female	65.2	55.2	44.4	***	***	***
Hispanic	10.3	12.8	10.9	^†		**
Black	31.2	12.7	11.5	***	***
Native American	3.1	3.7	2.8
Asian	1.6	2.8	4.9	**	***	***
White	63.3	76.8	78.9	***	***
Parent education <high school	10.2	8.3	4.6		***	***
Parent education =high school	37.5	29.2	22.8	**	***	***
Parent education >high school	38.1	50.2	63.4	***	***	***
Taught pregnancy in school	90.4	85.1	84.0	^†	**	^†
Ever repeated a grade	32.6	23.4	15.4	***	***	***
Income ≤15,000	21.7	11.3	8.0	***	***	***
Income >15,000 and ≤30,000	24.2	16.6	12.7	***	***	***
Income >30,000 and ≤45,000	19.1	19.5	17.9
Income >45,000 and ≤60,000	8.6	13.7	15.6	***	***
Income >60,000	8.1	18.3	28.3	***	***	***
Community poverty and connection
Community poverty (standardized)	0.3 (1.16)	−0.03 (1.04)	−0.2 (0.93)	***	***	***
Youth’s connection to community	3.36 (1.09)	3.37 (1.14)	3.39 (1.12)
Family structure
Single mother	32.5	18.8	17.4	***	***	***
Single father	2.6	2.2	2.2
Married, biological parents	46.8	65.2	68.4	***	***	***
Stepfamily	16.5	12.6	11.1	***	**	***
Biological parent + cohabiting partner	1.6	1.2	1.0
Observations	674	3,166	4,838	3,840	5,512	8,004

Note. MPF = multiple partner fertility; SPF = single partner fertility. Standard deviations for mean values are in parentheses.

No F indicates an individual with no biological children.

†

p < .10. *p < .05. **p < .01. ***p < .001.

Multilevel Modeling Results

Results of a mixed effects model can be found in Table 2. Statistics are presented in the form of ORs, so a coefficient less than one suggests a negative association between the variable of interest and likelihood of MPF as a young adult, and a coefficient greater than one suggests that the variable of interest increases the odds an individual will experience MPF as a young adult. The effect of community context is presented at the bottom of the table.

Table 2.

Associations of Family Structure and Community Poverty in Adolescence With Young Multiple Partner Fertility.

Key predictors	MPF Model 1	MPF Model 2	MPF Model 3
Community poverty and connection
Community poverty (standardized)	1.048 (0.0553)	1.124 (0.153)	1.056 (0.0788)
Youth’s connection to community	0.995 (0.0413)	1.000 (0.0426)	0.997 (0.0416)
Family structure (married bio parents as reference)
Single mother	1.067 (0.136)	1.065 (0.136)	1.053 (0.139)
Single father	1.016 (0.356)	1.017 (0.356)	1.050 (0.366)
Stepfamily	1.479*** (0.202)	1.479*** (0.202)	1.480*** (0.202)
Cohabiting	0.668 (0.287)	0.659 (0.284)	0.598 (0.278)
Interaction key predictors
Community poverty × youth community connection	—	0.979 (0.0375)	—
Community poverty × single father	—	—	0.819 (0.270)
Community poverty × single mother	—	—	1.022 (0.107)
Community poverty × cohabiting	—	—	1.344 (0.463)
Community poverty × stepfamily	—	—	0.874 (0.129)
Covariates
Female	1.608*** (0.170)	1.607*** (0.170)	1.611*** (0.170)
Race and ethnicity (White as reference)
Hispanic/Latino(a)	0.686** (0.103)	0.686** (0.103)	0.685** (0.103)
Black	1.666*** (0.207)	1.667*** (0.207)	1.663*** (0.207)
Native American	1.278 (0.299)	1.276 (0.299)	1.279 (0.299)
Asian	0.702 (0.191)	0.703 (0.191)	0.709 (0.192)
U.S. born	1.454 (0.623)	1.457 (0.624)	1.446 (0.620)
Participant age at baseline	1.099** (0.0408)	1.099** (0.0408)	1.098** (0.0408)
Use of birth control	0.993 (0.0734)	0.992 (0.0733)	0.989 (0.0732)
History of sexual abuse	1.058 (0.210)	1.057 (0.210)	1.065 (0.211)
Age started sexual activity(beginning after 20 years as reference)
Began sexual activity at or before 15 years	1.124 (0.160)	1.125 (0.160)	1.123 (0.160)
Began sexual activity aged 16 to 20 years	1.984*** (0.358)	1.981*** (0.358)	1.973*** (0.357)
First birth younger than 20 years	12.65*** (1.318)	12.65*** (1.318)	12.71*** (1.326)
Pregnancy would embarrass family	0.930* (0.0409)	0.930* (0.0409)	0.930* (0.0410)
Taught about pregnancy in school	1.376** (0.215)	1.374** (0.215)	1.365** (0.213)
Ever repeated a grade	1.108 (0.126)	1.109 (0.126)	1.101 (0.126)
Desire to attend college	0.960 (0.0549)	0.960 (0.0549)	0.961 (0.0550)
Perceived likelihood of college	0.888** (0.0484)	0.889** (0.0485)	0.888** (0.0484)
Positive parenting experiences	0.931 (0.0624)	0.932 (0.0624)	0.935 (0.0627)
Engagement with parents	1.051 (0.0557)	1.052 (0.0558)	1.048 (0.0556)
Parents education (more than high school as reference)
Parent education <high school	1.544** (0.284)	1.546** (0.284)	1.551** (0.286)
Parent education =high school	1.593*** (0.178)	1.594*** (0.178)	1.586*** (0.177)
Household income (>60,000 as reference)
Income ≤15,000	1.596** (0.315)	1.600** (0.315)	1.599** (0.316)
Income >15,000 and ≤30,000	1.815*** (0.318)	1.817*** (0.318)	1.829*** (0.322)
Income >30,000 and ≤45,000	1.427** (0.249)	1.427** (0.249)	1.430** (0.250)
Income >45,000 and ≤60,000	1.077 (0.210)	1.077 (0.210)	1.080 (0.211)
Income question missing	1.204 (0.235)	1.204 (0.235)	1.204 (0.235)
Income question refused	1.143 (0.246)	1.144 (0.246)	1.153 (0.248)
Missing on control variables	2.274*** (0.279)	2.271*** (0.279)	2.268*** (0.279)
Constant	0.00428*** (0.00377)	0.00417*** (0.00368)	0.00431*** (0.00380)
Census tract variance (cons)	0.08 (0.061)	0.08 (0.061)	0.08 (0.061)
Observations	8,678	8,678	8,678
Number of groups (census tracts)	1,316	1,316	1,316

Note. MPF = multiple partner fertility. Odds ratios presented. Standard errors are in parentheses.

p < .1. **p < .05. ***p < .01.

The random effect of living in a particular census tract accounted for 8% of variance in our models. This suggests that the specific community an individual is situated in during adolescence has a limited role in explaining fertility outcomes as a young adult. Moreover, our model suggests that other interpersonal, familial, and poverty contexts do have an association with future fertility outcomes for youth, even after community context is accounted for.

Model 1 shows the associations between community poverty, family structure, and MPF when all covariates are included in the model. Results show that beginning sexual activity in the age span of 16 to 20 years is associated with 98% increased risk of MPF as a young adult. Most notably, having a first birth as a teenager has a very large and significant association with increased odds of MPF (OR = 12.65). This provides further evidence for previous work showing that sexual activity early in life and teenage pregnancy greatly increase the chances of MPF (Carlson & Furstenberg, 2006; Guzzo & Furstenberg, 2007a). Interestingly, being taught about pregnancy in school is also associated with a significant, 38% increased risk of MPF.

With regard to demographic characteristics, being female increases the risk of MPF by 61% and being Black increases the risk of MPF as a young adult by 67%. This is a large increased risk of MPF for the Black demographic group and is consistent with previous literature citing a greater prevalence of MPF among Black individuals (Carlson & Furstenberg, 2006; Logan et al., 2006; Manlove et al., 2008). Beliefs and perceptions of youth also seem to have a longitudinal impact on fertility outcomes. Believing that pregnancy will embarrass your family reduces the risk of experiencing MPF as a young adult by 7%, and believing you are likely to go to college reduces the risk of MPF by 11%. Notably, youth’s involvement with the community does not show an association with MPF.

Economic factors suggest that having an annual income of less than or equal to $45,000 as a youth is associated with a 43% to 82% increase in the likelihood of experiencing MPF as a young adult. Living in a neighborhood of high community poverty does not show a significant association with MPF as a young adult, although the direction suggests greater community poverty increases the likelihood of MPF. However, the null association suggests that this proxy of neighborhood disadvantage is not as important as other interpersonal and familial factors for influencing the fertility outcomes of youth. Finally, all family structures show increased odds of MPF compared with a married biological family structure, with a significant 48% increased risk of MPF as a young adult if youth lived in a stepfamily structure. Overall, the mixed effects model of the associations between family structure, community poverty, and MPF shows significant and sizeable relationships between measures of sexual behavior, interpersonal beliefs, and race. Having a lower household income as a youth does appear to have a longitudinal association of increased risk for MPF as a young adult, and living in a nontraditional family structure during adolescence does appear to increase the odds of MPF, particularly living in a stepfamily structure.

Model 2 and 3 extend Model 1 by including interaction terms. Insofar that the impact of community poverty likely varies by how involved the youth is with the community and by the proximal processes of their family structure within that community, it is important to investigate potential interaction effects between youth involvement, family structure, and community poverty. Including the interaction terms did not significantly change any associations previously found in Model 1. Importantly, in Model 2, youth’s connection to the community did not have a unique association with MPF when interacted with community poverty, suggesting that the effect of community poverty on MPF does not differ by youth’s community involvement.

Additionally, the interactions of family structure with community poverty (see Model 3) did not have significant associations with MPF as a young adult. The size of the ORs suggests that cohabiting and single mother family structures add an increased risk for MPF if youth are residing in high-poverty neighborhoods. Interestingly, the size of the interaction coefficient for community poverty and family structure suggest a decreased risk of MPF for youth experiencing a stepfamily parent structure in high-poverty neighborhoods. However, the association between a stepfamily structure and MPF is still greater than one and significant in Model 3. So while having a two parent, married household in a high-poverty neighborhood may provide a small protective effect compared with single mother or cohabiting family structures, the nonsignificant finding suggests living in a stepfamily structure is a significant risk factor for MPF regardless of the level of community poverty. Overall, Model 1 and Model 2 demonstrate that family structure seems to have a more important role in determining fertility outcomes of youth than community poverty and youth involvement with the community. Moreover, the effect of community poverty does not vary by how connected the youth is to the community, suggesting that the saliency of distal community processes may not be an important factor for determining youth’s fertility outcomes. Our model does show that nontraditional family structures put youth at greater risk of MPF, particularly stepfamily structures. Thus, family structure appears to play a role in influencing the fertility outcomes of youth, even after accounting for community context.

Discussion

The current study offers unique insight into the longitudinal relationship between community and familial context as a youth and MPF as a young adult. The nested design of Add Health data enables us to use mixed effects modeling to account for community-level differences in order to determine the unique associations of community poverty and family structure with MPF. Moreover, the longitudinal approach lets us follow individuals from youth into young adulthood, which offers confidence in the timing of our measures. Thus, we can be certain our measures of family structure and community poverty were experienced by youth before experiences of MPF, making the direction of our associations more clear.

Descriptive results show that young adults with MPF were more disadvantaged as youth, and more likely to have lived in a nontraditional family. These findings support previous work showing MPF to be more common among the most disadvantaged. Thus, it is important to continue targeting prevention efforts at disadvantaged communities that are more likely to experience MPF.

Results of multilevel, mixed effects models suggest that use of birth control, perceptions that your family would be embarrassed by a pregnancy, and a perceived likelihood that you will attend college all reduce the odds of MPF as an adult. Beginning sexual activity earlier in life—between the ages of 16 and 20 years—is significantly associated with large increased odds of MPF, and experiencing a teen pregnancy is even more strongly associated with increased odds of MPF. The coefficient sizes for these interpersonal beliefs and behavior variables are largest for early sexual activity and teenage pregnancy, providing evidence for the importance of making contraception readily available to youth at an early age. It is important to note that being sexually active before or at the age of 15 years is associated with increased odds of MPF, but this association is not significant. This may be because the number of youth having sex at or before age 15 is smaller than the number having sex between age 16 and 20 (see Table 1). Regardless, the direction of the coefficients suggests that having sex earlier increases the odds of MPF later in life, which is not surprising.

Interestingly, the variable indicating if youth were taught about pregnancy in school is associated with a significant increase in the odds of MPF in all models. The reason for the increased risk due to sex education in schools is unclear, but it may be that the wide age range of the sample in Wave 1 drives this effect because those who had learned about pregnancy during Wave 1 were likely older and therefore have experienced more time in their childbearing years at the point of Wave 4 data collection. Thus, the finding in Table 2 is likely due to the young ages of some of the Wave 1 participants that have not yet received sex education in school, and we do not believe this finding suggests we should reconsider offering sex education in school.

Being female and Black is associated with significantly increased odds of MPF. Previous data from the Census suggests that women are more likely to experience MPF, so this finding is similar to what past research has found (Monte, 2017). Similarly, previous literature has shown a greater prevalence of MPF in Black communities (Carlson & Furstenberg, 2006), and our findings offer further evidence to support this trend. It is important to note that Hispanics as well as Native Americans and Asians are not significantly more likely to experience MPF than White individuals. Thus, it seems clear that Black individuals are at greatest risk for MPF, even after accounting for community-level effects. However, because we were interested in measuring community poverty and not all aspects of community disadvantage, the scope of this study does not account for all aspects of a community that may be salient for Black youth outcomes. To further understand the increased risk of MPF for Black youth, it is important for future studies to examine the impact of other important community-level factors, such as quality of schools and community violence. It is particularly imperative to investigate the mechanisms of this relationship in future studies in order to understand how to best intervene and work toward reducing the racial disparity in MPF outcomes.

Regarding community poverty and family structure, the coefficient of community poverty is not statistically significant and close to one in Model 1, with only a small increase in coefficient size in Model 2 and Model 3. Moreover, community-level random effects only account for 8% of variance in the model. Thus, in the current study, it seems that community context does not have a lasting impact on fertility outcomes of youth. Also, the null effects of community poverty do not change with levels of youth’s connection to their community or family structure, which suggests the saliency of the distal processes from the community (Bronfenbrenner & Morris, 2006) is not a significant factor in the fertility outcomes of youth. Rather, family structure appears to influence the young adult fertility of youth, even after accounting for community-level effects and community poverty. In all models, living in a stepfamily structure as a youth is associated with 48% increased odds of MPF. This suggests that youth exposed to stepfamily structures for at least 1 year during adolescence are at an increased risk of MPF compared with their peers in married, biological families. Possible explanations of this association are a potential greater likelihood of kids with stepparents to also have half-siblings in the family or other forms of blended families. Thus, it may be that growing up in blended families and potentially being exposed to MPF in parental figures increases the likelihood of MPF in young adulthood. Our intent is not to argue that the presence of half-siblings in the household has a direct influence on future MPF, but instead, that early experiences and exposure of youth to their own parent’s MPF might increase their likelihood of experiencing MPF themselves later in life. However, future work should examine the intergenerational impact of MPF, and particularly, the effect and mechanisms through which stepfamilies are associated with an increased likelihood of MPF for youth.

Additionally, while the current study was focused on the role of a static family structure on youth’s fertility outcomes, it is important for future research to expand the scope of this study by focusing on the unique impacts of family instability. Answering this question was outside the scope of the current study, but given that nontraditional family structures are also more likely to be unstable, it seems important to see how the frequency of family instability in adolescence influences fertility outcomes in young adulthood.

In sum, youth who spend time in stepfamily structures seem to be most at risk for MPF. This may be due to the relationship modeling seen by the relationship dissolution and formation likely seen in stepfamily structures (Ruggles & Kennedy, 2015), or due to parental MPF that may be present in these family structures. Both pathways warrant future research to understand the most effective targets of prevention and intervention. Generally, family structure seems to be more important for influencing the MPF outcomes of youth than community poverty and community involvement. Other interpersonal, sexual behavior, and demographic factors also have a role in predicting MPF outcomes (e.g., age of first sexual activity, race, and gender), which provides further evidence to support increasing contraception access and targeting sexual behavior interventions at more disadvantaged individuals in efforts to prevent MPF.

Limitations

Although the current study provides insight into the associations of adolescent experiences of communities and families with MPF in young adulthood, some limitations are notable. Results should be taken with caution as causality cannot be inferred from our models. It is possible that there are additional unobserved characteristics that affect MPF, family structure, and community poverty; and future research is needed to carefully examine the extent to which family structure, community poverty, and/or their interaction are a cause of MPF. In line with this limitation, it is relevant to mention the potential for omitted variable bias and measurement error in our models, which likely leads to biased estimates. Regarding omitted variable bias, we were unable to incorporate key predictors of MPF into our models, such as union status at the time of the birth (Cancian, Meyer, & Cook, 2011). This may contribute to an overestimation of the effects of family structure on MPF. In terms of measurement error, all our variables are self-reports, which may lead to an underestimation of the fertility patterns and sexual conduct of youth.

Furthermore, the age of our sample is relatively young, and we are gauging participants’ MPF in the early years of their childbearing age. Related to this, the sample ranges from 7th to 12th grade in Wave 1, and it is likely that older youth during Wave 1 are much more likely to have experiences of MPF in Wave 4 because they have experienced more childbearing years. We ran a sensitivity test excluding individuals younger than 16 years during Wave 1, and the results did not significantly change (see the appendix). This suggests that including the wide age range in our sample does not significantly bias our results. Additionally, there was a small sample (n = 70) of participants who already had a child during Wave 1. We conducted a sensitivity test excluding these individuals, and the findings remained consistent with our main results. Therefore, we do not believe these individuals are biasing our results and we decided to keep them in the final analysis sample (see the appendix).

Finally, while we wanted to focus only on youth who experienced at least 1 year of a stable family structure, we conducted a sensitivity test with all youth—regardless of a change in family structure between Wave 1 and Wave 2—to understand if excluding unstable families significantly influences our findings (see the appendix). All of the significant findings in this sensitivity test were consistent with our main models, except that the interaction between community poverty and a single father family structure showed a significant association less than one. This suggests that individuals in a single father family structure in high-poverty communities at Wave 1 were 40% less likely to experience MPF as a young adult. However, because we do not know the frequency of instability youths in a single father family structure at Wave 1 experienced, it is hard to interpret the implications of this finding. Thus, because very few associations changed by allowing youth with unstable families to remain in the model, we chose to continue excluding them from our analyses so we could be certain of the minimum dosage of a family structure and how that amount of exposure is associated with MPF in young adulthood.

In addition, our estimates of MPF are likely downward biased since we have considerable levels of attrition between waves, which we try to mitigate by using weights. However, we cannot incorporate the experiences of those exiting our sample, who are likely more disadvantaged and therefore more likely to experience MPF. Moreover, Add Health does not interview students who dropped out of school between Wave 1 and Wave 2. This is important because those individuals are typically at a greater risk of experiencing MPF (Klerman, 2007). We tested for significant differences in family structure and community poverty during adolescence between our analysis sample and individuals who were lost due to attrition. Not surprisingly, the missing sample was significantly more likely to experience nontraditional family structures and greater community poverty than the final analysis sample. This nonrandom missing information is important to remember when interpreting the results, as our sample is likely missing the most disadvantaged individuals, who in turn may be the most likely to experience MPF. Finally, we suffer from selection bias, as families often do not enter family structures and neighborhoods randomly. By accounting for community-level effects in the multilevel models, however, we hope to control for some of this selection bias.

Conclusion and Implications

Our findings have important implications for public policy and interventions. Given the continued increase in MPF and its negative consequences for the well-being of adults and children, there has been an interest in identifying potential ways to either prevent MPF or to support those individuals already having children with multiple partners. The results of the current study have the potential to inform prevention efforts oriented at supporting children and families in disadvantaged communities. The key takeaways include the importance of having an increased focus on familial and personal attitudes (e.g., sexual behaviors) as points of MPF prevention and intervention during adolescence. With regard to family structure and neighborhood disadvantage during adolescence, our findings suggest family-level factors (e.g., parents’ education, household income, and family structure) are much stronger risk factors for MPF as a young adult than community-level factors (e.g., community poverty and community involvement). These findings suggest that prevention efforts would benefit from directing resources to family-focused interventions targeting disadvantaged, nontraditional family structures.

Footnotes

Appendix

Sensitivity Tests.

Key predictors	Main models			Test 1^a			Test 2^b			Test 3^c
Key predictors	Model 1	Model 2	Model 3	Model 1	Model 2	Model 3	Model 1	Model 2	Model 3	Model 1	Model 2	Model 3
Youth’s connection to community	0.995 (0.0413)	1.000 (0.0426)	0.997 (0.0416)	0.989 (0.0375)	1.002 (0.039)	0.990 (0.0376)	1.000 (0.0423)	1.006 (0.0435)	1.002 (0.0425)	1.001 (0.0583)	1.005 (0.0597)	1.004 (0.0587)
Community poverty (standardized)	1.048 (0.0553)	1.124 (0.153)	1.056 (0.0788)	1.023 (0.0493)	1.222 (0.149)	1.066 (0.0758)	1.041 (0.0566)	1.140 (0.158)	1.064 (0.0816)	1.106 (0.0853)	1.182 (0.241)	1.115 (0.122)
Single mother	1.067 (0.136)	1.065 (0.136)	1.053 (0.139)	1.190 (0.137)	1.183 (0.136)	1.178 (0.140)	1.047 (0.136)	1.044 (0.136)	1.044 (0.140)	1.277 (0.229)	1.274 (0.229)	1.273 (0.235)
Single father	1.016 (0.356)	1.017 (0.356)	1.05 (0.366)	0.969 (0.278)	0.964 (0.277)	1.019 (0.290)	1.044 (0.366)	1.045 (0.367)	1.077 (0.377)	1.279 (0.557)	1.283 (0.558)	1.306 (0.566)
Stepfamily	1.479*** (0.202)	1.479*** (0.202)	1.480*** (0.202)	1.284** (0.161)	1.289** (0.161)	1.294** (0.162)	1.453*** (0.202)	1.453*** (0.201)	1.455*** (0.201)	1.623** (0.312)	1.623** (0.312)	1.623** (0.312)
Cohabiting	0.668 (0.287)	0.659 (0.284)	0.598 (0.278)	0.809 (0.209)	0.8 (0.207)	0.73 (0.204)	0.714 (−0.314)	0.704 (0.31)	0.654 (0.304)	0.646 (0.432)	0.639 (0.428)	0.490 (0.376)
Community poverty × youth community connection	—	0.979 (−0.0375)	—	—	0.947 (0.0327)	—	—	0.973 (0.0379)	—	—	0.980 (0.0568)	—
Community poverty × single father	—	—	0.819 (0.270)	—	—	0.606* (0.164)	—	—	0.819 (0.270)	—	—	0.783 (0.334)
Community poverty × single mother	—	—	1.022 (0.107)	—	—	0.980 (0.0947)	—	—	0.989 (0.106)	—	—	1.007 (0.159)
Community poverty × cohabiting	—	—	1.344 (0.463)	—	—	1.219 (0.259)	—	—	1.359 (0.480)	—	—	1.755 (0.918)
Community poverty × stepfamily	—	—	0.874 (0.129)	—	—	0.824 (0.107)	—	—	0.874 (0.131)	—	—	0.896 (0.196)
Observations	8,678	8,678	8,678	9,687	9,687	9,687	8,608	8,608	8,608	3,987	3,987	3,987
Number of groups	1,316	1,316	1,316	1,399	1,399	1,399	1,309	1,309	1,309	882	882	882

Test 1: Random effect logistic models without restricting the sample to those with a stable family structure between Wave 1 and Wave 2. ^bTest 2: Random effects logistic models excluding participants who had a baby before baseline data collection (n = 70). ^cTest 3: Models restricted only to youth ≥16 years old in baseline survey.

Acknowledgements

Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design.

Authors’ Note

Persons interested in obtaining Data Files from Add Health should contact Add Health, The University of North Carolina at Chapel Hill, Carolina Population Center, Carolina Square, Suite 210, 123 W. Franklin Street, Chapel Hill, NC 27516 (addhealth_contracts@unc.edu). No direct support was received from grant P01-HD31921 for this analysis.

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research uses data from Add Health, a program project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris, and funded by a grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies.

ORCID iD

Angela Guarin

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