The Opioid Epidemic and Children’s Living Arrangements in the United States, 2000–2018

Abstract

Although the detrimental effects of the opioid epidemic on health and wellbeing have been well documented, we know little about how it has affected the family contexts in which children live. Using data from the 2000 Census, the 2005–2018 American Community Survey (ACS) and restricted Vital Statistics, we assess how the opioid epidemic, as measured by a rise in the opioid overdose death rate, affected the rates of children living in different family arrangements: two married parents, two cohabiting parents, single mother, single father, or another configuration. According to local fixed-effects models, a higher opioid overdose death rate is associated with fewer children living with two married parents and an increase in children living in family structures that tend to be less stable, such as those led by cohabiting parents or a single father. These changes in family arrangements have potential long-term implications for the wellbeing of future generations.

In 2017, more than 11 million people misused some type of opioid in the United States (SAMHSA 2018). Although multiple studies have shown that opioid dependence has severe negative effects on individuals’ lives, we know very little about how it affects the family contexts in which children live (Peisch et al. 2018). Indirect evidence of the effect of the opioid epidemic on family structure may be gleaned from studies examining other closely related outcomes. For example, a recent study has shown that local opioid-overdose death rates are associated with an increase in nonmarital births (Caudillo and Villarreal 2021). There is also evidence of a positive association between opioid prescriptions and child removal rates (Bullinger and Ward 2021). Nonetheless, we do not know how the opioid epidemic has shaped children’s probability of living in different types of family arrangements. This question is fundamental to understanding the long-term and intergenerational effects of the opioid crisis because different family structures are associated with heterogeneous health, behavioral, and economic outcomes for children (Craigie, Brooks-Gunn, and Waldfogel 2012).

In this study, we use the 5 percent sample of the 2000 Census, the 2005–2018 1-year samples of the American Community Survey (ACS) and restricted Vital Statistics data for the same period to evaluate the effect of local opioid-overdose death rates (ODR) on the rates of children living under five different family structures: two married parents, two cohabiting parents, single mother, single father, and adults other than parents. We distinguish between children living with two married or two cohabiting parents because children of married parents tend to experience better educational and social outcomes, compared to those with cohabiting parents (Brown 2010). Children living with two married parents are the least likely to experience poverty, and housing and food insecurity, whereas those living with single or cohabiting parents are at higher risk of experiencing those conditions (Manning and Brown 2006). We also evaluate changes in the rates of children living with single mothers and fathers, because this type of family structure is the most economically vulnerable as there is usually only one adult who works and provides income (Brady, Finnigan, and Hübgen 2017).

We examine how children’s family arrangements have changed throughout the opioid epidemic using local area fixed-effects models, which account for any time-invariant community-level characteristics that might affect both opioid abuse and changes in family arrangements. We exploit variation in ODR from 2000 to 2018, a period that encompasses the three waves of the opioid epidemic (CDC 2021), which were driven by prescription opioids (late 1990s-2010), heroin (2010–2013), and synthetic opioids (2013-present). We estimate models for different age groups to infer the extent to which the opioid crisis affects child wellbeing through two different channels: first, by increasing the probability that children are born into family structures characterized by greater instability and disadvantage (Craigie, Brooks-Gunn, and Waldfogel 2012); and second, by increasing the probability that children’s family structures will become destabilized by the profound effects of opioid dependence on individuals, their families and communities (Birnbaum et al. 2006; Harris et al. 2019; Orford et al. 2013). We also explore whether the living arrangements of White, Black, and Hispanic children are heterogeneously affected by the opioid crisis.

Our findings show that areas that have been affected by the opioid crisis, as measured by ODR, have witnessed decreasing rates of children living with two married parents, and increasing rates of children living with two cohabiting parents, with a single father, and with adults other than their parents. Our analyses suggest that the living arrangements of babies and older children were affected by the opioid crisis at similar rates, which highlights the relevance of both reconfigurations in living arrangements and changes in the probability that children are born into different family structures. While children aged 1–14 were more likely to live in a variety of family structures other than with two married parents as a result of the opioid epidemic, children under age one were more likely to live with cohabiting parents. Finally, our findings show that the opioid epidemic has diversified the living arrangements of White children to a larger extent than those of their Black and Hispanic counterparts. In the following sections, we discuss what is known about how family structure and stability relate to child wellbeing, and the channels through which the opioid epidemic may affect the living arrangements of children.

Why Are Children’s Living Arrangements Important?

Evidence from the social sciences has long highlighted a strong relationship between with whom children live and how they fare in multiple dimensions of wellbeing. Findings from this body of literature have supported two interrelated aspects of children’s family contexts that strongly predict more desirable outcomes: the presence of two parents and the degree of stability in the family structure (Cavanagh and Fomby 2019; McLanahan and Percheski 2008). In particular, family structures that include two married biological parents are associated with better economic, behavioral, emotional and educational outcomes for children than alternative living arrangements, such as two cohabiting biological parents, married stepfamily, cohabiting stepfamily, single parents, or no parents (Brown 2010). These differences are largely driven by disadvantages in economic resources (Wimer et al. 2021), parental education and mental health (Artis 2007) in these alternative family structures, as well as by variation in father involvement (McLanahan, Tach, and Schneider 2013). Although the causal effect of family structure is notoriously difficult to ascertain, and estimates vary across studies (Bzostek and Berger 2017), an increase in the rates of children living in family structures other than with two married parents is likely to hinder their prospects for upward social mobility and deepen pre-existing inequalities (McLanahan and Percheski 2008).

Family structure and stability are closely related. Compared to those living with married parents, the children of cohabiting and single parents are more likely to experience transitions in family living arrangements (Brown 2010; Manning 2015). In turn, family transitions may bring about dramatic changes in available resources, disruptions in social ties, and stress (Cavanagh and Fomby 2019), and may result in lower financial commitment and investments in children (Hastings and Schneider 2021). Changes in family structure are therefore also associated with behavioral problems, poorer health and educational outcomes, and lower income mobility for children (Cavanagh and Fomby 2019). Although the relationship between family structure and children’s outcomes is partially explained by the socioeconomic characteristics of parents who select into those living arrangements, the inherent differences in stability that characterize each of these structures have important implications for child wellbeing and for societal-level processes such as the intergenerational transmission of disadvantage (Bloome 2017).

How May the Opioid Epidemic Affect Children’s Living Arrangements?

In 2017, there were approximately 3.2 million children in the United States living with an adult who was misusing opioids, and about 550,000 children living with an adult who had an Opioid Use disorder (OUD) (Bullinger and Wing 2019). This translated into about 623,000 parents with an OUD who were living with a child under 18 years of age (Clemans-Cope et al. 2019). Given the drastic increase in opioid overdose mortality in the last two decades (CDC 2021), the question of how this epidemic is affecting children should be central for both scholars and policy makers. The opioid epidemic may contribute to changing and destabilizing the family structures of children through at least three overlapping channels: by reducing the quality of family relations, by increasing the risk of Child Protective Services (CPS) involvement, and by increasing the rate of children born into disadvantaged family structures.

Parent-Child and Other Family Relationships

Opioid abuse is positively associated with individual and family-level outcomes that may strain family relations. First, it may hinder parents’ ability to fulfill their expected roles, such as contributing their earnings to the household, since individuals with OUD are less likely to have stable employment (Rhee and Rosenheck 2019). Opioid dependence may also impair parents’ ability to effectively contribute to the household economy by providing childcare. Substance Use Disorders (SUD) in general are associated with problematic parenting styles, including a reliance on inconsistent, abusive, or neglectful practices that often put children at risk of psychological and physical harm (Romanowicz et al. 2019). Individuals with OUD experience lower memory, attention, planning, and decision-making abilities, as well as substantial reductions in complex psychomotor abilities, such as driving or cooking (Wollman et al. 2019), all of which may render them less able to safely care for children. A parent’s inability to adequately fulfill caretaker or breadwinner roles due to an OUD may motivate other family members to seek separate living arrangements for their children.

Second, opioid misuse and abuse may reduce family members’ ability to resolve interpersonal conflicts in a healthy manner. A recent systematic review found that at least 15 percent of men who had an OUD had perpetrated severe physical intimate partner violence (IPV) or a threat involving a weapon in the last year, and at least 32 percent of women with an OUD had been victims of IPV in the same time frame (Stone and Rothman 2019). A study in Pennsylvania found that a higher number of hospitalizations due to opioid misuse at the ZIP-code level predicted more hospitalizations due to IPV and child maltreatment in 2004–2014 (Sumetsky, Burke, and Mair 2020). Opioid misuse and abuse may therefore fuel intrafamily conflict and increase the risk of related injuries for household members, therefore catalyzing the reconfiguration of family structures by motivating members to leave the household, or by attracting the involvement of CPS. Given the strong association between drug use and multiple forms of crime beyond domestic violence (Bennett, Holloway, and Farrington 2008), children of parents with OUD are also at higher risk of family separation due to parental involvement with the criminal justice system.

Third, the opioid epidemic may affect children’s living arrangements through their own opioid use and their behavioral responses to parental opioid dependence. Being raised by a parent with an OUD may elicit externalizing behaviors, such as delinquency and aggression, as well as mental health problems such as mood and anxiety disorders (Peisch et al. 2018). The children of opioid-dependent parents also face greater access to these substances at home, which may put them at higher personal risk of misuse and abuse. Parents of children and youth who exhibit disruptive behavior, mental health disorders, and substance abuse resulting from parental opioid dependence might be encouraged to delegate their care to other adults. Furthermore, parental opioid abuse could initiate a process of deterioration in family relations that may eventually motivate children and youth to escape the household environment.

Child Protective Services and Foster Care Entries

The proportion of foster care entries caused by parental drug use has grown from 15 percent in 2000 to 36 percent in 2017 (Meinhofer and Angleró-Díaz 2019). However, the extent to which the opioid epidemic has contributed to such trend remains unclear. At the national level, opioid-overdose death rates and hospitalizations due to opioid use were associated with an increase in foster care entry between 2011 and 2016 (Ghertner et al. 2018), and higher opioid prescription rates have been linked to increased child removal rates in states like California and Florida (Quast, Bright, and Delcher 2019; Quast, Storch, and Yampolskaya 2018). Along the same lines, the number of infants with neonatal opioid withdrawal syndrome predicted more infant foster care entries in a sample of counties across eight states of the US between 2009 and 2017 (Loch et al. 2021). On the other hand, a recent study found a null association between opioid overdose events and child removals in Washington State (Rebbe et al. 2020). Another study found heterogeneous effects of opioid prescriptions on child removals due to drug abuse at the national level, with this association being null for 12 states and negative for 15 states between 2010 and 2015 (Quast 2018).

Although useful, studies about the relationship between the opioid epidemic, CPS involvement and foster care entries provide limited information about how the family contexts of children are changing due to parental opioid abuse. When maltreatment is substantiated and a child is removed from their home, states are required by federal law to prioritize placing the child with relatives (Child Welfare Information Gateway 2018). While nearly a quarter million children are placed in foster care every year, about the same number are informally placed with relatives as a result of CPS involvement (Presser 2021). Therefore, foster care entry rates alone are unable to capture such changes in the child’s family structure. This is particularly true for child removals due to parental opioid abuse, since compared to children of parents that abuse other type of substances, these children face greater barriers to reunification and tend to spend a longer time away from their parents’ care due to the involvement of child welfare authorities (Moreland et al. 2021).

The meaning of CPS involvement for children’s living arrangements is further complicated by the fact that there is substantial variability in the legal criteria that define when parental drug use justifies child removal across states and over time (Quast 2018). CPS’ propensity to place children at risk of maltreatment in foster care may have also varied across different stages of the opioid crisis (Mackenzie-Liu 2021). Therefore, the correlation between foster care entries and other family reconfigurations driven by the opioids crisis may have changed across states and time. Futhermore, focusing on child removals may distort the extent to which different ethnoracial groups are experiencing changes in living arrangements because Black families are more likely to attract CPS involvement compared to Hispanics and Whites (Maguire-Jack et al. 2015). Finally, child removal cases may capture a very small portion of the change in children’s living arrangements caused by the opioid epidemic. For instance, only 5 percent of children being raised by their grandparents in 2016 were placed there by the foster care system (Generations United 2018).

Changes in the Rates of Children Born into Different Family Structures

The opioid epidemic may also affect the living arrangements of children by shifting the proportion of babies born into different types of family structures. A recent study showed that opioid overdose deaths and opioid prescription rates predicted more births among unmarried women, but not among their married counterparts, between 2000 and 2016 (Caudillo and Villarreal 2021). This increase in nonmarital fertility may be partially explained by the fact that unmarried women are at higher risk of OUD (Rhee and Rosenheck 2019), and opioid dependence is linked to a higher number of sexual partners and lower and less effective contraceptive use (Terplan et al. 2015). Given that opioid misuse and abuse predicts unemployment (Rhee and Rosenheck 2019) and a greater propensity to be involved in IPV (B. C. Moore, Easton, and McMahon 2011), opioid-dependent individuals may have fewer marriage prospects. Therefore, unmarried individuals who face a greater risk of pregnancy due to opioid abuse may also be less likely to get married after becoming pregnant. Being born into a family structure that is not led by two married parents may increase children’s exposure to instability and reconfiguration of their living arrangements throughout childhood (Kalil and Ryan 2010).

Data and Methods

We used data from the 2005–2018 1-year samples of the American Community Survey (ACS) and from the 5 percent sample of the 2000 census (Ruggles et al. 2019c) to calculate the rate of children living in different family arrangements, defined according to whether they were living with one or two parentsⁱ, or with adults other than their parents. Values for years 2001–2004 were interpolated (Ruggles et al. 2019c). Our aggregated dataset comprises 1,070 Consistent Public Use Microdata Areas (CPUMA) that were observed throughout the 19 years covered by our observation period. CPUMAs represent the smallest geographical unit that is consistently identified over time and encompass territories of at least 100,000 residents (Ruggles et al. 2019b; 2019a). We computed five outcomes of interest for each CPUMA-year: the yearly rates of children (per 100,000) who were living with two married parents, two cohabiting parents, single father, single mother, and adults other than their parents. The latter category included children living with other relatives, non-relatives, or in foster homes (see Table A1 in Online Appendix for a detailed breakdown). Children living in group quarters (less than 1% in 2018) were excluded from our analytical sample because they were not sampled in 2005. We focused on children aged 14 or younger because, by Census Bureau definitions, older teens may be heading families of their own.

We used the ODR per 100,000 residents to measure the intensity of the opioid epidemic in each CPUMA. We interpret ODR as a measure of the extent to which residents are misusing or abusing opioids in each CPUMA-year, and as an indicator of the probability that children will live with adults who misuse opioids, as opposed to merely a count of potential deaths in children’s families. Compared to other measures of the intensity of the opioid epidemic, the ODR reflects changes in both illegal and prescription opioid use, and it does not rely on personal reports, which may be subject to social desirability bias (Caudillo and Villarreal, 2021). In addition, ODR can be calculated separately by sex, which allows us to explore potential causal mechanisms in greater detail. We estimated the total number of opioid-overdose deaths in each county and year from restricted death certificate data (NVSS 2018) using CDC guidelines (Seth et al. 2018). Any death with underlying causes X40–44 (unintentional), X60–64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent) was classified as an opioid-overdose death if the multiple causes included any of the following: opium (T40.0), heroin (T40.1), natural/semisynthetic opioids (T40.2), methadone (T40.3), synthetic opioids other than methadone (T40.4), and other and unspecified narcotics (T40.6). To calculate population denominators, we used county-year population estimates from the U.S. Census Bureau by age and sex. We then converted our county-level ODR estimates to the PUMA level and then aggregated them to the CPUMA level to merge them with our estimates of family structure obtained from the ACS.ⁱⁱ

We took several measures to account for potential confounders in our analytical strategy. First, all our models include CPUMA fixed effects to control for potential unobserved confounders that are unique to each CPUMA but do not vary over time, and that may be associated with both opioid abuse and family structure. Unobserved factors may include stable cultural and economic characteristics, and geographical features. Second, we computed a series of time-varying control variables using the ACS 1-year samples, intended to capture changes in demographic and economic conditions over time within each CPUMA: the percentage of the population with and without completed high school; the percentage that is unemployed; the percentage receiving government income assistance (such as social security income, AFDC and general assistance); the percentage that is non-Hispanic Black, Hispanic, and foreign born; and the average household income. To capture changes in urbanization, we used the population density of the CPUMA (defined as the total population per square mile) as an additional control. Third, all our models include year fixed effects, which account for national variation in unobserved characteristics that may affect both opioid-use and family structure patterns over time, and that may not be already captured by our time-varying controls.

The social processes that link the opioid epidemic to changes in family structure are likely to unfold over several years. For instance, it may take anywhere from months to years of drug-induced inconsistent contraception for an opioid-dependent woman to experience a pregnancy, plus another nine months for a baby to be born. A parent’s opioid misuse and dependence may unfold over several years before leading to a divorce or separation, or before it catches the attention of Child Protective Services. In other cases, a family member’s death due to opioid overdose may lead to more immediate reconfigurations in children’s living arrangements. Therefore, the length of time needed for an increase in the local opioid misuse and abuse to disrupt children’s living arrangements is an empirical question. For this reason, we estimated models using ODR alternatively measured one, two and three years before the outcomes of interest. In each of these model variations, we lagged our time-varying covariates by one additional year relative to the year when the ODR was observed, so that they capture confounding factors preceding both changes in the opioid epidemic and family structures, instead of mediating processes caused by the former.

Although CPUMAs never cross state boundaries, some counties may include multiple CPUMAs. For this reason, there are cases in which multiple CPUMAs that belong to the same county are assigned the same ODR. To account for CPUMAs that are not independent within counties, we used clustered standard errors by county in all our models. Finally, to produce estimates that were representative of the national average, we weighted our models using the total population in each CPUMA in the baseline year, 2000.

Results

Table 1 shows descriptive statistics for all the aggregate outcome and predictor variables used in our analysis, weighted by CPUMA population size. Throughout the 2000–2018 period, the majority of children under age 15 (65 percent) were living with two married parents. The second most prevalent family structure was living with a single mother (21 percent). About 7 percent of children were living with two cohabiting parents, 4 percent lived with a single father, and 4 percent lived with adults other than their parents. The share of children living with two married parents is very similar when comparing those under age 1 to those aged 1–14 (64.2 versus 64.6 percent). In contrast, while 11 percent of children under one lived with two cohabiting parents, this share decreased to 7 percent among those aged 1–14, which underscores the fragility of cohabiting unions relative to marriages. In general, the share of children living in family structures other than with two-married-parents was higher among older children. The total opioid-overdose death rate throughout the observed period was 7.5 per 100,000 population, and it was higher for men (10 per 100,000 pop.) than it was for women (5 per 100,000 pop.). As shown in Figure 1, the trend in the total opioid overdose death rate in the United States has increased dramatically since 2000.

Table 1.

Descriptive statistics of CPUMAs, 2000–2018

	Mean	SD
Living arrangements of children 14 or younger (per 100,000 children)
Two married parents	64,593.2	11,330.4
Two cohabiting parents	6,946.3	3,142.4
Only mother	21,099.3	8,682.0
Only father	3,860.3	1,632.3
Adults other than parents	3,500.9	1,907.0
Living arrangements of children under age 1 (per 100,000 children)
Two married parents	64,227.4	15,628.7
Two cohabiting parents	10,663.2	8,016.9
Only mother	19,434.6	12,252.6
Only father	2,811.8	3,894.4
Adults other than parents	2,863.0	3,822.0
Living arrangements of children ages 1–14 (per 100,000 children)
Two married parents	64,620.2	11,321.1
Two cohabiting parents	6,690.2	3,088.8
Only mother	21,213.4	8,703.0
Only father	3,930.8	1,680.9
Adults other than parents	3,545.4	1,958.1
Opioid epidemic measures
Opioid-overdose death rate	7.5	7.1
Female opioid-overdose death rate	5.0	4.7
Male opioid-overdose death rate	10.2	10.1
Local demographic characteristics
Percent population with less than high school	20.2	5.5
Percent population with high school or some college	57.6	8.2
Percent population with bachelor’s degree or more	22.2	10.7
Percent of unemployed population	9.0	3.7
Average household income	53,617.6	15,627.3
Percent receiving government income assistance	1.6	1.0
Percent non-Hispanic White	65.5	21.3
Percent Hispanic	15.4	16.1
Percent non-Hispanic Black	13.1	12.3
Percent foreign-born	13.2	11.8
Population per square mile	29.1	85.5
Observations	20,330

Outcomes and local demographic controls were obtained from the 5 percent sample of the 2000 Census and the 2005–2018 1-year samples of the American Community Survey, with interpolations for the years in between (Ruggles et al. 2019c). Opioid-overdose death rates were calculated using data from the National Vital Statistics System (NVSS 2018).

Figure 1.

Opioid overdose death rate in the United States, 2000–2018

Data source: Author’s calculations with data from the 2000 Census, the 2005–2018 American Community Survey 1-year samples, and restricted Vital Statistics.

Table 2 shows results of CPUMA fixed-effects models using the total ODR to predict the rates of children under age 15 (per 100,000) living in each of the family structures mentioned above, while controlling for local sociodemographic conditions and accounting for year fixed effects. The table shows two models for each outcome, which alternate using the ODR measured one, two and three years in the past. The ODR has a negative and significant association with the rate of children living with two married parents. Every increase of 1 opioid-overdose death per 100,000 population is associated with 37 to 83 fewer children per 100,000 living with two married parents in a CPUMA (p<0.01). In contrast, the ODR has a positive association with the rates of children living with two cohabiting parents, with a single father, or with adults other than parents. For all these outcomes, the ODR coefficients are positive and statistically significant regardless of whether the ODR is measured one, two or three years in the past. An increase of 1 opioid-overdose death per 100,000 population observed three years in the past is associated with 24 more children (per 100,000) living with two cohabiting parents, 13 more children (per 100,000) living with a single father, and 29 more children (per 100,000) living with adults other than their parents. Although the associations between the ODR and living arrangement rates maintain the same sign across lags, their size and significance level grows larger as the number of years lagged increases, which suggests that the processes linking the opioid epidemic to changes in family structure may take a couple of years to unfold completely. The associations between the ODR and the rate of children living with a single mother were not statistically significant in these models.

Table 2.

CPUMA fixed effects models using total opioid-overdose death rates to predict the living arrangements of children aged 14 or younger (per 100,000 children), 2000–2018

	Two married parents			Two cohabiting parents			Single mother			Single father			Adults other than parents
	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
Opioid-overdose death rates (per 100,000 pop.) in CPUMA
ODR (y-1)	−37.09 **			14.32 *			8.36			5.41 +			9.00 *
	[12.41]			[6.19]			[9.03]			[3.15]			[4.05]
ODR (y-2)		−50.29 **			18.84 *			3.26			10.07 *			18.12 ***
		[15.54]			[7.99]			[10.62]			[4.30]			[4.72]
ODR (y-3)			−82.57 ***			23.95 **			16.49			13.07 *			29.05 ***
			[16.24]			[9.25]			[11.34]			[5.56]			[5.97]
Observations	16050	16050	16050	16050	16050	16050	16050	16050	16050	16050	16050	16050	16050	16050	16050

Outcomes and local demographic controls were obtained from the 1-year samples of the American Community Survey (Ruggles et al. 2018). Opioid-overdose death rates were calculated using data from the National Vital Statistics System (NVSS 2018). Analyses were conducted at the CPUMA-level. All models include CPUMA and year fixed effects and control for the percentage of the population with and without completed high school; the percentage that is unemployed; the percentage receiving government income assistance (such as social security income, AFDC and general assistance); the percentage that is non-Hispanic Black, Hispanic, and foreign born; the average household income, and the total population per square mile. Demographic controls are measured with an additional lag relative to opioid-overdose death rates. Standard errors are clustered by county and shown in brackets.

⁺p<0.10,

^*p<0.05,

^**p<0.01,

^***p<0.001

To better understand the causal mechanisms behind our findings, we tested separate models using gender specific ODR. Table 3 shows results of CPUMA fixed-effects models using female and male ODR to predict the rates of children under age 15 living in each of the family structures mentioned above, while controlling for the same local sociodemographic conditions and accounting for year fixed effects. Again, our regressions alternate between ODR measured one, two and three years in the past. Panels 1 and 2 show that when introduced separately, both female and male ODR have a negative and significant association with the rate of children living with two married parents, and a positive and significant association with the rates of children living with two cohabiting parents or adults other than parents. These coefficients are generally statistically significant across ODR lags. Nonetheless, they tend to be larger when a lag of three years is used, as was the case when the total ODR was used (Table 2).

Table 3.

CPUMA fixed effects models using female and male opioid-overdose death rates to predict the living arrangements of children 14 or younger (per 100,000 children), 2000–2018

	1. Using female ODR
	Two married parents			Two cohabiting parents			Single mother			Single father			Adults other than parents
	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
Opioid-overdose death rates (per 100,000 pop.) in CPUMA
Fem. ODR (y-1)	−49.26 **			17.60 *			10.63			4.32			16.71 **
	[16.22]			[8.44]			[11.95]			[4.47]			[5.55]
Fem. ODR (y-2)		−60.25 **			22.07 *			0.53			15.72 **			21.93 ***
		[19.50]			[10.45]			[13.35]			[5.80]			[6.13]
Fem. ODR (y-3)			−97.70 ***			29.27 **			10.77			25.48 ***			32.19 ***
			[20.74]			[10.89]			[14.58]			[6.47]			[7.52]
	2. Using male ODR
	Two married parents			Two cohabiting parents			Single mother			Single father			Adults other than parents
	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
Opioid-overdose death rates (per 100,000 pop.) in CPUMA
Male ODR (y-1)	−23.50 **			9.40 *			5.36			4.19 +			4.55 +
	[8.49]			[4.29]			[6.25]			[2.18]			[2.72]
Male ODR (y-2)		−32.13 **			12.16 *			2.92			5.49 +			11.56 ***
		[10.65]			[5.42]			[7.28]			[2.92]			[3.29]
Male ODR (y-3)			−49.82 ***			14.24 *			12.77 +			4.63			18.18 ***
			[11.15]			[6.48]			[7.53]			[3.88]			[4.07]
	3. Using female and male ODR
	Two married parents			Two cohabiting parents			Single mother			Single father			Adults other than parents
	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
Opioid-overdose death rates (per 100,000 pop.) in CPUMA
Fem. ODR (y-1)	−29.27 +			7.35				5.41		−4.01			20.52 **
	[17.15]			[11.27]				[14.69]		[6.66]			[6.89]
Male ODR (y-1)	−13.37			6.86				3.49		5.58+			−2.55
	[9.29]			[5.75]				[7.88]		[3.24]			[3.35]
Fem. ODR (y-2)		−32.48			10.99			−5.91			15.25 *			12.15
		[20.17]			[12.17]			[14.83]			[7.47]			[7.83]
Male ODR (y-2)		−21.20+			8.46			4.91			0.36			7.47 +
		[11.29]			[6.31]			[8.37]			[3.71]			[4.15]
Fem. ODR (y-3)			−69.16 **			21.81 +			−4.01			31.39 ***			19.97 *
			[23.02]			[12.52]			[16.49]			[7.65]			[8.16]
Male ODR (y-3)			−27.20 *			7.1			14.09			−5.64			11.65 **
			[11.92]			[7.43]			[8.58]			[4.55]			[4.36]
P-value of test H0: All ODR coefficients = 0	0.010	0.006	0.000	0.073	0.066	0.023	0.655	0.836	0.211	0.138	0.026	0.000	0.005	0.001	0.000

Outcomes and local demographic controls were obtained from the 1-year samples of the American Community Survey (Ruggles et al. 2018). Opioid-overdose death rates were calculated using data from the National Vital Statistics System (NVSS 2018). Analyses were conducted at the CPUMA-level. All models include CPUMA and year fixed effects and control for the percentage of the population with and without completed high school; the percentage that is unemployed; the percentage receiving government income assistance (such as social security income, AFDC and general assistance); the percentage that is non-Hispanic Black, Hispanic, and foreign born; the average household income, and the total population per square mile. Demographic controls are measured with an additional lag relative to opioid-overdose death rates. Standard errors are clustered by county and shown in brackets. Observations in all models are 16,050.

⁺p<0.10,

^*p<0.05,

^**p<0.01,

^***p<0.001

Panel 3 in Table 3 shows results from models that included both female and male ODR simultaneously. As was the case when female and male ODR were introduced separately, the coefficients of female ODR are generally larger than those of male ODR. Given that both indicators are highly correlated, they tend to remain statistically significant only when the ODR have the largest magnitudes, which is when they are lagged by three years. Both the female and male ODR (y-3) are negatively associated with the rate of children living with two married parents (p<0.05), and positively associated with the rate of children living with adults other than their parents (p<0.05), although again, the female ODR coefficients are larger. Overall, these results suggest that women’s opioid use is more consequential than men’s to explain changes in family structure brought about by the opioid epidemic. The simultaneous inclusion of these predictors results in relatively high levels of multicollinearity, making our estimates of the standard errors of each individual coefficient unstable (the Variance Inflation Factors for these two variables exceeds 7.0 in all models that include both predictors). Therefore, relying on the statistical significance of F tests to draw conclusions about the differences between male and female ODR coefficients would be misleading. Instead, the bottom panel of Table 3 therefore reports the results of an F-test for the hypothesis that the coefficients for the male and female ODR are both equal to zero. Consistent with our results from Table 2, we find that the coefficients for the male and female ODR are jointly significant predictors of the rates of children living in the different family structures, except for the rate of children living with mothers only.

Table 4 presents results from CPUMA fixed effects models using total ODR to predict the rates of children living in each of the family structures of interest only for children under age one. All models control for local demographic conditions and include year fixed effects. By restricting our analysis to children under age one we aim to distinguish changes in the distribution of family arrangements that are primarily explained by births occurring within those structures, as opposed to being driven by family reconfigurations. According to Table 4, the ODR observed three years in the past is positively and significantly associated with an increase in the rate of children under age one living with two cohabiting parents (p<0.05). Consistently, the ODR measured three years in the past has an association of similar magnitude—with an opposite sign—with the rate of children under age one living with married parents (p<0.10). When lagged by one or two years, the ODR has no significant associations with any of the living arrangements of young children. This suggests that the processes that link the opioid epidemic to the family structures of children younger than age one start at least a year before the time of conception. The fact that the local ODR predict an increase in the rate of children under age one living with cohabiting parents is consistent with evidence presented in Caudillo and Villarreal (2021), who found that the opioid epidemic increased nonmarital birth rates at the local level.

Table 4.

CPUMA fixed effects models using total opioid-overdose death rates to predict the living arrangements of children under age one (per 100,000 children), 2000–2018

	Two married parents			Two cohabiting parents			Single mother			Single father			Adults other than parents
	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
Opioid-overdose death rates (per 100,000 pop.) in CPUMA
ODR (y-1)	−3.78			28.94			−12.74			3.21			−15.63
	[29.08]			[20.78]			[21.93]			[9.54]			[9.94]
ODR (y-2)		9.02			16.07			−27.76			−5.58			8.26
		[39.38]			[24.70]			[26.72]			[12.09]			[12.65]
ODR (y-3)			−71.69 +			62.34 *			−14.06			2.96			20.45
			[41.84]			[27.43]			[33.36]			[13.78]			[13.60]
Observations	16050	16050	16050	16050	16050	16050	16050	16050	16050	16050	16050	16050	16050	16050	16050

Outcomes and local demographic controls were obtained from the 1-year samples of the American Community Survey (Ruggles et al. 2018). Opioid-overdose death rates were calculated using data from the National Vital Statistics System (NVSS 2018). Analyses were conducted at the CPUMA-level. All models include CPUMA and year fixed effects and control for the percentage of the population with and without completed high school; the percentage that is unemployed; the percentage receiving government income assistance (such as social security income, AFDC and general assistance); the percentage that is non-Hispanic Black, Hispanic, and foreign born; the average household income, and the total population per square mile. Demographic controls are measured with an additional lag relative to opioid-overdose death rates. Standard errors are clustered by county and shown in brackets.

⁺p<0.10,

^*p<0.05,

^**p<0.01,

^***p<0.001

Table 5 presents separate models for children aged 1–14. The magnitudes and signs of the ODR coefficients closely resemble those shown in Table 2, which included children in all age groups. Whereas the opioid epidemic predicts a large increase in the rate of babies living with cohabiting couples (Table 4), it predicts increases of smaller magnitudes in multiple types of alternative family structures among older children (Table 5). For both age groups, the ODR is associated with a lower rate of children living with two married parents. The ODR coefficients in the two-married-parent models for both age groups have similar sizes when opioid deaths are observed three years in the past, with the coefficient for children aged 1–14 being only slightly larger (−83 vs −72). Table A2 in the Online Appendix shows results from models that further break down this age group into ages 1–4, 5–10 and 11–14. The results from these ancillary models suggest that adolescents drive the positive association between ODR and children living with a single father that we see in the pooled analyses.

Table 5.

CPUMA fixed effects models using total opioid-overdose death rates to predict the living arrangements of children aged 1–14 (per 100,000 children), 2000–2018

	Two married parents			Two cohabiting parents			Single mother			Single father			Adults other than parents
	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
Opioid-overdose death rates (per 100,000 pop.) in CPUMA
ODR (y-1)	−38.45 **			13.06 *			9.07			5.81 +			10.52 *
	[12.44]			[5.98]			[9.08]			[3.26]			[4.11]
ODR (y-2)		−53.55 ***			18.57 *			4.94			11.52 **			18.52 ***
		[15.19]			[7.82]			[10.57]			[4.36]			[4.72]
ODR (y-3)			−82.68 ***			21.21 *			18.2			13.79 *			29.48 ***
			[16.06]			[9.04]			[11.46]			[5.55]			[6.28]
Observations	16050	16050	16050	16050	16050	16050	16050	16050	16050	16050	16050	16050	16050	16050	16050

Outcomes and local demographic controls were obtained from the 1-year samples of the American Community Survey (Ruggles et al. 2018). Opioid-overdose death rates were calculated using data from the National Vital Statistics System (NVSS 2018). Analyses were conducted at the CPUMA-level. All models include CPUMA and year fixed effects and control for the percentage of the population with and without completed high school; the percentage that is unemployed; the percentage receiving government income assistance (such as social security income, AFDC and general assistance); the percentage that is non-Hispanic Black, Hispanic, and foreign born; the average household income, and the total population per square mile. Demographic controls are measured with an additional lag relative to opioid-overdose death rates. Standard errors are clustered by county and shown in brackets.

⁺p<0.10,

^*p<0.05,

^**p<0.01,

^***p<0.001

Overall, these findings indicate that changes in the distribution of children’s family structures caused by the opioid crisis are plausibly driven by both family reconfigurations experienced later in childhood, and by changes in the union contexts in which children are born. Both types of processes have led to more children living in family structures other than with two-married-parents.

Table A3 in the Online Appendix reproduces our original models in Table 2, separately for White, Black, and Hispanic children. Models for Blacks and Hispanics include only those CPUMAs where the corresponding ethnoracial group represented at least 10 percent of the local population in 2000, to ensure that there is a minimum population size to estimate race-specific rates. Results for White children closely resemble those in Table 2, with the exception that in this group the ODR has a positive and significant association with the rate of White children living with single mothers, but not with single fathers. In contrast, the ODR is not significantly associated with living arrangements for Hispanic and, for the most part, Black children. More importantly, the ODR does not have a significant association with the rate of children living with two married parents in neither the Black or Hispanic sample. Together, results in this race-specific analysis suggest that the change in living arrangements caused by the opioid epidemic is primarily driven by White families.

Robustness Checks

Because outcome values for years 2001–2004 were interpolated in our main analyses, we conducted a robustness check using only non-interpolated years 2005–2018. Results are shown in Table A4 (Online Appendix), with coefficients and significance levels being very similar to those in our main analysis. This robustness check reassures us that our findings are not distorted by interpolation of our outcome variables.

The analyses described so far assume that children did not recently move to a different CPUMA and were therefore exposed to the ODR registered in their CPUMA of residence two or three years in the past, depending on the lag used for the ODR. This may not be a reasonable assumption because children may have moved to other geographical areas due to reconfigurations in their living arrangements (Cohen 2014; Cohen and Pepin 2018). As a sensitivity test, we replicated the analyses in Table 2 excluding children who were reported to have moved from one PUMA to another in the previous year.ⁱⁱⁱ This analysis is restricted to years 2005–2018 because migration variables are not available in the 2000 Census and are therefore first observed in the 2005 ACS 1-year sample (see descriptive statistics in Table A5 in Online Appendix). Despite the reduction in statistical power, results in Table A6 in the Online Appendix show a pattern very similar to that in Table 2. Specifically, results of the models excluding individuals who moved across PUMA boundaries also show a large reduction in the rates of children living with two married parents (−30 to −78, p<0.05), and an increase in children living in alternative family structures. Although the ACS does not have information about place of residence beyond 12 months in the past, this sensitivity test provides reassurance that our results are not biased by measurement error in the exposure to the opioid epidemic introduced by migration flows.

Fixed effects models are vulnerable to unobserved time-varying confounders that may affect both family structures and opioid misuse and abuse. Although year fixed effects in our models account for national trends in such confounders, there might be unobserved time-varying confounders that vary across smaller geographical areas. As a robustness check, we used the legalization of recreational marijuana markets as an instrumental variable. By providing a low-dependence alternative to opioids (Maharajan et al. 2020), marijuana may prevent some individuals from developing opioid addiction, and it may shield their families and communities from suffering its related disruptions. A detailed description of the instrument and a methodological discussion of this exercise are presented in Section 2 of the Online Appendix.

The overall conclusions in our main analyses (Table 2) are mirrored in this instrumental variable exercise (Table A8 in Online Appendix, Section 2), with a higher intensity of the opioid epidemic consistently predicting fewer children living with two married parents and more children living with cohabiting parents or with a single father. In addition, the association between ODR and children living with a single mother is statistically significant in these models. The magnitudes of the coefficients in these analyses are larger than those in Table 2, which suggests that unobserved endogeneity might be attenuating the association between ODR and children’s living arrangements in our main models. Therefore, our results in Tables 2–5 may represent lower bounds of the relationship between the opioid crisis and family structure. Nonetheless, as any other IV analysis, these models rely on strong assumptions and their results should be interpreted with caution.

What to Do?

In this study, we evaluated changes in a broad spectrum of children’s family structures caused by the opioid epidemic. Although much attention has been paid to the association between opioid use and child removals, foster care entries represent a small fraction of the family rearrangements that children have experienced during the opioid epidemic. Our study contributes to a better understanding of the consequences of the opioid crisis on family structures by assessing how the specific composition of children’s households has changed in areas with high levels of opioid abuse and misuse, as measured by opioid overdose death rates. Our findings show that opioid-overdose death rates predict lower rates of children under age 15 living with two married parents and suggest that these changes in the distribution of living arrangements are driven by two types of processes. First, transitions that move children into a variety of family structures other than living with two married parents, such as living with two cohabiting parents, a single father, or adults other than parents. Second, an increase in the number of children born to cohabiting parents, or to unmarried parents who decide to cohabit shortly after a pregnancy or birth, as suggested by our analysis of the living arrangements of children under age one. This is consistent with recent evidence that the opioid epidemic has increased nonmarital birth rates at the local level (Caudillo and Villarreal 2021). We build on such previous evidence by showing that nonmarital births caused by the opioid crisis are primarily occurring to cohabiting, as opposed to unpartnered, women.

Results from our analysis examining the association between opioid overdose deaths and the rate of children living with single mothers are mixed, which underscores the increased relevance that alternative family structures and caretakers are acquiring due to the opioid crisis. Our findings also suggest that White children have experienced more diversification in family structures due to the opioid epidemic than their Black or Hispanic counterparts. This conclusion is consistent with findings from previous studies, which have shown that the positive associations between the opioid crisis and nonmarital births (Caudillo and Villarreal 2021) and child removal rates (Quast, Storch, and Yampolskaya 2018) are primarily driven by changes occurring among the White population.

Given the sharp increase in opioid misuse and abuse in the last decade, primarily driven by synthetic opioid use, we may expect a corresponding rise in family disruptions experienced by children. Predictions from our models suggest that in 2018, at least 360,000 additional children under age 15 lived in something other than a married-couple family due to the opioid epidemic.^iv This means that at least 10 percent of the decrease in the number of children living with married parents in the US between 2000 and 2018 can be explained by increasing opioid overdose rates. For White children, this share would be at least 18 percent. Furthermore, recent studies suggest that substance use and opioid overdoses have accelerated during the COVID-19 pandemic (Haley and Saitz 2020; Kelley et al. 2021; Glober et al. 2020). By increasing social isolation and making OUD treatment less accessible, the COVID-19 pandemic has worsened an already dire situation (Linas et al. 2021). Policies intended to address the consequences of the pandemic should focus on improving access to OUD treatment and ameliorating the effect of opioid abuse on families.

We have found that the opioid epidemic has increased the probability that children will live in family structures other than with two-married-parents. This means that the opioid crisis is redistributing the burden of caring for and raising children towards families that are disproportionately vulnerable to poverty and material hardship, and might be especially unprepared to endure the economic and mental health shocks caused by the OUD of a family member. Strengthening the safety net around these families may alleviate potentially negative effects on children’s health and economic wellbeing. Public financial and in-kind support and public healthcare insurance represent a large share of unmarried parents’ resources in the US (Kalil and Ryan 2010; Stanczyk 2020) and could help reduce the detrimental intergenerational effects of the opioid epidemic. For instance, guaranteeing affordable childcare is key to protecting adults’ ability to remain in the labor force and to earn income after undertaking new or increased parenting responsibilities. Providing supplemental income through programs such as Temporary Assistance for Needy Families and the Earned Income Tax Credit (which expired in 2021), as well as Medicaid and food stamps may buffer negative financial shocks to families caring for children of parents who misuse or abuse opioids. Guaranteeing access to housing is also crucial considering the instability in living arrangements that characterizes fragile families. It is essential to strengthen these programs and promote access for families and children who have been directly affected by a relative’s opioid misuse or abuse. In particular, expanding eligibility to cover a broad diversity of family structures is key to ensure that the needs of children who face changes in their living arrangements are met.

Our analyses using gender specific ODR suggested that female opioid abuse was more strongly associated with children’s living arrangements. This is consistent with the disproportionate burden of parenting responsibilities shouldered by women in the US, which may render children of mothers with OUD more vulnerable to family reorganization relative to those with fathers with OUD. However, the aggregate nature of our analyses does not allow us to identify the opioid misusers in each household, or to directly observe stability and change in families where at least one family member misuses opioids or has died from an opioid overdose. Instead, we use opioid overdose death rates as a measure of the intensity of local opioid misuse and abuse.

In our analyses, the associations between opioid overdose death rates and children’s living arrangements have consistent signs across lag structures, but they tend to become stronger as the lag in opioid overdose death rates increases. This suggests that the opioid crisis is affecting family structures through processes that may take several years to unfold. It is possible that the changes in family structures that we find are partially explained by broader community-level disruptions that characterize illicit-drug epidemics, in addition to the individual- and family-level processes described in the Background section. Some of these problems are widespread violence, increased profitability of illegal activities such as drug dealing, decreased educational achievement and attainment, and a saturation of local governments’ capacity to address these and other social problems (T. Moore and Pacula 2020). For instance, as the opioid epidemic worsens, local governments may be forced to divert resources from programs that support families’ financial and psychological wellbeing to fund strategies against opioid misuse and abuse. Such repurposing of resources may increase pressure on families enduring psychological, emotional, and financial strain due to the opioid crisis and other factors, and may contribute to making family reconfigurations more likely. Federal financial support for local governments that have overextended their resources trying to mitigate the opioid crisis may reduce these indirect detrimental consequences.

A limitation of our study is the inability to accurately identify children living in foster care. In the American Community Survey, children can either be identified as relatives, non-relatives or foster children of the person who owns or rents the home. Because the survey allows for only one response category per household member, some of the children living with adults other than parents may be identified as relatives or non-relatives instead of foster children, even if they have been placed there by CPS. For this reason, we analyze children living with adults other than parents as a single category. An additional limitation in our analyses is that the outcomes in our models do not constitute direct measures of child wellbeing. Future studies should specifically examine the extent to which shifts in family structures caused by the opioid crisis have affected outcomes such as health, poverty, and education among children. Despite its limitations, we believe that this study delineates the extent to which children’s family contexts have been altered by the opioid crisis beyond the extensively researched trends in CPS involvement, and provides a starting point to discuss child-centered policy interventions other than increasing treatment for parental OUDs (Hall et al. 2016). The policy actions taken now to reduce the effect of family disruptions and vulnerabilities caused by the opioid epidemic will determine the extent to which this long-standing crisis will deepen social inequality and limit social mobility for future generations.