Population Research and Policy Review

, Volume 36, Issue 5, pp 739–760 | Cite as

Poverty and Problem Behaviors across the Early Life Course: The Role of Sensitive Period Exposure

Original Research

Abstract

Research routinely finds that children exposed to poverty exhibit more problem behaviors than their nonexposed counterparts. This research, however, lacks developmental specificity with regard to timing and the pathways by which poverty exposures manifest across the early life course. I utilized 15 years of prospective data from the Study of Early Child Care and Youth Development to assess how poverty exposures and financial strains at different ages (0–1, 2–5, and 15) were related to problem behaviors during early childhood (ages 2–5), late childhood (ages 5–12), and adolescence (age 15). Results show that poverty exposures during infancy and to a lesser extent early childhood were robust predictors of problem behaviors in early childhood, late childhood, and adolescence because they were linked to more problem behaviors at younger ages, which persisted over time. These associations partially operated through financial strain. Poverty during adolescence was mostly unrelated to problem behaviors during adolescence after taking into account exposures at younger ages. Overall, this study provided initial evidence that poverty exposure during infancy may have lasting implications for problem behaviors across the early life course.

Keywords

Problem behaviors Poverty Sensitive periods Life course Stress process 

Introduction

In 2014, approximately 5.5 million US children under age 6 years—nearly one in four—lived in a poor household (Jiang et al. 2016). Given this phenomenon, it is surprising that researchers know little about the long-term well-being of young children exposed to poverty (Avison 2010). Problem behaviors including internalizing and externalizing problems, with the former characterized by disturbances in emotions and moods (e.g., depression, withdrawal, and anxiety, feelings of inferiority, self-consciousness, shyness, hypersensitivity) and the latter by behaviors that are harmful and disruptive to others (e.g., problems with attention, self-regulation, antisocial and aggressive behaviors), are two particularly important impediments to well-being as they represent an early form of psychopathology.

An extensive body of research has established that children exposed to poverty exhibit more problem behaviors than their less disadvantaged counterparts (e.g., Strohschein 2005; McLeod and Shanahan 1996; Fanti and Henrich 2010; MacMillan et al. 2004; Dearing et al. 2006; Duncan and Brooks-Gunn 2000; Duncan et al. 2014; Morris et al. 2005; Evans and Cassles 2014; McLeod and Shanahan 1996; Strohschein 2005; Zachrisson and Dearin 2015; National Institute of Child Health and Human Development Early Child Care Research Network 2005). Moreover, recent work has indicated that this association is particularly pronounced if exposure occurred prior to age five (e.g., Duncan et al. 2012; Huston and Bentley 2010). This research, however, has largely ignored the role of timing within the first 5 years of life and how exposures unfold across the early life course (Doyle et al. 2009; Duncan et al. 2012; Avison 2010). This study fills this void by examining how poverty experienced at different periods during the early life course, including infancy and early childhood, is related to externalizing and internalizing behaviors through adolescence and to what extent financial strain, a chronic stressor that reflects satisfaction with and degree of worry regarding one’s financial situation, operates as a pathway connecting poverty exposure to problem behaviors. This study also tests whether financial strain is linked to problem behaviors, in its own right, independent of poverty exposure.

This study is important for at least two reasons. First, it is unique in its specificity regarding the timing of poverty exposure. Children may experience poverty differently depending on whether exposure occurs during infancy (ages 0–1) or early childhood (ages 2–5). Poverty research frequently combines these periods together despite wide acknowledgement that disparate phases of development occur at different ages within the first 5 years of life (Sheridan and Nelson 2009; Rosenblum et al. 2009). Some aspects of brain development, for instance, occur exclusively during infancy, such as neuronal pruning, and are influenced by one’s social environment (Wexler 2006). For this reason, it seems prudent to test if the connection between poverty exposure and problem behaviors also differs by the timing of exposure. Second, there is a paucity of work that examines the pathways by which early-life exposures unfold over time. For instance, it is unclear whether poverty exposure in early childhood will be related to adolescent problem behaviors directly or only through problem behaviors during late childhood (defined as ages 5–12). Overall, this study will provide critical insight into both when and why early-life poverty exposure leaves a lingering mark on behavioral tendencies.

This study integrates the concepts of stress proliferation (Pearlin et al. 2005) with those of sensitive periods (Knudsen 2004) and chains of risk (Rutter 1989) to understand why poverty may be linked to problem behaviors from infancy through adolescence. While prior research on the subject utilized these concepts separately (e.g., Avison 2010; Duncan et al. 2012; Elder et al. 2015), this study integrates them to better understand how early poverty and financial strain set in motion the adverse consequences that reverberate across the early life course. Moreover, social stress process theory provides the foundational justification for this study’s framework, as it details how socially derived stressors, such as poverty exposure, can shape one’s psychological well-being.

The Social Stress Process and Stress Proliferation

Social stress process theory addresses how stressors emanating from social sources are involved in a dynamic process, which influence individual well-being over time (Pearlin and Bierman 2013). A central feature of this framework is that exposure to stressors are rooted in one’s socioeconomic background and can influence emotions, cognitions, behaviors, and physiological functioning. Stressors refer to unwanted situations or incidents that are perceived to be threatening or burdensome (Pearlin 1989) and often produce maladaptive responses such as depression, anxiety, fear, anger, or aggression (Wheaton et al. 2013). Stress proliferation, which refers to the process by which an initial stressor begets a cascade of secondary stressors, is an import feature of the stress process model (Pearlin et al. 2005; Pearlin and Bierman 2013). For example, involuntary job loss was shown to increase depressive symptoms because job loss led to financial and marital strain (Pearlin et al. 1981). Other work has shown that stress proliferation can extend beyond the person that experiences the primary stressor to family members including children and parents (Menaghan 2009; Avison 2010; Milkie et al. 2008).

Living in poverty may produce different psychological consequences depending on whether and to what extent, it is perceived as threatening (Lazarus and Folkman 1984). Because parental interactions are a primary way that poverty shapes children’s lives, the caretaker’s objective economic situation may not be as important as their subjective perception. If the parents’ subjective level of financial stress is low, it would be less likely to influence parent–child interactions. This study, therefore, views poverty exposure as a primary stressor and financial strain, as well as the sequela it engenders for the child, as forms of secondary stressors. Stress proliferation is thought to occur because the lack of financial resources linked to poverty creates pressure, worry, and frustration when caregivers attempt to pay the bills and purchase basic necessities. This financial strain is argued to take a psychological toll on the caregiver and interfere with child–caretaker interactions (e.g., Masarik and Conger 2017; Conger et al. 1994; Conger and Donnellan 2007). For example, parents experiencing financial strain report providing more inconsistent and punitive discipline, less warmth, and less extensive monitoring to their children than their less-strained counterparts (Menaghan 2009).

Those exposed to poverty may exhibit more problem behaviors for reasons beyond financial strain. For instance, poverty may expose children to noise pollution, crowded housing, a chaotic home environment and other secondary stressors that occur independent of financial strain (Evans 2004). While the association between poverty exposure and problem behaviors is well established for children and adolescents (e.g., Masarik and Conger 2017; Neppl et al. 2016), researchers know less about how poverty exposures during biologically sensitive periods are related to problem behaviors at later ages.

Sensitive Periods

Sensitive periods are specific intervals during the early life course in which important developmental and biological changes occur among young children and when environmental exposures are especially likely to alter those changes (Knudsen 2004; Kuh et al. 2003). An important characteristic of sensitive periods is that the effects of environmental exposures can often extend across the life course. The earliest periods in life are particularly important for development because the brain is in a state of rapid growth, and environmental exposures during this period shape neural circuits and the behaviors they mediate (Knudsen 2004; Zeanah 2009). For example, children exposed to excessive amounts of stress in the first few years of life often experience dysregulation of their bodies’ stress response system (Gunnar and Quevedo 2007; McFarland and Hayward 2014) and such dysregulation can precede the onset of problem behaviors (Haltigan et al. 2011; Saridjan et al. 2014). The current study differentiates two sensitive periods, infancy and early childhood, but stops short of making hypotheses about the relative strength of their associations with problem behaviors.

The proposed model suggests that poverty experienced during infancy will potentially elicit early problem behaviors. Evidence indicates that caretakers living in poverty are often less responsive to their infants than those not living in poverty (Hart and Risley 1995). Infants are attuned to their caretakers emotions, and negative interactions with caregivers have been shown to produce signs of psychological distress and physiological stress among infants (in the form of elevated cortisol levels) when they experience less optimal interactions (Garner 1995; Haley 2011). A number of infant emotional behaviors have been linked to the emotional expressiveness, awareness of emotions, and negative affects of the parents including expressiveness, emotion-regulatory behaviors, and ability to soothe (Garner 1995; Gergely and Watson 1996). Less-optimal caretaker–infant interactions may impact an infant’s perception, shape cognitive processing, and influence future problem behaviors (Todorov et al. 2011).

Poverty and financial strain exposure during early childhood are connected to more problem behaviors during late childhood as well. During early childhood, kids develop the ability to regulate their actions and emotions and to gauge the emotions of others. These skills form the basis for all other learning and social relationships and children are especially dependent on caregivers in developing them (Yeung et al. 2002; Farkas and Beron 2004). Those who do not fully develop these skills will often exhibit problem behaviors as they engage with increasingly complex social environments and relationships. Poverty may impede the development of these skills and put children at risk of problem behaviors in the future.

Poverty exposure during sensitive periods may directly influence problem behaviors during late childhood and adolescence independently of problem behaviors during early childhood. Because socioemotional skills are thought to develop hierarchically, environmental exposures that hinder optimal developmental during sensitive periods may eventually lead to problem behaviors but only after more advanced skills, such as social competence, are needed (e.g., Knudsen et al. 2006; Heckman 2006). The ability to effectively regulate one’s emotions, for instance, is necessary to develop social competence, and when children lack social competence they often exhibit internalizing or externalizing problem behaviors as a result. The central point here is that the foundational neural networks children need for social and emotional regulation develop during sensitive periods as a function of their social environment (Knudsen et al. 2006; Knudsen 2004). A child may not exhibit problem behaviors until they enter a complex social environment, such as school, and experience troublesome social interactions.

An important point about this model is that the underlying mechanisms linking poverty and financial strain to the child likely vary by the child’s age as poverty may interfere with age-specific domains of development. For example, during infancy the development of a secure emotional attachment occurs when the infant and caretaker engage in a series of social and emotional exchanges (e.g., the mutual exchange of smiles, gazes, touches, and laughter). Poverty exposure may lead to less positive parent–infant exchanges and thereby shape attachment. Also, while the key underlying mechanisms linking poverty and financial strain to problem behaviors concern the home environment, especially caretaker–child interactions, the extent to which a child is exposed to the home environment likely varies by age. In particular, while parents and caregivers are the essence of infants’ and toddlers’ environment, they are only one part of adolescents’ lives as teenagers can access more enriching environments elsewhere (e.g., school, work) as they seek independence outside of the home. There are likely a multitude of ways in which poverty and financial strain can proliferate into future stressors for the child, the important point is that these mechanisms will likely vary by stage of development.

Poverty exposures during infancy and early childhood could also be indirectly related to problem behaviors across the early life course through problem behaviors at younger ages. In order to understand this potential process, I utilize the idea of chains of risk.

Chains of Risk

The proposed model suggests that early-life exposure to poverty can also be associated with problem behaviors during late childhood or adolescence because it is linked to problem behaviors that manifest in the early childhood and persist over time. The stability or propagation of problem behaviors throughout the early life course is a central component of this model. This study employs the concept of “chains of risk” (Rutter 1989) to better explicate this idea. Chains of risk are a sequence of probabilistic negative events that accumulate to increase the likelihood of problem behaviors at any one time. These chains of risk represent a life-course succession of risks and outcomes (Kuh et al. 2003; Ferraro and Shippee 2008).

Chains of risk in this study operate through pathways linking elevated problem behaviors at one point of time to more problem behaviors at later periods in time. The model asserts that the problem behaviors that result from poverty exposure during infancy or early childhood will continue even in the absence of future poverty. Once problem behaviors manifest their continuation is contingent upon a series of person–environment interactions. There are two types of person-environment interactions that chain together past and future problem behaviors (Caspi et al. 1989; Rutter 1989). First, the cumulative continuity perspective suggests the child may select and create environments consistent with their behavioral disposition. These newly selected or created environments will continue to sustain the child’s behavioral dispositions even if the initial precipitating factor that created that disposition is absent (e.g., poverty exposure). For example, suppose parental financial strain decreased self-regulation which prompted externalizing problems behaviors at age 12. This child may be more likely to form friends with similar dispositions and possibly create peer groups that value and encourage externalizing behaviors regardless of future parental financial well-being.

Second, the interactional continuity perspective suggests a continually dynamic transaction between the child and their environment that promotes the continuity of problem behaviors. For instance, at a behavioral level, a child with internalizing behaviors may find that by withdrawing socially from a situation they avoid criticism or anger from others and doing so reinforces their disposition toward more internalizing behaviors. At a cognitive level, social interaction can reinforce self-confirming expectations. For instance, hostile children may expect others to be hostile and behave in ways that elicits hostile behaviors. Experiencing this hostility may, in turn, reinforce the mindset that the world is a hostile place (Caspi et al. 1989). This type of continuity may help explain how the mental formations or social algorithms elaborated on by Bowlby (1982) extend from infancy to adulthood. Indeed, recent work in social neuroscience provides evidence for this perspective as it shows that early experiences creates new neural pathways whose strength ceases or desists based on future encounters that either confirm or deny previously established mental formations (Todorov et al. 2011).

Gender Differences

Previous research suggests that the relationship between poverty exposure and problem behaviors may vary by gender during early and middle childhood (e.g., Bolger et al. 1995). This research, however, is accentuated by an inconsistency of findings. A systematic review of studies that examined the associations between socioeconomic status and problem behaviors across the early life course spotlighted these inconsistencies and concluded that, while researchers often report gender differences, no consistent patterns can be inferred from the existing research (Reiss 2013). For instance, one study showed that males from poor households were more likely than females to exhibit more internalizing problem behaviors over time (Bolger et al. 1995), while another showed no gender differences (Amone-P’Olak et al. 2009).

There is more consistent evidence that female adolescents living in poverty, however, experience more problem behaviors than their male counterparts living in poverty (Letourneau et al. 2013), a phenomenon referred to as “double jeopardy” (McLeod and Owens 2004; Mendelson et al. 2008). This idea of double jeopardy suggests that living in poverty during adolescence is more challenging for girls. For example, female adolescents living in poverty experience more interpersonal-related stressors (Gore et al. 1992). In addition, female adolescents living in poverty may face more challenges and more severe stressors than male adolescents (e.g., sexual violence) and poverty may weaken the protective influences that would be most beneficial for buffering the deleterious effects of poverty (Formoso et al. 2000). Overall, prior research hints that gender may moderate the association between poverty exposure and problem behaviors and suggests female adolescents living in poverty will fare worse than their male counterparts.

Hypotheses

By integrating these concepts applied to the topic of poverty and problem behaviors, I make four predictions. First poverty exposure during infancy, early childhood, and adolescence will be positively associated with problem behaviors in early childhood, late childhood, and adolescence, respectively. Second, these associations will be partially explained by parental financial strain. Third, poverty exposure and parental financial strain will share enduring associations with problem behaviors over the early life course. In particular, each will share both direct associations that exist independent of problem behaviors at younger ages and indirect associations that operate through problem behaviors at earlier ages. Finally, the positive association between poverty exposure during adolescence and problem behaviors will be stronger among females than males. Using 15 years of prospective data ranging from infancy to adolescence, I evaluate these hypotheses.

Methods

Data

The NICHD Study of Early Child Care and Youth Development (SECCYD) is a longitudinal study that followed one cohort from birth through high school. In 1991, data collection began after recruiting families in the hospital after giving birth in 10 US cities: Little Rock, Arkansas; Irvine, California; Lawrence, Kansas; Boston, Massachusetts; Philadelphia, Pennsylvania; Pittsburgh, Pennsylvania; Charlottesville, Virginia; Morganton, North Carolina; Seattle, Washington; and Madison, Wisconsin. During a 24 hour sampling period, 8986 women were approached in the hospital to determine eligibility and willingness to participate in the study. Among these women, 3416 met the eligibility requirements and agreed to be telephoned in 2 weeks. The key eligibility requirements included the following: (1) the mother had to be older than 18 years of age and speak English; (2) the infant had to be healthy, not part of a multiple birth, and not being relinquished for adoption; and (3) the family could not be planning to move within the following year. At a follow-up telephone interview, 1353 either declined to participate or could not be reached. A total of 1364 families were recruited after randomly dropping 699 potential recruits. This process resulted in a 52% response rate. By design, more than 10% of the sample consisted of single mothers, mothers without high school educations, and nonwhite mothers. Among the 1364 originally enrolled in the study, 1009 were available in the final phase of the study during adolescence. Cases who did not have valid child behavior scores at age 15 were excluded from the analyses resulting in a final sample size of 973. While the SECCYD is a large national study, it is not nationally representative.

Measurement

Problem Behaviors

Problem behaviors in this study include externalizing and internalizing behaviors as assessed by the Child Behavior Checklist (CBCL) and reported by the mother or primary caretaker at 24, 36, and 54 months, kindergarten through 6th grade, and age 15. These checklists measure the same concept over time and are age appropriate (see Achenbach 1991). Moreover, the measures were standardized to allow for analysis over time. The CBCL is a widely used measure to assess the social competence and problem behavior of children and consists of approximately 100 items (the number of items varies by age). Each item asked how well a specific behavior describes the child with three possible responses: “not true,” “somewhat or sometimes true,” and “very or often true.” The externalizing scale taps into such problems as aggressive, delinquent, and antisocial behaviors. The internalizing scales tap into such problems as withdrawal, anxiety, and affect problems. Both measures have a possible range of 30–100. All indicators of problem behaviors were reported by the mother or caretaker and results therefore reflect the mother or caretaker’s perception of their child’s internalizing or externalizing behaviors. Behavior problem scales were averaged on three age groupings representing early childhood, late childhood, and adolescence: 2–4, 5–12, and 15 years of age.

Poverty and Financial Strain

Household or family income consists of the mother’s report at each period of collection. This included mother’s earnings, father or resident partner’s earnings, and all other sources of household income including public assistance. I utilized three poverty exposures variables from seven points in time. Income measures at 6 and 9 months reflected household income from 0 to 1 years of age, income at 15, 24, 36, and 54 months reflected household income from 2 to 5 years of age, and a single indicator reflected income at 15 years of age. Household income accounted for inflation via the consumer price index. I calculated an income-to-needs ratio for each of the seven time points by dividing the household income by the poverty threshold for a given family size at a specific point in time. An income-to-needs ratio less than or equal to one indicated the family was living in poverty during a given time. Specific dichotomous poverty indicators reflected whether an individual ever experienced poverty during infancy (i.e., 6 or 9 months), early childhood (i.e., 15, 24, 36, or 54 months) or adolescence. For example, if the respondent had an income-to-needs ratio less than one at 6 months but not 9 months, they would be considered to have experienced poverty during infancy.

Financial strain consists of three items asked of the mother at three points in time. The first item asked “Overall, how happy are you with your financial situation?” with responses ranging from 1 “very dissatisfied” to 5 “very satisfied.” The second and third items asked “how often do you worry about financial matters” and “do you know how much money you’ll have to live on from 1 month to the next,” respectively. Responses for these items ranged from 1 “almost never” to 5 “almost all the time.” All items were (re)coded such that lower scores indicated less financial strain to give a range of 3–15. The average levels of financial strain at 1 and 6 months (financial strain was not available at 9 months) comprised financial strain in infancy. Strain at 54 months represented early-childhood financial strain, and strain at 15 years represented financial strain in adolescence (Chronbach’s α = .69). Measures of financial strain were not available from ages 6–14.

Demographics

Race/ethnicity consists of three dichotomous variables: white/non-Hispanic, black non-Hispanic, and others. Child’s gender (1 = female) was measured dichotomously. Mother’s education at 9 months included categories for less than high school, high school, some college, bachelor’s degree, and master’s degree or higher. Marital status was coded married or not (1 = yes; 0 = no). Depressive symptoms of the mother at baseline were measured via the Center for Epidemiological Studies-Depression Scale (CES-D; Chronbach’s α = .88). Also, given that poverty exposure in utero, as opposed to infancy, may also be a sensitive period, controls for birth weight (in grams) and whether the mother experienced any medical complications during her pregnancy were included.

Analytical Strategy

I utilized a path analysis to examine the associations between poverty exposure and problem behaviors, conducted decomposition analyses to test for indirect associations, and performed subpopulation analyses to test for gender differences. I conducted all analyses separately for externalizing and internalizing problem behaviors. The path models utilized allow for the correlation of error terms for financial strain and poverty exposure. It also specified that exogenous control variables, such as education and race, shared a direct pathway to problem behaviors in adolescence (not shown).

I estimated all models using “full information maximum likelihood” (FIML) using the Analysis of Moment Structures (AMOS) software (Arbuckle 1999). This procedure used all available data and thereby effectively corrected for missing data due to nonresponse or attrition assuming the unobserved data were missing at random (MAR). Among the 973 cases with valid data for problem behaviors during adolescence, 72 were missing some information on problem behaviors at other ages, poverty exposure, or other covariates. FIML resulted in the inclusion of these additional 72 cases in the analysis. I calculated standard errors for indirect associations using a bias-corrected percentile method that utilized bootstrapping procedures (Arbuckle 1999). Because this bootstrapping method could not handle missing data, I calculated the standard errors using a sample derived from listwise deletion of missing cases as seen in past research (e.g., Mistry et al. 2002; Wadsworth et al. 2013). Those with missing data looked demographically similar to those with nonmissing data in terms of poverty exposure, financial strain, and problem behaviors. The model fit and coefficients did not differ in any meaningful way from the analyses presented here. The standard errors from the listwise deletion sample are likely conservative estimates of the standard error from the full sample because standard errors are inversely proportional to the sample size and the nonmissing data has 72 fewer cases.

Results

Table 1 presents the descriptive statistics for the key study variables, stratified by gender, as well as information on basic demographic and control variables. At 0–1 years of age approximately 23% of all infants experienced poverty (N = 213). This percentage decreased over time, and by age 15, approximately 7% of children were living in poverty (N = 68). Between ages 2 and 5, approximately 23% of children experienced living in poverty (N = 213). While the proportion of children living in poverty decreased with the time, the levels of financial strain reported by these children’s mothers remained fairly constant. The mean level of both externalizing and internalizing behaviors was the highest at ages 2–4 and decreased slightly from approximately 52 and 50 to 46 and 47, respectively, by age 15. The children in the sample are from mothers who are mostly white (82% white and 12% black). Mothers had an average age of 28, about one third had some college education (31%), and most (80%) were married. Table A in the appendix shows the correlation coefficients for all key study variables at different ages.
Table 1

Descriptive statistics by gender

 

Females (N = 487)

Males (N = 486)

Mean

SD

Min

Max

Mean

SD

Min

Max

Poverty 0–1(N  = 213)

0.23

0.42

0.00

1.00

0.22

0.42

0.00

1.00

Poverty 2–5(N  = 213)

0.21

0.40

0.00

1.00

0.24

0.43

0.00

1.00

Poverty 15 (N = 68)

0.07

0.26

0.00

1.00

0.08

0.26

0.00

1.00

Financial strain 0–1

7.73

2.45

3.00

15.00

7.98

2.52

3.00

15.00

Financial strain 2–5

7.83

2.23

3.00

15.00

8.02

2.40

3.00

15.00

Financial strain 15

7.68

2.74

3.00

15.00

7.76

2.78

3.00

15.00

Externalizing behaviors 2–4

51.75

7.69

32.67

79.50

51.86

7.77

30.00

74.67

Externalizing behaviors 5–12

47.74

9.00

32.00

79.17

47.01

8.32

30.00

73.33

Externalizing behaviors 15

45.97

11.08

33.00

94.00

45.05

9.80

32.00

77.00

Internalizing behaviors 2–4

49.60

7.85

31.00

80.00

49.53

8.04

30.00

82.67

Internalizing behaviors 5–12

48.02

7.67

32.60

71.17

48.02

8.41

33.60

78.50

Internalizing behaviors 15

46.96

9.69

31.00

83.00

46.32

10.02

32.00

73.00

White

0.82

0.39

0.00

1.00

0.82

0.39

0.00

1.00

Black

0.12

0.32

0.00

1.00

0.12

0.32

0.00

1.00

Other race

0.05

0.21

0.00

1.00

0.05

0.22

0.00

1.00

Less than high school

0.07

0.26

0.00

1.00

0.09

0.28

0.00

1.00

High school

0.18

0.39

0.00

1.00

0.22

0.42

0.00

1.00

Some college

0.33

0.47

0.00

1.00

0.31

0.46

0.00

1.00

College degree

0.25

0.43

0.00

1.00

0.22

0.42

0.00

1.00

Graduate degree

0.16

0.37

0.00

1.00

0.15

0.36

0.00

1.00

Married at birth

0.80

0.40

0.00

1.00

0.79

0.41

0.00

1.00

Age at 1 month

28.91

5.43

18.00

46.00

28.25

5.70

18.00

44.00

Postpartum depressive symptoms

10.72

8.50

0.00

46.00

11.78

9.09

0.00

53.00

Birthweight (km)

3.42

0.49

2.00

5.34

3.58

0.51

2.17

5.03

Any health problems during pregnancy

0.35

0.48

0.00

1.00

0.30

0.46

0.00

1.00

Figure 1 shows the number of children that entered, exited, or remained in poverty over time. The solid line shows these numbers for those that experienced poverty during infancy. For example, among the 213 that experienced poverty during infancy, 74 exited poverty by early childhood, 94 continued to experience poverty during early childhood but did not experience it during adolescence, and 43 experienced chronic poverty. Similarly, the dotted line shows the number of children that experienced poverty or no poverty over time for those that were not exposed to poverty during infancy. Overall, this figures highlights that children were exposed to poverty at various ages and for varying durations of time. Given this study’s focus on the timing of poverty, this pronounced heterogeneity in whether, when, and how long children were exposed to poverty is noteworthy.
Fig. 1

Number of children that entered, exited, or remained in poverty at different ages.

Bolded numbers represent the cross-sectional total number of children living/not living in poverty at different ages for the full sample. Non-bolded numbers represent the number of children that entered, exited, or remained in poverty if valid poverty indicators were available for at least two consecutive ages. For this reason, the sum of the pathways going away from or toward each cell will not always equal the bolded number

Poverty, Financial strain, and Externalizing Behaviors

Figure 2 provides the standardized path coefficients for the theoretical model with regards to externalizing behavior (CFI = .96; RMSEA = .06; NFI = .95). Each coefficient represents how much a standard deviation increase in the predictor variable was associated with a change in the outcome variable in terms of its standard deviation. For instance, a standard deviation increase in financial strain at 0–1 years of age was associated with a .175 standard deviation increase in externalizing behaviors at 2–4 years of age. There are at least seven components of the model that are noteworthy. First, poverty positively predicted financial strain at all ages. Second, poverty exposure during infancy shared a direct association with externalizing behaviors at 2–4 (B = .087; p = .008). Third, financial strain at 0–1 was positively associated with externalizing behaviors at 2–4 (B = .175; p < .001) while financial strain at 2–5 and 15 were unrelated to externalizing behaviors at 5–12 and 15, respectively. Fourth, externalizing behaviors positively predicted future behavior over time. The magnitude of these path coefficients were quite pronounced. For instance, a standard deviation increase in externalizing behaviors at 5–12 (i.e., a 9 unit increase) was associated with a 5.9 unit increase in externalizing behaviors at 15. Fifth, financial strain at 0–1 was positively associated with externalizing behaviors at 5–12 independent of problem behaviors at 2–4 (B = .090; p = .009). Sixth, poverty exposure (B = .012; p = .690) and financial strain (B = .040; p = .230) at ages 0–1 and 2–5 (B = −.014; p = .612; B = .011; p = .746) were unrelated to externalizing behaviors at 15 independent of externalizing behaviors at other ages. Finally, the relationships outlined in Fig. 2 did not differ by gender with one exception. Poverty exposure at age 15 was more strongly associated with externalizing behaviors at age 15 among females (B = .078 p = .031).
Fig. 2

Path model linking poverty and externalizing behaviors over time: standardized coefficients (Based on a subpopulation analyses, one pathways was freed to vary by gender).

*p ≤ .05, **p ≤ .01, ***p ≤ .001. The standardized coefficient for females appears above the coefficient for males in parentheses for the coefficient representing the association between poverty at 15 and externalizing behaviors at 15. This was the only pathway that varied by gender. Untrimmed models contained controls for race/ethnicity, mother’s education and age, mother’s depressive symptoms, and marital status at 1 and 54 months, and 15 years, as well as covariates for birth weight and health complications during pregnancy. These variables did not improve model fit and including them did not change any of the substantiative relationships presented here. Error terms for each endogenous variables were included in the model but not shown above. CFI = .96, RMSEA = .06, NFI = .95, N = 973

Poverty, Financial Strain, and Internalizing Behaviors

Figure 3 provides the standardized path coefficients for the structural model with regards to internalizing behaviors (CFI = .96; RMSEA = .08; NFI = .93). The results shown in Fig. 3 mostly parallel the results of the externalizing behaviors analysis seen in Fig. 2 with four exceptions. First, a negative direct association between poverty at 2–5 years of age and internalizing behaviors at 5–12 was found. While this finding was unexpected, it was relatively small in magnitude compared to the other significant associations in the model. Moreover, ancillary analysis showed that this coefficient was positive when financial strain at 2–5 years of age was not included in the model which suggests this negative association was not robust to alternative model specifications. Second, financial strain at 2–5 years of age was linked with more internalizing behaviors at 5–12 (B = .143; p < .001). Third financial strain at 0–1 was unrelated to internalizing behaviors at 5–12. Finally, poverty exposure at 15 was unrelated to internalizing behaviors at 15 for both males and females.
Fig. 3

Path model linking poverty and internalizing behaviors over time: standardized coefficients (Based on a subpopulation analyses, no pathways were freed to vary by gender). *p ≤ .05, **p ≤ .01, ***p ≤ .001. Untrimmed models contained controls for race/ethnicity, mother’s education and age, mother’s depressive symptoms, and marital status at 1 and 54 months, and 15 years, as well as covariates for birth weight and health complications during pregnancy. These variables did not improve model fit, and including them did not change any of the substantiative relationships presented here. Error terms for each endogenous variables were included in the model but not shown above. CFI = .95, RMSEA = .062, NFI = .93, N = 973

Decomposition Analyses

Table 2 shows the total and indirect associations of poverty exposure and financial strain with problem behaviors most temporally proximate to these exposures. Poverty exposure during infancy was associated with both more externalizing (B = .125; p = .004) and internalizing behaviors (B = .131; p = .007) at 2–4. The total associations of poverty exposure at 2–5 and 15 with problem behaviors at 5–12 and 15, respectively, were not statistically different from zero. The indirect associations of poverty at 0–1 with problem behaviors at 2–4 were positive (B = .038; p = .013; B = .038; p = .020). While the total association of poverty at 2–5 was unrelated to internalizing behaviors at 5–12, the indirect association shared a positive relationship with internalizing behaviors at 15. The null results for the total association make sense because the direct association of poverty at 2–5 was negative while the indirect association was positive (see Fig. 3).
Table 2

Total and indirect associations of poverty and financial strain with problem behaviorsa

  

Externalizing behaviors

Internalizing behaviorsc

Total effects

Indirect effectsb

Total effects

Indirect effectsb

Poverty 0–1

→Problem behaviors 2–4

.125**

(.123)**

.038*

(.036)

.131**

.038*

Poverty 2–5

→Problem behaviors 5–12

.022

(.023)

.008

−.057

.029*

(.009)

_

Poverty 15

→Problem behaviors 15

.076

(−.006)

−.001

.039

.007

(−.001)

aThe total and indirect associations for females are above those for males for models analyzing externalizing behaviors

bThe indirect effects must operate through financial strain pathways (see Figs. 1 and 2)

cAll parameters in this model were constrained to be equal across gender as this produced

the optimal model fit

* p < .05, ** p < .01; N = 973

Table 3 presents a decomposition of the associations of poverty and financial strain with problem behaviors during late childhood and adolescence. There are at least six important pieces of information shown in this table. First, both poverty and financial strain in infancy had positive indirect associations with externalizing and internalizing behaviors at 5–12. This suggests that poverty was related to problem behaviors via financial strain during infancy which was linked to more problem behaviors at 2–4. Problem behaviors at 2–4, in turn, were positively associated with problem behaviors at 5–12. Second, the coefficients for the externalizing behavior analysis were relatively large. For instance, the indirect association of financial strain (.117; p < .001) with externalizing behaviors was approximately 27% of the magnitude of the indirect association of externalizing behaviors at 2–4 (.438; p < .001).
Table 3

Indirect associations of poverty and financial strain with problem behaviors during late childhood and adolescence operating through problem behaviors at younger ages a

 

Externalizing behaviors

Internalizing behaviorsb

Poverty 0–1

→ Problem behaviors 5–12

.103**

(.099)**

.081**

Financial strain 0–1

→Problem behaviors 5–12

.117*

(.118)*

.100*

Poverty 0–1

→Problem behaviors 15

.089**

(.092)**

.020

Financial strain 0–1

→ Problem behaviors 15

.136*

(.147)*

.074*

Poverty 2–5

→ Problem behaviors 15

.017

(.019)

.028

Financial strain 2–5

→Problem behaviors 15

.027

(.031)

.091*

Problem behaviors 2–4

→ Problem behaviors 15

.438*

(.460)*

.362**

aThe indirect associations for females are above those for males for the analysis concerning externalizing behaviors

bAll parameters in this model were constrained to be equal across gender as this produced the optimal model fit

* p < .05, ** p < .01; N = 973

Third, while the findings were similar between the externalizing and internalizing models, the magnitude of the associations were more modest for the latter models. Fourth, poverty and financial strain during infancy shared indirect positive associations with externalizing behaviors at 15. Only financial strain at the same age shared a positive association with internalizing behaviors. Fifth, poverty at 2–5 years of age was unrelated to problem behaviors at 15 years of age. Finally, financial strain at 2–5 years of age was positively associated with internalizing but not externalizing behaviors at age 15.

Conclusion

This study established an integrative framework based on three distinct concepts—stress proliferation, sensitive periods, and chains of risk—which elucidates the ways in which poverty exposure may be related to problem behaviors over the early life course. This framework made four hypotheses. First, poverty exposures during infancy, early childhood, and adolescence will be positively associated with problem behaviors in early childhood, late childhood, and adolescence, respectively. Second, parental financial strain will partially explain these associations. Third, poverty exposure and parental financial strain will share enduring associations will problem behaviors over the early life course. In particular, each will share both direct associations that exist independent of problem behaviors at younger ages and indirect associations that operate through problem behaviors at earlier ages. Four, the positive association between poverty exposures during the adolescence and the problem behaviors will be larger in magnitude for females. The results of the analysis provided at least partial evidence for each of these hypotheses.

The results provided robust evidence that poverty exposure during infancy was linked to more externalizing and internalizing problem behaviors in early childhood. Poverty exposure during early childhood was only associated with internalizing behaviors. Exposure during adolescence was only associated with externalizing problem behaviors among females. These associations sometimes operated through financial strain with the strongest evidence appearing if exposure occurred during infancy.

Poverty exposure and financial strain also shared enduring associations with problem behaviors. Financial strain during infancy was directly associated with more externalizing behaviors during late childhood independent of externalizing behaviors during early childhood. Neither poverty exposure nor financial strain during infancy shared direct associations with internalizing behaviors at later ages. Poverty and financial strain during infancy shared positive indirect associations with both externalizing and internalizing behaviors during late childhood, and this indirect association extended to both problem behaviors at age 15 for financial strain. Poverty exposure during infancy shared a positive indirect association with externalizing but not internalizing behaviors at age 15. These associations did not vary by gender with one exception. Poverty exposure during adolescence was linked with more externalizing behaviors but only among females.

This study was consistent with a broader body of research showing that early poverty exposure is linked with more problem behaviors (e.g., Masarik and Conger 2017; Repetti et al. 2002; McLeod and Shanahan 1993, 1996). At least, five important points can be gleaned from this research. First, this study provides initial evidence that exposures during infancy may have lasting implications for problem behaviors across the early life course. While the idea that pernicious exposures during early life are particularly harmful is not new, this study showed that such exposures during infancy may be particularly salient. Previous research that examined exposures during sensitive periods has tended to combine early life into one sensitive period defined as ages 0–5. The implicit assumption of that approach is that exposures during differing periods within this interval do not have differentially sized influences. In this study, infancy (0–1) was differentiated from the early childhood (2–5). Indeed, exposures during infancy were more consistently linked with problem behaviors than those during early childhood. Future research would be well served to apply a finer focus on when exposures are most detrimental to later well-being than has previously been given.

Second, this study suggests that poverty exposure during sensitive periods may be related to more problem behaviors across the early life course because it sets in motions chains of risk that persist across childhood. Research on sensitive-period exposures tends to focus on direct influences by emphasizing lasting biological effects outside of the social context. Because sensitive-period exposure was largely linked to later problem behaviors due to the persistence of problem behaviors at younger ages, the results suggest the occurrence of these lasting biological deficits, if they are occurring, are happening within a social context surrounding these behaviors. The continuation of problem behaviors may be occurring through chains of risk in which behavioral dispositions propel children to select, create, or interact with their environment in ways that socially reinforce or elicit future problem behaviors. An important point is that these self-maintaining processes are likely occurring within, interacting with, and shaping encounters with dominant social institutions, especially schools (e.g., Bornstein et al. 2010). For example, hostility in children may prompt hostility from teachers thereby eliciting further hostile behavior. This hostile behavior may influence the characteristics of a child’s social networks which, in turn, can further instigate problem behaviors. Future research that provides insight into how these chains of risk might be broken within institutional contexts, such as schools, could have substantial policy implications.

Third, the findings pertaining to poverty exposure during adolescence are somewhat inconsistent with past research. Poverty exposure during adolescence was only associated with higher problem behaviors among females and only for externalizing behaviors despite a broader pattern in the literature of poverty exposure being linked to more problem behaviors during adolescence (e.g., Simons et al. 2016; Vandewater and Lansford 2005; Wadsworth and Berger 2006). There are two plausible explanations for this discrepancy. First, the lack of an association in the current study may reflect the relatively small number of children that experienced poverty during adolescence. The timing-specific associations between poverty exposure and problem behaviors were disproportionately represented by a relatively small group of children. For instance, only 11 children experienced poverty for the first time during adolescence. Second, because those that experienced poverty during adolescence were disproportionately likely to have experienced poverty in early life, it is possible that the relationship between poverty and problem behaviors in adolescence previously found in the research was driven by poverty exposure during infancy or early childhood and not adolescence.

Finally, the ways in which gender shapes the link between poverty and problem behaviors should continue to receive empirical attention. The gender difference in the association between poverty exposure during adolescence and externalizing behaviors has at least two plausible explanations. First, females may have been more likely to experience or be vulnerable to the stressors that resulted from living in poverty than their male counterparts. For example, Formoso et al. (2000) using a low-income sample of adolescents found that parental attachment and monitoring were protective of problem behaviors among females but not males. Second, sample attrition may have contributed to this finding, as male adolescents with mothers with less than a high school education were less likely to be in the study during adolescence than others. This potential problem of attrition may have contributed to a lack of an association between poverty and externalizing behaviors among male adolescents. Future study designs that simultaneously test for mediating mechanisms and address gendered attrition would be well positioned to further our knowledge about these potential gender differences.

This study should be interpreted within the context of its strengths and limitations. The study employed 15 years of prospective data from a national sample and utilized a modeling strategy to address issues of timing and associated pathways. Several limitations are also noteworthy. First, readers should consider this study’s relatively small cell sizes when interpreting the evidence for the study hypotheses, especially regarding poverty exposure during the adolescence. A more balanced research design would include more instances of people entering, exiting, and living in chronic poverty. Such designs would also facilitate direct tests of timing versus cumulative exposure perspectives. Second, while representing children throughout the nation, the sample was not nationally representative and did not allow for tests regarding racial/ethnic differences. Third, measures were not available to assess financial strain in mid-childhood (i.e., ages 6–14). These ages may have also played in important role in the development of problem behaviors. Fourth, my measure of poverty was based on the U.S. Government’s criteria for the physical necessities of life for a family of a given size. Although not discussed, poverty is a multidimensional phenomenon consisting of varying degrees of relative and absolute deprivation (Brady 2003). Multiple measures of both relative and absolute poverty may be essential in understanding the early-life origins of problem behaviors. Lastly, children are not static actors and will attempt to cope with environmental demands in different ways. Some children will be more successful than others at this. Moreover, a child’s level of resilience when faced with environmental stressors may vary by past exposures including those that occurred during sensitive periods. Future research that incorporates child resilience into conceptual and analytic models may produce a more accurate picture of how poverty exposure is related to problem behaviors across the early life course.

This study showed that when children experience poverty is of crucial importance for understanding the early-life origins of problem behaviors. The chains of risk linked to adolescent problem behaviors were anchored in infancy and early childhood. These sensitive periods may present a unique window in which social policy can gain substantial leverage in improving later problem behaviors and the complications that emanate from them. The timing of early poverty exposure and why it is related to problem behaviors across the early life course represents a promising line of inquiry that should be given greater attention to in the future study.

Notes

Acknowledgements

This research was supported by an infrastructure 5 R24 HD042849 and a training 5 T32 HD007081 Grants awarded to the Population Research Center at the University of Texas in Austin by the Eunice Kennedy Shriver National Institute of Child Health and Human Development. This study was also supported by training Grant T32HDOO1763 to the Bendheim-Thoman Center for Research on Child Wellbeing at Princeton University by the Eunice Kennedy Shriver National Institute of Child Health and Human Development.

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Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.Department of Sociology, Center for Demography and Population HealthFlorida State UniversityTallahasseeUSA

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