Chapter 9: Poverty, Early Experience, and Brain Development

CHAPTER 9

Poverty, Early Experience, and Brain Development

Luciane R. Piccolo Kimberly G. Noble

Currently, poverty affects 16 million children erty puts children at risk for a host of negative in the United States (DeNavas-Walt & Proctor, physical and mental health outcomes, as well as 2014), and a billion children globally. In the detrimental effects on achievement (Johnson, United States, poverty is an index defined by Riis, & Noble, 2016). In developing countries, the federal government based on annual family it is estimated that over 200 million children income, which varies according to the number under age 5 years do not develop properly due of adults and children in the home. The 2015 to the consequences of poverty (Grantham-Mc- poverty level for a family with two adults and Gregor et al., 2007). two children in the United States was $24,036. Importantly, while poverty is (currently) de- Interestingly, the official federal poverty level fined strictly according to income, socioeco- does not vary geographically, which means that nomic status (SES) typically comprises income, neither the local cost of living nor the gener- as well as educational attainment, occupational osity of government-sponsored social service prestige, and subjective social status, or where programs are taken into account, despite the one sees oneself within the social hierarchy fact that these factors vary remarkably across (McLoyd, 1998; Nobles, Ritterman Weintraub, the United States. Because of this, investigators & Adler, 2013). Childhood SES has been associ- are actively pursuing new metrics for measur- ated with a number of broad outcome measures ing and defining poverty. For example, the An- that are important for children’s cognitive de- chored Supplemental Poverty Measure, a more velopment, such as school achievement, grade complex poverty measure that includes addi- retention, literacy, IQ, and school graduation tional variables such as tax payments, work ex- rates (Brooks-Gunn & Duncan, 1997). penses, and governmental assistance, adjusted Indeed, longitudinal data suggest that the for geographic differences, has been proposed SES gap in cognitive development and academ- (Wimer, Nam, Waldfogel, & Fox, 2016). Using ic achievement tends to emerge early in child- this new measure, it was observed that childhood and to widen throughout the elementary hood poverty in the United States has been school years. For example, the British Cohort reduced over the past 50 years, mainly due to Study followed 17,200 children ages 2–10 in governmental initiatives, but substantial dis- the United Kingdom. In a compelling analysis, parities in the risk of poverty still remain by Feinstein (2003) demonstrated that children education level and family structure (Wimer from socioeconomically advantaged homes

Copyright @ 2019. The Guilford Press. All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law. et al., 2016). Regardless, growing up in pov- who were performing at the 10th percentile on

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a measure of cognitive development at age 2 nitive tests to children, with each task designed tended to show increases in their cognitive abil- to evaluate a particular brain function. Results ities, relative to other children of the same age, across studies have been remarkably consistent, over the course of childhood; by age 10, these suggesting particularly robust socioeconomic children’s cognitive performance was slightly disparities from early childhood through ado- above average. In contrast, children who start- lescence in language skills, memory, and ex- ed out at the 90th percentile at age 2 and came ecutive function. For example, in one sample, from socioeconomically disadvantaged homes by the start of school, children from higher tended to show much smaller gains over the socioeconomic backgrounds tended to out- course of childhood; by age 10, these children perform their peers from more disadvantaged were performing somewhat below average. backgrounds in language, memory, and execu- These findings imply that by age 10, child fam- tive functions, with effect sizes ranging from ily socioeconomic circumstances are a better 0.25 to 0.50 standard deviations (Noble, Mc- predictor of cognitive development than early Candliss, & Farah, 2007). Similar findings have cognitive skills. been replicated in many laboratories across The factors that have contributed to this gap the United States and in quite a few countries in cognition are likely multifactorial and may around the world (Arán-Filippetti, 2012; Ardila, be partly explained in terms of differences in Rosselli, Matute, & Guajardo, 2005; Farah et nutrition (Black, 2008; Kant & Graubard, 2012; al., 2006; Fernald, Weber, Galasso, & Ratsifan- Nyaradi, Li, Hickling, Foster, & Oddy, 2013), drihamanana, 2011; Fluss et al., 2009; Hackman prenatal care (Jedrychowski et al., 2009), peri- & Farah, 2009; Hackman, Farah, & Meaney, natal complications (De Haan et al., 2006), 2010; Hanson, Chandra, Wolfe, & Pollak, 2011; gestational age (Noble, Fifer, Rauh, Nomura, Lipina et al., 2013; Noble & McCandliss, 2005; & Andrews, 2012), drug exposure (Rauh et al., Piccolo, Arteche, Fonseca, Grassi-Oliveira, & 2004), the home language environment (Hart Salles, 2016; Raizada & Kishiyama, 2010; Vil- & Risley, 1995; Melvin et al., 2017; Suskind et laseñor, Sanz Martín, Gumá Díaz, Ardila, & al., 2015), early education differences (Lynch Rosselli, 2009). & Vaghul, 2005; Schweinhart et al., 2005) and Although research in this area has grown in family stress (Evans, Maxwell, & Hart, 1999); recent years, several questions about the asso- as well as genetic contributions (Guo & Harris, ciations between SES and child development 2000; Guo & Stearns, 2002; Tucker-Drob, Bri- remain unanswered. We address four of these ley, Engelhardt, Mann, & Harden, 2016; Tucker- questions in the remainder of this chapter. First, Drob & Harden, 2017; Tucker-Drob, Rhemtulla, how early in infancy or early toddlerhood are Harden, Turkheimer, & Fask, 2011). Each of socioeconomic disparities in child development these factors has been shown to contribute in detectable? Second, how do these differences part to the link between SES and children’s cog- related to differences in children’s brain struc- nitive skills. Of course, it rapidly becomes quite ture and function? Third, which experiences complicated to attempt to uncover causal links explain socioeconomic disparities in cognitive among these highly intercorrelated factors. One and brain development? Finally, how can re- way to begin to disentangle these associations search in this field inform interventions? is to recognize that broadband cognitive and achievement measures, such as IQ or school graduation rates themselves likely represent a Detecting Socioeconomic Disparities conglomerate of multiple-component cogni- in Child Development tive functions. Neuroscience tells us that dis- tinct brain networks support different cognitive Socioeconomic disparities in cognitive devel- skills. By taking a neuroscience framework, we opment have been reported as early as the first can investigate which particular cognitive skills or second year of life (Fernald, Marchman, & and corresponding brain networks are most Weisleder, 2013; Halle et al., 2009; Hoff, 2003a, strongly associated with socioeconomic back- 2003b; Noble, Engelhardt, et al., 2015; Rowe ground. & Goldin-Meadow, 2009). For example, by 18 In the last two decades, a number of studies months of age, children from lower SES fami- have taken this approach. Researchers have re- lies perform more poorly than their peers from cruited socioeconomically diverse families, and higher SES families on measures of language

Copyright @ 2019. The Guilford Press. All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law. have administered a series of varied neurocog- processing skill and vocabulary (Fernald et al.,

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2013). In the same study, Fernald and colleagues Fox, Levitt, & Nelson, 2010). While few stud- (2013) reported that by 24 months of age, there ies to date have investigated links between so- was a 6-month gap between low- and high-SES cioeconomic disparities and brain structure or groups in processing skills critical to language function in infancy or very early childhood, development. A study using data from the Early one study of 44 healthy African-American one- Childhood Longitudinal Study, Birth Cohort month-old infants did find that lower SES was (ECLS-B; Halle et al., 2009) reported dispari- associated with smaller cortical gray and deep ties between low and high SES infants on lan- gray matter volumes (Betancourt et al., 2016). guage and cognitive measures by 9 months. In Tomalski and colleagues (2013) reported asso- that study, by age 24 months there was a mean ciations between SES and resting brain activ- difference of 0.5 standard deviations between ity in infants as young as 6–9 months of age. SES groups on the Bayley Cognitive Assess- Intriguingly, however, using similar electro- ment (Halle et al., 2009). Another study found encephalographic measures of resting brain no detectable socioeconomic differences in function, Brito, Fifer, Myers, Elliott, and Noble language or memory performance between 9 (2016) found no socioeconomic disparities in and 15 months of age, but found that dramatic brain function at birth. While the small sam- disparities emerged in these skills between 15 ple and correlational nature of the study limit and 21 months of age (Noble, Engelhardt, et al., causal inference, these results are consistent 2015). By 21 months, children of more highly with the notion that SES-related differences educated parents scored approximately 0.8 in brain function may emerge over time in an standard deviations higher in both language and experience-dependent manner. memory tasks than children of less educated Altogether, a small but growing body of evi- parents (see Figure 9.1). dence suggests that socioeconomic disparities Experience-related differences in neural cir- in children’s cognitive and brain development cuitry are often evident well before general cog- may emerge early in infancy. This has implica- nitive or behavioral differences can be detected tions for the timing of both screening and inter- (Bosl, Tierney, Tager-Flusberg, & Nelson, 2011; vention efforts, as discussed below.

FIGURE 9.1. Children of more highly educated parents scored approximately 0.8 standard deviations higher on language tasks than their peers of less educated parents in language tasks at 21 months of age. Adapted from Noble, Engelhardt, et al. (2015, p. 12). Copyright @ 2019. The Guilford Press. All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.

Socioeconomic Disparities and Brain Structure amygdala during socioemotional processing and Function tasks (Gianaros et al., 2008; Kim et al., 2013). In language-supporting regions, researchers Numerous studies have now documented so- have reported socioeconomic disparities in the cioeconomic disparities in brain structure and function of frontal areas (Pakulak & Neville, function across the lifespan, using multiple 2010; Raizada, Richards, Meltzoff, & Kuhl, neuroimaging techniques (for reviews, see Brito 2008; Tomalski et al., 2013) as well as a moder- & Noble, 2014; Ursache & Noble, 2016). ating effect of SES in the activation of the left Socioeconomic disparities in brain function fusiform gyrus during a reading task (Noble, have been documented both behaviorally and in Farah, & McCandliss, 2006; Noble, Wolmetz, measures of brain physiology. From a behavior- et al., 2006). This emerging research suggests al perspective, individuals from disadvantaged that socioeconomic conditions may shape brain backgrounds tend to underperform relative to functioning on both behavioral and neurobio- their higher SES peers in numerous cognitive logical levels. tasks (Hackman & Farah, 2009; Hackman et Socioeconomic disparities have also been al., 2010; Johnson et al., 2016; Ursache & Noble, documented in the structure of the brain, in ad- 2016), such as language (Dearing, McCartney, dition to its function. The most commonly re- & Taylor, 2001; Engel, Santos, & Gathercole, ported finding is a positive association between 2008; Farah et al., 2006; Fernald et al., 2013; SES and the size of the hippocampus, which Fluss et al., 2009; Hart & Risley, 1995; Hoff, supports memory (Butterworth, Cherbuin, Sa- 2003b, 2006, 2013; Noble, Engelhardt, et al., chdev, & Anstey, 2011; Hair, Hanson, Wolfe, & 2015; Noble, Farah, & McCandliss, 2006; Noble Pollak, 2015; Hanson et al., 2011; Jednorog et & McCandliss, 2005; Noble, Norman, & Farah, al., 2012; Leonard et al., 2015; Luby et al., 2013; 2005; Noble, Tottenham, & Casey, 2005; Noble, Noble, Grieve, et al., 2012; Noble, Houston, Wolmetz, Ochs, Farah, & McCandliss, 2006; Kan, & Sowell, 2012; Piras, Cherubini, Cal- Pungello, Iruka, Dotterer, Mills-Koonce, & tagirone, & Spalletta, 2011; Staff et al., 2012). Reznick, 2009; Raviv, Kessenich, & Morrison, Additional links have been reported between 2004), memory (Akshoomoff et al., 2014; Farah socioeconomic factors and the structure of pre- et al., 2006; Herrmann & Guadagno, 1997; frontal regions important for self-regulation Noble, Engelhardt, et al., 2015; Noble et al., and attention (Hair et al., 2015; Hanson et al., 2007; Noble, Norman, & Farah, 2005; Turrell 2013; Leonard et al., 2015; Noble, Korgaonkar, et al., 2002; Waber et al., 2007), and executive Grieve, & Brickman, 2013), as well as between functions (Ardila et al., 2005; Blair et al., 2011; SES and left-hemisphere cortical regions that Evans & Fuller-Rowell, 2013; Evans & Rosen- are important for the development of language baum, 2008; Evans & Schamberg, 2009; Farah (Hair et al., 2015; Jednorog et al., 2012; Noble, et al., 2006; Hackman, Gallop, Evans, & Farah, Houston, et al., 2012, 2015). 2015; Hughes, Ensor, Wilson, & Graham, 2010; Much of this work has focused on examin- Leonard, Mackey, Finn, & Gabrieli, 2015; ing links between socioeconomic circumstance Lipina et al., 2013; Mezzacappa, 2004; Noble and cortical volume. While many studies have et al., 2007; Noble, Norman, & Farah, 2005; reported significant associations (Butterworth Raver, Blair, & Willoughby, 2013; Rhoades, et al., 2011; Cavanagh et al., 2013; Hair et al., Greenberg, Lanza, & Blair, 2011; Sarsour et al., 2015; Hanson et al., 2011, 2013; Jednorog et al., 2011; Wiebe et al., 2011). Similar findings have 2012; Liu et al., 2012; Luby et al., 2012; Noble, been reported on a neurobiological level. For Houston, et al., 2012; Staff et al., 2012), others example, socioeconomic disparities have been do not (Brain Development Cooperative Group, reported in individuals’ hippocampus function 2012; Lange, Froimowitz, Bigler, Lainhart, & during memory tasks (Czernochowski, Fabi- the Brain Development Cooperative, 2010). ani, & Friedman, 2008; Sheridan, How, Arau- Findings may be discrepant in part because dif- jo, Schamberg, & Nelson, 2013); as well as in ferent brain regions and ages have been investi- prefrontal cortex during executive functioning gated (Brito & Noble, 2014). Additionally, cor- tasks (D’Angiulli, Herdman, Stapells, & Hertz- tical volume represents a composite of surface man, 2008; D’Angiulli et al., 2012; Kishiyama, area and cortical thickness, two morphometric Boyce, Jimenez, Perry, & Knight, 2009; Sheri- properties that exhibit different developmen- dan, Sarsour, Jutte, D’Esposito, & Boyce, 2012; tal trajectories (Raznahan et al., 2011). Recent

Copyright @ 2019. The Guilford Press. All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law. Stevens, Lauinger, & Neville, 2009), and in the work has examined socioeconomic disparities

in these more specific structural properties of lobes of the brain; furthermore, greater corti- the developing cortex. cal thickness partially accounted for socioeco- In general, cortical thickness peaks around nomic discrepancies in reading and math test preschool age, then decreases with time, con- performance. In a follow-up study using the tinuing to thin through early adulthood (Brown sample of 1,099 children and adolescents ref- et al., 2012; Raznahan et al., 2011; Sowell, erenced earlier, Piccolo, Merz, and colleagues Thompson, & Toga, 2004; Walhovd, Fjell, (2016) reported that SES moderated patterns Giedd, Dale, & Brown, 2017). In a longitudinal of age-associated change in cortical thickness. study, Gogtay and colleagues (2004) reported a Specifically, at lower levels of SES, a curvilin- progressive sequence of cortical thinning that ear pattern of relatively steep age-related de- began around 4–8 years of age, with the matu- crease in cortical thickness was observed ear- ration, or thinning, of somatosensory and visual lier in childhood, with a subsequent leveling off cortices, followed by areas that support spatial during adolescence. In contrast, at higher levels orientation and language (parietal lobes). The of SES, associations between age and cortical last areas (frontal lobes) matured during ado- thickness were linear, with more gradual de- lescence, as complex cognitive abilities (e.g., creases in cortical thickness at younger ages, executive functions) emerge. In contrast, sur- with continued cortical thinning through late face area increases rapidly during childhood, adolescence. One possible explanation of these until aage 9–10 years, when it reaches a plateau, findings is that early adversity may narrow the followed by a midadolescent phase of decline time window when experience-dependent pro- (Brown et al., 2012; Raznahan et al., 2011). Tak- cess shapes development and/or accelerate mat- ing into account these differences in develop- uration (Callaghan & Tottenham, 2016). mental trajectories, it is most informative to Of note, many of these studies indicate wide study cortical thickness and surface area sepa- variation in brain development between indi- rately. viduals, even within socioeconomic strata. For To examine how SES relates to surface area, example, in a secondary analysis of the 1,099 Noble, Houston, and colleagues (2015) evalu- participants from Noble, Houston, and col- ated a socioeconomically diverse sample of leagues (2015), moderation analyses indicated 1,099 children and adolescents, and controlled that the impact of SES varies across cortical for genetic ancestry. Higher family income was thickness, with SES more strongly predictive of associated with larger cortical surface area in executive function skills among children with children’s brains. This relationship was particu- thicker cortices and more strongly predictive larly strong for areas that support language and of language skills among children with thinner executive functioning (Noble, Houston, et al., cortices (Brito, Piccolo, & Noble, 2017). Thus, 2015), and differences in surface area partially socioeconomic disparities—and the experi- accounted for socioeconomic differences in cer- ences for which they likely serve as a proxy— tain executive function skills. Furthermore, the represent just one mechanism that may lead to relationship between family income and sur- individual differences in brain development. face area was nonlinear, such that the steepest gradient was seen at the low end of the income spectrum; that is, dollar for dollar, differences Experiences Shaping Poverty-Related in family income were associated with propor- Differences in Cognitive and Brain Development tionately greater differences in brain structure among the most disadvantaged families. The link between family socioeconomic cir- Several studies have examined links between cumstance and children’s brain development SES and cortical thickness. For example, in a is likely based at least in part in differences in sample of 283 children and adolescents, Law- experience. As mentioned earlier, numerous son, Duda, Avants, Wu, and Farah (2013) ob- factors may contribute to these links (nutrition, served that parental education, but not family health care, material resources, etc.). Although income, was positively associated with cortical an exhaustive review of the different possible thickness in the right anterior cingulate gyrus mechanisms is beyond the scope of this chap- and left superior frontal gyrus. In a sample of ter, we next review the evidence for two pos- 58 early adolescents, Mackey and colleagues sible types of experience that may mediate (2015) reported that family income was posi- these links, namely, the home linguistic envi-

Copyright @ 2019. The Guilford Press. All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law. tively associated with cortical thickness in all ronment and family stress. Figure 9.2 illustrates

a theoretical model illustrating these putative ronment to brain development is in its infancy. links (Noble, Houston, et al., 2012). In brief, the However, some work suggests that one-on-one quantity and quality of language that children social interaction is critical for shaping the de- hear is likely important for shaping develop- velopment of language supporting brain func- ment of neural networks that support the de- tion (Kuhl, Tsao, & Liu, 2003). A recent study velopment of language skills. Simultaneously, investigated whether the home environment socioeconomic disadvantage is associated with might explain SES differences in brain struc- greater exposure to family stress. Stress, in ture. In a longitudinal design, cognitive stimu- turn, has important effects on the hippocampus lation in the home environment (as measured (supporting memory development), as well as by HOME Inventory) at age 4 predicted corti- the prefrontal–limbic circuitry that supports the cal thickness in temporal and prefrontal cortex development of self-regulation. in late adolescence (Avants et al., 2015). Future work is required to determine specific features SES, the Home Language Environment, of the home language environment that may ac- and Language Development count for these links.

It is well documented that children from lower SES, Stress, Memory, and Executive Function SES homes tend to be exposed to a lower quanti- Development ty and quality of linguistic input (Goldin-Mead- ow et al., 2014; Hart & Risley, 1995). It has been Although short-term response to stress can be estimated that by age 4, children from disadvan- adaptive, chronic exposure to high degrees of taged homes hear 30 million fewer words than stress contributes to the emergence of physical their more advantaged peers (Hart & Risley, disease and dysfunction (McEwen, 1998). Chil- 1995). In addition, the complexity of the verbal dren raised in families with lower socioeco- interactions, as well as the responsiveness or the nomic backgrounds are often exposed to a high- conversational nature of the verbal interactions, er degree of family stress, including increased seems to vary as a function of SES (Evans et exposure to neighborhood violence, chaos in al., 1999; Perkins, Finegood, & Swain, 2013). the home, mental health problems and unstable Furthermore, the number of words children hear relationships. When families are exposed to has been directly related to their vocabulary size high levels of stress, their familial relationships (Arriaga, Fenson, Cronan, & Pethick, 1998; Fer- tend to be characterized by conflict or emotion- nald et al., 2013; Hoff, 2003b, 2006, 2013). For al withdrawal rather than with the warm and example, the amount of speech parents direct to nurturing relationships that are important for their children before the age of 3 years accounts children’s development (Biglan, Flay, Embry, & for over half of the variance in children’s cogni- Sandler, 2012; Farah et al., 2008; Hackman et tive performance and vocabulary at 9 years of al., 2010). age (Hart & Risley, 1995). A number of reports suggest that disadvan- Research linking the home linguistic envi- taged children may have altered levels of stress

Linguistic Left-hemisphere Language environment language cortex

SES Hippocampus Memory

Family stress Cognitive and Prefrontal/limbic emotional circuitry regulation

FIGURE 9.2. Mechanisms underlying SES effects on structural and functional brain development: theoretical model. Adapted from Noble, Houston, Kan, et al. (2012, p. 2). Copyright @ 2019. The Guilford Press. All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.

hormones, such as cortisol (Blair & Raver, independent (Figure 9.2) (Evans et al., 1999; 2016; Juster et al., 2016; Lupien, King, Meaney, Perkins et al., 2013). For example, crowding in & McEwen, 2001; Vliegenthart et al., 2016). the home is associated with psychophysiologi- Several neural networks are particularly sen- cal stress (Evans, Lepore, Shejwal, & Palsane, sitive to cortisol. For example, high levels of 1998) and reduced language diversity (Evans et cortisol have been associated with impaired al., 1999). Parents from crowded homes spoke functioning of the hippocampus, amygdala, in less complex and sophisticated ways with and prefrontal areas, leading to impairments their children, and tended to be less verbally in memory, executive functioning, and emo- responsive to their children when compared to tion regulation (Blair et al., 2011; Gianaros et parents from less crowded homes. al., 2007; Liston, McEwen, & Casey, 2009; Lupien et al., 2001; Lupien, McEwen, Gunnar, & Heim, 2009; McEwen & Gianaros, 2010; Implications for Interventions Sheridan et al., 2013; Tottenham & Sheridan, 2009). It is therefore possible that socioeco- If we believe that SES disparities are likely lead- nomic disparities in exposure to stress may lead ing to differences in experience, which in turn to alterations in cortisol, which in turn have help to shape brain development and behavior, cascading effects on these neural systems and then the question is how such experiences can the cognitive and emotional skills they support. be modified, and what is the right level at which Additionally, studies have reported that the per- to intervene (see Figure 9.3)? ception of stress may drive these physiological Most commonly, interventions aimed at re- consequences. In general, there is evidence that ducing socioeconomic gaps in achievement perceived stress is associated with deleterious have been implemented in the form of educa- effects on cognitive outcomes (Aggarwal et al., tional interventions. High-quality early child- 2014; Korten, Comijs, Penninx, & Deeg, 2017; hood education can lead to dramatic improve- Merz, Tottenham, & Noble, 2018; Munoz, Sli- ments in children’s academic success and winski, Scott, & Hofer, 2015; Rubin et al., 2015) lifelong well-being (Lynch & Vaghul, 2005; as well as with decreased hippocampal volume Schweinhart et al., 2005). However, due in part (Gianaros et al., 2007; Lindgren, Bergdahl, & to the scarcity of publicly available programs, Nyberg, 2016; Luby et al., 2013; Pagliaccio et not all children receive high-quality early edu- al., 2014; Piccolo & Noble, 2018; Zimmerman et cation, and low-income children are the least al., 2016) and prefrontal cortex (Gianaros et al., likely to be enrolled (Meyers, Rosenbaum, 2007; Moreno, Bruss, & Denburg, 2017). The Ruhm, & Waldfogel, 2004). However, children association between perceived stress and amyg- from disadvantaged families are also more like- dala is controversial, and results vary accord- ly to benefit most from early education (Mag- ing to the studies’ analysis techniques, brain nuson & Waldfogel, 2005; Ruhm & Waldfogel, regions evaluated, and the gender and age of 2012). It has been estimated that policies target- the sample, with some work reporting increased ing low-income families and expanding access perceived stress related to smaller (Pagliaccio et to high-quality early education could close be- al., 2014) and other research with larger amyg- tween 20 and 36% of the school readiness gaps dala volume in children (Tottenhan et al., 2010) (Magnuson & Waldfogel, 2005). and other works finding no association between In this regard, one commonly cited example perceived stress and amygdala volume (Luby et is the High/Scope Perry Preschool study, which al., 2013; Piccolo & Noble, 2018). has followed 123 children born in poverty for Importantly, the home language environment more than 40 years. At ages 3 and 4, the sub- and family stress pathways are unlikely to be jects were randomly divided into a group that

FIGURE 9.3. Possible levels of intervention for SES disparities on cognitive development. Copyright @ 2019. The Guilford Press. All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.

received an intensive, high-quality preschool Nurse–Family Partnership home visiting pro- program and home visits, and a comparison gram has led to moderate improvements in chil- group that received no preschool program. At dren’s cognitive and behavioral outcomes (Olds age 40, participants who were randomized to et al., 2014). Other programs, such as Reach the treatment group had higher wages, were Out and Read (Zuckerman, 2009; Zuckerman & more likely to hold a job, had committed fewer Khandekar, 2010), and its expansions, such as crimes, and were more likely to have graduated the Video Interaction Project (Cates, Weisleder, from high school than adults who did not attend & Mendelsohn, 2016; Mendelsohn et al., 2007) preschool (Schweinhart et al., 2005). These have taken advantage of the fact that the pedi- benefits were quite cost-effective—for every atric primary care setting represents an acces- dollar invested, there was a return of nearly $13. sible, high-engagement, and potentially scal- However, the pragmatics of scaling up such in- able venue for interventional services. For even tensive programs to the larger population while the best-designed parent-focused programs, maintaining high quality is a frequently cited however, there are inherent challenges of up- concern. Other studies have suggested that a take and attrition when targeting disadvantaged less intensive (and potentially more scalable) families whose lives are often characterized by approach can still be beneficial. For example, psychological strain and lack of routines (Kalil, the Chicago School Readiness Project was a Duncan, & Ziol-Guest, 2016). classroom-based intervention providing Head A final avenue for directing interventions Start teachers with training on effectively man- may be at the most distal level, namely, through aging dysregulated behavior. In an evaluation changing SES itself. A great deal of work using using a cluster-randomized controlled trial de- longitudinal data and natural variation in fam- sign, investigators found that the program led ily income has suggested that early childhood to gains in not only executive functioning and differences in family income are robust predic- impulsivity but also preacademic skills, despite tors of children’s later achievement, educational the fact that these skills were not explicitly tar- attainment, and even adult earnings (Duncan, geted (Raver et al., 2011). Similarly, the Boston Yeung, Brooks-Gunn, & Smith, 1998; Duncan, Public Schools’ prekindergarten program has Ziol-Guest, & Kalil, 2010). Quasi-experimental used research-based curricula and coaching of evidence from the welfare-to-work experiments teachers’ approach. In a study with more than of the 1990s suggests that income increases led 2,000 4- to 5-year-old children enrolled in the to improved achievement and schooling out- program, Weiland and Yoshikawa (2013) found comes, with a $4,000 increase in annual income moderate to large improvements in children’s (in current dollars) for 2–3 years, increasing language and math performance, as well as school achievement by 0.18 standard deviations small impacts on executive and emotional de- (Duncan, Morris, & Rodrigues, 2011; Morris, velopment skills. Duncan, & Clark-Kauffman, 2005). Children High-quality early childhood education from families with increased income tended to clearly plays a critical role in reducing socio- spend more time in the labor market as adults economic disparities in achievement (Engle et (Dahl & Lochner, 2012), and even showed evi- al., 2011). However, when we consider that so- dence of improved health in adulthood (Ziol- cioeconomic disparities in language skills are Guest, Duncan, Kalil, & Boyce, 2012). already clearly apparent by the second year of In this regard, one promising approach may life (Fernald et al., 2013; Halle et al., 2009; Hoff, to be to focus on supplementation of income 2003b; Noble, Engelhardt, et al., 2015; Rowe & itself as a means to improve children’s devel- Goldin-Meadow, 2009), we can argue that if we opmental outcomes. Such unconditional or are waiting until children start formal school conditional cash transfers have been tested in to invest in interventional approaches, we are developing countries and have often produced likely waiting too late. significant improvements in children’s educa- To intervene earlier in childhood, one might tion and health (Fiszbein, Schady, & Ferreira, focus on changing children’s experiences 2009). Such a program might be expected to through, for example, parenting interventions. lead to changes in the family via two primary Indeed, dozens of such interventions have been pathways. First, increased opportunities for designed and evaluated, many of which tend to material investment may enable families to pur- be quite effective (Lundahl, Tollefson, Risser, chase more nutritious foods, buy more books

Copyright @ 2019. The Guilford Press. All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law. & Lovejoy, 2007). For example, the large-scale and toys, and afford better child care and better

housing in better neighborhoods. Second, extra tus and cognitive skills in school-age children: Pre- income may reduce psychological strain and dicting and mediating variables]. Psykhe (Santiago), stress that families and children experience, en- 21(1), 3–20. abling parents to be present and engaged with Ardila, A., Rosselli, M., Matute, E., & Guajardo, S. their children in warmer, more nurturing ways. (2005). The influence of the parents’ educational level on the development of executive functions. De- If an evaluation of such a cash transfer program velopmental Neuropsychology, 28(1), 539–560. did indeed produce meaningful results, find- Arriaga, R. I., Fenson, L., Cronan, T., & Pethick, S. J. ings could inform debates on the generosity or (1998). Scores on the MacArthur Communicative cuts to existing or new social service programs Development Inventory of children from low and that affect families with young children in the middle-income families. Applied Psycholinguistics, United States and around the world. While in- 19(2), 209–223. creased family income may not be the most im- Avants, B. B., Hackman, D. A., Betancourt, L. M., Law- portant factor in shaping children cognitive and son, G. M., Hurt, H., & Farah, M. J. (2015). Relation brain development, it may represent a highly of childhood home environment to cortical thickness scalable intervention to help children overcome in late adolescence: Specificity of experience and timing. PLOS ONE, 10(10), e0138217. the consequences of living in poverty. Betancourt, L. M., Avants, B. B., Farah, M. J., Brod- sky, N. L., Wu, J., Ashtari, M., et al. (2016). Effect of socioeconomic status (SES) disparity on neural Conclusions development in female African-American infants at age 1 month. Developmental Science, 19(6), The developing brain is intensely affected by 947–956. experiences in the first years of life. On the Biglan, A., Flay, B. R., Embry, D. D., & Sandler, I. 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EBSCO : eBook Collection (EBSCOhost) - printed on 7/2/2019 3:06 PM via TUFTS UNIV AN: 1843598 ; Zeanah, Charles H..; Handbook of Infant Mental Health, Fourth Edition Account: s3579704.main.ehost