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Early Childhood Research Quarterly 29 (2014) 538–549

Contents lists available at ScienceDirect

Early Childhood Research Quarterly

ocioeconomic status and receptive vocabulary development:eplication of the parental investment model with Chileanreschoolers and their families�

atherine H. Coddington ∗, Rashmita S. Mistry, Alison L. Baileyepartment of Education, University of California, Los Angeles (UCLA), Los Angeles, CA United States

r t i c l e i n f o

rticle history:eceived 9 July 2013eceived in revised form 25 May 2014ccepted 22 June 2014vailable online 2 July 2014

a b s t r a c t

Drawing on the first wave of data from the Chilean Longitudinal Study for Early Childhood the cur-rent study examined the relation between family socioeconomic status (SES) and children’s receptiveSpanish vocabulary, and whether these relations were mediated by physical features of the home envi-ronment, parent–child interactions, and participation in center-based child care. The results of pathanalyses (n = 1589) estimating direct and indirect effects of SES on children’s receptive vocabulary test

eywords:ESarent investmentsarly childhood

scores provided evidence of partial mediation through indices of standard of living and parents’ level ofcognitive and linguistic stimulation in the home. This study is among the first to replicate with a non-U.S. sample, a well-established linkage among SES, family-level conditions and processes, and youngchildren’s language outcomes.

© 2014 Elsevier Inc. All rights reserved.

eceptive vocabularyhile

ntroduction

The first Millennium Development Goal (MDG1), set by thenited Nations with global commitment in 1990, was to reduce

he number of people living below the international poverty line$1.25 per day) in half, or by 700 million people, by 2015 (Unitedations [UN], 2013). Since then, remarkable progress has beenade and MDG1 was pronounced “achieved” as of 2010, five years

head of schedule (UN, 2013). Despite these important advancesn poverty reduction, 1.2 billion people, a disproportionate num-er of whom are children, continue to live in conditions of povertyround the world (UN, 2013). In the United States and internation-lly, the adverse effects of low socioeconomic status (SES) on young

hildren’s language, cognitive, socioemotional and physical devel-pment is well established (Evans, 2004; McLoyd, 1998; Walkert al., 2007). However, additional research with non-U.S. samples

� This research was supported by a fellowship awarded to the first author fromhe Foundation for Psychocultural Research, Center for Culture, Brain and Develop-ent program at UCLA. Many thanks to Andrea Rolla San Francisco, Paola Leal and

olleagues at the Chilean Ministry of Education who provided feedback on draftsf this manuscript. This work was done in partial fulfillment of the first author’saster’s Degree, and we thank committee member Carollee Howes.∗ Corresponding author at: Department of Education, Moore Hall 3302A, 405 Hil-ard Avenue, Los Angeles, CA 90095-1521, United States. Tel.: +1 310 339 5206;ax: +1 310 206 6293.

E-mail address: cathycod@ucla.edu (C.H. Coddington).

ttp://dx.doi.org/10.1016/j.ecresq.2014.06.004885-2006/© 2014 Elsevier Inc. All rights reserved.

on the mechanisms through which SES is associated with children’sdevelopment is needed if policies and programs are to be craftedand implemented to effectively support vulnerable children andfamilies in all parts of the world (Britto, Yoshikawa, & Boller, 2011).

The current study sought to test the generalizability of theparental investment model (PIM) of SES influences on child devel-opment, which has robust empirical support based on diverse U.S.samples, in a novel and importantly non-U.S. context, Chile. Inthe United States, children’s language development, particularlyvocabulary, is positively associated with SES and is an impor-tant predictor of children’s later literacy development (Snow,2006). Furthermore, family processes such as investing time andresources in children’s cognitive and linguistic development havebeen shown to be important pathways by which SES matters forchildren’s language development (Conger, Conger, & Martin, 2010).Similar associations between SES and vocabulary developmenthave been documented in Latin America. For example in Ecuadorand Chile, lower levels of SES are associated with lower languagetest scores (Paxson & Schady, 2008; World Bank, 2012). However,the mechanisms by which this transmission occurs in Latin Ameri-can countries have been less well demonstrated (Schady, 2006). Toaddress these limitations, our study investigated the mechanismsthrough which SES was associated with children’s vocabulary test

scores using nationally representative data based on a sample ofpreschool-aged Chilean children. Our study’s findings have theoret-ical implications such as the degree of generalizability of relationsamong SES, family processes and child development across

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aried contexts and also practical implications for social policyesign particularly regarding the inclusion of parent education,

ncome transfers, and subsidies to families with young children.

omparing Chile with the United States

For the current study, we drew on research conducted in thenited States and internationally, particularly in Latin Ameri-an countries, to inform our conceptual model. Bridging thesewo bodies of research was necessary due to limited publishedesearch assessing the mechanisms through which SES influencesoung children’s development based on Latin American samples.onceptual frameworks and research findings on SES and childevelopment based on work in the United States are pertinento the Chilean context because, across several national-level eco-omic and education indicators, Chile and the United States look

airly similar. For example, Chile and the United States have similarverall and child poverty rates. Recent estimates for Chile indicatehat 21% of the overall population and 24% of the child populationre poor (Organization of Economic Cooperation and Development,OECD] 2011a). Comparable rates in the United States are 17%nd 22%, respectively (OECD, 2011a). Furthermore, both Chile andhe United States are characterized by dramatic inequality (OECD,011a).

With respect to social policies, there are important similaritiesnd differences when comparing the two countries. Chile has a longistory of social policies that target the most vulnerable popula-ions (Palma & Urzúa, 2005). Furthermore, young children wereecently given the highest priority on the country’s political agendahrough the creation of a national policy framework and program,hile Grows with You in 2006 (Vega, 2011). Chile Grows with Yourovides free access to early childhood care and education (ECCE)ervices for children starting at three months of age and continuinghrough age four and who also live in the poorest 60% of Chileanouseholds, while kindergarten education (for 5-year-olds) wasecently made compulsory and free (Chilean Ministry of Education,014; Peralta, 2011). Chile has slightly higher preschool enrollmentates for children ages 3–5 than the United States. In 2008, 63% ofhildren ages 3–5 were enrolled in preschool education services inhile, while the comparable enrollment rate was 56% in the Unitedtates for the same year (OECD, 2013).

ES and child development

In the current study, we include two commonly used indica-ors of SES, household income per capita and maternal education,hich are shown to exert differential impacts on children’s devel-

pment (Duncan & Magnuson, 2005). First, we review relevantiterature linking income to language development and then weurn to empirical work examining the relation between maternalducation and language development.

ncome and language developmentPoverty is negatively associated with a host of outcomes for

hildren (Duncan & Brooks-Gunn, 2000). There is robust evidence,cross diverse samples in the United States, linking adverse lan-uage development with low-income status for young childrenArraiga, Fenson, Cronan, & Pethick, 1998; Dearing, Berry, & Zaslow,006; Dearing, McCartney, & Taylor, 2001; McLoyd, 1998; Shonkoff

Phillips, 2000; Smith, Brooks-Gunn, & Klebanov, 1997; Stipek &yan, 1997). In Latin American countries, similar negative languageutcomes have been documented for young children growing up

n conditions of poverty, although fewer studies have been pub-ished linking SES to young children’s language development. Forxample, Paxson and Schady (2008) reported that poorer childrenn rural Ecuador had substantially lower vocabulary test scores

earch Quarterly 29 (2014) 538–549 539

relative to their less poor peers. To our knowledge, researchregarding the link between income and language development inChile has not yet been conducted.

Maternal education and language developmentSimilar to income, maternal education level is also positively

associated with a number of child outcomes across cognitive, lin-guistic, and behavioral domains (Raviv, Kessenich, & Morrison,2004; Roberts, Bornstein, Slater, & Barrett, 1999; Rosenzweig &Wolpin, 1994). In the case of language outcomes, maternal edu-cation is thought to be a more important determinant of children’sdevelopment than income (Hoff, 2013). In the United States, chil-dren whose mothers have lower levels of education experienceless complex language environments in terms of vocabulary, sen-tence structure, and contingent responses and also display slowerrates of vocabulary growth compared with peers whose mothershave higher levels of education (Hart & Risley, 1995; Hoff, 2003;Hoff-Ginsberg, 1998). While there is certainly substantial diversityin young children’s language development within low-SES popu-lations (Bailey & Moughamian, 2007) in general, lower levels ofeducation are adversely associated with young children’s develop-ment (Dearing et al., 2006).

Young children’s experiences with language also differ bymaternal education levels in Latin American countries. In Chile,higher parental education is associated with increased vocabu-lary test scores (World Bank, 2012). Rogoff (2003) observed thatGuatemalan Mayan mothers who had limited formal educationwere less likely to converse with their children and model the aca-demic discourse typically found in formal education systems ascompared to more highly educated Mayan mothers. Similar find-ings were reported for a sample of Mexican mothers, where highermaternal education level was observed to be positively associatedwith mothers’ verbal responses to their young children’s bids forattention (Richman, Miller, & LeVine, 1992). These findings aresignificant because in the United States, early vocabulary devel-opment is an important predictor of later language development(Rowe, Raudenbush, & Goldin-Meadow, 2012) and reading com-prehension (Snow, 2006) and, in turn, of academic success acrossthe elementary and high school years (Dickinson, 2011).

In summary, research conducted both in the United States andLatin American countries has consistently shown that SES is asso-ciated with young children’s language experience and outcomes,specifically vocabulary development. However, in the Latin Amer-ican context, much less attention has been paid to examining theprocesses by which SES influences children’s language outcomes,particularly in Chile.

Pathways through which SES affects child development: theparental investment model

The key to developing programs and policies that effectivelysupports young children from disadvantaged backgrounds lies inunderstanding the mechanisms through which SES is associatedwith early child development. PIM, which has garnered substan-tial empirical support in the United States, specifies a collection ofmediating pathways that account for variation in children’s lan-guage and cognitive test scores as a function of SES (for reviewssee Conger et al., 2010; Huston & Bentley, 2010; McLoyd, Mistry,& Hardaway, 2014). Research results on PIM indicate that associa-tions between SES and children’s cognitive and linguistic outcomesare mediated through the following three pathways: (1) a family’s

standard of living, (2) access to cognitively stimulating resourcesand activities inside the home, and (3) access to high qualityservices outside of home, including ECCE programs (Mistry &Wadsworth, 2011).

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One component of a family’s standard of living, the first path-ay specified in the PIM framework, is material deprivation,hich includes the lack of availability of consumer durables (e.g.,icrowave, refrigerator, etc.) and housing conditions (e.g., type

f flooring; OECD, 2006). In PIM research, standard of living haseen operationalized in different ways, but its conceptualizationocusing on the quality of the physical home environment haseen established as one conduit through which SES influences chil-ren’s cognitive development (Guo & Harris, 2000; Yeung, Linver,

Brooks-Gunn, 2002). Yeung et al. (2002) found that increasednancial resources were associated with improved physical homenvironments, which in turn positively influenced the warmth andducational content of parent–child interactions and subsequently,hildren’s scores on language and cognitive achievement tests. Poorousing quality not only negatively influences children’s health sta-us due to the increased risk of disease and injury, but may alsodversely affect children’s cognitive development by limiting chil-ren’s ability to explore and play in their homes (Bashir, 2002;ECD, 2011b). Therefore, our study focused on the physical qual-

ty of the home environment as a possible mediator of SES andhildren’s vocabulary test scores.

Support for the second PIM pathway, the provision of a cog-itively stimulating home environment, has also been observedcross several studies using nationally representative samples ofoung children in the United States. For example, researchersave shown that the extent to which parents engage in activi-ies that promote children’s learning, such as reading to childrennd purchasing educational toys that promote children’s cogni-ive development, mediates the relation between family incomend children’s achievement test scores (Gershoff, Aber, Raver, &ennon, 2007; Yeung et al., 2002) and cognitive ability (Linver,rooks-Gunn, & Kohen, 2002; Lugo-Gil & Tamis-LeMonda, 2008).ecent international evidence comes from a study conducted in Theetherlands, where parental language stimulation practices werebserved to account for the influence of SES on the Dutch and Turk-sh vocabulary outcomes of children of Turkish immigrants (Prevoot al., 2013). However, similar research drawing on Latin Americanamples has not been conducted to our knowledge.

Finally, in the United States, research evidence on ECCErograms, the third pathway commonly tested within a PIM frame-ork, shows that children who participate in center-based ECCErograms as compared with children cared for at home by fam-

ly members or in home-based ECCE programs, display betteranguage outcomes both concurrently and longitudinally as they

ove into the elementary school years and beyond (Barnett, 1993;ampbell & Ramey, 1994; Campbell, Ramey, Pungello, Sparling,

Miller-Johnson, 2002; Magnuson, Meyers, Ruhm, & Waldfogel,004; NICHD Early Child Care Research Network, 2002). Addition-lly, children from low-income backgrounds who participate inenter-based ECCE programs exhibit higher cognitive outcomes asompared with children who are in kith and kin care arrangementsLoeb, Fuller, Kagan, & Carrol, 2004). In Chile, research findingsemonstrate that children ages 2 through 4 who participate inublicly funded ECCE programs have higher reading, math andocial science test scores in fourth grade as compared with chil-ren who did not participate in any formal ECCE program (Cortázaraldés, 2011). Therefore, we aimed to assess whether participation

n center-based ECCE programs, as compared with home-based orore informal kith and kin care, mediated the association between

ES and vocabulary test scores for the Chilean children in our sam-le. Furthermore, we distinguished between publicly and privatelyunded ECCE programs in our study to explore whether relations

mong SES, center-based ECCE programs, and children’s vocabularycores held equally for both types of programs.

Recently, investment and enrollment in publicly funded ECCErograms has increased substantially in Chile due in part to the

earch Quarterly 29 (2014) 538–549

national policy, Chile Grows with You, which was established in2006. While access has risen dramatically, questions about thequality of these programs remain due to a lack of quality stan-dards and regulations (Economist Intelligence Unit, 2012). Thedistinction between publicly and privately funded programs wasexploratory, yet appropriate for two reasons. First, research find-ings suggest that higher SES families in Chile tend to use privateECCE programs rather than publicly funded programs (CortázarValdés, 2011; Staab & Gerhard, 2010). Second, publicly funded pro-grams may have lower structural quality despite highly trained staffdue to high teacher-child ratios and teacher turnover rates in manysites (Staab & Gerhard, 2010). Therefore, we modeled ECCE pro-gram participation comparing participation in publicly or privatelyfunded centers as compared with no center-based care. We definedpublicly funded programs as those that receive funding from thefederal government, which include entirely publicly funded munic-ipal programs and subsidized programs, which are administeredby the Integra Foundation and National Council of Nursery Schools(JUNJI), and are free for children from the lowest two income quin-tiles (Staab & Gerhard, 2010). Privately funded ECCE programs arethose that do not receive government funding such that familiespay tuition and fees.

Summary and current study

Compelling theoretical and empirical evidence, predominantlywith families and children in the United States, points to theinfluence of SES on children’s development through a multitudeof pathways. Parents with greater financial resources are ableto provide their children with a higher standard of living, pur-chase educational materials and activities for their children, andaccess center-based ECCE programs, all of which relate to improveddevelopmental outcomes for young children. However, the extentto which similar processes and conditions account for the linkbetween SES and child outcomes in a context other than the UnitedStates has not been as well researched. Testing the applicabilityof PIM with non-U.S. samples, but in contexts with comparablesocioeconomic landscapes is important to gauging the theoreticalreach of PIM. While we posit that Chile and the United States arecomparable in key ways that allow for a test of the generalizabilityof PIM beyond U.S. borders, we are also mindful that differences inthe sociopolitical and economic histories across the two countriesmay contribute to differences in the mechanisms that link SES andyoung children’s development in Chile as compared with the UnitedStates For example, many of the Chilean parents who have youngchildren today grew up during a time of military dictatorship andthen the transition back to democracy, which is a dramatically dif-ferent sociopolitical environment in which to be raised compared tothe United States. Also relevant is that Chilean women are less likelyto be employed outside of the home as compared with women inthe United States, in many cases due to traditional patriarchal val-ues (Contreras & Plaza, 2010). Both may have impacts on the natureof PIM as it is instantiated in the Chilean context.

As a first step in testing the generalizability of PIM, the cur-rent study sought to address the following questions using datadrawn from the first wave of the Chilean Longitudinal Study forEarly Childhood:

1. What is the association between SES (i.e., household income percapita and maternal education level) and Chilean preschool-agedchildren’s Spanish vocabulary test scores?

2. To what extent is this association mediated through a fam-ily’s standard of living, parents’ engagement in cognitive andlinguistic stimulation with their child at home, and children’senrollment in center-based ECCE programs?

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ata source

Data used for this study came from the first wave of the Lon-itudinal Study for Early Childhood (Encuesta Longitudinal paraa Primera Infancia: ELPI), conducted by the Chilean Ministry ofducation in 2009–2010 with the goal of better understandingociodemographic characteristics and developmental outcomes ofhilean children ages birth to five and their families. To be eligible

or inclusion, children had to be born between January 1, 2006 andugust 31, 2009; that is, children were newborns to almost age five

4 years, 11 months) when the study was conducted. The sampleas nationally representative of children from birth to age five dur-

ng the study period, based on birth records from the Chilean Civilegistry and Identification Service.

ampling frameworkThe sample for the ELPI study was constructed in two phases:

rst at the municipal level and then at the child level. At the munic-pal level, the 82 largest Chilean municipalities with more than0,000 residents in each were automatically included in the samp-

ing frame, and one additional municipality was included, despiteaving fewer than 60,000 residents, because it is a regional capi-al. Next, the remaining 263 municipalities in Chile were groupedy geographic region and rank ordered by income per capita andumber of children in the target age range. Thirty-three differentlusters of municipalities were then created such that each clusterontained approximately 100,000 residents. From each cluster, oneunicipality was randomly chosen. This process of selecting theunicipalities yielded a total of 116 municipalities representative

f the geographic, socioeconomic and demographic characteristicsf Chile from which the final study sample was drawn (Universityf Chile, 2010a). At the child-level, eligible children were stratifiedy gender and age and then drawn randomly from across the 116unicipalities.

rocedureData were collected by trained research assistants with post-

econdary education in psychology. From April through September010, research assistants conducted 15,175 interviews with pri-ary caregivers and 13,895 direct assessments with primary

aregivers and the focal child (67% response rate based on originalampling frame). In general, individuals from lower-SES municipal-ties were more likely to participate in the study than individualsrom higher-SES municipalities (University of Chile, 2010a). How-ver, additional information on the families who did not participaten ELPI is not available, making precise comparisons between those

ho did and did not participate not possible.The first contact with selected families by the interviewers

as made in person using information provided by the Chileanivil Registry and Identification Service. All interviews and assess-ents were conducted after receiving signed, voluntary consent.

he primary caregiver’s interview was conducted at the first meet-ng, typically in the home of the target child, or an appointment

as made for a later date. After conducting the interviews, thenterviewer made an appointment for the child and caregiverssessments, usually within two weeks of the interview. Researchssistants then returned to the home to conduct assessments ofrimary caregivers and children. During return visits, several par-icipants withdrew from the study for unknown reasons resultingn a total of 13,895 completed evaluations.

urrent study sample and descriptionThe current study draws on a sub-sample of children ages 4

ears to 4 years, 11 months (n = 1589) and their families because

earch Quarterly 29 (2014) 538–549 541

the vocabulary development of children at this age has been foundto be strongly related to multiple components of literacy such asprint awareness, phonological awareness, and reading comprehen-sion (Dickinson, McCabe, Anastasopoulos, Peisner-Feinberg, & Poe,2003; Snow, 2006; Whitehurst & Lonigan, 1998) and subsequentschool achievement (Dickinson, 2011). For our sub-sample a totalof 1589 primary caregivers completed the interview during the firstvisit made by the research team and, of those 1419 children andtheir caregivers participated in developmental assessments duringthe second visit made by the research team. There were a few differ-ences across the variables of interest in the current study comparingfamilies who completed both the interview and direct assess-ments with those who completed only the interview portion of thestudy. Specifically, those with both interview and assessment datahad higher maternal vocabulary test scores (M = 8.14, SD = 3.62;M = 5.45, SD = 2.66; p = .014, respectively), engaged in higher lev-els of cognitive stimulation (M = 2.85, SD = 1.96; M = 1.27, SD = 1.35;p = .003, respectively), and were more likely to live in rural commu-nities (M = 0.12, SD = 0.33; M = 0.08, SD = 0.27; p = .042, respectively)as compared with those who had only caregiver interview data.Our analytic approach includes two considerations designed to helpredress these differences across families with only caregivers’ dataand those with caregivers’ and developmental assessment data.First, all analyses included a fairly comprehensive set of covariates,including if families lived in rural versus urban communities. Sec-ond, as discussed in greater detail below, our analytic procedure,path analysis within a structural equation modeling (SEM) frame-work using Mplus v7 (Muthén & Muthén, 1998–2012), deals withissues of missing data by fitting the covariance structure modeldirectly to the observed raw data for each participant (Arbuckle,1996; Enders, 2006).

The current study sample included approximately equal num-bers of girls (51%) and boys (49%) whose ages ranged from 48 to58 months (M = 50.16 months, SD = 1.65). The average age of tar-get children’s primary caregivers was 31.85 years (SD = 7.65) andhousehold size ranged from 2 to 16 members (M = 4.82, SD = 1.64).Most children lived in two-parent households (75%) and motherswho worked outside of the home for pay (48%), worked on average36.99 hours in the week prior to data collection (SD = 15.86). Themajority of participants lived in urban areas (89%), and only 7% ofprimary caregivers self-identified as being of indigenous heritage.

Measures

All measures selected for the ELPI study were scrutinized bya panel of Chilean child development researchers with supportfrom international experts and were piloted extensively with 650participants to ensure cultural relevance, validity and reliability(University of Chile, 2010b).

Children’s vocabulary test scoresThe primary child outcome variable of interest was standard

scores on the Test de Vocabulario en Imágenes Peabody (TVIP; theSpanish adaptation of the Peabody Picture Vocabulary Test). TVIP isan individually administered test that has been widely used in earlychildhood research both in the United States and internationally toassess children’s receptive vocabulary (Dunn & Dunn, 1981; Dunn,Padilla, Lugo, & Dunn, 1986). Despite it being normed in Mexicoand Puerto Rico between 1981 and 1986, in Chile, the TVIP hasbeen found to be a valid and reliable measure of language (Strasser,Larraín, López de Lérida, & Lissi, 2010).

Socioeconomic statusBoth the primary caregiver’s (which in almost all cases was

the child’s biological mother) total years of formal education andhousehold income per capita were included in the current study

542 C.H. Coddington et al. / Early Childhood Research Quarterly 29 (2014) 538–549

Table 1Factor loadings for cognitive and linguistic stimulation indices.

Cognitive stimulation Linguistic stimulation

During the evaluation did the research assistant observe. . .?at least 10 books in the home 0.32 0.07the mother speaking to the child 0.34 0.07the mother encouraging the child to try new things that promote development 0.64 0.13the mother modeling how to use new toys with educational value 0.87 0.17the mother structuring the child’s play time 0.67 0.14the mother providing toys that challenge the child to develop new abilities 0.79 0.13

In the last 7 days did you or a family member. . .?read storybooks or look at illustrations with your child 0.16 0.62tell stories to your child 0.12 0.66

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ote: Factor loadings >0.30 are in boldface. The extraction method used was princip

s indicators of SES. For maternal education level, respondentsere asked the highest level of education in which they studied

nd how many grades were completed at that level. Combiningesponses from these two items resulted in a continuous variablef the mother’s total years of formal education.

Primary caregivers were also asked a series of questionsegarding household income, during the previous complete month,rom a variety of sources (e.g., work, pensions, government sub-idies, alimony, etc.) and for all members of the household. Thismount was divided by the total number of individuals living inhe household during the same time period yielding a per capitastimate of the household’s monthly income. To account for skew-ess and kurtosis, the raw household per capita income variableas transformed by adding one and taking the natural log (Mayer,

997).

arental investmentsParents’ investments in their young children were modeled

sing six variables. First, a standard of living index was constructedased on five items from the caregiver interview that related toonsumer durables and housing conditions. Each item was dichoto-ously coded (1 = yes, 0 = no) and summed to create a scale score

anging from 0 to 5. Items included if the family had a refrigera-or, washing machine, water heater, and microwave in the home,nd if the flooring in the home was concrete. A separate dichoto-ous variable denoting home ownership (1 = owned home) was also

ncluded in the analyses since home ownership is significant in thehilean context where it is considered an avenue to future savingsSalinas, 2011).

The next two variables focused on the educational and linguisticontent of parent–child interactions. Separate indices of cogni-ive simulation and linguistic stimulation were created using itemsrom The Home Observation for Measurement of the EnvironmentHOME; Caldwell & Bradley, 1984), which was administered as partf the caregiver interview. The HOME has been used in numeroustudies to capture stimulation and support for the child in the fam-ly context (Bradley, Corwyn, McAdoo, & Garcia Coll, 2001; NICHDarly Child Care Research Network, 2002). In Chile, the HOME haseen shown to be culturally relevant and reliable (Bustos Correa,errera, & Mathiesen, 2001). For the current study, we included0 items from the HOME that were conceptually related to cog-itively (e.g., having more than 10 books in the home and havingevelopmentally appropriate toys) and linguistically stimulatingractices (e.g., did someone in the home converse with or readooks to the focal child in the week prior to the interview) in arincipal axis factor analysis with a promax rotation. The factor

nalysis yielded a two-factor structure matrix (r = 0.19), with eigenalues greater than 1.0, with the first factor (i.e., cognitive stimula-ion; 6 items) accounting for 25.48% of the variance and the secondactor (i.e., linguistic stimulation; 4 items) explaining 10.15% of the

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variance. Cronbach’s alpha indicated adequate reliability for bothfactors ( = 0.78 and 0.56 for cognitive and linguistic stimulationindices, respectively). Table 1 provides a summary of the factoranalysis results.

Finally, enrollment in publicly or privately funded center-basedECCE (as compared with non-center-based care, that is, home care,or informal kith and kin care) was used to capture the natureof parents’ investments in their children outside of the home. Inthe United States, center-based care is generally associated withbetter cognitive and linguistic outcomes for young children whencompared to other caregiving arrangements (Loeb et al., 2004),especially during the toddlerhood years (NICHD Early Child CareResearch Network, 2002). For our purposes, we included two vari-ables in the analysis. The first variable denoted whether or nota child attended a publicly funded, center-based ECCE program(1 = attends) while the second variable assessed whether or notthe child attended a privately funded, center-based ECCE program(1 = attends). By including both of these dichotomous indicators inthe analysis, we were able to assess the influence of SES in parents’choices to enroll their child in a center-based program in com-parison to children who were cared for at home. In this sample,home-based care mostly consisted of care by close family members(e.g., mothers, grandmothers) and, less frequently, care by nanniesor other more informal arrangements.

CovariatesAll analyses included a standard set of covariates related to child,

primary caregiver, and household characteristics that are known torelate to young children’s development in the United States or LatinAmerican countries (Bornstein, 2002; Linver et al., 2002; Lugo-Gil & Tamis-LeMonda, 2008; UNESCO, 2010; Yeung et al., 2002).Primary caregivers’ linguistic ability (based on the vocabulary sub-scale of the Wechsler Adults Intelligence Scale, WAIS; Apfelbeck& Hermosilla, 2000), household size, family structure (1 = two-parent household, 0 = single-parent household) and the primarycaregiver’s current age were included as demographic controls. Inaddition, to account for mother’s employment status, we includeda continuous variable indicating the total number of hours moth-ers worked outside of the home for a wage during the week priorto data collection. Primary caregivers who did not work for a wageduring the week prior to data collection (52%) were coded as havingzero work hours. Finally, the primary caregiver’s identification asa member of one of the nine legally recognized indigenous groupsin Chile (1 = yes, 0 = no), child’s gender (1 = boy) and rural versusurban residency (1 = rural, 0 = urban) were included as covariatesin all analyses.

Analytic strategy

The primary analytic tool was structural equation modeling(SEM) using Mplus v7 (Muthén & Muthén, 1998–2012). More

C.H. Coddington et al. / Early Childhood Research Quarterly 29 (2014) 538–549 543

Table 2Weighted means and standard deviations for model variables.

Variable n M SD Min.–Max.

Child outcomeChild Vocabulary (TVIP Standard Score) 1419 107.67 16.79 63.00–145.00

Socioeconomic status – predictorsMaternal Education (total years) 1564 11.56 3.23 0.00–23.00Household Income per Capita (raw; Chilean Pesos)a 1589 76,400.00b 113,717.05 0.00–1,166,666.63

Parental investments – mediatorsStandard of Living Index (5 items; 0–5) 1586 3.73 1.35 0.00–5.00Home Ownership (1 = yes) 1584 0.60 0.49 0.00–1.00Cognitive Stimulation (6 items; 0–6) 1430 2.87 1.95 0.00–6.00Linguistic Stimulation (4 items; 0–4) 1589 3.63 0.74 0.00–4.00Publicly Funded Center-Based ECCE (1 = attends) 1172 0.38 0.49 0.00–1.00Privately Funded Center-Based ECCE (1 = attends) 1172 0.26 0.44 0.00–1.00

CovariatesMaternal Vocabulary (WAIS Standard Score) 1430 8.33 3.75 0.00–19.00Household Size 1589 4.82 1.64 2.00–16.00Two-Parent Household (1 = yes) 1589 0.75 0.44 0.00–1.00Maternal Age at Interview (years) 1430 31.85 7.65 17.00–71.00Maternal Employment (hours prior week)c 1582 17.97 21.59 0.00–100.00Indigenous Heritage (1 = indigenous) 1583 0.07 0.26 0.00–1.00Child Gender (1 = boy) 1430 0.49 0.50 0.00–1.00Rural Residency (1 = rural) 1589 0.11 0.31 0.00–1.00

Note: The variation in sample size is due to varied response rates across interviews and assessments as well as missing data. Weights account for response rates and probabilityof selection and allow for extrapolation to the population level for children born in Chile from January 2006 to August 2009.

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a In August of 2010, 76,400 Chilean pesos was equivalent to $148.99 USD.b Median income is reported here.c Maternal employment is based on all mothers, including those who were not w

pecifically, we used path analysis to test relations among thetudy constructs because it allows for the simultaneous testing ofirect and indirect (i.e., mediating) effects, which is not possibleith an OLS regression approach (Stage, Carter, & Nora, 2004).

EM also allows for a test of model fit – how well the hypothe-ized model fit the observed data, based on several goodness-of-fitndices. However, because we estimated a just-identified or fullyaturated model (i.e., has perfect fit; no remaining degrees of free-om), model fit indices are not applicable. Model fit tests (e.g.,FI, RMSEA) are meaningful when the constraints on the modelre identified a priori and are theoretically justified; that is, whenhe aim is to evaluate how well the hypothesized model fits aiven covariance matrix. In our case, estimation of a just-identifiedodel was most consistent with our hypothesized (i.e., conceptual)odel; while model trimming would have yielded tests of model

t, it would not accurately reflect specification of the model thate were most interested in testing. Furthermore, a just-identifiedodel also represented the most parsimonious (and only) way to

valuate the significance of the decomposition effects (i.e., total,irect, and indirect effects) of maternal education and household

ncome per capita on children’s vocabulary knowledge through theet of theoretically informed mediating processes. This can onlye evaluated by including the direct association between the two

ndicators of SES on children’s outcomes; hence our reliance on aust-identified model.

Full-information maximum likelihood (FIML) estimation proce-ures were used to deal with issues of missing data in the pathnalyses. FIML is one of the preferred methods that allow gen-ralization of results to the population while preserving the usef all available data and a default in Mplus (Muthén & Muthén,998–2012). FIML does not impute missing data; rather, it fits theovariance structure model directly to the observed raw data forach participant (Arbuckle, 1996; Enders, 2006). FIML results innbiased estimates when data can be assumed to be missing at

andom (MAR). For MAR to hold, the “cause” of the missing dataust also appear in the analysis (Enders, 2006); Mplus imposes this

ondition by excluding cases with missing data on the most exoge-ous, independent variables from the analysis. In this study, the

g outside of the home for pay.

most exogenous variables are the set of model covariates; 171 caseswere excluded from the analyses due to missing data, resulting ina final analytic sample size of 1418.

Results

Descriptive statistics

Table 2 presents the weighted means and standard deviationsof all study variables. Due to the complexity of the samplingframe and to ensure that our descriptive findings appropriatelyreflect population-level estimates we included appropriate weights(fexp test and fexp enc) to all descriptive statistics. Weights werecalculated to account for individual selection probability andresponse rates (University of Chile, 2010a). This allows interpreta-tion of the descriptive results to the population of Chilean children,ages 4 and 5, drawn from the larger ELPI sample.

Among the current study sample, children’s average TVIPscores were 107.67 (SD = 16.79). Average maternal education levelwas equivalent to just below high school completion (M = 11.56,SD = 3.23), and the median raw household income per capita forthe most recent full month prior to data collection was 76,400.00Chilean pesos (SD = 113,717.05), which was equivalent to $148.99USD in August of 2010. Each of the six indicators of parentalinvestments displayed substantial variability. On a 5-point scale,the average standard of living was 3.73 (SD = 1.35) and 60% ofstudy participants were homeowners. Average cognitive stimula-tion was right around the mid-point of a 6-point scale (M = 2.87,SD = 1.95), while the mean linguistic stimulation score was fairlyhigh (M = 3.63; SD = 0.74) on a 4-point scale. Finally, 38% of studyparticipants were enrolled in publicly funded, center-based ECCEprograms and 26% were enrolled in privately funded, center-basedECCE. Thirty-six percent of the sample was not enrolled in eitherform of center-based ECCE program, and were mostly cared for at

home by close relatives and nannies and in a few cases by neighbors.

Bivariate correlations revealed significant relations among keystudy variables. Children’s TVIP scores were most strongly asso-ciated with maternal education (r = 0.31, p < .01), standard of living

544 C.H. Coddington et al. / Early Childhood Research Quarterly 29 (2014) 538–549

Table 3Bivariate correlations among key model variables (excluding covariates).

1 2 3 4 5 6 7 8 9

1. Child Vocabulary (TVIP Standard Score) –2. Maternal Education (total years) .31** –3. LN Household Income per Capita .18** .25** –4. Standard of Living Index (5 items; 0–5) .31** .43** .21** –5. Home Ownership (1 = yes) .01 .03 .05* .13** –6. Cognitive Stimulation (6 items; 0–6) .23** .22** .09** .21** .01 –7. Linguistic Stimulation (4 items; 0–4) .15** .14** .03 .11** .01 .14** –8. Publicly Funded Center-Based ECCE (1 = attends) −.05 −.14** −.06* −.07* −.03 −.08* .02 –9. Privately Funded Center-Based ECCE (1 = attends) .18** .22** .12** .23** .05 .12** .02 −.46** –

Note: Bivariate correlations are based on pairwise deletion. Sample size ranges from 1037 to 1589. Correlations vary slightly from those obtained in the path analysis, whichu

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sed the full information maximum likelihood to correct for missing data.* p < .05, 2-tailed.

** p < .01, 2-tailed.

r = 0.31, p < .01), cognitive stimulation at home (r = 0.23, p < .01) andhe maternal WAIS scores (r = 0.36, p < .01). Maternal education andousehold income per capita were modestly correlated with eachther (r = 0.25, p < .01) and were significantly correlated with almostll of the parental investment variables (see Table 3). Bivariate cor-elations among covariates and the variables that comprise SES,arental investments, and children’s TVIP scores are presented inable 4.

irect and indirect effects of socioeconomic status

Results of the path analysis are summarized in Fig. 1. First,ith respect to maternal education level, the results indicated

hat, above and beyond the influence of the covariates, householdncome per capita, and the mediating variables, maternal educationevel was directly associated with children’s TVIP scores. Children of

others with higher levels of education exhibited higher levels of

panish receptive vocabulary as compared with children of moth-rs with lower levels of education. In addition, maternal educationas significantly associated with all of the mediating variables,ith the exception of home ownership, and in expected directions.

able 4ivariate correlations among covariates and child outcome, predictors and mediators.

ChildVocabulary(TVIP StandardScore)

MaternalEducation(total years)

LNHouseholdIncome perCapita

Standard ofLiving Indexitems; 0–5)

MaternalVocabulary(WAIS StandardScore)

.36** .47** .14** .36**

Household Size −.12** −.09** −.08** .02

Two-ParentHousehold(1 = yes)

.06* −.06* .01 .06*

Maternal Age atInterview (years)

.09** −.06* −.02 .09**

MaternalEmployment(hours priorweek)

.12** .24** .19** .17**

IndigenousHeritage(1 = indigenous)

−.09** −.08** −.04 −.14**

Child Gender(1 = boy)

−.05* −.03 −.04 −.02

Rural Residency(1 = rural)

−.16** −.21** −.07** −.27**

ote: Bivariate correlations are based on pairwise deletion. Sample size ranges from 1399sed the full information maximum likelihood estimation to correct for missing data.

* p < .05, 2-tailed.** p < .01, 2-tailed.

That is, higher levels of maternal education were associated withhigher levels of standard of living and engagement in cognitivelyand linguistically stimulating interactions with children. Higherlevels of maternal education also predicted lower rates of enroll-ment in publicly funded center-based ECCE (after accounting forthe covariates and enrollment in privately funded child center-based ECCE programs) and higher rates of enrollment in privatelyfunded center-based ECCE programs (again, after accounting for thecovariates and enrollment in publicly funded center-based ECCEprograms).

The direct effects of household income per capita on chil-dren’s TVIP scores and the set of mediating variables were lessrobust than those observed for maternal education. First, con-sistent with maternal education, we observed a persistent directeffect of income on children’s TVIP scores, after accountingfor all other modeled relationships, including maternal educa-tion (see Fig. 1). Second, whereas household income per capita

predicted higher levels of standard of living and children’senrollment in privately funded, center-based ECCE programs, itwas found to be unrelated to the remaining set of mediatingvariables.

(5HomeOwnership(1 = yes)

CognitiveStimulation(6 items;0–6)

LinguisticStimulation(4 items;0–4)

PubliclyFundedCenter-BasedECCE(1 = attends)

PrivatelyFundedCenter-BasedECCE(1 = attends)

.01 .26** .15** −.08* .20**

.10** −.04 −.08** .06* −.09**

−.05* .02 −.01 −.01 .01

.06* .04 .02 −.02 .02

.02 .09** −.002 −.04 .05

.03 −.06* −.03 −.02 −.05

.02 −.01 −.04 −.05 .03

.04 −.08** −.001 .01 −.11**

to 1589. Correlations vary slightly from those obtained in the path analysis, which

C.H. Coddington et al. / Early Childhood Research Quarterly 29 (2014) 538–549 545

Maternal Education(Total Years)

R2 = 28%

Household Incomeper CapitaR2 = 7%

Standard of LivingR2 = 29%

Home OwnershipR2 = 2%

Cognitive StimulationR2 = 8%

LinguisticStimulation

R2 = 4%

Publically-fundedCenter-based

ECCER2 = 3%

Privately-fundedCenter-based

ECCER2 = 7%

Child Vocabulary(TVIP Score)

R2 = 22%

.28***

.12***

.05

.05

.11***

.02

.10***

-.01

-.14***

-.03

.11**.07*

.10***

.06*

.11***-.01

.10***

.07**

.04

.08*

.18***

Fig. 1. Path analysis results for full sample (n = 1418). Shown coefficients are standardized path coefficients after accounting for the influence of the following covariates:maternal vocabulary (WAIS standard score), household size, two parent household (1 = yes), maternal age at interview, maternal employment (hours prior week), indigenousheritage (1 = indigenous), child gender (1 = boy), rural residency (1 = rural). Covariances among the mediators were also estimated, but not shown given model complexity.The following covariances were significant among mediators: standard of living with home ownership, cognitive stimulation and center-based private child care; cognitivestimulation with linguistic stimulation; and publicly funded, center-based child care with privately funded, center-based child care. Because the model was fully saturatedfi

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t statistics do not apply. ***p ≤ .001, **p ≤ .01,*p ≤ .05.

Turning next to the relations among the mediators and chil-ren’s TVIP scores, a majority of the mediators were significantlyredictive of children’s vocabulary test scores, and in the expectedirection. The exceptions were home ownership and children’snrollment in publicly funded, center-based ECCE, which were notredictive of TVIP scores. Higher levels of standard of living andarents’ engagement in cognitively and linguistically stimulatingctivities with their child, and children’s enrollment in privatelyunded, center-based ECCE predicted higher vocabulary test scoresmong children.

In addition to exploring the direct relations within our model,e also sought to determine the significance of the indirect

ffects through which each of our indicators of SES influencedhildren’s Spanish vocabulary test scores (see Table 5 for a sum-ary of the indirect effects). All inferences were based on theplus estimation of indirect effects, which estimates indirect

ffects with delta method standard errors (Muthén & Muthén,998–2012). Results indicated three pathways of significancehrough which maternal education influenced children’s vocabu-ary test scores – standard of living (standardized indirect effectsstimate: = 0.03, p < .000, cognitive stimulation (standardized

ndirect effects estimate: = 0.01, p = .009), and linguistic stimu-ation (standardized indirect effects estimate: = 0.01, p = .033).tandard of living was also observed to be a significant pathwayhrough which household income per capita influenced children’s

vocabulary test scores (standardized indirect effects estimate: = 0.01, p = .002).

Discussion

Research findings from multiple disciplines including neurobi-ology, psychology, and economics underscore the importance ofchildren’s early cognitive, linguistic, socioemotional, and physicaldevelopment for lifelong learning and success (Shonkoff & Phillips,2000). Low-SES is a well-documented risk factor for adverse earlydevelopment, including language development (Dearing et al.,2006). However, the majority of the research evidence document-ing what and how early experiences matter for children is basedon families and children living in the United States. Given the dis-proportionate number of children living in conditions of povertybeyond the United States’ borders and the continued global com-mitment to reduce poverty further even after achieving MDG1early (UN, 2013), the current study’s aim of replicating the parentalinvestment model in the Chilean context was opportune.

Differential influence of maternal education and income

Results from the current study contribute to and extend priorunderstanding of the pathways through which family SES mattersfor young children’s development. This study is one of the first to

546 C.H. Coddington et al. / Early Childhood Research Quarterly 29 (2014) 538–549

Table 5Decomposition of effects for final model predicting children’s vocabulary test scores based on full sample.

Predictor Dependent variable Standardized effect coefficients

Total effect Direct effect Indirect effect

Maternal Education Child Vocabulary (TVIP Standard Score) 0.15*** 0.10*** 0.05***

Standard of Living Index (5 items; 0–5) – – 0.03***

Home Ownership (1 = yes) – – −0.001Cognitive Stimulation (6 items; 0–6) – – 0.01**

Linguistic Stimulation (4 items; 0–4) – – 0.01*

Publicly Funded Center-Based ECCE (1 = attends) – – −0.01Privately Funded Center-Based ECCE (1 = attends) – – 0.01

Household Income per Capita Child Vocabulary (TVIP Standard Score) 0.08** 0.06* 0.02**

Standard of Living Index (5 items; 0–5) – – 0.01**

Home Ownership (1 = yes) – – −0.001Cognitive Stimulation (6 items; 0–6) – – 0.002Linguistic Stimulation (4 items; 0–4) – – 0.000Publicly Funded Center-Based ECCE (1 = attends) – – −0.001Privately Funded Center-Based ECCE (1 = attends) – – 0.01

Note: n = 1418. Tests of significance of total and indirect effects were conducted using Mplus.

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* p ≤ .05** p ≤ .01

*** p ≤ .001.

pply the PIM perspective to children’s developmental outcomesn a Latin American context. Consistent with our expectationsnd research conducted in the United States, findings indicatedhat SES influenced young children’s vocabulary test scores bothirectly and indirectly through several of the hypothesized path-ays. The observed associations between higher levels of SES,

hrough more educationally enriching and stimulating home envi-onments, and young children’s increased language developmenteported here support an extensive body of existing research con-ucted in the United States (Bradley & Corwyn, 2002; Dearing et al.,006; Gershoff et al., 2007; Linver et al., 2002; Yeung et al., 2002).he replication of these findings to a new economic and earlyhildhood policy context suggests that relations among SES, familyonditions and processes, and young children’s development areeneralizable to additional contexts outside of the United States.

A more novel contribution of the current study’s findings wasisentangling what about SES informs these downstream relationsith family conditions and processes and child outcomes. Our set

f mediating variables can be organized around those that addresshe structural conditions of the settings in which young childrenpend significant amounts of time (i.e., standard of living, homewnership, attending either publicly or privately funded center-ased ECCE), as well as the quality of the family processes insidehe home (i.e., cognitive and linguistic stimulation that parentsngage in during interactions with their young children). The find-ngs suggest different pathways of influence of SES through thesewo distinct features of children’s environments – in and outsidehe home. Maternal education, for example, was observed to have a

ore pervasive influence on children’s vocabulary outcomes bothhrough structural conditions of the home setting such as the fam-ly’s standard of living, and through family processes such as theuality of parent interactions with their children, including howhey talked with and engaged their child in cognitively and linguis-ically stimulating activities. Household income per capita, on thether hand, was observed to influence young children’s vocabularyutcomes exclusively through its influence on a family’s standard ofiving; that is, a structural feature of the home setting rather thanhe quality of parent interactions with their child. Prior researchtemming from a PIM perspective typically models the influencef family income exclusively on child outcomes, while controlling

or the influence of maternal education, and less often includestandard of living indices as mediators of this association (Guo &arris, 2000; Linver et al., 2002). However, our results highlight

he nuanced and divergent pathways of influence that maternal

education and household income per capita have on young chil-dren’s linguistic development.

A related contribution of the current study is the importance ofincluding measures of standard of living to capture more structuralconditions of the home setting as predictors of children’s develop-mental outcomes and correlates of SES. While not as consistentlymeasured or included in studies of SES influences on children, ourfindings and those from prior studies (Yeung et al., 2002) point tostandard of living differences as important considerations whendetermining how SES matters for young children’s development.This may be even more relevant when considering the impor-tance of standard of living differences in lower- as opposed tohigher-income countries because standard of living is positivelyassociated with a country’s gross domestic product (GDP; OECD,2006). Research literature on housing quality suggests that sub-standard housing conditions adversely impact health outcomes,which in turn impact children’s development (Bashir, 2002; Marsh,Gordon, Heslop, & Pantazis, 2000; OECD, 2011b). For example, onecomponent of our standard of living index, the presence of concreteflooring, likely has implications for children’s physical health, andsubsequently for children’s cognitive and linguistic development.Homes without concrete floors (i.e., with dirt flooring) in communi-ties with poor sanitation conditions pose a risk for soil-transmittedhelminth (parasitic worm) infections and lead poisoning, whichcan lead to negative physical, nutritional and cognitive conse-quences (Tong, von Schirnding, & Prapamontol, 2000; World HealthOrganization, 2010). Internationally, the adverse impact of com-promised health and nutritional status experienced during theearly childhood years on short- and long-term cognitive and phys-ical development is well documented (Walker et al., 2007). Whilehealth status was not tested as a pathway linking standard of livingand young children’s language development, it suggests a possi-ble direction for future research that further expands the rangeof conditions and circumstances through which SES accounts forvariability in children’s outcomes.

Our definition of standard of living also included several exam-ples of consumer durables (e.g., having a washing machine or amicrowave). Both indicators of SES predicted standard of livingsuggesting that having both the means to purchase such commodi-ties and awareness of their utility (via education) is important. On

the surface, greater access to such durable goods could alter howmothers spend their time at home by possibly reducing time spentengaged in daily household tasks and increasing time spent inter-acting with children. Research based on U.S. samples shows that

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C.H. Coddington et al. / Early Childho

igher-SES families are more likely to invest in the types of activi-ies that better prepare children for school such as reading to andonversing with children, or taking their children to novel places asompared with lower-SES families (Phillips, 2011). Examining theelation between standard of living and time spent with childrenn Chile is an avenue for future research. A second possible mech-nism linking standard of living to child development lies with theossibility that a greater standard of living reduces the level oftress mothers feel thereby allowing them to be more responsive inheir parenting. Future PIM research, both in the United States andnternationally should attend closely to both the process-orientedeatures of a child’s home environment as well as more structuralonditions of the home, including standard of living.

One point of departure in our study findings was the lack of con-lusive evidence that children’s participation in center-based ECCErograms mediated the influence of SES on children’s vocabularyest scores. It is important to note that, consistent with literaturen the United States and Chile, SES was predictive of children’s par-icipation in center based ECCE (Cortázar Valdés, 2011; Magnusont al., 2004). Moreover, maternal education level predicted usagef privately versus publicly funded ECCE programs in oppositeirections. Increased maternal education was positively relatedo children’s enrollment in privately funded ECCE center-basedrograms, and negatively associated with children’s enrollment

n publicly funded ECCE center-based programs. Privately fundedenter-based care participation was, in turn, a positive, significantredictor of children’s vocabulary test scores. It is possible that

n Chile, better educated mothers, and those with more financialesources, choose privately funded ECCE settings in part becausehey deem them to be of higher quality. Although this cannote confirmed (or disconfirmed) in the current study, it suggestshat another direction for future research in Chile is further pro-ing of the factors that contribute to parents’ decisions aroundCCE arrangements. In addition, recent increased investment inCCE, has led to a dramatic rise in participation rates (Staab &erhard, 2010). However, measuring the quality of these servicesill be important in better understanding how center-based ECCErograms impact children’s development concurrently and lon-itudinally and to ensure the most effective use of public fundsllocated for ECCE services.

tudy caveats and future directions

The results of the current study make important theoretical andractical contributions by extending the PIM model to a new con-ext; namely, Chile. However, the findings should be interpretedith the appropriate degree of caution in light of limitations of the

tudy design and measurement considerations. First, although ouronceptual model stipulates a causal model whereby SES influencesamily conditions and processes, which, in turn, affect children’sutcomes, the cross-sectional nature of the data limit our abilityo infer causation among our study constructs. For example, it isossible that chaotic home environments adversely impact fami-

ies’ socioeconomic status by threatening job security and earningotential. However, the theoretical model and prior research pointoward the feasibility that measures of socioeconomic status bencluded as the most exogenous variables in our model. Second,ur measure of household income per capita is somewhat limitedince it pertained to just one time point and may mask impor-ant fluctuations in income known to have differential associationsith child development outcomes (McLoyd, 1998). Additionally,

he ECCE component of the PIM conceptual model would be bet-

er measured by exploring the quality of ECCE settings throughirect observational assessments of the process quality of ECCErograms, (i.e., the warmth and responsiveness of teacher–child

nteractions), which is known to impact children’s development

earch Quarterly 29 (2014) 538–549 547

in the United States (Vandell & Wolfe, 2000). Such data were notavailable in the ELPI study, but are important considerations forfuture research incorporating ECCE settings within a PIM frame-work. Finally, our outcome measure, the TVIP, is somewhat limitedbecause it provides a restricted assessment of vocabulary devel-opment by exclusively focusing on receptive language and may beless culturally relevant in Chile, given its Puerto Rican and Mexicannorming samples drawn almost 30 years ago. Additional researchwith more comprehensive measures of children’s language out-come is an important next step.

We offer several suggestions for future studies that would aidmore complete understanding of the mechanisms through whichSES is associated with child development in Chile and beyond. Theinclusion of measures of financial stress or worry as well as par-ents’ mental health would offer another pathway through whichSES may influence children’s development and explain additionalvariation in child outcomes in Chile. Such pathways have beenrobustly tested in the United States through pathways complemen-tary to PIM and referred to as the “Family Economic Stress Model”(FESM; Conger et al., 2010; McLoyd et al., 2014). Recent studiesin the United States have shifted from testing one or the other ofthese pathways to examining their joint influence on children’s aca-demic, social, and behavioral outcomes. In this study, we chose tofocus on PIM because it has been shown to be a more robust predic-tor of children’s academic and cognitive outcomes (Gershoff et al.,2007; Yeung et al., 2002). Future studies that include examinationof children’s social and behavioral outcomes should also considerincluding tenets from the FESM perspective. Finally, there is a needfor additional longitudinal research to further current understand-ing of how SES affects children’s developmental trajectories outsideof the United States. Compelling evidence of the critical importanceof early childhood poverty in diminishing the future prospects andlife chances for children in the United States also fuels interest anda desire to better understand the life chances of children livingin poverty and other adverse environments in countries outsideof the United States as well. As additional waves of the ELPI databecome available (only the first wave was available at the timeof the current study), it will allow for more sophisticated longi-tudinal investigations of how SES matters for Chilean children’sdevelopment.

Conclusion

Policy implications of the current study’s findings highlightthe importance of increasing parent education levels to positivelyimpact young children’s linguistic development in Chile. Policyor program options range from incentivizing parents to return toschool to pursue formal education opportunities to strengtheningparent education components of ECCE programs such as the par-enting workshops offered by the Integra Foundation or JUNJI with afocus on oral language interactions. Policies that increase parents’standard of living either through housing subsidies or conditionalcash transfers are likely to allow families to better support theiryoung children’s development perhaps by increasing time parentscan spend with children or by improving children’s health statusalthough additional research is needed to better understand thispolicy lever.

In general, findings from the current study replicate impor-tant findings from the United States regarding the mechanismsthrough which SES is related to young children’s development ina nationally representative sample of Chilean preschool-aged chil-

dren. Similar to findings across diverse communities within theUnited States, in Chile it appears that SES’s influence on young chil-dren’s development is due in part to its impact on parents’ ability toprovide an adequate and stimulating home environment for their

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48 C.H. Coddington et al. / Early Childho

hildren, particularly in ways that promote children’s early vocabu-ary development. The current study’s two measures of SES tappednto different types of resources that parents bring to their phys-cal home structures and their interactions with young children.arents with higher, as compared with lower levels of education,rovided richer home environments for their young children inerms of both higher standards of living and more stimulating inter-ctions with their child, and parents with higher levels of incomeelative to those with lower levels of income provided their childrenith a higher standard of living. Although related, this study’s find-

ngs provide additional evidence of the differential impact of SES viaaternal education and household income per capita on children’s

arly developmental outcomes. Ultimately, we find that both aremportant contributors to healthy child development and shoulde attended to when considering social policy directives targetinghildren and families, both in the United States and internationally.

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