Home improvements: Within-family associations between income and the quality of children's home environments

Journal of Applied Developmental Psychology 28 (2007) 427 – 444

Home improvements: Within-family associations between income and the quality of children's home environments Eric Dearing a,⁎, Beck A. Taylor b a

140 Commonwealth Avenue, Department of Counseling, Developmental, and Educational Psychology, Lynch School of Education, Boston College, Chestnut Hill, Massachusetts 02467, USA b School of Business, Samford University, USA Available online 20 July 2007

Abstract Within-family associations between changes in income and changes in the home environment during infancy and early childhood were examined using data from the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development (n = 1364). Linear and nonlinear (i.e., semilog) specifications were estimated for family income. In addition, variations in income effects were estimated as a function of the level of developmentally stimulating resources in homes during early infancy. Increases in family income were positively associated with increases in the quality of children's home environments. The estimated effects of income were largest for families who had low incomes and low-quality early home environments. These results were evident for physical characteristics of the home that likely required monetary investments, as well as for psychosocial characteristics of the home. © 2007 Elsevier Inc. All rights reserved. Keywords: Income; Poverty; Home environment; Infancy; Early childhood

1. Introduction Risk associated with poverty pervades most developmental domains including cognitive, language, and social– emotional functioning. Much of this risk may be relayed to children via their home environments. Indeed, theory from both developmental psychology and economics gives primacy to the home environment as a mechanism transmitting the effects of family economic well-being to children (Becker, 1993; Becker & Tomes, 1979; Conger et al., 2002; Elder & Caspi, 1988). In particular, theorists have highlighted the fact that family economic resources allow parents to invest in developmentally stimulating physical and psychosocial resources within the home (Becker, 1993). Economic losses and deprivation may also increase the probability that children will have negative experiences within their homes, such as hostile parenting (Conger, Rueter, & Conger, 2000). Understanding the responsiveness of children's earliest home environments to family economics is of great importance to the study of poverty as a developmental context, because poverty experiences during early childhood appear exceptionally detrimental to children's life chances (e.g., Duncan, Yeung, Brooks-Gunn, & Smith, 1998). ⁎ Corresponding author. E-mail address: [email protected] (E. Dearing). 0193-3973/$ - see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.appdev.2007.06.008

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1.1. Family economics, the early home environment, and child development Following classic theory that emphasizes the role of developmental contexts as potential determinants of child well-being (i.e., via direct, indirect, and transactional processes such as those outlined by Bronfenbrenner, 1977, and Sameroff, 1975), nearly three decades of empirical work has documented a wide variety of ways that the early home environment may influence children's cognitive, language, and social–emotional development. During infancy and early childhood, for example, parents' sensitivity, warmth, and responsiveness to their children's needs have implications for contemporaneous and later social, emotional, and mental health outcomes (for a review, see Shonkoff & Phillips, 2000). In addition, the availability of learning materials in the home such as books, parental encouragement of learning through activities such as reading to children, and access to stimulating resources outside of the home such as libraries have life course implications for literacy and achievement (Shonkoff & Phillips, 2000). Importantly, family economic resources may be one determinant of the amount of physical and psychosocial resources to which children have access in their homes. Because many developmental supports within the home have monetary costs, providing these supports usually requires that families have the economic resources as well as the desire to invest in their children's development (Becker, 1993). All other things equal, families with fewer economic resources are less capable of investing in materials that may stimulate their children's development. The influence of economic resources, however, may extend beyond financial constraints on expenditures. Indeed, stress associated with low income or economic losses may result in constraints on psychosocial investments. A lack of financial resources, or a loss of financial resources, may impede parents' abilities to engage in positive interactions with their children and simultaneously increase the chance that parents will engage in negative interactions with their children (e.g., hostile and harsh parenting behaviors), primarily because stress associated with economic problems may limit parents' own psychosocial well-being (Conger et al., 2000; Dearing, Taylor, & McCartney, 2004). There is, in fact, considerable evidence that compared with families who are not poor, families in poverty have fewer developmentally appropriate physical and psychosocial resources within their homes (for reviews, see Brooks-Gunn & Duncan, 1997; Dearing, Berry, & Zaslow, 2006; Evans, 2004). Children living in poverty are, for example, less likely than their nonpoor peers to have access to books, age-appropriate toys, and computers in their homes (e.g., Bradley, Corwyn, McAdoo, & Coll, 2001; National Institute of Child Health and Human Development [NICHD] Early Child Care Research Network, 1997). Compared with their peers who are not poor, young children in poverty are also less likely to visit learning environments outside their homes such as museums (e.g., Bradley et al., 2001). Further, the physical structures of the homes of young children in poverty are often characterized by subpar construction, inadequate lighting conditions, and overcrowding (Evans, 2004). The psychosocial environments of children in poverty are less stimulating and supportive, on average, and filled with greater risk relative to those of children in higher-income families. For example, parents living in poverty spend less time engaged in learning-related activities with their young children, including less time reading to their children, less time teaching their children (e.g., teaching the alphabet), and less time talking with their children (Evans, 2004; Hart & Risley, 1995; Bradley et al., 2001; Hoff, 2003). In addition, parents living in poverty are more likely to use punitive parenting strategies and are less likely to demonstrate high levels of responsiveness to their young children (e.g., Bradley et al., 2001; McLeod & Shanahan, 1993). Of note, however, is the fact that most empirical work on the home environment has not been experimental, and, as such, there remain concerns about potential omitted variable bias (Blau, 1999; Duncan et al., 1998; Mayer, 1997). Specifically, questions remain as to whether poverty leads to poorer-quality home environments or whether an unobserved variable causes both. In addition, despite an extensive body of research from many disciplines comparing the home environments of poor children with the home environments of relatively wealthier children, little work has focused on whether poor families invest more in their children's home environments when given the financial opportunity to do so. In other words, the supposition that economic gains for poor families will translate into increased material and psychosocial investments in poor children is questionable based on the present knowledge base. Withinfamily analyses of longitudinal data on family income and the home environment can help address some of these questions.

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1.2. Strengths of within-family estimates Within-family analyses of longitudinal data can provide important advantages over between-family analyses of cross-sectional data, particularly when studies are nonexperimental. First, related to concerns of potential omitted variable bias, unobserved characteristics that are fixed within families cannot bias within-family estimates of the association between changes in family income and changes in the home environment (Duncan, Magnuson, & Ludwig, 2004; Hsaio, 2003). In other words, unobserved heterogeneity across families that is stable over time cannot bias associations between gains and losses in economic resources and any corresponding gains and losses in home resources, if such associations are estimated within families. Second, within-family analyses of income are policy relevant, because they help address whether families can recover from economic deprivation. Policy strategies aimed at increasing the economic standing of poor families to improve child outcomes are based, in large part, on the hope that parents will use economic gains to benefit their children by investing in the home environment, both financially and psychosocially (Magnuson & Duncan, 2004). Although it is clear that the home environments of poor children are deprived, it is not clear whether family investments in children are responsive to variations in family income; competing investment demands, family preferences, and other financial or psychological constraints may limit the effects of increased family income on poor children's home environments. For example, the fact that poorer families are, on average, less cognitively stimulating in interactions with their children than are wealthier families does not necessarily imply that increased income will result in increased cognitive stimulation. Thus, within-family analyses can add to existing knowledge from between-family analyses, because the former helps address whether or not economic gains are associated with improvements in the home environment. 1.3. The existing literature on income and home environment changes within families Despite the fact that family income is often in flux, particularly for families living in or near poverty (Bane & Ellwood, 1985; Duncan, 1988; Corcoran & Chaudry, 1997), few within-family analyses of income and the home environment exist. One notable exception, however, was conducted by Votruba-Drzal (2003), who estimated withinfamily associations for changes in income and changes in cognitive stimulation in the home between early childhood (3–4 years of age) and middle childhood (7–8 years of age). The author reported significant and positive associations, such that increased income was related to increased cognitive stimulation in the home and decreased income was related to the decreased cognitive stimulation in the home. Votruba-Drzal also noted that this association appeared to be nonlinear, such that changes in family income had the largest effects on home environments for the poorest families in the study, a result that has also been demonstrated in between-family analyses of the home environment (e.g., Taylor, Dearing, & McCartney, 2004). Even for the poorest families, however, changes in cognitive stimulation associated with changes in income appeared modest in absolute terms (i.e., a $10,000 increase in income was associated with approximately one-quarter of a standard deviation increase in cognitive stimulation [Votruba-Drzal, 2003]). 1.3.1. Variations in within-family income effects? Above and beyond the effects of contemporaneous income, a variety of economic and noneconomic forces likely affect the quality of children's early home environments. Family economic well-being prior to the birth of children, for example, may affect resources in the home once children are born via accumulated wealth (Campbell & Mankiw, 1990; Carroll, 2001; Friedman, 1957; Mayer, 1972). Families may also vary with respect to their marginal propensity to invest in their children for a variety of reasons, including parental preferences (Kalil & DeLeire, 2004). Families with fairly similar income levels may differ markedly with respect to the amount of income they allocate to developmentally stimulating resources. In addition, variations within economic strata with regard to parent personality and mental health likely influence the allocation of psychosocial resources to children. Some parents, for example, demonstrate resilient parenting strategies evidenced by their ability to provide adequate psychosocial resources to their children despite living in a deprived family economic context (Murry, Bynum, Brody, Willert, & Stephens, 2001). Further, some parents fail to invest an adequate level of psychosocial resources in their children despite living in a relatively affluent family economic context (Luthar & Latendresse, 2005).

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It is also important to note that income may be least likely to determine family investments during infancy compared with later life periods. Consider, for example, that many families experience planned changes in maternal employment following the birth of a child that lead to short-term income fluctuations; these income changes may have relatively small effects on overall family economic conditions if, for example, families rely on savings or forward spending that anticipates a return to work for the mother (Carroll, 1994; Carroll, Dynan, & Krane, 2003). Second, the time immediately preceding and following the birth of a child may be associated with exceptional investments in material resources for the child and psychosocial supports for the child (and parents) by extended family and friend networks. Ultimately, differences between families with respect to economic factors such as wealth and forward spending, as well as noneconomic factors such as parent personality, mental health, and preferences, could result in some families being relatively more reliant on income and some families being relatively less reliant on income for making investments in material and psychosocial resources in the home, particularly during infancy. Consider, for example, families who invest in high levels of developmentally stimulating resources for their children, even when family income is low. These families are unlikely to have much need for additional resources when they experience increased income (e.g., ceiling effects may limit improvements in these home environments). On the other hand, home environments with few developmentally stimulating resources may be particularly responsive to income changes because there is considerable room for improvement in the home with increased investments. Further, deprived home environments may be a signal that these families are less capable of compensating for income losses via stored wealth or resilient parenting strategies. If so, lower-income families may be, on average, more responsive to income changes than higher-income families, but income gains may be most strongly associated with improved home environments in families with both low levels of income and low levels of developmentally stimulating resources in the home. 1.4. The present study In the present study, we estimated within-family associations between changes in income and changes in the home environment. Following Votruba-Drzal (2003) and between-family studies that have demonstrated stronger associations between income and the home environment for families at the low end of the income distribution compared with wealthier families, we estimated both linear and nonlinear income specifications. Income changes were expected to be more strongly associated with home environment changes among poorer families compared with wealthier families, primarily because changes could have a relatively larger impact on the financial standing of poorer families (e.g., a $10,000 increase in income would be a 100% gain for families earning $10,000 per year and a 20% gain for families earning $50,000 per year). We also extended previous work on this topic in at least three ways. First, we examined an array of developmentally stimulating resources within the home, because theory linking family income and family investments in children addresses investments that are related to cognitive, health, language, and social–emotional domains of development. Related to this first contribution, we organized resources in the home environment around two types of parental investments: (1) developmentally stimulating physical and material resources that required monetary expenditures, and (2) developmentally stimulating psychosocial resources that did not necessarily require monetary expenditures. We expected changes in income to be positively associated with both of these types of parental investment. Second, we examined within-family associations between changes in income and changes in children's home environments during infancy and early childhood. Despite considerable empirical work demonstrating the developmental salience of both the home environment and family economic resources during this time, the responsiveness of the home environment to economic changes during infancy and early childhood has received little attention. Third, we examined the initial quality of the home environment in early infancy as a potential moderator of the association between changes in family income and changes in the home environment. Specifically, we expected changes in income to be most strongly associated with changes in the home environment for families that were low income and had relatively few developmentally stimulating resources in the home initially. 2. Method 2.1. Sample Data used in this investigation were from the first and second phases of the NICHD Study of Early Child Care and Youth Development (SECCYD). Shortly after giving birth in 1991, 1364 women and their recently born children

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Table 1 Sample descriptive statistics for time-varying indicators in the NICHD Study of Early Child Care and Youth Development

Family income Family size Always married or partnered Sometimes married or partnered Never married or partnered Maternal employment Partner employment AFDC Continuous receipt Intermittent receipt Never received Unemployment Continuous receipt Intermittent receipt Never received In-kind relief Continuous receipt Intermittent receipt Never received Child support Continuous receipt Intermittent receipt Never received

M (SD)/%

Average within-family Δ

$50,805 (39,665) 4.14 (1.11) 75% 16% 9% 21.98 (16.01) 37.00 (18.61)

$13,462 (16,621) .48 (.56)

9.39 (8.90) 9.53 (10.22)

2% 10% 88% 0.4% 13% 86% 6% 30% 64% 3% 16% 81%

living in or near 10 urban and suburban sites in the United States were recruited to participate in this study by use of a conditional random sampling method (for extensive recruitment and sampling details, see NICHD Early Child Care Research Network, 2001; NICHD Early Child Care Research Network & Duncan, 2003). Originally designed to study the developmental implications of early child care, the first and second phases of the SECCYD include longitudinal data (collected from birth through 54 months of age) on family economics and the quality of the home environment. Although the sample is not statistically representative of any population defined a priori, it is economically and geographically diverse (NICHD Early Child Care Research Network, 2001). 2.2. Measures 2.2.1. Study covariates When study children were 1 month old, their mothers reported their child's sex and ethnicity as well as their own years of education. We analyzed ethnicity using three effect-coded dummy variables that compared estimates for African-American, European-American, and Latino-American children with estimates for the grand mean (i.e., children of ethnicities other than these three groups were coded as − 1 on each of the three dummy variables). When children were 1, 6, 15, 24, 36, and 54 months old, several time-varying family indicators were assessed via maternal report, including: family size (i.e., total number of children and adults living in the household), maternal partner status (i.e., married, living with a partner, or single), maternal hours of employment, partner hours of employment, and receipt of Aid to Families with Dependent Children [AFDC], unemployment/disability insurance (e.g., Social Security Insurance), in-kind relief (e.g., food stamps, Special Supplemental Nutrition Program for Women, Infants, and Children [WIC Program]), and child support. Note that family size and the employment indicators were continuous variables, and the other time-varying indicators were dummy variables. For example, two time-varying dummy variables were created for partner status at each time point: married versus single and partnered (but not married) versus single. For continuous time-varying family indicators, there was considerable within-family variation over time (see Table 1). For example, maternal and partner hours of employment varied, on average, by more than 9 h, and families one standard deviation above the mean change experienced almost 20-hour changes in maternal and partner employment. For time-varying dummy variables, however, most families were stable over time. For example, only

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16% of families experienced a change in partner status and, only 10% of families experienced a change in AFDC receipt. 2.2.2. Family income When study children were 1, 6, 15, 24, 36, and 54 months old, mothers reported their families' annual income from all sources, including income earned from the employment of mothers and partners living in the household; income from AFDC, unemployment, or disability insurance; child support; in-kind transfers such as food stamps; and other income such as investment income.1 Total household income was divided by 10,000, so that estimated income coefficients represented the estimated change in home environment associated with a $10,000 change in income. In our statistical models, linear estimates of within-family associations between changes in family income and changes in the home environment were estimated using income levels (divided by 10,000); nonlinear estimates of within-family associations were made using the natural logarithm of income data (divided by 10,000). There was considerable within-family variation in family income across the study period (see Table 1). On average, for example, families experienced a $13,462 change in annual income, and families who were one standard deviation above the mean change experienced income changes of more than $30,000. Even for those families who averaged $10,000 or less in annual income across the study, the mean within-family change was $3658 (SD= $2729), with the greatest changes among these families exceeding $15,000. 2.2.3. Home environment Using the Home Observation for Measurement of the Environment (HOME), the quality of the home environment at 6, 15, 36, and 54 months (Caldwell & Bradley, 1984) was assessed. The infant/toddler version of HOME was administered at 6 and 15 months (45 items), and the early childhood version, at 36 and 54 months (55 items at 36 months and 57 items at 54 months). Using dichotomous yes/no items based on maternal responses to questions and interviewer observations, both versions assess a variety of household characteristics from quality of parent–child interactions (e.g., maternal responsiveness) to level of cognitive stimulating resources available and provided in the home (e.g., number of books child owns). Interviewers in the NICHD SECCYD were trained research assistants who demonstrated greater than 90% agreement with a certified HOME trainer and “gold standard” videotape. Both versions of this measure have been validated in economically diverse samples (e.g., see Caldwell & Bradley, 1984). The HOME represented an important advance in assessing children's homes as developmental contexts in that it was not designed as a comprehensive inventory of all characteristics of the home environment, but was designed to capture aspects of children's homes that are salient for their cognitive, language, and social–emotional development (for a discussion of this strength of HOME, see Ramey & Ramey, 2000). Consider, for example, that rather than indexing the total quantity of books in the home, HOME assesses the extent to which books are available to the child for developmental stimulation. As such, HOME provides an indication of family investments in the home environment that are likely to affect children's development directly. Although Caldwell and Bradley (1984) originally divided the infant/toddler version into six subscales and the early childhood version into eight subscales using exploratory factor analyses, Linver, Brooks-Gunn, and Cabrera (2004) recently argued that conceptually derived subscales are also likely useful when using HOME. Given that economic theory and developmental theory both highlight the role of family investments in developmentally stimulating physical and psychosocial resources as two of the primary means by which family economics may influence child development, we divided each version of HOME into two scales that are conceptually meaningful with respect to family investment in children's development: the physical environment and the psychosocial environment within the home. Items placed on the physical environment scale included characteristics of the home structure (interior and exterior; e.g., “Building appears safe and free from hazards.”), learning materials (e.g., “Ten or more children's books are available to the child.”), and outings or activities provided to the child (e.g., “Child has been on a trip of more than 50 1 As recommended by Cole and Currie (1994) for secondary analyses of income data, we disaggregated total income composites in the NICHD SECCYD into the specific items on which participants reported (e.g., maternal wages, paternal wages, cash benefits) so that obvious inconsistencies could be corrected. At 54 months, a small number of families reported total family incomes that were substantially less than the sum of the individual items for income from maternal employment, partner employment, and other income (e.g., the difference for two families was more than $100,000). For these families, the sum of maternal employment, partner employment, and other income items was used for analyses. The statistically significant results reported hereafter for income and its interactions with time and 6-month home resources were, however, also significant if we used the total income values for these families.

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Table 2 Descriptive statistics for HOME scales Scale

Physical environment 6 months 15 months 36 months 54 months Psychosocial environment 6 months 15 months 36 months 54 months

Number of items

α

18 18 22 24 27 27 33 33

Correlation 6 months

15 months

36 months

.57 .58 .79 .73

– .51⁎⁎⁎ .46⁎⁎⁎ .34⁎⁎⁎

– .51⁎⁎⁎ .34⁎⁎⁎

– .52⁎⁎⁎

.69 .70 .77 .72

– .49⁎⁎⁎ .35⁎⁎⁎ .35⁎⁎⁎

– .45⁎⁎⁎ .39⁎⁎⁎

– .55⁎⁎⁎

⁎⁎⁎p b .001.

miles in last year.”). Items placed on the psychosocial environment scale included indicators of parental warmth (e.g., “Mother caresses, cuddles, or kisses child during visit.”), responsiveness (e.g., “Mother usually responds verbally to child’s speech.”), learning stimulation (e.g., “Child is encouraged to learn the alphabet.”), and lack of hostility (e.g., “Mother does not scold or derogate or yell at child more than once during the visit.”). Because they were developed from a family investment perspective, one factor distinguishing items on these two scales was whether or not financial or psychosocial investments were needed (on the part of the parent/family) to make the resource available to children; when financial investments were judged to be necessary, the item was placed on the physical environment scale. Items that may have involved both financial and psychosocial investments (e.g., “Child has been taken to a museum during the past year.”) were included on the physical environment scale, primarily because financial investments would have likely been required for these items whether or not there were psychosocial investments. Table 2 provides an overview of the number of items, internal consistencies, percentages of items endorsed, and intercorrelations for these scales and the total HOME scale (i.e., a composite of all items) at each assessment time. In general, each of the three scales demonstrated good internal consistency and moderate to high intercorrelations. Although the physical environment scales from the infant/toddler version (i.e., at 6 and 15 months) had the lowest reliability scores (.57 and .58), internal consistency for these measures exceeded the minimum threshold of .50 suggested by Cohen and Cohen (1983). We were able to increase these reliability scores (to .65 for 6 months and .68 for 15 months) by removing four items that demonstrated low correlations with the scales, but using these more reliable scales in our analyses did not change the pattern of significant or null results presented hereafter. Further, as Bradley (2004) has noted, dropping items from HOME to improve scale reliability can come at the cost of failing to capture a complete range of child experiences in the home environment. Thus, all items were retained for analyses. In addition to the broad constructs represented by the physical and psychosocial environment and the total HOME scales, we also examined more narrow, domain-specific forms of family investment by dividing both the physical environment and the psychosocial environment into three domains. Physical environment items were divided into measures of home structure, learning materials, and outings and activities. Psychosocial environment items were divided into measures of warmth and lack of hostility, responsiveness, and learning stimulation. Although most of these six domain-specific scales included 10 or more items, there were two notable exceptions for which only 3 items were available at two or more time points: the home structure at 6 and 15 months and outings and activities at 6, 15, 36, and 54 months.2 At all time points and across all scale types, scores were negatively skewed such that, on average, more than 80% of items on HOME were endorsed. Although these nonnormal distributions of scores across families are of less concern for our within-family analyses than is usually the case for between-family analyses, the high percentage of endorsed items at 6 months is relevant to our interest in examining the early home environment as a moderator of the association 2 Although it is possible to divide HOME into more subscales than we have done in the present study, either empirically (e.g., Caldwell & Bradley, 1984) or conceptually (e.g., Linver et al., 2004), most of these divisions vary considerably across versions (e.g., infant/toddler HOME vs. early childhood HOME), making estimates of change less practical and more arbitrary.

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between changes in income and changes in the home environment. Specifically, in presenting the empirical results graphically, we chose to graph families that were at the mean, one standard deviation below the mean, and two standard deviations below the mean on the 6-month HOME scores. We chose not to graph estimates for families above the mean, because the mean scores represented relatively high values; in fact, less than two standard deviations above the mean represented impossible values on each of the HOME scales.3 Because the number and content of items varied over time, we transformed HOME scores using two strategies. First, we transformed raw scores into standard normal scores such that the mean score was equal to 0 with a standard deviation of 1. Second, the percentage of items endorsed relative to the total number of items on the scale was computed at each time point (e.g., 36 endorsed items at 6 months and 44 endorsed items at 36 months were both equal to a score of .80, or 80%). Given that the significant and null results for income reported hereafter were identical when using these alternative coding strategies, we report only those results using the percentage of items endorsed. Thus, in our linear specifications, estimated income coefficients represented changes in the percentage of items endorsed associated with a $10,000 change in income (e.g., a coefficient of 1.00 would indicate that a $10,000 increase in income was associated with an additional one percentage point of items endorsed). To assess the impact of income changes on the home environment in our nonlinear specifications, we had to divide the estimated income coefficients by the level of family income (e.g., a coefficient of 3.00 for a family with an income level of $20,000 would imply that a $10,000 increase in income was associated with an additional 1.50 percentage points of items endorsed). 2.3. Statistical analyses 2.3.1. Multiple imputation of missing values Of the NICHD sample, 95% (i.e., 1296 families) had sufficient nonmissing data for analysis in the present study. Yet, because recent methodological work (for a review, see Schafer & Graham, 2002) has indicated that multiple imputation is a preferred method for dealing with missing data compared with discarding participants and/or observations (e.g., listwise deletion), we conducted our inferential analyses using this preferred method. Multiple imputation replaces missing observations with values that are computed from multivariate analyses of participants' nonmissing data on other variables plus random variation. Because the imputed values vary somewhat each time they are estimated, it is recommended (e.g., Rubin, 1987; Royston, 2004) that this process be repeated until 5 to 10 complete data sets containing both the nonmissing and imputed data have been generated. Statistical analyses are conducted separately on each complete data set, and the resulting coefficients and standard errors are then combined according to “Rubin's rules” (Rubin, 1987; Schafer & Graham, 2002). For the present study, we used multiple imputation by chained equations (MICE) (Royston, 2004). Specifically, five complete data sets that combined observed and imputed values were generated. We then used the multiple imputation option in HLM 5.04 (Raudenbush, Bryk, & Congdon, 2001) to estimate multilevel models from these five complete data sets and combine estimates according to “Rubin's rules”. 2.3.2. Within-family estimates in multilevel models Within-family associations between family income and home resources were estimated in multilevel models. These within-family estimates were obtained by centering time-varying predictors such as income on each family's mean for predictors. This method has been referred to alternatively as within-person (e.g., Singer & Willett, 2003) and groupmean (e.g., Raudenbush & Bryk, 2002) centering. Consider the following level 1 model, yit ¼ β00 þ β10 ðxit − ― xi : Þ þ uit , for which the predictor, x, has been withinfamily centered. In this model, xit is the value of predictor x for family i at time t, and ― xi : is the average value of predictor x for family i across all time points. As such, β10 should be interpreted as the average within-family association between explanatory variable x and outcome y (e.g., the average within-family association between family income and the psychosocial environment), and β00 should be interpreted as the unadjusted average of outcome y for family i. The main effects of time-varying predictors may be estimated in a level 1 model such as this. Further, the 3

There were 64 families who were two standard deviations below the mean and another 116 families who were between one and two standard deviations below the mean for physical resources at 6 months. Of these 180 families, 44 had incomes of $10,000 per year or less at 6 months. There were 58 families who were two standard deviations below the mean and another 113 families who were between one and two standard deviations below the mean for psychosocial resources at 6 months. Of these 171 families, 56 had incomes of $10,000 or less at 6 months.

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effects of time-invariant predictors, as well as interactions between time-varying and time-invariant predictors, may be estimated by adding the time-invariant predictors at level 2 of the model. Importantly, multilevel models with estimates centered within families help control for unobserved characteristics of the family that are constant over time. This is an important advantage over between-family analyses of nonexperimental data, such as those often estimated using ordinary least squares regression, primarily because between-family estimates of nonexperimental data are susceptible to omitted variable bias due to unobserved characteristics of families (e.g., genetics) that are time invariant. It is important, however, to note that not all multilevel models provide within-family estimates, even when predictors are time varying. If, for example, time-varying predictors in multilevel models of longitudinal data on families have been left uncentered or have been centered on the grand mean (i.e., yit ¼ β00 þ β10 ðxit − ― x:: Þ þ uit , for which ― x:: is the average value of predictor x across all families and all time points), these predictors estimate a mix of within-family and betweenfamily effects. Of these three centering choices for time-varying predictors (i.e., uncentered, within-family centered, and grand-mean centered), only centering within families helps control for time-invariant omitted variable bias due to unobserved heterogeneity across families (Raudenbush & Bryk, 2002; Kreft & De Leeuw, 1998; Singer & Willett, 2003). 3. Results 3.1. Family income and home resources: linear and nonlinear within-family associations In multilevel models, we examined within-family associations between family income and children's physical and psychosocial home environments. Specifically, family income was specified in the first level of models as a timevarying predictor that was centered within families. For both the physical and the psychosocial home environment, two income specifications were estimated. In the first, family income was specified in levels to estimate linear associations (i.e., across the income distribution, a one-unit change in income was equivalent to a $10,000 change). In the second, the natural logarithm of family income was specified to estimate nonlinear associations (i.e., semilog functions). In the first level of each of these models, we also included four time-varying covariates that were centered within families: child age, child age squared, marital status (two dummy variables), and family size. Because these level 1 predictors were centered within families, the level 1 intercepts in our models provided estimates of the average quality of home environments across the study period. Specifically, the level 1 models took the following form: ―
―
―
yit ¼ ½π00 þ π10 Timeit − Timei þ π20 Time2it − Time2i þ π30 Marriedit − Marriedi ―
―
―
þπ40 Partneredit − Partneredi þ π50 FamSizeit − FamSizei þ π60 Incomeit − Incomei  þ ½ζ00  þ uit : In the second level of these models, we specified child sex, birth order, and ethnicity, as well as maternal age and education, as time-invariant predictors of the level 1 intercepts, child age, and child age squared. Thus, the level 2 models took the following form: π0i ¼ β00 þ β01 ðSexÞ þ β02 ðBirthOrderÞ þ β03 ðAfricanAmÞ þ β04 ðEuroAmÞ þ β05 ðLatinoAmÞ þβ06 ðMomAgeÞ þ β07 ðMomEdÞ þ r0i ; π1i ¼ β10 þ β11 ðSexÞ þ β12 ðBirthOrderÞ þ β13 ðAfricanAmÞ þ β14 ðEuroAmÞ þ β15 ðLatinoAmÞ þβ16 ðMomAgeÞ þ β17 ðMomEdÞ; π2i ¼ β20 þ β21 ðSexÞ þ β22 ðBirthOrderÞ þ β23 ðAfricanAmÞ þ β24 ðEuroAmÞ þ β25 ðLatinoAmÞ þβ26 ðMomAgeÞ þ β27 ðMomEdÞ; π3i ¼ β30 ; π4i ¼ β40 ; π5i ¼ β50 ; π6i ¼ β60 : Note that the linear and nonlinear time trends (i.e., child age and child age squared) controlled for naturally occurring changes over time in the physical and psychosocial qualities of children's homes, regardless of whether such

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Table 3 Multilevel models for physical and psychosocial home environments

Average home quality Sex Birth order African-American European-American Latino-American Maternal age Maternal education Child age Sex Birth order African-American European-American Latino-American Maternal age Maternal education Child age2 Sex Birth order African-American European-American Latino-American Maternal age Maternal education Married Partnered Family size

Family income

Physical environment

Psychosocial environment

51.90⁎⁎⁎ − .09 − 1.29⁎⁎⁎ − 6.37⁎⁎⁎ 4.20⁎⁎⁎ 1.44 .35⁎⁎⁎ 1.31⁎⁎⁎ − 1.57⁎⁎⁎ − .12 − .13⁎ .00 .08 .04 .01 .07⁎⁎⁎ .03⁎⁎⁎ .002 .002⁎⁎ .000 − .003⁎ .000 .000 − .001⁎⁎⁎ 3.61 .82 − .50

56.60⁎⁎⁎ − 1.36⁎⁎ − 1.38⁎⁎⁎ − 3.68⁎⁎⁎ 2.72⁎⁎⁎ 1.12 .29⁎⁎⁎ 1.01⁎⁎⁎ − .83⁎⁎ − .12⁎ − .09 .10 .06 .01 − .01 .05⁎⁎⁎ .02⁎ .002 .002⁎ − .001 − .002 − .001 .000 − .001⁎ 7.22⁎⁎ 2.12 −1.29⁎⁎

Linear

Semilog

Linear

Semilog

.23⁎

2.75⁎

.27⁎

3.96⁎⁎

Note. The coefficients for variables other than income were nearly identical under linear and nonlinear income specifications. Those estimated in models that included linear specifications for income are displayed. ⁎p b .05. ⁎⁎p b .01. ⁎⁎⁎p b .001.

changes were developmental in nature or simply measurement artifacts. The time-invariant predictors specified in the second level of models controlled for between-family differences in patterns of stability and change in the home environment. It is also worth noting that in addition to partner status and family size, we estimated several other timevarying covariates in alternative specifications, including maternal hours of employment, partner hours of employment, and receipt of AFDC, unemployment insurance, in-kind relief, and child support. All statistically significant main effects and interactions that we report for income hereafter remained significant when these additional covariates were included in our models, even when all covariates were estimated simultaneously. Only partner status and family size, however, proved to be statistically significant time-varying covariates in any of our models. Results from this first analytical step are summarized in Table 3. With respect to the average quality of children's home environments, both child birth order and African-American ethnicity were significant predictors such that, other things being equal, later-born children and African-American children had a lower percentage of developmentally stimulating resources in their physical and psychosocial home environments than other children. European-American ethnicity, maternal age, and maternal education were positively associated with the quality of children's physical and psychosocial home contexts such that, on average, European-American children, as well as children with relatively older and more educated mothers, had a higher percentage of developmentally stimulating resources in their homes compared with other children. In addition, child sex was significant for the psychosocial outcome; other things being equal, girls were exposed to higher-quality psychosocial environments in their homes compared with boys. In addition to these between-family differences with respect to average home environment, child birth order and maternal education significantly predicted rate of change in both the physical and psychosocial characteristics of

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Fig. 1. Semilog estimates of change in the physical and psychosocial home environment given a $10,000 change in family income as a function of families’ levels of annual income. Values along the x axis correspond to family income level; values along the y axis correspond to the estimated percentage point change in the home environment scores associated with a $10,000 change in family income.

homes, such that between-family differences in the quality of the home environment that were associated with child birth order and maternal education were larger in early childhood than in infancy. Further, the quality of psychosocial home environments declined linearly at a greater rate for boys than for girls, such that differences in the home psychosocial environment associated with sex were greater in early childhood than infancy. For the physical home environment, the estimated effects of marriage status, partner status, and family size were not statistically distinguishable from zero. For the psychosocial home environment, on the other hand, both marriage status and family size were statistically significant time-varying covariates. Specifically, a within-family change for mothers from single to married was associated with an increase of 7.22 percentage points in the psychosocial home environment, and a one-person increase in family size was associated with a decrease of 1.29 percentage points on this scale. Above and beyond the between-family differences and within-family changes in developmentally stimulating resources that were implied by model covariates, changes in family income significantly and positively predicted changes in both the physical and psychosocial home environments. Yet, the linear estimates of the effects of income were small. Indeed, a $10,000 increase in family income was associated with an increase of just .23 percentage points for the physical environment and just .27 percentage points for the psychosocial environment. These predicted changes amounted to less than 5% of the between-family standard deviation (i.e., 5.12 percentage points) for the average quality of the physical home environment and approximately 5% of the between-family standard deviation (i.e., 5.30 percentage points) for the average quality of the psychosocial home environment. As expected, however, for families at the low end of the income distribution, the semilog estimates of associations between income and the home environment were substantially larger compared with the linear estimates, particularly for the psychosocial environment. The semilog estimated impacts of $10,000 changes in family income are illustrated in Fig. 1 as a function of family income level.4 Based on these estimates, a $10,000 increase in family income for families at the low end of the income distribution (i.e., an annual income of $10,000) predicted an increase of 2.75 percentage points for the physical environment and an increase of 3.96 percentage points for the psychosocial environment, or approximately 54% and 75% of the between-family standard deviations for these two outcomes. For families with more income, however, the semilog estimates for changes in income were considerably smaller; in fact, for families with $50,000 annual income, the semilog estimates were similar in size to the linear estimates (i.e., a predicted change in the home environment of less than 1 percentage point).

4

In the semilog model, the incremental effect of additional income on the home environment is calculated by dividing the point estimate for the log of income by the income level. Thus, the incremental effect of income change varies by income level.

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Table 4 Moderating effects of early home environments

Family income (semilog) 6-month home environment

Physical environment

Psychosocial environment

18.56⁎ − .22+

26.91⁎⁎ − .29⁎

Note. Although all level 1 and level 2 covariates displayed in Table 3 were also included in these two models, only the coefficients relevant to interpreting cross-level interactions are displayed for brevity. + p b .10. ⁎p b .05. ⁎⁎p b .01.

3.2. The moderating effect of the early home environment To determine whether within-family associations between changes in income and changes in the home varied by the quality of children's earliest home environments, scores from the 6-month physical and psychosocial scales were added to our multilevel models as level 2 predictors of the semilog income parameters. Specifically, 6-month physical environment scores were included as a predictor of the within-family association between income and the physical context of the home; 6-month psychosocial environment scores were included as a predictor of the within-family association between income and the psychosocial context of the home. To control for potential variations in the estimated effects of marriage status, partner status, or family size due to the quality of the early home environment, we also included the 6-month home indicators as level 2 predictors of these level 1 parameters. The effects of all level 1 (e.g., child age) and level 2 (e.g., birth order) covariates previously specified were also estimated. All income results are summarized in Table 4. As expected, associations between family income and the psychosocial home environment did vary by the level of psychosocial investments that families made during early infancy. Specifically, family income changes predicted the largest psychosocial context changes in homes that were of the lowest quality when study children were 6 months old. In Fig. 2, semilog functions for the estimated impact of a $10,000 change in family income are displayed for families that were one or two standard deviations below the mean with respect to 6-month psychosocial environment scores. Comparing the semilog functions illustrated in Figs. 1 and 2 for psychosocial environments (note that the semilog function in Fig. 2 corresponds to the estimated impact of a $10,000 change in income for families with an average level of resources at 6 months) indicates that the estimated effect of income on the psychosocial quality of the home was 150% larger, at any given level of annual income, for families that were one standard deviation below the mean compared with families at the mean.

Fig. 2. Semilog estimates of change in the psychosocial home environment given a $10,000 change in family income for families either one or two standard deviations below the mean psychosocial home environment at 6 months. Values along the x axis correspond to family income level; values along the y axis correspond to the estimated percentage point change in the home environment scores associated with a $10,000 change in family income.

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Table 5 Changes in income and changes in domain-specific investments: main effects and moderating effects Investments in the physical environment Home structure

Learning materials

Outings and activities

Main effect model Interaction model Main effect model Interaction model Main effect model Interaction model Family income (semilog) − .16 6-month home environment

29.54⁎⁎⁎ − .42⁎⁎

4.32⁎⁎

29.39⁎⁎⁎ − .35⁎⁎⁎

2.39

33.00⁎ − .37⁎

Investments in the psychosocial environment Warmth and lack of hostility

Responsiveness

Cognitive stimulation

Main effect model Interaction model Main effect model Interaction model Main effect model Interaction model Family income (semilog) 7.45⁎⁎ 6-month home environment

31.35 − .27

4.81⁎⁎

28.65⁎⁎⁎ − .34⁎⁎

.22

27.29⁎⁎⁎ − .35⁎⁎⁎

Note. Although all Level 1 and Level 2 covariates displayed in Table 3 were also included in these two models, only the coefficients relevant to interpreting cross-level interactions are displayed for brevity. ⁎p b .05. ⁎⁎p b .01. ⁎⁎⁎p b .001.

The estimated impact of a $10,000 increase in income, however, was largest for families that had low-quality home environments during early infancy and low annual incomes. Consider, for example, that a $10,000 increase in family income predicted an increase of 6.04 percentage points for the psychosocial environment if families had developmentally stimulating resource levels that were one standard deviation below the mean at 6 months and annual incomes of $10,000. Comparatively, for families with an average level of developmentally stimulating resources in the home at 6 months and an annual income of $50,000, a $10,000 increase in family income predicted an increase of .79 percentage point in the psychosocial environments of children's homes. 3.3. Changes in income and changes in domain-specific investments As a final step in our analytical plan, we estimated within-family associations between changes in family income (semilog specification) and changes in the six domain-specific investment scales from HOME. As part of this final step, we estimated the potential moderating effect of the quality of the early home environment in each of the six domains, in the same manner that we estimated such moderating effects for the domain-general scales. For these analyses, we also estimated the same set of level 1 and level 2 covariates that were estimated in our other models. These results are summarized in Table 5. The main effect of changes in income was statistically significant for three of the six domain-specific investment scales: learning materials, warmth and lack of hostility, and responsiveness. Similar to the estimates for the domaingeneral physical and psychosocial investments of families, the estimated effects of changes in income were greatest for families at the low end of the income distribution. For families at the lowest end of the income distribution, in fact, a $10,000 gain in family income predicted increases of: 4.32 percentage points (110% of the between-family standard deviation) for learning materials, 7.45 percentage points (124% of the between-family standard deviation) for warmth, and 4.81 percentage points (80% of the between-family standard deviation) for responsiveness. On the other hand, for families with $50,000 in annual income, a $10,000 gain in income predicted increases in these three domains that were much smaller, ranging from .86 percentage point (22% of the between-family standard deviation) for learning materials to 1.49 points (25% of the between-family standard deviation) for warmth and lack of hostility. In addition, the interaction of income with the quality of the home environment in early infancy was statistically significant for five of the six domain-specific scales: home structure, learning materials, outings and activities, responsiveness, and cognitive stimulation. In other words, in these five investment domains, the estimated impact of a $10,000 increase in income was greatest for families that had low annual incomes and exceptionally deprived home environments during early infancy. Consider, for example, the estimated effects of income gains for learning material investments in the physical environment and responsiveness investments in the psychosocial environment, the two areas for which effect sizes were largest. A $10,000 gain in family income predicted a 9.25-percentage-point increase (235% of the between-family standard deviation) in learning materials and a 9.46-percentage-point increase (157% of the between-family standard deviation) in responsiveness for families with $10,000 in annual income and home

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environments that were one standard deviation below the mean at 6 months. For these families, the smallest estimated effect of a $10,000 gain was in the cognitive stimulation domain, for which the predicted increase was 4.43 percentage points (67% of the between-family standard deviation). 4. Discussion In the present study, we examined whether family investments in their children's home environments were responsive to economic changes. In so doing, we estimated within-family associations between changes in income and changes in the quality of children's home environments between infancy and early childhood. For both physical and psychosocial qualities of children's homes, changes in family income were significantly and positively associated with changes in the home environment such that gains in income predicted improvements in the quality of the home environment. Income gains were associated with the greatest improvements for families who were both at the low end of the income distribution and living with relatively few developmentally stimulating resources in the home when children were infants. 4.1. Within-family associations and between-family differences On average, gains in family income were positively associated with improvements in the quality of children's physical and psychosocial home environments, although the size of this association was small when we used linear estimates that constrained associations to be equal across families of varying income levels. In nonlinear specifications, however, it was evident that income gains were associated with relatively large improvements in the quality of the home environment for families at the low end of the income distribution. Indeed, for families at the lowest end of the income distribution (i.e., annual income of $10,000 or less), the predicted changes in the home environment associated with a $10,000 change in family income were approximately 54 and 75% as large as the between-family standard deviations for the physical and psychosocial environments, respectively. This pattern of nonlinearity in income effects was also evident when we examined domain-specific areas of the physical and psychosocial qualities of children's home environments. Specifically, significant nonlinear associations between income changes and home environment changes were evident for the level of learning materials in the physical environment and levels of both warmth and responsiveness in the psychosocial environment, with the largest estimated effect sizes for warmth. Overall, these results are consistent with nonlinear income effects that have been demonstrated in past research (e.g., Taylor et al., 2004; Votruba-Drzal, 2003), but to our knowledge, this is some of the first evidence that both physical and psychosocial investments in children's earliest home environments are responsive to income changes, particularly when families have little income to begin with. In addition, in our analyses, having little income was not the only indicator that families were likely to be exceptionally responsive to income changes. Having relatively low-quality home environments during early infancy also moderated the estimated effects of income on family investments, such that income gains predicted the largest improvements in the physical and psychosocial home environments for families with the lowest-quality home environments. Families that had low income and low-quality early home environments experienced the greatest improvements in the home following income gains. Although this interaction pattern was statistically significant only for the psychosocial home environment when domain-general scales were examined, this pattern was evident for all three domain-specific areas of children's physical home environments (i.e., home structure, learning materials, and outings and activities) and two of three domain-specific areas of children's psychosocial home environments (i.e., responsiveness and cognitive stimulation). Once income level and the quality of early home environments were considered, our largest estimated income effects were for families’ investments in learning materials. For families who were at the low end of the income distribution and had few learning materials in the home at 6 months, a $10,000 gain in income predicted increases in learning materials that were equivalent to 235% of the between-family standard deviation in this area. This exceptional sensitivity of home learning materials to family economics may be one reason that children's cognitive and language development appears especially sensitive to family economics (Dearing, Berry, & Zaslow, 2006; Duncan & BrooksGunn, 1997; McLoyd, 1998). Regardless, we think there are at least three reasons that the home environments of families who were both low income and had few resources appeared most malleable in response to changes in family income.

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First, the nonlinearity of associations between income and the home environment may be due, at least in part, to the size of income gains and losses relative to family income level. A $10,000 gain for a family previously earning $10,000, for example, would be a 100% increase in financial standing compared with a 20% increase in financial standing for a family previously earning $50,000. Second, compared with families who had at least average-quality home environments following childbirth, families who began with few resources were capable of more dramatic investment increases in response to income increases. In other words, for homes with higher levels of physical and psychosocial resources available to children, the potential for additional investments may have been limited by ceiling effects. Third, resource levels in the early home environment may have served as indicators of family risk and resiliency. Consider, for example, families who, despite low levels of income, had relatively high-quality home environments at 6 months. This relatively high marginal propensity to invest in their children within the context of poverty may have been evidence of either economic resources that were not captured by the income measures (e.g., wealth accumulated prior to the birth of the study child) or noneconomic resources such as resilient parents. Importantly, any economic or noneconomic resources leading to a developmentally stimulating early home environment despite low income may have also protected the family against losing physical or psychosocial resources if income declined. Savings, for example, could offset the potential investment effects of income loss. Consider, on the other hand, families who had low incomes and few physical or psychosocial resources in the home. These families' abilities to invest in their children appeared susceptible to low income, perhaps because they did not have access to compensatory mechanisms such as savings and resilient parenting strategies. In addition, for both lowincome and high-income families with few developmentally stimulating resources in the home, these low resource levels may have indicated economic constraints (e.g., debt) or noneconomic constraints (e.g., parent mental health problems) resulting in a relatively low marginal propensity to invest in physical and psychosocial resources for their children. If so, income losses could compound such problems, particularly for low-income families for whom losses would have the largest relative effects. It is reasonable to suspect, for example, that income losses were exceptionally likely to result in declines in psychosocial resources when parents had preexisting mental health problems (e.g., for a discussion of how negative life events can compound parent depressive problems and thereby negatively affect parenting, see Lyons-Ruth, Wolfe, & Lyubchik, 2000). 4.2. Strengths and limitations of the present study Our within-family estimates of associations between changes in family income and changes in the home environment have important advantages compared with between-family estimates from nonexperimental data, not the least of which are the ability to rule out time-invariant omitted variable bias and the ability to examine whether changes in income matter for families. It is, however, important to note that regardless of whether they are based on betweenfamily analyses of cross-sectional data or within-family analyses of longitudinal data, estimates of the association between family income and the home environment using nonexperimental data are susceptible to bias caused by reciprocal causation (i.e., simultaneity) and unobserved characteristics of families that are not static, but can vary over time. In addition, problems associated with measurement error can be compounded in studies of within-family change because these estimates rely on difference scores, although such compounded measurement error problems are less likely to occur when there is interfamily variation in the rate of change in explanatory variables such as family income (Hsaio, 2003; Rogosa, 1995). Regardless, the scientific and policy relevance of being able to rule out potential omitted variable bias due to fixed characteristics of families makes our within-family study a valuable addition to existing between-family studies linking income and the quality of the home environment. Regarding the impact of income on physical resources in the home, one should consider that we were not able to assess expenditures per se. When a family gained income, for example, our analyses did not address directly how much of that increased income was spent on child-specific resources. In fact, in our analyses, the estimated effects of income gains may have been limited for some families because of ceiling effects on the HOME. Modeling within-family associations between changes in family income and changes in spending on developmental resources could clarify further the extent to which family investments in children are responsive to economic gains and losses. Indeed, we expect the estimated effects of expenditure changes to be larger than those for income changes because of this added specificity regarding inputs into the home environment and differences across families in the propensity to allocate economic resources to children (for a further discussion of this issue, see Dearing et al., 2006).

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Specific to the NICHD SECCYD, it is also important to note that the sample is not representative of a population defined a priori, and participating families are predominantly European-American. Relatively stringent exclusionary criteria were used when initially selecting the sample. Nonnative English-speaking families and families living in exceptionally dangerous neighborhoods, for example, were not eligible to participate. Each of these factors may limit the extent to which our results may be generalized to ethnic minority, immigrant, and extremely deprived populations. Nonetheless, the NICHD SECCYD sample is economically and geographically diverse. Further, it is one of few prospective longitudinal studies to include multiple observations of the home environment beginning in infancy. 5. Summary and conclusions The present study contributes to the existing knowledge base on family investments in children's development in at least three ways. First, we extended previous evidence that income gains are associated with increased cognitive stimulation in the home by demonstrating that income gains are associated, more generally, with increased developmentally stimulating material resources that require monetary expenditures by families, as well as increased developmentally stimulating psychosocial resources that do not necessarily require monetary expenditures. As such, our results indicated that both physical and psychosocial dimensions of the home appear to be malleable developmental contexts in response to economic changes, a particularly important result considering the variety of cognitive, health, language, and social–emotional outcomes that these home environment features have been associated with in previous research. Second, we provided evidence that economic changes begin to affect the home environment during infancy and early childhood. Given that children's long-term achievement may be uniquely responsive to family economic context during early childhood (e.g., Duncan et al., 1998), the responsiveness of the home to changes in income during this time may have exceptionally salient implications for children's development. Families, however, may vary in how responsive their homes are to income changes during this time. Indeed, the third important contribution of the present study was evidence that income effects varied not only by level of family income, a result that other researchers have also demonstrated (e.g., Taylor et al., 2004; Votruba-Drzal, 2003), but also by the initial status of the home environment shortly after children were born. Large gains and losses in developmentally stimulating resources following income gains and losses occurred primarily for families that began with few physical and psychosocial resources in the home during infancy, particularly if these families also had low levels of income. In fact, for families that managed to provide a relatively high level of developmentally stimulating resources, income gains and losses had little or no effect on the home environment, even when income was low. Identifying families that are exceptionally responsive to economic changes could help target intervention efforts to those in greatest need. In this respect, early home environments may be a useful criterion. In addition, future research into the mechanisms that lead families to display a relatively high marginal propensity to invest in children despite low income could help identify both economic and noneconomic compensatory strategies for low-income families, thereby helping to alleviate economic risk transmitted to children via the home environment. Acknowledgements The authors thank Sarah Friedman, Katherine Magnuson, Kathleen McCartney, and two anonymous reviewers for their comments on previous drafts of this article. References Bane, M. J., & Ellwood, D. (1986). Slipping into and out of poverty: The dynamics of spells. Journal of Human Resources, 21, 1−23. Becker, G. S. (1993). Human capital: A theoretical and empirical analysis, with special reference to education (3rd ed.). Chicago: University of Chicago Press. Becker, G. S., & Tomes, N. (1979). An equilibrium theory of the distribution of income and intergenerational mobility. Political Economy, 87, 1153−1189. Blau, D. M. (1999). The effect of income on child development. Review of Economics and Statistics, 81, 261−276. Bradley, R. H. (2004). Chaos, culture, and covariance structures: A dynamic systems view of children's experiences at home. Parenting: Science and Practice, 4, 243−257. Bradley, R. H., Corwyn, R. F., McAdoo, H. P., & Coll, C. G. (2001). The home environments of children in the United States: Part I. Variations by age, ethnicity, and poverty status. Child Development, 72, 1844−1886.

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