sign in sign up
<<
Home , Grandparent

Composition and Outcomes: A Longitudinal Study using Child and Fixed Effects in Peru

Sarah Reynolds, Jere Behrman, Lia Fernald

April 6, 2018 Do not cite

ABSTRACT At least 41% of children in the Peru Young Lives Cohort have lived with at some point during their childhood up until age 12 years. Almost 30% no longer had their biological in the household by the time they reached age 12. We use a longitudinal survey of around 2,000 children and around 800 of their younger in Peru to examine associations between household composition and indicators of children’s development. Using child and family fixed effects, we examine if children in single- , mother- families, extended families, step-families, or no-mother families differ in height, vocabulary scores, or math scores from children in nuclear families. We find few differences in child development across family structure for the population as a whole, or when we stratify by child sex. Applying child fixed effects, we find that among rural children, when children are single mother , they are a quarter of a standard deviation lower in height (p<0.05) and perform 0.4 standard deviations worse in math (p<0.1) than when in nuclear families, in spite of having a higher wealth index. When cohabitating with a single mother and grandmother, children perform 0.77 standard deviations worse in math (significant) than when living in a . Children in extended families with grandparents and both , however, perform similarly as nuclear families across all outcomes. Differences in child development outcomes do not emerge by family structure for urban children. These results suggest policy may wish to target children of single and single mothers cohabitating with grandparents in rural settings. INTRODUCTION Household composition can be important for child development as different family members may make different economic resources or emotional support available to the child. and grandparents are examples of adults in the household who may perform different functions for child development. In America, the portion of intergenerational households is on the rise (Ruggles and Heggeness 2008), as it is in the US (R. E. Dunifon, Ziol-Guest, and Kopko 2014). Furthermore, children frequently live without their biological father in the household. Although much research has already been done on how father presence influences children, but less so in developing countries. Furthermore, the high rate of intergenerational residence between children and grandparents, with over 40% of children having cohabited with a grandparent by age 12 make Peru an ideal country to study how non-nuclear family structures are associated with child development outcomes. Much of the research on non-nuclear family structures has focused on absent fathers. Fathers may provide economic resources for the family that may improve nutritional and cognitive outcomes. There is consensus that parental union dissolution resulting in father absence is associated with economic disadvantage for children (Amato, 2010; McLanahan, Tach, & Schneider, 2013; Page & Stevens, 2004; Thomson, Hanson, & McLanahan, 1994). In the US, father absence is negatively associated with children’s academic achievement (Amato and Gilbreth 1999; McLanahan, Tach, and Schneider 2013). In the international context, where father separation has been most studied with relation to migration, there are mixed findings of associations between parental migration and children’s educational, health, and labor outcomes (Antman 2013). After a parental separation, a step- may enter the household. Ganong and Coleman’s review of the literature reveals that most children in step-families fare worse on a variety of measures (academic achievement, behavior, and interpersonal relationships) than children in two-parent households, however, the effect sizes found are generally quite small (2017). Research on extended families has found that grandparents can also be important for child development and can take on some of the roles that are typically associated with fathers. For example, grandparents can also be providers; they can reduce economic hardship for the grandchildren in single-mother headed families (Mutchler and Baker 2009). Several studies find the presence of grandparents is associated with better child outcomes in single-mother households; most studies do not find associations between grandparent presence and child outcomes when both parents are in the household (Aquilino, 1996; Deleire & Kalil, 2002; Dunifon & Kowaleski-Jones, 2007; Monserud & Elder, 2011). In a study examining heterogeneity of effects of grandparents by household wealth, there were significant benefits of grandparent presence for children born to single mothers in richer households, but no effects for poorer children born to single mothers (Augustine and Raley 2013). There is emerging evidence that associations between grandparent coresidence and child outcomes vary by culture. For example, studies on early childhood find that grandparent coresidence is associated with higher child development scores for and Black children, but not for White children in the US (Pilkauskas 2014; Mollborn, Fomby, and Dennis 2010). In South Africa and Brazil, grandparent pensions benefit coresident grandchildren; in South Africa, but not boys living with their grandmothers had improved height for age and weight for height (Duflo 2003). In Brazil, girls living with grandfathers had better literacy outcomes than Brazilian boys or children living with grandmothers (Ponczek, 2011). Using Taiwanese panel data, Pong and Chen (2010) find long-term coresidence with grandparents is associated with higher cognitive test scores in young adolescents; a recent transition into coresidence confers no such advantage. In contrast, data from rural China suggest that living with grandparents of low levels of schooling compared to the population does not affect children’s educational attainment, but living with relatively well-schooled grandparents is significantly associated with a lower likelihood of school dropout (Zeng and Xie 2014). Recent research on Peru finds that single-mother headed households are on the rise in Peru and multi-family households (including intergenerational households) are less likely to be poor in Peru, compared to single-family households (Cuesta, Rios-Salas, and Meyer 2017). Around 20% of females in Peru have their first child by age 19, but 20% of these are not living with a partner, but likely their own parents (Favara, Lavado, and Sanchez 2016). Although there is strong evidence that household composition is associated with child development, a number of gaps remain in the literature. Most research focuses on associations between one type of family structure (fathers or extended families) and child well-being. A notable exception is Reynolds, Fernald, and Behrman (Reynolds et al. under review), who find that, in Chile, grandparent coresidence supports young children’s vocabulary while father coresidence provides income support. Our study compares children in single-mother families, mother-grandparent families, extended families, step-families to nuclear families. We use within-child comparisons on over 1,400 children from Peru and their younger siblings. Compared to the analysis from Chile, this survey covers a longer time span of the children’s lives and uses more rounds of data, which allows for determining if the timing of family structure in the life of the child matters. Additionally, the ability to apply family fixed effects by using the data allows us to further control for unobserved traits. Results can inform how to target social policy to support child development in the family context.

DATA Data is from the Young Lives Study from Peru, which has been described in detail elsewhere.(Barnett et al. 2013) The present analysis uses data from the younger cohort, which had a mean age of 11.7 months in 2002. Follow-up data were collected in 2006 when children had a mean age of 5.3 years, in 2009 when children had a mean age of 7.9 years, and in 2013 when children had a mean age of 12.0 years. In round 3 and round 4, additional metrics were also collected on the study children’s next younger sibling. The Central University Research Ethics Committee of the University of Oxford and the Instituto de Investigación Nutricional in Peru reviewed Young Lives survey protocol. Consent was obtained collectively from communities and individually from and, when they were old enough to provide it, children. Participants were selected through a multi-stage sampling process beginning with 20 sentinel sites that were purposively selected to reflect the Young Lives study’s aims of examining the causes and consequences of childhood poverty and diversity of childhood experiences. Within the sentinel sites, approximately 100 children within the eligible age category were randomly sampled for participation (Brock 2011). Less than 2% of selected households refused to participate. There was only one study child per household. Additional details regarding country-specific sampling protocols and strategies can be found in country reports accessible at www.younglives.org.uk. Sample Size Young Lives surveyed 2,052 children in Round 1 with 1,813 children (88%) present in all rounds. Height data on the next younger sibling was taken in rounds 3 and 4. The younger sibling was also tested on vocabulary if 5 years of age or older. TVIP scores in Quechua (N=228 in round 2, N=276 in round 3, and N=64 in round 4) were excluded from the analysis due to lack of comparability. Table 1 presents sample sizes for each round and outcome. Outcome variables Height-for-age Z scores (HAZ) from all survey rounds were computed using the WHO Growth Standards (World Health Organization 2006) for children <60 months and the WHO Growth References(de Onis et al. 2007) for children >60 months. Because HAZ in round 1 is inversely correlated with age, all round 1 HAZ measurements were adjusted to their predicted value at age 12 months by calculating the difference between each child’s HAZ and the mean HAZ for children within 1 month of the child’s age in the same country. This value was then added to the mean HAZ for children aged 11-13 months. This adjustment is preferable to adding age as a covariate in the model because the adjustment does not assume a linear relationship between HAZ and age. This technique has been employed in previous analyses(Lundeen et al. 2014) (Crookston et al. 2013) (Andersen et al. 2015). Observations with HAZ beyond 6 standard deviations were coded as missing (N=30), unless they were predicted values, in which case they were replaced with 6 or -6 (N=1). Children were tested with a Mathematics Achievement Test and the Spanish Version of the Peabody Picture Vocabulary Test (TVIP) (Dunn et al. 1986) at ages 5, 8, and 12 years. Round-specific details about the tests, including selection of questions and implementation, can be found elsewhere [31,32]. To compare results over time, we age-normalized the raw scores. The means and standard deviations used to calculate the age and language standardized TVIP scores were generated applying a methodology similar to that used by Rubio-Codina et al., but with a lowess smoothing function instead of a polynomial. The non-parametric lowess smoothing function places more weight on observations closer to each age to estimate expected TVIP (Rubio-Codina et al. 2015). Based on visual inspection, we selected a bandwidth of 10% of the data to maintain functional flexibility while removing noise. For the age-conditional standard deviation, we squared the residuals of the lowess function, and again used the lowess smoothing method to generate age-conditional standard deviations used in standardize the TVIP scores. These smoothing functions are illustrated in Figure A1. Standardized values beyond 4 and -4 were replaced with 4 and -4 (N=5). The math scores were only standardized for the ages when the focus child was tested since this data was not collected on younger siblings. Since these data were denser, we did not use lowess, but rather linear fit with each round of data, which was confirmed best when testing multivariable fractional polynomial options with the Stata command mfp. For the age- conditional standard deviation, we squared the residuals of the expected value of the linear fit and applied a lowess smoothing function. These smoothing functions are illustrated in Figure A2. Standardized values beyond 4 and -4 were replaced with 4 and -4 (N=4). Although, not a child development measure, we also include a wealth index as an outcome variable as a measure of economic and environmental resources for the child. The wealth index included measures of housing quality, consumer durables, and services such as electricity, water and sanitation; these sub-indices are weighted equally in the composite index. Information on the exact variables included in the wealth index and their weights are available elsewhere (Escobal et al. 2003). The wealth index is included as a control variable for the child development outcomes. Household Composition Variables We created indicator variables for the type of family structure indicated by the presence of different family members on the household roster in each survey round. Nuclear families had a biological mother and biological father present. Extended with both parents included a mother, father, and at least one grandparent. Mother + grandparent families were households in which the child’s biological father was not present in the household but at least one grandparent was. Step families included a step-father of the child; due to the small number of observations (N=26), step-families with grandparents were grouped into this category. Children with a biological mother but without a biological father, grandparent, or were considered to be in single mother families. Families of children who did not have a biological mother in the household were coded no mother. In all cases, families could include other non-sibling adults other than those indicated, with the exception of nuclear families. Those we categorize as other. Although the relation questions on the household roster referenced the focus child, the household structure of half-siblings could be different if the younger sibling’s biological father is the focus child’s step-father. In this case, the younger sibling would be in a nuclear family while the focus child would be in a step family. In most cases, father’s absence from the household was permanent. For example, at age 12, only ll% of fathers and 13% of mothers who had left the household were temporarily absent. We did not count these members as present in the household since this information was not available at other rounds of data collection. Initially indicator variables for maternal and paternal grandfathers and grandmothers were generated based on the household roster. Tests of differential coefficients on maternal vs paternal grandparents and grandmothers vs grandfathers, as well as all four separately, concluded they were not statistically distinct in the regressions so we only present results for the variable “any grandparent present.” A description of these tests is in the Appendix. Control Variables Because we used child fixed effects for the longitudinal analysis, only time-variant control variables were needed. We included an indicator variable to indicate if the child had moved location between since the previous round. This variable is assigned the value 0 at age 1y. We include age in months as well as its square and cubic as controls.1 To account for other household members, we include a set of variables indicating the number of people of that category (children less than 6 y, boys and girls 7-15 y, men and women 16-55y, and men and women 56+ y). These do not include fathers, mothers, stepfathers, or grandparents and also do not include the child. Stratification Variables We perform the analyses for different subsets of the population to see if these transitions influence more vulnerable children differently. We compare girls and boys, urban and rural, children with mothers whose first language was indigenous and children of mothers whose first language was Spanish, and children of mothers less than 26 years of age and children of mothers age 26 years and older.

SAMPLE DESCRIPTION Change in grandparent and maternal grandparent presence is the household transition most experienced by these Peruvian children, emphasizing the importance of studying the impact

1Although outcomes are standardized, zhfa uses an external norm. Thus zhfa may change with age if the cohort is improving or decreasing nutritionally over time. Similarly, because we normalized TVIP continuously over age instead of separate normalization by rounds as we did for math we also find some age-dependence when we control for the round of data collection. This could be due to differences in knowledge of the focus children and younger siblings.

of grandparents on child development. 32% of focus children experienced some change in grandparent presence in the household over the four survey rounds (Table A1) and 21% experienced a change in father presence in the household over the four survey rounds; these numbers suggest sufficient variation in child experience to apply child fixed effects to the longitudinal data. The grandparent most likely to be in the household was the maternal grandmother. Rates of biological father presence for focus children changed from 83.9% (1y) to 71.9% (12y). As expected, biological mothers were present in the children’s households at higher frequency than biological fathers, although there was also a decline in presence as the children aged. The rates of transitions are relatively high, however, with 9.7% and 21% of focus children having a change in the father’s and mother’s presence respectively in the household over the four survey rounds. These relatively high rates of transitions indicate that there are instances of these household members entering the household; movement is not uni-directional. Patterns for younger siblings is similar, but of lower magnitude since there are fewer years during which they are surveyed (Table A1). Although around half of Young Lives children have a stable family structure throughout childhood, there are many and diverse transitions (Table 2 and Figure 1). The fraction of children living in step families, single mother families & families in which the child’s biological mother is not present increase as children age. Grandparents mostly “separate” from grandchildrens’ households but also “enter,” with the most transitions occurring between ages 1 & 5. The portions of focus children experiencing any change between categories is 0.27 between ages 1 & 5, 0.17 between ages 5 & 8, and 0.21 between ages 8 & 12. These numbers are roughly proportional with the number of years between rounds. However since the grandparent changes are more often earlier in life, the other changes must be more frequent later. Summary statistics by sample type (Table 3) indicate that children in nuclear families with grandparents present are taller and have more economic advantage than children in nuclear families both at age 1 and age 12. In addition, at age 12, the cognitive scores of children in extended families with both parents are higher than in other types of families. Children in households with single mothers or mother+grandparent households have similar access to economic resources as nuclear families.

LONGITUDINAL ANALYSIS & RESULTS We do a longitudinal analysis to see if these descriptive associations hold when children live in different household structures. We use child fixed effects (FEi) to estimate

YT k,i= α FT i + γXT i + ΣiτiFEi + T + uT i (1)

YT k,i represents one of three outcomes k for child i from round T: height for age z-score, age-

normalized vocabulary score, or age-normalized math score. FT i is a vector of dummy variables indicating family structure of child i’s household in round T, with the omitted case being the nuclear family. XT i is a vector time varying controls. ΣiτiFEi is child fixed effects. T is a set of indicator variables for survey round. Though the age-normalized outcome variables are centered at 0, the normalization used all the observations available, while the data analysis includes only children with all outcomes at all time periods. These survey round indicator variables adjust for this difference in population. We cluster errors at the household level; the error term is u. In an additional specification, to address possibilities of differential influences of family type at different ages, we interact the family structure indicator variable with child age in months. We find few differences in child development across family structure for the population as a whole (Table 4). Children in step families and single mother families are around 10% of a standard deviation shorter than when they are in nuclear families, however, the statistical significance is only marginal (p<0.1). Of a larger magnitude, children in mother and grandparent families perform around a quarter of a standard deviation worse in math compared to children in nuclear families, but again, the statistical significance is marginal. We find no differences by family type in vocabulary or wealth. Interactions with age did not prove to be significant, either (Regressions not shown). Surprisingly, the wealth and moving covariates are not correlated with child development outcomes. Of the additional household structure control variables, the number of girls—but not boys— ages 7-15 in the household is associated with lower height. Men and women ages 16-55 are associated with greater wealth while men age 56 and older are associated with less wealth. Stratification by child sex does not yield statistically significant differences in child development scores by family type with the exception that math scores are a quarter of a standard deviation lower for girls when living in a single mother home, but this result is only marginally significant. (Regressions not shown.) Family types are fairly proportional for children who lived in urban and rural populations at age 1 year with the caveat that there are slightly more nuclear households in rural areas. Among children who live in urban households, we found no differences in child development scores by family type. (Regressions not shown.) Among rural children, however, when children are single mother households, they are a quarter of a standard deviation lower in height (p<0.05) and perform 0.4 standard deviations worse in math (p<0.1) than when in nuclear families, in spite of having a higher wealth index (Table 5). When cohabitating with a single mother and grandmother, children perform 0.77 standard deviations worse in math (significant) than when living in a nuclear family. Children in extended families with grandparents and both parents, however, perform similarly as nuclear families across all outcomes (Table not shown). These results are similar for children whose mothers’ first language was indigenous, though not quite as strong, since many rural dwellers have this characteristic (62%) and only 19% of urban children had mothers whose first language was indigenous and not Spanish.

CONCLUSION We find households structure to be correlated with child development in rural Peru, but not in urban Peru. Children of single mothers may be at risk of low nutrition and poorer math development, even though they live in households with a higher wealth index. Children of single mothers living with grandparents in rural areas may also be at risk of low math development. There are several reasons as to why we find these differences in rural Peru and not in urban Peru. Rural settings are more based in agriculture, where women can have a lower comparative advantage resulting in fewer economic resources. In spite of higher wealth index and income, single mothers may not be able to grow or raise as many nutritious foods. Schools may be farther and thus more reliance on education at home may be required, resulting in a lower math score. Urban settings may allow for neighbors and family members outside the household to care for young children or elderly family members; that support network may not be in place for single mothers, especially single mothers with grandparents, in rural areas. There are several limitations to our study. Because there are so many distinct family types, we may have low representativeness of some of the family types to be able to richly characterize them. Although our longitudinal study reduces concern about selection bias through using child fixed effects, we still cannot fully account for all time-varying unobserved factors. Due to a lack of international or national standards for the PPVT and math tests, the child development outcome variables are not normalized to an absolute scale, and instead are relative to their peers in each survey year: results should be interpreted as relative changes rather than absolute changes. Finally, we do not have information on the exact timing of the change in household membership. Our study has a number of strengths. The high number of household transitions experienced by children in Peru between ages 1 and 12 years provides sufficient variation to compare child development outcomes within a large variety of non-nuclear family types: nuclear, single mother, extended, mother-grandparent, step, and no-mother families. We analyze three outcomes in complementary areas of child development: nutrition, vocabulary, and math. In spite of these limitations and topics to be explored in future research, our study results contribute to the body of literature that suggests that non-nuclear family members influence child development, even when controlling for wealth. Peruvian policy makers or NGOs supporting children and families may wish to target single rural mothers, particularly if still living with the child’s grandparents, and provide them with additional support for their children’s nutritional and cognitive development. Although we found little longitudinal evidence that living in an is associated with improved child development, summary statistics reveal that extended families have higher economic resources, and the children in these families have better development outcomes. There is room for additional improvement; directly involving grandparents may help family programs that support early childhood become more effective.

References

Amato, Paul R. 2010. “Research on : Continuing Trends and New Developments.” Journal of and Family 72 (3): 650–66. doi:10.1111/j.1741-3737.2010.00723.x. Amato, Paul R., and Joan G. Gilbreth. 1999. “Nonresident Fathers and Children’s Well-Being: A Meta-Analysis.” Journal of Marriage and Family 61 (3): 557–73. doi:10.2307/353560. Andersen, Christopher T, Sarah A Reynolds, Jere Behrman, Benjamin T Crookston, Kirk A Dearden, Javier A. Escobal, Subha Mani, Alan Sanchez, Aryeh D Stein, and Lia C.H. Fernald. 2015. “Participation in the Juntos Conditional Cash Transfer Program in Peru Is Associated with Changes in Child Anthropometric Status but Not Language Development or School Achievement.” Journal of Nutrition. Antman, Francisca M. 2013. “The Impact of Migration on Family Left Behind.” In International Handbook on the Economics of Migration, 293. https://books.google.com/books?hl=en&lr=&id=beAMAQAAQBAJ&oi=fnd&pg=PA29 3&dq=antman+francisca+migration&ots=PU6DxDyRq_&sig=fHBSfUvqVJq8dQ8JmLF 1sgDiZAM. Aquilino, William S. 1996. “The Life Course of Children Born to Unmarried Mothers: Childhood Living Arrangements and Young Adult Outcomes.” Journal of Marriage and the Family, 293–310. Augustine, Jennifer March, and R. Kelly Raley. 2013. “Multigenerational Households and the School Readiness of Children Born to Unmarried Mothers.” Journal of Family Issues 34 (4): 431–59. doi:10.1177/0192513X12439177. Barnett, Inka, Proochista Ariana, Stavros Petrou, Mary E. Penny, Le Thuc Duc, S. Galab, Tassew Woldehanna, Javier A. Escobal, Emma Plugge, and Jo Boyden. 2013. “Cohort Profile: The Young Lives Study.” International Journal of Epidemiology 42 (3): 701–8. doi:10.1093/ije/dys082. Brock, Karen. 2011. “Young Lives Methods Guide — Young Lives.” http://www.younglives.org.uk/content/young-lives-methods-guide-0. Crookston, Benjamin T., Whitney Schott, Santiago Cueto, Kirk A. Dearden, Patrice Engle, Andreas Georgiadis, Elizabeth A. Lundeen, Mary E. Penny, Aryeh D. Stein, and Jere R. Behrman. 2013. “Postinfancy Growth, Schooling, and Cognitive Achievement: Young Lives.” The American Journal of Clinical Nutrition 98 (6): 1555–1563. Cuesta, Laura, Vanessa Rios-Salas, and Daniel R. Meyer. 2017. “The Impact of Family Change on Income Poverty in Colombia and Peru.” Journal of Comparative Family Studies 48 (1): 67–96. Cueto, Santiago, and Juan Leon. 2012. “Psychometric Characteristics of Cognitive Development and Achievement Instruments in Round 3 of Young Lives.” Young Lives Technical Note 15. http://www.grade.org.pe/upload/publicaciones/Archivo/download/pubs/NDMtn25.pdf. Cueto, Santiago, Juan Leon, Gabriela Guerrero, and Ismael Muñoz. 2009. “Characteristics of Cognitive Development and Achievement Instruments in Round 2 of Young Lives.” http://grade.org.pe/download/pubs/psychometricround2younglives.pdf. Deleire, Thomas, and Ariel Kalil. 2002. “Good Things Come in Threes: Single-Parent Multigenerational Family Structure and Adolescent Adjustment.” Demography 39 (2): 393–413. doi:10.1353/dem.2002.0016. Duflo, Esther. 2003. “Grandmothers and Granddaughters: Old-Age Pensions and Intrahousehold Allocation in South Africa.” The World Bank Economic Review 17 (1): 1–25. Dunifon, Rachel E., Kathleen M. Ziol-Guest, and Kimberly Kopko. 2014. “Grandparent Coresidence and Family Well-Being Implications for Research and Policy.” The ANNALS of the American Academy of Political and Social Science 654 (1): 110–26. doi:10.1177/0002716214526530. Dunifon, Rachel, and Lori Kowaleski-Jones. 2007. “The Influence of Grandparents in Single- Mother Families.” Journal of Marriage and Family 69 (2): 465–81. Dunn, Lloyd M., Eligio R. Padilla, Delia E. Lugo, and Leota M. Dunn. 1986. “Manual Del Examinador Para El Test de Vocabulario En Imágenes Peabody.” Circle Pines: American Guidance Service. Escobal, Javier, Claudio Lanata, Sofia Madrid, Mary Penny, Jaime Saavedra, Pablo Suárez, Héctor Verastegui, Eliana Villar, and Sharon Huttly. 2003. “Young Lives Preliminary Country Report: Peru.” http://www.younglives.org.uk/sites/www.younglives.org.uk/files/YL-CR1-Peru- 2003_revised.pdf. Favara, Marta, Pablo Lavado, and Alan Sanchez. 2016. “Understanding Teenage Fertility, , and Marriage: The Case of Peru.” SSRN Scholarly Paper ID 2849755. Rochester, NY: Social Science Research Network. https://papers.ssrn.com/abstract=2849755. Ganong, Lawrence, and Marilyn Coleman. 2017. “Effects of Living on Children.” In Stepfamily Relationships, 175–89. Springer, Boston, MA. doi:10.1007/978-1-4899-7702- 1_9. Lundeen, Elizabeth A., Jere R. Behrman, Benjamin T. Crookston, Kirk A. Dearden, Patrice Engle, Andreas Georgiadis, Mary E. Penny, and Aryeh D. Stein. 2014. “Growth Faltering and Recovery in Children Aged 1–8 Years in Four Low-and Middle-Income Countries: Young Lives.” Public Health Nutrition 17 (09): 2131–2137. McLanahan, Sara, Laura Tach, and Daniel Schneider. 2013. “The Causal Effects of Father Absence.” Annual Review of Sociology 399 (July): 399–427. doi:10.1146/annurev-soc- 071312-145704. Mollborn, Stefanie, Paula Fomby, and Jeff A. Dennis. 2010. “Who Matters for Children’s Early Development? Race/Ethnicity and Extended Household Structures in the .” Child Indicators Research 4 (3): 389–411. doi:10.1007/s12187-010-9090-2. Monserud, Maria A., and Glen H. Elder. 2011. “Household Structure and Children’s Educational Attainment: A Perspective on Coresidence with Grandparents.” Journal of Marriage and Family 73 (5): 981–1000. doi:10.1111/j.1741-3737.2011.00858.x. Mutchler, Jan E., and Lindsey A. Baker. 2009. “The Implications of Grandparent Coresidence for Economic Hardship among Children in Mother-Only Families.” Journal of Family Issues 30 (11): 1576–97. doi:10.1177/0192513X09340527. Onis, Mercedes de, Adelheid W Onyango, Elaine Borghi, Amani Siyam, Chizuru Nishida, and Jonathan Siekmann. 2007. “Development of a WHO Growth Reference for School-Aged Children and Adolescents.” Bulletin of the World Health Organization 85 (09): 660–67. doi:10.2471/BLT.07.043497. Page, Marianne E., and Ann Huff Stevens. 2004. “The Economic Consequences of Absent Parents.” The Journal of Human Resources 39 (1): 80–107. doi:10.2307/3559006. Pilkauskas, Natasha V. 2014. “Living with a Grandparent and Parent in Early Childhood: Associations with School Readiness and Differences by Demographic Characteristics.” Developmental Psychology 50 (12): 2587–99. doi:http://dx.doi.org/10.1037/a0038179. Ponczek, Vladimir. 2011. “Income and Bargaining Effects on Education and Health in Brazil.” Journal of Development Economics 94 (2): 242–53. doi:10.1016/j.jdeveco.2010.01.011. Pong, Suet-ling, and Vivien W. Chen. 2010. “Co-Resident Grandparents and Grandchildren’s Academic Performance in Taiwan.” Journal of Comparative Family Studies 41 (1): 111– 29. Reynolds, Sarah, Lia Fernald, Julianna Deardorff, and Jere Behrman. Under review. “Family Structure and Child Development in Chile: A Longitudinal Analysis of Household Transitions Involving Fathers and Grandparents.” Rubio-Codina, Marta, Orazio Attanasio, Costas Meghir, Natalia Varela, and Sally Grantham- McGregor. 2015. “The Socioeconomic Gradient of Child Development: Cross-Sectional Evidence from Children 6–42 Months in Bogota.” Journal of Human Resources 50 (2): 464–483. Ruggles, Steven, and Misty Heggeness. 2008. “Intergenerational Coresidence in Developing Countries.” Population and Development Review 34 (2): 253–81. doi:10.1111/j.1728- 4457.2008.00219.x. Thomson, Elizabeth, Thomas L. Hanson, and Sara S. McLanahan. 1994. “Family Structure and Child Well-Being: Economic Resources vs. Parental Behaviors.” Social Forces 73 (1): 221–42. doi:10.1093/sf/73.1.221. World Health Organization. 2006. “WHO Child Growth Standards: Methods and Development.” EN technical report. http://www.who.int/childgrowth/standards/technical_report/en/. Zeng, Zhen, and Yu Xie. 2014. “The Effects of Grandparents on Children’s Schooling: Evidence from Rural China.” Demography 51 (2): 599–617. doi:10.1007/s13524-013-0275-4.

Figure 1: Sankey Diagram of Household Structure Changes of Focus Children*

1yr 5y 8y 12y

* The width of each band represents the portion of children in each family type. The final version of the diagram will have the step-extended combined with step (as has been done in the paper) and only include children who are present in every survey round.

Appendix: Justification of pooling grandparent types

In order to simplify the family structure types we are comparing, we use the categories that are defined by include any or no grandparents. For example, a single mother living with her child’s maternal grandmother or with her child’s paternal grandfather would both be included in our mother + grandparent family category. To come to this decision, we compared families with grandmothers to families with grandfathers and families with maternal grandparents to families with paternal grandparents. We also took into consideration the number of families with the different grandparent types. For example, there are only 4 families in which a single mother lives with paternal grandparents.

We performed our main specification (1) but substituted in different variables. We included a categorical variable for biological parents (both, mother only, father only, neither) and a categorical variable for grandparents, with two different categorizations for the grandparents (none, grandmother only, or grandfather only, both) (none, maternal grandparent only, paternal grandparent only, both). For either categorization of the grandparents, none indicated any significant differences between grandparnet types’ association with child development scores (Table A2 – Panel A). However, when we interacted the categorical variable for biological parents in the household with the categorical varaible for grandparents in the household, we found a few categories in which there were significant differences (Table A2 – Panel B).

Figure A1

The Peabody Picture Vocabulary Test (Spanish Version) Normalization by Age 150 100 50 0

55 155 Age in Months (integer)

TVIP Raw Scores (Spanish only) mean score for each month Lowess smoothed using 10% of the data Lowess smoothed standard deviation Source: Peru Young Lives Children (Rounds 1-4) & Sibling Data (Rounds 3-4)

Figure A2

Math Score by Age Linear Fit 30 20 Math Score 10 0

48 60 72 84 96 108 120 132 144 156 Age in Months

Table 1: Sample Size Height in Height in Height Sample R1 R2 R3 R4 all rounds any round

Focus Child 2033 1950 1937 1876 1813 2052 Younger XX 797 771 745 823 Sibling PPVT Sample PPVT in PPVT in R1 R2 R3 R4 (Spanish) all rounds any round

Focus Child X 1675 1712 1850 1488 1958 Younger XX 367 733 344 754 Sibling Math in Math in Math Sample R1 R2 R3 R4 all rounds any round

Focus Child X 1949 1881 1871 1790 1983 Table 2: Family Type by Survey Round Family Type 1 y 5 y 8 y 12 y Ever Always 1,156 1,239 1,219 1,133 1,467 843 Nuclear 64% 68% 67% 62% 81% 46% 77 99 139 210 311 22 Single Mom 4% 5% 8% 12% 17.0% 1% Extended, 363 209 152 115 472 49 Both Parents 20% 12% 8% 6% 26% 3% Mother + 201 148 122 97 303 39 Grandparent 11% 8% 7% 5% 17% 2% 6 42 77 111 132 0 Step Family 0% 2% 4% 6% 7% 0% 10 76 104 147 182 7 No Mother 1% 4% 6% 8% 10% 0% Focus children in with stable family structure: 960 53% Focus children with data in all rounds, N=1813 Table 3: Summary Statsics by Mean & SD at Age 1 yr Mean & SD at Age 12 yr Family Type Extended, Mother + Extended, Mother + Nuclear Overall+ Nuclear Overall+ Single Mom Both Parents Grandparent Single Mom Both Parents Grandparent Step Family No Mother HAZ -1.32 -1.41 -1.08*** -1.15* -1.26** -1.04 -1.04 -0.89 -0.80** -1.16 -1.22* -1.04 (0.04) (0.14) (0.06) (0.08) (0.03) (0.03) (0.07) (0.11) (0.13) (0.11) (0.09) (0.03) Vocabulary -0.04 0.07 0.40*** 0.03 0.07 -0.14 0.00*** Child (standardized) (0.03) (0.07) (0.09) (0.11) (0.08) (0.07) (0.02) Variables 0.03 0.01 0.30** -0.06 -0.08 -0.36*** 0.00* Math (standardized) (0.04) (0.09) (0.13) (0.14) (0.11) (0.11) (0.03) 0.50 0.52 0.51 0.48 0.50 0.52 0.47 0.52 0.46 0.44 0.50 0.50 Male (0.01) (0.05) (0.02) (0.03) (0.01) (0.01) (0.03) (0.05) (0.05) (0.05) (0.04) (0.01) Mother's years of 7.01 7.68 8.72*** 8.28*** 7.52** 7.27 7.76 8.91*** 8.90*** 7.97* 7.26 7.53** Mother schooling (0.12) (0.47) (0.20) (0.25) (0.10) (0.13) (0.26) (0.38) (0.39) (0.35) (0.31) (0.09) Variables Mother's native language 0.36 0.38 0.27*** 0.26*** 0.34* 0.35 0.28* 0.24** 0.19*** 0.23** 0.24** 0.33 was indigenous (0.01) (0.05) (0.02) (0.03) (0.01) (0.01) (0.03) (0.04) (0.04) (0.04) (0.03) (0.01) Wealth Indexa 0.42 0.44 0.48*** 0.43 0.43 0.59 0.59 0.63* 0.61 0.60 0.58 0.59* (0.01) (0.02) (0.01) (0.02) (0.01) (0.01) (0.01) (0.02) (0.02) (0.02) (0.02) (0.00) Housing quality index 0.39 0.41 0.43*** 0.42* 0.40 0.46 0.47 0.49 0.45 0.49 0.47 0.46 (0.01) (0.03) (0.01) (0.02) (0.01) (0.01) (0.02) (0.02) (0.02) (0.02) (0.02) (0.01) Access to services index 0.60 0.67* 0.66*** 0.59 0.61 0.83 0.86** 0.85 0.87* 0.84 0.82 0.83 (0.01) (0.04) (0.02) (0.02) (0.01) (0.01) (0.01) (0.02) (0.02) (0.02) (0.02) (0.01) Household Consumer durables index 0.27 0.24 0.35*** 0.29 0.28*** 0.48 0.43*** 0.54*** 0.51 0.47 0.45* 0.48*** SES variables (0.01) (0.02) (0.01) (0.02) (0.00) (0.01) (0.01) (0.02) (0.02) (0.02) (0.02) (0.00) Total land owned by the 5.55 0.58 1.11 0.63 0.55 31.89 6.39 hh, in hectares (4.24) (0.27) (0.44) (0.16) (0.17) (30.67) (3.72) Total monthly current 311.31 368.40** 298.30 273.49 337.18 258.48* 312.46* Incomplete data at age 1 y expenditure, in Soles (9.88) (22.36) (23.85) (21.05) (42.06) (14.66) (7.54) Total monthly 69.29 126.15*** 49.59** 53.61* 72.22 64.15 73.61*** expenditure per capita, (2.59) (9.69) (4.57) (5.00) (9.05) (4.97) (2.17) Rural 0.34 0.28 0.27** 0.32 0.32 0.29 0.19*** 0.21* 0.23 0.22 0.31 0.27 (0.01) (0.05) (0.02) (0.03) (0.01) (0.01) (0.03) (0.04) (0.04) (0.04) (0.04) (0.01) Region - Coast 0.33 0.27 0.41*** 0.39* 0.35** 0.35 0.41* 0.48** 0.49*** 0.44* 0.42 0.36 Geographic (0.01) (0.05) (0.02) (0.03) (0.01) (0.01) (0.03) (0.05) (0.05) (0.05) (0.04) (0.01) Variables Region - Jungle 0.16 0.22 0.12* 0.09** 0.15** 0.15 0.15 0.08** 0.06** 0.24** 0.22** 0.14* (0.01) (0.04) (0.02) (0.02) (0.01) (0.01) (0.02) (0.03) (0.02) (0.04) (0.03) (0.01) Region - Sierra 0.51 0.51 0.46 0.52 0.50 0.49 0.43 0.44 0.44 0.32*** 0.36*** 0.42 (0.01) (0.05) (0.02) (0.03) (0.01) (0.01) (0.03) (0.05) (0.05) (0.04) (0.04) (0.01) N 1306 90 409 228 2033 1181 220 119 99 120 163 1902 Proportion 0.64 0.04 0.20 0.11 1.00 0.62 0.12 0.06 0.05 0.06 0.09 1.00 Focus Children only Pvalue of the t-test comparing mean to nuclear families: significant at * 10% **5% ***1% +Pvalues for Overall sample means come from a joint test of differnce a Wealth index includes the housing, services and consumer durables indecces Table 4: Child Fixed Effects ZHFA PPVT Math Wealth Index Single Mother -0.08 -0.09+ 0.04 0.03 -0.13 -0.15 0 0 (0.05) (0.05) (0.08) (0.07) (0.10) (0.11) (0.01) (0.01) Extended (Both Parents) 0.05 0.05 0 0 -0.01 -0.05 0 0 (0.04) (0.04) (0.06) (0.06) (0.10) (0.10) (0.01) (0.01) Family Type - Mother + Grandparent(s) -0.01 -0.02 -0.1 -0.11 -0.18 -0.23+ 0.01 0 compared to (0.06) (0.06) (0.09) (0.09) (0.13) (0.14) (0.01) (0.01) Nuclear Step -0.12+ -0.13+ 0.13 0.08 0.13 0.12 0 0 (0.07) (0.07) (0.11) (0.11) (0.15) (0.15) (0.01) (0.01) No Mother -0.05 -0.07 0.1 0.1 -0.03 -0.07 0.01 0.01 (0.07) (0.07) (0.09) (0.10) (0.13) (0.14) (0.01) (0.01) 5y -0.28** -1.59** -0.34** 2.33** 0.02 -4.03** 0.09** 0.04 (0.03) (0.30) (0.03) (0.61) (0.04) (0.97) (0.00) (0.03) Round Fixed 8y 0.08** -1.91** 0 2.10** 0.01 -2.03** 0.14** 0.06 Effects (0.03) (0.46) (.) (0.38) (0.04) (0.64) (0.00) (0.06) 12y 0.26** -2.66** -0.08** 0 0 0 0.15** 0 (0.03) (0.71) (0.03) (.) (.) (.) (0.00) (0.09) Age in Months 0.03** -0.25** -0.23** 0 (0.01) (0.03) (0.06) (0.00) Age in Months Squared 0 0.00** 0.00* 0.00+ Child Age (0.00) (0.00) (0.00) (0.00) Age in Months Cubed 0 -0.00** -0.00+ 0 (0.00) (0.00) (0.00) (0.00) wealth index 0.01 0.23 0.05 Household (0.10) (0.16) (0.23) variables moved since last round 0.05 -0.02 -0.04 0.01 (0.03) (0.04) (0.06) (0.01) Number of children 0-6 -0.01 -0.01 -0.04 0 (0.02) (0.02) (0.03) (0.00) Household Number of boys 7-15 -0.02 0.02 0.06+ 0 Composition (0.02) (0.02) (0.04) (0.00) Controls - Number of girls 7-15 -0.05** -0.02 0.01 0 does not (0.02) (0.02) (0.03) (0.00) include Number of men 16-55 0.01 -0.01 -0.02 0.01* parents, (0.01) (0.02) (0.03) (0.00) grands, steps Number of women 16-55 0 -0.02 -0.01 0.01* or child of (0.02) (0.02) (0.03) (0.00) the Number of men 56+ 0.02 -0.07 0.03 -0.02** observation (0.05) (0.05) (0.08) (0.01) Number of women 56+ -0.04 0.03 -0.11 0 (0.04) (0.05) (0.07) (0.01) Constant -1.30** -1.43** 0.11** 5.12** 0.01 13.61** 0.43** 0.44** (0.02) (0.10) (0.02) (1.13) (0.04) (1.79) (0.00) (0.01) Adjusted R2 0.717 0.72 0.63 0.68 0.414 0.437 0.806 0.809 N 9358 9358 6336 6336 5700 5700 10244 10244 Number of children 2870 2870 2711 2711 1982 1982 2878 2878 Number of families 2052 2052 1962 1962 1982 1982 2052 2052 Standard Errors clustered by Family Pvalues: significant at * 10% **5% ***1% Table 5: Results for Rural Children Child Fixed Effects ZHFA PPVT Math Wealth Index Single Mother -0.22* -0.24** 0.22 0.16 -0.44+ -0.41+ 0.04* 0.04* (0.09) (0.09) (0.15) (0.15) (0.24) (0.24) (0.02) (0.02) Extended (Both Parents) 0.05 0.03 0.08 0.03 0.3 0.23 -0.01 -0.01 (0.07) (0.08) (0.15) (0.15) (0.18) (0.18) (0.01) (0.01) Mother + Grandparent(s) -0.08 -0.1 -0.26 -0.32 -0.77* -0.72* 0 0 (0.12) (0.13) (0.23) (0.24) (0.30) (0.31) (0.02) (0.02) Step -0.09 -0.07 0.07 0.06 -0.1 -0.03 0.02 0.02 (0.15) (0.14) (0.26) (0.26) (0.34) (0.34) (0.03) (0.03) No Mother -0.16 -0.20+ 0.31 0.18 -0.19 -0.12 0 0 (0.11) (0.12) (0.19) (0.20) (0.26) (0.28) (0.03) (0.03) Adjusted R2 0.671 0.672 0.523 0.55 0.408 0.414 0.633 0.637 N 2457 2457 1409 1409 1802 1802 2485 2485 Number of children 642 642 601 601 624 624 642 642 Standard Errors clustered by Family Pvalues: significant at * 10% **5% ***1% Table A1: Portion of children living with specific family members

FOCUS CHILDREN Age 1 Age 5 Age 8 Age 12 Ever % with transition* Biological Mother 99.4% 95.8% 94.3% 91.9% 99.6% 9.7% Biological Father 83.9% 81.2% 77.5% 71.9% 88.5% 21.4% Step Father 0.3% 2.4% 4.4% 6.3% 7.5% 7.5% Any Grandparent 31.7% 23.3% 19.9% 16.9% 41.1% 32.0% Maternal Grandmother^ 25.2% 15.9% 13.6% 11.3% 31.9% 25.8% Maternal Grandfather^ 18.9% 10.9% 8.4% 7.3% 22.9% 18.8% Paternal Grandmother^ 0.0% 5.0% 4.4% 3.6% 7.2% 7.2% Paternal Grandfather^ 0.0% 3.4% 3.1% 2.3% 5.0% 5.0% Focus children with data in all rounds, N=1,813 *a change in presence of the family member in the household ^Age 1 questionaire does not distinguish between maternal & paternal grandparents. Estimates were made based on identity of R2 grandparents. Unclassifyable grandparents (ie no longer in the hosuehold in R2) were assumed to be maternal.

YOUNGER SIBLINGS Round 3 Round 4 Ever % with transition Biological Mother 98.4% 95.6% 98.7% 3.4% Biological Father 88.2% 82.3% 89.9% 9.4% Step Father 5.6% 5.8% 7.0% 2.6% Any or no Grandparent 13.7% 12.9% 18.3% 9.9% Maternal Grandmother^ 8.5% 8.7% 12.1% 7.0% Maternal Grandfather^ 5.0% 5.4% 7.5% 4.7% Paternal Grandmother^ 3.2% 2.6% 4.2% 2.6% Paternal Grandfather^ 2.6% 2.1% 3.0% 1.2% Younger siblings with data in rounds 3 & 4, N=745 ^Age 1 questionaire does not distinguish between maternal & paternal grandparents. Estimates were made based on identity of R2 grandparents. Unclassifyable grandparents (ie no longer in the hosuehold in R2) were assumed to be maternal. Table A2: Child Fixed Effect Regressions modeled with separate grandparent categories ZHFA PPVT Math Wealth Index Child Fixed Effects No Controls Controls No Controls Controls No Controls Controls No Controls Controls Grandparent Grandmother 0.03 0.03 0.02 0.05 0.03 -0.02 0 0 indicator (0.04) (0.05) (0.09) (0.08) (0.10) (0.11) (0.01) (0.01) variables - Grandfather 0.02 0.04 0.03 -0.05 0.05 0.02 0.01 -0.01 base category (0.08) (0.08) (0.10) (0.10) (0.20) (0.19) (0.02) (0.01) is no Both 0.06 0.07 -0.1 -0.11 -0.09 -0.14 0.01 0 grandparents (0.05) (0.05) (0.07) (0.07) (0.11) (0.12) (0.01) (0.01) Mother only -0.06 -0.08+ 0.02 0.02 -0.13 -0.14 0 0 Parent (0.04) (0.05) (0.07) (0.07) (0.09) (0.09) (0.01) (.) indicator Father only -0.13 -0.14 -0.03 0 0.05 0.02 -0.01 0.02 categories - (0.10) (0.10) (0.10) (0.11) (0.19) (0.19) (0.02) (0.04) base category neither -0.04 -0.07 0.23+ 0.23+ -0.11 -0.09 0.01 0.04 is both parents (0.09) (0.09) (0.13) (0.12) (0.15) (0.16) (0.02) (0.06) r2_a 0.717 0.72 0.63 0.68 0.413 0.436 0.807 0.81 N 9357 9357 6335 6335 5699 5699 10243 10243 Focus Children 2870 2870 2711 2711 1982 1982 2878 2878 Younger Siblings 2052 2052 1962 1962 1983 1983 2052 2052 Pvalue* 0.87 0.95 0.93 0.44 0.93 0.85 0.86 0.90 *Pvalue is the test of equality of coefficients on Grandmother & Grandfather

ZHFA PPVT Math Wealth Index Child Fixed Effects No Controls Controls No Controls Controls No Controls Controls No Controls Controls Grandparent Paternal -0.03 -0.03 -0.08 -0.1 -0.08 -0.16 0 0 indicator Grandparent(s) (0.07) (0.07) (0.13) (0.12) (0.16) (0.17) (0.01) (0.01) variables - Maternal 0.06 0.04 0.07 0.08 0.08 0.04 0 0 base category Grandparent(s) (0.05) (0.05) (0.09) (0.09) (0.12) (0.12) (0.01) (0.01) is no Both 0.31 0.24 -0.18 -0.21 -0.44 -0.37 0 -0.01 grandparents (0.22) (0.25) (0.45) (0.44) (0.39) (0.32) (0.04) (0.04) Mother only -0.05 -0.06 0.06 0.06 -0.15 -0.16 0.01 0 parent (0.05) (0.05) (0.08) (0.07) (0.11) (0.10) (0.01) (0.01) indicator Father only -0.09 -0.1 0 0.04 -0.09 -0.1 -0.02 -0.02 categories - (0.11) (0.12) (0.12) (0.13) (0.23) (0.23) (0.02) (0.02) base category neither -0.01 -0.03 0.25 0.21 -0.26 -0.21 0.05* 0.05* is both parents (0.12) (0.13) (0.20) (0.20) (0.22) (0.23) (0.02) (0.02) r2_a 0.81 0.72 0.723 0.624 0.674 0.417 0.44 0.808 N 10243 8411 8411 5773 5773 5174 5174 9216 Focus Children 2878 2764 2764 2582 2582 1893 1893 2776 Younger Siblings 2052 1974 1974 1871 1871 1894 1894 1974 Pvalue* 0.90 0.34 0.41 0.31 0.20 0.42 0.33 0.92 *Pvalue is the test of equality of coefficients on Paternal & Maternal