# I. INTRODUCTION

Children living in poverty are exposed to a wide range of negative influences, such as poor housing, inadequate nutrition, parental stress, and lack of cognitive stimulation (Walker et al. 2011). Additionally, these children are often exposed to multiple stressors simultaneously and repeatedly over time (Najman et al. 2009). There is consistent evidence of socioeconomic gradients for child health outcomes in both high-income countries (Case et al. 2002; Currie 2009; Shonkoff et al. 2009) and low- to middle-income countries (Currie and Vogl 2013; Petrou and Kupek 2010).

Low socioeconomic status (SES) is also a risk factor for poor child development outcomes (e.g., language, cognition, and behavior), and children of parents with less income or education perform worse than children whose parents have more income or education (Cunha and Heckman 2008; Kelly et al. 2011), according to studies from high-income countries. There is evidence of socioeconomic gradients in child development outcomes from lower and middle-income countries as well, but existing data are primarily cross-sectional. For example, wealth gradients in child development scores are evident as early as 3–23 months in India, Indonesia, Peru, and Senegal (Fernald et al. 2012). Preschool children (3–6 years old) exhibit significant gaps in receptive language in Ecuador (Paxson and Schady 2007), several other Latin American countries (Schady et al. 2015), and the four countries of the Young Lives Study (Engle et al. 2011). Unfortunately, these studies are limited by the lack of panel data with repeated measures of child development outcomes across critical and sensitive periods. Cross-sectional studies cannot untangle issues of timing where age effects are confounded with cohort effects and where the role of early skills on later development cannot be modeled. With few exceptions (e.g., [Kobrosly et al. 2011]) and the Young Lives cohorts, longitudinal panels have primarily been focused in the US and Europe.

This paper is one of the few examples of a longitudinal cohort study set in a very low-income country in Sub-Saharan Africa that has assessed children with direct measures of cognitive development at two points in time: early childhood (3–6 years) and school age (7–10 years). The cohort was also evaluated with early math and literacy measures at school age. Previous work demonstrated that these same children showed significant differences across socioeconomic status in standardized scores for a wide range of cognitive and language tests; the largest gaps in the SES gradient were evident in receptive language and sustained attention scores (Fernald et al. 2011).

The primary objective of this paper is to take advantage of these longitudinal data and examine how the magnitude and age profile of SES gradients change over time, modeling skill formation as a cumulative and dynamic process. Differences in child development are assessed across wealth groups, and the evolution of the SES gradient across early childhood and into school age is documented. While other papers based on cross-sectional evidence have already documented that the SES gradient widens at early ages and then flattens out after age six during the school-age window (Schady 2011), this study relies on both the cross-sectional and the longitudinal aspect of the data to document the critical timing of the emergence and evolution of the SES differences in child development and learning outcomes.

We further add to the literature by assessing the magnitude of the SES gradients across _multiple_ developmental domains. Much of the current focus on children living in resource-poor circumstances is on height, weight, or morbidity outcomes or on a single dimension of child development (such as receptive language). In this study, multiple domains of child development are examined, including measures of cognition, language, and executive function, to examine whether the magnitude and evolution of SES gradients differ across domains. There is evidence that certain regions of the brain may be differentially vulnerable to the effects of SES (Hackman