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Long-term Associations of Attending Early Childhood Education in Low- and Middle-Income Countries and Vocabulary Skills

Thu, March 21, 12:30 to 2:00pm, Hilton Baltimore, Floor: Level 2, Key 11

Integrative Statement

Over 550 million children under age five reside in low and middle-income (LAMI) countries, and 22% live in poverty (Grantham-McGregor et al., 2007; Unesco, 2015). Research conducted primarily in the U.S. suggests that poverty and urbanicity interface in complex ways to shape children’s development, with children in large urban settings scoring below their counterparts in rural communities when it comes to academic achievement (Miller et al., 2013). In the developing world, there is some evidence to suggest that patterns differ, with children growing up in rural areas faring worse than their urban counterparts on education and health (Clifton, 2015; Smith et al., 2005). It is crucial to understand the range of factors that contribute to these disparities so that efforts to address them can be targeted effectively.

In recent years, governments in LAMI countries have turned to early childhood education (ECE) programs to address problems associated with child poverty (Engle et al., 2007; Myers, 1992, 2005; Ramey & Ramey, 1998; Unesco, 2015). Drastic expansion of government investment in preschool has increased the proportion of poor children attending preschool in LAMI countries from 22% in 1999 to 45% in 2010 (Unesco, 2012). However, evidence suggests that as ECE has spread, children in rural communities have less access to preschool (see Table 1). Differential access to early education may be one of the factors giving rise to disparities in poor children’s development across rural and urban communities.

Drawing data from the Young Lives Study, a longitudinal multi-method investigation following economically disadvantaged children in four different countries: India (N=2,011), Ethiopia (N=1,999), Peru (N=2,052), and Vietnam (N=2,000), this study examines associations between urbanicity and low-income children’s literacy skills at age 8. Furthermore, it considers whether early childhood education participation mediates skills disparities. The data include comprehensive measures of children’s health and academic outcomes as well as extensive information about children’s time use, family, school, and community characteristics. Multilevel structural equation modeling (MSEM) will be used to explore these associations because it combines the capabilities of multilevel models with those of structural equation modeling (SEM; Heck & Thomas, 2015), allowing the nesting of children (level-1) within communities (level-two). Subsequent analyses will consider a range of child, family, community, and school factors that may also act as mediators of the differences in low-income children’s development across urban and rural communities.

Preliminary analyses of receptive vocabulary, shown in Table 2, indicate that the urban-rural gap favors urban children. Mediation analysis suggests that differential participation in early childhood education programs partially explains relations between urbanicity and receptive vocabulary at age eight years in Peru and Ethiopia. In the future, these models will be developed further to understand the lack of significant mediation in India and Vietnam and to better take into account the differential selection of children and families into urbanicity and early care and education settings.

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