Abstract
This paper provides new evidence of sharp differences in cognitive development by socioeconomic status in early childhood for five Latin American countries using a common measure of receptive language ability. We find important differences in development in early childhood across countries and steep socioeconomic gradients within every country. For the three countries where we can follow children over time, there are few substantive changes in scores once children enter school. Our results are robust to different ways of defining socioeconomic status, to different ways of standardizing outcomes, and to selective nonresponse on our measure of cognitive development.
I. Introduction
Development in early childhood is an important predictor of success in adulthood in a number of domains. Research from multiple disciplines makes clear that outcomes in early childhood are malleable although the window of opportunity may be short, especially for cognitive outcomes and nutritional status. There is also evidence from developed and developing countries that investments in early childhood can positively affect long-term trajectories (Almond and Currie 2011, and Cunha et al. 2006 are reviews for the United States; Engle et al. 2007, 2011, and Behrman et al. 2013 are reviews for developing countries that focus primarily on the child development literature).
This paper provides new evidence of sharp differences in cognitive development by socioeconomic status in early childhood for five Latin American countries. It complements research from the United States that shows that gaps in early cognitive and noncognitive ability appear early in the life cycle. At age three, the difference in cognitive scores between children of college graduates and high school dropouts in the United States is almost 1.5 standard deviations, and this difference is stable until (at least) 18 years of age (Heckman 2008). At age five, children in the lowest income quartile have scores that are approximately 0.8 standard deviations lower than those in the highest income quartile on a math test (Cunha and Heckman 2007). Duncan and Magnuson (2013) report that average achievement gaps in math and reading between children in the top and bottom income quintiles are more than a full standard deviation at the beginning of kindergarten.
By and large, comparable evidence does not exist for developing countries. We are aware of only a handful of earlier studies that seek to measure socioeconomic differences in early childhood in developing countries. A study of poor children in rural Ecuador uses panel data to show that there are substantial differences in cognitive development at young ages—including in vocabulary, memory, and visual integration—between children of higher and lower socioeconomic status. The socioeconomic gradients in vocabulary (but not in other measures of cognitive development) appear to increase between three and five years of age (Schady 2011, which builds on Paxson and Schady 2007). Two other studies use single cross-sections of data from low-income countries, specifically Madagascar (Fernald et al. 2011) and Cambodia and Mozambique (Naudeau et al. 2011). These studies also find substantial differences in cognitive development at young ages, with increasing gaps in the cross-sections between ages three and five or six for some, but not all, indicators of cognitive development.
Our paper substantially extends earlier work on the subject. We highlight three important contributions. First, we present results that are comparable for five countries, based on a common outcome measure, child performance on the Test de Vocabulario en Imágenes Peabody (TVIP). In all five countries, we observe socioeconomic gradients in cognitive development (albeit of different magnitudes), which suggests that this pattern is not idiosyncratic, country-specific, or a result of data mining. Moreover, in the rural areas of all five countries, and in the urban areas of Chile and Colombia, the distribution of socioeconomic status in the surveys we use is broadly similar to the distribution of socioeconomic status in nationally representative household surveys, further suggesting that the results we report have external validity, at least in rural areas.1 Second, we show that our findings are robust to different ways of defining socioeconomic status, to different ways of standardizing outcomes, and to selective nonresponse on our measure of cognitive development. Finally, in three countries (Ecuador, Nicaragua, and Peru), we exploit the longitudinal structure of the data to analyze how deficits in receptive language ability observed at young ages evolve as children enter the early school years.
II. Data and Setting
We begin by describing the surveys that we use for our analysis in Table 1. The table shows that the surveys we use vary in sample sizes and coverage. The largest samples are found in the survey for Chile (approximately 5,400 children) and the smallest in Nicaragua and Peru (between 1,800 and 1,900 children each). The Nicaraguan survey only sampled children in rural areas, while the data for Chile, Colombia, Ecuador, and Peru covered both urban and rural areas. The age range of children in the surveys also varies. The test of child cognitive development we use, discussed in more detail below, is designed to be applied to children 30 months and older, and in most of our analysis we limit the sample to children ages 36–71 months. In practice, however, the oldest children in Chile are 57 months of age while the youngest children in Peru are 53 months of age.
Surveys Used in Analysis
Table 1 also shows that in three of the countries we analyze—Ecuador, Nicaragua, and Peru—there is a panel component in the data. In Peru, there are two waves of this panel, separated by approximately three years; in Nicaragua, there are three rounds of data collected over a four-year period; in Ecuador, finally, there are four rounds of data collected over a seven-year period.
A major strength of our study is the use of a common measure of child cognitive development: performance on the widely used Test de Vocabulario en Imágenes Peabody (TVIP), the Spanish version of the Peabody Picture Vocabulary Test (PPVT) (Dunn et al. 1986). Children are shown slides, each of which has four pictures, and are asked to identify the picture that corresponds to the object (for example, “boat”) or action (for example, “to measure”) named by the test administrator. The test continues until the child has made six mistakes in the last eight slides. The test is a measure of receptive vocabulary because children do not have to name the objects themselves and because children need not be able to read or write. Performance on the PPVT and TVIP at early ages has been shown to be predictive of important outcomes in a variety of settings.2
To analyze socioeconomic gradients in TVIP scores, we construct country-specific, age-specific z-scores by subtracting the month-of-age-specific mean of the raw score and dividing by the month-of-age-specific standard deviation, separately by country and by urban-rural place of residence (as in Cunha and Heckman 2007 and many others). As a robustness test, we also report results that use the tables given by the test developers to standardize the test (as done by Paxson and Schady 2007).
A fraction of children in every survey, ranging from 2 percent in Colombia to 18 percent in Nicaragua, did not take the TVIP. Although we do not have data that are comparable across all five countries on the reasons why these children did not take the test, it appears that most of them had difficulty understanding the instructions and making it past the practice items that are applied at the outset. Consistent with this, there are more children with missing test data at younger ages and more in the poorest country, Nicaragua. Earlier work on Ecuador has shown that children who miss a given test do worse on other tests, or on the same test in different survey waves, than other children with comparable wealth and parental schooling levels (Paxson and Schady 2010; Schady 2011). Because children who miss tests are likely to be “low performers,” we assign these children a test score of zero. We test the robustness of our results to this approach to handling missing data.
We construct a measure of household wealth by aggregating a number of household assets and dwelling characteristics using the first principal component. Similar wealth indices have been used extensively in the medical, demographic, nutritional, and economics literatures. The exact variables included in the wealth measures vary by country because of differences in the assets and dwelling characteristics that were collected in the surveys (see online Appendix Table 1 at http://uwpress.wisc.edu/journals). As a robustness check, we test whether our results are sensitive to using consumption or education as an alternative measure of socioeconomic status, or to using only a common set of assets to construct the wealth index in all countries.
There are substantial differences across the countries we study in their level of development. Chile is the richest of the five countries, with GDP per capita, adjusted for Purchasing Power Parity (PPP) in 2012, above US$22,000, and Nicaragua is the poorest, with GDP per capita equal to US$ 4,006. The other three countries—Colombia, Ecuador, and Peru—all have per capita GDP levels between US$9,500 and US$11,000.3 The average grades of completed schooling of adults in each country follows the same pattern as GDP per capita, with approximately seven more grades of schooling in Chile than in Nicaragua. Like other countries in Latin America, the countries we analyze are highly unequal. The Gini coefficient of household per capita income ranges from 0.48 for Peru to 0.56 for Colombia. In comparison, the Gini coefficient for Sweden is 0.25 and that for the United States is 0.41. The average Gini for OECD countries (excluding the two Latin American countries, Chile and Mexico) is 0.31.
III. Results
The aim of this paper is descriptive. In our main results, we simply compare the TVIP scores for children in the top and bottom quartiles of the distribution of wealth. Because associations between TVIP scores and wealth could differ between urban and rural areas, we calculate separate wealth indices and conduct separate analyses for urban and rural areas.
Table 2 shows that differences in language development between richer and poorer children within countries are statistically significant and large.4 Differences across quartiles are biggest in urban Colombia (1.23 standard deviations) and rural Ecuador (1.21 standard deviations). Online Appendix Table 2 shows that, as expected, the differences between children in the richest and poorest deciles (as opposed to quartiles) are substantially larger—in both urban Colombia and rural Ecuador they are 1.64 standard deviations.
Main Results
We next present the results from nonparametric (Fan) regressions (Fan and Gijbels 1996) of the difference in scores between children in the top and bottom quartiles, and the associated confidence intervals constructed by bootstrapping. Figure 1 suggests that the bulk of the difference between poorer and less-poor children is apparent by age three in all countries; Appendix Figure 2 shows that this is also the case in comparisons between the poorest and richest deciles. We note, however, that making comparisons of age gaps in test scores measured in standard deviations is not straightforward when the tests are measured with error. Suppose, as seems likely, that there is more measurement error in the TVIP at younger ages (for example, if younger children are more easily distracted). In this case, a finding of a constant gap in standard deviations of test scores as children age would be consistent with a decline in the actual (as opposed to measured) gap as children age.5
Age Patterns in Scores
Notes: Nonparametric regressions of average TVIP score on age in months, by wealth quartile. The bandwidth of the regressions is 7.5.
Panel Data Analysis
Notes: Nonparametric regressions of average TVIP score on age in months, by baseline wealth quartile. The bandwidth of the regressions is 7.5.
We conduct a number of robustness checks on our main results (Table 3). First, for four countries (Colombia, Ecuador, Nicaragua, and Peru), we present results in which we use a common set of household assets rather than the largest set of assets available in the surveys for each country to construct our measure of wealth. (We cannot do this for Chile because there are very few assets that are common to the Chilean and other data sets.) Second, for two countries in which consumption data are available (Colombia and Nicaragua), we sort households into quartiles using household per capita consumption rather than wealth. Third, we compare outcomes for children of mothers with incomplete primary education or less and those with complete secondary education or more. Fourth, we restrict the sample to children of monolingual parents.6 Fifth, we report results that use the norms provided by the test developers (rather than the internal z-scores we construct) to standardize the TVIP.7
Robustness Checks and Extensions
Table 3 shows that the patterns summarized above are robust. Results are very similar when only assets that are common across countries are used to construct the wealth index, or when we use consumption, rather than wealth, as a measure of well-being. There are substantial differences in child TVIP scores by mother schooling levels (incomplete primary or less, compared to complete secondary or more) in all countries. For example, in rural Ecuador the difference in outcomes between children of mothers with complete secondary schooling or more and those with incomplete primary schooling or less is 1.16 standard deviations. Excluding children in households where a language other than Spanish is spoken substantially increases the wealth gradient in rural Peru (from 0.77 to 0.95 standard deviations) but has little effect on the results for urban Peru or urban or rural Ecuador.
Results that use the norms provided by the test developers (the fifth row of the table) show similar wealth gradients as those we report in our main specification. Recall that the distributions of wealth in the data we use to calculate the TVIP scores are broadly similar to the distributions of wealth in nationally representative surveys for the rural areas of all five countries and for the urban areas of Chile and Colombia. We can therefore also use these results to make (cautious) comparisons across rural-urban areas in these two countries and across rural areas in all five countries.
First, limiting the sample to rural areas, mean scores are highest in Chile (90 points), substantially lower in Colombia and Ecuador (78 and 75 points, respectively), and lower still in Peru and Nicaragua (69 and 66 points, respectively). This means that, in Nicaragua and Peru, the average child in the poorest wealth quartile in rural areas has TVIP scores that are more than two standard deviations below the reference population that was used to norm the test. The results for Peru are particularly noteworthy because GDP per capita levels in Peru are roughly comparable to those found in Colombia and Ecuador and are approximately three times as high as those in Nicaragua. Second, children in urban areas have somewhat higher scores than those in rural areas in Chile (a difference of six points for those in the highest quartile) and substantially higher scores in Colombia (a difference of 26 points, more than 1.5 standard deviations, for those in the highest quartile). Of course, in interpreting these urban-rural comparisons, it is important to keep in mind that average income levels tend to be substantially higher in urban than in rural areas in most Latin American countries.
We also test the degree to which our results are sensitive to missing test data by calculating upper and lower bounds on the wealth gradients (last row of Table 3), in the spirit of Manski (1990) and Horowitz and Manski (2000). Specifically, we estimate the upper bound by excluding all children with missing test data in the richest wealth quartile, and assigning a score of 0 to all children in the poorest wealth quartile who were missing the TVIP, as before. Conversely, we estimate the lower bound by excluding all children with missing test data in the poorest wealth quartile and assigning a score of 0 to all children in the richest wealth quartile who were missing the TVIP, as before. Table 3 shows that the bounds that take account of missing test data are generally quite tight. For example, in urban Chile, our basic estimate suggests that the difference in outcomes between children in the top and bottom wealth quartiles is 0.78 standard deviations, the lower bound on this difference is 0.74, and the upper bound is 0.83. Only in Nicaragua, the country with the largest number of children with missing test data, are the bounds somewhat wider, with a lower bound for the difference of 0.58 standard deviations and an upper bound of 0.99 standard deviations.
Although the aim of our paper is descriptive, and the data we have do not allow us to establish causality from socioeconomic status (whether measured by wealth, consumption, or education) to child cognitive development, we make an attempt to deepen our understanding of the gradients we observe with some conditional associations. Specifically, in Table 4, we present the results from three regressions. In the first, Specification 1, only the dummy variable for children in the fourth (richest) quartile is included as an explanatory variable; in Specification 2, we add maternal schooling attainment; in Specification 3, finally, we also add location fixed effects.
Wealth, Mother ‘s Education, and Location
These results show that, by and large, the significant association between wealth and child cognitive development is robust to the inclusion of these additional covariates. In some cases (for example, rural Colombia) adding maternal schooling to the regression reduces the coefficient on wealth by relatively little while in others (for example, urban Peru), the coefficient on wealth goes down substantially. Similarly, adding location fixed effects has a large effect on the coefficient on wealth in some settings (for example, urban Colombia, the urban and rural areas of Ecuador) but not in others (for example, the urban and rural areas of Chile and Peru). In part, this is likely to reflect the much larger number of location fixed effects in some countries (for example, in Colombia, the location fixed effects correspond to municipalities in rural areas and neighborhoods in urban areas; there are 213 and 385 location fixed effects in urban and rural areas, respectively) than in others (in Chile, the location fixed effects correspond to much larger units, roughly comparable to states in the United States; there are 15 and 13 location fixed effects in urban and rural areas, respectively). It is also possible that residential sorting is more pervasive in some countries than in others. We conclude that further econometric or qualitative work would be necessary to better understand the causal pathways whereby wealth affects child cognitive development, and how these vary from country to country, but information would be required beyond what is available to us for such estimates.
Finally, we use longitudinal data from rural Ecuador, rural Nicaragua, and rural and urban Peru to analyze possible changes in the wealth gradients as children age. For this analysis, we limit the sample to children who took the TVIP in all four survey waves in Ecuador (85 percent of children who took the TVIP at baseline), three survey waves in Nicaragua (92 percent), and two survey waves in Peru (96 percent). Figure 2 shows that in all three countries the wealth gradients that are apparent among four-and five-year-old children are also apparent as these children age. In Ecuador, where the panel has the longest duration (seven years), differences in TVIP scores between wealthier and less wealthy children at 12–13 years of age, when children are of an age where they would be completing elementary school, are very similar to those found at five-six years of age. In all three countries, there is no evidence of catchup. On the other hand, the poorest children do not appear to fall further behind either.8
IV. Discussion and Conclusions
Early childhood development has long-lasting consequences for adult success. Long-term panels that have followed children from early ages into adulthood show that children with poor levels of nutrition, inadequate cognitive development, and low levels of socioemotional development tend to do badly in school, have higher levels of unemployment, earn lower wages (even controlling for schooling attainment), have a higher incidence of teenage pregnancy, are more likely to use drugs, are more likely to be involved in criminal activities, and have children with worse nutritional status.
Evidence on the extent to which there are shortfalls and socioeconomic gradients in cognitive development among young children in developing countries is very scarce. In this paper, we use data from five countries in Latin America to show that there are important differences in early language development between children in wealthier and poorer households. Latin America is generally regarded as the most unequal region in the world (World Bank 2005). Our analysis suggests that the differences in income levels and in other measures of well-being that are apparent in adulthood arise early in children’s lives.
Our study has limitations. The lack of nationally representative data for some countries and the lack of urban data for Nicaragua limit our comparisons. Also, our wealth measure is based on correlations of patterns of asset ownership and dwelling characteristics but does not include a complete list of assets and dwelling characteristics, and does not consider that such characteristics have different values (prices). Finally, we are able to compare only one measure of cognitive development across countries.
Nevertheless, the strengths of our study are considerable. It is the first systematic, multicountry comparison of wealth gradients in cognitive development for young children in the developing world over critical periods of their life courses. The gradients we observe are substantial. There are also large differences across countries in levels of child cognitive development. In the three countries where we can follow children over time, there do not appear to be substantive changes in the gradients once children enter school. This pattern, whereby socioeconomic gradients appear early and are largely unchanged after age six, is similar to findings from the United States (Brooks-Gunn at al. 2006; Carneiro and Heckman 2003; Cunha and Heckman 2007).
Our results have important policy implications. They reinforce with much more direct evidence the importance of programs directed toward poor young children in developing countries emphasized in a prominent recent survey (Engle et al. 2011). Nevertheless, they also lead us to be somewhat pessimistic about closing these gaps because the magnitudes of the differential we find are large relative to the program effects that have been estimated in the literature. Berlinski et al. (2009) estimates that preschool attendance improves cognitive development by 0.23 standard deviations in Argentina; cash transfers to very poor households improve cognitive development by 0.18 standard deviations in Ecuador (Paxson and Schady 2010), and 0.10 standard deviations in Nicaragua (Macours et al. 2012); home visits are estimated to improve cognitive development of young children by approximately 0.25 standard deviations in Colombia (Attanasio et al. 2014). In this paper, we estimate that the difference between children in the poorest and the richest quartile in the countries we study are bigger than one standard deviation in urban Colombia and rural Ecuador and larger than 0.75 standard deviations in the urban and rural areas of all five countries (with the exception of rural Colombia, where the difference is 0.57 standard deviations). Differences between children in the top and bottom deciles are of course even larger. The results in our paper underline the magnitude of the challenge faced by policymakers seeking to close the gaps in development in early childhood in Latin America and, we suspect, in many other developing countries.
Footnotes
Norbert Schady is the Principal Economic Advisor for the Social Sector at the Inter-American Development Bank.
Jere Behrman is the William R. Kenan, Jr. professor of economics at the University of Pennsylvania.
Maria Caridad Araujo is a Lead Economist in the Social Protection and Health Division of the Inter-American Development Bank.
Rodrigo Azuero is a PhD candidate in Economics at the University of Pennsylvania.
Raquel Bernal is professor and director of the Center for the Study of Economic Development at Universidad de los Andes.
David Bravo is professor and director of the Centro de Encuestas y Estudios Longitudinales at Pontificia Universidad Católica de Chile.
Florencia Lopez-Boo is a Senior Economist with the Social Protection and Health Division of the Inter-American Development Bank.
Karen Macours is associate professor at the Paris School of Economics and researcher at INRA.
Daniela Marshall is a PhD candidate in Demography at the University of Pennsylvania.
Christina Paxson is professor of economics and public policy and President of Brown University.
Renos Vakis is Lead Economist in the Development Research Group at the World Bank. This research was supported by the Inter-American Development Bank, the World Bank, the Eunice Shriver Kennedy National Institute of Child Health and Development (Grant R01-HD065436), Iniciativa Científica Milenio (Project NS100041), and Grand Challenges Canada (Grant 10036540). The data used in this article can be obtained beginning November 2015 through October 2018 from Norbert Schady, Inter-American Development Bank, norberts{at}iadb.org.
↵1. To make this comparison, we use nationally representative household surveys in each of the five countries, restrict the list of assets and dwelling characteristics to those that are common to both the nationally representative survey and the survey that was the basis for our analysis of the TVIP scores, and calculate wealth indices in the nationally representative surveys, separately for urban and rural areas. We then recalculate a wealth index in the surveys that we use to analyze the TVIP scores, giving each of the assets and dwelling characteristics the same weight that they receive in the calculation of the first principal component in the nationally representative survey. Finally, we graph kernel densities of the distribution of wealth in both surveys (See online Appendix Figure 1).
↵2. Some examples include Schady (2012), which shows that children with low levels of TVIP scores before they enter school are more likely to repeat school grades and have lower scores on tests of math and reading in primary school in Ecuador; Case and Paxson (2008), which shows that low performance on the PPVT at early ages predicts wages in adulthood in the United States; and Cunha and Heckman (2007), which uses the National Longitudinal Survey of Youth (NLSY) to show that, by age three, there is a difference of approximately 1.2 standard deviations in PPVT scores between children in the top and bottom quartiles of the distribution of permanent income in the United States, and that this difference is largely unchanged until at least age 14. More generally, there is a large literature that shows that vocabulary size in kindergarten and earlier predicts reading comprehension throughout school and into early adulthood (see the discussion in Powell and Diamond 2012, and the references therein).
↵3. The World Bank classifies Chile as a high-income country; Colombia, Ecuador, and Peru as upper-middle income countries; and Nicaragua as a lower-middle-income country.
↵4. There are substantial differences across countries in the age distribution of the sample, even for countries where nominally the coverage is the same (Colombia, Ecuador, Nicaragua). To ensure that any comparisons we make across countries are not affected by differences in the age composition of children, all of the results we report give equal weight to each month of age, within a country, and by place of residence (urban or rural). In practice, the unweighted results are very similar to those that use these weights. The t-statistics adjust for the possible correlation of errors at the level of communities or census tract in Colombia, Ecuador, Nicaragua, and Peru, and at the region level in Chile.
↵5. We thank an anonymous referee for pointing this out to us.
↵6. In Peru, the TVIP was translated into Quechua, an indigenous language spoken primarily in rural areas of the highlands, and children were given the option of taking the test in Spanish or Quechua. Twenty-two percent of children in rural areas, but only 0.1 percent of children in urban areas, chose to take the test in Quechua. Because children in households that speak Quechua or another indigenous language may have more limited vocabularies in any given language, and because the likelihood of being a non-Spanish speaker is correlated with household wealth, we exclude children with mothers who report they speak a language other than Spanish in Peru (56 percent and 17 percent in rural and urban areas, respectively) and Ecuador (2 percent in both urban and rural areas).
↵7. The TVIP has been standardized by the test developers on samples of Mexican and Puerto Rican children to have an average score of 100 and a standard deviation of 15 at all ages. The lowest possible standardized score is 55.
↵8. Of course, in the presence of noisy data, one must be cautious about interpreting these panel results for the same reason one must be cautious about interpreting age patterns based on a single cross-section.
- Received January 2013.
- Accepted March 2014.
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