basis of these comparisons, Gertler and Fernald argue that children in the evaluation sample appear to have very serious cognitive deficits. On average, they place in the 17<sup>th</sup> percentile for vocabulary on the TVIP, and in the 15<sup>th</sup> percentile for long-term memory, the 22<sup>nd</sup> percentile for short-term memory, and the 7<sup>th</sup> percentile for visual integration in the Woodcock-Johnson-Munoz test. The results presented by Gertler and Fernald are not disaggregated by the age of the child, so it is not clear whether the pattern observed by Fernald et al. (2005) using the Bayley scores— larger deficits for older children—is also apparent in this sample and with these outcomes.

Halpern et al. (1996) use the Denver Test to analyze deficits in gross and fine motor skills, language development, and adequate socialization of a sample of children born in hospitals in Pelotas, Rio Grande do Sul, Brazil (see also Victora et al. 1989). The authors conclude that about one-third (34 percent) of children show developmental deficits. There are clear gradients by household income, and these persist after corrections are made for birthweight. (Children born in households with lower socioeconomic status have lower birthweights.) Moreover, Halpern et al. find that the income gradients they observe become larger with the age of the child.

Paxson and Schady (2005) use data on a sample of poor children in Ecuador to study the determinants of child cognitive development, as measured by performance on the TVIP. They show that the age-normed TVIP score declines steadily between 3 and 6 years of child age. This decline in the mean is accompanied by an increase in dispersion. Paxson and Schady graph the 90<sup>th</sup> , 50<sup>th</sup> , 25<sup>th</sup> and 10<sup>th</sup> percentiles of the TVIP scores at each age. They show that the 90<sup>th</sup> percentile scores are relatively constant with age, and there are modest declines in the median; by contrast, scores for children at the 25<sup>th</sup> and 10<sup>th</sup> percentiles of the distribution decline sharply with age.

What factors are responsible for the increasing dispersion in test scores with age? Paxson and Schady (2005) first use simple graphs to analyze this question. Figure 1, taken from their paper, shows that the median TVIP score for children born to parents with low levels of education or low levels of household wealth falls dramatically with the age of the child. By contrast, the score remains roughly constant for children in wealthier households or with more educated parents. Finally, Figure 1 shows that there are no differences between the scores of urban and rural children in their sample. The effects of socioeconomic status on test performance are large: Paxson and Schady estimate that a child whose family falls at the 90<sup>th</sup> percentile for wealth, maternal education and paternal education is predicted to have a score that is approximately two standard deviations higher than a child at the 10<sup>th</sup> percentile for each of these variables. Paxson and Schady also consider the relationship between cognitive outcomes, socioeconomic status, and measures of child nutritional status. They show that nutritional status, in particular hemoglobin 14