provides summary statistics on subgroups within the final linked dataset for 4th-grade reading test scores. Of the total linked children: 43.1% were black (56.9% are white); 52.9% enrolled in the free or reduced lunch program; 8.1% had parents who did not complete high school; 45.3% had parents who completed high school; and 46.6% had parents who had more than high school education (college, graduate school, etc.). Blood lead levels for the linked children ranged from 1 to 16 μg/dL, with the mean and median levels of 4.8 and 4 μg/dL, respectively. The interquartile range was 3, with the 25th and 75th percentiles equal to 3 and 6 μg/dL, respectively. Average EOG test scores were lower for children enrolled in the free or reduced lunch program, children whose parents had lower levels of education, and children who were exposed to more lead. Black children were overrepresented in the “riskier” end of each of these variables.
Summary statistics on subgroups in the final linked 4th-grade reading dataset for 100 counties in NC (N = 57,678).
presents the results of the multivariate linear regression for reading EOG test scores, controlling for the covariates in and individual school system variability. The referent group is defined as white, female students, enrolled in the Wake County School System, who do not participate in the free or reduced lunch program, whose parents graduated high school, and who have a blood lead level equal to one. The coefficients of all the covariates are of the expected signs. We tested for interaction between blood lead levels and parental education or enrollment status in the free and reduced lunch program, but no significant interactions were found. We also tested for interaction between lead exposure and the age indicators, and again, the results were not significant. Therefore, to simplify interpretations, we did not include the interactions in the final model.
Results of multivariate linear regression for 4th-grade reading EOG score data (N = 57,678)a.
The coefficients of the indicator variables for each blood lead level are consistently negative and statistically significant (all p < .0001). They generally increase in absolute magnitude as blood lead levels increase. Thus, these results demonstrate a strong and statistically significant dose-response effect between early childhood lead exposure and performance on elementary school achievement tests (i.e., the higher the early childhood lead exposure, the lower the EOG test scores for the child).
also demonstrates a strong and statistically significant dose-response effect between parental educational attainment and performance on elementary school achievement tests (i.e., the more education completed by the parent, the higher the EOG test scores for the child). Parental educational attainment may be serving as a proxy for family socioeconomic status or parental IQ (or some combination of the two) (Mueller et al., 2001
; Davis-Kean, 2005
). The coefficient on participation in the free or reduced lunch program is negative and significant as expected.
While the linear regression results provide interesting and important insights, they focus on study subjects at the mean of the EOG distribution curves. We are interested in both the top and bottom tails of the distribution: location on the top tail determines placement into advanced and intellectually gifted programs, and location on the bottom tail determines grade advancement and class placement. To understand how the environmental and social stressors are affecting subjects/population located on different portions of the EOG curve, we employed quantile regression. Similar to the linear regression model, interaction effects among key explanatory variables were also tested in the quantile regression models, but were not found to be significant. displays three box and whiskers plots summarizing the results from the quantile regression.
Box plots of the estimated 4th-grade reading EOG scores from quantile regression models (N = 57,678).
The first panel of displays the distributions of EOG scores for children at five different blood lead levels. All five boxes are based on children not enrolled in free/reduced lunch and whose parents completed high school. EOG scores generally decrease as blood lead levels increase. For all quantiles, the effects of increasing blood lead level from 1 μg/dL to any blood lead level greater than 3 μg/dL are statistically significant (p < .05). The distributions for children with relatively high lead exposure are more spread out than those for children with relatively low lead exposure (e.g., the difference between the 95th and 5th percentiles is 24.6 for children with a blood lead level of 10 μg/dL or higher, and is 23.1 for children with a blood lead level of 1 μg/dL). Most of this additional spread occurs on the low end of the EOG distribution. For example, when going from a blood lead level of 1–10+ μg/dL, the 5th percentile drops about 2.3 points whereas the 95th percentile drops only 0.8 points. Lead exposure stretches out the lower tail of the test score distribution by more than it moves the middle portion or upper tail of the distribution. This differential effect is statistically significant (p = .04). Thus, lead effects may be even more potent in populations at the lower performance regions of the EOG curve.
The second panel of displays the distributions of EOG scores for children with differing parental education. All five boxes are based on children not enrolled in free/reduced lunch and who have a BLL = 1 μg/dL. EOG scores tend to decrease with lower parental educational attainment. For all quantiles, the effects of decreasing education level from any education level more than high school education to the level of less than high school education are statistically significant (p < .05). The distributions for parents with comparatively low educational attainment are more spread out than those for parents with higher educational attainment (e.g., the difference between the 95th and 5th percentiles is 23.4 when parents have less than high school education and is 20.7 when parents have graduate education). Akin to the effects of lead exposure, most of this additional spread occurs on the low end of the EOG distribution. There is an 8.0 point gap in the 95th percentile scores between parents who have less than high school education and parents who have graduate education, whereas the gap is 10.7 points in the 5th percentile scores. This differential effect is statistically significant (p < .00001).
, in the last panel, displays the distributions of EOG scores for children enrolled or not enrolled in the free and reduced lunch program. Both boxes are based on children whose parents have a high school degree only and who have a BLL = 1 μg/dL. EOG scores tend to be lower for children enrolled in the free and reduced lunch program (p < .05). The 95th percentile values for the two groups differ by 1.7 points, whereas the 5th percentile values differ by 2.6 points. This differential effect is statistically significant (p = .0002).
The negative effects shown in are for each variable holding the other variables constant. Some children are subject to multiple risk factors that would affect test performance. This is illustrated in , which compares the percentiles of test scores for children who have “lower risk” values on all three variables (parents who have completed college, not enrolled in lunch program, and BLL = 1 μg/dL) with the percentiles of test scores for children who have comparatively “higher risk” values on all three variables (parents who have only completed high school, enrolled in lunch program, and BLL = 5 μg/dL). For example, the 5th percentile of test scores for children in the “higher risk” group is about 10 points lower than the 5th percentile of test scores for children in the “lower risk” group (shown in the first bar in ).
Cumulative deficit: decrease in EOG scores by multiple risk factors (N = 57,678).
reveals that parental education differences account for the largest part of the test score decrement at any percentile (58–65% of total decrement), and that the lunch program and lead exposure account for 25–28% and 7–16% of the total test score decrement, respectively. Of course, as lead exposure increases, the proportion of the test score drop accounted for by lead exposure increases. also highlights the trend seen in : deficits in these variables impact low percentiles of the test score distribution more than they impact high percentiles. Again, the combination of risk factors has a greater impact on the population at the lower end of the EOG distribution.