Race, Income, And Test Scores: A Structural Model of the Determinants of Test Scores in Toledo Elementary Schools

In this study, we build a structural model for explaining variations in test scores among elementary schools in Toledo, Ohio. In the process of constructing this model we examine the assumptions underlying theories of the determinants of test scores, notably the family background, inequality, concentrated poverty, subculture, and racial theses. Using Census Tract and public school data for the city of Toledo, we test these assumptions and reach a number of conclusions. First, percapita income is the strongest variable associated with test scores. Second, although concentrated poverty negatively correlates with test scores, income is a stronger explanatory variable. Third, schools located in low-income neighborhoods are more likely to have teachers with less experience and less than a master’s degree. Fourth, these teacher characteristics make a small difference in test scores. Finally, race is not a factor in test scores when we control for poverty. The broader implication is that low income is the most serious problem with low-test scores. Scholars have long observed an association between poverty and low-test scores. However, they have debated explanations for this association. Four major position have dominated this debate: 1) The family background perspective, 2) The unequal educational resources view, 3) The concentrated poverty and social dislocation thesis, and 4) The culture of poverty theory, In this study, we reexamine some of the aspects of the debate and the underlying assumptions of these views and positions. Our primary purpose in this study is to construct a structural model for explaining The determinants of tests scores in Toledo public schools. Before building the model, we briefly reexamine some of the assumptions under girding the debate over the determinants of test scores. Our goal is not to provide comprehensive overview of this debate but simply to situate our study within the context of the broader literature. We identify a few of assumptions and test them. However, our goal is to create a structural model. In developing our model, we use census tract data for the city of Toledo and Toledo public school reports. Our model includes four factors: community, teacher profile, student profile, and test scores. Each factor consists of several variables, the households, and percapita income. Teacher profile variables involve teacher experience and teacher education. Student profile variables are the proportion of students entering and leaving the school and the percentage of students on the federal school lunch program. We use these data to test come of the assumptions arising from the debate over the determinants of test scores. Our ultimate goal is to develop a structural model explaining these determinants. We focus on the Toledo area because of its convenience, as we are located in Toledo, and because of its demographics. Its population of 332,943 to a large extent mirrors the nation. It has a poverty rate of 15.4 and mean income of $24,819, both equivalent to national figures. It has a black population of 65,659 or 19.7 percent of the city’s population, slightly above the national proportion of just above 12 percent. We proceed as follows: First, we briefly discuss these four major positions in the debate. Second, we develop four testable assumptions from our discussion. Third, we construct our model. Finally we test our assumption as we complete our model.