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The Effect of School Quality on Black-White Health Differences: Evidence From Segregated Southern Schools


This study assesses the effect of black-white differences in school quality on black-white differences in health in later life resulting from the racial convergence in school quality for cohorts born between 1910 and 1950 in southern states with segregated schools. Using data from the 1984–2007 National Health Interview Surveys linked to race-specific data on school quality, we find that reductions in the black-white gap in school quality led to modest reductions in the black-white gap in disability.

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  1. Many other explanations for the causes of racial differences have been explored, including physician discrimination (Balsa and McGuire 2003), the use of health care providers of differing quality (Baicker et al. 2005), the availability and type of insurance (Balsa et al. 2007; Currie et al. 2008), patient compliance with therapy (Simeonova 2008), medical knowledge (Aizer and Stroud 2010), residential segregation (Williams and Jackson 2005), and income (Williams and Jackson 2005).

  2. As Donohue et al. (2002) noted, migration from urban to rural areas within the South explains little of the convergence in quality of black and white schools.

  3. The Rosenwald Fund provided matching grants to build schools and required minimum standards for teachers’ wages and the length of the school year in order to receive funding (Donohue et al. 2002). Thus, the Fund required commitment from blacks in the local communities, which could reflect preferences for education. However, Aaronson and Mazumder (2011) found that the socioeconomic conditions of blacks were unrelated to the locations of Rosenwald schools. Because the Rosenwald Fund contributed to the building of new schools, the improved sanitation in these new schools could be correlated with the improvements in school quality for blacks during the 1920s. Although our primary measure of school quality was influenced by the Rosenwald Fund, this measure does not reflect the additional improvements in school quality specifically from the construction of new schools. To incorporate this additional component of school quality, we examine data, generously shared by Bhashkar Mazumder, on the construction of Rosenwald schools between 1919 and 1932. Because we do not have information on the total quantity of school buildings attended by white and black students, as opposed to just the number of Rosenwald schools, we add the average exposure to Rosenwald schools for black students for each state and for each cohort as a separate variable to the specification in Eq. (2). As shown in Table 6 in the appendix, we find that exposure to Rosenwald schools does not influence the primary result of the study, which is the influence of the convergence in school quality on racial differences in disability.

  4. Cotton, which relied significantly on slave labor, was the primary crop in the states with relatively large black populations, while tobacco was the primary crop in states with relatively smaller black populations (Daniel 1985; Fogel and Engerman 1974; Wright 1986).

  5. The emphasis on schooling and the decline in child labor was most pronounced among southern blacks, both in farm and nonfarm households (Tolnay 1999). For example, the percentage of black youths ages 10–14 and from southern farm households who were enrolled in school increased by 11 percentage points to 85 % between 1910 and 1940, while corresponding figures for white and northern black children remained constant (Tolnay 1999). The percentage of black adolescents in southern farm households who were working decreased by 37 percentage points to 43 % during this period; the decrease in child labor was most pronounced for females (Tolnay 1999). These trends reflected the changing labor market for blacks in the South: an increase in wage laborers instead of tenant farmers, an increase in agricultural productivity from technological advances, and an increase in off-farm employment (Tolnay 1999).

  6. The desegregation of hospitals in the South in the 1960s improved access to medical care for blacks and decreased infant mortality (Almond et al. forthcoming). The integration of hospitals occurred very rapidly throughout the region because of the Civil Rights Act and the Medicare Act (Almond et al. forthcoming). Thus, variation in exposure to integrated hospitals is reflected in the cohort fixed effects.

  7. The six excluded states are Alabama, Florida, Georgia, Louisiana, Mississippi, and North Carolina. The prevalence estimates, which were also used in Bleakley (2007) and reported by Kofoid and Tucker (1921), are based on a survey of U.S. Army recruits.

  8. We also estimate a model using the specification described by Eq. (2) (not reported) in which black-white differences in family income is the outcome. Although our specification differs from Card and Krueger’s (1992), consistent with their results, we find that a convergence in school quality results in a significant convergence in income.

  9. The southern states in our sample are Alabama, Arkansas, Delaware, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, Missouri, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia. The District of Columbia was also included; for simplicity, our references to “southern states” throughout the discussion of results also include the District of Columbia.

  10. There are 18 states of birth, four birth cohort groups, four survey year groups, and two sexes, creating a potential total of 576 cells that would measure black-white differences. We excluded 109 cells with missing values for IMR, which we interpolated but did not extrapolate, and with fewer than 10 blacks and 10 whites. Of the 109 cells, 63 were cells with fewer than 10 blacks and 10 whites; and 60 cells had missing IMR values, 14 of which also had fewer than 10 blacks and 10 whites. Five additional cells did not have data because of the coverage of the NHIS. One of the states, Delaware, was nearly always excluded, contributing only four cells out of a potential 32 cells. Approximately three-quarters of the excluded cells are from the 1910–1919 birth cohorts.

  11. The average number of days per school year could be viewed as a measure of the quantity of schooling; however, as noted earlier, we consider term length as a measure of school quality because the number of days per school year is a determinant of the amount of human capital gained within a year of completed schooling.

  12. We thank David Card for providing the detailed data used to construct the 10-year averages published in Card and Krueger (1992), which include state averages for 1915 and biennially from 1918 to 1966.

  13. Card and Krueger (1992) noted that the average teacher wages reported in the school quality data are nearly identical to the state- and race-specific average wages for teachers in the 1940, 1950, and 1960 censuses; however, the authors acknowledge the possibility of measurement error in these data, particularly in the early years. Because the school quality measures are assigned to individuals based on their years of schooling attended and then aggregated for each cohort in each state, the school quality measures are weighted averages of 20 years of school quality data, which reduces the influence of measurement error from a specific year. Related to measurement error concerns, the average pupil-teacher ratio is based on enrollment instead of attendance, so that these reported statistics may not accurately reflect the number of students present in a classroom. As Card and Krueger (1996) and Heckman et al. (1996) noted, using state-level measures of school quality could reduce the attenuation bias from school-level measures of school quality that are potentially measured with error or that do not reflect the quality of schooling received throughout all years of schooling. Further, using state averages of school quality masks variation in school districts within states, which will lead to a downward bias in the estimated impact of school quality (Donohue et al. 2002).

  14. The scoring coefficients for the summary measure of school quality indicate that term length receives the most weight (scoring coefficient = 0.45), followed by pupil-teacher ratio (scoring coefficient = −0.39), and teacher wages (scoring coefficient = 0.17). The “uniqueness” of the three individual school quality measures is 0.194 for pupil-teacher ratio, 0.396 for teacher wages, and 0.171 for term length, indicating that the summary factor measure explains most of the variance in all three individual school quality measures.

  15. A complete set of the estimates for the specifications shown in each table is available in Online Resource 1.

  16. Further supporting this claim are specifications that include an interaction term between the black-white difference in the proportion of the cell population that still lives in the South and the black-white difference in school quality.

  17. We also investigate whether veteran’s status mediates the effects. However, the NHIS significantly changed its measure of veteran status in 1997, making it difficult to interpret an analysis similar to that using the other mediating variables. Nevertheless, we do not find evidence that veteran status mediates the effects that we observe.

  18. We also investigated whether black-white differences in school quality were associated with aggregate mortality before we observed individuals in the NHIS data. Specifically, we used census data to look at death rates between 1960–1970 and 1970–1980 using the approach of Lleras-Muney (2005). We found no evidence that black-white differences in school quality were associated with black-white differences in mortality before we observed individuals in the NHIS data (results not shown), reducing concerns about selective mortality bias. On the other hand, it is interesting that differences in school quality are associated with disability but not with reductions in early mortality except when we exclude the oldest cohorts in the NHIS data. Possible explanations for this finding include measurement error in the calculation of death rates using successive censuses and advancements in medical care and medical technology that are relatively more likely to be used by individuals with greater education from higher-quality schools that extend longevity but do not reduce morbidity.


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This project was funded, in part, by the National Institute of Mental Health (T32 postdoctoral traineeship), the Robert Wood Johnson Foundation, Emory University Woodruff Funds, and the Emory Global Health Institute. We thank Al Headen, Ellen Meara, Frank Sloan, Jim Walker, and Ty Wilde; seminar participants at the Federal Reserve Bank of Atlanta and the University of Wisconsin; and participants at the American Society of Health Economists biennial conference, the Association for Public Policy Analysis and Management annual conference, the Census Restricted Data Center conference, and the Southeastern Health Economics Study Group for helpful comments. We thank David Card for sharing school quality data, Ken Chay for sharing hospital desegregation data, and Bhash Mazumder for sharing data about the Rosenwald schools. We are especially grateful to Patricia Barnes, Stephanie Robinson, and Deborah Rose at the National Center for Health Statistics for their assistance with the restricted-access National Health Interview Survey data. The findings and conclusions in this article are those of the authors and do not necessarily represent the views of the Research Data Center, the National Center for Health Statistics, or the Centers for Disease Control and Prevention.

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Correspondence to Ezra Golberstein.

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Table 5 The influence of black-white differences in school quality on black-white differences in health measures
Table 6 The influence of black-white differences in school quality and exposure to Rosenwald schools on black-white differences in health

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Frisvold, D., Golberstein, E. The Effect of School Quality on Black-White Health Differences: Evidence From Segregated Southern Schools. Demography 50, 1989–2012 (2013).

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  • Education
  • Health status
  • School quality
  • Health disparities