Search

At the request of the State of Pennsylvania, the School District of Philadelphia, in the summer of 2002, asked three for-profit firms to assume responsibility for 30 of its lowest-performing schools and it asked four nonprofit managers to assume the management of 16 other low-performing schools. A difference-in-differences analysis is used to estimate the impact of nonprofit and for-profit management on individual student achievement. Gains in test scores at the treated schools are estimated by comparing them with gains in other low-performing schools in the district. Students at schools under for-profit management outperformed those at schools under nonprofit management in all six years in both reading and math. Most estimations are statistically significant. Impacts of for-profit management relative to district management were positive in math, but no reading impacts could be detected. At nonprofits, students appear to have learned substantially less, especially in math, at nonprofit schools, than had their school remained under regular district management. However, impacts fell short of statistical significance. (Propensity Score Analysis is appended. Contains 21 endnotes and 9 tables.)

Max Weber attributed the higher economic prosperity of Protestant regions to a Protestant work ethic. We provide an alternative theory, where Protestant economies prospered because instruction in reading the Bible generated the human capital crucial to economic prosperity. County-level data from late 19th-century Prussia reveal that Protestantism was indeed associated not only with higher economic prosperity, but also with better education. We find that Protestants' higher literacy can account for the whole gap in economic prosperity. Results hold when we exploit the initial concentric dispersion of the Reformation to use distance to Wittenberg as an instrument for Protestantism. Two appendixes are included: (1) Census Data for Prussia in the 1870s/1880s; and (2) The Spread of Literacy from Lutheran Times to 1871. (Contains 32 footnotes, 4 figures, and 13 tables.)

On July 14, 2006, the U. S. Department of Education's National Center for Education Statistics (NCES) released a study that compared the performance in reading and math of 4th and 8th-graders attending private and public schools. Using information from a nationwide, representative sample of public and private school students collected in 2003 as part of the ongoing National Assessment of Educational Progress (NAEP), the NCES study reported that the performance of students attending private schools was superior to that of students attending public schools. After statistical adjustments were made for student characteristics, the private school advantage among 4th-graders was reported to give way to a 4.5-point public school advantage in math and school-sector parity in reading. After the same adjustments were made for 8th-graders, private schools retained a 7-point advantage in reading but achieved only parity in math. In this paper, the authors argue that NCES's measures of student characteristics were flawed by inconsistent classification across the public and private sectors and by the inclusion of factors open to school influence. Utilizing the same data as the original study but substituting better measures of student characteristics, improved, alternative models identify a private school advantage in 11 out of 12 public-private comparisons. In 8th-grade math, the private school advantage varies between 3 and 6.5 test points; in reading, it varies between 9 and 12.5 points. Among 4th graders, in math, parity is observed in one model, but private schools outperform public schools by 2 and 3 points in the other two models; in 4th-grade reading, private schools have an advantage that ranges from 7 to 10 points. However, although the alternative models constitute an improvement on the NCES model, no conclusions should be drawn as to causal relationships from these or any other results based on NAEP test scores, because they are too fragile to be used for such purposes. Inferring causality from observations at one point in time is highly problematic. Appended are: (1) Variable Descriptions and Summary Statistics; and (2) The Lubienskis' Study. (Contains 9 tables.) [This paper was prepared for the annual meetings of the American Political Science Association, Philadelphia, PA, August 2006.]