View Research by Author - Zeyu Xu

Publications

Differences in teacher quality would appear to be the most likely reason for disparities in the quality of high-poverty and lower-poverty schools. However, the linkages between teacher quality and socio-economic-based disparities in student achievement are quite complex. Using data from North Carolina and Florida, this paper examines whether teachers in high-poverty schools are as effective as teachers in schools with more advantaged students. Bottom teachers in high-poverty schools are less effective than bottom teachers in lower-poverty schools. The best teachers, by comparison, are equally effective across school poverty settings. The gap in teacher quality appears to arise from the lower payoff to teacher qualifications in high-poverty schools. In particular, the experience-productivity relationship is weaker in high-poverty schools and is not related to teacher mobility patterns. Recruiting teachers with good credentials into high-poverty schools may be insufficient to narrow the teacher quality gap. Policies that promote the long-term productivity of teachers in challenging high-poverty schools appear key.

The gold standard in making causal inference on program effects is a randomized trial. Most randomization designs in education randomize classrooms or schools rather than individual students. Such "clustered randomization" designs have one principal drawback: They tend to have limited statistical power or precision. This study aims to provide empirical information needed to design adequately powered studies that randomize schools using data from Florida and North Carolina. The authors assess how different covariates contribute to improving the statistical power of a randomization design and examine differences between math and reading tests; differences between test types (curriculum-referenced tests versus norm-referenced tests); and differences between elementary school and secondary school, to see if the test subject, test type, or grade level makes a large difference in the crucial design parameters. Finally they assess bias in 2-level models that ignore the clustering of students in classrooms.

This paper examines the effect of school mobility rates on the performance of different groups of students in North Carolina. We use detailed administrative data on North Carolina students and schools from 1996 to 2005 and follow four cohorts of 3rd graders for six years each. We find school mobility rates, were highest for minority and disadvantaged students, declined across successive cohorts for Hispanic students, but increased for Black students. Also, school mobility hurt the math performance of Black and Hispanic students, but not that of white students, and improved the reading performance of white and more advantaged students, but had no effect on the reading performance of minority students. “Strategic” school moves (cross-district) benefitted or had no effect on student performance, but "reactive" moves (within district) hurt all groups of students. White and Hispanic students were more likely to move to a higher quality school and Blacks students, to a lower quality school. Negative effects of school mobility increased with the number of school moves.

Teach for America (TFA) selects and places graduates from the most competitive colleges as teachers in the lowest-performing schools in the country. This paper is the first study that examines TFA effects in high school. We use rich longitudinal data from North Carolina and estimate TFA effects through cross-subject student and school fixed-effects models. We find that TFA teachers tend to have a positive effect on high school student test scores relative to non-TFA teachers, including those who are certified in-field. Such effects exceed the impact of additional years of experience and are particularly strong in math and science.

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