Josh Kinsler

Estimating spillovers using panel data, with an application to the classroom

Obtaining consistent estimates of spillovers in an educational context is hampered by at least two issues: selection into peer groups and peer effects emanating from unobservable characteristics. We develop an algorithm for estimating spillovers using panel data that addresses both of these problems. The key innovation is to allow the spillover to operate through the fixed effects of a student’s peers. The only data requirements are multiple outcomes per student and heterogeneity in the peer group over time. We first show that the non-linear least squares estimate of the spillover parameter is consistent and asymptotically normal for a fixed T. We then provide an iterative estimation algorithm that is easy to implement and converges to the non-linear least squares solution. Using University of Maryland transcript data, we find statistically significant peer effects on course grades, particularly in courses of a collaborative nature. We compare our method with traditional approaches to the estimation of peer effects, and quantify separately the biases associated with selection and spillovers through peer unobservables.

Assessing Rothstein's critique of teacher value-added models

Value-added models of teacher effectiveness yield consistent estimates of teacher quality under the assumption that students are randomly assigned to classrooms conditional on ability. Rothstein (2010) tested and rejected this underlying sorting assumption, casting doubt on the usefulness of the value-added framework. In this paper, I illustrate that the falsification tests employed by Rothstein perform quite poorly in small samples and I propose an alternative testing strategy that is less sensitive to sample size. I also show that none of the proposed falsification tests works well when the achievement production function is misspecified. Finally, I return to the same North Carolina sample employed by Rothstein and retest the assumption of conditional random assignment. Once I account for the “smallness” of the data and allowteacher inputs to persist at reasonable rates, I fail to reject conditional random assignment. Keywords. Teacher value-added, model testing. JEL classification. I20, C10.

School Discipline: A Source or Salve for the Racial Achievement Gap?

Racial disparities in school discipline are believed to contribute to the persistent achievement gap between black and white students. In this article, I estimate the relationship between school discipline and achievement within a structural model, taking into account the spillover effects of disruptive behavior. I find that discipline has an overall positive influence on student performance and that the racial gap in discipline stemming from cross‐school variation in discipline policies is consistent with achievement maximization. Integrating schools can close both the discipline and achievement gaps; however, overall achievement is reduced since schools are less able to target their discipline policies.

Family Income and Higher Education Choices: The Importance of Accounting for College Quality

In the examination of the determinants of educational choices, little attention has been devoted to the relationship between family income and the quality of higher education. Using the 1979 and 1997 waves of the NLSY, we show that family income significantly affects the quality of higher education, especially for high-ability individuals.While the impact of family income on college quality is significant in both samples, it has declined considerably over time for high-ability students. Overall, the trends we observe are highly consistent with increases in tuition across the quality spectrum, coupled with more generous merit-based aid at high-quality institutions.

The Specificity of General Human Capital: Evidence from College Major Choice

College graduates do not always pursue careers related to their major. Science majors working in jobs unrelated to their field of study earn approximately 30% lower wages than those working in related jobs. We develop a structural model of major choice and labor market outcomes that allows for skill uncertainty and differential accumulation of human capital across major. Our findings confirm that the average return to obtaining a science degree and working in a related job remains close to 30%. We also find that individuals are uncertain about their future productivity at the time of the college major decision.

Beyond Levels and Growth: Estimating Teacher Value-Added and its Persistence

The levels and growth achievement functions make extreme and diametrically opposed assumptions about the rate at which teacher inputs persist. I first show that if these assumptions are incorrect, teacher value-added estimates can be significantly biased. I then develop a tractable, cumulative model of student achievement that allows for the joint estimation of unobserved teacher quality and its persistence. The model can accommodate varying persistence rates, student heterogeneity, and time-varying teacher attributes. I implement the proposed methodology using schooling data from North Carolina, and find that only a third of the contemporaneous teacher effect survives into the next grade.

Productivity Spillovers in Team Production: Evidence from Professional Basketball

We estimate a model where workers are heterogeneous both in their own productivity and in their ability to facilitate the productivity of others. We use data from professional basketball to measure the importance of peers in productivity because we have clear measures of output and members of a worker’s group change on a regular basis. Our empirical results highlight that productivity spillovers play an important role in team production. Despite this, we find that worker compensation is largely determined by own productivity with little weight given to productivity spillovers.

Teacher Complementarities in Test Score Production: Evidence from Primary School

The existence of teacher complementarities across grades can have important implications for the efficient assignment of students to teachers and for the evaluation of teachers using standard value-added models (VAMs). In this paper, I extend the basic VAM to allow for interactive effects between teachers. Using data from North Carolina’s primary schools, I find that teacher complementarities within schools are extremely small. This finding suggests that there is little to gain by assigning students to particular teacher sequences. Moreover, measures of teacher effectiveness generated from a typical VAM are not likely to be biased by interactions with past teacher inputs.