Pension Reform and Labor Supply: Retention and Productivity under a Pension Cut

National Bureau of Economic Research · 2025-04-01

We examine the effect of a representative pension reform on the retention and productivity of workers.The reform cut pension annuities and early retirement benefits for public school teachers, projected to save eight percent of pension revenues.We leverage administrative records and a discontinuity in the reform to estimate its effect.Contrary to expectations, the cut increased retention by discouraging early retirement.Using idiosyncratic within-school variation in exposure, we find the reform somewhat increased student achievement by 0.01-0.03standard deviations, partly through reduced turnover.The reform thus maintained or improved both teacher retention and productivity.

Pension Reform and Labor Supply

SSRN Electronic Journal · 2021 · 4 citations

• Labour economics
• Business
• Economics

Replication data for: The Causes and Consequences of Test Score Manipulation: Evidence from the New York Regents Examinations

ICPSR Data Holdings · 2019-01-01

We show that the design and decentralized scoring of New York's high school exit exams—the Regents Examinations—led to systematic manipulation of test scores just below important proficiency cutoffs. Exploiting a series of reforms that eliminated score manipulation, we find heterogeneous effects of test score manipulation on academic outcomes. While inflating a score increases the probability of a student graduating from high school by about 17 percentage points, the probability of taking advanced coursework declines by roughly 10 percentage points. We argue that these results are consistent with test score manipulation helping less advanced students on the margin of dropping out but hurting more advanced students that are not pushed to gain a solid foundation in the introductory material.

The Causes and Consequences of Test Score Manipulation: Evidence from the New York Regents Examinations

American Economic Journal Applied Economics · 2019-06-27 · 81 citations

We show that the design and decentralized scoring of New York’s high school exit exams—the Regents Examinations—led to systematic manipulation of test scores just below important proficiency cutoffs. Exploiting a series of reforms that eliminated score manipulation, we find heterogeneous effects of test score manipulation on academic outcomes. While inflating a score increases the probability of a student graduating from high school by about 17 percentage points, the probability of taking advanced coursework declines by roughly 10 percentage points. We argue that these results are consistent with test score manipulation helping less advanced students on the margin of dropping out but hurting more advanced students that are not pushed to gain a solid foundation in the introductory material. (JEL H75, I21, I28)

Measuring the Impacts of Teachers: Reply to Rothstein

American Economic Review · 2017-01-01 · 1 citations

Using Lagged Outcomes to Evaluate Bias in Value-Added Models

American Economic Review · 2016-05-01 · 23 citations

Value-added (VA) models measure agents' productivity based on the outcomes they produce. The utility of VA models for performance evaluation depends on the extent to which VA estimates are biased by selection. One common method of evaluating bias in VA is to test for balance in lagged values of the outcome. We show that such balance tests do not yield robust information about bias in value-added models using Monte Carlo simulations. Even unbiased VA estimates can be correlated with lagged outcomes. More generally, tests using lagged outcomes are uninformative about the degree of bias in misspecified VA models. The source of these results is that VA is itself estimated using historical data, leading to non-transparent correlations between VA and lagged outcomes.

The Causes and Consequences of Test Score Manipulation: Evidence from the New York Regents Examinations

National Bureau of Economic Research · 2016-04-01 · 22 citations

In this paper, we show that the design and decentralized, school-based scoring of New York's high school exit exams -the Regents Examinations -led to the systematic manipulation of test sores just below important proficiency cutoffs. Our estimates suggest that teachers inflate approximately 40 percent of test scores near the proficiency cutoffs. Teachers are more likely to inflate the scores of high-achieving students on the margin, but low-achieving students benefit more from manipulation in aggregate due to the greater density of these students near the proficiency cutoffs. Exploiting a series of reforms that eliminated score manipulation, we find that inflating a student's score to fall just above a cutoff increases his or her probability of graduating from high school by 27 percent. These results have important implications for educational attainment of marginal high school graduates. For example, we estimate that the black-white graduation gap is about 5 percent larger in the absence of test score manipulation.

The Causes and Consequences of Test Score Manipulation: Evidence from the New York Regents Examinations. CEPA Working Paper No. 16-08.

2016-04-01 · 1 citations

Using Lagged Outcomes to Evaluate Bias in Value-Added Models

National Bureau of Economic Research · 2016-02-01 · 6 citations

Value-added (VA) models measure the productivity of agents such as teachers or doctors based on the outcomes they produce. The utility of VA models for performance evaluation depends on the extent to which VA estimates are biased by selection, for instance by differences in the abilities of students assigned to teachers. One widely used approach for evaluating bias in VA is to test for balance in lagged values of the outcome, based on the intuition that today's inputs cannot influence yesterday's outcomes. We use Monte Carlo simulations to show that, unlike in conventional treatment effect analyses, tests for balance using lagged outcomes do not provide robust information about the degree of bias in value-added models for two reasons. First, the treatment itself (value-added) is estimated, rather than exogenously observed. As a result, correlated shocks to outcomes can induce correlations between current VA estimates and lagged outcomes that are sensitive to model specification. Second, in most VA applications, estimation error does not vanish asymptotically because sample sizes per teacher (or principal, manager, etc.) remain small, making balance tests sensitive to the specification of the error structure even in large datasets. We conclude that bias in VA models is better evaluated using techniques that are less sensitive to model specification, such as randomized experiments, rather than using lagged outcomes.

1. Finance and Economics

Columbia University Press eBooks · 2016-12-31