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The paper presents a first global investigation of the longer-term inequality implications of COVID-19 by examining the effect of school closures on the ability of children from different countries and backgrounds to engage in continued learning throughout the pandemic, and their implications for intergenerational mobility in education. The analysis builds on the data from the Global Database of Intergenerational Mobility, country-specific results of the learning loss simulation model using weekly school closure information from February 2020 to February 2022, and high-frequency phone survey data collected by the World Bank during the pandemic to assess the incidence and quality of continued learning during periods of school closures across children from different backgrounds. Based on this information, the paper simulates counterfactual levels of educational attainment and corresponding absolute and relative intergenerational educational mobility measures with and without COVID-19 impacts, to arrive at estimates of COVID-19 impacts. The simulations suggest that the extensive school closures and associated learning losses are likely to have a significant impact on both absolute and relative intergenerational educational mobility in the absence of remedial measures. In upper-middle-income countries, the share of children with more years of education than their parents (absolute mobility) could decline by 8 percentage points, with the largest impacts observed in the Latin America region. Furthermore, unequal access to continued learning during school closures across children from households of different socioeconomic backgrounds (proxied by parental education levels) leads to a significant decline in relative educational mobility. [This report was prepared by the World Bank Poverty and Equity Global Practice & Education Global Practice.]

We use publicly available, longitudinal data from Washington state to study the extent to which three interrelated processes--teacher attrition from the state teaching workforce, teacher mobility between teaching positions, and teacher hiring for open positions--contribute to "teacher quality gaps" (TQGs) between students of color and other students in K-12 public schools. Specifically, we develop and implement an agent-based model simulation of decisions about attrition, mobility, and hiring to assess the extent to which each process contributes to observed TQGs. We find that eliminating inequities in teacher mobility and hiring across different schools would close TQGs within 5 years, while just eliminating inequities in teacher hiring would close gaps within 10 years. On the other hand, eliminating inequities in teacher attrition without addressing mobility and hiring does little to close gaps.

Over the last two decades, twenty-two states have moved away from traditional defined benefit (DB) pension systems and toward pension plan structures like the defined contribution (DC) plans now prevalent in the private sector. Others are considering such a reform as it is seen as a means of limiting future pension funding risk. It is important to understand the implications of such reforms for end-of-career exit patterns and workforce composition. Empirical evidence on the relationship between pension plan structure and retirement timing is currently limited, primarily because, most state pension reforms are so new that few employees enrolled in those alternative plans have reached retirement age. An exception, and the subject of our analysis, is the teacher retirement system in Washington State, which introduced a hybrid DB-DC plan in 1996 and allowed employees in its traditional DB plan to transfer into the new plan. Our analysis focuses on a years-of-service threshold, the crossing of which grants employees early retirement eligibility and, in many cases, a large upward shift in retirement wealth. The financial implications of crossing this threshold are far greater under the state's traditional DB plan than under the hybrid plan. We find that employees are responsive to crossing the yearsof-service threshold, but we fail to find significant evidence that the propensity to exit the workforce varies according to plan enrollment.

We investigate factors influencing student sign-ups for Washington State's College Bound Scholarship (CBS) program. We find a substantial share of eligible middle school students fail to sign the CBS, forgoing college financial aid. Student characteristics associated with signing the scholarship parallel characteristics of low-income students who attend 4-year colleges. Simulations suggest the program may address college enrollment gaps, increasing college-going by some disadvantaged groups, it also would reinforce inequalities in college-going that exist between sub-groups of low-income students. Finally, student sign-up rates are lower than has been previously reported. Importantly, we find a perception among program administrators that nearly all eligible students sign up, which shifts attention away from sign-ups to encouraging pledgees to follow through with program requirements. Principal/Guidance Counselor Interview Questions are appended.

We estimate school-choice preferences revealed by the rank-ordered lists submitted by more than 22,000 applicants to a citywide lottery for more than 200 traditional and charter public schools in Washington, DC. The results confirm previously reported findings that commuting distance, school demographics, and academic indicators play important roles in school choice, and that there is considerable heterogeneity of preferences. Simulations suggest segregation by race and income would be reduced and enrollment in high-performing schools increased if policymakers were to expand school choice by relaxing school capacity constraints in individual campuses. The simulations also suggest that closing the lowest-performing schools could further reduce segregation and increase enrollment in high-performing schools. The following are appended: (1) Findings on Neighborhood and Other Non-Academic Characteristics; (2) Detailed Analysis of Endogeneity and Independence of Irrelevant Alternatives; and (3) Supplemental Tables and Figures. [For "Market Signals: How Do DC Parents Rank Schools, and What Does It Mean for Policy? Policy Brief," see ED568708.]

Student growth measures are increasingly common in K-12 school and teacher accountability policy in the United States. At this time, it is still rare for educator preparation programs (EPPs) to be evaluated by similar measures for either low stakes program improvement or higher stakes accreditation processes. Growth in student achievement reflects the contributions of multiple people and multiple entities, including EPPs, via their roles in selecting educators into the profession and providing candidates with education and training. This paper presents a partial list of technical considerations that researchers, policy makers, and, ultimately, EPPs must address when trying to measure or manage performance. We identify two categories of considerations: those that affect the calculation of teacher value added, and those that affect the aggregation of teacher estimates to the EPP level. Consistent with the current literature, we distinguish the effects of preparation programs on teacher performance from the effects of selection into programs and into districts/schools. One of the most perplexing conclusions of several recent papers is the lack of correlation between teachers' training institution and on-the-job performance. We demonstrate how this result can be generated through a teacher-school matching process under certain labor market conditions. Support for this research was provided by the Council for the Accreditation of Educator Preparation.

We conduct an empirical simulation exercise that gauges the plausible impact of increased rates of college attainment on a variety of measures of income inequality and economic insecurity. Using two different methodological approaches--a distributional approach and a causal parameter approach--we find that increased rates of bachelor's and associate degree attainment would meaningfully increase economic security for lower-income individuals, reduce poverty and near-poverty, and shrink gaps between the 90th and lower percentiles of the earnings distribution. However, increases in college attainment would not significantly reduce inequality at the very top of the distribution. [This paper was prepared for the 2020 ASSA session, "The Race between Education and Technology Revisited." For the Policy Brief, see ED603920.]

Teacher and principal evaluation systems now emerging in response to federal, state and/or local policy initiatives typically require that a component of teacher evaluation be based on multiple performance metrics, which must be combined to produce summative ratings of teacher effectiveness. Districts have utilized three common approaches to combine these multiple performance measures, all of which introduce bias and/or additional prediction error that was not present in the performance measures originally. This paper investigates whether the bias and error introduced by these approaches erodes the ability of evaluation systems to reliably identify high- and low-performing teachers. The analysis compares the expected differences in long-term teacher value-added among teachers identified as high- or low-performing under these three approaches, using simulated data based on estimated inter-correlations and reliability of measures in the Gates Foundation's Measures of Effective Teaching project. Based on the results of our simulation exercise presented here, we conclude these approaches can undermine the evaluation system's objectives in some contexts. Depending on the way these performance measures are actually combined to categorize teacher performance, the additional error and bias can be large enough to undermine the district's objectives. An appendix presents: (1) Correlation Matrix; and (2) Estimated Coefficients under the Error-Minimizing Approach.

School districts and state departments of education frequently must choose between a variety of methods to estimating teacher quality. This paper examines under what circumstances the decision between estimators of teacher quality is important. We examine estimates derived from student growth percentile measures and estimates derived from commonly used value-added estimators. Using simulated data, we examine how well the estimators can rank teachers and avoid misclassification errors under a variety of assignment scenarios of teachers to students. We find that growth percentile measures perform worse than value-added measures that control for prior year student test scores and include teacher fixed effects when assignment of students to teachers is nonrandom. In addition, using actual data from a large diverse anonymous state, we find evidence that growth percentile measures are less correlated with value-added measures with teacher fixed effects when there is evidence of nonrandom grouping of students in schools. This evidence suggests that the choice between estimators is most consequential under nonrandom assignment of teachers to students, and that value-added measures controlling for teacher fixed effects may be better suited to estimating teacher quality in this case.

Empirical Bayes' (EB) estimation is a widely used procedure to calculate teacher value-added. It is primarily viewed as a way to make imprecise estimates more reliable. In this paper we review the theory of EB estimation and use simulated data to study its ability to properly rank teachers. We compare the performance of EB estimators with that of other widely used value-added estimators under different teacher assignment scenarios. We find that, although EB estimators generally perform well under random assignment of teachers to classrooms, their performance generally suffers under non-random teacher assignment. Under nonrandom assignment, estimators that explicitly (if imperfectly) control for the teacher assignment mechanism perform the best out of all the estimators we examine. We also find that shrinking the estimates, as in EB estimation, does not itself substantially boost performance. The report also includes an appendix containing three additional tables.

This paper investigates how the precision and stability of a teacher's value-added estimate relates to the characteristics of the teacher's students. Using a large administrative data set and a variety of teacher value-added estimators, it finds that the stability over time of teacher value-added estimates can depend on the previous achievement level of a teacher's students. The differences are large in magnitude and statistically significant. The year-to-year stability level of teacher value-added estimates are typically 25% to more than 50% larger for teachers serving initially higher performing students compared to teachers with initially lower performing students. In addition, some differences are detected even when the number of student observations is artificially set to the same level and the data are pooled across two years to compute teacher value-added. Finally, the paper offers a policy simulation which demonstrates that teachers who face students with certain characteristics may be differentially likely to be the recipient of sanctions in a high stakes policy based on value-added estimates and more likely to see their estimates vary from year-to-year due to low stability.

In a provocative and influential paper, Jesse Rothstein (2010) finds that standard value-added models (VAMs) suggest implausible future teacher effects on past student achievement, a finding that obviously cannot be viewed as causal. This is the basis of a falsification test (the Rothstein falsification test) that appears to indicate bias in VAM estimates of current teacher contributions to student learning. Rothstein's finding is significant because there is considerable interest in using VAM teacher effect estimates for high-stakes teacher personnel policies, and the results of the Rothstein test cast considerable doubt on the notion that VAMs can be used fairly for this purpose. However, in this paper, we illustrate--theoretically and through simulations--plausible conditions under which the Rothstein falsification test rejects VAMs even when there is no bias in estimated teacher effects, and even when students are randomly assigned conditional on the covariates in the model. On the whole, our findings show that the "Rothstein falsification test" is not definitive in showing bias, which suggests a much more encouraging picture for those wishing to use VAM teacher effect estimates for policy purposes. The following are appended: (1) When Falsification Tests Fail; and (2) Negatively Correlated Errors Need Not Cause Bias.

In a provocative and influential paper, Jesse Rothstein (2010) finds that standard value-added models (VAMs) suggest implausible future teacher effects on past student achievement, a finding that obviously cannot be viewed as causal. This is the basis of a falsification test (the Rothstein falsification test) that appears to indicate bias in VAM estimates of current teacher contributions to student learning. More precisely, the falsification test is designed to identify whether or not students are effectively randomly assigned conditional on the covariates included in the model. Rothstein's finding is significant because there is considerable interest in using VAM teacher effect estimates for high-stakes teacher personnel policies, and the results of the Rothstein test cast considerable doubt on the notion that VAMs can be used fairly for this purpose. However, in this paper, we illustrate--theoretically and through simulations--plausible conditions under which the Rothstein falsification test rejects VAMs even when students are randomly assigned, conditional on the covariates in the model, and even when there is no bias in estimated teacher effects. Appended are: (1) When Falsification Tests Fail; and (2) Negatively Correlated Errors Need Not Cause Bias. (Contains 1 table and 53 footnotes.)