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Zajonc, Tristan

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Zajonc

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Tristan

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Zajonc, Tristan
Author's Publications: search.filters.isAuthorOfPublication.bca0d95d-af39-4362-b162-307773fb536e

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    Essays on Causal Inference for Public Policy
    (2012-08-07) Zajonc, Tristan; Imbens, Guido W
    Effective policymaking requires understanding the causal effects of competing proposals. Relevant causal quantities include proposals' expected effect on different groups of recipients, the impact of policies over time, the potential trade-offs between competing objectives, and, ultimately, the optimal policy. This dissertation studies causal inference for public policy, with an emphasis on applications in economic development and education. The first chapter introduces Bayesian methods for time-varying treatments that commonly arise in economics, health, and education. I present methods that account for dynamic selection on intermediate outcomes and can estimate the causal effect of arbitrary dynamic treatment regimes, recover the optimal regime, and characterize the set of feasible outcomes under different regimes. I demonstrate these methods through an application to optimal student tracking in ninth and tenth grade mathematics. The proposed estimands characterize outcomes, mobility, equity, and efficiency under different tracking regimes. The second chapter studies regression discontinuity designs with multiple forcing variables. Leading examples include education policies where treatment depends on multiple test scores and spatial treatment discontinuities arising from geographic borders. I give local linear estimators for both the conditional effect along the boundary and the average effect over the boundary. For two-dimensional RD designs, I derive an optimal, data-dependent, bandwidth selection rule for the conditional effect. I demonstrate these methods using a summer school and grade retention example. The third chapters illustrate the central role of persistence in estimating and interpreting value-added models of learning. Using data from Pakistani public and private schools, I apply dynamic panel methods that address three key empirical challenges: imperfect persistence, unobserved student heterogeneity, and measurement error. After correcting for these difficulties, the estimates suggest that only a fifth to a half of learning persists between grades and that private schools increase average achievement by 0.25 standard deviations each year. In contrast, value-added models that assume perfect persistence yield severely downwardly biased and occasionally wrong-signed estimates of the private school effect.
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    Here Today, Gone Tomorrow? Examining the Extent and Implications of Low Persistence in Child Learning
    (2009) Andrabi, Tahir; Das, Jishnu; Khwaja, Asim; Zajonc, Tristan
    Learning persistence plays a central role in models of skill formation, estimates of education production functions, and evaluations of educational programs. In non-experimental settings, estimated impacts of educational inputs can be highly sensitive to correctly specifying persistence when inputs are correlated with baseline achievement. While less of a concern in experimental settings, persistence still links short-run treatment effects to long-run impacts. We study learning persistence using dynamic panel methods that account for two key empirical challenges: unobserved student-level heterogeneity in learning and measurement error in test scores. Our estimates, based on detailed primary panel data from Pakistan, suggest that only a fifth to a half of achievement persists between grades. Using private schools as an example, we show that incorrectly assuming high persistence significantly understates and occasionally yields the wrong sign for private schools’ impact on achievement. Towards an economic interpretation of low persistence, we use question-level exam responses as well as household expenditure and time-use data to explore whether psychometric testing issues, behavioral responses, or forgetting contribute to low persistence—causes that have different welfare implications.
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    Do Value-Added Estimates Add Value? Accounting for Learning Dynamics
    (2008) Andrabi, Tahir; Das, Jishnu; Khwaja, Asim; Zajonc, Tristan
    Evaluations of educational programs commonly assume that what children learn persists over time. The authors compare learning in Pakistani public and private schools using dynamic panel methods that account for three key empirical challenges to widely used value-added models: imperfect persistence, unobserved student heterogeneity, and measurement error. Their estimates suggest that only a fifth to a half of learning persists between grades and that private schools increase average achievement by 0.25 standard deviations each year. In contrast, estimates from commonly used value-added models significantly understate the impact of private schools' on student achievement and/or overstate persistence. These results have implications for program evaluation and value-added accountability system design.