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Replication is a critical component of scientific credibility as it increases our confidence in the reliability of the knowledge generated by original research. Yet, replication is the exception rather than the rule in economics. In this paper, we examine why replication is so rare and propose changes to the incentives to replicate. Our study focuses on software code replication, which seeks to replicate the results in the original paper using the same data as the original study and verifying that the analysis code is correct. We analyse the effectiveness of the current model for code replication in the context of three desirable characteristics: unbiasedness, fairness and efficiency. We find substantial evidence of "overturn bias" that likely leads to many false positives in terms of "finding" or claiming mistakes in the original analysis. Overturn bias comes from the fact that replications that overturn original results are much easier to publish than those that confirm original results. In a survey of editors, almost all responded they would in principle publish a replication study that overturned the results of the original study, but only 29% responded that they would consider publishing a replication study that confirmed the original study results. We also find that most replication effort is devoted to so called important papers and that the cost of replication is high in that posited data and software are very hard to use. We outline a new model for the journals to take over replication post acceptance and prepublication that would solve the incentive problems raised in this paper.
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We use a field experiment in Tanzania to compare the effectiveness on learning of two teacher performance pay systems. The first is a Pay for Percentile system (a rank-order tournament). The second rewards teachers based on multiple proficiency thresholds. Pay for Percentile can (under certain conditions) induce optimal effort among teachers, but our threshold system is easier to implement and provides teachers with clearer goals and targets. Both systems improved student test scores. However, the multiple-thresholds system was more effective in boosting student learning and is less costly.
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The idea that complementarities across policies can yield increasing returns from joint implementation has been posited in several economic settings. Yet there is limited, well-identified evidence of such complementarities in practice. We present results from a randomized experiment across a representative sample of 350 schools in Tanzania that studied the impact of providing schools with (a) unconditional school grants, (b) bonus payments to teachers based on student performance, and (c) both of the above. At the end of two years, we find (a) no impact on student test scores from providing school grants, (b) some evidence of positive effects from offering performance-linked bonuses to teachers, and (c) significant positive effects on learning from providing both programs. Most importantly, we find strong evidence of complementarities between the two programs, with the effect of joint provision being significantly greater than the sum of the individual effects. Our results suggest that accounting for complementarities between inputs and incentives could substantially improve the effectiveness of public spending on education.
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Factorial designs are widely used for studying multiple treatments in one experiment. While t-tests based on the "long" model (including main and interaction effects) provide valid inferences against "business-as-usual" counterfactuals, "short" model t-tests (that ignore interactions) yield higher power if the interactions are zero, but incorrect inferences otherwise. Out of 27 factorial experiments published in top-5 journals in 2007-2017, 19 use the short model. We reanalyze these experiments, and show that over half of their published results lose significance when interactions are included. We show that testing the interactions using the long model and presenting the short model if the interactions are not significantly different from zero leads to incorrect inference due to the implied data-dependent model selection. Based on recent econometric advances, we show that local power improvements over the long model are possible. However, if the main effects are of primary interest, leaving the interaction cells empty yields valid inferences and global power improvements. In addition, the sample size needed to detect interactions is substantially larger than that required to detect main effects, resulting in most experiments being under-powered to detect interactions. Thus, using factorial designs to explore whether interactions are meaningful can be problematic because interaction estimates are likely to considerably overestimate the magnitude of the true effect conditional on being significant.
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Replication is a critical component of scientific credibility as it increases our confidence in the reliability of the knowledge generated by original research. Yet, replication is the exception rather than the rule in economics. In this paper, we examine why replication is so rare and propose changes to the incentives to replicate. Our study focuses on software code replication, which seeks to replicate the results in the original paper using the same data as the original study and verifying that the analysis code is correct. We analyse the effectiveness of the current model for code replication in the context of three desirable characteristics: unbiasedness, fairness and efficiency. We find substantial evidence of "overturn bias" that likely leads to many false positives in terms of "finding" or claiming mistakes in the original analysis. Overturn bias comes from the fact that replications that overturn original results are much easier to publish than those that confirm original results. In a survey of editors, almost all responded they would in principle publish a replication study that overturned the results of the original study, but only 29% responded that they would consider publishing a replication study that confirmed the original study results. We also find that most replication effort is devoted to so called important papers and that the cost of replication is high in that posited data and software are very hard to use. We outline a new model for the journals to take over replication post acceptance and prepublication that would solve the incentive problems raised in this paper.
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We use a panel survey of ~19,000 primary-school-aged children in rural Tamil Nadu to study 'learning loss' after COVID-19-induced school closures, and the pace of recovery after schools reopened. Students tested in December 2021 (18 months after school closures) displayed learning deficits of ~0.7σ standard deviations in math and ~0.34σ standard deviations in language compared to identically-aged students in the same villages in 2019. Two-thirds of this deficit was made up within 6 months after school reopening. Further, while learning loss was regressive, the recovery was progressive. A government-run after-school remediation program contributed ~24% of the cohort-level recovery, and likely aided the progressive recovery.
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This paper presents the results of a large-scale randomized experiment conducted across 1,496 public primary schools in Mexico. The experiment identifies the impact on schools' managerial capacity and student test scores of providing schools with: (a) cash grants, (b) managerial training for school principals, or (c) both. The school principals' managerial training focused on improving principals' capacities to collect and use data to monitor students' basic numeracy and literacy skills and provide feedback to teachers on their instruction and pedagogical practices. After two years of implementing these interventions, the study finds that: (a) the cash grant had no impact on the student's test scores or the management capacity of school principals; (b) the managerial training improved school principals' managerial capacity but had no impact on students' test scores; and (c) the combination of cash grants and managerial training amplified the effect on the school principals' managerial capacity and had a positive but statistically insignificant impact on students' test scores.
Economics of Education --- Education --- Education Grants --- Education Quality --- Education Reform and Management --- Effective Schools and Teachers --- Managerial Training --- Primary Education --- Principals --- School Management --- Student Learning --- Test Scores
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We use a field experiment in Tanzania to compare the effectiveness on learning of two teacher performance pay systems. The first is a Pay for Percentile system (a rank-order tournament). The second rewards teachers based on multiple proficiency thresholds. Pay for Percentile can (under certain conditions) induce optimal effort among teachers, but our threshold system is easier to implement and provides teachers with clearer goals and targets. Both systems improved student test scores. However, the multiple-thresholds system was more effective in boosting student learning and is less costly.
Choose an application
We present results from a large-scale randomized experiment across 350 schools in Tanzania that studied the impact of providing schools with (a) unconditional grants, (b) teacher incentives based on student performance, and (c) both of the above. After two years, we find (a) no impact on student test scores from providing school grants, (b) some evidence of positive effects from teacher incentives, and (c) significant positive effects from providing both programs. Most importantly, we find strong evidence of complementarities between the two programs, with the effect of joint provision being significantly greater than the sum of the individual effects. Our results suggest that combining spending on school inputs (which is the default policy) with improved teacher incentives could substantially increase the cost-effectiveness of public spending on education.
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