Assessing the Performance of Nonexperimental Estimators for Evaluating Head Start

Griffen, Andrew S.; Todd, Petra

doi:10.1086/691726

Cited by 7 publications

(2 citation statements)

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“…3 Non-experimental ordinary least squares (OLS) estimates of the wage returns to training show an average return of 8.7% with a standard deviation of 5.7%. 4 To account for worker selection into training, some studies seek to elicit exogenous variation by 1 Other assessments of selection bias have concerned large-scale policy programs such as PROGRESA, an antipoverty program in rural areas of Mexico (Diaz and Handa, 2006;Bifulco, 2012), Head Start, an early childhood intervention program in the US (Griffen and Todd, 2017), programs to prevent dropping out of high-school (Agodini and Dynarski, 2004) and income gains from migration (McKenzie et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Assessing Selection Bias in Non-Experimental Estimates of the Returns to Workplace Training

Sauermann

Stenberg

2020

SSRN Journal

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Section: Introductionmentioning

confidence: 99%

Assessing Selection Bias in Non-Experimental Estimates of the Returns to Workplace Training

Sauermann

Stenberg

2020

SSRN Journal

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“…Some related programs, such as Head Start, are means-tested but they place the key emphasis on child development. The evidence on labor supply effects of Head Start remain mixed(Griffen and Todd, 2017). For a general review of programs related to early childhood education and their effects on child outcomes, see for instanceElango, Garcia, Heckman and Hojman (2016).…”

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confidence: 99%

Welfare Reform and the Labor Market

Chan

Moffitt

2018

Annu. Rev. Econ.

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This article reviews the basic theoretical models that are appropriate for analyzing different types of welfare reforms, as well as the related empirical literature. We first present the canonical labor supply model of a classical welfare program and then extend this basic framework to include in-kind transfers, incomplete take-up, human capital, preference persistence, and borrowing and saving. The empirical literature on these models is presented. The negative income tax, earnings subsidies, US welfare reforms with features that differ from those in other countries, and childcare reforms are then surveyed in terms of both the theoretical models and the empirical literature surrounding each.

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Within study comparisons and risk of bias in international development: Systematic review and critical appraisal

Villar

Waddington

2019

Campbell Systematic Reviews

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Executive SummaryBackground: Many systematic reviews incorporate nonrandomised studies of effects, sometimes called quasi-experiments or natural experiments. However, the extent to which nonrandomised studies produce unbiased effect estimates is unclear in expectation or in practice. The usual way that systematic reviews quantify bias is through "risk of bias assessment" and indirect comparison of findings across studies using meta-analysis. A more direct, practical way to quantify the bias in nonrandomised studies is through "internal replication research", which compares the findings from nonrandomised studies with estimates from a benchmark randomised controlled trial conducted in the same population. Despite the existence of many risks of bias tools, none are conceptualised to assess comprehensively nonrandomised approaches with selection on unobservables, such as regression discontinuity designs (RDDs). The few that are conceptualised with these studies in mind do not draw on the extensive literature on internal replications (within-study comparisons) of randomised trials.Objectives: Our research objectives were as follows:Objective 1: to undertake a systematic review of nonrandomised internal study replications of international development interventions.Objective 2: to develop a risk of bias tool for RDDs, an increasingly common method used in social and economic programme evaluation.Methods: We used the following methods to achieve our objectives.Objective 1: we searched systematically for nonrandomised internal study replications of benchmark randomised experiments of social and economic interventions in low-and middle-income countries (L&MICs). We assessed the risk of bias in benchmark randomised experiments and synthesised evidence on the relative bias effect sizes produced by benchmark and nonrandomised comparison arms.Objective 2: We used document review and expert consultation to develop further a risk of bias tool for quasi-experimental studies of interventions (ROBINS-I) for RDDs.---

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Assessing the Performance of Nonexperimental Estimators for Evaluating Head Start

Cited by 7 publications

References 68 publications

Assessing Selection Bias in Non-Experimental Estimates of the Returns to Workplace Training

Assessing Selection Bias in Non-Experimental Estimates of the Returns to Workplace Training

Welfare Reform and the Labor Market

Within study comparisons and risk of bias in international development: Systematic review and critical appraisal

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