Publication and Identification Biases in Measuring the Intertemporal Substitution of Labor Supply

Horváth, Roman; Elminejad, Ali; Havránek, Tomáš

doi:10.31222/osf.io/nshqx

Cited by 4 publications

(3 citation statements)

References 57 publications

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“…Be sure to make notes during the entire literature search process to facilitate replicability and construct a PRISMA diagram (see Havranek et al 2020, Moher et al 2015, Page et al 2021. See meta-analysis.cz/frisch (Elminejad et al 2022a) or metaanalysis.cz/risk (Elminejad et al 2022b) for an example of the diagram.…”

Section: Literature Searchmentioning

confidence: 99%

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Meta-Analysis of Social Science Research: A Practitioner’s Guide

Iršová¹,

Doucouliagos²,

Havránek³

et al. 2023

Preprint

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This paper provides concise, nontechnical, step-by-step guidelines on how to conduct a modern meta-analysis, especially in social sciences. We treat publication bias, p-hacking, and heterogeneity as phenomena meta-analysts must always confront. To this end, we provide concrete methodological recommendations. Meta-analysis methods have advanced notably over the last few years. Yet many meta-analyses still rely on outdated approaches, some ignoring publication bias and systematic heterogeneity. While limitations persist, recently developed techniques allow robust inference even in the face of formidable problems in the underlying empirical literature. The purpose of this paper is to summarize the state of the art in a way accessible to aspiring meta-analysts in any field. We also discuss how meta-analysts can use advances in artificial intelligence to work more efficiently.

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Section: Literature Searchmentioning

confidence: 99%

“…The dilution prior adds a weight that penalizes models with high collinearity. This model ensemble has been successfully employed in many meta-analyses (Bajzik et al 2021, Elminejad et al 2022a, 2022b, and an example of the code is available at meta-analysis.cz/students/students.do.…”

Section: Heterogeneity and Implied Estimatesmentioning

confidence: 99%

Meta-Analysis of Social Science Research: A Practitioner’s Guide

Iršová¹,

Doucouliagos²,

Havránek³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…First, as shown by Andrews & Kasy (2019) and Stanley & Doucouliagos (2014), publication bias can be a nonlinear function of the standard error. Second, as discussed by Havranek et al (2022), the assumption of no correlation between estimates and standard errors in the absence of publication bias can be problematic because of unobserved heterogeneity that affects both estimates and standard errors. To address these two problems, we employ recently developed nonlinear tests for publication bias: the selection model by Andrews & Kasy (2019), the weighted average of adequately powered estimates (Ioannidis et al, 2017), the stem-based technique (Furukawa, 2021), the endogenous kink model (Bom & Rachinger, 2019), and the p-uniform* technique (van Aert & van Assen, 2021).…”

Section: Calibrated Estimatedmentioning

confidence: 99%

Relative Risk Aversion: A Meta-Analysis

Elminejad¹,

Havránek²,

Iršová³

2022

Preprint

Self Cite

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We collect 1,021 estimates from 92 studies that use the consumption Euler equation to measure relative risk aversion and that disentangle it from intertemporal substitution. We show that calibrations of risk aversion are typically larger than estimates thereof. Moreover, reported estimates are typically larger than the underlying risk aversion because of publication bias. After correction for the bias, the literature suggests a mean risk aversion of 1 in economics and 2--7 in finance contexts. The reported estimates are systematically driven by the characteristics of data (frequency, dimension, country, stockholding) and utility (functional form, treatment of durables). To obtain these results we use nonlinear techniques to correct for publication bias and Bayesian model averaging techniques to account for model uncertainty.

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Health risk and the welfare effects of Social Security

Bagchi

Jung

2022

Macroecon. Dynam.

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We quantify the importance of idiosyncratic health risk in a calibrated general equilibrium model of Social Security. We construct an overlapping generations model with rational-expectations households, idiosyncratic labor income and health risk, profit-maximizing firms, incomplete insurance markets, and a government that provides pensions and health insurance. We calibrate this model to the US economy and perform two computational experiments: $\left (i\right)$ cutting Social Security’s payroll tax, and $\left (ii\right)$ modifying Social Security’s benefit-earnings rule. Our findings suggest that health risk amplifies the welfare implications of both experiments: downsizing Social Security always leads to higher overall welfare, but the welfare gain is larger when we account for health risk, and increasing the progressivity of Social Security’s benefit-earnings rule has a larger positive effect on welfare in the presence of health risk. We also find that allowing households additional tools to self-insure against health risk weakens the precautionary motive, so our experiments have similar welfare implications both with and without health risk.

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Publication and Identification Biases in Measuring the Intertemporal Substitution of Labor Supply

Cited by 4 publications

References 57 publications

Meta-Analysis of Social Science Research: A Practitioner’s Guide

Meta-Analysis of Social Science Research: A Practitioner’s Guide

Relative Risk Aversion: A Meta-Analysis

Health risk and the welfare effects of Social Security

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