Meta-Analytic Approaches in Economic Studies

Meta-analytic approaches in economics have evolved as a critical tool for synthesising empirical findings across heterogeneous studies. These methods aggregate quantitative estimates from multiple sources to arrive at consolidated measures of effect size, enabling researchers to reconcile divergent results and gauge the robustness of policy-relevant parameters. Core techniques include fixed-effects models, which assume a common true effect across studies, and random-effects models, which accommodate between-study variation and unobserved heterogeneity. Recent methodological refinements address pervasive challenges such as publication bias, selective reporting and model uncertainty. Selection models and contour-enhanced funnel plots assist in diagnosing and correcting for non-random study inclusion, while p-curve analysis and trim-and-fill techniques adjust pooled estimates for suspected biases. Advanced simulation studies have compared the performance of alternative estimators under realistic bias scenarios, guiding practitioners towards optimal analytic strategies. Applications span a broad spectrum of economic policy questions, from estimates of fiscal multipliers and labour-market elasticities to utility curvature and productivity effects. Meta-analysis has not only interrogated conventional wisdom by revealing systematic overestimation of effects but has also informed evidence-based policy by quantifying the global variation and contextual determinants of key parameters.

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Meta-Analytic Approaches in Economic Studies publication trend

The graph below shows the total number of articles in meta-analytic approaches in economic studies across all publications each year (not limited to Nature Index journals).

Technical terms

Meta-analysis: A statistical procedure for combining quantitative results from multiple studies to derive overall effect estimates.

Publication bias: The tendency for studies with significant or favourable results to be published more frequently than those with null or negative findings.

Fixed-effects model: An approach that assumes all studies estimate the same true effect size, with differences due only to sampling error.

Random-effects model: A framework that allows true effect sizes to vary across studies, accounting for between-study heterogeneity.

Model averaging: A method for incorporating uncertainty across different statistical models by weighting estimates according to model fit or predictive performance.

References

1. Conventional wisdom, meta‐analysis, and research revision in economics. Journal of Economic Surveys (2024).
2. Publication and Attenuation Biases in Measuring Skill Substitution. The Review of Economics and Statistics (2024).
3. A Monte Carlo Analysis of Alternative Meta-Analysis Estimators in the Presence of Publication Bias. Economics: The Open-Access, Open-Assessment E-Journal (2015).