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Spurious Precision in Meta-Analysis

Author

Listed:
  • Irsova, Zuzana
  • Bom, Pedro R. D.
  • Havranek, Tomas
  • Rachinger, Heiko

Abstract

Meta-analysis upweights studies reporting lower standard errors and hence more precision. But in empirical practice, notably in observational research, precision is not given to the researcher. Precision must be estimated, and thus can be p-hacked to achieve statistical significance. Simulations show that a modest dose of spurious precision creates a formidable problem for inverse-variance weighting and bias-correction methods based on the funnel plot. Selection models fail to solve the problem, and the simple mean can beat sophisticated estimators. Cures to publication bias may become worse than the disease. We introduce an approach that surmounts spuriousness: the Meta-Analysis Instrumental Variable Estimator (MAIVE), which employs inverse sample size as an instrument for reported variance.

Suggested Citation

  • Irsova, Zuzana & Bom, Pedro R. D. & Havranek, Tomas & Rachinger, Heiko, 2023. "Spurious Precision in Meta-Analysis," CEPR Discussion Papers 17927, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:17927
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    2. Zuzana Irsova & Hristos Doucouliagos & Tomas Havranek & T. D. Stanley, 2024. "Meta‐analysis of social science research: A practitioner's guide," Journal of Economic Surveys, Wiley Blackwell, vol. 38(5), pages 1547-1566, December.
    3. Pazzona, Matteo, 2024. "Revisiting the Income Inequality-Crime Puzzle," World Development, Elsevier, vol. 176(C).
    4. Matej Opatrny & Tomas Havranek & Zuzana Irsova & Milan Scasny, 2023. "Publication Bias and Model Uncertainty in Measuring the Effect of Class Size on Achievement," Working Papers IES 2023/19, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised May 2023.
    5. Rodolfo Campos & Mario Larch & Jacopo Timini & Elena Vidal & Yoto Yotov, 2024. "Does the WTO Promote Trade? A Meta-analysis," School of Economics Working Paper Series 2024-11, LeBow College of Business, Drexel University.
    6. Ichiro Iwasaki & Evžen Kočenda, 2024. "Quest for the general effect size of finance on growth: a large meta-analysis of worldwide studies," Empirical Economics, Springer, vol. 66(6), pages 2659-2722, June.
    7. Marvin Schütt, 2024. "Wind Turbines and Property Values: A Meta-Regression Analysis," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 87(1), pages 1-43, January.
    8. Kroupova, Katerina & Havranek, Tomas & Irsova, Zuzana, 2024. "Student Employment and Education: A Meta-Analysis," Economics of Education Review, Elsevier, vol. 100(C).
    9. Ali Elminejad & Tomas Havranek & Roman Horvath & Zuzana Irsova, 2023. "Intertemporal Substitution in Labor Supply: A Meta-Analysis," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 51, pages 1095-1113, December.
    10. Schneider, Florian, 2024. "Do robots boost productivity? A quantitative meta-study," MPRA Paper 123392, University Library of Munich, Germany.
    11. Tersoo David Iorngurum, 2023. "Method Versus Cross-Country Heterogeneity in the Exchange Rate Pass-Through," Working Papers IES 2023/16, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised May 2023.
    12. T. D. Stanley & Hristos Doucouliagos & Tomas Havranek, 2023. "Reducing the Biases of the Conventional Meta-Analysis of Correlations," Working Papers IES 2023/34, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Dec 2023.
    13. Yanxia Yu & Chenfei Qu & Tom Coupé & Mathilda Featherston-Lardeux & Andreas Loeschel & Arne R. Weiss & Da Zhang, 2025. "Did China’s Pilot Emissions Trading Scheme Reduce CO2 Emissions? Evidence from a Meta-Analysis," Working Papers in Economics 25/04, University of Canterbury, Department of Economics and Finance.

    More about this item

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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