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Economic importance and statistical significance: Guidelines for communicating empirical research


  • Jane Miller
  • Yana van der Meulen Rodgers


A critical objective for many empirical studies is a thorough evaluation of both substantive importance and statistical significance. Feminist economists have critiqued neoclassical economics studies for an excessive focus on statistical machinery at the expense of substantive issues. Drawing from the ongoing debate about the rhetoric of economic inquiry and significance tests, this paper examines approaches for presenting empirical results effectively to ensure that the analysis is accurate, meaningful, and relevant for the conceptual and empirical context. To that end, it demonstrates several measurement issues that affect the interpretation of economic significance and are commonly overlooked in empirical studies. This paper provides guidelines for clearly communicating two distinct aspects of “significance” in empirical research, using prose, tables, and charts based on OLS, logit, and probit regression results. These guidelines are illustrated with samples of ineffective writing annotated to show weaknesses, followed by concrete examples and explanations of improved presentation.

Suggested Citation

  • Jane Miller & Yana van der Meulen Rodgers, 2008. "Economic importance and statistical significance: Guidelines for communicating empirical research," Feminist Economics, Taylor & Francis Journals, vol. 14(2), pages 117-149.
  • Handle: RePEc:taf:femeco:v:14:y:2008:i:2:p:117-149 DOI: 10.1080/13545700701881096

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    References listed on IDEAS

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    8. McDowell, John M & Smith, Janet Kiholm, 1992. "The Effect of Gender-Sorting on Propensity to Coauthor: Implications for Academic Promotion," Economic Inquiry, Western Economic Association International, vol. 30(1), pages 68-82, January.
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    Cited by:

    1. Alice Wieland & James Sundali & Markus Kemmelmeier & Rakesh Sarin, 2014. "Gender differences in the endowment effect: Women pay less, but won't accept less," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 9(6), pages 558-571, November.
    2. Athena Engman, 2013. "Is there life after P>0.05? Statistical significance and quantitative sociology," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(1), pages 257-270, January.
    3. Lisa Kaida, 2015. "Ethnic Variations in Immigrant Poverty Exit and Female Employment: The Missing Link," Demography, Springer;Population Association of America (PAA), vol. 52(2), pages 485-511, April.
    4. Migheli, Matteo, 2014. "Preferences for government interventions in the economy: Does gender matter?," International Review of Law and Economics, Elsevier, vol. 39(C), pages 39-48.
    5. Julie A. Nelson, 2012. "Are Women Really More Risk-Averse than Men?," GDAE Working Papers 12-05, GDAE, Tufts University.
    6. Wesley Mendes-Da-Silva & Luciano Rossoni & Bruno Conte & Cristiane Gattaz & Eduardo Francisco, 2016. "The impacts of fundraising periods and geographic distance on financing music production via crowdfunding in Brazil," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 40(1), pages 75-99, February.

    More about this item


    Economic significance; regression analysis; statistical significance; writing; feminist economics; JEL Codes: Y1; A29; C10;

    JEL classification:

    • Y1 - Miscellaneous Categories - - Data: Tables and Charts
    • A29 - General Economics and Teaching - - Economic Education and Teaching of Economics - - - Other
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General


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