Skip to main content
SpringerLink
Log in

Gini’s mean difference offers a response to Leamer’s critique

  • Published:
METRON Aims and scope Submit manuscript

Abstract

Gini’s mean difference has decomposition properties that nest the decomposition of the variance as a special case. By using it is possible to reveal some of the implicit assumptions imposed on the data by using the variance. I argue that some of those implicit assumptions can be traced to be the causes of Leamer’s critique concerning the ability to manipulate the results of regressions. By requiring the econometrician to report whether those assumptions are violated by the data, we may be able to offer a response to Leamer’s critique. This will reduce the possibility of supplying “empirical proofs” which in turn may increase the trust in econometric research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. The standard errors are not reported because by replicating the data one can get any value of the standard errors.

  2. For an explanation see http://en.wikipedia.org/wiki/Degree_day.

  3. To enable replication by different software, two incomplete observations were omitted.

References

  1. Angrist, J., Pischke, J.-S.: The credibility revolution in empirical economics: how better research design is taking the con out of econometrics. J. Econ. Perspect. 24(2), 3–30 (2010)

  2. David, H.A.: Gini’s mean difference rediscovered. Biometrika 55, 573–575 (1968)

    MATH  Google Scholar 

  3. De Veaux, D.R.: Tight upper and lower bounds for correlation of bivariate distributions arising in air pollution modeling, Technical Report No. 5 (1976), Department of Statistics, Stanford University (1976)

  4. Denuit, M., Dhaene, J.: Simple characterizations of comonotonicity and countermonotonicity by extremal correlations. Belg. Actuar. Bull. 3, 22–27 (2003)

    Google Scholar 

  5. Einhorn, H.J.: Alchemy in behavioral science. Public Opin. Q. (Autumn) 36(3), 367–378 (1972)

  6. Golan, Y., Yitzhaki, S.: Who does not respond in the social survey: an Exercise in OLS and Gini regressions, draft. Presented at the 31st IARIW (2010). http://www.iariw.org/c2010.php

  7. Ioannidis, J., Doucouliagos, C.: What’s to know about the credibility of empirical economics? J. Econ. Surv. 27(5), 997–1004 (2013)

    Google Scholar 

  8. Lambert, P.J., Decoster, A.: The Gini coefficient reveals more. Metron LXIII 3, 373–400 (2005)

    MathSciNet  Google Scholar 

  9. Langel, M., Tillé, Y.: Variance estimation of the Gini index: revisiting a result several times published. J. R. Stat. Soc. A 176(2), 521–540 (2013)

  10. Leamer, E.: Let’s take the Con out of econometrics. Am. Econ. Rev. 73(1), 31–43 (1983)

    Google Scholar 

  11. Pittau, M.G., Yitzhaki, S., Zelli, R.: The “make-up” of a regression coefficient: gender gaps in the European labor market. Rev. Income Wealth (2014). doi:10.1111/roiw.12094

  12. Yitzhaki, S.: Stochastic dominance, mean variance, and the Gini’s mean difference. Am. Econ. Rev. 72(1), 178–185 (1982)

  13. Yitzhaki, S.: On using linear regression in welfare economics. J. Bus. Econ. Stat. 144, 478–86 (1996)

  14. Yitzhaki, S., Lambert, P.J.: The relationship between the absolute deviation from a quantile and Gini’s mean difference. Metron 71, 97–104 (2013)

  15. Yitzhaki, S., Lambert, P.J.: Is higher variance necessarily bad for investment. Rev. Quant. Financ. Account. 43(4), 855–860 (2014)

    Article  Google Scholar 

  16. Yitzhaki, S., Schechtman, E.: The Gini instrumental variable, or the “double instrumental variable” estimator. Metron LXII 3, 287–313 (2004)

  17. Yitzhaki, S., Schechtman, E.: Identifying monotonic and non-monotonic relationship. Econ. Lett. 116, 23–25 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  18. Yitzhaki, S., Schechtman, E.: The Gini Methodology: A Primer on a Statistical Methodology. Springer, New York (2013)

    Book  Google Scholar 

Download references

Acknowledgments

I am grateful to an anonymous referee, Giovanni Giorgi, Sergiu Hart, Peter Lambert, Ingram Olkin, Stéphane Mussard, Mark Schaffer and Carsten Schröder for helpful comments on former drafts of this paper. I am grateful to Mark Schaffer who programmed the Stata program for running the Gini regression. The SAS and STATA programs will be sent upon request.

Author information

Authors and Affiliations

  1. Department of Economics, Hebrew University, Jerusalem, Israel

    Shlomo Yitzhaki

Authors
  1. Shlomo Yitzhaki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shlomo Yitzhaki.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yitzhaki, S. Gini’s mean difference offers a response to Leamer’s critique. METRON 73, 31–43 (2015). https://doi.org/10.1007/s40300-014-0057-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40300-014-0057-9

Keywords

Search

Navigation