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

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  • Shlomo Yitzhaki

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. Copyright Sapienza Università di Roma 2015

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  • Shlomo Yitzhaki, 2015. "Gini’s mean difference offers a response to Leamer’s critique," METRON, Springer;Sapienza Università di Roma, vol. 73(1), pages 31-43, April.
    • Handle: RePEc:spr:metron:v:73:y:2015:i:1:p:31-43
    DOI: 10.1007/s40300-014-0057-9
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    References listed on IDEAS

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    1. M. Grazia Pittau & Shlomo Yitzhaki & Roberto Zelli, 2015. "The “Make-up” of a Regression Coefficient: Gender Gaps in the European Labor Market," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 61(3), pages 401-421, September.
    2. Joshua D. Angrist & Jörn-Steffen Pischke, 2010. "The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 24(2), pages 3-30, Spring.
    3. Peter J. Lambert & Andre' Decoster, 2005. "The Gini coefficient reveals more," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3), pages 373-400.
    4. Shlomo Yitzhaki & Peter Lambert, 2014. "Is higher variance necessarily bad for investment?," Review of Quantitative Finance and Accounting, Springer, vol. 43(4), pages 855-860, November.
    5. Yitzhaki, Shlomo, 1996. "On Using Linear Regressions in Welfare Economics," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 478-486, October.
    6. Shlomo Yitzhaki & Edna Schechtman, 2004. "The Gini Instrumental Variable, or the “double instrumental variable” estimator," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3), pages 287-313.
    7. Leamer, Edward E, 1983. "Let's Take the Con Out of Econometrics," American Economic Review, American Economic Association, vol. 73(1), pages 31-43, March.
    8. Yitzhaki, Shlomo, 1982. "Stochastic Dominance, Mean Variance, and Gini's Mean Difference," American Economic Review, American Economic Association, vol. 72(1), pages 178-185, March.
    9. Yitzhaki, Shlomo & Schechtman, Edna, 2012. "Identifying monotonic and non-monotonic relationships," Economics Letters, Elsevier, vol. 116(1), pages 23-25.
    10. John Ioannidis & Chris Doucouliagos, 2013. "What'S To Know About The Credibility Of Empirical Economics?," Journal of Economic Surveys, Wiley Blackwell, vol. 27(5), pages 997-1004, December.
    11. Matti Langel & Yves Tillé, 2013. "Variance estimation of the Gini index: revisiting a result several times published," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(2), pages 521-540, February.
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    1. Schröder, Carsten & Yitzhaki, Shlomo, 2017. "Revisiting the evidence for cardinal treatment of ordinal variables," European Economic Review, Elsevier, vol. 92(C), pages 337-358.
    2. Emanuela Raffinetti & Elena Siletti & Achille Vernizzi, 2017. "Analyzing the Effects of Negative and Non-negative Values on Income Inequality: Evidence from the Survey of Household Income and Wealth of the Bank of Italy (2012)," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 133(1), pages 185-207, August.

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    Keywords

    Gini; Variance; Regression;
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