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Estimation and Inference for Actual and Counterfactual Growth Incidence Curves

Listed author(s):
  • Ferreira, Francisco H. G.

    ()

    (World Bank)

  • Firpo, Sergio

    ()

    (Insper, São Paulo)

  • Galvao, Antonio F.

    ()

    (University of Iowa)

Different episodes of economic growth display widely varying distributional characteristics, both across countries and over time. Growth is sometimes accompanied by rising and sometimes by falling inequality. Applied economists have come to rely on the Growth Incidence Curve, which gives the quantile-specific rate of income growth over a certain period, to describe and analyze the incidence of economic growth. This paper discusses the identification conditions, and develops estimation and inference procedures for both actual and counterfactual growth incidence curves, based on general functions of the quantile potential outcome process over the space of quantiles. The paper establishes the limiting null distribution of the test statistics of interest for those general functions, and proposes resampling methods to implement inference in practice. The proposed methods are illustrated by a comparison of the growth processes in the United States and Brazil during 1995-2007. Although growth in the average real wage was disappointing in both countries, the distribution of that growth was markedly different. In the United States, wage growth was mediocre for the bottom 80 percent of the sample, but much more rapid for the top 20 percent. In Brazil, conversely, wage growth was rapid below the median, and negative at the top. As a result, inequality rose in the United States and fell markedly in Brazil.

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Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 10473.

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Length: 61 pages
Date of creation: Jan 2017
Handle: RePEc:iza:izadps:dp10473
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  1. Francois Bourguignon & Francisco H.G. Ferreira & Nora Lustig, 2005. "The Microeconomics of Income Distribution Dynamics in East Asia and Latin America," World Bank Publications, The World Bank, number 14844.
  2. Roger Koenker & Samantha Leorato & Franco Peracchi, 2013. "Distributional vs. Quantile Regression," CEIS Research Paper 300, Tor Vergata University, CEIS, revised 17 Dec 2013.
  3. B. Essama-Nssah & Saumik Paul & Léandre Bassolé, 2013. "Accounting for Heterogeneity in Growth Incidence in Cameroon Using Recentered Influence Function Regression," Journal of African Economies, Centre for the Study of African Economies (CSAE), vol. 22(5), pages 757-795, November.
  4. Alberto Abadie & Joshua Angrist & Guido Imbens, 1999. "Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings," Working papers 99-16, Massachusetts Institute of Technology (MIT), Department of Economics.
  5. Joshua Angrist & Victor Chernozhukov & Ivan Fernandez-Val, 2004. "Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure," NBER Working Papers 10428, National Bureau of Economic Research, Inc.
  6. Wojciech Kopczuk & Emmanuel Saez & Jae Song, 2010. "Earnings Inequality and Mobility in the United States: Evidence from Social Security Data Since 1937," The Quarterly Journal of Economics, Oxford University Press, vol. 125(1), pages 91-128.
  7. Fan, Yanqin & Park, Sang Soo, 2010. "Sharp Bounds On The Distribution Of Treatment Effects And Their Statistical Inference," Econometric Theory, Cambridge University Press, vol. 26(03), pages 931-951, June.
  8. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val, 2011. "Conditional quantile processes based on series or many regressors," CeMMAP working papers CWP19/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  9. Victor Chernozhukov & Christian Hansen, 2005. "An IV Model of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 73(1), pages 245-261, 01.
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