Estimation and Inference for Actual and Counterfactual Growth Incidence Curves

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...

Full description

Bibliographic Details
Main Authors: Ferreira, Francisco H. G., Firpo, Sergio, Galvao, Antonio F.
Format: Working Paper
Language:English
en_US
Published: World Bank, Washington, DC 2017
Subjects:
Online Access:http://documents.worldbank.org/curated/en/953201483623365115/Estimation-and-inference-for-actual-and-counterfactual-growth-incidence-curves
http://hdl.handle.net/10986/25942
id okr-10986-25942
recordtype oai_dc
spelling okr-10986-259422021-06-08T14:42:47Z Estimation and Inference for Actual and Counterfactual Growth Incidence Curves Ferreira, Francisco H. G. Firpo, Sergio Galvao, Antonio F. growth incidence curve potential outcome inference quantile process economic growth income distribution inequality wage growth 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 0 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. 2017-01-30T17:08:18Z 2017-01-30T17:08:18Z 2017-01 Working Paper http://documents.worldbank.org/curated/en/953201483623365115/Estimation-and-inference-for-actual-and-counterfactual-growth-incidence-curves http://hdl.handle.net/10986/25942 English en_US Policy Research Working Paper;No. 7933 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Policy Research Working Paper Latin America & Caribbean Brazil United States
repository_type Digital Repository
institution_category Foreign Institution
institution Digital Repositories
building World Bank Open Knowledge Repository
collection World Bank
language English
en_US
topic growth incidence curve
potential outcome
inference
quantile process
economic growth
income distribution
inequality
wage growth
spellingShingle growth incidence curve
potential outcome
inference
quantile process
economic growth
income distribution
inequality
wage growth
Ferreira, Francisco H. G.
Firpo, Sergio
Galvao, Antonio F.
Estimation and Inference for Actual and Counterfactual Growth Incidence Curves
geographic_facet Latin America & Caribbean
Brazil
United States
relation Policy Research Working Paper;No. 7933
description 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 0 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.
format Working Paper
author Ferreira, Francisco H. G.
Firpo, Sergio
Galvao, Antonio F.
author_facet Ferreira, Francisco H. G.
Firpo, Sergio
Galvao, Antonio F.
author_sort Ferreira, Francisco H. G.
title Estimation and Inference for Actual and Counterfactual Growth Incidence Curves
title_short Estimation and Inference for Actual and Counterfactual Growth Incidence Curves
title_full Estimation and Inference for Actual and Counterfactual Growth Incidence Curves
title_fullStr Estimation and Inference for Actual and Counterfactual Growth Incidence Curves
title_full_unstemmed Estimation and Inference for Actual and Counterfactual Growth Incidence Curves
title_sort estimation and inference for actual and counterfactual growth incidence curves
publisher World Bank, Washington, DC
publishDate 2017
url http://documents.worldbank.org/curated/en/953201483623365115/Estimation-and-inference-for-actual-and-counterfactual-growth-incidence-curves
http://hdl.handle.net/10986/25942
_version_ 1764460545312817152