Measuring Poverty Dynammics and Inequality in Transition Economies : Disentangling Real Events from Noisy Data

The author uses instrumental variable methods, and the decomposition of income into transitory and persistent components to distinguish underlying income inequality and changes in poverty from the effects attributable to measurement error or transi...

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Main Author: Luttmer, Erzo F.P.
Format: Policy Research Working Paper
Language:English
en_US
Published: World Bank, Washington, DC 2014
Subjects:
Online Access:http://documents.worldbank.org/curated/en/2001/02/1003132/measuring-poverty-dynammics-inequality-transition-economies-disentangling-real-events-noisy-data
http://hdl.handle.net/10986/19701
id okr-10986-19701
recordtype oai_dc
spelling okr-10986-197012021-04-23T14:03:44Z Measuring Poverty Dynammics and Inequality in Transition Economies : Disentangling Real Events from Noisy Data Luttmer, Erzo F.P. AVERAGE INCOME AVERAGE INCOMES CONSUMPTION EXPENDITURE COUNTRY AVERAGE DATA SETS DEMOGRAPHIC CHARACTERISTICS DEVELOPING COUNTRIES EARNINGS INEQUALITY EQUIVALENCE SCALE EQUIVALENT INCOME EXPLANATORY POWER GINI COEFFICIENT GOVERNMENT EXPENDITURES HIGH COST HOUSEHOLD BUDGET SURVEY HOUSEHOLD EXPENDITURE HOUSEHOLD INCOME HOUSEHOLD MEMBERS HOUSEHOLD SIZE INCOME INCOME INEQUALITY INCOMES INEQUALITY INEQUALITY MEASURES LIVING STANDARD LIVING STANDARDS LOG INCOME MEASURED INEQUALITY MEASUREMENT ERROR MEASURING POVERTY MONTHLY EXPENDITURE NORMAL DISTRIBUTION OPPORTUNITY COST POLICY RESEARCH POOR AREAS POOR COUNTRIES POSITIVE CORRELATION POVERTY DYNAMICS POVERTY ESTIMATES POVERTY REDUCTION PRIVATE SECTOR PUBLIC EXPENDITURES PUBLIC PROVISION RANDOM WALK RURAL AREAS STANDARD ERRORS STRUCTURAL CHANGE SURVEY DATA TAX SYSTEMS TAXATION TRANSITION ECONOMIES UNDERLYING PROBLEM UNEQUAL DISTRIBUTION URBAN AREAS URBAN HOUSEHOLDS VALUATION WAGES The author uses instrumental variable methods, and the decomposition of income into transitory and persistent components to distinguish underlying income inequality and changes in poverty from the effects attributable to measurement error or transitory shocks. He applies this methodology to household-level panel data for Russia and Poland in the mid-1990s. The author finds that: 1) Accounting for noise in the data reduces inequality (as measured by the Gini coefficient) by 10-45 percent. 2) Individuals in both countries face much economic insecurity. The median absolute annual change in income or spending is about fifty percent in Russia, and about 20 percent in Poland. But roughly half of these fluctuations reflect measurement error or transitory shocks, so underlying levels of income, and spending are much more stable than the data suggest. 3) The apparent high levels of economic mobility are driven largely by transitory events and noisy data. After transitory shocks are accounted for, about eighty percent of the poor in both Russia and Poland remain in poverty for at least one year. So there is a real risk of an entrenched underclass emerging in these transition economies. 2014-08-26T18:58:45Z 2014-08-26T18:58:45Z 2001-02 http://documents.worldbank.org/curated/en/2001/02/1003132/measuring-poverty-dynammics-inequality-transition-economies-disentangling-real-events-noisy-data http://hdl.handle.net/10986/19701 English en_US Policy Research Working Paper;No. 2549 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo/ World Bank, Washington, DC Publications & Research :: Policy Research Working Paper Publications & Research Europe and Central Asia Poland Russian Federation
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 AVERAGE INCOME
AVERAGE INCOMES
CONSUMPTION EXPENDITURE
COUNTRY AVERAGE
DATA SETS
DEMOGRAPHIC CHARACTERISTICS
DEVELOPING COUNTRIES
EARNINGS INEQUALITY
EQUIVALENCE SCALE
EQUIVALENT INCOME
EXPLANATORY POWER
GINI COEFFICIENT
GOVERNMENT EXPENDITURES
HIGH COST
HOUSEHOLD BUDGET SURVEY
HOUSEHOLD EXPENDITURE
HOUSEHOLD INCOME
HOUSEHOLD MEMBERS
HOUSEHOLD SIZE
INCOME
INCOME INEQUALITY
INCOMES
INEQUALITY
INEQUALITY MEASURES
LIVING STANDARD
LIVING STANDARDS
LOG INCOME
MEASURED INEQUALITY
MEASUREMENT ERROR
MEASURING POVERTY
MONTHLY EXPENDITURE
NORMAL DISTRIBUTION
OPPORTUNITY COST
POLICY RESEARCH
POOR AREAS
POOR COUNTRIES
POSITIVE CORRELATION
POVERTY DYNAMICS
POVERTY ESTIMATES
POVERTY REDUCTION
PRIVATE SECTOR
PUBLIC EXPENDITURES
PUBLIC PROVISION
RANDOM WALK
RURAL AREAS
STANDARD ERRORS
STRUCTURAL CHANGE
SURVEY DATA
TAX SYSTEMS
TAXATION
TRANSITION ECONOMIES
UNDERLYING PROBLEM
UNEQUAL DISTRIBUTION
URBAN AREAS
URBAN HOUSEHOLDS
VALUATION
WAGES
spellingShingle AVERAGE INCOME
AVERAGE INCOMES
CONSUMPTION EXPENDITURE
COUNTRY AVERAGE
DATA SETS
DEMOGRAPHIC CHARACTERISTICS
DEVELOPING COUNTRIES
EARNINGS INEQUALITY
EQUIVALENCE SCALE
EQUIVALENT INCOME
EXPLANATORY POWER
GINI COEFFICIENT
GOVERNMENT EXPENDITURES
HIGH COST
HOUSEHOLD BUDGET SURVEY
HOUSEHOLD EXPENDITURE
HOUSEHOLD INCOME
HOUSEHOLD MEMBERS
HOUSEHOLD SIZE
INCOME
INCOME INEQUALITY
INCOMES
INEQUALITY
INEQUALITY MEASURES
LIVING STANDARD
LIVING STANDARDS
LOG INCOME
MEASURED INEQUALITY
MEASUREMENT ERROR
MEASURING POVERTY
MONTHLY EXPENDITURE
NORMAL DISTRIBUTION
OPPORTUNITY COST
POLICY RESEARCH
POOR AREAS
POOR COUNTRIES
POSITIVE CORRELATION
POVERTY DYNAMICS
POVERTY ESTIMATES
POVERTY REDUCTION
PRIVATE SECTOR
PUBLIC EXPENDITURES
PUBLIC PROVISION
RANDOM WALK
RURAL AREAS
STANDARD ERRORS
STRUCTURAL CHANGE
SURVEY DATA
TAX SYSTEMS
TAXATION
TRANSITION ECONOMIES
UNDERLYING PROBLEM
UNEQUAL DISTRIBUTION
URBAN AREAS
URBAN HOUSEHOLDS
VALUATION
WAGES
Luttmer, Erzo F.P.
Measuring Poverty Dynammics and Inequality in Transition Economies : Disentangling Real Events from Noisy Data
geographic_facet Europe and Central Asia
Poland
Russian Federation
relation Policy Research Working Paper;No. 2549
description The author uses instrumental variable methods, and the decomposition of income into transitory and persistent components to distinguish underlying income inequality and changes in poverty from the effects attributable to measurement error or transitory shocks. He applies this methodology to household-level panel data for Russia and Poland in the mid-1990s. The author finds that: 1) Accounting for noise in the data reduces inequality (as measured by the Gini coefficient) by 10-45 percent. 2) Individuals in both countries face much economic insecurity. The median absolute annual change in income or spending is about fifty percent in Russia, and about 20 percent in Poland. But roughly half of these fluctuations reflect measurement error or transitory shocks, so underlying levels of income, and spending are much more stable than the data suggest. 3) The apparent high levels of economic mobility are driven largely by transitory events and noisy data. After transitory shocks are accounted for, about eighty percent of the poor in both Russia and Poland remain in poverty for at least one year. So there is a real risk of an entrenched underclass emerging in these transition economies.
format Publications & Research :: Policy Research Working Paper
author Luttmer, Erzo F.P.
author_facet Luttmer, Erzo F.P.
author_sort Luttmer, Erzo F.P.
title Measuring Poverty Dynammics and Inequality in Transition Economies : Disentangling Real Events from Noisy Data
title_short Measuring Poverty Dynammics and Inequality in Transition Economies : Disentangling Real Events from Noisy Data
title_full Measuring Poverty Dynammics and Inequality in Transition Economies : Disentangling Real Events from Noisy Data
title_fullStr Measuring Poverty Dynammics and Inequality in Transition Economies : Disentangling Real Events from Noisy Data
title_full_unstemmed Measuring Poverty Dynammics and Inequality in Transition Economies : Disentangling Real Events from Noisy Data
title_sort measuring poverty dynammics and inequality in transition economies : disentangling real events from noisy data
publisher World Bank, Washington, DC
publishDate 2014
url http://documents.worldbank.org/curated/en/2001/02/1003132/measuring-poverty-dynammics-inequality-transition-economies-disentangling-real-events-noisy-data
http://hdl.handle.net/10986/19701
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