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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2014
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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 |
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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 |
_version_ |
1764440394191339520 |