How Survey-to-Survey Imputation Can Fail
This paper proposes diagnostics to assess the accuracy of survey-to-survey imputation methods and applies them to examine why imputing from the Household Income and Expenditure Survey into the Labor Force Survey fails to accurately project poverty...
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| Format: | Policy Research Working Paper |
| Language: | English en_US |
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World Bank Group, Washington, DC
2014
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| Online Access: | http://documents.worldbank.org/curated/en/2014/07/19754254/survey-to-survey-imputation-can-fail http://hdl.handle.net/10986/19364 |
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okr-10986-193642021-04-23T14:03:51Z How Survey-to-Survey Imputation Can Fail Newhouse, D. Shivakumaran, S. Takamatsu, S. Yoshida, N. AVERAGE WAGES BIASES CALCULATION CHANGES IN POVERTY CONFIDENCE INTERVALS CONSUMPTION DATA CONSUMPTION EXPENDITURE CONSUMPTION EXPENDITURES DECLINE IN POVERTY DRINKING WATER DUMMY VARIABLES EMPLOYMENT INCOME EMPLOYMENT STATUS ESTIMATES OF POVERTY FOOD CONSUMPTION HOUSEHOLD BUDGET HOUSEHOLD CONSUMPTION HOUSEHOLD DEMOGRAPHICS HOUSEHOLD EXPENDITURE SURVEYS HOUSEHOLD HEAD HOUSEHOLD HEADS HOUSEHOLD INCOME HOUSEHOLD SIZE HOUSEHOLD SURVEY HOUSEHOLD SURVEYS HOUSEHOLD WELFARE HOUSING INCOME GROWTH INEQUALITY LIVING STANDARDS NATIONAL POVERTY NATIONAL POVERTY RATE PER CAPITA CONSUMPTION POOR POOR PROVINCES POVERTY ANALYSIS POVERTY ASSESSMENT POVERTY DATA POVERTY ESTIMATES POVERTY INDICATOR POVERTY LINES POVERTY MAPPING POVERTY MAPS POVERTY MEASUREMENT POVERTY MEASURES POVERTY RATE POVERTY RATES POVERTY REDUCTION PRECISION PREDICTION PREDICTIONS PROBABILITIES PROBABILITY REDUCTION IN POVERTY REDUCTION OF POVERTY REGIONAL DIFFERENCES REGIONAL LEVEL REGIONAL LEVELS REGIONAL PERSPECTIVE RELIABILITY RURAL RURAL AREAS RURAL POPULATION RURAL POVERTY RURAL PUBLIC RURAL SECTOR RURAL SECTORS SAMPLE DESIGN SAMPLING ERRORS SELF-EMPLOYMENT STANDARD DEVIATION STANDARD ERRORS UNEMPLOYMENT VILLAGE LEVEL WAGE INCOME WAGE RATES WAR WELFARE INDICATORS WELFARE MONITORING This paper proposes diagnostics to assess the accuracy of survey-to-survey imputation methods and applies them to examine why imputing from the Household Income and Expenditure Survey into the Labor Force Survey fails to accurately project poverty trends in Sri Lanka between 2006 and 2009. Survey-to-survey imputation methods rely on two key assumptions: (i) that the questions in the two surveys are asked in a consistent way and (ii) that common variables of the two surveys explain a large share of the intertemporal change in household expenditure and poverty. In addition, differences in sampling design can lead validation tests to underestimate the accuracy of survey-to-survey predictions. In Sri Lanka, the causes of failure differ across sectors. In the urban sector, the primary culprit is differences between the two surveys in the design of the questionnaire. In the rural and estate sectors, the set of common variables in the prediction model does not adequately capture changes in poverty. The paper concludes that in Sri Lanka, survey-to-survey imputation between the Household Income and Expenditure Survey and the Labor Force Survey cannot produce accurate poverty estimates unless the Labor Force Survey adds additional questions on assets and is redesigned to use a questionnaire that is compatible with the Household Income and Expenditure Survey. Alternatively, a new welfare-tracking survey that satisfies these conditions could be established. 2014-08-15T16:54:27Z 2014-08-15T16:54:27Z 2014-07 http://documents.worldbank.org/curated/en/2014/07/19754254/survey-to-survey-imputation-can-fail http://hdl.handle.net/10986/19364 English en_US Policy Research Working Paper;No. 6961 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo/ World Bank Group, Washington, DC Publications & Research :: Policy Research Working Paper Publications & Research South Asia Sri Lanka |
| 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 WAGES BIASES CALCULATION CHANGES IN POVERTY CONFIDENCE INTERVALS CONSUMPTION DATA CONSUMPTION EXPENDITURE CONSUMPTION EXPENDITURES DECLINE IN POVERTY DRINKING WATER DUMMY VARIABLES EMPLOYMENT INCOME EMPLOYMENT STATUS ESTIMATES OF POVERTY FOOD CONSUMPTION HOUSEHOLD BUDGET HOUSEHOLD CONSUMPTION HOUSEHOLD DEMOGRAPHICS HOUSEHOLD EXPENDITURE SURVEYS HOUSEHOLD HEAD HOUSEHOLD HEADS HOUSEHOLD INCOME HOUSEHOLD SIZE HOUSEHOLD SURVEY HOUSEHOLD SURVEYS HOUSEHOLD WELFARE HOUSING INCOME GROWTH INEQUALITY LIVING STANDARDS NATIONAL POVERTY NATIONAL POVERTY RATE PER CAPITA CONSUMPTION POOR POOR PROVINCES POVERTY ANALYSIS POVERTY ASSESSMENT POVERTY DATA POVERTY ESTIMATES POVERTY INDICATOR POVERTY LINES POVERTY MAPPING POVERTY MAPS POVERTY MEASUREMENT POVERTY MEASURES POVERTY RATE POVERTY RATES POVERTY REDUCTION PRECISION PREDICTION PREDICTIONS PROBABILITIES PROBABILITY REDUCTION IN POVERTY REDUCTION OF POVERTY REGIONAL DIFFERENCES REGIONAL LEVEL REGIONAL LEVELS REGIONAL PERSPECTIVE RELIABILITY RURAL RURAL AREAS RURAL POPULATION RURAL POVERTY RURAL PUBLIC RURAL SECTOR RURAL SECTORS SAMPLE DESIGN SAMPLING ERRORS SELF-EMPLOYMENT STANDARD DEVIATION STANDARD ERRORS UNEMPLOYMENT VILLAGE LEVEL WAGE INCOME WAGE RATES WAR WELFARE INDICATORS WELFARE MONITORING |
| spellingShingle |
AVERAGE WAGES BIASES CALCULATION CHANGES IN POVERTY CONFIDENCE INTERVALS CONSUMPTION DATA CONSUMPTION EXPENDITURE CONSUMPTION EXPENDITURES DECLINE IN POVERTY DRINKING WATER DUMMY VARIABLES EMPLOYMENT INCOME EMPLOYMENT STATUS ESTIMATES OF POVERTY FOOD CONSUMPTION HOUSEHOLD BUDGET HOUSEHOLD CONSUMPTION HOUSEHOLD DEMOGRAPHICS HOUSEHOLD EXPENDITURE SURVEYS HOUSEHOLD HEAD HOUSEHOLD HEADS HOUSEHOLD INCOME HOUSEHOLD SIZE HOUSEHOLD SURVEY HOUSEHOLD SURVEYS HOUSEHOLD WELFARE HOUSING INCOME GROWTH INEQUALITY LIVING STANDARDS NATIONAL POVERTY NATIONAL POVERTY RATE PER CAPITA CONSUMPTION POOR POOR PROVINCES POVERTY ANALYSIS POVERTY ASSESSMENT POVERTY DATA POVERTY ESTIMATES POVERTY INDICATOR POVERTY LINES POVERTY MAPPING POVERTY MAPS POVERTY MEASUREMENT POVERTY MEASURES POVERTY RATE POVERTY RATES POVERTY REDUCTION PRECISION PREDICTION PREDICTIONS PROBABILITIES PROBABILITY REDUCTION IN POVERTY REDUCTION OF POVERTY REGIONAL DIFFERENCES REGIONAL LEVEL REGIONAL LEVELS REGIONAL PERSPECTIVE RELIABILITY RURAL RURAL AREAS RURAL POPULATION RURAL POVERTY RURAL PUBLIC RURAL SECTOR RURAL SECTORS SAMPLE DESIGN SAMPLING ERRORS SELF-EMPLOYMENT STANDARD DEVIATION STANDARD ERRORS UNEMPLOYMENT VILLAGE LEVEL WAGE INCOME WAGE RATES WAR WELFARE INDICATORS WELFARE MONITORING Newhouse, D. Shivakumaran, S. Takamatsu, S. Yoshida, N. How Survey-to-Survey Imputation Can Fail |
| geographic_facet |
South Asia Sri Lanka |
| relation |
Policy Research Working Paper;No. 6961 |
| description |
This paper proposes diagnostics to
assess the accuracy of survey-to-survey imputation methods
and applies them to examine why imputing from the Household
Income and Expenditure Survey into the Labor Force Survey
fails to accurately project poverty trends in Sri Lanka
between 2006 and 2009. Survey-to-survey imputation methods
rely on two key assumptions: (i) that the questions in the
two surveys are asked in a consistent way and (ii) that
common variables of the two surveys explain a large share of
the intertemporal change in household expenditure and
poverty. In addition, differences in sampling design can
lead validation tests to underestimate the accuracy of
survey-to-survey predictions. In Sri Lanka, the causes of
failure differ across sectors. In the urban sector, the
primary culprit is differences between the two surveys in
the design of the questionnaire. In the rural and estate
sectors, the set of common variables in the prediction model
does not adequately capture changes in poverty. The paper
concludes that in Sri Lanka, survey-to-survey imputation
between the Household Income and Expenditure Survey and the
Labor Force Survey cannot produce accurate poverty estimates
unless the Labor Force Survey adds additional questions on
assets and is redesigned to use a questionnaire that is
compatible with the Household Income and Expenditure Survey.
Alternatively, a new welfare-tracking survey that satisfies
these conditions could be established. |
| format |
Publications & Research :: Policy Research Working Paper |
| author |
Newhouse, D. Shivakumaran, S. Takamatsu, S. Yoshida, N. |
| author_facet |
Newhouse, D. Shivakumaran, S. Takamatsu, S. Yoshida, N. |
| author_sort |
Newhouse, D. |
| title |
How Survey-to-Survey Imputation Can Fail |
| title_short |
How Survey-to-Survey Imputation Can Fail |
| title_full |
How Survey-to-Survey Imputation Can Fail |
| title_fullStr |
How Survey-to-Survey Imputation Can Fail |
| title_full_unstemmed |
How Survey-to-Survey Imputation Can Fail |
| title_sort |
how survey-to-survey imputation can fail |
| publisher |
World Bank Group, Washington, DC |
| publishDate |
2014 |
| url |
http://documents.worldbank.org/curated/en/2014/07/19754254/survey-to-survey-imputation-can-fail http://hdl.handle.net/10986/19364 |
| _version_ |
1764443722450206720 |