Poverty Imputation in Contexts without Consumption Data: A Revisit with Further Refinements
A key challenge with poverty measurement is that household consumption data are often unavailable or infrequently collected or may be incomparable over time. In a development project setting, it is seldom feasible to collect full consumption data for estimating the poverty impacts. While survey-to-s...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Washington, D.C
The World Bank
2021
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | A key challenge with poverty measurement is that household consumption data are often unavailable or infrequently collected or may be incomparable over time. In a development project setting, it is seldom feasible to collect full consumption data for estimating the poverty impacts. While survey-to-survey imputation is a cost-effective approach to address these gaps, its effective use calls for a combination of both ex-ante design choices and ex-post modeling efforts that are anchored in validated protocols. This paper refines various aspects of existing poverty imputation models using 14 multi-topic household surveys conducted over the past decade in Ethiopia, Malawi, Nigeria, Tanzania, and Vietnam. The analysis reveals that including an additional predictor that captures household utility consumption expenditures-as part of a basic imputation model with household-level demographic and employment variables-provides poverty estimates that are not statistically significantly different from the true poverty rates. In many cases, these estimates even fall within one standard error of the true poverty rates. Adding geospatial variables to the imputation model improves imputation accuracy on a cross-country basis. Bringing in additional community-level predictors (available from survey and census data in Vietnam) related to educational achievement, poverty, and asset wealth can further enhance accuracy. Yet, there is within-country spatial heterogeneity in model performance, with certain models performing well for either urban areas or rural areas only. The paper provides operationally-relevant and cost-saving inputs into the design of future surveys implemented with a poverty imputation objective and suggests directions for future research |
| Beschreibung: | 1 Online-Ressource (123 Seiten) |
| DOI: | 10.1596/1813-9450-9838 |
Internformat
MARC
| LEADER | 00000nmm a22000001c 4500 | ||
|---|---|---|---|
| 001 | BV049080854 | ||
| 003 | DE-604 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 230731s2021 xxu|||| o||u| ||||||eng d | ||
| 024 | 7 | |a 10.1596/1813-9450-9838 |2 doi | |
| 035 | |a (ZDB-1-WBA)072747587 | ||
| 035 | |a (OCoLC)1392146349 | ||
| 035 | |a (DE-599)KEP072747587 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 044 | |a xxu |c XD-US | ||
| 049 | |a DE-12 |a DE-521 |a DE-573 |a DE-523 |a DE-Re13 |a DE-19 |a DE-355 |a DE-703 |a DE-91 |a DE-706 |a DE-29 |a DE-M347 |a DE-473 |a DE-824 |a DE-20 |a DE-739 |a DE-1043 |a DE-863 |a DE-862 | ||
| 100 | 1 | |a Dang, Hai-Anh H. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Poverty Imputation in Contexts without Consumption Data |b A Revisit with Further Refinements |c Hai-Anh H. Dang |
| 264 | 1 | |a Washington, D.C |b The World Bank |c 2021 | |
| 300 | |a 1 Online-Ressource (123 Seiten) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 520 | 3 | |a A key challenge with poverty measurement is that household consumption data are often unavailable or infrequently collected or may be incomparable over time. In a development project setting, it is seldom feasible to collect full consumption data for estimating the poverty impacts. While survey-to-survey imputation is a cost-effective approach to address these gaps, its effective use calls for a combination of both ex-ante design choices and ex-post modeling efforts that are anchored in validated protocols. This paper refines various aspects of existing poverty imputation models using 14 multi-topic household surveys conducted over the past decade in Ethiopia, Malawi, Nigeria, Tanzania, and Vietnam. The analysis reveals that including an additional predictor that captures household utility consumption expenditures-as part of a basic imputation model with household-level demographic and employment variables-provides poverty estimates that are not statistically significantly different from the true poverty rates. In many cases, these estimates even fall within one standard error of the true poverty rates. Adding geospatial variables to the imputation model improves imputation accuracy on a cross-country basis. Bringing in additional community-level predictors (available from survey and census data in Vietnam) related to educational achievement, poverty, and asset wealth can further enhance accuracy. Yet, there is within-country spatial heterogeneity in model performance, with certain models performing well for either urban areas or rural areas only. The paper provides operationally-relevant and cost-saving inputs into the design of future surveys implemented with a poverty imputation objective and suggests directions for future research | |
| 650 | 4 | |a Asset Wealth | |
| 650 | 4 | |a Demographic and Health Survey | |
| 650 | 4 | |a Educational Achievement | |
| 650 | 4 | |a Employment | |
| 650 | 4 | |a Household Survey | |
| 650 | 4 | |a Inequality | |
| 650 | 4 | |a Living Standards | |
| 650 | 4 | |a Poverty Lines | |
| 650 | 4 | |a Poverty Measurement | |
| 650 | 4 | |a Poverty Reduction | |
| 650 | 4 | |a Survey-To-Survey Imputation | |
| 700 | 1 | |a Abanokova, Kseniya |e Sonstige |4 oth | |
| 700 | 1 | |a Carletto, Calogero |e Sonstige |4 oth | |
| 700 | 1 | |a Kilic, Talip |e Sonstige |4 oth | |
| 856 | 4 | 0 | |u https://doi.org/10.1596/1813-9450-9838 |x Verlag |z kostenfrei |3 Volltext |
| 912 | |a ZDB-1-WBA | ||
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-034342744 | |
Datensatz im Suchindex
| _version_ | 1812671843218227200 |
|---|---|
| adam_text | |
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Dang, Hai-Anh H. |
| author_facet | Dang, Hai-Anh H. |
| author_role | aut |
| author_sort | Dang, Hai-Anh H. |
| author_variant | h a h d hah hahd |
| building | Verbundindex |
| bvnumber | BV049080854 |
| collection | ZDB-1-WBA |
| ctrlnum | (ZDB-1-WBA)072747587 (OCoLC)1392146349 (DE-599)KEP072747587 |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| doi_str_mv | 10.1596/1813-9450-9838 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nmm a22000001c 4500</leader><controlfield tag="001">BV049080854</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">230731s2021 xxu|||| o||u| ||||||eng d</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1596/1813-9450-9838</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-1-WBA)072747587</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1392146349</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP072747587</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="a">xxu</subfield><subfield code="c">XD-US</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-12</subfield><subfield code="a">DE-521</subfield><subfield code="a">DE-573</subfield><subfield code="a">DE-523</subfield><subfield code="a">DE-Re13</subfield><subfield code="a">DE-19</subfield><subfield code="a">DE-355</subfield><subfield code="a">DE-703</subfield><subfield code="a">DE-91</subfield><subfield code="a">DE-706</subfield><subfield code="a">DE-29</subfield><subfield code="a">DE-M347</subfield><subfield code="a">DE-473</subfield><subfield code="a">DE-824</subfield><subfield code="a">DE-20</subfield><subfield code="a">DE-739</subfield><subfield code="a">DE-1043</subfield><subfield code="a">DE-863</subfield><subfield code="a">DE-862</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Dang, Hai-Anh H.</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Poverty Imputation in Contexts without Consumption Data</subfield><subfield code="b">A Revisit with Further Refinements</subfield><subfield code="c">Hai-Anh H. Dang</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Washington, D.C</subfield><subfield code="b">The World Bank</subfield><subfield code="c">2021</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (123 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">A key challenge with poverty measurement is that household consumption data are often unavailable or infrequently collected or may be incomparable over time. In a development project setting, it is seldom feasible to collect full consumption data for estimating the poverty impacts. While survey-to-survey imputation is a cost-effective approach to address these gaps, its effective use calls for a combination of both ex-ante design choices and ex-post modeling efforts that are anchored in validated protocols. This paper refines various aspects of existing poverty imputation models using 14 multi-topic household surveys conducted over the past decade in Ethiopia, Malawi, Nigeria, Tanzania, and Vietnam. The analysis reveals that including an additional predictor that captures household utility consumption expenditures-as part of a basic imputation model with household-level demographic and employment variables-provides poverty estimates that are not statistically significantly different from the true poverty rates. In many cases, these estimates even fall within one standard error of the true poverty rates. Adding geospatial variables to the imputation model improves imputation accuracy on a cross-country basis. Bringing in additional community-level predictors (available from survey and census data in Vietnam) related to educational achievement, poverty, and asset wealth can further enhance accuracy. Yet, there is within-country spatial heterogeneity in model performance, with certain models performing well for either urban areas or rural areas only. The paper provides operationally-relevant and cost-saving inputs into the design of future surveys implemented with a poverty imputation objective and suggests directions for future research</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Asset Wealth</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Demographic and Health Survey</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Educational Achievement</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Employment</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Household Survey</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Inequality</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Living Standards</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Poverty Lines</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Poverty Measurement</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Poverty Reduction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Survey-To-Survey Imputation</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Abanokova, Kseniya</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Carletto, Calogero</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kilic, Talip</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1596/1813-9450-9838</subfield><subfield code="x">Verlag</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-1-WBA</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-034342744</subfield></datafield></record></collection> |
| id | DE-604.BV049080854 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T22:27:58Z |
| indexdate | 2024-10-12T04:03:00Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034342744 |
| oclc_num | 1392146349 |
| open_access_boolean | 1 |
| owner | DE-12 DE-521 DE-573 DE-523 DE-Re13 DE-BY-UBR DE-19 DE-BY-UBM DE-355 DE-BY-UBR DE-703 DE-91 DE-BY-TUM DE-706 DE-29 DE-M347 DE-473 DE-BY-UBG DE-824 DE-20 DE-739 DE-1043 DE-863 DE-BY-FWS DE-862 DE-BY-FWS |
| owner_facet | DE-12 DE-521 DE-573 DE-523 DE-Re13 DE-BY-UBR DE-19 DE-BY-UBM DE-355 DE-BY-UBR DE-703 DE-91 DE-BY-TUM DE-706 DE-29 DE-M347 DE-473 DE-BY-UBG DE-824 DE-20 DE-739 DE-1043 DE-863 DE-BY-FWS DE-862 DE-BY-FWS |
| physical | 1 Online-Ressource (123 Seiten) |
| psigel | ZDB-1-WBA |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | The World Bank |
| record_format | marc |
| spellingShingle | Dang, Hai-Anh H. Poverty Imputation in Contexts without Consumption Data A Revisit with Further Refinements Asset Wealth Demographic and Health Survey Educational Achievement Employment Household Survey Inequality Living Standards Poverty Lines Poverty Measurement Poverty Reduction Survey-To-Survey Imputation |
| title | Poverty Imputation in Contexts without Consumption Data A Revisit with Further Refinements |
| title_auth | Poverty Imputation in Contexts without Consumption Data A Revisit with Further Refinements |
| title_exact_search | Poverty Imputation in Contexts without Consumption Data A Revisit with Further Refinements |
| title_exact_search_txtP | Poverty Imputation in Contexts without Consumption Data A Revisit with Further Refinements |
| title_full | Poverty Imputation in Contexts without Consumption Data A Revisit with Further Refinements Hai-Anh H. Dang |
| title_fullStr | Poverty Imputation in Contexts without Consumption Data A Revisit with Further Refinements Hai-Anh H. Dang |
| title_full_unstemmed | Poverty Imputation in Contexts without Consumption Data A Revisit with Further Refinements Hai-Anh H. Dang |
| title_short | Poverty Imputation in Contexts without Consumption Data |
| title_sort | poverty imputation in contexts without consumption data a revisit with further refinements |
| title_sub | A Revisit with Further Refinements |
| topic | Asset Wealth Demographic and Health Survey Educational Achievement Employment Household Survey Inequality Living Standards Poverty Lines Poverty Measurement Poverty Reduction Survey-To-Survey Imputation |
| topic_facet | Asset Wealth Demographic and Health Survey Educational Achievement Employment Household Survey Inequality Living Standards Poverty Lines Poverty Measurement Poverty Reduction Survey-To-Survey Imputation |
| url | https://doi.org/10.1596/1813-9450-9838 |
| work_keys_str_mv | AT danghaianhh povertyimputationincontextswithoutconsumptiondataarevisitwithfurtherrefinements AT abanokovakseniya povertyimputationincontextswithoutconsumptiondataarevisitwithfurtherrefinements AT carlettocalogero povertyimputationincontextswithoutconsumptiondataarevisitwithfurtherrefinements AT kilictalip povertyimputationincontextswithoutconsumptiondataarevisitwithfurtherrefinements |