What Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data?:
This paper implements a machine learning approach to estimate intra-generational economic mobility using cross-sectional data. A Least Absolute Shrinkage and Selection Operator (Lasso) procedure is applied to explore poverty dynamics and household-level welfare growth in the absence of panel data se...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Washington, D.C
The World Bank
2018
|
| Schriftenreihe: | World Bank E-Library Archive
|
| Online-Zugang: | Volltext |
| Zusammenfassung: | This paper implements a machine learning approach to estimate intra-generational economic mobility using cross-sectional data. A Least Absolute Shrinkage and Selection Operator (Lasso) procedure is applied to explore poverty dynamics and household-level welfare growth in the absence of panel data sets that follow individuals over time. The method is validated by sampling repeated cross-sections of actual panel data from Peru. In general, the approach performs well at estimating intra-generational poverty transitions; most of the mobility estimates fall within the 95 percent confidence intervals of poverty mobility from the actual panel data. The validation also confirms that the Lasso regularization procedure performs well at estimating household-level welfare growth between two years. Overall, the results are sufficiently encouraging to estimate economic mobility in settings where panel data are not available or, if they are, to improve panel data when they suffer from serious non-random attrition problems |
| Beschreibung: | 1 Online-Ressource (31 Seiten) |
| DOI: | 10.1596/1813-9450-8545 |
Internformat
MARC
| LEADER | 00000nmm a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV048274054 | ||
| 003 | DE-604 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 220609s2018 |||| o||u| ||||||eng d | ||
| 024 | 7 | |a 10.1596/1813-9450-8545 |2 doi | |
| 035 | |a (ZDB-1-WBA)NLM011149760 | ||
| 035 | |a (OCoLC)1334040962 | ||
| 035 | |a (DE-599)GBVNLM011149760 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 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 Lucchetti, Leonardo |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a What Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data? |c Lucchetti, Leonardo |
| 264 | 1 | |a Washington, D.C |b The World Bank |c 2018 | |
| 300 | |a 1 Online-Ressource (31 Seiten) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a World Bank E-Library Archive | |
| 520 | |a This paper implements a machine learning approach to estimate intra-generational economic mobility using cross-sectional data. A Least Absolute Shrinkage and Selection Operator (Lasso) procedure is applied to explore poverty dynamics and household-level welfare growth in the absence of panel data sets that follow individuals over time. The method is validated by sampling repeated cross-sections of actual panel data from Peru. In general, the approach performs well at estimating intra-generational poverty transitions; most of the mobility estimates fall within the 95 percent confidence intervals of poverty mobility from the actual panel data. The validation also confirms that the Lasso regularization procedure performs well at estimating household-level welfare growth between two years. Overall, the results are sufficiently encouraging to estimate economic mobility in settings where panel data are not available or, if they are, to improve panel data when they suffer from serious non-random attrition problems | ||
| 700 | 1 | |a Lucchetti, Leonardo |4 oth | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Lucchetti, Leonardo |t What Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data? |d Washington, D.C : The World Bank, 2018 |
| 856 | 4 | 0 | |u https://doi.org/10.1596/1813-9450-8545 |x Verlag |z kostenfrei |3 Volltext |
| 912 | |a ZDB-1-WBA | ||
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-033654249 | |
Datensatz im Suchindex
| _version_ | 1812671818901749760 |
|---|---|
| adam_text | |
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Lucchetti, Leonardo |
| author_facet | Lucchetti, Leonardo |
| author_role | aut |
| author_sort | Lucchetti, Leonardo |
| author_variant | l l ll |
| building | Verbundindex |
| bvnumber | BV048274054 |
| collection | ZDB-1-WBA |
| ctrlnum | (ZDB-1-WBA)NLM011149760 (OCoLC)1334040962 (DE-599)GBVNLM011149760 |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| doi_str_mv | 10.1596/1813-9450-8545 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nmm a2200000zc 4500</leader><controlfield tag="001">BV048274054</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">220609s2018 |||| o||u| ||||||eng d</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1596/1813-9450-8545</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-1-WBA)NLM011149760</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1334040962</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVNLM011149760</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="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">Lucchetti, Leonardo</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">What Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data?</subfield><subfield code="c">Lucchetti, Leonardo</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">2018</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (31 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="490" ind1="0" ind2=" "><subfield code="a">World Bank E-Library Archive</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This paper implements a machine learning approach to estimate intra-generational economic mobility using cross-sectional data. A Least Absolute Shrinkage and Selection Operator (Lasso) procedure is applied to explore poverty dynamics and household-level welfare growth in the absence of panel data sets that follow individuals over time. The method is validated by sampling repeated cross-sections of actual panel data from Peru. In general, the approach performs well at estimating intra-generational poverty transitions; most of the mobility estimates fall within the 95 percent confidence intervals of poverty mobility from the actual panel data. The validation also confirms that the Lasso regularization procedure performs well at estimating household-level welfare growth between two years. Overall, the results are sufficiently encouraging to estimate economic mobility in settings where panel data are not available or, if they are, to improve panel data when they suffer from serious non-random attrition problems</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lucchetti, Leonardo</subfield><subfield code="4">oth</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="a">Lucchetti, Leonardo</subfield><subfield code="t">What Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data?</subfield><subfield code="d">Washington, D.C : The World Bank, 2018</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1596/1813-9450-8545</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:BVB01-033654249</subfield></datafield></record></collection> |
| id | DE-604.BV048274054 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T20:00:10Z |
| indexdate | 2024-10-12T04:02:37Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033654249 |
| oclc_num | 1334040962 |
| 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 (31 Seiten) |
| psigel | ZDB-1-WBA |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | The World Bank |
| record_format | marc |
| series2 | World Bank E-Library Archive |
| spellingShingle | Lucchetti, Leonardo What Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data? |
| title | What Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data? |
| title_auth | What Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data? |
| title_exact_search | What Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data? |
| title_exact_search_txtP | What Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data? |
| title_full | What Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data? Lucchetti, Leonardo |
| title_fullStr | What Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data? Lucchetti, Leonardo |
| title_full_unstemmed | What Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data? Lucchetti, Leonardo |
| title_short | What Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data? |
| title_sort | what can we machine learn about welfare dynamics from cross sectional data |
| url | https://doi.org/10.1596/1813-9450-8545 |
| work_keys_str_mv | AT lucchettileonardo whatcanwemachinelearnaboutwelfaredynamicsfromcrosssectionaldata |