Big Data Measures of Well-Being: Evidence From a Google Well-Being Index in the United States
We build an indicator of individual subjective well-being in the United States based on Google Trends. The indicator is a combination of keyword groups that are endogenously identified to fit with the weekly time-series of subjective well-being measures disseminated by Gallup Analytics. We find that...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | , , |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Paris
OECD Publishing
2016
|
| Schriftenreihe: | OECD Statistics Working Papers
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | We build an indicator of individual subjective well-being in the United States based on Google Trends. The indicator is a combination of keyword groups that are endogenously identified to fit with the weekly time-series of subjective well-being measures disseminated by Gallup Analytics. We find that keywords associated with job search, financial security, family life and leisure are the strongest predictors of the variations in subjective well-being. The model successfully predicts the out-of-sample evolution of most subjective well-being measures at a one-year horizon |
| Beschreibung: | 1 Online-Ressource (37 Seiten) 21 x 29.7cm |
| DOI: | 10.1787/5jlz9hpg0rd1-en |
Internformat
MARC
| LEADER | 00000nam a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV047937156 | ||
| 003 | DE-604 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 220413s2016 xx o|||| 00||| eng d | ||
| 024 | 7 | |a 10.1787/5jlz9hpg0rd1-en |2 doi | |
| 035 | |a (ZDB-13-SOC)061243051 | ||
| 035 | |a (OCoLC)1312699495 | ||
| 035 | |a (DE-599)BVBBV047937156 | ||
| 040 | |a DE-604 |b ger |e aacr | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-384 |a DE-91 |a DE-473 |a DE-824 |a DE-29 |a DE-739 |a DE-355 |a DE-20 |a DE-1028 |a DE-1049 |a DE-188 |a DE-521 |a DE-861 |a DE-898 |a DE-92 |a DE-573 |a DE-19 | ||
| 100 | 1 | |a Algan, Yann |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Big Data Measures of Well-Being |b Evidence From a Google Well-Being Index in the United States |c Yann Algan ... [et al] |
| 264 | 1 | |a Paris |b OECD Publishing |c 2016 | |
| 300 | |a 1 Online-Ressource (37 Seiten) |c 21 x 29.7cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a OECD Statistics Working Papers | |
| 520 | |a We build an indicator of individual subjective well-being in the United States based on Google Trends. The indicator is a combination of keyword groups that are endogenously identified to fit with the weekly time-series of subjective well-being measures disseminated by Gallup Analytics. We find that keywords associated with job search, financial security, family life and leisure are the strongest predictors of the variations in subjective well-being. The model successfully predicts the out-of-sample evolution of most subjective well-being measures at a one-year horizon | ||
| 650 | 4 | |a Employment | |
| 650 | 4 | |a Economics | |
| 650 | 4 | |a United States | |
| 700 | 1 | |a Beasley, Elizabeth |4 ctb | |
| 700 | 1 | |a Guyot, Florian |4 ctb | |
| 700 | 1 | |a Higa, Kazuhito |4 ctb | |
| 856 | 4 | 0 | |u https://doi.org/10.1787/5jlz9hpg0rd1-en |x Verlag |z kostenfrei |3 Volltext |
| 912 | |a ZDB-13-SOC | ||
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-033318650 | |
Datensatz im Suchindex
| _version_ | 1818806115637395456 |
|---|---|
| adam_text | |
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Algan, Yann |
| author2 | Beasley, Elizabeth Guyot, Florian Higa, Kazuhito |
| author2_role | ctb ctb ctb |
| author2_variant | e b eb f g fg k h kh |
| author_facet | Algan, Yann Beasley, Elizabeth Guyot, Florian Higa, Kazuhito |
| author_role | aut |
| author_sort | Algan, Yann |
| author_variant | y a ya |
| building | Verbundindex |
| bvnumber | BV047937156 |
| collection | ZDB-13-SOC |
| ctrlnum | (ZDB-13-SOC)061243051 (OCoLC)1312699495 (DE-599)BVBBV047937156 |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| doi_str_mv | 10.1787/5jlz9hpg0rd1-en |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nam a2200000zc 4500</leader><controlfield tag="001">BV047937156</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">220413s2016 xx o|||| 00||| eng d</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1787/5jlz9hpg0rd1-en</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-13-SOC)061243051</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1312699495</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV047937156</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">aacr</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-384</subfield><subfield code="a">DE-91</subfield><subfield code="a">DE-473</subfield><subfield code="a">DE-824</subfield><subfield code="a">DE-29</subfield><subfield code="a">DE-739</subfield><subfield code="a">DE-355</subfield><subfield code="a">DE-20</subfield><subfield code="a">DE-1028</subfield><subfield code="a">DE-1049</subfield><subfield code="a">DE-188</subfield><subfield code="a">DE-521</subfield><subfield code="a">DE-861</subfield><subfield code="a">DE-898</subfield><subfield code="a">DE-92</subfield><subfield code="a">DE-573</subfield><subfield code="a">DE-19</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Algan, Yann</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Big Data Measures of Well-Being</subfield><subfield code="b">Evidence From a Google Well-Being Index in the United States</subfield><subfield code="c">Yann Algan ... [et al]</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Paris</subfield><subfield code="b">OECD Publishing</subfield><subfield code="c">2016</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (37 Seiten)</subfield><subfield code="c">21 x 29.7cm</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">OECD Statistics Working Papers</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">We build an indicator of individual subjective well-being in the United States based on Google Trends. The indicator is a combination of keyword groups that are endogenously identified to fit with the weekly time-series of subjective well-being measures disseminated by Gallup Analytics. We find that keywords associated with job search, financial security, family life and leisure are the strongest predictors of the variations in subjective well-being. The model successfully predicts the out-of-sample evolution of most subjective well-being measures at a one-year horizon</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Employment</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Economics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">United States</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Beasley, Elizabeth</subfield><subfield code="4">ctb</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guyot, Florian</subfield><subfield code="4">ctb</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Higa, Kazuhito</subfield><subfield code="4">ctb</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1787/5jlz9hpg0rd1-en</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-13-SOC</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-033318650</subfield></datafield></record></collection> |
| id | DE-604.BV047937156 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T19:35:07Z |
| indexdate | 2024-12-18T19:04:38Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033318650 |
| oclc_num | 1312699495 |
| open_access_boolean | 1 |
| owner | DE-384 DE-91 DE-BY-TUM DE-473 DE-BY-UBG DE-824 DE-29 DE-739 DE-355 DE-BY-UBR DE-20 DE-1028 DE-1049 DE-188 DE-521 DE-861 DE-898 DE-BY-UBR DE-92 DE-573 DE-19 DE-BY-UBM |
| owner_facet | DE-384 DE-91 DE-BY-TUM DE-473 DE-BY-UBG DE-824 DE-29 DE-739 DE-355 DE-BY-UBR DE-20 DE-1028 DE-1049 DE-188 DE-521 DE-861 DE-898 DE-BY-UBR DE-92 DE-573 DE-19 DE-BY-UBM |
| physical | 1 Online-Ressource (37 Seiten) 21 x 29.7cm |
| psigel | ZDB-13-SOC |
| publishDate | 2016 |
| publishDateSearch | 2016 |
| publishDateSort | 2016 |
| publisher | OECD Publishing |
| record_format | marc |
| series2 | OECD Statistics Working Papers |
| spelling | Algan, Yann Verfasser aut Big Data Measures of Well-Being Evidence From a Google Well-Being Index in the United States Yann Algan ... [et al] Paris OECD Publishing 2016 1 Online-Ressource (37 Seiten) 21 x 29.7cm txt rdacontent c rdamedia cr rdacarrier OECD Statistics Working Papers We build an indicator of individual subjective well-being in the United States based on Google Trends. The indicator is a combination of keyword groups that are endogenously identified to fit with the weekly time-series of subjective well-being measures disseminated by Gallup Analytics. We find that keywords associated with job search, financial security, family life and leisure are the strongest predictors of the variations in subjective well-being. The model successfully predicts the out-of-sample evolution of most subjective well-being measures at a one-year horizon Employment Economics United States Beasley, Elizabeth ctb Guyot, Florian ctb Higa, Kazuhito ctb https://doi.org/10.1787/5jlz9hpg0rd1-en Verlag kostenfrei Volltext |
| spellingShingle | Algan, Yann Big Data Measures of Well-Being Evidence From a Google Well-Being Index in the United States Employment Economics United States |
| title | Big Data Measures of Well-Being Evidence From a Google Well-Being Index in the United States |
| title_auth | Big Data Measures of Well-Being Evidence From a Google Well-Being Index in the United States |
| title_exact_search | Big Data Measures of Well-Being Evidence From a Google Well-Being Index in the United States |
| title_exact_search_txtP | Big Data Measures of Well-Being Evidence From a Google Well-Being Index in the United States |
| title_full | Big Data Measures of Well-Being Evidence From a Google Well-Being Index in the United States Yann Algan ... [et al] |
| title_fullStr | Big Data Measures of Well-Being Evidence From a Google Well-Being Index in the United States Yann Algan ... [et al] |
| title_full_unstemmed | Big Data Measures of Well-Being Evidence From a Google Well-Being Index in the United States Yann Algan ... [et al] |
| title_short | Big Data Measures of Well-Being |
| title_sort | big data measures of well being evidence from a google well being index in the united states |
| title_sub | Evidence From a Google Well-Being Index in the United States |
| topic | Employment Economics United States |
| topic_facet | Employment Economics United States |
| url | https://doi.org/10.1787/5jlz9hpg0rd1-en |
| work_keys_str_mv | AT alganyann bigdatameasuresofwellbeingevidencefromagooglewellbeingindexintheunitedstates AT beasleyelizabeth bigdatameasuresofwellbeingevidencefromagooglewellbeingindexintheunitedstates AT guyotflorian bigdatameasuresofwellbeingevidencefromagooglewellbeingindexintheunitedstates AT higakazuhito bigdatameasuresofwellbeingevidencefromagooglewellbeingindexintheunitedstates |