Why are some U.S. cities successful, while others are not? Empirical evidence from machine learning:
The U.S. population has become increasingly concentrated in large metropolitan areas. However, there are striking differences in between the performances of big cities: some of them have been very successful and have been able to pull away from the rest, while others have stagnated or even declined....
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | , , |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Paris
OECD Publishing
2020
|
| Schriftenreihe: | OECD Economics Department Working Papers
no.1643 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | The U.S. population has become increasingly concentrated in large metropolitan areas. However, there are striking differences in between the performances of big cities: some of them have been very successful and have been able to pull away from the rest, while others have stagnated or even declined. The main objective of this paper is to characterize U.S. metropolitan areas according to their labor-market performance: which metropolitan areas are struggling and falling behind? Which ones are flourishing? Which ones are staying resilient by adapting to shocks? We rely on an unsupervised machine learning technique called Hierarchical Agglomerative Clustering (HAC) to conduct this empirical investigation. The data comes from a number of sources including the new Job-to-Job (J2J) flows dataset from the Census Bureau, which reports the near universe of job movements in and out of employment at the metropolitan level. We characterize the fate of metropolitan areas by tracking their job mobility rate, unemployment rate, income growth, population increase, net change in job-to-job mobility and GDP growth. Our results indicate that the 372 metropolitan areas under examination can be categorized into four statistically distinct groups: booming areas (67), prosperous mega metropolitan areas (99), resilient areas (149) and distressed metropolitan areas (57). The results show that areas that are doing well are predominantly located in the south and the west. The main features of their success have revolved around embracing digital technologies, adopting local regulations friendly to job mobility and business creation, avoiding strict rules on land-use and housing market, and improving the wellbeing of the city's population. These results highlight that cities adopting well-targeted policies can accelerate the return to growth after a shock. |
| Beschreibung: | 1 Online-Ressource (21 p.) |
| DOI: | 10.1787/7f77c2e7-en |
Internformat
MARC
| LEADER | 00000cam a22000002 4500 | ||
|---|---|---|---|
| 001 | ZDB-13-SOC-068281161 | ||
| 003 | DE-627-1 | ||
| 005 | 20231204121348.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 210915s2020 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1787/7f77c2e7-en |2 doi | |
| 035 | |a (DE-627-1)068281161 | ||
| 035 | |a (DE-599)KEP068281161 | ||
| 035 | |a (FR-PaOEC)7f77c2e7-en | ||
| 035 | |a (EBP)068281161 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 084 | |a C38 |2 jelc | ||
| 084 | |a J61 |2 jelc | ||
| 084 | |a J11 |2 jelc | ||
| 084 | |a O51 |2 jelc | ||
| 084 | |a E24 |2 jelc | ||
| 100 | 1 | |a Azzopardi, Damien |e VerfasserIn |4 aut | |
| 245 | 1 | 0 | |a Why are some U.S. cities successful, while others are not? Empirical evidence from machine learning |c Damien, Azzopardi ... [et al] |
| 264 | 1 | |a Paris |b OECD Publishing |c 2020 | |
| 300 | |a 1 Online-Ressource (21 p.) | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 490 | 0 | |a OECD Economics Department Working Papers |v no.1643 | |
| 520 | |a The U.S. population has become increasingly concentrated in large metropolitan areas. However, there are striking differences in between the performances of big cities: some of them have been very successful and have been able to pull away from the rest, while others have stagnated or even declined. The main objective of this paper is to characterize U.S. metropolitan areas according to their labor-market performance: which metropolitan areas are struggling and falling behind? Which ones are flourishing? Which ones are staying resilient by adapting to shocks? We rely on an unsupervised machine learning technique called Hierarchical Agglomerative Clustering (HAC) to conduct this empirical investigation. The data comes from a number of sources including the new Job-to-Job (J2J) flows dataset from the Census Bureau, which reports the near universe of job movements in and out of employment at the metropolitan level. We characterize the fate of metropolitan areas by tracking their job mobility rate, unemployment rate, income growth, population increase, net change in job-to-job mobility and GDP growth. Our results indicate that the 372 metropolitan areas under examination can be categorized into four statistically distinct groups: booming areas (67), prosperous mega metropolitan areas (99), resilient areas (149) and distressed metropolitan areas (57). The results show that areas that are doing well are predominantly located in the south and the west. The main features of their success have revolved around embracing digital technologies, adopting local regulations friendly to job mobility and business creation, avoiding strict rules on land-use and housing market, and improving the wellbeing of the city's population. These results highlight that cities adopting well-targeted policies can accelerate the return to growth after a shock. | ||
| 650 | 4 | |a Economics | |
| 650 | 4 | |a United States | |
| 700 | 1 | |a Fareed, Fozan |e MitwirkendeR |4 ctb | |
| 700 | 1 | |a Lenain, Patrick |e MitwirkendeR |4 ctb | |
| 700 | 1 | |a Sutherland, Douglas |e MitwirkendeR |4 ctb | |
| 856 | 4 | 0 | |l FWS01 |p ZDB-13-SOC |q FWS_PDA_SOC |u https://doi.org/10.1787/7f77c2e7-en |3 Volltext |
| 912 | |a ZDB-13-SOC | ||
| 912 | |a ZDB-13-SOC | ||
| 951 | |a BO | ||
| 912 | |a ZDB-13-SOC | ||
| 049 | |a DE-863 | ||
Datensatz im Suchindex
| DE-BY-FWS_katkey | ZDB-13-SOC-068281161 |
|---|---|
| _version_ | 1804748639133761536 |
| adam_text | |
| any_adam_object | |
| author | Azzopardi, Damien |
| author2 | Fareed, Fozan Lenain, Patrick Sutherland, Douglas |
| author2_role | ctb ctb ctb |
| author2_variant | f f ff p l pl d s ds |
| author_facet | Azzopardi, Damien Fareed, Fozan Lenain, Patrick Sutherland, Douglas |
| author_role | aut |
| author_sort | Azzopardi, Damien |
| author_variant | d a da |
| building | Verbundindex |
| bvnumber | localFWS |
| collection | ZDB-13-SOC |
| ctrlnum | (DE-627-1)068281161 (DE-599)KEP068281161 (FR-PaOEC)7f77c2e7-en (EBP)068281161 |
| discipline | Wirtschaftswissenschaften |
| doi_str_mv | 10.1787/7f77c2e7-en |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03248cam a22004452 4500</leader><controlfield tag="001">ZDB-13-SOC-068281161</controlfield><controlfield tag="003">DE-627-1</controlfield><controlfield tag="005">20231204121348.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210915s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1787/7f77c2e7-en</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)068281161</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP068281161</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(FR-PaOEC)7f77c2e7-en</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(EBP)068281161</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">C38</subfield><subfield code="2">jelc</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">J61</subfield><subfield code="2">jelc</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">J11</subfield><subfield code="2">jelc</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">O51</subfield><subfield code="2">jelc</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">E24</subfield><subfield code="2">jelc</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Azzopardi, Damien</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Why are some U.S. cities successful, while others are not? Empirical evidence from machine learning</subfield><subfield code="c">Damien, Azzopardi ... [et al]</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Paris</subfield><subfield code="b">OECD Publishing</subfield><subfield code="c">2020</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (21 p.)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">OECD Economics Department Working Papers</subfield><subfield code="v">no.1643</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The U.S. population has become increasingly concentrated in large metropolitan areas. However, there are striking differences in between the performances of big cities: some of them have been very successful and have been able to pull away from the rest, while others have stagnated or even declined. The main objective of this paper is to characterize U.S. metropolitan areas according to their labor-market performance: which metropolitan areas are struggling and falling behind? Which ones are flourishing? Which ones are staying resilient by adapting to shocks? We rely on an unsupervised machine learning technique called Hierarchical Agglomerative Clustering (HAC) to conduct this empirical investigation. The data comes from a number of sources including the new Job-to-Job (J2J) flows dataset from the Census Bureau, which reports the near universe of job movements in and out of employment at the metropolitan level. We characterize the fate of metropolitan areas by tracking their job mobility rate, unemployment rate, income growth, population increase, net change in job-to-job mobility and GDP growth. Our results indicate that the 372 metropolitan areas under examination can be categorized into four statistically distinct groups: booming areas (67), prosperous mega metropolitan areas (99), resilient areas (149) and distressed metropolitan areas (57). The results show that areas that are doing well are predominantly located in the south and the west. The main features of their success have revolved around embracing digital technologies, adopting local regulations friendly to job mobility and business creation, avoiding strict rules on land-use and housing market, and improving the wellbeing of the city's population. These results highlight that cities adopting well-targeted policies can accelerate the return to growth after a shock.</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">Fareed, Fozan</subfield><subfield code="e">MitwirkendeR</subfield><subfield code="4">ctb</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lenain, Patrick</subfield><subfield code="e">MitwirkendeR</subfield><subfield code="4">ctb</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sutherland, Douglas</subfield><subfield code="e">MitwirkendeR</subfield><subfield code="4">ctb</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="l">FWS01</subfield><subfield code="p">ZDB-13-SOC</subfield><subfield code="q">FWS_PDA_SOC</subfield><subfield code="u">https://doi.org/10.1787/7f77c2e7-en</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-13-SOC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-13-SOC</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">BO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-13-SOC</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-863</subfield></datafield></record></collection> |
| id | ZDB-13-SOC-068281161 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-16T15:07:04Z |
| institution | BVB |
| language | English |
| open_access_boolean | |
| owner | DE-863 DE-BY-FWS |
| owner_facet | DE-863 DE-BY-FWS |
| physical | 1 Online-Ressource (21 p.) |
| psigel | ZDB-13-SOC |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | OECD Publishing |
| record_format | marc |
| series2 | OECD Economics Department Working Papers |
| spelling | Azzopardi, Damien VerfasserIn aut Why are some U.S. cities successful, while others are not? Empirical evidence from machine learning Damien, Azzopardi ... [et al] Paris OECD Publishing 2020 1 Online-Ressource (21 p.) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier OECD Economics Department Working Papers no.1643 The U.S. population has become increasingly concentrated in large metropolitan areas. However, there are striking differences in between the performances of big cities: some of them have been very successful and have been able to pull away from the rest, while others have stagnated or even declined. The main objective of this paper is to characterize U.S. metropolitan areas according to their labor-market performance: which metropolitan areas are struggling and falling behind? Which ones are flourishing? Which ones are staying resilient by adapting to shocks? We rely on an unsupervised machine learning technique called Hierarchical Agglomerative Clustering (HAC) to conduct this empirical investigation. The data comes from a number of sources including the new Job-to-Job (J2J) flows dataset from the Census Bureau, which reports the near universe of job movements in and out of employment at the metropolitan level. We characterize the fate of metropolitan areas by tracking their job mobility rate, unemployment rate, income growth, population increase, net change in job-to-job mobility and GDP growth. Our results indicate that the 372 metropolitan areas under examination can be categorized into four statistically distinct groups: booming areas (67), prosperous mega metropolitan areas (99), resilient areas (149) and distressed metropolitan areas (57). The results show that areas that are doing well are predominantly located in the south and the west. The main features of their success have revolved around embracing digital technologies, adopting local regulations friendly to job mobility and business creation, avoiding strict rules on land-use and housing market, and improving the wellbeing of the city's population. These results highlight that cities adopting well-targeted policies can accelerate the return to growth after a shock. Economics United States Fareed, Fozan MitwirkendeR ctb Lenain, Patrick MitwirkendeR ctb Sutherland, Douglas MitwirkendeR ctb FWS01 ZDB-13-SOC FWS_PDA_SOC https://doi.org/10.1787/7f77c2e7-en Volltext |
| spellingShingle | Azzopardi, Damien Why are some U.S. cities successful, while others are not? Empirical evidence from machine learning Economics United States |
| title | Why are some U.S. cities successful, while others are not? Empirical evidence from machine learning |
| title_auth | Why are some U.S. cities successful, while others are not? Empirical evidence from machine learning |
| title_exact_search | Why are some U.S. cities successful, while others are not? Empirical evidence from machine learning |
| title_full | Why are some U.S. cities successful, while others are not? Empirical evidence from machine learning Damien, Azzopardi ... [et al] |
| title_fullStr | Why are some U.S. cities successful, while others are not? Empirical evidence from machine learning Damien, Azzopardi ... [et al] |
| title_full_unstemmed | Why are some U.S. cities successful, while others are not? Empirical evidence from machine learning Damien, Azzopardi ... [et al] |
| title_short | Why are some U.S. cities successful, while others are not? Empirical evidence from machine learning |
| title_sort | why are some u s cities successful while others are not empirical evidence from machine learning |
| topic | Economics United States |
| topic_facet | Economics United States |
| url | https://doi.org/10.1787/7f77c2e7-en |
| work_keys_str_mv | AT azzopardidamien whyaresomeuscitiessuccessfulwhileothersarenotempiricalevidencefrommachinelearning AT fareedfozan whyaresomeuscitiessuccessfulwhileothersarenotempiricalevidencefrommachinelearning AT lenainpatrick whyaresomeuscitiessuccessfulwhileothersarenotempiricalevidencefrommachinelearning AT sutherlanddouglas whyaresomeuscitiessuccessfulwhileothersarenotempiricalevidencefrommachinelearning |