Abstract
A variety of approaches to delineating metropolitan areas have been developed. Systematic comparisons of these approaches in terms of the metro area landscape that they generate are however few. Our paper aims to fill this gap. We focus on Indonesia and make use of data on commuting flows, spatially fine-grained population, and remotely sensed nighttime lights to construct metropolitan areas using several approaches that have been developed in the literature. We find that the maps and characteristics of Indonesia's metro area landscape generated when using a commuting flow approach differ substantially from those generated using other approaches. Moreover, combining information on the metro areas generated by the different approaches with detailed micro-data from Indonesia's national labor force survey, we show that the estimated agglomeration wage premium for Java-Bali tends to fall when using a more restrictive definition of metro areas. This is not the case for the rest of Indonesia, for which we, moreover, find a much lower estimated agglomeration wage premium. We provide an explanation for these findings, and also tentatively probe the factors behind Indonesia's agglomeration wage premium.
| Original language | English |
|---|---|
| Article number | 103275 |
| Journal | Journal of Urban Economics |
| Volume | 125 |
| Early online date | 18 Jul 2020 |
| DOIs | |
| Publication status | Published - Sept 2021 |
Bibliographical note
Funding Information:The authors thank Katie McWilliams, Benjamin Stewart, Andrii Berdnyk and Brian Blankespoor for their outstanding GIS support, as well as Jonathan Hasoloan and Shaun Zhang for supplemental GIS support. They also thank Massimiliano Cali and Giorgio Presidente for the sharing of data on manufacturing TFP. They further thank Gilles Duranton and two anonymous referees for their excellent comments. Participants at the 2019 European Urban Economics Association, the World Bank's 2019 Land and Poverty, and the 2019 Singapore Management University (SMU) Urban and Regional Economics conferences likewise provided very helpful comments, as did participants at seminars at the Dutch Bureau for Economic Policy Analysis and the World Bank. Financial support from both the Swiss State Secretariat for Economic Affairs (SECO) and the UK's Department for International Development (DFID) is also very gratefully acknowledged.
Funding Information:
The authors thank Katie McWilliams, Benjamin Stewart, Andrii Berdnyk and Brian Blankespoor for their outstanding GIS support, as well as Jonathan Hasoloan and Shaun Zhang for supplemental GIS support. They also thank Massimiliano Cali and Giorgio Presidente for the sharing of data on manufacturing TFP. They further thank Gilles Duranton and two anonymous referees for their excellent comments. Participants at the 2019 European Urban Economics Association, the World Bank's 2019 Land and Poverty, and the 2019 Singapore Management University (SMU) Urban and Regional Economics conferences likewise provided very helpful comments, as did participants at seminars at the Dutch Bureau for Economic Policy Analysis and the World Bank. Financial support from both the Swiss State Secretariat for Economic Affairs (SECO) and the UK's Department for International Development (DFID) is also very gratefully acknowledged.
Publisher Copyright:
© 2020