Do monetary subjective well-being evaluations vary across space? Comparing continuous and discrete spatial heterogeneity
Mauricio Sarrias
Spatial Economic Analysis, 2019, vol. 14, issue 1, 53-87
Abstract:
Using subjective well-being estimations, this study analyzes whether compensating variations vary across space using a cross-sectional data set from Chile. To achieve this goal, it describes and compares two econometric ways of modelling unobserved spatial heterogeneity. Both approaches allow compensating variations to vary across spatial units by assuming some distribution a priori. One method assumes that the spatial heterogeneity can be represented by a discrete distribution (a group of regions that share the same coefficient) and the other that the preferences can be represented by a continuous distribution (each region has a different coefficient). The results show that focusing just on the average estimates of compensating variations, as the applied studies have done so far, masks useful local variation. More empirical studies are needed to assess the advantages and disadvantages of both econometric approaches and how their results compare across a wide range of conditions and samples.
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (6)
Downloads: (external link)
http://hdl.handle.net/10.1080/17421772.2018.1485968 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:specan:v:14:y:2019:i:1:p:53-87
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RSEA20
DOI: 10.1080/17421772.2018.1485968
Access Statistics for this article
Spatial Economic Analysis is currently edited by Bernie Fingleton and Danilo Igliori
More articles in Spatial Economic Analysis from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().