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Social Indicators Research

Erschienen in:

01.05.2011

Geography of European Life Satisfaction

verfasst von: Adam Okulicz-Kozaryn

Erschienen in: Social Indicators Research | Ausgabe 3/2011

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Abstract

The vast majority of studies analyze life satisfaction at individual and/or country level. This study contributes with analysis of life satisfaction at the (sub-national) province level across multiple countries. The purpose of this study is to call attention to spatial aspects of life satisfaction. Literature does not discuss the fact that life satisfaction in one province may be related to life satisfaction in other provinces. This study shows that there are well-defined happiness clusters in Europe, but also some outliers.

Vorheriger Artikel Socio-demographic Correlates of Subjective Well-being in Urban India

Nächster Artikel Empirical Research on Factors Related to the Subjective Well-Being of Chinese Urban Residents

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Psychologists usually use Analysis of Variance (ANOVA) and economists use various regression techniques.

This law, however, may be less true today than in was 1970s due to globalization and IT revolution. I thank anonymous reviewer for bringing this to my attention.

Data were merged with the GIS codes of European regions, so called NUTS (Nomenclature of Territorial Units for Statistics) shape file. Shape file contains spatial information, latitude and longitude that allows to display data in a map. In some cases it was unclear which regions are corresponding to each other, and these cases are shown in Table 1. In most cases EB regions are the same as NUTS second level regions. In case of Denmark and Finland NUTS I regions were aggregated to match EB regions.

GIS ArcMap software defined them. ArcMap picks break points that best group similar values and maximize the differences between classes.

Rook contiguity matrix makes less sense here, where the four neighbors of each cell in the cardinal directions are given the value 1, all others 0. I also experimented with distance-based matrices that produced similar results.

As a robustness check I also calculated distance-based weight matrix with cutoff set at 200 km. Using this alternative matrix specification, Moran’s I is also .66 (significant at .01).

Spatially lagged variable is simply a variable multiplied by weight matrix.

Note that data for Norway, The Netherlands, Switzerland, and Eastern Europe are missing and hence it influences results for neighboring provinces.

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Titel: Geography of European Life Satisfaction
verfasst von: Adam Okulicz-Kozaryn
Publikationsdatum: 01.05.2011
Verlag: Springer Netherlands
Erschienen in: Social Indicators Research / Ausgabe 3/2011
Print ISSN: 0303-8300
Elektronische ISSN: 1573-0921
DOI: https://doi.org/10.1007/s11205-010-9671-y

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