Abstract
Using data on individuals of age 50 and older from 11 European countries, we analyze two economic aspects of subjective well-being of older Europeans: satisfaction with household income, and job satisfaction. Both have been shown to contribute substantially to overall well-being (satisfaction with life or happiness). We use anchoring vignettes to correct for potential differences in response scales across countries. The results highlight a large variation in self-reported income satisfaction, which is partly explained by differences in response scales. When differences in response scales are eliminated, the cross-country differences are quite well in line with differences in an objective measure of purchasing power of household income. There are common features in the response scale differences in job satisfaction and income satisfaction. French respondents tend to be critical in both assessments, while Danish and Dutch respondents are always on the optimistic end of the spectrum. Moreover, correcting for response scale differences decreases the cross-country association between satisfaction with income and job satisfaction among workers.
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Notes
They control for unobserved heterogeneity by including the individual means over time of the time-varying explanatory variables as additional controls.
The assumption that the \( \nu_{i}^{k} \) are mutually independent may be too strong. Moreover, unobserved heterogeneity in the thresholds may also lead to correlated vignette evaluations. Sensitivity checks of Kapteyn et al. (2007) suggest that allowing for a richer covariance structure of the errors is a statistically significant improvement but has no effect on the substantive results.
We ignore here that income tax is collected in different ways across countries (In France, for example, tax is not withheld on earnings like in most other countries, but paid afterwards).
The amounts in vignette 1 were CK 24,000 in the Czech Republic, DKK 14,200 in Denmark, €1,550 in Germany, €1,200 in Greece, €1,450 in Italy, PZ 3,300 in Poland, €1,300 in Spain and SK 15,400 in Sweden. The amounts in vignette 2 were always twice as high. As pointed out by a referee, the different degrees of rounding might have effects on the responses, but we do not think this is a major issue.
Kapteyn et al. (2008) make the opposite assumption that the living standard is purely relative, and therefore use vignettes with multiples of country specific median incomes. Which assumption is better seems to depend on the interpretation of the living standard concept one is trying to measure.
Respondents of age 65 or older got vignettes on other daily activities.
Missing household incomes were imputed using, among other variables, an alternative measure of household income as one of the predictors. See Appendix for details.
The formula to derive the equivalence scale is the following: \( \frac{{y^{N} }}{{y^{1} }} = N^{{ - {{\beta_{loghhsize} }\mathord{\left/ {\vphantom {{\beta_{loghhsize} }{\beta_{loghhincome} }}} \right. \kern-\nulldelimiterspace}{\beta_{loghhincome} }}}} \). Where y N is the income that a household with N individuals should have to have the same income satisfaction as a single household with an income of y 1.
Adding age squared (in all equations) hardly improved the fit and did not change any of the substantive results. We therefore present the specification with a linear age term only, which is easier to interpret.
Estimates of the parameters determining the thresholds are not presented to save space. They are available in the online appendix.
The changes in the country ranking when correcting for DIF can be compared with results of Kristensen and Johansson (2008) for all workers. Similar to what we find, they find that the ranking of France improves, while that of Denmark worsens. Different from our findings, however, correcting for DIF substantially improves job satisfaction in the Netherlands and Greece and worsens it in Spain. Our other countries are not in their data set.
For each respondent, we replace the thresholds by thresholds of the average German respondent (i.e., with the average individual characteristics of the German sample).
The fit is not exact due to finite sample errors, simulation errors, and, possibly, the fact that the model may not fit the data perfectly well.
The Pearson correlation decreases from 0.75 to 0.24.
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Acknowledgments
We are grateful to two anonymous referees, Teresa Bago d’Uva, Didier Fouarge, Hendrik Juerges, Raymond Montizaan, and participants of the final COMPARE conference in Brussels for useful comments. This paper was written as part of the project COMPARE, funded by the European Commission through its 6th framework (project number CIT5-CT-2005-028857). Data collection and infrastructure for making data available to researchers was mainly funded by the European Commission through several SHARE related projects in the 5th and 6th framework programmes (CIT5-CT-2005-028857, QLK6-CT-2001-00360; RII-CT-2006-062193). Additional funding was provided by the US National Institute on Aging (grant numbers U01 AG09740-13S2; P01 AG005842; P01 AG08291; P30 AG12815; Y1-AG-4553-01; OGHA 04-064; R21 AG025169) and various national sources (see http://www.share-project.org for a full list of funding institutions).
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Appendix
Appendix
1.1 Construction of a household income measure
The measure of after-tax household income contains a substantial number of missing values and unreliable outliers. To get a reliable measure of after tax household income, we applied the following procedure. First, we ran a regression of log of household income last month, excluding the country-specific first and last percentile, on standard explanatory variables [country dummies, education, age, gender, log household size, employment status, and health status (numbers of chronic diseases and reported symptoms)]. Figure 7 presents the distribution of the residuals of this regression by country. It appears clearly that we have a high proportion of outliers in many countries (especially in Italy, Czech Republic). Based upon these results, we chose thresholds for each country to define observations as outliers.
In a second step, we used the valid measures of current household income to run a second regression including the standard explanatory variables and another measure of household income based on the information about personal income and the income of other household members received last year. Finally, we replaced the unreliable or missing values of the general household income by the prediction of the model using the other household income measure. This method has the advantage of providing information about household income for almost all observations in the SHARE sample.
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Bonsang, E., van Soest, A. Satisfaction with Job and Income Among Older Individuals Across European Countries. Soc Indic Res 105, 227–254 (2012). https://doi.org/10.1007/s11205-011-9879-5
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DOI: https://doi.org/10.1007/s11205-011-9879-5