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Predicting the Trend of Well-Being in Germany: How Much Do Comparisons, Adaptation and Sociability Matter?

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Abstract

Using longitudinal data from the German Socio-Economic Panel, we estimate the variation of subjective well-being experienced by Germans over the last two decades testing the role of some of the major correlates of people’s well-being. Our results suggest that the variation of Germans’ well-being between 1996 and 2007 is well predicted by changes over time of income, demographics and social capital. The increase in social capital predicts the largest positive change in subjective well-being. Income growth, also predicts a substantial change in subjective well-being, but it is compensated for about three fourths by the joint negative predictions due to income comparison and income adaptation. Finally, we find that aging of the population predicts the largest negative change in subjective well-being. This result appears to hinge on the large loss of satisfaction experienced by individuals in old age.

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Notes

  1. See Dolan et al. (2008) for a recent survey on the determinants of subjective well-being.

  2. Corazzini et al. (2011) show that also the perception of poverty is reference-based, with people in higher income countries giving more relevance to reference income.

  3. See Bartolini and Bonatti (2008), Antoci et al. (2012), and Bilancini and D’Alessandro (2011) recent theoretical investigations on the role of social capital.

  4. The data were extracted using the add-on package PanelWhiz v2.0 (Nov 2007) for Stata. PanelWhiz was written by Dr. John P. Haisken-DeNew (john@panelwhiz.eu). The PanelWhiz generated .do files to retrieve the SOEP data used here are available upon request to the authors. Any data or computational errors in this paper are our own. Please refer to Haisken-DeNew and Hahn (2010) for a detailed description of PanelWhiz.

  5. For more details on the SOEP see Frick et al. (2007) and Wagner et al. (2007).

  6. http://www.diw.de.

  7. As pointed out in Dolan et al. (2008), health is likely to be an important correlate of SWB. However, we cannot control for it in our regressions because the SOEP does not contain data on objective health. The SOEP does provide data on subjective health, but this variable has been shown to be a poor proxy of objective health (e.g., Kahneman and Riis 2005; Deaton 2008).

  8. For more details about the tunnel effect see Hirschman (1973) or, more recently, Senik (2004) and Caporale et al. (2009).

  9. In order to explore different non-linear relationships between age and SWB, we run further regressions where we include not only age and age squared, but also higher order variables. We found confirmation that in the very old age the relationship is strongly negative and concave.

  10. See, e.g., Clark and Oswald (2006), Blanchflower and Oswald (2008) and references therein for a full list of contributions supporting the U-shape hypothesis in economics. See instead Mroczek and Spiro (2005) for a recent contribution in the psychological literature which also supports the U-shape hypothesis. Interestingly enough, Mroczek and Spiro (2005) find that the age of minimum SWB is greater than the one typically found by economists of about 20 years (in the 60s instead of the 40s).

  11. See also Easterlin (2006) and Van Landeghem (2009) on this issue.

  12. In this respect, Becchetti et al. (2008) do not provide any indication since they do not allow for a non-linear relationship between age and SWB.

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Acknowledgments

Francesco Sarracino is supported by an AFR grant (contract PDR-09-075) by the National Research Fund, Luxembourg cofunded under the Marie Curie Actions of the European Commission (FP7-COFUND).

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Authors and Affiliations

  1. Department of Economics, University of Siena, Siena, Italy

    Stefano Bartolini

  2. Department of Economics, University of Modena and Reggio Emilia, Modena and Reggio Emilia, Italy

    Ennio Bilancini

  3. Population et emploi, CEPS/INSTEAD, Luxembourg, Luxembourg

    Stefano Bartolini & Francesco Sarracino

Authors
  1. Stefano Bartolini
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  2. Ennio Bilancini
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  3. Francesco Sarracino
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Correspondence to Ennio Bilancini.

Appendices

Appendix 1: Definition of Variables

Satisfaction with life a scale of 11 degrees ranging from 10, if respondent declares to be “completely satisfied”, to 0, if respondent declares to be “completely dissatisfied” (SOEP source variable: p1110107)

Married 1 if respondent reports to be currently married, 0 otherwise (SOEP source variable: d1110407)

Separated 1 if respondent reports to be currently separated, 0 otherwise (SOEP source variable: d1110407)

Divorced 1 if respondent reports to be currently divorced, 0 otherwise (SOEP source variable: d1110407)

Widowed 1 if respondent reports to be currently widowed, 0 otherwise (SOEP source variable: d1110407)

Female 1 if subject is female, 0 otherwise (SOEP source variable: d11102ll)

Age number of years since born, 0 otherwise (SOEP source variable: d1110107)

Age squared age to the power of 2, 0 otherwise (SOEP source variable: d1110107)

Household size number of reported household members, 0 otherwise (SOEP source variable: d1110607)

1 child 1 if in the household there is one child, 0 otherwise (SOEP source variable: d1110707)

2 children 1 if in the household there is two children, 0 otherwise (SOEP source variable: d1110707)

3 or more children 1 if in the household there is three or more children, 0 otherwise (SOEP source variable: d1110707)

Years of education number of years the respondent declared to have attended school, 0 otherwise (SOEP source variable: d1110907)

Living with parents at 16 1 if respondent declares to be living with own parents at 16 years old, 0 otherwise (SOEP source vari-able: xh61)

Absolute income natural logarithm of Adjusted Monthly Household Net Income (Euro 2000) as provided in the SOEP, 0 otherwise (variable name: ahinc07)

Reference income natural logarithm of average Adjusted Monthly Household Net Income (Euro 2000) for a reference group as provided in the SOEP, 0 otherwise (variable name: ahinc07)

Past income Lag3 3 years temporal lag of the natural logarithm of Adjusted Monthly Household Net Income (Euro 2000) as provided in the SOEP, 0 otherwise (variable name: ahinc07)

Monthly at church 1 if respondent reports to attend at least once a month church or religious institutions, 0 otherwise (SOEP source variable: xp0309)

Monthly at culture events 1 if respondent reports to attend at least once a month cultural events, 0 otherwise (SOEP source vari-able: xp0301)

Monthly at cinema 1 if respondent reports to go at least once a month to the cinema, 0 otherwise (SOEP source variable: xp0302)

Monthly doing sport activities 1 if respondent reports to participate at least once a month to sport activities, 0 otherwise (SOEP sourcevariable: xp0303)

Monthly at social gatherings 1 if respondent reports to visit at least once a month friends, relatives or neighbours, 0 otherwise (SOEP source variable: xp0305)

Monthly helping friends 1 if respondent reports to help at least once a month friends, relatives or neighbours, 0 otherwise (SOEP source variable: xp0306)

Monthly volunteering 1 if respondent perform volunteer work at least once a month in clubs, associations or social services, 0 otherwise (SOEP source variable: xp0307)

Monthly local political participation 1 if respondent reports to participate at least once a month in citizens’ action groups, politic-al parties, local government, 0 otherwise (SOEP source variable: xp0308)

Unemployed 1 if respondent declares to be unemployed, 0 otherwise (SOEP source variable:lfs07)

Student 1 if respondent declares to be student, 0 otherwise (SOEP source variable:lfs07)

Non working 1 if respondent declares to be non-working, 0 otherwise (SOEP source variable:lfs07)

Retired 1 if respondent declares to be retired, 0 otherwise (SOEP source variable:lfs07)

Military/civil service 1 if respondent declares to be in military of civil service, 0 otherwise (SOEP source variable:lfs07)

West 1 if respondent declares to live in West Germany, 0 otherwise (SOEP source variable: l1110207)

Appendix 2: Descriptives

See Table 5.

Appendix 3: Construction of reference groups

We defined reference group of individual i in year t as the sub-sample of individuals in Germany living in i’s region (west or east) in year t same year with education and age similar to i’s. More precisely, reference groups are defined using the following four different variables:

  • Year

    a scalar containing all years between 1984 and 2007;

  • West

    a dummy variable indicating living in West Germany;

  • \(\texttt{Age\_cls}\)

    a scalar indicating whether individuals are younger than 30, between 31 and 60 and 61 or older;

  • \(\texttt{Yreduc\_cls}\)

a three-value variable indicating whether individuals went through less than 11 years of education, between 11 and 12 years, or 13 or more years of education.

We used the following Stata 9.0 syntax to construct groups:

\(\texttt{egen Ref.Group = group(year west age\_cls2 yreduc\_cls2)}\)

\(\texttt{if !missing(year,west,age\_cls2,yreduc\_cls2)}\)

In this way, we generated a new variable assuming a different value for each possible group. This procedure generated 378 reference groups. Subsequently, we computed the income of the reference group as the mean value of the incomes of all the individuals in each reference group:

bys Ref.Group: egen ref.income = mean(income) if !missing(income)

Finally, we took the log of reference income:

gen lnref.income = ln(ref.income)

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Bartolini, S., Bilancini, E. & Sarracino, F. Predicting the Trend of Well-Being in Germany: How Much Do Comparisons, Adaptation and Sociability Matter?. Soc Indic Res 114, 169–191 (2013). https://doi.org/10.1007/s11205-012-0142-5

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