Problems and Shortcomings of the Study

'Age' as break variable. In the study, 'Age' was used as break variable according to 5-year age

brackets. However, how 'Age' brackets are chosen is relatively arbitrary. It is worthwhile to remember that other authors used either 10-year age brackets (Ferrer-i-Carbonell, 2005; Oshio et al., 2009) or an area age ranges within (plus/minus 5 years) (McBride, 2001). Furthermore, theses ways of defining 'Age' as break variable are not exhaustive. For example, it could be assumed that people compare themselves with others, who are in range of being 10 years younger to 10 years older than the respondent. Alternatively, the idea that people compare mostly upwards70 _{may be translated into a specification, where people compare only with}

people who are 5 or 10 years older. Of course, even more specifications are possible. Since the “true” age bracket or age range is not known, it remains unclear if the specification used here fits best. More research needs to be done on this question.

Household income. The income variable states household income of the respondents and not

individual income. Therefore, actual wealth of the respondent cannot be measured accurately, since a specific household income has a different value for an individual living alone or a

family with children. This asymmetry is partially captured by the introduction of the control variables for the family status (e.g. 'Married' or 'Children'), but still leaves a huge gap in the distribution of the household income within the household.

Income bands. Since there are only income bands available in the ESS data-set, actual

household income is not accurately measurable. This leads to imprecise results when generating reference income variables due to using midpoints of the income bands as a proxy.

Reference Income. Usually, reference income is stated as the share of individual income over

average income of the reference group or as individual income minus average income of the reference group. Here, since income bands were used ordinally as dummies for absolute income, the reference income was approximated solely by the average income of the reference group. As a consequence, it was econometrically – due to linear dependency with the control variables included - not possible to calculate the coefficients of the reference income, where break variables were only 'Country', 'Gender' or 'Education'.

Weights. To calculate the reference income, it was necessary to aggregate the data. Correctly,

it would be necessary to apply design and population weights to address imbalances in the sample. Since the weights are based on the original data-set, weights became incorrect after deleting 38 % of the original observations due to missing values and answers not interested in (e.g. 'Don't know'). Therefore, the calculation of the reference income – both with and without weights – may be somewhat biased.

Reference group specifications. The study focused on specifications containing 'Education',

'Gender', 'Age', 'Country', 'East/West' and/or 'Federal States' as break variable. Of course, these break variables are not exhaustive. It may be up to the researcher to use other break variables. As an example, one could argue that – as Van de Stadt et al. (1985) assume – people may compare most with people having the same employment status (Paid Work, Retired, Housekeeping etc.) as the respondent.

External reference group. The study focused only on the impact of an external reference

group on satisfaction. According to theory, people may also compare to an internal reference point such as expected own income or past income. To get more realistic results, both internal and external reference groups/points should be included in the regression as far as there is

data availability (ideally panel data).

Control variables. Although most of the important control variables are included, there are

theoretically more variables, which help to explain life satisfaction. On the socio-demographic level, Argyle (1999) reviews that ethnicity, leisure, social class, life events and activities and competencies have explanatory power. On a macroeconomic level, general unemployment, the crime rate, inflation and inequality have some additional explanatory power. The macroeconomic variables are partially addressed by country dummies.

7 Conclusion

In this master thesis, I investigated the specific role of reference groups by applying different specifications within an empirical analysis. The results show that different specifications may lead to different results, in particular regarding the significance of the coefficients.

On the multinational level, specifications turn mostly significant with 'Country' as break variable outlining that people mostly compare within their country. When looking for the tunnel effect on the multinational level, no clear evidence can be found, neither in the sub-set containing only young people nor in the sub-set containing transition countries.

On the German level, differentiation according to federal states or East- and West Germany improved only in some cases the significance of the coefficients. In contrast to the whole sample, evidence for the tunnel effect can be found in the sample containing only the young people, since the specification 'Education/Federal States' turned significantly positive while being significantly negative in the sub-sample containing the old respondents. This is also supported by the fact that specifications containing 'Age' and 'Gender' produce negative coefficients in the sub-sample containing the old, but becoming insignificant in the sub- sample containing the young.

Based on these observations, it is hard to say if there is a “best” specification for the reference group. The analysis showed that the explanatory power of the reference group specification depends also on the sub-sample treated (on the whole German sample, the specification 'Education/Federal States' does not improve the Pseudo-R2 _{visibly, but does in the sub-sample}

for only young German respondents). That the results depend on the sub-sample treated may also indicate that reference groups change as individuals become older and it may indicate that it depends on the region treated what the reference group of people really is.

care from deriving clear implications for personal life based on the results obtained, since a large part of the variety cannot be explained by the determinants. Instead, there may be other unobserved determinants, which may also have strong explanatory power. In addition, the study performed here had to face several obstacles, which could not be addressed. Ideally, a study to measure satisfaction should have panel data, continuous income bands, included internal reference points and an expanded set of control variables.

Appendix A - List of Variables