**Table 3. Well-being over the lifespan in a synthetic panel with group-fixed effects: LiTS data (2006–2010)**

||(1)|(2)|(3)|
|---|---|---|---|
|Age|-0.0141<sup>***</sup><br>(0.005)|-0.0783<sup>***</sup><br>(0.016)|-0.1813<sup>***</sup><br>(0.051)|
|Age2<br>Age3||0.0007<sup>***</sup><br>(0.000)|0.0029<sup>***</sup><br>(0.001)<br>-0.00001<sup>**</sup><br>(0.000)|
|Minimum||59.2|51.2|
|Maximum|||82.2|
|_Adj._R<sup>2</sup>|0.255|0.285|0.293|
|Number ofgroups|672|672|672|

_Note_ : Individuals are grouped by birth-year decades, country, gender, and survey year. The dependent variable is group average life satisfaction (5-point scale). Age ranges from 18 to 99. The individual controls are group averages of marital status (married versus unmarried as the reference), self-reported health (5-point scale), education (medium level education and high level education, with low level education as the reference), income quintiles, and employment (employed versus not employed as the reference). Robust standard errors in parentheses; * _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01.

# _3.3 Well-being in a panel data set for the Russian Federation_

The comprehensive RLMS panel data for the Russian Federation permit us to follow the same individuals over time and include individual fixed effects (Clark and Oswald, 2006; Clark, 2007; Gwozdz and Sousa-Poza, 2010). Because the first four RLMS waves are problematic and Wave 5 lacks important socio-demographic controls, for this analysis, we rely on RLMS rounds VI (1995) to XXI (2013). We first compare the results of the pooled OLS to those for the model with fixed effects (see Table 4) using specifications similar to the cross-sectional analysis above.<sup>14</sup> By comparing age as a linear, quadratic, and cubic term, as well as a dummy specification (age range of 14–104), we are able to quantify the differences in the results produced by the two methods and thereby show the robustness of the pattern in our other regressions. Although the linear specification yields a negative correlation between age and subjective well-being (column 1) that loses significance when fixed effects are included (column 2), when age is treated as linear, the cohort effects are significantly different from those of the comparison birth cohort (born 1919 and earlier). Interestingly, the coefficients on cohort dummies exhibit a U-shape, with the lowest wellbeing observed among those born in the 1960s, who are middle aged (37–52) during the survey period. This latter provides evidence of the cohort dummies picking up the residual variation left by the linear (as opposed to quadratic) age specification.

The quadratic and cubic results for the pooled OLS and FE specifications are fairly similar: the minima of the U-shape differ slightly, with the fixed effects regression indicating a minimum 5 years later than the pooled OLS. Here, the cohort effects mostly remain insignificant, although the cubic specification yields a maximum for the pooled OLS that is 10 years later in life than in

> 14 As we do not include survey weights into the cross-sectional regressions, we followed the recommendation of Heeringa (1997) and re-estimated the models including dummies for rural/PTG/urban and 176 areas to account for the non-random sampling procedure: the results remain robust and do not substantially differ from Table 4.

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