indicators. We adopt the 5-year clustering for two reasons: (i) to balance the trade-off between cell size and within-group heterogeneity in well-being and (ii) to facilitate the distinction between age groups, cohort effects, and time.<sup>6</sup> This specification, adopted in prior studies, also helps to avoid co-linearity problems between age, survey year, and birth year (the cohort effect).<sup>7</sup> As in Blanchflower and Oswald (2008), cohort effects are modelled as birth decades. �� represents the characteristics of individual _i_ (see Appendix 2 for a detailed description of the covariates included for each data source). Adding in self-reported health not only extends Blanchflower and Oswald’s (2008) model but is likely to reduce any bias in our estimates of age’s relation with well-being. _C_ designates country indicators, _Y_ denotes year dummies, and �� denotes the individual error term. Heteroskedasticity-adjusted standard errors are estimated for coefficients in all specifications.

# _2.2.2 Synthetic panel estimation: LiTS_

Following Deaton (1985) and Russell and Fraas (2005), we construct a synthetic (or pseudo) panel for the LiTS data that follows groups<sup>8</sup> of individuals over the 2006–2010 inter-wave period and uses their group-specific means for inference. To ensure equal sample means for each group, these groups (or cells) must be created based on time-invariant characteristics, which in our case are birth-year decades, gender, and country. Because the estimation requires a sufficiently large cell size, we exclude the earliest and latest birth-year decade and drop all countries not covered by both LiTS waves.<sup>9</sup> For this analysis, we create 672 cells based on 28 countries, 6 birth decades, 2 sexes, and 2 survey waves (2006 and 2010). For each cell, we estimate the mean of the covariates, including age, marital status, employment, education, self-reported health, and relative income. By assuming the existence of a fixed group that remains constant over both periods, we can apply a fixed-group effects estimator, a feasible step if the sample size in each cell is relatively large. To address any heteroskedasticity produced by unequal cell sizes, we also weight each cell by its sample size (Russell and Fraas, 2005).<sup>10</sup> The model used is as follows:

> 6 We assume that the well-being of a 50-year-old does not differ systematically from that of a 51-year-old and that a certain category size is needed to capture possible differences in well-being with age. In addition, smaller age groups would result in small cell sizes, especially at the extremes of the age distribution.

> 7 For a critical discussion on the approach and identification, see Baetschmann (2014).

> 8 Such groups are sometimes referred to as “cohorts,” but we avoid this term because this paper also examines birth cohorts.

> 9 The gender differences in life expectancy are notably substantial in some ECA countries. In the Russian Federation, for instance, women live on average more than 10 years longer than men, which must be taken into account when looking at the joint distribution. However, the gender split analysis of the RLMS data shows no major differences in well-being/age patterns between the sexes.

> 10 This weighted method has some limitations related to data availability. On the one hand, if the sample size within each group does not exceed thousands of observations, the measurement error can be large (Devereux, 2007). On the other, the groups must be sufficiently homogeneous for the assumption of fixed group effects to remain justifiable, meaning that clustering groups over different countries would introduce another bias. We thus treat the results of this approach with caution but compare them with our pooled cross-sectional analysis estimates to check robustness.

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