absence of valid instruments. In view of that, we follow O’Connor (2020) and Burger et al. (2021) and re-estimate model (1) using the Lewbel IV estimator (Lewbel, 2012) as a robustness check. The Lewbel IV estimator uses internally generated, heteroskedasticity-based instruments for crosssectional data to isolate the effect of the individual domain satisfactions on overall SWB. The Lewbel IV estimator is comparable to the difference generalized method of moments (GMM) and the system GMM estimators in panel data research (Arellano and Bond, 1991).

Apart from examining the main correlates of SWB in Brazil, we study how these effects vary along the income distribution and across genders, generations and space using split sample analysis (Koenker and Bassett Jr., 1978). This helps us to identify the heterogeneity in the domains correlated with the SWB of different segments of the population and to understand why some groups of people have been particularly hard hit by the economic crisis in the mid-2010s.

Finally, to examine temporal differences in SWB between the period before the economic crisis (2010-2014) and after the economic crisis (2015-2020), we make use of the Blinder-Oaxaca decomposition analysis (Blinder, 1973; Oaxaca, 1974). The Blinder–Oaxaca decomposition divides the differential of the SWB outcome into two parts: the first part shows the explained differences in SWB scores between the two periods (Q), while the second one refers to the unexplained part (U). More specifically: ΔSWB= [E(XA) – E(XB)] ́ β* + [E(XA) ́ (βA–β*) - E(XB) ́ (βB– β*)], (2) where ΔSWB is the difference in SWB between the two time periods, A (2015-2020) and B (20102014), βA and βB are vectors of coefficients estimated using weighted least squares for the respective periods (using sampling weights), and β* is a non-discriminatory vector of coefficients, estimated with a pooled regression and used to determine the deviation in the relative importance of each domain in the model between the two time periods. The explained part (Q=[E(XA) – E(XB)] ́β*)—or the “endowments effect”—shows how much of the overall differential in the average SWB can be attributed to differences in the level of the explanatory variables (X) between the two periods. Hence, this “endowment effect” reflects the differences in objective and subjective circumstances between the post-crisis period A and pre-crisis period B. The unexplained part (U=[E(XA) ́ (βA–β*) – E(XB) ́ (βB– β*)]) captures omitted variables as well as changes in the estimated coefficients between period A and B.

# **4. Empirical Results**

# _4.1. Factors underpinning SWB in Brazil_

Figure 4 shows the socio-demographic differences in SWB across respondents with different objective characteristics. The largest differences in SWB are observed between the poorest 20% (6.2) and richest 20% (7.1), those with only elementary education (6.4) and the college educated (7.1), the unemployed (6.2) and the full-time employed (6.8), and the young (<30 years, 6.9) and the middle-aged (45-69 years, 6.4). Gender and spatial differences as well as differences between people with and without a partner are more limited, although people in the North of Brazil have a significantly lower SWB (6.4) than the South (6.7) and the Southeast (6.7). The average difference in SWB between the pre-crisis (2010-2014) and post-crisis (2015-2020) period is 0.6 points.

Table 2 presents results from different specifications of model (1) in Brazil. Model specification (1), shown in column (1), includes only personal characteristics, based mainly on objective data. In line with the descriptive statistics provided in the previous section, we find a U-shaped relationship between age and SWB. However, this relationship loses its significance and becomes less pronounced in model specification (2) where we control for subjective economic situation. The respective differences between males and females and singles and individuals with partners are statistically significant and these results are robust to the inclusion of different controls. In all 13