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Subjective Well-Being at the Macro Level—Empirics and Future Scenarios


We estimate the impact of a large number of determinants of subjective well-being (SWB) across 143 countries, and project SWB across macro-regions for different socio-economic scenarios. We focus on the 23% of the variance in SWB that is explained by cross-country differences, as the remaining 77% is due to individual-specific factors. We estimate a mixed-effects model to quantify the contributions of various socio-demographic, environmental, energy-related, economic, and institutional factors in explaining SWB. We find that the contribution of institutions to SWB is as large as that of economic factors. We then generate projections on the evolution of SWB until 2100 based on the five Shared Socioeconomic Pathways (SSPs), a framework that facilitates the integrated analysis of future climate impacts, vulnerabilities, adaptation, and mitigation. Holding constant some institutional and economic factors for which SSP projections are not available, the results show significant differences in SWB across SSPs, of up to two points on the standard 0–10 scale of life satisfaction. The highest levels of projected material SWB are likely to occur in the Sustainable Development scenario (SSP1) and the conventional development scenario (SSP5) which lead to very similar SWB levels in material factors. Differences across regions are large. The OECD region and Latin America show the highest levels of SWB historically. The current projections reveal that Latin America could overtake the OECD countries in terms of subjective well-being. Overall, our results can provide valuable insights to policy evaluation in the context of climate change. Future work could expand these scenarios to include also further social and societal variables.

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  1. The SSPs are a scenario framework that was established by the climate change research community to facilitate the integrated analysis of future climate impacts, vulnerabilities, adaptation, and mitigation. They are based on five narratives describing alternative socio-economic developments, including sustainable development, regional rivalry, inequality, fossil-fueled development, and middle-of-the-road development (Riahi et al. 2017). They contain cross-country forecasts on population, gross domestic product, urbanization and life expectancy and many other dimensions.

  2. Notably from the World Happiness Report from which we obtain annual data on the dependent variable (SWB), but also from many other sources listed in Table 2.

  3. Concretely, Easterlin et al. (2010) suggest that whereas life satisfaction levels and income tend to move together in the short term, in the long term, defined as ten years or more, such relationship is nonexistent.

  4. In particular they consider annually-averaged mean monthly precipitation, annually-averaged mean temperature, mean temperature of the coldest month, mean temperature of the hottest month, mean precipitation of the driest month, mean precipitation of the wettest month, the number of cold months, the number of hot months, the number of dry months and the number of wet months.

  5. The panel has a total of 185 observations that are extracted from 67 countries during the period from 1972 until 2000.

  6. The study uses an air pollution index (API) including sulfur dioxide (SO2), nitrogen dioxide (NO2), and fine particulate matter smaller than 10 microns (PM10) using daily data. They include rich weather data to help isolate the impact of air pollution from weather patterns, including sunshine patterns.

  7. Estimations with region and years as fixed effects (specification (2) and (4) in Table 3) produces very similar results.

  8. Social, physical as well as personal activities, security, political voice, health, education, and material wealth and income, etc.

  9. However, only data on a specific subset of countries is provided, up to 60 in the last wave (2010–2014).

  10. Furthermore, initiatives such as the Social Progress Index (https://www.socialprogressindex.comSPI) provide new methodologies for understanding welfare that rely on non-economic dimensions.

  11. The RCP 1.9 was not in the original RCP database but has been implemented by IAMs in Rogelj et al. (2018), and we match it to the (closest) RCP2.6 climate projection.

  12. We interpret the 8.5 W/m2 RCP as the reference or baseline model implementation (“Ref”), which typically yields radiative forcing levels between 6.5 and 8.7 W/m2 at the end of the century, thus closely related to this RCP.

  13. While several SSPs have been implemented in many models, we chose the marker model to abstract from additional uncertainty due to the use of different models. We did, however, run the results also with all models (results are available from the authors upon request) and the results are very similar and SSP/RCP differences by far dominate model uncertainty in this application.

  14. This governance index is basically an average of the five World Governance Indicators of Kaufmann et al. (2010), scaled between zero and one.

  15. While a simple scenario exercise could be conceivable, as in Barrington-Leigh and Galbraith (2019), we feel that these variables are difficult to link in a meaningful way to the SSP storylines and hence refrain from assumptions here.

  16. More in detail, while the socio-economic drivers of the SSPs have been developed at the country level, consistent model implementations are available only at a more aggregated level: the five macro “R5” regions. The only exceptions are climatic variables, which we aggregated using the population of 2000 as weights, and inequality. For the latter, we computed the within-region Gini index based on the GDP scenario across regions, down-scaled baseline country-level GDP pathways, and recompute the regional Gini combining these GDP scenarios and country-level Gini scenarios assuming a lognormal distribution within countries to compute the total within-regional Gini index.

  17. We estimated the LME regression coefficients (Models 4 and 5) reported in Table 3 using Maximum Likelihood (ML) estimation. When we use our main specification (Model 4) to build projections, we fit the model using MCMC as described above in order to quantify uncertainty around our projections using the posterior distribution. The ML and MCMC generated estimates are virtually identical.

  18. Apart from increasing the degrees of freedom in the estimation, this approach is immediately consistent with the simulation exercise of the next section, where the data are also at this five-region aggregation (R5).

  19. We generated these uncertainty intervals using the posterior distribution of our main regression model. We generated future projections using all (4000) samples of the posterior distribution produced by the MCMC estimation and then identified the 2.5th and 97.5th percentiles of projections at each time point.

  20. Figure 15 in the “Appendix” shows the results for all SSPs.

  21. The link between the multiple SDGs and life satisfaction data has also been suggested by Sachs (2016) and Helliwell et al. (2018).


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We thank the participants of the CD-LINKS meeting 2018, New Delhi, and the IAMC 2018 conference, Sevilla, Sibel Eker, Eric Galbraith, Matthias Emmerling, and two anonymous referees for very valuable comments and suggestions. The usual caveat applies. This work acknowledges the CD-LINKS Project funded by the European Union’s Horizon 2020 program under the Grant Agreement No. 642147.

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Additional Figures

See Figs. 8, 9, 10, 11, 12.

Fig. 8
figure 8

Ranking of countries in 2017 according to the SDG performance index (Sachs et al. 2019) and SWB

Fig. 9
figure 9

The R5 regions

Fig. 10
figure 10

Socioeconomic assumptions across the 5 SSPs about global GDP per capita (a) and population size (b)

Fig. 11
figure 11

GDP per capita (a) and SWB projection for the SSP2 scenario (b), with (dashed line) and without (solid line) climate impacts on GDP based on Burke et al. (2015)

Fig. 12
figure 12

Decomposition of the contributions to SWB (in black) at the global average for SSP2 (the middle of the road scenario)

Sensitivity Analyses and Other Specifications

See Figs. 13, 14, 15 and Table 4

Fig. 13
figure 13

Standardized coefficients, all specifications (whiskers indicate 95% CIs), and filled colors indicate significant variables \((p=0.05)\). Note that the standardized coefficient for population density in model (3) is \(-5.9\) but not shown in this graph to keep the range of the x-axis relevant to most coefficient values

Fig. 14
figure 14

Full set of simulations

Fig. 15
figure 15

Full set of simulations comparing RCPs

Table 4 Comparing Log(GDP) versus Quadratic GDP

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Emmerling, J., Navarro, P. & Sisco, M.R. Subjective Well-Being at the Macro Level—Empirics and Future Scenarios. Soc Indic Res 157, 899–928 (2021).

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