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Social Indicators Research
Published in: Social Indicators Research 3/2021

13-04-2021 | Original Research

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

Authors: Johannes Emmerling, Paula Navarro, Matthew R. Sisco

Published in: Social Indicators Research | Issue 3/2021

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Abstract

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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Appendix
Available only for authorised users
Footnotes
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.​socialprogressin​dex.​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).
 
Literature
Adler, M. (2013). Happiness surveys and public policy: What’s the use? Duke Law Journal, 62(8), 1509–1601.
Andrijevic, M., Cuaresma, J. C., Muttarak, R., & Schleussner, C.-F. (2019). Governance in scenarios of future socio-economic development. Manuscript under review.
Arrow, K. (1951). Social Choice and Individual Values. Cowles Foundation Monographs: Wiley.
Atkinson, A. B. (1970). On the measurement of inequality. Journal of Economic Theory, 2(3), 244–263.CrossRef
Barrington-Leigh, C., & Escande, A. (2018). Measuring progress and well-being: A comparative review of indicators. Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 135(3), 893–925.CrossRef
Barrington-Leigh, C., & Galbraith, E. (2019). Feasible future global scenarios for human life evaluations. Nature Communications, 10(1), 161.CrossRef
Barrington-Leigh, C., & Wollenberg, J. T. (2019). Informing policy priorities using inference from life satisfaction responses in a large community survey. Applied Research in Quality of Life, 14(4), 911–924.CrossRef
Becchetti, L., Castriota, S., Corrado, L., & Ricca, E. G. (2013). Beyond the Joneses: Inter-country income comparisons and happiness. The Journal of Socio-Economics, 45, 187–195.CrossRef
Bergson, A. (1938). A Reformulation of Certain Aspects of Welfare Economics. The Quarterly Journal of Economics, 52(2), 310–334.CrossRef
Bjørnskov, C., Dreher, A., & Fischer, J. A. V. (2008). Cross-country determinants of life satisfaction: Exploring different determinants across groups in society. Social Choice and Welfare, 30(1), 119–173.CrossRef
Bjørnskov, C., Dreher, A., & Fischer, J. A. V. (2010). Formal institutions and subjective well-being: Revisiting the cross-country evidence. European Journal of Political Economy, 26(4), 419–430.CrossRef
Bjørnskov, C., Dreher, A., Fischer, J. A. V., Schnellenbach, J., & Gehring, K. (2013). Inequality and happiness: When perceived social mobility and economic reality do not match. Journal of Economic Behavior & Organization, 91, 75–92.CrossRef
Burke, M., Hsiang, S. M., & Miguel, E. (2015). Global non-linear effect of temperature on economic production. Nature, 527(7577), 235–239.CrossRef
Calvin, K., Bond-Lamberty, B., Clarke, L., Edmonds, J., Eom, J., Hartin, C., et al. (2017). The SSP4: A world of deepening inequality. Global Environmental Change, 42, 284–296.CrossRef
Clark, A. E., Frijters, P., & Shields, M. A. (2008). Relative income, happiness, and utility: An explanation for the easterlin paradox and other puzzles. Journal of Economic Literature, 46(1), 95–144.CrossRef
Cohen, M. A. & Vandenbergh, M. P. (2008). Consumption, Happiness, and Climate Change. Technical Report dp-08-39, RFF.
Cunningham, M. R. (1979). Weather, mood, and helping behavior: Quasi experiments with the sunshine samaritan. Journal of Personality and Social Psychology, 37(11), 1947–1956.CrossRef
Cuñado, J., & Gracia, F. P. d. (2013). Environment and happiness: New evidence for Spain. Social Indicators Research, 112(3), 549–567.
Dellink, R., Chateau, J., Lanzi, E., & Magné, B. (2017). Long-term economic growth projections in the Shared Socioeconomic Pathways. Global Environmental Change, 42, 200–214.CrossRef
Easterlin, R. A., McVey, L. A., Switek, M., Sawangfa, O., & Zweig, J. S. (2010). The happiness-income paradox revisited. Proceedings of the National Academy of Sciences, 107(52), 22463–22468.CrossRef
Easterlin, R. A., Morgan, R., Switek, M., & Wang, F. (2012). China’s life satisfaction, 1990–2010. Proceedings of the National Academy of Sciences, 109(25), 9775–9780.CrossRef
Elgar, F. J., Davis, C. G., Wohl, M. J., Trites, S. J., Zelenski, J. M., & Martin, M. S. (2011). Social capital, health and life satisfaction in 50 countries. Health & Place, 17(5), 1044–1053.CrossRef
Engelbrecht, H.-J. (2009). Natural capital, subjective well-being, and the new welfare economics of sustainability: Some evidence from cross-country regressions. Ecological Economics, 69(2), 380–388.CrossRef
Ferreira, S., Akay, A., Brereton, F., Cuñado, J., Martinsson, P., Moro, M., & Ningal, T. F. (2013). Life satisfaction and air quality in Europe. Ecological Economics, 88, 1–10.CrossRef
Fischer, J. A. (2009). The Welfare Effects of Social Mobility. OECD Social, Employment and Migration Working Papers.
FitzRoy, F., Franz-Vasdeki, J., & Papyrakis, E. (2012). Climate change policy and subjective well-being. Environmental Policy and Governance, 22(3), 205–216.CrossRef
Fleurbaey, M. (2016). Economics and economic justice.
Fricko, O., Havlik, P., Rogelj, J., Klimont, Z., Gusti, M., Johnson, N., et al. (2017). The marker quantification of the Shared Socioeconomic Pathway 2: A middle-of-the-road scenario for the 21st century. Global Environmental Change, 42, 251–267.CrossRef
Fujimori, S., Hasegawa, T., Masui, T., Takahashi, K., Herran, D. S., Dai, H., et al. (2017). SSP3: AIM implementation of Shared Socioeconomic Pathways. Global Environmental Change, 42, 268–283.CrossRef
Grün, C., & Grunewald, N. (2010). Subjective well being and the impact of climate change. Technical report.
Harris, I., Jones, P. D., Osborn, T. J., & Lister, D. H. (2014). Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. International Journal of Climatology, 34(3):623–642.
Hausmann (2008). The Philosophy of Economics. Cambridge University Press.
Helliwell, J. F., Layard, R., & Sachs, J. (Eds.). (2018). World Happiness Report 2018. Sustainable Development Solutions Network.
Jebb, A. T., Tay, L., Diener, E., & Oishi, S. (2018). Happiness, income satiation and turning points around the world. Nature Human Behaviour, 2(1), 33–38.CrossRef
Jiang, L., & O’Neill, B. C. (2017). Global urbanization projections for the Shared Socioeconomic Pathways. Global Environmental Change, 42, 193–199.CrossRef
Kaufmann, D., Kraay, A., & Mastruzzi, M. (2010). The Worldwide Governance Indicators: Methodology and analytical issues. SSRN Scholarly Paper ID 1682130, Social Science Research Network, Rochester, NY.
Kc, S., & Lutz, W. (2017). The human core of the shared socioeconomic pathways: Population scenarios by age, sex and level of education for all countries to 2100. Global Environmental Change, 42, 181–192.CrossRef
Knight, S. J. & Howley, P. (2017). Can clean air make you happy? Examining the effect of nitrogen dioxide (NO2) on life satisfaction. Technical Report 17/08, HEDG, c/o Department of Economics, University of York.
Kreps, D. (2008). Microeconomic Foundations I. Princeton University Press.
Kriegler, E., Bauer, N., Popp, A., Humpenöder, F., Leimbach, M., Strefler, J., et al. (2017). Fossil-fueled development (SSP5): An energy and resource intensive scenario for the 21st century. Global Environmental Change, 42, 297–315.CrossRef
Kristoffersen, I. (2018). Great expectations: Education and subjective wellbeing. Journal of Economic Psychology, 66, 64–78.CrossRef
Li, Z., Folmer, H., & Xue, J. (2014). To what extent does air pollution affect happiness? The case of the Jinchuan mining area, China. Ecological Economics, 99, 88–99.CrossRef
MacKerron, G., & Mourato, S. (2009). Life satisfaction and air quality in London. Ecological Economics, 68(5), 1441–1453.CrossRef
Mazur, A. (2011). Does increasing energy or electricity consumption improve quality of life in industrial nations? Energy Policy, 39(5), 2568–2572.CrossRef
Mentzakis, E., & Moro, M. (2009). The poor, the rich and the happy: Exploring the link between income and subjective well-being. The Journal of Socio-Economics, 38(1), 147–158.CrossRef
Mikucka, M., Sarracino, F., & Dubrow, J. K. (2017). When does economic growth improve life satisfaction? Multilevel analysis of the roles of social trust and income inequality in 46 countries, 1981–2012. World Development, 93, 447–459.CrossRef
O’Neill, B. C., Kriegler, E., Ebi, K. L., Kemp-Benedict, E., Riahi, K., Rothman, D. S., et al. (2017). The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Global Environmental Change, 42, 169–180.CrossRef
Popp, A., Calvin, K., Fujimori, S., Havlik, P., Humpenöder, F., Stehfest, E., et al. (2017). Land-use futures in the shared socio-economic pathways. Global Environmental Change, 42, 331–345.CrossRef
Powdthavee, N., Lekfuangfu, W. N., & Wooden, M. (2015). What’s the good of education on our overall quality of life? A simultaneous equation model of education and life satisfaction for Australia. Journal of Behavioral and Experimental Economics, 54, 10–21.CrossRef
Rao, S., Klimont, Z., Smith, S. J., Van Dingenen, R., Dentener, F., Bouwman, L., et al. (2017). Future air pollution in the Shared Socio-economic Pathways. Global Environmental Change, 42, 346–358.CrossRef
Rehdanz, K., & Maddison, D. (2005). Climate and happiness. Ecological Economics, 52(1), 111–125.CrossRef
Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C., Fujimori, S., et al. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change, 42, 153–168.CrossRef
Ribas, A., Lucena, A. F. P., & Schaeffer, R. (2017). Bridging the energy divide and securing higher collective well-being in a climate-constrained world. Energy Policy, 108, 435–450.CrossRef
Rogelj, J., Popp, A., Calvin, K. V., Luderer, G., Emmerling, J., Gernaat, D., Fujimori, S., Strefler, J., Hasegawa, T., Marangoni, G., Krey, V., Kriegler, E., Riahi, K., Vuuren, D. P. v., Doelman, J., Drouet, L., Edmonds, J., Fricko, O., Harmsen, M., Havlík, P., Humpenöder, F., Stehfest, E., & Tavoni, M. (2018). Scenarios towards limiting global mean temperature increase below 1.5c. Nature Climate Change, 1.
Sachs, J. (2016). Happiness and sustainable development: Concepts and evidence. In World Happiness Report 2016. Sustainable Development Solutions Network.
Sachs, J., & G., S.-T., Kroll, C., Lafortune, G., & Fuller, G. (2019). Sustainable Development Report 2019. Bertelsmann Stiftung and Sustainable Development Solutions Network (SDSN), New York. Technical report.
Sarracino, F. (2010). Social capital and subjective well-being trends: Comparing 11 western European countries. The Journal of Socio-Economics, 39(4), 482–517.CrossRef
Sekulova, F., & van den Bergh, J. C. J. M. (2013). Climate change, income and happiness: An empirical study for Barcelona. Global Environmental Change, 23(6), 1467–1475.CrossRef
Sørensen, J. F. L. (2014). Rural-urban differences in life satisfaction: Evidence from the European Union. Regional Studies, 48(9), 1451–1466.CrossRef
van Vuuren, D. P., Stehfest, E., Gernaat, D. E. H. J., Doelman, J. C., van den Berg, M., Harmsen, M., et al. (2017). Energy, land-use and greenhouse gas emissions trajectories under a green growth paradigm. Global Environmental Change, 42, 237–250.CrossRef
Verme, P. (2011). Life satisfaction and income inequality. Review of Income and Wealth, 57(1), 111–127.CrossRef
Vuuren, D. P. v., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J.-F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S. J., & Rose, S. K. (2011). The representative concentration pathways: An overview. Climatic Change, 109(1–2), 5.
Welsch, H. (2006). Environment and happiness: Valuation of air pollution using life satisfaction data. Ecological Economics, 58(4), 801–813.CrossRef
Zhang, X., Zhang, X., & Chen, X. (2017). Happiness in the air: How does a dirty sky affect mental health and subjective well-being? Journal of Environmental Economics and Management, 85, 81–94.CrossRef
Metadata
Title
Subjective Well-Being at the Macro Level—Empirics and Future Scenarios
Authors
Johannes Emmerling
Paula Navarro
Matthew R. Sisco
Publication date
13-04-2021
Publisher
Springer Netherlands
Published in
Social Indicators Research / Issue 3/2021
Print ISSN: 0303-8300
Electronic ISSN: 1573-0921
DOI
https://doi.org/10.1007/s11205-021-02670-2

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