Rethinking National Well‐Being: Introducing a Measure of Wealth‐Adjusted Life Satisfaction in 116 Countries

The study presents alternative measures for measuring the welfare of a country in the context of identifying relationships generated by the impact of changes in the income level, measured by gross domestic product (GDP), related to other welfare, measured by the Happy Planet Index (HPI).The analysis was conducted in Romania, during the period from 2012 to 2016.The research methodology involves simple linear regression and welfare descriptive variables such as GDP, GDP/capita, HPI and its subcomponents' indicators, namely life satisfaction, life expectancy and ecological footprint.Identification of aspects that have an impact on the welfare of citizens allows to compare levels of wellbeing experienced worldwide and to identify the main areas at the national level on which improvements can be made.The results indicate that, although there is no correlation between GDP and HPI, GDP/capita has great influence on both life satisfaction and life expectancy.Also, GDP has influence on the ecological footprint.Given these considerations, the main conclusion of the research is that, although the level of welfare, quantified using GDP, changes positively, this change is due to the increased life expectancy, life satisfaction, reduced ecological footprint rather than to changes in income levels.

The scientific debate on the relation between Gross Domestic Product (GDP) and self reported indices of life satisfaction is still open. In a well-known finding, Easterlin reported no significant relationship between happiness and aggregate income in time-series analysis. However, life satisfaction appears to be strictly monotonically increasing with income when one studies this relation at a point in time across nations. Here, we analyze the relation between per capita GDP and life satisfaction without imposing a functional form and eliminating potentially confounding country-specific factors. We show that this relation clearly increases in country with a per capita GDP below 15,000 USD (2005 in Purchasing Power Parity), then it flattens for richer countries. The probability of reporting the highest level of life satisfaction is more than 12% lower in the poor countries with a per capita GDP below 5,600 USD than in the counties with a per capita GDP of about 15,000 USD. In countries with an income above 17,000 USD the probability of reporting the highest level of life satisfaction changes within a range of 2% maximum. Interestingly enough, life satisfaction seems to peak at around 30,000 USD and then slightly but significantly decline among the richest countries. These results suggest an explanation of the Easterlin paradox: life satisfaction increases with GDP in poor country, but this relation is approximately flat in richer countries. We explain this relation with aspiration levels. We assume that a gap between aspiration and realized income is negatively perceived; and aspirations to higher income increase with income. These facts together have a negative effect on life satisfaction, opposite to the positive direct effect of the income. The net effect is ambiguous. We predict a higher negative effect in individuals with higher sensitivity to losses (measured by their neuroticism score) and provide econometric support of this explanation.

Gross Domestic Product (GDP) has been utilized by academics and policy makers to indicate the economic well-being of the people. However, economic growth measures cannot capture fully the overall well-being of the people. This paper has tested quality of economic growth in Japan after World War II as to whether it has brought about positive outcome in the well-being of its citizens. Comparison between GDP and GPI (Genuine Progress Index) has revealed that GDP does not fit as well with people's life satisfaction trend as GPI. Prefecture-based rankings on GDP, Human Development Index (HDI) and Life Satisfaction have shown that there are clear gaps between objective measures and subjective measures to indicate the overall well-being of the people. Also, analysis on major determinants for people's life satisfaction reveals that older people, women, non-employed people, and those who live in subsidized housings felt satisfied with their life.

The relationships between gross domestic product (GDP) per capita, life satisfaction, and male-female suicide ratio were analyzed for 25 industrialized countries. GDP per capita was the strongest correlate of male-female suicide ratio even when life satisfaction was controlled using partial correlations. Life satisfaction correlated significantly with GDP per capita but not with suicide rates.

In this study, we develop a theory of Monetary Intelligence, and explore satisfaction with pay and life from the virtuous money smart perspective: In order to enjoy high pay satisfaction and life satisfaction, money smart individuals must become good stewards and curb their love-of-money motive. We collect data from 6,586 managers in 32 geopolitical entities across six continents. After controlling for Gross Domestic Product (GDP) per capita and individual income, results support our theory: Money smart people simultaneously focus on their intellectual virtues of prudence and “fulfillment”—enhancing stewardship behavior (the works) and “contentment”—reducing affective motive (love of money) and keeping cognitive meaning in perspective which are associated with enviable higher pay satisfaction than life satisfaction. GDP is related to life satisfaction, but not to pay satisfaction; while income is related to both. Counter-intuitively, neither GDP nor income is related to the feelings that money brings happiness. Across three levels of the income pyramid, managers in high (low) GDP countries have higher (lower) life satisfaction than pay satisfaction. Our novel discoveries not only demonstrate the application of a new theory of monetary intelligence in a global context but also illustrate intra-personal, inter-personal, and cross-cultural differences in the pursuit of happiness in people’s lives.

OBJECTIVESIncreasing interest has emerged in the use of subjective well-being as a development indicator and for the evaluation of public policies. The aim of this study was to assess life and health satisfaction and their determinants in the adult population of Iran.METHODSWe conducted a survey of a sample of 3,150 adults at least 18 years of age in Tehran, the capital of Iran. The subjects were selected using a stratified random sampling method, and they were interviewed face-to-face at their usual residence by trained interviewers. Life satisfaction was used as a measure of subjective well-being. We used ordinary least square regression models to assess the associations of life and health satisfaction with socio-demographic variables.RESULTSOn a 0-10 scale, the mean (standard deviation) scores for life and health satisfaction were 6.93 (2.54) and 7.18 (1.97), respectively. The average score for life satisfaction in females was 0.52 points higher than in males. A U-shaped relationship was found between age and life satisfaction, with respondents 35 to 44 years of age having the lowest average level of life satisfaction. Satisfaction with life and health among divorced respondents was significantly lower than among never-married and married participants. The scores for life satisfaction in respondents who rated their health status as poor were 3.83 points lower than in those who rated their health status as excellent.CONCLUSIONSThe majority of the population of Tehran was satisfied with their life and health. Self-rated health status had the greatest impact on life satisfaction.

The aim of this study is to investigate whether income has different relationships to subjective well-being in richer countries compared to poorer ones. We report analyses based on interview data collected in the European Social Survey (n = 72,574) that examine how income relates to life satisfaction (LS) and emotional well-being (EWB) in 28 European countries, varying in gross domestic product (GDP) per capita. Our results indicate that the within-country correlations of income with LS and EWB decrease as GDP per capita increases. Partial correlations controlling for EWB are positive but do not vary with GDP per capita, whereas partial correlations controlling for LS vary inversely with GDP per capita. We hypothesise that the invariant income-LS relationships result from effects of relative income on social comparisons, while the varying income-EWB relationships result from the negative impacts of time scarcity in richer countries and the buffering of negative experiences in poorer ones.

GDP spatialization in Ningbo City based on NPP/VIIRS night-time light and auxiliary data using random forest regression

Background:Despite having one of the highest Gross Domestic Product (GDP) per capita levels, United States (US) ranks lower in subjective well-being, including happiness and life satisfaction, compared with European countries. Studies of the impact of happiness and life satisfaction on life expectancy and mortality in the US are limited or non-existent. Using a national longitudinal dataset, we examined the association between levels of happiness/life satisfaction and US life expectancy and all-cause mortality.Methods:We analyzed the 2001 National Health Interview Survey (NHIS) prospectively linked to 2001-2014 mortality records in the National Death Index (NDI) (N=30,377). Cox proportional hazards regression was used to model survival time as a function of happiness, life satisfaction, and sociodemographic and behavioral covariates.Results:Life expectancies at age 18 among adults with high levels of happiness and life satisfaction were, respectively, 7.5 and 8.9 years higher compared to those with low levels of happiness and life satisfaction. In Cox models with 14 years of mortality follow-up, all-cause mortality risk was 82% higher (hazard ratio [HR]=1.82; 95% CI=1.59,2.08) in adults with little or no happiness, controlling for age, and 36% higher (HR=1.36; 95% CI=1.17,1.57) in adults with little/no happiness, controlling for sociodemographic, behavioral and health characteristics, when compared with adults reporting happiness all of the time. Mortality risk was 107% higher (HR=2.07; 95% CI=1.80,2.38) in adults who were very dissatisfied with their life, controlling for age, and 39% higher (HR=1.39; 95% CI=1.20,1.60) in adults who were very dissatisfied, controlling for all covariates, when compared with adults who were very satisfied.Conclusions and Global Health Implications:Adults with higher happiness and life satisfaction levels had significantly higher life expectancy and lower all-cause mortality risks than those with lower happiness and satisfaction levels. These findings underscore the significance of addressing subjective well-being in the population as a strategy for reducing all-cause mortality.

The gridded spatial distribution data of Gross Domestic Product (GDP) has a wide range of application values in many fields, such as regional economic analysis, urban planning, sustainable utilization of resources, and disaster risk assessment. However, currently the publicly accessible GDP grid datasets face limitations in terms of temporal coverage, spatial extent, and accuracy. Therefore, based on the remote sensing data of land use and nighttime light, this study developed two methods: the factor averaging method (FAM) and grid averaging method (GAM), and used Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) algorithms to jointly construct the spatial model of GDP, so as to produce China’s 1 km gridded GDP in 2020. The experimental results show the following: (1) The GAM yields higher R2 values than the FAM in modeling the three industries, and therefore, it is adopted as the basis for GDP spatialization modeling. (2) XGBoost achieves higher R2 values than RF in modeling primary and secondary industries, but lower R2 values in modeling tertiary industry. Consequently, both methods are combined to construct the overall GDP spatialization model. (3) The accuracy of the GDP spatialization results is evaluated based on town-level GDP statistics, with an R2 value of 0.78, indicating its reliable predictive capability. (4) Compared with publicly available GDP datasets, our dataset exhibits consistent spatial distribution patterns and aggregation trends. Furthermore, our GDP dataset provides a more detailed depiction of variations within county-level administrative units. Therefore, the method proposed in this study offers a valuable option for generating a gridded GDP dataset, visually displaying the uneven economic development across various regions in China. It helps to uncover economic disparities among regions and provides data support for formulating differentiated support policies, so as to promote balanced regional development among regions. Furthermore, it contributes to promoting sustained, inclusive, and sustainable economic growth (SDG 8) and reducing inequalities within and among countries (SDG 10), thereby providing strong support for urban planning and sustainable development.

It has been hypothesized that the language of Twitter users is associated with the socioeconomic well-being those users experience in their physical com- munities (e.g., satisfaction with life in their states of residence). To test the relationship between language use and psychological experience, researchers textually processed tweets to extract mainly sentiment and subject matters (topics) and associated those two quantities with census indicators of well-being. They did so by focusing on geographically coarse-grained communities, the finest-grained of which were U.S. census areas. After briefly introducing those studies and describing the common steps they generally take, we offer a case study taken from our own work on geographically smaller communities: London census areas.

Prior literature suggests that, among the so-called ‘developed economies’, residing in urban contexts is associated with lower life satisfaction. Using data from the European Social Survey (ESS) and Eurostat, we contribute to this literature by focusing on three different indicators of urbanity (subjective domicile, population density and living in a dominant urban region) in a multilevel modelling context in order to define where exactly the relatively lower life satisfaction can be found. Moreover, we account for the level of economic development at both regional and national levels. The results show that subjective domicile is strongly associated with life satisfaction, whereas regional gross domestic product (GDP) and other urbanity indicators are insignificant. Our results also highlight the relatively higher life satisfaction in rural surroundings in more developed countries. We conclude by noting that future contributions to the literature on urban–rural life satisfaction differences should utilize panel data, making it possible to address the spatial sorting versus contextual effects debate, and focus on investigating the higher level determinants at the country level that define the existence of urban–rural differences in life satisfaction within a country.

Nighttime light imageries are widely used for mapping the gross domestic product (GDP) over large areas. However, nighttime light imagery is inappropriate to disaggregate agricultural GDP and inadequate to differentiate the GDP from the secondary and tertiary sectors. Points-of-interest, a kind of geospatial big data with geographic locations and textual descriptions of the category, can effectively distinguish industrial and commercial areas, and therefore have the potential to improve the precise GDP mapping from secondary and tertiary sectors. In this study, a machine learning method, random forest, was used to disaggregate the 2010 county-level census GDP data of mainland China to 1 km × 1 km grids. Six Random Forest models were constructed for different economic sectors to explore the non-linear relationships between various geographic predictors and GDP from different sectors. By fusing points-of-interest of varying categories, the spatial distribution of economic activities from the secondary and tertiary sectors was effectively distinguished. Compared to previous studies, the strategy of developing specific Random Forest models for different sectors generated a more reasonable distribution of GDP. Our results highlight the feasibility of using point-of-interest data in disaggregating non-agricultural GDP by exploiting the complementary features of the different data sources.

Most of the aggregate-level analyses of the relationship between objective and subjective measures for well-being have limited themselves to the measures of national gross domestic product (GDP) and mean life satisfaction. We develop this line of research by embedding the analysis into the context of 289 NUTS (Nomenclature des Unités Territoriales Statistiques) regions in Europe and replacing the simple life satisfaction measure with measures of active human functioning. We suggest that the measures of personal and social well-being, as they are operationalized in the 6th Round of the European Social Survey (ESS) questionnaire, can be treated as subjective indicators for social and human capital and, thereby, can be associated with the regional level GDP in cross-sectional analysis. The empirical analysis shows that the indicator for ‘social trust’ appears to have a positive and significant correlation with regional GDP. The analysis also distinguishes another form of social capital; ‘social contact and support’, reflecting the relative frequency and quantity of social support, which also shows a positive relationship with regional GDP. Concerning subjective human capital, the strongest predictor for regional GDP appears to be the aggregated sense of ‘competence and meaning’ in the regions. These effects proved robust after including the objective control variables (population density, intramural research and development (R&D) expenditure, share of tertiary-educated population and employment).

Advertising theory offers competing perspectives on how advertising might affect life satisfaction. For instance, advertising may have some negative effects by increasing materialism, or it may have some positive effects by reducing marketplace uncertainty. Yet research investigating these connections remains limited. We compile a data set of per capita advertising expenditure to investigate advertising’s relationship with life satisfaction within 76 countries from 2006 to 2019. We deal with several sources of endogeneity and account for other determinants of life satisfaction (e.g., gross domestic product (GDP) per capita, social support) in our analysis. Results from a within-country fixed-effect model indicate that per capita advertising expenditure is positively related to national average life satisfaction. Moderation analyses of this aggregate secondary data and two individual-level experiments provide mechanistic evidence that this occurs because of advertising’s ability to reduce marketplace uncertainty. However, supplemental analyses and an additional experiment indicate that this positive relationship is attenuated through a materialism pathway in certain situations (e.g., related to cultural, income, and subjective inequality factors) and can become negative. As such, we provide the first nuanced and multifaceted view of advertising’s complex relationship with life satisfaction in the marketing literature. History: Puneet Manchanda served as the senior editor for this article. Supplemental Material: The e-companion and data are available at https://doi.org/10.1287/mksc.2021.0136 .