Happiness Inequality Research Articles

Moving Towards Happiness?

AbstractWe add to the literature on the driving forces of international migration. While the existing literature establishes that income differences, migration costs and certain other factors (e.g. climate or human rights) affect the migration decision, we focus on the broader role of non‐pecuniary factors. We include well‐being measures in a standard model of bilateral migration flows and enrich the analysis further by testing the effects on migration of inequality in happiness within a country. Our findings that both the mean and standard deviation of happiness – in both origin and destination countries – help explain bilateral migration flows over and above any income effect, indicates the need to incorporate both pecuniary and non‐pecuniary factors when modelling migration choices.

Happiness Inequality in China

Along with China becoming an upper-middle-income country from a lower-middle-income one after 2009, happiness inequality in China has been enlarged. Based on the Chinese General Social Survey database (2003–2015), this paper investigates the determinants of happiness inequality in China and explores what factors contribute to its enlargement after 2009. We find that a rise of income inequality as well as the population share of middle age cohorts can widen China’s happiness inequality, while an increase in income or education level has a reducing impact. Being in employment also has happiness inequality reducing impacts. A decomposition analysis shows that the deterioration of China’s happiness inequality is mainly caused by coefficient effects, i.e., the relationships between happiness inequality and its influencing factors have changed, which reflects the dramatic change in the Chinese economy and society. Among the coefficient effects, regional heterogeneity plays an important role. Policies enhancing economic performance and education as well as reducing income inequality and regional inequality can help to mitigate happiness inequality and improve social harmony in China.

Analytical Study of Happiness Inequality in Egypt

Based on World value survey (3 waves: 2001, 2008 & 2012) the happiness inequality in Egypt has been enlarged. This paper illustrates the concept of happiness inequality and analysis the status of happiness inequality in Egypt. Also the paper aim to determine the main factors affecting happiness inequality in Egypt. This paper used RIF regression (Recentered influence function) to investigate the impacts of several factors like: age, educational level, employment status, gender, martial status and health status on happiness inequality in Egypt. We found that increase the population share of middle age people and increase unemployment rate can enlarge Egypt’s happiness inequality. While an increase in educational level, improvement in health and having good job has a reducing impact on happiness inequality.

Inequality and Welfare in Quality of Life Among OECD Countries: Non-parametric Treatment of Ordinal Data

The last few years have witnessed an increasing emphasis on going beyond GDP per capita when measuring a nation’s quality of life. Countries (e.g. UK, France, Canada, Germany, Italy, Japan, Korea, Spain) and international organizations (e.g. OECD) have been developing methods suitable for non-income indicators. However, this involves serious measurement challenges due to: (a) multidimensionality, and (b) ordinality (i.e. unlike income these indicators do not have a natural scale). This paper is the first summary of the methods developed in the last decade in the field of inequality and welfare measurement to address these challenges. Next, we utilize the presented methodology and provide evidence on the ranking of OECD countries in terms of welfare and inequality in education and happiness. We find that when dimensions are analysed separately, welfare dominance is frequent (42% of all comparisons in education and 31% in life satisfaction). The number drops to only 4.4% for bivariate dominance, which highlights the empirical relevance of multidimensional analysis. Greece, Portugal and Hungary feature the lowest joint welfare. Northern European countries are most often dominating and Southern European countries are most often dominated in both inequality and welfare analyses.

The Value of Happy Population for Relative Engagement in Vertical and Horizontal Entrepreneurship

In this study, we investigate the impact of a country’s population happiness level and happiness inequality on the level of entrepreneurship engagement that individual entrepreneurs in that country pursue. Entrepreneurship engagement is measured along two dimensions: horizontal entrepreneurship (expansion-focused, involving venture growth and internationalization) and vertical entrepreneurship (innovation- focused driven by opportunity). We argue that a country’s happiness level and happiness inequality are differently related to these two dimensions. By using an integrative dataset incorporating multi-country and individual-level data over the period 2007-2013, our empirical analysis supports the hypotheses we develop in the paper.

Moving Towards Happiness

We add to the literature on the driving forces of international migration. While the existing literature establishes that income differences, migration costs, and certain other factors (e.g. climate or human rights) affect the migration decision, we focus on the broader role of nonpecuniary factors. We include well-being measures in a standard model of bilateral migration flows and enrich the analysis further by testing the effects on migration of inequality in happiness within a country. Our findings that both the mean and standard deviation of happiness - in both origin and destination countries - help explain bilateral migration flows over and above any income effect, indicates the need to incorporate both pecuniary and non-pecuniary factors when modelling migration choices.

Inequality with Ordinal Data

The standard theory of inequality measurement assumes that the equalisand is a cardinal quantity, with known cardinalization. However, one often needs to make inequality comparisons where either the cardinalization is unknown or the underlying data are categorical. We propose an alternative approach to inequality analysis that is rigorous, has a natural interpretation, and embeds both the ordinal data problem and the well‐known cardinal data problem. We show how the approach can be applied to the inequality of happiness and of health status.

What Affects Happiness Inequality? Evidence from Japan

This paper examines the determinants of happiness inequality in Japan using unique data from the “Preference Parameters Study” of Osaka University, a nationally representative survey conducted in Japan. By estimating Recentered Influence Function regressions, we find that household income has a negative and significant effect on happiness inequality, as found for other advanced economies, though people’s perception of their relative standing in the income spectrum also matters for the dispersion of happiness. Moreover, the regression results show that the insecurity faced by people about their jobs and life after retirement is also significantly associated with the widening of happiness inequality. Such findings are cause for grave concern given that the share of irregular jobs, which tend to be low paid and insecure, in total employment significantly increased in Japan during the Lost Two Decades and that this increasing trend has not yet been reversed.

Happiness and Globalization: A Spatial Econometric Approach

Based on the data of Gallup World Poll Survey (GWP), this study employs a spatial 2SLS (two stage least square) model to examines the impact of globalization on the level of happiness across 145 nations. We observe endogeneity of the spatial lag term (Wh) on happiness as well as spatial dependencies in the independent variables (WX’s) which represents indirect effects from a change in X’s in the neighboring regions. We observe contrasting spillover effects (own v/s neighboring effect) of the same explanatory variable generating positive or negative effect, respectively, across space. Further, an inverted ‘U’ Kuznet curve reveals a non-linear relationship between average happiness and happiness inequality. At low levels, an increase in well-being appears to hurt the poor; but beyond a certain threshold, it seems to reduce inequality. We observed among countries that happiness inequalities for developed regions are more when compared to developing regions.

Chronotype and emotional intelligence in diurnal regulation of subjective arousal and pleasantness

We study the relationship between intelligence (IQ) and happiness inequality, measured by the standard deviation of life satisfaction, in a cross section of 81 countries. We find that higher levels of IQ are significantly correlated with lower levels of happiness inequality. This relationship is highly statistically significant even when we control for the level of economic development and a large set of control variables including economic freedom and social capital. We furthermore find suggestive evidence that economic development has a happiness equalizing effect, but this effect is, at least partially, mediated by the level of intelligence. Nations with higher levels of economic development and low IQ tend to experience less equal distribution of happiness compared to nations with higher levels of economic development and high IQ. The results are robust for a panel of 50 US states from 1972 to 2012 that uses alternative measure of intelligence from the US General Social Survey.

Are all smart nations happier?

We study the relationship between intelligence (IQ) and happiness inequality, measured by the standard deviation of life satisfaction, in a cross section of 81 countries. We find that higher levels of IQ are significantly correlated with lower levels of happiness inequality. This relationship is highly statistically significant even when we control for the level of economic development and a large set of control variables including economic freedom and social capital. We furthermore find suggestive evidence that economic development has a happiness equalizing effect, but this effect is, at least partially, mediated by the level of intelligence. Nations with higher levels of economic development and low IQ tend to experience less equal distribution of happiness compared to nations with higher levels of economic development and high IQ. The results are robust for a panel of 50 US states from 1972 to 2012 that uses alternative measure of intelligence from the US General Social Survey.

ECONOMIC FREEDOM & HAPPINESS INEQUALITY: FRIENDS OR FOES?

This article examines the relationship between economic freedom and happiness inequality for a large sample of countries. We find that economic freedom is negatively associated with happiness inequality and robust to several alternative measures of happiness inequality, including the standard deviation, mean absolute difference, coefficient of variation, and Gini coefficient. Among the economic freedom areas, legal system and sound money are negatively correlated with happiness inequality. Drawing on the Engerman‐Sokoloff hypothesis, we use a measure of factor endowments as an instrument for economic freedom to provide a further robustness test, finding a negative association between economic freedom and happiness inequality. (JEL D63, I31, P16)

The role of intelligence in the distribution of national happiness

We study the relationship between intelligence (IQ) and happiness inequality, measured by the standard deviation of life satisfaction, in a cross section of 81 countries. We find that higher levels of IQ are significantly correlated with lower levels of happiness inequality. This relationship is highly statistically significant even when we control for the level of economic development and a large set of control variables including economic freedom and social capital. We furthermore find suggestive evidence that economic development has a happiness equalizing effect, but this effect is, at least partially, mediated by the level of intelligence. Nations with higher levels of economic development and low IQ tend to experience less equal distribution of happiness compared to nations with higher levels of economic development and high IQ. The results are robust for a panel of 50 US states from 1972 to 2012 that uses alternative measure of intelligence from the US General Social Survey.

Economic Growth Evens Out Happiness: Evidence from Six Surveys.

In spite of the great U-turn that saw income inequality rise in Western countries in the 1980s, happiness inequality has fallen in countries that have experienced income growth (but not in those that did not). Modern growth has reduced the share of both the "very unhappy" and the "perfectly happy". Lower happiness inequality is found both between and within countries, and between and within individuals. Our cross-country regression results argue that the extension of various public goods helps to explain this greater happiness homogeneity. This new stylised fact arguably comes as a bonus to the Easterlin paradox, offering a somewhat brighter perspective for developing countries.

Welfare States and the Redistribution of Happiness

We use data from the 2002 International Social Survey Programme, with roughly 42,000 individuals nested within twenty-nine countries, to examine the determinants of happiness in a comparative perspective. We hypothesize that social democratic welfare states redistribute happiness among policy-targeted demographic groups in these countries. The redistributive properties of the social democratic welfare states generate an alternate form of “happiness inequality” in which winners and losers are defined by marital status, presence of children, and income. We apply multilevel modeling and focus on public social expenditures (as percentage of GDP) as proxy measures of state intervention at the macro level, and happiness as the specific measure of welfare outcome at the micro level. We find that aggregate happiness is not greater in the social democratic welfare states, but happiness closely reflects the redistribution of resources in these countries. Happiness is redistributed from low-risk to high-risk individuals. For example, women with small children are significantly happier, but single persons are significantly less happy in the welfare states. This suggests that the pro-family ideology of the social democratic welfare states protects families from social risk and improves their well-being at the cost of single persons. Further, we find that the happiness gap between high- versus low-income earners is considerably smaller in the social democratic welfare states, suggesting that happiness is redistributed from the privileged to the less privileged.

Index of Inequality-Adjusted Happiness (IAH) Improved: A Research Note

Eight years ago we proposed a new measure of happiness in nations, called Inequality-Adjusted Happiness (IAH). This measure indicates how successful nations are in combining a high level of happiness and a low degree of inequality in happiness among citizens. The index gives equal weight to the level of happiness measured using the mean and inequality of happiness measured using the standard deviation. Scores on this index are now available for 139 nations. In this paper we present a technical improvement for the method to calculate IAH.

Living Happily Ever After? The Effect of Taiwan’s National Health Insurance on the Happiness of the Elderly

The implementation of Taiwan’s National Health Insurance (NHI) in 1995 has created a quasi-experiment that permits us to draw causal inference for the effect of the NHI on the happiness and life satisfaction. The NHI is expected to have a positive effect on happiness because it not only improves the health and mitigates the uncertainty in financial losses due to health shocks, but also alleviates health inequality in the population through equal access to care. The results show that the NHI has a significant effect on happiness and life satisfaction. The effect ranges from 3 to 30 % depending on the measures of happiness. Nonetheless, the effect of the NHI in narrowing happiness inequality across socioeconomic status (SES) is significant for health and income groups only while its effect on education- and gender-happiness gradients are insignificant. The increase in the probability of being happy or satisfied with life among the least healthy group is 0.08 % points higher than that of the healthier ones. Earlier studies have shown that the effects of health insurance on health and disparity in health across SES groups are small. In contrast, our findings suggest that the welfare impact of the NHI is quite large.

The drivers of happiness inequality: suggestions for promoting social cohesion

The goal of this paper is to identify and quantify the contribution of a set of covariates in affecting levels and over time changes of happiness inequality. We make use of a recent methodology that allows decomposing the overall change in happiness inequality into composition and coefficient effects of each covariate. We focus on the increase in happiness inequality observed in Germany between 1991 and 2007 in the German SocioEconomic Panel (GSOEP) database, deriving the following findings. First, trends in happiness inequality are mainly driven by composition effects, while coefficient effects are negligible. Second, among composition effects, education has an inequality-reducing impact, while changes in labour market conditions and demographic composition contribute to explain the rise in happiness inequality. Third, the increase in income inequality cannot be considered as a driver of the increase in happiness inequality. A clear cut policy implication of our paper is that policies enhancing education and labour market performance are crucial to reduce happiness inequality and the potential social tensions arising from it.

The Drivers of Happiness Inequality: Suggestions for Promoting Social Cohesion

This paper identifies and quantifies the contribution of a set of covariates in affecting levels and over time changes of happiness inequality. Using a decomposition methodology based on RIF regression, we analyse the increase in happiness inequality observed in Germany between 1992 and 2007, using the German Socio-Economic Panel (GSOEP) database, deriving the following findings.First, trends in happiness inequality are mainly driven by composition effects, while coefficient effects are negligible. Second, among composition effects, education has an inequality-reducing impact, while the increase in unemployment contributes to the rise in happiness inequality. Third, the increase in average income has a reducing impact on happiness inequality, while the raise in income inequality cannot be considered as a driver of happiness inequality trends. A clear cut policy implication is that policies enhancing education and economic performance contribute to reduce happiness inequality and the potential social tensions arising from it.

Does it matter how happiness is measured? Evidence from a randomized controlled experiment

A continuous and a discrete rating scale were implemented for a single item happiness question in a representative survey. A randomized controlled experiment enables unique analyses on data quality and distributions, which suggest superiority of the continuous scale. Results raise doubts about earlier inferences drawn on correlates of happiness. So far only self-assessed discrete happiness data have been used for research into the determinants of happiness. However, distribution distortions were found for the numerically labeled discrete scale, especially for women. Through this discretization bias, the widely reported gender happiness inequality puzzle can be explained.