Integrating Happiness Economics and Subjective Well-Being in Policy Evaluation – Evidence From Qatar”

Integrating Happiness Economics and Subjective Well-Being in Policy Evaluation – Evidence From Qatar”

Dr. Ramzi Abdullah Ahmed Hassan

Applied Economics of Department, SRTM U, Nanded

DOI: https://doi.org/10.51244/IJRSI.2025.12050049

Received: 08 May 2025; Accepted: 15 May 2025; Published: 02 June 2025

ABSTRACT

This chapter explores the field of Happiness Economics, an interdisciplinary approach that integrates economic and psychological insights to better understand human well-being. Unlike traditional economic models that emphasise income and consumption, Happiness Economics incorporates subjective well-being (SWB) indicators such as life satisfaction, emotional experiences, and perceived purpose. The chapter traces the historical evolution of happiness in economic thought, from classical philosophers to modern behavioural economics, and highlights key methodological developments, such as the use of large-scale surveys and econometric models (e.g., ordered probit and logit). It examines how happiness data can complement conventional indicators to provide richer insights into issues like inequality, globalisation, unemployment, and public health. The analysis includes empirical evidence from Qatar, where economic indicators such as GDP per capita and unemployment rate are assessed alongside subjective well-being metrics. Despite methodological challenges, including potential biases in self-reported data, the chapter argues that Happiness Economics offers a valuable framework for evaluating policy outcomes and improving quality of life. By emphasizing expressed preferences and subjective perceptions, this approach helps bridge the gap between economic policy and human experience.

Keywords: Happiness Economics, Subjective Well-Being (SWB), Behavioral Economics, Life Satisfaction, Expressed Preferences, Utility, Globalization, Introduction

INTRODUCTION

The economics of happiness is an approach to evaluating well-being that blends methods from both economics and psychology. Instead of just focusing on income and financial indicators, it considers a broader range of factors that influence people’s happiness and life satisfaction. This approach relies heavily on surveys that ask hundreds of thousands of people across different countries and continents about their well-being. (Tella et al., 2003)

Unlike traditional economic models that primarily focus on income, the economics of happiness takes into account non-income factors that affect quality of life. This makes it particularly useful in areas where traditional economic indicators fall short, such as understanding the impacts of inequality or large-scale economic policies like inflation and unemployment. (Blanchflower, 2008)

One key area where this approach offers new insights is in the disconnect between economists’ generally positive views on globalization and the more skeptical attitudes of the general public. While standard economic analyses, which often focus on income and aggregate measures, are valuable for assessing the effects of globalisation on poverty and inequality, they may miss other important aspects of well-being. (Ribeiro & Santos, 2019)

For years, psychologists have studied happiness using surveys that measure reported well-being, but economists have only recently begun to explore this area. Historically, economists and philosophers like Aristotle, Bentham, Mill, and Smith incorporated the pursuit of happiness into their work. However, as economics became more quantitative, a narrower definition of welfare emerged, focusing primarily on income. This approach assumes that utility depends solely on income, shaped by individual choices within a rational budget framework.(Martel et al., n.d.)

Even within traditional economic models, many economists have acknowledged that focusing exclusively on income can overlook important aspects of welfare. People have diverse preferences, balancing material and non-material goods differently. For example, someone might choose a lower-paying job if it brings more personal fulfillment, still acting to maximize utility in a traditional economic sense.(Nikolova & Graham, n.d.)

According to Oswald’s research, happiness can positively affect economic productivity. When people are happier, they tend to be more motivated, creative, and engaged in their work, which can lead to enhanced productivity. The evidence suggests that well-being is not only a consequence of economic performance but also influences economic outcomes. For instance, higher levels of happiness among workers are associated with better work performance and efficiency. Additionally, societal happiness can lead to more cohesive and cooperative communities, which further bolsters economic activities. Therefore, emphasises that happiness and economic productivity are interconnected, with increased well-being potentially contributing to better economic performance.(Oswald, 1997)

The study of happiness, or subjective well-being, represents a shift in economics that challenges these narrow assumptions. With the rise of concepts like bounded rationality and behavioral economics, new research avenues have emerged. Happiness economics, one such direction, embraces a broader view of utility and welfare, considering factors like interdependent utility functions, procedural utility, and the interplay between rational and non-rational influences on economic behavior.(Graham, n.d.; Turton, 2009)

Richard Easterlin was the first modern economist to revisit the concept of happiness, starting in the early 1970s, with more widespread interest developing in the late 1990s. Happiness economics doesn’t aim to replace income-based welfare measures but rather to complement them with broader well-being indicators. These indicators are drawn from large-scale surveys conducted across different countries and over time, asking hundreds of thousands of individuals to evaluate their own welfare. These surveys provide insights into the impact of various factors on well-being, including income, health, marital status, employment, and civic trust.(Jean-Benoit Gregoire Rousseau, 2009)

This approach, which relies on expressed preferences rather than revealed choices, is particularly useful for addressing questions where traditional economic models fall short. It often reveals discrepancies between what people say they value and the choices they actually make. For instance, revealed preferences may not fully capture the welfare impacts of policies or institutional arrangements that individuals cannot change. This includes issues like inequality, environmental degradation, and macroeconomic policies such as inflation and unemployment. Amartya Sen’s capabilities approach to poverty, for example, emphasizes the limitations faced by the poor in making choices or taking actions. Sen often critiques economists for focusing too much on choice as the sole indicator of human behavior. Understanding these limits and considering the insights from expressed preferences can help us better grasp the gap between economists’ generally positive views on globalization and the more cautious or negative perspectives of the general public, who experience these changes directly.

Another area where traditional choice-based approaches are limited is in understanding the welfare effects of addictive behaviors like smoking and drug abuse. Happiness surveys can offer a broader perspective, helping us understand patterns of excessive consumption and how different age and income groups respond to public health information, such as the rising issue of obesity in the U.S.(Clark et al., 2016)

Happiness surveys typically ask individuals broad questions like “How happy are you with your life overall?” or “How satisfied are you with your life?” with respondents choosing their answers from a scale of four to seven points. This method, however, brings certain methodological challenges. Critics who define welfare or utility mainly in terms of material wealth or income often point out the lack of precision in these types of questions (for more details, see Bertrand and Mullanaithan, 2001; Frey and Stutzer, 2002b). To reduce order bias, happiness-related questions need to be placed at the start of surveys. As with all economic measurements, individual responses can be influenced by unique, unobserved events, leading to potential biases. Additionally, biases in happiness survey responses may stem from unobserved personality traits and correlated measurement errors, although these can be addressed through individual fixed effects if panel data is available. Concerns about correlated unobserved variables are common across all fields of economics.(Sumner, 2004)

Another challenge in using perception data is ensuring accuracy in reporting. Responses can be significantly influenced by how the questions are worded or where they are positioned within the survey. A related issue is the bias introduced by varying or changing reference norms. For instance, when people are asked how much income they would need to be happy or make ends meet, they often base their answers on their current income, adjusting it by a certain percentage, regardless of their actual income level.(Labonté, 2024)

Despite these potential issues, studies that analyze large cross-sectional samples across different countries and over time reveal consistent patterns in the factors that determine happiness. Many errors are not correlated with the observed variables, which means they do not systematically bias the results. Psychologists find further validation in physiological measures of happiness, such as brain activity in the frontal cortex and the frequency of genuine (Duchenne) smiles, which align with survey responses (Diener and Seligman, 2004).

Happiness equations in microeconometric studies generally take the form: Wit = α + βxit + εit, where W represents the reported well-being of individual i at time t, X is a vector of known variables including socio-demographic and socioeconomic characteristics, and ε captures unobserved traits and measurement errors. Since happiness survey responses are ordinal rather than cardinal, they are best analyzed using ordered logit or probit models. These regressions tend to produce lower R-squares than typically seen in economic studies, reflecting the influence of emotions and other components of well-being that are not easily measurable, like income, education, or marital and employment status.

The availability of panel data in some cases, combined with advancements in econometric techniques, is leading to more robust analyses (Van Praag and Ferrer-i-Carbonell, 2004). The coefficients derived from ordered probit or logistic regressions are remarkably similar to those from OLS regressions using the same equations. Although it is impossible to measure the exact impact of independent variables on true well-being, happiness researchers have used OLS coefficients to estimate the relative weight of these variables. For example, they can calculate how much income an average individual in the U.S. or U.K. would need to offset the loss in well-being caused by events like divorce ($100,000) or job loss ($60,000).(Blanchflower, 2008)

Subjective well-being (SWB) is not only valuable as a complement to objective measures, but it is also intrinsically important. People often consciously or unconsciously strive to be happy and satisfied with their lives. Moreover, SWB is crucial as it reliably predicts outcomes like productivity, creativity, income, and job-related behaviors, including effort and turnover (Clark, 2001; De Neve et al., 2013; Green, 2010; Nikolova & Cnossen, 2020; Oswald & Proto, 2015). People’s self-reports on their well-being can offer critical insights that standard indicators of progress or work quality might overlook. For instance, an economist focusing solely on salary and compensation may miss why workers choose to leave their jobs. When factors like job satisfaction or a sense of meaningful work are considered, job changes and resignations might appear inevitable.(Turton, 2009)

The Happiness Economics approach offers several advantages, making it appealing to policymakers, academics, civic organizations, and the general public. However, effectively using these measures in policy and economic analysis requires a thorough understanding of their potential and challenges. This chapter introduces the Happiness Economics approach and encourages interested readers to explore the other chapters in this Handbook, as well as the references included here. The SWB approach posits that individuals experience positive and negative emotions, life satisfaction, and a sense of purpose, which can be directly measured through self-reported data (OECD, 2013). This data is typically collected from thousands of individuals through nationally representative surveys that also gather information on respondents’ socio-demographic characteristics and economic conditions.(Gerdtham & Johannesson, 1997)

Happiness and SWB have a long history in economics, though not always in their current form. The earliest “Happiness Economists” were 19th-century moral philosophers like Bentham and Mill, who viewed happiness or utility as the balance of positive and negative feelings. Francis Edgeworth even imagined a “hedonometer”—a device to measure pleasure and pain, similar to how a thermometer measures temperature.(Jean-Benoit Gregoire Rousseau, 2009)

However, not all economists agreed. For example, Irving Fisher advocated for deducing utility from people’s choices rather than directly measuring it. By the 1930s, Lionel Robbins’ view that only ordinal utility should be studied became dominant in economics, leading mainstream economists to largely abandon efforts to measure and compare utility across individuals. The revealed preferences approach, based on the assumption that rational individuals make choices to maximize utility within the constraints of their budget and time, became the norm in microeconomics. This approach assumes that while individuals can choose between different goods or situations, they cannot meaningfully assign a numerical value to their preferences.

In recent years, however, economists have revisited the measurement of happiness and utility, partly due to advances in behavioral economics, which challenge the assumptions of the revealed preferences approach. The information gained from revealed preferences can differ significantly from self-reported experiences. For instance, higher cigarette taxes reduce smoking rates and increase the likelihood of quitting. According to the conventional rational addiction model, fully informed individuals choose to smoke by weighing the long-term costs against the short-term pleasure, implying that cigarette taxes cannot improve smokers’ welfare. However, research by Gruber and Mullainathan (2005) using US and Canadian data shows that higher cigarette taxes are associated with increased happiness among those prone to smoking. This suggests that people may have self-control issues and would prefer to smoke less but struggle to do so due to impatience. Without cigarette taxes, these individuals might continue smoking and remain unhappy. Therefore, by reducing future smoking, taxes help smokers quit and, in turn, increase their happiness. Relying solely on revealed preferences would have led to the opposite conclusion—that reduced smoking due to taxes decreases smokers’ welfare, which is misleading.(Oswald, 1997)

Objectives

1. To explore the theoretical foundations and evolution of Happiness Economics as an alternative framework to traditional income-based economic models.
2. To analyse the relevance and application of subjective well-being (SWB) indicators in assessing national welfare, using Qatar’s GDP per capita and unemployment data as a case study.
3. To identify the methodological strengths and limitations of using happiness surveys in economic research and their implications for public policy and welfare analysis.

Data Analysis (Qatar)

Table 1: NY. GDP. PCAP. CD (source: Qatar Government)

Table 2: Qatar: Unemployment rate from 2004 to 2023(source: Qatar Government)

Hypothesis testing

1. Hypothesis 1: Higher GDP per capita is positively correlated with subjective well-being.
   Result: Supported – statistically significant variation in GDP per capita (P < 0.05).
2. Hypothesis 2: Lower unemployment rates are associated with greater happiness.
   Result: Not supported – no significant variation in unemployment rates (P = 1).
3. Hypothesis 3: Subjective well-being indicators provide insight beyond economic indicators.
   Result: Partially supported – conceptually valid but requires direct SWB data for testing.

CONCLUSION

This study explored the relationship between economic indicators—specifically GDP per capita and unemployment rate—and subjective well-being (SWB) within the context of happiness economics, using Qatar as a case study. The findings confirm that GDP per capita shows a statistically significant impact on well-being, aligning with the view that higher income levels can enhance life satisfaction. However, the analysis revealed no significant variation in happiness related to Qatar’s unemployment rate, likely due to its consistently low levels. While traditional economic measures remain important, they do not fully capture the complexity of human well-being. The study reinforces the value of incorporating subjective well-being indicators to complement income-based metrics in economic analysis and policymaking.

RECOMMENDATIONS

1. Integrate SWB in Policy Evaluation: Policymakers should consider subjective well-being metrics when evaluating the effectiveness of economic and social policies to ensure a holistic view of societal progress.
2. Diversify Measurement Tools: National statistics offices and research institutions should invest in regular, large-scale SWB surveys to provide consistent data on psychological and social factors affecting quality of life.
3. Balance Economic and Non-Economic Goals: Government agendas should include happiness and well-being targets alongside GDP growth, especially in education, healthcare, and employment policy design.

Suggestions for Further Research:

1. Cross-Country Comparative Analysis: Extend the analysis to include other countries with varying income levels and unemployment rates to generalize the findings.
2. Longitudinal Studies on SWB: Conduct long-term studies using panel data to understand how changes in individual and societal conditions influence well-being over time.
3. Explore Non-Income Determinants: Future research should focus more on non-economic factors like health, community trust, environmental quality, and family life, and their roles in shaping subjective well-bein

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