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on Economics of Happiness |
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Issue of 2024‒10‒21
four papers chosen by Viviana Di Giovinazzo, Università degli Studi di Milano-Bicocca |
| By: | Greyling, Talita; Rossouw, Stephanié |
| Abstract: | It is well-established that a country's economic outcomes, including productivity, future income, and labour market performance, are profoundly influenced by the happiness of its people. Traditionally, survey data have been the primary source for determining people's happiness. However, this approach faces challenges as individuals increasingly experience "survey fatigue"; conducting surveys is costly, data generated from surveys is only available with a significant time lag, and happiness is not a constant state. To address these limitations of survey data, Big Data collected from online sources like Google Trends™ and social media platforms have emerged as a significant and necessary data source to complement traditional survey data. This alternative data source can give policymakers more timely information on people's happiness, well-being or any other issue. In recent years, Google Trends™ data has been leveraged to discern trends in mental health, including depression, anxiety, and loneliness and to construct robust predictors of subjective well-being composite categories. We aim to develop a methodology to construct the first comprehensive, near real-time measure of population-level happiness using information-seeking query data extracted continuously using Google Trends™ in countries. We use a basket of English-language emotion words suggested to capture positive and negative affect based on the literature reviewed. To derive the equation for estimating happiness in a country, we employ machine learning algorithms XGBoost and ElasticNet to determine the most important words and weight the happiness equation, respectively. We use the United Kingdom's ONS (weekly and quarterly) data to demonstrate our methodology. Next, we translate the basket of words into Dutch and apply the same equation to test if the same words and weights can be used in a different country (the Netherlands) to estimate happiness. Lastly, we improve the fit for the Netherlands by incorporating country-specific emotion words. Evaluating the accuracy of our estimated happiness in countries against survey data, we find a very good fit with very low error metrics. If we add country-specific words, we improve the fit statistics. Our suggested methodology shows that emotion words extracted from Google Trends™ can accurately estimate a country's level of happiness. |
| Keywords: | Happiness, Google Trends™, Big Data, XGBoost, machine learning |
| JEL: | C53 C55 I31 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:glodps:1493 |
| By: | Désiré Avom (Yaoundé, Cameroon); Itchoko M. M. Mwa Ndjokou (Maroua, Cameroon); Pierre C. Tsopmo (Yaoundé, Cameroon); Cherif Abdramane (Yaoundé, Cameroon); Simplice A. Asongu (Johannesburg, South Africa) |
| Abstract: | This article examines the effect of leader longevity in power on world happiness. To make the assessment, a sample composed of 135 countries observed over the period 2006 to 2018 was constituted. The results obtained from OLS estimates show that longevity in power reduces individual happiness. Furthermore, the negative effect is more amplified in democratic countries. Quantile regression reveals variability in the effect over the different intervals. These results are robust to the use of alternative estimation techniques. We also identify the quality of institutions and public spending as two potential transmission channels through which longevity in power influences well-being. These results invite political authorities to respect constitutional limits or implement constitutional reforms with the aim of limiting the duration of the mandate of the executive in order to reduce the harmful effect of an extension of the latter on individuals' well-being. |
| Keywords: | longevity in power, happiness, quality of institutions, public spending, quantile regression |
| JEL: | D72 H31 H52 I31 |
| Date: | 2024–01 |
| URL: | https://d.repec.org/n?u=RePEc:agd:wpaper:24/033 |
| By: | Jean-Michel Benkert, Shuo Liu, Nick Netzer |
| Abstract: | Response times contain information about economically relevant but unobserved variables like willingness to pay, preference intensity, quality, or happiness. Here, we provide a general characterization of the properties of latent variables that can be detected using response time data. Our characterization generalizes various results in the literature, helps to solve identification problems of binary response models, and paves the way for many new applications. We apply the result to test the hypothesis that marginal happiness is decreasing in income, a principle that is commonly accepted but so far not established empirically. |
| Keywords: | response times, chronometric effect, binary response model, non-parametric identification, decreasing marginal happiness |
| JEL: | C14 D60 D91 I31 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:ube:dpvwib:dp2407 |
| By: | Satoshi KAWAKATSU; Tomoki SEKIGUCHI |
| Abstract: | Recently, two published studies examined the relationship between psychological resilience and entrepreneurs’ well-being during the COVID-19 pandemic. One study demonstrated that entrepreneurs’ psychological resilience was related to their well-being during the COVID-19 pandemic period, whereas the other study showed that entrepreneurs’ psychological resilience was unnecessary in maintaining well-being during the same period. We argue that these seemingly contradictory findings can be resolved by using the set-theoretic approach rather than the regression-based approach, which allows the examination of multiple causal pathways to outcomes and to differentiate and identify necessary and sufficient conditions in causality. The results of qualitative comparative analysis (QCA) using the Japanese data demonstrated that psychological resilience is sufficient but not necessary condition for entrepreneurs’ well-being when a threat to the business is perceived by entrepreneurs, which is consistent with the findings of the two previous studies. Based on the findings, we propose future research to better understand the nature of entrepreneurial resilience. |
| Keywords: | entrepreneurship, resilience, well-being, COVID-19, fuzzy set qualitative comparative analysis (fsQCA) |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:kue:epaper:e-24-006 |