The impact of economic growth, inflation and unemployment on subjective financial satisfaction: A New global evidence

Baeyong Lee; Kim, Hoolda; Tavakoli, Assad Article The impact of economic growth, inflation and unemployment on subjective financial satisfaction: A New global evidence Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Baeyong Lee; Kim, Hoolda; Tavakoli, Assad (2023) : The impact of economic growth, inflation and unemployment on subjective financial satisfaction: A New global evidence, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 11, Iss. 2, pp. 1-25, https://doi.org/10.1080/23322039.2023.2287908 This Version is available at: https://hdl.handle.net/10419/304284 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/oaef20 The impact of economic growth, inflation and unemployment on subjective financial satisfaction: A New global evidence Baeyong Lee, Hoolda Kim & Assad Tavakoli To cite this article: Baeyong Lee, Hoolda Kim & Assad Tavakoli (2023) The impact of economic growth, inflation and unemployment on subjective financial satisfaction: A New global evidence, Cogent Economics & Finance, 11:2, 2287908, DOI: 10.1080/23322039.2023.2287908 To link to this article: https://doi.org/10.1080/23322039.2023.2287908 © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Published online: 30 Nov 2023. Submit your article to this journal Article views: 4191 View related articles View Crossmark data Citing articles: 2 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 GENERAL & APPLIED ECONOMICS | RESEARCH ARTICLE The impact of economic growth, inflation and unemployment on subjective financial satisfaction: A New global evidence Baeyong Lee 1 , Hoolda Kim 1 * and Assad Tavakoli 1 Abstract: Using the happiness survey data, a robust body of literature has supported that people’s subjective well-being is related to economic growth, employment, and inflation. Motivated by “Happiness Economics,” this paper focuses on financial satisfaction, a proxy of subjective well-being. It examines the relationship between people’s financial satisfaction and nations’ macroeconomic performances. We use the World Values Survey and inflation, unemployment, and economic growth data collected from 2010–2014 to 2017–2022. We use the ordered probit and ordinary least squares regressions for the analysis. The findings show that financial satisfaction has a negative relationship with inflation and unemployment and a positive relationship with economic growth. Heterogeneity checks indicate that the association’s strength and statistical significance vary by gender, age, household income, marital status, educational attainment level, and employment status. The overall results suggest policymakers should strive to mitigate the economic vulnerability of women, older adults, low-income earners, low-educated, those who are not married but live together, and those who are not in the labor force to maximize the financial satisfaction of individuals and thus promote subjective well-being of them. Subjects: Economics and Development; Economics; Welfare Keywords: financial satisfaction; inflation; unemployment; economic growth; subjective well-being JEL Classification code: D60; E23; E24; E31; I30 1. Introduction Economic development is an essential goal of countries as it is closely related to human well-being (Hayo & Seifert, 2003). Many countries strive to achieve their development goals through economic, social, cultural, political, and technological progress (Coccia, 2019a). Following Richard Easterlin (1974), one of the earliest economists who investigated the relationship between subjective well-being and economic growth, the body of literature on the economics of happiness has been substantially expanded. Over several decades, the social welfare function defined based on inflation and unemployment has been expanded to analyze their relationship with subjective wellbeing (Blanchard & Fischer, 1989; Burda & Wyplosz, 1993; DiTella et al., 2001; Hall & Taylor, 1997; Lee et al., Cogent Economics & Finance (2023), 11: 2287908 https://doi.org/10.1080/23322039.2023.2287908 Page 1 of 25 Received: 22 May 2023 Accepted: 21 November 2023 *Corresponding author: Hoolda Kim, College of Business and Economics, Fayetteville State University, Fayetteville, NC, USA E-mail: [email protected] Reviewing editor: Muhammad Shafiullah, Economics, BRAC University, Bangladesh Additional information is available at the end of the article © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. Persson & Tabellini, 1990). An extensive body of literature has suggested that subjective well-being is positively associated with economic growth (Deaton, 2008; Diener et al., 2013; Easterlin et al., 2021; Hagerty, 2000; Hagerty & Veenhoven, 2003; Sacks et al., 2010; Schyns, 2001; Stevenson & Wolfers, 2008). An opposed relationship has been found with other measures of macroeconomic performance such as inflation and unemployment (DiTella et al., 2001, 2003; Gandelman & Hernandez-Murillo, 2009; Ouardighi & Munier, 2019; Perovic, 2008; Welsch, 2007; Wolfers, 2003). A multitude of studies have reported that age, income, educational attainment, and employment status are evident predictors of wealth and be correlated with subjective well-being (Ackerman & Paolucci, 1983; Garrett & James, 2013; Gholipour et al., 2022; Hong & Swanson, 1995; Joo & Grable, 2004; Kalsi et al., 2022; Xiao et al., 2014). Marital status and gender have also been discussed as potential contributors to subjective well-being (Calasanti et al., 2021; Fan & Babiarz, 2019; Kalsi et al., 2022; Yamokoski & Keister, 2006). The strength and statistical significance of the associations have appeared heterogeneous based on the choice of subjective well-being measures, macroeconomic performance outcomes, countries, and periods. Poor economic conditions may trigger financial constraints and distress. Financial problems may induce bigger economic, social, and political problems (French & Vigne, 2019). Many scholars have considered financial satisfaction a good proxy for subjective well-being (Diener & Diener, 2009; Diener & Suh, 2000; Diener et al., 2002; Easterlin et al., 2010; Ng & Diener, 2014; Ngamaba, 2017). A large number of studies have found a significant impact of financial stress on mental, psychological, physical, and self-rated health (de Miquel et al., 2022; Karanikolos et al., 2013; Ryu & Fan, 2023; Sweet et al., 2013), food security (Balistreri, 2016), political stability (Funke et al., 2016), and socioeconomic conditions (Brown & Gray, 2016; Easterlin et al., 2010; Peiro, 2006). Several studies have even argued that low economic growth and high inflation may increase unemployment which may lead to lower levels of subjective well-being including financial satisfaction (Hongo et al., 2020; Lim et al., 2019; Tang & Bethencourt, 2017). Job losses, high unemployment and inflation, and recession may affect people’s psychological and emotional status in general and their financial satisfaction in particular. Especially after the pandemic, many countries have experienced radical economic changes. Unexpected economic changes demand a better understanding of how macroeconomic performance may affect people’s financial well-being. Because the extent of the economic impact may vary by the measure of subjective well-being chosen in the analysis (Hayo & Seifert, 2003), we narrow down our interest to financial satisfaction. To have a better understanding of the macroeconomic impact on people’s financial satisfaction and provide better guidance for policymakers to promote the financial wellbeing of their citizens, this study asks the following research question: To what extent do inflation, unemployment, and economic growth affect the financial satisfaction of individuals? To answer this research question, we use the recent World Values Survey (WVS) data collected in 2010–2014 and 2017–2022 and two empirical models: ordered probit and Ordinary Least Squares (OLS). We hypothesize that the inflation and unemployment rates of a country negatively affect financial satisfaction while the economic growth of a nation positively affects the financial satisfaction of the citizens. Moreover, we hypothesize that the impact of macroeconomic performances on financial satisfaction varies by sociodemographic characteristics of individuals. To assess the heterogeneity across different demographic and socioeconomic factors and identify the groups that show significant associations between financial satisfaction and macroeconomic measures, we stratify the sample by gender, age, household income, marital status, educational attainment, and employment status. The paper is organized as follows. We review the literature on the relationship between subjective well-being and macroeconomic outcomes in Section 2. Section 3 introduces data, measures of variables, and empirical strategies. Section 4 presents the results and performs heterogeneity and robustness checks. Section 5 interprets and discusses the findings, and we conclude the paper in Section 6. Lee et al., Cogent Economics & Finance (2023), 11: 2287908 https://doi.org/10.1080/23322039.2023.2287908 Page 2 of 25 2. Literature review Various socioeconomic, political, and institutional factors such as health, wealth, knowledge, and technology can contribute to development in society (Coccia, 2010, 2014a, 2014b, 2018b). Notably, economic advancement may positively affect the political system, standard of living, culture, governance, education, safety, and social security (Coccia, 2019a; Todaro & Smith, 2003). On the other hand, economic problems such as unemployment, debt, and poverty may cause financial strains and psychological distress and create even greater social and political problems (French & Vigne, 2019). Many studies have found potential negative consequences of financial dissatisfaction on mental, physical, and self-rated health and psychological distress (de Miquel et al., 2022; Karanikolos et al., 2013; Ryu & Fan, 2023; Sweet et al., 2013), food security (Balistreri, 2016), and political stability (Funke et al., 2016). Because economic development is closely linked to economic performances (e.g., gross domestic product) and non-economic indicators (e.g., the standard of living or subjective well-being), it is critical to examine how economic performances are associated with non-economic indicators. Following Easterlin’s (1974) study that examined the link between happiness and income, a growing number of studies have begun to scrutinize the relationship between subjective well-being and macroeconomic outcomes. An extensive body of literature has proved a positive impact of economic growth on life satisfaction (Degutis et al., 2010; Diener et al., 2013; Easterlin et al., 2021; Hagerty, 2000; Hagerty & Veenhoven, 2003; Veenhoven & Vergunst, 2014). Some studies have even found a statistically significant and positive impact of income on the subjective well-being of individuals within countries, across countries, and over time (Deaton, 2008; Sacks et al., 2010; Schyns, 2001; Stevenson & Wolfers, 2008). However, there is also a study that has found mixed results: a positive relationship between GDP (Gross Domestic Product) and life satisfaction in Latin America, North Africa, the Middle East, and Eastern Europe, no relationship in East Asia and Western Europe, and a negative relationship in North America, Oceania, and Africa (Opfinger, 2016). The strength of the association between subjective well-being and economic growth has differed by many factors, such as the classification of countries, time, and unit of observation. In addition to economic growth, a myriad of studies have examined the relationship between subjective well-being and other economic performances, such as inflation and unemployment. Several studies have found an inverse association between subjective well-being and unemployment and inflation (DiTella et al., 2001, 2003; Gandelman & Hernandez-Murillo, 2009; Ouardighi & Munier, 2019; Perovic, 2008; Welsch, 2007; Wolfers, 2003). Nonfinancial factors such as demographic and socioeconomic characteristics may also impact individuals’ subjective well-being (Frey & Stutzer, 2002). Several studies have investigated the determinants of subjective well-being at the individual level, including gender (Blanchflower & Oswald, 2004; Blanchflower et al., 2014; DiTella et al., 2001, 2003; Gerdtham & Johannesson, 2001; Hayo, 2007; Perovic, 2008), age (Frey & Stutzer, 2000; Hayo, 2007; Perovic, 2008), marital status (Gerdtham & Johannesson, 2001; Hayo, 2007; Perovic, 2008), income (Bhuiyan & Szulga, 2017; Blanchflower & Oswald, 2004; DiTella et al., 2001, 2003; Gerdtham & Johannesson, 2001; Hayo, 2007), educational attainment level (Gerdtham & Johannesson, 2001; Perovic, 2008), and employment status (Gerdtham & Johannesson, 2001; Namazie & Sanfey, 1999; Perovic, 2008). Furthermore, several recent studies have focused on marital status and gender and examined their contribution to subjective well-being and financial satisfaction. Calasanti et al. (2021) have found that males are likely to report higher financial satisfaction after retirement while females are not, implying gender disparity in post-retirement financial satisfaction. Ngamaba (2017) has suggested that various socio-demographic and political factors such as gender, health status, social activity, and political stability may contribute to subjective well-being in addition to several macroeconomic factors such as economic growth and unemployment although their effects vary by country and region. Kalsi et al. (2022) have argued that paid contribution through full-time employment determines subjective financial satisfaction, suggesting a potential gender disparity in financial satisfaction as males are more likely to work full-time than females. Gholipour et al. (2022) have found that individuals with low income, less education, and those who have full-time and non-government jobs tend to prefer higher economic growth in their nations than other socioeconomic groups. Lee et al., Cogent Economics & Finance (2023), 11: 2287908 https://doi.org/10.1080/23322039.2023.2287908 Page 3 of 25 Following the study of Diener and Diener (1995) that has analyzed the association between life satisfaction and financial well-being, many studies have claimed financial satisfaction as a significant predictor of subjective well-being (Brockmann et al., 2009; Ng, 2015; Ng & Diener, 2014; Ngamaba, 2017). Nonetheless, despite a close relationship between financial satisfaction and macroeconomic outcomes, limited research has used financial satisfaction as an indicator of subjective well-being. Peiro (2006) has examined the relationship between financial satisfaction and socioeconomic conditions, but not with measures of macroeconomic performance. Brown and Gray (2016) have claimed that the financial status of households determines their life satisfaction and financial well-being. However, they have not explored the relationship between financial wellbeing and macroeconomic measures. Easterlin et al. (2010) have examined the relationship between financial satisfaction and the annual growth rate of GDP in 17 Latin American countries but not with other economic measures such as inflation and unemployment. We shed light on the literature on the relationship between financial satisfaction and macroeconomics and examine their associations using three macroeconomic variables: inflation, unemployment, and economic growth. We test the following hypothesis based on a cross-country analysis of financial satisfaction data: Hypothesis 1: The financial satisfaction score of individuals is positively associated with the economic growth rate of countries and negatively associated with the unemployment and inflation rates of countries. Hypothesis 2: The strength of the association between the financial satisfaction score of individuals and the inflation, unemployment, and economic growth rates of countries varies by demographic and socioeconomic characteristics. As the associations between individuals’ financial satisfaction scores and countries’ macroeconomic performances may vary by individual demographic and socioeconomic characteristics, we conduct heterogeneity tests to identify the specific groups whose financial satisfaction level significantly changes as macroeconomic outcomes change. 3. Data and empirical strategy 3.1. Sample and data This study examines the relationship between financial satisfaction and macroeconomic outcomes. It is imperative to investigate their associations as economic policies aim to mitigate the negative consequences of financial dissatisfaction and promote individuals’ subjective well-being. We use the 2010–2014 and 2017–2022 WVS data. It has been collecting information on financial satisfaction since 1981; however, the countries selected for the survey have significantly varied between waves. If we set the period of our study long, it leaves us with a small number of countries for the analysis. It may cause a bias due to high attrition. It is also challenging to collect valid macroeconomic outcomes covering the long study period, particularly in developing countries. Considering our limitations, this study uses the sixth and seventh waves of the World Values Survey. The sixth and seventh waves comprise the surveys collected from 60 countries in 2010– 2014 (Inglehart et al., 2018) and 64 countries in 2017–2022 (Haerpfer et al., 2020), respectively. Each country has participated once during each survey period. The survey asks various questions related to social values, well-being, social capital, and economic values, in addition to general demographic information. Remarkably, the WVS contains one question related to individual financial satisfaction, and the survey asked, “How satisfied are you with the financial situation of your household?” 1 We use this financial satisfaction question to measure subjective well-being and examine its relationship with macroeconomic performance. Lee et al., Cogent Economics & Finance (2023), 11: 2287908 https://doi.org/10.1080/23322039.2023.2287908 Page 4 of 25 3.2. Measures of variables Previous studies have used various macroeconomic outcomes such as inflation, unemployment, long-term interest rate, GDP per capita, and the Gini coefficient to understand the relationship between subjective well-being and macroeconomics (Blanchflower et al., 2014; DiTella et al., 2001; Perovic, 2008; Sanfey & Teksoz, 2007; Welsch, 2007; Wolfers, 2003). Because the WVS surveys were collected from 60 countries once in 2010–2014 and from 64 countries once in 2017–2022, we match the year of the survey and the year that the macroeconomic statistics were computed. However, some developing countries’ macroeconomic data were unavailable in a particular year. To maximize the sample size, we select three macroeconomic measures that are most available across countries: inflation, unemployment, and real GDP growth rates. We collect these macroeconomic performance data primarily from the International Monetary Fund (IMF, 2023a, 2023b, 2023c). If the macroeconomic data in a particular year or country is unavailable through the IMF, we use the World Bank as a secondary data source (World Bank, 2023a, 2023b, 2023c). In addition to financial satisfaction used as a dependent variable, we select six demographic and socioeconomic factors from the WVS data as control variables: age, gender, educational attainment level, marital status, employment status, and household income level. Marital status is separated into three groups: married, not married but live together, and divorced, separated, widowed, or single. Educational attainment level is grouped into low education (from no education to second secondary education) and high education (from post-secondary education to doctorate). Employment status is categorized into three groups: employed, unemployed, and not in the labor force. Household income level is aggregated into three groups: low-income (from the first decile to the third decile), middle-income (from the fourth decile to the sixth decile), and high-income (from the seventh decile to the tenth decile). Appendix Table A1 lists the dependent, independent, and control variables used for the analysis. It summarizes the questions, responses, and the source of each variable. 3.3. Models and data analysis procedure Modern macroeconomics assumes that the social welfare function is defined based on inflation and unemployment (DiTella et al., 2001). Although the existence of a social welfare function has not been proved, it has mainly been used in the theoretical literature of macroeconomics. Unemployment, inflation, and GDP have been demonstrated to be the main determinants of satisfaction and happiness (Frey & Stutzer, 2002; Perovic, 2008). Unemployment is the most used macroeconomic variable in the study of subjective well-being as it affects both employed and unemployed. The unemployed may fear the possibility of not getting employed. Being unemployed may negatively impact occupational skills, cause social isolation, and eventually lower individual well-being and social welfare (Kassenboehmer & Haisken DeNew, 2009; Paul, 2001; Perovic, 2008). The happiness and life satisfaction of the employed can also be negatively affected by high unemployment. Employed people may feel bad about unemployed people, be anxious about losing their jobs, or dislike taking a significant burden of unemployment taxes (Frey & Stutzer, 2002; Luechinger et al., 2010). High unemployment may cause fear among employed individuals and indirectly lower their subjective well-being (Green et al., 2000; Perovic, 2008). When inflation is very high, it may harm the economy. It could be costly to reduce inflation as it often requires extra unemployment (DiTella et al., 2001). On the other hand, stable and predictable low inflation is not a significant economic problem. However, people’s views on inflation differ from those of economists. According to the study of Shiller (1996), people believe that inflation may lower their standard of living, cause corruption and political violence, create moral issues, and damage national reputation. To consider the different perspectives of economists and the general population, we include inflation as a determinant of subjective well-being. Lee et al., Cogent Economics & Finance (2023), 11: 2287908 https://doi.org/10.1080/23322039.2023.2287908 Page 5 of 25 The positive impact of GDP on subjective well-being has been observed in many countries over time (Degutis et al., 2010; Diener et al., 2013; Hagerty, 2000; Hagerty & Veenhoven, 2003; Veenhoven & Vergunst, 2014). Yet, high income is not the only contributor to higher happiness levels. Other factors may raise the level of happiness along with an increased income, such as health, human rights, freedom, and equality (Perovic, 2008). People may not report a higher level of happiness or satisfaction when their absolute income has increased but when their relative income position has improved (Frey & Stutzer, 2002). To consider the relative income impositions of households, we include three income groups (high, medium, and low-income) as a control variable in addition to the real GDP growth rate. To estimate the association between financial satisfaction and three macroeconomic outcomes, we follow the approach of DiTella et al. (2001): where FS ij is the self-rated level of financial satisfaction of an individual i in country j. Inflation j is the inflation rate of country j, Unemployment j is the unemployment rate of country j, and GDP j is the real GDP growth rate of country j. X ij includes demographic and socioeconomic characteristics of individual i in country j such as age, gender, educational attainment level, marital status, employment status, and household income. ε ij is the error term. α, β, and γ are the coefficients of interest that explain to what extent financial satisfaction is related to inflation, unemployment, and real GDP growth, respectively. These estimates provide the magnitude of the association for each macroeconomic outcome variable. Because the WVS was conducted one time between 2010 and 2014 and another time between 2017 and 2022, we select macroeconomic values computed in the same years that the WVS data were collected. Inflation, unemployment, and economic growth are presumed to be associated. If inflation, unemployment, and economic growth have a significant association, it may distort the results or cause multicollinearity. To address this concern, we have checked the correlation between macroeconomic outcomes variables and found that the correlation coefficients are less than 0.7. Also, there could be a multicollinearity problem as we use three macroeconomic outcomes as explanatory variables. We have employed a variance inflation factor to check the multicollinearity issue. The values between 1 and 2 suggest a moderate correlation but not severe. Our model does not include the county-fixed effect as we use macroeconomic variables that are collected at the country level. However, the standard errors are clustered at the country level, assuming independence across countries but not across individuals within each country. Clustering is designed for the data with many clusters and a relatively small number of observations in each cluster (Leoni, 2005). If the number of observations in each cluster is too large, it can inflate the standard errors and make the estimates severely biased downward (Moulton, 1990; Perovic, 2008). Since we have approximately 60 countries and 800 to 1200 individuals in each cluster in the sample, we present the regression models with and without the cluster options to compare the results. Financial satisfaction measured on a scale of 1 through 10 is a cardinal variable. We employ an ordered probit model to estimate the coefficients, α, β, and γ, in Equation (1). Several scholars have argued that using a cardinal variable as a subjective well-being measure in the ordered logit or probit models or in the OLS regression does not make a statistically significant difference (FerrerI-Carbonell & Frijters, 2004; Welsch, 2007), so we employ OLS to evaluate the robustness of the results. After examining the association between financial satisfaction and macroeconomic performances using the 2010–2014 and 2017–2022 cross-sectional data, we repeat the same analysis Lee et al., Cogent Economics & Finance (2023), 11: 2287908 https://doi.org/10.1080/23322039.2023.2287908 Page 6 of 25 using pooled data from two waves. Following the approach of Perovic (2008), we include the yearfixed effect, δ t , to capture the global changes that might have affected countries in each wave: The association between financial satisfaction and macroeconomic outcomes can be heterogeneous depending on individuals’ social and economic status within a country. We separate the sample by age, gender, household income, marital status, educational attainment level, and employment status and perform heterogeneity checks. We also use the Misery Index, the sum of the annual inflation and unemployment rates created by Arthur Okun in the 1970s, for a sensitivity check. We use STATA for statistical analysis. 4. Results 4.1. Descriptive statistics Table 1 presents descriptive statistics of individuals in each wave. From 2010–2014 to 2017–2022, the financial satisfaction score increased by 0.39 points while all three macroeconomic outcomes declined. The inflation rate decreased by 1.72%, the unemployment rate decreased by 1.21%, and the real GDP growth rate decreased by 0.09%. The real GDP growth rate reports a large standard deviation. It may partly be due to the inclusion of 60 countries with different levels of economic development. We find similar proportions of males and females between the two waves. While the share of married people is about the same, we find increasing shares of people who are not married but live together, divorced, or separated. Regarding the education attainment level, we observe an increase in the shares of people with primary, first-secondary, and second-secondary education. While there is a slight decrease in the share of people with no education, the share of people with doctoral degrees declines substantially. More people tend to work full-time or are self-employed, and fewer people are not in the labor force. While the share of workers in the public sector declines, the share in the private sector inclines simultaneously. We further present the descriptive statistics of financial satisfaction by demographic and socioeconomic characteristics in Table 2. The financial satisfaction scores of males are higher than those of females. Young adults and people in the high-income group report higher scores of financial satisfactions. Married couples tend to be financially more satisfied than people who are unmarried but live together, divorced, separated, widowed, or single. Individuals with high levels of education report higher scores of financial satisfactions. The financial satisfaction scores are about the same between the employed and those who are not in the labor force. 4.2. Association between financial satisfaction and macroeconomic measures Table 3 reports the results of the ordered probit model for the periods of 2010–2014 and 2017– 2022, respectively. From 2010 to 2014, when we cluster the standard errors, financial satisfaction shows a negative association with inflation and a positive association with real GDP growth, as we hypothesized. Without clustering, financial satisfaction consistently shows a statistically significant negative association with inflation and unemployment and a positive association with real GDP growth. For example, when the inflation rate increases by one percentage point, the predicted log odds of reporting a higher financial satisfaction score decrease by 0.022. When the unemployment rate increases by one percentage point, the predicted log-odds of a reporting higher financial satisfaction score decrease by 0.007. When the real GDP growth rate rises by one percentage point, the predicted log odds of reporting a higher financial satisfaction score increase by 0.039. From 2017 to 2022, we find similar results. With clustering standard errors, the results present financial satisfaction’s negative relationship with inflation and unemployment and a positive Lee et al., Cogent Economics & Finance (2023), 11: 2287908 https://doi.org/10.1080/23322039.2023.2287908 Page 7 of 25 less education tends to be affected by changes in macroeconomic performance than those with more education. Numerous studies find married couples as the happiest group (e.g., DiTella et al., 2003), while others find singles as the happiest group (Gerdtham & Johannesson, 2001; Perovic, 2008). We find couples who are not married but live together as the group whose financial satisfaction is more likely to be affected by the changes in macroeconomic measures. As for employment status, several studies suggest that the employed are happier than the unemployed (Gerdtham & Johannesson, 2001; Hayo, 2007; Hayo & Seifert, 2003), while others claim that those who are not in the labor force are happier than the employed people (Namazie & Sanfey, 1999; Perovic, 2008). When we examine the association between financial satisfaction and macroeconomic performances across all demographic and socioeconomic groups, the financial satisfaction of those who are not in the labor force is found to be affected the most by the changes in inflation and unemployment rates compared to employed or unemployed individuals. In contrast, the financial satisfaction of employed and unemployed individuals is more likely to be associated with economic growth than those not in the labor force. To our knowledge, this is the first study that assesses the variation in the strength of the association between financial satisfaction and macroeconomic outcomes by separating the microdata sample based on age, gender, household income, marital status, educational attainment level, and employment status. The overall results suggest potential gender disparity and socioeconomic inequality across countries as the study finds females, older adults, individuals with low income, couples who are not married but live together, individuals with less education, and those who are not in the labor force as the groups whose financial satisfaction are highly associated with macroeconomic performance. Gender and socioeconomic inequality may affect their financial satisfaction, happiness, and well-being and cause adverse environments and violence within society (Coccia, 2018a). Socioeconomic policies should be designed and implemented to promote happiness and well-being, particularly in countries where socioeconomic inequality and gender disparity are high. Furthermore, we should note that various economic, social, political, and cultural factors may play a role in the assessment of the association between financial satisfaction and macroeconomic performance. Particularly, cultural differences are closely related to various factors such as economic factors, religion, and social belongingness. These differences may lead people to evaluate their financial satisfaction and, to a more considerable extent, well-being and life differently (Noguchi, 2022). For instance, the happiness levels of people in economically advantaged societies or countries may not significantly change even with substantial success, such as those in European countries. People in societies and countries where religious faith or social belonging is highly valued may be more satisfied with their lives when they are religious and belong to their community, such as those in Asian and Central American countries (Noguchi, 2022). 6. Conclusion This study considers financial satisfaction as a measure of subjective economic well-being. The overall results imply that inflation, unemployment, and economic growth are significant factors that affect the subjective economic well-being of individuals. Nevertheless, the impact of the changes in macroeconomic performances on financial satisfaction varies by demographic and socioeconomic factors. The findings suggest that policymakers should be aware of the close relationship between people’s financial satisfaction and macroeconomic performance and that the magnitude of its relationship may vary by gender, social, and economic factors. Recognizing potential gender and socioeconomic disparities, targeted public policies can be formulated to promote subjective well-being. Tailored interventions for vulnerable groups, such as females, older adults, low-income households, people who are not married but live together, loweducation, and those who are not in the labor force can be effective in mitigating the effects of economic fluctuations on socioeconomic inequality and enhancing people’s financial well-being. There are a few caveats to consider when interpreting the results. WVS conducts the survey approximately every five years. We have found inconsistency in some questionnaires between the waves. Many countries participated in one wave but did not participate in the following wave. Lee et al., Cogent Economics & Finance (2023), 11: 2287908 https://doi.org/10.1080/23322039.2023.2287908 Page 14 of 25 Getting reliable macroeconomic data, particularly for low-income countries, has also been demanding. We use the two most recent waves to maintain consistency and sufficient sample size and run the regression using cross-sectional data. It limits us from exploring other dynamic regression analysis that needs multiple waves. Therefore, all the estimates presented in this study should be cautiously interpreted as they may not fully address all the potential issues that dynamic models may address with longitudinal data. When a new WVS dataset is available, we may use three waves and employ the dynamic model to consider the lagged effects. We may also explore other datasets that contain various subjective well-being measures, including financial satisfaction and reliable macroeconomic data, to check the consistency. We assess various socioeconomic factors that may drive financial satisfaction. However, other factors, such as health status, freedom of choice, and political stability, could be closely related to financial satisfaction but have yet to be tested. In future research, we will consider these factors and examine if they improve the financial satisfaction of individuals. Author details Baeyong Lee 1 Hoolda Kim 1 E-mail: [email protected] ORCID ID: http://orcid.org/0000-0001-8651-3728 Assad Tavakoli 1 1 College of Business and Economics, Fayetteville State University, Fayetteville, NC, USA. Disclosure statement No potential conflict of interest was reported by the author(s). Data availability statement The data that support the findings of this study are available from the corresponding author, Hoolda Kim ([email protected]), upon reasonable request. Citation information Cite this article as: The impact of economic growth, inflation and unemployment on subjective financial satisfaction: A New global evidence, Baeyong Lee, Hoolda Kim & Assad Tavakoli, Cogent Economics & Finance (2023), 11: 2287908. Note 1. It is measured on a scale of 0–10: (1-completely dissatisfied, 10-completely satisfied). References Ackerman, N., & Paolucci, B. 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Annual percentage change Percent IMF/World Bank Unemployment Unemployment rate, percent Percent IMF/World Bank Real GDP growth Real GDP growth, annual percentage change Percent IMF/World Bank Age This means you are _______ years old Age in two digits WVS Gender Respondent’s sex 1 - Male 2 - Female WVS Education What is the highest educational level that you have attained? 0 - Early childhood education 1 - Primary education 2 - Lower secondary education 3 - Upper secondary education 4 - Post-secondary nontertiary education 5 - Short-cycle tertiary education 6 - Bachelor or equivalent 7 - Master or equivalent 8 - Doctoral or equivalent WVS Martial status Are you currently 1 - Married 2 - Living together as married 3 - Divorced 4 - Separated 5 - Widowed 6 - Single WVS Employment status Are you employed now or not? If yes, about how many hours a week? If more than one job: only for the main job 1 - Full time employee 2 - Part time employee 3 - Self employed 4 - Retired/pensioned 5 - Housewife not otherwise employed 6 - Student 7 - Unemployed WVS Household income level On this card is an income scale on which 1 indicates the lowest income group and 10 the highest income group in your country. We would like to know in what group your household is. Please, specify the appropriate number, counting all wages, salaries, pensions and other incomes that come in. 1 - Lowest group 10 - Highest group WVS Lee et al., Cogent Economics & Finance (2023), 11: 2287908 https://doi.org/10.1080/23322039.2023.2287908 Page 19 of 25 Table A2. Relation between financial satisfaction and macroeconomic measures by gender Dependent variable Financial satisfaction Year 2010–2014 2017–2022 2010–2022 Panel A: Males Inflation −0.023*** −0.021*** −0.024*** (0.002) (0.002) (0.001) Unemployment −0.009*** −0.022*** −0.015*** (0.001) (0.001) (0.001) Real GDP growth 0.042*** 0.029*** 0.035*** (0.002) (0.002) (0.001) Number of observations 40,164 38,897 79,061 Panel B: Females Inflation −0.022*** −0.029*** −0.027*** (0.002) (0.002) (0.001) Unemployment −0.006*** −0.022*** −0.014*** (0.001) (0.001) (0.001) Real GDP growth 0.037*** 0.029*** 0.031*** (0.002) (0.001) (0.001) Number of observations 43,628 43,139 86,767 *, **, *** indicate statistical significance at the 0.10, 0.05, and 0.01 levels, respectively. Note: Standard errors are in parentheses. Lee et al., Cogent Economics & Finance (2023), 11: 2287908 https://doi.org/10.1080/23322039.2023.2287908 Page 20 of 25 Table A3. Relation between financial satisfaction and macroeconomic measures by age group Dependent variable Financial satisfaction Year 2010–2014 2017–2022 2010–2022 Panel A: Age 16–29 Inflation −0.017*** −0.029*** −0.023*** (0.002) (0.003) (0.002) Unemployment −0.003* −0.011*** −0.007*** (0.001) (0.002) (0.001) Real GDP growth 0.039*** 0.024*** 0.031*** (0.002) (0.002) (0.001) Number of observations 24,097 19,755 43,852 Panel B: Age 30–49 Inflation −0.018*** −0.021*** −0.022*** (0.002) (0.002) (0.001) Unemployment −0.004*** −0.019*** −0.011*** (0.001) (0.001) (0.001) Real GDP growth 0.041*** 0.031*** 0.034*** (0.002) (0.002) (0.001) Number of observations 33,100 32,956 66,056 Panel C: Age 50 or above Inflation −0.034*** −0.029*** −0.035*** (0.002) (0.003) (0.002) Unemployment −0.016*** −0.031*** −0.023*** (0.002) (0.002) (0.001) Real GDP growth 0.039*** 0.034*** 0.035*** (0.002) (0.002) (0.001) Number of observations 26,595 29,325 55,920 *, **, *** indicate statistical significance at the 0.10, 0.05, and 0.01 levels, respectively. Note: Standard errors are in parentheses. Lee et al., Cogent Economics & Finance (2023), 11: 2287908 https://doi.org/10.1080/23322039.2023.2287908 Page 21 of 25 Table A4. Relation between financial satisfaction and macroeconomic measures by household income Dependent variable Financial satisfaction Year 2010–2014 2017–2022 2010–2022 Panel A: Low-income households Inflation −0.035*** −0.012*** −0.028*** (0.002) (0.004) (0.002) Unemployment −0.027*** −0.026*** −0.025*** (0.002) (0.002) (0.001) Real GDP growth 0.037*** 0.037*** 0.037*** (0.002) (0.002) (0.001) Number of observations 22,900 20,095 42,995 Panel B: Middle-income households Inflation −0.024*** −0.022*** −0.026*** (0.002) (0.002) (0.001) Unemployment −0.006*** −0.022*** −0.013*** (0.001) (0.001) (0.001) Real GDP growth 0.037*** 0.036*** 0.035*** (0.002) (0.001) (0.001) Number of observations 42,363 43,791 86,154 Panel C: High-income households Inflation −0.006** −0.040*** −0.024*** (0.002) (0.003) (0.002) Unemployment 0.012*** −0.015*** −0.000 (0.002) (0.002) (0.001) Real GDP growth 0.042*** 0.006*** 0.020*** (0.003) (0.002) (0.002) Number of observations 18,529 18,150 36,679 *, **, *** indicate statistical significance at the 0.10, 0.05, and 0.01 levels, respectively. Note: Standard errors are in parentheses. Lee et al., Cogent Economics & Finance (2023), 11: 2287908 https://doi.org/10.1080/23322039.2023.2287908 Page 22 of 25 Table A5. Relation between financial satisfaction and macroeconomic measures by marital status Dependent variable Financial satisfaction Year 2010–2014 2017–2022 2010–2022 Panel A: Married Inflation −0.022*** −0.022*** −0.024*** (0.001) (0.002) (0.001) Unemployment −0.011*** −0.028*** −0.019*** (0.001) (0.001) (0.001) Real GDP growth 0.034*** 0.031*** 0.031*** I (0.001) (0.001) (0.001) Number of observations 47,286 46,455 93,741 Panel B: Not married but live together Inflation −0.032*** −0.099*** −0.046*** (0.006) (0.012) (0.006) Unemployment −0.003 0.010** −0.000 (0.003) (0.005) (0.002) Real GDP growth 0.053*** 0.011 0.045*** (0.005) (0.009) (0.004) Number of observations 5,591 6,402 11,993 Panel C: Divorced, separated, widowed, or single Inflation −0.023*** −0.026*** −0.026*** (0.002) (0.003) (0.001) Unemployment −0.004*** −0.015*** −0.009*** (0.001) (0.002) (0.001) Real GDP growth 0.043*** 0.025*** 0.034*** (0.002) (0.002) (0.001) Number of observations 30,915 29,179 60,094 *, **, *** indicate statistical significance at the 0.10, 0.05, and 0.01 levels, respectively. Note: Standard errors are in parentheses. Lee et al., Cogent Economics & Finance (2023), 11: 2287908 https://doi.org/10.1080/23322039.2023.2287908 Page 23 of 25