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Article

The Impact of Uncertain Welfare Quality on Equity Market Performance

by
Tarek Eldomiaty
1,*,
Islam Azzam
1,
Hoda El Kolaly
1,
Nermeen Youssef
2,
Marwa Anwar Sedik
3 and
Rehab ElShahawy
4
1
Onsi Sawiris School of Business, The American University in Cairo, AUC Avenue, P.O. Box 74, New Cairo 11835, Egypt
2
Faculty of Business, Economics and Political Science, The British University in Egypt, P.O. Box 43, Cairo 11837, Egypt
3
Faculty of Business Administration & International Trade, Misr International University, Al Obour 19648, Egypt
4
School of Business Administration, Canadian International College in Cairo, CIC Avenue, P.O. Box 59, New Cairo 11241, Egypt
*
Author to whom correspondence should be addressed.
Int. J. Financial Stud. 2025, 13(2), 67; https://doi.org/10.3390/ijfs13020067
Submission received: 6 March 2025 / Revised: 29 March 2025 / Accepted: 10 April 2025 / Published: 15 April 2025
Browse Figures
Versions Notes

Abstract

:
Welfare quality is usually a stochastic outcome, as attempts at improving social welfare cannot be predicted in advance. The advances in stock market participation conclude that equity market performance is able to reflect investors’ mass reactions and therefore can fairly reflect the empiricism of welfare quality. In this paper, the pillars of the Happy Planet Index (hereinafter HPI) are used as proxies for countries’ welfare quality. The data cover 57 countries where equity markets exist over the annual period of 2006–2020. The results indicate that (a) the three pillars of HPIs have historical positive impacts on market capitalization and stock turnover; (b) stochastically, life satisfaction has an expected positive impact on market capitalization and stock turnover; (c) firms located in high (low) HPIs, life satisfaction, and life expectancy have significant (insignificant) stochastic impacts on market capitalization; and (d) the historical ecological footprints have positive impacts on market capitalization and stock turnover, whereas stochastic impacts are statistically insignificant.

1. Introduction

Uncertainty is inherent in welfare quality as long as institutionalists’ initiatives and responses from individuals cannot be predicted in advance. This paper benefits from the research strand, which concludes that economic development and welfare quality must be inextricably associated (Kaldor, 1939; Hicks, 1939; Little, 1957; Atkinson, 1999; Dang, 2014; Lall, 1981; Sen, 1988, 1990; Basu et al., 2022; Nayak & Pradhan, 2024). This paper extends the contribution of theories of welfare quality to equity markets (Evans & Hnatkovska, 2007; Gromb & Vayanos, 2010; Molinari, 2014; Galina et al., 2015; Nazir et al., 2010; Wurgler, 2000) and further considers the empiricism of stochastic aspects of welfare quality (Le Breton, 1991; Bouacida & Martin, 2021). The Happy Planet Index (HPI), developed by the New Economic Foundation (2016), is the first composite indicator that includes a measure of ecological efficiency along with human well-being (Marks et al., 2006; Abdallah et al., 2009, 2012).

The Trends of Countries’ Welfare Uncertainty

The authors address the uncertainty of welfare quality by running Monte Carlo stochastic simulations for the three pillars of the HPI. The skewness of HPIs measures the potential of welfare quality. The skewness of a historical pillar is arranged in an ascending order and depicted against the associated Monte Carlo stochastic (using empirical mean and volatility) simulated estimates across the three respective pillars. Figure 1, Figure 2, Figure 3 and Figure 4 show the skewness of the historical and stochastic scores of the three pillars of the HPI across 57 countries.
Figure 1, Figure 2 and Figure 3 show the interesting potential of welfare quality. In most countries, the negative skewness of a historical pillar is associated with an expected positive skewness reflecting the potential for improvement. The opposite is also true, highlighting a plausible motivation for further examination. These results reflect the realities of countries’ welfare. That is, it is fairly argued that countries are trying to improve declining welfare, but countries associated with higher welfare quality may experience an expected decline. This argument can be further supported by depicting the historical and stochastic skewness of the aggregate HPI ranks following the same procedures. This is shown in Figure 4.
This paper aims to compare the effects of historical as well as expected (stochastic) HPI pillars on widely known indicators of equity market performance, namely, the equity market risk premium, equity market capitalization as a percentage of GDP, and the market capitalization of the total number of listed domestic companies’ value of stocks traded as a percentage of GDP and total listed domestic companies.
This paper contributes to the existing literature in terms of dealing with the impacts of HPI stochastically as far as institutional uncertainty is considered. That is, it is plausible to assume that past institutional efforts are subject to change as priorities of institutional changes are not known in advance. Therefore, the stochastic treatment of the components of HPI offers forward directions to policymakers.
The paper is organized as follows. Section 2 compares the effects of rational versus behavioral effects on the performance of financial markets. Section 3 discusses the effects of welfare dimensions on financial markets and develops relevant hypotheses. Section 4 discusses the common indicators of equity market performance. Section 5 describes the data and the variables. Section 6 discusses the results for the historical and stochastic effects of the three pillars of the HPI on four indicators of equity market performance. Section 7 concludes.

2. Investors’ Sentiment and Stock Returns

A coherent justification of the factors driving stock price movements has been one of the most sophisticated obstacles facing financial practitioners. The classic finance theory postulates that investors are rational and that their interests are homogenous, which is referred to as the “Efficient Market Hypothesis”. Therefore, there will be no role of investor sentiment (Sharpe, 1964; Lintner, 1965). However, the critics of classic finance theory created a behavioral paradigm that advocates investor sentiment in terms of optimistic or pessimistic expectations about asset prices (Keynes, 1936). De Long et al. (1990) and Akerlof and Shiller (2009) extended this paradigm to investor irrationality (noise trading), arguing that the instability of asset prices is due to behavior such as animal spirit. Kahneman and Tversky (1972) and Barberis et al. (2001) stated that human behavior in prospect theory is more strongly associated with loss than with gain, hence surplus volatility. Nevertheless, Lee et al. (1991) and Baker and Wurgler (2006) conclude that investors’ optimism is positively related to the returns of the stocks that are held by noisy traders.
The abovementioned attempts have evolved to focus on the role of investor sentiment. Merkle and Egan (2015) offer an early attempt at the effect of welfare and equity returns, concluding that return variability for happy investors is due to overconfidence and financial literacy. S. H. Kim and Kim (2014) and Aggarwal et al. (2019) conclude that gross national happiness statistically predicts fluctuations in equity returns and trading volume.

3. Welfare Dimensions of Financial Markets

Loewenstein and Small (2007) and Rick and Loewenstein (2008) examine the impact of emotions on economic behavior. The authors of the present paper argue that investors who live in a quality environment are able to exercise expected and immediate emotions in an observed way. Risius et al. (2015), Zhang et al. (2016), and Porshnev et al. (2016) reported that negative emotions such as fear, anger, and depression have a greater explanatory effect on the volatility of stock prices. As far as uncertainty surrounds public decisions, the authors argue that further examinations of the quality of a country’s welfare help reduce potential uncertainty (Mandler, 2014). Sgroi et al. (2017) examined the institutional perspective of well-being and happiness and noted that there is no direct relationship between governments’ aims and individual happiness. Happiness is not precisely well defined, as Easterlin (2003) considers variables such as happiness, well-being, welfare, utility, and life satisfaction interchangeable. Deaton (2008) concludes that wealth has a positive relationship with well-being, as high-income countries have greater life satisfaction and degree of economic security than do low-income countries. Moreover, Smales (2014) confirms that US household well-being is significantly correlated with variations in equity markets in the short term. Larson and Wilford (1979) and Van Hoorn (2008) find that leisure has a greater direct impact on happiness than does GDP per capita. Deaton (2012) examines the association between well-being and the movement of stock prices and concludes that many Americans do not have financial interest in the equity market. Accordingly, testable hypotheses can be developed as follows.
H1. 
A significant relationship exists between the pillars of HPIs and the equity market capitalization of listed domestic companies as a percentage of GDP.
H2. 
A significant relationship exists between pillars of HPIs and the value of equity market capitalization of listed domestic companies (in current USD).
H3. 
A significant relationship exists between pillars of HPIs and the total value of traded stocks as a percentage of GDP.
In terms of life expectancy, Boersch-Supan and Winter (2001), Liu and Spiegel (2011), and Fellowes (2016) conclude that aging societies play an important role in changing people’s saving behavior and portfolio structure. Choi (2017), Oyinlola and Adedeji (2019), and Harris (2010) conclude that when the proportion of active people in aging societies decreases, the decline in productivity results in decreases in the expected financial return. Delis and Mylonidis (2015) conclude that the effect of happiness is inversely related to the investment decisions of households, such as investment in the stock market or buying insurance. Engelberg and Parsons (2016) reported that there is a significant negative relationship between hospitalization rates and daily stock returns. However, Acemoglu and Johnson (2007) claim that there is no significant correlation between life expectancy and economic growth, although this finding aligns with the universal effort to improve poor health states in less developed countries, which is recognized as a significant reason for poverty.
According to the New Economic Foundation (2016), the ecological footprint is associated with human demand for ecological resources. In this sense, Costanza et al. (2014) and Stubbs et al. (2013) conclude that natural capital, which is usually associated with ecosystem services, plays an important role in human well-being and offers support for the establishment of sustainable stock markets for funding sustainability. Therefore, the implementation of sustainability indices plays an essential role in sustainable practices and financial market development, which relies on a country’s sustainable ecosystem (Vives & Wadhwa, 2012). Accordingly, a testable hypothesis can be developed as follows.
H4. 
A significant relationship exists between the number of pillars of HPIs and the number of listed domestic companies.

4. Indicators of Equity Market Performance

The quality of a country’s welfare is usually linked to equity market performance, as the latter offers financing for economic growth in both developing and developed countries (Ho & Lyke, 2017; Murphy, 2016). Accordingly, a testable hypothesis can be developed as follows.
H5. 
A significant relationship exists between the pillars of HPIs and the turnover ratio of domestic shares.
Moreover, Salomons and Grootveld (2003) and S. W. Kim and Lee (2008) conclude that, compared with developed countries, emerging countries are usually associated with a higher equity risk premium. Accordingly, a testable hypothesis can be stated as follows.
H6. 
A significant relationship exists between the pillars of HPIs and the equity market risk premium.

5. Data and Variables

5.1. Data

The data include 57 countries that are listed in the HPI and have an active trading equity market. The annual data covers the years 2006–2020. The descriptive statistics are reported in Appendix A.

5.2. Dependent Variables

The dependent variables include various equity market performance indicators, namely, the equity market risk premium, the market capitalization of listed domestic companies as a percentage of GDP, the market capitalization of listed domestic companies (in current USD), the total value of stocks as a percentage of GDP, and the turnover ratio of domestic stocks (Pagano, 1993; Demirguc-Kunt & Levine, 1996; Levine & Zervos, 1998; Garcia & Liu, 1999; Thorsten & Levine, 2003; Seetanah & Ramessur, 2008; Barajas et al., 2013). The data are available from the World Bank database (https://data.worldbank.org/indicator/CM.MKT.LCAP.CD, accessed on 1 August 2024).

5.3. Independent Variables

The independent variables include the three pillars of HPIs, namely, life satisfaction (or well-being), life expectancy, and the ecological footprint (https://happyplanetindex.org, accessed on 18 January 2023). The HPIs imply elements of sustainability that offer extended benefits to further understand the economic behavior of equity markets (Bondarchika et al., 2016; Robinson, 2019; Brulé, 2022). Although Benjamin et al. (2023) raised concerns about the use of happiness data (self-reported well-being, SWB) drawn from the USA only, which holds credit for itself, the HPI actually offers fair elements of construct validity (Nunnally & Bernstein, 1994), as coverage of 57 economies is included in this paper. Larger populations also offer further construct validity (Smith et al., 2009; Straussi & Smithii, 2009).
A roadmap of the methodology can be described as follows. First, a quantification of welfare quality is to be developed and tested statistically. The objective is to make sure that different classes of quality are distinct, thus offering content validity of the statistical estimates. Second, a comparative analysis is carried out between the observed (or historical) and stochastic impacts of HPIs on different equity market indicators. The objective is to compare the impacts of historical (or observed) and stochastic (or expected) institutional efforts impeded in HPIs.

6. Results and Discussion

As quality is a qualitative aspect, it requires a differentiation between different classes of quality. Accordingly, welfare quality requires the classification of HPIs into classes, namely low, medium, and high. In so doing, the authors arranged the HPI data in ascending order and then divided it into quartiles. The 1st quartile refers to a low HPI, the 2nd and 3rd quartiles refer to a medium HPI, and the 4th quartile refers to a high HPI. To that end, the three classes of welfare quality must be tested to ensure they are independent and distinct. The authors use two relevant non-parametric tests, namely the Kruskal and Wallis (1952) test to compare medians and the Levene test (Gastwirth et al., 2009; Levene, 1960) to compare variances of the three levels of HPI ranks. The results are reported in Table 1.
The results in Table 1 show that the three levels of HPI differ significantly, thus reflecting the information content of welfare quality.
As the main objective of this paper is to examine the uncertainty of welfare quality, the validity of the results requires a comparison with the effects of the historical welfare quality. Therefore, two separate analyses are carried out. The first (second) analysis examines the historical (uncertain using Monte Carlo simulated estimates) effects of the three pillars of HPI on different indicators of equity market performance. Table 2 reports the results of the historical effects of welfare quality on equity market performance.

6.1. Effects of Welfare Quality on Market Capitalization as a Percentage of GDP

In Table 2, the positive relationship with life satisfaction indicates that the elements of a satisfactory social life motivate stockholders to trade stocks; thus, market capitalization appreciates (Bernheim & Rangel, 2009; Bernheim, 2009; Hong et al., 2004; Brown & Taylor, 2010; Liang & Guo, 2015). The mechanism of life satisfaction and stock trading is illustrated through the role of mood in investment decision-making, being an integral component of the psychology of investing (Kahneman & Tversky, 1973; Kahneman & Riepe, 1998; Shleifer, 2000; Isen, 2000). That is, a satisfactory life is associated with a better mood, which motivates stock trading with higher expected prices; thus, market capitalization improves.
The same is true in the case of life expectancy. It seems that increases in life expectancy call investors for active trading that promotes greater market capitalization (Blau, 2018). A number of studies in the literature argue that the volatility of life expectancy is significantly associated with longevity risk, which reflects changes in individuals’ consumption, investment decisions, and volatility of the stock market in general.
Notably, the inclusion of low and high HPI ranks serves as an indication of the robustness of the estimates of the index pillars. The results show that the negative association with a high HPI rank supports the abovementioned argument that shareholders located in high HPIs are not concerned with high stock prices or thus high market capitalization, as they are concerned with high welfare quality (Siganos et al., 2017). Notably, the contribution of life expectancy ( R ¯ 2 = 0.1429) is greater than the contribution of the ecological footprint ( R ¯ 2 = 0.0422). It is quite apparent that shareholders’ sentiments matter.

6.2. Effects of Welfare Quality on the Market Capitalization of Listed Domestic Companies

The results show that the ecological footprint and market capitalization of listed domestic companies are positively associated. A plausible interpretation is that the development of an equity market that includes sustainability-oriented firms plays an effective role in capital allocation (D. Kim et al., 2019). In addition, the positive result for the high HPI rank and market capitalization of listed domestic companies is a significant indication of investor sentiment (Blau, 2018).

6.3. Effects of Welfare Quality on the Turnover Ratio of Domestic Stocks

The results for life expectancy show a negative association with the turnover ratio of domestic stocks. This result indicates that, for example, low life expectancy motivates individual investors to be involved in more trading. That is, shareholders’ consumption significantly influences equity market liquidity (Liu & Spiegel, 2011).

6.4. Expected Welfare Quality and Equity Market Performance: Implications from Stochastic Simulation

The three pillars of HPIs offer a very relevant chance to examine their expected role as indicators of welfare quality. As the measurement of each component reflects countries’ institutional efforts to improve quality of life, examining the expected outcomes of improvements in welfare quality remains relevant (Feduzi & Runde, 2011). To that end, the authors run a stochastic Monte Carlo simulation on the three pillars to produce the expected stochastic values. Therefore, the regression of the stochastic values on the same equity market indicators as reported in Table 2 produces the effects of the expected changes in HPIs on equity market indicators. The results are reported in Table 3.
The results in Table 3 offer significant insights into the effects of the HPI pillars. The results show that the stochastic and expected effects of “life expectancy” and “ecological footprint” are not statistically significant. Nevertheless, the historical and stochastic effects of “Life Satisfaction” are positive and significant, reflecting the underlying effects of welfare quality. That is, the more that investors enjoy welfare quality, the greater their willingness to participate in equity trading (Orlitzky, 2005). As Bramoullé and Treich (2009) reported a negative relationship between uncertainty and emissions (as an indicator of the ecological footprint), the results of the present study suggest that the expected effect is statistically insignificant. This result offers a significant call for public policymakers to improve elements of life satisfaction, which eventually leads to significant progress in equity market participation in aggregate financing.

7. Conclusions

This paper offers further insights into institutional uncertainty as efforts for improving the three pillars of HPI remain usually expected and uncertain. Therefore, the relevant treatment being carried out in this paper compares the impacts of historical and stochastic simulation of the impacts of HPI on equity market development indicators.
The methodology examines the cross-country associations between HPI composite indicators and equity market performance indicators across 57 countries covering the annual period from 2006 to 2020. The pillars of HPIs are treated in this paper as measures of a country’s welfare. The findings of this paper address the behavioral and institutional dimensions of equity market performance and development. These two dimensions can be summarized as follows.

7.1. The Behavioral Perspectives of Equity Market Development

This dimension refers to the effects of welfare quality on aspects of equity market development. The overall results show that a country’s rank is significantly related to investors’ sentiment. That is, progress in life satisfaction leads to increases in equity market participation, thus increasing market capitalization.

7.2. Institutional Perspectives on Equity Market Development

The aggregate results identify the determinants of stock market participation. The improvement in equity holders’ life satisfaction tends to widen the pool of equity market participants. The results of this paper offer an extended call for policymakers to consider improvement in the quality of social life as a determinant of the size of the equity market, which is an ultimate objective for any country apart from being developed or developing. This conclusion parallels the conclusions reached by Angeletos and Pavan (2009) and Karp and Traeger (2024) that policymakers must have robust knowledge about the significant factors that enhance welfare quality. Given that firms may consider the ecological footprint when making investment decisions, the stochastic results show that the ecological footprint may not have a statistically significant impact. Equity holders with low HPIs are not heavily involved in the protection of the environment or well-being.
The historically significant (and stochastic simulated insignificant) impacts of ecological footprints on market capitalization and stock turnover offer insightful institutional perspectives. That is, policymakers must exert continuous efforts supporting the ecological impact on financial investment.

Author Contributions

Conceptualization, T.E. and I.A.; methodology, T.E.; software, H.E.K.; validation, M.A.S., R.E. and N.Y.; formal analysis, T.E.; investigation, H.E.K.; resources, N.Y.; data curation, T.E.; writing—original draft preparation, I.A.; writing—review and editing, T.E.; visualization, H.E.K.; supervision, T.E.; project administration, T.E.; funding acquisition, T.E., I.A., H.E.K., N.Y., M.A.S. and R.E. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data of this study has been collected from Bloomberg Finance (https://www.bloomberg.com), Google Finance (https://www.google.com/finance), EDGAR (https://www.sec.gov/edgar.shtml), the Happy Planet Index (https://happyplanetindex.org) and the World Bank (https://www.worldbank.org) (all accessed on 1 August 2024). The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

The authors acknowledge the comments received from the participants of the Brown Bag seminar at the American University in Cairo. The authors extend sincere appreciation to the editor of IJFS and two anonymous reviewers for insightful comments and understanding without which advances in welfare quality could have been very limited.

Conflicts of Interest

All the authors of this manuscript declare that they have no conflicts of interest. The authors confirm that there are no relevant financial or non-financial competing interests to report.

Appendix A

Table A1. Descriptive Statistics.
Table A1. Descriptive Statistics.
Market Capitalization as a Percentage of GDPMarket Capitalization of Listed Domestic Companies (ln)Y6 = Turnover Ratio of Domestic StocksLn Life Satisfaction ScoreLn Life Expectancy ScoreLn Ecological Footprint ScoreAfricaMiddle East & North AfricaCaribbean & West PacificWestern WorldCentral & South AmericaAsiaFormer Communist CountriesSub-Saharan Africa
Mean1.44525.73250.9271.8314.3101.1400.0090.1110.1110.3160.0310.0360.1600.027
Standard Error0.3180.0641.9330.0060.0030.0220.0030.0110.0110.0160.0060.0060.0120.005
Mode0.03821.11560.7001.9324.2970.18200000000
Kurtosis120.933−0.33212.564−0.1907.423−0.268109.94.2454.245−1.37427.81623.7121.50033.289
Skewness10.038−0.2603.072−0.648−2.322−0.76910.532.4922.4920.7995.4385.0501.8675.916
Range124.2578.506348.2080.8590.6552.78511111111
Minimum−2.92621.1151.8031.2813.770−0.55400000000
Maximum121.33029.622350.0112.1404.4262.23111111111
Count869869869869869869869869869869869869869869

Appendix B

Table A2. Results for the Augmented Dickey–Fuller Algorithm.
Table A2. Results for the Augmented Dickey–Fuller Algorithm.
Market Capitalization as a Percentage of GDPLn Market Capitalization of Listed Domestic CompaniesTurnover Ratio of Domestic Stocks
F stat (MacKinnon one-sided p values)167.4 ***137.9 ***485.6 ***
*** Significant at the 1% level.

Appendix C

Table A3. Results of the linearity test (RESET).
Table A3. Results of the linearity test (RESET).
Stock Market Performance IndicatorsTest Statistic (Significance)
Market capitalization of listed domestic companies (% of GDP)F (3, 793) = 2.8538 **
LN Market capitalization of listed domestic companies (current USD)F (3, 793) = 5.5654 ***
Turnover Ratio of Domestic StocksF (3, 793) = 4.7783 ***
** Significant at the 5% level; *** Significant at the 1% level.

Appendix D

Table A4. The Results for the Breusch–Pagan–Godfrey Test.
Table A4. The Results for the Breusch–Pagan–Godfrey Test.
Stock Market Performance IndicatorsMarket Capitalization as a Percentage of GDPLN Market Capitalization of Listed Domestic CompaniesTurnover Ratio of Domestic Stocks
Test Statistic (significance) χ 2 (3) = 0.9851 χ 2 (3) = 4.157 χ 2 (3) = 5.83

Appendix E

Table A5. The Results for Testing Fixed and Random Effects (Hausman Test).
Table A5. The Results for Testing Fixed and Random Effects (Hausman Test).
Stock Market Performance IndicatorsTest Statistic (Significance)
Market capitalization of listed domestic companies (% of GDP) χ 2 (3) = 8.32446 **
LN Market capitalization of listed domestic companies (current USD) χ 2 (3) = 9.66267 **
Turnover Ratio of Domestic Stocks χ 2 (3) = 9.76335 **
** Significant at 5%.

Appendix F

Table A6. Results of the Endogeneity Test.
Table A6. Results of the Endogeneity Test.
Pillars of Happy Planet IndexMarket Capitalization as a Percentage of GDPLN Market Capitalization of Listed Domestic CompaniesTurnover Ratio of Domestic Stocks
Ln Life Satisfaction score−2.3766 **−0.67662.55911 **
Ln Life Expectancy score0.5119 *2.13256 **−1.2289
Ln Ecological Footprint score1.196021.268952.438 **
** Significant at 5%, * Significant at 10%.

Note

1
It is worth noting that two indicators of equity market development turn out to be impractical in this paper as they are insignificant statistically across the three pillars of HPI. The two indicators are (1) Total Value of Traded Stocks as percentage of GDP and (2) Total Number of Listed Domestic Companies

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Figure 1. Fluctuations in Historical versus Stochastic Life Satisfaction across 57 Countries.
Figure 1. Fluctuations in Historical versus Stochastic Life Satisfaction across 57 Countries.
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Figure 2. Fluctuations in Historical versus Stochastic Life Expectancy across 57 Countries.
Figure 2. Fluctuations in Historical versus Stochastic Life Expectancy across 57 Countries.
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Figure 3. Fluctuations in the historical versus stochastic Ecological Footprints across 57 countries.
Figure 3. Fluctuations in the historical versus stochastic Ecological Footprints across 57 countries.
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Figure 4. The Skewness of Historical versus Stochastic HPI Ranks.
Figure 4. The Skewness of Historical versus Stochastic HPI Ranks.
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Table 1. Significance of the Differences between HPI Ranks.
Table 1. Significance of the Differences between HPI Ranks.
TestTest Value
Levene’s Test for Equality of Variances(F = 81.57); p value = 0.00
Kruskal–Wallis Test for equality of MediansChi-Square, df (153.9, 2); p Value = 0.00
Table 2. Historical Effects of HPIs on Equity Market Indicators1.
Table 2. Historical Effects of HPIs on Equity Market Indicators1.
VariablesLife SatisfactionLife ExpectancyEcological Footprint
Y1Y2Y3Y1Y2Y3Y1Y2Y3
Constant−0.035
(−2.7) ***		−0.007
(−0.371)		−0.025
(−1.38)		0.43
(1.1)		−0.041
(−0.92)		1.012
(0.972)		0.445
(1.51)		0.003
(0.171)		−0.27
(−0.32)
Ln Life Satisfaction score0.589
(2.533) **		−0.463
(−1.43)		0.296
(1.991) **
Ln Life Expectancy score 0.1326
(1.911) **		3.007
(1.226)		−0.66
(−2.39) ***
Ln Ecological Footprint score 0.217
(2.937) **		0.183
(2.698) **		7.27
(0.69)
Low HPI (Dummy)−0.036
(−2.96) **		−0.0014
(−0.047)		−0.021
(−2.06) **		0.3799
(0.53)		0.0075
(0.405)		2.784
(1.153)		−0.401
(−2.63) **		−0.016
(−0.46)		0.413
(0.191)
High HPI (Dummy)0.029
(0.643)		0.125
(2.78) ***		0.037
(0.79)		−0.154
(−2.75) ***		0.101
(2.27) **		−1.38
(−0.64)		−0.169
(−0.85)		0.111
(2.65) **		−1.852
(−0.834)
Regions (Dummies)YesYesYesYesYesYesYesYesYes
Time Effect (Dummy)YesYesYesYesYesYesYesYesYes
N812812812812812812812812812
R ¯ 2 0.1350.1150.1430.14290.33340.1530.04220.13320.14
S.E. of regression0.21110.23930.21023.4540.241416.9923.4500.23716.59
J-statistic (Prob.)8.72
(0.068)		6.449
(0.167)		9.548
(0.048)		9.8849
(0.0424)		4.622
(0.3282)		13.697
(0.0083)		1.269
(0.866)		15.88
(0.0071)		6.884
(0.195)
Durbin-Watson stat2.31021.9492.4712.0031.83682.9421.9971.9292.968
Notes: Y1 = Market capitalization as a percentage of GDP; Y2 = Market capitalization of listed domestic companies; Y3 = Turnover ratio of domestic stocks. The estimation GMM equation includes lagged (first difference) fixed effects and nonlinear explanatory random variables and dummy variables that follow. y tk = α k + ∑ i = 1 k β ik ( ln D X itk ) + ∑ i = 1 k β ik Z itk + λ k + υ tk , where t = 1, …, n; k = number of countries in each group; y tk = equity market performance indicators; D X itk = independent variables measured as lagged (first difference) values of the main pillars of HPIs; Z itk = dummy variables (binary values) that capture the differences between countries (Westerlund, 2007) as well as differences between high, medium, and low HPI ranks; λ k = individual effect; and υ tk = random error. The Hansen (1982) J test is estimated under the hypotheses that follow. H0: The overidentification restrictions are valid. H1: The overidentification restrictions are not valid. Hayashi (2000) and Hall (2005) provide detailed coverage of GMM estimation. The results of the augmented Dickey‒Fuller test (Appendix B) show that the data have to be lagged (first difference). The results of the RESET test in (Appendix C) show that nonlinearity fits the data; therefore, cubic transformation is applied (Ramsey, 1969; Thursby & Schmidt, 1977, 1979; Sapra, 2005; Wooldridge, 2025). The results for examining heteroskedasticity via the Breusch–Pagan–Godfrey test (Appendix D) reveal that homoskedasticity is plausible. The results of the Hausman test in (Appendix E) show that fixed effects fit the estimation process (Hausman, 1978; Hausman & Taylor, 1981). The results of testing for endogeneity (Appendix F) show that the three pillars of the HPI are not endogenous. The results show that the estimated Ys are free from misspecifications when adequate instrumental variables are used (Hill et al., 2011). The medium level of HPIs is excluded to avoid the problem of orthogonality. In this case, the 1st and 4th levels of HPIs offer a fair reflection of welfare quality. ** significant at the 5% level, *** significant at the 1% level.
Table 3. Stochastic effects of HPIs on equity market indicators.
Table 3. Stochastic effects of HPIs on equity market indicators.
VariablesLife Satisfaction Life ExpectancyEcological Footprint
Y1Y2Y3Y1Y2Y3Y1Y2Y3
Constant−0.1612
(−3.17) ***		0.0628
(1.874) **		0.035
(0.11)		0.7527
(1.447)		−0.004
(−0.04)		0.4428
(0.514)		1.1685
(0.597)		0.1835
(0.593)		1.7290
(0.300)
Ln Life Satisfaction2.78
(2.68) ***		1.162
(2.103) **		1.2192
(3.367) ***
Ln Life Expectancy −29.795
(−0.823)		3.12582
(0.414)		−40.559
(−0.5939)
Ln Ecological Footprint −7.8612
(−0.38)		−1.871
(−0.581)		−18.386
(−0.308)
Low HPI (Dummy)0.072
(2.71) **		−0.032
(−0.540)		−0.065
(−2.84) **		2.147
(1.385)		−0.0309
(−0.525)		6.0715
(1.049)		1.710
(0.550)		−0.1698
(−0.546)		5.889
(0.701)
High HPI (Dummy)−0.0102
(−0.096)		0.094
(3.340) ***		0.0870
(0.497)		0.21889
(0.5538)		0.0846
(2.29) **		0.4115
(0.594)		0.326
(0.28)		0.2217
(1.05)		0.9543
(0.294)
Regions (Dummies)YesYesYesYesYesYesYesYesYes
Time Effect (Dummy)YesYesYesYesYesYesYesYesYes
N812812812812812812812812812
R ¯ 2 0.16250.40240.19710.13310.15500.13090.17270.12900.1529
S.E. of regression0.36790.28130.31889.40940.243935.099.58680.469535.467
J-statistic (Prob.)3.9382
(0.047)		4.1878
(0.0407)		4.17977
(0.0401)		0.1377
(0.7105)		1.5358
(0.215)		1.2303
(0.2673)		0.0814
(0.7752)		0.9868
(0.320515)		1.05204
(0.305)
Durbin-Watson stat2.21151.90392.2442.00821.90412.01121.96351.92161.998
Notes: Y1 = Market capitalization as a percentage of GDP; Y2 = Market capitalization of listed domestic companies; Y3 = Turnover ratio of domestic stocks. ** significant at the 5% level, *** significant at the 1% level.
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Eldomiaty, T.; Azzam, I.; El Kolaly, H.; Youssef, N.; Sedik, M.A.; ElShahawy, R. The Impact of Uncertain Welfare Quality on Equity Market Performance. Int. J. Financial Stud. 2025, 13, 67. https://doi.org/10.3390/ijfs13020067

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Eldomiaty T, Azzam I, El Kolaly H, Youssef N, Sedik MA, ElShahawy R. The Impact of Uncertain Welfare Quality on Equity Market Performance. International Journal of Financial Studies. 2025; 13(2):67. https://doi.org/10.3390/ijfs13020067

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Eldomiaty, Tarek, Islam Azzam, Hoda El Kolaly, Nermeen Youssef, Marwa Anwar Sedik, and Rehab ElShahawy. 2025. "The Impact of Uncertain Welfare Quality on Equity Market Performance" International Journal of Financial Studies 13, no. 2: 67. https://doi.org/10.3390/ijfs13020067

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Eldomiaty, T., Azzam, I., El Kolaly, H., Youssef, N., Sedik, M. A., & ElShahawy, R. (2025). The Impact of Uncertain Welfare Quality on Equity Market Performance. International Journal of Financial Studies, 13(2), 67. https://doi.org/10.3390/ijfs13020067

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