Income Distribution, Inequality and Poverty: Evidence, Explanations and Policies

Clementi, Fabio (Ed.) Book Income Distribution, Inequality and Poverty: Evidence, Explanations and Policies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Clementi, Fabio (Ed.) (2024) : Income Distribution, Inequality and Poverty: Evidence, Explanations and Policies, ISBN 978-3-7258-2442-7, MDPI - Multidisciplinary Digital Publishing Institute, Basel, https://doi.org/10.3390/books978-3-7258-2442-7 This Version is available at: https://hdl.handle.net/10419/321945 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. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. 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/ mdpi.com/journal/economies Special Issue Reprint Income Distribution, Inequality and Poverty Evidence, Explanations and Policies Edited by Fabio Clementi Income Distribution, Inequality and Poverty: Evidence, Explanations and Policies Income Distribution, Inequality and Poverty: Evidence, Explanations and Policies Editor Fabio Clementi Basel •Beijing •Wuhan •Barcelona •Belgrade •Novi Sad •Cluj •Manchester Editor Fabio Clementi University of Macerata Macerata Italy Editorial Office MDPI AG Grosspeteranlage 5 4052 Basel, Switzerland This is a reprint of articles from the Special Issue published online in the open access journal Economies (ISSN 2227-7099) (available at: https://www.mdpi.com/journal/economies/special issues/JTG6GF449G). For citation purposes, cite each article independently as indicated on the article page online and as indicated below: Lastname, Firstname, Firstname Lastname, and Firstname Lastname. Article Title. Journal Name Year, Volume Number, Page Range. ISBN 978-3-7258-2441-0 (Hbk) ISBN 978-3-7258-2442-7 (PDF) doi.org/10.3390/books978-3-7258-2442-7 © 2024 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) license. Contents About the Editor ..............................................vii Fabio Clementi Income Distribution, Inequality and Poverty: Evidence, Explanations and Policies Reprinted from: Economies 2024,12, 276, doi:10.3390/economies12100276 .............. 1 Jibran Hussain, Saeed Siyal, Riaz Ahmad, Qaiser Abbas, Yu Yitian and Liu Jin Impact of Energy Crises on Income Inequality: An Application of Piketty’s Hypothesis to Pakistan Reprinted from: Economies 2024,12, 259, doi:10.3390/economies12100259 .............. 5 Molepa Seabela, Kanayo Ogujiuba and Maria Eggink Determinants of Income Inequality in South Africa: A Vector Error Correction Model Approach Reprinted from: Economies 2024,12, 169, doi:10.3390/economies12070169 .............. 28 Knut Lehre Seip and Frode Eika Sandnes The Timing and Strength of Inequality Concerns in the UK Public Debate: Google Trends, Elections and the Macroeconomy Reprinted from: Economies 2024,12, 135, doi:10.3390/economies12060135 .............. 45 Enzo Valentini Patterns of Intergenerational Educational (Im)Mobility Reprinted from: Economies 2024,12, 126, doi:10.3390/economies12060126 .............. 62 Elisabetta Croci Angelini, Francesco Farina and Silvia Sorana The Impact of the Great Recession on Well-Being across Europe Ten Years On: A Cluster Analysis Reprinted from: Economies 2024,12, 115, doi:10.3390/economies12050115 .............. 75 Adriana AnaMaria Davidescu, Oana-Ramona Lobont¸ and Tamara Maria Nae The Fabric of Transition: Unraveling the Weave of Labor Dynamics, Economic Structures, and Innovation on Income Disparities in Central and Eastern Europe Nations Reprinted from: Economies 2024,12, 68, doi:10.3390/economies12030068 .............. 92 Adriana AnaMaria Davidescu, Tamara Maria Nae and Margareta-Stela Florescu From Policy to Impact: Advancing Economic Development and Tackling Social Inequities in Central and Eastern Europe Reprinted from: Economies 2024,12, 28, doi:10.3390/economies12020028 ..............105 Lidia de Castro Romero, V´ıctor Mart´ın Barroso and Rosa Santero-S´anchez Does Gender Equality in Managerial Positions Improve the Gender Wage Gap? Comparative Evidence from Europe Reprinted from: Economies 2023,11, 301, doi:10.3390/economies11120301 ..............130 Michele Fabiani Unraveling the Roots of Income Polarization in Europe: A Divided Continent Reprinted from: Economies 2023,11, 217, doi:10.3390/economies11080217 ..............153 Jarle Aarstad and Olav Andreas Kvitastein Wage Inequality’s Decreasing Effect on Enterprise Operating Revenues Reprinted from: Economies 2023,11, 178, doi:10.3390/economies11070178 ..............171 v George Petrakos, Konstantinos Rontos, Chara Vavoura and Ioannis Vavouras The Impact of Recent Economic Crises on Income Inequality and the Risk of Poverty in Greece Reprinted from: Economies 2023,11, 166, doi:10.3390/economies11060166 ..............179 vi About the Editor Fabio Clementi Fabio Clementi, Ph.D., is a Professor of Economics at the University of Macerata (Italy). His main research interests focus on the size distribution of income, wealth and firms, business cycle analysis, and the empirical validation of agent-based economic models with real-world data. He has published several papers in peer-reviewed international journals and book chapters on topics related to his scientific activity and serves as a referee for various international journals. He has also been involved in a number of national and international research projects aligned with his areas of expertise. He has presented communications at many international meetings, also as an invited speaker, and has contributed as a member of the scientific and/or organizing committee of some international conferences. Most recently, he has acted as a consultant for the World Bank on matters related to income distribution and inequality. vii Economies 2024,12, 259 or to lifting the lower and middle classes. This ongoing issue of resource distribution and its historical roots needs in-depth investigation. Moreover, the political stability crisis was vital in contributing to income inequality, as well as the energy crisis, during this period. The political climate was not stabilized through any remarkable or notable institutional capacity-building programs in the years 2000 to 2007; rather, ad hoc adjustment policies continued to be enforced as a popular method of running state affairs. The energy fabric and system collapsed during the recession, led by the energy crisis in 2007, and there was not much that political and economic institutions could offer to society (Zaidi 2015). The country is still struggling with the aftermath of the policy-incapacitated outcomes of the past. As a result, inequality kept gradually increasing; the share of total income of the poorest 20 percent decreased during the period from 1997 to 2021, while the share of the wealthiest 20 percent increased. The share of the poorest 20 percent increased slightly during the 1980s. However, their share started to decline again in the 1990s and reached 6.37 percent by 2004. On the other hand, the share of the wealthiest 20 percent increased during the period from 1997 to 2021 and reached 50.02 percent in the period from 2009 to 2010 (World Bank Group 2014). Although the average income level in Pakistan is slightly increasing, along with economic growth of almost 5 percent, the gap between socioeconomic classes has not improved significantly. In this context, this article examines the factors beyond income levels that shape equality in society, with a special focus on the availability and affordability of energy resources. This article shows that there are factors other than income levels and economic growth that have significant roles in shaping equality in society. Two of these critical factors are affordable energy services and access to energy resources. The growing and creeping crisis in the energy sector over the years, as a result of persistent political uncertainty in the country, has been decreasing the capacity of the masses to access energy resources; for Murtaza and Faridi (2015), it has increased energy poverty, which reflects the persistency of the lack of energy supplies and the inability of the masses to access resources. At the same time, the limited availability of energy supplies and services has been reducing the real income of the masses over the years. According to Cheema and Sial (2012), this increasing income inequality despite increasing economic growth rates is due to the unfair distribution of resources among provinces and the biased distribution between rural and urban areas. In the case of rural–urban polarization, a similar conclusion was reached by Awan et al. (2013), in that the ability to access energy services in rural areas is relatively lower than in urban areas. Moreover, Haq and Shirazi (1998) concluded that, in terms of nonfood expenditure, economic welfare levels in rural areas are comparable to those of the urban sector, and they proposed that policies should be directed toward enhancing the expenditure capacity of the poorest groups. Nwosu et al. (2018) showed that nonfood expenditure is a major source of inequality in household consumption expenditures in both urban and rural areas and that variables such as living in rural areas, household size, type of household dwelling, and household dwelling characteristics account for significant proportions of inequality in food and nonfood expenditures. Murtaza and Faridi (2015) blamed income polarization for expanding income poverty and energy poverty, which reflects the decreasing consumption expenditures. Pakistan is among the weakest countries in the world in terms of the Sustainable Energy Development Index (SEDI), the Human Development Index (HDI), and the Energy Poverty Index (EPI) tested globally. This implies that the average household in Pakistan has to spend a large portion of its income to acquire energy supplies and services every time there is an energy crisis or oil prices increase. As over-dependency on external energy supplies shifts wealth from the local economy to the foreign, leaving less for the locals, the growth in household final consumption expenditure has decreased over the years in the country (Iddrisu and Bhattacharyya 2015). Kuznets (1955) contributed to the issue of income and resource distribution by empirically analyzing income inequality. He believed that countries face serious inequality issues when the economy is in the ‘take-off’ stage 6 Economies 2024,12, 259 and moving toward industrialization; however, the issue of inequality stabilizes after the achievement of the steady-state level. Piketty (2014), on the other hand, dived into the wealth and tax data for France, Germany, Japan, the United Kingdom, and the USA, from 1810 to 2010, to study the resource distributions and income inequalities in these countries. He believes that the concept of inequality in the field of economics is insufficient, especially while using the relationship between the economic growth rate and income shares. He expands his detailed discussion and proposes that, if the rate of return from capital (r) exceeds the growth rate of the economy (g) over a steady period, it gives rise to the problem of income inequality (Piketty 2015). Despite expanding the critique on his theory of income inequality, he proposes that, in addition to the r>ghypothesis for explaining income inequality, institutional changes and political shocks remain as integral and indigenous parts of this income inequality function (Piketty 2015). The extension of Piketty’s hypothesis presents a valid fit in the case of Pakistan, where political instability over the years has been responsible for this reduction in institutional capacities; as a result, despite increasing growth rates and income levels, income inequality is on the rise, especially after the 1990s. Lakner (2016) noted that the crucial aspects of measure Piketty’s (2014) that make it more relevant and appropriate are as follows: First, it does not underestimate the incomes and expenditures of wealthy households, as Atkinson et al. (2011) feared that most of the household surveys do a poor job capturing the incomes and consumptions of the richest. Second, it captures the capital incomes and profits of the entrepreneurial process, which household surveys in developing countries fail to incorporate (Alvaredo and Gasparini 2015). Third, consumption surveys, in use primarily in underdeveloped and developing countries, tend to undermine the actual picture of the living standards of the wealthy class, who tend to save more than the bottom fractions of a society (Aguiar and Bils 2015). Only 40 percent of the total population of Pakistan has access to clean fuel and technologies for cooking, and this is biased toward urban areas, where almost 98 percent of consumers have access to electricity, whereas only 90 percent of consumers in rural areas have the same access (World Bank 2018). The access, availability, and affordability of energy supplies, services, and technologies have wide disparities between the urban and rural, rich and poor, and haves and have-nots in Pakistan. This increasing inequality in energy resource distribution, energy supply disparities (Mahmood and Shah 2017), and income and energy poverty (Murtaza and Faridi 2015) in the presence of a persistent, unstable political system is causing increasing income inequality in Pakistan (Shehzadi et al. 2019). The existing literature provides a foundation for understanding the relationship between energy access and income inequality. Studies have shown that unequal access to energy can exacerbate income disparities (Bazilian and Yumkella 2015; Bouzarovski and Herrero 2017). In Pakistan, energy poverty has been identified as a significant issue affecting low-income households (Mahmood and Shah 2017). This study builds on these findings by applying Piketty’s hypothesis of resource distribution to the context of Pakistan’s energy crisis. The Kuznets curve has been a popular benchmark for understanding the relationship between economic growth and income inequality. This popularity was particularly notable during the mid-20th century. Acemoglu and Robinson (2013) provide a deeper insight into situations in which there is an abundance of energy but high inequality. Their work suggests that inclusive political institutions are necessary to ensure equitable resource distribution. Atkinson’s contributions, particularly his previous research, have similar conclusions about the relationship between inequality and resource distribution. This highlights the need for equitable energy policies. Piketty’s explanation of the correlations between resource distribution, growth, and income inequality suggests that unequal resource distribution leads to increased inequality. Resource distribution, in this context, includes access to energy, which is essential for economic growth and reducing inequality. Grunewald et al. (2014) imply that a reduction 7 Economies 2024,12, 259 in CO 2 emissions, energy usage, or energy supply increases the difference between energy supply and demand, causing energy crises. Shahbaz (2010) tested Kuznets’ theory in the context of energy and income inequality in Pakistan. The measure of income inequality used in these studies differs from Piketty’s r-g model. (See Figure 1) Income Ine q ualit y A pp lication of Pikte yy s Model Figure 1. Application of Piketty’s hypothesis on income inequality, 1997–2021. A comparison of rural and urban areas shows that 93 percent of the urban population compared to 63 percent of the rural population in Pakistan have access to electricity (Mahmood and Shah 2017). The misery of the energy disparity and deprivation reached an alarming situation, with 19 percent of the urban and 71 percent of the rural populations not being able to afford energy services or supplies. Despite increasing average income levels in Pakistan, along with an average economic growth rate of almost five percent, household consumption expenditure is decreasing and income inequality is barely improving (Awan et al. 2013). It is evident, from the fact that, despite an increasing per capita income, the Gini coefficient increased from 30 in 1997 to 37 in 2021, and household consumption expenditure is showing a decreasing trend (World Bank 2022). The impact of the energy crisis on the rural poor is significantly more severe than that on the urban masses, and the underprivileged and the poor are spending a more significant share of their incomes on energy supplies. The energy crisis is contributing to aggravated income inequality by widening the gap between the haves and the have-nots in the country, and hampering the ability of masses to have access to energy services and supplies, by reducing the real income of the lower classes, and thus their capacity to live. This paper makes a significant contribution by linking historical and contemporary issues of resource distribution in Pakistan with income inequality, particularly through the lens of the energy crisis. By examining the impact of policies like the Industrial Policy (1948) and the Green Revolution (1959), and their role in fostering socioeconomic disparities, the paper highlights how historical decisions have perpetuated income inequality and energy poverty. It builds on the existing literature by applying Piketty’s hypothesis on resource distribution and income inequality to Pakistan’s context, demonstrating that despite economic growth, the unequal distribution of energy resources exacerbates socioeconomic divides. This study underscores that beyond income levels and economic growth, access to affordable energy services is crucial in shaping societal equality. By integrating various theoretical perspectives, including Kuznets’ curve and Piketty’s r-g model, with empirical data on energy access and income inequality, the paper offers a nuanced analysis of how 8 Economies 2024,12, 259 persistent energy crises and political instability contribute to worsening income inequality in Pakistan. The rest of this paper is as follows: Section 2 contains the literature review; Section 3 covers methodology; and Section 4 covers data analysis followed by conclusions and recommendations for future directions. 2. Literature Review Income inequality is one of the most debated topics in the literature on socioeconomic welfare. The theory of income inequality reflects the fundamental issues of resource distribution and share, and the access of different fractions of society to national and collective resources. In every era, prominent researchers have presented their theories on income inequality, such as Smith (1776) who provided an essential and significant foundational understanding of income inequality through the concept of resource distribution between the laborers, the capitalists, and the landowners. He theorized that the landowner becomes richer with the growth of free markets and society as an consequence of the laborers hard work, this is the foundation of inequality in terms of resource distribution and the exploitation of the laboring class. The foundational work of Smith paved the way for Marx (1818–1883) to propose his famous theory of socioeconomic inequalities. He stated that the capitalist exploitation of resources is responsible for polarizing society and motivates the masses toward revolution as a form of class struggle. His thesis promised equality and justice in society and cured the issue of inequality with the prevalence of a dominant socialist society. On the contrary, Skocpol (1977) believed in the fundamental forces of market stabilization as a solution to the polarities in resource distribution and prevailing income inequalities. Wherein, Polinsky (1974) also conceded the possibility of fair distribution of resources through a theory of equity in factors of production. On the other hand, Keynes (1973) portrayed a pessimistic view of the consequences of income inequality and cautioned about the more significant damage that inequality could bring to society and the economy. Energy supply is a crucial factor for social development and growth, as well as advancements and transformations toward modernization. Limiting the supply of energy would create a crisis in societies. The sustainable flow of energy and energy use is fundamental for industrial economies and social organizations. Therefore, energy deprivation is a ‘threat multiplier’ and ‘threat instigator’ in military parlance; yet, unfortunately, it took several years for the international community and organizations to acknowledge the importance of energy sustainability and security. This energy deprivation is considered to be a clear catalyst for unrest, instability, youth unemployment, and urbanization in societies. Income inequality is an essential determinant of socioeconomic welfare, a vital theoretical affair for policymakers and economists. It reaches resource economics with the hope of determining the redistribution of scarce resources in a way that could reduce inequalities in society. The theory of inequality by Kuznet (1955) remains a popular benchmark to measure the level of welfare through income distribution among different income fractions in society. Notwithstanding the popularity of Kuznets’s thesis, the application of his measure of income inequality to developing countries raises valid and contradicting questions; most such objections are debatable, yet much is left unexplained by his theory, such as cases in which urbanization, industrialization, increasing improvements in energy resources and economic growth occur side by side along with increasing income inequality, such as in Russia (Lyubimov 2017). Similarly, Acemoglu and Robinson (2013) explained the resource richness (energy) of countries (such as Nigeria and Middle Eastern countries) experiencing increasing inequalities, thus showing that resource distribution is not justified or rational. The problem of income inequality was addressed in the groundbreaking work of Atkinson et al. (2011). He proposed a solution to the measurement of income inequality and the distribution of income, consumption, and wealth in terms of subjective social function. He measured income inequality within income distribution through ‘social loss’, which can be reflected through income, consumption, or wealth. Therefore, the debate 9 Economies 2024,12, 259 generated about socioeconomic welfare discussed earlier in this section is justified herein to explain the concept and measurement of income inequality. He believed that, as a traditional measure of income inequality, the Gini coefficient fails to explain the welfare aspects imbibed within income inequality, such as the social loss in unjust resource distribution and income inequalities. Therefore, the Gini coefficient cannot be trusted as an ultimate, complete, and reliable measure of income inequality. The concept of social loss first originated in the work of Dalton (1920), who defined income inequality as a loss of socioeconomic welfare as a result of the unjust and uneven distribution of resources and incomes within a society. Sen (1992) re-examined the concept and measurement of income inequality on the paradigm of capability functions such as elementary function (health, nourishment, and shelter) and social function (self-respect and community life). The realization of these sets of capabilities, as preferred by any individual, reflects his/her capability and standing in society. Therefore, the failure to provide access to and the availability of these functions for a different fraction of society reflects the prevailing level of income inequality, which hinders human freedom to enjoy the basic needs and wants. Hence, his theory explained inequality in terms of social welfare, where people compromise their ability to retain a respectable justified level of life due to nonavailability, a lack of access to collective resources, and a lack of capability. This theory enhances the concepts of social loss and socioeconomic welfare while expanding the definition and scope of the concept of income inequality. However, the promising contemporary theory of inequality from Piketty (2014, 2015) proposed a whole new dimension of income inequality and filled gaps left by earlier theories, especially in the context of macroeconomic dynamics. The proposed theoretical basis is that, when the rate of return on capital (r) is higher than the economic growth rate (g) over a reasonable period, it leads toward inequality in the society (r>g=IE). The theoretical architecture of this theory is based on the primary and fundamental laws of capitalism. For example, the first law says that the product of the capital–income ratio ( β ) and return on capital (r) are equal to the share of capital in the national income ( α ). The second law of capitalism asserts that β is the outcome of the ratio between national saving (s) and economic growth (g). Hence, the rate of return on capital is equal to the product share of capital within the national income and ratio between economic growth and the national savings (r= α *g/s). Piketty’s successful explanation of the correlations between resource distribution, growth, and income inequality over a greater extent of time gives a new dimension to the field. The major concern of the theory is that rising inequalities lead the wealthy class to influence the political and economic institutions in their favor; this aspect of the theory has already gained theoretical endorsement by Acemoglu and Robinson (2013) in that extractive economic institutions favor the elite to obtain personal benefits from the state and weaken the institutions’ and nations’ failures despite having abundant resources. Galvin (2019) proposed that countries with weak institutions cannot prevent the impact of external shocks, such as energy shocks, and such economies are prone to experiencing an economic collapse. Hence, unjust resource distribution (energy) leads to income inequality. The theoretical base between energy used/energy supply and income inequality is not widely explored or researched. However, some researchers have used carbon dioxide (CO 2 ) emissions as a proxy for energy supply and developed a theoretical relationship with income inequality to lay down foundations for their theories, such as the ‘equality hypothesis’, which states that income inequality could be positively associated with CO 2 emissions, implying that energy-use enhancement hints toward the inequality of energy supplies and, therefore, income inequality (Boyce 1994). On the contrary, some theorists believed that increasing CO 2 emissions play a fair role in bringing inequality downward, and are thus negatively associated with income inequality (Heerink et al. 2001). Similarly, the comprehensive theoretical justification for the relationship between CO 2 emissions/energy usage and inequality proposed by Grunewald et al. (2014), that, in the case of developing and underdeveloped countries, CO 2 emissions are negatively associated with income inequality, thus implies that more energy being used in developing countries 10 Economies 2024,12, 259 would decrease income inequality, as the amount of energy used is an indicator of economic growth and development. Hence, an increase in energy supplies improves economic growth, which therein extends to decreasing inequalities. The authors included Pakistan in their analysis, which, therefore, implies that a reduction in CO 2 /energy usage/energy supply increases the difference between energy supply and demand, which causes energy crises. This crisis puts downward pressure on economic growth, and the decreasing economic growth deteriorates the resource distribution and income inequality in Pakistan. This theory, therefore, provided the foundations for exploring the role of an energy crisis in explaining the variations in income inequality in the case of Pakistan. On the other hand, Khan and Heinecker (2018) laid the foundations for a theoretical relationship between income inequality and energy supply, while reinvigorating the debate on energy efficiency and income disparity. Their proposed theory states that an increase in energy use is associated with increasing disparity and is the result of an increase in the energy cost of having ‘good regulation’ of the system. As the system becomes unequal, the supply of energy resources and services becomes increasingly biased toward the high-income fractions of society, where the top 20 percent of the wealthiest fraction have more access to and use of energy than the rest. The links between energy and reducing inequalities may be seen most clearly in the context of access to energy, but there are also cases of energy poverty related to fuel poverty. In situations in which people have access to energy, it is often the poorest that end up using disproportionate shares of their income to pay for energy, in part because the higher upfront costs of investments in energy services are more difficult to bear for low-income households (Simcock et al. 2017). This thus validates the claim of Piketty (2015) that when resource distribution favors the capitalists more than the majority, the return on capital increases more than growth, hence, creating income inequality. Energy supply is a crucial factor in social development and growth and the advancement of the transformation of society toward modernization. A limit on the supplies of energy would create a crisis in societies. The sustainable flow of energy and energy use is fundamental for industrial economies and social organizations. Therefore, to avoid the energy crisis, industrial societies are requisite to find alternative means of energy, York et al. (2004). Access to energy resources and supplies is a prerequisite for development, such that the distribution of energy supplies and its access may have social repercussions and cause economic inequalities. Awan et al. (2013) argued that the majority of households rely on fuel consumption and noted that they face almost a 55 percent deprivation in terms of energy access, especially rural households. While reporting the bearing of the energy crisis by society, Arslan et al. (2014) noted that, with just the nonavailability of CNG fuel, the social and economic wellbeing of people in Pakistan is affected adversely. Moreover, they showed that the energy crisis is responsible for affecting the lifestyles of people in general. Similarly, Simcock et al. (2017) reported that the energy crisis has adversely affected all spheres of the wellbeing of people and has escalated the cost of living, especially for the low-income groups. Equality and welfare both rely on a sustained and stable energy supply to all. Economic growth and development is a step toward eradication of energy deprivation and inequality, and without sustainable energy supplies, it is difficult to reach the targets of economic development. The gap between the haves and have-nots in terms of access to modern energy services is widening day by day (World Bank 2013). Economic development has proven to be a significant necessary condition to enhance the energy use of the masses; thus, it is a necessary condition to reduce an energy crisis experienced by any society, York et al. (2004). Moreover, energy is the most crucial and integral part of production processes and services that enable societies to move forward on the path of growth, toward development and prosperity. Access to energy resources is a serious global issue. Today more than half of the world’s population is deprived of access to energy, energy-related services, appliances, and facilities. This deprivation is a significant hurdle and creates restraints for creating jobs, business opportunities, access to health, and education (World Bank 2013). However, the situations and scenarios in developing and underdeveloped coun11 Economies 2024,12, 259 tries such as Pakistan are getting worse. Moreover, countries with humongous populations and population growth rates face energy shortages. Some of the features of developing and overpopulated countries are increasing population and urbanization. Alongside other benefits, access to energy services and supplies is better, which attracts the rural population to migrate. In his cross-country study, Liddle (2013) found that urbanization increases with energy use. Today more than one billion people around the world have no access to electricity. This situation is forecasted to be worse by 2030, when this number will increase by as much as two-fold. The conditions in underdeveloped and developing countries are particularly terrible (Toman and Jemelkova 2003). Moreover, rural electrification has been proven to reduce energy poverty, which consequently improves energy equity and, thus, reduces inequality. Furthermore, the amount required to mitigate the gap between the haves and have-nots in terms of access to energy services, as well as to provide the deprived masses around the world with necessary energy supplies and services, is almost USD 40 billion annually, which will rise continually until 2030 worldwide (Schroeter 2013). Pao et al. (2014) stressed that global initiatives are inevitable for the provision of necessary energy supplies and services to produce opportunities for growth and prosperity for underprivileged and deprived people. Unfortunately, the masses with the least access to resources cannot lift themselves out of the abyss of misery, poverty, and income inequality without access to modern energy supplies and services. The reliance on expensive oil and fuel is not going to cater to this severe issue affecting the poor of the world. Even though the solution to this crisis of income inequality and uneven access to energy resources has been proven to lead to massive progress and development in renewable energy technologies and services, the adaptation of such technologies to developing and underdeveloped nations is near nonexistent, which is a sustained threat of increasing income inequality. The bulk of energy sources being used around the globe are not sustainable (International Atomic Energy Agency 2005); therefore, energy deprivation is a ‘threat multiplier’ and ‘threat instigator’ in military parlance; yet unfortunately, it took several years for international communities and organizations to acknowledge the importance of energy sustainability and security. This energy deprivation is considered a clear catalyst for unrest, instability, youth unemployment, and urbanization in societies (Bazilian and Yumkella 2015). The literature on energy usage/energy supply and income inequality is scarce, primarily due to the ambiguous theoretical paradigm between the two variables (Berthe and Elie 2015), and empirical studies on the energy crisis and income inequality are almost nonexistent, around the world in general, and in Pakistan in particular. Therefore, CO 2 emissions can be considered as a proxy of energy use, owing to the fact that more than 77 percent of CO 2 emissions are from energy sources (EIA, USA 2019; Ceyhan and Saribas 2022). As such, Shahbaz (2010), while exploring the environmental Kuznets curve and the role of energy consumption in Pakistan, stated that energy consumption increases CO 2 emissions. As per Grunewald et al. (2014), increasing CO 2 reduces inequality in developing countries. Therefore, with increasing CO 2 emissions, income inequality may reduce in Pakistan. In purview of this, an increase in energy consumption signals an increase in energy supplies, and if access to energy services have increased, and if this expansion includes the rural and deprived areas, it certainly provides room for us to infer that an energy supply has become available to the deprived. Therefore, in order to find to a shred of justified empirical evidence and to investigate a plausible association between energy use and income inequality, in many studies CO 2 emissions can be relied upon and inferred to be the energy usage. As such, Heerink et al. (2001) tested the relationship between CO 2 emissions and income inequality in 65 countries and found that a negative association between income inequality and CO 2 emissions exists. These results insinuated that the increase in CO2emissions reduces income inequality. Similar results are also endorsed by Ravallion et al. (2000) in their study using data for 42 countries from 1975 to 1992, and they proved that there is a static trade-off between CO 2 emissions and income inequality. On the 12 Economies 2024,12, 259 contrary, a rather comprehensive and more convincing empirical study in this aspect was completed by Grunewald et al. (2014), from 1980 to 2008, using a sample of 90 countries to test the relationship between CO 2 emissions and income inequality. They concluded that countries with lowand middle-income statuses possess a negative relationship between CO 2 emission and income inequality, and, on the contrary, this relationship is positive in the case of developed and high-income countries. Reaffirming the above results, in their study, Grunewald et al. (2017) found that, for lowand middle-income economies, higher income inequality is associated with lower carbon emissions, while in upper-middle-income and high-income economies, higher income inequality increases with per capita emissions. Since Pakistan is included in the group of low-income countries by the authors, it is implied that there is a negative relationship between CO2emissions and income inequality. The studies mentioned above and their results imply that income inequality decreases as CO 2 emissions increase. And the increase in CO 2 emissions is the imposition of the fact that more energy is being used at large (EIA, USA 2019). Therefore, the possibility that more people are using more energy and have greater access to energy services with the enhanced capacity to buy energy resources may indicate a reduction in income inequality. Hence, it provides an avenue of justification that energy use is a vital tool for reducing income inequality. Moreover, the reduction in CO 2 emissions signals in a drop in energy use. A sustainable supply of energy resources and services is a necessary condition for the development stages of societies. However, with the increasing utilization of energy resources, and a growing population, the stock and resources of energy supplies worldwide are decreasing significantly, raising fears for greater energy deprivation and polarity among nations and risking the share of energy for future generations (Sahir and Qureshi 2007). As such, access to energy supplies and services affect the freedom of millions around the globe to access economic opportunities. One of the reasons for areas of the world having remained underdeveloped is the lack of energy supply and services to initiate economic activities (Bazilian and Yumkella 2015). Income inequality, energy poverty, and poverty move in the same direction, as changes in energy consumption’s impact are a reliable indicator of prosperity. This is why the negative correlation between modern energy services and energy deprivation is well established and proven. In order to reduce income inequality and energy deprivation, access to modern energy services by the masses must be improved (International Energy Agency 2017). Awan et al. (2013) investigated the situation of energy deprivation and energy poverty in Pakistan by using the Multidimensional Energy Poverty Index (MEPI). They found a high intensity of energy deprivation and poverty throughout the country. The comparative analysis and results between rural and urban populations showed that the urban population in the country enjoys greater access to energy services than the rural population. As much as 71 percent of the population in rural areas of Pakistan are deprived of energy services and supplies compared to 29 percent in urban areas. This situation is a reflection of the prevalence of disparities in this society, in which urban populations not only have greater access to energy supplies but also socioeconomic opportunities to explore and with a higher per capita income compared to the rural populace. This also suggests that the rural populace comparatively experience a higher-pressure energy crisis, first based on their limited access to energy supplies and services, and, secondly, their ability to buy more energy when it is expensive remains low due to their income. Therefore, the persistent energy crises over the years has contributed to widening these gaps in polarity and income inequality in the country. This study employs the dynamic ordinary least squares (DOLS) method to analyze the impact of the energy crisis on income inequality from 1997 to 2021. The DOLS method is chosen for its ability to handle endogeneity and serial correlation issues, providing unbiased and efficient estimates (Lütkepohl 2001). This method is particularly relevant for our study as it allows for the incorporation of long-term equilibrium relationships among variables, which is essential for understanding the persistent effects of energy crises on income inequality. 13 Economies 2024,12, 259 Although, MEPI as an index for energy deprivation and poverty, it is also an excellent way to measure the income inequality derived through energy supply and services; however, the authors believe that it does not integrate all of the elements of sustainable energy. As such, advancement in the field of energy economics is required. Mahmood and Shah (2017) also conceived similar results, while extending the application of MEPI to the rural and urban areas of Pakistan. Their study remained focused on a comparative analysis and investigation of the differences in energy deprivation and access to energy resources and services between rural and urban areas of the country. They found that, on average, a household in Pakistan is 26.4 percent deprived of essential energy services and supplies. The alarming nature of this situation is that this deprivation is chronic and persistent, with the deprivation in rural areas being massively greater compared to the level of energy deprivation in urban areas of the country. With these results, it can be argued that the polarity and deprivation in access to primary energy supply and services are the foundational cause of persistent income inequality and the energy crisis in the country. They believe that the lack of governing wisdom and political will are responsible for bringing society to this edge. Murtaza and Faridi (2015) already produced similar results and showed that the progress in energy development in Pakistan increased during the first half of this century but at a plodding pace. Unfortunately, even this slow and meagre energy development and growth started decreasing after the energy shocks and crises of 2007 and 2011, resulting in increased energy poverty, income inequality, and disparities of the country. The level of income inequality is rooted in the overall economic progress and development of a country. An increasing economic performance is considered to have an impact on the level of earnings of households, which eventually transforms into households’ increasing ability to access to modern services and energy resources. Such an idea is proposed by Kuznet (1955), who believed that structural adjustment of economic growth can reduce the deprivation and income inequalities in society, as society crosses different levels of growth and development. He further contemplated that the very early and initial stages of economic growth yield income inequality, and later, when a steady state is achieved, income inequality is potentially reduced, based on the premise of industrialization, urbanization, and the trickle-down effect of economic growth to the lowest fraction of the society. Over the years, economists all over the world have studied the application of Kuznets theory in different scenarios without considering the collected historical data or improving the empirical validity of the theory to their respective societies (Piketty 2014). Shahbaz (2010) witnessed a similar application of Kuznets’s theory of income inequality in Pakistan and showed that economic growth leads to a reduction in inequality. However, the author failed to align his results and outcomes with the growth stages of the underpinning theory. On the contrary, Zouhaier and Karim (2012) found a negative effect of economic growth on inequality and proposed the need for a better methodological understanding to obtain more accurate results. Sharafat et al. (2014) reached similar results and found that increasing economic growth is associated with the increase in income inequality in Pakistan. Sen (1992) raised a point on the moral validity of researchers’ approaches toward studying inequality rather than using objective-driven research. Similar yet rigorous results are forwarded by Checchi and García-Peñalosa (2008). They studied 21 countries and reached the conclusion that labor market institutions are the prominent driving force in the determination of inequality; a more reliable institution would result in decreasing inequality. Sustained and sufficient energy resource distribution is predominantly agreed to rely on the factor of socioeconomic prosperity. However, energy resource allocation, efficiency, and conservation have long been critical elements in the energy policy dialogue. They have taken on renewed importance as concerns about global climate change and energy security have intensified in a country where 51 million or more people are still without electricity, with the national electrification rate being almost 73 percent (World Bank Group 2014). Many advocates and policymakers maintain that reducing the demand for energy is essential to meeting the challenges of energy deprivation and inequalities, and analyses tend to find that reductions in demand can be cost-effective means of addressing the 14 Economies 2024,12, 259 concerns of a shortage in energy supplies in the country and hence can curtail the ongoing energy crisis, being known as energy conservation plans. However, there is a difference in reducing demand and curtailing it. For instance, rural areas in Pakistan barely have a 63 percent electrification rate compared to 90 percent in urban areas, showing the inequality in energy resource distribution. Therefore, any conservation program that reduces energy demand indifferently between the urban and rural masses would hamper the social and economic activities of the country and eventually increase the threat to socioeconomic growth and welfare (World Bank Group 2014). The energy resource distribution and income inequality hypothesis proved that, in the case of developing countries, an increase in energy use decreases income inequality. Therefore, any policy that reduces energy demand helps to enhance income inequality in developing countries and, thus, serves as a threat to the welfare level of the public by increasing deprivation and has a direct bearing on income inequality (Mahmood and Shah 2017). 3. Methodology, Variables, and Model Income inequality (IE) is the dependent variable, energy crisis (EC) is an independent variable, and oil price shocks (OPS), gross domestic product (GDP), and population (POP) are controlled variables. Interaction terms are introduced to capture the indirect impact of other independent variables. The role of the energy crisis (EC) as a determinant of income inequality (IE) is grounded in the theoretical and empirical findings of Grunewald et al. (2017), Awan et al. (2013), and Mahmood and Shah (2017). These studies collectively establish that s scarcity of energy production is a fundamental cause of the energy crisis, which, in turn, exacerbates income inequality. The theory of inequality (Piketty 2014) is advanced to be employed for understanding inequality at the national level. This theory states that, if the returns on capital remain greater than economic growth, this could lead to IE. The household final consumption expenditures (HFCE) are negatively affected due to the increase in EC; as a result, households’ capacity and ability to access energy supplies is reduced, causing inequalities. As such, Bazilian and Yumkella (2015) believe that a lack of access to energy increases economic deprivation and decreases business opportunities, and this deprivation translates into IE. Similar propagation is contemplated by Iddrisu and Bhattacharyya (2015), who theorized that energy deprivation is a vital source of IE in a society, if not justified based on a rational resource distribution mechanism. The following Table 1 contains the list of variables with units of measurement and sources. Table 1. Variables’ measurement and sources. Variable Unit of Measurement Data Source Energy Crisis Kilos of Oil Equivalent World Bank Data Bank, Pakistan Energy Year Books, Pakistan Economic Survey, Bureau of Statistics Political Instability Index (0–100) International Country Risk Guide Inflation Rate Percentage Pakistan Bureau of Statistics, Economic Survey of Pakistan Population Numbers Pakistan Bureau of Statistics, Economic Survey of Pakistan Income Inequality Rate/Percentage Pakistan Bureau of Statistics, Economic Survey of Pakistan, House Hold Integrated Economic Survey of Pakistan, Ministry of Finance Gross Domestic Product United State Dollars Pakistan Bureau of Statistics, Economic Survey of Pakistan, Ministry of Finance Household Final Consumption Expenditure United State Dollars Pakistan Bureau of Statistics, Economic Survey of Pakistan, House Hold Integrated Economic Survey of Pakistan, World Bank Data Bank Oil Price Shocks Net Oil Price Increase World Bank Data Bank 15 Economies 2024,12, 259 Table 4. Long-run dynamic estimation results. Variable Coefficient Std. Error t-Statistic Prob. EC 1.00 0.22 4.36 0.01 * PIS −8.40 2.04 −4.11 0.01 * OPS −4.08 1.62 −2.52 0.06 ** INF 6.11 1.68 3.63 0.02 * HFCE 8.80 4.05 2.17 0.09 ** GDP −3.19 ×10−96.97 ×10−10 −4.57 0.01 * POP 3.11 ×10−61.01 ×10−63.08 0.03 * C−1882.1 413.29 −4.55 0.01 * R-Squared Adjusted R-Squared S.E. of Regression Long-Run Variance 0.96 0.68 11.46 53.26 Mean Dependent Var. S.D. Dependent Var. Sum Squared Resid. 11.41 20.45 525.54 *, ** Denote significance at 5 percent and 10 percent, respectively. Table 5. Null hypothesis: D (DOLSRESIDUALS) has a unit root. t-Statistic Prob. Augmented Dickey–Fuller Test Statistic −6.26 0.00 * Test Critical Values: 1% level −3.65 5% level −2.95 10% level −2.61 * Denotes significance at the 5 percent. Table 6. Dynamic DOLS residual ADF test. Variable Coefficient Std. Error t-Statistic Prob. D(DOLSRESIDUALS(-1)) −8.80 1.40 −6.26 0.00 ** D(DOLSRESIDUALS(-1),2) 6.47 1.28 5.04 0.00 ** D(DOLSRESIDUALS(-2),2) 5.21 1.07 4.84 0.001 ** D(DOLSRESIDUALS(-3),2) 3.91 0.85 4.57 0.001 ** D(DOLSRESIDUALS(-4),2) 2.65 0.63 4.20 0.003 ** D(DOLSRESIDUALS(-5),2) 1.48 0.40 3.69 0.001 ** D(DOLSRESIDUALS(-6),2) 0.50 0.16 3.07 0.005 * C 0.15 0.58 0.27 0.788 R-Squared 0.91 Mean Dependent var. −0.37 Adjusted R-Squared 0.89 S.D. Dependent var. 10.02 S.E. of Regression 3.31 Akaike Info Criterion 5.44 Sum Squared Resid. 263.63 Schwarz Criterion 5.81 Log-Likelihood −79.14 Hannan–Quinn Criteria 5.56 F-Statistic 37.08 Durbin–Watson Stat. 2.02 Prob. (F-Statistic) 0.000 *, ** Denote significance at the 5 percent and 10 percent, respectively. Table 7. Wald Test for DOLS. Test Statistic Value df Prob. F-statistic 14.80 (6, 4) 0.01 * ሆ288.83 6 0.00 * Null Hypothesis: C(2) = C(3) = C(4) = C(5) = C(6) = C(7) = 0 * Denotes significance at 5 percent. 22 Economies 2024,12, 259 Figure 2. CUSUM. Figure 3. CUSUMSQ. 5. Conclusions and Policy Recommendations The links between energy and reducing inequalities have been witnessed in an evident manner in the forms of the access to and the supply of energy. In Pakistan, the majority of people may have access to energy supplies. However, it is often the fractions of society that are underprivileged, below the extreme poverty line, and the middle classes who are using disproportionate amounts of their incomes to pay for energy supplies and services; to some extent, this is because of the higher upfront prices of energy supplies, expensive products, and expensive imported energy appliances. The nonavailability of low-cost energy supplies mainly affects underdeveloped regions, which have the majority of low-income households. However, this is equally applicable to middleand lower-income groups of the country, who generally compose the majority living in underprivileged rural areas, for whom energy is an inelastic fundamental necessity of living, and a significant fraction of their energy consumption is to ensure basic survival. Therefore, augmenting and extending the supplies of modern, domestic, cleaner, indigenous, and low-cost types of energy are significant for this stratum of society. At the same time, the equally inevitable requirement is to diminish the excessive share of these groups’ expenditures on energy supplies and services. The analysis of the results in the previous section shows that in most of the country, low-income groups spend a more significant share of their income on energy products, supplies, and services than higher-income fractions. Fair and equal access to energy supplies and services is less likely to reduce income inequality if it is not cost efficient, which is an unfortunate fact in the case of Pakistan. The electricity tariff systems create nonproductive indications for the provision of low-cost 23 Economies 2024,12, 259 supplies of energy instead of leading and facilitating access to energy sources. Cautious deliberation regarding energy tariff structures is inevitable, but at the same time, the safety nets and social security programs for the poorest needed to be expanded. At this stage, the energy supply is targeting energy prices that will substantially fulfil the objectives of reducing polarities and increasing real incomes, whereas it is equally important to expand cost-effective and efficient household instruments related to housing, transportation, agriculture, small domestic-scale production processes, and water pumping. Initiatives on energy policy are required to transform from a binary understanding to a qualitative paradigm regarding access, availability, provision of energy, and income inequality. There is a need to quantify the actual fraction of the total population that is deprived of basic, as well as cost-effective, energy supplies and services, and, at the same time, to encompass the barriers to providing cheap energy supplies to rural areas and constraints of smalland medium-household investments, through the Scheme for Financing Renewable Projects (State Bank of Pakistan 2016), and the associated risks. Therefore, the first plan of action for the government should be to facilitate the private sector and maintain the ease of doing business by reducing security and the related investment risks so that small-household financial policy de-risking instruments may be introduced and technical services may be provided geared toward the installation of low-cost renewable energy products for the low-income households, as per the Alternative and Renewable Energy Policy (2019). Ideally, it should start in rural areas which have fewer opportunities to access modern energy services and supplies. The factors that influence universal access to energy services, supplies, and quality, as well as equity should be the fundamental objective of socioeconomic and sociopolitical decision-making institutions. The political elite should moderate their consensus around the provision of affordable energy supplies to reduce energy poverty, energy deprivation, and inequalities. This may require the implementation of strict transparency measures in the energy sector, harmonizing the provinces, including Gilgit-Baltistan, Azad Jammu Kashmir, private stakeholders, and provincial governments, for integrated energy policies for a grassroots-level transformation. The draft of an inclusive, unified, integrated energy policy to be enforced for private investors, public enterprises, and federal and local governments alike to expand and offer special energy services and supplies to rural areas and the underprivileged via clean and green initiatives should be developed. Meanwhile, regulators can devise a strategy to link energy tariffs and taxes to income levels and regulate the pricing accordingly. Future research should focus on evaluating the impact of electricity pricing structures on different income groups and the effectiveness of social safety nets in reducing high electricity prices. It is also important to examine the barriers to the use of renewable energy solutions that benefit rural areas. In addition, research should examine how political ideology and understanding affect sustainable energy policy and explore how the private sector can contribute to enhancing public performance in improve energy access and affordability. Author Contributions: Conceptualization, S.S.; data curation, R.A., Y.Y. and Q.A.; formal analysis, S.S. and J.H.; funding acquisition, L.J.; methodology, J.H., R.A. and S.S.; project administration, L.J.; resources, S.S. and Y.Y.; writing—original draft, S.S., Q.A. and R.A.; writing—review and editing, Y.Y., S.S. and Y.Y. All authors have read and agreed to the published version of the manuscript. Funding: Ministry of Science and Technology, China. National Foreign Expert Project High-End Foreign Expert Introduction Plan. Educational traffic congestion management in China’s megacities for the “carbon peaking and carbon neutrality” Strategy—Machine learning model based on National Big Data for New Energy Vehicles (NDANEA) (QN2022178002L). Institutional Review Board Statement: Not Applicable. Informed Consent Statement: Not Applicable. Data Availability Statement: The data can be obtained upon request from the corresponding authors. Conflicts of Interest: The authors declare no conflicts of interest. 24 Economies 2024,12, 259 References Acemoglu, Daron, and James Robinson. 2013. Why Nations Fail: The Origins of Power, Prosperity, and Poverty. New York: Crown Publishing Group. Aguiar, Mark, and Mark Bils. 2015. Has consumption inequality mirrored income inequality? American Economic Review 105: 2725–56. [CrossRef] Ali, Sharafat. 2014. Inflation, Income inequality, and economic growth in Pakistan: A cointegration analysis. International Journal of Economic Practices and Theories 4: 33–42. Alternative and Renewable Energy Policy. 2019. Available online: https://climate-laws.org/document/alternative-and-renewableenergy-policy-2019_991c (accessed on 19 June 2023). Alvaredo, Facundo, and Leonardo Gasparini. 2015. Recent trends in inequality and poverty in developing countries. In Handbook of Income Distribution. Edited by Anthony B. Atkinson and François Bourguignon. Amsterdam: Elsevier, vol. 2. Arslan, Muhammad, Rashid Zaman, and R. K. Malik. 2014. Impact of CNG load shedding on daily routine: A study of Pakistan. International Letters of Social and Humanistic Sciences 42: 110–20. [CrossRef] Atkinson, Anthony B., Thomas Piketty, and Emmanuel Saez. 2011. Top incomes in the long run of history. Journal of Economic Literature 49: 3–71. [CrossRef] Awan, Rehmat Ullah, Falak Sher, and Akhtar Abbas. 2013. An investigation of multidimensional energy poverty in Pakistan. The Pakistan Development Review 4: 65–75. Bazilian, M., and K. Yumkella. 2015. Why Energy Poverty is the Real Energy Crisis. Global Governance. Available online: https: //www.weforum.org/agenda/2015/03/why-energy-poverty-is-the-real-energy-crisis (accessed on 19 June 2023). Bernanke, Ben S., Mark Gertler, Mark Watson, Christopher A. Sims, and Benjamin M. Friedman. 1997. Systematic monetary policy and the effects of oil price shocks. Brookings Papers on Economic Activity 1997: 91–157. [CrossRef] Berthe, Alexandre, and Luc Elie. 2015. Mechanisms explaining the impact of economic inequality on environmental deterioration. Ecological Economics 116: 191–200. [CrossRef] Bouzarovski, Stefan, and Sergio Tirado Herrero. 2017. The energy divide: Integrating energy transitions, regional inequalities and poverty trends in the European Union. European Urban and Regional Studies 24: 69–86. [CrossRef] Bourguignon, Thierry, Anne-Lorraine Bouquiaux-Stablo, Pascal Candolfi, Alain Mirza, Claudia Loardi, Marc-Antoine May, Rym El-Khoury, Michel Marchand, and Michel Aupart. 2015. Very long-term outcomes of the Carpentier-Edwards Perimount valve in aortic position. The Annals of Thoracic Surgery 99: 831–37. [CrossRef] Boyce, James. 1994. Inequality as a cause of environmental degradation. Ecological Economics 11: 169–78. [CrossRef] Checchi, Daniele, and Cecilia García-Peñalosa. 2008. Labour market institutions and income inequality. Economic Policy 23: 602–49. [CrossRef] Cheema, Ahmed Raza, and Maqbool H. Sial. 2012. Poverty, income inequality, and growth in Pakistan: A pooled regression analysis. The Lahore Journal of Economics 17: 137–57. [CrossRef] Ceyhan, Gaye D., and Deniz Saribas. 2022. Research trends on climate communication in the post-truth era. Educational and Developmental Psychologist 39: 5–16. [CrossRef] Dalton, Hugh. 1920. The measurement of the inequality of incomes. The Economic Journal 30: 348–61. [CrossRef] Easterly, William. 2003. The political economy of growth without development. In In Search of Prosperity: Analytic Narratives on Economic Growth. Princeton: Princeton University Press, pp. 439–72. EIA, USA. 2019. Available online: https://www.eia.gov/energyexplained/energy-and-the-environment/where-greenhouse-gasescome-from.php (accessed on 19 June 2023). Galvin, Ray, ed. 2019. Inequality and Energy: How Extremes of Wealth and Poverty in High Income Countries Affect CO 2 Emissions and Access to Energy. New York: Academic Press. Godfrey, Leslie G. 1988. Mis-Specification Tests in Econometrics: The Lagrange Multiplier Principle and Other Approaches. Cambridge: Cambridge University Press. Godfrey, Leslie G. 1996. Mis-specification tests and their uses in econometrics. Journal of Statistical Planning and Inference 49: 241–60. [CrossRef] Grunewald, Nicole, Michael Jakob, and Ioanna Mouratiadou. 2014. Decomposing inequality in CO 2 emissions: The role of primary energy carriers and economic sectors. Ecological Economics 100: 183–94. [CrossRef] Grunewald, Nicole, Stephan Klasen, Inmaculada Martínez-Zarzoso, and Chris Muris. 2017. The trade-off between income inequality and carbon dioxide emissions. Ecological Economics 142: 249–56. [CrossRef] Haider, Adnan, Qazi Masood Ahmed, and Zohaib Jawed. 2014. Determinants of energy inflation in Pakistan: An empirical analysis. The Pakistan Development Review 53: 491–504. [CrossRef] Hamilton, James D. 1983. Oil and the macroeconomy since World War II. The Journal of Political Economy 91: 228–48. [CrossRef] Haq, Rashida, and Nasim Shah Shirazi. 1998. Trends in Inequality and Welfare in Consumption Expenditure: The Case of Pakistan. The Pakistan Development Review 37: 765–79. [CrossRef] Heerink, Nico, Abay Mulatu, and Erwin Bulte. 2001. Income inequality and the environment: Aggregation bias in environmental Kuznets curves. Ecological Economics 38: 359–67. [CrossRef] Hendry, Nico, Abay Mulatu, and Erwin Bulte. 1980. Econometrics: Alchemy or science? Economica 47: 387–406. 25 Economies 2024,12, 259 Iddrisu, Insah, and Subhes C. Bhattacharyya. 2015. Sustainable energy development index: A multi-dimensional indicator for measuring sustainable energy development. Renewable and Sustainable Energy Reviews 50: 513–30. [CrossRef] International Atomic Energy Agency. 2005. Energy Indicators for Sustainable Development: Guidelines and Methodologies. United Nations, Department of Economic and Social Affairs, International Energy Agency. Vienna: International Atomic Energy Agency. International Energy Agency. 2017. Energy Access Outlook 2017. Paris: OECD. Keynes, John Maynard. 1973. The Collected Writings of John Maynard Keynes. 7, The General Theory. London: Macmillan. Khan, Fouad, and Paul Heinecker. 2018. Inequality and energy: Revisiting the relationship between disparity of income distribution and energy use from a complex systems perspective. Energy Research & Social Science 42: 184–92. Khan, Muhammad Arshad. 2015. Modelling and forecasting the demand for natural gas in Pakistan. Renewable and Sustainable Energy Reviews 49: 1145–59. [CrossRef] Krämer, Walter, Harald Sonnberger, Walter Krämer, and Harald Sonnberger. 1986. Diagnostic checking in practice. The Linear Regression Model Under Test, 123–55. Kuznet, Simon. 1955. Economic growth and income inequality. The American Economic Review 45: 1–28. Kuznets, Simon. 1955. International differences in capital formation and financing. In Capital Formation and Economic Growth. Princeton: Princeton University Press, pp. 19–111. Lakner, Christoph. 2016. Global Inequality: The Implications of Thomas Piketty’s Capital in the 21st Century. World Bank Policy Research Working Paper, (7776). Washington, DC: The World Bank. Liddle, Brantley. 2013. Impact of population, age structure, and urbanization on carbon emissions/energy consumption: Evidence from macro-level, cross-country analyses. Springer Science, Business 35: 286–304. [CrossRef] Lipset, Seymour Martin. 1960. Party systems and the representation of social groups. European Journal of Sociology/Archives Européennes de Sociologie 1: 50–85. [CrossRef] Lutzenhiser, Loren. 1993. Social and behavioral aspects of energy use. Annual Review of Energy and the Environment 18: 247–89. [CrossRef] Lütkepohl. 2001. Vector autoregressions. In Companion to Theoretical Econometrics’, Blackwell Companions to Contemporary Economics. Oxford: Basil Blackwell, pp. 678–99. Lyubimov, Ivan. 2017. Income inequality revisited 60 years later: Piketty vs. Kuznets. Russian Journal of Economics 3: 42–53. [CrossRef] Mahmood, Rafat, and Anwar Shah. 2017. Deprivation counts: An assessment of energy poverty in Pakistan. The Lahore Journal of Economics 22: 109–32. [CrossRef] Malik, Afia. 2012. Power Crisis in Pakistan: A Crisis in Governance? No. 2012: 1. Islamabad: Pakistan Institute of Development Economics. Malik, Kashif, Haram Ajmal, and Muhammad Umer Zahid. 2017. Oil price shock and its impact on the macroeconomic variables of Pakistan: A structural vector autoregressive approach. International Journal of Energy Economics and Policy 7: 83–92. Marx, Karl, and Friedrich Engels. 2008. The Communist Manifesto, 3rd ed. New York: Pathfinder Press. Masih, Abul MM, and Rumi Masih. 1996. Energy consumption, real income and temporal causality: Results from a multi-country study based on cointegration and error-correction modelling techniques. Energy Economics 18: 315–34. [CrossRef] Murtaza, Ghulam, and Muhammad Zahir Faridi. 2015. Causality linkages among energy poverty, income inequality, income poverty and growth: A system dynamic modelling approach. The Pakistan Development Review 54: 407–25. Nazir, Sidra, and T. Hameed. 2015. Impact of oil price and shocks on economic growth of Pakistan: Multivariate analysis (sectoral oil consumption). Business and Economics Journal 6: 1. Nwosu, Emmanuel O., Obed Ojonta, and Anthony Orji. 2018. Household consumption expenditure and inequality: Evidence from Nigerian data. International Journal of Development Issues 17: 266–87. [CrossRef] Pao, Hsiao-Tien, Yi-Ying Li, and Hsin-Chia Fu. 2014. Clean energy, non-clean energy, and economic growth in the MIST countries. Energy Policy 67: 932–42. [CrossRef] Piketty, Thomas. 2014. Capital in Twenty First Century. London and Cambridge: The Belknap Press of Harvard University Press. Piketty, Thomas. 2015. Capital in the twenty-first century. The American Economic Review 105: 48–53. [CrossRef] Polinsky, A. Mitchell. 1974. Economic Analysis as a Potentially Defective Product: A Buyer’s Guide to Posner’s” Economic Analysis of Law”. Harvard Law Review 87: 1655–81. [CrossRef] Ravallion, Martin, Mark Heil, and Jyotsna Jalan. 2000. Carbon emissions and income inequality. Oxford Economic Papers 52: 651–69. [CrossRef] Schroeter. 2013. Time to Get Serious About the Energy Crisis. Cologny: World Economic Forum, p. 2. Sen, Amartya. 1992. Inequality Re-Examined. New York: Clarendon Press, p. 155. Shahbaz, Muhammad. 2010. Income inequality-economic growth and non-linearity: A case of Pakistan. International Journal of Social Economics 37: 613–36. [CrossRef] Sahir, Mukhtar H., and Arshad H. Qureshi. 2007. Specific concerns of Pakistan in the context of energy security issues and geopolitics of the region. Energy Policy 35: 2031–37. [CrossRef] Shehzadi, Iram, Hafiz Muhammad Abubakar Siddique, and M. Tariq Majeed. 2019. Impact of political instability on economic growth, poverty and income inequality. Pakistan Business Review 20: 825–39. Sharafat, A. L. I., Humayun Rashid, and Muhammad Aamir Khan. 2014. The role of small and medium enterprises and poverty in Pakistan: An empirical analysis. Theoretical and Applied Economics 18: 593. 26 Economies 2024,12, 259 Sharif Razavian, Ali, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson. 2014. CNN features off-the-shelf: An astounding baseline for recognition. Paper presented at the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Columbus, OH, USA, June 23–28; pp. 806–13. Simcock, Neil, Harriet Thomson, Saska Petrova, and Stefan Bouzarovski. 2017. Energy Poverty and Vulnerability: A Global Perspective. Hague and Abingdon: Routledge. Skocpol, Theda. 1977. Wallerstein’s world capitalist system: A theoretical and historical critique. American Journal of Sociology 82: 1075–90. [CrossRef] Smith, Adam. 1776. An Inquiry into the Nature and Causes of the Wealth of Nations. Glasgow Edition of the Works and Correspondence of Adam Smith. Edited by Nicole Woolsey Biggart. New York: Oxford University Press, vol. II. Available online: http: //www.ibiblio.org/ml/libri/s/SmithA_WealthNations_p.pdf (accessed on 19 June 2023). State Bank of Pakistan. 2016. Annual Report 2016. Available online: https://www.sbp.org.pk/reports/annual/arFY16/Anul-indexeng-16.htm (accessed on 19 June 2023). Stock, James H., and Mark W. Watson. 1993. A simple estimator of cointegrating vectors in higher order integrated systems. Econometrica: Journal of the Econometric Society 61: 783–820. [CrossRef] Tabassam, Aftab Hussain, Shujahat Haider Hashmi, and Faiz Ur Rehman. 2016. Nexus between political instability and economic growth in Pakistan. Procedia: Social and Behavioral Sciences 230: 225–34. [CrossRef] Toman, Michael A., and Barbora Jemelkova. 2003. Energy and economic development: An assessment of the state of knowledge. The Energy Journal 24: 93–112. [CrossRef] Valasai, Gordhan Das, Muhammad Aslam Uqaili, HafeezUr Rahman Memon, and Saleem Raza Samoo. 2017. Overcoming electricity crisis in Pakistan: A review of sustainable electricity options. Renewable and Sustainable Energy Reviews 72: 734–45. [CrossRef] York, Richard, Eugene A. Rosa, and Thomas Dietz. 2004. The ecological footprint intensity of national economies. Journal of Industrial Ecology 8: 139–54. [CrossRef] White, Halbert. 1994. Estimation, Inference and Specification Analysis. Cambridge: University Press. World Bank. 2013. Toward a Sustainable Energy Future for All: Directions for the World Bank Group’s Energy Sector. World Bank’s Group Energy Sector. Washington, DC: World Bank. Available online: http://documents.worldbank.org/curated/en/74560146816052 4040/pdf/795970SST0SecM00box377380B00PUBLIC0.pdf (accessed on 19 June 2023). World Bank. 2018. Available online: https://documents.worldbank.org/en/publication/documents-reports/documentdetail/630671 538158537244/the-world-bank-annual-report-2018 (accessed on 19 June 2023). World Bank. 2022. Available online: https://www.worldbank.org/content/dam/meetings/external/annualmeeting-1/AM22-WBGPublications-Catalog.pdf (accessed on 19 June 2023). World Bank Group. 2014. World Bank. Available online: http://data.worldbank.org/indictor (accessed on 19 June 2023). Zaidi, Syed. Akbar. 2015. Issues in Pakistan’s Economy, 3rd ed. Oxford: Oxford University Press USA. Zouhaier, Hadhek, and Kefi Mohamed Karim. 2012. Democracy, investment and economic growth. International Journal of Economics and Financial Issues 2: 233–40. Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. 27 Citation: Seabela, Molepa, Kanayo Ogujiuba, and Maria Eggink. 2024. Determinants of Income Inequality in South Africa: A Vector Error Correction Model Approach. Economies 12: 169. https://doi.org/ 10.3390/economies12070169 Academic Editor: Fabio Clementi Received: 20 April 2024 Revised: 3 June 2024 Accepted: 5 June 2024 Published: 1 July 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). economies Article Determinants of Income Inequality in South Africa: A Vector Error Correction Model Approach Molepa Seabela, Kanayo Ogujiuba * and Maria Eggink School of Development Studies, University of Mpumalanga, Mbombela 1200, South Africa *Correspondence: [email protected] Abstract: The issue of income disparity has long plagued South Africa because of the political environment that existed before the country’s 1994 democratic transition. Based on the widely used Gini index, which gauges global inequality, the nation routinely has some of the highest rates of income disparity in the world. Income inequality in South Africa keeps rising even after a number of frameworks and policies have been put in place, which has a big influence on society. Thus, it is essential to comprehend the causes of income disparity and put suitable policies in place to remedy it. The purpose of this study is to look into the relationship between South Africa’s income disparity and its determinants. Using the Vector Error Correction Model (VECM) approach, this study empirically examines the effects of government spending on social grants, gross savings, population growth, and economic growth on income inequality from 1975 to 2017. Data on the Gini index are sourced from the Standardized World Income Inequality Database (SWIID). Findings reveal a statistically significant negative correlation between government spending on social grants and income inequality. Moreover, income inequality demonstrates a negative relationship with both gross savings and economic growth. However, population growth exhibits a positive correlation with income inequality. This study highlights the significance of implementing a comprehensive strategy to address income inequality in South Africa. This strategy should involve augmenting government expenditure on social grants, cultivating a savings culture within households, and enacting policies that incentivize job creation, particularly in areas with rapid population growth. In addition to making a substantial contribution to the body of evidence already available on income disparity, this study offers insightful information to policymakers working to improve the socioeconomic climate in South Africa. Keywords: income inequality; government spending on social grants; Gini coefficient; VECM; South Africa JEL Classification: C51; D63; H53 1. Introduction In recent years, there has been a growing global focus on income inequality, particularly its significant impact on developing countries such as South Africa. This study delves into the complex dynamics of income inequality within South Africa irrespective of a myriad of political and socioeconomic challenges confronting it. It examines the intricate relationship between government spending on social grants and income inequality. Using the Vector Error Correction Model (VECM), this study provides a detailed analysis of these dynamics. Ever since the country’s democratic transition in 1994, South Africa has faced growing anxiety over the problem of increasing wealth disparity. The persistent upward trend in income inequality highlights the complexity of addressing its underlying causes through policy and intervention methods, even in the face of widespread implementation of social spending measures. The political environment that South Africa finds itself in right now presents challenges to these endeavors. However, the available empirical academic research indicates conflicting relationships between the amount of money the government spends Economies 2024,12, 169. https://doi.org/10.3390/economies12070169 https://www.mdpi.com/journal/economies 28 Economies 2024,12, 169 on social grants and income disparity. Additionally, there are still unanswered questions about the relationships between gross savings, population increase, economic expansion, and income inequality. To comprehend income inequality both between and within groups, various inequality measures come into play such as the Gini coefficient, a widely utilized measure ranging from zero (indicating perfect equality) to one (indicating perfect inequality). Drawing from the Standardized World Income Inequality Database (SWIID), this study examines Gini indices for income inequality, considering both gross and net incomes from 1975 to 2017. The South African government through the National Development Plan (NDP) aims to reduce income inequality from 0.70 to 0.60 by the year 2030. Despite the various drafted and implemented policies, South Africa remains the most unequal country globally (IMF 2020). Severe income inequality has persisted over the last century, indicating the necessity of comprehensive economic reforms either through policy or legal prescripts. As Leibbrandt and Shipp (2019) opined, reducing inequality requires targeted policies that address disparities in earnings and employment prospects and ongoing support to diminish gender and racial income inequalities. Irrespective of the political climate and orientation held by those in authority, this study contends that existing policies and frameworks fall short in the face of growing income inequality, urging a re-evaluation of the contributing factors to income inequality. This study focuses on South Africa, a country known for its struggle with income inequality. It provides valuable insights that can inform national policy and global discourse on income inequality. This study’s inclusion of data spanning from 1975 to 2017 allows for a comprehensive analysis of long-term trends and dynamics, providing a strong foundation for policy recommendations and future research directions. Considering the aforementioned background, this study investigated how government socioeconomic spending, particularly on social grants, affects income inequality. Additionally, the roles of gross savings, population growth, and economic growth in shaping income inequality were analyzed. The paper follows the following layout: Section 2 critically analyzes the literature, encompassing both the theoretical framework and empirical studies on the relationship between government spending on social grants and income inequality. This section delves into existing research to provide a comprehensive understanding of the topic. Section 3 outlines the data and methodology utilized in the study. Section 4 presents the findings and engages in a detailed discussion. Finally, Section 5 concludes the study, offering policy recommendations based on the results of the study. 2. Related Literature Income inequality, a pressing issue in both developed and developing economies, is influenced by a variety of factors. This literature review consolidates insights from seminal works, globaland emerging-market studies, and specific studies focused on Africa, particularly South Africa, to comprehensively understand how each factor affects income inequality. These factors are not only crucial in the context of income inequality but are also connected to important works in economics that offer both theoretical foundations and empirical evidence for understanding the complex relationships between them and income inequality. 2.1. Seminal Works Although exactly what causes income inequality cannot be determined due to the variety of factors that play a role, in this study some of the crucial factors are identified. Financial development, economic growth, external trade, and government initiatives can help mitigate income inequality, whereas inflation worsens it (Kapingura 2017). A fundamental component of Keynesian economics, government expenditure on social welfare programs plays a crucial role in managing economic fluctuations and lessening income inequality (Vo et al. 2019). Such policies can stabilize aggregate demand, achieve full employment, and mitigate income inequality (Vo et al. 2019). In his influential work 29 Economies 2024,12, 169 during the Great Depression, John Maynard Keynes established the framework for Keynesian economics, highlighting the significance of aggregate demand in propelling economic activity (Jahan et al. 2014). Keynes proposed government intervention through expansionary fiscal policies to address economic fluctuations, attain full employment, stabilize prices, and diminish income inequality (Jahan et al. 2014; Alamanda 2020). Fishburn and Willig (1984) extended Dalton’s principle of transfer for income redistribution, demonstrating that socially desirable transfers, coupled with inverse transfers at higher income levels, yield positive social benefits. They linked these transfer principles to measures of income inequality and social welfare. Lerman and Yitzhaki (1995) developed a method to decompose changes in the Gini coefficient into components that narrow the income gap and reorganize income rankings. When this approach was used to analyze U.S. taxes and transfers for 1991, it was demonstrated that fiscal policies have the potential to lessen inequality by reducing income gaps and reordering income rankings. Apart from government intervention in the form of redistribution of income, savings can also play a role in income inequality. The relationship between savings and income inequality is complex. While savings can contribute to wealth inequality, they can also serve as a safety net and reduce poverty (Vo et al. 2019). Shen and Zhao (2022) found that the impact of savings on income inequality varies across different subgroups and economies. Serven and Schmidt-Hebbel (1999) found no evidence that income inequality affects aggregate saving across countries, whereas a study by Schmidt-Hebbel and Serven (2000) highlighted the theoretical ambiguity in the relationship between income inequality and aggregate savings, with empirical results showing no systematic effect. The relative income hypothesis (RIH) according to Duesenberry (1949) and the permanent income hypothesis (PIH) proposed by Friedman (1957) are two important theories for understanding consumption patterns, indicating that consumption patterns are influenced by relative income and expected lifetime income, respectively. In modern societies, social status and relative income have a major impact on consumption patterns. A study by Bisset and Tenaw (2020) showed that low-income individuals adjusted their consumption to keep pace with others, showing strong “proof effects” and “ratchet effects”. A study by Palley (2010) found that wealthy households save more permanent income than poor households, suggesting that relative income influences consumption patterns. Stable, long-term income policies can reduce income inequality by stabilizing consumption patterns (Yun et al. 2023). Another component that is identified by the literature as playing a role in income inequality is population growth. High population growth is generally associated with a less equal income distribution (Ram 1984; Oyekale et al. 2004; Kaasa 2005). Reducing population growth tends to increase the income share of the poorest segments of the population (Rodgers 1983; Oyekale et al. 2004). Lower population growth and limited migration may contribute to increased national and global economic inequality (Peterson 2017). Although economic growth may seem important for the reduction of income inequality, the relationship between income inequality and economic growth is more complex. Simon Kuznets (1955) proposed the Kuznets curve, which shows an inverted U-shaped relationship between economic development and income inequality. This suggests that inequality is a temporary phase in the development process. Arthur Lewis’s (1954) dualsector model explains economic development through labor transfer from a traditional to a modern sector, initially increasing inequality but eventually decreasing it as more workers transition to higher-paying industrial jobs (Sumner 2018). Lewis stressed the importance of government intervention to facilitate this transition and ensure fair income distribution (Sumner 2018). Piketty (2014) argues that returns on capital exceed the rate of economic growth and capital returns are higher than wages. He suggests that income inequality increases because wages grow more slowly than returns on capital. Piketty’s work emphasizes the need for progressive taxation and policies to promote equal access to education and opportunities in order to address income inequality (Sawyer 2015). Mo (2000) developed a theoretical framework revealing that income inequality negatively influences GDP growth, particularly through the transfer channel. Empirical studies in30 Economies 2024,12, 169 dicate that while economic growth can reduce poverty, income inequality can intensify poverty and exacerbate the impact of growth on poverty (Amponsah et al. 2023). Economic growth exhibits poverty-reduction properties, but income inequality intensifies poverty and aggravates the impact of growth on poverty (Adeleye et al. 2020). The impact of GDP growth on poverty reduction diminishes with higher initial inequality, with a smaller poverty-reduction response in sub-Saharan Africa (Fosu 2009). Evidence from empirical studies to determine how these theories apply and how these factors of income inequality play a role will be discussed further. 2.2. Africa/South African Studies Leibbrandt et al. (2012) discovered that social transfers, particularly child support grants and old-age pensions, played a crucial role in decreasing poverty and income inequality in South Africa. Woolard et al. (2015) demonstrated that progressive taxes and pro-poor social spending significantly reduce income inequality in South Africa. The findings indicate a negative relationship between progressive taxes and pro-poor social spending. Additionally, Schiel et al. (2014) found that while social grants have helped alleviate poverty, they have not significantly reduced income inequality in South Africa. Household composition decomposition techniques revealed that changes have significantly reduced the direct impact on inequality through changes in household composition. Consequently, the relationship between government expenditures and income inequality is deemed insignificant. Despite the vital role played by social grants in reducing South Africa’s persistently high levels of inequality, greater efforts are needed to further reduce income inequality as it remains relatively high. The impact of savings on economic growth in South Africa is negative in the long run but positive in the short run (Van Wyk and Kapingura 2021). The development of the financial sector, especially when inclusive, can reduce income inequality, making financial inclusion crucial for benefiting disadvantaged groups (Kapingura 2017). According to Yun et al. (2023), the government may consider introducing a policy that allows tax deductions for retirement savings. Additionally, the government can design welfare and social security programs to provide individuals with a steady income over the long term rather than shortterm cash payments. High population growth in low-income countries, including many in Africa, may slow economic development and exacerbate income inequality (Peterson 2017). Limited migration and lower population growth could increase economic inequality both nationally and globally. Studies by Nwosa (2019) and Ullah et al. (2021) support the positive relationship between population size and income inequality. The relationship between economic growth and income inequality in African countries, including South Africa, has been the subject of various studies on the Kuznets curve. The Kuznets hypothesis, which suggests an inverted U-shape relationship between economic growth and income inequality, has been challenged in several empirical studies. These studies rejected the hypothesis because the data used were cross-sectional, meaning that the countries analyzed were at different stages of development. For example, Wahiba and Weriemmi (2014), Niyimbanira (2017), Nwosa (2019), Mdingi and Ho (2021), and Chude and Chude (2022) re-evaluated the relationship between income inequality and economic growth and found that variations in income distribution are more related to country-specific characteristics than data comparability issues. In South Africa, high income inequality has been shown to have a negative impact on long-term economic growth (Mdingi and Ho 2021). Niyimbanira (2017) found that in the Mpumalanga province of South Africa, economic growth was associated with poverty reduction but did not significantly affect income inequality, contrary to theoretical expectations. Chude and Chude (2022) and Nwosa (2019) found no significant effect of income inequality on economic growth in Nigeria. Wahiba and Weriemmi (2014) found that economic growth had a positive impact on income inequality in Tunisia. Zungu et al. (2021) discovered that lower growth is associated with lower income inequality in the SADC region. Despite efforts to address income inequality through economic growth and redistributive policies, South Africa 31 Economies 2024,12, 169 Gross savings has a significant positive relationship with income inequality, with a 1% significance level. This means that when households save more, it leads to an increase in income inequality. Darku (2014) found that increased income inequality results in increased consumption by individuals in all income groups, leading to declining personal savings rates. According to Palley (2010), wealthy households save a higher percentage of their permanent income than poor households, leading to disproportionate wealth accumulation and investment returns and exacerbating income inequality. Similarly, empirical studies by Maaboudi et al. (2023) and Tran et al. (2020) found a positive relationship between gross savings and income inequality. However, studies by Van Wyk and Kapingura (2021), Yildirim (2020), and Deniz and Ozturkler (2010) found a negative relationship, while Halim et al. (2016) found no significant association between gross savings and income inequality. The theoretical studies that support these findings are Yun et al. (2023), Friedman (1957), and Duesenberry (1949). When applying the Relative Income Hypothesis (Duesenberry 1949) to this study, it is found that individuals with lower incomes might save less to keep up with the consumption patterns of wealthier individuals, leading to lower wealth accumulation and higher income inequality over time. On the other hand, the Permanent Income Hypothesis (Friedman 1957) suggests that wealthier households save more, leading to faster wealth accumulation than that of poorer households, resulting in increasing income inequality. Yun et al. (2023) suggest that policy intervention aimed at stabilizing income can reduce income inequality by stabilizing consumption patterns. The findings indicated that the population growth coefficient at 1% was statistically significant. As a result, population growth had a negative impact on income inequality over time. This suggests that as the population increases, income inequality is likely to also increase. The potential positive impact of population growth could be attributed to the fact that if state resources do not increase in line with the population, the allocation for social programs, healthcare, and education may result in fewer resources per person. This could strain society and ultimately lead to a rise in income inequality. Therefore, these results are consistent with other studies by Ullah et al. (2021), Nwosa (2019), Peterson (2017), and Anyanwu (2016), which found that population growth in the long-term leads to increases in income inequality. In an ideal scenario, an increasing population can lead to more entrepreneurial activity, job creation, and overall economic growth. However, in South Africa, several factors complicate these relationships. Issues such as structural inequality, limited access to education and resources, regulatory barriers, and economic instability can hinder entrepreneurial efforts and business growth. Historical differences in South Africa, including the impact of apartheid, have resulted in persistent economic inequality that disproportionately affects marginalized communities. High unemployment rates, skill shortages, and inadequate infrastructure further impede the development of entrepreneurs and economic expansion. Addressing these fundamental problems is crucial to creating a conducive environment for entrepreneurship and business growth while harnessing the potential benefits of a larger population. While social subsidies are essential in addressing immediate challenges of poverty and income inequality, long-term sustainable solutions must focus on encouraging inclusive economic growth and empowering individuals and communities to participate meaningfully in the economy. To achieve this, a comprehensive approach is needed to address both the supply-side constraints facing businesses and the broader socio-economic factors that contribute to inequality and exclusion. Prioritizing policies that support entrepreneurs and economic empowerment can help South Africa create a more equitable and prosperous society for all citizens. Policymakers may need to re-evaluate population-related policies like immigration, family planning, and resource allocation strategies. Addressing the potential negative impact of population growth on income distribution necessitates a holistic approach that accounts for demographic trends and social policy frameworks. This study’s findings show that annual GDP growth rates have a positive impact on income inequality in the long term. The commonly held view that economic growth automatically leads to improved income distribution is not always true in most developing 38 Economies 2024,12, 169 countries, including South Africa. This indicates that the relationship between economic growth and income inequality is more complex and can vary depending on various factors, such as policy interventions and labor market dynamics. This is consistent with a study by Wahiba and Weriemmi (2014) that also found a positive relationship. Empirical studies by Mdingi and Ho (2021), Nambie et al. (2023), Jianu et al. (2021), Vo et al. (2019), Royuela et al. (2019), and Caraballo et al. (2017) found a negative relationship between economic growth and income inequality. Chude and Chude (2022), Nwosa (2019), and Niyimbanira (2017) found no significant effect of income inequality on economic growth contrary to theoretical expectations. The dummy variable representing economic crises shows a negative relationship with income inequality. This means that in the long-run equation of the VECM, the dummy variable has a negative and significant impact on income inequality in South Africa, particularly capturing the global financial crisis. This study’s significant error correction term of − 0.063277, which falls between zero and negative, indicates a stable long-run equilibrium. The negative error correction term also suggests a stable and statistically significant cointegration relationship. Policymakers may need to reconsider the connection between economic growth and income distribution, focusing on inclusive growth strategies that prioritize equitable wealth distribution. This finding underscores the importance of targeted interventions to ensure that economic prosperity benefits all members of society. 4.6. Robustness Check Performing diagnostic tests is an essential part of this study since it indicates whether or not there is an issue with the model’s estimation. If an issue is found, it indicates that the model is inefficient, which may also imply that the findings are skewed (Wooldridge 2001). Tests for normality, heteroscedasticity, and serial correlation were among the diagnostic procedures carried out to determine whether the model utilized in this investigation reasonably fits the data. The results of the diagnostic tests conducted for this study indicate that the model is quite well described. Table 6 shows that the residuals have a combined probability for the Jarque–Bera of 0.1107 and are normally distributed. The likelihood of 0.5015 for LM-Stat indicates that the residuals are not serially correlated. Furthermore, no heteroskedasticity has been discovered, as shown by a joint Chi-square probability of 0.5603. Table 6. Diagnostic tests. Test Null Hypothesis t-Statistics Probability Jarque–Bera (JB) There is a normal distribution 4.401542 0.1107 Langrage Multiplier (LM) No serial correlation 45.54572 0.5015 White (CH-sq) No conditional heteroskedasticity 33.09478 0.5603 5. Conclusion and Recommendations This study concludes by summarizing findings, providing recommendations, and outlining limitations in Sections 5.1–5.3. 5.1. Conclusions South Africa has been struggling with the issue of income inequality for a long time, even before the onset of democracy. According to the widely accepted measure of global inequality, the Gini index, South Africa has the highest income inequality in the world. Despite implementing policies and frameworks, South Africa has seen a rise in income inequality, which has a profound impact on society. The purpose of this study was to analyze the relationship between specific economic indicators in South Africa using the Vector Error Correction Model (VECM) on income inequality. This is fundamental because 39 Economies 2024,12, 169 assessing the factors contributing to income inequality assists in finding appropriate measures to mitigate it. By evaluating government spending on social grants, gross savings, population growth, economic growth, and the dummy variable to capture economic crises, this study sheds light on their impact on income inequality from 1975 to 2017 in the South African context. This study revealed that government spending on social grants has a negative impact on income inequality. This means that as government spending on social grants increases, income inequality is expected to decrease in the long term. Additionally, this study found that gross savings have a positive impact on income inequality, with a significance of 1%. This suggests that wealthier households tend to save a higher percentage of their permanent income compared to poorer households, leading to disproportionate wealth accumulation and exacerbating income inequality. Furthermore, this study’s results indicated that economic growth has a positive impact on income inequality in the long run. However, it is worth noting that economic growth does not always lead to improved income distribution in developing countries like South Africa, as the relationship is influenced by factors such as policy interventions and labor market dynamics. Moreover, population growth is statistically significant at 1% and positively impacts income inequality in the long term. This implies that an increase in population over time can lead to a surge in income inequality. However, it is important to recognize that population growth can also stimulate entrepreneurship, create job opportunities, and contribute to overall economic development. In the long run, the dummy variable representing economic crises demonstrates a negative and significant relationship with income inequality in South Africa. This suggests that it has a notable impact on income inequality, particularly in the context of capturing the global financial crisis. The results of this study add to the existing literature on the relationship between government spending, gross savings, population growth, economic growth, and income inequality. The findings highlight the importance of government spending on social grants and the negative impact of gross savings on income inequality. These findings can inform policy decisions to reduce income inequality in South Africa. To tackle the pressing problem of income inequality, policymakers are advised to adopt a versatile approach that includes policies aimed at economic growth and equitable income distribution. Such measures may include boosting government spending on social welfare programs and revamping social security policies, both of which have shown to be effective in addressing income inequality. In a South African context, a prime example was the successful implementation of the social relief of distress grant during the COVID-19 pandemic. Although increasing spending on social welfare programs can help to reduce poverty and income inequality in the short term, there are some potential disadvantages to consider. For instance, relying too heavily on social grants without implementing measures to promote economic growth and employment could lead to dependency instead of encouraging self-reliance. In addition, inefficient administration and corruption can undermine the effectiveness of social welfare programs, resulting in misallocated resources and worsening income inequalities. This study’s findings emphasize the significance of savings and economic growth in addressing income inequality. Encouraging people to save and invest can lead to economic growth and create opportunities for accumulating wealth. Policies that incentivize saving behavior, such as tax breaks and tax-free investment incentives, can effectively promote these endeavors without adversely affecting government income. Furthermore, implementing policies to strengthen economic growth, such as infrastructure development, innovation incentives, and trade facilitation, can stimulate job creation and income generation. Supporting small and medium-sized enterprises (SMEs) and entrepreneurship can also foster inclusive economic growth. In addition, investing in population programs, including family planning initiatives and reproductive health services, can effectively manage population growth, therefore leading to less government spending on social grants. By empowering individuals to make informed choices about family planning, these programs can positively impact income 40 Economies 2024,12, 169 inequality by encouraging smaller family sizes. This, in turn, reduces pressure on resources and promotes economic development. 5.2. Recommendations This study’s findings highlight the importance of balancing individual saving behaviors with broader socio-economic goals, which can potentially influence financial regulation and social welfare policies. The following recommendations are made based on the results of this study: 1. This study proposes implementing strategies to curb income inequality, including increasing government spending on social welfare programs and reforming social security policies. These measures have been demonstrated to be effective in mitigating income disparities, as exemplified by the success of the social relief of distress grant implemented during the COVID-19 pandemic in South Africa. 2. Policymakers are encouraged to address the fundamental causes of income inequality, acknowledging the essential role of labor supply and job creation in alleviating income inequality; policies should focus on employment expansion. Initiatives such as skill development programs can enhance the workforce’s employability. 3. To balance population growth with inclusive economic development, policymakers are encouraged to develop policies that stimulate job creation and economic opportunities in regions experiencing rapid population growth. This should also foster an environment conducive to entrepreneurship and small business development to absorb the growing workforce and minimize the exacerbation of income inequality over the long term. 4. This study highlights the importance of policies geared towards improving gross savings. Encouraging a culture of saving and implementing incentives for individuals and businesses by the government can contribute to economic stability and resilience in the long term. 5.3. Limitations of This Study and Recommendation for Future Studies This study had some limitations due to a shortage of relevant data and materials. This study is restricted to a specific time frame because of the availability of data. The study period is from 1975 to 2017, which means that there is a five-year time lag in terms of the data since this study concluded in 2023. The unemployment data were not included in the model due to the methodological constraints of the Vector Error Correction Model (VECM) and data limitations. The unemployment rate data are considered to be integrated as order zero (I(0)), indicating that it is already stationary. On the other hand, for the Vector Error Correction Model (VECM) to establish cointegrating relationships, all variables need to be integrated as order one (I(1)). If an I(0) variable is included in a VECM, it can result in model misspecification and unreliable results. The near singular matrix error occurred when the lagged Gini coefficient was introduced, indicating perfect collinearity. This means that the lagged Gini coefficient was highly correlated with the current Gini coefficient, making it redundant in the model. Including perfectly collinear variables violates the assumptions of the VECM and can lead to unstable estimates. Furthermore, despite several efforts that were made to collect relevant data from different sources, this study is constrained due to the limited amount of data on income inequality in South Africa. It is recommended that: • Future research studies should investigate whether the results of this study would vary if the income inequality data were available over a more extended period. • Also, future research could incorporate different categories of social transfers as separate variables, enabling a more nuanced examination of their effects on income inequality. Author Contributions: Conceptualization, M.S. and K.O.; methodology, M.S.; validation, M.E., K.O. and M.S.; formal analysis, M.S.; investigation, M.S. and M.E.; data curation, M.E.; writing—original draft preparation, M.S. and K.O.; writing—review and editing, M.S., K.O. and M.E.; visualization, M.E.; supervision, K.O. All authors have read and agreed to the published version of the manuscript. 41 Economies 2024,12, 169 Funding: This research received no external funding, and the APC was funded by the University of Mpumalanga, South Africa. Informed Consent Statement: Not applicable. Data Availability Statement: Available on request. Conflicts of Interest: The authors declare no conflict of interest. References Adeleye, Bosede Ngozi, Obindah Gershon, Adeyemi Ogundipe, Oluwarotimi Owolabi, Ifeoluwa Ogunrinola, and Oluwasogo Adediran. 2020. Comparative investigation of the growth-poverty-inequality trilemma in Sub-Saharan Africa and Latin American and Caribbean Countries. Heliyon 6: e05631. [CrossRef] Ahmad, Mahyudin. 2017. Economic freedom and income inequality: Does political regime matter? Economies 5: 18. [CrossRef] Alamanda, Alamanda. 2020. The effect of government expenditure on income inequality and poverty in Indonesia. Journal Info Artha 4: 1–11. [CrossRef] Amponsah, Mary, Frank W. Agbola, and Amir Mahmood. 2023. The relationship between poverty, income inequality and inclusive growth in Sub-Saharan Africa. Economic Modelling 126: 106415. [CrossRef] Anderson, Edward, Maria Ana Jalles D’Orey, Maren Duvendack, and Lucio Esposito. 2017. Does government spending affect income inequality? A meta-regression analysis. Journal of Economic Surveys 31: 961–87. [CrossRef] Andrei, Dalina Maria, and Liviu C. Andrei. 2015. Vector error correction model in explaining the association of some macroeconomic variables in Romania. Procedia Economics and Finance 22: 568–76. [CrossRef] Anyanwu, John C. 2016. Empirical Analysis of the Main Drivers of Income Inequality in Southern Africa. Annals of Economics & Finance 17: 337–64. Arshad, Sumera, and Amajd Ali. 2016. Trade-off between Inflation, Interest and Unemployment Rate of Pakistan: Revisited. Bulletin of Business and Economics (BBE) 5: 193–209. Asari, F. F. A. H., Nurul Syuhada Baharuddin, Nurmadihah Jusoh, Zuraida Mohamad, Norazidah Shamsudin, and Kamaruzaman Jusoff. 2011. A vector error correction model (VECM) approach in explaining the relationship between interest rate and inflation towards exchange rate volatility in Malaysia. World Applied Sciences Journal 12: 49–56. Bhorat, Haroon, David Tseng, and Benjamin Stanwix. 2014. Pro-poor growth and social protection in South Africa: Exploring the interactions. Development Southern Africa 31: 219–40. [CrossRef] Bisset, Tazeb, and Dagmawe Tenaw. 2020. Keeping up with the Joneses: The relevance of Duesenberry’s relative income hypothesis in Ethiopia. Research Square. [CrossRef] Caraballo, M ª Ángeles, Carlos Dabús, and Fernando Delbianco. 2017. Income inequality and economic growth revisited. A note. Journal of International Development 29: 1025–29. [CrossRef] Chude, Nkiru Patricia, and Daniel Izuchukwu Chude. 2022. Effect of Income Inequality on the Economic Growth of Nigeria. International Journal of Scientific and Management Research 1: 22–35. [CrossRef] Darku, Alexander. 2014. Income inequality, status seeking, and savings rates in Canada. Canadian Studies in Population 41: 88–104. [CrossRef] Deniz, Pinar, and Harun Ozturkler. 2010. Effect of Income Inequality on Aggregate Saving: A Panel Analysis. Paper presented at Turgut Özal International Conference on Economics and Politics–I: Global Crises and Economic Governance, Malatya, Turkey, May 15–16; Available online: https://studys.ssrn.com/sol3/studys.cfm?abstract_id=3299364 (accessed on 10 February 2022). Duesenberry, James Stemble. 1949. Income, Savings, and the Theory of Consumer Behavior. Cambridge: Harvard University Press. Fishburn, Peter C., and Robert D. Willig. 1984. Transfer principles in income redistribution. Journal of Public Economics 25: 323–28. [CrossRef] Fosu, Augustin Kwasi. 2009. Inequality and the impact of growth on poverty: Comparative evidence for Sub-Saharan Africa. Social Science Research Network. [CrossRef] Francis, David, and Edward Webster. 2019. Inequality in South Africa. Development Southern Africa 36: 733–34. [CrossRef] Friedman, Milton. 1957. The permanent income hypothesis. In A Theory of the Consumption Function. Princeton: Princeton University Press. Halim, Mohammed Abdul, Sadia Sultana, Asma Akter, and Ataur Rahaman. 2016. Savings and Income Inequality in Bangladesh (Working Study Series: WP No 1617). Available online: https://www.bb.org.bd/pub/research/workingstudy/wp1617.pdf (accessed on 15 March 2020). International Monetary Fund. 2020. Six Charts Explain South Africa’s Inequality. Available online: https://www.imf.org/en/News/ Articles/2020/01/29/na012820six-charts-on-south-africas-persistent-and-multi-faceted-inequality (accessed on 27 March 2024). Jahan, Sarwat, Ahmed Saber Mahmud, and Chris Papageorgiou. 2014. Back to Basics: What Is Keynesian Economics?—The central tenet of this school of thought is that government intervention can stabilize the economy. Finance & Development 51: 53–54. Jianu, Ionu t , , Marin Dinu, Drago s , Huru, and Alexandru Bodislav. 2021. Examining the Relationship between Income Inequality and Growth from the Perspective of EU Member States’ Stage of Development. Sustainability 13: 5204. [CrossRef] 42 Economies 2024,12, 169 Johansen, Søren. 1991. Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica 59: 1551–80. [CrossRef] Kaasa, Anneli. 2005. Factors of Income Inequality and their Influence Mechanisms: A Theoretical Overview. Social Science Research Network. [CrossRef] Kapingura, Forget Mingiri. 2017. Financial sector development and income inequality in South Africa. African Journal of Economic and Management Studies 8: 420–32. [CrossRef] Kuznets, Simon. 1955. Economic growth and income inequality. The American Economic Review 45: 1–28. Leibbrandt, Murray, Arden Finn, and Ingrid Woolard. 2012. Describing and decomposing post-apartheid income inequality in South Africa. Development Southern Africa 29: 19–34. [CrossRef] Leibbrandt, Murray, and Timothy Shipp. 2019. Reducing Inequalities in South Africa: Progress on Equality Thwarted by Slow Growth and Success of Top Earners. (UN-WIDER Policy Brief 2019/2). Available online: https://www.wider.unu.edu/publication/ reducing-inequalities-south-africa (accessed on 7 July 2022). Lerman, Robert I., and Shlomo Yitzhaki. 1995. Changing Ranks and The Inequality Impacts of Taxes and Transfers. National Tax Journal 48: 45–59. [CrossRef] Lewis, W. Arthur. 1954. Economic Development with Unlimited Supply of Labor. The Manchester School of Economic and Social Studies 22: 139–91. [CrossRef] Maaboudi, Reza, Mohammad Hassan Fotros, and Zeynab Dare Nazari. 2023. The Effect of Income Inequality on the National Savings in Iran. Stable Economy Journal 4: 69–91. Mdingi, Kholeka, and Sin-Yu Ho. 2021. Literature review on income inequality and economic growth. MethodsX 8: 1–12. [CrossRef] [PubMed] Mo, Pak Hung. 2000. Income inequality and economic growth. Kyklos 53: 293–315. [CrossRef] Nambie, Nicholas Bamegne, Philomena Dadzie, and Dorcas Oye Haywood-Dadzie. 2023. Measuring the Effect of Income Inequality, Financial Inclusion, Investment, and Unemployment, on Economic Growth in Africa: A Moderating Role of Digital Financial Technology. International Journal of Economics and Financial Issues 13: 111–24. [CrossRef] Niyimbanira, Ferdinand. 2017. Analysis of the impact of economic growth on income inequality and poverty in South Africa: The case of Mpumalanga Province. International Journal of Economics and Financial Issues 7: 254–61. Nwosa, Philip Ifeakachukwu. 2019. Income inequality and economic growth in Nigeria: Implication for Economic Development. Acta Universitatis Danubius Oconomica 15: 108–16. Oyekale, A. S., A. I. Adeoti, and T. O. Ogunnupe. 2004. Sources of income inequality and poverty in rural and urban Nigeria. Paper presented at 3rd Annual Workshop of Poverty and Economic Policy (PEP) Network, Dakar, Senegal, June 11–20. Palley, Thomas I. 2010. The Relative Permanent Income Theory of Consumption: A Synthetic Keynes–Duesenberry–Friedman Model. Review of Political Economy 22: 41–56. [CrossRef] Peterson, E. Wesley F. 2017. The role of population in economic growth. SAGE Open 7: 215824401773609. [CrossRef] Piketty, Thomas. 2014. Capital in the Twenty-First Century. Cambridge: Harvard University Press. Ram, Rati. 1984. Population increase, economic growth, educational inequality, and income distribution. Journal of Development Economics 14: 419–28. [CrossRef] Rodgers, Gerry. 1983. Population growth, inequality and poverty. International Labour Review 122: 443–60. Royuela, Vicente, Paolo Veneri, and Raul Ramos. 2019. The short-run relationship between inequality and growth: Evidence from OECD regions during the Great Recession. Regional Studies 53: 574–86. [CrossRef] Sanchez, A. Angeles, and Antonio L. Perez-Corral. 2018. Government social expenditure and income inequalities in the European Union. Hacienda Pública Española 227: 133–56. [CrossRef] Sawyer, Malcolm. 2015. Confronting inequality: Review article on Thomas Piketty on Capital in the 2st Century. International Review of Applied Economics 29: 878–889. [CrossRef] Schiel, Reinhard, Murray Leibbrandt, and David Lam. 2014. Assessing the Impact of Social Grants on Inequality: A South African Case Study. (UN-WIDER Working Study 2014/160). Available online: https://www.wider.unu.edu/publication/assessing-impactsocial-grants-inequality (accessed on 24 June 2019). Schmidt-Hebbel, Klaus, and Luis Serven. 2000. Does income inequality raise aggregate saving? Journal of Development Economics 61: 417–46. [CrossRef] Serven, Luis, and Klaus Schmidt-Hebbel. 1999. Income Inequality and Aggregate Saving: The Cross-Country Evidence. World Bank Policy Research Working Paper. Washington, DC: World Bank. [CrossRef] Shen, Chengfang, and Xindong Zhao. 2022. How does income inequality affects economic growth at different income levels? Ekonomska Istraživanja/Ekonomska IstražIvanja 36: 864–84. [CrossRef] Solt, Frederick. 2020. Measuring income inequality across countries and over time: The Standardized World Income Inequality Database. Social Science Quarterly 101: 1183–99. [CrossRef] Stats SA (Statistics South Africa). 2023. Census 2022 (Statistical Release P0301.4). Available online: https://census.statssa.gov.za/ assets/documents/2022/P03014_Census_2022_Statistical_Release.pdf (accessed on 28 March 2024). Sumner, Andy. 2018. Trickle along Not Trickle Down? Inequality and the Lewis Model of Economic Development. (ESRC GPID Research Network Briefing Paper 13). Available online: https://gpid.univie.ac.at/wp-content/uploads/2018/04/Brief_13.pdf (accessed on 22 May 2024). 43 Economies 2024,12, 169 Tran, Nhan Dang, Chuong Nguyen Ong, and Quy Dinh Le Nguyen. 2020. The relationship between income inequality and savings: Evidence from household-level panel data in Vietnam. Journal of Applied Economics 23: 709–28. [CrossRef] Ullah, Atta, Zhao Kui, Saif Ullah, Chen Pinglu, and Saba Khan. 2021. Sustainable utilization of financial and institutional resources in reducing income inequality and poverty. Sustainability 13: 1038. [CrossRef] Van Wyk, Bianca Flavia, and Forget Mingiri Kapingura. 2021. Understanding the nexus between savings and economic growth: A South African context. Development Southern Africa 38: 828–44. [CrossRef] Vo, Duc Hong, Thang Cong Nguyen, Ngoc Phu Tran, and Anh The Vo. 2019. What Factors Affect Income Inequality and Economic Growth in Middle-Income Countries? Journal of Risk and Financial Management 12: 40. [CrossRef] Wahiba, Nasfi Fkili, and Malek El Weriemmi. 2014. The Relationship between Economic Growth and Income Inequality. International Journal of Economics and Financial Issues 4: 135–43. Woolard, Ingrid, Rebecca Metz, Gabriela Inchauste, Nora Lustig, Mashekwa Maboshe, and Catriona Purfield. 2015. How Much Is Inequality Reduced by Progressive Taxation and Government Spending. Available online: http://www.econ3x3.org/study/howmuch-inequality-reduced-progressive-taxation-andgovernment-spending (accessed on 27 March 2024). Wooldridge, Jeffrey M. 2019. Correlated random effects models with unbalanced panels. Journal of Econometrics 211: 137–50. [CrossRef] Wooldridge, Jeffrey M. 2001. Diagnostic testing. In A Companion to Theoretical Econometrics. Edited by Badi H. Baltagi. Hoboken: Blackwell Publishing Ltd., pp. 180–200. Yildirim, Ahmet Eren. 2020. The Relationships between Income Inequality & Savings: A Study on Households in Turkey. B˙ ILTÜRK Journal of Economics and Related Studies 2: 370–80. Yun, Wong Sing, Siti Hajar Samsu, Jain Yassin, Shairil Izwan Taasim, and Faerozh Madli. 2023. Household Consumption Function Based on Permanent Income Hypothesis: A Systematic Review. International Journal of Academic Research Economics and Management Sciences 12: 441–52. Zungu, Lindokuhle Talent, Lorraine Greyling, and Nkanyiso Mbatha. 2021. Economic growth and income inequality: A non-linear econometrics analysis of the SADC region, 1990–2015. African Journal of Economic and Management Studies 12: 285–301. Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. 44 Citation: Seip, Knut Lehre, and Frode Eika Sandnes. 2024. The Timing and Strength of Inequality Concerns in the UK Public Debate: Google Trends, Elections and the Macroeconomy. Economies 12: 135. https://doi.org/ 10.3390/economies12060135 Academic Editor: Fabio Clementi Received: 3 April 2024 Revised: 2 May 2024 Accepted: 21 May 2024 Published: 30 May 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). economies Article The Timing and Strength of Inequality Concerns in the UK Public Debate: Google Trends, Elections and the Macroeconomy Knut Lehre Seip 1,* and Frode Eika Sandnes 2 1Faculty of Technology, Art and Design, Oslo Metropolitan University, P.O. Box 4, St. Olavs Plass, Pilestredet Park 33, 0176 Oslo, Norway 2Department of Computer Science, Oslo Metropolitan University, 0167 Oslo, Norway; [email protected] *Correspondence: knut.lehr[email protected] Abstract: Inequality among people has several unwanted effects, in humanistic, social and economic contexts. Several studies address distributional preferences among groups, but little is known about when inequality issues are focused and when and why inequality abatement measures are brought on the political agenda. We show that during the period 2004 to 2023, inequality issues were focused during elections to the EU and UK parliament and with greatest strength during the elections to the EU parliament in May 2004 and to the UK parliament in May 2015. Periods with high unemployment and inflation cause the discussion on inequality to be followed by discussions on inequality measures. However, when the discussion of inequality is followed very closely by the discussions of abatement measures, inequality aversion becomes more strongly associated with the macroeconomic variables inflation and GDP (recessions) than with unemployment and more strongly associated with the concerns for fairness than concerns with war and crime. The results were obtained examining Google Trends and scholarly studies. Keywords: inequality; abatement measures; United Kingdom; Google Trends; parliament elections; recessions; unemployment; inflation 1. Introduction Inequality (INE) among people within countries and worldwide has been hypothesized to cause political instability, e.g., Roe and Siegel (2011), decrease economic growth, e.g., Michalek and Vybostok (2019), and contrast with what people regard as morally right, Rözer et al. (2022). Peoples’ perception of inequality of pay has also been found to be strongly connected to their political standpoint (Bratanova et al. 2016; Fisman et al. 2017; Sandnes et al. 2023). However, Hoy et al. (2024, data from Australia) show that the opinions of right-leaning voters change when misconceptions are corrected. A popular survey of relations between inequality and population “spirit” is given in Wilkinson and Pickett (2010, data sources pp. 280–83). Three sets of questions with regard inequality can be distinguished: A first set addresses the factors that contribute to increasing or decreasing inequality. A second set addresses which of these factors are the result of economic policy to stabilize the economy, and which factors can be managed directly to address inequality, e.g., Malla and Pathranarakul (2022). The third set addresses the type of inequality concerns expressed by low-, middleand high-income populations. Most of the current literature on inequality appears to address the concerns expressed by the low-income population. Recently, Google Trends have been used to identify people’s sentiments in the public conversation about important issues. For example, Timoneda and Wibbels (2022, pp. 3–6) discussed the use of Google Trends and examined the frequency of “protests” using Google Trends. Connor et al. (2019) searched for links between “inequality” and “racial-bias” but found no conclusive evidence for such a link. Here, we examine if the two issues, “inequality” (INE) and “inequality measures” (IMEs), were related to particular events or Economies 2024,12, 135. https://doi.org/10.3390/economies12060135 https://www.mdpi.com/journal/economies 45 Economies 2024,12, 135 macroeconomic economic states in the United Kingdom (UK). The UK was chosen because it ranks among the highest in Europe in terms of income inequality (Berisha et al. 2021), and Filippin and Nunziata (2019, p. 116) include the UK among the high-inequality cluster of European states. We developed three hypotheses. Hypotheses. We hypothesized that Hypothesis 1 (H1). Conversations about INE and measures to reduce inequality, IME, would be particularly strong around elections to the UK and the European union (EU) parliaments. The raationale is that when inequality becomes a focused issue in the public conversation, the need for measures to abate inequality will be stronger and therefore follow in strength in the conversation. For example, Fisman et al. (2017) argues that redistribution versus anticipated GDP growth was an important issue during the US presidential election in 2012. We evaluate the hypothesis by identifying time windows in Google Trends series where frequencies are larger than one half the series’ average frequency; in the following, these are denoted by INE+ and IME+. The technique of identifying windows in time series where values that are higher and lower than the average value is common in climate research; e.g., Gong et al. (2020). Second, we hypothesized that Hypothesis 2 (H2). INE would be leading IME during unfavorable economic conditions. Such periods could be periods with high unemployment (UE), high inflation (INF) and low central bank interest rate (CBI). CBI is low when the economy is underperforming. We evaluate this hypothesis by (i) applying a high-resolution lead-lag method (HRLL) to paired Google time series and (ii) embedding the LL relations in a principal component analysis (PCA) “map” of the UK economy. This allows us to identify which economic conditions are associated with a leading relation for INE to IME, that is, from inequality discussions to inequality abatement measures. Third, we hypothesized that Hypothesis 3 (H3). A major reason for conflicts related to inequality would be “fairness” issues. We evaluated this hypothesis by comparing Google Trends series for “inequality” with Trends series for “fairness”. If the two series should correlate, fairness is an important attribute of inequality. Since the political events associated with a parliament election last for a relatively short period, whereas economic changes typically last for the length of economic business cycle times, e.g., 4–8 years (Burns and Mitchell 1946), we disentangle the time series into a high-frequency, rapid component and a low-frequency, slow component. We find that inequality in the UK is in focus during parliament elections, and during periods with recessions and inflations, more than with periods of unemployment. Inequality is more strongly associated with fairness than with crime and war. The present study distinguishes itself from most other studies in that it identifies time windows during the economic and social development of a single country, the UK, whereas most other studies find patterns in inequality by studying and comparing inequality among several countries. We use a high-resolution lead-lag method (HRLL) that allows us to strengthen the causal interferences over what correlation alone would support. HRLLs are calculated over short time periods (3 months and 9 months are sufficient to establish significance). Furthermore, we add context to our results by making a PCA score plot for the UK economy from 2004 to 2023 based on five macroeconomic variables. The rest of the manuscript is organized as follows. Section 2 presents the time series used. Section 3 describes the methods, with emphasis on a high-resolution lead-lag (HRLL) method. Section 4 shows the results, and Section 5 discusses the results. Section 6 presents concluding remarks. 46 Economies 2024,12, 135 2. Data We used Google Trends https://trends.google.com/trends/?geo=NO, accessed on 5 January 2024. From that database, we retrieved time series for the frequency of the terms “inequality”, “poor” and “rich” for the period 2004 to 2024. We used the colloquial terms “poor” and “rich” instead of lowand high-income people because the second set of terms did not have enough data to construct meaningful time series. We retrieved time series for “conflict”, “political conflict” and “inequality measures” to express possible results of inequality on conflicts and abatement measures. We used the expression “Inequality measures” despite its relatively low overall scores because we then could examine if “inequality” would lead to the discussion of “inequality measures”. We also tried several other expressions for inequality measures, but none yielded enough data to construct meaningful time series. To put our results into an economic context for the UK, we used data on unemployment (UE), monetary supply (M1), inflation (INF) and industrial production (IP). The data were extracted from St. Louis Federal Reserve in 5 January 2024 (https://fred.stlouisfed.org/). Data for UK and EU parliament elections were downloaded from official governmental websites. There were eight election dates: October 2004 (EU election), May 2005 (UK election), June 2009 (EU-election) 2010 May (UK election), May 2015 (UK election), June 2017 (UK election), May 2019 (EU election), December 2019 (UK election). The UK left the EU on 31 January 2020. We retrieved an index for inequality, the Gini’s index from https://fred.stlouisfed.org/series/SIPOVGINIGBR and from https: //www.statista.com/statistics/872472/gini-index-of-the-united-kingdom/, the latter to find the most recent scores. Both data sources were retrieved on 5 January 2024. The Gini index is an economic equality score calculated for all countries in the world by the World Bank (https://data.worldbank.org/indicator/SI.POV.GINI, accessed on 5 January 2024). Data for concerns related to inequality were retrieved from Google Trends, Google Scholar and Web of Science. 3. Methodology In this section, we first disentangle the Google Trends time series into short-term and long-term components based on power spectral density (PSD) results for the Google series. We then explain how the series are detrended and smoothed to extract the features of interest. Thereafter we explain briefly the high-resolution lead-lag HRLL method used in the study, cycle periods (CP) and phase shifts (PS). Fourth, we briefly explain how we use some algorithms that are common in several statistical software packages. 3.1. Data Preprocessing We disentangled the time series by identifying time series’ components using LOESS smoothing. The LOESS smoothing algorithm has two parameters. The parameter (f) is the fraction of the time series that is used as a moving window, and the parameter (p) is the order of the polynomial equation used to interpolate the series. Because we always use p=2 , we use the nomenclature LOESS(f) to identify the smoothing degree in the rest of the manuscript. We LOESS(0.4)-smoothed the raw time series to identify the low-frequency time series and subtracted the low-frequency time series from the raw time series to identify the high-frequency time series. To remove high-frequency noise from the high-frequency series, we, in addition, used LOESS(0.2) to smooth the series. 3.2. Scoring Time Series Events To calculate scores, we identified the number of months during the two recessions and during the eight elections where (i) “inequality” and (ii) “inequality measures” showed high frequency and (iii) where “inequality” was leading “inequality measures”. Recessions lasted 16 and 6 months, respectively, and the election periods were defined as 2 months before and 1 month after the election month. This is the same time window as Timoneda and Wibbels (2022) use before and after a “protest” month. 47 Economies 2024,12, 135 Figure 5. United Kingdom economy and elections to EU and UK parliaments. ( a ) Macroeconomic time series centered and normalized to unit standard deviation. ( b ) PCA loading plot for UK economy described by the five time series in ( a ). ( c ) PCA score plot for UK economy. Red lines show when “inequality” (INE) leads “inequality measures” (IME) with high-frequency time series. ( d ) PCA score plot for UK economy. Red lines show where INE leads IME with low-frequency time series. ( e ) Same as ( c ), but the leading relation is strongly established, LL(INE,IME) >0.9 (range 0–1.0), ( f ) Same as ( d ), but the lead time between INE and IME is short. (range 0–100 months). UE = unemployment, M1 = monetary supply, INF = inflation expressed as consumer price index, CBI = central banks short-term interest rate. Blue dots are elections in UK to the UK or EU parliaments. Yellow dots show the beginnings of recessions 2008 and 2020. The PCA plots for the UK economy are shown as a loading plot in Figure 5b and as score plots in Figure 5c–f. The PCA score and loading plots are interpreted as follows: The position of an observation in a score plot (in our case, the state of the UK economy at a certain year) is associated with the position of the explanatory variables in the loading plot (in our case, the values of the five macroeconomic variables). Blue dots show parliament elections and yellow dots show recessions. The loading plot shows that the time series for the five time series matches a traditional pattern for an economy. For example, unemployment (UE) varies inversely to industrial production (IP), as it should according to Okun’s law, e.g., Maza (2022), Seip and Zhang (2022). High-frequency results. The red lines in the left score plots in Figure 5c,e show the time windows where the high-frequency INE is a leading variable to the high-frequency IME. The graph in Figure 5d shows results when LL relations are positive (>0; range − 1to +1), and the graph in Figure 5c shows results when LL relations are greater than 0.9, that is, the requirement for persistently tighter relations between INE and IME is strengthened. Visually, the plot suggests that INE leads IME during periods just before, during or just after an election event, and the tighter requirement emphasizes the recession and elections during the years 2004 to 2008. Low-frequency results. Figure 5d,f show scores where the low-frequency INE leads the low-frequency IME. The graph in Figure 5d shows the result when LL relations are positive, but for the graph in f, we have added the restriction that the lead time between INE and IME should be less than 20 months (range 0 to 100 months). The red curves in the upper right part of Figure 5d corresponds to states where INF, CBI and M1 have high values. (M1 is a technical variable expressing the amount of money in the society.) 54 Economies 2024,12, 135 High values of M1 and CBI suggest that the Bank of England tries to slow down the economy or reduce INF. The red curve in the upper left part corresponds to economic states where UE is high. The graph in Figure 5f with the tighter requirement between INE and IME shows that the years from 2004 to 2008 are emphasized together with the time after the UK left the EU and the inflation period after the pandemic. The period 2011–2014 with high UE is no longer marked as an important event for discussing inequality issues. Figure 5c–f show that inequality concerns were less pronounced during the actual pandemic in 2020 than during the following inflation period up to 2023. 4.3. Inequality Concerns Figure 6 shows two graphs that identify concerns that can be related to inequality. The first graph is based on the worldwide use of the concern terms—“fairness”, “GDP growth”, “crime” and “war”—in Google Trends, Google Scholar and the Web of Science. The second graph shows a PCA plot for the five Google Trends series: inequality and the four concern terms in the UK. ,QHTXDOLW\DQGFRQFHUQVZRUOG*RRJOH7UHQGV *RRJOH6FKRODUDQG:HERI6FLHQFH &RQFHUQV ,1(48$/,7< :$5 )$,51(66 &5,0( *'3 6FRUHQRUPDOL]HG         7UHQGV 6FKRODU :R6 6LPLODULW\EHWZHHQ*RRJOH7UHQGVIRU LQHTXDOLW\DQGSRVVLEOHFRQFHUQVLQ8. 3&         3&          ,1(48$/,7< )$,51(66 *'3 :$5 &5,0( (a)ȱ(b)ȱ Figure 6. Concerns for the effects of inequality. ( a ) Concerns worldwide. “Trends” is Google Trends; “Scholar” is number of results in Google Scholar; “WoS” is number of results in Web of Science. ( b ) Google Trends 2004 to 2022. Co-movement between “inequality” and “crime”, “fairness”, “GDP growth” and “war”. 5. Discussion We first outline the general economic history of the UK. We thereafter discuss when the frequency of inequality concerns is high. Third, we discuss the results that show when discussions on inequality are followed by discussions on inequality abatement measures, expressed by a positive lead-lag relation LL(INE, IME) value. Fourth, we examine the possible consequences of inequality, such as sentiments of unfairness in the UK population and increases in crime. Last, we discuss the use of Google Trends and the HRLL methodology. 5.1. UK Economic History 2008–2023 A rough, stylized sketch of the UK economic history can be read by comparing trajectories in the PCA “map”, Figure 5c–f, with the position of the macroeconomic variables in Figure 5b. 55 Economies 2024,12, 135 As trajectories in the panels of Figure 5c–f move towards the position for an acronym in panel Figure 5b, the variables represented by that acronym increase in importance for the UK economy. Following the abrupt beginning of the recession in 2008, the economy, IP, slowed down and UE increased until 2012–2013, then UE fell, and IP grew until the COVID-19 pandemic recession in 2020. Following that recession (and the UK leaving the EU), INF rose rapidly until 2022 and then slowed down in about the same phase until the present. It is interesting that combating the 2008 and the 2020 recessions resulted in quite different trajectories. The trajectory following the 2008 recession resulted in high UE, whereas the trajectories following the 2020 recession and pandemic resulted in high INF. However, the two recessions were handled differently using different monetary and fiscal policies. 5.2. High-Intensity Discussions of Inequality Issues Interest in inequality, expressed as the relative frequency of the search terms “inequality”, “poor” and “rich” as reported by Google Trends, were shown in Figure 2. The term “INE” declines before the recession but increases after the recession in 2008 for 6 to 9 months. The reason may be that there is an increase in employment before a recession but a “jobless recovery” after the recession (Seip and Zhang 2022). INE is on preponderance of probability or better associated with elections to the EU and UK parliaments over short-term time spans ≈ 24 months. “Preponderance of probability” here means that “inequality” and “inequality measures” are mentioned with higher frequency than the average during all 4 months around an election. The term IME was only associated with UK elections. Our hypothesis, H1, was therefore only partly supported; both INE and IME were used more frequently than expected (random) during EU and UK elections. However, IME was used less than INE, and the terms were not used persistently during all eight election events. Inequality and its abatement measures were discussed most consistently during the EU elections in October 2004 and the UK elections in May 2005 (black EU and UK columns in Figure 4). 5.3. Lead-Lag Relations The term INE was leading IME during elections both to the EU and the UK parliament over short time spans. By embedding the results in the “map” of the UK economy, it is seen that INE leads IME both during elections and during the recessions in 2008 and in 2020. Over the low-frequency time spans, ≈ 72 months, the economic “map” showed that INE was leading IME during periods with high unemployment and high inflation and during recessions. Requiring that the LL relations between INE and IME are tight, the intensity of inequality conversations around the 2008 recession and the inflation period after the COVID-19 pandemic were emphasized. Two reasons may be important: (i) there is an increased urgency to achieve inequality abatement measures, or (ii) the driving force must last for some time (>20 months), corresponding to the cutoff value for the time between high INE and IME frequencies. Thus, our hypotheses H2 was supported—a leading role for INE to IME is related to political events, that is, either to elections for the UK or the EU parliaments or to harmful economic conditions expressed by unemployment and inflation. For all terms, we tried several synonyms, but all alternatives failed to produce sufficient data. 5.4. Inequality Concerns and Inequality Abatement Measures Inequality concerns address the effects of inequality that reduce the quality of life or the affluence of the society. Abatement measures address what policies can be enacted to reduce either “objective” inequality, (e.g., expressed by the Gini index) or “subjective” inequality. Lambert et al. (2003, p. 1073) and Davidescu et al. (2024) include macroeconomic variables (GDP per capita) but also socioeconomic variables, such as public expenditures (schools) and gender policies, to explain subjective and real inequality concerns. 56 Economies 2024,12, 135 5.4.1. Inequality Concerns There are two dichotomies with respect to “inequality”. One set is if the inequality concerns relate to socioeconomic stress among the lower income individuals or if it relates to a discussion of measures to convince the rich to contribute more to the welfare system. The other set is if the rich get richer by unfair means and luck or by merit. We believe that concerns for socioeconomic stress would be dominant during recessions and during inflation events. Furthermore, discussions on stress would probably be more frequent on the internet, and discussion on how the rich could contribute more to welfare economy would probably be more frequent in reports and government hearing notes. However, in the UK, one could anticipate that discussion about contributions from the rich could be frequent during the Prime Minister period of the strongly conservative Liz Tuss from 6 September to 25 October 2022, (Tosun and Lucey 2023), but there was no pattern in the Google Trends that distinguished the period. To examine what type of concerns are most associated with inequality, we tried to combine the term “inequality” with the concern terms “fairness”, “GDP growth”, “crime” and “war”, but UK Google Trends just reported “not sufficient data”. However, time series were successfully established for the world. Figure 6a showed a comparison of the average frequency of the concern terms in Google Trends, Google Scholar and Web of Science. The first relates to public conversations, the second to scholarly research and discourses and the third to scientific publications. The concern most associated with inequality worldwide was war and, thereafter, crime. This holds for Google Trends, as well as for Google Scholar. For the Web of Science, all four concerns yielded similar results (the bars are of equal height). For the concern terms in the UK (not paired with inequality), we obtained full series for all terms. Comparing the time series for the four terms to the series for inequality with a PCA analysis, we found that the series for fairness and GDP growth were most like the time series for inequality, Figure 6b. The overall results indicate that during elections, inequality gives reasons for discussions of abatement measures, and fairness and GDP growth are the main concerns during the discussions. For the inequality concerns, like fairness, GDP growth, crime and war, most of the available literature gave information that was generic. The effects of inequality on economic growth were studied by Naguib (2015, p. 38 Appendix). The author examined countries that are members of the Organization for Security and Co-operation in Europe (OSCE) and found a statistically significant positive effect from inequality on GDP growth (a 1% increase in inequality gave a 1.2–1.5% increase in GDP, and the UK is included in the sample). In a model study, Lambert et al. (2003, pp. 1078, 1079) found that inequality aversion increased with the growth rate until it reached about 2%, but that growth above 2% reduced inequality aversion. It is not clear why it would not decrease persistently with growth rate. In economic terms, the Gini index for optimal growth rate is 38.2%; Lambert et al. (2003, Figure 2; model study) found it to be higher than the UK. Gini is 35.3 ± 1.2 for the period 2004 to 2020. Kelly (2000, p. 533) found that inequality had a strong and robust impact on violent crime in the United States, and Nafziger and Auvinen (2002) summarized findings, including their own studies, and found that inequality exacerbates the vulnerability of populations to humanitarian emergencies (war). However, the study addressed a selection of developing countries and thus did not include countries such as the UK. 5.4.2. Inequality Abatement Measures Inequality abatement measures are outside the scope of the present study. However, during the COVID-19 pandemic in 2020, many countries implemented government support measures that alleviated the economic effects of the pandemic, (e.g., in Sweden, Angelov and Waldenström (2023)), and this may explain why the focus on inequality was less during the actual pandemic and stronger during the following inflation period. An interesting concept of a “natural rate of inequality” has been put forward by Lambert et al. (2003). The natural rate may refer to inequality sentiments of a population or to an optimal output rate. 57 Economies 2024,12, 135 5.5. How Economic Policies May Create Greater Inequality Among economic states that solicit strong discussions of inequality are inflation and unemployment. Among the effects listed in the literature that would increase inequality are (i) increased profits for firms, (ii) reduced trade among countries and (iii) increased innovations. A micro-mechanism that could cause increasing inequality is the differences in consumer baskets for lowand high-income people (US data, Jaravel (2021, pp. 603, 605)) with households headed by single woman at the low end (OECD data, Azzollini et al. (2023)). Food is a larger part of the basket (food and energy prices tend to increase more than the average in a consumer basket). Finally, Filippin and Nunziata (2019, p. 119) suggest that perceived inflation is higher than actual inflation, but that there is a “keeping up with the Jones” effect along the whole income distribution. To reduce inflation, a key tool for the central banks is to increase their short-term interest rates. However, increasing the interest rate may be associated with a higher profit for large firms and, again, affect low-income people more than high-income people through the consumer basket argument (Weber and Wasner 2023). Increasing trade may have contrasting effects on inequality. Low prices on traded goods, e.g., tools and machinery from China, will in principle favor low-income people, but the results do not seem to support this conjecture; see, for instance, Jaravel (2021, pp. 600, 6011, 6015). However, Rajaguru et al. (2023, p. 487) suggest that economic globalization aggravates income inequality (and led to the Brexit vote in 2016). Barth et al. (2023, p. 11) examined the political parties’ election platforms for 169 European countries and found that increased import exposure decreased the welfare state support. Innovations and high patent frequency may increase the demand for skilled (and educated) workers, whereas the demand for unskilled workers decreases (Díaz et al. 2020); (Jaravel 2021, p. 600). Since the unskilled workers belong to the low-income group, inequality would increase. 5.6. The Method Most studiers of inequality address inequality and its effects by comparing effects of inequality among several countries; thus, ordinary linear regression (OLR), multiple regressions (MR) and panel data techniques are used. For example, Lambert et al. (2003) examined inequality across 96 countries, and Malla and Pathranarakul (2022) examined inequality across 68 countries. Some studies strengthen causality interferences by applying the Granger causality, (Granger 1969) or cross-correlation techniques (Kestin et al. 1998) to their data sets. However, both techniques require long data sets ( ≈ 30 samples) and thus often find bi-directional causalities, e.g., the Ogbeide and Agu (2015) study on poverty and inequality in Nigeria and Cetin et al. (2021) on income inequality and technological innovation. If we averaged over long time series, we would also have found bi-directional causality for our time series; see Figure 3. 5.7. Robustness Our focus was on the terms inequality and inequality abatement measures, but we could have searched for additional terms describing the effects of inequality as an issue in the political conversation. However, we found no terms that better described our intention with the study and that gave significant Google Trend series. We used the terms “fairness”, “GDP growth”, “crime” and “war” to identify the concerns associated with inequality. The terms were selected by comparing them to other similar terms in the Microsoft Word thesaurus. We originally wanted to use the term “morale”, but fairness gave a more complete time series. The HRLL method we use has been applied to sine functions with equal cycle periods but shifted in time relative to each other. It is then easily seen that it works as intended. However, in an application to forecasting algorithms in economics, it identified the forecast series as leading the observation about 80% of the time, and the economy was shown to be 58 Economies 2024,12, 135 anomalous when the forecasting series was not leading (Seip et al. 2019). Thus, we believe that the HRLL method identifies real (observed) LL relations correctly. 5.8. Further Work The terms we use are exploratory, and it may be possible to find terms that better express people’s sentiments. Our results for the UK could be generalized to other countries. For example, we downloaded inequality expressions for the US and found that several terms that did not deliver Google time series for the UK gave adequate time series for the US. Further studies should address abatement measures for unwanted consequences of inequality. A third issue is if it is possible to replace the model study by Lambert et al. (2003) on a “natural rate of inequality” by an empirical investigation based on UK data. Finally, we have discussed inequality on interannual and decadal scales, but inequality increases in many countries over multidecadal scales, and this could be the objective of further studies. 6. Conclusions Inequality among people is a challenging issue in many countries and is hypothesized to cause political conflicts around themes including fairness, economic growth, crime, and war. In contrast to most other studies on inequality, we study the timing and strength of interest in a single country, the United Kingdom. We show, using Google Trends 2004 to 2022, that the term “inequality” precedes the term “inequality measures” around UK and EU parliament elections and during periods with unfavorable economic conditions (e.g., high inflation). Our results suggest that abating unwanted effects of inequality would be effective around parliament election times and when inflation and unemployment is high. However, since inflation and unemployment are the results of economic policy choices, it may be possible to implement abatement measures before inequality issues become serious. Author Contributions: Conceptualization, K.L.S. and F.E.S.; methodology, K.L.S.; software, K.L.S.; validation, K.L.S. and F.E.S.; formal analysis, K.L.S.; investigation, K.L.S. and F.E.S.; resources, K.L.S. and F.E.S.; data curation, F.E.S.; writing—original draft preparation, K.L.S. writing—review and editing, K.L.S. and F.E.S.; visualization, K.L.S.; supervision, F.E.S.; project administration, K.L.S. and F.E.S.; funding acquisition, K.L.S. and F.E.S. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Oslo Metropolitan University, grant number 1. Informed Consent Statement: Not applicable. Data Availability Statement: All data and all calculations are available from the first author. Conflicts of Interest: The authors declare no conflicts of interest. References Angelov, Nikolay, and Daniel Waldenström. 2023. COVID-19 and income inequality: Evidence from monthly population registers. The Journal of Economic Inequality 21: 351–79. [CrossRef] Azzollini, Leo, Richard Breen, and Brian Nolan. 2023. Demographic behaviour and earnings inequality across OECD countries. The Journal of Economic Inequality 21: 441–61. [CrossRef] Barth, Erling, Henning Finseraas, Anders Kjelsrud, and Kalle Moene. 2023. Openness and the welfare state: Risk and income effects in protection without protectionism. European Journal of Political Economy 79: 102405. [CrossRef] Berisha, Edmond, David Gabauer, Rangan Gupta, and Chi Keung Marco Lau. 2021. Time-varying influence of household debt on inequality in United Kingdom. Empirical Economics 61: 1917–33. [CrossRef] Bratanova, Boyka, Steve Loughnan, Olivier Klein, and Robert Wood. 2016. The rich get richer, the poor get even: Perceived socioeconomic position influences micro-social distributions of wealth. Scandinavian Journal of Psychology 57: 243–49. [CrossRef] [PubMed] Burns, G. W., and W. C. Mitchell. 1946. Measuring Business Cycles. Cambridge, MA: National Bureau of Economic Research. Cetin, Murat, Harun Demir, and Selin Saygin. 2021. Financial Development, Technological Innovation and Income Inequality: Time Series Evidence from Turkey. Social Indicators Research 156: 47–69. [CrossRef] 59 Economies 2024,12, 135 Chroufa, Mohamed Ali, and Nouri Chtourou. 2022. Inequality and Growth in Tunisia: New Evidence from Threshold Regression. Social Indicators Research 163: 901–24. [CrossRef] Connor, Paul, Vasilis Sarafidis, Michael J. Zyphur, Dacher Keltner, and Serena Chen. 2019. Income Inequality and White-on-Black Racial Bias in the United States: Evidence from Project Implicit and Google Trends. Psychological Science 30: 205–22. [CrossRef] [PubMed] Davidescu, Adriana AnaMaria, Tamara Maria Nae, and Margareta-Stela Florescu. 2024. From Policy to Impact: Advancing Economic Development and Tackling Social Inequities in Central and Eastern Europe. Economies 12: 28. [CrossRef] Díaz, Guillermo Arenas, Andrés Barge-Gil, and Joost Heijs. 2020. The effect of innovation on skilled and unskilled workers during bad times. Structural Change and Economic Dynamics 52: 141–58. [CrossRef] Filippin, Antonio, and Luca Nunziata. 2019. Monetary effects of inequality: Lessons from the euro experiment. The Journal of Economic Inequality 17: 99–124. [CrossRef] Fisman, Raymond, Pamela Jakiela, and Shachar Kariv. 2017. Distributional preferences and political behavior. Journal of Public Economics 155: 1–10. [CrossRef] Gong, Yuhan, Tim Li, and Lin Chen. 2020. Interdecadal modulation of ENSO amplitude by the Atlantic multi-decadal oscillation (AMO). Climate Dynamics 55: 2689–702. [CrossRef] Granger, C. W. J. 1969. Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica 37: 424. [CrossRef] Hoy, Christopher, Russell Toth, and Nurina Merdikawati. 2024. A false divide? Providing information about inequality aligns preferences for redistribution between rightand left-wing voters. The Journal of Economic Inequality 2024: 1–39. [CrossRef] Jaravel, Xavier. 2021. Inflation Inequality: Measurement, Causes, and Policy Implications. Annual Review of Economics 13: 599–629. [CrossRef] Kelly, Morgan. 2000. Inequality and Crime. The Review of Economics and Statistics 82: 530–39. [CrossRef] Kestin, Tahl S., David J. Karoly, Jun-Ichi Yano, and Nicola A. Rayner. 1998. Time–Frequency Variability of ENSO and Stochastic Simulations. Journal of Climate 11: 2258–72. [CrossRef] Lambert, Peter J., Daniel L. Millimet, and Daniel Slottje. 2003. Inequality aversion and the natural rate of subjective inequality. Journal of Public Economics 87: 1061–90. [CrossRef] Malla, Manwar Hossein, and Pairote Pathranarakul. 2022. Fiscal Policy and Income Inequality: The Critical Role of Institutional Capacity. Economies 10: 115. [CrossRef] Maza, Adolfo. 2022. Regional Differences in Okun’s Law and Explanatory Factors: Some Insights from Europe. International Regional Science Review 45: 555–80. [CrossRef] Michalek, Anton, and Jan Vybostok. 2019. Economic Growth, Inequality and Poverty in the EU. Social Indicators Research 141: 611–30. [CrossRef] Nafziger, E. Wayne, and Juha Auvinen. 2002. Economic Development, Inequality, War, and State Violence. World Development 30: 153–63. [CrossRef] Naguib, Costanza. 2015. The relationship between inequality and GDP growth: An empirical approach. In LIS Working Paper Series. Luxembourg: Luxembourg Income Study (LIS). Ogbeide, Evelyn Nwamaka Osaretin, and David Onyinyechi Agu. 2015. Poverty and Income Inequality in Nigeria: Any Causality? Asian Economic and Financial Review 5: 439–52. [CrossRef] Rajaguru, Gulasekaran, Sadhana Srivastava, Rahul Sen, and Pundarik Mukhopadhaya. 2023. Does globalization drive long-run inequality within OECD countries? A guide to policy making. Journal of Policy Modeling 45: 469–93. [CrossRef] Roe, Mark J., and Jordan I. Siegel. 2011. Political instability: Effects on financial development, roots in the severity of economic inequality. Journal of Comparative Economics 39: 279–309. [CrossRef] Rözer, Jesper, Bram Lancee, and Beate Volker. 2022. Keeping Up or Giving Up? Income Inequality and Materialism in Europe and the United States. Social Indicators Research 159: 647–66. [CrossRef] Sandnes, Frode Eika, Anders Örtenblad, and Einar Duengen Bøhn. 2023. For or Against Equal Pay? A Study of Common Perceptions. Compensation & Benefits Review 55: 87–110. [CrossRef] Seip, Knut L., Oyvind Gron, and Hui Wang. 2018. Carbon dioxide precedes temperature change during short-term pauses in multi-millennial palaeoclimate records. Palaeogeography, Palaeoclimatology, Palaeoecology 506: 101–11. [CrossRef] Seip, Knut Lehre, and Dan Zhang. 2022. A High-Resolution Lead-Lag Analysis of US GDP, Employment, and Unemployment 1977–2021: Okun’s Law and the Puzzle of Jobless Recovery. Economies 10: 260, Corrected in Economies 11: 75. [CrossRef] Seip, Knut Lehre, and Robert McNown. 2007. The timing and accuracy of leading and lagging business cycle indicators: A new approach. International Journal of Forecasting 23: 277–87. [CrossRef] Seip, Knut Lehre, Yunus Yilmaz, and Michael Schröder. 2019. Comparing Sentimentand Behavioral-Based Leading Indexes for Industrial Production: When Does Each Fail? Economies 7: 104. [CrossRef] Timoneda, Joan C., and Erik Wibbels. 2022. Spikes and Variance: Using Google Trends to Detect and Forecast Protests. Political Analysis 30: 1–18. [CrossRef] Tosun, Onur Kemal, and Brian Lucey. 2023. Growth... What growth? Finance Research Letters 52: 103594. [CrossRef] 60 Economies 2024,12, 135 Weber, Isabella M., and Evan Wasner. 2023. Sellers’ inflation, profits and conflict: Why can large firms hike prices in an emergency? Review of Keynesian Economics 11: 183–213. [CrossRef] Wilkinson, Richard, and Kate Pickett. 2010. The Spirit Level: Why Greater Equality Makes Societies Stronger. New York: Bloomsbury Press. Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. 61 Citation: Valentini, Enzo. 2024. Patterns of Intergenerational Educational (Im)Mobility. Economies 12: 126. https://doi.org/10.3390/ economies12060126 Academic Editor: Gheorghe H. Popescu Received: 27 March 2024 Revised: 13 May 2024 Accepted: 17 May 2024 Published: 21 May 2024 Copyright: © 2024 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). economies Article Patterns of Intergenerational Educational (Im)Mobility Enzo Valentini Department of Political Science, Communication and International Relations, University of Macerata, 62100 Macerata, Italy; [email protected] Abstract: Intergenerational education mobility is a key dimension of social mobility and explores the extent to which educational attainment is transmitted across generations within a society. The implications of low education mobility concern both equity (everyone should have the same opportunities) and efficiency (it would be good for the economy and society if the most gifted and deserving young people were to study and not the children of the already educated). The literature identifies several drivers that can influence the level of social mobility in general and education mobility specifically, including characteristics of educational systems, public spending, degree of urbanisation, informal frictions, and beliefs. This paper seeks to identify ‘patterns of intergenerational education (im)mobility’ through a cluster analysis that takes into account the level of intergenerational mobility in education and a number of variables concerning its possible drivers, considering data on 82 countries (with different levels of development). The advantage of cluster analysis lies in the possibility of identifying regularities, but avoiding reasoning ‘on average’, i.e., safeguarding the possibility that different social patterns may exist. The results also allow us to speculate on possible policies to increase school mobility, highlighting, among other things, the ‘equalising’ role played by public spending on education. Keywords: intergenerational education mobility; social mobility; cluster analysis; public spending in education 1. Introduction The study of the intergenerational transmission of socio-economic statuses is interesting and topical in several respects. On the one hand, it helps us delve into the well-known theme of ‘equality of what?’ (Sen 1980). In fact, it is quite evident that a strong correlation of socio-economic statuses between parents and children is a sign of some lack of equality of opportunity. From this point of view, inequality of outcomes (e.g., income) can be more or less socially acceptable if it is accompanied, or not, by equality of opportunity. Nonetheless, high income inequality can itself be an obstacle to a real equality of opportunity (Corak 2013). On the other hand, mobility across generations is not only an issue of equality, but also of efficiency, especially when defined in terms of educational mobility (D’Addio 2007). It is important that it is the most able and willing people who study (and gain access to the resulting social roles) and not (only) people from rich families. Otherwise, we would end up with people with inadequate abilities in key roles (indeed, as often seems to be the case), and we would lose the opportunity to socially take advantage of the abilities of gifted people just because they come from ‘disadvantaged’ families or social backgrounds (Glomm and Ravikumar 1992; Lloyd-Ellis 2000; Staffolani and Valentini 2007). The focus of the research proposed in this paper is precisely on educational mobility, which among other things is considered to be a key element of overall economic mobility across generations (Feinstein et al. 2006; D’Addio 2007; Jerrim and Macmillan 2015; Narayan et al. 2018; Stuhler 2018). The literature identifies several factors that may influence the level of educational intergenerational mobility (and, through it, intergenerational income mobility). Economies 2024,12, 126. https://doi.org/10.3390/economies12060126 https://www.mdpi.com/journal/economies 62 Economies 2024,12, 126 Public spending in education is a key element. It can not only improve the quality of education in general, but also benefit those who would be at risk of starting off disadvantaged. From a theoretical point of view, Solon (2004) proposes a model according to which intergenerational income elasticity increases as the return on investment in human capital increases, but which decreases with the progressivity of public investment in human capital. The model of Davies et al. (2005) analyses intergenerational earning mobility in a framework where human capital plays a crucial role and concludes that mobility is higher with public than with private education. Herrington (2015) developed an overlapping generations model and calibrated it in order to compare the US and Norway empirically and quantitatively, reaching the conclusion that public education spending plays a key role in intergenerational earning persistence. Lee and Seshadri (2019) present a model of human capital investments that explains the intergenerational persistence of earnings, wealth, and college attainment and conclude that education subsidies can reduce the intergenerational persistence of economic status. Mayer and Lopoo (2008) focused on human capital investment. They used data from the Panel Study of Income Dynamics and the US Census of Governments and found greater intergenerational mobility in high-spending states compared with low-spending states. Neidhöfer et al. (2018) computed several indexes of intergenerational education mobility for 18 Latin American countries, finding significant cross-country differences also associated with public educational expenditures. Balcazar et al. (2015) used data on 48 countries that participated in the Programme for International Student Assessment (PISA) in 2012, finding evidence of a correlation between public spending on schooling and inequality of opportunity in achieving basic proficiency in reading, mathematics, and science. In Narayan et al. (2018), regressions involving data on richer economies showed that higher public spending on education is associated with higher relative intergenerational mobility in education, and the authors concluded that ‘[t]his is consistent with the theory that public spending helps equalise opportunities through investments that compensate for the gap in private investments between children of rich and poor parents’ (Narayan et al. 2018, p. 19). Moreover, if public spending on education is specifically directed towards individuals from disadvantaged economic backgrounds and is financed through taxes that particularly fall on individuals from wealthy families (e.g., bequests taxation), it can be particularly effective in rebalancing opportunities for access to education itself, fostering equity and efficiency gains (Staffolani and Valentini 2007). Public intervention in this field can also concern regulation, and the presence and duration (in years) of compulsory education can certainly influence educational mobility, particularly in the most disadvantaged contexts. An uneducated (and therefore more likely to be ‘poor’) parent might decide not to make their child study (or they may study less than they should). If a certain number of years of education is compulsory, this choice is limited. It is important to emphasise that the years of compulsory education in general are somewhat related to the level of public spending on education; however, they represent a slightly different aspect of public intervention directly related to regulation rather than the level of spending. Socio-economic segregation is another factor that can strongly influence educational mobility. Van der Weide et al. (2021), using data on 153 countries, found that proxies for segregation are negatively correlated with intergenerational education mobility. A specific definition of this concept concerns spatial/residential segregation. Recent research suggests that more residentially segregated areas (i.e., where families with different socioeconomic backgrounds and races live in separate neighbourhoods) tend to have lower intergenerational mobility. Chetty et al. (2014) used administrative records of more than 40 million children in the US and, with OLS (Ordinary Least Squares) regressions across 700 areas, found that high-mobility areas have less residential segregation. Connolly et al. (2019) linked microdata from the US and Canada, and their OLS estimates suggest that ‘inequalities between whites and blacks likely play an important role in understanding why the United States has lower rates of intergenerational mobility’ (Connolly et al. 2019, 63 Economies 2024,12, 126 is low in both clusters E and F) and the family of origin does not matter much when the parents are all equally poor. When the level of education and income begins to increase, the gap in opportunities between the children of poor and wealthy families may begin to widen; however, in wealthy economies, it may narrow when the state invests in public education. All these mechanisms seem to be confirmed by the results of the analysis in this article. On the other hand, it is interesting to note that greater intergenerational mobility seems to be favoured in poor countries by a higher degree of urbanisation and longer periods of compulsory education (which, on the contrary, does not seem important in rich countries). These results are in line with the considerations stated in the introduction (which illustrates the possible mechanisms in place). Equally, the importance given to children’s independence is more important to improving intergenerational mobility in advanced economies, in which households have additional resources they may devote to private investment in education. The same line of reasoning can be applied to the fact that greater income inequality is associated with lower intergenerational mobility in rich countries (in which household income can have a greater influence on children’s outcomes through private investment), which is not the case in poor countries. 5. Conclusions In drawing conclusions, it is important to emphasise that the analysis performed in this study allows us to derive an association between the mean values of the variables in the different clusters but does not allow us to make any statements regarding causality. In the case of income inequality, the dilemma is obvious: Does lower intergenerational mobility favour the persistence of income inequality? Or does high income inequality undermine the equality of opportunity? The topic is widely debated, but on balance it does not seem far-fetched to assume that the risk of a vicious circle is present in mediumand high-GDP countries. This vicious circle can, however, be broken by public investment in education as the results of this analysis show. In this case, the causality, although not formally demonstrated, seems clear: it is easier to imagine that a higher level of public spending encourages greater intergenerational mobility rather than the other way around. The direction of the relationship between intergenerational mobility and the importance given to children’s independence is also ambiguous. Does fostering children’s independence promote mobility (because rich families do not indulge their children too much)? Or do I want my child to be independent because I know he or she has the opportunity? While bearing in mind the fact that the lack of identification of causal links is a strong limitation, based on the (almost descriptive) evidence obtained it is possible to extract some indications in terms of policies that could help to improve intergenerational mobility. When differentiating between lessand more-developed countries (a distinction that is one of the key elements of this study), the following conclusions can be drawn: - For less-developed countries, public policies should limit the effects of physical, social, and economic segregation and increase the number of years of compulsory education. Public spending on education (as a percentage of GDP) does not seem to be directly relevant (in line with some considerations stated in the literature and pointed out in the introduction). However, it becomes relevant again through the above two channels (less segregation and more compulsory education) because both would still require public intervention at their own cost; - For more-developed countries, increasing the general level of education and increasing the general level of public spending on education are key elements and, of course, can go hand in hand. Income redistribution policies can also be important (they act through the channel of redistributing income-related opportunities). The importance given to children’s independence is a cultural trait that seems to play a role in developed countries; however, it does not seem to be an area where direct public intervention is possible or appropriate. Indirectly, policies that increase equality of opportunity could be helpful so that, over time, parents feel able to rely on their children’s 70 Economies 2024,12, 126 independence. It is clear, however, that this discourse opens up considerations of virtuous or vicious circles as pointed out above. The greatest utility of this analysis lies in the identification of ‘patterns’ by grouping countries according to the level of intergenerational education mobility (or ‘immobility’) and other variables that may be associated with it. This can help us understand the phenomenon of intergenerational mobility in education. The results are in line with those highlighted in the literature and thus support and strengthen them, including by highlighting the non-monotonicity of mechanisms and relationships among countries with different levels of development. This analysis also emphasises the ‘equalising’ role played by public spending on education in middleand high-income countries. Finally, an element new to the empirical literature was introduced into this analysis: the explicit consideration of the role played by cultural attitudes with respect to children’s independence. In terms of directions for future research, two aspects can be highlighted. The first is the need to deepen the causal relationships between intergenerational mobility and the factors that may influence it while differentiating between moreand less-developed countries. The second is the need to take cultural aspects into account, both theoretically and empirically. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data used in the article were all taken from free, publicly accessible databases. In addition, the data will be made available by the author on request. Conflicts of Interest: The author declares no conflicts of interest. Appendix A Table A1. Countries included in the analysis, sorted according to the value of MOB_EDU (‘Expected rank of a child whose parents rank in the bottom half of the education distribution’). Country MOB_EDU Country MOB_EDU Country MOB_EDU Cyprus 49.8 Portugal 40.4 Iran 37.2 Denmark 47.1 Hungary 40.3 Malaysia 37.1 Maldives 46.7 Serbia 40.2 Austria 37.1 Philippines 46.1 Switzerland 40.0 Azerbaijan 37.0 Israel 45.6 Ukraine 39.9 Uruguay 37.0 Russian Fed. 45.5 Kyrgyz Rep. 39.8 Greece 36.9 Zambia 45.1 Egypt 39.8 Iraq 36.8 Iceland 44.9 Korea, Rep. 39.6 Guatemala 36.7 Slovenia 44.4 Sweden 39.6 El Salvador 36.6 Germany 43.8 South Africa 39.1 Kenya 36.6 Slovak Rep. 43.1 Thailand 39.0 China 36.5 Finland 42.8 Belarus 38.9 Brazil 36.3 Belgium 42.8 Italy 38.8 Mexico 36.0 Dominican Rep. 42.5 Poland 38.4 Bangladesh 35.8 Turkey 42.3 Vietnam 38.4 Chile 35.7 Uzbekistan 41.9 Bulgaria 38.4 Ireland 35.6 71 Economies 2024,12, 126 Table A1. Cont. Country MOB_EDU Country MOB_EDU Country MOB_EDU United Kingdom 41.9 Armenia 38.3 Peru 35.6 Albania 41.9 Moldova 38.3 United States 35.5 France 41.8 Australia 38.2 Romania 35.3 Estonia 41.7 Croatia 38.2 Pakistan 35.0 Japan 41.7 Argentina 38.0 Ecuador 34.9 The Netherlands 41.3 Spain 37.8 Nigeria 34.4 Latvia 41.3 Georgia 37.5 Colombia 34.3 Norway 41.0 Czech Rep. 37.4 Bolivia 34.2 Indonesia 41.0 Kazakhstan 37.4 Lebanon 33.5 Jordan 40.9 Mongolia 37.3 Ghana 32.7 Canada 40.8 Tunisia 37.3 Lithuania 40.8 India 37.3 Appendix B Additional technical information on the variables relevant to the reproducibility of the analysis. Codes of the indicators in the World Bank World Development Indicator (WDI) database (https://datacatalog.worldbank.org/search/dataset/0037712, accessed on 20 July 2023): - GDP per capita (Constant 2017 Int. Dollars): NY.GDP.PCAPP.PP.KD - Educational attainment (% of the population 25+ who at least completed upper secondary school): SE.SEC.CUAT.UP.ZS - Urban population (% of tot. population): SP.URB.TOTL.IN.ZS - Gini Index: SI.POV.GINI - Government expenditure on education (% GDP): SE.XPD.TOTL.GD.ZS - Compulsory education (years): SE.COM.DURS The variable ‘Share of parents indicating ‘Children’s Independence’ as an important quality in children (%)’ comes from the European Values Study—World Values Study Integrated survey 1981–2021 (https://www.worldvaluessurvey.org/WVSEVStrend.jsp, accessed on 27 July 2023). The Integrated Values Survey (IVS) dataset 1981–2021 can be constructed by merging the EVS Trend File 1981–2017 (doi:10.4232/1.13736) and the WVS time series 1981–2021 dataset (doi:10.14281/18241.15). It is based on the Common EVS/WVS Dictionary (2021). It is also possible to find the IVS merge syntax for Stata at following link: https: //www.worldvaluessurvey.org/WVSEVStrend.jsp, accessed on 20 July 2023. The code of the variable used to derive the percentage of parents who value their children’s independence is A029 ‘Important child qualities: independence’ (see the IVS Common EVS–WVS dictionary at https://www.worldvaluessurvey.org/WVSEVStrend.jsp for further details). The individual weight used in order to obtain the country average (for each available year) is the variable whose code is S017. The variable regarding intergenerational mobility in education (‘Expected rank of a child whose parents rank in the bottom half of the education distribution’) comes from the World Bank Global Database on Intergenerational Mobility (GDIM): https://datacatalog. worldbank.org/search/dataset/0050771/Global-Database-on-Intergenerational-Mobility, accessed on 17 July 2023. In that database, the variable name/code is ‘MU050_randomtiebreak’. 72 Economies 2024,12, 126 Appendix C Countries falling within the clusters identified in Table 2. Cluster A (High GDP, lower intergenerational mobility): Australia, Belgium, Canada, France, Germany, Hungary, Ireland, Italy, Latvia, The Netherlands, Spain, Switzerland, United Kingdom, United States. Cluster B (High GDP, higher intergenerational mobility): Austria, Cyprus, Denmark, Finland, Iceland, Israel, Japan, Lithuania, Norway, Slovenia, Sweden. Cluster C (Middle GDP, higher intergenerational mobility): Armenia, Azerbaijan, Belarus, Bulgaria, Croatia, Czech Republic, Estonia, Georgia, Greece, Jordan, Kazakhstan, Korea, Rep., Kyrgyz Republic, Moldova, Mongolia, Poland, Romania, Russian Federation, Serbia, Slovak Republic, Ukraine, Uzbekistan. Cluster D (Middle GDP, lower intergenerational mobility): Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, El Salvador, Guatemala, Mexico, Peru, South Africa, Uruguay. Cluster E (Low GDP, higher intergenerational mobility): Dominican Republic, Maldives, Philippines, Turkey, Zambia. Cluster F (Low GDP, lower intergenerational mobility): Albania, Bangladesh, China, Egypt, Arab Rep., Ghana, India, Indonesia, Iran, Iraq, Kenya, Lebanon, Malaysia, Nigeria, Pakistan, Portugal, Thailand, Tunisia, Vietnam. References Balcazar, Carlos Felipe, Ambar Narayan, and Sailesh Tiwari. 2015. Born with a Silver Spoon: Inequality in Educational Achievement across the World. Policy Research Working Paper 7152. Washington, DC: World Bank. Becker, Gary S., and Nigel Tomes. 1979. An equilibrium theory of the distribution of income and intergenerational mobility. Journal of Political Economy 87: 1153–89. Available online: https://www.jstor.org/stable/1833328 (accessed on 3 February 2024). [CrossRef] Becker, Gary S., Scott Duke Kominers, Kevin M. Murphy, and Jörg L. Spenkuch. 2018. A Theory of Intergenerational Mobility. Journal of Political Economy 126: S7–S25. [CrossRef] Campbell, Mary, R. Haveman, G. Sandefur, and Barbara Wolfe. 2005. Economic inequality and educational attainment across a generation. Focus 23: 11–15. Chetty, Raj, Nathaniel Hendren, Patrick Kline, and Emmanuel Saez. 2014. Where is the land of opportunity? The geography of intergenerational mobility in the United States. The Quarterly Journal of Economics 129: 1553–623. [CrossRef] Connolly, Marie, Miles Corak, and Catherine Haeck. 2019. Intergenerational Mobility between and within Canada and the United States. The Journal of Labor Economics 37: S595–S641. [CrossRef] Corak, Miles. 2013. Income Inequality, Equality of Opportunity, and Intergenerational Mobility. Journal of Economic Perspectives 27: 79–102. [CrossRef] Corak, Miles. 2021. The Canadian Geography of Intergenerational Income Mobility. The Economic Journal 130: 2134–74. [CrossRef] D’Addio, Anna Cristina. 2007. Intergenerational Transmission of Disadvantage: Mobility or Immobility across Generations? OECD Social, Employment and Migration Working Papers, No. 52. Paris: OECD Publishing. [CrossRef] Davies, James Byron, Jie Zhang, and Jinli Zeng. 2005. Intergenerational Mobility under Private vs. Public Education. Scandinavian Journal of Economics 107: 399–417. [CrossRef] Duncan, Greg J., and Richard J. Murnane. 2012. Whither Opportunity? Rising Inequality, Schools, and Children’s Life Changes. New York, NY: Russell Sage Foundation. Durlauf, Steven N., and Ananth Seshadri. 2018. Understading the Great Gatsby Curve. In NBER Macroeconomics Annual 2017. Edited by Martin S. Eichenbaum and Jonathan Parker. Chicago: University of Chicago Press, vol. 32. Feinstein, Leon, Ricardo Sebates, Tashweka Anderson, Annik Sorhaindo, and Cathie Hammond. 2006. The Effects of Education on Health: Concepts, Evidence and Policy Implications. A Review for the OECD Centre for Educational Research and Innovation (CERI). Paris: CERI. GDIM (Global Database on Intergenerational Mobility). 2023. Development Research Group, World Bank. Washington, DC: World Bank Group. Available online: https://datacatalog.worldbank.org/search/dataset/0050771/Global-Database-on-IntergenerationalMobility (accessed on 17 July 2023). Glomm, Gerhard, and B. Ravikumar. 1992. Public versus Private Investment in Human Capital: Endogenous Growth and Income Inequality. Journal of Political Economy 100: 818–34. [CrossRef] Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. New York: Springer. Herrington, Cristopher Michael. 2015. Public education financing, earnings inequality, and intergenerational mobility. Review of Economic Dynamics 18: 822–42. [CrossRef] Jerrim, John, and Lindsey Macmillan. 2015. Income Inequality, Intergenerational Mobility, and the Great Gatsby Curve: Is Education the Key? Social Forces 94: 505–33. [CrossRef] 73 Economies 2024,12, 126 Kaufmann, Leonard, and Peter J. Rousseeuw. 1990. Finding Groups in Data: An Introduction to Cluster Analysis. New York, NY: Wiley. Kearney, Melissa S., and Phillip B. Levine. 2016. Income Inequality, Social Mobility, and the Decision to Drop Out of High School. Brookings Papers on Economic Activity Spring 333–96. Available online: http://www.nber.org/papers/w20195 (accessed on 20 February 2024). Lee, Sang Yoon (Tim), and Ananth Seshadri. 2019. On the Intergenerational Transmission of Economic Status. Journal of Political Economy 127: 855–921. [CrossRef] [PubMed] Leone, Tharcisio. 2019. Intergenerational Mobility in Education: Estimates of the Worldwide Variation. UNRISD Occasional Paper— Overcoming Inequalities in a Fractured World: Between Elite Power and Social Mobilization, No. 2. Geneva: United Nations Research Institute for Social Development (UNRISD). ISBN 978-92-9085-103-5. Lloyd-Ellis, Huw. 2000. Public Education, Occupational Choice, and the Growth-Inequality Relationship. International Economic Review 41: 171–202. Available online: http://www.jstor.org/stable/2648828 (accessed on 25 February 2024). [CrossRef] Makles, Anna. 2012. Stata tip 110: How to get the optimal k-means cluster solution. The Stata Journal 12: 347–51. [CrossRef] Mayer, Susan E. 1997. What Money Can’t Buy: Family Income and Children’s Life Chances. Cambridge: Harvard University Press. Mayer, Susan E., and Leonard M. Lopoo. 2008. Government Spending and Intergenerational Mobility. Journal of Public Economics 92: 139–58. [CrossRef] McLanahan, Sara, ed. 2020. How Cultural Factors Shape Economic Outcomes. Princeton: Princeton and Brookings, The Future of Children, p. 190. Available online: https://www.jstor.org/stable/e27074970 (accessed on 20 March 2024). Narayan, Ambar, Roy Van der Weide, Alexandru Cojocaru, Christoph Lakner, Silvia Redaelli, Daniel Gerszon Mahler, Rakesh Gupta N. Ramasubbaiah, and Stefan Thewissen. 2018. Fair Progress?: Economic Mobility Across Generations Around the World. Washington, DC: World Bank Publications. Neidhöfer, Guido, Joaquin Serrano, and Leonardo Gasparini. 2018. Educational Inequality and Intergenerational Mobility in Latin America: A New Database. Journal of Development Economics 134: 329–49. [CrossRef] Piketty, Thomas. 2000. Chapter 8: Theories of persistent inequality and intergenerational mobility. In Handbook of Income Distribution. Amsterdam: Elsevier, vol. 1, pp. 429–76. Reardon, Sean. 2011. The widening academic achievement gap between rich and poor: New evidence and possible explanations. In Whither Opportunity? Rising Inequality, Schools, and Children’s Life Changes. Edited by Greg J. Duncan and Richard J. Murnane. New York: Russell Sage Foundation, pp. 91–115. Reddy, Amarender, and Vikas Singh. 2021. Do parents’ education predict child education and occupation? IASSI-Quarterly 40: 644–63. Restuccia, Diego, and Carlos Urrutia. 2004. Intergenerational Persistence of Earnings: The Role of Early and College Education. American Economic Review 94: 1354–78. [CrossRef] Sen, Amartya. 1980. Equality of What? In Tanner Lectures on Human Values. Edited by Sterling M. McMurrin. Cambridge: Cambridge University Press, vol. 1. Solon, Gary. 2004. A Model of Intergenerational Mobility Variation over Time and Place. In Generational Income Mobility in North America and Europe. Edited by M. Corak. Cambridge: Cambridge University Press, chp. 2, pp. 38–47. Staffolani, Stefano, and Enzo Valentini. 2007. Bequest Taxation and Efficient Allocation of Talents. Economic Modelling 24: 648–72. [CrossRef] Stuhler, Jan. 2018. A Review of Intergenerational Mobility and Its Drivers. JRC Research Reports, JRC112247. Seville: European Commission, Joint Research Centre (Seville site). Van der Weide, Roy, Christoph Lakner, Daniel Gerszon Mahler, Ambar Narayan, and Rakesh Ramasubbaiah. 2021. Intergenerational Mobility around the World. Policy Research Working Paper; No. 9707. Washington, DC: World Bank. Van der Weide, Roy, Christoph Lakner, Daniel Gerszon Mahler, Ambar Narayan, and Rakesh Gupta. 2023. Intergenerational mobility around the world: A new database. Journal of Development Economics 166: 103167. [CrossRef] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. 74 Citation: Croci Angelini, Elisabetta, Francesco Farina, and Silvia Sorana. 2024. The Impact of the Great Recession on Well-Being across Europe Ten Years On: A Cluster Analysis. Economies 12: 115. https://doi.org/10.3390/ economies12050115 Academic Editor: Bruce Morley Received: 1 April 2024 Revised: 30 April 2024 Accepted: 7 May 2024 Published: 10 May 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). economies Article The Impact of the Great Recession on Well-Being across Europe Ten Years On: A Cluster Analysis Elisabetta Croci Angelini 1,*, Francesco Farina 2and Silvia Sorana 3 1Center for Interdisciplinary Studies in Economics, Psychology and Social Sciences (CISEPS), University of Milano-Bicocca, 20126 Milan, Italy 2 Centre for Investigation and Modelling of Experimental Observations (CIMEO), Sapienza University of Rome, 00161 Rome, Italy 3COOSS Marche Onlus, 60121 Ancona, Italy *Correspondence: elisabettacr[email protected] Abstract: To evaluate variations in the well-being dimensions of European citizens, we rely upon Principal Component Analysis methodology, whereby a large set of interrelated indicators are reduced to a small number of aggregate synthetic variables. We find that the 2008 crisis impinged differently on the various dimensions of well-being. The evolution of the indicators has affected different clusters of countries in various ways. Most importantly, we observe that there has been a shift of the principal component from the poor in terms of material deprivation to the risk of poverty for the worsening conditions in the labor market. Keywords: European Union; Great Recession; well-being; Principal Component Analysis 1. Introduction The Great Recession had widespread consequences. During the 2008 financial crisis the interplay between the mutual exposure of banks and governments to the other party’s insolvency risk greatly distressed the balance sheets of the banks, finally leading to a credit crunch. A severe recession in advanced countries, with rising unemployment and negative growth rates, caused a lower demand level. In Europe, diminishing earnings for households and declining profits for firms coupled with the slackened functioning of automatic stabilizers posited the Eurozone GDP dynamics on a lower path (De Grauwe and Ji 2013). To counter rising public deficit and public debts with respect to GDP, austerity policies were implemented through restrictive impulses of fiscal policy. The less efficient countries participating in the European Monetary Union have been exposed to the divergent impact of the common monetary policy and of the common fiscal rules. Due to the austerity policies meant to recover competitiveness, real devaluation ensued, with both lower employment rates and substantial wage cuts. Rocketing risk premia increased the spread of the Eurozone’s sovereign bonds vis-à-vis the German 10-year bund, with particularly high hikes in peripheral countries due to a contagion effect triggered by the partial default of the Greek public debt (Croci Angelini et al. 2016). The burden of the labor market adjustment has disproportionally fallen on the lowskilled labor force through lower job protection and lower pay, and to a larger extent on non-standard jobs. The widening top–bottom income inequality has been due more to increasing unemployment than to an enlarging distance between the bottom section of the wage distribution and the average wage, although in a few countries the reduction in earnings—larger for high-income than for low-income households—has slightly reduced income inequality (ILO 2015). Cuts in social expenses reduced the public provision of both monetary transfers and in-kind services. Not only were pensions and unemployment benefits reduced, but the degree of coverage, targeting, and generosity shrank too. These developments impinged not only on the earnings dimension, but also on quality of life, as Economies 2024,12, 115. https://doi.org/10.3390/economies12050115 https://www.mdpi.com/journal/economies 75 Economies 2024,12, 115 shown by relevant variations in the main non-monetary well-being dimensions in different countries. Enduring consequences for living conditions were registered in crucial dimensions of well-being, such as health, education, and also social inclusion (Jenkins et al. 2013). In 1985, the Council of Ministers of the European Union defined the “poor” as: “the persons whose resources (material, cultural and social) are so limited as to exclude them from the minimum acceptable way of life in the Member State to which they belong” (EU Council of Ministers 1985). As an effect of the lowering disposable income caused by countercyclical fiscal impulses and more flexible labor markets, the vulnerability of low-skilled workers and the precarious conditions of the labor force with part-time jobs expanded. Empirical evidence shows that in two-thirds of OECD countries, income inequality has been growing hand in hand with relative poverty; in all of them the risk of poverty, and in some of them also the intensity of poverty, has been soaring (Atkinson et al. 2010). The percentage of materially deprived people ranged from 3% in Luxembourg and 6% in Sweden and the Netherlands up to 45% in Latvia. These distances were much wider than the dispersion of poverty risk, ranging from 10% to 21% (Fusco et al. 2010, pp. 137–38 ). However, the heterogeneity in living conditions across European countries extends beyond the income-based indicators of poverty and inequality. An Index of Economic Well-being, constructed by incorporating information on consumption, wealth, inequality, and economic insecurity for the OECD countries, shows that the economic crisis more negatively affected well-being in the Peripheral Eurozone, vis-à-vis the other countries in the sample. Low-income people and the poor were also disproportionately affected by the intensification in non-material deprivation. The risk of poverty soared mainly in the sub-group of involuntarily part-time workers (Horemans et al. 2015). While the most dramatic fall in income was seen in the poor in Greece, empirical evidence shows that in many countries low-income groups and the poor were more severely hit during the Great Recession in terms of socio-economic attributes (Whelan and Maître 2012). Between 2008 and 2012 the fall in equivalized household income was very large in Iceland (40%), Greece (30%), and Ireland (20%), and to a lesser extent in the United Kingdom, Spain, and Portugal, where reductions ranged from 13% to 10%. To convey the well-being distance across people concerning material resources, the at-risk-of-poverty indicator has been employed. In 2008, 19 million people were living in severely materially deprived households in Europe; 17 million individuals aged 0–59 were living in jobless households; 49.6 million were living in households at risk of poverty, but were neither jobless nor severely materially deprived; 40 million were living in jobless households and/or materially deprived even if not at risk of poverty; whereas 6.9 million were living in jobless households, at risk of poverty, and severely materially deprived. “The rate for the 12 ‘new’ Member States (NMS12) was 17.3 per cent, a little but not much higher than for EU-15 with a rate of 16.4 per cent. It is certainly not the case that those at risk of poverty on the EU definition are mostly to be found in the New Member States: of the 80+ million at risk of poverty in EU-27, 64 million are to be found in the EU15. In Germany, alone, there are 12 1 2 million; in the United Kingdom 11 1 2 million; in Italy 11 million; and France and Spain together account for a further 17 million. In the largest New Member State, Poland, the number of people at risk of poverty is about 111 2million” (Atkinson and Marlier 2010, p. 106). The Europe 2020 Agenda, adopted by the European Union (European Commission 2010), pointed at making substantive progress, among other goals, in the promotion of social inclusion. The objectives set in Lisbon in 2000 were neither entirely accomplished by all member states, nor by the EU as a whole (Grimaccia 2021). Also due to the COVID-19 pandemic, the “strategy for a smart, inclusive and sustainable growth” did not deliver its promises. The 2020 target in the area of poverty and social exclusion was defined on the basis of three indicators: (1) the number of people considered ‘at-risk-of-poverty’ according to the EU definition, where the poverty risk threshold set at 60% of the national household equivalized median income; (2) the number of materially deprived people; and (3) the number of people aged 0–59 living in ‘jobless’ households, where no member aged 18–59 is working, or where members aged 18–59 have, on average, very limited work attachment. 76 Economies 2024,12, 115 The target was set to reduce by 25% the number of Europeans living below national poverty lines, by lifting around 20 million people out of poverty. The problem of fulfilling the objectives for social inclusion set in the Europe 2020 Agenda was considerably exacerbated (Atkinson and Marlier 2010). The Great Recession hit European countries differently by increasing the risk of poverty and inequality and, within those countries, hit individual households in terms of material and non-material deprivations, which include health and education as well as other dimensions relevant for the quality of life—a concept whose content is still debated, but on whose multidimensionality there is no doubt. Our paper investigates how the real devaluation ensuing the Great Recession affected the multidimensional well-being in European countries through Principal Component Analysis (PCA). The main idea is to summarize in a few major points the differences, if any, encountered by households after the crisis, how their well-being was affected at that time, and whether or not they found it difficult to recover. While there is no lack of studies addressing single issues at the country level, especially from a macroeconomic point of view, not many are based on microdata and explore household behavior in a multidimensional framework by applying PCA. The paper is organized as follows: Section 2 reviews some empirical literature meant to frame the setting and where the relevance of multidimensional well-being has been addressed; Section 3 discusses the methodological choice of analyzing the impact of the Great Recession on European well-being through PCA, also compared with other methods. The results are presented in Section 4 and discussed in Section 5 where a comparison of our results with previous research is provided. The 2008 crisis impinged differently on the various dimensions of well-being. All in all, our findings very much differentiate depending on the indicators and on the different groups of countries. Section 6 concludes the paper. 2. Literature Review The concern about income distribution and social exclusion in private households in the EU compellingly emerged at the turn of the century. The European Union Statistics on Income and Living Conditions (EU-SILC) dataset was established in the framework of the Open Method of Coordination within the Programme of Community Action meant to encourage cooperation between Member States to counteract social exclusion. It covers European countries, not necessarily members of the EU, and aims at issuing comparable statistics through an integrated design in this area of inquiry. A very wide literature appeared focusing on concepts, measurement, and evaluations of different aspects of inequality. By their very nature, socio-economic phenomena are the joint product of a variety of micro-economic characteristics (e.g., individual material deprivation) and/or macroeconomic conditions (e.g., an underemployment equilibrium with jobless households and/or individuals at high risk of poverty) impinging on the well-being of society at large while interrelations across the most relevant variables are difficult to disentangle. Following the “capability approach” (Sen 1985), in recent decades the research on well-being has turned towards multidimensionality. Indeed, quality of life is a multifaceted concept (Nussbaum and Sen 1993) to be achieved through a series of functionings, consisting of opportunities in terms of personal capacities. The empirical strand of literature on social indicators has increased enormously in the last three decades, suggesting that, to obtain a comprehensive evaluation of well-being, more dimensions need be added to the standard monetary dimension (Nolan and Whelan 2014). Divergent per capita GDP across the EU member states, and the dispersion of socio-economic status within the population, are bound to impinge on health conditions ( Crimmins et al. 2009 ). According to EU-SILC data (which refer to self-perceived health status, longstanding illness or disability, unmet medical and dental treatments, and limitations on daily activity), health limitations impaired activity levels between up to 77 Economies 2024,12, 115 around one-fifth in Cyprus, Poland, Sweden, and the United Kingdom, and over one-third in Estonia, Finland, and Latvia (Hernández-Quevedo et al. 2010). An analysis conducted by the EUROMOD team shows that the key factor in protecting a household from a drop in income is the presence of more than one single member of the household earning an income (Figari et al. 2010). Two individuals with the same income can have very different living standards if the resources available to each of them differ because of different national provision of public transfers (Fusco et al. 2010). Welfare institutions were crucial in the reduction of the risk of poverty within the European Union, ranging from under 15% to over 60%, with an average value of 38% (Whelan et al. 2014). While the evolution of jobs, especially for low-income households, is certainly relevant in the cross-country comparison of well-being in Europe before and after the crisis, the effect of the switch towards a more flexible labor market is difficult to assess. During the Great Recession, unemployment in the OECD labor force rose from 6.6% to 8%, with youth unemployment doubling on average, and reaching a peak of 50% in Greece and Spain (OECD 2015). Recent estimates convey the message that a possible positive impact on the employment rate very much depends on the initial degree of rigidity and the mix of institutional reforms (Sologon and O’Donoghue 2014). As for accommodation, an OECD Statistical Brief reports that housing prices, along with the savings ratio, represent a key driver of the level of household wealth, as the positive correlation between the median net wealth of households and the annual real growth rate of house prices is strong in the long run (Murtin and Mira d’Ercole 2015, p. 4). The relationship between income and wealth is also influenced in Europe by the varying impact across clusters of EU countries of the different forms of housing tenure (Kemeny 2001; Croci Angelini 2015). To compare the standard of living of owner-occupiers and tenants, the method adopted by Eurostat consists in the ”imputation” of a rent to owners (having subtracted the actual housing costs). Overall, the adjustment performed by means of the inclusion of imputed rent reduces the degree of income inequality. In particular, the at-risk-of-poverty rate would fall by five percentage points in Ireland and the United Kingdom, four in Estonia and Spain, and more than two in Belgium, Greece, Latvia, and Portugal (Sauli and Törmälehto 2010). Although home-ownership disproportionately affects the well-being of high-income versus low-income households, the impact of house property on a household’s financial balances is heterogeneous. On the one hand, the owner-occupier benefits from a higher income, as he gains a hidden rent (corresponding to the saved rent, which would have been paid to a landlord); also, a retired worker living in his own flat, but on the brink of poverty because of a low pension, could use his house to obtain a loan from a bank so as to improve a poor lifestyle. On the other hand, the loss in household equivalized income during a recession is countered in some countries by offering home-ownership as the collateral to borrowing, while in other countries the mortgage associated with home-ownership may worsen already distressed household finances, mainly depending on the income level of households and the national percentage of home-ownership (Sierminska 2012). In some EU countries the indicators for housing conditions were found to be highly correlated with income, while the indicators of material deprivation usually present a stronger relationship with income than with housing conditions, mainly as an effect of financial stress (Nolan and Whelan 2010). Furthermore, the sudden fall in short-term income impacts everyday life, and a declining long-term income counts more for housing conditions and the social environment; similarly, the degree of deprivation is higher for financial distress than for worsening social environment and housing conditions (Fusco et al. 2010). In the research effort aimed at evaluating multidimensional well-being (MWB), a methodological issue has to be tackled. The dimension-by-dimension approach—a “large and eclectic dashboard” (Stiglitz et al. 2009), or a “portfolio of indicators” (Atkinson et al. 2002)—aims at preserving the information on the interpersonal dispersion of well-being in each dimension. A synthetic indicator may summarize the overall well-being at the cost of ignoring possible interactions across dimensions. On this issue, the empirical evidence 78 Economies 2024,12, 115 stemming from the EU-SILC database is unclear. On the one hand, the estimate of three indicators—being at risk of poverty, living in a jobless household, and suffering from material deprivation—shows that one-third of the individuals are “disadvantaged” in more than one dimension (Atkinson et al. 2010, p. 127). On the other hand, everywhere there is a low correlation between the income level and the level of deprivation; in particular, in some countries a high level of deprivation is associated with a low level of poverty (Atkinson and Marlier 2010). To compute an index for each dimension avoids two critical issues: the normative evaluation of the weight to be attributed to each dimension, and the assessment of the degree of substitutability among them (Decancq and Lugo 2013). 3. Methodology Multidimensional well-being (MWB) indices seek the impact on well-being stemming from the mutual reinforcement of conditions often characterized by a high degree of complementarity. The variables they rely upon seldom enjoy orthogonality, a characteristic the lack of which hinders many quantitative analyses. The inputs face the problems of identifying the relevant dimensions, find indicators able to describe them, and aggregate the indicators into a single figure meant to aptly describe the multidimensional phenomenon. From a theoretical point of view, several characteristics are needed, a requirement that has been coped with by axiomatic methodologies (Weymark 2006). Empirically, they are often based on surveys, such as the European Quality of Life Survey (EQLS), where questions are posed by Eurofound to thousands of selected individuals. The queries are obviously designed to fit the purpose of the survey, yet independent researchers may use the data for their own investigations. To compare well-being in European countries before and after the Great Recession we use data from the EU-SILC dataset for the years 2007 and 2012. The data—covering 26 countries , among which 24 belong to the European Union and two are non-EU countries (Norway and Iceland)—are complete, i.e., all relevant variables exist for both years and all countries. Although Bulgaria, Croatia, Malta, and Romania are EU members today, they have been excluded for incomplete availability of data. Our units of analysis are these 26 European countries: for each of them the dataset includes several thousand entries, based on both households and individuals, from which each country’s information is calculated . The Principal Component Analysis (PCA) searches for the unknown factors which are at the roots of the well-being outcomes. This methodology consists of the computation of mutually orthogonal principal components (through the linear combinations of the original variables, i.e., the different indicators considered for each dimension). A large number of initial, possibly correlated, indicators are transformed into mutually uncorrelated linear combinations. The principal components are extracted with the aim of identifying hidden, unobservable variables able to explain a major portion of the variance. Hence, dispersed information about each individual entry is concentrated in principal components, each one summarizing the information conveyed by a larger set of indicators. The construction of composite indices from individual indicators helps in comparing across time and space the performance of a unit based on a large amount of information (Freudenberg 2003). The PCA is a reliable method meant to overcome the trade-off between comprehensiveness (which compresses the variety of dimensions of life into a synthetic index) and meaning (whereby the focus on the impact of the crisis on well-being prompts preserving the distinct short-term evolutionary path of well-being in each dimension) and so helps in weighting performances and devising policies (Nardo et al. 2008). To evaluate unobservable variables such as well-being or quality of life, an alternative method is the fuzzy set approach (Betti 2016) where the methodological focus is on the appropriate weights, while another method is by axiomatic measurement, which keeps a desirable decomposition characteristic and was proposed in a previous paper (Croci Angelini and Michelangeli 2012). Our methodological choice in favor of PCA was based on the reduction of variables aimed at understanding the structure underlying a large list of interrelated indicators in order to reduce them to a small number of aggregate synthetic variables. The search 79 Economies 2024,12, 115 reacted to the crisis by struggling to keep the labor market afloat while reducing both deprivations and so reach a position near to zero at the crossing of the two components. The poor performance of Southern European countries stands out, as the crisis struck them worse of all. Italy and Greece are in the “bad” quadrant in 2007 and are still there in 2012, joined by Cyprus, and aggravate their conditions further away from the zero. Spain and Portugal show improvements in the labor market at the expense of more deprivations caused by deregulation. In these countries the crisis is far from over also in the following years. Although it has not been possible to compute a recent comparable PCA for the lack of some variables, Southern countries need at least three more years (Portugal) to recover material deprivation levels, while Greece so far has not yet recovered. As for the labor market-related component, unemployment rates in Spain and in Greece were still over 10% in 2018. No other country shows the same record. Our results are also largely coherent with the findings of Ivanováet al. (2022) who explore EU quality of life through 19 EU-SILC based variables and employ PCA to reduced them to five factors (material-economic conditions, social contacts and existential issues, environmental issues and quality of environment, health limitations, and crime), which are the most important factors affecting EU inhabitants’ quality of life. Their study shows the maximum positive correlation (0.93) between households making ends meet with great difficulty and arrears (mortgage or rent, utility bills, or hire purchase); also, the quality of life has been found to be substantially negatively influenced by social insecurity mainly as an effect of economic safety. The research work by Mazurek (2016) carried out on quarterly macrodata and interested in the magnitude and shape of the Great Recession on 25 EU countries confirms our findings that the periphery of Europe (i.e., mainly East and South) has been the area mostly hit by the Great Recession. Finally, as for the remaining health-related components, it is perhaps worth mentioning that the performance of none of the Southern countries was in danger in 2007 (together with the Nordic and some other countries), while it was in Portugal in 2012. All in all, our analysis suggests that the countries with both most valuable economic structure and socio-economic indicators—again the Western continental and the Nordic countries—have shown a remarkable resilience during the period following the financial crisis, confirming their performance both for component 1 and 2. A significant exception is the UK, which is singled out for the worsening of component 2. Indeed, as shown in Betti (2016) the most relevant change happens in each single group for quality of life rather than in the overall index. 6. Conclusions The above discussion on the evolution of well-being in Europe indicates that the Great Recession has consolidated the division across the four groups of countries. On the one hand we see that the Central-Western and Nordic countries were able to react to the worsening socio-economic conditions; on the other hand the Central-Eastern countries’ convergence has stopped and the Southern countries have further been left behind. The UK is a case in point as its performance has worsened after Brexit in 2016. In other words, the crisis has negatively impinged on the capacity of national and supranational institutions in sustaining the market integration process. The evolution of the well-being conditions has been limited to a strengthening of market integration within the four groups of countries. Due to the impact of the crisis the objective of socio-economic convergence has been set aside. The purpose of this paper was to evaluate variations in aggregate economic welfare within 26 European countries, by connecting the impact on the main dimensions of wellbeing of the Great Recession, which was very heterogeneous across Core, Peripheral, and Central-Eastern Europe in particular at the bottom of the income distribution. 86 Economies 2024,12, 115 In some dimensions, such as risk of poverty, unemployment, and material deprivation, where a recession typically provokes negative effects in the short run, evidence shows that the crisis has worsened the well-being of a sizable number of households, mainly in Southern and Central-Eastern Europe. In some other dimensions, such as health conditions, educational achievements, and housing, the impact of the crisis is not particularly relevant. In the European Union, these indicators regard mainly publicly provided services, which are less subject to the decay that has hit automatic stabilizers after the negative fiscal impulses imposed by Brussels to repair distressed public finances. Typically, the possibly impact of the Great Recession on these dimensions could be assessed only in the medium term, in case the size of the cut to public expenditures would be so large as to gravely worsen the provision of these essential merit goods. The results of our paper can be compared with those reached by studies aimed at the evaluation of the effects of the Great Recession on European countries (e.g., Mazurek 2016; Fedotenkov et al. 2024) and of the countries’ performance towards the EU2020 targets ( e.g., Grimaccia 2021 ). Overall, our results are compatible with them, although the aims, scope, and methods may differ. As for the method, PCA was applied by Ivanováet al. (2022) over 19 variables to assess the quality of life in member countries of the European Union in eight dimensions reduced to the five most important factors. The advantage of this method lies in the way that a wide amount of information is summarized in few major components which may be more important for policy advice rather than a single figure that is only able to rank the countries. Author Contributions: Conceptualization, E.C.A., F.F. and S.S.; methodology, E.C.A. and S.S.; software, S.S.; validation, E.C.A. and S.S.; formal analysis, S.S.; investigation, S.S.; resources, S.S.; data curation, E.C.A.; writing—original draft preparation, F.F.; writing—review and editing, F.F.; visualization, S.S.; supervision, E.C.A.; project administration, E.C.A.; funding acquisition, E.C.A. and S.S. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Data Availability Statement: All EU-SILC data employed in this paper are available at https://ec. europa.eu/eurostat/web/income-and-living-conditions/information-data (accessed on 6 May 2024 ). Acknowledgments: While taking joint responsibility for this paper the authors gratefully acknowledge the support, discussion, and valuable comments by Fabio Clementi, Cristina Davino, and Enzo Valentini. We also thank the anonymous referees. Conflicts of Interest: The authors declare no conflicts of interest. Appendix A In 2007, the 11 indicators listed in the first column of Table A1 were reduced into the three components whose explanatory power is presented in Table 4. Table A1. Rotated component matrices afor the years 2007 band 2012 c. 1st Component 2nd Component 3rd Component 4th Component 2007 2012 2007 2012 2007 2012 2007 2012 1. Severe housing deprivation 0.750 0.095 0.269 0.772 0.324 0.376 - 0.173 2. Suffer from chronic illness 0.021 −0.224 −0.144 −0.102 0.870 0.264 - 0.835 3. Unmet medical treatment 0.900 0.126 0.011 0.320 0.177 0.826 - 0.133 4. Unmet dental treatment 0.896 0.241 −0.196 0.087 −0.033 0.899 -−0.101 5. Activities limited by bad health 0.232 0.028 0.186 0.181 0.834 −0.231 - 0.895 87 Economies 2024,12, 115 Table A1. Cont. 1st Component 2nd Component 3rd Component 4th Component 2007 2012 2007 2012 2007 2012 2007 2012 6. Poverty risk 0.555 0.749 0.291 0.376 −0.076 0.055 - −0.076 7. Extreme material deprivation 0.825 0.208 0.334 0.891 0.190 0.275 - −0.001 8. Unemployment −0.051 0.883 0.855 0.269 0.368 0.122 - −0.062 9. Underemployment 0.372 0.691 0.642 0.236 0.027 0.428 - −0.177 10. Temporary jobs −0.092 0.730 0.735 −0.395 −0.297 0.126 - 0.004 11. NEETs 0.344 0.071 0.792 0.535 0.034 −0.001 - −0.014 Source: Own calculations on EU-SILC 2007 and EU-SILC 2012 dataset. a Numbers in bold identify the items composing the different components after the convergence by Varimax method of rotation and Kaiser normalization. brotation reached convergence criteria in 4 iterations. cRotation reached convergence criteria in six iterations. Table A2 shows how the 26 countries contribute to the three (2007) or four (2012) components. Table A2. Countries’ contributions to the principal components. Country 2007 1st Component 2012 1st Component 2007 2nd Component 2012 2nd Component 2007 3rd Component 2012 3rd Component 2012 4th Component AT Austria −0.8119 −0.7708 −0.05874 0.2812 0.04249 −1.20.05 0.11156 BE Belgium −0.91417 −0.26396 0.52803 −0.28846 −0.37991 −0.87335 −1.05017 CY Cyprus 0.53777 0.41449 −0.37921 0.19816 −1.00462 0.18321 −0.74536 CZ Czechia −0.83171 −0.86943 0.69367 0.09863 −0.01061 −0.52352 −0.43346 DE Germany −0.2344 −0.05314 −0.20694 −0.65822 1.24639 −0.74793 1.45954 DK Denmark −0.51385 −0.6477 −1.49942 −0.37235 −0.30065 −0.14205 0.08512 EE Estonia 1.07845 −0.47987 −1.03448 0.7549 1.5694 0.32549 1.36469 EL Greece 0.52409 1.98103 1.6437 1.08254 −1.72526 −0.04791 −0.82843 ES Spain −0.60796 3.14244 1.63703 −1.26.12 −1.08996 −0.27919 −0.43654 FI Finland −1.24456 0.04651 0.38496 −0.90573 1.95109 0.02104 2.58969 FR France −0.5454 −0.07848 0.75137 −0.78641 0.07443 0.39423 0.25036 HU Hungary 0.79412 −0.45669 0.85781 2.01621 0.91443 0.58656 0.18496 IE Ireland −0.47203 0.5942 0.40587 −0.0914 −0.94683 −0.33155 −1.40245 IS Iceland 0.15807 −1.18332 −1.64814 −1.17504 −2.09799 1.47602 −0.74238 IT Italy 0.46134 0.77587 0.72078 0.86241 −0.92076 −0.14621 −0.3447 LT Lithuania 1.21276 −0.0457 −0.29146 1.82744 0.36067 −1.18555 −0.40005 LU Luxemburg −0.6931 −0.5774 −0.91574 −0.45614 −0.45531 −0.83776 −1.5566 LV Latvia 3.31357 −0.2101 −0.41543 1.97079 0.7569 2.7584 0.5087 NL Netherlands −0.64051 −0.82843 −1.51746 −0.9206 0.0243 −0.78992 0.82626 NO Norway −0.2299 −1.36951 −0.96034 −1.1438 −0.54461 0.52326 −1.20646 PL Poland 1.38332 0.36321 1.47278 0.36934 −0.0028 1.02713 0.33069 PT Portugal 0.16762 1.45223 1.26733 −0.96893 0.53069 1.33337 0.26141 SE Sweden 0.22784 −0.53582 −0.88212 −1.39502 −0.0403 1.49465 −0.41712 SI Slovenia −1.31893 0.4255 0.29688 −0.11817 1.7861 −1.32586 1.71076 SK Slovakia −0.47952 0.03088 0.34155 0.39451 0.30933 −0.70355 0.4685 UK United Kingdom −0.32102 −0.85603 −1.19227 0.68525 −0.04663 −0.98394 −0.58852 Source: Own calculations on EU-SILC 2007 and EU-SILC 2012 dataset. In Figure A1 the first and second component of both years are plotted in the scatter diagrams 2007 (a) and 2012 (b) into four quadrants. Countries that have the heaviest problems are represented in the upper right quadrant, while countries that fare better are in the double negative lower left quadrant. The two diagrams compare the two years with a warning: in 2007, the first and second components together explain more than half the variance (56%), while in 2012 they only reach 43,5%. A three-dimensional diagram for 2012 would reach 62% (and nearly 73% in 2007) but would not be as reader-friendly. 88 Economies 2024,12, 115 ȱ (a)ȱ2007ȱ(b)ȱ2012ȱ Figure A1. Scatterplot of the first and second component for the 26 countries, year 2007 ( a ) and 2012 (b). The first component is measured on the horizontal axis and the second component on the vertical axis. Countries represented on the left quadrants show negative values for the first components (i.e., they tend not to be deprived in 2007, nor have major problems on the labor market in 2012), while countries in the lower quadrants show negative values for the second component. Therefore, countries having both negative values tend to fare better than all the rest, while countries having both positive values tend to be more miserable than the remaining countries. References Atkinson, Anthony Barnes, and Eric Marlier, eds. 2010. Income and Living Conditions in Europe. Luxembourg: Publications Office of the European Union. Atkinson, Anthony Barnes, Anne-Catherine Guio, and EricMarlier, eds. 2017. Monitoring Social Inclusion in Europe; Luxembourg: Eurostat. Atkinson, Anthony Barnes, Bea Cantillon, Eric Marlier, and Brian Nolan. 2002. Social Indicators: The EU and Social Inclusion. Oxford: Oxford University Press. Atkinson, Anthony Barnes Eric Marlier, Fabienne Monatigne, and Anne Reinstadler. 2010. Income poverty and income inequality. In Income and Living Conditions in Europe. Edited by Anthony Barnes Atkinson and Eric Marlier. Luxembourg: Publications Office of the European Union. Ayllón, Sara, and Andras Gábos. 2017. The Interrelationships between the Europe 2020 Poverty and Social Exclusion Indicators. Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement 130: 1025–49. [CrossRef] Betti, Gianni. 2016. What impact has the economic crisis had on quality of life in Europe? A multidimensional fuzzy approach. Quality and Quantity 51: 351–64. [CrossRef] Crimmins, Eileen, Jung Kj Kim, and Teresa Seeman. 2009. Poverty and Biological Risk: The Earlier “Aging” of the Poor. Journal of Gerontology: Medical Sciences 64A: 286–92. [CrossRef] [PubMed] Croci Angelini, Elisabetta. 2015. Titolo di godimento della casa ed eterogeneitàdelle condizioni economiche e abitative nei paesi europei. In La casa. il benessere e le diseguaglianze. Edited by Francesco Farina and Maurizio Franzini. Milano: Egea. Croci Angelini, Elisabetta, and Alessandra Michelangeli. 2012. Axiomatic measurement of multidimensional well-being inequality: Some distributional questions. The Journal of Socio-Economics 41: 548–57. [CrossRef] Croci Angelini, Elisabetta, Francesco Farina, and Enzo Valentini. 2016. Contagion across Eurozone’s sovereign spreads and the Core-Periphery divide. Empirica 43: 197–213. [CrossRef] Croci Angelini, Elisabetta, Francesco Farina, and Enzo Valentini. 2020. Wage and employment by skill levels in technological evolution of South and East Europe. Journal of Evolutionary Economics 30: 1497–514. [CrossRef] Decancq, Koen, and Maria Ana Lugo. 2013. Weights in Multidimensional Indices of Wellbeing: An Overview. Econometric Reviews 32: 7–34. [CrossRef] 89 Economies 2024,12, 115 De Grauwe, Paul, and Yuemei Ji. 2013. Self-fulfilling crises in the Eurozone: An empirical test. Journal of International Money and Finance 34: 15–36. [CrossRef] EU Council of Ministers. 1985. Council Decision of 19 December 1984 on Specific Community Action to Combat Poverty (85/8/EEC). OJEC. L2. Brussels: EU Council of Ministers. Eurofound. 2018. Social Insecurities and Resilience. Luxembourg: Publications Office of the European Union. European Commission. 2010. Europe 2020. A Strategy for Smart, Sustainable and Inclusive Growth. Brussels: COM(2010) 2020 final. Available online: https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2010:2020:FIN:en:PDF (accessed on 6 May 2024). Fedotenkov, Igor, Virmantas Kvedaras, and Miguel Sanchez-Martinez. 2024. Employment protection and labour productivity growth in the EU: Skill-specific effects during and after the Great Recession. Empirica 51: 209–62. [CrossRef] Figari, Francesco, Andrea Salvatori, and Holly Sutherland. 2010. Policy simulation across countries using EUROMOD: Stress testing European welfare systems for unemployment. In Income and Living Conditions in Europe. Edited by Anthony Barnes Atkinson and Eric Marlier. Luxembourg: Publications Office of the European Union. Freudenberg, Michael. 2003. Composite Indicators of Country Performance: A Critical Assessment. OECD Science, Technology and Industry Working Papers, No. 2003/16. Paris: OECD Publishing. [CrossRef] Fusco, A., Anne-Catherine Guio, and Eric Marlier. 2010. Characterising the income poor and the materially deprived in European countries. In Income and Living Conditions in Europe. Edited by Anthony Barnes Atkinson and Eric Marlier. Luxembourg: Publications Office of the European Union. Grimaccia, Elena. 2021. Europe 2020 Strategy for a Smart, Inclusive and Sustainable Growth: A First Evaluation. Rivista Italiana di Economia Demografia e Statistica 75: 65–77. Hernández-Quevedo, Cristina, Cristina Masseria, and Elias Mossialos. 2010. Socio-economic determinants of health in Europe. In Income and Living Conditions in Europe. Edited by Anthony Barnes Atkinson and Eric Marlier. Luxembourg: Publications Office of the European Union. Horemans, Jeroen, Ive Marx, and Brian Nolan. 2015. Hanging in, but Only Just. Part-Time Employment and in-Work Poverty throughout the Crisis. CSB Working Paper n.3. Antwerp: Herman Deleeck Centre for Social Policy, University of Antwerp. ILO. 2015. Wage and Income Inequality. Global Wage Report 2014/15. Geneva: ILO. Ivanová, Monika, Radoslav Klamár, and Erika Feckova Škrabul’áková. 2022. Identification of Factors Influencing the Quality of Life in European Union Countries Evaluated by Principal Component Analysis. Geographica Pannonica 26: 13–29. [CrossRef] Jenkins, Stephen P., Andrea Brandolini, John Micklewright, and Brian Nolan. 2013. The Great Recession and the Distribution of Household Income. Oxford: Oxford University Press. Kemeny, Jim. 2001. Comparative housing and welfare: Theorising the relationship. Journal of Housing and the Built Environment 16: 53–70. [CrossRef] Mazurek, Jiri. 2016. The Evaluation of Recession Magnitudes in EU Countries during the Great Recession 2008–2010. Review of Economic Perspectives 16: 231–44. [CrossRef] Murtin, Fabrice, and Marco Mira d’Ercole. 2015. Household Wealth Inequality across OECD Countries: New OECD Evidence. OECD Statistical Brief. n.21. Paris: OECD. Nardo, Michela, Michaela Saisana, Andrea Saltelli, Stefano Tarantola, Anders Hoffmann, and Enrico Giovannini. 2008. Handbook on Constructing Composite Indicators: Methodology and User Guide. OECD JRC47008. Paris: OECD. Nolan, B., and Christopher T. Whelan. 2010. Using Non-Monetary Deprivation Indicators to Analyse Poverty and Social Exclusion: Lessons from Europe? Journal of Policy Analysis and Management 29: 305–25. [CrossRef] Nolan, Brian, and Christopher T. Whelan. 2014. The Social Impact of Income Inequality: Poverty, Deprivation and Social Cohesion. In Changing Inequalities in Rich Countries: Analytical and Comparative Perspectives. Edited by Wiemer Salverda, Brian Nolan, Daniele Checchi, Ive Marx, Abigail McKnight, Istaván György Tóth and Herman van de Werfhorst. Oxford: Oxford University Press. Nussbaum, Martha, and Amartya Kumar Sen, eds. 1993. The Quality of Life. Oxford: Oxford University Press. OECD. 2015. Employment Outlook. Paris: OECD. Sauli, Hannele, and Veli-Matti Törmälehto. 2010. The distributional impact of imputed rent. In Income and Living Conditions in Europe. Edited by Anthony B. Atkinson and Eric Marlier. Luxembourg: Publications Office of the European Union. Sen, Amartya Kumar. 1985. Commodities and Capabilities. Amsterdam and Oxford: North-Holland. Sierminska, Eva. 2012. Wealth in the Crisis. Social Situation Observatory. Income Distribution and Living Conditions. Research Note 9. Brussels: European Commission. Sologon, Denisa Maria, and Cathal O’Donoghue. 2014. Shaping Earnings Insecurity: Labour Market Policy and Institutional Factors. Review of Income and Wealth 60: S205–32. [CrossRef] Stiglitz, Joseph E., Amartya Kumar Sen, and Jean-Paul Fitoussi. 2009. Report by the Commission on the Measurement of Economic Performance and Social Progress. New York: Columbia University. Weymark, John A. 2006. The normative approach to the measurement of multidimensional inequality. In Inequality and Economic Integration. Edited by Francesco Farina and Ernesto Savaglio. London: Routledge, pp. 303–28. 90 Economies 2024,12, 115 Whelan, Christopher T., and Bertrand Maître. 2012. Understanding Material Deprivation in Europe: A Multilevel Analysis. Research in Social Stratification and Mobility 30: 489–503. [CrossRef] Whelan, Christopher T., Brian Nolan, and Bertrand Maitre. 2014. Multidimensional Poverty Measurement in Europe: An Application of the Adjusted Headcount Approach. Journal of European Social Policy 24: 183–97. [CrossRef] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. 91 Citation: Davidescu, Adriana AnaMaria, Oana-Ramona Lobon¸t, and Tamara Maria Nae. 2024. The Fabric of Transition: Unraveling the Weave of Labor Dynamics, Economic Structures, and Innovation on Income Disparities in Central and Eastern Europe Nations. Economies 12: 68. https://doi.org/10.3390/economies 12030068 Academic Editor: Fabio Clementi Received: 12 December 2023 Revised: 28 February 2024 Accepted: 4 March 2024 Published: 14 March 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). economies Article The Fabric of Transition: Unraveling the Weave of Labor Dynamics, Economic Structures, and Innovation on Income Disparities in Central and Eastern Europe Nations Adriana AnaMaria Davidescu 1,2,*, Oana-Ramona Lobon¸t 3and Tamara Maria Nae 4,5 1Department of Statistics and Econometrics, The Faculty of Economic Cybernetics, Statistics and Informatics, The Bucharest University of Economic Studies, 010552 Bucharest, Romania 2 Department of Education, Training and Labour Market, National Scientific Research Institute for Labour and Social Protection, 010643 Bucharest, Romania 3Department of Finance, Faculty of Economics and Business Administration, West University of Timisoara, 300223 Timisoara, Romania; [email protected] 4Department of Economics and Economic Policies, Faculty of Theoretical and Applied Economics, Bucharest University of Economic Studies, 010552 Bucharest, Romania; [email protected] 5Research and Analysis Department, Ministry of Finance, 050706 Bucharest, Romania *Correspondence: adriana.alexandr[email protected] Abstract: In recent years, the issue of income inequality has ascended to the forefront of national and international agendas, underscored by the urgency to navigate the complexities of market-driven economies without exacerbating social disparities. These challenges are particularly pronounced in the post-communist nations of Central and Eastern Europe, where the transition legacy and the marketization forces present unique dynamics in the evolution of income disparities. This research investigates the intricate mechanisms through which marketization impacts income inequality within the Central and Eastern European countries context, aiming to uncover how economic transformations influenced by global sustainability goals can contribute to narrowing the income gap. By employing panel data estimation techniques and Generalized Method of Moments (GMM) analysis, this study highlights the enduring nature of income disparities and the critical roles played by economic growth, education investment, labor market reforms, globalization, and governance quality in shaping equitable income distributions. Findings reveal that, despite the competitive nature of market economies potentially creating disparities, strategic policy interventions in education, economic policy, and labor market regulations can mitigate the adverse effects of marketization on income inequality. Additionally, this research emphasizes the importance of strong institutional frameworks and the nuanced role of the informal economy in influencing income distribution dynamics. Keywords: income inequality; determinants; CEE countries; panel data approach; GMM; social progress; convergence 1. Introduction Inequality has been the subject of a great debate at all times. This phenomenon has gained particular interest among economists since the economic downturns that hit Europe after the most significant wave of accession. Concurrently, there has been a heightened emphasis in research on income distribution, which is currently emerging as a progressively urgent economic and social concern. This is especially notable in emerging European nations, where inequalities surpass the mean observed in the European Union (EU). In the aftermath of the collapse of communist regimes in Central and Eastern Europe, the interplay between income inequality and marketization has witnessed significant changes. The shift from centrally planned to market-driven economies in the region has presented both opportunities and challenges. While marketization has stimulated economic growth, fostering an overall increase in prosperity and the emergence of a Economies 2024,12, 68. https://doi.org/10.3390/economies12030068 https://www.mdpi.com/journal/economies 92 Economies 2024,12,68 growing middle class, the swift implementation of market reforms has concurrently given rise to varying levels of income inequality. Certain segments of the population have been more adept at leveraging new economic opportunities, leading to disparities that need careful consideration. Reducing income inequality in Central and Eastern European countries holds paramount significance for fostering sustainable development, social cohesion and resilience. High levels of income inequality can undermine a region’s economic potential by limiting access to education, healthcare, and opportunities for a significant portion of the population. Addressing inequality contributes to political stability and a more inclusive society. Moreover, a more equitable distribution of income can stimulate domestic demand, fostering a robust and resilient economy. As CEE nations continue to navigate the challenges of transition and marketization, prioritizing policies that reduce income disparities becomes crucial for building a prosperous and harmonious future for their citizens. The genesis of this study is rooted in the persistent challenges pertaining to inequality, notably discernible within the European Union and accentuated within CEE countries. The research is motivated by the identification of a comprehensive set of policy recommendations designed to ameliorate the social landscape in CEE nations, with a strategic aim of mitigating and ultimately eradicating the socio-economic disparities between Eastern Europe and the rest of European Union. This paper contributes significant added value to the existing literature by offering a nuanced examination of the complex interplay between marketization, income inequality, and institutional quality, with a specific focus on post-communist countries in CEE. The study not only investigates the impact of marketization on income distribution but also integrates the crucial dimension of institutional quality, providing a more comprehensive understanding of the factors influencing inequality dynamics in the CEE region. By identifying key policy recommendations tailored to the unique socio-economic landscape of CEE countries, this paper offers actionable insights for policymakers striving to address and mitigate income disparities. This paper adopts a systematic structure, commencing with an insightful introduction that articulates the research problem’s significance and delineates the study’s objectives. Subsequently, this literature review meticulously examines existing scholarship, establishing a robust theoretical framework. The Data and Methodology section outlines the research design, data sources, and analytical approach, encompassing the nuanced exploration of random effects, fixed effects, and rigorous endogeneity testing techniques. Moving forward, the Results and Discussion section synthesizes empirical findings, differentiating between random and fixed effects. The conclusion succinctly summarizes key findings, underscores their contributions to the existing body of knowledge, and propounds avenues for future research. Additionally, the paper culminates with judicious policy recommendations, deriving practical implications from the study’s insights and offering guidance for decision makers in relevant domains. This cohesive structure ensures a logical progression of ideas, facilitating a comprehensive and impactful presentation of the research. 2. Literature Review 2.1. Marketization and Income Inequality: A Complex Nexus The relationship between marketization and income inequality is highly contextual, showing variance across different countries and regions. This variance is largely shaped by specific policy measures, institutional frameworks, and socio-economic conditions unique to each context. Scholars have underscored the critical importance of considering the distributive impacts of market-oriented policies, which include changes in access to education, social protection, and employment opportunities. The consensus from these studies suggests an intricate and multifaceted link between income inequality and marketization, highlighting the necessity for a nuanced understanding that accommodates a broad spectrum of contextual factors. 93 Economies 2024,12,68 2.2. Labor Market Dynamics and Their Disparate Impacts The labor market plays a pivotal role in influencing income inequality, with technological advancements and shifts in the demand for skilled labor contributing significantly to wage disparities. Acemoglu and Autor (2011) and Piketty (2014) provide evidence of how economic growth periods and technological shifts exacerbate income inequality. Goldin and Katz (2007) further this discussion by emphasizing the exacerbating role of education and skill differentials within the labor market. The importance of labor market policies, such as minimum wage regulations and social protection measures in shaping income distribution, is highlighted by Atkinson and Morelli (2010), with Chetty et al. (2014) discussing the persistence of inequalities across generations due to labor market opportunities. Card and Krueger (1994) and Autor et al. (2008) delve into the effects of minimum wage policies and labor market polarization, underscoring the significance of educational composition in the workforce and its impact on income disparities. The distribution of employees across industries, as discussed by Goos and Manning (2007), demonstrates how technological advancements have led to the decline in middle-skilled jobs, further increasing income inequality. 2.3. Economic Performance, Structure, and Income Inequality The intricate relationship between economic growth and income inequality has captured scholarly attention, with mixed findings regarding its impacts. Barro (2000) and Forbes (2000) explore this relationship, while Berg and Ostry (2011) suggest that extreme income disparities may disrupt economic stability. Persson and Tabellini (1994) underscore the mediating role of institutional quality in this relationship, proposing that well-designed institutions can alleviate the adverse effects of inequality on development. 2.4. Openness of the Economy: A Double-Edged Sword The interplay between economic openness and income inequality has been extensively studied, with Rodrik (1997) and Milanovic (2005a, 2005b) discussing how trade liberalization and globalization can initially increase income inequality. Bergstrand and Egger (2007), along with Firebaugh and Goesling (2004), emphasize the contingent nature of this relationship on factors like development level and institutional quality. 2.5. Shadow Economy and Income Disparities The shadow economy’s role in influencing income inequality is highlighted by Schneider and Enste (2000) and Torgler and Schneider (2007), noting the informal sector’s contribution to wage disparities and the growth of informal employment driven by income inequality. Buehn and Schneider (2012) stress the importance of the institutional context in understanding these dynamics. 2.6. Technological Advancements and High-Tech Exports The literature on high-tech exports and income inequality presents a nuanced view, with Lin and Li (2011) and Gouvea and Wang (2019) discussing the sector’s potential to both exacerbate and mitigate income disparities. The importance of investments in education and technology in narrowing skill differentials is noted by Barro (2000), with Li and Liu (2005) cautioning about the uneven benefits of high-tech exports. 2.7. Governance, Institutional Quality, and Income Distribution The role of governance and institutional quality in addressing income inequality is emphasized by Acemoglu and Robinson (2012) and Kaufmann et al. (2010), highlighting the importance of strong institutions in promoting equitable resource distribution. Murtin and Wacziarg (2014) provide empirical evidence linking improvements in institutional quality to reductions in income inequality. Despite extensive research on individual marketization factors and their impact on income inequality, there exists a notable gap in studies that provide a holistic analysis 94 Economies 2024,12,68 integrating these elements into a unified framework. The current literature often examines these aspects in isolation, lacking a comprehensive understanding of the synergies and interactions among marketization components and their collective influence on income distribution. There’s a critical need for research that bridges these gaps, offering an integrated perspective that encompasses the dynamic interplay among labor market dynamics, economic performance, technological shifts, and institutional frameworks in shaping income inequality outcomes. Addressing this void is imperative for policymakers and scholars seeking a thorough comprehension of marketization’s multifaceted impact on income distribution. The description of the variables that will be used in the empirical analysis can be studied in (Table 1). Table 1. List of the variables and data source. Variable Source Sign Endogenous Gini Coefficient (pp) Eurostat data base − Exogenous variable Economic Performance and Labor market Minimum monthly wage (%) Eurostat data base − Strictness of employment protection index, individual and collective dismissals (%) Employment Protection Database, OECD − Gross domestic product per capita (euro/cap.) Eurostat data base (+/−) Economic growth/cap. (%) Eurostat data base (+/−) Employed population with tertiary education (%) Eurostat data base −/+ Employees in the industry (%) Eurostat data base − Education expenditure (% GDP) Eurostat data base (−) Informal economy (% GDP) Global Economy (−/+) Globalization Share of high-tech exports (%) of Total Exports Eurostat data base +/− Innovation index (%) Global Economy (+/−) Openness of the economy (% GDP) Eurostat data base (+/−) Quality of institutions Regulatory quality (pp) World Bank (−) Rule of law (pp) World Bank (−) Control of Corruption (pp) World Bank (−/+) 3. Data and Methodology Addressing social issues and enhancing fair income distribution necessitates a deep dive into the factors influencing income disparity. This exploration is crucial for fostering broader socio-economic inclusion, elevating the general quality of life, and ensuring economic and social stability, which in turn, strengthens socio-economic cohesion and resilience against future crises. The empirical component of this study zeroes in on the determinants of income inequality within ten CEE countries, excluding Croatia due to data limitations. Utilizing panel data regression analysis, the research covers annual data from 2008 to 2019, dissecting the influence of identified determinants across four main categories: labor market institutions, economic development, globalization, and governance. The model employed is given by: 95 Economies 2024,12,68 Table 3. Cont. M1 M2 M3 M4 M5 M6 Quality of institutions No. of instruments (groups) 10 10 10 10 10 10 Obs.no. 120 120 120 120 120 120 Note: Within the table, the coefficients are displayed together with standard errors and the probabilities within (). Standard errors are typically displayed in parentheses right below the coefficients to indicate they are related but distinct values. 5. Conclusions The investigation into the effects of marketization on income inequality across CEE offers critical insights, synthesizing empirical evidence with dynamic panel data and GMM analysis. This refined understanding leads to several key conclusions: The pronounced persistence of income inequality, as highlighted by the lagged income inequality variable’s significance, underscores the chronic nature of disparities within CEE nations. This revelation underscores the imperative for enduring, strategic policy interventions designed to combat income inequality effectively. Confirming the pivotal roles of economic growth and increased allocations for education, this study advocates for policies that bolster economic development while significantly investing in education. Such initiatives promise to foster equitable income distributions, enhance job quality, and broaden educational opportunities. The nuanced impacts of minimum wage adjustments and the unequivocally positive influence of employment protection and active labor market initiatives illustrate the essential nature of thoughtful labor market policies. These findings advocate for measures that uplift low-income workers and promote inclusivity within the labor market. The association between reduced income inequality with high technology exports and economic openness speaks to globalization’s multifaceted role in fostering equitable income distribution. This highlights the opportunities globalization presents for inclusive growth, emphasizing strategic global integration. This study illuminates the indispensable role of governance, with strong institutions marked by rule of law and anti-corruption efforts emerging as crucial for mitigating income disparities. Strengthening governance and institutional integrity is framed as a cornerstone strategy in addressing income inequality. The shadow economy’s complex influence on income inequality highlights the intricate balance needed in integrating the informal sector with the formal economy. Crafting strategies that harness the informal sector’s potential while curbing its adverse effects is pivotal for equitable growth. In sum, this study not only enriches the academic dialogue on income inequality within the context of CEE countries but also provides actionable insights for policymakers. By delineating the mechanisms through which marketization factors influence income distribution, it underscores the critical need for targeted, integrated policy interventions that span economic, educational, labor, and governance domains to cultivate a more inclusive, equitable economic landscape. While this study contributes valuable insights into income inequality within CEE countries, certain limitations must be acknowledged. The exclusion of countries like Croatia due to insufficient data highlights the broader issue of data limitations in post-communist nations, emphasizing the challenges associated with comprehensive regional analyses. Additionally, despite the study’s extensive coverage, it may not capture all relevant marketization factors and their nuanced interactions. Factors such as technological innovation, demographic shifts, and international trade dynamics warrant further exploration for a comprehensive understanding. Furthermore, the focus on CEE countries, while providing essential regional context, raises concerns about the generalizability of the findings to other global contexts. The unique historical, economic, and social trajectories of CEE nations underscore the need for caution when applying these results beyond the studied region. 102 Economies 2024,12,68 Future research endeavors in the realm of income inequality within CEE countries could prioritize enhanced data collection efforts, particularly in nations currently facing data limitations. Additionally, future research could delve into evaluating the effectiveness of specific policy interventions within the CEE context, contributing to a more targeted and evidence-based approach to reducing income inequality in the region. Social implications: The findings highlight the importance of inclusive growth that benefits a broader segment of the population. By addressing income inequality, countries can improve social cohesion, reduce poverty rates, and enhance the overall quality of life for their citizens. This approach aligns with the pursuit of the Sustainable Development Goals, particularly Goal 10, which focuses on reducing inequalities. Economic implications: Addressing income inequality is not just a matter of social justice but also economic efficiency. High levels of inequality can hinder economic growth, create economic instability, and waste human capital. Policies that foster a more equitable income distribution can lead to a more sustainable and robust economic system. Author Contributions: Conceptualization, A.A.D. and O.-R.L. and A.A.D.; methodology, A.A.D. software, A.A.D., T.M.N.; validation, A.A.D., O.-R.L. and T.M.N.; formal analysis, O.-R.L.; investigation, A.A.D.; resources, A.A.D.; data curation, T.M.N.; writing—original draft preparation, A.A.D., T.M.N.; writing—review and editing, A.A.D., T.M.N.; visualization, O.-R.L.; supervision, A.A.D.; project administration, A.A.D.; funding acquisition, A.A.D. All authors have read and agreed to the published version of the manuscript. Funding: This research study has been elaborated within the Data Science Research Lab for Business and Economics of the Bucharest University of Economic Studies. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data can be available upon request. Conflicts of Interest: The authors declare no conflicts of interest. Abbreviations Acronyms for the variable. Acronym The Name of the Variable GINI COEF Gini Coefficient MMWBI Minimum monthly salary, annual average GDP/cap. Gross domestic product per capita ECG/cap. Economic growth/cap TERED Employed population with tertiary education EMP_IND Employees in the industry ED_SPEND Education expenditure INNOV Innovation index HIGHTECHXP Share of high-tech exports OPENESS Openness of the economy REG_QUAL Regulatory quality RULE_OF_LAW Rule of law CONT_CORR Control of Corruption SHADOW_EC Informal economy URB Urbanization degree EMP_SEV Employees in the services sector References Acemoglu, Daron, and David Autor. 2011. Skills, tasks, and technologies: Implications for employment and earnings. Handbook of Labor Economics 4B: 1043–171. Acemoglu, Daron, and James A. Robinson. 2012. Why Nations Fail: The Origins of Power, Prosperity, and Poverty. New York: Crown Business. Atkinson, Anthony Barnes, and Salvatore Morelli. 2010. Inequality and labor market institutions. Handbook of Income Distribution 2: 1059–143. 103 Economies 2024,12,68 Autor, David H., Lawrence F. Katz, and Melissa S. Kearney. 2008. Trends in U.S. Wage Inequality: Revising the Revisionists. The Review of Economics and Statistics 90: 300–23. [CrossRef] Barro, Robert J. 2000. Inequality and Growth in a Panel of Countries. Journal of Economic Growth 5: 5–32. [CrossRef] Berg, Andrew, and Jonathan D. Ostry. 2011. Inequality and Unsustainable Growth: Two Sides of the Same Coin? International Monetary Fund Staff Discussion Note No. 11/08. Washington, DC: International Monetary Fund. Bergstrand, Jeffrey H., and Peter Egger. 2007. A Knowledge-and-Physical-Capital Model of International Trade Flows, Foreign Direct Investment, and Multinational Enterprises. Journal of International Economics 73: 278–308. [CrossRef] Buehn, Andreas, and Friedrich Schneider. 2012. Shadow economies around the world: Novel insights, accepted knowledge, and new estimates. International Tax and Public Finance 19: 139–71. [CrossRef] Card, David, and Alan B. Krueger. 1994. Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania. The American Economic Review 84: 772–93. Chetty, Raj, Nathaniel Hendren, Patrick Kline, Emmanuel Saez, and Nicholas Turner. 2014. Is the United States still a land of opportunity? Recent trends in intergenerational mobility. American Economic Review 104: 141–47. [CrossRef] Firebaugh, Glenn, and Brian Goesling. 2004. Accounting for the Recent Decline in Global Income Inequality. American Journal of Sociology 110: 283–312. [CrossRef] Forbes, Kristin J. 2000. A Reassessment of the Relationship between Inequality and Growth. American Economic Review 90: 869–87. [CrossRef] Goldin, Claudia, and Lawrence F. Katz. 2007. The Race between Education and Technology. Cambridge: Harvard University Press. Goos, Maarten, and Alan Manning. 2007. Lousy and Lovely Jobs: The Rising Polarization of Work in Britain. The Review of Economics and Statistics 89: 118–33. [CrossRef] Gouvea, Raphael, and Wei Wang. 2019. Innovation and income inequality: Evidence from China’s high-tech industry. Structural Change and Economic Dynamics 51: 133–44. Kaufmann, Daniel, Aart Kraay, and Massimo Mastruzzi. 2010. The worldwide governance indicators: Methodology and analytical issues. Hague Journal on the Rule of Law 2: 220–46. [CrossRef] Li, Xiaoyig, and Xiaming Liu. 2005. Foreign direct investment and economic growth: An increasingly endogenous relationship. World Development 33: 393–407. [CrossRef] Lin, Frank, and John Li. 2011. Exports and income inequality: Further evidence from China. China Economic Review 22: 80–91. Medina, Leandro, and Friedrich Schneider. 2018. Shadow Economies around the World: What Did We Learn over the Last 20 Years? Washington, DC: International Monetary Fund. Milanovic, Branko. 2005a. Can we discern the effect of globalisation on income distribution? Evidence from household surveys. The World Bank Economic Review 19: 21–44. [CrossRef] Milanovic, Branko. 2005b. Worlds Apart: Measuring International and Global Inequality. Princeton: Princeton University Press. Murtin, Fabrice, and Romain Wacziarg. 2014. The democratic transition. The Quarterly Journal of Economics 129: 267–340. Persson, Tabellini, and Guido E. Tabellini. 1994. Is Inequality Harmful for Growth? American Economic Review 84: 600–21. Piketty, Thomas. 2014. Capital in the Twenty-First Century. Cambridge: Cambridge Belknap Press. Rodrik, Dani. 1997. Has Globalization Gone Too Far? Washington, DC: Institute for International Economics. Schneider, Friedrich, and Dominik H. Enste. 2000. Shadow Economies: Size, Causes, and Consequences. Journal of Economic Literature 38: 77–114. [CrossRef] Torgler, Benno, and Friedrich Schneider. 2007. Shadow economy, voice and accountability, and corruption. In Growth, Inequality, and Poverty: Prospects for Pro-Poor Economic Development. Edited by Anthony F. Shorrocks and Rolph Van Der Hoeven. Oxford: Oxford University Press, pp. 153–71. Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. 104 Citation: Davidescu, Adriana AnaMaria, Tamara Maria Nae, and Margareta-Stela Florescu. 2024. From Policy to Impact: Advancing Economic Development and Tackling Social Inequities in Central and Eastern Europe. Economies 12: 28. https://doi.org/10.3390/ economies12020028 Academic Editor: Fabio Clementi Received: 5 December 2023 Revised: 17 January 2024 Accepted: 19 January 2024 Published: 24 January 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). economies Article From Policy to Impact: Advancing Economic Development and Tackling Social Inequities in Central and Eastern Europe Adriana AnaMaria Davidescu 1,2,*, Tamara Maria Nae 3,4 and Margareta-Stela Florescu 5 1Department of Statistics and Econometrics, The Bucharest University of Economic Studies, 010552 Bucharest, Romania 2 Department of Education, Training and Labour Market, National Scientific Research Institute for Labour and Social Protection, 010643 Bucharest, Romania 3Department of Economics and Economic Policy, Bucharest University of Economic Studies, 010552 Bucharest, Romania; [email protected]o 4Research and Analysis Department, Ministry of Finance, 050706 Bucharest, Romania 5Department of Administration and Public Management, Bucharest University of Economic Studies, 010374 Bucharest, Romania; margareta.flor[email protected] *Correspondence: adriana.alexandr[email protected] Abstract: This study challenges the traditional reliance on GDP as the sole indicator of the success of the EU’s cohesion policy, aligning with the evolving academic discourse that calls for a broader spectrum of metrics incorporating social factors. The research aims to assess the impact of cohesion on economic performance and social progress at the regional level in Central and Eastern European countries, using regression analysis on panel data. Inspired by the call to move beyond GDP-focused assessments, this research re-evaluates cohesion policy within an expanded framework that prioritizes economic and social dimensions. Specifically, it addresses the escalating concerns of income disparity and poverty in Central and Eastern European nations. Utilizing panel data regression models, this study scrutinizes data from 2007 to 2018, covering two recent programming periods, to offer a comprehensive, multifaceted analysis of the impact of cohesion policy. It underscores the policy’s dual role in spurring economic growth and fostering social progress, particularly in mitigating income inequality and reducing poverty. The findings reveal that cohesion policies positively affect both economic performance and social progress, with notable impacts on narrowing the income gap and alleviating poverty in these regions. However, the economic benefits for poverty reduction materialize over a prolonged period, reflecting the gradual nature of policy impact and the time needed for investments to materialize. The study emphasizes the need for a long-term strategic vision in implementing cohesion policies. This includes enhanced data collection, a deeper focus on the social ramifications of policies, streamlined policy processes, capacity building, institutional strengthening, and prioritizing equitable opportunities to bridge income gaps effectively. This comprehensive approach aims to maximize the dual benefits of cohesion policies, promoting balanced economic and social progress across Central and Eastern Europe. Keywords: cohesion policy; economic development; social progress; regional analysis; beyond the GDP 1. Introduction The final objective of the European Social Model is to simultaneously ensure economic growth and social cohesion. One of the central goals of the EU (European Union), stipulated in the Maastricht Treaty, is promoting social and economic progress by strengthening economic and social cohesion. It is widely recognized that the promotion of cohesion is one of the most prominent and important of the EU’s many political responsibilities; this importance is founded on several aspects, such as: (i) the fact that it has acquired increased importance over time with regard to the budgetary expenses of the European Union; Economies 2024,12, 28. https://doi.org/10.3390/economies12020028 https://www.mdpi.com/journal/economies 105 Economies 2024,12,28 (ii) cohesion policy is quite visible in its broad remit, covering a very wide range of EU policy activities, including infrastructure, telecommunications, research and development, competitiveness, vocational training, employment and social inclusion, as well as objectives to promote environmental sustainability and digitization—objectives that are in line with the perspectives of the green and digital transition; (iii) the contribution to the consolidation of the process of historical enlargement and the EU deepening; (iv) cohesion policy involves a large number of political actors at European level, also managing to include governmental and non-governmental decision-makers at regional and local levels. With the emergence of the Europe 2020 Strategy, the objectives of the cohesion policy have acquired a multidimensional characteristic, paying increasing attention to social objectives. Thus, the analysis to be developed, as part of quantitative research, aims to expand the assessment of the cohesion policy on economic development by adding the social dimension to identify the effects in terms of achieving the two objectives that are complementary in the European model: economic growth and social cohesion. This study explores the hypothesis that cohesion policy positively affects economic growth and social outcomes. The research probes into the impact of cohesion policy on economic performance and social progress by analyzing regional-level data from Central and Eastern European (CEE) countries during the 2007–2013 and 2014–2020 programming periods using panel data regression models. This choice stems from the EU’s financial allocation strategy based on regional GDP (gross domestic product) and the increasing recognition of the social dimension in both EU and global strategies, like the UN’s Sustainable Development Goals. Central and Eastern European countries exhibit diverse economic performances and social landscapes. Before the COVID-19 pandemic, many countries like Poland, Hungary, Czech Republic, Slovakia, and Romania experienced GDP growth rates between 3% and 5% annually, reflecting relative economic robustness compared to Western Europe. However, varying levels of unemployment persist, with rates around 6.1% in Poland, 4.1% in Hungary, 3.6% in the Czech Republic, 7.7% in Slovakia, and 4.3% in Romania as of 2021. Income disparities remain a challenge, particularly between rural and urban areas, accentuating economic inequalities. While some countries have improved healthcare systems and social welfare, demographic shifts, including aging populations and the outward migration of skilled workers, pose ongoing concerns. EU funding has aided infrastructure development and education, and digital transformation efforts are underway to bolster innovation and competitiveness. The COVID-19 pandemic has brought varied economic impacts to CEE nations. Some experienced significant contractions due to lockdowns and disruptions in global supply chains, while others demonstrated resilience. These countries face ongoing challenges in managing income disparities, unemployment, and demographic shifts while striving to modernize infrastructure, improve healthcare, and enhance digital capabilities. A defining feature of the CEE nations that justifies grouping them for analysis, anticipating relatively consistent outcomes, is their shared history as former communist states. These nations underwent challenging transitional phases, leaving them with various economic, social, and institutional vulnerabilities. Consequently, their administrative capabilities in securing external financial support and their methods of utilizing these funds have not achieved the anticipated progress levels. The ERDF (European Regional Development Fund) stands out as one of the most pivotal tools, championing goals like economic expansion and employment generation and fostering close territorial collaboration. Additionally, the ERDF lends support to regions facing inherent demographic or geographical challenges. This includes regions like thinly populated areas or those dominated by mountainous terrains where homes are widely dispersed from the main community. For the 2014–2020 programming duration, out of the total budget earmarked for the cohesion policy (around EUR 350 billion), the ERDF was allocated EUR 199 billion, earmarked for specific thematic goals. This study aims to extend the assessment of cohesion policy’s impact on economic development by adding a social dimension, thus exploring the dual objectives of economic 106 Economies 2024,12,28 growth and social cohesion, which are complementary in the European model. Thus, the study aims to respond to several relevant research questions: How does the EU’s cohesion policy impact economic growth and social outcomes in Central and Eastern European (CEE) countries? How effective are EU cohesion policies in reducing social inequities, specifically income disparity and poverty, in CEE regions? Is there a measurable correlation between the implementation of EU cohesion policies and improvements in both economic performance and social progress at the regional level in CEE countries? How does the time-lagged nature of cohesion policy’s impact influence its effectiveness in achieving economic and social objectives? However, it should be noted that (which is characterized by an alternation of starting and stopping the reforming processes) (Dinu et al. 2005), which is why one of the methodological difficulties identified at the level of this analysis is that the effects of cohesion policy on economic performance and social progress cannot be observed immediately but with delays. Therefore, the effects of cohesion policy take time to become apparent as investments are spread over several years, and some of their results are then reinvested, which delays the expected effects. At the same time, some regions are more dynamic in economic activity, and others have a narrower concentration of activity, which determines different rhythms in terms of the emergence of results. Thus, the structure and implementation rules of the cohesion policy made it vulnerable to criticism (Barca 2009). Hence, there is a need to identify the efficiency or inefficiency of the cohesion policy as a contribution to the discussions on its reformation. Thus, in the framework of the analysis, models with a dynamic structure were developed to capture the delays in the appearance of the results. In enhancing the discourse, this study pushes boundaries beyond traditional GDP-focused evaluations, advocating for more holistic indicators that encapsulate social progress. This endeavor augments the academic dialogue and informs policy decisions for upcoming programming periods. A review of the existing literature suggests a pressing need to explore cohesion policy’s dual impact on economic development and social progress. Moving beyond the traditional metric of GDP growth, there is a compelling case for incorporating broader indicators that offer a comprehensive insight into social dimensions. While GDP growth remains a vital benchmark, it is not the sole aim of cohesion policy. Evaluating its effects on diverse socio-economic markers deepens academic discourse and bolsters the underpinnings of policy decisions. Such a holistic approach could enhance the efficiency of cohesion fund allocations in upcoming programming periods. Cohesion funds are EU subsidies for the development of the social and industrial infrastructure of certain member nations. Incorporating social indicators, such as the SPI (Social Progress Index), into the evaluation of cohesion policy is crucial as it provides a more comprehensive understanding of policy impacts; traditional economic metrics like GDP are limited in scope and fail to fully capture the nuances of societal advancement, especially in the realms of income inequality and poverty reduction. The Opportunities pillar of the SPI, focusing on aspects like access to education, information, and advanced healthcare, serves as a vital complement to economic data, offering a deeper insight into how cohesion policies foster equitable opportunities and directly contribute to mitigating social disparities. By integrating robust social indicators alongside economic metrics, we can gain a more holistic view of cohesion policy outcomes, ensuring that assessments are not just about economic efficiency but also about their effectiveness in creating inclusive, equitable societies where opportunities for advancement and poverty reduction are realistically appraised and addressed. This study explores the hypothesis that cohesion policy positively affects economic growth and social outcomes. It examines the impact of cohesion policy on economic performance and social progress at the regional level in CEE countries during the 2007–2013 and 2014–2020 programming periods using panel data regression models. This approach is rooted in the EU’s financial allocation strategy based on regional GDP and acknowledges 107 Economies 2024,12,28 the increasing recognition of the social dimension in global strategies, such as the UN’s Sustainable Development Goals. CEE countries present diverse economic performances and social landscapes, with various challenges and achievements in different sectors. The COVID-19 pandemic has further complicated these scenarios, having varied economic impacts on the nations. The shared history of these countries as former communist states provides a common thread for analysis, anticipating relatively consistent outcomes across the region. From this point of view, the paper makes several contributions to the literature. Firstly, a holistic evaluation of cohesion policy: While much of the existing literature focuses on the economic impacts of the cohesion policy, primarily gauging success through GDP growth, this study provides a more comprehensive assessment; by incorporating the social dimension, the research presents a multidimensional perspective on the policy’s outcomes. Secondly, an in-depth regional analysis of CEE countries: The study delves deep into the specific context of these countries. Given that the regions of CEE countries have traditionally been underrepresented in similar research, this work fills a significant gap. Thirdly, dynamic models: recognizing the time-lagged impacts of cohesion policies, the study employs dynamic models to account for the delays in outcomes. This approach ensures a more accurate depiction of the policy’s real-time and long-term effects. Fourthly, the study acknowledges and validates other significant indicators reflecting social progress by venturing beyond the conventional GDP metric. This broader approach offers a more rounded picture of the policy’s impacts and underscores the need for varied evaluation metrics in the future. Fifthly, implications for policy reformation: highlighting the cohesion policy’s efficiency or inefficiency contributes valuable insights that could shape discussions on policy reformation. Sixthly, bridging literature gaps: the research takes a bold step in analyzing and challenging the existing literature. It addresses the dearth of studies on the social impact of cohesion funds, further enriching academic discussions and offering fresh avenues for future research. This paper contributes to the literature by providing a holistic evaluation of cohesion policy, delving into the specific context of CEE countries, employing dynamic models to capture time-lagged impacts, and advocating for a broader range of evaluation metrics. It addresses the need for policy reformation and aims to bridge literature gaps by analyzing the social impact of cohesion funds. The paper unfolds as follows: Initially, we outline our core objectives and the rationale behind the impact analysis, grounding our research within the European landscape. Next, we provide a literature review, spotlighting the scant research on the effectiveness of cohesion policy, particularly in the context of CEE regions. In the subsequent section, we delve into our research methodology, detailing the data transformation processes to ensure accurate outcomes. Our findings highlight the correlation between cohesion funds, economic growth, and social progress indicators. We then evaluate the recent evolution of cohesion policy tools in CEE over the past two programming periods. The paper concludes with key takeaways and actionable recommendations for enhancing the efficacy of cohesion fund allocation. 2. Literature Review Over time, cohesion policy has proven effective in helping many states reduce their regional gaps and gaps with other Member States. It substantially improved the economies of the states in the PIGS group (Portugal, Ireland, Greece and Spain), having a GDP per inhabitant value below 90% of the European Community average, thus being eligible for the granting of cohesion funds and subsequently enjoying a substantial catching-up effect. Through its actions, cohesion policy has significantly contributed to accelerating growth and prosperity in the EU, reducing certain economic, social and territorial disparities. In the last few decades, Fiaschi et al. (2018) and similar studies have provided valuable insights into the economic dimensions of cohesion policy. They predominantly center on regional GDP, neglecting a comprehensive analysis of crucial social outcomes such as employment rates, poverty levels, and social inclusion measures, thereby leaving a 108 Economies 2024,12,28 notable gap in understanding the full spectrum of policy effects. The prevalent focus on economic indicators in the existing literature overlooks the multifaceted nature of social progress, failing to adequately evaluate how cohesion policies influence more nuanced social dimensions, including the effectiveness of these policies in directly reducing poverty, enhancing job opportunities, and fostering inclusive social environments. This oversight in the literature highlights a critical need for expanded research that goes beyond economic metrics, offering a more holistic view of cohesion policy impacts by incorporating assessments of direct social benefits, thereby providing a clearer picture of how these policies contribute to or fall short in addressing key societal challenges like income inequality and social exclusion. In addition to the merits associated with cohesion policy, it has been subjected to a series of criticisms over time, such as: (i) failure to meet the objectives established by the EU Treaties, (ii) insufficient emphasis on economic growth (Sapir 2003), and (iii) turned into a captivating policy without a clear mission, with complex and overly bureaucratic administration (Manzella and Mendez 2009). Later, criticism narrowed down to the results, with studies identifying a positive impact on economic growth (Crescenzi and Giua 2016; Rodriguez-Pose and Novak 2013; Tomova et al. 2013). In contrast, other studies identified a negative impact (Dall’Erba and Le Gallo 2008) or no impact (Boldrin and Canova 2001), and others suggested that there is no consensus regarding the effectiveness of cohesion policy (Ederveen et al. 2003; Darvas and Wolff 2018). There are divergent conclusions about the effectiveness of cohesion policy because the methods and hypothetical approaches used in determining the results are extremely different. Analyses of the impact of cohesion policy on European regional performance mainly focus on the economic dimension, measured by GDP per capita and occasionally by the employment rate (Becker et al. 2010; Rodríguez Pose and Novak 2013; Giua 2017; Fiaschi et al. 2018; Crescenzi and Giua 2020) or the level of education and health (Calegari et al. 2021). Although there is a rich literature base on the effects of cohesion, especially on economic performance, it is inconclusive. Contrasting results derive from the choice of spatial and temporal considerations, variables used, and impact estimation methodologies. Farrell (2004) identifies a positive impact of structural funds on regional economic growth in Ireland and Spain. The results are supported by Lolos (2009), who analyzes the case of Greece. Some studies identify a positive impact on economic growth, with more pronounced effects for developed regions (Crescenzi and Giua 2016; Calegari 2020). The possible explanations for these results are, on the one hand, that the regions behind have less negotiation skills to attract more funds (Charron 2016; Fratesi and Wishlade 2017), and on the other hand, that these regions present a reduced capacity to absorb the allocated funds, which leads to the paradoxical situation where the regions entitled to receive considerable amounts from the structural funds cannot use them (Becker et al. 2013; Surubaru 2017; Cerqua and Pellegrini 2018). Also, some studies have not identified any effect of cohesion policy on economic growth performance (Ederveen et al. 2006), which does not identify “conclusive agreements on the impact of EU cohesion policy in the existing literature” Medeiros (2014). Other studies suggest that significant transport and infrastructure investments supported by cohesion policy do not affect economic growth (Crescenzi and Rodríguez-Pose 2012). Some conclude that there is no significant impact of the absorption rate of EU funds on growth in the EU countries in the short term. However, even a negative impact can be identified (Albulescu and Goyeau 2013). Other studies identify a negative impact, such as Dall’Erba and Le Gallo (2008), who evaluated the impact of structural funds on the convergence process between 145 European regions from 1989 to 1999. They identified the convergence process, but the funds did not impact it. Although studies on the impact of cohesion funds have focused on the effects exerted on economic growth, with a limited contribution to the literature regarding the influence on other indicators that reflect the social dimension, recently, an increasingly significant wave of research explores the effect of cohesion policy on other types of non-economic outcomes as well (Ferrara et al. 2022; Albanese et al. 2021). 109 Economies 2024,12,28 For example, Calegari et al. (2021) goes beyond GDP and analyzes the impact of cohesion policy on GDP per capita and societal well-being through a modified version of the adapted human development index. The results of the study indicate that cohesion policy has significantly increased overall well-being in low-performing regions that have used cohesion funds, with the results being particularly visible in improving the level of education. Attempts to overcome the GDP barrier were also approached by Crescenzi and Giua (2018), who found that the cohesion policy exerted a positive and significant impact at the EU level both on regional economic growth and on employment, suggesting that the positive effect on regional employment survived the Great Depression and supported less developed regions during the recovery. Maucorps et al. (2020) analyzed the effects of EU cohesion policy on the economic growth of 276 European NUTS-2 regions between 2008 and 2016, using a structural equation model consisting of a measurement component (with two latent variables) and a structural component. The study’s results support the existence and purpose of cohesion policy, where EU funding is essential for the economic development of European regions without other abundant sources of funding, focusing on mitigating structural deficiencies that prevent the effective use of convergence investments. It is well known in the literature (Acemoglu and Robinson 2012) that economic growth, often stimulated by effective cohesion policies, lays the foundation for social progress, as increased regional prosperity can lead to enhanced public services, better infrastructure, and improved living standards, all of which are crucial for comprehensive social development. While economic advancements under cohesion policy have the potential to yield significant social benefits, such as reduced poverty rates and greater income equality, there remains a conspicuous gap in the current research that explicitly links these economic gains to tangible improvements in these specific social conditions (Stiglitz et al. 2010). According to Piketty (2014), the assumption that economic growth automatically translates into social progress is overly simplistic; without deliberate measures and targeted policies, the benefits of increased regional GDP may not effectively trickle down to address core issues of poverty and income disparity. To truly understand the impact of cohesion policy, research must extend beyond economic indicators and rigorously examine how these economic improvements correlate with, and possibly contribute to, key social outcomes like enhanced employment opportunities, poverty alleviation, and narrowed income gaps (Sen 1999). This lack of explicit linkage in the current literature between economic growth and its potential social benefits underscores the need for a more integrated approach to policy evaluation—one that considers how economic advancements under cohesion policies are practically reflected in the daily lives of individuals, particularly those in economically disadvantaged segments (Sachs 2015). While the literature has established the economic effectiveness of cohesion policy, particularly for the PIGS countries, there is a discernible need to delve into the social impacts of such policies. The current research corpus, which predominantly centers on GDP growth, does not sufficiently address the broader social dimensions such as employment, poverty alleviation, and social inclusion, leaving a gap our research aims to fill. We propose a dual-focused analysis considering economic and social indicators to provide a more nuanced understanding of cohesion policy outcomes. The contrasting conclusions drawn from existing studies on the economic impact of cohesion policy, ranging from positive to non-significant effects, suggest a methodological divergence that our study seeks to reconcile. By employing a robust, integrative methodology, we aim to offer clarity to the debate and contribute a comprehensive perspective on the effectiveness of cohesion policy in both economic and social terms. Furthermore, while some research points to the benefits of cohesion policy on regional development and growth, the social implications of such policies are not as well documented. Our study extends the scope of analysis beyond GDP, incorporating broader indicators of societal well-being, such as the modified HDI (Human Development Index), 110 Economies 2024,12,28 as examined by Calegari et al. (2021), and employment, as highlighted by Crescenzi and Giua (2018). We acknowledge the literature’s call for an integrated evaluation approach that tracks economic progress under cohesion policy and critically examines how such advancements translate into social benefits. By doing so, we aim to bridge the gap identified by seminal thinkers like Stiglitz et al. (2010) and Piketty (2014) and provide a more holistic assessment of cohesion policy’s impact on improving living standards and reducing social disparities. In essence, our research addresses the lacuna in the existing literature by exploring the direct linkage between economic growth facilitated by cohesion policy and its tangible social benefits, offering new insights into how increased regional prosperity under such policies can lead to substantial social improvements, particularly for those in less advantaged economic brackets. To contextualize our study within the broader academic dialogue and to illustrate the contribution this research makes to existing scholarship, Table 1 below presents a curated overview of major previous studies related to the impact of cohesion policy on economic growth and social outcomes. Table 1. An overview of the most relevant studies in the literature. Authors Title Journal Year Fratesi, Ugo, and Fiona G. Wishlade The Impact of European Cohesion Policy in Different Contexts Regional Studies 2017 Gagliardi, Luisa, and Marco Percoco The Impact of European Cohesion Policy in Urban and Rural Regions Regional Studies 2017 Crescenzi, Riccardo, and Mara Giua One or Many Cohesion Policies of the European Union? On the Differential Economic Impacts of Cohesion Policy across Member States Regional Studies 2020 Bradley, John Evaluating the Impact of European Union Cohesion Policy in Less-Developed Countries and Regions Regional Studies 2006 Pîrvu, Ramona, et al. The Impact of the Implementation of Cohesion Policy on the Sustainable Development of EU Countries Sustainability 2019 The existing literature on cohesion policy in the EU has predominantly concentrated on economic indicators, such as regional GDP, neglecting crucial social dimensions like employment rates, poverty levels, and social inclusion. To address this gap, our proposed research takes a dual-focused approach, simultaneously considering economic and social indicators. In doing so, we aim to offer a more comprehensive and holistic understanding of the outcomes of cohesion policy. Additionally, there is a recognized methodological divergence in existing studies regarding the economic impact of cohesion policy. Our research contributes by proposing the use of a robust, integrative methodology to reconcile these differences, providing a comprehensive perspective on the effectiveness of cohesion policy in both economic and social terms. This approach aims to bridge the gap in the literature and enhance the understanding of the multifaceted impacts of cohesion policies in the European Union. 3. Data and Methodology This research aims to assess the impact of cohesion on economic performance and social progress at the regional level in CEE countries using regression analysis on panel data. The study focuses on 54 regions of 10 CEE countries (Bulgaria, Czech, Estonia, Latvia, Lithuania, Hungary, Poland, Romania, Slovakia and Slovenia), for the period 2007–2018— this period was chosen according to the last two programming periods of the Multiannual 111 Economies 2024,12,28 The EQI, as delineated by the European Commission, reflects citizens’ collective perceptions and encounters concerning corruption and the quality and fairness of pivotal public services—namely health, education, and law enforcement—within their residing regions. This index is constructed from the most expansive survey ever conducted to gauge the quality perception of governance within the EU. This monumental survey amassed insights and firsthand experiences in public health, education, and law enforcement from over 129,000 participants across 208 regions spanning all 27 EU Member States, assessed at either the NUTS1 or NUTS2 level. The survey’s inquiries are rooted in a comprehensive, multifaceted understanding of government quality, encompassing high standards of impartiality and public service delivery paired with minimal corruption. Figure 4 provides an overview of the quality of public services in the EU regions, highlighting a segregation between South-East and North-West Europe that can be associated with the level of development of the regions as the South-East area is characterized by transition countries that are less developed. In contrast, the North-West area represents the hard core of the EU. The EQI level is of considerable importance in the context of the absorption and use of European funds, as a lower value of government quality can undermine the efficiency of the use of financial resources or even generate a deviation from the cohesion objective. The lower EQI values observed in the Southeastern regions, associated with transition countries, are supported by various studies, including those by Acemoglu and Robinson (2012), who emphasize the impact of governance quality on economic outcomes. Additionally, works by Zaman and Georgescu (2009), Crescenzi and Giua (2016), and Calegari (2020) reinforce the idea that government quality is instrumental in determining the success of regional development policies. Figure 4. European Government Quality Index (EQI 2017). 118 Economies 2024,12,28 Furthermore, the discussion surrounding the segregation between South-East and North-West Europe is rooted in the historical context of EU expansion, with the Southeastern regions often facing challenges in catching up with their more developed counterparts. Empirical data from various studies, such as those by Rodríguez-Pose and Hardy (2015) and Becker et al. (2013), support the notion that the disparities in government quality contribute to variations in the absorption and effective utilization of European funds. The future direction of the cohesion policy in 2021–2027 will focus mainly on the green and digital transition. The 2021–2027 multiannual financial framework was geared towards rapid recovery and resilience, strengthening convergence between EU regions, given the uneven territorial impact of the crisis. In this context, the legislative package on cohesion policy for the 2021–2027 programming period entered into force on July 1, 2021. The newly reformed rules were designed to increase the focus of cohesion policy on a “smarter” and “greener” Europe and to create favorable conditions for investment with simplified delivery mechanisms and closer links to structural reforms. Cohesion policy will thus contribute to implementing the EU political agenda, particularly promoting the green transition and digital transformation. The EU has committed to becoming the world’s first climate-neutral bloc by 2050. A Just Transition Fund has been set up under cohesion policy to ensure that the transition to a climate-neutral economy equitably takes place, leaving no region behind. 4.2. Evaluation of the Impact of Cohesion Policy Instruments on the Economic and Social Performance of CEE Countries Different specifications have been tested in terms of components and total value to assess the impact of cohesion policy on economic growth and social progress. In every one of the ten models evaluated, we employed the Hausman test to determine the nature of the effects (be it fixed or random). The outcomes consistently pointed to the existence of random effects across all models. Additionally, even though the models were deemed valid, adjustments were made to account for heteroscedasticity. In the case of economic growth, the empirical results highlighted in all four analyzed models the positive and significant impact of the investments of the funds specific to the cohesion policy (ERDF, CF and ESF) on economic growth, both in terms of components and in total value (Appendix B, Table A2). Thus, if we refer to the total investment of cohesion policy funds per inhabitant, an increase in this indicator by 1% leads to an increase in economic performance, ceteris paribus, by 0.05%. CF investments have the most pronounced impact in terms of investment type, they lead to an increase in economic performance, ceteris paribus, by 0.04%. The positive coefficients associated with the specific funds highlight the importance of European funds in driving regional economic growth. This implies that the strategic allocation of these funds can foster positive economic outcomes for regions that receive them. Each fund had a distinct impact on economic growth, as evidenced by the varying coefficients across models. This suggests that while all funds contribute positively, they have different areas of focus or effectiveness. While the ERDF primarily focuses on economic and social cohesion by correcting regional imbalances, the CF targets environmental and trans-European transport infrastructure in countries with a lower GDP. Given that specific funds like the ERDF support regions with natural handicaps or those that are demographically disadvantaged, the positive impact on real GDP per capita underscores the importance of such funds in bridging regional disparities and ensuring more equitable growth across regions. The positive impact of these funds on GDP per capita might reinforce the need for policymakers to continue prioritizing them. Moreover, understanding which funds have the most significant impact can guide future budgetary allocations. While the funds have a positive impact, the magnitude of the impact depends on how efficiently these funds are utilized. Efficient project implementation and fund utilization can maximize the economic benefits. 119 Economies 2024,12,28 In essence, the observed positive impacts of specific European funds on real GDP per capita stress the pivotal role these funds play in regional economic development. It emphasizes the need for continued investment, effective utilization, and strategic allocation to maximize the desired economic outcomes. The initial GDP per capita (from the previous period) has a positive and statistically significant impact on the current real GDP per capita. A 1% increase in the initial GDP per capita leads to increases in the current real GDP per capita, ranging from 0.34% to 0.4% across models. This suggests that regions with a higher initial GDP maintain economic strength over time. The EQI has a consistently positive and statistically significant effect on the Real GDP per capita across all models. The influence of EQI ranges from 0.13% to 0.18% for a unit increase, highlighting the importance of governance quality in positively influencing economic performance. Higher values of the EQI, indicating better governance and public service delivery, are associated with higher real GDP per capita. In summary, as measured by the EQI, good governance quality and investments from various European funds, especially when initiated in the previous period, have a positive and significant impact on the economic performance of regions. This highlights the importance of effective governance and strategic investment in economic growth. In terms of capturing the impact of the ESIF (European Structural and Investment Fund) investments on social progress, the empirical results revealed a positive and statistically significant impact that was preserved in all four models (both in total and by components of ESIF investments). The greatest impact on social progress was manifested by CF investment, ceteris paribus. The results indicate that the investments made by the ESIF positively influence social progress in the regions. This means that as the funding from the ESIF increases or is efficiently utilized, there is a consequent improvement in social indicators like education, health, equality, and overall well-being. The statement emphasizes that this positive effect is consistent across all four models they analyzed. Whether looking at the total investments or breaking them down into specific components, the positive relationship holds. This consistency reinforces the reliability of the findings. Among the ESIF components, the CF has the most pronounced positive effect on social progress. This might be because the CF specifically targets areas like environmental projects and trans-European transport networks, which can have direct or trickle-down effects on the populace’s well-being. Also, the initial value of real GDP/capita and the EQI value showed the same positive and statistically significant impact. So, the regions that registered a good economic start and benefited from high-quality institutions achieved increased social progress. Regions that began the period with a higher GDP per capita (a general measure of economic well-being) also saw greater social progress. This suggests a positive feedback loop where regions with a good economic base can better leverage ESIF investments to further social advancement. The quality of institutions, as measured by the EQI, also played a significant role. Regions with better governance, lower corruption, and higher quality public services (like health, education, and law enforcement) were more successful in translating ESIF investments into social progress. Good governance can ensure the efficient and effective use of funds, leading to tangible improvements in the lives of the citizens. The findings underscore the importance of financial investments from funds like the ESIF and foundational factors like initial economic conditions and institutional quality in driving regional social progress. This research conducted a thorough robustness assessment, further probing the effects of ESIF investments on the various facets of the social progress indicator (as seen in Table A3, Appendix C). The empirical results demonstrating a positive and significant influence on Basic Human Needs and Fundamental Well-being underscore the effectiveness of cohesion policies in enhancing aspects such as healthcare, education, and environmental quality, which are essential for the foundational welfare of individuals in CEE nations. Also, 120 Economies 2024,12,28 this positive impact reflects the direct benefits individuals in CEE regions experience in their daily lives, such as better access to basic services and improved living conditions, validating the targeted approach of cohesion policies towards addressing fundamental human necessities. However, the lack of a significant impact on the Opportunities segment of the SPI, particularly in areas related to access to higher education and information technology, points to a potential disconnect between the economic growth facilitated by cohesion policies and the translation of this growth into real opportunities for individuals, especially in terms of advancing equity and reducing income disparities. The consistently lower scoring of CEE nations in the SPI’s Opportunities dimension highlights a critical area for policy improvement, suggesting that while basic needs and well-being are being addressed, the policies are less effective in creating environments where individuals can capitalize on economic growth to improve their personal and professional prospects. This gap in policy impact, especially in the realm of reducing income inequality and tackling poverty, calls for a re-evaluation of the current policy frameworks and strategies, emphasizing the need to not only foster economic growth but to also ensure that such growth translates into equitable opportunities and tangible benefits for all segments of society, particularly the most vulnerable. This observation echoes Atkinson’s assertion (Atkinson 2015) that disparities in outcomes observed today pave the way for unequal opportunities in the future. This concept is particularly relevant in the context of income inequality and poverty. When current disparities in income and wealth are not addressed, they perpetuate a cycle of poverty and limit opportunities for upward social mobility for future generations. Therefore, the focus on enhancing interventions in the Opportunities sector of the SPI is crucial. By actively reducing income disparities and improving access to education, technology, and fair employment, we can create a more equitable foundation that supports the well-being and socio-economic advancement of upcoming generations. This approach is not just about reducing present inequalities; it is a strategic investment in preventing the entrenchment of poverty and ensuring fairer, more equitable opportunities for all, thereby breaking the cycle of poverty and income inequality that can otherwise persist across generations. 5. Discussions and Policy Implications While evaluating the effects of cohesion policy is complex due to its multidimensional nature, targeting both economic and social goals, it is crucial to note that these varied interventions have historically emphasized economic development over direct social impacts, particularly in addressing income inequality and poverty. The challenge lies in distinguishing the nuanced effects on social outcomes, such as reducing income disparities and enhancing social welfare, often overshadowed by the primary focus on economic indicators like GDP. The academic discourse, including works by (Zaman and Georgescu 2009; Antonescu 2012; Crescenzi and Giua 2016; Calegari 2020; Maucorps et al. 2020), predominantly examines the economic impacts of EU cohesion policy. However, this focus leaves an empirical gap regarding the policy’s effectiveness in combating social issues like poverty and inequality. While some studies (Pinho et al. 2015; Crescenzi and Giua 2016; Cerqua and Pellegrini 2018; Fidrmuc et al. 2019; Di Caro and Fratesi 2022) suggest a positive economic impact, the direct correlation with social improvements, particularly in reducing poverty and narrowing income gaps, remains underexplored (Dall’Erba and Fang 2017; Ehrlich and Overman 2020). Our study’s alignment with the literature, such as Farrell (2004) and Lolos (2009), confirming positive economic impacts, also highlights a need to deepen the understanding of how these economic benefits translate into tangible social progress. While economic development is a positive outcome, its success should also be measured by its ability to alleviate poverty and improve income equality. As with the study by Maucorps et al. (2020), our results, which took into account the impact of the three funds specific to the cohesion 121 Economies 2024,12,28 policy on economic development reflected through GDP per capita at the regional level (NUTS2) (elements that were taken into account in both studies), identify a positive impact. Regarding the impact of cohesion policy on social progress, our study identified a positive impact on the aggregate SPI and the two pillars of the SPI (Basic Human Needs and Basic Well-being). Still, for the countries considered (CEE), an impact on the Opportunities pillar was not identified. This gap is particularly critical when considering income inequality and poverty, as the Opportunities pillar directly relates to aspects like access to higher education and personal rights, which are key in breaking the cycle of poverty and ensuring equitable growth. It is difficult to compare the results with other studies as the literature relating to the impact on the SPI is limited. However, some studies have considered some of the variables included in the SPI. Most of the literature related to the impact of European funds on the employment rate (Becker et al. 2010; Rodríguez Pose and Novak 2013; Giua 2017; Fiaschi et al. 2018; Crescenzi and Giua 2018; Crescenzi and Giua 2020) predominantly identify a positive impact, considering that European funds contribute to increasing the employment rate. Other social aspects that the researchers focused on were the level of education and the modified version of the adapted human development index (Calegari 2020), where a positive impact was also identified. Therefore, the consistent positive impact associated with cohesion policy on economic growth emphasizes the crucial role of this policy in fostering regional development. Both economic growth and social progress have benefited from the funds, implying that wellstructured policy interventions can produce positive outcomes in these areas. Regarding the impact on economic growth and social outcomes, this study confirms a positive correlation between the EU’s cohesion policy and economic growth in CEE countries, aligning with the existing literature, such as Farrell (2004) and Lolos (2009). However, when it comes to the policy’s impact on social outcomes, particularly in reducing poverty and income disparity, the findings reveal a more complex scenario. While there is a positive impact on the aggregate Social Progress Index (SPI) and its components of Basic Human Needs and Well-being, the gap in the Opportunities pillar suggests that the policy’s effectiveness in addressing deeper social inequities needs more focus. In terms of the correlation between policy implementation and regional improvements, the empirical results indicate that regions with a stronger economic foundation (higher initial GDP per capita) tend to leverage the benefits of cohesion funds more effectively, thereby amplifying their economic and social gains. This finding underscores the need for quality governance and efficient institutions, which are essential for optimizing fund utilization. Concerning the time-lagged nature of policy impact, this study suggests that the impact of cohesion policy on social progress, especially in terms of improving personal rights and access to higher education, is not immediate but evolves over time. This highlights the need for a long-term strategic vision in policy implementation and assessment. The CF significantly impacts economic performance among the funds examined. This underscores the CF’s pivotal role in addressing disparities and promoting convergence among EU regions, especially in environmental projects and trans-European transport networks. Regions with a good economic start (higher initial GDP per capita) tend to maintain and amplify their economic strengths. Further, the consistent positive impact of the EQI underscores the importance of quality governance in driving economic outcomes. Efficient institutions that maintain low corruption levels and provide high-quality public services pave the way for the better utilization of funds and stronger economic growth. The results indicate that regions effectively utilizing ESIF investments witness marked social progress. A holistic approach, combining financial investments from funds like ESIF with a solid initial economic base and high institutional quality, can catalyze substantial social advancements. The research findings carry substantial significance for shaping the trajectories of social and economic progress in CEE economies. By confirming the consistent positive impact of EU cohesion policy on economic growth in the region, this study underscores 122 Economies 2024,12,28 the pivotal role of targeted interventions and continued support in fostering regional development. This insight is particularly crucial for policymakers as it provides evidence of the effectiveness of cohesion policy in narrowing economic disparities and promoting overall economic advancement in CEE countries. The research shows a gap in the Opportunities segment of the social progress indicator for CEE countries. This suggests a need for targeted interventions in these regions, particularly focusing on personal rights, electoral choices, tolerance, and access to higher education. As emphasized by Atkinson’s observation (Atkinson 2015), addressing today’s inequalities is crucial for ensuring equal opportunities for future generations. Taking into account the results of the study, we consider it important that, for CEE countries and regions, the following aspects are taken into account in the management of the cohesion policy: • Increase Absorption of European Funds for Economic and Social Welfare: This research emphasizes the need for greater efforts to increase the absorption degree of European funds. This recommendation aligns with the findings that regions effectively utilizing ESIF allocations witness marked social progress. The suggestion to augment absorption aims to leverage these funds more effectively to contribute to both economic development and social welfare. • Enhance Institutional Quality and Transparency: The study acknowledges the positive impact of efficient institutions on economic outcomes. Therefore, the recommendation to focus on measures and reforms to enhance institutional quality aligns with the research findings. It emphasizes the importance of quality governance in driving economic growth and ensuring better utilization of funds. • Streamline Bureaucratic Procedures: The suggestion to streamline public administration processes and reduce bureaucratic barriers resonates with the research findings calling for de-bureaucratization. Simplifying access to European funds can facilitate their efficient utilization, which, as the research indicates, contributes to regional economic and social progress. • Concentrate Investments in Social Areas with Poor Infrastructure: The research highlights a gap in the Opportunities segment of the SPI for CEE countries, particularly in areas like access to higher education and personal rights. The recommendation to concentrate investments in social areas with poor infrastructure aligns with this finding, aiming to reduce regional economic and social discrepancies and ultimately promote increased cohesion. • Targeted Interventions to Address Inequalities: The research underscores the need for targeted interventions in specific aspects such as personal rights, tolerance, and access to higher education. The policy recommendation advocating for a more nuanced approach to cohesion policy in CEE countries aligns with the findings, emphasizing the necessity of targeted interventions to directly address social disparities and ensure equal opportunities. Given these findings, our research advocates for a more nuanced approach to cohesion policy in CEE countries, emphasizing the need for targeted interventions that boost economic development and directly address social disparities. This includes improving institutional quality and transparency, reducing bureaucratic barriers to fund access, and prioritizing investments in social infrastructure. Such a comprehensive strategy is essential for bridging economic and social gaps, leading to a more cohesive society where economic growth translates into reduced poverty and greater income equality. 6. General Conclusions The empirical analysis supports the notion that well-structured EU cohesion policy interventions can produce positive outcomes in both economic and social domains. The significant impact of funds like the European Regional Development Fund (ERDF) on regional economic performance points to their crucial role in promoting convergence and addressing disparities, particularly in environmental and transport projects. 123 [Document text truncated for crawler view.]