Examining the Role of Structural Change in Income Inequality: Insights from Quantile ARDL Modeling in the United States

Examining the Role of Structural Change in Income Inequality: Insights from Quantile ARDL Modeling in the United States | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Examining the Role of Structural Change in Income Inequality: Insights from Quantile ARDL Modeling in the United States Cumali Marangoz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4797904/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract This study attempts to examine the dynamics of the U.S. manufacturing sector and income inequality using a Quantile Autoregressive Distributed Lag (QARDL) model from 1965q1 to 2019q4. We analyze the impact of urbanization, economic growth, human capital, manufacturing, and human capital on income distribution. An inverted U-shaped Kuznets curve indicates that as the manufacturing sector progresses the income disparity diminishes. Limiting our analysis to the industrial value contributed to GDP might cause a harder discern the overall effect on income inequality. A higher rate of GDP might not guarantee equal incomes, which may show an increase in certain sectors. Looking at the manufacturing sector's share of total employment, which reflects both employment opportunities and revenue sources, allows more direct reflection of income disparity. This study contributes to the literature by enhancing our comprehension of the intricate processes of income distribution, offering valuable insights for policymakers and researchers to better tackle income inequality. Manufacturing Sector Dynamics Structural Change Income Inequality Kuznet Curve Quantile ARDL Modeling Figures Figure 1 1. Introduction Income inequality, the disproportionate wealth of the top 1% of earners relative to the stagnation or decline in real wages of a median worker, is a major issue in the US (Saez & Zucman, 2016 ). This trend has started to harm the middle class, declining economic opportunities, and decreased social mobility. Scholars determine many factors that affect this phenomenon. Many studies examine income inequality in the US, especially in terms of structural shifts in the economy. The seminal work in understanding structural shift is Kuznets' hypothesis. The hypothesis states that there is an inverse-U shape relationship between income disparity and economic growth (Piketty & Saez, 2003 ). An essential component in income distribution is structural change, defined as changes in the sectoral composition of output and employment over the long run (Andersson & Palacio, 2016 ; Elhini & Hammam, 2021 ). To many scholars, wages have become more unequal because of economic reorganization, leading to the loss of high-paying industrial jobs (Yang et al., 2020 ). As a result of economic transformation, wage disparities have widened and have had far-reaching effects on the labor market due to the oversupply of skilled workers in the service industry relative to unskilled labor. As the United States shifted its economic base from agriculture to manufacturing, a large-scale movement to urban centers drove urbanization. Initially the exodus helped to even out income distribution by increasing the demand for low-skilled workers in the manufacturing sector. Yet, the subsequent deindustrialization caused a meteoric rise in urban populations as economic agents have shifted their focus to the service sector. Due to technological progress, the need for a more educated workforce caused a schism in the labor market. As the demand for skilled people surpassed that for unskilled positions, this moves exacerbated income disparities. Income inequality tends to worsen as workers shifted from manufacturing to the service sector (Scheiring et al., 2020). Overall inequality rises as workers leave the relatively equal and high-wage industrial sector for the more unequal tertiary sector (Moller et al., 2009). Industry, urbanization, human capital, and wealth disparities all interact intricately throughout structural change, as these processes demonstrate. The Industrial Revolution in the United States, as per the New Economic Geography Theory, commenced in the Northern states, such as New England, and gradually expanded to other Northern states since the early 1800s (Yazgan et al, 2022 ). The second industrial revolution, occurring from approximately 1870 until 1914, was a period of significant advancement related primarily to advances in chemicals and electricity (Engelman, 2015). This period marked a transformative phase in the USA and Germany, leading to substantial economic and technological progress. Furthermore, the third industrial revolution, which has been occurring since 1973, is closely associated with information and communication technologies, particularly in the USA and East Asia (Engelman, 2015). The deindustrialization process in the United States during the 1960s and 1970s led to a significant increase in mass unemployment among young, Black employees (Like & Cobbina, 2018 ). This phenomenon has been associated with the widening of income inequality and high unemployment in Europe, with some attributing it to the globalization of markets (Lee, 2011). The shift of manufacturing to countries with cheaper labor left low-skill American workers to seek employment in the service sector, leading to concerns about the nature, extent, and implications of deindustrialization (Autor et al. 2008 and Piore 2018 ). Authors have examined the deindustrialization process, emphasizing the relocation of production and the hollowing of firms in the United States (Benería & Santiago, 2001 ). Furthermore, the increase in service production and deindustrialization in the United States has been linked to a lengthening of the employment recovery from recessions by about 40% (Olney & Pacitti, 2017 ). The ongoing effects of deindustrialization in the United States have been associated with entrenched levels of urban disadvantage, racialized inequality, gang activity, and homicide (Fraser & Clark, 2021 ). Furthermore, offshore, or the migration of American corporations' activities to China in the 1980s and 1990s, had far-reaching consequences for domestic employment and income distribution in the United States. Offshoring manufacturing and production jobs from the United States to China had a huge influence on the domestic labor market and income distribution, as well as rapid deindustrialization. Figure 1 displays the Gini index (calculated based on current (2024) USD index) for the United States from 1965 to 2019 from St. Louis FED (FRED) database. During the late 1960s and early 1970s, the index remained in the mid to high 30s, indicating a lower level of income disparity. However, from the late 1970s to the early 1990s, income inequality appears to have gradually increased, as evidenced by the index's increasing trend. The association between deindustrialization and income inequality is further confirmed by the observation that income gaps expanded considerably in the United States beginning in the 1980s, with top earners receiving a rising share of total income (Appelbaum, 2011 ). Additionally, income disparity increased between 2000 and 2011, coinciding with the process of deindustrialization (Meyer & Sullivan, 2013 ). Previous studies have primarily utilized the structural change variable, focusing on indicators such as industrial employment rates (Kollyemer, 2018; Hillbom, & Bolt, 2015 ; Kum, 2008 ), the labor force in the agricultural sector (Andersson & Chavera, 2016), industry sector ratios (Dartanto et al., 2017 ), and the value added of the manufacturing sector to GDP (Ali et al., 2021 ). These studies support the classical view of the reducing effect of structural change, specifically the transformation from agriculture to the manufacturing sector, on income inequality. However, these approaches have two main limitations. Firstly, the variables used in previous studies are generally broad and do not specifically focus on manufacturing, which indeed plays a crucial role as the main engine of industry. Secondly, relying solely on indicators such as manufacturing sector growth or the number of workers in manufacturing may not provide a comprehensive understanding of their impact on income inequality. Therefore, incorporating multiple indicators simultaneously may yield more accurate results. Unlike previous studies, this study seeks to examine the factors that contribute to income disparity in the United States by analyzing structural change, human capital, economic growth, and urbanization. We employe a quantile autoregressive distributed lag (ARDL) model. The findings succinctly summarize that the U-shaped Kuznets curve remains relevant, showing that the growth of the manufacturing sector has a moderating impact on income disparity. The contribution and extension of the study to the literature are threefold. Firstly, to the best of the authors' knowledge, this research employs two alternative models to analyze not only manufacturing value added but also changes in employment in manufacturing compared to overall employment, based on the authors' current understanding. This enables us to evaluate if the expansion of the manufacturing industry has an impact on the income levels or employment patterns of workers. The findings suggest that increases in the manufacturing employment to total employment ratio have a mitigating effect on income inequality. Second, we also include urbanization and human capital in our models because of the impact of structural change on these components. As a third point, we employ the Quantile ARDL model, which has many advantages over the more traditional linear ARDL models. The method takes into account many thresholds that are determined by the quantiles. It allows for the addition of many regressors and combines all the advantages of standard ARDL approach (Bertsatos et al., 2020). Additionally, this method offers a thorough description of the general relationship between the listed factors and incorporates numerous quantiles. Conventional models disregard this, including quantile regression, OLS, and ARDL (Wang et al., 2021 ). The paper continues as follows: Section 2 discusses the literature review. Section 3, explains and details the methodology and data. Section 4 presents the findings and Section 5 concludes. 2. Literature Review This section attempts to summarize and survey the literature on the variables such as GDP growth, human capital, urbanization, and structural change that impact income inequality. Two fundamental gauges of a country's social and economic health are economic growth and income inequality. Academics, politicians, and economists have been arguing passionately about the link between these two phenomena for a long time. According to Kuznets's (1955) inverted U-shaped hypothesis, income inequality gets worse in the beginning stages of economic development and gets better as per capita income gets higher in the later stages. Several empirical studies support this inverted U-shaped hypothesis. Younsi and Bechtini (2020) examined BRICS countries, Kim, and Lin ( 2023 ) focused on both developed and developing countries, Jovanovic (2018) studied 26 ex-socialist countries, Barro ( 2008 ) conducted a global-level analysis, Galbraith and Kum ( 2002 ) delved into industrialized countries, Comin (2019) explored Brazil, Utari and Cristina ( 2015 ) investigated Indonesia, and Jin ( 2016 ) examined China. All these studies provide confirmation of the inverted U-shaped relationship between income inequality and economic development. However, studies using economic growth as a determinant instead of relying on Kuznets' inverted U-shaped hypothesis have yielded mixed results. For instance, some studies have found a positive relationship between income inequality and economic growth, as observed in Forbes ( 2000 ), Sameti and Rafie ( 2010 ), and Roine et al. ( 2009 ). On the contrary, other studies have indicated a negative correlation, such as Muinelo-Gallo and Roca-Saales (2013), and Sabir et al. (2015). Nevertheless, the evidence regarding the relationship between income disparity and economic development is inconsistent. Some research has failed to find a meaningful relationship; for instance, Szczepaniak (2022) and Shin et al. ( 2008 ). For several reasons, studies examining the interaction between income disparity and GDP growth have produced conflicting results. Research employs diverse methodologies, draws from diverse data sources, and spans distinct time periods, all of which contribute to the diverse variety of outcomes. Due to the complex and multi-faceted nature of economic systems and the dynamic nature of sociopolitical circumstances in different regions, the identified relationships vary greatly. Another factor that could influence the relationship between income inequality and economic growth is the institutional makeup and level of development of a country. How H.C. impacts income inequality is another important and well-studied element. Considering human capital is essential for getting to the bottom of income inequality. Human capital, a person's formal and informal learning, as well as education and job experience, affects income disparity. Investments in training and education lead to pay raises, reduced income and increased productivity in the workplace. Income inequality might be mitigated through several means. For instance, better educated employees have the ability to exploit technological innovation boosting the economy and creates more job opportunities (Acemoglu & Autor, 2011; Hanushek et al., 2015 ). Differences in access to high-quality education deepen preexisting income gaps (Chetty et al., 2014 ). Disparities in opportunity may widen due to the skill-biased technological advancements favoring those with more education and experience. Global economic developments, shifts in the labor market, and the relative availability of various skill sets all contribute to interaction between human capital and income inequality. While human capital might have both positive and negative effects on economic inequality, the majority of studies show that it reduces income inequality. Evidence of a positive association has been observed in multiple studies; for instance, Lee and Lee ( 2018 ), Suhendra et al. ( 2020 ), Lee et al. (2019), Molla ( 2021 ), Aqil and Wahyuniati ( 2021 ), Kim ( 2013 ), and Abdullah et al. (2015). Nevertheless, there is a handful of research that offers a contrasting perspective, including Sequeira et al. ( 2017 ) and Yang et al. ( 2020 ). The transition to structural change stage determines how urbanization affects income disparity. There seems to be a balance between economic growth and income distribution in the first stage, when urbanization is accelerated by the rapid transition from agriculture to the manufacturing sector (Kuznet, 1955). In the second phase, urban slums emerge as a result of economic shifts from low- to high-labor-intensive sectors and the industrialization-to-service-sector transition. At the same time, it might be more difficult for city dwellers to get the same degree. Because of this change, the relationship between urbanization and economic disparity is becoming less favorable. The literature has contradictory findings since urbanization has different impacts at different phases. The link between urbanization and economic inequality is complicated, as studies have conflicting results. Several studies have confirmed a favorable link; for example, Sulemana et al. ( 2019 ), Le et al. (2021), Kanbur and Zhuang ( 2013 ), and Adams and Klobodu ( 2018 ). However, a negative correlation was confirmed by Wan et al. ( 2022 ) and Guo et al. ( 2016 ). Overall, the contradictory empirical evidence about the link between urbanization and income disparity is likely due to the fact that different phases of urbanization are associated with distinct impacts. The concept of structural change has been widely used in the literature since Kuznets' influential work in 1955, and several indicators have been developed to quantify it. For instance, Kollyemer (2018), Hillbom and Bolt ( 2015 ), and Kum ( 2008 ) have examined industrial employment rates, while Andersson and Chavera (2016) focused on the labor force in the agricultural sector. Dartanto et al. ( 2017 ) analyzed industry sector ratios, and Ali et al. (2022) studied the value added by the manufacturing sector to GDP. These empirical studies support the classical view of the reducing effect of structural change (specifically, the transformation from agriculture to the manufacturing sector) on income inequality. Kollyemer (2018) utilizes time-series regression models with national-level data from 1947 to 2015 to investigate how industrialization interacts to shape income distribution. The findings suggest that industrialization yields more pronounced distributional effects on income inequality. Hillbom and Bolt ( 2015 ) examine the evolution of inequality in colonial and post-colonial Botswana, noting a rise beginning in the 1940s and peaking in the mid-1970s coinciding with the shift from a cattle-based (agriculture) to a diamond-driven economy ( industrial), followed by a subsequent decline since the 1990s. Dartanto et al. ( 2017 ) investigate the relationship between structural transformation and inequality in Indonesia, employing Theil's L decomposition and panel data analysis of provincial macroeconomic datasets. The study reveals that Indonesia has undergone an agriculture–service transition before the maturity of the industry sector, with the presence of an Inverted U Kuznet curve. Findings from both static and dynamic decomposition indicate that structural transformation contributing to growing inequality and the increasing share of the service sector to the national gross domestic product has mitigated the growth rate of inequality over the observation period. Kum ( 2008 ) introduces an updated dataset on inequality in structures of manufacturing pay spanning the years 1963 to 2002, utilizing the standard methods of the University of Texas Inequality Project (UTIP). It further examines these measures in conjunction with evidence on structural change, specifically changes in the shares of agriculture, manufacturing, and services in total employment notable finding is the close association between low inequality and low variability in inequality over time, with the movement out of agriculture correlating with heightened variability in the inequality of manufacturing pay. Overall, when assessing the reduction of income inequality resulting from the structural change from agriculture to industry, the variables mentioned above are often utilized. However, relying solely on these indicators to measure structural change proves insufficient due to two main reasons. First, the scope of these variables may not fully capture the multifaceted nature of structural transformation. An economy's ability to undergo fundamental transformation is greatly influenced by the manufacturing sector. Some have argued that this industry is a "growth-enhancing" one since it draws resources away from less productive pursuits and encourages structural change, an essential component of contemporary economic expansion.(Rodrik, 2016; Yazgan et al., 2022 ). Second, relying merely on these factors may not be sufficient to assess their impact on income inequality because they may signal manufacturing sector growth but not necessarily its impact on employment. This dual aspect underscores the limitations of studies that rely entirely on these factors. Table 1 summarizes studies on the drivers of income inequality in the United States using the Environmental Kuznets Curve (EKC) framework. The research given in the table is divided into two categories: studies that investigate the EKC hypothesis and those that seek to uncover factors leading to income disparity. We gain a more complex picture of the interplay among industrialization, human capital, economic growth, and income distribution as a result of the varied methodologies and results presented in each study. To begin with, studies that deal with the EKC hypothesis, including Kuznet (1955) and Ram ( 1991 ), shed light on how income disparity and economic growth are related. Unlike Kuznets’s study which supports the typical inverted U-shaped Kuznet curve, Ram ( 1991 ) shows that economic growth reduces inequality, but not in the classic EKC pattern. Second, some studies have zeroed in on specific reasons of income inequality (Nielsen and Alderson, 1997 ; Chevan and Stokes, 2000 ). According to Nielsen and Alderson an increase in the manufacturing labor force and income reduces income inequality, while deindustrialization increases it according to Chevan and Stokes. Frank ( 2009 ), Goldin and Katz ( 2009 ), and Dincer and Gunalp (2011) provide additional understanding of the elements driving income inequality (2012). Frank shows that human capital does not affect economic growth in the long run, which exacerbates wealth inequality. Unlike Goldin and Katz's arguments Dincer and Gunalp indicate that wealth increases income inequality, and industrialization and human capital both have adverse effects on it. Moreover, Yang and Greaney ( 2017 ), Kollmeyer ( 2018 ), and Lee and Lee ( 2018 ) enlighten the complex processes of income inequality. Yang and Greaney confirm an N-shaped pattern of income disparity and show that trade significantly reduces it. Lee and Lee ( 2018 ) indicate that human capital ameliorates income disparity, while Kollmeyer argues that deindustrialization worsens income inequality. Lastly, Zheng et al. ( 2022 ) and Hertz and Silva ( 2019 ) analyze the effects of economic growth and income distribution, respectively. Hertz and Silva argue that income inequality is on the rise regardless of urban or rural areas, with the latter experiencing a more significant acceleration at critical junctures. Zheng et al. ( 2022 ) states that income inequality is reduced while the economy expands. In sum, Table 1 clarifies the United States’ economic inequality briefly. Numerous aspects should be considered, such as industrialization, human capital, economic growth, and other types of economic growth to comprehend the dynamics of income distribution. Table 1 Summary of Literature on Income Inequality in the USA. Authors Sample Period Methodology Findings Kuznet (1955) 1929–1950 Descriptive Inverted U-shaped Kuznet curve is validated Ram ( 1991 ) 1947–1987 Regression Increasing economic growth causes a decrease in inequality, although the traditional inverted U-shaped Kuznet curve is not valid Nielsen and Alderson. (1997) 1970–1990 Panel Regression The increase in the labor force in manufacturing and income contributes to a reduction in income inequality. Chevan and Stokes ( 2000 ) 1970–1990 Descriptive statistics Deindustrialization causes an increase in income inequality. Gallet and Gallet (2004) 1947–1987 Regression Inverted U-shaped Kuznet curve is not valid. Frank ( 2009 ) 1945–2004 Autoregressive Distributive Lag Economic growth increases income inequality, and human capital does not affect income inequality in the long run. Goldin and Katz ( 2009 ) 1980–2005 Descriptive statistics Human capital erosion leads to a decline in skills and education, contributing to increased income inequality. Dincer and Gunalp (2012) 1981–1997 Generalized Method of Moments Human capital and industrialization increase Income Inequality while income reduces it. Yang and Greaney ( 2017 ) 1960–2012 Engle–Granger two-step ECM N-shaped income inequality is confirmed. Trade reduces income inequality. Kollmeyer ( 2018 ) 1947–2015 Regression Deindustrialization is a key determinant of income inequality. The increase in industrialization exacerbates income inequality. Lee and Lee ( 2018 ) 1980–2015 Panel Regression Human capital reduces income inequality. Hertz and Silva ( 2019 ) 1975–2015 Descriptive statistics and Decomposition method Income inequality is increasing in both urban and non-urban areas. However, inequality is rising more rapidly in rural areas during certain periods Zheng et al. ( 2022 ) 2016–2020 Regression Economic growth reduces income inequality Wallace et al., (2022) 2000 Descriptive Statics Deindustrialization contributes to the deterioration of income distribution 3. Data and Methods 3.1. Sample and Description of Data To examine the impact of structural changes on income inequality in the United States, this paper employs a quantile autoregressive distributed lag (QARDL) model over the period from 1965q1 to 2019q4. For robustness checks, we employ two variables related to structural changes: manufacturing value added to GDP (Model 1) and the ratio of employment in manufacturing to total employment (Model 2). Additionally, we incorporated human capital GDP and urbanization variables. In more detail, annual data is collected from the sources listed in Table 2 , and then, following Shahbaz et al. (2017), we convert annual data to quarterly data using the quadratic match sum method. This method adjusts for seasonal variations in the data when converting the data from low frequency to high frequency by reducing the point-to-point data variability (Razzaq et al., 2020 ; Sbia et al., 2014). In order to fully understand how manufacturing affects income inequality, it is crucial to look at more than simply the manufacturing value added to the GDP rate. The rate does not provide a comprehensive view of its effects on income distribution. Considering the manufacturing employment rate helps to clarify the relationship between income disparity and employment. Atkinson, Piketty, and Saez ( 2011 ) find that income inequality must be measured using employment statistics. The distribution of income and employment possibilities across economic sectors is inseparably related to income distribution. Therefore, focusing on the manufacturing employment percentage offers a more direct correlation to income inequality, since it mirrors the actual dynamics of the labor market and the distribution of income. World Bank's World Development Indicators database is one of the main sources of the interaction between manufacturing jobs and income inequality. The effect of manufacturing on income distribution in countries might be investigated by comparing statistics on the proportion of total employment in manufacturing. This gauge and method offers a more comprehensive picture of how manufacturing employment adds to income inequality by considering variations in economic frameworks, labor market regulations, and social policies. According to Autoret al. ( 2013 ) technological change and globalization have impacted employment and income distribution in manufacturing differently. The authors argue that income disparity has changed as a result of structural changes that have altered the makeup of industrial jobs. Therefore, in order to get a better picture of how changes in manufacturing affect income distribution, especially considering technological advancements and trade dynamics, it's best to analyze the manufacturing sector's share of total employment. Finally, to fully capture manufacturing's effect on income inequality, it is necessary to supplement the value added to GDP rate with the proportion of total employment in manufacturing. Table 2 summarizes the variables. Accordingly, Gross Domestic Product (GDP) is the total monetary value of all final goods and services produced inside a nation. The GDP is divided by the population to get the average economic output per person. The Income Inequality Index (or the Gini coefficient) measures how evenly distributed a community's income is. A value of 0 shows full equality and a value of 1 displays perfect inequality. The Income Inequality Index is computed using the Lorenz curve. Human Capital Index's (HCI) evaluates the workforce's health, education, and skill levels and encompasses factors such as life expectancy, number of years spent in school, and test scores. The index is between a value of zero and one. the Urbanization Index (URB) is one way to quantify the population in cities. It is calculated by dividing the total population by the number of individuals residing in urban areas. By determining the proportion of manufacturing value contributed to total GDP and displaying it as a percentage, the SCm index measures the influence of the manufacturing sector on GDP. Lastly, one measure that shows what percentage of the overall workforce is employed in manufacturing is the SCe, which stands for Manufacturing Employment in overall Employment Index. It is expressed as a percentage and is calculated by dividing the total number of employed persons by the number of people working in manufacturing. These indexes provide a holistic view of the societal and economic factors that influence income disparity. Their computation follows standard methods, and they are sourced from credible datasets. Table 2 Variable Source and Description Variable Description Unit Source of data Gini Income Inequality Index https://fred.stlouisfed.org/ GDP Economic Growth GDP per capita (current (2024)USD) https://www.worldbank.org/ H.C. Human Capital Index https://www.worldbank.org/ URB Urbanization Index https://www.worldbank.org/ SC m Manufacturing value added to GDP Index https://ourworldindata.org/ SC e Manufacturing employment in total employment Index https://ourworldindata.org/ Table 3 shows descriptive statistics. Specifically, the variables Lngini, Lnhc, Lnurb, Lngdp, and Lngdp2 exhibit a positive meaning with a narrow range between its minimum and maximum, accompanied by a low standard deviation, suggesting relative stability. However, it is essential to highlight a distinctive pattern in the variables associated with structural change, wherein the averages are negative, and both minimum and maximum values are similarly negative and proximate. Notably, the Jarque-Bera test further highlights this observation, indicating that these variables deviate from a normal distribution. Table 3 Descriptive Statistics and Unit Root Test Variable Mean Minimum Maximum Std.dev. Jarque-Bera ADF Level ADFΔ Lngini 0.913 0.885 0.931 0.014 19.960 a -2.571 -4.508 a Lngdp 2.487 2.053 2.773 0.212 19.322 a -1.968 -3.427 b Lngdp 2 24.933 25.722 30.774 4.143 17.753 a -1.067 -3.571 b Lnhc 0.308 0.311 0.330 0.017 67.831 b -3.445 b - Lnurb 1.085 1.068 1.103 0.010 238.775 b -3.573 b - Lnsc m -0.446 -0.570 -0.331 0.065 12.434 a − .3.356 -3.716 b Lnsc e -0.472 -0.671 -0.321 0.097 16.206 a -1.734 -4.017 a Note: 'a', 'b.' 'c' indicates a significance level of 1%, 5%, and 10%, respectively. Furthermore, we use the Augmented Dickey-Fuller (ADF) unit root test to identify the stationarity of a time series variable. The outcome of the ADF test shows that Lnhc and Lnurb are stationary at their levels, suggesting a lack of unit root. However, for the remaining variables, stationarity is achieved when taking the first difference. However, for the remaining variables, stationarity is achieved when taking the first difference 3.2. Quantile Autoregressive Distributed Lag (QARDL) Model To investigate the factors influencing income inequality in the United States, we are utilizing the Quantile Autoregressive Disturbed Lag (QARDL) model introduced by Cho et al. in 2015. The QARDL model, an extended version of the ARDL model, has several advantages. Firstly, like the classical ARDL, it provides short and long-term results, but uniquely presents these results according to quantiles. Secondly, it could capture the asymmetric effects of macroeconomic variables. Thirdly, it is less sensitive to outliers (Hashmi et al, 2023). The basic structure of the Autoregressive Distributed Lag (ARDL) model is as follows: $$\:{Gini}_{t}=\mu\:+\sum\:_{i=1}^{P}{\omega\:}_{i}{Gini}_{t-i}+\sum\:_{i=0}^{q}{\varPhi\:}_{i}{GDP}_{t-i}+\sum\:_{i=0}^{r}{\varOmega\:}_{i}{GDP}_{t-i}^{2}+\sum\:_{i=0}^{s}{\lambda\:}_{i}{HC}_{t-i}+\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:$$ $$\:\sum\:_{i=0}^{m}{ϴ}_{i}{URB}_{t-i}+\sum\:_{i-o}^{n}{\varPsi\:}_{i}{SC}_{t-i}+{\epsilon\:}_{t}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)$$ where \(\:{\epsilon\:}_{t}\) signifies the error (residual) term which is expounded as \(\:{Gini}_{t}-E\left[\frac{{Gini}_{t}}{{\gamma\:}_{t-1}}\right]\left[{Gini}_{t}\left|{\gamma\:}_{t-1}\right|\right]\) where \(\:\:\:{{\gamma\:}}_{\text{t}-1}\:\:\text{b}\text{e}\text{i}\text{n}\text{g}\:\text{s}\text{m}\text{a}\text{l}\text{l}\text{e}\text{s}\text{t}\:{\sigma\:}\:\text{f}\text{i}\text{e}\text{l}\text{d}\:\text{g}\text{e}\text{n}\text{e}\text{r}\text{a}\text{t}\text{e}\text{d}\:\text{b}\text{y}\:{D}_{t}\) , \(\:{GDP}_{t}\) , \(\:{GDP}_{t}^{2}\) , \(\:{HC}_{t}\) , \(\:{URB}_{t},\:{SC}_{t},\:{GİNİ}_{t-1}\) \(\:{GDP}_{t-1},{GDP}_{t-1}^{2},{HC}_{t-1},{URB}_{t-1},\:{SC}_{t-1}\) The lag orders p, q, r, s, m, and n are determined based on the Schwarz Information Criteria (SIC). In the above equations (1), GINI represents income inequality, GDP stands for gross domestic product per capita, GDP 2 refers to the square of GDP, H.C. denotes human capital, URB signifies urbanization, and S.C. represents structural change. The augmented form of the basic autoregressive distributed lag (ARDL) model, as presented in Eq. 1 above, has been extended to incorporate quantile aspects by Cho et al. in 2015, referred to as Quantile ARDL (QARDL) with parameters (p, q, r, s, m, n). Quantile ARDL model can be expressed as follows: $$\:{Q}_{G{ini}_{t}}=\mu\:\left(\tau\:\right)+\sum\:_{i=1}^{P}{\omega\:}_{i}\left(\tau\:\right){Gini}_{t-i}+\sum\:_{i=0}^{q}{\varPhi\:}_{i}{\left(\tau\:\right)GDP}_{t-i}+\sum\:_{i=0}^{r}{\varOmega\:}_{i}\left(\tau\:\right){GDP}_{t-i}^{2}+\sum\:_{i=0}^{s}{\lambda\:}_{i}\left(\tau\:\right){HC}_{t-i}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:$$ $$\:+\sum\:_{i=0}^{m}{ϴ}_{i}{\left(\tau\:\right)URB}_{t-i}+\sum\:_{i-o}^{n}{\varPsi\:}_{i}{\left(\tau\:\right)SC}_{t-i}+{\epsilon\:}_{t}\left(\tau\:\right)\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(2\right)$$ where \(\:{\epsilon\:}_{t}\left(\tau\:\right)={Gini}_{t}-{Q}_{{Gini}_{t}}\left(\tau\:|{\epsilon\:}_{t-1}\right)\) and \(\:0<\tau\:<1\) represents quantile Additionally, considering the likelihood of serial correlation, the QARDL model, as presented in the above equations (2), is extended as follows: \(\:{Q}_{\varDelta\:G{ini}_{t}}=\mu\:+\rho\:{Gini}_{t-1}+{\beta\:}_{GDP}{GDP}_{t-1}+{\beta\:}_{{GDP}^{2}}{GDP}_{t-1}^{2}+{\beta\:}_{HC}{HC}_{t-1}+{\vartheta\:\beta\:}_{URB}{URB}_{t-1}\) \(\:+{\beta\:}_{SC}{SC}_{t-1}+\sum\:_{i=1}^{P-1}{\omega\:}_{i}\varDelta\:{Gini}_{t-i\:}+\sum\:_{i=0}^{q-1}{\varOmega\:}_{i}\varDelta\:{GDP}_{t-i}+\sum\:_{i=0}^{r-1}{\varOmega\:}_{i}\varDelta\:{GDP}_{t-i}^{2}+\sum\:_{i=0}^{s-1}{\lambda\:}_{i}\varDelta\:{HC}_{t-i}\:+\sum\:_{i-0}^{m}{ϴ}_{i}{\varDelta\:URB}_{t-i}\) $$\:+\sum\:_{i=o}^{n}{\varPsi\:}_{i}{\varDelta\:SC}_{t-i}+{\epsilon\:}_{t}\left(\tau\:\right)\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(3\right)\:\:$$ Utilizing the delta method, the cumulative short-term impact of income inequality is determined by calculating \(\:{\beta\:}_{*}=\sum\:_{i-1}^{p-1}{\beta\:}_{1}\) , Furthermore, the combined short-term impact of both concurrent and preceding income inequality is captured by \(\:{\beta\:}_{*}=\sum\:_{i-1}^{n-1}{\beta\:}_{6}\) . Also, coefficient \(\:\:\rho\:\:\) is the speed of adjustment must be negative and significant in Eq. (3) (Cho et al., 2015 ). 4. Empirical Results 4.1. Pre- Estimation test Results The outcomes of the Jarque-Bera test, indicating a departure from normal distribution in the variables, prompted an investigation into the linearity of these variables. To further assess the characteristics of the variables, we applied the Broock, Dechert, and Scheinkman (BDS,1996) test, revealing non-linear behaviors in all variables (refer to Table 3 for details). Table 4 BDS test Results Variable m = 2 P-value m = 3 P-value m = 4 P-value m = 5 P-value m = 6 P-value Lngini 65.958 (0.000) 70.246 (0.000) 75.747 (0.000) 83.862 (0.000) 95.052 (0.000) Lngdp 56.809 (0.000) 0.354 (0.000) 66.214 (0.000) 73.818 (0.000) 0.581 (0.000) Lngdp 2 60.781 (0.000) 65.115 (0.000) 70.612 (0.000) 78.600 (0.000) 89.522 (0.000) Lnhc 44.399 (0.000) 47.570 (0.000) 51.56 (0.000) 57.356 (0.000) 65.275 (0.000) Lnurb 69.963 (0.000) 75.120 (0.000) 81.767 (0.000) 91.442 (0.000) 104.747 (0.000) Lnsc m 71.738 (0.000) 76.701 (0.000) 83.343 (0.000) 93.196 (0.000) 106.748 (0.000) Lnsc e 73.271 (0.000) 78.625 (0.000) 85.504 (0.000) 95.540 (0.000) 109.316 (0.000) Next, before applying the Quantile Autoregressive Distributed Lag (QARDL) model, it is crucial to conduct quantile unit root tests to assess the stationary properties of the variables. The rationale for choosing quantile unit root tests over conventional ones such as Augmented Dickey Fuller, Phillips, and Perron lies in the non-normal distribution of the data. To reduce the possibility of biased outcomes, it is best to use quantile unit root approaches, which provide more robust inference—particularly when working with data that does not follow a normal distribution (Koenker and Xiao, 2004 ). Human Capital (H.C.) is stationary at all quantiles, according to Table 5 , but urbanization is stationary at some quantiles and, for all other variables, it's the first difference. The data shown in Table 3 above are in accord with this outcome. According to these findings, urbanization is stable at certain quantiles, but human capital is stationary across all quantiles, suggesting a steady long-term relationship. However, after differencing, other variables display stationarity. This verification of stationary qualities is critical for further QARDL estimations since it ensures the model's trustworthiness in capturing the relationships between variables. Table 5 Quantile Unit root test results Quantiles (τ) GINI GDP HC URB SC m SC e α (τ) t-stats α (τ) t-stats α (τ) t-stats α (τ) t-stats α (τ) t-stats α (τ) t-stats 0.05 0.993 -0.272 1.002 0.131 0.996 -2.230 1.004 1.610 0.905 -0.935 1.000 0.004 0.10 1.015 0.927 0.996 -0.366 0.996 -15.553 1.005 -0.040 0.923 -1.115 1.001 0.584 0.20 1.016 1.924 0.997 -0.899 0.994 -7.347 0.993 -4.886 0.978 -0.592 0997 -1.030 0.30 1.009 1.974 0.995 -1.546 0.990 -12.597 0.988 -4.338 0.976 -1.077 0.997 -1.777 0.40 1.003 0.702 0.996 -1.497 0.990 -13.475 0.966 -0.747 0.982 -1.042 0.999 -0.557 0.50 1.002 0.613 0.996 -1.688 0.989 -34.799 0.999 -0.057 0.987 -0.837 0.997 -2.031 0.60 0.999 -0.194 0.995 -1.706 0.989 -29.999 1.006 1.021 0.985 -0.940 0.999 -1.060 0.70 0.991 -1.386 0.996 -1.111 0.988 -20.738 1.004 0.549 0.992 -417 0.998 -1.460 0.80 0.987 -1.530 0.997 -0.993 0.988 -18.262 0.980 -3.467 0.993 -0.299 0.999 -0.119 0.90 0.977 -1.405 0.993 -1.649 0.989 -17.770 0.992 -3.258 0.952 -1.106 1.000 0.258 0.95 0.998 -0.022 0.995 -0.326 0.988 -7.685 0.993 -1.549 0.968 -0.574 1.003 0.757 Note: Coefficients and t-stats in bold and rest represent I(0) and I(1), respectively. 4.2. QARDL Results Investigating the causes of income inequality in the United States is the primary goal of this study. Quantile Autoregressive Distributed Lag (QARDL) is the model we utilize to do this. Economic growth, urbanization, human capital, structural changes, and other independent factors are examined in separate models in our analysis. The QARDL model is useful because it allows us to study correlations across multiple quantiles of the dependent variables, examining both short-term and long-term dynamics (Hashmi et al., 2022 ). As previously stated, our model, targeted at finding factors of income inequality, integrates two distinct datasets to quantify structural change: manufacturing value added to GDP (SCm) (model 1) and the ratio of manufacturing employment to total employment (SCe) (model 2). Consequently, we present the results of two QARDL analyses in Table 6 and Table 7 . Table 6 depicts the long-run and short-run effects of quantile ARDL results, with the income inequality (GINI) index serving as the dependent variable. Meanwhile, human capital (HC), urbanization (URB), and structural change (SC m ) are employed as explanatory variables (model 1). Statistically, the p-value and the speed of adjustment parameter should be negative and significant (Anwar et al.,2021). As seen in Table 6 , except for the 0.30 quantile, all quantiles exhibit a negative and significant speed of adjustment, providing supporting evidence for the accuracy of our model. In the long run, the relationship between GDP and income inequality, as well as GDP squared and income inequality, is significant across all quantiles. Moreover, these relationships are positively and negatively oriented, respectively. This implies that the inverted U-shape is confirmed. Human capital is significant in all quantiles and negative signs show that human capital reduces income inequality in the USA. Urbanization is significant across quantiles, with a negative sign at the 0.05 quantile and positive signs at other quantiles. This suggests that, overall, urbanization increases income inequality in the majority of quantiles. Table 6 Results of Quantile autoregressive distributed lag (QARDL) for model 1 Quantiles α(τ) ρ(τ) β GDP (τ) β GDP 2 (τ) β HC (τ) β URB (τ) β SCm (τ) ω (τ) Φ 1 (τ) Ω 0 (τ) λ 0 (τ) ϴ 0 (τ) Ψ 0 (τ) 0.05 -1.737 a (0.000) -0.087 b ( 0.011) 0.855 a (0.000) -0.035 a (0.000) -2.511 a (0.000) -1.985 a (0.000) -0.065 (0.226) 0.583 a (0.000) 2.602 a (0.000) -0.131 a (0.000) -4.473 a (0.000) -0.868 (0.298) -0.125 a (0.000) 0.10 -1.863 a (0.000) -0.079 b (0.060) 0.852 a (0.000) -0.035 a (0.000) -2.538 a (0.000) 2.120 a (0.000) -0.036 (0.226) 0.586 a (0.000) 2.351 a (0.000) -0.118 a (0.000) -4.200 a (0.000) 0.093 (0.896) -0.116 a (0.007) 0.20 -2.132 a (0.000) -0.060 b (0.082) 0.965 a (0.000) -0.042 a (0.000) -2.658 a (0.000) 2.274 a (0.000) -0.091 b (0.036) 0.436 a (0.000) 0.509 (0.332) -0.025 (0.324) -1.920 b (0.040) 0.519 (0.507) -0.036 (0.334) 0.30 -2.211 a (0.000) -0.034 (0.105 1.064 a (0.000) -0.047 a (0.000) -2.818 a (0.000) 2.268 a (0.000) -0.135 a (0.000) 0.478 a (0.000) 0.469 (0.269) -0.020 (0.318) -1.704 a (0.008) 0.277 (0.628) -0.033 (0.363) 0.40 -2.015 a (0.000) -0.052 a (0.003) 0.943 a (0.000) -0.041 a (0.000) -2.601 a (0.000) 2.183 a (0.000) -0.090 b (0.031) 0.502 a (0.000) 0.576 (0.125) -0.026 (0.166) -1.643 a (0.000) 1.028 (0.140) -0.010 (0.762) 0.50 -1.932 a (0.000) -0.053 a (0.000) 0.902 a (0.000) -0.039 a (0.000) -2.451 a (0.000) 2.126 a (0.000) -0.075 b (0.030) 0.500 a (0.000) 0.343 (0.385) -0.014 (0.439) -1.099 (0.067) 1.108 (0.110) -0.003 (0.991) 0.60 -1.639 a (0.000) -0.052 a (0.002) 0.807 a (0.000) -0.033 a (0.000) -2.445 a (0.000) 1.952 a (0.000) -0.053 (0.143) 0.505 a (0.000) 0.345 (0.224) -0.014 (0.311) -1.043 b (0.015) 1.367 b (0.011) -0.015 (0.678) 0.70 -1.781 a (0.000) -0.060 b (0.012) 0.818 a (0.000) -0.034 a (0.000) -2.523 a (0.000) 2.096 a (0.000) -0.022 (0.677) 0.518 a (0.000) 0.000 (0.999) 0.003 a (0.845) -0.602 (0.169) 1.360 b (0.079) -0.041 (0.513) 0.80 -2.252 a (0.004) -0.090 a (0.000) 1.071 b (0.020) -0.045 b (0.045) -2.879 a (0.000) 2.331 a (0.000) 0.003 (0.978) 0.565 a (0.000) -0.020 (0.949) 0.005 (0.764) -0.632 c (0.096) 2.346 a (0.000) -0.103 a (0.001) 0.90 -2.521 a (0.000) -0.114 a (0.001) 1.340 a (0.000) -0.058 a (0.000) -3.430 a (0.000) 2.377 a (0.000) -0.100 (0.163) 0.542 a (0.000) -0.586 (0.219) 0.034 (0.170) 0.328 (0.526) 2.755 a (0.007) -0.108 a (0.000) 0.95 -2.221 a (0.000) -0.146 a (0.000) 1.315 a (0.000) -0.057 a (0.000) -3.458 a (0.000) 2.118 a (0.000) -0.137 b (0.014) 0.689 a (0.000) -1.088 (0.140) 0.056 (0.123) 1.518 c (0.080) 4.132 a (0.007) -0.062 (0.241) Note: 'a', 'b.' 'c' indicates a significance level of 1%, 5%, and 10%, respectively. We displayed only 11 out of 19 quantiles due to space constraints, and a full table report will be provided by the authors upon request. The short-run dynamics depict an inverted U-shaped relationship between income inequality and economic growth, confirmed only for the lowest quantile (0.05–0.10), implying that the impact of income inequality on economic growth is most pronounced for the lowest income groups. The impact of human capital on income inequality is significant within the quantiles of 0.05 to 0.60, as well as at 0.80 and 0.95. Among the significant quantiles, human capital exhibits a positive effect only at the 0.95 quantiles while demonstrating a negative impact on income inequality at other significant quantiles. When examining the urbanization variable, it is observed that it is significant and positively affects income inequality in the 0.60 to 0.95 quantiles. This indicates an increase in inequality within this range. Structural change has a weaker impact on income inequality in the short run compared to the long run. In the quantiles 0.05–0.10 and 0.80–0.95, there is only a statistically significant and inequality-reducing effect. Table 7 introduces a refinement to the model presented in Table 6 by employing the variable "structural change" differently. In this iteration, the focus is on employment in manufacturing as a percentage of total employment, enhancing the robustness of our analysis. As mentioned earlier, it is expected that, statistically, the p-value and the speed of adjustment parameter should be negative and significant. In this context, the same has been achieved here, with a negative sign and significance observed in all quantiles. In the long run, similar to Model 1, Model 2 consistently confirms an inverted U-shaped Kuznets curve across all quantiles, as indicated by the positive coefficient for GDP and the negative coefficient for GDP squared. In Model 2, the influence of human capital is consistently positive and significant across all quantiles, implying a uniformly positive effect on reducing income inequality. Urbanization, in this case, consistently exhibits a positive effect in all quantiles, indicating an overall increase in income inequality. Structural change has a consistent negative impact on reducing income inequality across most quantiles (0.20–0.95). When compared to Model 1, the results suggest that the structural change variable has a more pronounced effect in the current context. In the short run, from the 0.05 to 0.20 and 0.95 quantiles, GDP exhibits a positive effect, while GDP squared shows a negative and statistically significant impact. This pattern suggests the presence of an inverted U-shaped Kuznets curve in these quantiles. Specifically, it indicates that GDP initially contributes to an increase in income inequality, which is then reversed, demonstrating the dynamic effects of economic growth on income distribution within these quantiles. Human capital has a negative effect, implying that it reduces income disparity, particularly within the 0.05 to 0.70 quantiles, which encompass the low and middle quantiles.. This suggests that human capital exerts a beneficial impact on improving income equality within these quantiles. Urbanization not only sustains its impact in the long run but also exhibits a consistent effect in the short run. Specifically, within the quantiles ranging from 0.20,0.50 and from 0.60 to 0.95, urbanization demonstrates a significant and positive effect, indicating a substantial increase in income inequalities. This implies that, over time and across various quantiles, urbanization consistently contributes to a noteworthy escalation in income inequality. Finally, structural change significantly and consistently reduces income inequality across quantiles, ranging from 0.20 to 0.95, in a manner akin to its impact in the long run. Table 7 Results of Quantile autoregressive distributed lag (QARDL) for model 2 Quantiles α(τ) ρ(τ) β GDP (τ) β GDP 2 (τ) β HC (τ) β URB (τ) β SCe (τ) ω (τ) Φ 1 (τ) Ω 0 (τ) λ 0 (τ) ϴ 0 (τ) Ψ 0 (τ) 0.05 -1.027 b (0.042) -0.125 a (0.000) 0.643 a (0.000) -0.023 a (0.001) -2.455 a (0.000) 1.539 a (0.000) -0.038 (0.152) 0.709 a (0.000) 1.502 a (0.000) -0.084 a (0.000) -1.844 c (0.064) -1.872 (0.112) 0.103 (0.133) 0.10 -1.621 a (0.000) -0.125 a (0.000) 0.782 a (0.000) -0.031 a (0.000) -2.544 a (0.000) 1.971 a (0.000) -0.021 (0.343) 0.521 a (0.000) 1.290 (0.002) -0.063 a (0.005) -3.419 a (0.000) -0.219 (0.769) -0.047 (0.346) 0.20 -1.974 a (0.000) -0.060 a (0.010) 0.976 a (0.000) -0.004 a (0.000) -2.796 a (0.000) 2.112 a (0.000) -0.077 (0.000) 0.438 a (0.000) 0.883 a (0.000) -0.041 a (0.001) -3.081 a (0.000) 1.092 b (0.058) -0.068 b (0.036) 0.30 -1.863 a (0.000) -0.033 (0.106) 0.964 a (0.000) -0.041 a (0.000) -2.881 a (0.000) 2.074 a (0.000) -0.082 a (0.000) 0.408 a (0.000) 0.560 (0.174) -0.024 (0.226) -2.140 a (0.000) 0.108 (0.862) -0.081 b (0.014) 0.40 -1.737 a (0.000) -0.449 a (0.009) 0.912 a (0.000) -0.039 a (0.000) -2.592 a (0.000) 1.962 a (0.000) -0.087 a (0.000) 0.491 a (0.000) 0.379 (0.322) -0.015 (0.415) -1.553 a (0.009) 0.158 (0.815) -0.069 c (0.089) 0.50 -1.890 a (0.000) -0.053 a (0.001) 0.952 a (0.000) -0.042 a (0.000) -2.581 a (0.000) 2.062 a (0.000) -0.080 a (0.001) 0.467 a (0.000) 0.467 c (0.078) -0.022 (0.122) -1.512 a (0.000) 1.126 c (0.054) -0.044 (0.192) 0.60 -1.752 a (0.000) -0.043 a (0.001) 0.949 a (0.000) -0.041 a (0.000) -2.580 a (0.000) 1.934 a (0.000) -0.077 a (0.002) 0.479 a (0.000) 0.385 (0.266) -0.015 (0.352) -1.212 b (0.017) 0.973 c (0.092) -0.077 c (0.061) 0.70 -2.030 a (0.000) -0.058 a (0.000) 1.101 a (0.000) -0.048 a (0.000) -2.892 a (0.000) 2.090 a (0.000) -0.095 a (0.002) 0.501 a (0.000) 0.205 (0.478) -0.006 (0.669) -1.049 a (0.007) 1.405 a (0.010) -0.086 b (0.071) 0.80 -2.859 a (0.000) -0.077 a (0.002) 1.519 a (0.000) -0.068 a (0.000) -3.470 a (0.000) 2.520 a (0.000) -0.091 b (0.037) 0.538 a (0.000) -0.070 (0.866) 0.008 (0.735) -0.843 (0.122) 1.636 b (0.015) -0.132 b (0.037) 0.90 -3.053 a (0.000) -0.119 a (0.000) 1.671 a (0.000) -0.076 a (0.000) -3.701 a (0.000) 2.579 a (0.000) -0.119 a (0.002) 0.626 a (0.000) -0.554 (0.231) 0.034 (0.142) -0.394 (0.497) 2.216 a (0.000) -0.014 a (0.000) 0.95 -3.316 a (0.000) -0.123 a (0.006) 1.958 a (0.000) -0.091 a (0.000) -4.098 a (0.000) 2.581 a (0.000) -0.213 a (0.002) 0.696 a (0.000) 1.462 b (0.019) -0.081 a (0.000) 0.550 (0.526) 2.679 a (0.000) -0.131 a (0.000) Note: 'a', 'b.' 'c' indicates a significance level of 1%, 5%, and 10%, respectively. We displayed only 11 out of 19 quantiles due to space constraints, and a full table report would be provided by authors upon request. As a result, both Table 6 and Table 7 explore the long-run and short-run effects of quantile ARDL results, using the income inequality (GINI) index as the dependent variable. In both models, human capital (H.C.), urbanization (URB), and structural change (S.C.) serve as explanatory variables, providing insights into their respective influences on income inequality across different quantiles. In terms of similarities, both models validate the presence of an inverted U-shaped Kuznets curve. The consistent significance of the relationship between GDP and income inequality across all quantiles, characterized by positive and negative coefficients for GDP and GDP squared, respectively, aligns with findings from earlier studies, including Kuznet (1955), Nielsen and Alderson ( 1997 ). This suggests that income inequality increases at the outset of economic growth, reaches a peak, and then declines when a more balanced distribution of income is the consequence of further economic progress. This trend illustrates the characteristics of developing economies throughout their transition to industrialization, when disparities in wealth tend to expand. Nonetheless, progress and expansion lead to a more distributed and fair wealth in the long run. Further, in both models, human capital has a negative effect on income disparity, suggesting that income equality is generally on the rise. This highlights the critical role that education and skill development play in reducing economic inequality. From a monetary point of view, investing in people has three main effects: increasing labor productivity, fostering innovation, and creating a workforce with advanced skills that can demand greater wages. Because of this, the income gap is narrowing. These findings are in line with those of Molla ( 2021 ) and Suhendra et al. ( 2020 ), which provide more evidence that education and skill development are important ways to achieve a more equitable distribution of income. The impacts of structural change, however, varied significantly. Using a different technique and concentrating on manufacturing jobs, Table 7 confirms a more dramatic effect than Table 6 , which shows a negative impact on income inequality in most quantiles. Although structural change helps reducing income inequality, the exact mechanism by which it operates may differ in its efficacy. Focusing on manufacturing employment provides a more direct connection to income inequality and the job market from an economic perspective, since it precisely reflects the available job opportunities and revenue streams within the industry. Hence policies on increasing manufacturing employment, rather than merely increasing manufacturing output, may be more effective in reducing income disparity. Consistent with the literature, both tables suggest strong evidence of long-run dynamics and showing that structural change considerably reduces income disparity across a variety of quantiles. For instance, Suhendra et al. ( 2020 ) and Molla ( 2021 ) demonstrate that human capital has a substantial and detrimental impact on income disparity. This long-term finding points to the importance of structural changes in the economy on income distribution, such as the shift from agriculture to industry and services. There would be less income inequality as a result of new jobs and opportunities created as a result of economic growth and diversification. Hence, our findings and the literature are in line with the claim that human capital consistently exhibits a negative impact on income inequality. Moreover, focusing on manufacturing value added to the GDP rate alone may not capture the full extent of its impact on income inequality. While an increase in manufacturing value added to the GDP rate may signify growth in the sector, it does not necessarily guarantee equitable distribution of the generated income. Manufacturing-led economic expansion can result in increased GDP, but its benefits may not be distributed evenly across all groups, potentially worsening income inequality if the advantages are concentrated among a few. In contrast, examining the percentage of total employment in manufacturing provides a more direct link to the labor market and income inequality, as it reflects the actual employment opportunities and income sources within the sector. Since this strategy targets the distribution of income through employment, it follows that policies that encourage job creation in manufacturing are more likely to effectively reduce income disparity. Table 8 presents the results of Wald tests, which examine asymmetric effects in both short- and long-run scenarios. In general, the speed of adjustment parameter (ρ) is found to be significant across all situations. Furthermore, significant long-run asymmetric relationships are identified in both models, particularly evident in variables such as (GINI-GDP- GDP 2 HC, URB, and SCe, and SCm), where the null hypothesis of parameter constancy is rejected across all quantiles. However, in the short run, relationships among variables show a different picture in the case of model 1; the outcome confirms that null hypotheses related to parameter constancy were rejected. In the case of the second model, the asymmetric relationship is significant only for economic growth and human capital. These findings enable us to conclude that there is an asymmetric relationship on only in the long run between Gini inequality, economic growth urbanization, human capital, and structural change in the United States. Additionally, the results highlight the prevalence of asymmetrical relationships, particularly in the long term, between Gini inequality, economic development, urbanization, human capital, and structural change in the United States. Understanding and addressing these imbalances is crucial for developing effective methods to reduce income disparity and promote sustainable economic growth, as the enduring consequences show. Further examination and discussion are required, particularly in relation to policy development, given the significant and far-reaching consequences of the unequal long-term impacts. These findings highlight the need for policies that seek to increase economic growth, foster human capital development, and execute structural reforms to consider the uneven effects on income disparities over the long run. For example, there could be varying long-term impacts on income distribution resulting from efforts to enhance workers' education and training, as well as from initiatives that drive fundamental changes in the economy. This highlights the significance of thorough planning and precise implementation of policies. While short-term solutions may address immediate disparities, long-term strategies that take these persistent inequities into account are necessary for equitable and sustainable economic growth. To get to the bottom of what causes these long-term imbalances, more research is required. Furthermore, it is critical to assess how well specific policies mitigate unfavorable effects and foster positive outcomes. The need of addressing long-term policy solutions is highlighted by the long-term asymmetries that have been discovered between Gini inequality, economic growth, urbanization, human capital, and structural change. In order to achieve comprehensive and equitable economic growth, the United States must address these unequal relationships. Table 8 Results of the Wald Test for The Constancy of Parameters for Both Models Variable Wald-statistics Variable Wald-statistics Model 1 Model 2 ρ 14.373 (0.000) ρ 16.188 a (0.000) β GDP 50.203 (0.000) β GDP 68.288 a (0.000) β GDP 2 34.577 (0.000) β GDP 2 49.773 a (0.000) β HC 135.091 (0.000) β HC 164.190a (0.000) β URB 113.296 (0.000) β URB 108.267a (0.000) β SCm 4.727 (0.030) β SCe 10.699 a (0.001) gini 26.706 (0.000) gini 36.562 a (0.000) Φ 1 0.755 (0.385) Φ 1 3.130 a (0.007) Ω 0 0.599 (0.439) Ω 0 2.402 (0.121) λ 0 3.386 (0.067) λ 0 12.586 a (0.000) ϴ 0 0.000 (0.999) ϴ 0 1.708 (0.192) Ψ 0 2.570 (0.110) Ψ 0 3.734 b (0.053) To summarize, the results show that structural change factors, which represent the growth of the manufacturing sector, reduce income inequality. This is indicated by the presence of an inverted U-shaped Kuznets curve. Importantly, by using many structural change variables, the research becomes more robust and offers a more comprehensive picture of how it affects income distribution. In line with earlier studies, human capital always has a negative effect on income disparity. Yet, urbanization has contradictory consequences; it widens income gaps at the top quantiles while narrowing them at the bottom. Gross domestic product (GDP) shows an inverse U-shaped relationship with income inequality, indicating that income distribution is dynamic and subject to change over time. 5. Conclusion In the 1970s and 1980s, deindustrialization caused significant structural changes in the US. This shift contributed to the widening income disparity by reducing manufacturing employment, which hit the manufacturing sector more than other sectors. The objective of this study is to examine the relationship between income inequality, urbanization, human capital, and economic growth from 1965q1 to 2019q4 using a novel method called Quantile ARDL. We include manufacturing value added to GDP and manufacturing employment as a percentage of total employment as factors to strengthen the analysis. This method is chosen because the widely used variable of manufacturing value added in the literature might not provide a complete picture of its effect on income distribution when used alone. Understanding the correlation between income inequality and the labor market necessitates looking at the manufacturing sector's share of the overall workforce. Income inequality follows an inverted U-shaped pattern as the manufacturing sector grows. Showing that systemic reforms help bring about a decrease in income disparity. Urbanization has conflicting impacts: in some regions it widens income inequality, while narrowing it in other regions. Human capital tends to decrease income disparity. These findings support previous research and show the complicated form of structural changes in relation to income disparity. To illustrate, Autor, Dorn, and Hanson ( 2013 ) show that employment losses in manufacturing have had a negative influence on local labor markets, especially for individuals with lower levels of education. The results indicate a widening gap between the incomes of highly talented and unskilled individuals and an increase in overall income inequality. There has been a dramatic change in the distribution of income since the 1970s due to the transition from manufacturing to the service sector in the US and the outmigration of industrial companies to countries like China that have cheaper labor costs. Governments and policymakers might address this by considering several policy options. In order to foster innovation and stimulate expansion in domestic manufacturing, policymakers could consider offering tax incentives and various forms of financial assistance. Investments in advanced manufacturing technology and incentives for R&D are commonly associated with a high level of expertise and a focus on professionalism. The results of Acemoglu and Autor (2011) are in line with these incentives. Another way to encourage innovation and make the US manufacturing sector more competitive is to improve cooperation between public and private organizations. Collaborations can greatly enhance technological progress by pooling together resources and expertise. It would be beneficial for individuals to enhance their readiness for careers in high-tech manufacturing industries by ensuring that STEM education is more accessible at all levels of schooling. As per Chetty et al. ( 2014 ), education plays a crucial role in addressing income inequality and fostering economic mobility. One way to prepare individuals for success in emerging industrial roles is by developing specialized vocational training programs that are tailored to the specific requirements of these industries. Furthermore, workers will be able to adapt to new technology and shifting job demands if lifelong learning efforts are promoted. To reduce the abuse of cheap foreign labor, trade agreements should include environmental standards and labor standards. Domestic workers' rights can be protected, and fair competition ensured by these agreements. A more just and equitable competitive environment can be achieved by enforcing laws that require foreign producers to adhere to fair labor standards. One effective approach to mitigate the negative impacts of outsourcing is to promote the adoption of ethical sourcing practices by companies. Investing in modern transportation, communication networks, and energy systems can greatly enhance the efficiency and competitiveness of domestic manufacturing. To facilitate intricate manufacturing processes, enhancing infrastructure is crucial. Domestic manufacturing becomes increasingly feasible with the implementation of intelligent manufacturing technologies such as data analytics and automation. These technologies increase productivity while decreasing production costs. Similarly, a progressive tax system can help reduce inequality by redistributing income and funding social programs. Education and social safety nets can be financed by the revenue generated by progressive taxes. Further, people affected by economic changes can find a way to become financially stable, which reduces poverty and income fluctuations, by looking at Universal Basic Income (UBI) or other types of direct income assistance. Financial security can be improved by advocating for reasonably priced healthcare and housing. In order to achieve goals related to reducing income disparity and encouraging economic growth, it may be helpful to continuously perform research to evaluate the effectiveness of current policies. For a comprehensive analysis of how policies impact income inequality and economic mobility over time, future studies could consider employing longitudinal approaches. Furthermore, exploring the impact of emerging technologies such as AI and automation on income inequality and the labor market.By implementing these practical options and pursuing additional study, policymakers can work towards developing a more robust and fair economy. Given the current economic climate, this will promote inclusive growth and help address income inequality. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4797904","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":335326077,"identity":"744148b8-803c-40a0-beb2-336e3da36b4a","order_by":0,"name":"Cumali Marangoz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYFAD9gYgYQDEB4jWwnMAroWxgTgtEglQBiEt/NMOP/vwc0etnPzMx49fVxQwyPHdSGB/XIHP7NtpxjN7zxw3ZpydZmZ5xoDBWPJGAmPjGTxaDKQTjBl4244lNksnmBk2GDAkbgBpwecyA+n0z4x/gVraJI9/A2mpJ0JLjjEzb1tNYo8Ej/FDoJYEA0JaJG7nFDPLth0wluDJKWNsMJAwnHnmYeNMfFr4Z6dvZnzbVicn335888eGPzbyfMeTD3zEpwUKDoMINgmgrUCauJisAxHMH4hROgpGwSgYBSMPAADZqE6qM9l0tQAAAABJRU5ErkJggg==","orcid":"","institution":"Ağrı İbrahim Çeçen University","correspondingAuthor":true,"prefix":"","firstName":"Cumali","middleName":"","lastName":"Marangoz","suffix":""}],"badges":[],"createdAt":"2024-07-24 22:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4797904/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4797904/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63278614,"identity":"5012e320-9ac8-4d61-99bb-156d02cb6f69","added_by":"auto","created_at":"2024-08-26 12:36:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":64798,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe Gini index for the United States from 1965 to 2019\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4797904/v1/9dc19dbada4955424a0e4708.png"},{"id":63279673,"identity":"ef228081-eef3-4e6b-99e2-6a4a03d14d7c","added_by":"auto","created_at":"2024-08-26 12:44:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1363663,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4797904/v1/ab8afd4d-cc15-469d-ac87-10f77cd5637c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Examining the Role of Structural Change in Income Inequality: Insights from Quantile ARDL Modeling in the United States","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIncome inequality, the disproportionate wealth of the top 1% of earners relative to the stagnation or decline in real wages of a median worker, is a major issue in the US (Saez \u0026amp; Zucman, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This trend has started to harm the middle class, declining economic opportunities, and decreased social mobility. Scholars determine many factors that affect this phenomenon. Many studies examine income inequality in the US, especially in terms of structural shifts in the economy. The seminal work in understanding structural shift is Kuznets' hypothesis. The hypothesis states that there is an inverse-U shape relationship between income disparity and economic growth (Piketty \u0026amp; Saez, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). An essential component in income distribution is structural change, defined as changes in the sectoral composition of output and employment over the long run (Andersson \u0026amp; Palacio, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Elhini \u0026amp; Hammam, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To many scholars, wages have become more unequal because of economic reorganization, leading to the loss of high-paying industrial jobs (Yang et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As a result of economic transformation, wage disparities have widened and have had far-reaching effects on the labor market due to the oversupply of skilled workers in the service industry relative to unskilled labor.\u003c/p\u003e \u003cp\u003eAs the United States shifted its economic base from agriculture to manufacturing, a large-scale movement to urban centers drove urbanization. Initially the exodus helped to even out income distribution by increasing the demand for low-skilled workers in the manufacturing sector. Yet, the subsequent deindustrialization caused a meteoric rise in urban populations as economic agents have shifted their focus to the service sector. Due to technological progress, the need for a more educated workforce caused a schism in the labor market. As the demand for skilled people surpassed that for unskilled positions, this moves exacerbated income disparities. Income inequality tends to worsen as workers shifted from manufacturing to the service sector (Scheiring et al., 2020). Overall inequality rises as workers leave the relatively equal and high-wage industrial sector for the more unequal tertiary sector (Moller et al., 2009). Industry, urbanization, human capital, and wealth disparities all interact intricately throughout structural change, as these processes demonstrate.\u003c/p\u003e \u003cp\u003eThe Industrial Revolution in the United States, as per the New Economic Geography Theory, commenced in the Northern states, such as New England, and gradually expanded to other Northern states since the early 1800s (Yazgan et al, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The second industrial revolution, occurring from approximately 1870 until 1914, was a period of significant advancement related primarily to advances in chemicals and electricity (Engelman, 2015). This period marked a transformative phase in the USA and Germany, leading to substantial economic and technological progress. Furthermore, the third industrial revolution, which has been occurring since 1973, is closely associated with information and communication technologies, particularly in the USA and East Asia (Engelman, 2015).\u003c/p\u003e \u003cp\u003eThe deindustrialization process in the United States during the 1960s and 1970s led to a significant increase in mass unemployment among young, Black employees (Like \u0026amp; Cobbina, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This phenomenon has been associated with the widening of income inequality and high unemployment in Europe, with some attributing it to the globalization of markets (Lee, 2011).\u003c/p\u003e \u003cp\u003eThe shift of manufacturing to countries with cheaper labor left low-skill American workers to seek employment in the service sector, leading to concerns about the nature, extent, and implications of deindustrialization (Autor et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e and Piore \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Authors have examined the deindustrialization process, emphasizing the relocation of production and the hollowing of firms in the United States (Bener\u0026iacute;a \u0026amp; Santiago, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Furthermore, the increase in service production and deindustrialization in the United States has been linked to a lengthening of the employment recovery from recessions by about 40% (Olney \u0026amp; Pacitti, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The ongoing effects of deindustrialization in the United States have been associated with entrenched levels of urban disadvantage, racialized inequality, gang activity, and homicide (Fraser \u0026amp; Clark, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, offshore, or the migration of American corporations' activities to China in the 1980s and 1990s, had far-reaching consequences for domestic employment and income distribution in the United States. Offshoring manufacturing and production jobs from the United States to China had a huge influence on the domestic labor market and income distribution, as well as rapid deindustrialization.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays the Gini index (calculated based on current (2024) USD index) for the United States from 1965 to 2019 from St. Louis FED (FRED) database. During the late 1960s and early 1970s, the index remained in the mid to high 30s, indicating a lower level of income disparity. However, from the late 1970s to the early 1990s, income inequality appears to have gradually increased, as evidenced by the index's increasing trend. The association between deindustrialization and income inequality is further confirmed by the observation that income gaps expanded considerably in the United States beginning in the 1980s, with top earners receiving a rising share of total income (Appelbaum, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Additionally, income disparity increased between 2000 and 2011, coinciding with the process of deindustrialization (Meyer \u0026amp; Sullivan, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePrevious studies have primarily utilized the structural change variable, focusing on indicators such as industrial employment rates (Kollyemer, 2018; Hillbom, \u0026amp; Bolt, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kum, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), the labor force in the agricultural sector (Andersson \u0026amp; Chavera, 2016), industry sector ratios (Dartanto et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and the value added of the manufacturing sector to GDP (Ali et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These studies support the classical view of the reducing effect of structural change, specifically the transformation from agriculture to the manufacturing sector, on income inequality. However, these approaches have two main limitations. Firstly, the variables used in previous studies are generally broad and do not specifically focus on manufacturing, which indeed plays a crucial role as the main engine of industry. Secondly, relying solely on indicators such as manufacturing sector growth or the number of workers in manufacturing may not provide a comprehensive understanding of their impact on income inequality. Therefore, incorporating multiple indicators simultaneously may yield more accurate results.\u003c/p\u003e \u003cp\u003eUnlike previous studies, this study seeks to examine the factors that contribute to income disparity in the United States by analyzing structural change, human capital, economic growth, and urbanization. We employe a quantile autoregressive distributed lag (ARDL) model. The findings succinctly summarize that the U-shaped Kuznets curve remains relevant, showing that the growth of the manufacturing sector has a moderating impact on income disparity.\u003c/p\u003e \u003cp\u003eThe contribution and extension of the study to the literature are threefold. Firstly, to the best of the authors' knowledge, this research employs two alternative models to analyze not only manufacturing value added but also changes in employment in manufacturing compared to overall employment, based on the authors' current understanding. This enables us to evaluate if the expansion of the manufacturing industry has an impact on the income levels or employment patterns of workers. The findings suggest that increases in the manufacturing employment to total employment ratio have a mitigating effect on income inequality. Second, we also include urbanization and human capital in our models because of the impact of structural change on these components. As a third point, we employ the Quantile ARDL model, which has many advantages over the more traditional linear ARDL models. The method takes into account many thresholds that are determined by the quantiles. It allows for the addition of many regressors and combines all the advantages of standard ARDL approach (Bertsatos et al., 2020). Additionally, this method offers a thorough description of the general relationship between the listed factors and incorporates numerous quantiles. Conventional models disregard this, including quantile regression, OLS, and ARDL (Wang et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe paper continues as follows: Section 2 discusses the literature review. Section 3, explains and details the methodology and data. Section 4 presents the findings and Section 5 concludes.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eThis section attempts to summarize and survey the literature on the variables such as GDP growth, human capital, urbanization, and structural change that impact income inequality.\u003c/p\u003e \u003cp\u003eTwo fundamental gauges of a country's social and economic health are economic growth and income inequality. Academics, politicians, and economists have been arguing passionately about the link between these two phenomena for a long time. According to Kuznets's (1955) inverted U-shaped hypothesis, income inequality gets worse in the beginning stages of economic development and gets better as per capita income gets higher in the later stages. Several empirical studies support this inverted U-shaped hypothesis. Younsi and Bechtini (2020) examined BRICS countries, Kim, and Lin (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) focused on both developed and developing countries, Jovanovic (2018) studied 26 ex-socialist countries, Barro (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) conducted a global-level analysis, Galbraith and Kum (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) delved into industrialized countries, Comin (2019) explored Brazil, Utari and Cristina (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) investigated Indonesia, and Jin (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) examined China. All these studies provide confirmation of the inverted U-shaped relationship between income inequality and economic development.\u003c/p\u003e \u003cp\u003eHowever, studies using economic growth as a determinant instead of relying on Kuznets' inverted U-shaped hypothesis have yielded mixed results. For instance, some studies have found a positive relationship between income inequality and economic growth, as observed in Forbes (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), Sameti and Rafie (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and Roine et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). On the contrary, other studies have indicated a negative correlation, such as Muinelo-Gallo and Roca-Saales (2013), and Sabir et al. (2015). Nevertheless, the evidence regarding the relationship between income disparity and economic development is inconsistent. Some research has failed to find a meaningful relationship; for instance, Szczepaniak (2022) and Shin et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor several reasons, studies examining the interaction between income disparity and GDP growth have produced conflicting results. Research employs diverse methodologies, draws from diverse data sources, and spans distinct time periods, all of which contribute to the diverse variety of outcomes. Due to the complex and multi-faceted nature of economic systems and the dynamic nature of sociopolitical circumstances in different regions, the identified relationships vary greatly. Another factor that could influence the relationship between income inequality and economic growth is the institutional makeup and level of development of a country.\u003c/p\u003e \u003cp\u003eHow H.C. impacts income inequality is another important and well-studied element. Considering human capital is essential for getting to the bottom of income inequality. Human capital, a person's formal and informal learning, as well as education and job experience, affects income disparity. Investments in training and education lead to pay raises, reduced income and increased productivity in the workplace. Income inequality might be mitigated through several means. For instance, better educated employees have the ability to exploit technological innovation boosting the economy and creates more job opportunities (Acemoglu \u0026amp; Autor, 2011; Hanushek et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Differences in access to high-quality education deepen preexisting income gaps (Chetty et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Disparities in opportunity may widen due to the skill-biased technological advancements favoring those with more education and experience. Global economic developments, shifts in the labor market, and the relative availability of various skill sets all contribute to interaction between human capital and income inequality.\u003c/p\u003e \u003cp\u003eWhile human capital might have both positive and negative effects on economic inequality, the majority of studies show that it reduces income inequality. Evidence of a positive association has been observed in multiple studies; for instance, Lee and Lee (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Suhendra et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Lee et al. (2019), Molla (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Aqil and Wahyuniati (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Kim (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and Abdullah et al. (2015). Nevertheless, there is a handful of research that offers a contrasting perspective, including Sequeira et al. (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Yang et al. (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe transition to structural change stage determines how urbanization affects income disparity. There seems to be a balance between economic growth and income distribution in the first stage, when urbanization is accelerated by the rapid transition from agriculture to the manufacturing sector (Kuznet, 1955). In the second phase, urban slums emerge as a result of economic shifts from low- to high-labor-intensive sectors and the industrialization-to-service-sector transition. At the same time, it might be more difficult for city dwellers to get the same degree. Because of this change, the relationship between urbanization and economic disparity is becoming less favorable. The literature has contradictory findings since urbanization has different impacts at different phases. The link between urbanization and economic inequality is complicated, as studies have conflicting results. Several studies have confirmed a favorable link; for example, Sulemana et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Le et al. (2021), Kanbur and Zhuang (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and Adams and Klobodu (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, a negative correlation was confirmed by Wan et al. (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Guo et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, the contradictory empirical evidence about the link between urbanization and income disparity is likely due to the fact that different phases of urbanization are associated with distinct impacts.\u003c/p\u003e \u003cp\u003eThe concept of structural change has been widely used in the literature since Kuznets' influential work in 1955, and several indicators have been developed to quantify it. For instance, Kollyemer (2018), Hillbom and Bolt (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and Kum (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) have examined industrial employment rates, while Andersson and Chavera (2016) focused on the labor force in the agricultural sector. Dartanto et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) analyzed industry sector ratios, and Ali et al. (2022) studied the value added by the manufacturing sector to GDP. These empirical studies support the classical view of the reducing effect of structural change (specifically, the transformation from agriculture to the manufacturing sector) on income inequality.\u003c/p\u003e \u003cp\u003eKollyemer (2018) utilizes time-series regression models with national-level data from 1947 to 2015 to investigate how industrialization interacts to shape income distribution. The findings suggest that industrialization yields more pronounced distributional effects on income inequality. Hillbom and Bolt (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) examine the evolution of inequality in colonial and post-colonial Botswana, noting a rise beginning in the 1940s and peaking in the mid-1970s coinciding with the shift from a cattle-based (agriculture) to a diamond-driven economy ( industrial), followed by a subsequent decline since the 1990s. Dartanto et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) investigate the relationship between structural transformation and inequality in Indonesia, employing Theil's L decomposition and panel data analysis of provincial macroeconomic datasets. The study reveals that Indonesia has undergone an agriculture\u0026ndash;service transition before the maturity of the industry sector, with the presence of an Inverted U Kuznet curve. Findings from both static and dynamic decomposition indicate that structural transformation contributing to growing inequality and the increasing share of the service sector to the national gross domestic product has mitigated the growth rate of inequality over the observation period. Kum (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) introduces an updated dataset on inequality in structures of manufacturing pay spanning the years 1963 to 2002, utilizing the standard methods of the University of Texas Inequality Project (UTIP). It further examines these measures in conjunction with evidence on structural change, specifically changes in the shares of agriculture, manufacturing, and services in total employment notable finding is the close association between low inequality and low variability in inequality over time, with the movement out of agriculture correlating with heightened variability in the inequality of manufacturing pay.\u003c/p\u003e \u003cp\u003eOverall, when assessing the reduction of income inequality resulting from the structural change from agriculture to industry, the variables mentioned above are often utilized. However, relying solely on these indicators to measure structural change proves insufficient due to two main reasons. First, the scope of these variables may not fully capture the multifaceted nature of structural transformation. An economy's ability to undergo fundamental transformation is greatly influenced by the manufacturing sector. Some have argued that this industry is a \"growth-enhancing\" one since it draws resources away from less productive pursuits and encourages structural change, an essential component of contemporary economic expansion.(Rodrik, 2016; Yazgan et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Second, relying merely on these factors may not be sufficient to assess their impact on income inequality because they may signal manufacturing sector growth but not necessarily its impact on employment. This dual aspect underscores the limitations of studies that rely entirely on these factors.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes studies on the drivers of income inequality in the United States using the Environmental Kuznets Curve (EKC) framework. The research given in the table is divided into two categories: studies that investigate the EKC hypothesis and those that seek to uncover factors leading to income disparity. We gain a more complex picture of the interplay among industrialization, human capital, economic growth, and income distribution as a result of the varied methodologies and results presented in each study.\u003c/p\u003e \u003cp\u003eTo begin with, studies that deal with the EKC hypothesis, including Kuznet (1955) and Ram (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), shed light on how income disparity and economic growth are related. Unlike Kuznets\u0026rsquo;s study which supports the typical inverted U-shaped Kuznet curve, Ram (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) shows that economic growth reduces inequality, but not in the classic EKC pattern.\u003c/p\u003e \u003cp\u003eSecond, some studies have zeroed in on specific reasons of income inequality (Nielsen and Alderson, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Chevan and Stokes, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). According to Nielsen and Alderson an increase in the manufacturing labor force and income reduces income inequality, while deindustrialization increases it according to Chevan and Stokes.\u003c/p\u003e \u003cp\u003eFrank (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), Goldin and Katz (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and Dincer and Gunalp (2011) provide additional understanding of the elements driving income inequality (2012). Frank shows that human capital does not affect economic growth in the long run, which exacerbates wealth inequality. Unlike Goldin and Katz's arguments Dincer and Gunalp indicate that wealth increases income inequality, and industrialization and human capital both have adverse effects on it. Moreover, Yang and Greaney (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Kollmeyer (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and Lee and Lee (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) enlighten the complex processes of income inequality. Yang and Greaney confirm an N-shaped pattern of income disparity and show that trade significantly reduces it. Lee and Lee (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) indicate that human capital ameliorates income disparity, while Kollmeyer argues that deindustrialization worsens income inequality.\u003c/p\u003e \u003cp\u003eLastly, Zheng et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Hertz and Silva (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) analyze the effects of economic growth and income distribution, respectively. Hertz and Silva argue that income inequality is on the rise regardless of urban or rural areas, with the latter experiencing a more significant acceleration at critical junctures. Zheng et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) states that income inequality is reduced while the economy expands.\u003c/p\u003e \u003cp\u003eIn sum, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e clarifies the United States\u0026rsquo; economic inequality briefly. Numerous aspects should be considered, such as industrialization, human capital, economic growth, and other types of economic growth to comprehend the dynamics of income distribution.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eSummary of Literature on Income Inequality in the USA.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample Period\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethodology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFindings\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKuznet (1955)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1929\u0026ndash;1950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescriptive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInverted U-shaped Kuznet curve is validated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRam (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1991\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1947\u0026ndash;1987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncreasing economic growth causes a decrease in inequality, although the traditional inverted U-shaped Kuznet curve is not valid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNielsen and Alderson. (1997)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1970\u0026ndash;1990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePanel Regression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe increase in the labor force in manufacturing and income contributes to a reduction in income inequality.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChevan and Stokes (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2000\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1970\u0026ndash;1990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescriptive statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeindustrialization causes an increase in income inequality.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGallet and Gallet (2004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1947\u0026ndash;1987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInverted U-shaped Kuznet curve is not valid.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrank (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1945\u0026ndash;2004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAutoregressive Distributive Lag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEconomic growth increases income inequality, and human capital does not affect income inequality in the long run.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoldin and Katz (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1980\u0026ndash;2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescriptive statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman capital erosion leads to a decline in skills and education, contributing to increased income inequality.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDincer and Gunalp (2012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1981\u0026ndash;1997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGeneralized Method of Moments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman capital and industrialization increase Income Inequality while income reduces it.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYang and Greaney (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1960\u0026ndash;2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEngle\u0026ndash;Granger two-step ECM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN-shaped income inequality is confirmed. Trade reduces income inequality.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKollmeyer (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1947\u0026ndash;2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeindustrialization is a key determinant of income inequality. The increase in industrialization exacerbates income inequality.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLee and Lee (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1980\u0026ndash;2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePanel Regression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman capital reduces income inequality.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHertz and Silva (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1975\u0026ndash;2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescriptive statistics and Decomposition method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncome inequality is increasing in both urban and non-urban areas. However, inequality is rising more rapidly in rural areas during certain periods\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZheng et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2016\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEconomic growth reduces income inequality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWallace et al., (2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescriptive Statics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeindustrialization contributes to the deterioration of income distribution\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"3. Data and Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Sample and Description of Data\u003c/h2\u003e \u003cp\u003eTo examine the impact of structural changes on income inequality in the United States, this paper employs a quantile autoregressive distributed lag (QARDL) model over the period from 1965q1 to 2019q4. For robustness checks, we employ two variables related to structural changes: manufacturing value added to GDP (Model 1) and the ratio of employment in manufacturing to total employment (Model 2). Additionally, we incorporated human capital GDP and urbanization variables. In more detail, annual data is collected from the sources listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and then, following Shahbaz et al. (2017), we convert annual data to quarterly data using the quadratic match sum method. This method adjusts for seasonal variations in the data when converting the data from low frequency to high frequency by reducing the point-to-point data variability (Razzaq et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sbia et al., 2014).\u003c/p\u003e \u003cp\u003eIn order to fully understand how manufacturing affects income inequality, it is crucial to look at more than simply the manufacturing value added to the GDP rate. The rate does not provide a comprehensive view of its effects on income distribution. Considering the manufacturing employment rate helps to clarify the relationship between income disparity and employment. Atkinson, Piketty, and Saez (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) find that income inequality must be measured using employment statistics. The distribution of income and employment possibilities across economic sectors is inseparably related to income distribution. Therefore, focusing on the manufacturing employment percentage offers a more direct correlation to income inequality, since it mirrors the actual dynamics of the labor market and the distribution of income. World Bank's World Development Indicators database is one of the main sources of the interaction between manufacturing jobs and income inequality. The effect of manufacturing on income distribution in countries might be investigated by comparing statistics on the proportion of total employment in manufacturing. This gauge and method offers a more comprehensive picture of how manufacturing employment adds to income inequality by considering variations in economic frameworks, labor market regulations, and social policies. According to Autoret al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) technological change and globalization have impacted employment and income distribution in manufacturing differently. The authors argue that income disparity has changed as a result of structural changes that have altered the makeup of industrial jobs. Therefore, in order to get a better picture of how changes in manufacturing affect income distribution, especially considering technological advancements and trade dynamics, it's best to analyze the manufacturing sector's share of total employment. Finally, to fully capture manufacturing's effect on income inequality, it is necessary to supplement the value added to GDP rate with the proportion of total employment in manufacturing.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the variables. Accordingly, Gross Domestic Product (GDP) is the total monetary value of all final goods and services produced inside a nation. The GDP is divided by the population to get the average economic output per person. The Income Inequality Index (or the Gini coefficient) measures how evenly distributed a community's income is. A value of 0 shows full equality and a value of 1 displays perfect inequality. The Income Inequality Index is computed using the Lorenz curve. Human Capital Index's (HCI) evaluates the workforce's health, education, and skill levels and encompasses factors such as life expectancy, number of years spent in school, and test scores. The index is between a value of zero and one. the Urbanization Index (URB) is one way to quantify the population in cities. It is calculated by dividing the total population by the number of individuals residing in urban areas. By determining the proportion of manufacturing value contributed to total GDP and displaying it as a percentage, the SCm index measures the influence of the manufacturing sector on GDP. Lastly, one measure that shows what percentage of the overall workforce is employed in manufacturing is the SCe, which stands for Manufacturing Employment in overall Employment Index. It is expressed as a percentage and is calculated by dividing the total number of employed persons by the number of people working in manufacturing. These indexes provide a holistic view of the societal and economic factors that influence income disparity. Their computation follows standard methods, and they are sourced from credible datasets.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eVariable Source and Description\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSource of data\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGini\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncome Inequality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fred.stlouisfed.org/\u003c/span\u003e\u003cspan address=\"https://fred.stlouisfed.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic Growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGDP per capita (current (2024)USD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldbank.org/\u003c/span\u003e\u003cspan address=\"https://www.worldbank.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH.C.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman Capital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldbank.org/\u003c/span\u003e\u003cspan address=\"https://www.worldbank.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrbanization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldbank.org/\u003c/span\u003e\u003cspan address=\"https://www.worldbank.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSC\u003csub\u003em\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManufacturing value added to GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ourworldindata.org/\u003c/span\u003e\u003cspan address=\"https://ourworldindata.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSC\u003csub\u003ee\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManufacturing employment in total employment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ourworldindata.org/\u003c/span\u003e\u003cspan address=\"https://ourworldindata.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows descriptive statistics. Specifically, the variables Lngini, Lnhc, Lnurb, Lngdp, and Lngdp2 exhibit a positive meaning with a narrow range between its minimum and maximum, accompanied by a low standard deviation, suggesting relative stability. However, it is essential to highlight a distinctive pattern in the variables associated with structural change, wherein the averages are negative, and both minimum and maximum values are similarly negative and proximate. Notably, the Jarque-Bera test further highlights this observation, indicating that these variables deviate from a normal distribution.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eDescriptive Statistics and Unit Root Test\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd.dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJarque-Bera\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eADF Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eADFΔ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLngini\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.960\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-4.508\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLngdp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.322\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-3.427\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLngdp\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.753\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-3.571\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLnhc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.831\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.445\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLnurb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e238.775\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.573\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLnsc\u003csub\u003em\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.434\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.3.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-3.716\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLnsc\u003csub\u003ee\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.206\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-4.017\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: 'a', 'b.' 'c' indicates a significance level of 1%, 5%, and 10%, respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFurthermore, we use the Augmented Dickey-Fuller (ADF) unit root test to identify the stationarity of a time series variable. The outcome of the ADF test shows that Lnhc and Lnurb are stationary at their levels, suggesting a lack of unit root. However, for the remaining variables, stationarity is achieved when taking the first difference. However, for the remaining variables, stationarity is achieved when taking the first difference\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Quantile Autoregressive Distributed Lag (QARDL) Model\u003c/h2\u003e \u003cp\u003eTo investigate the factors influencing income inequality in the United States, we are utilizing the Quantile Autoregressive Disturbed Lag (QARDL) model introduced by Cho et al. in 2015. The QARDL model, an extended version of the ARDL model, has several advantages. Firstly, like the classical ARDL, it provides short and long-term results, but uniquely presents these results according to quantiles. Secondly, it could capture the asymmetric effects of macroeconomic variables. Thirdly, it is less sensitive to outliers (Hashmi et al, 2023). The basic structure of the Autoregressive Distributed Lag (ARDL) model is as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{Gini}_{t}=\\mu\\:+\\sum\\:_{i=1}^{P}{\\omega\\:}_{i}{Gini}_{t-i}+\\sum\\:_{i=0}^{q}{\\varPhi\\:}_{i}{GDP}_{t-i}+\\sum\\:_{i=0}^{r}{\\varOmega\\:}_{i}{GDP}_{t-i}^{2}+\\sum\\:_{i=0}^{s}{\\lambda\\:}_{i}{HC}_{t-i}+\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\sum\\:_{i=0}^{m}{ϴ}_{i}{URB}_{t-i}+\\sum\\:_{i-o}^{n}{\\varPsi\\:}_{i}{SC}_{t-i}+{\\epsilon\\:}_{t}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003e signifies the error (residual) term which is expounded as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Gini}_{t}-E\\left[\\frac{{Gini}_{t}}{{\\gamma\\:}_{t-1}}\\right]\\left[{Gini}_{t}\\left|{\\gamma\\:}_{t-1}\\right|\\right]\\)\u003c/span\u003e\u003c/span\u003ewhere\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:{{\\gamma\\:}}_{\\text{t}-1}\\:\\:\\text{b}\\text{e}\\text{i}\\text{n}\\text{g}\\:\\text{s}\\text{m}\\text{a}\\text{l}\\text{l}\\text{e}\\text{s}\\text{t}\\:{\\sigma\\:}\\:\\text{f}\\text{i}\\text{e}\\text{l}\\text{d}\\:\\text{g}\\text{e}\\text{n}\\text{e}\\text{r}\\text{a}\\text{t}\\text{e}\\text{d}\\:\\text{b}\\text{y}\\:{D}_{t}\\)\u003c/span\u003e\u003c/span\u003e,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{GDP}_{t}\\)\u003c/span\u003e\u003c/span\u003e,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{GDP}_{t}^{2}\\)\u003c/span\u003e\u003c/span\u003e,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{HC}_{t}\\)\u003c/span\u003e\u003c/span\u003e,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{URB}_{t},\\:{SC}_{t},\\:{GİNİ}_{t-1}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{GDP}_{t-1},{GDP}_{t-1}^{2},{HC}_{t-1},{URB}_{t-1},\\:{SC}_{t-1}\\)\u003c/span\u003e\u003c/span\u003e The lag orders p, q, r, s, m, and n are determined based on the Schwarz Information Criteria (SIC). In the above equations (1), GINI represents income inequality, GDP stands for gross domestic product per capita, GDP\u003csup\u003e2\u003c/sup\u003e refers to the square of GDP, H.C. denotes human capital, URB signifies urbanization, and S.C. represents structural change. The augmented form of the basic autoregressive distributed lag (ARDL) model, as presented in Eq.\u0026nbsp;1 above, has been extended to incorporate quantile aspects by Cho et al. in 2015, referred to as Quantile ARDL (QARDL) with parameters (p, q, r, s, m, n). Quantile ARDL model can be expressed as follows:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{Q}_{G{ini}_{t}}=\\mu\\:\\left(\\tau\\:\\right)+\\sum\\:_{i=1}^{P}{\\omega\\:}_{i}\\left(\\tau\\:\\right){Gini}_{t-i}+\\sum\\:_{i=0}^{q}{\\varPhi\\:}_{i}{\\left(\\tau\\:\\right)GDP}_{t-i}+\\sum\\:_{i=0}^{r}{\\varOmega\\:}_{i}\\left(\\tau\\:\\right){GDP}_{t-i}^{2}+\\sum\\:_{i=0}^{s}{\\lambda\\:}_{i}\\left(\\tau\\:\\right){HC}_{t-i}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:+\\sum\\:_{i=0}^{m}{ϴ}_{i}{\\left(\\tau\\:\\right)URB}_{t-i}+\\sum\\:_{i-o}^{n}{\\varPsi\\:}_{i}{\\left(\\tau\\:\\right)SC}_{t-i}+{\\epsilon\\:}_{t}\\left(\\tau\\:\\right)\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{t}\\left(\\tau\\:\\right)={Gini}_{t}-{Q}_{{Gini}_{t}}\\left(\\tau\\:|{\\epsilon\\:}_{t-1}\\right)\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:0\u0026lt;\\tau\\:\u0026lt;1\\)\u003c/span\u003e\u003c/span\u003e represents quantile Additionally, considering the likelihood of serial correlation, the QARDL model, as presented in the above equations (2), is extended as follows:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{Q}_{\\varDelta\\:G{ini}_{t}}=\\mu\\:+\\rho\\:{Gini}_{t-1}+{\\beta\\:}_{GDP}{GDP}_{t-1}+{\\beta\\:}_{{GDP}^{2}}{GDP}_{t-1}^{2}+{\\beta\\:}_{HC}{HC}_{t-1}+{\\vartheta\\:\\beta\\:}_{URB}{URB}_{t-1}\\)\u003c/span\u003e \u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:+{\\beta\\:}_{SC}{SC}_{t-1}+\\sum\\:_{i=1}^{P-1}{\\omega\\:}_{i}\\varDelta\\:{Gini}_{t-i\\:}+\\sum\\:_{i=0}^{q-1}{\\varOmega\\:}_{i}\\varDelta\\:{GDP}_{t-i}+\\sum\\:_{i=0}^{r-1}{\\varOmega\\:}_{i}\\varDelta\\:{GDP}_{t-i}^{2}+\\sum\\:_{i=0}^{s-1}{\\lambda\\:}_{i}\\varDelta\\:{HC}_{t-i}\\:+\\sum\\:_{i-0}^{m}{ϴ}_{i}{\\varDelta\\:URB}_{t-i}\\)\u003c/span\u003e \u003c/span\u003e \u003cdiv id=\"Eque\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:+\\sum\\:_{i=o}^{n}{\\varPsi\\:}_{i}{\\varDelta\\:SC}_{t-i}+{\\epsilon\\:}_{t}\\left(\\tau\\:\\right)\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(3\\right)\\:\\:$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eUtilizing the delta method, the cumulative short-term impact of income inequality is determined by calculating \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{*}=\\sum\\:_{i-1}^{p-1}{\\beta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e, Furthermore, the combined short-term impact of both concurrent and preceding income inequality is captured by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{*}=\\sum\\:_{i-1}^{n-1}{\\beta\\:}_{6}\\)\u003c/span\u003e\u003c/span\u003e. Also, coefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\rho\\:\\:\\)\u003c/span\u003e\u003c/span\u003eis the speed of adjustment must be negative and significant in Eq.\u0026nbsp;(3) (Cho et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Empirical Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Pre- Estimation test Results\u003c/h2\u003e \u003cp\u003eThe outcomes of the Jarque-Bera test, indicating a departure from normal distribution in the variables, prompted an investigation into the linearity of these variables. To further assess the characteristics of the variables, we applied the Broock, Dechert, and Scheinkman (BDS,1996) test, revealing non-linear behaviors in all variables (refer to Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for details).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBDS test Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003em\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003em\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003em\u0026thinsp;=\u0026thinsp;4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003em\u0026thinsp;=\u0026thinsp;5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003em\u0026thinsp;=\u0026thinsp;6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLngini\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e75.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e83.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e95.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLngdp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e66.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e73.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLngdp\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e78.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e89.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLnhc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e57.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e65.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLnurb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e81.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e91.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e104.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLnsc\u003csub\u003em\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e83.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e93.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e106.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLnsc\u003csub\u003ee\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e85.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e95.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e109.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNext, before applying the Quantile Autoregressive Distributed Lag (QARDL) model, it is crucial to conduct quantile unit root tests to assess the stationary properties of the variables. The rationale for choosing quantile unit root tests over conventional ones such as Augmented Dickey Fuller, Phillips, and Perron lies in the non-normal distribution of the data. To reduce the possibility of biased outcomes, it is best to use quantile unit root approaches, which provide more robust inference\u0026mdash;particularly when working with data that does not follow a normal distribution (Koenker and Xiao, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Human Capital (H.C.) is stationary at all quantiles, according to Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, but urbanization is stationary at some quantiles and, for all other variables, it's the first difference. The data shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e above are in accord with this outcome.\u003c/p\u003e \u003cp\u003eAccording to these findings, urbanization is stable at certain quantiles, but human capital is stationary across all quantiles, suggesting a steady long-term relationship. However, after differencing, other variables display stationarity. This verification of stationary qualities is critical for further QARDL estimations since it ensures the model's trustworthiness in capturing the relationships between variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eQuantile Unit root test results\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"17\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eQuantiles (τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eGINI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eURB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c14\" namest=\"c12\"\u003e \u003cp\u003eSC\u003csub\u003em\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c17\" namest=\"c15\"\u003e \u003cp\u003eSC\u003csub\u003ee\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eα (τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003et-stats\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eα (τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et-stats\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eα (τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003et-stats\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eα (τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003et-stats\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eα (τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003et-stats\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003eα (τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003et-stats\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.996\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-2.230\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.996\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-15.553\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-1.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.994\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-7.347\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.993\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-4.886\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-1.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.990\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-12.597\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.988\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-4.338\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-1.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-1.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.990\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-13.475\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-1.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.989\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-34.799\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-2.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.989\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-29.999\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-1.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.988\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-20.738\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-1.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.988\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-18.262\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.980\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-3.467\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.989\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-17.770\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-3.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-1.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.988\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-7.685\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"17\"\u003eNote: Coefficients and t-stats in bold and rest represent I(0) and I(1), respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.2. QARDL Results\u003c/h2\u003e \u003cp\u003eInvestigating the causes of income inequality in the United States is the primary goal of this study. Quantile Autoregressive Distributed Lag (QARDL) is the model we utilize to do this. Economic growth, urbanization, human capital, structural changes, and other independent factors are examined in separate models in our analysis. The QARDL model is useful because it allows us to study correlations across multiple quantiles of the dependent variables, examining both short-term and long-term dynamics (Hashmi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs previously stated, our model, targeted at finding factors of income inequality, integrates two distinct datasets to quantify structural change: manufacturing value added to GDP (SCm) (model 1) and the ratio of manufacturing employment to total employment (SCe) (model 2). Consequently, we present the results of two QARDL analyses in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e depicts the long-run and short-run effects of quantile ARDL results, with the income inequality (GINI) index serving as the dependent variable. Meanwhile, human capital (HC), urbanization (URB), and structural change (SC\u003csub\u003em\u003c/sub\u003e) are employed as explanatory variables (model 1). Statistically, the p-value and the speed of adjustment parameter should be negative and significant (Anwar et al.,2021). As seen in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, except for the 0.30 quantile, all quantiles exhibit a negative and significant speed of adjustment, providing supporting evidence for the accuracy of our model. In the long run, the relationship between GDP and income inequality, as well as GDP squared and income inequality, is significant across all quantiles. Moreover, these relationships are positively and negatively oriented, respectively. This implies that the inverted U-shape is confirmed. Human capital is significant in all quantiles and negative signs show that human capital reduces income inequality in the USA. Urbanization is significant across quantiles, with a negative sign at the 0.05 quantile and positive signs at other quantiles. This suggests that, overall, urbanization increases income inequality in the majority of quantiles.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eResults of Quantile autoregressive distributed lag (QARDL) for model 1\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuantiles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eα(τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eρ(τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ\u003csub\u003eGDP\u003c/sub\u003e(τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ\u003csub\u003eGDP\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e(τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ\u003csub\u003eHC\u003c/sub\u003e(τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eβ\u003csub\u003eURB\u003c/sub\u003e(τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eβ\u003csub\u003eSCm\u003c/sub\u003e(τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eω (τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eΦ\u003csub\u003e1\u003c/sub\u003e (τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eΩ\u003csub\u003e0\u003c/sub\u003e (τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eλ\u003csub\u003e0\u003c/sub\u003e(τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eϴ\u003csub\u003e0\u003c/sub\u003e (τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eΨ\u003csub\u003e0\u003c/sub\u003e(τ)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.737\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.087\u003csup\u003eb (\u003c/sup\u003e0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.855\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.035 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.511\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.985 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.065 (0.226)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.583 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.602 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.131\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-4.473\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.868 (0.298)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.125\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0.10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.863\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.079\u003csup\u003eb\u003c/sup\u003e (0.060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.852 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.035 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.538\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.120 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.036 (0.226)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.586 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.351 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.118\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-4.200\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.093 (0.896)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.116\u003csup\u003ea\u003c/sup\u003e (0.007)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.132\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.060\u003csup\u003eb\u003c/sup\u003e (0.082)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.965 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.042\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.658\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.274 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.091\u003csup\u003eb\u003c/sup\u003e (0.036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.436\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.509 (0.332)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.025 (0.324)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-1.920\u003csup\u003eb\u003c/sup\u003e (0.040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.519 (0.507)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.036 (0.334)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0.30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.211\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.034 (0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.064 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.047 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.818\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.268\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.135\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.478\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.469 (0.269)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.020 (0.318)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-1.704\u003csup\u003ea\u003c/sup\u003e (0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.277 (0.628)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.033 (0.363)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0.40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.015\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.052\u003csup\u003ea\u003c/sup\u003e (0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.943 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.041 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.601\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.183 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.090 \u003csup\u003eb\u003c/sup\u003e (0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.502 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.576 (0.125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.026 (0.166)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-1.643\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.028 (0.140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.010 (0.762)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.932\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.053\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.902\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.039\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.451\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.126\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.075\u003csup\u003eb\u003c/sup\u003e (0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.500 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.343 (0.385)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.014 (0.439)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-1.099 (0.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.108 (0.110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.003 (0.991)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0.60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.639\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.052\u003csup\u003ea\u003c/sup\u003e (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.807\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.033\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.445\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.952\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.053 (0.143)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.505 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.345 (0.224)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.014 (0.311)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-1.043\u003csup\u003eb\u003c/sup\u003e (0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.367\u003csup\u003eb\u003c/sup\u003e (0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.015 (0.678)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0.70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.781\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.060\u003csup\u003eb\u003c/sup\u003e (0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.818\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.034\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.523\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.096\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.022 (0.677)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.518\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000 (0.999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.003\u003csup\u003ea\u003c/sup\u003e (0.845)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.602 (0.169)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.360\u003csup\u003eb\u003c/sup\u003e (0.079)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.041 (0.513)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0.80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.252\u003csup\u003ea\u003c/sup\u003e (0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.090\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.071\u003csup\u003eb\u003c/sup\u003e (0.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.045\u003csup\u003eb\u003c/sup\u003e (0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.879\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.331\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003 (0.978)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.565\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.020 (0.949)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.005 (0.764)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.632\u003csup\u003ec\u003c/sup\u003e (0.096)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.346\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.103\u003csup\u003ea\u003c/sup\u003e (0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0.90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.521\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.114\u003csup\u003ea\u003c/sup\u003e (0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.340\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.058\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.430\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.377\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.100 (0.163)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.542\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.586 (0.219)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.034 (0.170)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.328 (0.526)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.755\u003csup\u003ea\u003c/sup\u003e (0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.108\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0.95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.221\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.146\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.315\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.057\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.458\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.118\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.137\u003csup\u003eb\u003c/sup\u003e (0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.689\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.088 (0.140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.056 (0.123)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.518\u003csup\u003ec\u003c/sup\u003e (0.080)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4.132\u003csup\u003ea\u003c/sup\u003e (0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.062 (0.241)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003eNote: 'a', 'b.' 'c' indicates a significance level of 1%, 5%, and 10%, respectively. We displayed only 11 out of 19 quantiles due to space constraints, and a full table report will be provided by the authors upon request.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe short-run dynamics depict an inverted U-shaped relationship between income inequality and economic growth, confirmed only for the lowest quantile (0.05\u0026ndash;0.10), implying that the impact of income inequality on economic growth is most pronounced for the lowest income groups. The impact of human capital on income inequality is significant within the quantiles of 0.05 to 0.60, as well as at 0.80 and 0.95. Among the significant quantiles, human capital exhibits a positive effect only at the 0.95 quantiles while demonstrating a negative impact on income inequality at other significant quantiles. When examining the urbanization variable, it is observed that it is significant and positively affects income inequality in the 0.60 to 0.95 quantiles. This indicates an increase in inequality within this range. Structural change has a weaker impact on income inequality in the short run compared to the long run. In the quantiles 0.05\u0026ndash;0.10 and 0.80\u0026ndash;0.95, there is only a statistically significant and inequality-reducing effect.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e introduces a refinement to the model presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e by employing the variable \"structural change\" differently. In this iteration, the focus is on employment in manufacturing as a percentage of total employment, enhancing the robustness of our analysis. As mentioned earlier, it is expected that, statistically, the p-value and the speed of adjustment parameter should be negative and significant. In this context, the same has been achieved here, with a negative sign and significance observed in all quantiles. In the long run, similar to Model 1, Model 2 consistently confirms an inverted U-shaped Kuznets curve across all quantiles, as indicated by the positive coefficient for GDP and the negative coefficient for GDP squared. In Model 2, the influence of human capital is consistently positive and significant across all quantiles, implying a uniformly positive effect on reducing income inequality. Urbanization, in this case, consistently exhibits a positive effect in all quantiles, indicating an overall increase in income inequality. Structural change has a consistent negative impact on reducing income inequality across most quantiles (0.20\u0026ndash;0.95). When compared to Model 1, the results suggest that the structural change variable has a more pronounced effect in the current context. In the short run, from the 0.05 to 0.20 and 0.95 quantiles, GDP exhibits a positive effect, while GDP squared shows a negative and statistically significant impact. This pattern suggests the presence of an inverted U-shaped Kuznets curve in these quantiles. Specifically, it indicates that GDP initially contributes to an increase in income inequality, which is then reversed, demonstrating the dynamic effects of economic growth on income distribution within these quantiles. Human capital has a negative effect, implying that it reduces income disparity, particularly within the 0.05 to 0.70 quantiles, which encompass the low and middle quantiles.. This suggests that human capital exerts a beneficial impact on improving income equality within these quantiles. Urbanization not only sustains its impact in the long run but also exhibits a consistent effect in the short run. Specifically, within the quantiles ranging from 0.20,0.50 and from 0.60 to 0.95, urbanization demonstrates a significant and positive effect, indicating a substantial increase in income inequalities. This implies that, over time and across various quantiles, urbanization consistently contributes to a noteworthy escalation in income inequality. Finally, structural change significantly and consistently reduces income inequality across quantiles, ranging from 0.20 to 0.95, in a manner akin to its impact in the long run.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eResults of Quantile autoregressive distributed lag (QARDL) for model 2\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuantiles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eα(τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eρ(τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ\u003csub\u003eGDP\u003c/sub\u003e(τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ\u003csub\u003eGDP\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e(τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ\u003csub\u003eHC\u003c/sub\u003e(τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eβ\u003csub\u003eURB\u003c/sub\u003e(τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eβ\u003csub\u003eSCe\u003c/sub\u003e(τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eω (τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eΦ\u003csub\u003e1\u003c/sub\u003e (τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eΩ\u003csub\u003e0\u003c/sub\u003e (τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eλ\u003csub\u003e0\u003c/sub\u003e(τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eϴ\u003csub\u003e0\u003c/sub\u003e (τ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eΨ\u003csub\u003e0\u003c/sub\u003e(τ)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.027\u003csup\u003eb\u003c/sup\u003e 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align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.782\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.031\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.544\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.971\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.021 (0.343)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.521\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.290 (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.063\u003csup\u003ea\u003c/sup\u003e (0.005)\u003c/p\u003e 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(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.467 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.467\u003csup\u003ec\u003c/sup\u003e(0.078)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.022 (0.122)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-1.512\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.126 \u003csup\u003ec\u003c/sup\u003e (0.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.044 (0.192)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0.60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.752\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.043\u003csup\u003ea\u003c/sup\u003e (0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.949 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.041 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.580\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.934 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.077 \u003csup\u003ea\u003c/sup\u003e (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.479\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.385 (0.266)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.015 (0.352)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-1.212\u003csup\u003eb\u003c/sup\u003e (0.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.973\u003csup\u003ec\u003c/sup\u003e (0.092)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.077\u003csup\u003ec\u003c/sup\u003e (0.061)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0.70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.030\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.058\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.101\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.048\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.892\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.090\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.095\u003csup\u003ea\u003c/sup\u003e (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.501\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.205 (0.478)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.006 (0.669)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-1.049\u003csup\u003ea\u003c/sup\u003e (0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.405\u003csup\u003ea\u003c/sup\u003e (0.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.086\u003csup\u003eb\u003c/sup\u003e (0.071)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0.80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.859\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.077\u003csup\u003ea\u003c/sup\u003e (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.519 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.068 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.470\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.520 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.091\u003csup\u003eb\u003c/sup\u003e (0.037)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.538 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.070 (0.866)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.008 (0.735)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.843 (0.122)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.636\u003csup\u003eb\u003c/sup\u003e (0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.132\u003csup\u003eb\u003c/sup\u003e (0.037)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0.90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.053\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.119\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.671\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.076\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.701\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.579\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.119\u003csup\u003ea\u003c/sup\u003e (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.626\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.554 (0.231)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.034 (0.142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.394 (0.497)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.216\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.014\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0.95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.316\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.123\u003csup\u003ea\u003c/sup\u003e (0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.958 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.091 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.098\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.581 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.213 \u003csup\u003ea\u003c/sup\u003e (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.696 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.462\u003csup\u003eb\u003c/sup\u003e (0.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.081\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.550 (0.526)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.679 \u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.131\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003eNote: 'a', 'b.' 'c' indicates a significance level of 1%, 5%, and 10%, respectively. We displayed only 11 out of 19 quantiles due to space constraints, and a full table report would be provided by authors upon request.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs a result, both Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e explore the long-run and short-run effects of quantile ARDL results, using the income inequality (GINI) index as the dependent variable. In both models, human capital (H.C.), urbanization (URB), and structural change (S.C.) serve as explanatory variables, providing insights into their respective influences on income inequality across different quantiles.\u003c/p\u003e \u003cp\u003eIn terms of similarities, both models validate the presence of an inverted U-shaped Kuznets curve. The consistent significance of the relationship between GDP and income inequality across all quantiles, characterized by positive and negative coefficients for GDP and GDP squared, respectively, aligns with findings from earlier studies, including Kuznet (1955), Nielsen and Alderson (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). This suggests that income inequality increases at the outset of economic growth, reaches a peak, and then declines when a more balanced distribution of income is the consequence of further economic progress. This trend illustrates the characteristics of developing economies throughout their transition to industrialization, when disparities in wealth tend to expand. Nonetheless, progress and expansion lead to a more distributed and fair wealth in the long run. Further, in both models, human capital has a negative effect on income disparity, suggesting that income equality is generally on the rise. This highlights the critical role that education and skill development play in reducing economic inequality. From a monetary point of view, investing in people has three main effects: increasing labor productivity, fostering innovation, and creating a workforce with advanced skills that can demand greater wages. Because of this, the income gap is narrowing. These findings are in line with those of Molla (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Suhendra et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which provide more evidence that education and skill development are important ways to achieve a more equitable distribution of income. The impacts of structural change, however, varied significantly. Using a different technique and concentrating on manufacturing jobs, Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e confirms a more dramatic effect than Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, which shows a negative impact on income inequality in most quantiles. Although structural change helps reducing income inequality, the exact mechanism by which it operates may differ in its efficacy. Focusing on manufacturing employment provides a more direct connection to income inequality and the job market from an economic perspective, since it precisely reflects the available job opportunities and revenue streams within the industry. Hence policies on increasing manufacturing employment, rather than merely increasing manufacturing output, may be more effective in reducing income disparity. Consistent with the literature, both tables suggest strong evidence of long-run dynamics and showing that structural change considerably reduces income disparity across a variety of quantiles. For instance, Suhendra et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Molla (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) demonstrate that human capital has a substantial and detrimental impact on income disparity. This long-term finding points to the importance of structural changes in the economy on income distribution, such as the shift from agriculture to industry and services. There would be less income inequality as a result of new jobs and opportunities created as a result of economic growth and diversification. Hence, our findings and the literature are in line with the claim that human capital consistently exhibits a negative impact on income inequality.\u003c/p\u003e \u003cp\u003eMoreover, focusing on manufacturing value added to the GDP rate alone may not capture the full extent of its impact on income inequality. While an increase in manufacturing value added to the GDP rate may signify growth in the sector, it does not necessarily guarantee equitable distribution of the generated income. Manufacturing-led economic expansion can result in increased GDP, but its benefits may not be distributed evenly across all groups, potentially worsening income inequality if the advantages are concentrated among a few. In contrast, examining the percentage of total employment in manufacturing provides a more direct link to the labor market and income inequality, as it reflects the actual employment opportunities and income sources within the sector. Since this strategy targets the distribution of income through employment, it follows that policies that encourage job creation in manufacturing are more likely to effectively reduce income disparity.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents the results of Wald tests, which examine asymmetric effects in both short- and long-run scenarios. In general, the speed of adjustment parameter (ρ) is found to be significant across all situations. Furthermore, significant long-run asymmetric relationships are identified in both models, particularly evident in variables such as (GINI-GDP- GDP\u003csup\u003e2\u003c/sup\u003e HC, URB, and SCe, and SCm), where the null hypothesis of parameter constancy is rejected across all quantiles. However, in the short run, relationships among variables show a different picture in the case of model 1; the outcome confirms that null hypotheses related to parameter constancy were rejected. In the case of the second model, the asymmetric relationship is significant only for economic growth and human capital. These findings enable us to conclude that there is an asymmetric relationship on only in the long run between Gini inequality, economic growth urbanization, human capital, and structural change in the United States. Additionally, the results highlight the prevalence of asymmetrical relationships, particularly in the long term, between Gini inequality, economic development, urbanization, human capital, and structural change in the United States. Understanding and addressing these imbalances is crucial for developing effective methods to reduce income disparity and promote sustainable economic growth, as the enduring consequences show.\u003c/p\u003e \u003cp\u003eFurther examination and discussion are required, particularly in relation to policy development, given the significant and far-reaching consequences of the unequal long-term impacts. These findings highlight the need for policies that seek to increase economic growth, foster human capital development, and execute structural reforms to consider the uneven effects on income disparities over the long run. For example, there could be varying long-term impacts on income distribution resulting from efforts to enhance workers' education and training, as well as from initiatives that drive fundamental changes in the economy. This highlights the significance of thorough planning and precise implementation of policies. While short-term solutions may address immediate disparities, long-term strategies that take these persistent inequities into account are necessary for equitable and sustainable economic growth. To get to the bottom of what causes these long-term imbalances, more research is required. Furthermore, it is critical to assess how well specific policies mitigate unfavorable effects and foster positive outcomes. The need of addressing long-term policy solutions is highlighted by the long-term asymmetries that have been discovered between Gini inequality, economic growth, urbanization, human capital, and structural change. In order to achieve comprehensive and equitable economic growth, the United States must address these unequal relationships.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eResults of the Wald Test for The Constancy of Parameters for Both Models\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWald-statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald-statistics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eρ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.373 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eρ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.188\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ\u003csub\u003eGDP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.203 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003csub\u003eGDP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.288\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ\u003csub\u003eGDP\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.577 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003csub\u003eGDP\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.773\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ\u003csub\u003eHC\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135.091 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003csub\u003eHC\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e164.190a (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ\u003csub\u003eURB\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113.296 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003csub\u003eURB\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108.267a (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ\u003csub\u003eSCm\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.727 (0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003csub\u003eSCe\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.699\u003csup\u003ea\u003c/sup\u003e (0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egini\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.706 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003egini\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.562\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΦ\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.755 (0.385)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eΦ\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.130\u003csup\u003ea\u003c/sup\u003e (0.007)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΩ\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.599 (0.439)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eΩ\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.402 (0.121)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλ\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.386 (0.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eλ\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.586\u003csup\u003ea\u003c/sup\u003e (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eϴ\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000 (0.999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eϴ\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.708 (0.192)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΨ\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.570 (0.110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eΨ\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.734\u003csup\u003eb\u003c/sup\u003e (0.053)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo summarize, the results show that structural change factors, which represent the growth of the manufacturing sector, reduce income inequality. This is indicated by the presence of an inverted U-shaped Kuznets curve. Importantly, by using many structural change variables, the research becomes more robust and offers a more comprehensive picture of how it affects income distribution. In line with earlier studies, human capital always has a negative effect on income disparity. Yet, urbanization has contradictory consequences; it widens income gaps at the top quantiles while narrowing them at the bottom. Gross domestic product (GDP) shows an inverse U-shaped relationship with income inequality, indicating that income distribution is dynamic and subject to change over time.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn the 1970s and 1980s, deindustrialization caused significant structural changes in the US. This shift contributed to the widening income disparity by reducing manufacturing employment, which hit the manufacturing sector more than other sectors. The objective of this study is to examine the relationship between income inequality, urbanization, human capital, and economic growth from 1965q1 to 2019q4 using a novel method called Quantile ARDL. We include manufacturing value added to GDP and manufacturing employment as a percentage of total employment as factors to strengthen the analysis. This method is chosen because the widely used variable of manufacturing value added in the literature might not provide a complete picture of its effect on income distribution when used alone. Understanding the correlation between income inequality and the labor market necessitates looking at the manufacturing sector's share of the overall workforce.\u003c/p\u003e \u003cp\u003eIncome inequality follows an inverted U-shaped pattern as the manufacturing sector grows. Showing that systemic reforms help bring about a decrease in income disparity. Urbanization has conflicting impacts: in some regions it widens income inequality, while narrowing it in other regions. Human capital tends to decrease income disparity. These findings support previous research and show the complicated form of structural changes in relation to income disparity. To illustrate, Autor, Dorn, and Hanson (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) show that employment losses in manufacturing have had a negative influence on local labor markets, especially for individuals with lower levels of education. The results indicate a widening gap between the incomes of highly talented and unskilled individuals and an increase in overall income inequality.\u003c/p\u003e \u003cp\u003eThere has been a dramatic change in the distribution of income since the 1970s due to the transition from manufacturing to the service sector in the US and the outmigration of industrial companies to countries like China that have cheaper labor costs. Governments and policymakers might address this by considering several policy options. In order to foster innovation and stimulate expansion in domestic manufacturing, policymakers could consider offering tax incentives and various forms of financial assistance. Investments in advanced manufacturing technology and incentives for R\u0026amp;D are commonly associated with a high level of expertise and a focus on professionalism. The results of Acemoglu and Autor (2011) are in line with these incentives. Another way to encourage innovation and make the US manufacturing sector more competitive is to improve cooperation between public and private organizations. Collaborations can greatly enhance technological progress by pooling together resources and expertise. It would be beneficial for individuals to enhance their readiness for careers in high-tech manufacturing industries by ensuring that STEM education is more accessible at all levels of schooling. As per Chetty et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), education plays a crucial role in addressing income inequality and fostering economic mobility. One way to prepare individuals for success in emerging industrial roles is by developing specialized vocational training programs that are tailored to the specific requirements of these industries. Furthermore, workers will be able to adapt to new technology and shifting job demands if lifelong learning efforts are promoted. To reduce the abuse of cheap foreign labor, trade agreements should include environmental standards and labor standards. Domestic workers' rights can be protected, and fair competition ensured by these agreements. A more just and equitable competitive environment can be achieved by enforcing laws that require foreign producers to adhere to fair labor standards. One effective approach to mitigate the negative impacts of outsourcing is to promote the adoption of ethical sourcing practices by companies. Investing in modern transportation, communication networks, and energy systems can greatly enhance the efficiency and competitiveness of domestic manufacturing. To facilitate intricate manufacturing processes, enhancing infrastructure is crucial. Domestic manufacturing becomes increasingly feasible with the implementation of intelligent manufacturing technologies such as data analytics and automation. These technologies increase productivity while decreasing production costs. Similarly, a progressive tax system can help reduce inequality by redistributing income and funding social programs. Education and social safety nets can be financed by the revenue generated by progressive taxes. Further, people affected by economic changes can find a way to become financially stable, which reduces poverty and income fluctuations, by looking at Universal Basic Income (UBI) or other types of direct income assistance. Financial security can be improved by advocating for reasonably priced healthcare and housing. In order to achieve goals related to reducing income disparity and encouraging economic growth, it may be helpful to continuously perform research to evaluate the effectiveness of current policies.\u003c/p\u003e \u003cp\u003eFor a comprehensive analysis of how policies impact income inequality and economic mobility over time, future studies could consider employing longitudinal approaches. Furthermore, exploring the impact of emerging technologies such as AI and automation on income inequality and the labor market.By implementing these practical options and pursuing additional study, policymakers can work towards developing a more robust and fair economy. Given the current economic climate, this will promote inclusive growth and help address income inequality.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAs the sole author of this manuscript, Cumali Marangoz was responsible for all aspects of the study, including the conceptualization, data collection, analysis, and writing of the main manuscript text.Cumali Marangoz also prepared all figures and tables, and reviewed the manuscript in its entirety.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdullah, A. J., Doucouliagos, H., \u0026amp; Manning, E. (2013). Does Education Reduce Income Inequality? 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Is there a Kuznets curve in China\u0026apos;s rural Area?\u0026mdash;An empirical analysis on provincial panel data. \u003cem\u003eModern Economy\u003c/em\u003e, \u003cem\u003e07\u003c/em\u003e(04), 391\u0026ndash;398. https://doi.org/10.4236/me.2016.74042\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-journal-of-economic-inequality","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joei","sideBox":"Learn more about [The Journal of Economic Inequality](http://link.springer.com/journal/10888)","snPcode":"10888","submissionUrl":"https://submission.nature.com/new-submission/10888/3","title":"The Journal of Economic Inequality","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Manufacturing Sector Dynamics, Structural Change, Income Inequality, Kuznet Curve, Quantile ARDL Modeling","lastPublishedDoi":"10.21203/rs.3.rs-4797904/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4797904/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study attempts to examine the dynamics of the U.S. manufacturing sector and income inequality using a Quantile Autoregressive Distributed Lag (QARDL) model from 1965q1 to 2019q4. We analyze the impact of urbanization, economic growth, human capital, manufacturing, and human capital on income distribution. An inverted U-shaped Kuznets curve indicates that as the manufacturing sector progresses the income disparity diminishes. Limiting our analysis to the industrial value contributed to GDP might cause a harder discern the overall effect on income inequality. A higher rate of GDP might not guarantee equal incomes, which may show an increase in certain sectors. Looking at the manufacturing sector's share of total employment, which reflects both employment opportunities and revenue sources, allows more direct reflection of income disparity. This study contributes to the literature by enhancing our comprehension of the intricate processes of income distribution, offering valuable insights for policymakers and researchers to better tackle income inequality.\u003c/p\u003e","manuscriptTitle":"Examining the Role of Structural Change in Income Inequality: Insights from Quantile ARDL Modeling in the United States","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-26 12:36:52","doi":"10.21203/rs.3.rs-4797904/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-04T16:12:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-31T07:33:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"91643866206322946257048963877191201438","date":"2024-08-02T16:25:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"339953023000008820073867339182288407281","date":"2024-08-02T10:53:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-02T09:29:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-30T07:31:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-30T07:30:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Journal of Economic Inequality","date":"2024-07-24T22:14:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-journal-of-economic-inequality","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joei","sideBox":"Learn more about [The Journal of Economic Inequality](http://link.springer.com/journal/10888)","snPcode":"10888","submissionUrl":"https://submission.nature.com/new-submission/10888/3","title":"The Journal of Economic Inequality","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"89323f44-cbdc-4658-8a8b-760a51dbed1f","owner":[],"postedDate":"August 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-15T11:54:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-26 12:36:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4797904","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4797904","identity":"rs-4797904","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}