Is the Technology-Oriented Kuznets Curve Hypothesis Valid in Türkiye? An Assessment in the Context of SDG-10

Abstract

Technological advancements around the world have generated important discussions about their impact on income distribution, the type of economic growth, and social welfare. These improvements are critical for both economic development and social inequality in developing countries such as Türkiye. The paper examines the long-run impact of technological innovation on income inequality (IEQ) in Türkiye by testing the Technological Kuznets Curve (TKC) hypothesis. The model uses data from 1990 to 2021 and represents IEQ by the Gini coefficient, technological innovation by patent applications, along with public expenditures used as control variables. The findings of the Fourier ADL cointegration test support the validity of the TKC hypothesis for Türkiye. This suggests that technological innovation increases inequality until the critical turning point in 2008, when the threshold number of 2015 patent applications was exceeded. Using Fourier FMOLS, DOLS, and CCR methods for robustness checks, the main results show public expenditures as a significant factor stabilizing long-term income dynamics. These results imply that growth strategies in the area of technology development should not merely favor innovation but also include measures to increase social welfare in Türkiye. This requires not just the stabilizing role of public spending, but technological growth supported by investment in education, skills, and social welfare.

1. Introduction

The effects of technological innovation on economic growth have long been debated in the economics literature. At the beginning of the 20th century, Kondratiev argued that technological breakthroughs are the cause of long-run economic cycles [1], and Solow [2] proved that technological progress is responsible for output growth that cannot be explained by capital and labor productivity. In contrast, Lucas [3] and Romer [4] argued that technology promotes long-run growth through the diffusion of knowledge (spillover effects), which is not only an economic phenomenon but also improves social welfare, ranging from education to health, infrastructure to income distribution. However, it is also emphasized that technological progress does not reduce income inequality. Some studies argue that technology worsens income inequality (IEQ) by widening the gap between skilled and unskilled labor in terms of its effects on labor productivity and wage structure [5,6,7,8]. This has led to a debate in the economic literature on the Kuznets Curve and whether economic growth leads to income inequality.

Kuznets [9] argued that higher income levels and economic growth will lead to higher inequality in the early stages of industrialization and economic growth, while a long-run inverse U-shaped relationship in IEQ follows as a country develops and approaches a steady state of growth. The Kuznets Curve (KC), which is widely accepted in the literature, has shaped the development literature for many years and encouraged other studies. However, it is insufficient to explain the new income distribution dynamics created by technological change. Therefore, the Technological Kuznets Curve (TKC) hypothesis was developed to understand the time-varying effects of technological developments on IEQ [10].

TKC refers to an approach that highlights that the effects of technological advancement on IEQ are not static but dynamic over time [11]. Technological innovations in this framework can thus temporarily exacerbate income inequalities because they confer a competitive edge on high-skilled labor and capital-intensive industries. But over time, this asymmetry tended to diminish as technology became more widely distributed throughout the economy, workers learned new skills, and public policies intervened [12]. As the widespread effects of technology modify not just growth but income distribution too, the TKC approach stresses the need to address technological transformation processes from a social justice perspective [13]. Here, it is argued that the two sides of the TKC (technology and knowledge) interact, in the sense that the increase in technological innovation increases productivity and incomes; however, when the initialization terms have an exclusive nature, without access mechanisms, inequality is left behind [14]. Accordingly, based on the literature on knowledge spillovers and skill-biased technological change (SBTC), it is argued that while technological innovations support productivity and income growth, these gains can be unequally distributed. While inequality can increase without inclusive mechanisms for access to technology, technology can be a powerful tool for reducing inequality when supported by public policies such as skills development, social support, and digital inclusion [8]. So, it is argued that developed countries and developing countries have to think of innovative areas, and social and economic components of policy in order to reduce inequality alongside innovative components. In this framework, the TKC approach brings to the agenda not only the economic impacts of technological development, but also the social inclusiveness of technological transformation, in line with Sustainable Development Goals (SDGs), one of the global goals for reducing inequalities.

The SDGs are a set of global goals created by the United Nations. They aim to make development more inclusive and fair. The programme has 17 targets to achieve by 2030. One of these targets is SDG-10, entitled “Reducing Inequalities”, which includes targets to reduce inequalities in income, opportunity, and access between and within nations. The subcomponents of this goal include SDG-10.1, which states that the income growth rate of the bottom 40 percent of society should be higher than the average income growth rate and SDG-10.2, which aims at promoting equal opportunities for all individuals to participate in social life, increasing their economic, social and political inclusion. In line with the TKC hypothesis, the extent to which the opportunities created by technological innovations can be utilized (inclusiveness and efficiency) is directly related to the targets of SDG-10. In this context, it is crucial that public policies are shaped in line with SDG-10 targets so that technology can be a tool that balances inequality rather than deepens it. At this point, the TKC hypothesis, which argues that the impact of technological development on inequality can change over time and can be reversed when guided by appropriate policies, provides a powerful theoretical framework to explain this process.

The TKC hypothesis suggests that the impact of technological progress on IEQ may change over time, and that this relationship may follow an inverted U-shaped course, initially increasing inequality, but after a certain threshold, it may produce decreasing effects. This dynamic structure emphasizes that the impact of economic growth on inequality is not static, and that the direction of this process largely depends on the quality of public policies. In this respect, SDG-10 (Reducing Inequalities) has a strong parallelism with the TKC hypothesis. In fact, growth itself is not the only way to reduce inequality; redistributing growth across all segments of society is also key to reducing inequality. While technological advances may initially increase inequalities by favoring those who can offer highly skilled labor and have wealth in the form of capital, such effects can be avoided in the long run through targeted policy action. In realizing this balance, directing public spending towards education, skills development, and social protection in line with SDG-10, which aims to reduce income inequality, plays a critical role [15]. Furthermore, expanding financial access, making digital transformation policies inclusive, and integrating low-income groups into innovation processes will ensure that technology creates opportunities for the whole of society, not just for capital owners and the highly skilled [16]. Thus, the increase in welfare generated by technological progress can become a stabilizing factor rather than deepening income inequality. In this context, growth policies need to be shaped by technological transformation processes that prioritize not only economic development but also social justice. The success of this transformation depends on the orientation of inclusive public spending. Therefore, technological progress needs to be considered not only in the context of growth but also in the context of SDG-10 by emphasizing the stabilizing role of public spending. In line with this perspective, this study is primarily related to SDG-10, namely “Reducing Inequalities” and aims to empirically examine whether technological innovations in Türkiye follow a course in line with the TKC hypothesis, as well as to assess the stabilizing role of public expenditures.

The approach to the relationship between growth and inequality developed by Kuznets in 1955 has been updated several times in the intervening years, adapted to different dimensions, and reinterpreted in the light of various social, economic, and technological transformations. In particular, in the 2020s, when digitalization, automation, and the transition to a knowledge-based economy are accelerating, the TKC hypothesis, developed to explain the impact of this transformation on income distribution, offers a dynamic and policy-interactive framework that goes beyond the classical model. In this context, testing the validity of the TKC hypothesis for Türkiye, which is a G20 country and continues its full membership negotiations with the European Union, is extremely important not only at the theoretical level but also in terms of guiding development policies. Although the Turkish economy has periodically regained growth momentum, this has largely been due to cyclical fluctuations, and it still lags behind advanced economies in the widespread and equitable use of technology at the societal level. Depending on the sophistication of technology and the extent to which it is used in society, this can exacerbate income inequality. Analyzing the effectiveness of public spending in this context, while testing the TKC hypothesis in the case of Türkiye, contributes to the development of a policy perspective that is directly in line with SDG-10. Therefore, the priority of this study is to carry forward the inequality debates established in 1955 to the 2020s through the TKC hypothesis, to reveal the impact of technological transformation on income distribution in Türkiye, to analyze the stabilizing role of public expenditure in this process, and to relate the findings obtained to SDG-10 and its subcomponents. Thus, the aim is to provide policymakers with an analysis of the impact of technological transformation on IEQ and the determinant role of public expenditure in a developing economy in a current and local context.

This study aims to examine whether the TKC hypothesis is valid in Türkiye. In this respect, it can be argued that the study can make several important contributions. The first one is that the study provides a contribution to the literature on the TKC approach, and the second one is to fill the gap in the absence of a study in this context for the Turkish economy. Secondly, investigating the impact of public expenditures in Türkiye in line with the goal of reducing IEQ, which is one of the SDGs, is also among the findings that can be obtained. For this purpose, in line with the related literature, the Gini coefficient is chosen as an indicator of IEQ and the number of patents as an indicator of technological innovation in Türkiye for the period 1990–2021. Third, in order to take into account the neglected structural breaks and to obtain stronger empirical results, the methodological framework based on Fourier approximations is adopted in the empirical analysis of the study. Lastly, the Fourier DOLS and Fourier CCR methods are used to test the robustness of the long-term coefficient estimates. These estimators, in addition to taking structural breaks into account by using Fourier functions, provide robust and consistent estimates in terms of avoiding autocorrelation and endogeneity problems.

The structure of this paper is outlined as follows. The next section will focus on a review of the relevant theoretical and empirical literature, the contribution of the study to the literature, and the development of hypotheses. The methodology is described in Section 3. Empirical findings are displayed in Section 4. In Section 5, the results are discussed, and the paper is concluded by providing some conclusions and policy recommendations in Section 6.

2. Conceptual Framework/Theoretical Background and Empirical Literature

This section of the study aims to present the theoretical background and empirical literature that can explain the relationship between technological development and IEQ and provide a conceptual framework. First, the TKC hypothesis and the Skills-Based Technological Change (SBTC) approaches are discussed as the theoretical backgrounds to explain the relationship in question. Then, empirical studies in the literature parallel to the subject of the study are evaluated, and gaps in the literature are identified. In this context, while the impact of technological innovations on IEQ is discussed in the context of the TKC hypothesis, the stabilizing role of public expenditures is mentioned with the SBTC approach. Finally, the contribution of the study to the literature is explained, and hypotheses are presented in line with the theoretical and empirical literature.

2.1. TKC Hypothesis and SBTC Concept

Simon Kuznets [9] formulated the KC hypothesis, a pivotal analysis elucidating the relationship between IEQ and economic development. The revolutionary theory of KC posits that income disparity initially rises during economic development but subsequently declines upon reaching a certain income threshold. This relationship is elegantly expressed by an inverted-U-shaped curve. Inequality initially increases due to rural-urban migration triggered by industrialization, the growth of capital-intensive sectors, and changes in skill demand. However, it decreases in later stages through policies such as education, social policies, and public expenditures.

When this approach is analyzed in the context of technological innovations, it is seen that the relationship between technological innovation and IEQ is expressed by the concept of TKC, which was developed on the basis of the KC, which suggests that the relationship between technological innovation and IEQ is in an inverted-U shape. According to the TKC, in the initial phase of technological innovations, individuals and companies that access these innovations realize significant gains. However, due to patents, intellectual property rights, and capital-intensive innovations, income growth is concentrated in a certain segment. In the maturation stage of technologies, these innovations start to spread to a wider audience over time, and low-skilled workers adapt to new technologies through various policies (education). As this process of adaptation progresses, the increase in IEQ starts to slow down. In the final stage of the process, the advanced stage, the widespread adoption of technology, productivity growth, and widespread innovation processes make the distribution of income more equitable, stabilize the labor market, and increase the earnings of low-income groups. Thus, technology-based economic growth creates mechanisms for a more equitable distribution of social welfare [8,17,18,19].

In times of major technological breakthroughs, inequality grows initially, but so does social mobility. This process leads to a concentration of highly skilled individuals in technologically advanced fields. This enables future technology developments and economic growth. However, as access to a technology increases with time, the inequality (in with mobility) associated with it tends to decrease but become increasingly persistent [20]. This is supported by Shin et al. [21], who, using data from OECD member countries, show that new technological inventions increase IEQ, but inequality decreases with the integration of these technologies into the overall economy. It should also be noted that these assessments are largely in line with the Solow [2] growth model, which emphasizes technological progress as the main determinant of long-term growth.

A new global process is brought with rapid change, and technological innovation brings KC to a new vision. It brings the concept of SBTC to the forefront. In this context, technological developments might raise IEQ through rising demand for skilled labor. But over the long term, IEQ characterizes the spread of innovation and public policy responses that may equalize it. Broadly, advances in technology have benefited high-skilled workers and disadvantaged low-skilled workers [8,22]. This has the potential to increase IEQ initially. This is because technological progress, while increasing the demand for high-skilled labor, may negatively affect the incomes of low-skilled workers and deepen inequality. Indeed, Katz and Murphy [23] have shown that skill-oriented technological changes deepen IEQ by increasing the demand for skilled labor. On the other hand, Autor et al. [22] argue that technology has led to a decline in routine jobs, especially at the middle level, and that workers with higher skills have an advantageous position. Schumpeter [24], within the framework of the concept of creative destruction, argues that innovation initially creates winners and losers, but over time it increases productivity and creates wider prosperity. On the other hand, according to Robinson’s [25] innovation paradox, firms with monopoly power may use innovation to consolidate their dominance and generate more profits, rather than to promote general welfare growth. Indeed, under capitalism, innovation often increases inequality, as owners of capital receive the bulk of the benefits rather than workers or society at large. In this context, monopolizing technology, concentration of wealth, and deepening inequality can cause the traditional inverted U-shaped TKC to become N-shaped in the long run.

Given the effects of technology growth on inequality, the question unavoidably arises: can government help to improve these? In fact, the KC argues that as economies develop, inequality first increases and then decreases in the first stage, while public expenditures play a role in the last stage. Although the impact of public expenditures on inequality varies according to the level of development of countries [11], it is observed that well-targeted public expenditures can reduce inequality, while poorly managed expenditures can have neutral or even negative effects [12].

2.2. Empirical Literature and Research Gap

Empirical studies reveal that the relationship between technological innovation and IEQ is multidimensional and contextual. While technological progress promotes economic growth, it also carries the risk of deepening inequalities in income distribution. Factors such as public expenditures and financial inclusion are important factors shaping this relationship. The literature emphasizes that while technological innovation may increase inequality in some contexts, it can strengthen social and economic balances with appropriate policy interventions. Especially in developing economies, technological innovation is shown to tend to increase IEQ.

2.2.1. TKC Hypothesis and Technological Innovation—IEQ Interaction

Between 1995 and 2011, Antonelli and Gehringer [6] studied 39 nations to see how technological innovation affected IEQ. Rapid technological advancement decreases economic inequity through creative destruction, as the study proves (Schumpeterian theory). Weak growth leads to income disparity, the study claims. In another study, Aghion et al. [7] studied how innovation affected US income disparity from 1980 to 2011. While patents are used as an innovation indicator, the study examines how innovation affects the top 1% of income. The study found that innovation directly affects the top 1% income and worsens IEQ. Josifidis and Supic [26] utilized patents to analyze the impact of innovation on wealth inequality in the United States. In a manner like to Aghion et al. [7], innovation increases the income of the high-income group and alters the distribution of income.

There are studies in the literature that contribute to the interaction between technological innovation and IEQ by analyzing the Turkish case. Çetin et al. [10] contribute to the literature by assessing the effects of technology and financial development on IEQ in the Turkish setting. Findings show that IEQ in Türkiye is negatively affected by technology development. Biyase et al. [27] explored the association between technological innovation, financial development, and IEQ using the panel data of BRICS countries over the period of 1990–2017. In the study, the estimation methods of DOLS and FMOLS are employed to show how technological innovation, following from patent applications, increases inequality and how financial development may help to stabilize the income distribution. In terms of the TKC hypothesis, Elfaki and Ahmed [15] tested the TKC hypothesis based on the PMG/ARDL framework in the analysis and examined the relationship between technological innovation and financial development and its impact on income inequality in a sample of Asian countries. Apart from the challenges of financial inclusion as a public expense, this study supports the TKC hypothesis because one of the main public features of the SDG-10 framework.

The linkage between technological development, globalization, and financial development has been widely researched on empirical grounds. Jaumotte et al. [28] examine how technical, global, and financial development affect income disparity in 51 nations. The analysis shows that technical and financial progress worsen IEQ, but globalization reduces it. Giri et al. [29] also find that technical and financial progress raise income disparity while globalization decreases it.

Technological advances affect IEQ non-linearly depending on the nation’s development. According to certain investigations, the TKC theory describes this association as an inverted U-shaped curve. Technology may raise economic disparity at first, but it may decrease and reverse later. Gravina and Lanzafame [13] examined the non-linear effects of financial development, globalization, and technology on IEQ with a panel data set covering 90 developed and developing countries for the period 1970–2015. The results derived from the GMM dynamic panel data model indicate that investment-specific (ICT) and general-purpose technological innovations (GPT) can exert varying directional effects on IEQ, contingent upon the developmental stage of the country. On the other hand, Canh et al. [30] analyzed 87 countries and concluded that improvements in communication technologies reduce IEQ both in the short and long run. On the other hand, some studies point to an S-shaped relationship and draw attention to changing inequality dynamics at different stages of technological progress. Using the ARDL model, Yau and Tan [31] explored the linkages between economic growth and IEQ from 1970 to 2028 in Malaysia and tested the S-shaped Kuznets curve hypothesis. The study suggests that if IEQ can be narrowed by the diffusion of information and communication technologies (ICT), it is possible to drive a model that will reduce that inequality. This shows that technology brings income equality and faster economic growth. Wang et al. [14] contradicts TKC. Patent applications increase China’s income gap. It is evaluated 1985–2019 data using long-run time series estimators, including ARDL, FMOLS, DOLS, and CCR, to determine if new technologies affect income disparities.

2.2.2. SBTC Argument and the Balancing Role of Public Expenditures

Enhancing skills, implementing institutional reforms, and promoting market activities contribute to mitigating the IEQ effects of technological innovation. The SBTC argument is utilized in empirical studies to articulate this process and assert that technology has the potential to augment skilled labor. Santos et al. [32] employed Pesaran’s joint correlated effects estimator to analyze technological adoption and IEQ through the application of country-specific coefficients. The study found that outmoded ICT and transportation technologies exacerbate income disparity. Modern ICT has a more complex and variable impact. Technical progress widens inequality more in rich than in poor countries. These data suggest that the effects of technological innovation on income distribution are highly contingent on the technology and the economic context. Kharlamova et al. [33] found that within EU countries, technological progress leads to diverse changes in the income distribution, conditioned by the development level. While technical advancement tends to have little effect on the degree of IEQ in industrialized nations with strong economies, this phenomenon distorts income distribution in less developed nations with weaker economies. Law et al. [34], in 23 developed countries, reported that technological progress has a positive effect on IEQ. In contrast, Shukla [35] employed quantitative data analysis utilizing regression models to explore the correlation between innovation indicators (i.e., patents and R&D expenditures) and productivity trends, IEQ, and employment polarization in industrialized nations from 1990 to 2020. This question is in contrast to technological innovation being consistent with the TKC hypothesis, and the conclusion is that technological innovation increases inequality instead of decreasing it. In particular, it has been found to exacerbate wage divergence between both high-skilled and low-skilled labor, augment employment polarization, and weaken the economic position of the middle-income group. This helps to explain why the income distribution effect of technological innovation is ultimately shaped not just by the rate of diffusion, but also by the shape of the labor market and policy responses.

However, consistent with the SBTC explanation, there are other studies that look at the role of technological progress in IEQ and wage inequality. Barua and Hgosh [36] study the impact of technological innovations on skilled and unskilled labor wages in India. The analysis shows that on the way to IEQ, technological developments also disproportionally increase the wages of skilled labor. Similarly, Borrs and Knauth [37] analyze the impact of technological development on skilled and unskilled labor wages. In the study, the impact of technological development and foreign trade with Eastern countries on wages is considered together. As a result of the analysis, it is revealed that technological development does not have a great impact on IEQ, but it has a small effect on reducing inequality for low-skilled workers.

Some studies on the relationship between technological progress and IEQ within the TKC framework argue that this relationship is U-shaped, suggesting that technological innovation initially reduces inequality, but income gaps widen again with the increase in skill requirements and the expansion of capital-intensive sectors. Through the development of the TKC model, Kim [12] provides evidence for the idea that the impact of technological progress on IEQ may reflect a U-shaped structure to the extent that empirical data analysis is performed. Furthermore, the study highlights the importance of economic decision-makers and legislators concentrating their attention on redistributive policies. If this is not performed, technological advancement may only benefit some parts of the population, which would further widen the income gap. In a similar vein, Tang et al. [11] analyzed the causal relationship between technological innovation (patent applications) and IEQ by estimating a panel data regression with the Generalized Least Squares (GLS) method with a data set of 73 countries. It also investigates the roles of public spending, manufacturing, agricultural employment, and export diversification in this relationship. The theory implies that whether a country’s income is over-endowed with technological innovation is determined by public spending. These findings point to public expenditure as a key policy tool capable of reducing technology-fueled inequality.

There are many studies in the literature about this aspect of public expenditures that could bring stability into the relationship between technological innovation and IEQ. Education and social protection expenditure are particularly important in reducing inequality, allowing disadvantaged individuals to access the skills and opportunities they need and protecting low-income groups in the event of economic shock. Sánchez and Pérez-Corral [16] conducted a dynamic panel data analysis of the 2005–2014 data for 28 EU countries and found that public social expenditures generally reduce IEQ. However, this impact is likely to be conditional on a country’s economic structure and social policy goals. Such findings indicate that public social spending represents an effective policy instrument in inequality reduction.

2.2.3. Literature Contribution

Research in the empirical literature shows that technological innovation by itself does not ensure less inequality. However, it indicates that as the financial systems become more inclusive, the economy gets more integrated into the global economy, and public spending becomes more efficient, the overall status of this relationship may change for the better. In sum, the literature shows that the impact of technological innovation on IEQ is context-dependent and mediated by economic, political, and social mechanisms. Thus, policy designs to address inequality must consider both short-term shock stabilizing policies and long-term inclusive growth strategies. Balanced management of public expenditures and financial development policy, in particular, can help account for the adverse effects of technological innovation on income distribution and thus could be conducive to a more inclusive economic growth process between countries. In this regard, the challenge of conceptualizing innovation policies through a public size and social justice lens comes out as a pressing need in order to avoid technology-fueled inequalities.

The Fourier ADL cointegration test is employed in this study to analyze the connection between technological innovation and IEQ in Türkiye. The theoretical hypothesis will be empirically tested within the framework of the TKC hypothesis. By exploring this relationship, the study also makes a methodological contribution to the literature. In fact, the role of technological innovation on IEQ can be disparate across different countries with different levels of development [13]. Specifically, studies like those of Biyase et al. [27] and Wang et al. [14] show that technological innovation, measured as patent applications, can either worsen or reduce IEQ in developing nations contingent on the inclusivity of their financial systems. The literature contribution of this study will be analyzing the impact of technological innovation on IEQ, considering public expenditures on the Fourier ADL cointegration test in the Turkish context, and discussing this impact in terms of the TKC hypothesis. This research builds upon this framework, examining the impact of policy interventions on income distribution and the stabilizing role of public spending within the intervention–distribution relationship. Among them, Tang et al. [11] is the most relevant as it investigates the role of public expenditure in the context of technological innovation and IEQ. The fact that the study extends these models by providing a unique perspective in the Turkish context and makes a methodological contribution to the literature by using the Fourier ADL method increases the importance of the study and its contribution to the field.

2.2.4. Hypothesis

As for the empirical literature, it is known that the impact of technological development on IEQ differs by the level of the development of the countries and by the structure of the policies in terms of their effectiveness/inclusiveness. In this framework, the leading purpose of this paper is to analyze the direction of the long-run effect of technological innovation on income distribution in Türkiye and how this relationship has changed through time. Additionally, whether public expenditures stabilize this relationship is also analyzed. The hypotheses developed in this direction are presented under three headings in relation to the empirical literature.

Hypothesis 1.

While technological innovation increases represented by patent applications in Türkiye have a negative impact on income distribution measured by the GINI coefficient at the beginning, inequalities in income distribution decrease after a certain break parallel to the improvements in patent applications.

Testing the TKC hypothesis with Hypothesis 1 in the case of Türkiye contributes to the theory and is important in terms of country-specific policy implications. This hypothesis is in line with the findings of Gravina and Lanzafame [13], who argue that the impact of technology on inequality varies according to a country’s development. On the other hand, Wang et al. [14] show that patent applications increase IEQ in the case of the Chinese economy, suggesting that this relationship should be analyzed on a country basis and/or by level of development. On the other hand, the impact of technological innovations on IEQ tested through Hypothesis 1 may be shaped depending on the direction of public policies. Hypothesis 2 tests whether the increase in public expenditures in Türkiye plays a stabilizing role in this relationship over time.

Hypothesis 2.

The rise of public expenditures in Türkiye leads the initial negative link between technological innovation and IEQ to weaken in the long term, leading to decreasing income inequalities in the long run.

Public expenditures, especially in areas such as education, skill development, and social protection, may have an inequality-reducing effect by increasing the access of low-income groups to technology. In line with Hypothesis 2, Sánchez and Pérez-Corral [16] emphasize the income inequality-reducing effect of social public spending in their study on EU countries. Similarly, Tang et al. [11] have shown that the effects of technological innovations can be offset by public expenditures. These studies on the stabilizing role of public expenditures suggest that the existence of a long-run relationship between patent applications, IEQ, and public expenditures representing technological development in the Turkish economy should be tested (Hypothesis 3). Thus, by taking into account the structural breaks in the Turkish case, the impact of public policies on IEQ will become clearer.

Hypothesis 3.

There is a long-run interaction between quantitative developments in patent applications, changes in public expenditures, and IEQ in Türkiye due to the presence of structural breaks identified by the Fourier ADL model.

In the test of Hypothesis 3, the Fourier ADL cointegration test allows us to analyze the long-run relationships between patent applications, income inequalities, and public expenditures by taking structural breaks in time into account. This also points to the methodological contribution of the study. Apart from Tang et al. [11], there are empirical studies that support the stated hypothesis from different points. At this point, patent applications mentioned in Hypothesis 3 were also used as an indicator of technological development in the analysis of Biyase et al. [27] and Wang et al. [14].

3. Materials and Methods

3.1. Data

This study seeks to evaluate the effect of technological innovation on IEQ in Türkiye, aligning with the SDG-10 target, based on the TKC hypothesis. The study additionally accounts for the influence of government expenditures on IEQ. The annual time series data for the dependent and independent variables from 1990 to 2021 are presented in Table 1, in accordance with the stated objective. The period range is constrained to 1990–2021 due to the availability of observation values for the technological innovation variable in Türkiye within these dates. In the OECD [38] report, patents are defined as ‘outputs of technological innovation’ with a focus on their relevance for international comparisons. The report also emphasizes that patents provide important data on the R&D performance of enterprises and countries and that patents are a vital source for assessing the effectiveness of technological innovation strategies. Similarly, Griliches [39] argued that patents act as a ‘valuable signal’ in the empirical examination of technological innovation, pointing in particular to their effectiveness in observing innovative activities over time. Therefore, the study utilizes patents as an indicator of technological innovation.

3.2. Model

In accordance with SDG-10, this research will use the variables listed in Table 1 to determine whether the TKC hypothesis claims validity for Türkiye. Although technical advancements may widen income gaps in the early phases of economic growth, the TKC theory predicts that these gaps will narrow with time [12]. Here, the empirical model in Equation (1) is built using the work of Kim [12] and Elfaki and Ahmed [15].

(1)$\ln GIN{I}_t={\delta }_0+{\delta }_1\ln TEC{H}_t+{\delta }_2\ln TEC{H}_t^2+{\delta }_3\ln GO{V}_t+u_t$

The natural logarithm of the dependent and independent variables in Equation (1) above is taken, and Equation (2) below is obtained. Taking the natural logarithm of the model in Equation (1) is important to avoid the possible variable variance problem and to obtain the elasticities of the variables, thus eliminating unit differences [42,43]. The natural logarithm of the model can be written as follows:

(2)$\ln GIN{I}_t={\delta }_0+{\delta }_1\ln TEC{H}_t+{\delta }_2\ln TEC{H}_t^2+{\delta }_3\ln GO{V}_t+u_t$

where ln = natural logarithm, ${\delta }_0$ = constant of the model, ${\delta }_1$, ${\delta }_2$, and ${\delta }_3$ = long-run elasticities, u = error term, GINI = income inequality, TECH = technological innovation and GOV = government expenditure. If ${\delta }_1$ > 0, ${\delta }_2$ < 0 and both coefficients are statistically significant, the TKC hypothesis holds. The validity of the TKC hypothesis assumes that an increase in TECH increases GINI up to a certain point in time, after which an additional increase in TECH decreases GINI. Based on Dinda’s [44] study, the other conditions specified in model 2 can be explained as follows:

${\delta }_1={\delta }_2=0$. There is no relationship between TECH and GINI.

${\delta }_1>0 \mathrm{and} {\delta }_2=0$ There is a monotonically increasing functional relationship between TECH and GINI.

${\delta }_1<0 \mathrm{and} {\delta }_2=0$ There is a monotonically decreasing functional relationship between TECH and GINI.

${\delta }_1<0 \mathrm{and} {\delta }_2>0$ There is a U-shaped functional relationship between TECH and GINI.

As stated in the theoretical and empirical literature section of the study, the effect of GOV on IEQ is negative. Therefore, the sign of ${\delta }_3$ is expected to be negative.

3.3. Empirical Methodology

This study employs a four-stage empirical strategy, as depicted in Figure 1. The order of integration of the variables is established through the Fourier ADF unit root test. The Fourier ADL test is employed to assess the cointegration among the variables. During the third stage, the estimation of long-run coefficients is conducted using the Fourier FMOLS estimator. The final stage involves conducting a robustness check of the Fourier FMOLS approach using the Fourier DOLS and Fourier CCR estimators.

3.3.1. Fourier ADF

To account for structural breaks and asymmetric relationships in the variable, the Fourier Extended Dickey–Fuller (FADF) unit root test incorporates trigonometric terms into the traditional Dickey–Fuller (ADF) test [45]. In fact, incorrect unit root conclusions could result from disregarding structural breaks in the variable. The FADF was first published in 2012 by Enders and Lee, who used a Fourier-based unit root test to replace more traditional methods. To reduce the effect of structural changes on the test’s power, the FADF records slow, steady changes in the series. As a first step, the FADF test verifies if the Fourier terms are statistically significant. The trigonometric terms are considered significant after the significance test; if not, the FADF test is applied [46]. The output of this analysis is given by Equation (2).

(3)$\Delta Y_t=\rho Y_{t-1}+c_1+c_2+c_3\sin (2\pi kt/T)+c_4\cos (2\pi kt/T)$

The optimum frequency (k) in the above equation takes a value between $1\le k\le 5$. In Enders and Lee (2012) [46], critical values for the FADF are tabulated.

3.3.2. Fourier ADL

This model, known as Fourier ADL (FADL), incorporates Fourier functions into the classic autoregressive distributed lag (ADL) framework. An unknown type of nonlinear break is accounted for in this model by including a Fourier function in the deterministic term. In cointegration tests, a large number of estimated dummy variables may lead to issues such as low predictive power, which can be avoided with the FADL model. Furthermore, this model has predictive ability even when there are structural breaks or nonlinear trends in the data. Quite recently, Banerjee et al. [47] have shown that as the sample size increases, the predictive power of the test is more pronounced. This method can be expressed mathematically as follows to test the effect of the independent variables lnTECH and lnGOV on the dependent variable lnGINI in the context of the TKC hypothesis:

(4)$\begin{array}{ll}\Delta \ln GIN{I}_t& ={\alpha }_0+{\eta }_1\sin \left({\displaystyle \frac{2k\pi t}{T}}\right)+{\eta }_2\cos \left({\displaystyle \frac{2k\pi t}{T}}\right)\\ & +{\eta }_3\ln GIN{I}_{t-1}+{\eta }_4\ln TEC{H}_{t-1}+{\eta }_5\ln TEC{H}_{t-1}^2+{\eta }_6\ln GO{V}_{t-1}+e_t\end{array}$

In this equation, ${\alpha }_0$, ${\eta }_1$, ${\eta }_2$, ${\eta }_3$, ${\eta }_4$, ${\eta }_5$, and ${\eta }_5$ are the parameters, i is the lag length, q and p are the maximum lag lengths and e_t is the disturbance term. Here, k = frequencies, t = trend, T = total observations, sin and cos denote trigonometric functions, and π = 3.1416. The appropriate k in the equation can be ascertained by evaluating the minimum of the sum of squared errors associated with $1\le k\le 5$. The selection of the optimal lag length is determined by identifying the minimum value of the AIC. Once k is determined, the null hypothesis that there is no cointegration $\left(H_0:{\eta }_3=0\right)$ is tested against the alternative hypothesis that there is cointegration $\left(H_1:{\eta }_3\ne 0\right)$. In instances where the t-value ${\eta }_3$ of $\left(t_{ADL}^F\left(\hat{k}\right)\right)$ is less than the AIC values identified by Banerjee et al. [47], the null hypothesis is deemed rejected, thereby indicating the existence of cointegration.

4. Empirical Findings

At the initial stage of the empirical analysis, the unit root tests of the variables were performed with ADF and FADF tests and are offered in Table 2. Accordingly, only for the lnTECH series, the Fourier terms are significant according to the F test. Therefore, FADF results for the lnTECH series and ADF results for other series are interpreted. These results show that all series become are I(1). From this point of view, the FADL approach can be used for cointegration.

The second stage of the empirical analysis focuses on the examination of the cointegration relationship among the variables, employing the FADL approach as outlined in Table 3. The data shown in Table 3 demonstrate that the absolute value of the test statistic for the ADL (2,1,1,1,2) model surpasses the critical value at the 5% significance level. It is concluded that a cointegration relationship exists between the variables in this context. Accordingly, Hypothesis 3 cannot be rejected. There is a long-run interaction between quantitative developments in patent applications, changes in public expenditures, and IEQ in Türkiye.

Following the identification of the cointegration relationship, the long-run coefficients of this cointegration relationship are tested with the Fourier FMOLS estimator and reported in Table 4. Accordingly, the effects of all independent variables on the dependent variable lnGINI in the long run are statistically significant. A 1% increase in lnTECH increases lnGINI by 0.036%, while a 1% increase in lnTECH2 decreases lnGINI by 0.002%. This result confirms that the TKC hypothesis is valid for Türkiye in the long run. In addition, a 1% increase in lnGOV decreases lnGINI by 0.013% in the long run. Following the identification of the cointegration relationship, the long-run coefficients of this cointegration relationship are tested with the Fourier FMOLS estimator and reported in Table 4. Accordingly, the effects of all independent variables on the dependent variable lnGINI in the long run are statistically significant. An increase of 1% in lnTECH results in a 0.036% increase in lnGINI, whereas a 1% increase in lnTECH2 leads to a 0.002% decrease in lnGINI. This finding supports the validity of the TKC hypothesis for Türkiye over the long term. A 1% increase in lnGOV results in a 0.013% decrease in lnGINI in the long run. Therefore, Hypothesis 1 and Hypothesis 2 cannot be rejected. In Türkiye, while technological innovation represented by patent applications initially has a negative impact on income distribution measured by the GINI coefficient, inequalities in income distribution decrease after a certain break in line with the improvements in patent applications. In other words, the increase in public expenditures in Türkiye contributes to the reduction in income inequalities in the long run by weakening the initial negative effect between technological innovation and IEQ.

The robustness check of the findings obtained from the Fourier FMOLS estimator is performed with the Fourier DOLS and Fourier CCR estimators and presented in Table 5 and Table 6, respectively. According to the Fourier DOLS results in Table 5, the effect of all independent variables on the dependent variable lnGINI is statistically significant at the respective significance levels. In the long term, lnTECH has a positive effect on lnGINI that is equal to about 0.116%. lnTECH2 has a negative effect on lnGINI, which is about 0.007%. Based on this result, the TKC theory seems to be true in Türkiye. Finally, it has been observed that lnGOV has a negative long-term effect on lnGINI, equal to about 0.0244%.

The findings of the Fourier CCR estimator in Table 6 show that the effects of all independent variables on the dependent variable lnGINI in the long run are statistically significant. The long-run findings show that lnTECH and lnTECH2 have positive (0.018%) and negative (0.001%) effects on lnGINI, respectively. This result proves the existence of the TKC hypothesis in Türkiye in the long run. Moreover, in the long run, a 1% increase in lnGOV decreases lnGINI by 0.007%.

The results of the Fourier FMOLS estimator complement those of the Fourier DOLS and Fourier CCR estimators. This suggests consistency of the estimations. The overall findings of the research are given in Figure 2 below.

5. Discussion

The long-run results of this study’s estimated Fourier ADL model indicate that lnTECH and lnTECH2 are significant and positively and negatively affect lnGINI, respectively, in Türkiye. The results indicate that the TKC hypothesis might hold for Türkiye, and the effects of technological innovation on IEQ tend to vary with time. Unlike the results shown in Biyase et al. [27], Shukla [35], Wang et al. [14], this finding aligns with results from Elfaki and Ahmed [15], Gravina and Lanzafame [13], Kim [12], and Tang et al. [11].

The properness of the TKC hypothesis is crucial for Türkiye, one of the world’s top 20 economies, and negotiating EU membership. Because technological innovation can be an important argument that can be used to reduce the GINI. At this point, determining the turning point of the TKC will be important in helping to develop interpretations and policies to reduce the GINI of technological innovation and, in this context, to combat IEQ. In the findings of the study for the case of Türkiye, the turning point in the number of patent applications in 2008 indicates that while technological innovation initially increases IEQ, this effect may reverse when the number of patent applications approaches 2015. The fact that 2008 coincided with the global financial crisis reveals how technological innovation in Türkiye is shaped by economic fluctuations and the vulnerabilities of the financial system. Moreover, the 2015 (units) critical threshold value determined in the number of patent applications indicates that technological progress has started to become more inclusive and reduce IEQ.

The result that technological innovation, as measured by patent applications, initially leads to an increase in IEQ is consistent with the SBTC explanation. This is because, in the early phases of technological innovation, high-skilled laborers and capital proprietors gain, but low-skilled workers fail to readjust to such evolution and hence fall behind. But as soon as the estimated threshold value of criticality is exceeded, the diffusion of technology, the acceleration of skills acquisition processes, and of public spending, etc., all come into play and have a leveling effect on inequality. This result is aligned with the TKC hypothesis on which the study is theoretically grounded, indicating that technological innovation can be more balanced beyond a threshold. Policymakers may derive this very threshold to handle technological transformation processes in a way to form a more equitable income distribution in society.

The common finding of Fourier FMOLS, DOLS, and CCR is that in the long run, public expenditures have a reducing effect on IEQ in the study. These outcomes are consistent with the results of Kim [12], Sánchez and Pérez-Corral [16], and Tang et al. [11]. As it accumulates, the TKC hypothesis posits that technological progress enables a skill-biased transformation, benefits high-skilled labor, but this inequality erodes over time as expansion of access to technology occurs, leading to public policies such as education, infrastructure, and social protection. Consequently, the findings of the study are very much in line with the notion that public spending acts as a stabilizing force during technological innovation and can reduce IEQ in the long run within this framework. For policymakers, these findings imply that simply relying on technological innovation will not reduce inequality, as its potentiality, the process can and should be governed, and the benefits made more stable through the wise use of public expenditures. Public investments in education, infrastructure, and social support mechanisms, in particular, could help equalize out the imbalances created by technological progress and make income distribution more equitable in the long run. Hence, this finding might suggest that the TKC hypothesis should be supported by public expenditures, and that the technological progress based on market-free dynamics could deepen inequalities.

Another implication of the empirical findings in the light of public expenditures is that economic decision-makers can transform the impact of technological innovation on IEQ over time through public policies such as education reforms, R&D support, and digital transformation strategies. Thus, it is understood that the public sector can eliminate technology-induced inequalities. In the interaction between technological innovation and the GINI coefficient, the 2015 critical threshold value calculated for the number of patent applications in 2008 supports this empirically. In conclusion, the empirical findings showing that the TKC hypothesis may be valid in Türkiye strongly suggest that technological progress needs to be evaluated in terms of social justice. In this context, the findings of this study suggest that Türkiye’s technology-oriented growth policies can be considered as a critical tool not only in promoting economic growth but also in combating IEQ.

On the other hand, our research findings are directly related to the core principles of SDG-10. In the short term, technology can increase inequality; for example, due to the digital divide, high-skilled individuals may earn more income, while low-skilled individuals may be left behind. In the longer term, however, a more equitable balance in income distribution can be achieved through widespread technology adoption and the implementation of effective policies. This is in line with the sub-targets of SDG-10. It is directly linked to SDG-10.1 and SDG-10.2. In this context, technological innovation can exacerbate inequality if not driven and supported. But if driven by inclusive public policy, it can become a powerful tool to narrow inequality gaps. This suggests that SDG-10 can be addressed not only through redistribution but also through proactive technological and educational strategies [15].

6. Conclusions and Policy Recommendations

Overall, this study is to test the effect of lnTECH and lnGOV on lnGINI in the scope of the TKC hypothesis for Türkiye. The empirical model is tested using the ADF unit root, ADL cointegration, and the FMOLS estimator anchored on Fourier functions. To test the robustness of the long-run coefficients estimated with the Fourier FMOLS estimator, the Fourier DOLS and CCR estimators are also employed. The results of the study show that the TKC hypothesis is confirmed in the long term in Türkiye. In addition, the empirical evidence also demonstrates that lnGOV decreases the lnGINI and, as such, decreases IEQ in Türkiye. A set of policy recommendations is made based on the findings from the study.

Policies also need to be designed to mitigate the adverse effects of technological advancements on IEQ and achieve more equitable economic growth in Türkiye. This includes increasing skills training and creating specialized workforce programs for low-skilled laborers. Scholarships and incentives for students studying in STEM (science, technology, engineering, and mathematics) fields should be increased, and continuing education programs should be supported for the existing workforce to adapt to technology. In addition, individuals’ adaptation to technological developments should be facilitated through lifelong learning programs. Promoting research and development (R&D) and innovation plays an important role in reducing inequality. Special support should be provided to increase access to technology for small and medium-sized enterprises (SMEs), and the start-up ecosystem should be strengthened to foster the development of innovative enterprises. There should be mechanisms to incentivize patent applications, and policies should be implemented that ensure the diffusion of technology production in different segments. The process will also be facilitated by the strategic orientation of public expenditures. Investments in education, infrastructure, and social support mechanisms can mitigate the inequalities created by technological progress and create a more equitable distribution of income. However, this increased access will require urgent supportive action from both the state and the public. The digital world gets access to low-income groups through free digital education platforms, access to the internet, and technology hardware. A public–private partnership funding model for digital transformation promotes collaborative industry-academia projects that encourage technology-based solutions.

These policy recommendations provide a comprehensive strategy for reducing inequalities in line with the core principles of SDG-10. Our recommendations are of particular relevance to Goal 10’s subdimensions 10.1 and 10.2. In the context of Target 10.1 (ensuring fairness in income distribution), concrete solutions are proposed, such as developing workforce programs for low-skilled workers and increasing skills training. STEM scholarships and continuing education programs have the potential to accelerate income growth for the bottom 40 percent of society by increasing economic mobility through education. Redirecting public spending towards education, infrastructure, and social support mechanisms can go a long way towards reducing income inequalities by enabling low-income groups to benefit more from economic opportunities. Implementation of these recommendations, which are also critical for Target 10.2 (Strengthening Social, Economic and Political Inclusion), will promote social and economic inclusion. Increasing access to technology for small and medium-sized enterprises (SMEs) and supporting innovative start-ups broadens economic inclusion and strengthens equal opportunities.

This study has some fundamental limitations. First of all, the study was conducted only for Türkiye. In addition, this study offers several opportunities for researchers interested in research on development economics. The TKC, which lies at the main focus of the study, is a relatively new topic. Hence, in future studies, a similar topic can be studied for different countries or country groups on the basis of high-value-added products using different current econometric approaches such as quantile and wavelet.

Author Contributions

Conceptualization, Ö.B., S.Ç., Ö.U.B., S.T. and S.Ç.; methodology, S.Ç. and S.T.; software, S.T.; validation, Ö.B., and S.Ç.; formal analysis, M.A., D.Ö., Ö.B. and Ö.U.B.; investigation, Ö.U.B. and M.A.; resources, S.Ç.; data curation, Ö.U.B. and D.Ö.; writing—original draft preparation, M.A., Ö.U.B., S.T., Ö.B., D.Ö. and S.Ç.; writing—review and editing, S.Ç.; visualization, D.Ö.; supervision, D.Ö.; project administration, M.A.; funding acquisition, M.A., Ö.U.B., S.T., Ö.B., D.Ö. and S.Ç. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1 Empirical flow. Source: Created by the authors.

Figure 2 Visual summary of empirical findings. Source: Created by the authors.

Table 1

Variables.

Symbol	Defining Variables	Unit	Source
GINI	Income inequality	Index	Solt [40]
TECH	Patent applications	Number of resident patents	WDI [41]
GOV	Government spending	General government final consumption expenditures (% of GDP)	WDI [41]

Table 2

ADF and FADF results.

Variables	FADF		ADF
	Level		Level	First Difference
	F Test	Test Statistic	Test Statistic	Test Statistic
lnGINI	10.034	-	−1.993 {1} (0.288)	−1.771 * {6} (0.072)
lnTECH	11.176 ***	−0.111 {2} [1]	0.318 {0} (0.975)	−4.604 *** {1} (0.000)
lnTECH2	9.612		0.750 {0} (0.991)	−4.523 *** {0} (0.001)
lnGOV	4.370	-	−1.265 {5} (0.631)	−5.040 *** {4} (0.000)

Notes: ***; 1% and *; 10%. ( ), { } and [ ] denote p-values, lag lengths, and frequencies. Critical values for the F test and FADF test are obtained from Enders and Lee (2012) [46]. The critical values for F and FADF 1% are 10.35 and Fourier −4.42, respectively. Source: Calculated by the authors.

Table 3

FADL findings.

Model	${\mathit{t}}_{\mathit{A}\mathit{D}\mathit{L}}^{\mathit{F}}\left(\hat{\mathit{k}}\right)$	$\hat{\mathit{k}}$	Delays	AIC	Conclusion
lnGINI= ƒ(lnTECH, lnTECH2, lnGOV)	−4.631 **	1	ADL(2, 1, 1, 2)	−9.773	✓

Note ** indicates 5% significance level. Critical values for the FADL (2017) [47] cointegration test (r = 3, k = 1). The critical value for the 5% significance level is −4.51. Source: Calculated by the authors.

Table 4

Fourier FMOLS results.

Variables	Coefficients	Standard Error	t-Statistic	p-Value
lnTECH	0.036 **	0.017	2.127	0.042
lnTECH2	−0.002 *	0.001	−1.987	0.056
lnGOV	−0.013 **	0.004	−2.703	0.011
c	3.607 ***	0.059	60.558	0.000
cos	0.012 ***	0.002	5.513	0.000
sin	0.036 ***	0.001	19.871	0.000
Turning point (TECH *)	Logarithmic value	7.608	Numerical value	2015.304

Notes: ***; 1%, **; 5% and *; 10%. Source: Calculated by the authors.

Table 5

Fourier DOLS findings.

Variables	Coefficients	Standard Error	t-Statistic	p-Value
lnTECH	0.116 ***	0.036	3.166	0.005
lnTECH2	−0.007 ***	0.002	−3.097	0.006
lnGOV	−0.024 **	0.010	−2.283	0.035
c	3.359 ***	0.124	26.927	0.000
cos	0.027 ***	0.004	6.569	0.000
sin	0.042 ***	0.004	10.148	0.000

Notes: ***; 1% and **; 5%. Source: Calculated by the authors.

Table 6

Fourier CCR findings.

Variables	Coefficients	Standard Error	t-Statistic	p-Value
lnTECH	0.018 **	0.007	2.390	0.023
lnTECH2	−0.001 *	0.001	−2.033	0.051
lnGOV	−0.007 ***	0.001	−6.263	0.000
c	3.653 ***	0.027	135.291	0.000
cos	0.008 ***	0.001	8.163	0.000
sin	0.036 ***	0.001	67.700	0.000

Notes: ***; 1%, **; 5% and *; 10%. Source: Calculated by the authors.

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