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Economic Complexity and Income Inequality: Does Country Risk Matter?

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Chien-Chiang Lee at Nanchang University
Chien-Chiang Lee
En-Ze Wang at Qingdao University of Science and Technology
En-Ze Wang
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Abstract

This research sets out to determine the nexus among economic complexity (ECI; which reflects a country’s productivity), income inequality, and country risk and whether country risk affects the complexity-inequality nexus. By applying balanced panel data of 43 countries from 1991 to 2016 to a data-driven econometric methodology-finite mixture model, we provide fresh insight into this relationship from the perspective of country risk. The results indicate that the two-group finite mixture model is able to best fit our data, and that increasing economic complexity has no impact on income inequality in group A, whereas improving the structure of productivity mitigates the income gap in group B. Furthermore, country risk and the subcomponents of the former (i.e., economic risk, financial risk, and political risk) all exert effects on the complexity-inequality nexus. Specifically speaking, an increase in ECI is associated with more equal income distribution in a country with low country risk, while the improvement in productive structure cannot improve an unequal income distribution in countries under high country risk. Finally, it is noteworthy that the finite mixture model also captures information about the transformation of this nexus, with evidence demonstrating that 5 countries experience a variation in their complexity-inequality relationship over the sample period.
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Vol.:(0123456789)
Social Indicators Research (2021) 154:35–60
https://doi.org/10.1007/s11205-020-02543-0
1 3
ORIGINAL RESEARCH
Economic Complexity andIncome Inequality: Does Country
Risk Matter?
Chien‑ChiangLee1,2· En‑ZeWang3
Accepted: 1 November 2020 / Published online: 9 November 2020
© Springer Nature B.V. 2020
Abstract
This research sets out to determine the nexus among economic complexity (ECI; which
reflects a country’s productivity), income inequality, and country risk and whether country
risk affects the complexity-inequality nexus. By applying balanced panel data of 43 coun-
tries from 1991 to 2016 to a data-driven econometric methodology-finite mixture model,
we provide fresh insight into this relationship from the perspective of country risk. The
results indicate that the two-group finite mixture model is able to best fit our data, and that
increasing economic complexity has no impact on income inequality in group A, whereas
improving the structure of productivity mitigates the income gap in group B. Furthermore,
country risk and the subcomponents of the former (i.e., economic risk, financial risk, and
political risk) all exert effects on the complexity-inequality nexus. Specifically speaking,
an increase in ECI is associated with more equal income distribution in a country with low
country risk, while the improvement in productive structure cannot improve an unequal
income distribution in countries under high country risk. Finally, it is noteworthy that the
finite mixture model also captures information about the transformation of this nexus, with
evidence demonstrating that 5 countries experience a variation in their complexity-inequal-
ity relationship over the sample period.
Keywords Income inequality· Economic complexity· Country risk· Finite-mixture
model· Heterogeneity
JEL classification O15· O4· G00· G51
The authors are listed in alphabetical order and share first authorship. This paper is supported by the
National Science Foundation of Jiangxi Province of China [20202BAB201006] and the 2020 Jiangxi
Humanities and Social Sciences Project of University.
* En-Ze Wang
enzewang2008@gmail.com
Chien-Chiang Lee
cclee6101@gmail.com
1 Research Center oftheCentral China forEconomic andSocial Development, Nanchang
University, Nanchang, China
2 School ofEconomics andManagement, Nanchang University, Nanchang, China
3 Economics andManagement School, Wuhan University, Wuhan, Hubei,
People’sRepublicofChina
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... However, widening income inequality will be a quite serious problem. In fact, income inequality is an important concern for the economy (Lee & Wang, 2021). ...
... In addition, another study applying SYS-GMM estimates based on data from 88 countries in 2002-2007 revealed that the economic complexity index is associated with higher income inequality (Chu & Hoang, 2020). Another study using mixed models in 43 countries over the period 1991-2016 also reported a positive influence of the economic complexity index on income inequality (Lee & Wang, 2021). ...
... These findings align with the studies conducted by Lee & Wang (2021) and Hartmann et al. (2017). The economic complexity index has the potential to reduce income inequality by improving the employment structure, expanding employment and educational prospects, and producing high-value products (Lee & Wang, 2021). ...
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This research examines the influence of the economic complexity index, GDP per capita, gross higher education participation ratio, government spending on education, and fertility rates on income inequality in G20 Forum member countries from 2010 to 2019. The dynamic panel data regression method with the two-step System Generalized Method of Moments (SYS-GMM) estimation technique is used to analyze the data in this study. The findings reveal that the economic complexity index, GDP per capita squared, government spending on education, and fertility levels have a negative and significant effect on income inequality. Meanwhile, GDP per capita and gross enrollment rates in tertiary education have a significant positive effect on income inequality. This study shows that the economic complexity index and the quality of human resources can reduce income inequality. Therefore, policies that focus on improving the quality of human resources need to be considered to encourage innovation, increase GDP per capita, and ultimately reduce income inequality.
View
... In the income inequality cluster, researchers mainly studied the impact of economic complexity on reducing inequality from various angles (Hartmann et al., 2017;Sbardella et al., 2017;Lee & Wang, 2021a, 2021b and mitigating greenhouse gas emissions (Boleti et al., 2021;Neagu & Teodoru, 2019;Romero & Gramkow, 2021;Shahzad et al., 2021). In addition, a few studies examined the role of technological innovation (Yu et al., 2022), financial stability (Ashraf, 2022), and globalization (Nan et al., 2022) on CO 2 emissions. ...
... For instance, Nguyen C.P. published papers related to "Economic growth" (Nguyen, 2021(Nguyen, , 2022Nguyen et al., 2021) and "Diversification" (Nguyen & Schinckus, 2022). In addition, Lee C.-C. published papers related to "Ecological footprint" You et al., 2022) and "Income inequality (Lee & Wang, 2021a, 2021b. Lapatinas A. published papers related to "Diversification" (Adam et al., 2023;Lapatinas, 2019), "Ecological footprint" , and "Income inequality" . ...
... reveals that the publications of each country are related to different clusters. For instance, in China, the publications are related to "Ecological footprint" (such as Numan et al., 2022;Shahzad et al., 2023), "Income inequality" (such as Lee & Wang, 2021a, 2021bLi et al., 2023), "Economic growth" (such as Tabash et al., 2022;Zhu & Li, 2017), and "Diversification" (such as Gao et al., 2021;Shou et al., 2017). Similarly, in the United States, the publications are related to different clusters including "Ecological footprint" (such as Additionally, a co-authored country analysis was applied to identify the main country groups that have contributed to the publication on economic complexity. ...
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The growing literature on the issues of economic complexity makes it challenging to achieve a comprehensive multidimensional picture of the current problem for beneficiaries, policymakers, and future research. Therefore, this study aims to conduct a bibliometric analysis of 272 documents published in the field of economic complexity since 2007 and extracted from the Scopus database. Results are presented through figures, tables, maps of past trends and research directions using keyword analysis, global citation analysis of authors, organizations, countries, journals, articles, references, content analysis, and other bibliometric analysis via VOSviewer, CiteSpace, and WordStat software. A bibliometric review was applied to identify four clusters: Economic Growth, Diversification, Income Inequality, and Ecological Footprint. Finally, the state of the art in economic complexity research is discussed, and directions for future research are provided.
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... Finally, concerning point (iv), Lee and Wang (2021) provide econometric evidence that the ECI-inequality nexus depends on country risk. According to the authors, country risk can negatively and positively affect the ECI-inequality relationship, depending on whether economic, financial and political risk-as the three main elements of country risk-affect the diversity or the exclusivity (i.e. ...
... To attract, coordinate and remunerate networks of qualified workers, high-quality and inclusive institutions are also required. In this respect, the scope for future research is still large: understanding which specific types of institutions, either formal or informal, shape the Non-linear and significant relation between ECI and income inequality, but only in urban and high-GDP states Lee and Wang (2021) 43 countries, 1991-2016 Two-group finite mixture model Negative relationship between ECI and income inequality only in low-risk countries, no relation in high-risk countries Source: author's elaborations complexity-inequality relationship is still subject to anecdotal and case-based evidence instead of being based on solid empirical evidence. Identifying true causal linkages is another issue that needs to be properly addressed, using not only instrumental variable techniques but also more recent approaches in the field of quasi-natural experiments, counterfactual analyses, difference-in-differences regressions or even machine learning algorithms. ...
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... Tracking the progress and the determinants of economic complexity has attracted great attention among policymakers and academia. An extensive body of the existing literature was concerned with the key determinants of the economic complexity: Foreign direct investment (Sadeghi et al., 2020;Antonietti and Franco, 2021;Saqib and Dinca, 2023); institutional governance (Hartmann et al., 2017); human capital accumulation (Shahbaz et al., 2019), income inequality (Hartmann et al., 2017;Lee and Wang, 2021), gender equality found to serve as key drivers of economic complexity. However, less attention has been paid to global risk factors such as geopolitical risk, oil price volatilities, and global economic policy uncertainties as potential determinants of economic complexity. ...
... The interactions among geopolitical risk, economic uncertainty, and economic complexity have become a central issue for academics, scholars, and policymakers investigations. Studies for instance, Lee and Wang (2021) assessed the impact of country risk on the economic complexity-income inequality for panel data of 43 countries from 1991 to 2016 using the finitemixture model. The authors uncover that upgrading economic complexity positively correlates to low uncertainty and equal income distribution. ...
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Enhancing future economic growth through higher economic complexity is vital for sustainable growth strategies. Even though most studies are concerned with the key factors governing the economic complexity process, there needs to be more studies about the impact of risk factors on economic sophistication evolvement, particularly for oil-rentier economies. The present paper intends to delve into the dynamic connectedness between some risk factors, such as economic, political, and geopolitical risks and oil price volatility and the progress of economic complexity. We utilized quarterly data (1995Q1-2021Q4) for Saudi Arabia as a heavy oil-rich economy. As the first study to delve into the short-long term connections at various time scales and frequencies within a multivariable setting, we contribute to the literature by offering a spotless picture of economic complexity risk factors. In doing so, we resort to a novel wavelet local multiple correlation method, which can explore the varying patterns of time periods in the interconnections between the variables. Our findings disclose that oil volatility, geopolitical risk, and global uncertainties are positively connected to economic complexity over the long run. In contrast, their short-term effect is weak and mostly insignificant. These results indicate the resiliency of the Saudi economy to external to oil volatility shocks and intensification of global and local uncertainties. These outcomes offer policymakers new insights and prominent policy recommendations when designing economic strategies to achieve higher economic sophistication and sustainable growth.
View
... Finally, concerning point (iv), Lee and Wang (2021) provide econometric evidence that the ECI-inequality nexus depends on country risk. According to the authors, country risk can negatively and positively affect the ECI-inequality relationship, depending on whether economic, financial and political risk-as the three main elements of country risk-affect the diversity or the exclusivity (i.e. ...
... To attract, coordinate and remunerate networks of qualified workers, high-quality and inclusive institutions are also required. In this respect, the scope for future research is still large: understanding which specific types of institutions, either formal or informal, shape the Non-linear and significant relation between ECI and income inequality, but only in urban and high-GDP states Lee and Wang (2021) 43 countries, 1991-2016 Two-group finite mixture model Negative relationship between ECI and income inequality only in low-risk countries, no relation in high-risk countries Source: author's elaborations complexity-inequality relationship is still subject to anecdotal and case-based evidence instead of being based on solid empirical evidence. Identifying true causal linkages is another issue that needs to be properly addressed, using not only instrumental variable techniques but also more recent approaches in the field of quasi-natural experiments, counterfactual analyses, difference-in-differences regressions or even machine learning algorithms. ...
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... We therefore apply FMMs to estimate the relationship between elections and military expenditures. This estimation technique is a semi-parametric method for modeling unobserved heterogeneity in the sample that allows us to relax the assumption of one data-generating process as parameters may vary across groups of observations, which makes it possible to determine the conditions under which competing theories apply (see also Bagozzi et al., 2014;Lee & Wang, 2021;Wang & Lee, 2021). Mixture models generate endogenous group membership and permit explaining group membership with several concomitant variables (Van Horn et al., 2015). ...
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... Based on endogenous growth theory which taking knowledge and technology as key economic drivers, economic complexity is proposed to reversely estimating the production capacity of an economy with exporting products information as carrier, which has more effectiveness of predicting economic growth than classical indicators in traditional political economy (Hidalgo, 2021), and has been successfully used in research on economic development . Ma et al. 2022;Britto et al. 2019;Sepehrdoust et al. 2019), green economy (Mealy and Teytelboym 2022;Rafique et al. 2021;Neagu 2019;Pérez-Hernández et al. 2021), social inequality (Hartmann et al. 2017;Chu and Hoang 2020;Lee and Wang 2021;Sepehrdoust et al. 2022), to provide information about the current state of economic development. In the realm of economic complexity, the primary models for assessing the state of economic development include classic measurement methods such as the economic complexity index (ECI) and the product complexity index (PCI) based on Method of Reflections (the following abbreviation "MR"), as well as fitness complexity index (FCI) and product complexity index (FPI) (Hidalgo 2021). ...
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Selecting suitable trading partners in the globalized trade landscape remains critical. Traditional selection processes driven by factors like comparative advantage and trade costs are cumbersome and incomplete. Economic complexity offers a more precise measure of a country’s economic development and product capacity, facilitating future-oriented choices. Leveraging international trade data from 2001 to 2015, this paper employs economic complexity theories to analyze Chinese and the US’ trade networks in the global market. It explores multi-level considerations for exporting countries’ partner selection, revealing varying product diversification challenges and market clustering tendencies. China shows increasing overall product proximity relationships, specifically notable in textile-related products, while the US exhibits decreasing product proximities. Additionally, trading positions and product dependencies vary across markets. Some countries, like Japan and Germany, maintain stable positions, while others fluctuate. Notably, countries like Nigeria, Bangladesh, and Cambodia have higher positions in the Chinese market, suggesting promising long-term trade partnerships. Conversely, countries like Yemen, Kazakhstan, Kuwait, and Sudan have higher positions in the US market, indicating significant disparities. Products that are needed in the Chinese market and occupy higher complexity positions are primarily concentrated in the field of non-ferrous metals, while the US market relies more on raw materials. This highlights distinct market dynamics, particularly pronounced in the US market. Hence, there is a greater likelihood of obtaining better economic benefits by trading these products in their respective markets.
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... However, farmers' participation in agribusiness institutions' activities has mostly been limited to production activities, and it has not been fully optimized [5]. The agricultural sector is essential in economic development, particularly in developing countries [4,6] a priority [3], and an essential part of domestic economies [7] as well as a strategic sector at the national and regional levels [8,9]. Furthermore, the sector is strategically responsible for accomplishing the first MDGs, which aim to eradicate extreme poverty and hunger [10]. ...
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