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Social Indicators Research

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09-11-2020 | Original Research

Economic Complexity and Income Inequality: Does Country Risk Matter?

Authors: Chien-Chiang Lee, En-Ze Wang

Published in: Social Indicators Research | Issue 1/2021

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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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The data section offers a more detailed definition and explanation about this concept.

For instance, in terms of the subcomponents of political risk, the risks of government stability, corruption, law and order, democratic accountability and so on all relate to the quality of a country’s institutions. Therefore, we believe that country risk is able to capture the vital features of its various institutions.

i.e., economic risk, political risk, and financial risk.

There is a large volume of published literature describing the role of demand on economic growth (economic strength) (Feldman et al. 1987; Walker and Vatter 1999; Nalewaik 2006) and highlighting the positive effect of improvement of the demand structure on economic growth (Echevarria 1997; Kongsamut et al. 2001; Matsuyama 2002; Buera and Kaboski 2012). Overall, the studies presented here indicate the fact that a country with high economic strength is more likely to be associated with a better demand structure.

In a country with low financial risk, there usually exists lower default risk, which will prompt investors to demand a low return relative to their investment and producers can pay a lower financial cost.

If we assume that \({z}_{g}\) follows a normal distribution, then the Eq. (2) with G = 1 can be simplified to a general linear regression model where the explained variable is e, the explanatory variable (control variable) includes both z and x and \(\varepsilon\) is normally distributed error term with variance \({\sigma }^{2}\):

e = \(z{\alpha }_{1}+ x{\alpha }_{2}+\varepsilon , \varepsilon \sim N(0,{\sigma }^{2})\)

Furthermore, we suppose that \({z}_{g}\) follows a normal distribution, then the Eq. (2) with G = 2 can be detailed as:

\(\left\{ {\begin{array}{*{20}c} {Group1:e = z\beta_{1} + x\beta_{2} + \varepsilon_{1} , \varepsilon_{1} \sim N\left( {0,\sigma_{1}^{2} } \right)} \\ {Group2:e = z\beta_{4} + x\beta_{4} + \varepsilon_{2} , \varepsilon_{2} \sim N\left( {0,\sigma_{2}^{2} } \right)} \\ \end{array} } \right.\)

where e, z and x are the explained variable, the explanatory variable and control variable respectively. \({\varepsilon }_{1}\) and \({\varepsilon }_{2}\) are independent and identical normally distributed error terms within each group with variances \({\sigma }_{1}^{2}\) and \({\sigma }_{2}^{2}\), respectively. Note that, the FMM with the joint normal distribution where each group is not yielded by the identical data-generating procedure (Owen, 2009; Liu et al., 2020).

BIC = 2LL + Jlogn, CAIC = − 2LL + J log (N + 1), AIC3 = − 2LL + 3 J, and AIC = − 2LL + 2 J, where LL represents the log-likelihood value, N represents observations, and J represents parameters.

More detailed definitions and calculation about ECI can be seen in Hidalgo and Hausmann (2009).

Sub-samples are based on the country classification of the International Monetary Fund. This classification is not based on strict criteria, economic or otherwise, and has evolved over time. More detailed information about the classification can be found in World Economic Outlook 2019.

Before estimating Fmm, we perform the group tests according to four information criteria (i.e.AIC, AIC3, BIC, and CAIC). The results (not reported) mean that two-group Fmm can describe our data the best. Based on a series of specification tests (not listed), we conclude two-group Fmm can also be suitable for the following robustness tests. All results are provided upon request.

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Title: Economic Complexity and Income Inequality: Does Country Risk Matter?
Authors: Chien-Chiang Lee
En-Ze Wang
Publication date: 09-11-2020
Publisher: Springer Netherlands
Published in: Social Indicators Research / Issue 1/2021
Print ISSN: 0303-8300
Electronic ISSN: 1573-0921
DOI: https://doi.org/10.1007/s11205-020-02543-0

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