The Impact of Capital Account Openness on Income Inequality: Empirical Evidence from Asia

Abstract

The relationship between income inequality and capital account openness is empirically investigated in this study, where macroeconomic variables have opposing effects. Panel data used in the study from the KAOPEN Index and World Bank consists of 28 Asian countries and has been examined; it contains annual observations from 1970 to 2018. The data is examined using a random-effect model based on GMM estimates. Income inequality and capital account openness are positively and significantly related, according to our findings. Overall, the findings imply that increasing income gaps reduced capital investment in nations with large discrepancies. The growing economic discrepancy is being caused by the rich's increasing income share at the expense of the poor. In Asia, inward capital account openness exacerbates income inequality, while outward capital account openness exacerbates it. As a result, income inequality slows economic growth, leading to inflation, unemployment, and increased government spending in several Asian countries. Our control factors, GDP, and other secondary school enrolments, all had a statistically significant negative relationship with income inequality. Income disparity has a positive and statistically significant association with government spending, inflation, population, trade openness, and unemployment. Income disparity has a negative association with capital account openness, gross domestic product, and secondary school enrollment.

1. Introduction

Rapid economic globalization has been observed over the last three decades. Financial integration among many countries is changing the sector of finance as a result of rapid economic globalization. Through technology innovation and transfer, resource allocation, capital flows, and capital accumulation, financial integration is projected to boost economic growth. Financial integration has three effects on economic growth (i) financial integration increases trade among countries, affecting economic growth. (ii) More financial integration increases productivity growth, increasing economic growth. (iii) Through foreign direct investment, a country’s volume of capital and economic growth increases. Capital account openness is currently one of the top debated topics for policymakers globally. How to liberalize a capital account? To what extent a nation, especially in Asian countries, can open its capital account? Thus, many researchers found interesting answers.

Rapid financial and economic integration recorded income inequality in many countries. Gini coefficient for countries shows that since 1980 income inequality has risen in high-income and middle-income countries. Slow economic growth and economic inefficiencies are often linked with income inequality. The financial liberation and development increase financial market integration, capital account openness, and foreign bank ownership in host countries. The liberalization and integration widen the income inequality gap across countries in the same period. The capital account liberalization increases the Gini coefficient in advanced and developing market economies and much more in emerging economies. There are two reasons; first, capital liberalization increases income inequality; second, capital liberalization is the mean of capital volatility. Second, in reality, capital account liberalization favors the rich to have more access to capital (Furceri & Loungani, 2018).

Some researchers examined the impact of financial liberalization on income inequality (de Haan & Sturm, 2017; Jung & KIim, 2018). The hypothesis of Kuznets (1955) noted that economic development and income inequality have a nonlinear relationship at the beginning of financial development; the gap of income inequality increases when the country reaches a steady situation, then the income inequality gap starts to decrease. Kuznets presumes an inverse-U shape relationship of income inequality and GDP per capita. Piketty and Saez (2003) suggested that income inequality has risen in most developed economies during the economic development process since the 1970s. The study evaluated how capital account openness and economic growth affect income inequality in Asian economies? What is the correlation between the dependent and independent variables?

Compared to the broad swathe of research on the effects of capital account openness on economic development and financial uncertainty, the distributional ramifications have received much less attention. The simultaneous rise in income inequality and capital account openness has emerged as a significant phenomenon in recent decades. A cursory examination of capital account openness and income inequalities patterns shows a meaningful positive relationship. Results suggest that income inequality marches in lockstep with capital account openness in Asia from 1970 to 2018, with a positive correlation. Simultaneously, capital account liberalization significantly impacts income disparity.

The remainder of the research is structured as follows. Section 2 examines the pertinent literature. The data and variables and econometric model construction used in this analysis are described in Section 3. The estimated findings and interpretation are presented in Section 4. Section 5 is about the conclusion.

2. Literature Review

2.1. Financial Openness and Economic Growth

Financial openness and economic integration lead to capital accumulation, productivity growth, and trade, affecting growth. Economic integration and financial liberalization increase foreign direct investment (FDI) and research and development, stimulating growth. Moreover, openness to capital account affects economic growth in two ways. First, external saving, economies with more capital account liberalization are more funds to finance their deficits in current accounts and enhance external saving. Second, countries reduce restrictions on capital flow and increase productivity (Edwards, 2001). The endogenous growth model study concluded that economic integration improves foreign direct investment, research, and development in the industrial sector and increases world growth. The results suggest a positive link between FDI flow and economic growth has not inferred any causal link; instead, both respond endogenously to economic integration (Gao, 2005). By financial liberalization, countries get “potential collateral benefits” important for developing financial markets, institutions, governance, and macroeconomic factors.

Theoretical literature reveals the role of financial liberalization in the economies’ economic growth and development process. Some emerging economies have made fast progress in the financial integration process in the last few decades. Financial integration has the property to encourage capital allocation, production specialization, international consumption risk-sharing, and economic growth (Gehringer, 2015; Saafi et al., 2016). Moreover, financial liberalization increases productivity factors through better efficiency in resource allocation and free access to investment opportunities, which lead to improving economic growth (Gehringer, 2013). Besides, by increasing competition and the import of financial services, financial integration speeds up the development and operations of the domestic financial sector and promotes more investment and growth.

Some researchers found an insignificant or negative effect of financial liberalization and financial distress on economic growth (Ben Naceur et al., 2007; Narayan & Narayan, 2013). (RATNAWATI, 2020) All factors of financial stability have a substantial impact on economic growth, poverty, income inequality, and financial stability at the same time. However, in some Asian countries, the partial impact of the financial inclusion factors on economic growth, poverty reduction, income inequality, and financial stability has not been optimal.

Researchers have closely examined the nexus of financial liberalization and economic growth through empirical and theoretical studies. The mixed results of these researches make the nexus of financial liberation and economic development more controversial and ambiguous (Bekaert et al., 2005). De Nicolò and Juvenal (2014) documented the positive effect of capital account openness on economic growth. Bumann et al. (2013), Rodrik (1998), Ahmed (2011, 2016), and Eichengreen and Leblang (2003) concluded the insignificant negative effect of financial integration on economic growth.

2.2. Financial Openness and Income Inequality

The contribution of financial openness in economic growth is well defined and understandable through various research studies. However, the relationship between financial liberalization and income distribution is still a new and exploring area. Research theories have mixed results some literature theories predict an inverted – U shape the relationship of financial liberalization and income inequality; however, some estimate a linear relationship. Galor and Moav (2004) confirmed a linear relationship between financial liberalization and income inequality. More financial development removes the credit restrictions, which benefits low-income groups through human resource and capital accumulation channels (Bumann & Lensink, 2016). Empirical results showed that capital account liberalization reduces income inequality of economies with the financial sector depth level. However, the theoretical model suggests that financial liberalization improves banking efficiency and reduces borrowing costs. The financial market keeps the equilibrium; there is an upward moment in deposit rates and affects the income of savers and investors, which reduces income inequality.

Contradictory results on the financial-inequality relation- ship; empirical research proposes that financial development positively impacts poverty reduction and inequality (Beck et al., 2007). Increasing private credit can positively lead the economic growth for the lower-income groups and decrease income inequality, negating the. Therefore, these studies explored the one-dimension ratio of private credit to GDP as a degree of financial development. This indictor explains only one side of economic development: financial system deepening while ignoring the financial efficiency and stability.

Claessens and Perotti (2007) and Demirgüç-Kunt and Levine (2008) investigated different dimensions of financial development and noted the significance of financial openness in reducing poverty and inequality. Nguyen et al. (2020) used the financial liberalization index to cover the capital account liberalization aspects and found that financial openness and international integration affect income inequality in three ways: first economic growth, second access to credit, and third financial services; and financial crises. Ang (2010) examined that due to removal of restrictions from foreign direct investment, interest rate ceiling, capital account liberalization, and stock market liberalization. Increased capital inflow and investment in developing countries and technological development increased productivity, are pushing economic growth. Economic growth has a positive impact on regions, sectors, and productivity factors, which are sources of employment for the poor segments of society. It enhanced the financial resources to improve the poorer and raise public spending and transfers. Batuo and Asongu (2015) assumed that least developed countries have low-interest rates due to their administrative setup, which slows the economic growth rate and reduces savings. Financial openness and liberalizing the interest rates can drive the poorer of society for savings and enhance the credit supply available in the economy to increase economic growth and reduce income inequality.

Some financial inclusion provides more access to people to get finances on low borrowing costs to invest more and more in business (Ang, 2010). (Ali et al., 2021)Financial inclusion has a small but statistically significant negative influence on income inequality at the micro-level. In some countries, the macro-level index and all specific indices of financial inclusion do not affect income inequality. Bumann and Lensink (2016) stated that financial liberalization increases the bank’s efficiency and reduces borrowing costs. Economic market equilibrium occurs with a rising deposit rate, minimizing the gap between deposits and interest rates. It benefited both savers and investors, narrowing the gap of income inequality. The level of economic development determines financial liberalization’s distributional effect. At the beginning of the development process, high-income groups have access to financial services due to the fixed cost of financial services, causing more income inequality (Arestis & Caner, 2004). As the economy grows, access to financial services becomes easy and cheap to lower groups because human capital replaces physical capital as the main factor to stimulate growth.

Furceri and Loungani (2018) found that capital account openness harms income inequality; a more open domestic financial system may worsen income inequality in the short run and medium-run. A country’s economic development, financial inclusion, and financial crises are the three channels of financial openness that affect the income inequality of an economy, but job performance improves. Zhang and Naceur (2016) suggested that financial development help to reduce poverty and income inequality, while external financial openness has the opposite effect on the world economy. Inflation is found to have a negative impact on lower-income groups. Financial inequality is affected by economic growth, financial inclusion, and financial instability (Ang, 2010; Arestis & Caner, 2004).

2.3. Capital Account Liberalization

Capital account liberalization occurs when a country’s government decides to switch from a closed capital account regime, in which capital cannot freely travel internationally, to a liberalized capital account system, in which capital can freely enter and exit the country. Capital account liberalization and financial openness have a variety of effects on inequality, including the impact of capital account openness on risk-sharing, the impact of capital account openness on the likelihood of financial crises, the impact of increased foreign direct investment on economies, and the impact of financial liberalization on the labor share of income.

Financial liberalization refers to the loosening of financial markets and institutions (Kaminsky & Schmukler, 2008). Financial liberalization entails reducing the govern-ment’s interventionist role and enhancing the influence of financial markets. Inequality has risen in developed, emerging, and developing countries, with no consistent causes identified in research investigations. The most important factor affecting income inequality around the world is financial liberalization. Financial openness elevates the role of financial markets and reduces government intervention in financial markets and institutions. Delis et al. (2014) and Li and Yu (2014) analyzed that financial openness has an essential role in reducing income inequality. Jaumotte and Osorio (2015) and Zhang and Naceur (2016) examined that financial liberalization is harmful to the income inequality between the rich and poor. Capital account openness increases income inequality. They link the impact of financial openness to economic progress at the country level (Furceri & Loungani, 2018). Income equality is harmed by capital account openness combined with a high level of financial depth. The theoretical model argues that financial exposure improves bank performance, lowers borrowing costs, and raises the bank deposit rate, allowing low-income people to save more and reduce income disparity (Bumann & Lensink, 2016). According to Li and Su (2019), capital account liberalization widens the income disparity in emerging economies. Liberalization of the inside capital account causes more income distortion than liberalization of the outward capital account.

3. Research Methodology

This study discusses income inequalities, capital account openness, economic growth, and control variables. We have analyzed 28 Asian countries. The collection of data is principally from secondary sources from the World Bank. It contains GDP per capita, government expenditures, total population, Education (school enrollment secondary), trade openness, inflation, and unemployment. We have measured the capital account openness using the KAOPEN index 2008 by china.

Moreover, the most conventional measure of income inequality is the Gini coefficient is used in this study. A Gini coefficient ranges from 0 to 100, with 0 representing perfect equality and 100 representing perfect inequality. The Gini coefficients used in this study are taken from the Estimated Household Income Inequality (EHII) database compiled by the University of Texas Inequality Project (UTIP). We picked the EHII dataset for Gini coefficient datasets measures. Income inequality is measured by gross income Gini (as employed by, for example, (Bumann & Lensink, 2016; de Haan et al., 2017; Li & Yu, 2014; Li & Su, 2019). The time frame of this study is from 1971 to 2018, primarily focused on data availability. Thus, the projected model is itemized as source data collected from the World Bank organized by the author. Below, Table 1 displays the measurement and details of the variables.

Table 1: Variables Details and Data Resources

3.1. Model Construction

This research adopts the stochastic influence of regression on the income inequalities, capital account openness, and economic growth model paradigm. The model equation is defined below.

II = f (CAO_it, GDP_it, CONTR_it, )       (1)

II = f (CAO_it, GDP_it, Σ_it GE, TP, SES, TRO, INF, UNP)       (2)

Secondary data used in this research and sets are derived from multiple sources. Income Inequalities (II) are the dependent variable, and independent variables include capital account openness (CAO) and the gross domestic product per capita (GDPP). Other control variables include government expenditures on education, total population, school enrollment secondary, trade openness, inflation, and unemployment. The variables are converted into a natural per capita logarithm method.

II_it = β₁ + β₂CAO_it + β₃GDPp_it + β₄CONT_it + µ_it       (3)

II_it = β₁ + β₂CAO_it + β₃GDPp_it + β₄ Σ_it + GE + TP + SES + TRO + INF + UNP + µ_it       (4)

Where β₀ are coefficients of dependent variables, ε is an error term with a standard distribution, i.e., zero mean and constant variance, I denote cross-section, and t denotes time panel. Income inequalities (A Gini coefficient ranges from 0 to 100 with 0 representing perfect equality and 100 representing perfect inequality.), capital account openness (KAOPEN Index), economic growth (GDP per capita), and control variables measurement are depicted in above table one. The panel data period is 1971–2018 and extracted from World Income Inequality Database, KAOPEN index, and World development indicators (WDI) online available on the World Bank’s website.

3.2. Panel Unit Root Test

For all chosen variables, the panel unit root tests have been performed. The test guarantees that the panel data is not vulnerable to spurious regression. The main reason for the panel unit root test is to overcome the common influence problem while ADF and Im, Pesaran, and Shin W-stat are used. The estimate’s dependability may be interrogated, as the intense power of the unit root test in series-time and panel experiments is less than 50 (Hausman, 1978; Yair Mundlak, 1978). Panel unit root test can solve this issue as it has more influence and standard asymptotic distribution. Consistent suggestions ought to be given by the assessment. Besides, as reported by Levin et al. (2002), the test is more effective associated with the unit root test panel data. These approaches have been widely used. Equation of Unit root test is as:

\(\Delta y_{i t}=\Delta \varnothing_{i t}+\beta_{i} x_{i t-1}+\rho_{i} T+\sum_{j-1}^{n} \theta_{i j} \Delta x_{i t-j}+\varepsilon_{i t}\)       (5)

Where, Ø_it, x_it, ρ_i, Δ, Τ, and ε_it symbolize the intercept, the analysis variables, the difference operator, the period, and the disorder term correspondingly. This second-generation unit root test is commonly used in the literature as unit root analysis based on first-generation methods yields inaccurate conclusions.

3.3. Panel Estimation

The estimator PMG should have a short-run estimate to be heterogeneous, considering the intercept, change speediness, and error update. For long runs, the slope coefficient is constrained to be homogeneous. The advantage of using the ARDL methodology is that long-run relationships are more accurate and consistent. It does, however, permit the expression that is an error correction and its coefficient to be less than two and negative. The important assumption is that precision is required in the calculation and that there should be no serial correlation in the residual error correction model, ensuring exogenous explanatory variables. Both the dependent (p) and autonomous (q) variables’ lags (p, q) have been added. The requirements are attainable. This method necessitates huge sizes of T and N, with T having to be greater than N. The number of N countries is close to 20–30 (Pesaran et al., 1999). The second estimator is MG, which was implemented by (Pesaran & Smith, 1995). The estimator is helpful since it performs various regressions for each country and constant. PMG will vary to some degree, and it will not be limited to estimator steps. It may produce different and heterogeneous coefficients for a single country in both random and fixed processes. It decreases the speed shift, making the short-run coefficient comparable, and allows for a specific panel coefficient.

GMM Estimation

II_it = β₁ + β₂CAO_it + β₃GDPp_it + β₄CONT_it + µ_it        (6)

β_1i = β₁ + ε_i        (7)

Random Effect Estimation

II_it = β₁ + β₂CAO_it + β₃GDPp_it + β₄CONT_it + ω_it        (8)

ω_i = µ_it + ε_i       (9)

4. Results and Discussions

4.1. Descriptive Statistics

Table 2 postulates the conclusions of descriptive statistics. These statistics are employed for demonstrating the complete explanation of the used dataset in detail. From table two, it has been shown that the average income inequality is 42.597, while the lowest and most significant values are 30.700 and the maximum is 52.000, respectively. Capital account openness average value is 0.466 with a minimum and maximum 0 and 1 Gini coefficient. The value of GDP per capita is 3.250, with 12.88 and –8.741 being the importance range. The government expenditure on education is noted as 3.788 with 8.018 and 0.998. The inflation is about 13.752, with 373.21 and –2.983. Secondary school enrollment recoded 71.744, with 107.248 and 16.538 percent. The total population is 79275608 with 1.27E+09 and 753334. Simultaneously, the average trade openness is 30.730, with 342.133 and –2462.025. The standard unemployment rate is 4.996, with 13.25 and 0.140. They are registered, respectively.

Table 2: Descriptive Statistics

4.2. Coefficient of Correlation

Table 3 demonstrates the pairwise relationships between the regressor variables, namely, income disparities, and the repressors, namely, capital account openness, GDP per capita, and Control variables. Wealth disparities show a strong and statistically significant link with independent variables rather than GDP per capita, according to paired analyses. It shows how increasing capital account openness has a major impact on income inequality. Increasing income inequality, on the

Table 3: Coefficient of Correlation

other hand, has a negative influence on GDP per capita. Many factors have large positive and negative effects, which are consistent with economic models and suit the theory. Many factors have significant positive and negative impacts consistent with economic models and fit the theory.

4.3. Unit Root Test

The results of two commonly used unit root metrics are presented in Table 4. It is necessary to examine each variable’s unit root before applying the ARDL model. The variables considered are not stationary at I(2); otherwise, the data would point to adverse outcomes. The unit root (nonstationary) is the underlying series, according to the null hypothesis of Im, Pesaran, and Shin (IPS) and ADF - Fisher Chi-square tests. These tests show that some parameters, such as GDP per capita, inflation, total population, and trade openness, have a unit root, whereas others do not. For income equality, capital account openness, government education spending, secondary school enrolment, and unemployment at the first difference, there is no root unit. Both variables are genuine, according to the Harris-Tzavalis conclusions, which are based on a unit root test. While no unit root is at I(0), income equality, capital account openness, government expenditure on education, secondary school enrolment, and unemployment all have level stationary at I(0). At the same time, GDP per capita, inflation, total population, and trade openness are all at the same level (I)(0).

Table 4: Unit Root Test Results

4.4. GMM Estimations

The GMM estimator tests the robustness of our variables, and Table 5 presents all the data. In contrast to the one-step system GMM, it is generally accepted that two-step GMM results are robust. Besides, instruments are structured in the system GMM by busing both degree and first difference equations. This technique provides detailed sample size information and enlarges the number of instruments. Even in the presence of endogenous regressors, the efficiency of a two-step GMM method allows for initial conditions to be informative and appropriate. Finally, Hansen J-test and second-order autocorrelation tests are used to analyze the instrument’s quality based on equations of level and difference and validity of over-identification constraint, respectively.

Table 5: GMM Estimator Results

Star *, **, *** shows the significant level of variables at less than 1, 5, and 10 percent

Two-step system GMM results demonstrate that capital account openness has a significant positive relationship with income inequalities, illustrating that capital account openness enhances income inequalities in Asia countries. More specifically, a one percent increase in capital account openness increases the income inequalities by 6.722 percent. In the case of GDP per capita, it negatively impacts income inequalities, which demonstrates II reducing the economic growth in Asian countries. One percent decrease in GDP per capita increases income inequality by 0.235 percent and assumes that all elements are constant. In contrast, all control variables except SES show a significant positive relationship with income inequality, whereas SES indicates a meaningful negative relationship with income inequality. More precisely, GE, INF, TP, TRO, and UNP increase the income inequality in Asian countries, and However, SES expansion in these nations will reduce the income inequality.

4.5. Hausman Test

The results of the Hausman test in Table 6 below show a probability value of greater than 5%, allowing us to firmly establish the random effect model’s application. After the Hausman test validation of the model specification, the study proceeded with the panel’s random effect regression and presented the findings; the findings are described in table six. The findings revealed that rising income disparities had a positive and significant impact on capital account openness while having a negative impact on GDP per capita in the sample nations. The capital account openness proxy variables grew by 3.029 percent (GDP per capita), 0.028 percent, as though income inequality increased by 1%. Furthermore, three control factors (government education spending, total population, and unemployment) have a positive impact on the rest of the variables while having a negative impact on the regressors.

Table 6: Hausman Test Results

4.6. Panel Random Effect Estimation

Income inequalities had a positive and significant effect on capital account openness, according to Table 7. It was calculated that a 1% increase in income disparities may raise capital account openness to 3.029 percent and have a considerable influence on GDP per capita. In the Asian countries studied, economic disparities have a positive impact on government spending on education, population, and unemployment. Inflation, school enrolment, and trade openness are all negatively affected, with –0.006272, –0.010941, and –0.000118, respectively.

Table 7: Panel Random Effect Estimation Results

Star *, **, *** shows the significant level of variables at less than 1, 5, and 10 percent.

Surprisingly, the results presented in table seven reflect the theoretical predictions made earlier in the study: Especially, Income equalities impact positively, and significantly that is desired result as per the above hypothesis.

5. Conclusion

Financial globalization plays a key role in generating a number of co-benefits that help countries achieve long-term growth. It’s unclear whether these economic benefits are distributed equally across the population. Over the previous three decades, income inequality has risen in most countries and regions, and this era coincided with increasing financial integration.

The impact of capital account openness on income inequality in Asia is investigated in this study. We find that capital account openness is connected to a statistically significant and sustained increase in income inequality using the panel fixed-effect model for 149 counties using data from 1970 to 2018. We find that a one percent rise in capital account openness leads to a 3.029 percent increase in the Gini measure of income inequality on average. Second, the type and direction of capital openness are critical. We also discovered that nations with developed financial systems and open capital accounts for inclusion have a lower impact on income disparity. Inward capital account openness widens the gap in income inequality more than outward capital account openness, and equal market integration enriches the wealthy while undermining the poor.

This finding does not rule out the possibility of countries opening financial accounts, but it does raise new concerns. Countries, where lowering inequality is a major policy aim, may need to develop liberalization policies and regulations that balance this concern with other factors.

The study presents the following policy suggestions: To attain better growth rates, governments should lower government spending on final consumption. To mitigate the negative effects of capital account liberalization, Asian countries must improve financial inclusion and financial institutions.

Furthermore, Asian corporations must prioritize investment and invest more in human capital, as both appear to accelerate economic growth.

Different times and evaluating changes in inequality could widen the study and give additional light on the impact on growth. Finally, further research is needed to better understand the influence of regional disparity on local economic growth.