Do fiscal and monetary policies contribute to income distribution? Empirical evidence from Central Asian Countries

doi:10.21203/rs.3.rs-1756885/v1

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Do fiscal and monetary policies contribute to income distribution? Empirical evidence from Central Asian Countries

https://doi.org/10.21203/rs.3.rs-1756885/v1

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This study examines the impacts of macroeconomic policies (i.e., fiscal policy by government consumption spending, and total tax revenue, and monetary policy by broad money and real interest rate) along with some other regressors namely inflation, gross domestic product (GDP) per capita, remittances, exchange rate, and financial development on income distribution in four Central Asian countries over 1995–2020. After employing the unit root tests, we implemented the Autoregressive Distributed Lag (ARDL)/Pooled Mean Group (PMG) approach for empirical analysis. Empirical result reveals that both fiscal and monetary policies have positive and negative impacts on income inequality. Empirical results also exhibit that inflation increases income inequality, while financial development, per capita income, and more inflow of foreign remittances decrease inequality in the panel and individual countries analysis. These empirical findings suggest that coordinated policies need to be adopted along with the increase in remittances, financial sector development to attenuate income inequality.

JEL Codes: E62; E63; D63

Fiscal policy

monetary policy

income distribution

ARDL/PMG

Central Asia

Income inequality refers to the uneven distribution of income among populations or groups. In today’s world, income inequality is among the most contentious concerns, as it exists to a certain level across the globe (Muhibullah & Das, 2019). In the last decade, the growing levels of inequality in the developed economies have raised issues about the sustainability of more unequal countries. In this regard, extensive literature debated the factors determining income inequality (see, Andersen et al. 2021). The governments focused heavily on addressing the problem of income inequality (Yellen, 2014), as it rose as a result of the 2008 global financial crisis and subsequent recession (see, Martinez-Vazquez et al. 2012). This evidence is also supported by Mahmood (2017) that income inequality has risen rapidly in the post-financial-crises period. Accordingly, this dramatic increase in income inequality causes poverty, unemployment, and worsens many other macroeconomic factors. Particularly, income inequality affects economies, families, societies, and communities differently. Evidence of increasing income and wealth inequality among developed and developing countries, has generated debates regarding its economic implication. However, there is no consensus on the link between inequality and economic growth but increasing income inequality can be a serious economic issue (Domanski et al., 2016).

Smith (1776, p.316) documented that “Wherever there is great prosperity, there is great inequality. For one very rich man, there must be at least five hundred poor, and the affluence of the few supposes the indigence of the many”. Consequently, it is believed that income inequality which harms people`s living standards, is a complicated problem that is deeply embedded in most Asian countries. Though income inequality exists in all countries with different ranks and measurements but there is a great visible disparity between the haves and have-nots in developed, developing, and emerging economies. Moreover, in developing countries, the gap between the haves and have-nots is comparatively wider than in developed countries. The unequal distribution of income widens the income gap and decreases interest in work, as well as causes high unemployment, political uncertainty, and massive corruption (Dehshiri et al., 2020). It is also claimed by Alvaredo and Gasparini (2015) that income disparity in emerging economies is nowadays worse than it was three decades ago. These increasing trends in inequality bring inefficiency in resources allocation, restrict aggregate demand, and reduce consumption and investment (Berg et al., 2018). Moreover, more unequal distribution of income may also increase household debt, fuel asset market boom, and economic uncertainty (Perugini et al., 2016).

Income and wealth inequality can be influenced by monetary policy, whereas inequality could also affect the efficiency of monetary policy (O`Farrell & Rawdanowicz, 2017). Rising income inequality is causing a significant issue, and various fiscal policy indicators are being considered to discuss it. Monetary policy, on the other hand, can also influence income distribution; however, its income redistributive effects have received little attention (Davtyan, 2016). Political economy claims that some parameters between inequality and economic growth are commonly used to define the redistributive mechanism (Muinelo-Gallo & Roca-Sagales, 2011). The redistributive of income is implied in political economy arguments to be incorporated via fiscal policy, including government expenditure and taxes. Moreover, income is spread through monetary policy as well. Monetary and fiscal policies regulate economic activities. Even though monetary and fiscal policy regulate the economic activities, particularly used for significantly different macroeconomic goals (for instance, increasing the size of the economy and controlling inflation), they both have also an impact on the same economic activities, including re-distribution, and are constantly interconnected. According to Coibion et al. (2017) the influence of monetary policy on the distribution of income is unclear as various types of the transmission of monetary policy function work in different directions. It is important to correctly grasp the distributional effects of monetary policy because determining its overall impact on incomes distribution and overall economic performance is unavoidable. Amaral (2017) mentioned that the Fed’s monetary policy does not directly aim to reduce inequality but focuses to reduce unemployment, stabilize prices and establish a reasonable long-term interest rate. Accordingly, changes in the different tools of monetary policy may influence income inequality indirectly.

The findings of existing empirical studies regarding the impact of monetary policy on income inequality are yet elusive. However, many prior studies have been documented the negative consequences of inequality on macroeconomic outcomes (Perugini et al., 2016). Every country aims to achieve inclusive and sustainable economic growth while boosting equity. The broadening income disparity between the affluent and the destitute has increased, wish to know the causes of inequality and poverty, and to devise appropriate interventions to diminish poverty and close income disparity. Fiscal policy has been adopted by many countries to tackle the widening income inequality and poverty (ADB, 2014).

Inequality in income and wealth has become increasingly obvious as a threat to economic actions and personal unity. Since the world financial crisis, central banks have used tight monetary policy and unconventional tools to combat sluggish growth and inflation. The widespread adoption of unconventional monetary policy tools and their possible influence on disparity has raised the attention of central banks’ position in wealth & income inequalities (Saiki &Frost, 2020). On the contrary, fiscal policy's role in income distribution is critical. It has three aims; (i) to keep economic stability; (ii) the government should give public goods inside the occurrence market distortion, and (iii) inside the face of persistently high inequality, it is necessary to transfer wealth. Particularly, fiscal policy tools (expenditure and tax) influence the distribution of income in both the short and long run. Taxes impact consumer and firm behaviors by changing the supply of labor, consumption, and investment decision, to minimize income inequality (Bakija, 2014). On the other hand, spending on education and infrastructure, as well as other transfers, have also an impact on income disparity (Berg et al. 2018). Accordingly, fiscal policy can influence the distribution of income, both positively and negatively, different in each country's case. The income inequality in selected central Asian countries is shown in Fig. 1.

Here, we determine the unequal distribution of incomes or wealth across the entire dispersion of households in the Central Asian countries. Amaral (2017) claims that yet there is no clear consequence on the impacts of conventional monetary policy appears from the consideration of hypothetical explanation through various channels but needs to measure these impacts in the data. Likewise, Martinez-Vazquez et al. (2012) argued that too little study has been carried out on how variation in taxes and government expenditures have truly affected the distribution of income, particularly in the developing world. Though, some research reveals that fiscal policies do impact trends in income distribution. Some prior studies including Caminada & Goudwaard (2001), observed that decreases in government expenditure on social programs have resulted in less substantial welfare systems and sharp upsurges in income inequality, while the causality has not been established in UK and Netherland. Similar results on the impact of fiscal policy on social welfare and inequality have also been observed in the case of developing countries like Indonesia (Keuning & Thorbecke, 1989), and Latin America (Ocampo, 1998). While the study of Schuknecht and Tanzi (2005) revealed a weak relationship between changes in government expenditures and income disparity. Moreover, Harberger (2006) observed the overall ineffectiveness of taxes in income distribution.

There has been increasing interest regarding the controversy between monetary and fiscal policies and income distribution. However, apart from previous work on income distribution and growth that motivated this study, the majority of the empirical evidence presented here is also relevant to fiscal and monetary policies, including others, income and inequality (Persson & Tabellini 1994); GDP per capita and inequality (Barro 2000); unemployment, inflation, real interest rate and income inequality (Jantti & Jenkins 2001); infrastructure expenditure and income inequality (Calderon & Serven 2004); Globalization and trade openness and income inequality (Dollar & Kraay 2004); higher education and income inequality (Breen & Garcia-Penalosa 2005). Thus, in this study, we intend to accomplish to investigate the link between fiscal and monetary policies and income distribution in the case of selected central Asian countries, while also addresses two questions; (i) what is the effect of fiscal policy on income distribution? (ii) What is the impact of monetary policy on income distribution?

The prime focus of this study is on the possible role of fiscal and monetary policies in income distribution, and to know to what extent changes in monetary and fiscal policies in the central Asian countries have contributed to condensing an ongoing worsening of income distribution patterns. Based on the importance of income inequality, and poverty where the gap between the haves and have-nots widens gradually, it is indispensable to conduct an empirical study and examine the impacts of fiscal and monetary policies on income distribution in four Central Asian countries over the period ranging from 1995 to 2020. This study has a novel contribution to the macroeconomic policy by encompassing all the literature regarding fiscal and monetary policy relationship with income inequality, particularly to the central Asian region. The findings of this study will guide the policymakers to mitigate income inequality through the effective implementation of fiscal and monetary policies. Thus, this study certainly contributes to the literature to detect the policy that might decrease income inequality.

The research study is structured as follows. Section 2 deals with the theoretical and empirical literature on the association between fiscal policy, monetary policy, and income distribution. Data description and empirical methodologies are given in section 3. The empirical results with discussions are reported in section 4. Concluding remarks are presented in section 5.

Theoretical Literature

One of the World`s most serious issues is rising income inequality everywhere. The key role that income distribution plays on the macroeconomic level of a country has primarily discoursed in a seminal study by Kuznets (1955) where he tried to establish the association between growth rate in income and income inequality. As per the World Inequality Report (2018), global income disparity has risen sharply since 1980, due to increased earnings of the world`s top one percent and the lower growth of the world`s bottom 50 percent. Inequality can rise or fall as a result of the quantitative effect of different policy tools. There is a strong argument to be made that unconventional monetary policy executed and even after the recent financial crisis has increased financial assets prices, further increasing inequality. However, the economists are divided on whether and how much unconventional monetary policy causes inequality. Monetary policy`s influence on inflation can also influence inequality. In the case of income inequality, theoretical processes and empirical findings are ambiguous regarding the way of the impacts, and they usually show the impacts are insignificant for low-income countries with low inflation (Bulir, 2001).

Many prior studies theoretically vindicated the relationship between monetary policy tools and income inequality (Coibion et al., 2017; Bulir, 2001). The monetary authorities have no direct control over inflation or output and employment; rather, they have an indirect effect on them by changing interest rates, buying, and selling bonds in an open market operation, and increasing or decreasing bank rates for bank reserve. These indirect influences on the gross domestic product (GDP), while monetary policy influences the income distribution. Wage and income inequality have been worsened by skill-based education and improvements in technology, changes in the household system, job status, and profession, labor market structural reforms, globalization, increased international trade (Feenstra & Hanson, 2008). In their study, Kharroubi et al. (2021) mentioned that inequality decreases the efficiency of monetary policy. The various effects of convenient monetary conditions along heterogeneous households can indeed influence monetary policy transmission when the income level is high. Wealthier families are less likely to consume, and thus their spending is even less responsive to monetary stimulation. Lower-income families, on the other hand, do not gain from extremely easy credit conditions since they have little or no assets or appropriate credit scores, making it impossible for them to borrow.

For a long time, fiscal policy was considered to be an effective tool for affecting income and wealth disparity, total demand, and growth`s ability to increase production (Musgrave, 1959). The effectiveness of the fiscal policy on income inequality has been limited by Neo-Classical growth models, which imply that fiscal policy has only a short-term effect on GDP, making assessing the influence of fiscal policy on income disparity in an economy impossible Ramos and Roca-Sagales (2008). However, if taxes are regressive, then this expenditure will not help to mitigate inequality; but if taxes are progressive then this spending will help to reduce inequality (Ali & Ahmed, 2010).

Empirical Literature

Even though the monetary policy had been neglected as a driver of growing income inequality, there were few empirical studies on the relationship of monetary policy with income inequality. For example, in the case of Korea, Yue (2011) found that income inequality and economic growth has a long-run link during 1980–2002. In the case of Japan, Saiki and Frost (2014) found that unconventional monetary policy enhances inequality from 1981 to 2008. Davtyan (2016) revealed that contractionary monetary policy reduces income disparity in the United State of America during 1979–2012. Coibion et al. (2017) observed that expansionary monetary policy reduces inequality in the U.S. Munir and Sultan (2017) conclude that per capita GDP and income disparity has a positive association in India and Pakistan during 1973–2015. In the cases of OECD countries, O`Farrell and Rawdanowicz (2017) concluded that there is a small impact of unconventional monetary policy on inequality. In their study, Mumtaz and Theophilpoulou (2017) observed that contractionary monetary policy rises income inequality in the United Kingdom from 1969–2012. The study of Adeleye (2020) observed that interest rate reduces inequality in Nigeria during 1980–2015. In the long run, however, they show that smart (prudent) monetary policy focused on low inflation and constant economic growth is related to increased living standards of poor people and higher income inequality (Park, 2021).

Similarly, Martinez-Vazquez et al. (2012) revealed that in a sample of 150 countries over 1970–2006, both progressive personal and corporate income taxes diminish inequality of income. They also observed that higher public spending on wellbeing, health, education, and housing has a favorable effect on income inequality. They concluded that both taxes and public expenditures have a significant influence on the Gini coefficient. In the case of Pakistan, Khan and Hashmi (2015) explored that development expenditure reduces income disparity. Cevik and Correa-Caro (2015) showed that income inequality is increased by government expenditure in China and other 33 countries during 1980–2013. They also highlighted that tax reduces income disparity. Raza et al. (2017) revealed that the effect of military expenditure on income inequality is positive in Pakistan during 1972–2012. Martínez-Aguilar et al. (2017) found four results that indicate the overall positive net impact of fiscal interventions on inequality and poverty in Chile during 2013. First, subsidies have a positive, yet small impact effect on inequality and poverty, while direct handovers are progressive, balancing, and decreasing the poverty headcount by 4–5 percentage points. Second, though social contributions are unbalancing and poverty-growing, direct taxes on personal income are balancing and poverty-impartial, while indirect taxes are poverty-growing. Third, social expenditure on tertiary education is marginally balancing but it is not pro-poor, opposing to the impacts of social expenditure on health, basic and secondary education, which are not only balancing but also pro-poor. Finally, the extent of monetary fiscal benefits is substantially higher than that of fiscal solvency.

In their study, Shahbaz et al., (2017) examined the impacts of financial development along with other regressors on income inequality in Kazakhstan over 1990–2014. They found that income inequality is reduced by financial development in Kazakhstan. Balseven and Tugcu (2017) found that in developing countries, the effect of economic growth on income distribution is negative, while in developed countries the effect is positive for 17 developed and 30 developing countries during 1990–2014. The authors also disclosed that in developing countries tax revenue reduces income disparity, while in advanced countries social benefit reduces income inequality. Alamanda (2020) found that infrastructure spending harms income disparity, whereas social aid and subsidy have an insignificant effect on income inequality in Indonesia over 2005–2017. Aman et al. (2021) observed a significant and positive link between income inequality and economic growth, while, FDI inflows and exports have a negative link with income inequality in 36 Asian economies over 2001–2019. Recently, in their study, Digdowiseiso et al. (2022) found that fiscal decentralization has a significant impact on ethnic inequality and distribution of income in 33 developing economies over 1990–2014.

The forgoing studies that examined the effect of fiscal and monetary policy on income distribution have focused on developed and some developing countries, but no perceptible study exists on the impact of fiscal and monetary policies on income distribution concerning the Central Asian countries. Thus, the focus of this study is on Central Asian countries to investigate the influence of fiscal and monetary policy on income distribution using updated data and holistic empirical strategy.

Table 1

Selected previous studies on the impact of fiscal and monetary policies on income distribution
Author(s)	Data, Countries, Method	Regressand	Regressors	Findings
Khan &Padda (2021)	2000–2017, Pakistan, Instrumental Variable, Fixed-effect	Income inequality	Total tax, indirect tax, fiscal deficit, development outlay, human capital Direct tax, current outlay, GDP per capita, civil liberty	(+) (-)
Indongo& Robinson (2021)	1996–2016, Namibia, ARDL model	Income inequality	Government spending, Tax on income and wealth Tax on products/VAT; corporate income tax; customs, excise duties, Government spending on health	(-) (+)
Hayrullahoglu&Tuzun (2020)	2002–2019, Turkey and OECD, PARDL	Income inequality	Inflation, and unemployment Tax revenue and GDP per capita;	(+) (-)
Taghizadeh–Hesary et al., (2020)	2002Q1–2017Q3, Japan, VAR, VECM	Income inequality	Money supply (M₁), interest rate Total tax, and real GDP	(+) (-)
Berisha et al., (2020)	2001–2015, CCE, MG, BRICS	Income inequality	Inflation, Income, Interest rate	(+)
Law & Soon (2020)	1987– 2014, 65 DC and LDCs, GMM	Income inequality	Inflation, trade openness, financial development, Institutional quality; unemployment.	(+) (-)
Cevik& Correa–Caro (2020)	1980–2013, 33countries with China, IV, 2SLS, GMM	Income inequality	Government expenditure Trade openness, Tax revenue, Human capital, financial development)	(-) (+)
Yoshino et al. (2018)	2002Q1–2016Q4, Japan, VAR, VECM	Income inequality	Money stock (M1), stock price index Real GDP, government income tax	(+) (-)
Siami-Namini& Hudson (2019)	1990–2014, 24 DCs and 66 LDCs, VECM, Toda Yamamoto	Income inequality	GDP per capita, Inflation, urbanization index Unemployment, and trade openness	(-) (+)
Monnin, (2014)	1971–2010, 10 OECD countries, Pooled regression	Income inequality	Inflation, GDP per capita, union density, Unemployment, trade openness skill-based technological changes,	(-) (+)
Muinelo-Gallo & Roca-Sagales, (2013)	1972–2006, 21 high income OCED, SUR SEM	Income inequality	Distributive expenditure, and direct tax	(-)
Source: Authors compilations

The Empirical Model

Kuznets (1955) established a relationship between economic growth and income inequality, which is known as the Kuznets curve and is a hypothetical curve that depicts how economic growth and income inequality acts together. According to the Kuznets curve, when economies grow up and income levels rise, income inequality would rise at first, then reach to peak, and finally, diminish after certain key development stages and income levels. The focus of this research is confined to four central Asian countries namely Azerbaijan, Kazakhstan, Kyrgyzstan, and Tajikistan. To investigate the impact of fiscal and monetary policies on income distribution, we use the following multivariate regression model based on prior studies (includes Furceri et al., 2021; Siami-Namini & Hudson, 2019; Azam & Raza, 2018).

$${\text{G}\text{i}\text{n}\text{i}}_{\text{i}\text{t}}= {{\beta }}_{0}+{{\beta }}_{1}{\text{M}2}_{\text{i}\text{t}}+{{\beta }}_{2}{\text{Y}}_{\text{i}\text{t}}+{{\beta }}_{3}{\text{P}}_{\text{i}\text{t}}+{{\beta }}_{4}{\text{E}\text{R}}_{\text{i}\text{t}}+{{\beta }}_{5}{\text{R}\text{R}}_{\text{i}\text{t}}+{{\beta }}_{6}{\text{G}\text{E}}_{\text{i}\text{t}}+{{\beta }}_{7}{\text{T}\text{T}\text{R}}_{\text{i}\text{t}}+{{\beta }}_{8}{\text{R}\text{M}}_{\text{i}\text{t}}+{{\beta }}_{9}{\text{F}\text{D}}_{\text{i}\text{t}}+{{\mu }}_{\text{i}\text{t}}$$

Where${\beta }_{1}\dots {\beta }_{9}$represent the slope coefficients, and the term β₀ is the intercept. $Gini, M2, Y, P, ER, RR, GEX, TTR, RM$ and $FD$represents income inequality; broad money (% of GDP), real GDP per capita, Inflation rate, real effective exchange rate index, real Interest rate (%), general government final consumption spending (% of GDP), total tax revenue (% of GDP), foreign remittances inflows, and financial development index respectively. Lastly, ${\mu }_{it }$represent the stochastic error term.

On the monetary side, Saiki and Frost, (2014) shows that income inequality is increased by expansionary monetary policy, while Coibion et al. (2017) reveals that inequality is decreased by expansionary monetary policy. Accordingly, this study hypothesizes that expansionary monetary through money supply (helps to mitigate) increases inequality. Similarly, many studies (including, Adeleye, 2020; Marasli, 2016) find that interest rate has a significant negative effect on income inequality. On the fiscal side, a research study concluded that the effect of fiscal policy by government expenditures (infrastructure expenditure) on income inequality is negative (Alamanda, 2020), and by tax revenue on income inequality is negative as well (Hayrullahoglu &Tuzun, 2020; Taghizadeh-Hesary et al., 2020).

Further, many studies noted mixed, positive, and negative, the effect of GDP per capita on income inequality (Munir & Sultan, 2017; Hayrullahoglu & Tuzun, 2020). However, an increase in per capita GDP is a positive indicator of the progress of an economy. Therefore, this study hypothesizes a negative link between GDP per capita and income inequality. Furthermore, previous literature unveiled a positive link between inflation with income inequality (Berisha et al., 2020; Law & Soon, 2020; Bulir, 2001). Consequently, this study postulate that a rise in inflation will increase income inequality significantly. Moreover, some of the empirical research favors the argument that exchange rate and income inequality have a positive relationship (for instance, Deyshappriya (2017). On the contrary, the empirical study revealed a negative link between these both which means that an increase in exchange rate decreases the income inequality. In addition, Koczan & Loyola, (2021) and Bang et al., (2016) argue that foreign remittances decrease income inequality. Similarly, Emirguc-Kunt and Levine (2010) state that financial development has also a negative effect on income inequality.

In Eq. (1), we hypothesized that expansionary monetary through money supply (helps to mitigate) increases income inequality, and fiscal policy by government expenditures reduces it, while by tax revenue increases income inequality. We also hypothesized, that the effect of GDP per capita, foreign remittances and financial development have a negative link with the income inequality, while exchange rate and inflation rate have a positive link with the income equality.

Data and its sources

This study uses annual data of four selected Central Asian countries from 1995 to 20201. The data on income inequality has been obtained from the Standardized World Income Inequality Database (SWIID, 2021). However, Broad money, GDP per capita, inflation, government expenditure, and remittances data are taken from World Development Indicators (WDI, 2022). Meanwhile, data for Real interest rates are obtained from International Monetary Fund (IMF, 2022), while the real effective exchange rate data is taken from Bruegel (Darvas, 2021). The total tax revenue data has been collected from the International Centre for Tax and Development (ICTD, 2022). Financial development data are obtained from World Bank global financial development (2021). Further, we create an index of financial development for four central Asian countries by using the principal component approach (PCA) on the major two proxies of financial development i.e., Liquid Liabilities and Private credit by deposit money.

Empirical Strategy

Construction of financial development index

There are two types of approaches used in the literature to examine the effect of financial reform on the effectiveness of any economy. To begin, the researchers used various proxies of financial sector development to evaluate the effect of various financial reforms qualities and characteristics on economic performance (see Hye, 2011; Ahmed, 2007). In their study, Hye and Islam (2013) expounds that some scholars used the principal component approach to create a financial development index for their economies using the major proxies of financial sector development. This study used the principal component approach to construct the financial development index for four (04) central Asian countries by using the principal component analysis (PCA) on the major of two proxies of financial development available in the literature: Liquid Liabilities and Private credit by deposit money. The PCA is a multivariate method for analyzing associations between for multiple dependent variables. This PCA method is commonly employed in various field of research, particularly globalization and environmental index computation (Agenor, 2004). In terms of methodology, each dataset with p variables can have up to p principal component (PC), each of which is a linear combination of the original variables, with coefficients equaling the eigenvalues of the correlation of the covariance matrix. The principal component is then categorized by Eigen-values, which are equal to the component variance, in descending order. It`s worth noting that the Eigen-vectors are all of the same length (see Table 2).

Table 2

Construction of financial development index
Number	Value	difference	proportion	Cumulative proportion
1	1.73984	1.47969	0.8699	0.8699
2	0.260157		0.1301	1.0000
Eigen vectors (loading)

Variables	PC1		PC2
ll	0.7071		0.7071
pcd	0.7071		-0.7071

Source: Author estimations

Unit root test

The non-stationary characteristic of variables is very well recognized in the extant literature, which can then be minimized by transforming them to 1st difference. Regressing a random walk series on another, according to data and Kumar (2011), would produce an incorrect result. In this regard, the Augmented Dickey-Fuller (ADF) approach, proposed by (Dickey & Fuller, 1979; 1981), is applied to detect the non-stationarity. The ADF technique is required because it leads the study`s selection of the most suitable techniques for such investigation. The inquiry of the ADF test is based on the following regression.

$${\varDelta \gamma }_{t}={\beta }_{0}+{\gamma }_{t}+{\delta \gamma }_{t-1}+{\varSigma }_{t-i}^{n}{\alpha }_{t}{\varDelta \gamma }_{t-i}+{\mu }_{t}$$

The test assumes that the coefficient of the level lagged variable in Eq. (2) should be zero for stationarity i.e., δ = 0, and for unit root δ = 1. Whenever the null hypothesis is failed to reject, then the series is stationary and when the null hypothesis is rejected, the series is non-stationary.

Panel unit root test

For panel data analysis, Levin et al. (2002) and Im et al. (2003) proposed a panel unit root method. According to Mercan et al. (2013), these techniques (LLC and IPS) are based on the model given below as Eq. (3).

$${\varDelta \gamma }_{it}={\beta }_{i}{\gamma }_{it-1}+{\varSigma }_{j=1}^{n}{\alpha }_{ij}{\varDelta \gamma }_{it-j}+{\chi }_{it}\sigma +{\mu }_{it}$$

Levin et al. and Im et al. test have the null hypothesis that “for all cross-section unit β_i = 0 (nonstationary)” against the alternative of “for relatively one unit β_i < 0 (stationary)”.

If the considered variables are detected as a mixture of $I\left(0\right)$ and$I\left(1\right)$, then the panel Autoregressive Distributed Lag (ARDL) approach is applicable, and if all variables are stationary at 1st difference i.e. $I\left(1\right)$ then we can also apply panel ARDL model.

ARDL model

To assess the robustness of the estimations, the pooled mean group (PMG) estimator introduced by Pesaran & Shin (1999) and Pesaran et al. (1999; 2001) is employed. As per Pesaran et al., (2001) the ARDL method proposed in Pesaran and Shin (1999) is valid regardless of whether variables are purely I(0), I(1), or mutually cointegrated. According to (Pesaran et al., 2001), the ARDL approach has numerous advantages over other co-integration approaches: (i) it is efficient in performing short- and long-term association among various variables that do not have the same integration order. Assuming that such variables are stationary in I(0) and I(1). (ii) ARDL approach can eliminate issues like as omitted variables and auto-correlation. (iii) This approach can be advantageous in situations with a limited sample size. The ARDL approach, notably the Pooled Mean Group estimator, gives accurate coefficients despite the possibility of endogeneity in response lag regressors. According to Pesaran et al., (1999), the ARDL model`s unrestricted error correction for the response variable (GINI) can be expressed as follows.

$${\text{G}\text{I}\text{N}\text{I}}_{\text{i}, \text{t}}={{\Sigma }}_{\text{j}=1}^{\text{p}}{{\delta }}_{\text{i}\text{j}} {\text{G}\text{I}\text{N}\text{I}}_{\text{i}, \text{t}-\text{j}}+{{\Sigma }}_{\text{j}=0}^{\text{q}}{\text{Ϭ}}_{\text{i}\text{j}}{\text{X}}_{\text{i}, \text{t}-\text{j}}+{{\mu }}_{\text{i}}+{\text{є}}_{\text{i}\text{t}}$$

The subscript i and t in Eq. (4) denote the country (group) and time, accordingly. As a result, the time period t = 1,…T (1995–2020) and countries i = 1,… N (in this situation, N = 4). GINI is income inequality as a dependent variable; X (Kₓ 1) is the independent variables such as broad money (M2), GDP per capita, inflation, real effective exchange rate, real interest rate, Government expenditure, total tax revenue, remittances, and financial development for country i; u_i represents fixed effects. δ_ij indicates the coefficient of lagged dependent variable; Ϭ_ij is (K ₓ 1) coefficient of lagged explanatory variables, and є denotes error term. T must be large enough so that each country may be calculated individually.

The reparametrized form of the Equation shown below can be used to accomplish the study`s set of goals, which are dependent on the nature of the data.

$${\varDelta GINI}_{it}={\partial GINI}_{i, t-1}+{\beta }_{i}{Y}_{it}+{\varSigma }_{j=1}^{p-1}{\delta }_{i,j}{\varDelta GINI}_{i, t-j}+{\varSigma }_{j=0}^{q-1}{\phi }_{ij}{\varDelta Y}_{i, t-j}+{u}_{i}+{є}_{i, t}$$

Where,${\partial }_{i}=-⟮1-{\varSigma }_{j=1}^{p}{\delta }_{i,j}⟯$,${\beta }_{i}+ ={\varSigma }_{j=0}^{q}{\phi }_{i,j}$

${\delta }_{it}= -{\varSigma }_{m=j+1}^{p}{\delta }_{im}$ j = 1,2,…..., p-1. (6)

and

${\phi }_{it}= -{\varSigma }_{m=j+1}^{p}{\phi }_{im}$ j = 1,2…. q -1.

$\partial$ in Eq. (5) is the coefficient of the speed of adjustment towards the long-run equilibrium.

[1] Data availability statement: Data used in this study for empirical examination have been obtained from the World Development Indicators (2021), the World Bank database, Standardized World Income Inequality (2021), Darvas (2021) “Timely measurement of real effective exchange rates’, Working Paper 15/2021, Bruegel”, and International Centre for Tax and Development (ICTD) (2022).

Descriptive statistics and correlation matrix analysis based on the panel data from 1995–2020 are given in Table 3. The maximum value of Gini is 56.05 and the minimum value is 15.16. Moreover, the maximum value of broad money (M2) is 47.67 and the minimum value is 6.72. The maximum value of GDP per capita is 11402.76 and the minimum value is 383.60. The descriptive statistics in Table 3 also indicate that the maximum value of inflation is 201.23 and the minimum value is -26.35. However, the maximum value of the real effective exchange rate is 139.56 and the minimum value is 65.34. The maximum value of the real interest rate is 60.00 and the minimum value is -8.15. The descriptive statistics in Table 3 reveal that the maximum value of government expenditure is 20.10 and the minimum value is 8.26. Furthermore, the maximum value of total tax revenue is 28.15 and the minimum value is 5.10. In addition, the maximum value of remittances is 2.42 and the minimum value is 44.12. Moreover, the maximum value of the financial development index is 3.16 and the minimum value is -1.45.

Table 3

Descriptive statistics/ correlation matrix
	Gini	M2	Y	P	ER	RR	GE	TTR	RM	FD
Mean	33.02000	24.19218	3252.636	87.81414	100.8688	14.85400	13.22472	18.32210	9.876651	1.73E-08
Median	33.69000	22.54463	1187.525	81.91755	100.0000	12.94338	12.22873	17.81790	2.423353	-0.264918
Maximum	56.05000	47.67290	11402.76	201.2394	139.5600	60.00000	20.10585	28.15691	44.12622	3.166695
Minimum	15.16000	6.723581	383.6092	-26.35286	65.34000	-8.150463	8.264269	5.100491	-14.25240	-1.451216
Std. Dev.	6.359852	10.65711	3283.850	50.73734	15.23632	11.99940	3.280203	4.773132	14.38233	1.000000
Skewness	-0.433630	0.154044	1.115360	0.191937	0.276197	0.945834	0.479847	-0.053047	0.938553	1.172822
Gini	1
M2	-0.4532	1
Y	-0.3446	0.4845	1
P	-0.4342	0.5947	0.3787	1
ER	-0.2556	0.1149	-0.1868	-0.2061	1
RR	0.2128	-0.1654	-0.1373	-0.2347	-0.1082	1
GE	0.3029	0.0831	-0.4767	-0.1223	0.0366	0.3438	1
TTR	-0.0432	0.4269	0.2378	0.4662	-0.0909	-0.1613	0.0443	1
RM	0.0894	0.0696	-0.4368	0.4651	0.1452	-0.2001	0.1876	0.4514	1
FD	-0.3113	0.6791	0.6851	0.4184	0.1534	-0.3125	-0.3426	0.4718	-0.0308	1

A summary of the unit root tests analysis is presented in Table 4a. The result of the unit root test in Azerbaijan revealed that the Gini coefficient, real interest rate, total tax revenue, and financial development index are stationary at level, whereas broad money, GDP per capita, inflation, real effective exchange rate, general government final expenditure, and remittances are stationary at 1st difference. The result of the unit root test of Kazakhstan shows that Gini coefficient, inflation, real interest rate, government expenditure final expenditure, and remittances are stationary at level, while broad money, GDP per capita, real effective exchange rate, total tax revenue, and financial development index are stationary at 1st difference. Similarly, in the case of Kyrgyzstan the unit result demonstrates that at level Gini coefficient, broad money, inflation, real interest rate, general government final expenditure, total tax revenue, and financial development index are stationary, whereas GDP per capita, real effective exchange, and remittances rate is stationary at 1st difference. Moreover, in Tajikistan, the unit root test also found that the Gini coefficient, inflation, real effective exchange rate, and financial development index are stationary at level, while the other variables broad money, GDP per capita, real interest rate, general government final expenditure, total tax revenue, and Remittances are stationary at 1st difference. The LLC and IPS tests are used for the selected variables at the level and 1st difference in this study and shown the result of panel unit root test in Table 4b. The test is used to ensure either variable is stationary at level or first difference. The result of the Panel unit root test revealed that all variables are stationary at the level and first difference. The result of LLC tests shows Gini coefficient, real effective exchange rate, real interest rate, general government final expenditure, and total tax revenue are stationary at a level while broad money, GDP per capita, Inflation, remittances, and financial development index are stationary at 1st difference. The panel unit root test result of (IPS) in Table 4b reveals that GINI coefficient, real interest rate, government expenditure, total tax revenue, and financial development index are stationary at level, while broad money and GDP per capita, inflation, real effective exchange rate, and remittances are stationary at 1st difference.

Table 4

a: Unit root test results (individual countries)
Azerbaijan				Kazakhstan
Variables	At Level	1st ∆	Decision	At Level	1st ∆	Decision
Gini	0.0345	---	I(0)	0.0215	---	I(0)
M2	0.9500	0.0014	I(1)	0.5608	0.0016	I(1)
Y	0.1704	0.0320	I(1)	0.6816	0.0055	I(1)
P	0.9982	0.0449	I(1)	0.0001	--	I(0)
ER	0.2971	0.0244	I(1)	0.3053	0.0002	I(1)
RR	0.0028	---	I(0)	0.0032	---	I(0)
GE	0.4486	0.0276	I(1)	0.0198	---	I(0)
TTR	0.0113	--	I(0)	0.3852	0.0005	I(1)
RM	0.2970	0.0000	I(1)	0.0115	--	I(0)
FD	0.0289	--	I(0)	0.1055	0.0344	I(1)
Kyrgyzstan				Tajikistan
Gini	0.0408	---	I(0)	0.0191	--	I(0)
M2	0.0329	---	I(0)	0.1594	0.0007	I(1)
Y	0.5251	0.0059	I(1)	0.9999	0.0000	I(1)
P	0.0245	--	I(0)	0.0031	--	I(0)
ER	0.4313	0.0030	I(1)	0.0099	---	I(0)
RR	0.0476	----	I(0)	0.2626	0.0056	I(1)
GE	0.0057	---	I(0)	0.0799	0.0005	I(1)
TTR	0.0015	--	I(0)	0.5551	0.0055	I(1)
RM	0.8321	0.0006	I(1)	0.2842	0.0277	I(1)
FD	0.0253	--	I(0)	0.0058	--	I(0)

Table 4

b: Panel unit root tests results
Tests	Level			1st ∆
Tests	Variables	τ-test	ρ –value	τ-test	ρ –value
Levin, Lin & Chu t*(LLC)	Gini_it	-3.8291^***	0.0001	-2.9784	0.0014
	M2_it	0.4191	0.6624	-4.2520^***	0.0000
	Y_it	-0.0049	0.4980	-2.2649^**	0.0118
	P_it	1.9016	0.9714	-3.0295^***	0.0012
	ER_it	-2.6515^***	0.0040	-2.4049	0.0081
	RR_it	-4.4006^***	0.0000	-7.3463	0.0000
	GE_it	-1.8239^**	0.0341	-2.7094	0.0034
	TTR_it	-2.6216^***	0.0044	-3.4653	0.0003
	RM_it	-0.9249	0.1775	-1.5112^*	0.0654
	FD_it	-1.5062	0.0660	-2.1421^**	0.0161
Im, Pesaran and Shin (IPS)	Gini_it	-3.2517^***	0.0006	-4.5266	0.0000
	M2_it	0.9987	0.8410	-4.3386^***	0.0000
	Y_it	1.2228	0.8893	-2.3082^**	0.0105
	P_it	5.0034	1.0000	-2.3305^***	0.0099
	ER_it	-0.5967	0.2754	-4.4184^***	0.0000
	RR_it	-2.7638^***	0.0029	-7.5302	0.0000
	GE_it	-2.9525^***	0.0016	-5.1863	0.0000
	TTR_it	-1.9551^**	0.0253	-3.3448	0.0004
	RM_it	-0.7329	0.2318	-3.9015^***	0.0000
	FD_it	-1.4263^*	0.0769	-3.0077	0.0013

All variables are stationary at the level I(0) and 1st difference I(1). Based on these results, we used the Panel ARDL model to check the long-run and short-run impact of fiscal and monetary policies on income distribution.

The ARDL result

Table 5 shows the overall reports on the estimation results from Eq. 1, illustrating the impact of macroeconomic policies (i.e., fiscal & monetary) policies along with other regressors on income distribution. This study employed the Gini coefficient as a dependent variable in the equation with broad money, per capita income, inflation, real effective exchange rate, real interest rate, government expenditure, total tax revenue, remittances, and financial development index as the independent variables. The panel data approach was also conducted to examine the long-term and short-term influence of the independent variables on the dependent variable. The ARDL model chosen for this study is ARDL (3, 1, 1, 1, 1, 1, 1, 1, 1, 1) with the Akaike info criterion employed as a lag selection criterion. According to the results, the model has a log-likelihood of -41.5166 and a standard deviation of 1.6797.

The empirical results of the ARDL model in the cases of individual countries namely Azerbaijan, Kazakhstan, and Kyrgyzstan reveal that the impacts of broad money on income inequality are negative and statistically significant in the long run. The estimated coefficients found for Azerbaijan, Kazakhstan, and Kyrgyzstan are − 0.5336; -0.4941%, and − 0.3474 respectively. These results imply that monetary policy reduces income equality. These findings are consistent with the findings by (Coibion et al., 2017). While the results are opposite in the case of a panel of four countries, which are in accord with the findings by (Saiki & Frost 2014).

The impact of GDP per capita on income inequality has been found negative and significant in the long run. These results show that with an increase in the GDP per capita, the inequality is supposed reduced in the selected Central Asian countries. Our findings are like the findings by (Siami-Namini& Hudson, 2019; Monnin, 2014; Hayrullahoglu&Tuzun, 2020).

The impact of inflation on income inequality found is significantly positive in the long run. These results indicate that income inequality increases with an increase in inflation in the panel countries and individual countries namely Azerbaijan and Kazakhstan. Our results are similar to the findings by (Balcilar et al., 2018; Law & Soon, 2020). However, the effect of inflation in the case of Kyrgyzstan, and Tajikistan on income distribution found are negative. These findings are consistent with the findings by (Siami-Namini & Hudson, 2019; Monnin, 2014).

Nevertheless, the result of the study revealed that a real effective exchange rate has a significantly positive impact on income inequality in the long run. the estimated coefficient value indicates that when the real effective exchange rate increases by 1 percent in the long run, income inequality will also be increased by (0.034%) in panel countries and the case of individual countries namely Kazakhstan (0.0193%) and Tajikistan (0.0294%). This result is similar to the finding by Deyshappriya (2017). Whereas the impact of the real effective exchange rate in the case of Azerbaijan (-0.0314) and Kyrgyzstan (-0.3064%) is negative on income inequality. However, our result is in accord with findings by (Min et al., 2015).

Furthermore, the empirical results of the ARDL model in the cases of individual countries namely Azerbaijan, Kazakhstan, and Kyrgyzstan show that the effects of real interest rate on income inequality are positive in the long run. The estimated coefficients found for Azerbaijan, Kazakhstan, and Kyrgyzstan are 0.0086%; 0.1581%, and 0.10% respectively. These results suggest that the real interest rate increases income inequality. These findings are consistent with the findings by (Berisha et al., 2020; Taghizadeh-Hesary et al., 2020). While the results are opposite in the case of a panel of four countries (-0.12%) and also in Tajikistan (-0.0185%), indicates that the real interest rate reduced income inequality, which is consistent with the findings by (Adeleye, 2020; Marasli, 2016).

The empirical results exhibit that the effect of government expenditure on income distribution, in the long run, is positive and significant in the panel of four countries. The coefficient of government expenditure, in the long run, reveals that an increase in government expenditure by 1% will increase income inequality by (0.314%). Our findings are similar to those (Khan & Padda, 2021). However, individual results of the simple ARDL model for Azerbaijan reveal that government expenditure (0.936%) has an insignificantly positive impact on income inequality, while in Kyrgyzstan government expenditure (1.6579%) has a significantly positive effect on income inequality. This result is accord to the findings by (Khan & Padda, 2021). The effect of estimated coefficient (-0.5067%), (-0.082%) sign of government expenditure on income inequality is negative in Kazakhstan and Tajikistan. This result is consistent with Alamanda (2020) result. They found that government expenditure decreases income inequality.

Further, the result of the study also explored that in the long-run total tax revenue has a significantly positive impact on income inequality in the panel of four countries and an insignificantly positive effect on income inequality in individual countries namely Azerbaijan and Tajikistan. Result shows that an increase in total tax revenue by 1% increases income inequality by (0.72%) in panel countries, and (0.112%) in Azerbaijan, and (0.0369%) in Tajikistan. Similarly, for Kazakhstan the estimated coefficient value (-0.0913%) indicates that the effect of total tax revenue on income inequality is negative and insignificant, whereas negative and significant (-0.8266) in Kyrgyzstan. These results are similar to the finding by (Hayrullahoglu &Tuzun, 2020; Taghizadeh-Hesary et al., 2020).

However, the findings of the study demonstrated that the impact of remittances on income inequality, in the long run, is negatively significant in the panel of four countries (-0.12%) and in the case of individual countries namely Azerbaijan and Tajikistan − 2.85 and − 0.036% and insignificantly negative effect on income inequality in Kazakhstan, and Kyrgyzstan − 4.59% and − 0.19% respectively. Our findings are consistent with (Koczan & Loyola, 2021; Bang et al., 2016) results. They observe that remittances decrease income inequality.

Nevertheless, the study also found that in the long run, the financial development index has a negative but significant influence on income inequality in panel countries (-2.07%) and Kazakhstan (-8.5027%) and insignificantly negative impact of financial development index on income inequality in Azerbaijan (-1.0867%), Kyrgyzstan (-2.168%), and Tajikistan (-0.1368%). These results are consistent with (Azam & Raza, 2018; Demirguc-Kunt& Levine, 2010) findings.

Table 5

The ARDL/PMG results
	Country(s)/ Variables	Panel-4	Azerbaijan	Kazakhstan	Kyrgyzstan	Tajikistan
	Country(s)/ Variables	Coefficient	Coefficient	Coefficient	Coefficient	Coefficient
Long run estimates	M2	0.1124^*** (0.0000)	-0.5336^** (0.0374)	-0.4941^** (0.0156)	-0.3474^* (0.0534)	0.02664^** (0.0384)
	Y	-0.0039^*** (0.0000)	-0.0057^** (0.0178)	-0.0109^** (0.0135)	-0.0126 (0.3638)	-0.0089^** (0.0382)
	P	0.0192^*** (0.0000)	0.1509 (0.3778)	0.2116^* (0.0518)	-0.1076^* (0.0847)	-0.0312^** (0.0148)
	ER	0.0342^*** (0.0000)	-0.0314 (0.7399)	0.0193 (0.6970)	-0.3064^*** (0.0051)	0.0294^** (0.0124)
	RR	-0.1165^*** (0.0006)	0.0086 (0.8987)	0.1581^** (0.0267)	0.1000 (0.1646)	-0.0185^* (0.0646)
	GE	0.3139^*** (0.0000)	0.9360 (0.1110)	-0.5067^* (0.0442)	1.6579^** (0.0186)	-0.0818 (0.0146)
	TTR	0.7204^*** (0.000)	0.1120 (0.8565)	-0.0913 (0.2562)	-0.8266^* (0.0945)	0.0369 (0.4248)
	RM	-0.1236^*** (0.0000)	-2.8531^** (0.0111)	-4.5918 (0.1647)	-0.1952 (0.1833)	-0.0357^** (0.0110)
	FD	-2.0742^*** (0.0000)	-1.0867 (0.8206)	-8.5027^*** (0.0065)	-2.1687 (0.3907)	-0.1368 (0.6013)
Short run estimates	ECM	-0.3752^** (0.0143)	-1.8567^*** (0.0001)	-3.3699^*** (0.0002)	-0.1521^*** (0.0000)	-0.1648^*** (0.0000)
	∆(GINI (-1))	-0.4238 (0.1127)	0.8135^*** (0.0007)	1.9689^*** (0.0003)	-0.7008^*** (0.0000)	-0.3381^*** (0.0054)
	∆ (GINI (-2))	0.4238 (0.5834)	0.7062^*** (0.0011)	0.6179^*** (0.0008)
	∆ (M2)	-0.0500 (0.7093)		-0.4941^*** (0.0002)	-0.3474^*** (0.0002)	0.0312^*** (0.0001)
	∆ (Y)	0.0081 (0.1247)		-0.0109^*** (0.0002)		-0.0040^*** (0.0015)
	∆ (P)	-0.1162^* (0.0928)	-0.1509^*** (0.0026)	0.2116^*** (0.0008)	-0.10756^*** (0.0008)	-0.0312^*** (0.0000)
	∆ (ER)	-0.0440^* (0.0917)	-0.03138 (0.1252)	0.0193^*** (0.0205)	-0.3064^*** (0.000)	0.0294^*** (0.0000)
	∆(RR)	0.0494 (0.3714)	0.0086 (0.4353)	0.1581^*** (0.0007)	0.10007^*** (0.0016)
	∆ (GE)	0.0745 (0.5152)	0.9360^*** (0.0008)	-0.5067^*** (0.0015)		0.0182^** (0.0296)
	∆ (TTR)	-0.3082 (0.3062)			-0.8266^*** (0.0001)	0.0369^* (0.0789)
	∆ (RM)	-0.4311 (0.1594)		-4.5918^*** (0.0015)	-0.1952^*** (0.0019)	0.0014 (0.6317)
	∆ (FD)	2.4272^*** (0.0018)	-1.0867^* (0.0654)	8.0259^*** (0.0002	-2.1697^** (0.0187)	-0.1368 (0.1607)
	C	7.2055^*** (0.0054)	59.1735^** (0.0265)	112.6961^*** (0.0000)	-1.9014^*** (0.0000)	5.4882^*** (0.0000)
		(3, 1, 1, 1, 1, 1, 1, 1, 1, 1)	(3, 0, 0, 1, 1, 1, 1, 0, 0, 1)	(3, 1, 1, 1, 1, 1, 1, 0, 1, 1)	(2, 1, 0, 1, 1, 1, 0, 1, 1, 1)	(1, 0, 1, 1, 0, 1, 1, 1, 1, 1)
Note: model selection method: Akaike info criterion (AIC). Asterisks (*), (), and (*) show statistically significant at 1%, 5%, and 10% respectively. ECM = Error Correction Coefficient.

The error correction coefficient, which indicates the model`s speed of adjustment toward the long-run equilibrium, is revealed by the short-run equation. The ECM is estimated as (-0.3752%), which is statistically significantly negative at the 5% level. These results demonstrate that the model`s annual adjustment to the long-run equilibrium is nearly 37%. These findings reveal that the speed of adjustment is fast, at 37% per year, and will take nearly 2.8 years to reach long-run equilibrium (Azam, 2020). Consequently, the ECM`s statistically significantly negative results strongly support theoretical predictions. Furthermore, the negatively significant error correction term indicates a trend of the long-run equilibrium (see Table 5). Overall results in a summarized form on the panel and individual countries are shown in Fig. 2.

This study aims to add to the existing literature by examining the relationship between fiscal and monetary policies along with some other regressors (GDP per capita, real effective exchange rate, foreign remittances inflow, and financial development index) and income distribution in four central Asian countries (Azerbaijan, Kazakhstan, Kyrgyzstan, and Tajikistan from 1995 to 2020. The study employed a panel unit root test and panel ARDL model. The result of the study shows that fiscal policy by (govt. expenditure and total tax revenue) increases income inequality in the long run. The result of the Panel ARDL approach reveals that the impact of government expenditure on income inequality is positive and significant in the long run, while insignificantly positive in the short run. The result also reveals that the influence of total tax revenue on income inequality is positive and significant. Also in the short run, the effect of total tax revenue on income inequality is negative and insignificant. Results explore that monetary policy by broad money increases income inequality, while monetary policy by (real interest rate) decreases income inequality in the long run. The finding of the study shows that broad money has a significantly positive impact on income inequality in the long run, whereas insignificant and negative effect on income inequality in the short run. Result also demonstrates that the real interest rate on income inequality is significantly negative and insignificantly positive effects on income inequality in the short run. Furthermore, findings of the ARDL approach also indicate that broad money reduces income inequality significantly in Azerbaijan, Kazakhstan, Kyrgyzstan, and Tajikistan. The study also demonstrates that per capita GDP decreases income inequality in the long run while increasing in the short run. Moreover, the result of the simple ARDL method reveals that GDP per capita reduces income inequality in Azerbaijan, Kazakhstan, Kyrgyzstan, and Tajikistan.

Furthermore, the result noted that inflation enhances income inequality in the long run and mitigates income inequality in the short run. In the long run, inflation has a positive and significant impact on income inequality whereas the negative but significant impact on income inequality in the short run. Nevertheless, the result of the ARDL approach suggests that inflation has insignificantly increased income inequality in Azerbaijan, and significantly increases in Kazakhstan, whereas inflation significantly reduces income inequality in Kyrgyzstan, Tajikistan.

The panel ARDL estimates exhibit that the impact of real effective exchange rate on income inequality, in the long run, is significantly positive, whereas negative in the short run. The study said that a real effective exchange rate increases income inequality in the long run while in the short run it decreases income inequality. In addition, the result of the simple ARDL technique demonstrates that the real effective exchange rate insignificantly diminishes income inequality in Azerbaijan, while significantly decreasing income inequality in Kyrgyzstan and the also real effective exchange rate has insignificantly increased income inequality in Kazakhstan whereas significantly enhancing income inequality in Tajikistan.

Results also noted that the real interest rate insignificantly increases income inequality in Azerbaijan and Kyrgyzstan and significantly increases in Kazakhstan, while the real interest rate significantly decreases income inequality in Tajikistan. However, the simple ARDL model shows that government expenditure insignificantly widens income inequality in Azerbaijan and significantly increases in Kyrgyzstan, whereas significantly reducing income inequality in Kazakhstan and Tajikistan. Result also reveals that total tax revenue insignificantly enhances income inequality in Azerbaijan and Tajikistan, whereas insignificantly mitigates income inequality in Kazakhstan and significantly decreases income inequality in Kyrgyzstan. The panel ARDL estimates suggest that the impact of remittances on income distribution is significantly negative in the long run while in the short run the effect is insignificantly negative. Results also suggest that remittances have a significantly negative effect on income inequality in Azerbaijan and Tajikistan, whereas the insignificantly negative impact on income inequality in Kazakhstan and Kyrgyzstan. In addition, the result reveals that financial development has a significantly negative effect on income distribution in the long run and an insignificantly positive effect on income distribution in the short run. The result of the ARDL model indicates that financial development has a negative and insignificant effect on income inequality in Azerbaijan, Kyrgyzstan, and Tajikistan, while in Kazakhstan the impact of the financial development index on income inequality is negative but significant.

Data availability statement

Data used in this study for empirical examination have been obtained from the World Development Indicators (2021), the World Bank database, Standardized World Income Inequality (2021), Darvas (2021) “Timely measurement of real effective exchange rates’, Working Paper 15/2021, Bruegel”, and International Centre for Tax and Development (ICTD) (2022), which are a freely available database.

Disclosure statement

The authors declare that they have no conflict of interest.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Do fiscal and monetary policies contribute to income distribution? Empirical evidence from Central Asian Countries

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1. Introduction

2. The Existing Theoretical And Empirical Studies

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