Corruption,income inequality and decline in South Asia

Abstract

By using panel data over the 2001–2016 time-period, this study investigates the dynamic relations between corruption, income inequality and the decline in South Asia. Namely, the panel data are subjected to the Chow probability test, the fixed-effect model, random-effect GLS regression and the Hausman test. The empirical results show that economic growth is adversely affected by income inequality, i.e., they show the significant effect of the Gini coefficient on economic decline. It is evident that income maldistribution leads to decline, yet corruption seems to have a positive effect on economic growth. Despite its limitations, the study yields both future-research directions and policymaking recommendations toward curbing the decline in South Asia.

Keywords

Corruption income inequality economic growth fixed-random effect model South Asia

Kashif Iqbal is a Ph.D Candidate at the School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China. He completed his post-graduation in the field of Economics. His research interests are world economy and human development.

Hui Peng is working as a professor at Beijing University of Post and Telecommunication, Beijing, China. She is completed Ph.D and P. Doc from Renmin University in Beijing, China. Her research interest is risk management and E-commerce.

Danish is an associate professor at Guangdong University of Foreign Studies Guangzhou, China. He did his Ph.D degree in Business Administration. He published several SSCI and SCI papers. His research interests are renewable and non-renewable energy, Co2 emission, and Information System.

Muhammad Hafeez is currently pursuing his Ph.D degree in Management Science and Engineering and works at the School of Economics and Management, Beijing University of Posts and Telecommunications. His research interests are education economics, financial economics, environmental economics, and green development.

Khurshaid Ahmad is a permanent faculty member at the Department of International Relations, University of Peshawar. He has ten years of teaching and research experience at different universities. He has got distinction as a gold medalist in master studies. He has completed MPhil in international relations and now working on Ph.D. His areas of expertise are terrorism, strategy, foreign policy, and geopolitics. He has published eight research papers in various research journals. He holds administrative experience too at the university.

Liang Tang is an Associate Professor in school of Finance and Accounting at Nichols College USA. His areas of expertise are finance, accounting and economics.

1 Introduction

For decades, income inequality is seen as a serious issue. Previous studies have tried to identify the determinants of income inequality. Simon’s [1] “Inverted U hypothesis” is pioneering on assessing the relation between income inequality and economic growth. And Kuznets’s Inverted U-hypothesis states that inequality increases as growth rate per capita GDP increases. Angelsen [2] studied some developing and developed countries and found a negative relation between income inequality and economic growth. The researcher’s interest turned to the inequality-growth nexus after presenting Kuznets’s inverted U-hypothesis to the American Economic Association [3 –5]. N.C. Georgantzas [6] posits that the only possible way of human and economic development is self-development. In this way, all business enterprises and other societal organisations can achieve economic self-development by facilitating their employee, customer, division and partner self-development. Enterprises and other societal organisations can help to achieve economic self-development by facilitating their employee, customer, division and partner self-development [7].

The relation between inequality and economic growth has been widely discussed in the literature. According to the neo-classical economists Delbianco F. [8] and Fadi Fawaz [9], higher saving rates are associated with lower income inequality [10]. They found a large gap between marginal propensity to save between poor household and rich household. The saving rates in a society with inequality will be higher. Inequality is positively associated with rising economic growth rates [11]. A wave of empirical literature reinvestigated the inequality-growth nexus and claimed that inequality harms economic growth, and their relation is negative. These studies explored several channels relating to growth and inequality like rent-seeking, market imperfections, policy responses to inequality (redistributive policies) and political instability [12 –15]. All these channels imply a negative impact of inequality on growth. It is argued that a society with inequality is more inequitable and more open to rent-seeking activities. These rent-seeking activities demolish the productive ability of society by disturbing the economic environment. These activities are considered to suppress the institutional development in an economy and hence are detrimental to economic growth [16 –18]. There are sizable disparities in income distribution both within a country and among the nations. Shin I. [19] states that only 13 percent of the world’s population is holding 72 percent of total resources. In most of the empirical studies, a negative and insignificant relation is found between inequality and growth. However, a positive impact of inequality on growth is observed in the case of developed economies [20].

The corruption is intertwined with inequality and growth. In more unequal societies, there is higher pressure on governments to adopt and implement the redistributive policies. These redistributive policies are financed by heavy taxation. This higher taxation can lead to the discouragement of work, saving and investment [21]. The voting model proposed by Persson T. [22] also predicted that; in more unequal societies, the median income is higher than the average income, the people’s demand for redistributive policies is much higher. A number of studies suggest that the channel through which inequality affects growth is that of corruption [9 , 24]. It is suggested by some studies, including Dobson S. [25], that in societies with high inequality is more open to violent and criminal activities, and more vulnerable to corruption. The work of Kunieda T [26] found that inequality is one of the primary sources of corruption [27].

There are two schools of thought with respect to the relations between inequality and economic growth. One school of thought argue the positive relations exist among income inequality and economic growth [3, 28], while, another school of thought argued for negative relations [4 , 29], which also investigates corruption as a channel through which inequality affects growth. This contradiction in different findings is attributable to the measures chosen, the period of the study and group of countries selected for the analysis. This controversy motives us to further study this important issue. We strive to clarify the literature ambiguity on the relation between income inequality (IE) and economic growth through the channel of corruption (C). To this end, we employ a panel data of developing South Asian economies to test the hypothesis that income inequality fuels the corruption in an economy which in turn reduces the economic growth. To the best of our knowledge, this study is the first to empirically estimate the relations between corruption, income inequality and economic growth in South Asian countries. Therefore, this study volition is to investigate the relation between variables. Our findings offer insight to policymakers for dealing with the issue related to corruption, income inequality and decline in South Asia. Furthermore, emphasizing limitation of the article, this study is limited to South Asia so that future researchers could expand the study to other countries.

The rest of the article is organized as follows. Section 2 reviews of the literature. Section 3 provides information on our model specification and data source. Section 4 contains our results and discussion. Finally, Section 5 concludes the study.

2 Literature review

2.1 Income inequality and economic growth

Inequality growth nexus a burning issue for last many decades. The “Inverted U-hypothesis” of [1] and “Kaldorian view” of Nicholas Kaldor” are the pioneering work on the inequality-growth relation. Both, Simon Kuznets [1] and [30] argued that initial income inequality is associated to the higher growth rates, i.e. countries may face a trade-off between reducing income inequality and promoting economic growth [8 , 22]. Another view is that inequality hinders economic growth and thus associated negatively with economic growth [31]. Results indicated a positive relation between the share of the middle class and economic growth in a democratic environment. However, the results were insignificant for non-democratic countries. Thus, it is concluded that growth is affected by equality as investment promotes with equality [19, 32].

Retarding impact of inequality on economic growth is recorded in the case of low-income countries. However, in rich countries, this relation is recorded positive [9, 33]. It is concluded that the cost of reducing income inequality is to forgo the improving growth rate performance [32]. Hence, there is a trade-off between improving economic growth rate and reduction of income inequality [34]. Estimation results of the estimator Davtyan K. [35] show that different Gini values (based on different percentile ratios) have a different impact on growth. That is: 90/75 percentile ratio and 50/10 percentile rate have an insignificant impact when used individually, but when used collectively they show joint significance. Thus, the study suggests that a single measure of inequality will be unable to capture the relation between inequality and growth.

2.2 Corruption and economic growth

As economists study the institutional setup of top-down imposed social order that influences national budgets, more than sixty nations deem corruption as the most extreme hindrance to advancement and self-development [36]. In the context of corruption, Mo [23] has investigated the relation between income inequality and economic growth. A previous study shows that inequality and corruption have a significant negative impact on the risk perceptions of potential investors, and economic growth [37]. The empirical result is inconsistent with the hypothesis that instability and corruption impede economic growth. From the empirical results, no evidence is found to verify the negative causal relation between corruption and economic growth [5, 29]. Previous studies suggest that corruption impedes economic efficiency, which could slow or even shrink economic growth. Which is inversely affect the income classes, especially the most vulnerable [38, 39].

Furthermore, some wealthy and well-connected citizens may attempt to influence government through both legal (lobbying), and illegal (bribery and favoritism) means to tilt government expenditures and the incidence of taxes in their favor [24 , 41]. To the extent that corruption fosters both tax evasion and exemptions favoring the wealthy and well-connected, it lowers tax revenues and makes taxations less progressive. Moreover, government expenditures in real terms could shrink both because of the loss in tax revenue and also because corruption raises the cost of government programs [4 , 28].

3 Methodology

To express the nature of relations among economic growth, income inequality and corruption we express the empirical specification as follow. $Growth = α_{ij} + β_{1 i} Gin i_{ij} + β_{2 i} Corrp + ɛ_{ij}$ (1) where growth, gini, and corrp stands for economic growth, gini coefficient, and corruption respectively while α, β1, and β2 indicate intercept, the impact of income inequality on economic growth, and the impact of corruption on economic growth subsequently.

Data is collected from the World Bank and the transparency index for South Asian countries for the year 2001 to 2016. The choice of time period selection is based on the data availability. We focus on the developing economies (South Asia) only due to the similarities in their socio-economic structure, common social and economic problems and volition [42]. The economic growth is measured in GDP per capita (constant 2010 USD). The income inequality is measure through gini coefficient. The transparency index measure through 1 to 10 (From 1 less corrupt country to 10 more corrupt country).

4 Results and discussion

In this section, we explain the econometric methods used in the study, such as Chow poolability test; Fixed-effect model; Random-effect GLS regression and Hausman test.

4.1 Chow probability test

Data for this study consist of South Asian countries for the time period from 2001–2016. We have a combination of both cross-section and time-series which is also known as panel data or sometimes known as pooling of data [23]. For this reason, we need to know whether we can pool our data or not. Some tests may utilise to solve such problems. Chow poolability test is one of the important methods to find that whether we can pool the data or not. If the data is not poolable, then we will move to run the simple OLS as we do in cross section by removing the years. If we find that, the data can be pooled then standard panel estimations will be practiced [34].

Procedure:

Run regression (OLS) without any country dummies. It will give us restricted RSS.

Then run regressions for all countries individually. It will give us unrestricted RSS.

Then apply the Chow Probability formula.

$F - Stat = \frac{(RSSr - Rssu) / (i - 1) (K - 1)}{RSSu / (i (n - k - 1)}$ (2)

While in equation 2, i is the number of countries while K is a number of variables.

The value of F-statistics is 0.25 which is less that thumb rule 2.5. So, we accept the null hypothesis of this test that data can be pooled. Therefore, we have pooled out data by combining each country’s time series.

Next important step is to decide whether to use the fixed-effect model or random-effect model to explain the relation between the variables. To this end; we estimated both the models to choose the best one through the Hausman test.

4.2 Fixed-effect model

The results of the fixed-effect model in Table 1 show that growth is adversely affected by inequality. The coefficient value (–0.2141) shows the significant negative impact of Gini on growth. However, the coefficient value of corruption shows a positive relation with growth, which is consisted of the finding of [38]. This seemingly puzzling result is possible if corruption pushes the government to bypass bureaucracy to some degree and take action on approving various projects more quickly.

Table 1
Fixed-effect model Results

Dependent Variable Growth

Independent Variables β SE t-stats Prob.

Inequality –0.2141* 0.0746 –2.87 0.005

Corruption 0.5257 0.6662 0.79 0.031

Cons 12.27 3.6225 3.39 0.001

No. of groups 07

No. of observation 91

Diagnostics statistics

Wald stats for model 5.45

Wald stats Prob. 0.0001

Dependent Variable	Growth
Inequality	–0.2141*	0.0746	–2.87	0.005
Corruption	0.5257	0.6662	0.79	0.031
Cons	12.27	3.6225	3.39	0.001
No. of groups	07
No. of observation	91
Diagnostics statistics
Wald stats for model	5.45
Wald stats Prob.	0.0001

Note: β indicates the coefficient, *shows 1 % level of significance.

In the case of the random-effect model, Table 2 shows that direction of the result is the same but we found a difference in the coefficient values. The same negative relation was recorded between growth and inequality, while a positive relation was recorded between corruption and growth [21]. The results are significant as per suggested by the p-values. The effect of corruption on growth shown in the random model is high than the fixed effect model, which is suggested a much higher impact of the said variable on the growth.

Table 2

Random-effect model Results

Dependent Variable	Growth
Independent Variables	β	SE	t-stats	Prob.
Inequality	–0.1746**	0.0703	–2.48	0.013
Corruption	0.9401	0.5465	1.72	0.035
Cons	9.58	3.2222	2.98	0.003
No. of groups	07
No. of observation	91
Diagnostics statistics
Wald stats for model	9.29
Wald stats Prob.	0.0096

Note: β indicates the coefficient, **shows 5% level of significance.

Our results suggest that income inequality drives economic decline through the inefficient uses of resources. It is evident that the distribution of income is not equal leads to discourage the effective use of resources and economic growth. The surprising positive relation between corruption and growth is consisted with the findings of [22 , 26].

4.3 Hausman test

Hausman test is used to select the suitable model among the random and fixed effects models. It is the endogeneity test. The purpose of this test is to find out whether the result of the random coefficient model is unbiased or not. The null hypothesis of the Hausman test is that there is no endogeneity or random model co-efficient are unbiased. The decision of bias and unbiased is depended on the p-value. The estimated results of the model presented in Table 3 suggest that the random-effect model is more suitable than the fixed-effect model.

Table 3
Hausman test results

coefficients

Variables Fixed(b) Random(B) Difference (b-B)

Inequality –0.2141 –0.1746 –0.0394

Corruption 0.5275 0.9401 –0.4125

Test hypothesis Ho: Difference in coefficients not systematic

Chi² 2.53

Prob > Chi² 0.2822

	coefficients
Inequality	–0.2141	–0.1746	–0.0394
Corruption	0.5275	0.9401	–0.4125
Test hypothesis	Ho: Difference in coefficients not systematic
Chi²	2.53
Prob > Chi²	0.2822

5 Conclusion and policy suggestions

This study demonstrates the statistical importance of corruption in determining income inequality, shows a negative relation between corruption and income equality. This confirms that an increase in the corruption index worsens the income equality of a country, in particular, corruption follows the increases in government spending. Our evidence suggests that highly corrupt countries have high-income inequality [21].

The corruption and income inequality relation as evidenced by our study have significate policy implications as follows: The government law-enforcement agencies must take necessary action against corruption. They should also pay attention to reforming the role of government in the economy. The government officials must improve the political process, the role of decentralization of power to grassroots, decision-making, monitoring, planning, and execution to curb this menace. Moreover, the anti-corruption strategy should be pluralistic and holistic where players in public sector, the corporate private sector, and civil society jointly share responsibility by addressing the issues of accountability, transparency, participation, openness and the rule of law. International pressure on corrupt countries, including criminalizing, bribing foreign officials by multinational firms, is useful. However, the success of any anti-corruption campaign ultimately depends on the reform of domestic institutions in corrupt countries. Thus, law enforcement agencies need to formulate and implement a strategy that does the following:

(a) Encourage the reduction of rents by means of economic liberalization, deregulation, tax simplification, de-monopolization, and macroeconomic stability; (b) Reduce discretion through administrative and civil service reform, including meritocratic recruitment and decentralization; (c) Honest and visible commitment by the leadership to fight against corruption. The leadership must show zero tolerance for it; and (d) Increase accountability – by building up institutions such as auditing and accountancy units, through legal reforms such as judicial strengthening, by encouraging public oversight through parliament and a more vibrant civil society; (e) equal punishment and reward.

6 Limitation and future research direction

This study examines relations between income inequality, corruption and economic growth using South Asian countries data and future research could expand the study to other countries. Despite its limitations, this study sheds important light as to how policymakers can find ways to curb economic an decline in South Asian countries.

References

Simon

. Economic Growth and Income Inequality Simon Kuznets. Am Econ Rev. 1955;45:1–28.

Angelsen

, Wunder

. Poverty and Inequality: Economic Growth Is Better Than Its Reputation. 2006:1–20.

Dincer

, Gunalp

. Corruption, Income Inequality, and Poverty in the United States. Fond Eni Enrico Mattei. 2008:1–38.

Mendonça

HF de

, Fonseca AO

. Corruption, income, and rule of law: Empirical evidence from developing and developed economies. Rev Econ Política. 2012;32:305–14. doi: 10.1590/S0101-31572012000200009

Gyimah-Brempong

. Corruption, economic growth, and income inequality in Africa. Econ Gov. 2002;3:183–209. doi: 10.1007/s101010200045

Georgantzas

. Humanizing HSM to benefit human systems. Hum Syst Manag. 2018;37:1–5. doi: 10.3233/HSM-18283

Georgantzas

. On business-enterprise organization: A praxis research. Hum Syst Manag. 2018;37:257–70. doi: 10.3233/HSM-180368

Delbianco

, Dabús

, Caraballo

MÁ

. Income inequality and economic growth: New evidence from latin america. Cuad Econ. 2014;33:381–98. doi: 10.15446/cuad.econ.v33n63.45338

Fawaz

Fadi

, Rahnama

, Valcarcel

. A refinement of the relationship between economic growth and income inequality. Appl Econ. 2014;46:3361–27. doi: 10.1080/00036846.2014.929624

10.

Harttgen

, Klasen

. A Household-Based Human Development Index. World Dev. 2012;40:878–99. doi: 10.1016/j.worlddev.2011.09.011

11.

Yang

, Greaney

. Economic growth and income inequality in the Asia-Pacific region: A comparative study of China, Japan, South Korea, and the United States. J Asian Econ. 2017;48:6–22. doi: 10.1016/j.asieco.2016.10.008

12.

Latif

, Xin

, Khan

, Iqbal

, Pathan

, Salam

, et al. Ict and sustainable development in south asian countries. Hum Syst Manag. 2017;36:353–62. doi: 10.3233/HSM-17166

13.

Wan

, Lu

, Chen

. The inequality – growth nexus in the short and long run: Empirical evidence from China. 2006;34:654–67. doi: 10.1016/j.jce.2006.08.004

14.

José

, Tabosa

, Carvalho

PU De

. Brazil, 1981-2013: The effects of economic growth and income inequality on poverty. 2013;120:153–70.

15.

Khattak

, Muhammad

, Iqbal

. Determining the Relationship between Income Inequality, Economic Growth & Inflation. 2014;1:104–14.

16.

Hartmann

, Guevara

, Jara-figueroa

, Aristara

. Linking Economic Complexity, Institutions, and Income Inequality. World Dev. 2017;93:75–93. doi: 10.1016/j.worlddev.2016.12.020

17.

Sahli

, Rejeb

J Ben

. The Environmental Kuznets Curve and Corruption in the Mena Region. Procedia - Soc Behav Sci. 2015;195:1648–57. doi: 10.1016/j.sbspro.2015.06.231

18.

Capasso

, Santoro

. Active and passive corruption: Theory and evidence. Eur J Polit Econ. 2018;52:103–19. doi: 10.1016/j.ejpoleco.2017.05.004

19.

Shin

. Income inequality and economic growth. Econ Model. 2012;29:2049–57. doi: 10.1016/j.econmod.2012.02.011

20.

Rubin

, Segal

. The effects of economic growth on income inequality in the US. J Macroecon. 2015;45:258–73. doi: 10.1016/j.jmacro.2015.05.007

21.

Dzhumashev

. Corruption and growth: The role of governance, public spending, and economic development. Econ Model. 2014;37:202–15. doi: 10.1016/j.econmod.2013.11.007

22.

Persson

, Tabellini

. Is inequality harmful for growth? Am Econ Rev. 1994;84:600–21. doi: 10.1108/eb018853

23.

. Corruption and Economic Growth. J Comp Econ. 2001;29:66–79. doi: 10.1006/jcec.2000.1703

24.

Corrado

, Rossetti

. Public corruption: A study across regions in Italy. J Policy Model. 2018. doi: 10.1016/j.jpolmod.2018.01.001

25.

Dobson

, Ramlogan-Dobson

. Is there a trade-off between income inequality and corruption? Evidence from Latin America. Econ Lett. 2010;107:102–4. doi: 10.1016/j.econlet.2009.12.038

26.

Kunieda

, Okada

, Shibata

. Corruption, capital account liberalization, and economic growth: Theory and evidence. Int Econ. 2014;139:80–108. doi: 10.1016/j.inteco.2014.03.001

27.

Akçay

. Corruption and Economic Growth: Across-National Study. Afyon Kocatepe Üniversitesi Iktis ve Idari Bilim .... 2002. doi: http://dergiler.ankara.edu.tr/dergiler/42/464/5289.pdf

28.

Alesina

, Angeletos

. Corruption, inequality, and fairness. J Monet Econ. 2005;52:1227–44. doi: 10.1016/j.jmoneco.2005.05.003

29.

Agostino

, Dunne

, Pieroni

. Government Spending, Corruption and Economic Growth. WORLD Dev. 2016;xx. doi: 10.1016/j.worlddev.2016.03.011

30.

Kaldor

Nicholas

. A Model of Economic Growth. Econ J. 1957;67:591–624. doi: 10.2307/2227704

31.

Mikucka

, Sarracino

, Dubrow

. When Does Economic Growth Improve Life Satisfaction? Multilevel Analysis of the Roles of Social Trust and Income Inequality in 46 Countries, 1981-2012. World Dev. 2017;93:447–59. doi: 10.1016/j.worlddev.2017.01.002

32.

Naguib

. The Relationship between Inequality and GDP Growth: An Empirical Approach. 2015.

33.

Cingano

. Trends in income inequality and its impact on economic growth. vol. Working Pa. 2014. doi: https://dx-doi-org.web.bisu.edu.cn/10.1787/5jxrjncwxv6j-en

34.

Dabla-norris

, Kochhar

. Causes and Consequences of Income Inequality: A Global Perspective. IMF Staff Discuss Note. 2015:1–39. doi: https://www.imf.org/external/pubs/ft/sdn/2015/sdn1513.pdf

35.

Davtyan

. “Interrelation among Economic Growth, Income Inequality, and Fiscal Performance: Evidence from Anglo-Saxon Countries. ” Res Inst Appl Econ. 2014:1–42. doi: 10.7866/HPE-RPE.16.2.2

36.

, Xu

, Zou

. Corruption, Income Distribution, and Growth. Econ Polit. 2000;12:155–81.

37.

Ertimi

, Dowa

, Albisht

, Oqab

. The Impact of Corruption on Economic Growth in OIC Countries. Int J Econ Financ. 2016;8:91. doi: 10.5539/ijef.v8n9p91

38.

Farooq

, Shahbaz

, Arouri

, Teulon

. Does corruption impede economic growth in Pakistan? Econ Model. 2013;35:622–33. doi: 10.1016/j.econmod.2013.08.019

39.

Saha

, Gounder

. Corruption and economic development nexus: Variations across income levels in a non-linear framework. Econ Model. 2013;31:70–9. doi: 10.1016/j.econmod.2012.11.012

40.

Pieroni

, d’Agostino

. Corruption and the effects of economic freedom. Eur J Polit Econ. 2013;29:54–72. doi: 10.1016/j.ejpoleco.2012.08.002

41.

Bagchi

, Svejnar

. billionaire wealth, income distribution, and poverty R. J Comp Econ. 2015;000:1–26. doi: 10.1016/j.jce.2015.04.002

42.

Policardo

, Carrera

EJS

. Corruption causes inequality, or is it the other way around? An empirical investigation for a panel of countries. Econ Anal Policy. 2018. doi: 10.1016/j.ea2018.05.001

	coefficients
Variables	Fixed(b)	Random(B)	Difference (b-B)
Inequality	–0.2141	–0.1746	–0.0394
Corruption	0.5275	0.9401	–0.4125
Test hypothesis	Ho: Difference in coefficients not systematic
Chi²	2.53
Prob > Chi²	0.2822