Investigating the Factors Affecting Administrative Corruption in Selected Oil-producing Countries with a Regional Development Planning Approach: Using the Quantile Panel Model

Subject Areas : Regional Planning

reza homaunifar ¹ (PhD student in International Economics, Islamic Azad University,)
jalil totonchi ² (Assistant Professor of Economics, Islamic Azad University, Yazd Branch)

Received: 2020-05-10 Accepted : 2020-06-17 Published : 2021-04-21

Keywords: democracy, Regional Development, Corruption, Quantum Model, Oil-producing Countries,

Abstract :

Corruption reduces economic growth by reducing investment, reducing the use of public services, and increasing inequality, with negative economic, political, and social consequences. Therefore, the study of corruption and the factors affecting it is one of the issues that have been considered by economists and regional development policy makers. The purpose of this paper is to investigate the factors affecting corruption in oil-producing countries in the period 1996-2017 with a view to regional development, and in this regard, the Quantile panel method has been used. Because the countries in question have different income levels, they are divided into two groups; Countries with high per capita incomes and countries with low incomes are classified. Research shows that increasing economic growth, democracy and improving the distribution of income in both groups of countries can reduce corruption. On the other hand, the increase in the two variables "government-to-GDP ratio" and "bureaucratic index" in low-income oil-producing countries increases corruption; While in high-income oil-producing countries, it leads to an improvement in the corruption index.


Corruption reduces economic growth by reducing investment, reducing the use of public services, and increasing inequality, with negative economic, political, and social consequences. Therefore, the study of corruption and the factors affecting it is one of the issues that have been considered by economists and regional development policy makers. The purpose of this paper is to investigate the factors affecting corruption in oil-producing countries in the period 1996-2017 with a view to regional development, and in this regard, the Quantile panel method has been used. Because the countries in question have different income levels, they are divided into two groups; Countries with high per capita incomes and countries with low incomes are classified. Research shows that increasing economic growth, democracy and improving the distribution of income in both groups of countries can reduce corruption. On the other hand, the increase in the two variables "government-to-GDP ratio" and "bureaucratic index" in low-income oil-producing countries increases corruption; while in high-income oil-producing countries, it leads to an improvement in the corruption index.
 Keywords: Corruption, Democracy, Regional Development, Bureaucracy, Quantum Model, Oil-producing Countries
Extended abstract
 Introduction:
 Due to the significant negative consequences of corruption in recent years, the problem of corruption and the factors affecting it have been considered by many economists and regional development policymakers. Corruption reduces economic growth by reducing investment, reducing the use of public services, and increasing inequality, with negative economic, political, and social consequences. Therefore, the present study examines the factors affecting the corruption index using the Quantile regression approach in oil-producing countries. The main advantage of Quantile regression is that it determines the effect of factors affecting corruption during different parts of the corruption index. According to the purpose of this study, the study tests the following hypotheses:
 Economic growth has a negative and significant impact on corruption in oil-producing countries.1
 Income inequality has a positive and significant effect on corruption in oil-producing countries.2
 Democracy has a negative and significant impact on corruption in oil-producing countries.3
 Bureaucracy has a negative and significant impact on corruption in oil-producing countries.4
 The size of the government has a positive and significant effect on corruption in oil-producing countries
Methodology:
 In this study, the method used is the Quantile panel method. Because the countries in question have different income levels, they are divided into two groups; Countries with high per capita incomes and countries with low incomes are classified. Research shows that increasing economic growth, democracy and improving the distribution of income in both groups of countries can reduce corruption. On the other hand, the increase in the two variables "government-to-GDP ratio" and "bureaucratic index" in low-income oil-producing countries increases corruption; while in high-income oil-producing countries, it leads to an improvement in the corruption index.
: Consider a simple panel model
  
 Where i represent countries and t is time.   And  represents the economic growth and removal of regressors, respectively. β And u also indicate the removal of parameters and the disruption component, respectively. In Quantile regression, conditional distribution is used to combine several different functions. Each multiple regression also provides a unique point of conditional distribution. By placing the regression of several polygons together, a complete distribution of the original conditional distribution is provided. Indicates ɵ conditional Quantile from the  levels given by.
In this connection, the c is the effect of displacement depends on the conditional variable Quantile. It is important to study polygonal regression in studies that have asymmetric distribution, distribution with broad sequences.
Discussions and Findings:
 The findings show that in low-income oil-producing countries, the economic growth index in the second and third sections (fluctuations) has a positive and significant effect on the corruption reduction index. The ratio of government expenditures to GDP, has a negative and significant effect on the Corruption Reduction Index until the sixth section. Therefore, it can be said that in low-income oil-producing countries, increasing government spending has not been effective and has increased corruption.
 Also, the effect of the bureaucratic index on the corruption reduction index in all parts (sections), with the exception of the second and third sections is negative and significant. This result shows that in low-income oil-producing countries, the policies implemented to improve bureaucracy are ineffective due to the government's significant role. The effect of democracy on the issue of reducing corruption in oil-producing countries with low per capita income is positive in all sections except the first one and its mean full at a 99% percent confidence level. In other words, increasing democracy, which can manifest itself in media freedom and economic transparency, can pave the way for a reduction in the corruption index. The effect of the Gini coefficient index is on reducing negative and significant administrative corruption (with the exception of Decade 9). Therefore, increasing the Gini coefficient, which means more inequality in income distribution, will lead to an increase in corruption.
Conclusion:
 Based on the estimates of the quantum panel model for oil-producing countries with high per capita income, the variable of economic growth has a significant and significant effect on reducing corruption in the sixth to ninth quarters (sections). Therefore, increasing economic growth in the last few weeks could lead to a reduction in corruption. The ratio of government expenditures to GDP in all parts, with the exception of the second and fifth sections, has a positive and significant effect on the Corruption Reduction Index. In other words, government management and spending in oil-producing countries with high per capita incomes is targeted and can help reduce corruption. In this regard, considering the effectiveness of government policies, the effect of bureaucracy on reducing administrative corruption in all sections, with the exception of the fifth section, is positive and significant.
 The Democracy Index also has a positive and significant effect on reducing corruption in all sections except the first and fifth sections. Thus, increasing democracy through various factors such as media freedom can help reduce corruption. In the end, the Gini coefficient in all sections with the exception of the first and fifth sections has a negative effect and reduces the level of corruption in the level of 99% confidence. Thus, an increase in the Gini coefficient, which means greater inequality, will increase corruption in high-income oil-producing countries.

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