Next Article in Journal
Determinants of Related and Unrelated Export Diversification
Previous Article in Journal
Do Technological Innovations Affect Unemployment? Some Empirical Evidence from European Countries
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Consequences of Corruption on Inflation in Developing Countries: Evidence from Panel Cointegration and Causality Tests

1
Department of Economics, Necmettin Erbakan University, Konya 42090, Turkey
2
Department of Economics, Ahi Evran University, Kırsehir 40100, Turkey
*
Author to whom correspondence should be addressed.
Economies 2017, 5(4), 49; https://doi.org/10.3390/economies5040049
Submission received: 6 October 2017 / Revised: 20 November 2017 / Accepted: 30 November 2017 / Published: 11 December 2017

Abstract

:
Up until the 1980s, studies on corruption were dominated by disciplines of public administration and sociology. In the following years, however, economists have also provided a good amount of research on this issue. According to Transparency International Agency, corruption, which has a negative impact on most macroeconomic indicators, is “the abuse of entrusted power for private gain”. Even though the disruption of corruption causing weak growth and investment rates has long been examined, there is little evidence regarding its impact on inflation. In this study, the nexus between corruption and inflation was investigated for 20 countries over the period 1995–2015. Estimation results indicated that high corruption increased inflation rates, and that there was a unidirectional causal relationship from corruption to inflation for ten countries in the sample.
JEL Classification:
D73; E31; C33

1. Introduction

Corruption is a multidimensional issue with far-reaching effects and is highly related to disciplines of sociology, political science, history, economics, and public administration. Thus, it is quite difficult to make an exact and comprehensive definition of corruption. Corruption has been defined as “the use of public office for private gain” (Gray and Kaufman 1998; Rose-Ackerman 1999; Lambsdorff 2007). In another definition by Shleifer and Vishny (1993), corruption was defined as the “sale by government officials of government property for personal gain”. However, with recent developments, it is inadequate to define corruption only as the abuse of public office. Corruption, which has a more comprehensive scope, is based on the transfer of benefits among the parties and the return offered. The International Transparency Agency defines corruption as “the abuse of entrusted power for private gain”, and evaluates both the public and private sector. As understood from these definitions, to speak about corruption, one needs to exercise their power, and some people need to benefit from the use of this power. Based on this definition, all crimes such as bribery, embezzlement, dishonesty, misconduct, and favoritism can be considered as corruption.
Until recently, studies evaluated corruption as a practice against public morality within the framework of criminal law. Within this scope, studies have mainly focused on the reasons behind it, and the social consequences of corruption. However, with the measurement of corruption, studies started being conducted to reveal the relationship between corruption and macroeconomic variables such as economic growth and investments. The main difficulty faced by the researchers while conducting studies to measure the economic effects of corruption is the way in which corruption is measured and the reliability of this measurement. The measurement of corruption is mainly made through the examination of perception levels of some segments of the population. The best indexes developed in this respect are the Bribe Payers Index (BPI), the Global Corruption Barometer (GCB), the Control of Corruption Index, the Corruption Perceptions Index (CPI), the Freedom from Corruption, and Business International.
The role of the state in economic and government policies, the structure of the tax system, inequality in income distribution, poverty, inflation, low wages, commercial limitations, low rates of employment, competitive power of the economy, and level of openness and informal economy are among the economic factors that may lead to corruption (Akçay 2001, pp. 44–45). The most destructive effect of corruption on the economy is seen in investments, economic growth, and economic development. High rates of corruption lead to a decrease in investments and slow down economic growth. As a result, companies may postpone their investments in the country, or they may shift their investments to another state. Furthermore, corruption reduces the efficiency of public expenditure and the quality of the existing infrastructure, increases the cost of doing business, and eventually hampers economic growth. Other adverse effects of corruption include damage to income distribution, the prevention of the efficient use of economic resources, and inflation (Al-Marhubi 2000, p. 199).
In addition to these negative effects, some views have proposed that corruption helps save the time lost in bureaucracy and thus leads to efficiency and development. Corruption, which mainly occurs in the form of bribery in countries with a complex bureaucratic system and a weak institutional structure, shortens the time spent on bureaucratic practices and accelerates investments. According to Leff (1964) and Lui (1985), since corruption is a transfer payment from the bribe giver to the bureaucrats, it does not create a social cost.
Although there has been no consensus on the positive and negative effects of corruption on economic indicators, the general view is that its negative effects will outweigh the positive ones in the long run. Corruption emerges as a negative construct that erodes property rights, harms political institutions and complicates the nature of economic development (Hodge et al. 2011). To eliminate the destruction in societies caused by corruption, which was described as a “cancer” by Wolfensohn, the former president of the World Bank, it is of great significance to implement effective policies and methods against corruption (Bhargava 2006, p. 341).
This study aimed to determine the direction of the effects of corruption on inflation by using the data from 20 selected developing countries for the period between 1995 and 2015. Within the scope of this study, following the introduction section, which gives the fundamental definitions about the topic, the theoretical background on the relationship between corruption and inflation is outlined in Section 2. Section 3 presents a review of the literature, and Section 4 describes the dataset and empirical model. Section 5 includes explanations of the econometric method and comments on the empirical findings. The study is concluded with a general evaluation.

2. Theoretical Background on the Relationship between Corruption and Inflation

The studies that examined the relationship between corruption and inflation found a positive and significant relationship between the change in the rate of inflation and corruption. In this respect, it is possible to cite many reasons that may lead to the relationship between corruption and inflation. Some of these reasons are listed below:
Nordhaus (1975) stated that voters were sensitive to inflation and unemployment rates, and that their choices during the election period were shaped by these two indicators. According to Nordhaus, opportunist political parties used their existing public authority and increased government spending in order to be re-elected in the short term and to remain in power. Politicians financed the budget deficits created by the increase in government overspending taxes, which hurts the voters. Thus, the political authority chooses to finance budget deficits through emission to eliminate this negative impact. Increasing the volume of money through emission leads to inflation. According to Catao and Terrones (2005), in countries with high corruption levels and weak prosecution system, seigniorage becomes the primary source of income for governments.
Moreover, corruption reduces efficiency by preventing the efficient distribution of economic and public resources. The transfer of these resources from productive fields to less-controllable and manipulated projects negatively affects financial performance. This efficiency loss in public space reflects negatively on the private sector after some time and decreases economic performance. To cover the costs (which increase due to corruption), domestic and foreign borrowing is applied, which in turn increases inflation rates and the risk level of the country. This process has a direct deterrent effect on foreign investors and leads to an increase in exchange rates and current account deficit problems. As a result of this vicious circle, the increase in the volume of emission to cover the costs also triggers inflationist pressures.
Therefore, high levels of public deficits and an ineffective tax system in a country render seigniorage necessary to finance these deficits. Governments generate income through the monetization of the fiscal deficit under the name of seigniorage and inflation tax. According to optimal tax theory, tax evasion and tax collection costs may cause governments to turn to inflation tax as a source of income. In particular, it is possible to witness this situation in countries where tax evasion and tax collection costs are at high levels. In their study conducted by using data from 169 countries for the period between 1960 and 1999, Aisen and Veiga (2008) found that the income developing countries earned from seigniorage was five times as high as the income the developed nations received.
On the other hand, an increase in the general level of prices is expected to create more opportunities for illegal and unethical behaviors such as theft and fraud (Braun and Di Tella 2004, p. 80). High inflation is a problem, which reduces the income of individuals and groups, impairs income distribution, and increases uncertainty in the economy. Due to the rapid and continuous decrease in purchasing power, people may seek ways to illegally generate income (Al-Marhubi 2000, p. 200). Thus, high inflation rates determine how public resources are used, and increasing the activities of rent-seeking, reaping a speculative profit, and lobbying may lead to economic degeneration (Rahmani and Yousefi 2009, p. 3).
Wrong macroeconomic policies pursued to finance high public expenditures cause high budget and current account deficits and trigger inflation. The weak institutional structure of a country creates further pressure, particularly in periods of high inflation. In such an environment, investors cannot protect their property rights, which creates an environment conducive to corruption (Samimi et al. 2012, p. 392).
When a general evaluation is made, it can be said that a two-way relationship exists between inflation and corruption. As corruption increases in a country, capital flows to other countries and taxable resources decrease. A reduction in tax revenue and the increase in public expenditure render emission necessary, which creates an inflationist effect. On the other hand, an acceleration in inflation and continuously changing prices enhance economic uncertainty, reduce purchasing power, and cause people to be involved in corruption to generate extra income.

3. Literature Review

Bliss and Di Tella (1997), Ades and Di Tella (1999), Choi and Thum (1999), and Svensson (2005) outlined an extensive theoretical background on the economic causes of corruption and its effects. However, empirical tests on the effect of corruption on economic activities were conducted later due to the difficulties experienced in measuring the corruption. With the measurement of corruption and the increase in the number of databases, it became possible to analyze the economic effects of corruption, which led to the construction of literature on this topic. The majority of the studies in this field have focused on the relationship between corruption and macroeconomic indicators such as the rate of economic growth, GDP per capita, the structure of the market, the rate of investment, public expenditures, the volume of foreign direct investment, inflation, and international trade.
Knack and Keefer (1995), Mo (2001), Leite and Weidmann (2002), Neeman et al. (2004), Pellegrini and Gerlagh (2008), Méon and Sekkat (2005) and Ahmad (2008) revealed that corruption negatively affected economic development and GDP per capita. As a result of the analysis conducted for 68 countries, Mauro (1995) found that corruption had adverse effects not only on economic development, but also on investments and the structure of official institutions. On the other hand, Aidt et al. (2008) argued that corruption negatively affects economic development in countries with high institutional quality, whereas corruption did not have any impact on economic growth in countries with low institutional quality. In their study conducted in Nigeria, Aliyu and Elijah (2008) found that corruption negatively affected economic development, the development of human capital, and total employment. Dridi (2013) investigated the effect of corruption on macroeconomic indicators such as investment, human capital, political instability, inflation, public expenditure, and openness using data from 82 countries for the period between 1980 and 2002. The findings of the study revealed that corruption negatively affected growth through low human capital and political instability.
According to Abed and Davoodi (2002) and Smarzynska and Wei (2000), corruption had a dissuasive effect—similar to tax—on foreign direct investments. The increase in the corruption index created a negative impact on foreign direct investments. Bandeira et al. (2001) maintained that capital efficiency, human capital efficiency, and the total efficiency factor decreased as a result of corruption. Barreto (2001), Li et al. (2000), and Gyimah-Brempong (2002) found a significant positive relationship between corruption and income inequality. Furthermore, Li et al. (2000) tried to reveal the rate at which the increase in corruption increased income inequality.
In addition to the negative impacts, some arguments in favor of corruption are also seen in the literature. Such arguments claim that corruption eliminates the bureaucratic structure that delays the financial decisions and prevents investments, thus accelerating growth. According to Leff (1964), Nye (1967), Huntington (1968), and Gerni et al. (2012), bribery may decrease bureaucratic costs and accelerate efficient public administration. These studies highlight that as bureaucratic procedures are stimulated and bureaucratic costs drop, economic growth is promoted.
Although there are numerous studies that have examined the effect of corruption on economic indicators and growth, few studies have investigated the relationship between corruption and inflation. There are two different approaches regarding the relationship between corruption and inflation. The first approach maintains that corruption leads to inflation. According to the analysis conducted by Al-Marhubi (2000) in 41 countries for the period from 1980–1995, there was a positive relationship between corruption and inflation. Piplica’s (2011) study, which focused on 10 transition economies in Central and Eastern Europe, revealed that corruption increased inflation. In their research on transition economies, Abed and Davoodi (2002) also found a positive relationship between corruption and inflation. Similarly, Smith-Hilman (2007) and Samimi et al. (2012) indicated the significant positive impact of corruption on inflation. Piplica and Praksa (2011) used the data from 10 EU countries for the period 1995–2008 to reveal that corruption positively affected inflation.
Mohammadishirkolaei (2014) measured the effect of corruption on inflation using the data for MENA (Middle East and North Africa) countries for the 2003–2010 period and the variables of openness degree, per capita income, final consumption expenditures, and equity growth rate. The results of the analysis revealed that openness level, per capita income, and corruption perception index affected inflation rates negatively, whereas equity growth rate and final consumption expenditures had a significant positive effect on inflation.
Yousefi (2015) tested her hypothesis that corruption led to an increase in monetary expansion and thus inflation rate using the data for 164 countries between 1995 and 2010 period. The findings of the study pointed to a significant positive relationship between corruption and inflation for the entire sample. It was further observed that the parameter coefficients for the OECD (Organisation for Economic Co-operation and Development) countries were lower when compared to the non-OECD countries. Thus, the impact of corruption on inflation in developed countries was lower in comparison to the developing countries. Another significant finding of the study was that the impact of corruption on inflation in countries that did not export oil was statistically significant, while this impact was insignificant in oil exporting countries.
From another standpoint regarding the relationship between corruption and inflation, inflation increases corruption. In their study, which examined the relationship between corruption and economic factors, Getz and Volkema (2001) found that economic uncertainties increased the general level of prices, and the increase in inflation triggered corruption. The study by Paldam (2002), which investigated the economic and cultural determinants of corruption, revealed that an increase in inflation rates gave rise to corruption in the short term. Tosun (2002) examined the economic determinants of corruption in 44 countries using data from the 1982 to 1995 period, which pointed to a statistically significant and positive relationship between corruption and inflation.
Braun and Di Tella (2004) examined the effect of inflation on corruption by using data for 75 countries from the period between 1982 and 1994, and by integrating political rights and import/GDP variables into the model. The results of the study revealed that the increase in the general level of prices aggravated corruption. In their study conducted with data from 97 countries from different income groups for the 2002–2010 period, Akca et al. (2012) investigated the effect of economic variables such as inflation, economic growth, effectiveness of government, political stability, and trade deficit on corruption. As their findings suggested, inflation had a statistically significant positive effect on corruption for all countries in the model. Ben Ali and Sassi (2016) analyzed the data for the period between 2000 and 2012 for 100 countries and found that in particular, countries with a weak institutional structure tended to use seigniorage as a source of income and the expansion of money supply increased inflation rates through other channels.
As a result, the literature on corruption suggested that there was a significant positive relationship between corruption and inflation. While some studies have indicated that corruption leads to inflation, other studies have argued that inflation causes corruption.

4. Data Set and the Empirical Model

This study examined the relationship between corruption and inflation using data from 20 developing countries for the 1995–2015 period. These countries were selected based on the availability of data in the IMF (International Monetary Fund) emerging and developing economies list. Although there were no precise and objective measures of corruption described in the literature, some international institutions have developed some indicators. The Corruption Perception Index developed by Transparency International, Freedom from Corruption presented by the Heritage Foundation, Business International used by Mauro (1995), and the World Bank Control of Corruption Index are the most widely used corruption measurement tools. All of these indices measure perception and based on surveys conducted by private firms and international organizations. However, these are not the perfect criteria for corruption, and should only be perceived as substitute tools that could be used in measuring corruption (Rohwer 2009).
Serra (2006) pointed out that aggregate corruption indicators such as the Corruption Perception Index (CPI) and Freedom from Corruption would be more reliable than the indices obtained from individual sources such as the ICRG (International Country Risk Guide) and the World Bank (Serra 2006, p. 229). This study used the Freedom from Corruption data set developed by the Heritage Foundation to measure corruption. In this index, which uses a scale between 0 and 100, large numbers indicate an improvement in the level of corruption. The Freedom from Corruption Index, which was developed based on the Corruption Perception Index (CPI) prepared by Transparency International, is one of the ten indicators of the Economic Freedom Index. The Corruption Perception Index obtains a value between 0 and 10, where high values indicate low levels of corruption, while low values point to high levels of corruption. The Freedom from Corruption Index by the Heritage Foundation, on the other hand, is a revised version of the Corruption Perception Index, where values between 0 and 100 are assigned. High values in the Freedom from Corruption Index point to low levels of corruption in a country, so each value in this index is deducted from 100 and reversed for ease of interpretation. Thus, the freedom from corruption variable, which was reconstructed for econometric analysis, is expected to have a positive sign (Al-Marhubi 2000; Abed and Davoodi 2002; Braun and Di Tella 2004; Piplica 2011; Smith-Hilman 2007; Piplica and Praksa 2011; Samimi et al. 2012; Ben Ali and Sassi 2016).
Annual percentage changes in consumer prices were used for the inflation rate, which was the dependent variable of the estimated long-term equation in the study. Additionally, some control variables (which are determinants of inflation) were included in the long-run equation to prevent the problem of omitted-variable bias. To this end, based on Romer (1993), Lane (1997), Terra (1998), Gokal and Hanif (2004), Wynne and Kersting (2007), Badinger (2009), Blackburn and Powell (2011), Evans (2012), Ben Ali and Sassi (2016), GDPGAP to measure the difference between actual output and potential output, OPEN for the economic integration level of a country into the world, and M2 for price stability and the decisions of the monetary authority were added to the regression. Econometric analyses were conducted using EViews 9 and Gauss10 software. The regression model to be estimated to determine the relationship between corruption and inflation is given in Equation (1):
INFit = β0 + β1GDPGAPit + β2OPENit + β3M2it + β4CORit + eit, for i = 1,2,…, N, t = 1,2…T
In this equation, i represents the cross-sectional units, while t is time and eit is the error term that is independently identically distributed with zero mean and constant variance. Furthermore, INF, GDPGAP, OPEN, M2, and COR represent the inflation rate, the output gap, the share of total imports and exports of goods and services in GDP, annual broad money growth rate, and the freedom from corruption index, respectively. The GDPGAP, OPEN, M2 and INF variables were compiled from the World Bank WDI 2015 data set, while COR was obtained from the Heritage Foundation data. β1, β2, β3, β4 represent the long-term coefficients for each variable. Detailed information on the variables in the long-term equation is given in Table 1.
There have been different theories regarding the long-run coefficient of the output gap on inflation rates. According to the New Keynesian Phillips curve, which is based on the rational expectations hypothesis, a positive output gap leads to a boosting effect on inflation rates (Gali and Gertler 1999; Dewan et al. 1999; Paul 2009; Tiwari et al. 2014; Abbas et al. 2016). In this case, β1 will have a positive sign.
Romer (1993), Lane (1997), Wynne and Kersting (2007), and Evans (2012) argued that economic integration has a direct and indirect price effect on inflation rates. Trade openness means easier access to cheap final goods and imported inputs. As a result of the decrease in production costs, domestic prices and inflation rates will also decrease. Furthermore, the intense competition in trade with the external world pushes the domestic firms to manufacture in areas where they have comparative advantages, which results in an increase in productivity and a decrease in price levels (Wynne and Kersting 2007, pp. 8–9). Grossman and Helpman (1991) pointed out four possible ways where increasing trade openness led to an increase in domestic productivity. In this respect, openness leads to an increase in productivity as technical knowledge transfer among the countries increases; creates pressure on the firms to develop new products in order to not lose their competitive advantage; provides a wider market opportunity through which successful innovative activities may be utilized; and causes an increase in productivity by encouraging countries to specialize in certain sectors. Given the direct and indirect price effects, trade openness will decrease domestic prices and a country’s long-run inflation rates, and OPEN is expected to take a negative sign in the long run (β1 < 0). Romer (1993) maintained that unanticipated monetary expansion was an important variable that led to an increase in inflation rates. In this respect, it is predicted that β3 will have a negative sign.

5. Methodology and the Empirical Findings

To determine the relationship between corruption and inflation, the stationary of variables was first tested through the unit root methods, and then the presence of a long-term relationship was investigated with co-integration tests. However, as global integration has reached significant dimensions both commercially and financially, particularly after the 1980s, a shock in one country eventually spreads to other countries. Thus, before conducting an econometric analysis in empirical studies, the effect of countries with cross-sectional units on each other must be tested with cross-sectional dependence methods. The findings of the cross-sectional dependence test helped us determine the unit root, co-integration, and causality methods most appropriate to the structure of the dataset.
The Breusch and Pagan (1980) LM (Lagrange Multiplier), Pesaran (2004), CD, CDLM, and Pesaran et al. (2008) LMadj (bias-adjusted CD) tests are the most commonly used methods to test the cross-sectional dependence. In each of these methods, the null hypothesis states that there is no cross-sectional dependence. Among these methods, LM yields valid results in panels where the cross-sectional dimension is small and the time dimension is very large. Under the null hypothesis, the LM statistic has asymptotic chi-square distribution with N(N − 1)/2 degrees of freedom. Under the condition that time dimension is greater than cross-sectional dimension, another method, the CDLM test developed by Pesaran (2004) is used with samples where both time and cross-sectional dimensions take large values ( T and then N ). This test statistic has a standard normal distribution. Pesaran (2004) also developed the CD test to be used in large panels without any conditions (Menyah et al. 2014, p. 390). Calculations in Pesaran (2004) CDLM and CD statistics are made with the formulas in Equations (2) and (3):
C D L M = 1 N ( N 1 ) i = 1 N 1 j = i + 1 N ( T ρ ^ i j 2 1 )
C D = 2 T N ( N 1 ) ( i = 1 N 1 j = i + 1 N ρ ^ i j )
In addition to these methods, the LMadj (bias-adjusted CD) test was developed by Pesaran et al. (2008) as a different version of the LM test. This method yields meaningful results for large panels including first T and then N . The LMadj statistic, which has asymptotic normal standard distribution, is calculated with the formula given in Equation (4):
L M a d j = 2 N ( N 1 ) i = 1 N 1 j = i + 1 N ( T k ) ρ ^ i j 2 μ T i j ϑ T i j
Table 2 shows the results of the cross-sectional dependency test for 20 developing countries. The findings in the table indicated that the null hypothesis of no cross-sectional dependence could not be rejected at a 99% significance level for all methods. This result meant that a shock in one of the countries in the sample was not expected to have the same effect on the other countries.
The fact that the cross-sectional dependence test pointed to cross-sectional independence among the countries in the panel meant that some first-generation methods could be used in the unit root, co-integration, and causality analyses. Thus, three different unit root tests were used to determine the integration levels of the series. Among these methods, Levin et al. (2002, hereinafter LLC) proposed a test that made a homogeneity hypothesis, while Im et al. (2003, after this IPS) was based on heterogeneity hypothesis. These two tests can be used to check the null hypothesis of stationary and the presence of the unit root in the alternative hypothesis. On the other hand, in contrast from the first two methods, the Hadri (2000) test, which was used as the third unit root method, examines the presence of unit root in the null hypothesis and the stationary of the series in the alternative hypothesis.
The results of the unit root test are displayed in Table 3. According to the test results, although the inflation rate (CPI) and money supply (M2) had a unit root at the level according to the LLC and Hadri tests, they were stationary at the level according to the IPS. The variables of corruption (COR) and output gap (GDPGAP) were not stationary at the level according to the IPS and LLC, while the openness (OPEN) had a unit root at the level according to all the unit root tests. The results of the differenced series showed that each series became stationary at the first difference. This indicated that the series were integrated to the order of one, I ( 1 ) , according to at least one of the unit root tests (IPS, LLC, and Hadri). Following these findings, the presence of a long-term relationship among the series had to be investigated through co-integration methods. To this end, co-integration analysis was conducted with the Pedroni (1999) and Kao (1999) techniques, which are among the first-generation co-integration methods.
Pedroni (1999), who used the Engle and Granger (1987) co-integration method as the basis, made seven statistical calculations for the heterogeneous panel, four of which were within-dimension and three of which were between-dimension. The within-dimension statistics proposed by Pedroni were panel ν, panel rho, panel PP, and panel ADF-statistics, while the between-dimension statistics were Group rho, Group PP, and Group ADF. This method tests the absence of a long-term relationship in the null hypothesis and the existence of a long-term relationship in the alternative hypothesis.
Another co-integration test that used the Engle and Granger (1987) approach as the basis was developed by Kao (1999). Unlike Pedroni (1999), this approach tested the null hypothesis of no long-term relationship among the variables by assuming that the slope coefficients of all the cross-sectional units were homogeneous. The Pedroni (1999) and Kao (1999) panel co-integration test results are reported in Table 4.
The results of the Pedroni cointegration test, which are presented in Table 4, showed that the null hypothesis of no cointegration could be rejected according to five of the seven statistics. This result indicated that there was a long-term relationship among the variables in Equation (1). The findings of Kao’s (1999) test also verified this result. The t statistics in the constant model of the Kao (1999) residual co-integration test showed that the null hypothesis was rejected at the 99% significance level. Eventually, it can be concluded that there exists a long-run relationship between inflation and the independent variables in Equation (1).
As a result of the finding that the variables in Equation (1) moved together in the long term, the parameter coefficients were obtained with the Pesaran and Smith (1995) Mean Group estimator (MG) in the next stage. The Pesaran and Smith (1995) MG estimator gives heterogeneous coefficients for each country in the panel under cross-sectional independence. The results of the panel MG estimator are presented in Table 5.
The Mean Group estimator showed that in 14 of the 20 developing countries, corruption positively affected inflation rates, while in six countries, the results were statistically significant. The positive relationship between inflation rates and freedom from corruption also indicated that corruption increased the inflation rates in a country, which was in accordance with Al-Marhubi (2000), Braun and Di Tella (2004), and Ben Ali and Sassi (2016). The GDPGAP, which accounts for the effect of output gap on inflation rates, had a positive and significant sign in six of the countries in the panel. According to the New Keynesian Phillips curve, the positive effect of output gap on inflation rates meant that the current GDP level was below the potential GDP and thus, the increase in output had a boosting effect on inflation (Gali and Gertler 1999; Abbas et al. 2016; Ben Ali and Sassi 2016). This finding coincided with the findings of Paul (2009), Tiwari et al. (2014), and Valadkhani (2014), who maintained that the output gap was positively related to the inflation rate. The effect of the M2 variable, which shows monetary expansion, on inflation rates was, as expected, positive in all countries, and these results were statistically significant in nine countries. It was seen that the OPEN variable, which represents trade openness, had a positive coefficient in six countries, while it had a negative and significant coefficient in three countries. Thus, it was found that openness had a lowering effect on inflation rates in only Brazil, Hungary, and Poland through the availability of cheap imported input, or productivity increase.
Engle and Granger (1987) showed that if two variables were integrated to order one and cointegrated, there might be a causal relationship at least in one direction. In this study, the presence of a causality relationship between corruption and inflation rate was investigated through the Canning and Pedroni (2008) causality analysis. In this method, which used the dynamic error correction model as the basis, the long-term equation expressed in Equation (1) was estimated and based on this equation, and e ^ i t was calculated in Equation (5).
e ^ i t = I N F i t β ^ 0 β ^ 1 G D P G A P i t β ^ 2 O P E N i t β ^ 3 M 2 i t β ^ 4 C O R i t
Following Equation (5), the error correction models seen in Equations (6) and (7) were constructed.
Δ I N F i t = β 1 i + λ 1 i e ^ i t 1 + j = 1 K φ 11 i j I N F i , t j + j = 1 K φ 12 i j C O R i , t j + ε 1 i t
Δ C O R i t = β 2 i + λ 2 i e ^ i t 1 + j = 1 K φ 21 i j I N F i , t j + j = 1 K φ 22 i j C O R i , t j + ε 2 i t
In the error correction models, εit shows how distant the variables are from the equilibrium. For a long-term relationship between the variables, at least one of the λ1i and λ2i adjustment coefficients must be different from zero (Canning and Pedroni 2008, p. 512). The significance test of λ1i may be interpreted as an investigation of whether corruption has a long-term effect on inflation. Similarly, the significance test of λ2i is also a test to identify the effect of inflation on corruption in the long run. On the other hand, −λ1i/λ2i, gives information about the direction of the long-term effect of inflation on corruption (Canning and Pedroni 2008, p. 513).
Group mean based and Lambda-Pearson based tests were developed to choose between the null hypothesis of no causality, and the alternative hypothesis, which proposed the causal relationship. Group mean panel test and Lambda-Pearson statistics were calculated with the formulas in Equations (8) and (9) (Canning and Pedroni 2008, pp. 518–19).
t ¯ λ 1 = N 1 i = 1 N t λ 1 i
P λ 1 = 2 i = 1 N ln P λ 1 i
The tλ1 in Equation (8) is the t statistics about the λ1i = 0 null hypothesis of no causality in country i . In Equation (9), lnPλ1i is the logarithmic value pertaining to the p probability value of t statistics in country i (Canning and Pedroni 2008, pp. 518–19). The causality relationship between corruption and inflation rates, which were the two main variables of our study, was two-directional: from inflation rate to corruption, and from corruption to inflation rate. Rejection of the null hypothesis at the observed significance levels implied a causal relationship between variables.
The Canning and Pedroni (2008) causality test results in Table 6 showed that there was no causal relationship between INF and COR according to the Lambda-Pearson statistics for the whole panel. However, when the results were evaluated by country, it was seen that the null hypothesis, which indicated that inflation rates did not cause corruption, was rejected in only five of the 20 countries in the panel. In this respect, the inflation rate was found to be a statistically significant variable leading to corruption in Brazil, Bulgaria, Indonesia, Kazakhstan, and Mexico. When the second analysis was done with the null hypothesis, it was seen that there was a causality relationship for more countries. It was found that there was causality from the level of corruption to inflation rates in 10 of the 20 countries in the panel. Another significant finding obtained from the Canning and Pedroni (2008) test was that there was a bilateral causality relationship between the inflation rates and corruption at the 99% significance level in Indonesia.

6. Conclusions

Corruption, as an economic malady, is seen as one of the reasons behind weak economic performance. The negative effect of corruption on economic development has been the topic of many theoretical and empirical studies. There is fairly extensive literature stating that corruption negatively affects economic growth and development, reduces investments, and leads to a waste of resources. Corruption also impairs the competition between people and institutions and leads to unfair social, political, and economic structures. On the other hand, some researchers have argued that corruption may positively contribute to economic growth in countries with weak institutional structure. Studies that support this view argue that in countries with a complex and stagnant bureaucratic structure, corruption in the form of bribery accelerates investments and increases economic growth.
Although many studies have so far examined the effect of corruption on economic development, few studies have investigated the effect of corruption on the inflation rate. According to theoretical explanations, corruption increases inflation, particularly as governments in countries without an efficient tax system choose to compensate for the loss of income through seigniorage. The increase in money supply with emission leads to inflation. Moreover, bribery payments increase the general level of prices as an additional cost. Similarly, corruption harms the effective distribution of the financial and public resources, and negatively affects productivity and economic performance.
This study examined the relationship between corruption and inflation using data from 1995–2015 for 20 developing countries. The Corruption Perception Index, Freedom from Corruption, World Bank Control of Corruption, and Business International indices are cited as the most reliable corruption tools in the literature to measure the degree of corruption. This study used the Freedom from Corruption data set developed by the Heritage Foundation as the corruption measurement tool, and long-term coefficients were obtained using the first-generation panel data methods. The findings obtained through the Mean Group estimator showed that there was a positive and statistically significant relationship between inflation and corruption in 6 of the 20 developing countries. Accordingly, an increase in corruption caused a high level of the inflation rate. This result verifies the general view in the literature. Moreover, the Canning and Pedroni (2008) causality test revealed that there was causality from the corruption levels to inflation rates in 10 out of 20 developing countries. Accordingly, it can be argued that controlling corruption with effective policies can also help in reducing the inflation rate and achieving price stability.

Author Contributions

Both authors contributed equally to this work.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Abbas, Syed K., Prasad Sankar Bhattacharya, and Pasquale Sgro. 2016. The New Keynesian Phillips Curve: An Update on Recent Empirical Advances. International Review of Economics and Finance 43: 378–403. [Google Scholar] [CrossRef]
  2. Abed, George T., and Hamid Davoodi. 2002. Corruption, Structural Reforms, and Economic Performance in the Transition Economies. In Governance, Corruption, & Economic Performance. Edited by George T. Abed and Sanjeev Gupta. Washington: International Monetary Fund, Publication Services. [Google Scholar]
  3. Ades, Alberto, and Rafael Di Tella. 1999. Rents Competition and Corruption. American Economic Review 89: 982–93. [Google Scholar] [CrossRef]
  4. Ahmad, Naved. 2008. Corrupt Clubs and the Convergence Hypothesis. The Pakistan Development Review 45: 1001–9. [Google Scholar]
  5. Aidt, Toke, Jayasri Dutta, and Vania Sena. 2008. Governance Regimes, Corruption and Growth: Theory and Evidence. Journal of Comparative Economics 36: 195–220. [Google Scholar] [CrossRef]
  6. Aisen, Ari, and Francisco J. Veiga. 2008. Political Instability and Inflation Volatility. Public Choice 135: 207–23. [Google Scholar] [CrossRef] [Green Version]
  7. Akca, Haşim, Ahmet Yılmaz Ata, and Coşkun Karaca. 2012. Inflation and Corruption Relationship: Evidence from Panel Data in Developed and Developing Countries. International Journal of Economics and Financial Issues 2: 281–95. [Google Scholar]
  8. Akçay, Selcuk. 2001. Economic Analysis of Corruption in Developing Countries. Unpublished Ph.D. thesis, Afyon Kocatepe University, Institute of Social Sciences, Afyon, Turkey. (In Turkish). [Google Scholar]
  9. Aliyu, Shesu U. R., and Akanni O. Elijah. 2008. Corruption and Economic Growth in Nigeria: 1986–2007. MPRA Paper, No: 12504. Munich, Germany: Munich Personal RePEc Archive. [Google Scholar]
  10. Al-Marhubi, Fahim A. 2000. Corruption and Inflation. Economics Letters 66: 199–202. [Google Scholar] [CrossRef]
  11. Badinger, Harald. 2009. Globalization, the Output-Inflation Tradeoff and Inflation. European Economic Review 53: 888–907. [Google Scholar] [CrossRef]
  12. Bandeira, Andrea C., Fernando Garcia de Freitas, and Marcos F. G. Silva. 2001. How does Corruption Hurt Growth? Evidences about the Effects of Corruption on Factors Productivity and Per Capita Income. Escola de Economia de São Paulo Discussion Paper, No. 103. Sao Paolo, Brazil: University of Sao Paolo. [Google Scholar]
  13. Barreto, Raul A. 2001. Endogenous Corruption, Inequality and Growth: Econometric Evidence, School of Economics. Working Paper, No. 01-2. Adelaide, Australia: Adelaide University. [Google Scholar]
  14. Ben Ali, Mohamed Sami, and Seifallah Sassi. 2016. The Corruption-Inflation Nexus: Evidence from Developed and Developing Countries. BE Journal of Macroeconomics, De Gruyter 16: 125–44. [Google Scholar] [CrossRef]
  15. Bhargava, Vinay. 2006. Curing the Cancer of Corruption. In Global Issues for Global Citizens: An Introduction to Key Development Challenges. Edited by Vinay Kumar Bhargava. Washington: The World Bank, pp. 341–70. [Google Scholar]
  16. Blackburn, Keith, and Jonathan Powell. 2011. Corruption, Inflation and Growth. Economics Letters 113: 225–27. [Google Scholar] [CrossRef]
  17. Bliss, Christopher, and Rafael Di Tella. 1997. Does Competition Kill Corruption? Journal of Political Economy 105: 1001–23. [Google Scholar] [CrossRef]
  18. Braun, Miguel, and Rafael Di Tella. 2004. Inflation, Inflation Variability, and Corruption. Economics & Politics 16: 77–100. [Google Scholar]
  19. Breusch, Stanley T., and Adrian Pagan. 1980. The Lagrange Multiplier Test and Its Applications to Model Specification in Econometrics. The Review of Economic Studies 47: 239–53. [Google Scholar] [CrossRef]
  20. Canning, David, and Peter Pedroni. 2008. Infrastructure, Long-run Economic Growth and Causality Tests for Cointegrated Panels. Manchester School 76: 504–27. [Google Scholar] [CrossRef]
  21. Catao, Luis, and Marco Terrones. 2005. Fiscal Deficit and Inflation. Journal of Monetary Economics 52: 529–54. [Google Scholar] [CrossRef]
  22. Choi, Jay Pil, and Marcel Thum. 1999. The Economics of Repeated Extortion. The Rand Journal of Economics 35: 203–23. [Google Scholar] [CrossRef]
  23. Dewan, Edwin, Shajehan Hussein, and Steven Morling. 1999. Modelling Inflation Processes in Fiji. Working Paper No: 99/02. Suva, Fiji: Economics Department Reserve Bank of Fiji. [Google Scholar]
  24. Dridi, Mohamed. 2013. Corruption and Economic Growth: The Transmission Channels. Journal of Business Studies Quarterly 4: 121–52. [Google Scholar]
  25. Engle, Robert F., and Clive W. J. Granger. 1987. Co-integration and Error Correction: Representation, Estimation, and Testing. Econometrica 55: 251–76. [Google Scholar] [CrossRef]
  26. Evans, Richard W. 2012. Is Openness Inflationary? Policy Commitment and Imperfect Competition. Journal of Macroeconomics 34: 1095–110. [Google Scholar] [CrossRef]
  27. Gali, Jordi, and Mark Gertler. 1999. Inflation Dynamics: A Structural Econometric Analysis. Journal of Monetary Economics 44: 195–222. [Google Scholar] [CrossRef]
  28. Gerni, Mine, Ömer Selçuk Emsen, Dilek Özdemir, and Özge Buzdağlı. 2012. Determinants of Corruption and their Relationship to Growth. Paper present at the International Conference on Eurasian Economies, Almaty, Kazakhstan, October; vol. 1, pp. 131–39. (In Turkish). [Google Scholar]
  29. Getz, Kathleen A., and Roger J. Volkema. 2001. Culture, Perceived Corruption and Economics: Model of Predictors and Outcomes. Business and Society 40: 7–30. [Google Scholar] [CrossRef]
  30. Gokal, Vikesh, and Subrina Hanif. 2004. Relationship between Inflation and Economic Growth. Economics Department Reserve Bank of Fiji Working Paper. Available online: http://rbf.gov.fj/docs/2004_04_wp.pdf (accessed on 20 July 2017).
  31. Gray, Cheryl W., and Daniel Kaufman. 1998. Corruption and Development. Finance and Development 35: 7–10. [Google Scholar]
  32. Grossman, Gene M., and Elhanan Helpman. 1991. Innovation and Growth in the Global Economy. Cambridge: MIT Press. [Google Scholar]
  33. Gyimah-Brempong, Kwabena. 2002. Corruption, Economic Growth, and Income Inequality in Africa. Economics of Governance 3: 183–209. [Google Scholar] [CrossRef]
  34. Hadri, Kaddour. 2000. Testing for Stationarity in Heterogenous Panel Data. Econometrics Journal 3: 148–61. [Google Scholar] [CrossRef]
  35. Hodge, Andrew, Sriram Shankar, D. S. Prasada Rao, and Alan Duhs. 2011. Exploring the Links between Corruption and Growth. Review of Development Economics 15: 474–90. [Google Scholar] [CrossRef]
  36. Huntington, Samuel P. 1968. Political Order in Changing Societies. New Haven: Yale University Press. [Google Scholar]
  37. Im, Kyung So, M. Hashem Pesaran, and Yongcheol Shin. 2003. Testing for Unit Root in Heterogenous Panels. Journal of Econometrics 115: 53–74. [Google Scholar] [CrossRef]
  38. Kao, Chihwa. 1999. Spurious Regression and Residual-Based Tests for Cointegration in Panel Data. Journal of Econometrics 90: 1–44. [Google Scholar] [CrossRef]
  39. Knack, Stephen, and Philip Keefer. 1995. Institutions and Economic Performance: Cross-Country Tests Using Alternative Institutional Measures. Economics and Politics 7: 207–27. [Google Scholar] [CrossRef] [Green Version]
  40. Lambsdorff, Johann Graf. 2007. The Institutional Economics of Corruption and Reform: Theory, Evidence and Policy. Cambridge: Cambridge University Press. [Google Scholar]
  41. Lane, Philip. 1997. Inflation in Open Economies. Journal of International Economics 42: 327–47. [Google Scholar] [CrossRef]
  42. Leff, Nathaniel H. 1964. Economic Development through Bureaucratic Corruption. American Behavioral Scientist 8: 118–32. [Google Scholar] [CrossRef]
  43. Leite, Carlos, and Jens Weidmann. 2002. Does Mother Nature Corrupt? Natural Resources, Corruption, and Economic Growth. In Governance, Corruption, & Economic Performance. Edited by George T. Abed and Sanjeev Gupta. Washington: International Monetary Fund, Publication Services. [Google Scholar]
  44. Levin, Andrew, Chien-Fu Lin, and Chia-Shang James Chu. 2002. Unit Root Tests in Panel Data: Asymptotic and Finite-Sample Properties. Journal of Econometrics 108: 1–24. [Google Scholar] [CrossRef]
  45. Li, Hongyi, Lixin Colin Xu, and Heng-fu Zou. 2000. Corruption, Income Distribution, and Growth. Economics and Politic 12: 155–82. [Google Scholar] [CrossRef]
  46. Lui, Francis T. 1985. An Equilibrium Queuing Model of Bribery. Journal of Political Economy 93: 760–81. [Google Scholar] [CrossRef]
  47. Mauro, Paolo. 1995. Corruption and Growth. Quarterly Journal of Economic 110: 681–712. [Google Scholar] [CrossRef]
  48. Menyah, Kojo, Saban Nazlioglu, and Wolde-Rufael Yemane. 2014. Financial Development, Trade Openness and Economic Growth in African Countries: New Insights from a Panel Causality Approach. Economic Modelling 37: 386–94. [Google Scholar] [CrossRef]
  49. Méon, Pierre-Guillaume, and Khalid Sekkat. 2005. Does Corruption Grease or Sand the Wheels of Growth? Public Choice 122: 69–97. [Google Scholar] [CrossRef]
  50. Mo, Pak Hung. 2001. Corruption and Economic Growth. Journal of Comparative Economic 29: 66–79. [Google Scholar] [CrossRef]
  51. Mohammadishirkolaei, Momammadzaman. 2014. Estimating the Impact of Corruption Perception Index on Inflation Rate Case Study: MENA Region Countries. International Journal of Economy, Management and Social Sciences 3: 476–80. [Google Scholar]
  52. Neeman, Zvika, Daniele Paserman, and Avi Simhon. 2004. Corruption and Openness, Department of Economics. Hebrew University Working Paper, No. 353. Jerusalem, Israel: Hebrew University. [Google Scholar]
  53. Nordhaus, William D. 1975. The Political Business Cycle. Review of Economic Studies 42: 169–90. [Google Scholar] [CrossRef]
  54. Nye, Joseph S. 1967. Corruption and Political Development: A Cost-Benefit Analysis. The American Political Science Review 61: 417–27. [Google Scholar] [CrossRef]
  55. Paldam, Martin. 2002. The Cross country Pattern of Corruption: Economics, Culture and Seesaw Dynamic. European Journal of Political Economy 18: 215–40. [Google Scholar] [CrossRef]
  56. Paul, Biru P. 2009. In Search of the Phillips Curve for India. Journal of Asian Economics 20: 479–88. [Google Scholar] [CrossRef]
  57. Pedroni, Peter. 1999. Critical Values for Cointegrating Tests in Heterogeneous Panels with Multiple Regressors. Oxford Bulletin of Economics and Statistics 61: 653–670. [Google Scholar] [CrossRef]
  58. Pellegrini, Lorenzo, and Reyer Gerlagh. 2008. Causes of Corruption: A Survey of Cross Country Analyses and Extended Results. Economics of Governance 9: 245–63. [Google Scholar] [CrossRef]
  59. Pesaran, M. Hashem. 2004. General Diagnostic Tests for Cross Section Dependence in Panels. Cambridge Working Papers in Economics, 435. Available online: https://doi.org/10.17863/CAM.5113 (accessed on 21 January 2017).
  60. Pesaran, M. Hashem, and Ron Smith. 1995. Estimating Long-run Relationships from Dynamic Heterogeneous Panels. Journal of Econometrics 68: 79–113. [Google Scholar] [CrossRef]
  61. Pesaran, M. Hashem, Aman Ullah, and Takashi Yamagata. 2008. A Bias-Adjusted LM Test of Error Cross-Section Independence. Econometrics Journal 11: 105–27. [Google Scholar] [CrossRef]
  62. Piplica, Damir. 2011. Corruption and Inflation in Transition EU Member Countries. Ekonomska Mısao I Praksa 2: 469–506. [Google Scholar]
  63. Piplica, Damir, and E. M. Praksa. 2011. Corruption and Inflation in Transition EU Members. Journal of Public Administration 8: 475–505. [Google Scholar]
  64. Rahmani, Teymur, and Hana Yousefi. 2009. Corruption, Monetary Policy and Inflation: A Cross Country Examination. Unpublished manuscript. [Google Scholar]
  65. Rohwer, Anja. 2009. Measuring Corruption: A Comparison between the Transparency International’s Corruption Perceptions Index and The World Bank’s Worldwide Governance Indicators. CESifo Research Report 3/2009. Available online: https://www.cesifo-group.de/DocDL/dicereport309-rr2.pdf (accessed on 20 May 2017).
  66. Romer, David. 1993. Openness and Inflation: Theory and Evidence. Quarterly Journal of Economics 108: 869–903. [Google Scholar] [CrossRef]
  67. Rose-Ackerman, Susan. 1999. Corruption and Government: Causes, Consequences and Reform. Cambridge: Cambridge University Press. [Google Scholar]
  68. Samimi, Aahmad Jafari, Maryam Abedini, and Mehrnoosh Abdollahi. 2012. Corruption and Inflation Tax in Developing Countries. Middle-East Journal of Scientific Research 11: 391–95. [Google Scholar]
  69. Serra, Danila. 2006. Empirical Determinants of Corruption: A Sensitivity Analysis. Public Choice 126: 225–56. [Google Scholar] [CrossRef]
  70. Shleifer, Andrei, and Robert W. Vishny. 1993. Corruption. The Quarterly Journal of Economics 108: 599–617. [Google Scholar] [CrossRef]
  71. Smarzynska, Beta K., and Shang-Jin Wei. 2000. Corruption and Composition of Foreign Direct Investment: Firm Level Evidence. NBER Working Paper, No. 7969. Cambridge, UK: National Bureau of Economic Research. [Google Scholar]
  72. Smith-Hilman, A. Vindelyn. 2007. Competition Policy, Inflation and Corruption: Evidence from African Economies. Applied Economics Letters 14: 653–56. [Google Scholar] [CrossRef]
  73. Svensson, Jakob. 2005. Eight Questions about Corruption. Journal of Economic Perspectives 19: 19–42. [Google Scholar] [CrossRef]
  74. Terra, Cristina T. 1998. Openness and Inflation: A New Assessment. Quarterly Journal of Economics 113: 641–48. [Google Scholar] [CrossRef]
  75. Tivari Aviral K., Cornel Oros, and Claudiu Tiberiu Albulescu. 2014. Revisiting the Inflation–Output Gap Relationship for France Using a Wavelet Transform Approach. Economic Modelling 37: 464–75. [Google Scholar]
  76. Tosun, Umur. 2002. A Public Failure Product: Corruption. In Corruption and Efficient State. Edited by S. Cingi, C. Guran and M. U. Tosun. Ankara: Ankara Chamber of Commerce Publication. [Google Scholar]
  77. Valadkhani, Abbas. 2014. Switching Impacts of the Output Gap on Inflation: Evidence from Canada, the UK and the US. International Review of Economics and Finance 33: 270–85. [Google Scholar] [CrossRef]
  78. Wynne, Mark A., and Erasmus Kersting. 2007. Openness and Inflation. Staff Paper No. 2. Dallas, TX, USA: Federal Reserve Bank of Dallas. [Google Scholar]
  79. Yousefi, Hana. 2015. Corruption and Inflation. In A Thesis for the Degree of Doctor of Philosophy in Economics. Exeter: University of Exeter. [Google Scholar]
Table 1. Definition of variables and data sources.
Table 1. Definition of variables and data sources.
VariablesExplanations of VariablesData Source
INFConsumer prices (annual %)World Bank, WDI
CORFreedom from corruption indexHeritage Foundation
GDPGAPReal GDP minus detrended GDP with Hodrick-Prescott FilterWorld Bank, WDI
OPENExports plus imports of goods and services (% of GDP)World Bank, WDI
M2Broad money growth (annual %)World Bank, WDI
Table 2. Cross-sectional dependence test results.
Table 2. Cross-sectional dependence test results.
TestStatisticsp-Value
LM (Breusch and Pagan 1980)183.20.62
CDLM (Pesaran 2004)−0.3450.36
CD (Pesaran 2004)−1.2240.11
LMadj (Pesaran et al. 2008)−0.1170.54
Table 3. Unit root tests results.
Table 3. Unit root tests results.
VariableLLCIPSHadri
LevelFirst Diff.LevelFirst Diff.LevelFirst Diff.
CPI6.10 ***−13.1−5.18−16.26.32 ***23.2
GDPGAP−6.53−10.69−5.80−11.181.4410.43
OPEN−2.07 *−6.53−1.17 ***−6.227.66 ***4.70
M2−1.62 ***−9.43−2.94−14.15.54 ***16.2
COR−15.6−14.5−14.1−14.66.70 ***6.14
Note. *** and * indicate that the variable has unit root at 1 and 10% levels of significance, respectively. Newey-West bandwidth selection with Bartlett Kernel is used for unit root analysis. The SIC is used to determine the optimal lag lengths. Estimations are made with the inclusion of constant and trend.
Table 4. Pedroni and Kao co-integration tests results.
Table 4. Pedroni and Kao co-integration tests results.
CountryConstantProbabilityConstant and TrendProbability
Pedroni (1999)
Panel v1.0720.14−0.6010.72
Panel rho−2.5380.00−1.2820.09
Panel pp−17.050.00−18.220.00
Panel adf−16.920.00−18.980.00
Group rho0.9520.821.8140.96
Group pp−10.950.00−13.200.00
Group adf−9.3830.00−11.360.00
Kao (1999)
t statistics−5.8450.00
Note: Lag selection is based on the SIC with the maximum of 3 lags.
Table 5. Panel mean group estimation results for 20 developing countries.
Table 5. Panel mean group estimation results for 20 developing countries.
CountryGDPGAPM2COROPEN
Argentina0.60 ***0.24 ***0.060.48 ***
Azerbaijan3.640.826.72 **−1.87
Brazil−0.580.252.09 ***−1.89 ***
Bulgaria5.152.96 ***2.610.16
China1.57 *0.29 *0.12−0.10
Egypt−0.400.02−0.140.19 **
Hungary0.84 *0.26−0.41−0.15 **
India−0.010.10−0.170.12 *
Indonesia2.46 ***0.140.46 **0.13
Kazakhstan3.120.70 ***−1.06−0.40
Malaysia0.130.060.03−0.001
Mexico−0.390.140.97 ***−0.35
Pakistan0.760.050.101.03 ***
Poland0.010.37 ***0.10−0.19 ***
Romania2.35 **1.16 ***0.590.47
Russia1.591.74 ***−1.62−1.18
South Africa0.190.070.32 **0.18 **
Thailand0.29 **0.22 **0.10 **0.02
Tunisia−0.110.06−0.060.04 *
Turkey−0.950.81 ***0.38−0.73
PANEL0.85 **0.51 ***−0.015−0.20
Note: *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.
Table 6. Canning and Pedroni (2008) causality test results.
Table 6. Canning and Pedroni (2008) causality test results.
CountryCOR→INF λ1 p-ValueINF→COR λ2p-ValueRatio − λ1i2i
Argentina−1.528 ***0.01−0.1520.52−0.09
Azerbaijan−0.770 **0.04−0.0320.89−0.04
Brazil0.1490.650.737 ***0.00−4.95
Bulgaria0.0040.81−0.028 **0.036.33
China−0.818 *0.10−0.5260.31−0.64
Egypt−0.2460.43−0.1600.45−0.64
Hungary−0.2400.40−0.5160.13−2.14
India−0.2110.33−0.0960.70−0.45
Indonesia−0.793 ***0.00−0.269 ***0.01−0.33
Kazakhstan−0.1330.35−0.265 *0.05−2.00
Malaysia−1.195 **0.040.5960.540.49
Mexico−0.1900.130.818 ***0.004.31
Pakistan−0.3440.15−0.1610.63−0.46
Poland−0.1410.620.0120.970.08
Romania−0.147 **0.02−0.0480.51−0.32
Russia−0.305 **0.02−0.0550.19−0.17
South Africa−2.074 ***0.010.2610.780.12
Thailand−1.584 **0.04−0.2690.70−0.16
Tunisia−0.4810.34−2.1820.11−4.53
Turkey−0.141 *0.10−0.0040.93−0.02
PANEL−0.5590.34−0.1160.39−0.20
Note: ***, ** and * indicate the rejection of the null hypothesis of no causality at 1, 5, and 10 percent levels of significance, respectively.

Share and Cite

MDPI and ACS Style

Özşahin, Ş.; Üçler, G. The Consequences of Corruption on Inflation in Developing Countries: Evidence from Panel Cointegration and Causality Tests. Economies 2017, 5, 49. https://doi.org/10.3390/economies5040049

AMA Style

Özşahin Ş, Üçler G. The Consequences of Corruption on Inflation in Developing Countries: Evidence from Panel Cointegration and Causality Tests. Economies. 2017; 5(4):49. https://doi.org/10.3390/economies5040049

Chicago/Turabian Style

Özşahin, Şerife, and Gülbahar Üçler. 2017. "The Consequences of Corruption on Inflation in Developing Countries: Evidence from Panel Cointegration and Causality Tests" Economies 5, no. 4: 49. https://doi.org/10.3390/economies5040049

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop