DOES THE INTERNET USAGE REDUCE THE CORRUPTION IN ...

Acta Aerarii Publici, ISSN 1336-8818 Vol. 12, issue 1, 2015 pp. 22-43

DOES THE INTERNET USAGE REDUCE THE CORRUPTION IN PUBLIC SECTOR? THE SHORT RUN AND LONG RUN CAUSALITY. ZNIŽUJE POŽÍVANIE INTERNETU KORUPCIU VO VEREJNOM SEKTORE? KRÁTKODOBÁ A DLHODOBÁ KAUZALITA. JÁN HUŇADY Ing. Ján Huňady, PhD., Katedra financií a účtovníctva, Ekonomická fakulta Univerzity Mateja Bela, Tajovského 10, 975 90 Banská Bystrica, e-mail: [email protected]

MARTA ORVISKÁ prof. Ing. Marta Orviská, PhD., Katedra financií a účtovníctva, Ekonomická fakulta Univerzity Mateja Bela, Tajovského 10, 975 90 Banská Bystrica, e-mail: [email protected]

Abstract The main aim of this paper is to test the potential relationship between Internet usage and public sector corruption. Our analysis is based on panel data, including 86 developed and developing countries and covering the time period from year 1998 to 2012. Several suitable control variables have been also included in the models. In the first stage we have used the Granger causality test, fixed-effects and random effects models. We have found significant one way Granger causality from Internet usage to corruption. Furthermore, we have applied panel data cointegration regression using DOLS and FMOLS to test the possible long-run relationship between both variables. Moreover, a vector error correction model has been used in the final step of the analysis in order to verify the short run relationship and examine the stability of the long-run relationship and the speed of adjustment. Our results suggest that Internet usage could significantly contribute to a reduction of public sector corruption. This effect could be explained primarily by better access to information, better control mechanisms and automation of certain processes in the public sector arising from higher Internet usage. The relationship seems to be evident in short-run as well as in long-run, though the long-run relationship seems to be even more intensive. Keywords: internet usage, corruption, public sector, panel data cointegration. JEL Codes: D73, O30, L86.

Introduction The Internet is perhaps the most influential technology of the last decades. Its power is related to the power of information, which is nowadays very important. We assume that Internet usage could have a significant and positive effect on corruption reduction in the public sector. This could happen due to better access to information, potential higher transparency and automation of certain processes in public administration via the Internet. However, only little attention was paid to this issue in the theoretical and empirical literature of corruption so far. Despite this fact there are some studies trying to empirically testing this or some similar relationships. However, they are mostly based on crosssectional datasets and done without taking into account time dynamics or potential long-run relationships. In contrast to this, we used various types of panel data regression methods to identify the assumed relationships in the short-run as well as in the long-run based on a rather extensive panel data sample including 85 countries covering the period from 1998 to 2012. We applied fixed and random

Acta Aerarii Publici, ISSN 1336-8818 Vol. 12, issue 1, 2015

effects panel regressions and further estimated the long-run coefficient using panel DOLS and FMOLS cointegration regression. Moreover, a vector error correction model was used in the final stage to test the robustness of these results and identify the short-run and the long run relationships.

2. Literature review According to Tanzi (1999) the most popular and simplest definition of corruption is that it is the abuse of public power for private benefit. He also added that from this definition it should not be concluded that corruption cannot exist within the private sector, because especially in large private enterprises corruption clearly does exist. However, corruption is usually directly related to public sector activities and therefore we decide to focus our attention only on this kind of corruption. The potential determinants, causes and effects of corruption in the public sector have been widely reported in the literature so far. There are several different potential consequences of corruption on the economy reported in the literature from lowering the level of investment and economic growth (Mauro, 1995) or increasing economic inequality and poverty (Gupta, Davoodi and Alonso-Terme, 2002) to various more specific effects such as higher number of traffic fatalities (Anbraci and Escalears, 2006) or more earthquake deaths (Escaleras and Anbarci, 2007). Moreover, Orviska, Caplanova, and Hudson, (2014) have also recently reported that lower level of corruption is associated with higher degrees of satisfaction with democracy. Despite this, there are also some studies reporting the effect of corruption on foreign direct investment or economic growth as insignificant or even positive (Drury, Krieckhaus and Lusztig, 2006; Egger and Winner, 2005). However, the potential consequences of corruption appear to be slightly clearer than its causes, and possible solutions. There are several theoretical and empirical studies focused on the determinants of corruption so far. For example Tanzi (1998) distinguished between direct and indirect causes of corruption. Direct causes include regulations, taxation, public expenditures, provision for goods and services and financing of the political parties. On the other hand indirect causes are quality of bureaucracy, level of wages in public sector, penalty system, institutional control, transparency rules and examples by the leadership. Leite and Weidemann (1999) find out, that the natural resource abundance together with government policies and concentration of bureaucratic power have significant impact on the incidence of corruption. Corruption seems to be also negatively correlated with trade openness as confirmed for example by Gerring and Thacker (2005) or Ades and Di Tella (1999). Corruption in the water supply and sanitation public services in South Asia countries have been analyzed by Davis (2004). He stated that a shift in the accountability networks of service providers, and a change in the work environment that increases the moral cost of misconduct, could significantly reduce the rate of corruption. Based on the results of Ata and Arvas (2011) derived from data on 25 EU countries inflation, economic development, economic freedom and income distribution seems to be statistically significant determinants of corruption. According to the authors particularly important are high inflation and a skewed income distribution in causing a rise in corruption. Several other determinants have been found by Rehman and Naveed (2007). Their results indicate that GDP per capita, unemployment rate, public spending and secondary school enrolments are the most significant factors influencing the level of corruption. To some extent similar conclusions have been achieved by the analysis on micro-level data done by Mocan (2008). The results show that both personal and country characteristics are important in determining the risk of exposure to bribery. On one hand, significant factors at a personal level are, especially, gender, income, education, marital status and city size. On the other hand, the unemployment rate, average education, and the strength of the institutions are important determinants of corruption at the country level. Brunetti and Weder (2003) tested the impact of freedom of press on corruption based on crosssectional data. Besides the observed negative effect of free press on the level of corruption, they also found that inflation, government consumption, different measure of openness and different measure of human capital are further determinants of corruption. Furthermore, there is evidence that military expenditures and arms import could also have a positive impact on corruption growth as reported by Hudson and Jones (2008). There is also the discussion about the relationship between the corruption and shadow economy. Buehn and Schneider (2009) identified a positive two-way relationship between the shadow economy and corruption. Their results show that the shadow economy influences


corruption more than corruption influences the shadow economy. Hence, the size of the shadow economy could be seen as one of the determinants of corruption in the country. According Hudson, Williams, Orviska and Nadin (2009) the corruption and the desire to avoid corrupt governments officials are the final key drivers of businesses into the informal economy. Thus, we can say that the problems of the shadow economy and corruption are closely linked together and a reduction in corruption could have positive effect on reducing the shadow economy and vice versa. Better access to information is often a neglected factor in the most of the literature, but in our view it could have substantial impact on corruption. DiRienzo, Cassandra, Das, Cort and Bubridge (2007) found that the greater the access to information via information and communication technologies, the lower the corruption in the country. This could be at present particularly related to higher Internet usage. As stated by Orviska and Hudson (2009) the Internet has the potential to reduce past disadvantages, such as lack of access to services by those in remote areas. Results also suggest that the Internet access as well as internet banking usage in the EU is the highest in the Scandinavian countries of Finland, Denmark and Sweden as well as in the Netherlands. Interestingly, particularly in these countries the level of corruption is the lowest in the EU, based on the data from Transparency international. There are also some empirical studies in some way supporting this kind of potential relationship. Andersen, Bentzen, Dalgaard and Selaya (2011) stated that Internet could reduce corruption in several ways. First of all because the Internet is the major source of information, it may increase risk of corruption detection and thus making corrupt behaviour less attractive. Second, the Internet is the chief vehicle for the provision of E-government, which allows citizens access to government services online and thus limiting the interaction between potentially corrupt officials and the public. Moreover, online systems require standardized rules and procedures and this could reduce bureaucratic discretion and increase transparency. A study done by Andersen (2009) has focused specifically on the impact of changes in E-government on corruption. Using data for 149 countries for two time observations (years 1996, 2006) he found significant and economically interesting effect. His results proved that by the most conservative estimate, moving from the 10th percentile to the 90th percentile in the E-government distribution implies a reduction in corruption equivalent to moving from the 10th to 23rd percentile in the control of corruption distribution. According to cross-sectional regression done by Vinod (1999), Internet together with schooling, GDP per capita and income inequality seem to be significant determinants of corruption. Garcia-Murillo (2010) identified a positive impact of Internet access in the country on reducing the corruption based on the cross-section regression of approximately 170 countries. Different approach was used by Goel, Nelson and Naretta (2012). They assumed that greater corruption awareness could reduce corruption. The internet was considered the main source of information about corruption. Authors analyzed the searches on Google and Yahoo about corruption based on the cross-country data and found that Internet hits about corruption per capita correlate negatively with corruption perceptions and corruption incidence. Lio, Liu and Ou (2011) study the effects of Internet adoption on reducing corruption by using panel data of 70 countries covering the period from 1998 to 2005. They found bi-directional causality between Internet adoption and corruption. They also stated that OLS is likely to over-estimate the effects of the Internet on corruption reduction. Therefore dynamic panel data models were used in their paper.

3. Data and Methodology The level of corruption in the public sector is in our case based on the Transparency International’s Corruption perception index (CPI). Transparency International each year score countries on how corrupt their public sectors are according to expert assessments and opinion surveys and publish the results on their own website. The index has been calculated in the range between the zero and ten. The higher the value the less corruption can be expected in the public sector of the country. Since 2012 the index was slightly modified to take the values from zero to one hundred, but the main methodology remains unchanged. Thus, we multiplied values from previous years by ten to ensure comparable measures. Based on the CPI we have constructed indicator labeled as corruption as follows: Corruption = 100 – CPI. We wanted to capture the level of corruption, thus this slight transformation has been used to reach better clarity and more straightforward interpretations. This indicator was applied in all regression models as the dependent variable.


Our main aim is to test the null hypothesis of no effect of Internet usage on corruption in the public sector. This will be tested in the short run as well as in the long run. As the main independent variable we used the indicator of Internet users per 100 inhabitants referenced in the World Development Indicators database freely available at the World Bank. Other independent variables used in the models as controls are also available on the World Bank database. We included the high-tech exports share of manufactured exports as the proxy for technology level in the countries. This could control for different level of technology in different countries and improve the real informative value of the Internet usage effect. This variable is also not so strongly correlated with Internet usage as for example GDP per capita or electricity consumption, which we also considered for possible inclusion in the model. In this case we expect a negative sign of the estimated coefficient, because higher technology level in the country could have adverse effect on corruption. On the other hand, we expected a positive sign for the variables reflecting unemployment and inflation, because in general the uncertain economic environment should encourage corruption. Moreover, the people that are afraid of losing their jobs or people trying to find their job could use some kind of bribes for public sector officials. The openness of the economy and general government final consumption expenditure could either increase or decrease the public sector corruption. It is more straightforward to assume that a higher level of general government consumption expenditures could lead to higher corruption in public sector, because this could typically means more resources for direct distribution in the public sector, more government contracts and perhaps also more public procurements. Despite this fact, a high level of public consumption expenditure can also go hand in hand with higher quality of public services and better control mechanisms. However, our research in this paper has been focused on examining the effect of Internet usage on public sector corruption therefore the number of variables in the models used is somewhat limited. The decision on the inclusion of certain variables was also considerably influenced by the data availability. All variables used in the models are summarized in Table 1. We decided to use the panel data because we consider them more suitable for this type of analysis. Moreover, panel data captures the cross-section dimension along with time-series dimensions and therefore reduces the potential co-linearity among explanatory variables, improves the efficiency of the estimates and provides control for omitted variables bias. Our sample includes annual data for 86 countries covering the period 1998-2012. Hence, together at most 1290 observations are available. The countries included in the sample are listed in the table in annex 1. Table 1 Variables used in the models Variable Description

Source

Dependent variable: Corruption

Corruption = 100 – Corruption perception index.

Transparency International. Retrieved from:

Internet users (on 100 inhabitants) High technology exports (as % of manufactured exports)

World bank database (WDI). Available at: World bank database (WDI). Available at: World bank database. (WDI). Available at: World bank database (WDI). Available at:

Independent variables: Internet users High tech export share UNEMPT OPENNESS General gov. final consumpt. exp. Inflation

Source: Authors.

Unemployment rate (in %) Openness of economy (Export + Import/GDP) General government final consumption expenditures (as % of GDP) Annual inflation rate measured in consumer prices (annual % change)

World bank database. (WDI). Available at: World bank database. (WDI). Available at:


In order to verify the assumed causal links between Internet usage and corruption in the public sector we have conducted the panel data regression analysis. In first stage we have applied static fixedeffects and random-effects model to estimate short-run causalities. In the next step we use panel cointegrating regressions as well as vector error correction models (VECM). These methods have been applied due to the non-stationarity of the data used as well as the need to capture dynamic and longrun relationship. Based on the chosen methodology we are able to analyze assumed causalities in short-run and long-run using non-stationary dynamic panels without facing potential problems of spurious regression or endogeneity. Before using cointegrating regression models we have to test for weak stationarity and order of integration. We used the Levin-Lin-Chu (2002), Im, Pesaran and Shin (2003) and Breitung (2000) tests as well as Fisher ADF and PP tests defined by Madalla and Wu (1999) and Choi (2001). These panel unit root test are similar to a unit root test carried out on a single time series. In the case of variables, when we managed to satisfactorily demonstrate the same level of integration we further tested the existence of cointegration. The cointegration between the dependent and independent variables have been tested using panel cointegration tests developed by Pedroni (2004) and Kao (1999). They test the null hypotheses of no cointegration between selected variables and both of them are commonly used for these purposes in the empirical literature. Pedroni (2004) cointegration tests use eleven different statistics. Eight of them are panel cointegration statistics based on the within approach and three of them are group panel cointegration which are based on the between approach. The Kao (1999) test specifies the intercept and homogenous coefficient from the regression and tests the null hypothesis of non-stationary in the residuals from this regression. The Pedroni and Kao tests both allow us to test for the presence of cointegration, but these methods alone could not properly estimate the potential long-run relationships. The cointegrated panel regression needs to be used for this. Thus, when we managed to confirm the cointegration between analyzed variables, we further estimated the long run parameters using the Dynamic OLS (DOLS) and Fully Modified OLS (FMOLS) as panel cointegration estimators. The DOLS estimator developed by Kao and Chiang (2000) and FMOLS estimator introduced by Phillips and Moon (1999) and Pedroni (2000) are often used in the empirical literature to estimate the cointegrating vectors using the panel data. Both the FMOLS and DMOLS methods are based on the standard OLS considering the fixedeffects panel regression model that can be written as: Yit   i   i X it  uit , i = 1,..., N, t = 1,...,T where Yit is a vector of the dependent variable, β is a vector of slopes dimension, α is an individual fixed effect and uit are stationary disturbance terms. It is assumed that the Xit vector are integrated processes of order one for all i, where: Xit=Xit-1+εit FMOLS estimator than can be expressed as follows: 1

ˆFMOLS

N T  N  T    ( xit  xi )    ( xit  xi ) yˆ it  Tˆ  ,  i1 t 1   i1  t 1 

ˆ  serial correlation term that gives covariance matrix of the residuals corrected for where   

autocorrelation and yˆ it is the transformation of dependent variable yit in order to achieve the endogeneity correction. The DOLS estimator is obtained from the following equation: j q2

yit   i  X it   cij X i ,t  j  uit , j q1

where cij is the coefficient of leads and lags of first differenced independent variables. The DOLS estimates of long-run coefficients β are super-consistent. Then we can estimate the β long run coefficient by the following equation:

ˆ



DOLS



1

T  T     zit zit   zit yˆ it  , i 1  t 1   t 1  N

where zit  xit  xi , xit q ,..., xit q is 2(q+1)×1 vector of regressors.


As stated, both estimators account for the potential serial-correlation and endogeneity problems of the standard OLS estimators, which is also beneficial in our analysis. The FMOLS estimator solves this problems by nonparametric corrections, while DOLS estimators add leads and lags of differenced repressors into regression as parametric corrections. Finally we applied panel vector error correction model (VECM) using the residuals from DOLS or FMOLS cointegration equations as an error correction term (ECT). While cointegration coefficients reflect the long run balanced relationship, the VECM model is also able to capture the correcting mechanism of short term deviations from long run equilibrium. Thus, this approach is suitable to identify the sort-run as well as the long-run relationship between variables. In our case we can identify potential the short-run relationships between Internet usage and corruption and also test for the significance of the long-run relationship estimated by DOLS and FMOLS. We can also estimate the speed of adjustment to long-run equilibrium according to the coefficient of error correction term. The VECM model can be expressed as follows: m

m

k 1

k 1

Yi ,t  1,i   1ik Yi ,t k   1,ik X i ,t k  1,i ECTi ,t 1  u1,t , where ECTi,t-1 in our case are the lagged residuals derived from the long-run DOLS or FMOLS cointegrating regressions. Thus, we can summarize that our analysis consists of three main stages. In the first stage we used fixed effects and random effects models to estimate the static short-run coefficients between selected variables. The second stage consists of panel cointegration tests and estimates of long-run coefficients by DOLS and FMOLS cointegrating regressions. In the last stage we applied the dynamic vector error correction models. All these steps are done in order to identify different types of potential causalities between corruption and Internet usage.

4. Analysis and Results The basic descriptive statistics of corruption and Internet users as the two main variables included in the models are summarized in Table 2. Table 2 The basic descriptive statistics of two main variables users used in the models Corruption

Internet users

Mean 51.78837 29.22727 Median 59.00000 19.80000 Maximum 90.00000 96.21000 Minimum 0.000000 0.009244 Std. Dev. 23.62027 27.85072 Skewness -0.632514 0.737145 Kurtosis 2.117121 2.237372 Observations 1290 1289 Source: World bank : World Development Indicators and Transparency international (CPI). At first we analyzed the possible causalities in the Granger sense between these two variables using the Granger causality test. As we can see in Table 3 there is relatively strong evidence for Granger causality from Internet usage to corruption but no evidence for Granger causality interacting backwards. This result is in some extent also important in respect to possible endogeneity issues between these variables especially in the case of fixed effects and random effects models. Thus, we can say that it is likely that Internet usage Granger causes corruption in the selected period and sample of countries.


Table 3 Results of Pairwise Granger Causality Tests Number of lags: 1 Null hypothesis: ΔINTERNET-USERS does 6.48** not Granger Cause ΔCORRUPTION Null hypothesis: ΔCORRUPTION does not Granger Cause 1.30 ΔINTERNET-USERS Observations 1115 Source: Authors.

2

3

4

5.16***

3.32**

1.99*

1.02

0.89

0.95

1029

943

857

In order to identify possible relationships and estimate the regression coefficients we have to use the panel regression models. We have tested the variables for weak stationarity using panel unit-root tests to prevent spurious regression and other potential problems. The results are summarized in the annex 2. Both the corruption and Internet usage variables seem to be non-stationary at their levels. We applied first differences for the variables that we based on the unit-root tests assumed to be the I(1) processes at their levels. Then we have to decide whether to use fixed effect or random effects models in the first stage of our analysis. The result of Hausman test suggests us to use the random effects in our case. However, the nature of our data is slightly more suitable for the fixed-effects model. Thus, we decide to use both types of panel regression models and compare the results. Moreover, we applied the cross section fixed effects, period fixed effects as well as both cross-section and period fixed effects models. The results of panel regressions are summarized in Table 4. Table 4 Results of regression Dependent variable: Δ Corruption

C (Fixed effects) Δ Internet users share LogOpenness Δ Unemployment rate Log Gen. gov. final consumption exp. Inflation High-tech exports share Observations R2 DW stat Source: Authors.

Cross-section fixed effects White diagonal standard errors & cov.

Period fixed effects

Cross-section & period fixed effects

5.33 (1.59) -0.05*** (-2.62) -0.55 (-0.93) 0.1* (1.70) -1.08 (-1.57) -5 -1.2x10 (1.03) 0.0005 (0.02)

-1.89** (-2.01) -0.06*** (-2.50) 0.1 (0.59) 0.1 (1.52) 0.49** (2.02) -5 4.6x10 (0.94) 0.01* (1.71)

-1.97 (-0.5) -0.06** (-2.16) 0.74 (0.95) 0.09 (1.36) -0.42 (-0.59) -5 -7.8x10 (-1.12) -0.006 (-0.27)

-0.05** (-2.15) 0.03 (0.16) 0.11* (1.83) 0.46* (1.85) -5 4.26x10 (0.85) 0.01* (1.79)

1186 0.07 1.99

1186 0.06 2.00

1186 0.11 2.1

1186 0.01 1.97

Crosssection random effects X

As we can seen the variable reflecting the Internet usage is significant at the 5% significance level in all models used. Moreover, it is also significant at the 1% level in the case of the cross-section and period fixed effects models. The estimated coefficient of Internet usage variable has a negative sign in all models. Thus, we can say that the results suggest that higher internet usage could reduce corruption in the country. Despite this fact the coefficient of determination is very small in all models. This is due


to insignificance of most of controls variables included in the models. The usage of differenced variables could lead to loss of information and harm the overall relevance of the models. These results reflect only the static and short-run causalities between the variables. However we have a good reason to believe that Internet usage does not affect the corruption only in short-run but there is even more important relationship in the long-run. In accordance with this assumption and due to the non-stationarity of our main variables we decided to apply cointegrating regression in the second stage of our analysis. First of all we have to check the order of integration in the case of all variables used. This was done previously by panel unit root tests (see Annex 2). The weak stationarity after first differences have been quite clearly proved for corruption and internet usage variables by all of the panel unit root tests. However, we still have got mixed results when we tested the variables at their levels. To make sure that both of the key variables (corruption and internet users share) are first-order integrated we analyze the weak stationarity also for individual time series of each country in the sample. Subsequently, we exclude those countries which, on the basis of individual unit-root tests appeared to be stationary at level. This concerns seven countries: Columbia, Denmark, Germany, Luxembourg, Paraguay, Tunisia and United States. Thus, the cross-section dimension in our sample was slightly reduced to 79 countries, and the sample includes together 1185 observations. The results of unit-root tests for the variables after above mentioned adjustment are shown in annex 3. The panel unit root tests this time clearly confirmed the fact that variables corruption and internet users are the I(1) processes and thus can be further tested for cointegration by the Pedroni and Kao tests. The other included variables seem to be mostly stationary in levels. The results are mixed for variables reflecting the rate of unemployment and general government final consumption expenditures share on GDP. Therefore, based on the available panel unit root test we cannot be completely sure whether these two variables are integrated of order 1 or 0. In accordance with results of panel unit-root tests we applied the panel cointegration tests firstly on the corruption and Internet usage variables. The results support our assumption about cointegration between these two variables (see Table 5). The large majority of the Pedroni tests as well as Kao tests reject the null hypothesis of no cointegration at the 1% significance level.

Table 5 The results of cointegration tests using corruption and internet users variables Cointegration: CORRUPTION, INTERNET USERS (individual intercept) Statistic Weighted Statistic Panel v-Statistic,within dimension 5.65*** 6.39*** Pedroni Panel rho-Statistic, within dimension -3.11*** -4.19*** (Engle-Granger based) tests Panel PP-Statistic, within dimension -4.82*** -6.15*** – individual intercept Automatic lag length Panel ADF-Statistic, within dimension -7.14*** -7.64*** selection based on SIC Group rho-Statistic, between dimension -0.25 Null Hypothesis: Group PP-Statistic, between dimension -5.05*** no cointegration Group ADF-Statistic, between dimension -8.08*** Kao cointegration test Null Hypothesis: ADF-Statistic -2.50*** no cointegration Cointegration: CORRUPTION, INTERNET USERS (individual intercept and trend) Statistic Weighted Statistic Panel v-Statistic, within dimension 0.37 0.14 Pedroni Panel rho-Statistic, within dimension 0.42 0.58 (Engle-Granger based) tests Panel PP-Statistic, within dimension -5.26*** -6.61*** – individual intercept Automatic lag length Panel ADF-Statistic, within dimension -8.60*** -9.47*** selection based on SIC Group rho-Statistic, between dimension 2.87 Null Hypothesis: Group PP-Statistic, between dimension -5.33*** no cointegration Group ADF-Statistic, between dimension -10.61***

Source: Authors. Consequently, we also analyzed the cointegration between the corruption and unemployment as well as between the corruption and general government final expenditure. The results of these panel cointegration tests are summarized in Table 6 and Table 7.


Table 6 The results of cointegration tests using corruption and unemployment variables Cointegration: CORRUPTION, UNEMPLOYMENT (individual intercept) Statistic Weighted Statistic Panel v-Statistic, within dimension 2.00** 3.07*** Pedroni Panel rho-Statistic, within dimension -0.21 -0.85 (Engle-Granger based) tests Panel PP-Statistic, within dimension -1.63* -2.14** – individual intercept Automatic lag length Panel ADF-Statistic, within dimension -3.16*** -3.26*** selection based on SIC Group rho-Statistic, between dimension 1.77 Null Hypothesis: Group PP-Statistic, between dimension -1.44* no cointegration Group ADF-Statistic, between dimension -4.89*** Kao cointegration test Null Hypothesis: ADF-Statistic -2.43*** no cointegration Cointegration: CORRUPTION, UNEMPLOYMENT (individual intercept and trend) Statistic Weighted Statistic Panel v-Statistic, within dimension 1.30* 1.76** Pedroni Panel rho-Statistic, within dimension 1.21 0.28 (Engle-Granger based) tests Panel PP-Statistic, within dimension -2.92*** -4.60*** – individual intercept Automatic lag length Panel ADF-Statistic, within dimension -7.00*** -7.70*** selection based on SIC Group rho-Statistic, between dimension 3.34 Null Hypothesis: Group PP-Statistic, between dimension -4.38*** no cointegration Group ADF-Statistic, between dimension -9.70***

Source: Authors. Despite the fact that we get mixed results most of the tests reject the null hypothesis of no cointegration for both doubles of variables. Table 7 The results of cointegration tests using corruption and general government final consumption expenditures variables Cointegration: CORRUPTION, LOG(general government final consumption expenditure) Statistic Weighted Statistic Panel v-Statistic, within dimension 2.91*** 4.36*** Pedroni Panel rho-Statistic, within dimension -0.81 -2.19** (Engle-Granger based) tests Panel PP-Statistic, within dimension -2.02** -3.28*** – individual intercept Automatic lag length Panel ADF-Statistic, within dimension -3.62*** -4.24*** selection based on SIC Group rho-Statistic, between dimension 0.72 Null Hypothesis: Group PP-Statistic, between dimension -2.42*** no cointegration Group ADF-Statistic, between dimension -5.16*** Kao cointegration test Null Hypothesis: ADF-Statistic -2.44*** no cointegration Cointegration: CORRUPTION, UNEMPLOYMENT (individual intercept and trend) Statistic Weighted Statistic Panel v-Statistic, within dimension 0.04 0.84 Pedroni Panel rho-Statistic, within dimension 1.55 0.19 (Engle-Granger based) tests Panel PP-Statistic, within dimension -2.53*** -4.56*** – individual intercept Automatic lag length Panel ADF-Statistic, within dimension -7.47*** -8.51*** selection based on SIC Group rho-Statistic, between dimension 3.36 Null Hypothesis: Group PP-Statistic, between dimension -3.65*** no cointegration Group ADF-Statistic, between dimension -8.82***

Source: Authors. Thus, we can say that it is likely that there is a cointegration between corruption and unemployment. The same is true for the cointegration between corruption and general government


final expenditure share on GDP. However, these results are somewhat more doubtful than in the case of cointegration between the corruption and Internet usage. We further applied DOLS and FMOLS cointegration regressions, in order to estimate the longrun coefficient of Internet usage and its impact on corruption. The results of various types of DOLS and FMOLS regressions are summarized in Table 8. The long-run coefficients of Internet usage are statistically significant and have negative signs in all models. The coefficient value is higher when we include the linear trend in the models. There is only very little differences between DOLS and FMOLS estimates in general. The coefficient of determination is always higher than 0.97. Thus, we can say that there is relatively strong evidence for a long-run relationship between corruption and Internet usage. Based on these results, it is likely the higher share of internet users in a population have in the long-run downward impact on public sector corruption. Table 8 The results of DOLS and FMOLS models using only internet users as independent variable DOLS (constant) DOLS (linear trend) DOLS FMOLS FMOLS automatic leads and (constant) (constant) automatic leads and (linear lags based on AIC crit. 1 lead, 1 lags based on AIC crit trend) lag Internet -0.044*** -0.031** -0.045*** -0.16*** -0.15*** users (-3.62) (-2.06) (-3.77) (-6.03) (-5.43) 2 R 0.981 0.987 0.97 0.987 0.985 Adj. R2 0.976 0.979 0.968 0.984 0.982 Long-run 19.31 13.41 32.12 9.88 13.06 variance Source: Authors. In the next step we include in the model two other control variables, which seem to be cointegrated. This is mostly done to control for ensure the robustness of our results. Anyhow, we have to be aware of the fact that the unit root test gave us somewhat mixed results about the order of integration and the cointegration was not unambiguously proved for these two variables. The results of cointegrating regression models can be seen in Table 9. Table 9 The results of DOLS and FMOLS models using three independent variables DOLS DOLS DOLS (constant) (constant) (linear trend) FMOLS automatic leads Fixed: automatic leads (constant) and lags based 1 lead, and lags based on on AIC crit. 1 lag AIC crit. -0.054*** -0.046*** -0.037*** -0.16*** Internet users (-5.24) (-3.21) (-3.16) (-6.62) 0.31*** 0.78*** 0.42*** 0.23*** Unemployment (3.50) (5.56) (4.76) (3.09) Log (gen.gov. final -3.30*** -3.98** -4.7*** -0.41 consumption exp.) (-2.82) (-2.42) (-3.08) (-0.43) R2 0.983 0.998 0.975 0.987 Adj. R2 0.980 0.993 0.969 0.984 Long-run variance 14.66 1.26 30.37 9.88 Source: Authors.

FMOLS (linear trend) -0.13*** (-5.43) 0.33*** (4.39) -1.27 (-1.21) 0.985 0.983 12.51

The statistical significance of Internet usage remains unchanged as well as the negative sign of its coefficient. Unemployment seems to be also statistically highly significant based on all models used. The results of models with only a constant suggest that the variable reflecting the general government final consumption expenditure is also statistically significant. However, when we included a linear trend to the model this variable became statistically insignificant. The positive sign of coefficient by


rate of unemployment is in line with our expectations. Thus, the higher unemployment could cause the higher level of corruption in the long-run. On the other side the estimated negative effect of the variable reflecting the share of general government final consumption expenditure on corruption could be somewhat surprising. In the last stage of the analysis we apply vector error correction model (VECM) to identify shortrun dynamic causalities as well as to estimate the speed of adjustment to long-run equilibrium. This approach could be also used for verification of the significance and the stability of long-run relationship indented by cointegration regression. The residuals from each cointagrating regression between Internet usage and corruption (see Table 8) have been used asan error correction term (ECT) in different VECMs. The lagged and differenced values of corruption and Internet users have been included in the models. The two period lag was used as optimum lag length in the models taking into account the Schwarz criterion. As we can see in Table 8, the level of public sector corruption is affected by its past values as expected. Moreover, based on the results of Wald test, we can also say that both coefficients of lagged Internet usage are jointly statistically significant in all models except the last VECM. The error correction term is statistically highly significant in all models. The coefficient of ECT is negative and lower than zero, which is fully in line with the assumption of a long-run stable relationship between corruption and Internet usage. The speed of adjustment to the long run equilibrium between both variables is relatively fast, lying between 37% to 84 % a year. Table 10 The results of VECMs using different error correction terms ECT from ECT from DOLS DOLS ECT from (constant) (constant) FMOLS automatic leads 1 lead, (constant) and lags 1 lag ΔCorruption-1

C(1)

ΔCorruption-2

C(2)

ΔInternet users-1

C(3)

ΔInternet users-2

C(4)

ECT(-1)

C(5)

0.14*** (4.12) 0.15*** (4.45) -0.04 (-1.55) -0.03 (-1.43) -0.42*** (-13.71)

0.12*** (3.53) 0.12*** (3.62) -0.05* (-1.94) -0.03 (-1.27) -0.43*** (12.46)

ECT from DOLS ((linear trend)

ECT from FMOLS (linear trend)

0.13*** (3.92) 0.14*** (3.42) -0.03 (-1.09) -0.04 (-1.61) -0.37*** (-13.86)

0.31*** (9.92) 0.26*** (8.87) -0.05** (-2.47) 0.01 (0.66) -0.84*** (-22.45)

0.33*** (10.83) 0.28*** (9.67) 0.005 (0.25) -0.01 (-0.63) -0.84*** (-23.82)

R2 AIC

0.18 4.75

0.15 4.85

0.18 4.82

0.36 4.58

0.38 4.53

SC Wald test: Null hypothesis: C(3) = C(4) = 0 Wald test: F-statistic

4.78

4.88

4.85

4.60

4.56

6.06***

7.1***

4.99***

3.71**

0.21

12.12***

14.19***

9.99***

7.42**

0.42

Wald test: Chi-square Source: Authors.

The results of the VECM again support our assumption about the short-run as well as the long-run effect of Internet usage on public sector corruption. In both cases this effect is negative, which means that corruption in public sector could be in some extent, reduced by higher usage of Internet.

Conclusions Reducing the level of corruption in the public sector is one of the main challenges for governments in most countries, especially in the case of those which are less developed. There are


several determinants of corruption stated in the literature. The potential effect of Internet connection on corruption is mentioned in several literature sources and some empirical papers have also looked at this topic. However the comprehensive empirical analysis of this problem is rather unique so far. Our analysis put some new empirical insights into this problem using different panel data regression approaches to identify potential long run as well as short run relationships. Moreover the relatively wide panel data set for developed as well as developing countries has been used. Based on the results we can reject the null hypothesis about no significant effect of Internet usage on corruption in the public sector. The results suggest strong evidence for a positive relationship between Internet usage and the reduction of corruption in the public sector. The effect acts from Internet usage to corruption, but no significant inverse relationship has been detected. This relationship seems to be significant both in the short run as well as in the long run. However, the longrun effect seems to be even more evident. The fixed-effects and random effects models perform rather poorly in explaining the overall variability. Even though, the Internet usage appears to be significant variable here. The potential long run causality has been further analyzed by DOLS and FMOLS panel cointegration regressions, which have shown significant and robust long run negative effect of Internet usage on the level of corruption. In generally, we can say that a higher share of Internet users in the country could cause a significantly lower level of public sector corruption in the long run. This has been proved in all models used in our analysis. The results also suggest that the long run relationship seems to be stable in time and the speed of adjustment to long-run equilibrium is relatively fast. We assume that the negative effect of Internet usage on corruption is the consequence of better access to information and better control mechanisms. Furthermore, the higher Internet usage among people allows the government to implement the automation of certain processes in public sector. Thus, this could partly avoid a direct client’s contact with officials and thus reduce the possibility of corruption. The other potential variables influencing the corruption are the unemployment rate and general government final consumption expenditures. While the higher rate of unemployment in the country could lead to more corruption, higher general government final consumption expenditures could reduce the corruption, which is a surprising finding. Thus, the support of Internet connection and Internet usage by government could lead to lower levels of corruption in the country. This could be especially interesting suggestion for developing countries, where the access to information is rather low and the level of corruption is relatively high.

Acknowledgements: We would like to thank Prof. John Hudson from Department of Economics, University of Bath for his valuable comments and suggestions. This research is supported by LIPSE Learning from Innovation in Public Sector Environments project funded by the 7th Framework Programme of the European Union under contract no. 320090.

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Annex 2 The results of unit-root tests 1998-2012 x 86 countries LLC test Corruption - individual intercept Corruption - intercept & trend ΔCorruption - individual intercept ΔCorruption - intercept & trend Internet users - individual intercept Internet users - intercept & trend ΔInternet users – individual intercept ΔInternet users intercept & trend Log Openness - individual intercept Log Openness - intercept & trend Unemployment - intercept Unemployment - intercept & trend ΔUnemployment intercept ΔUnemployment intercept & trend Log General government final consumption expenditure - intercept Log General government final consumption expenditure - intercept & trend CPI inflation - intercept CPI inflation - intercept & trend High-tech exports intercept High-tech exports intercept & trend

Source: Authors.

Breitung

-2.29** -0.024**

4.21

IPS test

ADF test

PP test

-1.51*

202.19*

178.90

-3.10***

237.41***

173.01

-17.73***

617.46***

613.35***

-13.6***

479.49***

463.61***

14.80

186.73

206.44**

7.48

124.56

125.58

-7.42***

366.65***

386.79***

-10.39***

401.50***

436.44***

-2.62***

224.68***

199.71*

-4.77***

255.55***

220.30***

-4.18***

259.79***

186.24

-1.60*

217.34**

175.30

17.15***

610.85***

630.63***

-13.18***

475.28***

489.65***

-3.45***

252.87***

2221.76**

Order of integration

I(1) -15.14*** -13.97***

-3.19***

-2.23 0.08

9.91

I(1) -9.81*** -13.85***

-6.89**

-4.08***

I(0) -4.31***

-1.56*

-5.92*** -2.06**

-2.63

-17.81*** -15.99**

10.47***

-6.09***

I(0) or I(1)

I(0) -6.83***

0.34

-17.35*** -14.25***

-4.28***

-1.70**

-5.11***

279.85***

171.05

15.16***

531.32***

597.96***

-13.13***

452.74***

502.16***

-1.22

240.80***

217.61***

-3.45***

240.23***

225.04***

I(0)

I(0) -2.55***

1.27


Annex 3 Results of unit-root tests, after the adjustment for cointegration 1998-2012 x 79 countries LLC test Corruption - individual intercept Corruption - intercept & trend ΔCorruption - individual intercept ΔCorruption - intercept & trend Internet users - individual intercept Internet users - intercept & trend Δ Internet users individual intercept Δ Internet users intercept & trend Log Openness - individual intercept Log Openness - intercept & trend Unemployment - intercept Unemployment - intercept & trend ΔUnemployment intercept ΔUnemployment intercept & trend Log General government final consumption expenditure - intercept Log General government final consumption expenditure - intercept & trend Δ Log General government final consumption expenditure - intercept Δ Log General government final consumption expenditure - intercept & trend CPI inflation - intercept CPI inflation - intercept & trend High-tech exports intercept High-tech exports intercept & trend

Source: Authors.

Breitung

-1.36* -0.028

4.3

IPS test

ADF test

PP test

-1.19

177.55

157.41

-1.43

180.92

142.07

-16.46***

552.07***

556.87***

-12.93***

437.56***

425.98***

14.74

164.74

180.03

7.03

121.37

-7.49***

346.75***

362.75***

-9.62***

360.66***

388.88***

-2.57***

210.67***

186.55*

-4.6***

235.55***

197.76**

-4.16***

244.59***

175.74

-1.53*

202.35***

166.07

16.51***

564.53***

588.98***

-12.87***

443.02***

462.58***

-3.37***

237.23***

200.20**

-4.46***

245.32***

159.54

Order of integrat.

I(1) -14.60*** -13.53***

-3.12***

-2.32 0.28

8.28

123.64

I(1) -9.56*** -12.93***

-6.20***

-3.73***

I(0) -4.33***

-1.08

-5.59*** -1.8**

-2.99

-17.07*** -15.50***

-9.70***

-6.12***

-6.05***

-0.16

-19.84***

-16.52***

11.27***

-16.43*** -14.24***

-3.84***

-1.90**

-18.66***

614.95***

595.37***

-14.14***

473.89***

472.40***

-14.21***

473.86***

532.94***

-12.53***

413.54***

440.88***

-1.55*

231.48***

208.63***

-3.49***

222.64***

210.33***

I(0) or I(1)

I(0) or I(1)

I(0)

I(0) -2.36***

1.31