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Another way to choose the best model for our specification is by employing a Hausman test. We tested our specification to select the best model between fixed effects and random effects models. The coefficient result of the Hausman test showed that the p-value was 0. The coefficient indicated that the null hypothesis of the random effects model was the preferred model; therefore, the random effects model was utilized in our estimation strategy. Moreover, the random effects model may be subject to a variety of biases if there is a causality direction assumed in the model or the presence of the omitted variable bias.
Furthermore, in the second model, we acknowledge that the random effects model does not consider the dynamic panel data regression, as the corruption incidence in the past year is most likely correlated with corruption in the current year. Introducing one-year lagged corruption as an independent variable correlates with the error term, and this creates an endogeneity issue. Additionally, the causality relationship between fiscal decentralization and corruption may pose an endogeneity problem because an increase in the degree of fiscal decentralization may affect corruption and vice versa.
System GMM allows the lags in the endogenous variables to serve as instrumental variables. The panel data analysis and system GMM model are favorable for a country that has limited corruption data at the subnational level. The advantage of using a panel data analysis is that it can capture the change in provinces over time, and a system GMM estimator is an efficient method for a large number of observations and short period samples. Additionally, because the system GMM estimator accounts for dynamics in a model, we utilized it to solve the endogeneity issue resulting from causality between corruption and fiscal decentralization.
The estimation results for the random effects model are displayed in Table 3 , and the results from the system GMM model are in Table 4. The model specifications in columns 1 through 6 of Table 3 and Table 4 are similar. The two proxies of fiscal decentralization, which served as the main independent variables, are divided into columns 1 through 3 , using expenditure decentralization, and columns 4 through 6 capture tax revenue decentralization. In more detail, in columns 1 and 4 , the baseline specification, we included government size, ln rgdp per capita, and education as control variables.
Columns 2 and 5 consist of the baseline specification and two socioeconomic indicators: the ethnolinguistic and political indices. Lastly, columns 3 and 6 examine the baseline specification and a demographic variable productive age population. Table 3 presents the estimation results using a random effects model. From the results, the degree of fiscal decentralization represented by the expenditure decentralization was revealed to facilitate an increase of corruption cases in columns 1 through 3.
A higher degree of authority in the fiscal sector from the central government to local governments appeared to decrease the incidence of corruption, and the result showed a five percent significance level. In an economic scale, a one percent greater degree of expenditure shares of local governments was more likely to increase corruption incidence by 0. The findings confirmed the hypothesis that a higher degree of expenditure authority may likely facilitate an increase in corruption cases.
Our estimation results confirm the findings of Treisman and Alfano et al. In contrast, if we use tax revenue collected by the province as the fiscal decentralization proxy, the results in columns 4 through 6 revealed negative effects of tax revenue decentralization on corruption, yet these were statistically insignificant. The results contradict the findings of Shon and Cho , who found a positive and statistically significant relationship between revenue decentralization and corruption conviction in U.
One possible reason for the discrepancy is that the capacity of local governments in Indonesia to collect tax revenue remains low because of the lack of capable human resources, limited discretion from central government on revenue collection, and a higher dependency on intergovernmental grants as their revenue sources. Regarding government size, economic scale, and education effects on corruption, a greater portion of total government revenue relative to regional GDP is expected to reduce corruption.
The economic scale represents the wealth of the provinces denoted by regional GDP per capita. We expect that a rich province will tend to maintain efficient governance and be less corrupt; in contrast, a poor province is more likely to suffer from high corruption incidence, which might hinder its regional development.
Many studies control for human resource development because a province with a higher education level typically manages itself efficiently and practices good governance and accountability compared with a province with a lower education level. From the estimation results, government size had a negative effect on corruption in all specifications, yet the result in column 6 was statistically insignificant.
The findings confirm the economic benefits of attempting to reduce corruption incidence in government.
Laboratory federalism and intergovernmental grants
However, the economic scale cannot capture the impact of the wealth of the province on corruption, indicated by the statistically insignificant results in all specifications. This finding agrees with the hypothesis that provinces with higher education levels are less likely to engage in corrupt practices because they understand the consequences and risks. Introducing socioeconomic indicators to control for corruption, the ethnolinguistic fractionalization index attempts to answer whether a province with a heterogeneous population, reflected by a higher index, may discourage corruption, while a homogeneous province encourages corruption.
Mauro argued that, in cross-country analyses, a country with a heterogeneous population is found to have lower corruption. Another socioeconomic indicator that we accounted for is political stability, to control for the varying degrees of stability across provinces.
From the estimation results, ethnolinguistic variables were statistically insignificant in all specifications. Political stability was found to limit corruption incidence in columns 2 and 5. Moreover, a demographic indicator was represented by the productive age population of those between the ages of 15 and 64 years, yet the results in columns 3 and 6 were statistically insignificant.
We now turn to the second model, dealing with the dynamic effects of corruption by using the system GMM model. Corruption in the past year is believed to influence current corruption incidence. Bureaucrats learn the circumstances of corruption over time, but a slow judiciary process and a low number of corruption convictions are two indicators of low quality in a judicial institution.https://nibavorsvets.tk
Advanced Public Economics: Fiscal Federalism - Kurt Schmidheiny - Uni Basel
In contrast, a higher number of corruption cases may strongly discourage bureaucrats from getting involved in corrupt practices because of effective anticorruption efforts. In the empirical analysis, a dynamic effect can be a threat and a severe endogeneity problem if the lagged value of the dependent variable is placed as the independent variable. We used a system GMM model to solve this issue by following equation 2.
The estimation results of the system GMM model are displayed in Table 4 , and the model specifications are similar to the one employed in the random effects model. Before discussing the results, it is essential to check the validity of the instruments and the absence of serial correlation of second-order in the first-differences error term.
We checked the validity of the instruments using the Hansen test for overidentifying restrictions. All coefficients of the p -value Hansen test in columns 1 through 6 indicate high values; therefore, we cannot reject the null hypothesis that the instruments are valid. Another test is for second-order autocorrelation in the first-differences error term, and all coefficients of the p -value AR-2 show high values.
These findings generally indicate that we cannot reject the null hypothesis of the absence of second-order serial correlation in the first-differences error. The test results support the consistency of the system GMM estimator in our model. The corruption cases in the previous year were more likely to facilitate corruption cases in the following year, only in the baseline model in column 1 and after controlling for the productive age population in column 6.
Acta Univ. Agric. Silvic. Mendelianae Brun. 2018, 66, 755-766
The lagged corruption seemed to have no effect on current corruption cases, and it was statistically insignificant in columns 2 through 5. After controlling the dynamic of corruption, the effect of the degree of expenditure decentralization revealed a positive impact on corruption in columns 1 and 3. Similarly, tax revenue decentralization demonstrated a corruption-increasing impact in column 4.
This result correlates with the finding of Shon and Cho Both expenditure and revenue decentralization confirmed that an increase in the degree of fiscal decentralization was likely to promote corruption incidence. Regarding the economic scale indicator, the results improved and were statistically significant in most specifications. Moreover, an increase in regional GDP per capita was found to increase corruption incidence.
This finding suggests that a rich province has more financial resources that motivate bureaucrats to extract rent for private gains. Different from the random effects model, the productive age population revealed a positive effect on corruption in columns 3 and 6.
The probability of an increase in corruption in the productive age population was based on the data that the largest demographic in Indonesia consisted of people between 15 and 64 years of age; therefore, people in the productive age range were more likely to actively engage in production and economic activities. In this section, we need to check whether our main estimation results remain consistent with the other fiscal decentralization variable form.
An alternative estimation strategy utilizes a percentage change to measure the degree of fiscal decentralization. This functional form is intended to analyze how much the percentage change of the degree of fiscal decentralization, both expenditure and revenue sides, affects corruption, rather than measuring the fiscal decentralization in level units. For a robustness check, we estimated the degree of fiscal decentralization measured in the percentage change using a system GMM estimator. Understanding the impact of a percentage increase or decrease of the degree of fiscal decentralization on corruption can help us to provide better analyses for local governments when allocating budgets.
Evaluation Structure of Local Public Expenditures in the European Union Countries
To differentiate the fiscal decentralization unit measurement used in the percentage change and in level units utilized in the previous system GMM model, the expenditure decentralization is abbreviated as expenditure dec growth rate, and tax revenue decentralization is tax revenue dec growth rate. Corruption and the control variables are the same as the previous system GMM model. The baseline equation and model estimations are the same as the ones used in the previous discussion of system GMM estimations. The estimation results are presented in Table 5. In Table 5 , the main estimation results still hold.
The one-year corruption revealed a positive effect on current corruption, and it was statistically significant at one percent in columns 1 through 6. The magnitudes of expenditure decentralization and tax revenue decentralization are consistent with our previous findings. An increase in the degree of tax revenue decentralization revealed an increase in the number of corruption cases in columns 4 and 5. At the economic scale, an increase of one percent change in the degree of expenditure decentralization revealed the number of corruption cases at the 0. A consistent finding between the estimation results, using a percentage change form in the robustness check section and the main results, influences our conclusion of these findings.
Existing studies have made inconclusive findings on the relationship between the degree of fiscal decentralization and corruption. The main difference in these studies is the choice of corruption measures such as corruption perception index and objective measures the number of corruption cases and the number of corruption convictions.
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Using a specific-country study in Indonesia, this study utilizes unique corruption data: the number of corruption cases investigated by KPK an independent body under state law , as well as extensive financial data from the provincial level, obtained from the Indonesia Ministry of Finance.
Employing those corruption and financial data, this research contributes an original analysis on the degree of fiscal decentralization and corruption in Indonesia at the provincial level. A higher degree of expenditure decentralization revealed a positive, robust, and statistically significant effect on corruption. Based on data from KPK, the number of corruption cases at the provincial level rose over the sample period of to Similarly, tax revenue decentralization was found to facilitate an increase in corruption cases in local governments.
This finding contradicts the hypothesis that a higher degree of tax revenue decentralization may hinder bureaucrats from extracting rent and getting involved in corrupt practices. Hansen, Nico A. American Economic Review , 91, — Schmidheiny, Kurt , Income segregation from local income taxation when households differ in both preferences and incomes.