Gross Regional Domestic Product GRDP

Generally, the economic development of every districtscities was flourishing with good direction as explained by the contribution that developed relatively stable until 2013. This means that the alignment in the ability to produce goods and services from every districtscites to the total creation of goods and services in Riau growing good. The economic contribution, especially in the districts cities division, such as Kuansing district, Pelalawan, Siak, Rokan Hulu, Rokan Hilir, Kota Dumai continues to grow the economy stable Riau Province. Likewise, the same thing experienced by the districts cities. Source: Pendapatan Regional KabupatenKota Menurut Lapangan Usaha FIGURE 4.1 The Distribution of GRDP without Oil and Gas for DistrictsCities in Riau Province at 2014 The economic structure of Riau Province is determined by the three sectors, the primary agriculture and mining, secondary industry sector, Pekanbaru , 16.92 Siak, 11.01 Indragiri Hilir, 10.87 Bengkalis, 10.2 Kampar, 9.41 Others, 41.59 electricity and gas, waste management, water supply, and construction and tertiary sectors of trade, transport, provision of accommodation to eat and drink, information and communications, financial services, real estate, etc.. In 2012-2014, the primary sector dominates the economy moving society districtscities in Riau. This happened in Kuantan Singingi, Indragiri Hilir, Kampar, Rokan Hulu, Bengkalis and Rokan Hilir. Secondary sector annually highly viscous dominated in the area of Siak, Pelalawan and Pekanbaru, respectively for the year 2014 recorded the role in the economy of the district amounted to 57.67 percent, 53.07 percent and 50.23 percent. This shows that the three districtscities are dominated by the industry sector in running the economy. For the tertiary sector, Dumai City has a big contribution in the economy of Riau. However, the tertiary sector is still unstable due to the ability of community resources is not equitable and stable. Furthermore, in general the economy of the districts cities will be reflected clearly in the economic structure of Riau. Basically the economy of Riau society is agrarian, which engaged in the primary sector and began a slight shift to the secondary sector. In 2014 the secondary sector was recorded at 40.17 percent and 39.29 percent into the primary sector. 2. GRDP with Oil and Gas After the oil and gas elements are included in the GDP districts cities in Riau Province, the seeming diversity of economic growth of the districtscities, especially the districtscities that have oil and gas resources. The growing diversity of economic districtscities is arising as sufficient fluctuation of production and added value of the oil and gas revenue. During the 2012-2014 economic growth tends to weaken. At that time the world oil prices fluctuate and the level of oil and gas production that has many obstacles both technical and non-technical influence economic movement Riau. In addition, conditions some oil wells have begun to ripen also affect oil and gas production. Table 4.9 The Economic Growth of DistrictsCities in Riau Province 2012-2014 DistrictsCities 2012 2013 2014 Kuansing 5,93 5,46 5,34 Inhul 8,39 6,21 5,62 Inhil 7,91 7,16 6,92 Pelalawan 3,02 5,55 6,08 Siak 2,07 -2,56 -0,71 Kampar 5,82 6,25 3,21 Rohul 6,12 5,93 6,78 Rohil 3,65 2.49 4,01 Bengkalis -0,65 -3,48 -3,50 Kep.Meranti 6,70 4,05 4,65 Pekanbaru 7,82 5,73 6,79 Dumai 3,66 3,72 3,53 Riau 3,76 2,49 2,62 Source: Pendapatan Regional KabupatenKota Menurut Lapangan Kerja The weakening of economic growth due to the instability of oil and gas in 2014 amounted to 2.62 percent, generally affecting the economic growth at the districtscities that has large oil and gas resources. Bengkalis growth rate became -3.50, Siak became -0.71, Kampar grew by 3,21 and Rokan Hilir grew by only 4.01. The magnitude contribution of oil and gas which is dominant in the economy of a districtscities, so, when they were in an instability condition, it will have a big impact to economic growth for the districtscities that produce oil and gas. The economic structure is very clearly illustrates the economic contribution of the districtscities that has oil and gas resources to the economy of Riau. This indicates how much oil and gas sector has a big opportunity in the economy. For that, the districtscities that have an oil and gas clearly seem to give much contribution to the economy of Riau. It’s mean, with the existence of oil and gas production, so the rising ability of districtscities would let them dominantly to produce more goods and services in Riau Province. Therefore, with the condition of decreasing levels in oil and gas production that very quickly, the government has to evaluate all the problems that happened. And there is a possibility to rearrange an assessment of Natural Resources SDA to be more effective and efficient, so it will positively impact the economic and social development in Riau. Generally, the role of Natural Resources Oil and Gas in districtscities was change the composition of economic contribution without oil and gas by districtscities towards the economy of Riau. Pekanbaru is the largest donator for the economy of Riau, but Siak is only donating 12.68. Source: Pendapatan Regional KabupatenKota Menurut Lapangan Kerja FIGURE 4.2 Economic Structure with Oil and Gas For DistrictsCities in Riau Province , 2014 However, there are some districts which have oil and gas, but their contributions to the economy of Riau are relatively low. This is due to the quantity of oil and gas production that managed by the districts is lower than the total oil and gas production of Riau. Some districts such Indragiri Hulu about 5.02, Pelalawan about 5.62, Rokan Hulu about 3.73 , Meranti Island about 2.41 , and Dumai City about 3.50. Bengkalis, 24.15 Inhil, 7.1 Inhu, 5.02 Kampar, 10.09 Kuansing, 3.56 Pelalawan, 5.62 Rohil, 11.09 Rohul, 3.37 Siak, 12.68 Dumai, 3.5 Pekanbaru, 11.05 Kep. Meranti, 2.41 68 CHAPTER FIVE ANALYSIS AND DISCUSSION In this chapter the author analyzes the effects of Total Population, Government Spending and Gross Regional Domestic Product GRDP towards Local Revenue PAD in the Districts Cities in Riau Province in 2010-2014. Data panel is a tool that used to analyze the influence of variables independent Total Population, Government Spending and Gross Regional Domestic Product GRDP to dependent variable Local Revenue PAD with Fixed Effect as a model analysis, and settlement using the program statistical computer from Eviews8. The results of processing data I present in this chapter are the best estimation results and were qualified in economic theory, statistics and econometrics. With the hope, the results of this estimation can provide answers from the hypothesis proposed in this study. There are two approach methods that used in the regression of panel data, both are Fixed Effect Model and Random Effect Model, which previously tested with Chow Test and Hausman Test to determine which model is better used in this study.

A. Classic Assumption Test

1. Multicollenearity Test In this study will detect the presence or absence multicollinearity with zero-degree test or view the partial correlations between the independent variable. As a rough rule Rule of Thumb, if the correlation coefficient is high enough above 0.85 then presumably multicollinearity is in the model. Conversely, if the correlation coefficient is relatively low, we assume that the model does not contain multicollinearity Widarjono, 2005 in Sucipto, 2012. In this test, multicollinearity, made an approach to the value of R 2 and the significance of the variables used. The data used include the time series and cross-section, and multicollinearity usually occurs in time series data on the variables used. By combining the data time series and cross-section, actually, issues that cause multicollinearity can be reduced, which means one variant that has nothing to do as has been suggested is a combination of time series and cross section, or better known as Data Pooling. Thus, technically there is no problem of multicollinearity. Table 5.1 Multicollinearity Test Coef Covariance Matrix C LOGTotal Pop. LOGGov. Spending LOGGRDP C 17.56625 -0.718486 -0.590862 0.106311 LOGTotal Pop. -0.718486 0.147999 -0.071874 0.001698 LOGGov. Spend -0.590862 -0.071874 0.099828 -0.010533 LOGGRDP 0.106311 0.001698 -0.010533 0.002894 Source: Processed with Eviews8.0 From the test above, it can be concluded that the partial correlation between independent variables are no multicollinearity issues. Because, the value of correlation matrix less than 0.85. 2. Heteroscedasticity Test Heteroscedasticity explained that there is a difference in a model of residual variance on observation. In this case, the problem comes from variations that used in cross section. Heteroscedasticity means every variants of disturbance term which limited by a specific value in independent variables are the form of a constant value equal to σ 2 or in the same variants. Heteroscedasticity test aims to test whether the regression model occurred inequality variants of residuals in the observations to other observations which fixed, then it is called homocedasticity and if it different it is called heteroscedasticity Sucipto, 2012. In this test, to detect heteroscedasticity in panel data by using Park Test, where the probability value of all independent variables are not significant at level 5. These circumstances explain the presence of the same variant or an existence of homocedasticity between the values of independent variables with the residuals of each variable itself. The table below, we can see the result of homocedasticity test using Park Test: Table 5.2 Heteroscedasticity Test with Park Test Variables Prob. C 0.1596 LogTotal_Population? 0.0690 LogGovernment_Spending? 0.6330 LogGRDP? 0.3974 Source: Processed with Eviews8.0 The result from the test above, we see that probability value of all independent variables are greater than 5 0.05, therefore the regression data from this study has no heteroscedasticity issues, all the independent variables are free from heteroscedasticity.

B. Model Selection

In analyzing the panel data model, there are three approaches that can be used, such Ordinary Pooled Least Square, Fixed Effect Model and Random Effect Model. First of all, the test statistic that used to determine the model is using Chow test. Chow test determines which method is most appropriately used in the regression of panel data, between Pooled Method and Fixed Effect Method. Data Panel Method was tested to all sample data ten districts and two cities. In Chow Test, if the value of F statistic is significant, then Hausman Test is done to choose whether Fixed Effect Model or Random Effect Model is most appropriate to use. So the result of Hausman Test with a probability value which less than Alpha is significant, it means Fixed Effect Method is a chosen method to process Panel Data. The Method is testing with Fixed Effect and Random Effect then the mixing of Cross-section, Period, and the combination of cross-sectionperiod. 1. Chow Test Chow test is a test to determine whether the Fixed Effect Model or Random Effect Model that is most appropriately used in estimating panel data. The hypothesis test in Chow is: H : Common Fixed Model or Pooled OLS H 1 : Fixed Effect Model Basic rejection of the hypothesis above is by comparing the calculation of the F-statistic and F-table. If the result of F-statistic larger than the F-table, then H0 is rejected, which means the most appropriate model used is the Fixed Effects Model. Vice versa, if the F-statistic is smaller than F-table then H0