{'fraction_non_alphanumeric': 0.025327063643558825, 'fraction_numerical': 0.014991386936077098, 'mean_word_length': 4.981620718462824, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 5, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Study of Macro Variables in Excogitating Stability of Indonesian Economic Growth as OECD’s Development Center: An Error Correction Model Approach

OECD (Organization for Economic Cooperation and Development) stands for promoting the economic welfare of its members. It also coordinates its effort to help developing countries outside its membership. Indonesia is selected to become DC (Development Center). The goal of DC-OECD as a liaison for developing countries and emerging countries to help decision-making find policy solutions to stimulate economic growth and improve living conditions. Indonesian economic growth is likely to be increased, but the discrepancy of infrastructure is still high. Furthermore, this research aims to analyze macro variables effect to economic growth. Method used quantitative analysis by using Error Correction Model approach. The result shows in long term, BI Rate has significant and negative effect to economic growth, Taxation has negative and significant effect to economic growth, in the meantime Government Expenditure has positive and significant effect to economic growth. In short term, BI rate has positive and not significantly to economic growth, Taxation has positive and not significantly to economic growth, and government expenditure has positive and not significantly effect to economic growth.


INTRODUCTION
OECD (Organization for Economic Cooperation and Development) is international cooperation organization encompass economic and development. OECD established on 31 September 1961 to find solutions to stimulate economic growth and reaching out society's well-being of each country (Wino, 2019). For about 40 years, OECD has taken into account and become huge world data statistical source. OECD does not collect the data only, it also supervises trend and world economic development's forecasting and another aspect, comprised of social, environment, trade, education, agriculture, technology, taxation, so on. Not only with members, since 2007 OECD has been having relationship with Indonesia. This relationship known as Development Center (DC). DC-OECD as a liaison for developing countries and emerging countries to help decision-making find policy solutions to stimulate economic growth and improve living conditions. Indonesian economic growth is likely to be increased followed by the decline of discrepancy. According to OECD survey (2018) robust economic expansion and good government policy, the level of poverty and discrepancy increasingly decreased, by contrary accessibility of public services more widespread. Income per capita has grown, but the discrepancy of infrastructure was still high. Indonesian economic growth remains stable approximately 5 percent per 2013, which encouraged by consumption and infrastructure spending.

Figure 1. Consumption and Infrastructure Spending
Based on the figure above, economic growth projection will be good. The enhancement of income and public trust or consumer will be sustaining incremental of household's consumptions. On the other hand, there are another macro variable which analyzed to offset growth and stability, such as interest rate. In 2016-2017, interest rate declined in sustaining economic growth. But nowadays, that interest rate increasing to slow capital outflow down.
Debt is still become another funding source to infrastructure spending, medical, and social aid. Even 2017 ago, the growth of civil-servant expenditure had been restricted moreover district transfer started to improve. However, subsidy was re-increasing. Enhancement of income is still become fiscal threat. In which taxation income is relatively lower than another developing country ( Figure 2). There has been increasing number of assessable, but the awareness to pay on-time is still low. In excogitating stability of Indonesian economic growth, there some macro variables observed, such as interest rate, taxation, and government expenditure. As stated Kasmir in Wensy (2018), interest rate is a recompense to customer which conventional-based after buying or using their products. Shift of interest rate, will be affecting aggregate demand (Aryaningsih, 2008). Then, taxation income as cited in Juwita in Suyanto (2016), taxation income is government income from tax sector whereby the purposes comprises domestic and abroad in financing government expenditure. In government expenditure, development theory and government expenditure initiated by Rostow and Musgrave, there is correlation amongst of government expenditure and economic development phase. The phase divided into 3 (three) steps, they are preliminary phase, middle phase, and advanced phase.

METHODS
The

Stage of Data Analysis
Data stationarity is an important requirement in the econometrics model for time series data. Stationary data is data that has mean, variance and auto variance (in lag variations), in other words the data is more stable. Widarjono (2013) states the data stationarity test can be done through coleogram by looking at the coefficients of ACF and PACF. If the data used in the model is not stationary, then the data is reconsidered in terms of its validity and stability, because the regression results from non-stationary data will cause spurious regression. Spurious regression is a regression that has a high R2, but there is no meaningful relationship between the two (Gujarati, 2012).
Data stationarity test data is done through a unit root test (unit root test). This unit root test, developed by David Dickey and Wayne Fuller, is known as the Augmented Dickey-Fuller (ADF) Test. The procedure for determining the stationarity of data can be done by comparing the statistical value of DF with the value of the statistical t distribution. DF value of statistics greater than the value of t statistics shows the data is stationary and vice versa. Basically, to carry out ADF-test as like: Whereby: p = selected interval; 0, 2, = estimated value; = error term The criteria for decision making in these hypothesis are: 0: = 0, which means the data is not stationary (contains the root unit) 1: <0, which means stationary data (does not contain a root unit)

Unit Root Test in First Difference
This test is a continuation of the unit root test as a consequence of not fulfilling the assumption of stationarity at zero degree. This test is very important to do for each variable in order to know the stationarity of research data. And to find out how many times the data must be done in a certain degree difference in order to produce a stationary variable. All existing variables will be differed to a certain degree until all variables are stationary to the same degree.

Cointegration Test
The concept of cointegration associated with two or more non-stationary time series variables which will be co-integrated if the combination is also linear over time, although it can occur that each variable is non-stationary. The time series variable is co-integrated, so there is a stable relationship in the long run (Gujarati, 2012).

Advances in Economics, Business and Management Research, volume 152
Cointegration test is a test of whether there is a long-term relationship between the independent variable and the dependent variable. This test is a continuation of the stationary test. The main purpose of this cointegration test is to find out whether stationary cointegration residuals or not. If the variables are co-integrated, there is a stable relationship in the long run. By contrary, if there is no cointegration between variables, the implication is that there is no relationship in the long run. The term cointegration is also known as the error, because the deviation of the long-run equilibrium is corrected gradually through a series of partial short-term adjustments. It is assumed that y is the dependent variable and the independent variables x1, x2, x3 with the equation: Partial Test (t-test) t-test aims to see the influence of each independent variable to dependent variable (Gujarati, 2012). In another theoretical t-test utilized to recognize the quality of regression significance of each independent variable (X), whether there is influence or not to dependent variable (Y), t-test is as follows: t test =  (Gujarati, 2004). The statistical hypohesis as follows:  H0 = b1 b2 b3 b4 = 0, there is no influence between independent variable to dependent variable  Ha = b1 b2 b3 b4  0, there is an influence between independent variable to dependent variable

Coefficient of Determination (R²)
To know how much independent variable's influence or strength to dependent variable as whole, so used analysis of variance by calculating the coefficient of determination. Coefficient of determination (Gujarati, 2004) shows the ability of independent variables to explain variations in the dependent variable. Notable: Multicolllinearity is a linear relationship (perfect or imperfect) between some or all of the independent variables in the regression model (Gujarati, 2012). Another notion declares that Multicollinearity is the situation of the correlation or relationship between independent variables. Multicollinearity is essentially a regression sample phenomenon. The independent variable has a high correlation among the independent variables although regression coefficient values can be obtained, but most likely any independent variables are not statistically significant. Additionally, high multicollinearity symptoms can cause signs of regression coefficient containing signs contrary to theoretically predicted ones. If multicollinearity is perfect in the sense of the regression coefficients of the X variables are indeterminate or inconstant and their standard errors are infinite. If multicollinearity is less than perfect, the regression coefficients although determinate possess large standard errors (in relation to the coefficients themselves) which means the coefficients can not be estimated with great presision or accuracy.
Autocorrelation test performed by using the Series Correlation LM Test Breusch-Godfrey method by looking at the comparison of Prob. F or Prob. Chi-Square with alpha set at 5%. If the Chi-Square probability > α is 5%, it is free from the autocorrelation problem. Meanwhile, if the Chi-Square probability < α 5%, it can be said that the model in the study has an autocorrelation problem.
Normality test is carried out to analyze whether the data in the study are normally distributed or not. This test can be carried out by looking at the value of Jaeque-Bera (JB) and probability. Data can be stated normal distribution is when the probability is more than α 5%. Vice versa, when the probability value < α 5%, then the data is not normally distributed.
Heteroscedasticity test is performed for the purpose of analyzing whether the interference variable has a non-constant variant. This test is carried out using the white heteroscedasticity method. Where can be seen through the Chi-Square probability. If Prob. Chi-Square> α 5%, then free of heteroscedasticity. Whereas when Prob. Chi-Square <α 5% then there is no heteroscedasticity problem.

Stationarity Test
In utilizing ECM analysis tool, there are several steps that must be done, first by conducting a unit root test or stationarity test. In this study it was determined that alpha (α) used was 5%. If the probability value is at level> 5%, it can be concluded that the data is not stationary so the requirements to obtain the first ECM equation are met. By the contrary, if the probability is > 5%, the stationary data means that the formulation of the ECM cannot be continued. The condition is that each variable must not be significant at the level of level but must be significant at the level of 1st difference or 2nd difference and the data of each variable must be stationary at the same level. Kindly find the results of the stationarity test below:  Stationarity test in this study used the ADF (Augmented Dickey Fuller) procedure. Based on the results of the stationarity test conducted, using the Augmented Dicky Fuller (ADF) method it is found that GDP when compared to alpha 5% (0.05) is not stationary at the level, but stationary at the level of 1st difference, so the variables in this study pass the stationarity test and qualify to be able to use the ECM model in this study.

Cointegration Test
After all variables have passed the stationarity test, the next step is the cointegration test, this test is conducted to determine the behavior of research Advances in Economics, Business and Management Research,volume 152 data in the long term whether or not to cointegrate. And to find out the integration of longterm balance between variables. This cointegration test also determines whether research can use the ECM model as an analysis tool. Johansen's cointegration test results based on the Trace value indicate that there is one cointegration on the variable. In addition, the results of this cointegration test can also be obtained from forming residuals obtained through regression of the independent variables to the dependent variable with the Ordinary Least Square (OLS) method whereby the residual must be stationary at the level of level in order to obtain co-integrated results. Based on the result above, ECT is found at the level, where the probability of ECT is significant at 0.0007, smaller than alpha (0.05). It shows the existence of cointegration in research.

Advances in Economics, Business and Management Research, volume 152
Based on the estimation results, it is obtained that in the short term the BI Rate, TAX (Taxation) and GE (government expenditure) affect positively but not significantly to economic growth (GDP). According to the results of the processed data, a coefficient (C) of 62.50222 is obtained, it means when BI Rate, taxation and government expenditure are assumed to be constant, GDP will increase of 62.5 billion rupiah. Based on the regression results also found the magnitude of the coefficient of ECT that is equal to -0.162260. This figure means that if there is a past imbalance of 100%, then GDP will adjust to decrease by 16%. Then for each variable regression coefficient.
BI Rate which has a regression coefficient value of 37.07236, it means that when taxation and government expenditure are assumed to be constant, if the BI Rate increases by 1%, it will also have an impact on increasing GDP by 37.07236 billion rupiah. The taxation regression coefficient which has a value of 0.363673 means that, if the BI Rate transaction and government expenditure are assumed to be fixed / unchanged, each tax increase of 1% will also have an impact on an increase in GDP of 0.363673 billion rupiah. The regression coefficient of government expenditure which has a value of -0,000243 means that if the BI Rate and taxes are assumed to be constant and unchanged, if there is an increase in government expenditure by 1 billion rupiahs, it will have an impact on increasing GDP by 0,000243 billion rupiahs.
The results of the short-term analysis show that the interest rate shows is positive and significant does not affect GDP. This means that an increase in interest rates in the short term cannot increase Indonesia's GDP. Research conducted by Yusuf, et al (2019) found that in Nigeria interest rate did not significantly influence economic growth in the short term using the ECM method. The absence of a link between interest rates and economic growth implies that the velocity of money in the country is so low that an increase in interest rates is not able to increase the amount of community savings in financial institutions, in so doing in the short term interest rates did not significantly affect GDP. The short-term analysis results show that the increase in taxation rates in the short term has no effect on GDP. This is different from the study conducted by Ahmad, et al (2018) who studied research on the impact of direct taxes on economic growth in Pakistan. The results found that the indirect tax variable did not have a significant effect in the long term had no significant effect on economic growth, so that the determination of government policy in the short term would not affect the increase in GDP. It means that the lack of taxation collected system. The results showed that the government expenditure had no significant effect on GDP. It means that an increase in government expenditure in the short term has no effect on increasing GDP. It is caused by government expenditure in the short term such as construction of facilities and infrastructure is a long-term investment, so that in the short term government expenditure does not affect the increase in GDP. The same condition also occurs in the country. Research conducted by Sari and Sholeh (2013) who found that in the province of Bengkulu in the short term government spending has not been able to increase economic growth.

Estimation Result in Long Term
Kindly find the effect of BI Rate, Taxation, and Government Expenditure to economic growth (GDP) below (Processed Data, 2020): Advances in Economics, Business and Management Research,volume 152  Based on the result above, the long-term model is: GDPt = 3094,213 -250,78BI Ratet -9,18TAXt + 0,01GEt + Based on the estimation results, it is found that in the long term the BI Rate and taxation affect GDP negatively and significantly. Whereas government expenditure positive significantly affecting GDP. According to the results of the processed data, a coefficient (C) of 3,094,213 is obtained, it means when the BI Rate, taxation and government expenditure are assumed to be constant, GDP will increase of 3,094,213 billion rupiah. Then for each variable regression coefficient: BI Rate which has a regression coefficient value of -250.7843, it means that when taxation and government expenditure are assumed to be constant, the increase in the BI Rate of 1% will have an impact on GDP decline of -250.7843 billion rupiah. The taxation regression coefficient which has a value of -9.186343 means that if BI Rate and government expenditure are assumed to be constant, each taxation increases of 1 billion rupiah will impact on GDP reduction of -9,186343 billion rupiah.
Analysis of estimation result in long term interest rate is negative and significantly affect GDP with the probability of a 0.0000 interest rate being significant at alpha one percent. It means that an increase in interest rates will reduce GDP in the long term, by the contrary decrease in interest rates by the monetary authority will have an impact on increasing economic growth. Based on the results of research conducted by Research carried out by Joseph, et al (2018) who analyzed the impact of interest rates on economic growth in Nigeria using the multiple regression method. The results found that interest rates significantly influence economic growth. Increased interest rates will reduce the amount of investment; this will certainly have an impact on reducing GDP that can be produced by a country. The regression coefficient of government expenditure which has a value of 0.014084 means that if the BI Rate and taxation are assumed to be constant, if government expenditure increases by 1 billion rupiahs will also have an impact on GDP of 0.014084 billion rupiah. Taxation in the long term has a significant negative effect on GDP with a negative relationship. It means that an increase in tax by the government will reduce the amount of GDP that can be collected in the long term. The results of this study are the same as in Africa. Research conducted by Gashi, (2018) found that there is a negative relationship between taxes and economic growth. One of the policies implemented by the government is to reduce indirect taxes and raise direct taxes to increase economic growth. Government expenditure in the long run has a significant effect on GDP. This means that increasing government policy spending will have an impact on increasing GDP. Research conducted by Hassan and Misra, (2016) who found that there is a positive and significant relationship between government spending and GDP in Jammu and Kasmir countries. This indicates that government spending will improve the construction of facilities and infrastructure, so that it will have an impact on increasing GDP. Government expenditure in the long term has a significant effect on GDP. It means that increasing government policy expenditure will have an impact on increasing GDP. Research carried out Advances in Economics, Business and Management Research,volume 152 by Hassan and Misra, (2016) who found that there was a positive and significant relationship between government expenditure and GDP in Jammu and Kasmir countries. This indicates that government spending will improve the construction of facilities and infrastructure, so that it will have an impact on increasing GDP.

CONCLUSIONS
Based on the results, macro variables in creating stability of economic growth in Indonesia as DC OECD, it can be concluded as follows. In long term, BI Rate has significant and negative effect to economic growth, Taxation has negative and significant effect to economic growth, in the meantime Government Expenditure has positive and significant effect to economic growth. In short term, BI rate has positive and not significantly to economic growth, Taxation has positive and not significantly to economic growth, and government expenditure has positive and not significantly effect to economic growth.