Impact of Interest Loan, Growth of Regional Gross Domestic Product, Inflation and Economic Growth on Loans at Credit Union in West Kalimantan, Indonesia

Loans or credits offered by Kopdit credit unions are a potential source of funds that need to be developed, to help accelerate the home industry and the micro and small economies. Therefore, we want to see the impact of several conditions such as the loan interest rate, GDP per capita growth, inflation rate and economic growth. Quite a number of studies have looked at the impact of interest rates, GDP growth, inflation rates and economic growth on loans or credits to banks or banking institutions. We do not look at credit or loans from banks, but on Kopdit credit unions (CU). The results of our research show that simultaneously the loan interest rate, GDP growth, inflation rate and economic growth have a strong enough influence on loans at Credit Union Credit Unions, namely 79.2454%. Partially the variable of loan interest rate, GDP growth per capita, inflation rate affects outstanding loans, while economic growth partially has no effect on outstanding loans.


Introduction
Economic finance for small and medium businesses is an extremely scarce and costly commodity. Rare when financial services are unable to reach socially deprived individuals. Since financial organizations are unable to reach socially vulnerable individuals, moneylenders take advantage of this state and circumstance. Loan sharks charge very high interest rates, making lending to small and micro enterprises an extremely costly commodity. The truth is that banking institutions are unable to enter the broader population, especially the economically disadvantaged, particularly those residing in rural areas. As a consequence, citizens who are economically disadvantaged and reside in rural areas lack access to bank-provided resources. Banks will only represent those who are economically secure. Apart from serving only the middle and upper classes, banking entities can only represent the population up to the subdistrict level, and even then, only some banks such as the People's Credit Bank (BPR) and Bank Rakyat Indonesia can serve the community (BRI).
In addition to banks' narrow geographical scope, banks must meet additional conditions in order to open savings accounts. For instance, the bank mandates a minimum purchase amount. It also needs investments to be kept to a minimal level. It's clear that banks need a minimum sum of savings, as the profit earned on investments must be larger than the monthly administration charge. If the interest earned on the investment is smaller than the monthly operating costs, the existing savings will drop, since the principal of the savings will be utilized to cover the monthly administrative costs. As a result, it's natural for the bank to demand a According to Livingston and Ord (1994), many aspects influence the quantity desired by customers. Taste, the price of an item, and alternative commodities are all examples of these influences. If a product's price is prohibitively high, an individual would suggest limiting their intake. In the other hand, as costs decline, buyers want to raise their intake of these goods. The cost of borrowing (interest cost), which is called the price of the debt in the credit system, is a consideration to weigh when deciding whether or not to borrow. It is heavily affected, in addition to taste and demand rates, by the availability of alternative products, in this case a loan bid from another entity. Loans can be offered by official commercial banks, government departments, or informal entities. In this case, the cheapest option is chosen.
Numerous experiments have been conducted to determine the association between interest rates on loans and credit demand. Reduced interest rates, according to Weller and Radha (2004), potentially raise lending and private spending. According to Weller and Radha's study, interest rates and credit demand have a negative or inverse association. Credit interest rates can be used as a predictor indicator to determine how the private sector reacts to interest rate increases. Huzaynah et al. (2011) discovered that interest rates and inflation had little discernible impact on bank credit demand. Utility refers to a product's capacity to fulfill consumer needs. Commodities that fulfill customer preferences have a higher use benefit. If a loan or credit may be used to satisfy household financial requirements, it is claimed to have utility or usage worth (Saleemi, 2000).
As Amonoo et al. (2003) discovered, there is no correlation between interest rates and credit demand. Similarly, the findings of a study performed by (Maiti et al., 2020) indicate that interest rates have no impact on credit demand in the short and long run. According to Mudida (2003), as an individual's income rises, the market for the majority of commodities increases as well. Small-scale buyers often focus on the same, primarily low-quality goods with low income expectations. Poor product output translates into a low income goal, which results in low market returns. As a consequence, if revenue is poor, company owners are unable to obtain credit from formal institutions.
Other scholars, including Kao (1999), Maddala andWu (1999), andWesterlund (2007), examined the connection between GDP per capita and domestic credit in the banking sector using panel results. The study's findings indicate that in Latin American countries, per capita GRDP and domestic credit have a significant long-term relationship. The same research discovered an equal causal association between GRDP per capita and domestic credit. Madagascar Zeller (1994) is another researcher who examined the association between income levels and credit demand using a regression model. The Zeller model presupposes that credit demand is solely determined by internal variables.
According to Zeller's study, the risk of qualifying for credit rises substantially as one's academic age or duration of schooling increases, as one's age increases, and as one's income increases. For low-income individuals that depend heavily on short-term credit. Zeller determines when credit application is seen as a decision-making mechanism that starts with an individual's decision whether or not to apply for credit. Livingston and Ord (1994), who assess the quantity of goods requested by customers based on a variety of criteria. Individual demand is influenced by a variety of variables, including the size of one's profits. If a person's income rises, the demand for credit increases; conversely, if income reduces, the quantity of goods demanded declines as well. Meanwhile, Mpuga (2004) examined credit demand in rural Uganda and discovered that weak rural households are at a disadvantage and are less inclined to seek credit than inhabitants in urban areas.
Credit demand is often affected by other variables, such as the pace of inflation. According to Garoufalis (2017), the inflation rate is negatively related to credit demand. Maiti et al., All., (2020) concluded that the amount of credit required by the private sector in the short run is contingent upon the pace of inflation. In the near term, lower inflation rates stimulate an increase in credit demand. Personally, one would consider the impact of higher inflation when deciding the loan's duration. In Sub-Saharan Africa, Onwe and Olarenwaju (2014) discovered a strong negative association between corporate spending and inflation. Another school of thought holds that inflation has a detrimental effect on bank credit demand; as inflation rises, demand for bank credit decreases, and vice versa. This inverse relationship exists since inflation erodes wages in order to keep up with the costs of living (Aryaningsih, 2008). Another finding is that inflation has a partly negative and substantial impact on bank lending. Boyd et al., 2001;Aryaningsih, 2008;Vazakidis et al., 2011;Du, 2011;Kholisudin, 2012;Tarigan, 2012Tarigan, (2012. The findings contradict those of Sukarti (2008), Haryati (2009), andEgert (2006), who assert that inflation has a positive and important impact on the volume of credit disbursed in portion.
Gross Regional Domestic Product has an effect on or has the potential to drive credit expansion (Sihombing, 2010;Novembinanto, 2009;Olusanya et al., 2012;Al Daia et al., 2011;Du, 2011;Vazakidis et al., 2011;Yusuf, 2009). However, other studies have discovered the inverse association, with DRB or economic development having little discernible impact on lending growth. (2012) (Mahayoga et al.). Numerous observational studies support the theory that there is a favorable association between economic development (GDP) and credit demand. Kashyap, Stein, and Wilcox (1993) claim that economic growth enhances private agents' capacity to absorb higher levels of debt, allowing them to fund expenditure through credit. Rifai (2007) argued that gross domestic product has a positive and substantial impact on bank credit demand, implying that when GDP rises, bank credit demand increases as well. That as people's incomes rise, demand for products and services rises, allowing business players to expand their operations or create new ones. A loan can be used to fund company expansion or the

Chow Test
Chow test to determine the Common Effect (OLS) or Fixed Effect model that is most appropriate to use in estimating panel data in research. The criteria in determining the decision are also the same, namely: If the probability (Prob) on the Cross-Section F <0.05, the better model is Fixed effect. If the probability (Prob) on the Cross-Section F> 0.05 then the better model is the Common effect. The results of the Chow test conducted using Eviews 8.8 software can be seen in Table 1  The probability value of Chi square is 1.0000 > 0.05, so the best model is random effect.

Langrange Multiplier Test
The next step is to perform the Langrange Multiplier test, to determine the best common effects or random effects model. The results of the Langrange Multiplier test calculation can be seen in Table 3 below: From the data from the calculation of the multicollinearity test in Table 4, it is known that the centered VIF value of loan interest is 3.515925, the centered VIF value of GDP per capita growth is 4.389570, the centered VIF value of inflation is 2.811351, and the centered VIF value of economic growth is 4 , 803850. All the independent variable centered VIF values have a value less than 10. The centered VIF value of loan interest is 3.515925 <10, the centered VIF value of GDP per capita growth is 4.389570 <10, centered VIF from the inflai 2.811351 <10 , and the centered VIF value of economic growth is 4.803850 <10. Because the centered VIF value of all independent variables is less than 10, the independent variables in the regression model in the study are not related or multicollinearity does not occur.

Heteroscedasticity Test
Because the data used in this study is panel data, which is closer to the characteristics of cross section data compared to time series data, it is necessary to carry out a heteroscedasticity test.
In a good regression model, there should not be an unequal variance of the residuals for all observations. So, it is necessary to identify whether in the regression equation heteroscedasticity problems occur. To see if there is a heteroscedasticity problem, we use the Glejser test, which is to regress the absolute value (AbsRes) with the independent variable, which can be seen in Table 5 below: From Table 5, it is known that the Chi square probability value is 0.9509. in accordance with the provisions in the heteroscedasticity test, if the probability value of Chi square> 0.05, it means that there is no heteroscedasticity problem in the model. Thus, because the Chi square probability value is 0.9509> 0.05, it means that there is no heteroscedasticity problem.

Panel Data Regression Analysis with Random Effect Method
The following will show the results of panel data regression calculations using the Crosssection random effects method, which can be seen in Table 6 below:

Determination Analysis (Adjusted R Square)
Based on the calculated data in

Conclusion
We find that loan interest rates, GDP growth, inflation and economic growth simultaneously have a strong enough effect of 0.792454 or 79.25%, the remaining 20.75% is influenced by other factors that we do not take into account. The loan interest rate variable (X1) partially affects outstanding loans (Y) at the Credit Union Credit Union in West Kalimantan, because the calculated t value is smaller than the t table (-16,307 <-1,972). The per capita GRDP growth variable (X2) partially affects the loan (Y) at the Credit Union Credit Union in West Kalimantan, because the t-count value is greater than the t table (2.030> 1.972). Inflation rate variable (X3) partially has no effect on outstanding loans (Y) at Credit Union Credit Union, because -t count> -t table (-0.289> -1,972). Finally, the economic growth variable (X4) partially has no effect on outstanding loans (Y) at Kopdit credit unions in West Kalimantan, because the value of t count <t table (1.605 <1.972).