The Effects of Credit and Labor on Economic Growth in Indonesia
Ikram Khairun1, Aliasuddin, Srinita1*
1 Faculty of Economics and Business, Universitas Syiah Kuala, Banda Aceh, Indonesia
*Corresponding Author: [email protected] Accepted: 15 February 2023 | Published: 1 March 2023
DOI:https://doi.org/10.55057/ijaref.2023.5.1.9
_________________________________________________________________________________________
Abstract: This study examines the effect of productive credit, consumptive credit, and the effective employment ratio on economic growth in Indonesia. The data used is time series data in the form of the quarter 2001Q2 – 2021Q4, and the ARCH (Autoregressive Conditional Heteroskedasticity) method is used to estimate the model in this study. The estimation results show that the consumptive credit variable and the effective labor ratio influence economic growth, while productive credit does not. It is hoped that the government can increase the allocation of credit, especially consumer credit in Indonesia, so that this credit can increase economic growth in Indonesia and also for practitioners in banking to evaluate the use of credit so that it is right on target so that this credit can optimize economic growth in Indonesia.
Keywords: productive credit, consumptive credit, economic growth, Indonesia ___________________________________________________________________________
1. Introduction
Economic growth is a macroeconomic indicator that describes the success or failure of economic development (Wulandari et al., 2020). The economic progress of a region is determined by the growth resulting from changes in the region's output. Development activities accompany economic growth to increase people's welfare, where economic growth dramatically influences goods and services. Economic growth also measures an increase in people's income, meaning that people's income and purchasing power increase so that goods and services are more affordable and the poor population decreases. Economic conditions can be described through an increase in the GDP of a region. Bank Indonesia data shows that since 2010 the amount of Indonesia's GDP has continued to increase, almost doubling in 2021 where it was in 2010.
Figure 1 shows that Indonesia's economic growth has declined since 2011. Even though there was an increase in economic growth from 2016 to 2018, it was not as big as the economic growth in 2011. In contrast, in 2020, Indonesia's economic growth was negative due to the Covid-19 pandemic. One factor that influences economic growth is the amount of money used by the community for economic activities. In this case, credit is one of the facilities that can be obtained by the community so that they have the capital to carry out business activities or meet their daily needs. Currently, many financial institutions provide money lending services through credit with conditions determined by these financial institutions. Households and businesses can utilize banking credit for productive and consumptive activities.
In 2010 the amount of credit extended by banks was 56,426 billion rupiahs, of which productive credit was 31.825 billion rupiahs and consumptive credit was 24.601 billion rupiahs. The amount of credit issued by banks will continue to increase until 2021. The amount of credit extended by banks is 351,783 billion rupiahs, of which productive credit is 273,000 billion rupiahs, and consumer credit is 78,783 billion rupiahs.
Based on Figure 1, there was a decrease in credit growth, but economic growth continued to increase in 2012, 2016, 2017, and 2018. Meanwhile, in 2013, there was a decrease in credit growth which was also accompanied by a decrease in economic growth, and only in 2019 was credit growth also accompanied by a growth in the economy.
Figure 1: Economic Growth, Productive Credit Growth, and Consumptive Economic Growth of Indonesia, 2010–2021
Source: Bank of Indonesia, 2022.
The relationship between economic growth, productive credit, and consumer credit in Indonesia differs from previous studies. Research by Narayan & Narayan (2013), which examined the impact of the financial system on economic growth in 65 developing countries, found that financial developments, including bank credit, had a different impact, where the banking sector did not contribute to economic growth except in Asia. Yakubu & Affoi (2014) analyzed the impact of commercial bank credit on economic growth in Nigeria from 1992 to 2012. This study found that commercial bank credit significantly affected economic growth in Nigeria. Furthermore, Ananzeh (2016) argued in his research on the relationship between bank credit with economic growth in Jordan using the Vector Error Correction Model (VECM) and the Granger Causality Test. The thesis shows that the efficiency of bank credit facilities in the main economic sectors has an essential role in Jordan's economic growth. Bank credit has a long-term balance relationship with economic growth in the agriculture, industry, construction, and tourism sectors.
Guerra (2017), evaluating the causality and short-term effects between bank credit and economic growth in Mexico, shows that from 2001Q1 to 2016Q4, GDP growth had a positive
effect on the rate of growth of bank credit. Still, there is no evidence of causality or any effect of bank credit on GDP. Camba & Camba (2020) examined the relationship between domestic credit services and stock market liquidity on economic growth in the Philippines from 2005 to 2018, finding that domestic credit had a short-term and significant causal relationship with GDP growth.
The results of the research by Narayan & Narayan (2013), Yakubu & Affoi (2014), Ananzeh (2016), Guerra (2017), Camba & Camba (2020) do not distinguish between productive credit and consumptive credit. So based on this description, the authors are interested in identifying the impact of productive and consumptive credit on economic growth in Indonesia.
2. Literature Review
Credit has a different impact on economic growth. Camba & Camba (2020) examined the relationship between domestic credit and stock market liquidity on economic growth in the Philippines from 2005 to 2018. This study found that domestic credit had a short-term and significant causality relationship with GDP growth. Bui (2020) analyzed the impact of non- linear domestic credit on economic growth in ASEAN countries from 2004 – 2017, showing that increasing domestic credit increased the economy. Still, credit exceeding the optimal threshold of 97.5 percent had a negative effect on economic growth.
Mondragon's research (2018) explains that shrinking the supply of credit to households causes a decrease in credit flows, housing spending, non-housing spending, and employment. Guerra (2017), evaluating the causality and short-term effects between bank credit and economic growth in Mexico, shows that from 2001Q1 to 2016Q4, GDP growth had a positive effect on the rate of growth of bank credit. Still, there is no evidence of causality or any effect of bank credit on GDP.
Basmar et al. (2017) tested the effect of bank credit on the financial crisis in Indonesia and also determined the effect of credit on GDP for the period 1990 – 2014. This study found that bank credit had a positive and significant effect on economic growth and the financial crisis in Indonesia. The crisis in Indonesia in 1997 and 2008 was caused by the disbursement of bank credit, which was primarily channeled to large industrial sectors but did not use the precautionary principle.
Ananzeh (2016) argued in research on the relationship between bank credit and economic growth in Jordan, showing the efficiency of bank credit facilities in the primary economic sector has an essential role in Jordan's economic growth, that bank credit has a long-term balance relationship with economic growth in the agricultural, industrial and industrial sectors.
, construction and tourism. Thierry et al. (2016) analyzed the relationship between bank credit and economic growth using the Vector Error Correction Model (VECM) in Cameroon, showing that there is a unidirectional causal relationship from domestic credit to the private sector by banks and bank deposits as proxies for bank credit development and economic growth. This result is also consistent with several previous studies which found a causal relationship between bank credit to GDP and imply that monetary policy that is pro-bank credit will boost Cameroon's economy.
Gozgor (2015) empirically tested the causality relationship between economic growth and domestic credit in 58 developed and developing countries from 1970 to 2010. This study found significant causality from domestic credit to economic growth in only seven developing
countries. In addition, there is unidirectional causality from economic growth to domestic credit in five developed and ten developing countries.
Duican & Pop (2015) analyzed the relationship between credit and economic growth in Romania at the regional level, using data from 2005 to 2014. This analysis shows that credit has a significant influence on the evolution of GDP in Romania, an increase in one monetary unit of credit will determine an increase of 1.47 monetary units in GDP, so it can be concluded that banks must continue to finance the economy through credit because they make a significant contribution to GDP growth in Romania.
Sassi & Gasmi (2014) empirically assessed the effect of corporate credit and household credit on economic growth using a sample of 27 European countries from 1995 – 2012. The empirical results show that corporate credit positively affects economic growth, while household credit has a negative effect.
Nwakanma et al. (2014) evaluated the long-term relationship between bank credit and the private sector and economic growth in Nigeria and the causality between them. Resulting in a significant long-term relationship between variables but has a significant causality. Banu (2013), in research on the impact of credit on economic growth in the context of the global crisis, this study found a relationship between GDP, credit offered to public administration, and credit offered to households. The analysis shows that credit offered to households contributes more to GDP than public administration.
Narayan & Narayan (2013), in research on the short-term relationship between the financial system and economic growth: new evidence from a regional panel that examines the impact of the financial system on economic growth in 65 developing countries, found that financial developments, including bank credit, have a different impact where the banking sector does not contribute to economic growth except in Asia.
Krishnankutty (2011) analyzed the relationship between bank credit and economic growth in northeast India, using panel data from 1999 to 2007. This study found that bank credit to various sectors in northeast India did not have much impact on economic growth, where the main reasons are default and lack of supervision.
On the other hand, human capital, in this case, labor, is a factor that significantly influences economic growth. The neoclassical theory explains that technological progress does not mean replacing human labor with machines, but qualitative changes in production, such as an increase in the education level of workers (Sharipov, 2015). J. Cao et al. (2020) also find that higher factor labor supply, education levels, and labor force participation result in a practical labor input of 0.40 percent in 2015 – 2030 and project GDP growth of 5.80 percent for 2015 – 2030 compared to a GDP growth of 5.23 percent if these factors are ignored. Previously, Jajri
& Ismail (2010) revealed that capital and labor ratios significantly contribute to Malaysia's economic growth and labor productivity.
From various previous studies, it can be concluded that between credit and economic growth and the ratio of effective employment to economic growth. This study hypothesizes that productive credit, consumptive credit, and the effective labor ratio positively affect economic growth.
3. Research Method
This study looks at the effect of productive credit, consumptive credit, and the effective employment ratio on economic growth in Indonesia. d. The time series quarterly data from 2001-2021 are used in this study with a sample of 83 observations. The economy, namely Indonesia's nominal GDP in rupiah, is sourced from the Economic Research Federal Reserve Bank of St. Louis. Productive credit is nominal productive credit in billions of rupiah sourced from Bank Indonesia. Consumer credit is nominal consumptive credit in billions of rupiah sourced from Bank Indonesia. The effective workforce ratio, namely the total labor force to the total population in percent, is sourced from the Central Bureau of Statistics and the World Bank. The models formed in this study are as follows:
0 1 2 3 1
t t t t
E = + PC + CC + ELF+e
(1)
Where 𝛽1, 𝛽2, 𝛽3 are coefficients, E is nominal GDP, PC is productive credit in nominal terms;
CC is consumptive credit in nominal terms, ELF is the effective labor ratio, and 𝜀t is the error component at time t.
The estimation steps in this research are as follows. First, this study will provide a descriptive statistical picture of the research data. In the second step, the research data will be tested for stationarity; the stationarity test is used to avoid spurious regression on time series data ( ). The third stage is a model estimation. In general, model estimation is carried out using the OLS method. Fourth stage. A classic assumption test was carried out to determine whether there is a relationship between the independent and dependent variables. The classic assumption test includes tests for normality, multicollinearity, and heteroscedasticity. Other methods are used to estimate the data model if the classical assumptions are violated. The final stage is to test the hypothesis to see the effect of productive credit, consumptive credit, and the labor ratio on economic growth.
4. Finding and Discussion Descriptive Statistics
This section will describe credit and economic growth data in Indonesia for the period 2001Q2 to 2021Q4. The description is carried out by providing an overview of the ratio of productive credit to GDP and the ratio of consumptive credit to GDP. The presentation of research data descriptions can be seen in Table 1.
Table 1: Descriptive Statistics of the Variables
Statistics PC CC
Mean 0,002549 0,001264
Median 0,002044 0,001257
Maksimum 0,007254 0,003751
Minimum 0,000681 0,000301
Std. Deviasi 0,001749 0,000632
Source: Estimated Results, 2022.
The productive credit variable has the highest ratio to GDP of 0.007254 percent, the lowest is 0.000681 percent, and the average is 0.002044 percent. The standard deviation is 0.001749, which means that the tendency for productive credit data in Indonesia during the research time series has a deviation rate of 0.0017. The consumer credit variable has the highest ratio to GDP of 0.003751 percent, the lowest is 0.000301 percent, and the average is 0.001264 percent. The standard deviation is 0.000632, which means that the tendency for productive credit data in Indonesia during the research time series has a deviation rate of 0.0006. Descriptive statistics for the sample period show that productive credit has a higher ratio to GDP than consumptive credit, even when compared to the maximum, minimum, and average values of productive credit are two times greater than consumptive credit.
Stationarity Test
The stationarity test is used to avoid spurious regression on time series data. The stationarity test is determined from the probability results of the Augmented Dickey-Fuller statistical test.
If the value is less than 0.05, then the data is stationary. Otherwise, the data is not stationary if it is more significant than 0.05. Table 2 shows the results of the stationarity test from the Augmented Dickey-Fuller probability test statistic for each study sample.
Table 2: Test for Stationarity
Variable Prob. At level Prob. 1st difference
Prob. 2nd difference
Stationer
LogE 0,9288 0,0000*** - 1st difference
LogPC 0,0459** - - at level
LogCC 0,1041 0,0000*** - 1st difference
ELF 0,3551 0,4590 0,0000*** 2nd difference
Source: Estimated Results, 2022.
The stationary test results show that the E variable is stationary at the 1st difference, the PC variable is stationary at the level, the CC variable is stationary at the 1st divergence, and the ELF variable is stationary at the 2nd difference. The model that is more suitable for this case is nonlinear because there are variables that are second differences. Furthermore, heteroscedasticity is tested to make sure whether the OLS is applicable on not in this case. The results show heteroscedasticity, so the appropriate model is Autoregressive Conditional Heteroscedasticity (ARCH).
The Estimated ARCH Model
The results of the stationarity test show that there is a variable that is stationary in the second difference, and the heteroscedasticity test shows that there is heteroscedasticity. Hence, the model that is estimated in this study is the ARCH model. This ARCH model meets the requirements because the ARCH coefficient is significant, so that it can be used in this study.
The estimation results for this ARCH model are in Table 3.
The estimation results show that the effect of productive credit is not significant even though the coefficient of this variable is positive. This indicates that productive credit allocation has not been able to encourage economic growth in Indonesia. This result indicates that the allocation of productive credit is still relatively small, so it has not yet affected economic growth. The non-effect of productive credit on economic growth can be caused by the disbursement of bank credit extended to large industrial sectors that overuse the precautionary principle (Basmar et al. 2017) and lack of supervision which can lead to default (Krishnankutty, 2011).
Table 3: The Estimated ARCH Model
Variable Coefficient Std. Error z-Statistic Prob.
Log(PC) 0.010770 0.007398 1.455765 0.1455
Log(CC) 0.101011 0.009968 10.13312 0.0000***
ELF 0.162419 0.003459 46.96133 0.0000***
C 12.44613 0.120281 103.4751 0.0000***
Variance Equation
C 0.000227 0.000150 1.512880 0.1303
RESID(-1)^2 1.264913 0.399524 3.166047 0.0015
R-squared 0.926271
Source: Estimated Results, 2022.
Consumptive credit has a positive and significant effect on economic growth. This can happen because, with an increase in consumption, one component of aggregate demand increases so that economic growth increases. In addition, part of this consumer credit can be used for economic activities, so consumer credit significantly affects economic growth. Mondragon (2018) explains that shrinking the supply of credit to households can affect housing spending, non-housing spending, and employment. Banu's research (2013) shows that credit offered to households contributes to GDP.
5. Conclusion
This study examines the effect of productive credit, consumptive credit, and the effective employment ratio on economic growth in Indonesia. The estimation results using ARCH state that consumptive credit and effective labor have a positive and significant effect on economic growth in Indonesia, which can be interpreted that increasing consumptive credit and effective labor ratios can increase economic growth in Indonesia.
Consumer credit affects economic growth; if productive credit increases, economic growth will also increase. This can be caused by consumer credit, mainly used for productive activities such as activities that generate added value or purchases that result in the creation of new production sectors. Likewise, with the effective labor ratio, where the more people work, the more it indicates that the quality of the people has increased and will impact economic growth.
Given that productive credit does not affect economic growth, the Government needs to oversee the overall credit allocation in Indonesia so that it is right on target so that these loans can optimize economic growth in Indonesia.
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