The Effect of Credit among Micro, Small, and Medium Enterprises on Income Inequality in Indonesia

Income inequality has become a growing global concern during the last two decades. High income inequality can hinder economic growth, and in the case of Indonesia, the Gini coefficient continues to show an increasing trend. One of the factors that increase the level of income inequality is inequality in business opportunities and access to capital. To address this issue, the government created a credit program to benefit micro, small, and medium enterprises (MSMEs). MSME loans are basically designed to increase equality through the promotion of business opportunities. The authors seek to study the impact of MSME credit on income inequality in Indonesia using panel data with random effects in 33 provinces from 2005-2016. The results of this study are expected to provide an overview of whether the existing MSME credit program has been running effectively or not.

Income inequality has become a growing global concern during the last two decades.High income inequality can hinder economic growth, and in the case of Indonesia, the Gini coefficient continues to show an increasing trend.One of the factors that increase the level of income inequality is inequality in business opportunities and access to capital.To address this issue, the government created a credit program to benefit micro, small, and medium enterprises (MSMEs).MSME loans are basically designed to increase equality through the promotion of business opportunities.The authors seek to study the impact of MSME credit on income inequality in Indonesia using panel data with random effects in 33 provinces from [2005][2006][2007][2008][2009][2010][2011][2012][2013][2014][2015][2016].The results of this study are expected to provide an overview of whether the existing MSME credit program has been running effectively or not.

INTRODUCTION
In recent years, microcredit has emerged as a powerful tool to alleviate poverty and promote financial inclusion in developing countries.One of the key promises of microcredit is its potential to reduce income inequality by providing financial resources to those who are traditionally excluded from the formal banking sector.Indonesia, a country where a large proportion of the population lacks access to formal financial services, has seen a rapid expansion of microcredit programs in the past few decades.
One example of microcredit programs in Indonesia is the provision of credit to micro, small, and medium enterprises (MSMEs), which the Indonesian government has been actively expanding.This can be seen in the growth of MSME loans, which grew by 231 percent, from 388 billion in 2010 to around 898 billion in 2016 (Figure 1).
Research by Pitt and Khandker (1998) suggests that access to microcredit can lead to increased household income and asset accumulation among the poor, contributing to a more equitable distribution of wealth.However, the impact of microcredit on inequality is not uniform, and its effectiveness varies depending on factors such as program design, borrower characteristics, and local economic conditions (Navajas et al., 2000).
From the results of existing studies, microcredit can reduce the level of income inequality in a country (Morduch and Haley, 2002).In line with the above study, Bangoura et al. (2016) found that the intensity of microcredit activities has a significant effect on reducing income inequality.However, this effect depends on the targeting strategy used by each microcredit provider institution.Kasali et al. (2015) added that the application of microcredit would be more effective if the government created an environment in which microcredit could work inclusively.
Indonesia, as a result of its rapidly growing microcredit programs, is not included in the success stories of Southeast Asian countries in tackling income inequality.Indonesia experienced an increase in income inequality by 10 percent from 1990 to 2014 (Figure 2).Several factors may explain this increasing trend.Source: MSME Credit Data (Bank Indonesia, 2018) According to Wicaksono et al. (2017), this increase has a negative effect on income inequality and the Gini coefficient.This finding is in line with the rapid growth in Indonesia's per capita income, which has also led to a significant reduction in the poverty rate in Indonesia since 1978.
In addition to growth in per capita income, various subsidy policies and regulations that are not on target and are more profitable for the middle-and upper-income distribution groups may increase income inequality (Rhee et al., 2014).For example, fertilizer subsidies are misdirected, as 65 percent of the total poor farmers enjoy only 3 percent of the total subsidies, while 90 percent of these subsidies go to the top 5 percent of farmers (Osorio et al., 2011).
The depth of the financial sector also plays a large role in determining the level of income inequality.
However, expansion of the financial sector in developing countries must be carried out with caution, because it can have a negative impact on reducing income inequality.This is because the low development of an inclusive financial sector causes most of the funds to be misdirected and only benefit large companies or the upper class.In contrast, countries that have better access to credit funds can channel those funds to micro-, small-, and mediumscale companies (Dabla-Norris et al., 2015).
Knowing this, it is necessary to understand the impact of high income inequality.On a micro scale, income inequality can limit a person's opportunity and ability to achieve a better social

Literature Review
There is much debate about the sources of income inequality.Some argue that globalization is the main contributing factor to the increased inequality.
From a political viewpoint, protectionist sentiments assert that, in OECD countries, most of the benefits from increased productivity due to globalization in the last two decades have been enjoyed by skilled and highly educated workers, thereby excluding low-skilled workers (OECD, 2011).From a conceptual point of view, globalization increases trade integration, which is associated with higher relative wage levels in affluent countries resulting in higher levels of income inequality in these countries (Kremer and Masking, 2006).
However, evidence shows that the impact of globalization on income inequality varies across countries.Lindert and Williamson (2001) argue that countries that can adapt their policies to take advantage of globalization can reduce their income inequality.China, for example, tends to experience large reductions in income inequality between cities and rural areas when cities have trade openness that adapts to globalization (Wei and Wu, 2001).Other studies show that trade integration has increased inequality in high-and low-wage countries (Milanovic and Squire, 2005).
Globalization is not the only determinant of increasing inequality; another possible explanation is the distribution of market incomes.Technological progress is also often mentioned as a distortion in the distribution of market income.Information systems and high technology are also often described as biased skilled industries, which are considered a factor in increasing income inequality (OECD, 2011).
According to Harjes (2007), income inequality within countries is more adversely affected by technological advances than globalization.
One of the factors that influence the level of income inequality in a country is the deepening of the financial sector.Financial sector deepening is a process in which the efficiency, depth, breadth, and reach of financial markets increase (Ekberg et al., 2015).However, this occurs because efforts to deepen the financial sector are not followed by efforts to increase financial inclusion so that the benefits of the financial sector deepening are felt more acutely by the upper class.According to Ekberg et al. (2015), financial sector deepening is an important component to support sustainable economic growth.In addition, the deepening of the financial sector is also a key for Indonesia to achieve the 2030 target, which is to become one of the G7 economies.
In order to achieve this target, the Government A study by Hermes (2014) also reveals that higher microfinance participation is associated with reduced income inequality.The results of other studies also show that the higher the number of institutions providing microcredit, the lower the level of income inequality in that country (Tchouassi, 2011).According to Bangoura et al. (2016), increasing access to microcredit, which can be measured by increasing the number of active borrowers, can increase the income of the poor and reduce inequality.However, these objectives cannot be achieved if the existing financial services are not inclusive.
According to the existing research results, even though microcredit expansion has been carried out, low-income households still have limited access to financial services such as savings products, transfer payment products, insurance, and pension programs (Bird et al., 2011).Therefore, access to At the higher education stage, a positive relationship was found again, and income inequality increased as school enrollment increased.This finding is in line with the findings of Blanden and Macmillan (2016), who stated that school enrollment rates at high levels of achievement are still below the level needed to reduce income inequality.This may be caused by differences in the participation of poor and rich families in higher education (Crawford et al., 2018).These researchers also revealed a relationship between financial ability, intergenerational mobility, and income inequality.
The data leads to the conclusion that the amount of school enrollment at a certain level determines the effect of the school enrollment rate on income inequality.This study does not include educational attainment to capture the quality of students because data at the provincial level is very limited.
Economic growth is also considered one of the determining factors in inequality.A study by Lyubimov (2017) compared two types of relationship between income inequality and economic growth.According to Ulu (2018), government social spending also has a negative relationship with income inequality, in line with Anderson et al.
(2016), who found a negative relationship between government social spending and income inequality.
Therefore, social security is used as a control variable in the model.
Globalization, as discussed earlier, is considered a contributor to inequality.Therefore, the degree of trade openness is used as a control variable.Initially, the model to be used included exports plus imports relative to GDP as a measure of trade integration.
However, import data at the provincial level is not available.Thus, the ratio of exports per GDP is used as a substitute.
The results of the study show that population growth generally has a positive correlation with inequality (Rougoor and Van Marrewijk, 2015).It is argued that the rapid increase in population can be attributed to a higher youth dependency ratio.
As a result, the economic growth of countries with high populations tends to be slower than countries with low populations.To ensure that the results of the regression are robust, this paper conducts sensitivity tests using the Fama-MacBeth two-step regression and weighted least squares regression.The logarithmic basic model is robust if the relationship between the dependent variable and the independent variable is consistent across regression models.

RESULTS AND DISCUSSION
According to the RE regression results, MSME loans affect income inequality negatively at a very significant level, where ⍺ = 0.1 percent.This  ***: significant at 0.1 percent, **: significant at 1 percent, *: significant at 5 percent.The authors hope that the results of this research can be used as a basis for the Indonesian government's policy to increase the coverage and budget of the MSME credit interest subsidy scheme.Thus, MSMEs in Indonesia can continue to grow and, in the end, can reduce the income gap.
However, in this study, the net credit expansion data uses the MSME credit net expansion as a whole.
Thus, the specific relationship between credit per business scale (micro, small, and medium sized) and income inequality could not be seen in the model.In other words, the authors could not conclude whether the impact of microcredit across business scales caused variance in income inequality.Therefore, there is a need for further research that examines the impact of credit per business scale on income inequality in Indonesia, which can assist the government in formulating related policies.

Figure 2 .
Figure 2. Changes in Global Income Inequality from 1990-2014 Source: Inequality in Asia and the Pacific in the Era of the 2030 Agenda for Sustainable Development (United Nations ESCAP, 2018) inclusive financial services is one of the supporting factors for the success of microcredit in reducing income inequality.Of course, microcredit and microfinancing are not without drawbacks.For example, research conducted by Phan et al. (2017) revealed that national pro-poor targeted programs (NTPs) had no effect or even had unintended results.NTPs represent several strategies, policies, and investments that are devoted to improving the welfare of the most economically vulnerable people.These programs include hunger elimination, training, and job creation.Using the econometric method, providers actually increase inequality within a province after implementing this program.Research offers several possible causes for this result, including bad governance and policy implementation and processes that are too complicated to make NTPs more complex and not transparent.Corruption also presents a problem.According to a series of policies from the European Bank for Reconstruction and Development (EBRD) released in 2015, microcredit does not significantly increase household income and does not succeed in lifting poor households out of poverty.This is due to the distorted use of microcredit loans, only some of which are used for business and some for personal consumption.Another reason is that not all who receive loans are reliable entrepreneurs.Only a few of the borrowers experienced an increase in profit.Government spending on education and school enrollment rates are used as control variables because education is a basic factor in measuring inequality of opportunity.According to Huber et al. (2019), government spending on education has succeeded in consistently reducing income inequality.This was confirmed by a policy experiment conducted by Yang and Qiu (2016), in which early education subsidies for poor families significantly reduced income inequality.In other words, these variables tend to have a negative correlation with income inequality.Traditionally, educational expansion is considered important in supporting economic growth and is also considered effective in eliminating the transfer of poverty between generations and reducing income inequality(Coady and Dizioli, 2017).The educational expansion referred to in this research is an increase in the level of participation at each level of education.However, empirically, education and income inequality have a complex relationship.According toWicaksono et al. (2017), unequal access to education leads to higher income inequality.Blanden and Macmillan (2016) found that the relationship between educational expansion and educational inequality has an inverted U-shaped relationship.This means that, in the early stages, educational inequality increases with the number of children at one level of education and decreases when the proportion of poor families reaches a certain number.In addition, the direction of the relationship between education and income inequality can also be separated according to the school enrollment rate at each level.According toKeller (2010), elementary school participation has a positive relationship with income inequality, which means that the expansion of education at the elementar y school level increases income inequality.Different results were obtained at the junior and senior secondary levels, at which expansion of education could significantly reduce income inequality.
Lyubimov says thatKuznets (1955)  sees economic growth as having an inverted U-shaped relationship with inequality, as inequality increases when poor countries develop and then inequality decreases when the country is more prosperous.However, this is difficult to prove during the period under study, due to the lack of available data.In 2013, Thomas Pikkety published a book stating that inequality did not decrease as the country became more affluent but also increased and formed an S curve rather than the U, as Kuznets proposed.A followup study byYang and Greaney (2017) supported the S-curve proposed by Pikkety.However, this study also says that there is a positive relationship between economic growth and inequality in America, Japan, and China but that South Korea saw a negative relationship.This shows the complexity of the relationship between economic growth and inequality.Therefore, GDP per capita is used as a control variable.For a developing country like Indonesia, the agricultural sector is still the backbone for the poor.According to a study conducted by Gordon Gonzales and Resosudarmo (2017), the agricultural sector has a negative correlation with income inequality.The results of their research suggest that an increase in the ratio of agricultural products to GDP is associated with an increase in spending for the bottom 20 percent of income.In this case, it is appropriate to use the ratio of agricultural products per GDP as a determinant of inequality.According to a journal from the Journal of Economic Surveys(Anderson, Jalles D'Orey, Duvendack, and   Esposito, 2017), government spending does affect income inequality.However, the magnitude and direction of this relationship depend on the type of government spending and the method of measuring inequality used, such as the Gini coefficient or other methods.For example, government social spending has the most negative relationship with income inequality.To avoid collinearity, MSME credit subsidies and public education are excluded from the total government budget per capita.
indicates that a one percent increase in MSME loans may reduce income inequality by 0.03 percent, holding other variables constant.These results are in line with the research hypothesis, under which MSME loans are believed to reduce income inequality.Moreover, Figure3shows that homoscedasticity is present in the model.This suggests that the variance of the lgini is the same for all of the data.

Figure 3 .
Figure 3. RE residuals versus fitted values Source: Authors' regression results

Table 2 .
Regression Results with the OLS, random effects, fixed effects