Measuring Inequality Using J-Bonet Index: What Can We Learn from Regional Data?

This paper aims to analyze Inequality Using J-Bonet Index Distribution based on regions dimension and its determinant factors. This study uses a quantitative research approach with Quantile Regression (QR) analysis. The results showed that overall, there was no noticeable difference in income inequality among regions. However, according to the status of regions and regions, the expansion area in eastern Indonesia is twice as high as regional income inequality in the parent area in the same region. Other findings, economic growth and poverty cause high-income inequality in eastern Indonesia, while in the western region of Indonesia has no significant effect. In western Indonesia, fiscal decentralization is the cause of high-income inequality between regions, while in eastern Indonesia has no significant effect. Human development has no real impact on income inequality. It is a preliminary study of inequality using J-Bonet and its determinants in Indonesia based on regions dimension with Quantile Regression (QR) as an analysis tool. It can add empirical evidence about the inequality and region dimension.


INTRODUCTION
Income inequality makes the economy and social phenomena worse (Zhang, 2021). Economic Inequality is not just about income but also about overlapping features that contribute to quality of life and social welfare (Cahyono, Subroto, & Anwar, 2017;Postoiu & Bușega, 2015). Investigating inequality is a continuing concern within the development goals of developing countries, such as Indo nesia. One of the particular concerns and major problems in sustainable development goals is income inequality. Inequality is the main factor limiting sustainable development. The development of different industries and the resulting distribution of income is the main reason for income inequality (Piketty & Saez, 2003). Income inequality makes the economy and social phenomena worse. Income distribution in Indonesia often indicates that income inequality is relatively low due to the 'propoor' policies taken by the government (Leigh & van der Eng, 2009). However, data shows that income inequality still exists in Indonesia (see figure 1). Much uncertainty still exists about the measurement and determinants of Inequality based on empirical and evidence studies in Indonesia.

Figure 1. The Condition of Province-based Economy in Indonesia in 2019
Source: Statistic Indonesia, various years (processed) Figure 1. is managed using the data derived from statistics Indonesia in 2019. The lines represent economic growth, and the columns contain the Gross Regional Domestic Product (GRDP) over the constant basic price in 2010, per capita, and areas representing the per capita GRDP. This graph presents the growth interestingly since each province's GRDP and per-capita GRDP differ. The next important point is the economic condition that is centralized on Java island, particularly DKI Jakarta, West Java, Central Java, and East Java. It is indicated by the highest value of GRDP and per-capita GRDP compared to other provinces among islands or areas in Indonesia. Interestingly, it is found in the figure 1 that several provinces with small GRDP and per capita GRDP experience larger GRDP growth compared to other provinces with higher GRDP, such as DI. Yogyakarta, Bali, Central Kalimantan, North Kalimantan, Central Sulawesi, South Sulawesi, Southeast Sulawesi, Gorontalo and North Maluku. Therefore, the above explanation reveals the existence of a development gap, in terms of income, among regions in Indonesia. It might be triggered by different resources, demographic factors, development fund allocation or area's financial condition, and concentration of economic activities. However, the gap is considered a severe problem for Indonesian development. It is crucial to deal with the designated issue immediately because inequality limits sustainable development in Indonesia.
Several studies analyzed the income inequality based on the area's dimension in Indonesia, such as research conducted by Indra et al (2018), Nugraha & Prayitno (2020), and Farida et al (2021). First, Indra et al. (2018) analyzed the gap and expenses polarization in Indonesia, in terms of regional dimension. The regional dimension is classified into city and village, western and eastern areas, and provinces with lots of resources and lack of resources. The findings demonstrate the gap and polarization in Indonesia move unidirectional with the inclining trend. In the area context, a high increase in the initial inequality and low polarization of expenses is detected.
The trend in each regional dimension indicates a convergence pattern. Second, Nugraha & Prayitno (2020) stated that in terms of area in Indonesia, western Indonesia suffers a higher income inequality than eastern areas. Third, Farida et al., (2021) analyzed the determining factors for a gap, measured with the Williamson index. (Farida et al., 2021) solely studied eastern Indonesia empirically at the province level. At the same time, Nugraha & Prayitno (2020) analyzed the determining factors of the income gap, both in the eastern and western areas of Indonesia, using the data panel regression for provincelevel observation. So as Indra et al. (2018) conducted the study on certain regional dimensions, such as village and city, western and eastern areas, as well as the area with lots of resources and lack of resources, at a province level. Both studies exclude the area dimension based on new autonomy regions and parent regions.
This research contributes to two aspects. First, the research gap associated with income inequality and its determinants that cover the entire regencies and cities in Indonesia is reviewed based on the area's status. The area's status is referred to the current autonomy and parent areas, as well as the classification of areas, which are the western and eastern Indonesia. All this time, the studies on a negative aspect of expenses distribution in Indonesia are mostly dominated by the gap issues by employing the standard measurement, such as Gini index, Williamson index, and Theil index. This research offers another proxy in the gap measurement from the income perspective, using the J-Bonet index, first developed by Bonet (2006). Second, the quantile regression technique is used to ensure the effects of the independent variable over the dependent variable, based on the data distribution structure. Then, this research aims to analyze the income inequality and its determinant among regions based on the area status in Indonesia.
Previous research analyzed the correlation between human development and inequality, which revealed a negative relationship conducted by Farida et al (2021); Kuncoro & Murbarani (2016); Lessmann & Seidel (2017); Li & Westlund (2013). As a development indicator, human capital plays an important role in equitable development (Lessmann & Seidel, 2017;Li & Westlund, 2013). The human development index as the proxy of human development quality gives a real impact on equitable development (Farida et al., 2021). By referring to the endogenous growth theory, human capital is considered the critical factor and the main source of economic growth; hence a high level of human development could boost the economic growth to diminish the gap in development (Kuncoro & Murbarani, 2016). The correlation between the two variables is more considerable considering the different empirical results. The findings from Amrullah et al (2020) signified that human development enhancement leads to an increase in inequality.
The existing studies acknowledge the government's important role related to regional autonomy or decentralization. Regional autono-my is closely related to fiscal decentralization. Several studies indicate that fiscal decentralization influences inequality negatively. A study carried out by (Aritenang, 2014) showed the spatial effect of fiscal decentralization could lessen the inequality among regions measured by using Theil index and convergence coefficient. Moreover Rodriuez-Pose & Ezcurra (2010) discovered the decreasing inequality due to the decentralization policy in high-income areas or countries.
Based on several other empirical studies, different findings are exposed. Fiscal decentralization increases the interregions inequality (Liu, Martinez-Vazquez, & Wu, 2017;Rodriguez-Pose & Ezcurra, 2010;Shahzad & Yasmin, 2016). Decentralization positively influences inequality that only happens in lower-middle-income countries or areas (Rodriguez-Pose & Ezcurra, 2010). Furthermore, Shahzad & Yasmin (2016) explained that fiscal decentralization leads to the improvement of income inequality. Yet, the presence of appropriate institutions altogether with fiscal decentralization could reduce the negative consequence of fiscal decentralization towards inequality of income. Besides Liu et al (2017) suggested the existence of fiscal equities that could expand the detrimental impact of fiscal decentralization on the interregions gap.
Previously, the relationship between economic growth and inequality has already been observed in the hypothesis of Kuznets. An empirical study by Kuncoro & Murbarani, (2016) indicated that the Kuznets hypothesis occurred in Indonesia. Furthermore, that study also revealed that Gross Regional Domestic Product (GRDP) influences the increase of inequality positively and significantly. At the same time, the variable of squared GRDP influences the decrease of inter-provinces inequality in Indonesia. A positive correlation between percapita GRDP and income inequality is caused by the improvement of people's uneven per capita incomes, in another word, the increase in per capita income tends to be concentrated only in certain regions. In most developed countries, capital utilization is more emphasized, so only those with access to capital can relish the economic benefits. It is aligned with the Neo-Classic hypothesis that in early development, the gap tends to increase and later decrease at the following stage. At a particular point, the gap will re-increase to be finally re-decrease, recreating the event.
A study by Hindun et al (2019) suggested the higher poverty, the worse the inequality level. Therefore, a policy or development strategy is required to accelerate equitable development. Likewise, Hassan et al (2015) demonstrated a positive correlation between poverty with short-term and long-term inequality. The positive correlation is stated in terms of development level and government policy. The low Gross Domestic Product (GDP) level in developing countries leads to higher income inequality and increases the poverty gap. Therefore, a positive correlation is detected between income equality and poverty. Developed countries with high GDP have low inequality in income, limit the poverty gap, and push economic growth. Therefore, in the economic chain, it gives a negative correlation between income inequality and poverty. On the other side, in terms of government policy, if it is targeted at lower-income people, basic education, and agriculture, this effort will increase the level of the poor's basic income through job opportunities. Consequently, the enhancement in poverty level leads to an increase in income inequality and vice versa. Another result from Chotia & Rao (2014) showed the absence of a correlation between poverty and income inequality.

METHOD
This study uses a quantitative approach to analyze income inequality between regions in Indonesia and its determinants. The data used is secondary data with cross-sectional type with research locus in 508 regencies and cities of Indonesia, excluding the administrative area of the DKI Jakarta province as the capital of the country because the region is the centre of economic activity which has high logical consequences as the nation's capital, and consider aspects of the completeness of the data for each variable. Data for this research was retreived from the Indonesian Central Bureau of Statistics.
The standard of inequality is based on the concept of relative per capita GDP. To meet the perfect equality or the ideal equality condition, unit per capita GDP (regency/city) must be equal with the average of reference (province) for all regions at a certain year. Then, the inequality can be formulated as the distance of the relative part to the same and perfect part. The bigger the absolute distance, the higher inequality of regional incomes. Overall data are sourced from Statistics Indonesia and Region's financial report. Koenker and Bassett (1978) introduced the quantile regression to test how far the economic factors or a variable in the economy depends on other factors/ variables to examine the designated "dependency' structure. The advantage of having a dataset in the form of a cross-section is the variability and irregularity since each cross-section has its own intercept value. Lee and Li (2012) confirmed that a quantile regression panel could be employed to observe further one variable's "dependency" with other variables in the form of a cross-section. The equation is formulated as follows: In which τ represents the quantile in the structure of 0 < τ < 1, _ ( | , ). Notation of τ represents the quantile condition of Yi, β_τ is the parameter value of an equation, and X is the independent variable that is assumed to be influenced by the dependent variable in a structure of quantile regression condition. This research employs the following equation: It is determined as an inequality index that is measured using the J-Bonet index approach, IPM is defined as human development index, DFPAD as fiscal decentralization, EG is economic growth, POVR represents the poverty level, and τ is quantile condition. The difference in the equation of quantile regression and OLS illustrated as follows: The difference between OLS and QR formula is an error term in OLS.

RESULTS AND DISCUSSION
Region expansion (new autonomous regions) after the enactment of Law number 22 of 1999 concerning regional government, reflecting regional autonomy, has occurred a lot in Eastern Indonesia. A total of 127 new autonomous regions were formed in the Eastern Region of Indonesia, while in the western region of Indonesia, there were 65 new autonomous regions. Creating a new autonomous region shows regional euphoria to carry out proliferation in accelerating the achievement of development goals. On average, there are regional differences in income inequality between regions according to the region and regional status in Indonesia. The highest inequality occurs in the expansion areas in eastern Indonesia, which is equal to 0.547 and the lowest in the parent regions in the same region. However, if we look at each region and the status of the regions, it shows significant inter-regional income inequality. In the parent regions in the western region of Indonesia, income inequality occurs, shown in varying values between regions with the lowest inequality of 0.001 and the highest of 6.535. In the eastern region of Indonesia, the divisional regions show varying income inequality, with the lowest inequality of 0.004 and the highest of 5.688. At the same time, the parent regions in the same region also experience inequality but not as severe as in the divisional regions. Based on this phenomenon, it can be concluded that income inequality between regions according to the region and regional status still occurs in Indonesia. Differences in the potential of resources and the ability to manage potential owned by the regions cause the high-income inequality between regions. Income inequality as a result of calculating the J-Bonet Index in Indonesia by Region and Regional Status is shown in table 1. Based on the data gathered from Statistics Indonesia in 2019, there are 508 regencies and cities classified into 276 regencies and cities located in western Indonesia (excluding 5 cities and 1 administrative regency of Jakarta Province) and 232 regencies and cities situated in eastern Indonesia. The measurement of income inequality for regions in Indonesia employs J-Bonet index and its determinants, referring to the human development index, fiscal decentralization, economic growth, and poverty level. It also refers to the perspective of the area's status in the dummy variable designated for the expansion and parent areas, as well as the western and eastern areas of Indonesia. All variables are descriptively shown in Table 2. Table 2 demonstrates the descriptive statistics of variables used to analyze the inequality determinants in Indonesia's regional income. The table simulates the average inequality for regional income in Indonesia of 0.4268 with a minimum value of 0.0013 and a maximum value of 6.5354. J-Bonet index indicates that inequality of inter-regions income is not too high, even though the index utilization is considered flexible without categorization. Human development index and fiscal decentralization are recorded with an average of 69.4112, ranging from 30.7500 to 86.6500 and 10.6258 ranging from 0,2223 to 80.3914, respectively. The average economic growth and poverty level are found to be 5.3174 with a minimum value of 0.13% to 38.52% and 12.0551 ranging from 1.68% to 43.65%, respectively. At the same time, it can be verified that 37.79% are included in the expansion area in Indonesia, and the remaining 54.33% are located in the western Indonesia. The analysis for inequality model, conducted in two steps, first was OLS estima-tion and second was Quantile regression. Decomposition method was used both in OLS estimation and quantile regression. The estimation result for inequality model exposes in Table  3 and 4.   A robustness check is employed to solve the problem of spurious regression. In addition, the option "robust" in STATA was used to produce robust standard errors in both models. The results confirm that the models are robust and the estimation coefficient is consistent, as shown in Tables 3 and 4. Table 4 illustrates the estimation result of the determinant model parameter on income inequality in 2019 in Indonesia by using the double linear regression with robustness. The estimation result signifies that parameter estimators of several independent variables are signifi-cant in the significance level of 1% up to 5%. By referring to inequality using J-Bonet index, it resulted in the absence of income inequality among regions in Indonesia, whether parent area, expansion area, or status of an area. J-Bonet index is employed as an inequality measurement that applies the absolute value of shared per capita income of regency/ city towards the province's per capita income in a certain area. The consistency should be considered compared to other inequality proxies.    Source: data processed From Figure 2, The quantiles of the dependent variable are on the horizontal axis, and the coefficients are on the vertical axis. The regression coefficients of the quantiles are plotted as lines that vary across quantiles with confidence intervals around them. Overall, the quantile regression coefficient graph can be explained as follows: The quantile coefficient for HDI for inequality differs significantly between quantiles (the line that passes through the confidence interval plot) with a negative effect (negative coefficient or below 0). The quantile regression plot shows that HDI decreases in areas with higher inequality or HDI has a significant impact in areas with lower inequality. The quantile coefficients of fiscal decentralization, economic growth and poverty rates on inequality differ between quantiles with a positive effect. The effect of fiscal decentralization, economic growth and poverty rates increased in areas with higher inequality. The actual effects of decentralization, growth and poverty rates occur in regions with higher inequality even though the effect will exacerbate inequality between regions. Table 3 and 4 describe that, averagely, the human development index and fiscal decentralization has no significant influence on interregions income inequality in Indonesia. Uneven human development is still insignificant to overcoming such income inequality in Indonesia. The improvement in human resource quality is still unable to boost the equity of people's per capita income. It requires the appropriate policy and strategy to improve the quality of resources to be better and evenly distributed. The development and equal access to proper and adequate facilities and infrastructure should be enhanced, particularly in education and health. Not only in terms of physical/ asset capital but also in terms of human resources' competence. The outermost or suburban area must be put as one of the considerations related to health and educational access, while for developing and developed areas, the capacity must be considered. Regarding people's expenses, the stimulus must be accommodated to enhance the purchasing power. The government, private sector, and people can contribute to each portion in improving the quality of human resources.

Tabel 1. J-Bonet Income inequality Index in Indonesia by Region and Regional Status
The role of fiscal decentralization is expected to improve the convergence of regions' development in Indonesia, in which the underdeveloped and developing regions will run faster in catching up with the economic lag. Yet, this research shows the opposite result and is consistent with Fadli (2016), who suggested that fiscal decentralization (Regional Original Revenue) has no direct impact on income inequality. The research conducted by Fadli (2016) used the proxy of Williamson index, while this research applies J-Bonet index.
Fiscal decentralization proxied with the ratio of Regional Original Revenue towards total revenue has no real impact on the decrease of income inequality among regions in Indonesia. Generally, regional original revenue in several regions in Indonesia is still relatively low. It influences the regional expenses in fulfilling the internal needs. This condition indicates the lack of local resource utilization. It takes the optimization and the deeper searching of existing potencies and local resources to increase the revenue. Hence the dependency of the region on the central government could be reduced. The local government could enhance the collaboration with the private sector and public in managing the local resources to promote the people's income and welfare based on sustainability. A region with a lack of resources cannot be abandoned since it will become stagnant or left behind, while a region with lots of resources keeps on developing. This condition worsens the development gap among regions.
Moreover, the regional original revenue can be optimized for development equity and for promoting local people's welfare through the provision of public goods. Fiscal decentralization can enhance the economic performance and welfare level since local government is considered more efficient in producing or accommodating the people with the public goods. The provision of public goods for the region is not necessarily the same as what people needs. Through local government, output and outcome from public goods, which are prepared as needed, will bring more benefits and satisfaction to local people. The appropriateness between necessities with something earned makes an effective government budget.
Although the inequality measurement turns out differently, this research result aligns with a study by Kuncoro & Murbarani (2016) demonstrated that economic growth intensifies inequality. Economic growth positively and significantly influences income equality among regions in eastern Indonesia but not western Indonesia. Besides, this research only observes 1 short-term period, 1 year to be exact, and applies to all regencies and cities in Indonesia, except for regencies and city of province DKI Jakarta. Based on the hypothesis of Kuznets, at the early time of development, the inequality increased, which is caused by economic growth. It is due to distinguished resource potencies owned by regions located in eastern Indonesia that lead to the development gap. The regions that originated with rich resources could exploit them to promote local people's welfare. While the regions with limited resources or not yet explored optimally, it takes time for processing that impacts people's welfare.
Poverty has a positive and significant influence on income inequality among regions that only happens in the Eastern area of Indonesia. In contrast, in western Indonesia, poverty has an insignificant influence. Poverty without proper handling will become a barrier to development that refers to limitations and an individual's lack of productivity. The poor have lower skills, narrowing the job opportunity and resulting in less income and poor work quality. It also impacts the region with low economic performance compared to areas with low poverty levels and high productivity. A small value of the