Central and Local Taxes: Which Plays a Greater Role in Improving the Human Development Index in Indonesian Regencies and Cities?

Abstract

Nur Farida Liyana1, Rizqi Haniyah2, Arief Budi Wardhana3

123Polytechnic of State Finance STAN, Banten, Indonesia Correspondence: [email protected]

Abstract

Taxation plays a fundamental role in financing public services and promoting human development through fiscal decentralization. However, empirical evidence comparing the contributions of central and local taxes to human development remains limited. This study examines whether central tax revenue sharing (DBH) or locally generated tax revenues (LTR) play a greater role in improving the Human Development Index (HDI) across Indonesian regencies and municipalities. Using balanced panel data from 486 regencies and municipalities during 2018–2022, panel data regression is employed to estimate the effects of tax-based fiscal resources on HDI, controlling for gross regional domestic product (GRDP), government expenditure, population, and unemployment. The results show that both DBH and LTR have positive and statistically significant effects on HDI, indicating that taxation supports human development through fiscal equalization and local fiscal autonomy. The larger coefficient of LTR suggests a stronger marginal association with HDI than DBH. GRDP positively affects HDI, while government expenditure is negatively associated with HDI, highlighting the importance of expenditure quality rather than magnitude. Population is not statistically significant, whereas unemployment has a relatively small effect. These findings extend the fiscal decentralization literature by demonstrating the complementary roles of central and local taxation: DBH strengthens local fiscal capacity through revenue redistribution, while LTR enhances fiscal autonomy and responsiveness to local priorities. Improving human development therefore requires effective revenue sharing, stronger local revenue mobilization, and more efficient public expenditure, particularly for education, healthcare, and human capital development.

Introduction

human development index; revenue sharing fund; local taxes revenue; panel data; taxes.

Introduction

Taxes are one of the main sources of state revenue used to finance development and public services. In the context of Indonesia's regional development, taxes play a very important role. Funds obtained from taxes are used to finance state expenditures, such as infrastructure, education, health, and defense. Taxes play a role in development, and, ultimately, the community's welfare becomes a national goal. However, the economic conditions of the community are highly unequal across regions in Indonesia. This inequality in welfare is evident in the uneven development across Indonesia's regions. Many areas still need increased access to electricity and energy, while others no longer do. According to Raharti et al. (2021), Indonesia's development inequality across provinces between 2005 and 2019 is quite high. This inequality is influenced by factors such as the poverty rate.

development outcomes to achieve sustainable economic welfare. Azzahra et al. (2022) supports this opinion, stating that HDI negatively influences poverty; the higher the HDI, the lower the poverty rate. Therefore, improving HDI must be the government's responsibility. However, inequality in HDI again occurs. For example, based on data from Central Bureau of Statistics (2021), there is a significant difference between Jakarta's HDI and Papua's HDI. Jakarta's HDI is 80, while Papua's is 60. This occurs for various reasons, including differences in regional financial capacity during the era of regional autonomy and differences in the quality of education across regions.

One solution to limited funds is tax collection. The income redistribution function of taxes can help improve community welfare. However, with regional autonomy, which gives regions greater authority to collect local taxes, this is still not enough to cover the regional budget. For this reason, the central government issued a Revenue Sharing policy consisting of Tax Revenue Sharing and Natural Resource Revenue Sharing. With the existence of the Tax Revenue Sharing Fund (DBH), it is expected to achieve welfare equalization by playing the tax function as income redistribution. The purpose of this DBH is to minimize the HDI gap between regions in Indonesia.

Previous studies have widely examined the relationship between intergovernmental transfers, local taxation, and human development outcomes. Overall, the literature suggests that DBH and other fiscal transfers are expected to contribute positively to improvements in the HDI by enhancing public service provision. This positive relationship is supported by studies such as Juniantari & Purwadinata (2025) and Rizkiyah & Anitasari (2023) who find that fiscal transfers have a significant positive effect on HDI. Similarly, Harahap (2017) shows that the General Allocation Fund, Special Allocation Fund, and Revenue Sharing Fund jointly influence HDI.

However, the empirical evidence is not fully consistent. Some studies report either insignificant or different effects of DBH on developmental outcomes. For instance, Arrosyid & Bawono (2025) and Rabiyah et al., (2026)found that DBH does not significantly affect HDI in Indonesia and Riau Province. This inconsistency suggests that the effectiveness of fiscal transfers may depend on regional conditions and the efficiency of public fund allocation. A similar pattern is observed in the literature on local taxes. Several studies, including Sari & Rochmatullah (2023) and Agus (2020) found that local taxes have a positive and significant role in increasing HDI. However, Sofyan (2023) found other results. Sofyan revealed that owned-source regional revenue (PAD), which includes local taxes, does not influence HDI.

Methods

This research uses a quantitative approach to measure the relationship between independent and dependent variables. The dependent variable is the HDI. The independent variable in this study is revenue from the taxation sector, for central taxes represented by DBH and LTR. The existence of the separation of taxation authority between the central government and local governments has resulted in taxation sector revenues not being the domain of the central government (Zhang et al., 2024). There is a side to tax revenue collected by local governments, both at the provincial and regencies/city levels (Dewi et al., 2022). For this reason, the independent variables of this study include all components of central and local tax revenues, both analyzed in aggregate and separately (Balunywa et al., 2014). In addition, there are control variables that potentially affect HDI, generally divided into four groups, namely government spending, unemployment rate, Gross Regional Domestic Product (GRDP), and population (UNDP, 2020).

The data used in this study consist of quantitative panel data from 514 districts/cities in Indonesia over the period 2018–2022. The dataset was obtained from official government sources, including Statistics Indonesia (BPS), the Directorate General of Taxes (DGT), and the Directorate General of Fiscal Balance (DJPK) of the Ministry of Finance. However, several observations were excluded to ensure data consistency and comparability. The provinces of DKI Jakarta, Gorontalo, and Maluku were excluded due to incomplete or inconsistent reporting of key fiscal variables over the study period, which may affect panel balance and the reliability of the estimates. In addition, several newly established or administratively fragmented districts namely Tolikara, Central Mamberamo, Puncak Intan Jaya, and Pegunungan Arfak were excluded due to structural breaks in the time-series data resulting from regional expansion, frequent data gaps, and noncomparable fiscal indicators over time. These conditions could introduce measurement bias and distort regression estimates. Thus, the initial balanced panel consists of 514 districts/cities observed over five years (2018–2022), resulting in 2,570 potential observations. The final dataset comprises 486 districts/cities across Indonesia, which includes 2,430 observations after excluding cases with incomplete data, representing the administrative units for which complete and consistent data were available throughout the study period. This procedure ensures data consistency and estimation reliability without materially affecting the dataset's representativeness. Although some districts/cities were omitted due to data limitations, the resulting dataset retains extensive national coverage and a substantial number of observations.

Given that the data used in this study are secondary data, the author collected and compiled publicly available datasets from relevant government agencies. The data were obtained from official publications and open-access databases of BPS, DGT, and DJPK. For certain variables for which complete data were not publicly available, the author obtained additional administrative data directly from the relevant agencies, subject to data confidentiality regulations. All datasets used in the final analysis were harmonized into a balanced panel structure for the period 2018–2022.

This study uses three models to provide an overview of the relationship between the HDI of central tax revenue represented by DBH, local tax revenue, and other factors as follows: 𝐻𝐷𝐼𝑖𝑡= 𝛽0 + 𝛽1ln⁡(𝐷𝐵𝐻𝑖𝑡) + 𝛽2ln⁡(𝐺𝑅𝐷𝑃𝑖𝑡) + 𝛽3ln⁡(𝑃𝑂𝑃𝑖𝑡) + 𝛽4𝑈𝑁𝐸𝑀𝑃𝑖𝑡+ 𝛽5ln⁡(𝐺𝐸𝑋𝑃𝑖𝑡) + 𝜀𝑖𝑡…………………………………….(1)

Source: Processed by author

governments, comprising shared revenues from personal income taxes (Articles 21, 25, and 29), land and building taxes for plantation, forestry, and mining sectors, and tobacco excise revenue-sharing funds. 𝐺𝑅𝐷𝑃𝑖𝑡⁡denotes regional gross domestic product, 𝑃𝑂𝑃𝑖𝑡⁡is the total population, 𝑈𝑁𝐸𝑀𝑃𝑖𝑡 represents the unemployment rate, and 𝐺𝐸𝑋𝑃𝑖𝑡 denotes government expenditure on infrastructure and education. The subscripts 𝑖⁡and 𝑡⁡refer to district/city and year, respectively. The variables 𝐷𝐵𝐻, 𝐺𝑅𝐷𝑃, POP, and GEXP are transformed into natural logarithms to reduce heteroscedasticity, mitigate scale differences across districts, and allow the estimated coefficients to be interpreted as elasticities. Finally, 𝜀𝑖𝑡 denotes the stochastic error term that captures unobserved factors affecting the HDI. 𝐻𝐷𝐼𝑖𝑡= 𝛽0 + 𝛽1𝐿𝑛(𝐿𝑇𝑅𝑖𝑡) + 𝛽2𝐿𝑛(𝐺𝑅𝐷𝑃𝑖𝑡) + 𝛽3𝐿𝑛(𝑃𝑂𝑃𝑖𝑡) + 𝛽4𝑈𝑁𝐸𝑀𝑃𝑖𝑡+ 𝛽5𝐿𝑛(𝐺𝐸𝑋𝑃𝑖𝑡) + 𝜀𝑖𝑡.........................................(2)

Model 2 examines the effect of local tax revenue on HDI, while controlling for regional economic and socioeconomic characteristics. Equation (2) is specified as follows: where 𝐻𝐷𝐼𝑖𝑡⁡denotes the HDI of district/city 𝑖⁡in year 𝑡; 𝐿𝑇𝑅𝑖𝑡⁡represents local tax revenue; 𝐺𝑅𝐷𝑃𝑖𝑡⁡denotes gross regional domestic product; 𝑃𝑂𝑃𝑖𝑡⁡represents the total population; 𝑈𝑁𝐸𝑀𝑃𝑖𝑡⁡denotes the unemployment rate; and 𝐺𝐸𝑋𝑃𝑖𝑡⁡⁡represents government expenditure on infrastructure and education. The subscripts 𝑖⁡and 𝑡⁡indicate district/city and year, respectively. The variables 𝐿𝑇𝑅, 𝐺𝑅𝐷𝑃, 𝑃𝑂𝑃, and 𝐺𝐸𝑋𝑃⁡are transformed into natural logarithms to reduce heteroscedasticity, account for differences in scale across districts/cities, and facilitate the interpretation of the estimated coefficients as elasticities. Finally, 𝜀𝑖𝑡⁡denotes the stochastic error term that captures unobserved factors affecting the Human Development Index. 𝐻𝐷𝐼𝑖𝑡= 𝛽0 + 𝛽1𝐿𝑛(𝐷𝐵𝐻𝑖𝑡) + 𝛽2𝐿𝑛(𝐿𝑇𝑅𝑖𝑡) + 𝛽3𝐿𝑛(𝐺𝑅𝐷𝑃𝑖𝑡) + 𝛽4𝐿𝑛(𝑃𝑂𝑃𝑖𝑡) + 𝛽5𝑈𝑁𝐸𝑀𝑃𝑖𝑡+ 𝛽6𝐿𝑛(𝐺𝐸𝑋𝑃𝑖𝑡) +.....................(3)

Model 3 is employed to examine the simultaneous relationship between central tax transfers and local tax revenue on HDI, while controlling for regional economic output, population size, unemployment rate, and government expenditure. In Equation (3), 𝐻𝐷𝐼𝑖𝑡 denotes the Human Development Index of district/city (i) in year (t); 𝐷𝐵𝐻𝑖𝑡⁡represents tax revenue-sharing funds; 𝐿𝑇𝑅𝑖𝑡⁡represents local tax revenue; 𝐺𝑅𝐷𝑃𝑖𝑡⁡denotes regional gross domestic product; 𝑃𝑂𝑃𝑖𝑡⁡refers to total population; 𝑈𝑁𝐸𝑀𝑃𝑖𝑡 denotes the unemployment rate; and 𝐺𝐸𝑋𝑃𝑖𝑡⁡represents government expenditure on infrastructure and education. The subscripts (i) and (t) indicate district/city and year, respectively, while 𝜀𝑖𝑡 is the stochastic error term. Variables DBH, LTR, GRDP, POP, and GEXP are transformed into natural logarithms to improve normality, reduce heteroscedasticity, and facilitate coefficient interpretation.

collecting secondary data, the data were analyzed with panel data regression in Stata to test whether the hypothesis is accepted. Table 1 presents the results of the panel data model selection tests for the three estimated models. The Chow test results indicate that all models significantly favor the Fixed Effect Model (FEM) over the Common Effect Model (CEM), as evidenced by p-values of 0.0000. For Model 1, the Hausman test yields a p-value of 0.9314 (> 0.05), indicating that the Random Effect Model (REM) is more appropriate than the FEM; this result is further supported by the significant Breusch–Pagan LM test (p-value = 0.0000), which confirms that REM is preferred over CEM. In contrast, the Hausman test results for Models 2 and 3 are both statistically significant (p-value = 0.0000), indicating that FEM is preferred over REM. Accordingly, the final estimation model is REM for Model 1 and FEM for Models 2 and 3.

Result and Discussion

Descriptive Statistics

Indonesia's Human Development Index increased from 2018 to 2022. As shown in Figure 1, from 71.39 to 72.91 in 2022. If we look at these results, there has been a numerical improvement in Indonesia's human quality from year to year. Indirectly, this quality improvement leads to improvements in education, health, and income, as these are the determining factors in the HDI.

Indonesia’s HDI shows a general upward trend over time. During the 2018–2022 study period, HDI across Indonesian provinces continued to improve, reflecting gradual progress in human development outcomes. For instance, during this period, DKI Jakarta consistently recorded the highest HDI, reaching 81.65 in 2022, while D.I. Yogyakarta remained in the “very high” category (HDI ≥ 80). In contrast, Papua recorded one of the lowest HDI levels at 61.39 in 2022, highlighting persistent regional disparities in development outcomes. Several provinces also experienced notable improvements, such as Central Papua, which moved from the “low” to “medium” category, and West Sulawesi, which progressed from the “medium” to “high” category during the observed period. Although outside the main study period, the most recent available data show that Indonesia’s national HDI reached 75.02 in 2024, an increase of 0.63 points (0.85%) compared to 2023. This indicates that Indonesia continues to experience steady improvement in human development beyond the analytical period of this study. However, these 2024 figures are presented only as supplementary background information and are not included in the regression analysis. Overall, these patterns indicate persistent disparities in human development across regions in Indonesia, underscoring the need for continued policy attention to reduce regional inequality.

The varying HDI figures require the government to play a role in equalizing development. For this reason, as explained in the previous chapter, the government is present for the welfare of the people and utilizes tax funds that have an income redistribution function to help equalize Indonesia's human development. One of the policies implemented during the era of regional autonomy is the Tax Revenue Sharing Funds (Kementerian Keuangan Republik Indonesia, 2022). DBH is an important instrument in Indonesia's regional financial system. DBH serves to distribute a portion of state revenue derived from resources in the regions to local governments (Hidayat et al., 2024; Pidhegso et al., 2025). It aims to improve fiscal justice and support regional development. The function and purpose of DBH is to provide a share of state revenue to resource-producing regions as compensation for the exploitation of natural resources or taxes collected in the region (Ashari et al., 2026). DBH also functions to increase regional financial capacity, reduce fiscal imbalances between regions, and provide incentives for regions to increase their production or contribution to state revenue (Hanindya & Bintoro, 2024).

In this study, DBH is derived solely from tax revenuesharing funds sourced from Income Tax (PPh), Value Added Tax (VAT), Land and Building Tax (PBB), and Fees on Acquisition of Land and Building Rights (BPHTB). Based on Table 2, the highest DBH was recorded in 2021 at IDR 63 T.

Problems and challenges in DBH implementation include disbursement delays (Fahrezy et al., 2025). In some cases, DBH disbursement is often late, which can hamper the implementation of regional development programs. Regional dependency is also an obstacle to DBH. Many regions become highly dependent on DBH, leading to less innovation in identifying sources of PAD (Pramudya & Marseto, 2025). However, DBH has a positive impact on regional development in Indonesia. DBH assists regions in financing infrastructure, public services, and community welfare programs (Mulyani Utami & Diantimala, 2023).

for example, to address the impact of externalities, such as Tobacco Excise.

Meanwhile, regarding GRDP, data for regencies/cities in Indonesia show an upward year-over-year trend, as shown in Table 2. Based on Table 2 for the period 2018-2022, many regions show positive developments, especially in sectors such as trade, services, and manufacturing. However, inter-regional variations in growth remain significant, especially between urban and rural areas and between Java and the rest of Indonesia. This is due to different regional growth. Urban areas such as DKI Jakarta, Surabaya, and Medan continue to show stable growth, supported by the dominance of the services and industrial sectors. Outside Java, cities such as Balikpapan and Makassar also recorded positive growth, largely driven by the natural resources and logistics sectors. Regions that depend on the primary sector (agriculture and mining) exhibit greater fluctuations in growth than those with a more diversified economic base. Furthermore, some regions in Papua and Maluku still have relatively low per capita GRDP, despite recent increases.

Increased infrastructure investment across various regions has helped some regions increase their GRDP. This infrastructure investment is reflected in government capital expenditure. Based on Table 2, government expenditure fluctuates from year to year. Capital expenditure can represent government spending on investment in fixed assets such as infrastructure, health facilities, and education, playing an important role in driving economic growth and improving HDI.

Capital expenditure allocated to infrastructure development (such as roads, bridges, and transportation) improves regional connectivity, supports economic activity, and attracts private investment (Kadyraliev et al., 2022). This increases economic output in the region. Development projects increase people's income through job creation in the construction sector, which in turn encourages further consumption and investment. Capital expenditure can help regions dependent on the primary sector (such as agriculture and mining) expand into the secondary (industry) and tertiary (services) sectors (Kadyraliev et al., 2022). Investments in educational infrastructure, such as schools, laboratories, and learning facilities, improve access to education, thereby increasing Expected Years of Schooling and Average Years of Schooling, components of HDI (Sanusi et al., 2023). In addition, the construction of hospitals, health centers, and sanitation facilities improves public health (Huang et al., 2025). This increases Life Expectancy, one of HDI indicators. Capital spending on economic infrastructure improves people's access to jobs and income, supporting better living standards (Patimat et al., 2025). By 2022, increased capital spending on education and health in some provinces directly contributes to higher HDI, especially in eastern regions such as Papua, which is beginning to move from the "low" to the "medium" HDI category. Regions with high infrastructure capital expenditure, such as West Java and East Java, show improvements in both GRDP and HDI as improved connectivity increases economic activity and access to public services.

Furthermore, Indonesia is a large country with a population of more than a quarter of a billion people. This is certainly very important for Indonesia's human development. A large population can create significant domestic market potential for products and services. It can drive economic investment, increase growth, and provide resources to improve quality of life, such as education and healthcare. A large population is also a source of labor that supports the growth of the industrial and service sectors, which indirectly supports HDI components such as decent living standards (Arshed et al., 2023).

The number of unemployed people in February 2024 was recorded at 7.2 million people (Huruta, 2024). This decrease was due to an increase in employment in various sectors, especially food and beverage accommodation, trade, and government administration. Overall, the number of workers increased by 3.55 million people in the past year. The Labor Force Participation Rate also increased, reaching 69.8% of the total working-age population of 214 million people. This trend reflects stronger economic conditions, which encourage job creation and reduce unemployment. Nonetheless, challenges remain, such as the high proportion of informal workers who still dominate the labor force structure. However, the downward trend from 60.12% in February 2023 to 59.17% in February 2024 indicates an improvement in the quality of employment. The way forward involves strengthening labor-intensive sectors of the economy and optimizing fiscal policy to continue supporting inclusive growth.

This fiscal policy is evident in Indonesia's existing taxation system. When looking at what happens in the regions, local taxes are a very important instrument. Local taxes are among the main instruments for increasing local fiscal capacity, which reflects the ability of local governments to manage and use their financial resources (Kiziltan & Yereli, 2023). PAD including from local taxes, is an important factor in supporting local development and reducing dependence on central transfers (Hasanah et al., 2025). Increased local taxes enable local governments to provide better public services and to encourage infrastructure development.

Although local taxes contribute significantly to PAD, many regions have not optimally utilized their tax potential. Some regions still depend on central transfers, particularly the general allocation fund (DAU) and special allocation fund (DAK), to finance their spending. There is a large gap in local tax revenue between developed regions such as Java and Sumatra and eastern Indonesia. Developed regions have a stronger economic base, access to adequate infrastructure, and better tax administration, which can maximize local tax revenue.

The 2024 Central and Local Government Financial Relations Law (HKPD Law) is an important step to improve the governance of local taxes and levies. Local governments are now encouraged to use trend analysis to set realistic tax targets and improve tax collection efficiency.

Overall, regions such as DKI Jakarta and Bali show excellent local tax performance, while provinces in eastern Indonesia still face major challenges in increasing their tax potential. Improving fiscal capacity and tax administration is key to narrowing this gap.

Regression Analysis and Interpretations The Role of Central Taxes in Improving HDI in Regenciess/Cities in Indonesia

Central taxes play an important role in improving HDI in Indonesian regencies/cities by allocating funds from the central government to the regions. In this study, central tax is represented by DBH. DBH plays an important role in improving HDI in Indonesian regencies/cities. DBH is part of the tax revenue distributed by the central government to the regions, including Income Tax, Land and Building Tax, and Fees for Acquisition of Land and Building Rights.

DBH provides additional revenue to regions that can be allocated to basic services relevant to HDI, such as education, health, and welfare. DBH is used to build or rehabilitate schools, improve teacher capacity, and provide access to free education. In addition, these funds support the provision of public health services such as community health centers, immunization, stunting, and malnutrition. Finally, DBH is expected to help improve welfare by improving the quality of clean water, sanitation, and social protection programs. DBH redistributes revenue from regions with high economic potential to less-developed regions, helping equalize access to basic services that affect the HDI. Regions that previously lacked fiscal capacity can access funds to improve the quality of education and health services.

DBH provides fiscal space for regions to fund economic empowerment programs, such as job training, MSME development, and labor-intensive programs. Improved economic welfare will be reflected in the per capita expenditure indicator, one of the components of HDI. Local governments can use DBH to implement programs tailored to local needs, such as eradicating illiteracy in certain areas or constructing health facilities in remote areas.

𝐻𝐷𝐼= 51.35 + 0.11𝐿𝑛𝐷𝐵𝐻+ 3.56𝐿𝑛𝐺𝑅𝐷𝑃−0.002𝐿𝑛𝑃𝑂𝑃

+ 0.043𝑈𝑁𝐸𝑀𝑃−0.641𝐿𝑛𝐺𝐸𝑋𝑃 With 2,430 observations, we ran the data in Stata, yielding an R-squared of 34%, indicating that approximately 34% of the variation in the HDI is explained by the independent variables in the model (Table 3). A P-value of 0.00 suggests that the model is jointly significant. From these results, all variables are statistically significant except LnPOP (population), with the following explanation: 1. The constant value of 51.36 means that without the

influence of other variables, the HDI of regencies and cities in Indonesia is 51.36. 2. DBH has a statistically significant positive effect on HDI,

with a coefficient of 0.108 implying that an increase in tax revenue-sharing funds is associated with an improvement in HDI. 3. Meanwhile, GRDP has a significant positive effect on HDI.

When GRDP in Indonesian regencies and cities increases by 1%, HDI will increase by 3.56. 4. The population, which is expected to have a significant

positive relationship, has an insignificant relationship in this model. 5. The Unemployment Rate, which is predicted to have a

significant negative impact, actually has a positive and significant impact. If the unemployment rate increases by 1 point, the HDI will also increase by 0.043. 6. Meanwhile, government spending, in this case capital

expenditure, also has a significant negative impact. If capital expenditure increases by 1%, the HDI decreases by -0.64. The results of this study certainly contradict theoretical expectations, as DBH is generally thought to improve public services, such as education and health, which improve HDI (Siregar, 2024). The results indicate that DBH have a positive and statistically significant effect on HDI, suggesting that central government tax revenues contribute to human development through fiscal decentralization. As a mechanism for redistributing centrally collected taxes, DBH strengthens local fiscal capacity, enabling local governments to finance essential public services such as education, healthcare, and infrastructure that directly support improvements in HDI. This finding demonstrates that the developmental benefits of taxation extend beyond revenue collection, as tax revenues redistributed to subnational governments can enhance human capital and improve social welfare.

through higher income and productive economic activities. In contrast, government expenditure shows a negative coefficient, suggesting that higher spending alone does not necessarily translate into better human development, possibly reflecting inefficiencies in expenditure allocation or the concentration of spending in less-developed regions. Population does not have a significant effect, implying that population size itself is not a key determinant of HDI after controlling for economic and fiscal factors. Meanwhile, unemployment is negatively associated with HDI, although the effect is relatively small, suggesting that limited employment opportunities may reduce household welfare and access to education and healthcare.

Overall, these findings underscore that while regional economic performance and labor market conditions influence human development, tax revenue-sharing transfers play a pivotal role by linking central tax collection with improvements in local public services and human development outcomes. This highlights the importance of designing an effective taxand-transfer system to ensure that national tax revenues translate into tangible improvements in quality of life at the local level. The Role of Local Taxes in Improving HDI in Regency / City in Indonesia.

Source: Data processed by the author

and metropolitan areas such as Jakarta, Surabaya, and Bandung) have much greater tax potential than remote or rural areas. Tax sources such as motor vehicle tax (PKB), hotel and restaurant tax, and billboard tax are higher in urban areas with intensive economic activities. Regions with high tax potential have larger budgets to finance education, health, and basic infrastructure, which improves HDI. In contrast, regions with low tax potential (for example, agrarianbased or remote economies) face limitations in financing HDI improvement programs.

Many regions and cities still depend on transfers from the central government (such as the General Allocation Fund and Revenue Sharing Fund) because the contribution of local taxes to total local revenue is relatively small. This dependency is particularly evident in regions with low local revenue, where local taxes only contribute a small portion to the total budget. This dependency limits the flexibility of local governments to allocate budgets according to their regionspecific needs, which can slow down improvements in HDI. Some regions show low tax-collection efficiency due to limited administrative capacity, weak taxpayer databases, or a lack of technological innovation. More developed regions tend to have better tax systems, such as digital-based taxes, while lagging regions still rely on conventional methods. Low tax collection reduces the budget for public services essential to education, health, and welfare.

Most local tax revenues are allocated to routine expenditures, such as employee salaries, rather than capital expenditures that support education and health services. Only a few regions rely heavily on local taxes for programs that directly affect HDI, such as improving health or education facilities. Untargeted budget allocation results in a lack of direct impact of local taxes on HDI improvement. Some regions provide tax incentives to attract investment, which often reduces tax revenues in the short term. Regions that are successful in attracting investment tend to have a higher HDI because such investment often has a positive impact on the local economy and on infrastructure provision. Improperly targeted tax incentive policies can reduce local revenues and hinder efforts to improve HDI.

In this study, the role of local taxes on HDI is in accordance with the theory. The following is the resulting model:

𝐻𝐷𝐼= 43.77 + 0.2𝐿𝑛𝐿𝑇𝑅+ 3.91𝐿𝑛𝐺𝑅𝐷𝑃+ 0.09𝐿𝑛𝑃𝑂𝑃

jointly significant, as indicated by the F-statistic of 552.66 (p < 0.001). The model explains approximately 58.76% of the within-region variation in the HDI, as reflected by the within Rsquared of 0.5876. 1. The constant (43.77) states that when all independent

variables are zero (although unrealistic), HDI is estimated at 43.77. This constant represents the baseline HDI, excluding the contribution of the other variables. 2. The coefficient of local taxes is 3.91. 1% increase in local

taxes is associated with a 3.19-point increase in HDI. This positive relationship can be explained by the relatively direct fiscal link between locally generated revenues and the provision of public services. This mechanism is consistent with development theory, which suggests that higher locally generated revenues enhance local governments' capacity to deliver quality public services, thereby contributing more directly to improvements in human development outcomes. 3. GRDP coefficient: 3.91. A 1% increase in GRDP increases

HDI by 3.91. GRDP reflects the region's economic activity. Higher economic growth positively impacts people's wellbeing, including access to education and healthcare. This is consistent with economic development theory, which emphasizes the positive relationship between economic growth and people's quality of life. 4. Population (LnPOP) has a negative coefficient but is not

statistically significant at the 5% level (p-value = 0.069), implying that population size alone does not substantially influence HDI after controlling for fiscal and economic conditions. 5. Unemployment Rate coefficient 0.05. A 1-point increase in

the unemployment rate increases HDI by 0.32. This relationship seems contrary to expectations, as unemployment is usually associated with a decline in welfare. This positive coefficient may reflect that regions with high unemployment have additional income, such as from the informal sector, or that subsidy policies support an increase in HDI despite high unemployment. This requires further analysis to understand the true relationship. 6. Government Expenditure, in this case represented by

on non-priority projects (e.g., grandiose infrastructure that is irrelevant to the basic needs of the people). High capital expenditure in underdeveloped regions, resulting in low HDI despite increased investment. HDI is a key indicator of human-centered development, which includes education, health, and welfare. Positive local taxes and GRDP reflect increased government capacity to finance services that improve people's quality of life. Malthusian Theory states that a negative relationship between population and HDI indicates population pressure on local resources, which is consistent with Malthusian theory. Capital expenditure should have a positive impact on HDI if managed properly. However, the negative relationship in this model may indicate inefficiency or suboptimal allocation, consistent with criticisms in public expenditure management theory. A high GRDP usually leads to increased community welfare. The positive relationship between GRDP and HDI suggests that economic growth supports human development. For this reason, it is necessary to optimize the collection and use of local taxes.

Strengthening tax administration to increase local revenue and allocating local taxes to priority programs that support education and health will certainly improve HDI. Efficient management of capital expenditure is equally important. It is necessary to focus on infrastructure projects that directly impact HDI, such as health and education facilities, and to improve transparency and accountability in the use of capital expenditure. It is necessary to capitalize on economic growth. With this, it is expected to encourage economic inclusion to ensure GRDP growth impacts all levels of society and develop labor-based economic sectors to absorb unemployment.

The model shows the relationship of economic variables with HDI. The theoretically sound results (Local Tax and GRDP) indicate the importance of income efficiency for human development. However, anomalies in the Unemployment Rate and Capital Expenditure suggest that expenditure management and employment policies require further evaluation to ascertain their impact on HDI.

These results show that the role of local taxes is crucial for improving HDI. However, several challenges, including administrative capacity, still need to be improved. There are still regions with limited capacity to optimally manage and collect local taxes. Regions with a weak economic base may have low tax revenues, affecting their ability to improve HDI. Local tax allocation is often suboptimal, with most of the budget spent on routine expenditures rather than on human development investments. Surabaya City, for example, has a large local tax potential, mainly from hotel, restaurant and vehicle taxes, which is used to fund free education, quality health services and infrastructure development. This has resulted in a relatively high HDI. Meanwhile, Regencies in Disadvantaged Areas with low local tax potential have limited budgets for education and health, which results in a stagnant HDI.

also continue by increasing the efficiency of PAD management, so that regions are more independent in financing development programs. Finally, there is a need for fiscal redistribution. The central government can help regions with low tax potential through a fairer policy for redistributing transfer funds.

Local tax conditions in Indonesia vary widely and directly affect the ability of Regencies/cities to improve HDI. Regions with high tax potential tend to have better HDI because they can finance education, health, and community welfare services. However, regions with low tax potential require fiscal redistribution policies and capacity-building to maximize their capacity to support human development.

Between central and local taxes, which one has a greater role in improving the HDI of Regenciess/cities in Indonesia?

To assess whether DBH tax or local tax has more role in improving Human Development Index (HDI) in regencies/cities in Indonesia, we need to analyze its characteristics, management, and impact on human development based on economic theory and practice in the field. 1. DBH tax is a transfer of funds from the central government

to the regions, based on tax revenue in the region. DBH is part of fiscal transfers, so these funds are relatively stable and certain to be received by regions every year. Then the impact on HDI is because DBH Tax is used to support local government spending, including education and health. In accordance with the principle of fiscal decentralization, DBH increases regions' capacity to provide public services, especially in regions with limited PAD. The drawback of DBH is the fiscal dependency of regions. Regions that rely too much on DBH tend to have no incentive to increase ownsource revenue. 2. Local taxes are part of the PAD, which comes from motor

vehicle tax, hotel tax, restaurant tax, and others. The advantage of local taxes is fiscal independence. Local taxes grant local governments autonomy to manage their own revenues, allowing flexibility in planning development programs. The impact on HDI is that high local taxes are often associated with developed regions that are able to fund free education, health facilities, and basic infrastructure development. Local governments can directly allocate taxes to priority sectors that improve HDI, such as education and health services. The principle of financial decentralization encourages regions to be more innovative in increasing their tax revenue and allocating the budget optimally. Regarding local tax shortfalls, there is an imbalance in tax potential. Regions with a weak economic base have low tax potential, so their contribution to HDI is relatively small compared to developed regions. Many regions still face obstacles to managing taxes effectively due to administrative and technological limitations. The author has run a panel regression in Stata to compare the roles of DBH and local taxes in HDI. The results are shown in Table 5 below.

The Fixed Effects regression model is jointly significant, as indicated by the F-statistic of 470.26 with a p-value of 0.000. The model explains approximately 59.28% of the within-region variation in the HDI, as reflected by the within R-squared of 0.5928. However, the effect of each independent variable on HDI is as follows: 𝐻𝐷𝐼= 43.2 + 0.09𝐿𝑛𝐷𝐵𝐻+ 0.2𝐿𝑛𝐿𝑇𝑅+ 3.7𝐿𝑛𝐺𝑅𝐷𝑃+ 0.08𝐿𝑛𝑃𝑂𝑃

+ 0.04𝑈𝑁𝐸𝑀𝑃−0.6𝐿𝑛𝐺𝐸𝑋𝑃 This model explains the influence of economic and demographic variables on the HDI. The following is an analysis of each component of the regression model. 1. The constant indicates that if all independent variables are

Source: Data processed by the author

2. DBH Tax, with a coefficient of 0.094. A 1% increase in DBH

Pajak reduces HDI by 0.094. It already meets the theory. DBH, including Tax DBH, should have a positive impact by increasing regions' fiscal capacity to fund public services. 3. Local Tax Revenue with a coefficient of 0.2. A 1% increase

in local taxes increases HDI by 0.2 points. Local taxes are the main source of local own-source revenue (PAD), providing local governments with flexibility to finance priority programs. This positive relationship suggests that increasing local fiscal independence through local taxes directly impacts sectors that support HDI, such as education, health, and basic infrastructure. 4. GRDP with a coefficient of 2.48. A 1% increase in GRDP

increases HDI by 3.7. GRDP represents the economic strength of a region. This positive relationship reflects that economic growth contributes to improving people's quality of life, including access to education and health. Human development theory states that economic growth provides greater resources for investment in human development. 5. Population has a positive but statistically insignificant

coefficient, indicating that population size alone does not significantly influence HDI after accounting for fiscal and economic factors. 6. Unemployment Rate with coefficient: 0.04. 1-point

increase in the unemployment rate increases HDI by 0.04. This relationship is contrary to expectations, as unemployment usually has a negative impact on people's well-being. This positive coefficient may reflect special phenomena, such as government support and educated unemployment. Regions with high unemployment rates may receive welfare-enhancing social assistance. Then in some cases, a high unemployment rate may reflect the high education level of the community, which contributes to the HDI even though the labor force has not been fully absorbed. 7. The estimated coefficient for capital expenditure is -0.6.

reflect several underlying factors. This may indicate inefficiencies in expenditure allocation, a concentration of spending on administrative or recurrent expenditures, or the tendency for governments to allocate larger budgets to relatively underdeveloped regions. The results indicate that both central tax revenue transfers (DBH) and local tax revenues (LTR) make positive and statistically significant contributions to improving the Human Development Index (HDI). However, the estimated coefficient of local tax revenue (β = 0.2) is larger than that of tax revenuesharing transfers (β = 0.09), suggesting that ceteris paribus, locally generated tax revenues have a stronger marginal association with HDI than transfers from centrally collected taxes. This finding may reflect the greater flexibility and fiscal autonomy associated with local tax revenues. Unlike DBH, which is allocated according to nationally determined formulas and regulations, locally generated taxes can be more directly aligned with local development priorities. Greater fiscal autonomy enables local governments to respond more effectively to local needs by financing education, healthcare, and infrastructure projects that directly contribute to human development.

Nevertheless, the positive and significant effect of DBH remains an important finding. As an instrument of fiscal decentralization, tax revenue-sharing transfers redistribute centrally collected taxes to local governments, particularly those with limited fiscal capacity. Consequently, DBH promotes horizontal equity by ensuring that less affluent regions have sufficient resources to provide essential public services. In this sense, central taxation contributes to human development not through direct expenditure, but through the redistribution of fiscal resources across regions.

Therefore, the findings should not be interpreted as indicating that local taxes are more important than central taxes, but rather that the two are complementary. Local taxes strengthen fiscal autonomy, while central tax revenue-sharing strengthens fiscal equalization. Sustainable improvements in HDI require both an effective local revenue system and an equitable intergovernmental transfer system. Together, these fiscal instruments enhance local governments' capacity to invest in education, healthcare, and public infrastructure, thereby improving human development outcomes.

taxes are more effective at enhancing development where fiscal capacity already exists, whereas central tax transfers ensure that regions with weaker tax bases can also finance essential public services. Accordingly, improving human development in Indonesia depends not on choosing between central and local taxation, but on strengthening the complementarity between fiscal equalization through tax revenue sharing and fiscal autonomy through local revenue mobilization.

The findings provide several policy implications for improving human development through Indonesia's fiscal decentralization framework. First, the positive effect of DBH suggests that the central government should continue strengthening the intergovernmental fiscal transfer system to reduce regional disparities in fiscal capacity. However, increasing the amount of DBH alone is insufficient. Transfer policies should increasingly emphasize performance-oriented allocation mechanisms, in which a portion of the transfer is linked to measurable improvements in education, healthcare, and other HDI-related outcomes. Such an approach would encourage local governments to utilize transferred resources more effectively while maintaining fiscal equalization across regions.

Second, the larger coefficient of local tax revenue indicates that strengthening local revenue mobilization can generate greater improvements in HDI. Therefore, local governments should enhance tax administration through digitalization, a broader tax base, improved taxpayer compliance, and more efficient tax collection, rather than increasing tax rates. Higher local revenues expand fiscal autonomy, allowing local governments to finance development programs that are more responsive to local needs.

Third, the negative coefficient of government expenditure suggests that increasing public spending alone does not guarantee better human development outcomes. Policymakers should therefore shift their focus from the quantity of expenditure to its quality. Budget allocations should prioritize productive spending on education, healthcare, sanitation, and basic infrastructure while reducing inefficient or low-impact expenditures. Strengthening expenditure planning, monitoring, and evaluation is also essential to improve the effectiveness of public spending.

Finally, the complementary effects of central tax transfers and local tax revenues indicate that improving HDI requires a balanced fiscal strategy. Central government transfers remain essential for ensuring fiscal equity across regions, particularly for areas with limited revenue-generating capacity, while stronger local tax systems enhance fiscal autonomy and accountability. Accordingly, future fiscal decentralization reforms should integrate both objectives, namely fiscal equalization through tax revenue sharing and fiscal sustainability through stronger local revenue mobilization to maximize improvements in human development.

Conclusion

References

that both DBH and LTR have positive and statistically significant effects on HDI, confirming that taxation contributes to human development through two complementary fiscal channels. However, the larger coefficient of LTR suggests that local taxes play a greater marginal role in improving HDI, reflecting the importance of fiscal autonomy in enabling local governments to finance development programs that are more responsive to local needs. Nevertheless, central tax revenue-sharing remains indispensable for strengthening the fiscal capacity of regions with limited revenue potential and reducing regional disparities in human development. Among the control variables, GRDP positively influences HDI, highlighting the role of regional economic growth in improving education, health, and living standards. In contrast, government expenditure is negatively associated with HDI, suggesting that the effectiveness of public spending depends more on the quality and allocation of expenditure than on its magnitude. Population size is not a significant determinant of HDI, while unemployment has only a relatively small effect after controlling for fiscal and economic conditions.

This study contributes to the fiscal decentralization and public finance literature by demonstrating that taxation's role in human development extends beyond revenue generation. While central taxes promote human development through fiscal equalization by redistributing tax revenues across regions, local taxes contribute through fiscal autonomy by enabling governments to tailor public spending to local priorities. Rather than substituting for one another, these fiscal instruments perform complementary functions in improving human development outcomes. From a policy perspective, enhancing HDI requires an integrated fiscal strategy that combines an effective and performance-oriented tax revenue-sharing system with stronger local revenue mobilization and more efficient public expenditure. Strengthening local tax administration, improving the targeting of intergovernmental transfers, and prioritizing productive spending on education, healthcare, and basic infrastructure will maximize the developmental impact of both central and local tax revenues.

This study is subject to several limitations. It focuses primarily on tax-based fiscal resources and does not explicitly consider institutional quality, governance, or the composition of public expenditure, all of which may influence how fiscal resources are translated into human development outcomes. Future research should incorporate these institutional dimensions and examine whether the effectiveness of central and local taxes varies across regions with different levels of fiscal capacity and governance quality. Such analyses would provide a deeper understanding of how fiscal decentralization can more effectively promote sustainable and inclusive human development.

Author Contributions

Nur Farida Liyana was responsible for data collection, data analysis, and manuscript writing. Rizqi Haniyah contributed to data collection and manuscript revision, while Arief Budi Wardana was involved in manuscript revision and proofreading.

Acknowledgements

The authors would like to express their gratitude to PKN STAN for providing the opportunity to conduct this research, as well as to all parties who contributed to the completion of this study.

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