* Corresponding author: [email protected]
Infrastructure Spending in the Characteristics of the Regions:
Towards the Achievement of Sustainable Development Goals
DIANA SULISTYOWATI*
Directorate General of Fiscal Balance, Indonesia PUJI WIBOWO
Polytechnic of State Finance STAN, Indonesia
Abstract: Indonesia is faced with challenges by the increasing infrastructure competitiveness globally. In 2019, Indonesia's competitiveness rank declined to 72nd from 141 countries compared to the previous year (World Economic Forum, 2019).
Another challenge is also depicted in achieving Sustainable Development Goals (SDGs) 9 dealing with the infrastructure as shown at two indicators that still require special attention. So far, the gaps in infrastructure development still exist in Indonesia after seeing the region's characteristics. The government also supports infrastructure development with a fiscal decentralization system through the Balancing Fund instrument. This study is aimed to analyze the effect of Owned-Source Revenues (OSR) and Balancing Fund Income on infrastructure development in Indonesia by considering mandatory infrastructure spending and aspects of regional characteristics comprising institutional status, geographical conditions, and economic structure. This study analyzes four-panel data regression modeling and the Generalized Method of Moment (GMM) method. The results showed that government intervention in mandatory infrastructure spending could moderate the influence of OSR and Balancing Fund income on infrastructure development. In testing based on regional characteristics, the impact of OSR and Specific Grant (Dana Alokasi Khusus/DAK) on infrastructure development is higher. It has a positive value in regions with better financial independence (urban, Java-Sumatra, and metropolitan). Otherwise, the influence of Block Grant (Dana Alokasi Umum/DAU) and Shared Nontax Revenues (Dana Bagi Hasil/DBH) on infrastructure development is more substantial. It has a significant positive value in regions with a lower financial independence category (district, non- Java-Sumatra, and non-metropolitan). This research also provides novelty in an analysis of mandatory spending on infrastructure. The central government can use this study's results to prioritize or maximize the potential of Subnational Governments (SNGs) in infrastructure development, and the ultimate goal is the achievement of the Infrastructure SDGs in Indonesia.
Keywords: Infrastructure, Fiscal Decentralization, Regional Characteristics, GMM
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Abstrak: Indonesia dihadapkan pada tantangan terkait peningkatan daya saing infrastruktur di dunia. Pada tahun 2019 Indonesia mengalami penurunan peringkat dari tahun sebelumnya menjadi peringkat ke-72 dari 141 negara (World Economic Forum, 2019). Tantangan lain juga tercermin dalam capaian Sustainable Development Goals (SDGs) 9 terkait infastruktur berupa 2 indikator masih memerlukan perhatian khusus. Masih terdapat kesenjangan dalam pembangunan infrastruktur di Indonesia jika dilihat dari karakteristik wilayah. Pemerintah turut mendukung pembangunan infrastruktur dengan sistem desentralisasi fiskal melalui instrumen Dana Perimbangan.
Penelitian ini bertujuan untuk menguji pengaruh dari Pendapatan Asli Daerah (PAD) dan pendapatan Dana Perimbangan terhadap pembangunan infrastruktur di Indonesia dengan mempertimbangkan mandatory spending infrastruktur dan aspek karakteristik wilayah meliputi status institusional, kondisi geografis dan struktur ekonomi. Pengujian tersebut dilakukan dengan empat pemodelan regresi data panel dan menggunakan metode Generalized Method of Moment (GMM). Hasil penelitian menunjukkan, intervensi pemerintah melalui mandatory spending infrastruktur mampu memoderasi pengaruh PAD dan pendapatan Dana Perimbangan terhadap pembangunan infarstruktur. Pada pengujian berdasarkan karakteristik wilayah, pengaruh PAD dan Dana Alokasi Khusus (DAK) terhadap pembangunan infrastruktur lebih tinggi dan bernilai positif di daerah dengan tingkat kemandirian keuangan yang lebih baik (perkotaan, Jawa-Sumatra, dan metropolitan). Sebaliknya, pengaruh Dana Alokasi Umum (DAU) dan Dana Bagi Hasil (DBH) terhadap pembangunan infrastruktur lebih tinggi dan bernilai positif pada daerah dengan kategori tingkat kemandirian keuangan yang lebih rendah (kabupaten, non Jawa-Sumatra, dan non-metropolitan). Penelitian ini turut memberikan kebaruan dalam bentuk analisis mandatory spending infrastruktur. Hasil penelitian ini dapat digunakan oleh pemerintah pusat agar membuat prioritas yang lebih baik untuk memaksimalkan potensi pemerintah daerah dalam pembangunan infrastruktur dan tujuan akhirnya adalah ketercapaian SDGs Infrastruktur di Indonesia.
Kata Kunci: Infrastruktur, Desentralisasi Fiskal, Karakteristik Daerah, GMM
1. Introduction
The infrastructure spending budget in Indonesia is increasing every year. On the main points of the Indonesian State Budget 2020, total infrastructure spending in 2017 amounted to Rp379,7 Trillion, the budget raised to Rp394 Trillion in 2018, and Rp423,3 Trillion in 2019. The infrastructure budget is increasing every year, but the competitiveness of the Indonesian infrastructure in the world still needs to be attentive.
Based on data from The Global Competitiveness Report of the World Economic Forum (WEF), the competitiveness of Indonesian infrastructure in 2017-2018 ranked 52nd of 137 countries. In 2018, there was a decline in 71st of 140 countries. Again, in 2019
331 Indonesia was ranked 72nd of 141 countries. Another challenge in developing infrastructure in Indonesia is also reflected in achieving the Sustainable Development Goals (SDGs) 9 about the industry, innovation, and infrastructure. In 2019, out of six indicators related to infrastructure on SDGs 9, four indicators of progress and two indicators still required special attention (Bappenas, 2020). Therefore, the infrastructure budget still needs to be managed better to improve the competitiveness of Indonesia's infrastructure and the achievement of the SDGs 9.
Another indicator that shows the size of the infrastructure development in Indonesia is The Economic Development Inclusive Index (Indeks Pembangunan Ekonomi Inklusif/IPEI). IPEI is a tool to measure Indonesia's national, provincial, and district/city development level. The index's components measuring infrastructure development are in pillar 1 (Growth and Development of the Economic). Assessment on pillar 1 influences the highest overall index (about 50%), while pillars 2 and 3 are 25%. Figure 1 shows that pillar 1 has the lowest value compared with the two pillars and is under the value of the IPEI average. Although it has been given a high weight, the economic infrastructure assessment components still cannot contribute to the value of the IPEI average.
Figure 1.
The Economic Development Inclusive Index per Pillar
Source: inclusif.bappenas.go.id (processed data) 5,43 5,46
5,37
5,48 5,85
6,30
6,42
6,57
5,87 5,83 5,91
6,09
5,64 5,75 5,75
5,89
5,00 5,20 5,40 5,60 5,80 6,00 6,20 6,40 6,60 6,80
2016 2017 2018 2019
PILLAR 1: Economic Growth and Development (50%)
PILLAR 2: The Income Equalization and Poverty Reduction (25%) PILLAR 3: Expanding Access and Opportunity (25%)
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Indonesia has 542 Subnational Governments (SNGs) with different characteristics, so the local level's development priorities could differ from the national level. Many infrastructures in Indonesia are still concentrated in Java and Sumatra islands (Aritenang, 2019). This phenomenon is also shown by the data IPEI at the district/city level. The translation of this index is used to compare positions between districts/cities in Indonesia.
Based on Figure 2, it can be observed that the urban SNGs have an average value of IPEI greater when compared to the district SNGs. The Java-Sumatra region has a larger IPEI compared to the non-Java-Sumatra region. The average value of the IPEI for three years in metropolitan areas is also higher compared to non-metropolitan areas.
The data may indicate that there are still gaps in the development of the characteristics of the Indonesian district.
Figure 1.
The Comparison of IPEI Based on the Characteristics of the Region
Source: inclusif.bappenas.go.id (processed data)
The achievement of SDGs targets remains a national development priority. The SDGs targets at the national level have been aligned with Indonesia's Medium-Term Development Plans (Rencana Pembangunan Jangka Menengah Nasional/RPJMN) for 2015-2019 and the RPJMN for 2020-2024 (Bappenas, 2020). National targets in the 2015-2019 RPJMN for developing and rehabilitating irrigation networks have not been achieved. Then there was the order of the Ministry of Public Works and Housing
4,50 5,00 5,50 6,00 6,50
Urban District Java Sumatra Non-Java Sumatra Metropolitan Non-Metropolitan
2019 2018 2017
333 (Pekerjaan Umum dan Perumahan Rakyat/PUPR) to strengthen the monitoring and evaluation of the implementation of infrastructure development by the regional authorities (Ministry of PUPR, 2020). The targets of the 9th SDGs in line with the 2020- 2024 policy are the advancement of Information and Communication Technology infrastructure, increased industrialization, and the strengthening of economic infrastructure through road, rail, sea, air, and land connectivity.
The development of infrastructure in Indonesia is also supported by fiscal decentralization through the allocation of the Balancing Fund. The fund consists of the block grant (DAU), Shared Nontax Revenues (DBH), and the Specific Grant (DAK).
The General Transfer Fund (Dana Transfer Umum/DTU), which consists of DAU and DBH, is allocated in the State Budget to be used by the region's authority to implement decentralization. Since Law number 18/2016 about APBN 2017 was passed, the government directed the utilization of the DTU by at least 25% for the regional infrastructure spending directly related to the acceleration of the construction of public services facilities and the economy.
The policy of mandatory spending infrastructure sourced from DTU is expected to accelerate the availability of public services to improve well-being. Table 1 shows that the percentage of SNGs from 2017 to 2019 that do not meet the policy of mandatory spending infrastructure sourced from DTU is still quite large, about 48.5%. However, the percentage (25%) might not be ideal for any SNGs because the conditions and needs of the infrastructure of each district are also different.
Table 1.
The Compliance of Mandatory Spending Infrastructure Sourced from DTU
The Compliance (25% DTU)
2017 2018 2019
SNGs %
compliance SNGs %
compliance SNGs %
compliance
already fulfilled 25% 230 42,4% 253 46,7% 354 65,3%
not fulfilled 25% 312 57,6% 289 53,3% 188 34,7%
Total 542 100% 542 100% 542 100%
Average does not fulfil
25% 48,5%
Source: DJPK-Kemenkeu RI (processed data)
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Indonesia still faces challenges in improving the infrastructure's competitiveness at the national and global levels. Increased OSR and Balancing Fund income significantly stimulate SNGs infrastructure spending. The fiscal capacity of SNGs is still limited, and the dependence on Intergovernmental Transfer (IGT) is still high. In 2019, the portion of the OSR only contributed 24.5% to the total revenue of SNGs (APBD realization 2019). The fiscal capacity of the majority of SNGs in Indonesia is still low. Based on the Minister of Finance Regulation (PMK) No. 126/PMK.07/2019, 271 SNGs (50% of the total SNGs in Indonesia) in Provincial and District/cities is low fiscal capacity and very low.
Only limited studies explore the determinants of infrastructure spending (Suhardjanto et al., 2009; Lewis and Oosterman, 2011; Lewis, 2013; Aritenang, 2019).
Such studies use variable data OSR and Balancing Fund income before 2014, so they have not covered the new policy of IGT. Then, there are some contradictions in the results of such research. Fiscal decentralization through a proxy OSR, balancing funds and other income simultaneously affects public spending (Suhardjanto et al., 2009).
However, the research results (B. Lewis, 2013) revealed that DAU less significantly affects the SNGs' capital expenditure, while DAK has a significant effect. Then, research (Aritenang, 2019) found that the balancing fund income of infrastructure spending in the districts is not uniform but varies across different regions. The differences between the DAK and DAU effects were also found when considering regency and urbanization types and size.
This study is aimed to analyze the effect of Owned-Source Revenues (OSR) and Balancing Fund Income on infrastructure development in Indonesia by considering mandatory infrastructure spending and aspects of regional characteristics comprising institutional status, geographical conditions, and economic structure. Based on the problems, the current issue, emerging challenges, and issues in previous research, the author researched the influence of OSR and Balancing Fund Income on the development of infrastructure in Indonesia and deepened the discussion by considering the region's characteristics of the SNGs. Modeling the characteristics of the region is analyzed based on three clusters: the status of the institutional (urban and municipality), geographical
335 conditions (Java/Sumatra and Non-Java/Sumatra), and the structure of the economy (the area of metropolitan and non-metropolitan).
2. Theoretical Basis and Hypothesis Development
2.1 The Theory of Fiscal Federalism and Fiscal Decentralization
Fiscal Federalism is the theory about the division of budgetary functions between levels of government with the level of decision-making centralized and decentralized, where the choice is made at each level of provision of public services and is determined by the demand of the population to fulfill the service (Oates, 1972). Decentralization is a transfer or delegation of legal authority and politics to plan, make decisions, and manage the public's interests under the central government's direction (Rondinelli, 1981). Fiscal decentralization is the delegation of responsibilities and the division of power and authority in fiscal covering aspects of revenue and expenditure (Hastuti, 2018). The function of fiscal decentralization can increase economic growth (Oates, 1993). Fiscal delegation to lower-level governments is more closely related to society.
It has more information than centralized policy formulation so that SNGs can provide truly public services needed in the region (Wibowo, 2008).
Research (Schulze and Sjahrir, 2014) states that decentralization has provided financial resources for better public services. However, the effect is not uniform because the distribution of income per capita and the capacity of SNGs to allocate such resources remains uneven. The theory of public finance states that the response of the local expenditure is high when the SNGs earn a revenue fund balance than the revenue itself (the flypaper effect). In decentralization, the IGT funds received by the SNGs significantly influence expenditure compared to the money generated by OSR (Aritenang, 2019).
2.2 The Classification of Infrastructure and the SDGs 9
There are several theories related to the classification of infrastructure.
(Shadmanov, 2015) classify the infrastructure into two main categories, namely social infrastructure and industry. According to (Palei, 2015), infrastructure consists of two elements: infrastructure that contains aspects of capital and the element of public
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interest. Conclusion: there are infrastructure assets that are so large in their capitalization but have a low intensity against the interests of the public. (Baldwin and Dixon, 2008) divides the infrastructure into three groups: machinery and equipment, buildings, and engineering structures. According to (Grigg, 1998), infrastructure is the physical system that provides a means of drainage, irrigation, transportation, buildings, and other public facilities. That is needed to meet a wide variety of basic human needs, whether it is the need the social and economic conditions.
The measurement of the quality of infrastructure in The Global Competitiveness Report issued by the World Economic Forum (WEF) is almost similar to the achievement indicators of the target of the existing infrastructure on the SDGs 9.
Similarities are found in the indicators in the field of transport infrastructure. Based on Regional Expenditure and Revenue Budget (APBD), the components of the infrastructure spending are a combination of infrastructure indicators according to the WEF and the indicator on the SDGs 9. The scope of the infrastructure used in this research is the social infrastructure or for the public needs, which is the expenditure nomenclature on APBD.
Figure 2.
The Interconnectedness of the Infrastructure Various Indicators
Source: Data processed by the author Indicators of the Global
Competitiveness Report- WEF
•Transport Infrastructure (quality and road connectivity, density and efficiency of the train service, connectivity to the airport, port efficiency and connectivity of delivery of the ship)
•Utility Infrastructure (access and quality of electricity supply, water availability and exposure to drinking water dirty)
Indicators of the SDGs 9th
•The steady conditions of national road
•the length of the toll road construction
•the number of airport
•the number of strategic port
•the length of railway lines
•the amount of the pier crossing
Infrastructure Spending in the APBD
•drinking water, buildings and water networks, irrigation
•office buildings, land building construction, sanitation
•the road, the network or the installation of electricity and telephone, bridge
•health and education infrastructure
•monument/historic buildings
337 2.3 Teori Tax-Spend Hypothesis and Spend-Tax Hypothesis
Milton Friedman conceived the theory of Tax-Spend Hypothesis in 1978 (Young, 2009), which states that income has a causal relationship that is positive toward expenditure. More (Young, 2009) also noted that spending would increase if the income tax increases. The increase in income tax will only lead to increased spending and reduce the budget deficit. This hypothesis also has another version, namely the Spend- Tax Hypothesis. This hypothesis's theory states that government spending changes will affect income (Payne, 1998). On the Spend-Tax Hypothesis, the reduction in government spending will lead to a reduction in the deficit. (Payne, 2003) Again, the income tax adjustment also determines the government spending decision.
2.4 The Development of Fiscal Decentralization and the SDGs Infrastructure in Indonesia
Changes to the fiscal decentralization policy are significant after the reform started by issuing Law Number 25 of 1999 as amended by Law No. 33 of 2004 on the Financial Balance between Central and Regional Governments. The law explained that the sources of financing for regional development from the fund balance include DBH, DAU, and DAK. In 2016, the Fund Balance was grouped into the General Transfer Fund (DTU/which consists of DAU and DBH) and Specific Grant (DAK), including DAK Physical and DAK Non-Physical.
According to (Bailey, 1999), intergovernmental transfer consists of block and specific grants. Block grants are the transfer of funds from the central government used by the government priority areas, and there are no restrictions on the central government's use. A specific grant is a type of transfer of funds already appropriated to finance particular activities according to the needs of the SNGs in line with national priorities. The central government can direct the use of block grants through the APBD.
Since 2017, the government has led the use of the block grant sourced from DTU to construct public infrastructure by at least 25% of the DTU after deducting the Village Fund Allocation (ADD) received by the region. The policy of mandatory spending infrastructure sourced from DTU can affect the use of OSR or the fiscal capacity of SNGs.
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DAK Physical and DAK Non-Physical allocated facilitate public access to essential services, and public distribution is made per field with the period distribution different. DAK Physical is allocated based on the proposal of the region's needs, which is aligned with the national priorities for the provision of infrastructure and public services. Based on the technical guidelines related to ministers, DAK Non-Physical can also be used to maintain the infrastructure. As shown in Figure 4, the realization of DAK Physical in total is lower than that of the DAK Non-Physical. Every year from 2017 to 2019, the realization of DAK Non-Physical is continuously increased. It is different from the realization of DAK Physical, which declined in 2018 but increased in 2019.
Figure 3.
Comparison of the Realization of the DAK Physical and DAK Non-Physical Year 2017-2019 (in Trillion Rupiah)
Source: DJPK-Kemenkeu RI (processed data of the author)
Fiscal decentralization on SNGs revenue in the APBD consists of the OSR, the Fund Balance income, and other legitimate local revenue. OSR on every SNGs includes local tax, local retribution, and the results of the wealth management area separated.
Table 2 shows that the OSR only contributes about 20% of total SNGs revenues. It indicates the presence of the dependence of the SNGs on the central government. Fund Balance is still the primary revenue source, more than 50% each year. From 2015 to 2019, more than 30% of DAU's revenue was realized. On the contrary, the proportion of DAK tends to be lower, at 6% to 15,3% in the same period. By type of expenditure,
62,10 58,15 64,17
105,56 114,95 122,23
- 20,00 40,00 60,00 80,00 100,00 120,00 140,00
2017 2018 2019
DAK Physical Realisation DAK Non-Physical Realisation
339 the proportion of spending on goods and services by SNGs is 20% - 25% of total expenditures, and only less than 20% of SNGs' spending is used for capital expenditure.
Table 2.
The Percentage of the Components of Revenue and Expenditure in 2015-2019
Description
Year
2015 2016 2017 2018 2019
Revenue Type 100% 100% 100% 100% 100%
Owned-Source Revenues (OSR) 23,8% 22,9% 25,4% 24,6% 24,5%
The Fund Balance 53,7% 62,5% 59,2% 59,7% 58,3%
Shared Non-Tax Revenues (DBH) 8,6% 8,8% 7,3% 8,3% 8,1%
Block Grant (DAU) 39,1% 38,4% 37,0% 36,2% 34,9%
Specific Grant (DAK) 6,1% 15,4% 15,0% 15,1% 15,3%
Other Legitimate Revenues 22,4% 14,6% 15,3% 15,7% 17,2%
Expenditure Type 100% 100% 100% 100% 100%
Personnel Spending 36,0% 35,0% 33,7% 33,7% 32,4%
Good and Service Spending 21,1% 20,8% 23,4% 24,5% 25,6%
Capital Spending 22,8% 22,0% 20,0% 18,6% 18,4%
Other Spending 20,2% 22,1% 22,9% 23,2% 23,5%
Source: DJPK Kemenkeu RI (data realization of the APBD 2015-2019) 2.5 The Level of Financial Independence of SNGs
This study analyzed the ratio of the SNGs' independence based on the region's characteristics. The purpose is to determine whether forming a specific pattern in the SNGs in each region's characteristics in using Income OSR and Fund Balance to realize infrastructure spending. DJPK – Ministry of Finance (2011) stated that the financial independence of the SNGs is calculated through the ratio of the OSR to the total income or the ratio of the intergovernmental transfer fund (IGT) to the total revenue. These two ratios have different meanings. The greater the ratio of OSR to the entire regional income, the more significant the level of regional financial independence. On the other hand, the more significant the transfer ratio, the smaller the level of regional financial independence.
Several studies discuss the connection between OSR and the Fund Balance on the level of financial independence. (Andriana, 2020) suggests that the DAU and DBH did not significantly affect the level of independence of the regions. On the contrary, DAK has a significant and positive influence on the level of independence of the regions.
Other research results (Lestari et al., 2016) and (Amalia N. and Haryanto, 2019) mention
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that the OSR significantly and positively influences financial independence. However, DAU has a significant and negative effect on financial independence.
The greater the percentage of OSR, the more significant the financial independence of the regions. Figure 5 shows the ratio between the OSR with the total income of the SNGs. The data show that the SNGs urban area has an average percentage of local independence, which is high compared with the district government. Then the SNGs area of Java-Sumatra has a level of financial independence higher than the SNGs in the non-Java-Sumatra. The SNGs area of the metropolitan also has a percentage of local independence, which is higher than local non-metropolitan.
Figure 4.
The Financial Independence Level of each Region's Characteristic
Source: Data processed by the author
2.6 Previous Research and Research Gap
Research gap in previous research, there has been no analysis related to the implementation of the latest balancing fund policies, such as mandatory infrastructure spending sourced from DTU. There are also different results in the previous study.
Based on the research (Aritenang, 2019; Suhardjanto et al., 2009) obtained the result that the OSR and the income of the Fund Balance have a positive effect on infrastructure spending, but the research (Lewis, 2013) stated that the OSR negatively affect the
0,00%
5,00%
10,00%
15,00%
20,00%
25,00%
30,00%
2017 2018 2019
341 infrastructure spending of SNGs. Further, research (Aritenang, 2019; Lewis, 2013) stated that the DTU could not stimulate SNGs infrastructure spending. The influence of the DAU on infrastructure spending is low compared with DAK because the use of DAU is significant for the operational expenditure of the SNGs (Lewis, 2013).
Related analysis based on the characteristics of the territory of the SNGs (Aritenang, 2019) mentions that there are significant differences in the development plan of the SNGs. Infrastructure spending of SNGs is unique and depends on the local policy and the vision of the head of the region. Findings (Aritenang, 2019) mention that for the SNGs located on Java Island, the influence of the OSR and DAK on infrastructure spending is lower than the SNGs located outside Java Island. The effect of DAK on infrastructure spending is high on the government of the territory of the urban areas. (Lewis, 2013) explains that the influence of DBH depends on the potential tax and natural resources of the SNGs. The study (Aritenang, 2019) found that DBH's influence on infrastructure spending is not higher in Java Island and metropolitan districts.
2.7 Hypothesis Development
The development of the hypothesis in this research is classified into three parts. In the first part (H1-H3), on the hypothesis with modeling without regional characteristics, the theoretical rationalization is described for the influence of PAD, DAU, DBH, and DAK (independent variables) on infrastructure development (dependent variable) of SNGs throughout Indonesia. This hypothesis was made to determine the impact of independent variables on the dependent variable before the connection based on the region's characteristics. The second part (H4-H5) developed a hypothesis on how the variable mandatory spending infrastructure sourced from DTU can moderate the influence of independent variables on the dependent variable. This hypothesis was designed to determine whether a variable in moderation can act as a pure moderator variable. Then the hypothesis of the third part (H6-H14), which is at the core of this research, is the modeling based on the region's characteristics. This hypothesis is arranged to know more detail how the influence of independent variables on the
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dependent variable after connected with a moderating variable on each region's characteristics (institutional, geography, and economic structure).
2.7.1 The Hypothesis in the Modeling without the Characteristics of the Region The high OSR can also enlarge the authority of the SNGs in implementing the policy autonomy that aims to improve public services and the economy of the area. One way to improve public service is to do the shopping for the benefit of the investment to be realized through capital expenditure (Solikin, 2010). The better the OSR of the SNGs, the greater the capital expenditure allocation (Ardhani, 2011). (Suhardjanto et al., 2009) also stated in their research that the OSR has a significant effect on public spending.
Therefore the hypothesis is developed on code H1.
H1: OSR has a significant positive impact on the infrastructure development of SNGs in Indonesia.
Empirical research (Holtz-Eakin et al., 1985 in Hariyanto; Adi, 2007) stated a correlation between the IGT from the central government and capital expenditure. The results of the research (Suhardjanto et al., 2009) said that there is a significant relationship between the DTU with capital spending. Therefore, the constructed hypothesis is that the DTU significantly impacts SNGs infrastructure spending on code (H2a and H2b).
H2a: DAU has a significant positive impact on the infrastructure development of SNGs in Indonesia.
H2b: DBH has a significant positive impact on the infrastructure development of SNGs in Indonesia.
The utilization of DAK is directed at developing, procuring, improving, and repairing facilities and physical infrastructure of public services. The DAK's goal was to reduce the cost of specific activities that the SNGs must bear. The utilization of DAK to realize the program is expected to improve public services (Ardhani, 2011). It indicates a relationship between DAK and the construction of the area's infrastructure, so we developed a hypothesis on code H3.
H3: DAK has a significant positive impact on the infrastructure development of SNGs in Indonesia.
343 2.7.2 The Hypothesis of the Variable Moderation
An OSR of SNGs also has the responsibility to provide good service to the community through infrastructure spending that can be budgeted in capital expenditures or maintenance expenditures in the APBD each year. The policy of mandatory spending infrastructure sourced from DTU can affect the use of OSR or the fiscal capacity of SNGs. However, mandatory spending policy can also benefit local authorities experiencing social inequality and the economy is high enough (Amir, Hidayat, and Tenrini, RH., 2013). Therefore, this study constructed a hypothesis as on code H4.
H4: Variable mandatory spending infrastructure sourced from DTU can moderate the influence of the OSR against the construction of infrastructure.
Since 2017, the Central Government, through Law No. 18 of 2016 (State Budget for the fiscal year 2017), has been directing the use of General Funds Transfer (DTU) for the construction of public infrastructure by at least 25% of the DTU received by the area after the deduction of the Village Fund Allocation (Alokasi Dana Desa/ADD). To analyze the influence of the policy of mandatory spending on this infrastructure, we developed the hypothesis on codes H5a and H5b.
H5a: Variable mandatory spending infrastructure sourced from DTU can moderate the DAU's influence on infrastructure development.
H5b: Variable mandatory spending infrastructure sourced from DTU can moderate DBH's influence on infrastructure development.
2.7.3 The Hypothesis Based on the Characteristics of the Region
When spending funded from specific grants increases or decreases, the matching funds that the SNGs must be provided are also adjusted. In such a situation, the SNGs may consider the addition or subtraction of OSR. The research results (Amalia N. and Haryanto, 2019; Lestari et al., 2016) stated that the OSR significantly and positively affects financial independence. Then, referring to Table 3, the urban area, the Java- Sumatra, and the metropolitan SNGs have an average level of financial independence higher than the district area, non-Java-Sumatra, and the non-metropolitan. (Aritenang, 2019) states that SNGs located on the islands of Java and Sumatra have a lower OSR influence on infrastructure spending than SNGs outside Java and Sumatra. However,
344
SNGs with a higher level of financial independence receive a smaller allocation of balancing funds than SNGs with a lower level of financial independence. Therefore, local governments with higher levels of financial independence need more OSR support to sustain their economies. Meanwhile, SNGs with a low level of financial independence use more IGT. Based on the theory and the data, the hypothesis can be constructed (on code H6-H8) that the SNGs of urban areas, the Java-Sumatra, and the metropolitan area need more OSR roles to support the economy.
H6: OSR has a significant positive impact on infrastructure development and is higher on the urban areas SNGs.
H7: OSR has a significant positive impact on infrastructure development and is higher on the Java-Sumatra SNGs.
H8: OSR has a significant positive impact on infrastructure development and is higher on the metropolitan SNGs.
Table 3.
The Average Level of Financial Independence of each Characteristic of the Region
Urban District Java
Sumatra
Non- Java Sumatra
Metropolitan Non- Metropolitan
2017 22,4% 10,5% 14,9% 10,5% 27,3% 10,6%
2018 20,8% 9,1% 13,5% 9,1% 25,3% 9,2%
2019 20,9% 9,1% 13,5% 9,2% 25,6% 9,3%
Average 21,3% 9,6% 13,9% 9,6% 26,1% 9,7%
Difference 11,8% 4,3% 16,4%
Source: processed by the author
The SNGs with a level of financial independence that will either get the allocation of DTU is low compared with the SNGs with a level of financial independence that is lacking. The purpose of DTU is to equitable distribution of the financial capacity of interregional (Law No 33 of 2004 on Financial Balance). Based on previous research (Andriana, 2020) stated that the DTU has no significant effect on the level of financial independence. Then the research results (Amalia N. and Haryanto, 2019) show that the DAU affects financial independence significantly and negatively. Therefore, the constructed hypothesis (code H9a-H11b) is that the SNGs with a financial independence level will lower their dependence on DTU.
345 H9a: DAU has a significant positive impact on infrastructure development and is lower on the urban areas SNGs.
H9b: DBH has a significant positive impact on infrastructure development and is lower on the urban areas SNGs.
H10a: DAU has a significant positive impact on infrastructure development and is lower on the Java-Sumatra SNGs.
H10a: DBH has a significant positive impact on infrastructure development and is lower on the Java-Sumatra SNGs.
H11a: DAU has a significant positive impact on infrastructure development and is lower on the metropolitan SNGs.
H11b: DBH has a significant positive impact on infrastructure development and is lower on the metropolitan SNGs.
With more than 500 municipalities and districts, there is a significant difference in the district's development plan. The activity of urban areas, such as manufacturing, trade, services, and hospitality, dominates the municipality. Meanwhile, the district's economic activity is driven by the rural-based, such as agriculture and agribusiness (Nurcholis 2005). So, capital expenditure for infrastructure by SNGs is unique and dependent on regional policy and the vision of the head area (Aritenang, 2019). Further, the study also stated that the influence of DAK on infrastructure spending is higher in the urban area than in the district. Based on these explanations, we developed a hypothesis as code H12.
H12: DAK has a significant positive impact on infrastructure development and is higher on the scope of SNGs urban areas.
The mapping of data APBD 2020 Semester I based on the nomenclature of activities obtained information that the SNGs in Java Island and Sumatra Island are dominant shopping increases rather than development expenditure (DJPK-Mof, 2020).
It is different with the SNGs in the outer Islands of Java and Sumatra, which are doing a lot of development expenditure rather than spending increase. Then, DAK's effect on capital expenditures is lower for regions located on Java and Sumatra islands than in areas outside Java-Sumatra Island (Aritenang, 2019). Therefore, we develop a
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hypothesis (H13) that the influence of DAK on infrastructure spending government is higher on the scope of SNGs outside Java Island and Sumatra Island.
H13: DAK has a significant positive impact on infrastructure development and is higher on the scope of SNGs outside Java-Sumatra Island.
The research results (Aritenang, 2019) show that the influence of DAK on infrastructure spending is low in metropolitan areas compared with the non- metropolitan. Metropolitan areas have more financial gain in reducing their dependence on the transfer of DAK compared to non-metropolitan. Based on these explanations, we then developed a hypothesis on code H14.
H14: DAK has a significant positive impact on infrastructure development and lowers the scope of SNGs in the category of the metropolitan area.
3. Research Methods
3.1 The Process of Data Collection
This study used the data from 508 SNGs (Regency/City Government) and one province of DKI Jakarta within three years (2017 s.d. 2019). Data for the other 33 provinces was not used because it could be redundant. Provincial Government data is a compilation of Regency/City Government data. In contrast, the Province of DKI Jakarta is still being used because there is no specific APBD on any city in the Province of DKI Jakarta. Furthermore, the metropolitan area refers to the data released by Bappenas (National Development Planning Agency) in the RPJMN (National Medium-Term Development Plans) 2015-2019 and is updated on the RPJMN 2020-2024. The secondary data was obtained from the Directorate General of fiscal Balance (Direktorat Jenderal Perimbangan Keuangan/DJPK). Such data includes the realization of the APBD, data infrastructure spending of SNGs, and budget data Mandatory Spending Infrastructure of the fiscal Year 2017-2019. The data on Gross Regional Domestic Product (GRDP) and the population in 2017-2019 per region were obtained from the Central Bureau of Statistics.
347 3.2 Operationalization of Variables
This study used three dummy variables for modeling the regression test based on the region's characteristics. There are three types of dummy variables, the first is a cluster of urban/district, the second is a cluster of SNGs of Java-Sumatra and Non-Java- Sumatra, and the third cluster is SNGs of metropolitan and non-metropolitan. The variables in this study consist of an independent variable, dependent variable, moderating variable, control variable, and dummy variables.
Table 4.
Summary of the Proxy Variable
Type Variable Proxy
Dependent Variable
Infrastructure spending of SNGs BLJ_INFRA = The infrastructure spending realization
Independent Variable
Owned-Source Revenues (OSR) OSR = The OSR realization/Population General Purpose Grant (DAU and
DBH)
DAU= The DAU realization/Population DBH DBH= The DBH realization/Population Specific Grant (DAK) DAK= The DAK realization/Population Moderating
Variable
Infrastructure Mandatory Spending MDT_INFRA = The allocation of
Infrastructure Mandatory Spending/population Control
Variable
Gross Regional Domestic Product (GRDP)
GRDP = GRDP/ population
Population Density Pop_Density = Ln (Population Density) Labor Force Participation Rate
(LFPR)
LFPR = Ln (LFPR) Source: Data is processed from various studies
3.3 Methods of Data Analysis
The data analysis used in this research is quantitative and uses E-Views 9. This research regression model uses the Generalized Method of Moment (GMM) to test the influence of independent variables on the dependent variable. GMM is used because there is an assumption about the correlation between the residual (error term) value and the dependent variable's lag (Ekananda, 2016). Then (Aritenang, 2019) mentioned that the method of GMM is used because one or more independent variables correlate with the value of the residual (error term) that causes endogeneity. For consideration in selecting the model GMM, this research tests endogeneity. The test results show that the probability of a lag between the dependent variable and the independent variable (DAU, DBH, and DAK) has a significant value because it is less than the value of α (0.05). Only variables of the OSR do not have a significant relationship with the value
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of the residual. It can also be seen that the value of the probability of the F-statistic is also significant (0,0000). Therefore, it can be concluded that the equation using the Ordinary Least Square (OLS) generally occurs an endogeneity problem. GMM was selected because Instrument Variables in the general estimator in this model can overcome endogeneity problems (Ekananda M., 2016).
The next testing model GMM is to test the significance of the parameters (Wald Test) and Test Sargan. Wald test is used to determine whether there is a relationship in the model or simultaneously test the regression model's significance (Damaliana, A.T.
& Setiawan, 2016). The results show that the value of the F-statistic Wald Test is obtained by 153,4923 and the value of p-value 0,0000. The value of α used in this study amounted to 0.05, so at least one variable is significant to the model. Test Sargan is necessary to determine the validity of instrument variables (Damaliana, A.T., and Setiawan, 2016) where the amount exceeds the parameters estimated (the conditions of the overidentifying restriction). The study further explained that in addition to testing the validity of instrument variables, Test Sargan could also be used to see whether the residual data experience heteroscedasticity. To analyze the Sargan Test, the value of the J-Statistic (Sargan Statistic) and the value of the instrument rank detail the estimation results of the model GMM (Ekananda M., 2016). Statistics Sargan calculated through the statistical chi-square c(p-k), where p is the instrument rank and k is the estimated number of parameters. P-value calculated using Microsoft Excel with the formula
"=CHISQ.DIST.RT(the value of the J-statistic;p-k]). The calculation of the p-value is as follows: the J-statistic = 25,12025, the value of (p-k) = 524-9 = 515. The numbers inputted into the formula are "=CHISQ.DIST.RT(25,12025; 515) = 1,0000. Since the obtained p-value of 1,0000, where the value is greater than 0.05 (the value of α), the problem does not occur heteroscedasticity in the model estimation of the GMM.
3.4 Research Model
This study refers to the model of the previous research regarding the influence of local revenue and fund balance against infrastructure spending government area of research (Aritenang, 2019; Blane Lewis, 2013). Then this study added analysis related
349 to implementing the fund balance policies of the latest mandatory spending infrastructure sourced from DTU. The framework of this study is shown in Figure 6.
Figure 6.
Research Framework
Source: processed by the author
The research models used by the author are as follows:
Model 1: GMM method for analyzing the influence of OSR and Fund Balance on SNGs' infrastructure development throughout Indonesia.
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Blj_INFRAit = α + β1 OSRit + β1(OSRit x MDT_INFRA it)+ β2 DAUit + β2(DAUit x MDT_INFRAit) + β3 DBHit + β3(DBHit x MDT_INFRAit) + β4 DAKit + β5 MDT_INFRA it + γ1 Zit + γ2 Blj_INFRAit-1 + ε it
Description:
BLJ_INFRA i,t = the construction of the infrastructure of the region i in year t
OSR i,t = the realization of OSR of SNGs i in the year t DAU i,t = the realization of DAU of SNGs i in the year t DBH i,t = the realization of DBH of SNGs i in the year t DAK i,t = the realization of DAK of SNGs i in year t
OSR i,t × MDT_INFRA i,t = moderating variable realization of the OSR against the mandatory spending the infrastructure of the region i in year t
DAU i,t × MDT_INFRA i,t = moderating variable realization of the DAU against the mandatory spending the infrastructure of the region i in year t
DBH i,t × MDT_INFRA i,t = moderating variable realization of the DBH against the mandatory spending the infrastructure of the region i in year t
Z i,t = control variables socio-economy region i in year t β1 – β5 = the regression coefficient of independent variables
α = intercept /constant value
γ1 = the coefficients of the control variables
γ2 = the coefficient of the lag of the dependent variable
ε i,t = error region i in year t
Model 2 to 4: To analyze the influence of OSR and Fund Balance of spending on the infrastructure of the SNGs based on the region's characteristics.
Blj_INFRAit = α + β1 OSRit + β1 OSRit x DXit + β1(OSRitx MDT_INFRAit) + β1 (OSRit x MDT_INFRAit)x DXit + β2 DAUit + β2 DAUit x DXit + β2(DAUit x MDT_INFRAit) + β2
351 (DAUit x MDT_INFRAit) x DXit + β3 DBHit + β3 DBHit x DXit + β3(DBHit x MDT_INFRAit) + β3 (DBHit x MDT_INFRAit)x Dit + β4 DAKit + β4 DAKit x Dit + β5
MDT_INFRAit + γ1 Zit + γ2 Blj_INFRAit-1 + ε it Description of dummy variable (DX):
Variable D1/ Model 2: testing based on the status of institutional/ urban and district (D1 = 1 to the territory of urban and D1 =0 to the territory of the district).
Variable D2/ Model 3: testing based on the geographical conditions/ Java-Sumatra and Non-Java-Sumatra (D2 = 1 for Java-Sumatra and D2 = 0 for Non-Java- Sumatra).
Variable D3/ Model 4: testing based on the structure of the economy/ the metropolitan and non-metropolitan (D3 = 1 for the metropolitan region and D3 = 0 for the region of non-metropolitan).
4. Results and Discussion
4.1 Testing the Research Variables without the Characteristics of the Region 4.1.1 Test Results GMM
Based on Table 5, the variable OSR has a probability value of 0,3470. With a significance level of 5% (95% confidence), it can be concluded that the variable OSR in the regression model for the region of SNGs in Indonesia has no significant effect on the variables of infrastructure spending. It means that hypothesis H1 is rejected. Then the variables DAU and DBH have a probability value of 0,0000. With a significance level of 5% (95% confidence). It can be concluded that the variable DAU and DBH in the regression model for the region of SNGs in Indonesia can have a significant effect on the variable infrastructure spending. These results are consistent with hypotheses H2a and H2b, so the hypothesis is accepted. Then the variable DAK has a probability value of 0,0000. With a significance level of 5% (95% confidence), it can be concluded that the variables of the DAK in the regression model for the region of SNGs in Indonesia can significantly affect the variable infrastructure spending. However, DAK has a coefficient of -0,5135. Because DAK has a negative value, then hypothesis H3 is rejected.
352 Table 5.
Coefficient and the Value of the Probability-t on the Model 1 Variable Generalized Method of Moment
Coefficient t-Prob.
OSR -0,0694 0,3470
DAU 0,9261 0,0000
DBH 0,7109 0,0000
DAK -0,5135 0,0000
MDT_INFRA 0,0625 0,2844
MV_PAD 7,81E-07 0,0000
MV_DAU -3,66E-08 0,0000
MV_DBH -3,14E-08 0,0283
Source: Processed data from E-Views 9 4.1.2 Testing Moderating Variable
One of the methods to identify the presence or absence of the moderator variable is to test the Moderated Regression Analysis (MRA). According to (Ghozali, 2016), the test of interaction, commonly referred to as MRA, is a particular method used in multiple linear regression. The regression equation contains elements of interaction (multiplication between the moderating variable with multiple independent variables).
The model analysis of MRA in this study is expressed in two forms of the equation as follows:
Equation 1 (before there is a moderating variable):
Blj_INFRAit = α + β1 OSR it + β2 DAU it + β3 DBH it + β4 DAK it + β5 Z it + ε i,t Persamaan 2 (setelah terdapat variabel moderasi):
B_INFRAit = α + β1 OSR it + β2 DAU it + β3 DBH it + β4 DAK it + β5 MDT_INFRA it + β6 (OSR it × MDT_INFRA it) + β7 (DAU it × MDT_INFRA it) + β8 (DBH it × MDT_INFRA it) + β9 Z it + ε it
The interaction of a moderating variable (MDT_INFRA) with independent variables (OSR, DAU, DBH) in Table 5 is symbolized by MV_OSR, MV_DAU, and MV_DBH. From the results of the testing of the model, equations are listed in the table, with a significance level of 5% (95% confidence), it is known that the interactions between the OSR, DAU, and DBH with variable mandatory spending infrastructure sourced from DTU significant effect on the realization of infrastructure spending.
353 Based on Table 6 (adjustments on the value of t-Test), Adjusted R-Squared equation 1 by 95,29% and Adjusted R-Squared equation 2 by 95,68%. It is known that an increase in Adjusted R-Squared after a moderating variable is inserted. In equation 2, the variable MDT_INFRA has a coefficient of 0,062541 and a probability value of 0,2844. The probability value is greater than the value of α (0.05) so that it does not affect the independent variable (the realization of infrastructure spending). Variable OSR was originally not significant in equations 1 and 2, the change becoming significant after connecting with a moderating variable. Then the probability value of the moderating variable interaction other than DAU and DBH also showed a considerable value.
Table 6.
The Results of the Regression Test MRA with the Method of GMM
Variable Equation 1 Equation 2
Coefficient Prob. Coefficient Prob.
OSR 0,320949 0,0552 -0,069376 0,3470
DAU 0,786550 0,0000 0,926126 0,0000
DBH 0,692915 0,0000 0,710898 0,0000
DAK -0,525640 0,0000 -0,513548 0,0000
MDT_INFRA - - 0,062541 0,2844
MV_PAD - - 7,81E-07 0,0000
MV_DAU - - -3,66E-08 0,0000
MV_DBH - - -3,14E-08 0,0283
R-Squared 0,976812 0,978894
Adj. R-Squared 0,952930 0,956811
J-Statistic 25,12025 60,00082
Prob. (J-Statistic) 0,000722 0,000000
Source: processed Data the Author of the Application of E-Views
Through the test results of the MRA, the moderating variable does not directly affect the independent variable. However, the results of the interaction of a moderating variable with the independent variables related to having a significant effect. If referring to (Sharma et al., 1981 in Ghozali, 2016) associated with the grouping of the moderator variable, it can be concluded that the moderating variable in this study is classified as a pure moderator. This result means that hypotheses H4, H5a, and H5b are acceptable.
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4.2 The Results of the Testing Variable OSR Based on the Characteristics of the Region
Value probability t-Test results (adapted for testing a one-tailed) variable OSR based on the region's characteristics are shown in Table 7. Numbers with green highlights show the value of the significant probability of statistic t. The influence of variable OSR against infrastructure spending before it is given the interaction effect with the dummy variable or a moderating variable has a value of the probability of significant characteristics of the region D2 (Java-Sumatra and Non-Java-Sumatra).
However, the t-Test value on the region's characteristic D1 (cities and districts) and the characteristics of the region D3 (metropolitan and non-metropolitan) do not show significant results. Nevertheless, after a variable OSR is connected with the moderating variable, the probability statistic-t value in each SNGs' characteristic becomes significant. Likewise, after the variable OSR is given the effect of the interaction with the dummy variable, the value of the probability statistic t in the SNGs code of D1 and D2 has significant results. Only SNGs with the D3 has a probability value of the t-Test that is not significant (hence hypothesis H8 is rejected).
Table 7.
The Value of t-Test Variable OSR Based on the Characteristics of the Region
Independent Variable OSR
Dummy Variable Code D1 D2 D3
Region's Characteristics Urban Districts JS Non-
JS Met Non Met Independent Variable t-prob. 0,0564 0,0283 0,3141 Interaction of Moderating Variable
with Independent Variable t-prob. 0,0000 0,0000 0,0000 Dummy
Variable Effect t-
prob.
a.
Interaction of Dummy Variable with Independent Variable
0,0005 0,0045 0,3060 b. Interaction of Dummy
Variable with Moderating Variable
0,0000 0,0001 0,1878
Source: Processed data by the author
Based on Table 8 regression results variable OSR against infrastructure spending on the region's characteristics with the dummy D1, the SNGs of urban areas have a larger coefficient and a positive value when compared to the SNGs of the district, which have a negative value (hence the hypothesis H6 is accepted). Then on the characteristics
355 of the territory of the SNGs with the dummy D2, the value of the coefficient of the variable OSR is large and positive in the SNGs area of Java-Sumatra compared with SNGs outside the region of Java-Sumatra (mean hypothesis H7 is accepted). The regression variables OSR against infrastructure spending on the region's characteristics with the dummy D3, the SNGs of metropolitan has a large coefficient and positive value if compared with the SNGs of the non-metropolitan area, which have a negative coefficient.
Table 8.
The Coefficient of the Variable OSR Base on the Characteristics of the Region
Description OSR
D1 D2 D3
A Independent Variable -0,3196 -0,4065 -0,1587
B Interaction of Moderating Variable with Independent Variable
0,0000 0,0000 0,0000
C Total (A+B) -0,3196 -0,4065 -0,1587
D Dummy Variable Effect
Region's Characteristics Urban Districts JS Non- JS
Met Non- Met Da Interaction of
Dummy Variable with Independent Variable
1,5902 0,0000 1,1612 0,0000 0,3338 0,0000
Db Interaction of Dummy Variable with Moderating Variable
0,0000 0,0000 0,0000 0,0000 0,0000 0,0000
E Total Dummy Var. Effect (Da+Db) 1,2706 -0,3196 0,7547 -0,407 0,1751 -0,1587 Source: Data is processed by the author
4.3 The Results of the Testing Variable DTU Based on the Characteristics of the Region
Value probability t-Test results (it has been adapted for testing a one-tailed) variable DTU based on the region's characteristics shown in Table 9. Numbers with green highlights show the value of the significant probability of statistic t. The table shows that the p-value variables DAU and DBH have the same pattern. The influence of variable DTU against infrastructure spending before it is given the effect of the interaction with the dummy variable or a moderating variable has a significant probability value in each of the region's characteristics. It is similar to the results of the t-Test variable DTU for general modeling throughout Indonesia. Likewise, after the
