metadata: authors: 0: author: Dita,Astrid
1: author: Qibthiyyah, Riatu
2: author: Shah,Anwar M.
count: Indonesia
docna: 0: docna: General purpose central-provincial-local
            transfers (DAU) in Indonesia : from gap filling to ensuring
            fair access to essential public services for all
docty: Policy Research Working Paper
subtopic: Regional Governance,Urban Housing and Land Settlements,Mining & Extractive Industry (Non-Energy),Public Sector Administrative & Civil
            Service Reform,Urban Governance and Management,Urban Housing,Social Accountability,Municipal Management and Reform,Democratic Government,Local Government,Public Sector Administrative and Civil
            Service Reform,De Facto Governments
teratopic: Governance,Public Sector Development,Macroeconomics and Economic Growth,Finance and Financial Sector Development
abstracts: cdata!: Indonesia has come a long way from
            centralized governance to decentralized local governance,
            and today Indonesia ranks among the most decentralized
            developing countries. The Government of Indonesia is
            revisiting all aspects of local governance to make
            appropriate legal and institutional adjustments based on
            lessons leaarned during the past decade. An important area
            of this re-examination and possible reform is the central
            financing of subnational expenditures. The system of
            intergovernmental finance represents one of the most complex
            systems ever implemented by any government in the world. The
            system is primarily focused on a gap-filling approach to
            provincial-local finance in an objective manner to ensure
            revenue adequacy and local autonomy but without
            accountability to local residents for service delivery
            performance. This paper takes a closer look at Dana Alokasi
            Umum -- the most dominant program of unconditional central
            transfers to finance provincial-local government
            expenditures in Indonesia. The paper also presents
            illustrative simulations of alternative programs and
            compares these with the existing Dana Alokasi Umum
            allocations. The paper concludes that super complexity leads
            to lack of transparency, inequity, and uncertainty in
            allocation. Simpler alternatives are available that have the
            potential to address autonomy and equity objectives while
            also enhancing efficiency and citizen-based accountability.
            Such alternatives would represent a move away from the
            complex gap-filling approach to simple output-based
            transfers to finance operating expenditures. Capital grants
            would deal with infrastructure deficiencies. And the
            alternatives would institute fiscal capacity equalization as
            a residual program with an explicit standard to ensure that
            all local jurisdictions have adequate means to deliver
            reasonably comparable levels of public services at
            reasonably comparable levels of tax burdens across the country.
geo_reg_key: 517198,517191
repnme: repnme: General purpose central-provincial-local
            transfers (DAU) in Indonesia : from gap filling to ensuring
            fair access to essential public services for all
display_title: General purpose
            central-provincial-local transfers (DAU) in Indonesia : from
            gap filling to ensuring fair access to essential public
            services for all
listing_relative_url: /research/2012/06/16338364/general-purpose-central-provincial-local-transfers-dau-indonesia-gap-filling-ensuring-fair-access-essential-public-services-all
topicv3: Local Governance m1326016 737,Mining Services m1326410 1701,Mining Sector m1327113 554,Land and Housing m1327648 817,Public Administration m1327723 723
publishtoextweb_dt: 2012-06-06T00:00:00Z
historic_topic: Governance,Public Sector Development,Macroeconomics and Economic Growth,Finance and Financial Sector Development
no_of_pages: 36
author_keys: 0: author_key: 1165032
1: author_key: 93058
model_extractions: WPS6075 Policy Research Working Paper 6075 General Purpose Central-provincial-local Transfers (DAU) in Indonesia From Gap Filling to Ensuring Fair Access to Essential Public Services for All _Anwar Shah Riatu Qibthiyyah Astrid Dita_ The World Bank Poverty Reduction and Economic Management Unit World Bank Office, Jakarta June 2012
Policy Research Working Paper 6075

# **Abstract**

Indonesia has come a long way from centralized governance to decentralized local governance, and today Indonesia ranks among the most decentralized developing countries. The Government of Indonesia is revisiting all aspects of local governance to make appropriate legal and institutional adjustments based on lessons leaarned during the past decade. An important area of this re-examination and possible reform is the central financing of subnational expenditures. The system of intergovernmental finance represents one of the most complex systems ever implemented by any government in the world. The system is primarily focused on a gapfilling approach to provincial-local finance in an objective manner to ensure revenue adequacy and local autonomy but without accountability to local residents for service delivery performance. This paper takes a closer look at _Dana Alokasi Umum_ —the most dominant program of unconditional central transfers to finance provincial-local government expenditures in Indonesia. The paper also presents illustrative simulations of alternative programs and compares these with the existing _Dana Alokasi Umum_ allocations.

The paper concludes that super complexity leads to lack of transparency, inequity, and uncertainty in allocation. Simpler alternatives are available that have the potential to address autonomy and equity objectives while also enhancing efficiency and citizen-based accountability. Such alternatives would represent a move away from the complex gap-filling approach to simple output-based transfers to finance operating expenditures. Capital grants would deal with infrastructure deficiencies. And the alternatives would institute fiscal capacity equalization as a residual program with an explicit standard to ensure that all local jurisdictions have adequate means to deliver reasonably comparable levels of public services at reasonably comparable levels of tax burdens across the country.

This paper is a product of the Poverty Reduction and Economic Management Unit, World Bank Office, Jakarta. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at shah.anwar@gmail.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **General Purpose Central-provincial-local Transfers (DAU) in Indonesia: From Gap Filling to Ensuring Fair Access to Essential Public Services for All**

_Anwar Shah (World Bank, SWUFE,China and ADB)_ , Riatu Qibthiyyah ( University of Indonesia) and Astrid Dita (University of Indonesia)

**Key words:** grants, fiscal transfers, fairness, equalization, public services, provinciallocal finances, decentralization, results based accountability, incentives, Indonesia reforms

**Sector Boards** : EP, PSG, PR, ARD, URBAN, INFRASTRUCTURE, SOCIAL, SDN, HDN
# **General Purpose Central-provincial-local Transfers (DAU) in Indonesia: From Gap Filling to Ensuring Fair Access to Essential Public Services for All**

Anwar Shah<sup>1</sup> (Center for Public Economics, China, World Bank and the Asian Development Bank), Riatu Qibthiyyah (University of Indonesia) and Astrid Dita (University of Indonesia)

## _17 April 2012_

## **1. Introduction**

Provincial and local government reforms continue to dominate the national policy agenda today in Indonesia inspite of the sweeping legislative and administrative changes affecting the organization, functions and finance of sub-national government undetaken since 1999. During the past decade, Indonesia has come a long way from a centralized governance to decentralized local governance. By the big bang in 2001, Indonesia has leap-frogged to the community of decentralized nations, and today Indonesia ranks among the most decentralized developing countries (see Ivanyna and Shah, 2011; see Figure 1). With this transformation, the Central Government still accounts for 91% of revenue collection and 64% of direct spending (2011 figures). Provinces and local governments account for 9% of revenue collection and 36% of expenditures. Provincial governments are relatively less important than local governments and command only 9% of national expenditures. Local government expenditure share (27%) in Indonesia compares favorably with most developing and industrial countries (see Figure 2).

> 1This paper was presented at the World Bank/GOI-MOF  Conference on  ‘Alternative Visions for Decentralization in Indonesia’  held at Jakarta from March 12-113, 2012 and the Asian Development Bank/GOI-MOF workshop on Intergovernmental Finance in Indonesia from April 4-5, 2012 in Bali.  The authors are grateful to  Pak Dr. Marwanto Harjowiryono, Pak Dr. Heru Subiyantoro, Pak Putut, Ibu Erny Murniasih, Pak Aditya Nuryuslam of the Ministry of Finance , Indonesia, Wismana Adi Suryabrata, BAPPENAS, Raksaka Mahi, University of Indonesia, Gutat Setyo Tamtomo Yudo Baroto, MOHA, Juan Luis Gomez, ADB  and Blane Lewis, University of Singapore, Shubham Chauduri, William Wallace, Anna Gueorguieve, Pak Daan , World Bank and participants of the Bali workshop for helpful comments. The views presented here are those of the authors alone and may not be attributed to the institutions they represent. Comments may please be addressed to Anwar Shah (shah.anwar@gmail.com).

.

2
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paper takes a closer look at the most dominant program of central transfers to finance provincial-local government expenditures in Indonesia.

The paper concludes that super complexity leads to a lack of transparency, inequity and uncertainty in allocation. Simpler alternatives are available that have the potential to address equity objectives while also enhancing efficiency and citizen-based accountability. Such alternatives would represent a move away from complex gap filling and special allocation approaches to simple output based transfers to finance operating expenditures, complemented by capital grants to deal with infrastructure deficiencies and instituting fiscal capacity equalization as a residual program with an explicit standard to ensure that all local jurisdictions have adequate means to deliver reasonably comparable levels of public services at reasonably comparable levels of tax burdens across the country. The paper argues that such an alternative system of intergovernmental finance would preserve autonomy, while enhancing equity, simplicity, objectivity, transparency and accountability.

The rest of the paper is organized as follows. Section 2 provides a bird’s eye view of central transfers to sub-national governments. Section 3 presents a critical review of general allocation transfers ( _Dana Alokasi Umum_ —DAU). Section 4 presents simulations of alternative approaches to such transfers. Section 5 discusses the merits and demerits of a popular proposal to set up an independent grants commission. A final section presents concluding remarks and presents a forward-looking view about the reform of intergovernmental finances in Indonesia.

# **2. Central Transfers to Finance Provincial-Local Public Services in Indonesia – A Review**

_Central transfers to provincial-local governments in Indonesia—a brief synopsis_ Central transfers are the most important source of revenues for sub-national governments in Indonesia. These financed 90% of sub-national governments, 54% of provincial, 86% of cities and 93% of districts expenditures in 2010. Major transfers (Balance Grants or _Dana Perimbangan_ ) to finance provincial and local expenditures are as follows.

**Table 1.** **<u>Central-Provincial/Local Transfers in Indonesia (2010)</u>**

|**Transfer**|**Share of total transfer in**<br>**2010**|**Share of sub-national expenditures in**<br>**2010**|
|---|---|---|
|**Tax sharing**|25%|20%|
|**Gap filling (DAU)**|56%|46%|
|**Special Allocation Grant**<br>**(DAK)**|6%|5%|
|**Other specificpurpose**|13%|10%|
|**All**|100%|90% (Provinces: 54%; Cities: 86% and<br>Districts: 93%)|

Source: Ministry of Finance, Indonesia _Tax by tax sharing (Dana Bagi Hasil—_ DBH _)_ . Central Government collects taxes on personal income, property, and renewable aand non-renewable natural resources and returns by origin a pre-defined share of the revenues to the originating jurisdiction. These transfers accounted for 25% of total central ransfers in 2010 and financed 20% of sub-national expenditures.

4
_Transfers to deal with vertical and horizontal fiscal gaps_ . Central government provides a basic allocation for wages and salaries and a fiscal gap transfer ( _Dana Alokasi Umum_ or DAU) if a jurisdiction’s revenues fall short of calculated expenditure needs using macro indicators. These transfers accounted for 56% of total central transfers and financed 46% of sub-national expenditures.

_Specific Purpose Grants_ . These grants include the Special Allocation Grant ( _Dana Alokasi Khusus_ or DAK), Special Autonomy grants for Aceh, Papua and Papua Barat, Adjustment Fund compensation, and Special Incentives Grants ( _Dana Insentif Daerah_ or DID) and _Hibah_ . DAK is intended to influence local government spending on areas of national priority. It accounts for 6% of central transfers and finances 5% of sub-national expenditures. Adjustment Fund compensation ( _Dana Penyesuaian_ or DP) provides special ad-hoc assistance e.g. for school operational assistance (BOS), allowances for certified teachers etc. Special Autonomy grants ( _Dana Otonomi Khusus_ or DOK) are intended to provide special and preferential support to Aceh and Papua provinces. DID is a small grant program accounting for less than 1% of total transfers and are granted to better performing provinces and cities on public financial management, tax effort, having higher HDI relative to fiscal capacity, higher economic growth, higher reductions in poverty, unemployment and inflation. _Hibah_ transfers are primarily financed by external assistance and are intended to finance sub-national infrastructure and social development expenditures. Specific purpose transfers in total accounted for 19% of central transfers in 2010 and financed 15% of sub-national expenditures (see Qibthiyyah, 2011).

The following paragraphs present a review of the general-purpose transfer (DAU).

# **3. The General Purpose Gap Filling Transfer—** **_Dana Alokasi Umum_ (DAU): An Introductory Overview**

The general-purpose unconditional transfers, DAU, constitute the dominant sources of revenues for provincial and local governments in Indonesia. As part of the DAU transfers, the Central Government of Indonesia provides a basic allocation for wages and salaries and a fiscal gap transferif a jurisdiction’s revenues fall short of calculated expenditure needs using macro indicators. These transfers accounted for 56% of total central transfers and financed 46% of sub-national expenditures in 2010.

These transfers according to Law 33 (2004) are intended to balance revenue means with expenditure needs for sub-national governments providing central financing in ―proportionate, democratic, fair and transparent manner‖ by taking into account ―local potential (fiscal capacity) and conditions and local needs‖. Total pool of these transfers is arbitrarily set at of 26% of central revenues net of tax sharing transfers in 2011. The 20% of the total pool is allocated to provinces and the remaining 80% to all cities and districts. The DAU provides a basic allocation to cover wages of provinces cities and districts. The remaining funds are allocated by formula that determines fiscal gap based upon the differences between fiscal needs and fiscal capacity. Formula factors for both provinces and cities are the same but receive differential weights due to the peculiar application to DAU allocation of the weighted coefficient of variation- the so called Williamson’s Index.

Fiscal capacity of a province is determined by summing up 50% of own source revenues, 80% of non-resource tax sharing and 95% of resource and mining tax sharing. Fiscal capacity of a city or district government on the other hand is based upon 93% of own source revenues, 100% of non-resource tax revenue sharing and 63% of resources and mining tax revenue sharing.

5
DAU = Basic Allocation + Fiscal Gap (Fiscal needs minus Fiscal Capacity)

|~~es se~~<br>~~Cn~~<br>~~Cn~~<br>~~rs se~~<br><br><br>|
|---|
|~~ee~~<br>~~ee ee~~<br>~~ee~~|
|~~Ce~~<br>~~PC~~<br>|
|~~rs se~~<br>~~Rr es~~|
|**Cities/Districts**|**DAU 2006**|**DAU 2007**|**DAU 2008**|**DAU 2009**|**DAU 2010**|**DAU 2011**|
|---|---|---|---|---|---|---|
|**DAU variable weight**<br>**Fiscal Need Variables**<br>|||||||
|**Index of population**|30.00%|30.00%|30.00%|30.00%|30.00%|30.00%|
|**Index of area**|15.00%|15.00%|15.00%|15.00%|13.25%|13.50%|
|**Index of construction**<br>**price index**|30.00%|30.00%|30.00%|30.00%|30.00%|30.00%|
|**Index of inverse of HDI**|10.00%|10.00%|10.00%|10.00%|11.00%|10.00%|
|**Index of inverse per**<br>**capita GRDP**|15.00%|15.00%|15.00%|15.00%|15.75%|16.50%|
|**Fiscal Capacity Variables**<br>|<br>||||||
|**Own source revenue**|100.00%|75.00%|75.00%|70.00%|93.00%|93.00%|
|**Tax revenue sharing**|100.00%|75.00%|75.00%|73.25%|100.00%|80.00%|
|**Resource revenue**<br>**sharing**|100.00%|50.00%|50.00%|100.00%|100.00%|63.00%|

Source: Ministry of Finance, Government of Indonesia

# **DAU – An Evaluation**

In the interest of tax harmonization, limiting differentials in sub-national fiscal capacities and lowering tax administration costs, Indonesia has adopted a highly centralized tax system with the Central Government raising 91% of total revenues. This creates a large vertical fiscal gap (almost 90%) that is filled by revenue sharing and transfers. Revenue sharing by origin while reducing vertical fiscal gap accentuates horizontal fiscal inequalities. DAU is the foremost program to bridge horizontal inequities. It is an objective formula based transfer program that partially compensates for civil service wages and partially tries to limit the differentials the fiscal capacity across jurisdictions by focusing on reducing the variations in regional allocation of transfers as measured by the weighted coefficient of variation. This results in reducing overall inequality in fiscal capacities and some redistribution of income across provinces and cities and districts as shown by Eckardt and Shah (2007). However, the current program has a number of important limitations.

_One size fits all approach leads to fiscal inequity._ The foremost concern is that the program equalizes jurisdictions with widely dissimilar responsibilities and characteristics. This is especially true when you group metropolitan areas, cities of varying population sizes and rural municipalities or districts of varying geographical areas as done under the current program. This violates the most fundamental dictum of transfers that ―one size does not fit all‖. The Constitutional Court of Indonesia in South Sulawesi case had earlier ruled that, ―Uniform treatment of different entities causes injustice‖. Indeed it would be a travesty of justice to consider that a small town with a tiny population such as Kabupaten Puncak has the similar fiscal needs and capacities as a large city like Kota Bandung or for that matter a small district in terms of area, Tangaran has similar revenue means and expenditure needs as a large district of Tangerang. The existing program ignores the fiscal capacity or fiscal need differentials of various size and class of municipalities and assumes that they all have equal per capita needs and revenue means other than from those factors explicitly considered in the formula. If one examines local finance in other countries, there are wide justifiable variations in per capita revenues and spending across various size and urban/rural class of municipalities in view of the diversity of needs and preferences and responsibilities. Equalizing unequals leads to injustice 7
for all. One cannot possibly have the same standard and access and diversity of services in a small remote district as opposed to a large city.

_A complex and opaque standard of equalization_ . A second important concern has to do with the choice of the Williamson’s index as the equalization standard. Most industrial countries adopt a simple, transparent but an explicit standard to reach a broad political and social consensus on overall amount of equalization payments (see Table 4). This is important because equalization programs can have important efficiency and equity tradeoffs. An excessive standard of equalization can lead to adverse impacts on growth just as too little equalization can create potential for succession. While equalization standards vary in terms of their relative emphasis on fiscal capacity versus fiscal need equalization, all bear some affinity to providing reasonably comparable levels of public services at reasonably comparable levels of tax burdens across all jurisdictions to ensure a common political and economic union. The central focus of an equalization program is to help disadvantaged jurisdictions have comparable public service standards to allow them to integrate with the wider economy. Indonesia is unique in selecting complex statistical criterion—the weighted coefficient of variation or the Williamson’s index— as the equalization standard. This choice is unfortunate as it introduces complexity and lack of transparency in the allocation criteria. Further the Williamson’s index has relatively greater sensitivity to outliers. For example, it is possible to redistribute income among the two top quintiles and have a lower value of the Williamson’s index while there may be no significant redistribution to the poorest quintile. Its use in determining factor weights is particularly worrisome as multiple distributions of component weights can yield the same index. The use of this index in determining factor weight introduces uncertainty and inequity in allocations as without any material changes in need and capacity factors, changes in weights alters the allocation of transfers across jurisdictions. In a developing country context, complexity is sometimes cited as a way to hold the politics at bay as policy makers may not fully comprehend the limitations of a complex design and may hold fire. The Indonesia program cannot be justified on this basis as Table 3 shows that while policy makers may not understand the working of such an index, they have not refrained from forcing the choice of higher variations in inequality in a subsequent year if the resulting allocations lead to more satisfactory outcomes for their jurisdictions of interest.

**Table 4. The** **<u>Choice of An Equalization Standard: Indonesia in the International Context</u>**

|_Standard_<br>_of_<br>_Equalization_|_None_<br>_(general_<br>_revenue sharing)_|<br>_National_<br>_Average_<br>_(Per Capita)_|_Complex_<br>_criteria_|_statistical_|
|---|---|---|---|---|
|Determines<br>grant<br>pool|||||
|Determines allocation<br>to jurisdictions|UK<br>and<br>most<br>developing countries<br>e.g.<br>Brazil,<br>India,<br>Thailand|Australia,<br>China,<br>Russia, Switzerland|Indonesia<br>Coefficient<br>Variation<br>called<br>Index)|(weighted<br> <br>of<br>– the so-<br>Williams’|
|Determines both the<br>pool and the alloca-<br>tion||Canada,<br>Germany,<br>Finland,<br>Denmark,<br>Sweden|||

_An erroneous view of fiscal capacity_ . A third concern has to do with how fiscal capacity is measured. Various sources of revenues are given arbitrary and differential weights for provinces and cities/districts and revenues from specific purpose transfers (DAK) are excluded. This gives an erroneous view of fiscal capacities of various jurisdictions.

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application of the Williamson’s index as multiple distributions of relative weights can lead to the same value of the index. The use of the Williamson’s index in determining formula factor weights also causes complexity, non-transparency, uncertainty and inequity in individual allocations. Individual jurisdictions entitlements can change from year to year and relative to others for no apparent justification.

_Non-neutrality to amalgamation and incorporation decisions._ The expenditure need determination component formula is also non-neutral as to the amalgamation and incorporation decisions. Using composite index indicator and referencing the transfer not to per capita but to average of total fiscal need or fiscal capacity may contribute to non-neutrality to fragmentation of jurisdictions. Amalgamation of existing jurisdictions leads to lower central transfers for amalgamating jurisdictions and break up of existing jurisdictions benefits all in terms of higher per capita central transfers (see Table 6 from Marwanto Harjowiryono, 2011). No wonder, three new provinces have been created and the number of cities/districts mushroomed from 336 in 2001 to 502 in 2010.

**Table 6. Existing DAU Allocation Rewards Jurisdictional Fragmentation**

|**Province**|**Number of**<br>**Districts/Cities**<br>**in 2001**|**Number of**<br>**Districts/Cities**<br>**in 2011**|**Total DAU for**<br>**Districts/Cities**<br>**in 2001**<br>**(billion Rp)**|**Total DAU for**<br>**Districts/Cities**<br>**in 2011**<br>**(billion Rp)**|**% change**<br>**in DAU:**<br>**2001-2011**|
|---|---|---|---|---|---|
|**Kalteng**|6|14|0.9|5.5|528%|
|**Yogyakarta**|5|5|0.9|2.7|216%|

Source: Marwanto Harjowiryono (2011) _Inequity for large urban and rural jurisdictions._ Finally and most importantly expenditure need determination uses a one size fits all approach and assumes per capita fiscal needs of large cities are similar to those of a small town or a rural district when it uses aggregate average spending for all local governments as a reference point. This creates tremendous injustice for large urban and large rural areas.

_Complex, non-tranparent and inequitable allocations._ In sum, the gap filling approach is unnecessarily complex, non-transparent and uses a macro approach that is not well grounded in the local realities to ensure inter-jurisdictional equity. These manna from heaven transfers also create an incentive and accountability structure that is not conducive to responsible, responsive, fair and accountable local governance. Simpler alternatives as outlined in the following paragraphs have the potential to enhance efficiency and equity of such transfers mechanisms.

# **4. DAU Simplification Reform Alternatives**

In the following three alternative options for the reform of DAU are presented. Common elements of these three alternatives are:

- One size does not fit all. Requires grouping (clustering) of local governments by population size, area and class of local governments.

- The adopted formula must have a sunset clause of five years but interim changes in the formula should not be allowed.

- The formula must have ceilings and floors to keep yearly entitlements stable and predictable.

11
- National average (by size and class) gap filling or equalization standard to replace Williamson’s index. Pool can be adjusted by affordability and allocation determined by standard.

- Gap filling or equalization by size and class of local governments and possibly equal per capita grants to villages.

- Fiscal capacity measurement based upon potential revenues plus tax shares and other transfers and 50% of resource revenues.

- Fiscal needs measurement to discontinue wages compensation (basic allocation), discontinue use of HDI and the Williamson’s index and other indexes. Instead consider need measurement based upon service/client population for each service category.

# **_Alternative 1: A Simpler Yet More Objective Gap Filling Approach_**

To simplify DAU and to ground it in local comparative context, one requires meaningful grouping of local governments. One alternative in this regard is to have the following classes or clusters. Please note that the use of sophisticated statistical techniques in clustering as recently advocated by the World Bank and the Indonesian academics simply adds more complexity without having any clear advantages to a simpler and transparent approach advocated here.

**Table 7. Characteristics of Each Cluster: Province, C1-C4, and D1-D4**

|**Cluster**|**No.**<br>**of**<br>**juris-**<br>**dicti-**<br>**ons**|**Total**<br>**popu-**<br>**lation**<br>**(mil.)**|**Min.**<br>**pop.**<br>**(mil.)**|**Max.**<br>**pop.**<br>**(mil.)**|**Group**<br>**ave-**<br>**rage**<br>**pop.**|**Min.**<br>**area**<br>**(km**<sup>**2**</sup>**)**|**Max.**<br>**area**<br>**(km**<sup>**2**</sup>**)**|**Group**<br>**average**<br>**area**<br>**(km**<sup>**2**</sup>**)**|**Per**<br>**capita**<br>**2009**<br>**own**<br>**revenues**<br>**(IDR)**|**Per capita**<br>**2009**<br>**expendi-**<br>**ture**<br>**(IDR)**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Provinces**|32|228|0.76|43.1|7.12|3,133|319,036|59,696|164,181|666,348|
|**Cities**|||||||||||
|C1|11|19.2|1.02|2.63|1.74|119|683|302|148,971|853,093|
|C2|13|8.8|0.52|0.88|0.68|39|2,938|589|168,587|1,405,204|
|C3|58|12.4|0.10|0.50|0.21|16|2,400|352|189,369|2,493,247|
|C4|11|0.7|0.03|0.10|0.07|23|9565|1426|221736|4336555|
|<br>**Districts**|||||||,|,|,|,,|
|D1|100|71.3|0.01|3.68|0.71|425|1,891|1,232|100,068|1,725,989|
|D2|99|52.1|0.01|4.09|0.52|1,901|3,802|2,710|94,200|2,345,628|
|D3|99|34.4|0.01|2.43|0.35|3,803|7,155|5,218|122,226|3,383,613|
|D4|100|26.1|0.02|1.54|0.26|7,248|49,958|19,603|202,034|5,431,698|

Notes: Cluster classification: **Provinces** —one group: P1: Provinces excluding Jakarta. **Cities** ( _Kota_ )—4 groups: C1 with population over 1 million; C2 with population 500K to 1 million; C3: Population 100500 K; C4: Population under 100K. **Districts** ( _Kabupaten_ )—4 groups: D1: vast area—top quartile in area; D2: large area—2<sup>nd</sup> quartile in area; D3: medium area—3<sup>rd</sup> quartile in area; D4: small area—4<sup>th</sup> quartile in area.

12
Please note that urban _kabupatens_ that are part of large metropolitan areas may be treated just like cities ( _kotas_ ) and grouped with cities. Meanwhile, the fiscal capacity and fiscal needs would be calculated as follows:

**Fiscal capacity** is defined to include potential revenues from own sources (PAD) if the jurisdiction applied group average tax effort to own bases plus tax shares and transfers (DBH Pajak) plus potential natural resource revenues (DBH SDA) plus other grants. A simple way to calculate potential own source revenues would be to apply national average effective tax rate to local nonresource GRDP (in this simulation, it is proxied by using non-oil and gas GRDP). Potential resource revenues will be similarly calculated by applying national average effective resource tax rate to local resource based GRDP only (in this simulation, it is proxied by using oil and gas GRDP). Due to instability of resource reveues, and higher public expenditure needs with resource exploitation and exhaustible nature of some resources only 50% of resource revenues will be counted towards revenue capacity of resource rich regions as done in Canada. Revenues from specific purpose grants will be fully included. Capital grants and loans earmarked to finance spefic projects to finance centrally determined infrastructure deficiencies will  be excluded from such calculations.

**Fiscal need** is calculated for a representative expenditure system of about 10 functions comprising most of local operating expenditures. This expenditure system will be dfferentiated by size class of local governments and will have service population indicators as determinants with weights based upon aggregate group local government expenditure for the specified function based on a 3 or 5 years group moving average. Table 8 provides illustrative expenditure functions, service indicators and weights based upon 2008 expenditures only. Since the data for public housing is unavailable, its weight is distributed to other field of services.

The allocation for Alternative 1 is then determined by a simple gap-filling for the per capita fiscal gap, that is the difference of per capita fiscal capacity and per capita fiscal need. The per capita fiscal gap for each region is multiplied by the service population to give the fiscal gap or DAU allocation of the first alternative. Total allocation of DAU is, however, capped at 26% of net GOI revenues as prescribed in the law. This means a proportionate reduction of grant funds for all jurisdictions based upon the availability of funds factor.

**Table 8. DAU Fiscal Need Compensation: Alternatives—Provinces, Cities (C1...C4), Districts (D1...D4)**

|**Expenditure function**|**Need Indicator**||**Weight (%)**||
|---|---|---|---|---|
|||**Provinces**|**Cities**|**Districts**|
|**General administration**|population (66.6%) and area<br>(33.3%)|52.1|29.3|35.0|
|**Law and order**|population|0.6|||
|**Law, order, and fire protection**|||1.0|1.0|
|**Education**|school age population|7.3|30.4|20.0|
|**Health**|weighted population of age group<br>with higher weights for age groups<br>0-5 and 65+|6.0|9.2|17.5|
|**Social protection and welfare**|no. of unemployed|1.0|2.3|1.5|
|**Housing**|no. of people in public housing or<br>no. of units ofpublic housing|Annulled|Annulled|Annulled|
|**Roads and transportation**|km of roads|10.0|10.0|10.0|
|**Agriculture and forestry**|area|6.0|||

13
|**Water and sewer**|no. of residential and<br>commercial/industrial units||9.2||
|---|---|---|---|---|
|**Other services**|population|17.0|8.6||
|**Rural services**|area|||15.0|

Results from simulations of this alternative are presented in Table 9 for provinces, in Table 10 for cities and in Table 11 for districts. Figures 5-13 report the redistributive impact of various alternatives. These tables and figures show that on most indicators of fairness, Alternative 1 offers superior results for most jurisdictions. These superior results in fairness are achieved while having simpler, more meaningful and transparent allocation of resources. The suggested refinements of the existing DAU offers significant improvements over the existing program. These include simpler and more meaningful and easily understood indicators. The deteremination of allocation by group leads to equal treatment of equals. Factor weights are objective and would be stable as these are determined by taking moving average of aggregate spending by the group as a whole. There is more clear and transparent need equalization. Both pool and allocation are determined by formula. Total pool , however, can be constrained by affordability. Nevertheless, there is one major drawback of the proposed design— unconditonal fiscal gap compensation strengths autonomy but without accountability to local residents. A second alternative retains this drawback but moves the current program from gap filling to an equalization program.

|**Table 9. Results for the Provinces Group under Various Simulation Scenarios***|
|---|

||**Current DAU**<br>**Formula**|**Alternative 1**|**Alternative 2**|**Alternative 3**|
|---|---|---|---|---|
|**No. of Observed Provinces**|32|32|32|32|
|**No. of Receiver**|31|30|10|32|
|**Average DAU allocation**<br>**(million IDR)**|622,547|979,405|693,084|905,339|
|**Average per capita DAU**<br>**allocation (IDR)**|209,547|236,475|379,869|171,008|
|**Maximum per capita DAU**<br>**allocation (IDR)**|796,794|956,711|1,347,027|306,176|
|**Minimum per capita DAU**<br>**allocation (IDR)**|10,629|63,671|22,309|75,320|
|**Max to min ratio of per**<br>**capita DAU allocation**|74.96|15.03|60.38|4.07|
|**Maximum per capita**<br>**revenue after transfer**<br>**(IDR)**|3,617,952|3,777,869|4,168,184|3,124,595|
|**Minimum per capita**<br>**revenue after transfer**<br>**(IDR)**|170,194|228,693|71,730|240,342|
|**Max to min ratio of per**<br>**capita revenue after**<br>**transfer **|21.26|16.52|58.11|13.00|
|**Coef. of variation**|1.031|1.102|1.447|0.944|
|**Weighted coef. of variation**|1.051|1.013|1.411|0.838|
|**Relative mean deviation**|0.310|0.338|0.431|0.288|

14
|**Gini Index**|0.423|0.428|0.546|0.371|
|---|---|---|---|---|
|**Weighted Gini Index**|0.381|0.332|0.435|0.293|

Notes:

*) Excluding non-receivers, inequality measures calculated for after transfer per capita revenue

15
# **Table 10. Results for the** **<u>Cities Group under Various Simulation Scenarios*</u>**

||**Current DAU**<br>**Formula**|**Alternative 1**|**Alternative 2**|**Alternative 3**|
|---|---|---|---|---|
|**No. of Observed Cities**|93|93|93|93|
|**No. of Receiver**|91|92|59|93|
|**Average DAU allocation (million)**|318,295|346,842|62,647|397,776|
|**Average per capita DAU allocation**|1,404,176|1,226,520|234,929|1,372,161|
|**Maximum per capita DAU**<br>**allocation (IDR)**|7,301,673|2,594,625|530,996|3,242,229|
|**Minimum per capita DAU**<br>**allocation (IDR)**|147,101|232,243|18214|379,163|
|**Max to min ratio of per capita**<br>**DAU allocation **|49.64|11.17|29.15|8.55|
|**Maximum per capita revenue after**<br>**transfer (IDR)**|9,943,591|5,115,631|5,769,565|7,029,313|
|**Minimum per capita revenue after**|510,582|671,978|283,775|736,689|
|**transfer (IDR)**|||||
|**Max to min ratio of per capita**<br>**revenue after transfer **|19.48|7.61|20.33|9.54|
|**Coef. of variation**|0.611|0.437|0.742|0.467|
|**Weighted coef. of variation**|0.598|0.488|0.739|0.507|
|**Relative mean deviation**|0.208|0.154|0.228|0.160|
|**Gini Index**|0.299|0.230|0.327|0.235|
|**Weighted Gini Index**|0.291|0.249|0.312|0.250|
|**No. of Observed C1 Group Region**|11|11|11|11|
|**No. of Receiver**|11|11|5|11|
|**Average DAU allocation (million)**|599,780|772,744|171,417|926,020|
|**Average per capita DAU allocation**|352,450|449,996|114,966|537,202|
|**Maximum per capita DAU**<br>**allocation (IDR)**|506,550|543,150|170,570|634,337|
|**Minimum per capita DAU**<br>**allocation (IDR)**|153,056|314,452|60,271|379,163|
|**Max to min ratio of per capita**<br>**DAU allocation**|3.31|1.73|2.83|1.67|
|**Maximum per capita revenue after**<br>**transfer (IDR)**|1,233,261|1,346,061|985,273|1,473,621|
|**Minimum per capita revenue after**<br>**transfer (IDR)**|510,582|671,978|288,977|736,689|
|**Max to min ratio of per capita**<br>**revenue after transfer **|2.42|2.00|3.41|2.00|
|**Coef. of variation**|0.240|0.197|0.365|0.192|
|**Weighted coef. of variation**|0.233|0.198|0.364|0.195|
|**Relative mean deviation**|0.092|0.070|0.137|0.070|
|**Gini Index**|0.126|0.101|0.183|0.097|
|**Weighted Gini Index**|0.128|0.106|0.193|0.103|
|**No. of Observed C2 Group Region**|13|13|13|13|
|**No. of Receiver**|13|13|9|13|
|**Average DAU allocation (million)**|367,534|484,375|86,086|578,064|

16
|**Average per capita DAU allocation**|544,808|710,779|128,791|851,087|
|---|---|---|---|---|
|**Maximum per capita DAU**<br>**allocation (IDR)**|810,767|887,157|322,956|1,053,690|
|**Minimum per capita DAU**<br>**allocation (IDR)**|147,101|330,507|49,547|642,642|
|**Max to min ratio of per capita**<br>**DAU allocation **|5.51|2.68|6.52|1.64|
|**Maximum per capita revenue after**<br>**transfer (IDR)**|2,932,250|3,442,057|2,811,546|3,452,077|
|**Minimum per capita revenue after**|854,038|910,819|324,900|967,542|
|**transfer (IDR)**|||||
|**Max to min ratio of per capita**<br>**revenue after transfer **|3.43|3.78|8.65|3.57|
|**Coef. of variation**|0.395|0.423|0.730|0.400|
|**Weighted coef. of variation**|0.371|0.397|0.693|0.375|
|**Relative mean deviation**|0.135|0.148|0.260|0.147|
|**Gini Index**|0.189|0.199|0.346|0.195|
|**Weighted Gini Index**|0.181|0.193|0.339|0.190|
|**No. of Observed C3 Group Region**|58|58|58|58|
|**No. of Receiver**|56|57|39|58|
|**Average DAU allocation (million)**|272,053|271,306|50,566|303,549|
|**Average per capita DAU allocation**|1,387,206|1,285,945|269,172|1,455,062|
|**Maximum per capita DAU**<br>**allocation (IDR)**|2,308,378|1,526,098|519,377|1,973,908|
|**Minimum per capita DAU**<br>**allocation (IDR)**|361,909|232,243|58,569|914,632|
|**Max to min ratio of per capita**<br>**DAU allocation **|6.38|6.57|8.87|2.16|
|**Maximum per capita revenue after**<br>**transfer (IDR)**|5,769,565|5,115,631|5,769,565|7,029,313|
|**Minimum per capita revenue after**<br>**transfer (IDR)**|927,534|924,709|283,775|1,644,595|
|**Max to min ratio of per capita**<br>**revenue after transfer **|6.22|5.53|20.33|4.27|
|**Coef. of variation**|0.333|0.275|0.757|0.367|
|**Weighted coef. of variation**|0.345|0.282|0.750|0.350|
|**Relative mean deviation**|0.118|0.083|0.205|0.107|
|**Gini Index**|0.168|0.124|0.299|0.150|
|**Weighted Gini Index**|0.178|0.127|0.302|0.143|
|**No. of Observed C4 Group Region**|11|11|11|11|
|**No. of Receiver**|11|11|6|11|
|**Average DAU allocation (million)**|214,035|149,815|15,376|153,297|
|**Average per capita DAU allocation**|3,557,909|2,304,629|271,529|2,385,820|
|**Maximum per capita DAU**<br>**allocation (IDR)**|7,301,673|2,594,625|530,996|3,242,229|
|**Minimum per capita DAU**<br>**allocation (IDR)**|2,291,657|1,991,547|18,214|1,743,818|
|<br>**Max to min ratio of per capita**<br>**DAU allocation **|3.19|1.30|29.15|1.86|

17
|**Maximum per capita revenue after**<br>**transfer (IDR)**|9,943,591|4,956,495|2,660,132|5,114,642|
|---|---|---|---|---|
|**Minimum per capita revenue after**<br>**transfer (IDR)**|3,004,333|2,882,973|712,676|2,464,611|
|**Max to min ratio of per capita**<br>**revenue after transfer**|3.31|1.72|3.73|2.08|
|**Coef. of variation**|0.380|0.206|0.499|0.263|
|**Weighted coef. of variation**|0.307|0.185|0.489|0.248|
|**Relative mean deviation**|0.128|0.090|0.220|0.117|
|**Gini Index**|0.171|0.108|0.260|0.135|
|**Weighted Gini Index**|0.139|0.098|0.258|0.130|

Notes:

*) Excluding non-receivers, inequality measures calculated for after transfer per capita revenue

18
**Table 11. Results for the Districts** **<u>Group under Various Simulation Scenarios*</u>**

||**Current DAU**<br>**Formula**|**Alternative 1**|**Alternative 2**|**Alternative 3**|
|---|---|---|---|---|
|**No. of Observed Districts**|398|398|398|398|
|**No. of Receiver**|391|397|126|398|
|**Average DAU allocation (million)**|370,746|357,357|90,961|371,465|
|**Average per capita DAU allocation**|1,903,119|1,278,589|1,449,887|1,136,520|
|**Maximum per capita DAU**<br>**allocation (IDR)**|21,675,276|21,037,218|34,552,332|5,095,879|
|**Minimum per capita DAU**<br>**allocation (IDR)**|136,837|32,246|4,320|333,629|
|**Max to min ratio of per capita**<br>**DAU allocation **|158.40|652.39|7,998|15.27|
|**Maximum per capita revenue after**<br>**transfer (IDR)**|36,078,212|24,734,346|35,392,980|21,921,194|
|**Minimum per capita revenue after**<br>**transfer (IDR)**|356,598|560,275|120,262|537,761|
|**Max to min ratio of per capita**<br>**revenue after transfer **|101.17|44.15|294.30|40.76|
|**Coef. of variation**|1.346|1.091|1.889|0.966|
|**Weighted coef. of variation**|1.089|0.940|2.137|0.860|
|**Relative mean deviation**|0.370|0.324|0.491|0.303|
|**Gini Index**|0.500|0.440|0.639|0.412|
|**Weighted Gini Index**|0.383|0.337|0.538|0.329|
|**No. of Observed D1 Group**|100|100|100|100|
|**No. of Receiver**|100|100|31|100|
|**Average DAU allocation (million)**|437,288|386,566|84,570|392,470|
|**Average per capita DAU allocation**|1,172,551|806,197|1,110,100|714,346|
|**Maximum per capita DAU**<br>**allocation (IDR)**|15,242,422|5,084,624|7,834,006|3,777,651|
|**Minimum per capita DAU**<br>**allocation (IDR)**|171,044|292,594|14,443|333,629|
|**Max to min ratio of per capita**<br>**DAU allocation**|89.11|17.38|542.41|11.32|
|**Maximum per capita revenue after**<br>**transfer (IDR)**|22,708,516|12,550,717|15,300,098|10,197,022|
|**Minimum per capita revenue after**<br>**transfer (IDR)**|356,598|560,275|120,262|537,761|
|**Max to min ratio of per capita**<br>**revenue after transfer **|63.68|22.40|127.22|18.96|
|**Coef. of variation**|1.513|1.069|2.034|0.934|
|**Weighted coef. of variation**|0.686|0.495|1.289|0.432|
|**Relative mean deviation**|0.328|0.275|0.480|0.242|
|**Gini Index**|0.438|0.362|0.612|0.321|
|**Weighted Gini Index**|0.229|0.165|0.332|0.152|
|**No. of Observed D2 Group**|99|99|99|99|
|**No. of Receiver**|97|98|40|99|
|**Average DAU allocation (million)**|376,778|340,249|56,915|348,238|

19
|**Average per capita DAU allocation**|1,576,243|948,519|765,176|840,495|
|---|---|---|---|---|
|**Maximum per capita DAU**<br>**allocation (IDR)**|21,675,276|3,966,180|5,555,236|3,184,663|
|**Minimum per capita DAU**<br>**allocation (IDR)**|273,026|353,957|15,913|461,592|
|**Max to min ratio of per capita**<br>**DAU allocation **|79.39|11.21|349.10|6.90|
|**Maximum per capita revenue after**<br>**transfer (IDR)**|32,266,992|14,557,896|16,146,952|11,791,707|
|**Minimum per capita revenue after**<br>**transfer (IDR)**|520,858|606,292|136,799|744,153|
|**Max to min ratio of per capita**<br>**revenue after transfer**|61.95|24.01|118.03|15.85|
|**Coef. of variation**|1.477|0.962|1.695|0.848|
|**Weighted coef. of variation**|0.871|0.594|1.395|0.522|
|**Relative mean deviation**|0.315|0.254|0.433|0.228|
|**Gini Index**|0.445|0.355|0.574|0.322|
|**Weighted Gini Index**|0.312|0.218|0.418|0.192|
|**No. of Observed D3 Group**|99|99|99|99|
|**No. of Receiver**|99|99|31|99|
|**Average DAU allocation (million)**|331,227|292,833|55,243|306,006|
|**Average per capita DAU allocation**|2,051,840|1,185,130|1,205,038|1,061,774|
|**Maximum per capita DAU**<br>**allocation (IDR)**|16,397,758|5,053,894|7,083,893|2,240,744|
|<br>**Minimum per capita DAU**<br>**allocation (IDR)**|340,037|49,799|22,213|440,941|
|**Max to min ratio of per capita**<br>**DAU allocation **|48.22|101.49|318.91|5.08|
|**Maximum per capita revenue after**<br>**transfer (IDR)**|36,078,212|24,734,346|26,764,344|21,921,194|
|**Minimum per capita revenue after**<br>**transfer (IDR)**|547,832|797,903|169,158|648,736|
|**Max to min ratio of per capita**<br>**revenue after transfer**|65.86|31,00|158.22|33.79|
|**Coef. of variation**|1.298|1.142|1.886|1.061|
|**Weighted coef. of variation**|1.013|0.686|1.666|0.608|
|**Relative mean deviation**|0.339|0.285|0.466|0.255|
|**Gini Index**|0.462|0.385|0.609|0.351|
|**Weighted Gini Index**|0.388|0.253|0.510|0.232|
|**No. of Observed D4 Group**|100|100|100|100|
|**No. of Receiver**|95|100|24|100|
|**Average DAU allocation (million)**|335,726|408,791|202,092|438,260|
|**Average per capita DAU allocation**|2,850,914|2,166,975|3,346,229|1,925,755|
|**Maximum per capita DAU**<br>**allocation (IDR)**|19,862,532|21,037,218|34,552,332|5,095,879|
|**Minimum per capita DAU**<br>**allocation (IDR)**|136,836|32,246|4,320|891,093|

20
|**Max to min ratio of per capita**<br>**DAU allocation **|145.15|652.39|7,998.02|5.72|
|---|---|---|---|---|
|**Maximum per capita revenue after**<br>**transfer (IDR)**|27,022,350|21,877,864|35,392,980|12,559,045|
|**Minimum per capita revenue after**<br>**transfer (IDR)**|551,144|1,244,735|194,263|1,124,271|
|**Max to min ratio of per capita**<br>**revenue after transfer **|49.03|17.58|182.19|11.17|
|**Coef. of variation**|1.041|0.783|1.531|0.621|
|**Weighted coef. of variation**|0.963|0.725|1.732|0.606|
|**Relative mean deviation**|0.342|0.274|0.466|0.230|
|**Gini Index**|0.465|0.361|0.603|0.309|
|**Weighted Gini Index**|0.411|0.299|0.600|0.279|

Notes:

*) Excluding non-receivers, inequality measures calculated for after transfer per capita revenue

21
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redistribute small sums. While expenditure need equalization may redistribute more resources, as shown by calculations presented in Tables 9-11, overall redistributive impact of this program is relatively smaller than actual DAU and other alternatives presented here (see Figures 5-13) and as a result the program does not fare well on comparative indicators of fiscal equity.

# **_Alternative 3: Back to the Future - An Almost Ideal Approach:  Fiscal Capacity Equalization Supplemented by Output-based Operating Grants for Merit Services_**

Under this option, fiscal capacity equalization follows the same approach as under alternative #2. However expenditure need compensation is done through output-based operating transfers for merit public services only where allocation is based upon the share of service population and there is no conditionality on spending but instead customized conditions on service delivery performance in terms of service access and quality for individual jurisdictions and providers for continuation of the grant program. The design of such transfers are spelled out in Shah (2007, 2010, 2011). In addition to these operating transfers, there would also be a need to have a capital grant and capital market access program (the former for poorer and the latter for richer jurisdictions) for merit services based upon a planning view to deal with infrastructure deficiencies pertaining to a national minimum standard.

Our simulation of this alternative combines a fiscal capacity equalization grant with output based grants for education, health, transportation and social welfare and protection only. We have not included capital grants to overcome infrastructure deficiencies as such grant program must be a separate grant program based upon a planning view and must not be formula based grant program available to all. This alternative further simplifies the determination of grant pool and allocations. But the most important advantage of this alternative is that it preserves local autonomy while enhancing accountability to local government residents.

Tables 9-11 present simulations of allocations using this approach and Figures 5-13 depict redistributive impacts. The results combine fiscal capacity equalization using group average standard for each cluster/group and output based grants for education, health, infrastructure and social welfare and protection using relevant service populations. The results demonstrate clear superiority of this approach in establishing fair financing allocation to ensure fair and equitable access to merit services to all Indonesians regardless of their place of residence. In addition, this approach improves simplicity of allocation criteria, preserves local government autonomy against higher-level undue interference and enhances local government accountability to local residents. The approach is home grown as it embodies rich and successful past Indonesian experience with INPRES grants.

# **5.  Does Indonesia Need a Grants Commission?**

For determining the system of grants, one finds four stylized types of models used in practice (see Shah, 2005, for a formal framework for evaluating such institutional arrangements).

The first and the most commonly used practice is for the federal/central government alone to decide on it. This has the distinct disadvantage of biasing the system towards a centralized outcome whereas the grants are intended to facilitate decentralized decision-making. In India, the federal government is solely responsible for the Planning Commission transfers and the 27
centrally sponsored schemes. These transfers have strong input conditionality with potential to undermine state and local autonomy. The 1988 Brazilian constitution provided strong safeguard against federal intrusion by enshrining the transfers’ formulae factors in the constitution. These safeguards represent an extreme step as they undermine flexibility of fiscal arrangements to respond to changing economic circumstances.

The second approach used in practice is to set up a quasi-independent body, such as a grants commission, whose purpose is to design and reform the system. These commissions can have a permanent presence as in South Africa and Australia or they can be brought into existence periodically to make recommendations for the next five years as done in India. These commissions have proven to be ineffective in some countries largely because many of the recommendations have been ignored by the government and not implemented as in South Africa. In other cases, while the government may have accepted and implemented all they recommend, they have been ineffective in reforming the system due to the constraints they have imposed on themselves as is considered to be the case in India. In some cases, these Commissions become too academic in their approaches and thereby contributing to the creation of an overly complex system of intergovernmental transfers as has been the case with the Commonwealth Grants Commission in Australia (Shah, 2004, 2007).

The third approach found in practice is to use executive federalism or central-provincial-local committees or forums to negotiate the terms of the system. Such a system is used in Canada andGermany. In Germany, this system is enhanced by having state governments represented in _Bundesrat_ —the upper house of the parliament. This system allows for explicit political input from the jurisdictions involved and attempts to develop a common consensus.

The fourth approach is a variation on the third approach and uses an intergovernmental-cumlegislative-cum-civil society committee with equal representation from all constituent units but chaired by the federal/central government to negotiate changes in the existing arrangements. The so-called Finance Commission in Pakistan and the Indonesian DPOD represent this model. This approach has the advantage that all stakeholders—donor, recipients, civil society and experts are represented on the commission. Such an approach keeps the system simple and transparent. It is important that in such forums only the donors and recipients be the voting members (principals) with civil society members and experts to serve as observers (non-voting members) and provide feedback and technical assistance to the principals of these forums. An important disadvantage of this approach is that if unanimity rule is adopted, such bodies may be deadlocked forever as was witnessed in Pakistan in the 1990s.

The Indonesian DPOD is an important forum for decisions on grant determination. The DPOD’ role in grant determination can be strengthened by requiring that only the cabinet ministers, governors and mayors can serve as members and vote on matters relating to fiscal transfers and all these decisions must require three-fourth majority of the DPOD.

In conclusion, there appears to be no clear advantage in creating an independent grant commission in Indonesia. Grant determination role is better served by the intergovernmental forum such as the DPOD supported by a technical secretariat at the Ministry of Finance.

**6. Concluding Remarks on Long-Term Reform Options for Central-Provincial-Local Fiscal Relations in Indonesia**

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During the past decade Indonesia has made a remarkable transformation from centralized rule to decentralized and democratic local governance. This transformation can be sustained if the intergovernmental finance create the right incentives and accountability regimes for responsive and accountable local governance. Prior to the 2000 reforms, Indonesia had an intergovernment finance system the so called INPRES (presidential instruction) grants system, that was simple, transparent and focused on results based accountability. This paper has called for Indonesia to return to its roots and implement reform options that represent a ―back to the future‖ approach—an approach that draws upon rich and successful Indonesian experiences that have often been cited in the public finance literature as examples of better practices in central transfers (see Shah, 1994, 1998 and Boadway aand Shah, 2009).

To strengthen accuntable local governance, Indonesia needs to consider the following reform options:

- _Tax decentralization and tax base sharing._ Tax base sharing is feasible for personal income taxes on residence principle. Tax decentralization may be feasible for royalties, fees, severance, production, output and property taxes, sin taxes (gambling, liquor and massage parlors) and local environmental taxes and charges.

- _Output  based per capita operating (non-matching) grants for setting national minimum standards for merit services such as education, health and infrastructure._ These grants should embody simple allocation criteria to local governments based on service population, e.g. school operating grant based upon school age population. Local governments would disburse these grants to all providers – government and non government as done in Canada, Brazil, Chile, Finland and Thailand. Continuity of finance can be assured by maintaining or improving upon existing standards of access and service quality. Such transfers will preserve local autonomy while enhancing simplicity, transparency and citizens’ based accountability for service delivery performance. Indonesia in the past had a measure of succces with a grant program (INPRES grants) that embodied at least some of these features.

- _Fiscal capacity equalization grants based upon national average standard for each cluster/_ group to enable all jurisdictions to provide reasonably comparable levels of public services at reasonably comparable levels of tax burdens. Adoption of fiscal capacity equalization grants would also simplify the use of indirect fiscal capacity equalization on specific grants.

- _Capital (with 10 to 90% matching) grants to fiscally disadvantaged jurisdictions_ to overcome infrastructure deficiencies in setting national minimum standards for merit services. These grants should be based upon a planning view of identified infrastructure deficiencies and should contain matching requirements that vary inversely with per capita fiscal capacity. These grants combined with ouput based operating grants will create a level playing field and enable poorer juridictions to integrate with the broader national economy and help reduce regional income and fiscal disparities.

- _Assistance for responsible capital market access_ to richer local jurisdictions.

The above mentioned reforms will result in an intergovernmental finance system that is more transparent, objective, predictable and simpler with a sharper focus on objectives. These reforms may be considered as integral elements of any effort at fine tuning the existing fiscal system of multi-order governance in Indonesia.

This paper has demonstrated the need for simpler and transparent allocation criteria for greater fairness and accountability. Complex systems compromise both fairness and accountability as has been happing under the current DAU system.

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