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INDONESIA 2005INDONESIA 2005

STATISTICS INDONESIA

2009


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FINANCIAL SOCIAL

ACCOUNTING MATRIX

INDONESIA 2005

BPS - STATISTICS INDONESIA AND BANK INDONESIA

2009


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FINANCIAL SOCIAL ACCOUNTING MATRIX

INDONESIA 2005

BPS Catalogue : 9503004

Publication Number : 07230.0901

I S B N : 978-979-064-083-2

Edition : Indonesian Edition, May 2008

English Edition, July 2009

Book Size : 21 cm x 29.7 cm

Number of Pages : 100 pages

Manuscript : Directorate of Expenditure Account - BPS

Directorate of Production Account - BPS

Directorate of Economic and Monetary Statistics - BI

Cover Design : Eko Ariantoro and Fayota Prachmasetiawan

Published by : BPS - Statistics Indonesia and Bank Indonesia

Printed by : Directorate of Economic and Monetary Statistics - BI

May be cited with reference to the source.


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FSAM INDONESIA 2005

COMPILERS

BPS-STATISTICS INDONESIA BANK INDONESIA

BOARD OF ADVISORS

Slamet Sutomo 1. Triono Widodo2. Halim Alamsyah

PROJECT EXPERTS

1. Supriyanto2. Abdul Rachman3. Nursinah Amal Urai

1. Mohammad M. Toha2. Wijoyo Santoso3. Wiwiek Sisto Widayat

WORKING GROUP

1. Sri Soelistyowati2. Setianto3. Nina Suri Sulistini4. Sodikin Baidowi5. Dianawati

6. Emil Azman S.7. Buyung Airlangga8. Yomin Tofri9. Rudiansyah10. Budi Cahyono

1. Eko Ariantoro2. Prijono3. Pujiastuti4. Widyastuti Noviandari5. Wishnu Mahraddika

6. Nurcholis7. M. Anwar Bashori8. Aulia Fadly9. Ganjar Wicaksono

TECHNICAL STAFFS

1. Widodo2. Wikaningsih

3. Joni Kasmuri4. Triana M. Aritonang5. Erlia Rahmawati6. Ika Virnaristanti7. Fayota Prachmasetiawan8. Suryani Widarta9. Pipit Hely Sorayan10. Dyah Soendhari11. Wisnu Winardi12. Hadi Susanto13. Niti Rosika Febriyanti

14. Tantri Herawati Lestari15. Wembri Suska


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16. Budhi Wibowo17. Lilia Endrian18. Muhammad Irkham19. Puji Agus Kurniawan20. Eko Oesman

21. Widdia Angraini22. Murdiono23. Lien Suharni24. Endah Riawati25. Suryadiningrat26. Budi Prawoto27. Muji Lestari28. Margo Yuwono29. Rerta Mastiani30. Sri Setyarini31. Etjih Tasriah

32. Tri Isdinarmiati33. Fathi Ilhami34. Urip Widiyantoro35. Deden Achmad Sunarjo36. Yezua Harnold F. Hermanus37. Harni Dwi Prikasih38. Ratih Widayanti39. Busminoloan40. Suryadi41. Ari Sugih Mulia

CONTRIBUTORS

1. Slamet Sutomo2. Supriyanto3. Abdul Rachman4. Nursinah Amal Urai5. Sri Soelistyowati6. Setianto7. Emil Azman S.8. Buyung Airlangga

9. Rudiansyah10. Yomin Tofri11. Sodikin Baidowi12. Nina Suri Sulistini13. Widodo14. Joni Kasmuri

1. Triono Widodo2. Wiwiek Sisto Widayat3. Anni V. L. Herman4. Eko Ariantoro5. Prijono6. Pujiastuti7. Nurcholis8. Netri Yunera

9. Widyastuti Noviandari10. Wishnu Mahraddika11. Ganjar Wicaksono


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FOREWORD

One of the tasks of the BPS-Statistics Indonesia, as the agency officially

mandated with providing statistics in Indonesia, is to disseminate a variety of

statistical information that has been comprehensively collated and compiled into

publications. The publication of this Financial Social Accounting Matrix (FSAM) of

Indonesia 2005 is just one example of information propagation as mentioned.

This publication is a collaborative effort between BPS-Statistics Indonesia

and Bank Indonesia (BI) to compose Indonesias FSAM 2005 or FSAM Indonesia

2005. FSAM is translated in Indonesian as Sistem Neraca Sosial Ekonomi

Finansial (SNSEF). A joint team from BPS-Statistics Indonesia and BI prepared the

2005 FSAM covering two time horizons, namely 2006/2007 and 2007/2008. The

data framework of FSAM was built to explain interdependency between financial

sector performance and real sector performance. The condition of the Indonesian

economy, particularly in recent periods, has been greatly influenced not merely by

real sector performance, but also financial sector performance. By integrating

financial sector performance into a data framework of real sector performance, a

range of financial transmission channels from the financial sector that shape real

sector performance can be inferred and analyzed through a more inclusive

structure.

At the operational level, the data framework of FSAM integrates two existingframeworks compiled and published by BPS-Statistics Indonesia, more specifically

the Social Accounting Matrix (SAM or Sistem Neraca Sosial Ekonomi) and Flow of

Funds Accounts (FoF or Neraca Arus Dana). By inserting the FoF framework,

particularly into the capital sub matrix of SAM, the data framework of FSAM can be

formulated. However, developing FSAM data framework requires further

complementary measures, such as adjustments to classifications and figures,


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among others, as well as the reconciliation of SAM and FoF accounts. A data

framework for SAM has been periodically compiled, every five years, by BPS-

Statistics Indonesia since 1975; whereas the data framework for FoF has been

compiled with data support from BI, the Ministry of Finance (MoF) and other

relevant institutions or agencies, annually and quarterly.

The publication of Indonesias FSAM 2005 or FSAM Indonesia 2005 is

expected to enrich the variety of data sources and assist the analysis of statistical

information and policy analysis. Through this medium, the Chairman of BPS-

Statistics Indonesia would like to convey heartfelt gratitude and sincere

appreciation to BI, which facilitated this collaboration, Prof. Iwan J. Azis from

Cornell University who generously provided technical assistance, and also to all

members of the team of contributors and editors, from BPS-Statistics Indonesia

and BI, as well as to all who have poured their most diligent efforts in to publishing

the original data framework of Indonesia FSAM. We genuinely expect that this

publication will benefit data users, especially for the government, academics,

research agencies and other stakeholders.

Jakarta, July 2009

BPS Statistics Indonesia

Chief Statistician,

Rusman Heriawan


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PREFACE

Providing proficient data and information is pivotal for the compiling of

macroeconomic assessments to support economic and monetary policy

formulation. As one provider of statistical information, BI continuously strives to

improve the quality of data and information, by improving more sophisticated and

integrated system and methodologies in data collection. Through collaboration with

other relevant institutions, BI regularly conducts various statistical analyses todevelop new indicators in order to yield comprehensive, reliable, accurate, timely

and accessible policy indicators.

The Indonesian FSAM 2005 publication represents one tangible result of

collaborative efforts between BI and BPS-Statistics Indonesia. The compilation of

Indonesias FSAM was initiated by BI and BPS-Statistics Indonesia through a pilot

project in constructing Indonesias FSAM 2000, with technical assistance from

Prof. Iwan J. Azis of Cornell University, USA. Over the subsequent two years, thepublication of Indonesias FSAM 2005 was organized into two stages, namely

Stage I (2006) in which the concept was prepared and primary data were collected

through surveys; and Stage II (2007) during which the Indonesian FSAM was

finalized.

FSAM is the result of a balanced and consistent integration between data

from the SAM, which describes real sector activities, and the FoF that explains

financial sector activities. In the future, FSAM is expected to assist the structural

explanation of monetary policy transmission channels to the real sector and

distortions that may happen. Consequently, FSAM can be utilized to support

monetary policy formulation effectively.

FSAM 2005 is the first FSAM publication. In subsequent periods, the

Indonesia FSAM will be compiled and published every five years. Hopefully, this


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publication can complement the wealth of statistics in Indonesia and extensively

benefit policymakers, academicians and practitioners in related fields.

Jakarta, July 2009

Bank Indonesia

Acting Governor,

Miranda S. Goeltom


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PREFACE

In the early 2000s, while conducting research in Tokyo I was approached by

Bank Indonesia (BI) to help improve their research and teaching quality. One of the

main topics being suggested was about the link between financial sector and the

real sector. Indeed, like many other emerging markets, Indonesia experienced a

disconnect between the two sectors: lower interest rate with a lack of credit growth

(e.g., 2002-2004), and growing output with low employment elasticity (since 2004),

making it more difficult for the countrys socio-economic conditions to improve, i.e.,

unemployment rate continued to be high, the rate of poverty reduction slowed. A

standard credit-channel problem, disproportional investment in financial sector, and

the analytical link between macro policy and unemployment and poverty

immediately came to mind. This suggests the importance of establishing a

connection between credit-channel problems and the countrys socio-economic

indicators that include welfare and incomes of different household groups (urban-

rural, poor-rich, skilled-unskilled etc). It was at this juncture BI came to realize that

in conducting monetary policy a financial social accounting matrix (FSAM) is

needed. As a data system, FSAM connects the real sector, macro-financial sector,

and social variables in a systematic and highly consistent manner. Soon a joint

project with BPS was set up. This document is the final product of that joint project.

Broadly speaking, information in the FSAM is derived from three sources:

the input-output table, the socio economic survey, and the flow-of-fund. Realizing

their comprehensiveness, the difficulty and the tedious work required to link them

systematically, the joint project was rather large scale and time-consuming. It took

more-than two years before this 2005 FSAM was finally published. Given all the

energy, time, expertise, and resources spent, those involved in the project can

stand proud of this publication. But more importantly, the quality of research,

analysis, and policy debate can now be enhanced by utilizing the incredibly

important information contained in this FSAM.


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I feel fortunate to have the opportunity to advise on the technical substance

of the project and interact with many bright, hard-working and dedicated colleagues

at BI and BPS during the period. I hope this publication is useful not only for BI and

BPS but also for a wider audience in general. It is critical to realize, however, that

this should be seen as the beginning of a process whereby Indonesian FSAM will

be produced on a regular basis.

Ithaca, July 2009

Professor and Director of Graduate Studies

Regional Science Program

Cornell University,

Iwan J. Azis


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TABLE OF CONTENTS

FOREWORD ............................................................................................................. i

PREFACE ............................................................................................................... iii

PREFACE ................................................................................................................ v

TABLE OF CONTENTS ......................................................................................... vii

LIST OF APPENDIXES ........................................................................................... ix

LIST OF TABLES ..................................................................................................... x

LIST OF FIGURES .................................................................................................. xi

ABSTRACT ............................................................................................................ xii

CHAPTER I - INTRODUCTION ...............................................................................1

1.1. Background ............................................................................................... 1

1.2. Goal and Objectives .................................................................................. 4

1.3. Publication Outlines ................................................................................... 4

CHAPTER II - DESCRIPTION OF THE INDONESIAS FSAM 2005 ........................ 6

2.1. FSAM: Integration of SAM and FoF ........................................................... 6

2.2. Country Experiences in FSAM Compilation ............................................. 10

2.3. Basic Framework of Indonesias FSAM 2005 .......................................... 11

2.4. Classification of Indonesias FSAM 2005 ................................................. 13

CHAPTER III - COMPILATION METHOD OF INDONESIAS FSAM 2005 ............18

3.1. Transformation of SAM into FSAM .......................................................... 18

3.2. Inserting FoF Data into FSAM Framework .............................................. 19

3.3. Supporting Surveys ................................................................................. 20

3.3.1. Household Savings and Investment Survey (SKTIR-Survei

Khusus Tabungan dan Investasi Rumah Tangga) ............... 20

3.3.2. Input-Output Survey (SKIO-Survei Khusus Input-Output) .... 21


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3.3.3. Trade and Services Sector Survey (SKSPJ-Survei Khusus

Sektor Perdagangan dan Jasa) ............................................ 22

3.3.4. Private Non Financial Corporation Survey (SKPS-SurveiKhusus Perusahaan Swasta) ............................................... 22

3.4. SAM and FoF Reconciliation .................................................................... 23

CHAPTER IV - ANALYSES OF INDONESIAS FSAM 2005 ................................. 25

4.1. The Result: Indonesias FSAM 2005 ........................................................ 25

4.2. Descriptive Analysis ................................................................................. 25

4.2.1. Indonesias Economic Structure 2005 .................................. 25

4.2.2. Structure of Income and Expenditure by Institution .............. 28

4.2.3. Saving Rate .......................................................................... 33

4.2.4. Saving-Investment Gap ........................................................ 34

4.2.5. Financial Analysis Based on Indonesias FSAM 2005 .......... 36

4.3. Application of Indonesias FSAM in Economic Analysis ........................... 39

CHAPTER V - CONCLUSION ............................................................................... 41

REFERENCES ...................................................................................................... 43


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LIST OF APPENDIXES

Appendix 1. Country Experiences in FSAM Compilation.. A1-1

Appendix 2. Classification of The Indonesias FSAM 2005.......... A2-1

Appendix 3. Concepts and Definitions Used in The Indonesias FSAM 2005 A3-1

Appendix 4. The Indonesias FSAM 2005 (79x79 Matrix)................ A4-1


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LIST OF TABLES

Table 4.1. The Indonesias FSAM 2005 (22x22 Matrix) ......................................... 26

Table 4.2. Indonesias Economic Structure 2005 .................................................. 27

Table 4.3. Production Structure by Industrial Sectors in Indonesia ....................... 28

Table 4.4. Income Allocation by Institution ............................................................ 30

Table 4.5. Share of Sources of Income by Institution ............................................ 31

Table 4.6. Saving Rate by Institution ..................................................................... 34

Table 4.7. Gross Saving and Physical Investment by Institution ........................... 35

Table 4.8. Domestic and Rest of The World Financial Accounts ........................... 37

Table 4.9. Financial Account of Households .......................................................... 38

Table 4.10. Financial Account of Other Institutions ............................................... 39


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LIST OF FIGURES

Figure 2.1. Basic framework of SAM ........................................................................ 7

Figure 2.2. Inter-account Relationships in SAM .......................................................8

Figure 2.3. Basic Framework of The Indonesias FoF ..............................................9

Figure 2.4. Basic Framework of The Indonesias FSAM ........................................ 12

Figure 2.5. Data Format of The Indonesias FSAM 2005 ....................................... 13

Figure 3.1. The Construction Process of The Indonesias FSAM 2005 .................. 24


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ABSTRACT

In various discourses of economic policy analyses, decision-makers are

often confronted with difficult task of explaining the impacts of a certain monetary

or fiscal policy on the behavior of economic agents. Accordingly, it highlights the

importance of filling the research needs in this area which is currently limited. But it

should be noted that such studies require accurate information regarding the

transaction channel, including its transmission mechanisms to provide a

comprehensive macro analysis. Therefore, it will not only enrich the analysis

conducted by policymakers but also contributes, more broadly, the efforts to

improve social welfare.

Based on this specific requirement for information, BI and BPS-Statistics

Indonesia collaborated to establish a data system known as FSAM. It comprises of

a matrix that provides comprehensive and consistent information on the

interdependency between the financial and real sectors based on available

statistical data. The provision of FSAM data for Indonesia corresponds to the long

standing efforts taken by BI to seek information concerning monetary policy

transmission to the real sector through financial transaction channels. The

publication of FSAM is based on the idea to provide a data framework that can

relate economic variables in the real sector to variables in the financial sector.

Statistically, FSAM data represents an integration of SAM data, which

portrays real sector data, with the FoF that records financial activities in an

economy. The BPS-Statistics Indonesia officially has made available these data

sources at regular intervals. However, to integrate both data sets into one

statistical product that provides comprehensive information involves a complicated

process and requires proper resources.

Efforts to compile FSAM began in 2005 through a FSAM pilot project using

simulations based on data for the year 2000. Simulation results from the FSAM


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2000 were considered successful despite several limitations, in particular the

unavailability of specific data needs, for example the holding of financial

instruments by households and non-financial institutions, as well as data on the

allocation of domestic real investment. Therefore, to fill the gaps, proxies were

used.

Then in 2006 and 2007, BI again collaborated with BPS-Statistics Indonesia

to compile the Indonesias FSAM 2005. In general, the FSAM was compiled by

integrating SAM and FoF data into a comprehensive, balanced and consistent

FSAM matrix using the Saving-Investment Gap mechanism, reconciling between

gross savings data and domestic real investment data. The mechanism represents

a key in understanding of how information from the real sector is transmitted to the

financial sector, and vice versa.

The FSAM framework is compiled in the form of a symmetric matrix

classified into nine components, namely Production Factors, Institutions, Industrial

Sectors, Trade and Transport Margins, Commodities, Capital, Indirect Taxes and

Subsidies, Financial Instruments, and Rest of the World. Disaggregation of the

individual components is conducted depending on analysis needs and the

availability of supporting data. The resulting disaggregation is Indonesias FSAM

2005 framework which has 79 component dimensions.

The primary data sources used to compile FSAM 2005 are the Input-Output

(I-O) Table, SAM and FoF, which were supported by the results of specific surveys,

such as Survey on Input and Output (SKIO-Survei Khusus Input-Output), Survey

on Household Savings and Investments (SKTIR-Survei Khusus Tabungan dan

Investasi Rumah Tangga), and Survey on Private Businesses (SKPS-Survei

Khusus Perusahaan Swasta).

Macroeconomic analyses can be performed based on the results of FSAM

in order to describe the interdependency among economic sectors, as well as

among institutions, economic variables, and financial instruments. FSAM based

analysis can be conducted in three forms according to level of complexities,


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starting from: 1) descriptive analysis to describe the static phenomena of the

economy (economic structure) in 2005; to 2) behavioral analysis to explain the

impacts of a particular economic policy on various economic variables (often

described as policy analysis); and 3) economic modeling and forecasting, which

applies FSAM to develop economic models, for instance a model of general

equilibrium.

As experienced by other economies which have started to prepare FSAM

data (Euro Area, China, Cameroon, Turkey and Pakistan), the compilation of

FSAM data in Indonesia is facilitated by the availability of SAM and FoF data as

well as the need to better understand the interactions between such data. The

FSAM 2005 is the first FSAM published in Indonesia. Compilation of the FSAM

based on data from the year 2005 was only conducted in the year 2006-2007 due

to complexity of data needs. Going forward, it is expected that FSAM can be

routinely produced in every five years, in conjunction with the availability of I-O

table and SAM publications.

The Indonesias FSAM 2005, a collaborative effort between BI and BPS-

Statistics Indonesia, is published to serve wider publics interest. It is expected that

FSAM data can be used by policy-makers and academics alike in various analyses

and policy simulations such as Structural Path Analysis (SPA) and Financial

Computable General Equilibrium (FCGE) model.


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CHAPTER I - INTRODUCTION

1.1. Background

Some issues that plague economies, particularly in developing countries

including Indonesia, are the prevailing disparity in income distribution and still high

the unemployment rate despite continued economic growth. There have been a

number of researches in this area to address these issues, such as the ones

conducted by Kutznets (1956, 1963), Adelman and Morris (1971, 1973), Ahluwalia

(1976), etc.

To unravel the interrelations between economic growth on the one hand and

the twin issues of unequal income distribution and unemployment on the other,

experts specializing in economic development and statistics have devoted their

efforts into building a data framework that can provide better insight into the

interdependency among these three issues (economic growth, income inequality

and unemployment). A data framework that can explain such critical issues is

SAM.

Through joint cooperation among three institutions, namely BPS-Statistics

Indonesia, the Institute of Social Studies (ISS) in The Hague as well as Cornell

University in Ithaca, USA, a SAM for 1975 and 1980 was successfully compiled for

Indonesia. Afterwards, composing a SAM for Indonesia has become a regular task

for BPS-Statistics Indonesia every five years. The most recent publication of SAM

by BPS-Statistics Indonesia is for the year 2005.

In the aftermath of the economic crisis that hit Indonesia in 1997/1998, one

of the economic phenomena that have occurred is a disconnect between the

financial and real sectors. On the one hand, financial sector indicators

demonstrated positive performance, for instance growth in the equity and money


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markets, yet on the other hand, this growth did not subsequently translate into real

sector growth.

This paradox spurred interest in exploring the interdependency between the

financial sector and real sector in a comprehensive and integrated data framework.

The data framework is expected to better explain the transmission channels from

the real sector to the financial sector, as well as the impacts of the financial sector

on the real sector.

Conceptually, SAM data framework is capable of explaining all

interconnected economic activities in a country, both real sector activities andfinancial sector activities. However, SAM framework has its limitation especially

when explaining financial sector activities. Under this framework, the relationship

between real sector and financial sector performance is explained using capital

account that records the gross saving information of institutions (households, the

government and businesses) operating in an economy. Gross saving is the

difference between income and expenditure available for financing physical

investment.

In practice, however, the gross saving of economic actors is not only spent

on financing physical investment, such as building a house, office space, main

roads, warehouse, etc. Saving could also be spent on financing non-physical

investment (portfolio investment), such as purchasing stocks, time deposits, foreign

exchange, etc. The source of funds for real and financial investment not only stems

from gross saving but can also originate from other sources, such as loans, bond

issuances, or the raised from another source, for instance the withdrawal of

deposits held at a bank. Such interaction requires transactions, which stimulates

dynamics in the asset and liability in the balance sheets of economic actors.

If the relationship between gross saving and real investment, as well as

between financial sources and uses within SAM framework can be obtained, this

will provide more comprehensive understanding of the movement of financial

sector in relation to real sector activities, and vice versa. This fundamental notion


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provides the basis for the compilation of an FSAM, a term widely known in the

International Best Practices. FSAM data is compiled to explain interactions

between the financial and real sectors, by disaggregating the capital account in

SAM. Hence, FSAM data framework is expected to elucidate interdependency

between the financial and real sectors in an effort to clarify a range of financial

transmission channels in the economy. FSAM data framework is expected to allow

better understanding over the impacts of various monetary policies on real sector

performance, and vice versa, as well as the possibility of identifying more structural

distortions.

Capital account is rearranged in SAM to better elaborate information on

saving and investment, as well as the assets and liabilities of economic actors.

This is performed by incorporating FoF data into capital sub matrix of SAM. It is

worth mentioning that the FoF data in Indonesia have been compiled through

cooperation among BI, MoF as well as BPS-Statistics Indonesia on quarterly and

annual basis.

Acknowledging the importance of an FSAM data framework, BI and BPS-

Statistics Indonesia launched an initiative to construct an FSAM data in 2005

through an FSAM simulation pilot project using the year 2000 data. These

simulation results were completed despite several limitations due to

data/information unavailability; therefore, a number of proxies were used to fill in

the gaps. Efforts to compile FSAM were continued through cooperation between BI

and BPS-Statistics Indonesia using 2005 data, which was conducted in two

phases, namely 2006 (Phase I) and 2007 (Phase II).

Phase I focused on formulating the concept, acquiring primary data through

surveys and consolidating secondary data. This resulted in limited and partial data

for the FSAM 2005 (temporary data). There were limitations stemming from

incomplete data sources, particularly data unavailability on Non-Financial

Corporations and the Structure of Industrial Input-Output. Therefore a proxy from

the available data was set up for the estimations. Another work to be done is


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reconciling macroeconomic equilibrium in FSAM with other aggregate macro data,

such as the I-O Table. Consequently, statistical discrepancies are put in the

balancing items.

Phase II focused on the Phase I FSAM data continuation, to produce final

FSAM 2005. In addition, some descriptive analysis simulations are performed. The

Indonesia FSAM 2005 represents a final, comprehensive, balanced and consistent

result that covers all transactions among sectors related to the I-O Table, including

the distribution of income and expenditure from SAM data, and financial

transactions from FoF data. All data were reconciled into an integrated matrix that

kept its consistency and balance. The integration of I-O Table, SAM and FoF data

was performed through the reconciliation of saving and domestic real investment

data. Such data integration provides a comprehensive picture of the

interdependency between the real sectors and financial sectors.

1.2. Goal and Objectives

The publication of the Indonesias FSAM 2005 is aimed at providing

data/information on interdependency between the real and financial sectors as wellas explaining the transmission mechanism of a particular policy on economic

structure and vice versa. Data included in the Indonesia FSAM 2005 is presented

in a comprehensive, integrated and consistent data framework.

It is hoped that this publication will meet the general public best interest,

more specifically provide the long awaited tool to assist policy analysis as well as

better equip deeper economic study.

1.3. Publication Outlines

This publication consists of five chapters that generally cover the following

areas of interest:

1. Chapter I: Introduction, explaining the background, goal and objectives of the

publication as well as the outlines.


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2. Chapter II: Description of the Indonesias FSAM 2005, including the concept of

SAM and FoF integration, the form and meaning of the FSAM 2005 framework

as well as its classifications.

3. Chapter III: Compilation process of FSAM 2005.

4. Chapter IV: Analyses of FSAM 2005.

5. Chapter V: Conclusion.


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CHAPTER II - DESCRIPTION OF THE INDONESIAS FSAM 2005

2.1. FSAM: Integration of SAM and FoF

The FSAM data framework represents integration between SAM, which

covers transactions in the real sector, and FoF that reflects financial transactions

among institutions. In order to get better grasp of FSAM as an independent matrix,

at first the basic concepts of each data system used to build FSAM should be

described. The capital account components consisting of investment and saving

serve as the building block primary connecting two data systems in such a way that

SAM and FoF are integrated and consistent.

The core data required to construct SAM comes from the Indonesia I-O

Table of 2005. Several adjustments are made to industrial classification to make it

in line with the SAM classifications which had been made consistent earlier with

the FSAM data framework. In addition, supplementary data sources related to the

account information of institutions was also included (see SAM publication and

Indonesia I-O Table for detailed explanation).

As with other statistical products of national accounts, the concept of SAM

and FoF publications are also based on the System of National Accounts (SNA)

1993. However, as the concept of FSAM publication is not explicitly explained in

SNA 1993, the current compilation was compiled with reference to international

best practices, namely those from the European Union, Cameroon, Turkey,

Pakistan and Peoples Republic of China.


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Figure 2.1. Basic framework of SAM

Source:Adapted from Indonesian FSAM Technical Assistance material by Prof. Iwan J. Azis, CornellUniversity.Note:

Economic transaction flowsXij Notation of expenditure transaction from column j received by row i.

The notion that SAM is a data system that can be used as an economic

analysis tool is based on the economic circular flow concept. As shown in Figure

2.1, production activities generate value added as a production factor income at

the amount (X13). Thereafter, production factor income will be distributed to the

institutional sectors in the form of income distribution (X21), which is further used by

the institutional sectors to consume commodities produced by industrial sectors

(X32). Meanwhile, in terms of production activity, there are transactions among the

industrial sectors (X33), and in the activity of income distribution there will be

redistribution (transfer) transactions among the institutional sectors (X22). Such a

circular flow of economic transactions forms the basis of SAM analysis to facilitate

the study of interdependency among industrial sectors, production factors and

institutional sectors due to production, income distribution and transfer activities, as

well as consumption, saving and investment.


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The SAM data framework system provides picture of transactions in the real

sector. In general, SAM is a square matrix that describes linkages between

production factor accounts, institutional accounts, industrial sector accounts and

other accounts. Rows in the SAM matrix indicate income and columns show

account expenditure. Therefore, the contents of a SAM matrix can highlight inter-

account relationships. The descriptions and meanings of inter-account

relationships under the Indonesian SAM framework are explained in Figure 2.2.

Figure 2.2. Inter-account Relationships in SAM

FoF is a data system designed to show financial transactions among various

institutional sectors, for example the government, state-owned companies,

insurance agents, commercial banks, non-financial private companies, etc.

Monetary and Financial Statistics Manual (MFSM) 2000 states that FoF is a

consolidated account of the financial institution sectors which also records financial

activities of other institutional sectors.

Each sector in FoF has a set of sources and uses of funds, which are

reflected in the activities of buying and selling financial instruments, such as time


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deposits, bonds, loans, etc. These instruments represent either assets or liabilities

of each sector. As the system also includes the rest of the world sector, it is also

known as an open system for each transaction. In other words, each financial

instrument bought in one sector will have a mirror image in the form of selling

activities in another sector. FoF can also be viewed as a data set designed to show

how saving is connected with surplus and deficit sectors (Figure 2.3).

FoF data is presented in matrix form. The columns represent sectors and

the rows identify various types of financial instruments. In the FoF, sectors are

institutions that perform financial transaction activities. Each sector has two

columns, the first describes changes of assets (uses of funds) and the second

shows changes of liabilities (sources of funds). Increases in assets or liabilities of a

sector are portrayed by a positive financial flow, whereas any decreases are

indicated by a negative financial flow. In general, the FoF scheme is outlined in

Figure 2.3.

Figure 2.3. Basic Framework of The Indonesias FoF

Remarks:Financial flows from surplus sectors (net lending) to deficit sectors (net borrowing)Inter-sectoral financial flows through financial instruments


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2.2. Country Experiences in FSAM Compilation

As discussed earlier, FSAM is relevant in explaining the relationships

among variables related to financial and non financial sectors. FSAM as a data

framework, reflecting an integration of data systems deriving from SAM and FoF,

had been developed in a number of countries/regions such as Europe, Turkey,

China, Pakistan, and Cameroon, just to name a few. (see appendix 1)

For Europe region, it is known as Euro Area Accounting Matrix (EAAM).

EAAM provides a picture of production structure, inter-institutions flows and

financial flows within Euro region. The first version of annual EAAM was completed

using 1999 data. EAAM can be used to analyze economic structure, including the

development of financial transaction. EAAM helps provide a better understanding

of the transmission mechanism of monetary policy in the Euro region. EAAM also

offers a consistent and uniform statistical framework both for real and financial

sector economic activities, supported by harmonious statistical concept and proper

classification of economic activities and financial assets.

FSAM based analyses using EAAM revealed that financial institutions

constituted the largest institution to pay and earn interest and hold more than half

of financial assets and liabilities. Meanwhile, non financial institutions are reported

to have financial investments largely in the form of debt securities and stocks.

Meanwhile, Turkey had developed FSAM using its 1996 data. The FSAM

compilation is conducted using a range of data sources, among others, input-

output table, household income and consumer survey, income distribution survey,

banking sector balance sheets, and the balance sheet of Turkeys central bank.

The purpose of FSAM compilation is to build various models for their economy.

Likewise, Cameroon employed FSAM to develop an Integrated

Macroeconomic Model for Poverty Analysis (IMMPA), as a part of an integrated-

quantitative model of macroeconomic analysis to help investigate the impact of

external shocks and government policy on income distribution, job creation and


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poverty. Additionally, IMMPA is also utilized to support and evaluate the strategy of

poverty alleviation.

Meanwhile, Pakistan used 1999/2000s data to develop FSAM. The aim of

constructing FSAM is to produce a core database for an FCGE model applied

specifically to analyze the behavior of public debt.

The construction of FSAM in China began by conducting a broad review on

changes on financial sector, which results in the establishment of a consistent

accounting system for Chinese economy. This study moves further in which

Chinas FSAM is used as an analytical tool in formulating economic policy in China.

The study used multiplier analysis with the major finding was the links between real

economy and financial sector, as well as the contribution of modern financial

system development to economic growth.

The Chinas study uncovered the growing role of households in countrys

asset accumulation especially financial asset. In addition, it was found that non

financial institution met their financing needs through financial system notably in

the form of bank loans. The substantial amount of capital transfers by government

to non financial institutions suggests that government remains a major player in

allocating financial resources in China. Lastly, it showed that financial sectors

continued their leading role in intermediating capital from household sector to non

financial institutions.

2.3. Basic Framework of Indonesias FSAM 2005

The building blocks of Indonesian FSAM 2005 are classified into Production

Factors, Institutions, Production Sector, Capital and Financial. The details aredescribed in Figure 2.4.


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Figure 2.4. Basic Framework of The Indonesias FSAM

FSAM is a data framework that can bridge the limitations found in SAM and

FoF because FSAM presents integrated real and financial sector information. In

general, the format of Indonesian FSAM data framework is classified into nine

components (9x9 matrix), namely: Production Factors, Institutional Sectors,

Industrial Sectors, Trade and Transport Margin, Commodities, Capital, IndirectTaxes and Subsidies, Financial Instruments and Rest of the World (Figure 2.5).

Further disaggregation of the Indonesian FSAM building blocks, from

dimension of 5x5 (Figure 2.4) into 9x9 (Figure 2.5), is aimed at providing better

perspective on the structure of the economy regarding the trade and transport

margin, and taxes and subsidies, as well as separating industries and

commodities. Therefore, transaction interdependency between industries and

commodities can be observed more closely.


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Figure 2.5. Data Format of The Indonesias FSAM 2005

2.4. Classification of Indonesias FSAM 2005

The data framework of Indonesias FSAM 2005 emphasizes interlinkages of

economic activities among institutions (financial and non-financial), with further

emphasis on income distribution. For this purpose, the classification of institutions

in the FSAM framework is further disaggregated into poor and non-poor

households in rural and urban areas. Meanwhile, financial transactions do not

differentiate between transactions in rupiah and foreign currencies.

Even though the basic framework of FSAM is derived from SAM, the

classifications of SAM adopted in the construction of the Indonesias FSAM 2005

are slightly different than the SAM classifications published by BPS - StatisticsIndonesia in the following senses:

The capital account in FSAM was compiled by disaggregating the SAM

capital account and aggregating the financial instruments in the FoF

framework.


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Institutional account was disaggregated to central bank, bank and non bank

financial corporations, non-financial corporations, government and

households. Furthermore, households are classified as poor and non-poor

households in rural and urban areas.

In more detail, the classification of Indonesia FSAM 2005 consists of a 79 x

79 matrix (row x column) are as follows:

1. Production Factors:

a. Labor (1)

b. Non-Labor (2)

2. Institutional Sectors:

a. Central Bank (3)

b. Corporations:

i. Financial Corporations

1. Bank (4)

2. Non-Bank (5)

ii. Non-Financial Corporations (6)

c. Government (7)d. Households

i. Rural area

1. Poor (8)

2. Non-Poor (9)

ii. Urban

1. Poor (10)

2. Non-Poor (11)

3. Industrial Sectors

a. Agriculture, Livestock, Forestry and Fishery

i. Formal (12)

ii. Informal (13)

b. Mining and Quarrying

i. Formal (14)


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ii. Informal (15)

c. Manufacturing Industries

i. Oil and Gas

1. Formal (16)

2. Informal (17)

ii. Non-Oil and Gas

1. Formal (18)

2. Informal (19)

d. Electricity, Gas and Water Supply

i. Formal (20)ii. Informal (21)

e. Construction

i. Formal (22)

ii. Informal (23)

f. Trade, Hotels and Restaurant

i. Formal (24)

ii. Informal (25)

g. Transport and Communication

i. Formal (26)

ii. Informal (27)

h. Finance, Real Estate and Business Services

i. Formal (28)

ii. Informal (29)

i. Other Services

i. Formal (30)

ii. Informal (31)

4. Trade Margin and Transport Cost (32)

5. Commodities

a. Domestic Products

i. Agriculture, Livestock, Forestry and Fishery (33)


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ii. Mining and Quarrying (34)

iii. Manufacturing Industry (35)

iv. Electricity, Gas and Water Supply (36)

v. Construction (37)

vi. Trade, Hotel and Restaurant (38)

vii. Transport and Communication (39)

viii. Finance, Real Estate and Business Services (40)

ix. Other Services (41)

b. Import Products

i. Agriculture, Livestock, Forestry and Fishery (42)ii. Mining and Quarrying (43)

iii. Manufacturing Industry (44)

iv. Electricity, Gas and Water Supply (45)

v. Construction (46)

vi. Trade, Hotel and Restaurant (47)

vii. Transport and Communication (48)

viii. Finance, Real Estate and Business Services (49)

ix. Other Services (50)

6. Capital

a. Central Bank (51)

b. Corporations:

i. Financial Corporations

1. Bank (52)

2. Non-Bank (53)

ii. Non-Financial Corporations (54)

c. Government (55)

d. Households

i. Rural

1. Poor (56)

2. Non-Poor (57)


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ii. Urban

1. Poor (58)

2. Non-Poor (59)

7. Taxes and Subsidies

a. Indirect Taxes (60)

b. Subsidies (61)

8. Financial Instruments

a. Official Reserves Assets (62)

b. Currencies (63)

c. Demand Deposits (64)d. Saving Deposits (65)

e. Time Deposits (66)

f. Bank Indonesia Certificates (67)

g. Government Bonds (68)

h. Other Long-Term Securities (69)

i. Short-Term Securities (70)

j. Working Capital Credits (71)

k. Investment Credits (72)

l. Consumption Credits (73)

m. Non-bank Credits (74)

n. Trade Credits (75)

o. Shares and Equities (76)

p. Insurance and Pension Fund Reserves (77)

q. Others (78)

9. Rest of the World (79)


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CHAPTER III - COMPILATION METHOD OF

INDONESIAS FSAM 2005

3.1. Transformation of SAM into FSAM

As a data framework that integrates the SAM matrix and FoF table into the

FSAM format, at first SAM must be transformed according to the classifications of

FSAM data framework. The SAM data framework used to compile FSAM was

transformed to correspond to the purposes and objectives of FSAM.Transformations were predominantly made to the classification of production

factors, institutional sectors, industrial sectors, commodities (domestic and import)

and capital account. No changes were applied to other accounts (foreign account,

indirect taxes and subsidies).

Production factor account used in FSAM compilation was divided into two

types of account, namely labor and capital account. Institutional sectors

classification was expanded, particularly for institutions and household accounts.Institutional sector accounts in SAM classification were specified as financial

corporation accounts and non-financial corporation accounts. Financial institutions

are described as central bank, banks and non bank financial corporations.

According to SAM, households are classified as agricultural and non agricultural

households; also as rural and urban households. Meanwhile, in FSAM, households

are classified into poor and non-poor (based on poverty criteria from BPS -

Statistics Indonesia) as well as rural and urban areas.

As a data framework that will be used as a tool to guide policy formulation in

alleviating poverty and empowering the informal sector, therefore, the classification

of production sector in FSAM was capable of providing accurate picture of the

informal economy. To this end, the production sector under SAM classification was

grouped into ten sectors, namely: Agriculture, Livestock, Forestry and Fishery;

Mining and Quarrying; Oil Manufacturing Industry; Non-oil Manufacturing Industry;


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Electricity, Gas and Water Supply; Construction; Trade, Hotel and Restaurants;

Transports and Communications; Finance, Real Estate and Business Services and

Other Services. These sectors are further disaggregated into formal and informal

sectors. As regards to the classification of commodities, they were grouped

according to their sources (domestic and import), consistent with the nine

commodity groups categorized in the production sectors.

3.2. Inserting FoF Data into FSAM Framework

As an extension of SAM, FSAM derived by disaggregating the capital

account and inserting intra-institutional financial transactions from FoF. For thepurpose of compiling FSAM, adjustments were made to several sectors and

financial transactions. Several sectors/institutions were merged even though data

for each sector was available, whereas certain sectors were disaggregated to

make it in line with FSAM classifications.

Financial transactions in the Indonesias FoF generally include all financial

instruments available in the current Indonesia economy. Most of these financial

instruments are traded in the market, while others are not. In FSAM compilation,

some financial instruments are presented individually, some are presented in

aggregates of similar instruments, and others will be merged as one instrument.

Financial instruments used in FSAM are slightly different than those used in

FoF. This was to avoid misinterpretation in the analysis. Financial instruments

presented in detail are:

Other Securities: Short-term and Long-term Securities

Non-bank Credit: Other Institution Credit and Trade Credit

FoF components that have been adjusted according to FSAM classification

then inserted into Capital Account, Financial Instruments and Rest of the World.

The intersection of Capital Account row (including Rest of the World) with Financial

Instruments column shows the existence of sources of fund (liabilities) according to

the financial instruments of each institution (including Rest of the World).


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Meanwhile, the intersection of Financial Instrument row with Capital Account

column (including Rest of the World) evidences uses of funds (asset) or financial

investment by each institution (including Rest of the World). The difference

between financial assets and liabilities represents the difference between savings

and physical investment, which in turn reflecting capital outflow/inflow with the Rest

of the World sector.

3.3. Supporting Surveys

Several supporting surveys were conducted to complement the information

and data requirement in FSAM 2005 compilation are:

3.3.1. Household Savings and Investment Survey (SKTIR-Survei Khusus

Tabungan dan Investasi Rumah Tangga)

SKTIR was conducted to obtain data on household transactions, seeking

the ways households create and manage their savings. The survey is also used to

gather information on household investment, both in physical and financial terms.

Household institution concept applied in Indonesia FSAM 2005 covers all

household income (wage/salary and mixed income from a business venture with

inseparable financial reports).

The SKTIR 2006 was conducted in ten provinces: Riau, South Sumatera,

West Java, DKI Jakarta, Central Java, DI Yogyakarta, Bali, West Kalimantan,

North Sulawesi and Central Sulawesi. The sample size for the SKTIR was 5,000

households.

Sampling framework used to select the census block originated from the

National Socio-Economic Survey (SUSENAS-Survei Sosial Ekonomi Nasional)

2005, which differentiates urban and rural areas in each selected regency/city.

Meanwhile to select the households, sample household cluster was classified into

high, middle and low income category (recipient of the government cash transfer

program or BLT-Bantuan Langsung Tunai).


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A household was classified as poor if its expenditure fell within the poverty

threshold established according to their residences in either urban or rural areas.

The poverty threshold, taking the consumption module panel of SUSENAS in

February 2005 as a reference, was set at the expenditure of Rp150,799 for urban

areas and Rp117,259 for rural areas per capita per month.

The SKTIR results were used in FoF and SAM construction. In FoF

compilation, SKTIR results were used to calculate saving ratio and to gain

information on changes in financial instrument, either in assets or liabilities. In SAM

construction, SKTIR results were mainly used to identify income allocation of labor

and non-labor production factor to households (income from factors ownership) as

well as household transfers, saving and physical investment.

3.3.2. Input-Output Survey (SKIO-Survei Khusus Input-Output)

SKIO was conducted to obtain basic data required in Input-Output Table (IO

2005) compilation, including data on input structure (costs), allocation of goods and

product distribution for particular economic activities, information on employment

structure, production indicators, prices and other supporting information.

Business activities covered in SKIO 2006 include several activities in goods

and services industry by legal entities (formal industry) and non-legal entities

(informal industry) or non-directory corporations (PND-Perusahaan Non-Direktori)

and cottage industries. Activities in the goods industry consist of agriculture,

mining, unincorporated enterprises and construction. Meanwhile, activities in the

services industry consist of supporting transportation services (loading docks,

terminal/parking lots, travel agents), business services (advertising, consultancy

and legal), private community and social services (hospitals, clinics), private

entertainment and recreation services (recreation parks, nightclubs and karaoke),

individual and household services (hair salons, beauty salons, laundry, tailors and

household appliances repair).

SKIO 2006 was conducted in 15 provinces, namely North Sumatera, West

Sumatera, Riau, South Sumatera, Lampung, DKI Jakarta, Central Java, DI


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Yogyakarta, East Java, West Nusa Tenggara, West Kalimantan, South

Kalimantan, Central Sulawesi and South Sulawesi. Sample consists of 3,100

respondents, of which 2,200 were from the goods industry and 900 from the

services industry. Sample distribution was based on population activities

information from the Agricultural Census (ST-Sensus Pertanian) 2003 for

agriculture related activities and Integrated Business Survey (SUSI-Survei Usaha

Terintegrasi) for mining, manufacturing and construction related activities. This

survey was used particularly to complete the data for the IO table.

3.3.3. Trade and Services Sector Survey (SKSPJ-Survei Khusus Sektor

Perdagangan dan Jasa)

SKSPJ data collection began in 1994 but the activities involved changed

every year due to the numerous activities classified as trade and services industry.

The objective of SKSPJ was to improve coverage, methodology, quality of

production and price indicators, input structure and output allocation, revision of

GDP (annual and quarterly) estimation and initial IO table preparation for 2005.

SKSPJ data was used to fill the gaps in commodity margin and total margin in

FSAM 2005.

3.3.4. Private Non Financial Corporation Survey (SKPS-Survei Khusus

Perusahaan Swasta)

SKPS was conducted to figure out the role of economic players/private

institutions in production, consumption and investment activities in the national

economy, by compiling the year-end balance sheets, as well as profit and loss

statements from private institutions.

The objective of SKPS is to identify information on private non-financial

corporations characteristics, namely: type of business, structures of assets,

liabilities, input, output, etc. In addition, SKPS was conducted to elicit information

on the business transactions of private non-financial corporations through financial

reports including the year-end balance sheets and profit and loss statements.


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SKPS, performed from February to December 2007, was intended to

complete private corporation data in FoF compilation, namely structure of

corporate sources and uses of funds. By using SKPS results, we could observeprivate non-financial corporation primary balance sheets structures classified

according to their type of business. SKPS results are used in constructing FoF

structure for private non-financial corporation, which is subsequently consolidated

into FoF.

3.4. SAM and FoF Reconciliation

FSAM construction was preceded by SAM and FoF compilation; meanwhile

SAM construction was preceded by IO table construction.

Following the completion of SAM and FoF 2005, and both were successfully

balanced, they were transformed into a 79 x 79 FSAM framework. Reconciliation is

performed by integrating both matrices to make the FSAM matrix balanced.

Data from SKTIR was used to calculate savings data and the formation of

gross fixed capital for households. Some adjustments were applied to SKTIR

results, using several related data sources. For example, the subsidies/transfers

received by households were reconciled with Government Budget, and transfers inthe form of interest received on savings by households were cross checked with

bank data.

In FSAM, rest of the world savings in the FoF table, consisting of net export

revenue plus net factor income, were added with net transfer value originating from

Balance of Payments data.

Balanced FoF data is subsequently integrated into the FSAM framework.

The value of gross savings is placed at the intersection of capital account rows (51-59) and institution account columns (3-11), whereas investment data is located at

the intersection of commodity account rows (33-50) and capital account columns

(51-59).

Financial instrument data, originating from uses of funds in FoF, is located

at intersecting cell between financial instrument account rows (62-78) and capital

account and rest of the world account columns (51-59 and 79). Financial


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instrument data from FoF sources of fund is located at the intersection of capital

and rest of the world account rows (51-59 and 79) and financial instrument account

columns (62-78). Next step is to sum all cells in each columns and rows. Totalvalue of each row and column in FSAM must be balanced and consistent.

Compiling process of Indonesia FSAM 2005 as previously described,

generally performed in three stages, namely data collection, tabulation and

reconciliation (Figure 3.1). Data is collected from readily available sources such as

GDP, Government Budget and other regularly issued data. In addition, FSAM data

is also supplemented by supporting surveys for which the results are subsequently

tabulated and adjusted according to the data requirement of FSAM.

Figure 3.1. The Construction Process of The Indonesias FSAM 2005

FLOW OF

FUNDS

INPUT OUTPUT

TABLE

SOCIAL

ACCOUNTING

MATRIX

CENTRAL BANK

ACCOUNT

GOVERNMENT

ACCOUNT

BANK ACCOUNT

OTHER DOM.

ACCOUNT

REST OF THE

WORLD ACCOUNT

FSAM 2005

Specification of

FSAM Indonesia

RECONCILIATION

INCOME

DISTRIBUTION

R

E

C

O

N

C

I

L

I

A

T

I

O

R

E

C

O

N

CI

L

I

A

T

I

O

N

National Socio-

Economic Survey

Survey of Large and

Medium

Manufacturing

Industry & Survey of

Small Scale &

Household Industr

National Labor Force

Surveys

WagesSurvey

Intercensal

Population Survey

Gross Domestic

Product

Input Output

Survey

Private

Corporation

Survey

Household

Saving and

Investment

Survey

Capital Formation

Integrated Business

Surve

Trade &

Services Survey

Primary Data SUPPORTINGSURVEY

Agriculture Census

Government Budget

International

Investment Position

Financial Statement

DATA COLLECTION TABULATION


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CHAPTER IV - ANALYSES OF INDONESIAS FSAM 2005

4.1. The Result: Indonesias FSAM 2005

The Indonesias FSAM 2005 is published in two versions, namely an

aggregated 22x22 FSAM matrix (Table 4.1) and 79x79 matrix (Appendix 4). The

22x22 matrix is the aggregated version of the 79x79 matrix with classifications that

can be found in Appendix 2. Despite its more aggregated form, the 22x22 matrix

nevertheless provides complete macro descriptions of Indonesias economicstructure. For those interested in exploring the more detailed descriptions of

Indonesias macro economy, they can look at the 79x79 matrix.

Disaggregation of the 22x22 matrix into 79x79 matrix is conducted by

elaborating the institutional components (banks, non-bank financial corporations

and non-financial corporations), households (rural or urban; poor or non-poor),

production sector (formal or informal), commodities (domestic or import) as well as

17 financial instruments.

4.2. Descriptive Analysis

For a complete descriptive analysis, derivative tables were produced as can

be seen below.

4.2.1. Indonesias Economic Structure 2005

Indonesias economic structure based on FSAM 2005 revealed thateconomic output in 2005 was primarily used for intermediate input. The remaining

was gross value added (GVA) on production at factor costs, which consist of

payments for labor (wage and salary) and operating surplus.


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Table4.1.

TheIndone

siasFSAM2

005(22x22Matrix)

()


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In 2005, total output based on FSAM stood at Rp5,637.7 trillion. From that

amount, Rp2,760.8 trillion (48.97%) was intermediate input and Rp2,876.9 trillion

(51.03%) was GVA (see Table 4.2). Therefore, GDP can be calculated by adding

GVA with import duties in the amount of Rp62.3 trillion and subtracted by import

subsidies in the amount of Rp42.2 trillion. Hence, the difference between FSAM

and data published on GDP value which was recorded at Rp2,896.9 trillion is 4.4%.

This difference is largely attributable to the broader coverage of GDP data in FSAM

compared to the published GDP.

Table 4.2. Indonesias Economic Structure 2005

ItemsAmount

(Billions of Rp)%

Intermediate Input 2,760,764 48.97

Factor Income of Labor 1,486,179 26.36

Factor Income of Non-Labor 1,344,475 23.85

Indirect Taxes 112,164 1.99

Domestic Subsidies -65,926 -1.17

Total Output 5,637,656 100.00

In Indonesias FSAM 2005 framework, labor factor income consist of

compensation of employees covering wages and salaries of paid workers as well

as imputed wages and salaries from unpaid workers, including farmers (owners of

farm), self-employed entrepreneurs assisted by permanent workers or temporary

workers, and self-employed entrepreneurs helped by their family. Around 26.36%

of total output or Rp1,486.2 trillion is compensation of employees, meanwhile non-

labor factor income (e.g. rent, interest) is recorded at Rp1,344.5 trillion or 23.85%.


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Table 4.3. Production Structure by Industrial Sectors in Indonesia(Percentage)

Industrial Sectors IntermediateInput

ValueAdded

Total

Agriculture, Livestock, Forestry and Fishery 26.41 73.59 100.00

Mining and Quarrying 18.73 81.27 100.00

Manufacturing Industry 62.63 37.37 100.00

Electricity, Gas and Water Supply 64.40 35.60 100.00

Construction 65.08 34.92 100.00

Trade, Hotels and Restaurants 41.69 58.31 100.00

Transport and Communications 51.53 48.47 100.00

Finance, Real Estate and Business Services 32.49 67.51 100.00

Other Services 40.73 59.27 100.00

Total 49.38 50.62 100.00

Looking at economic structure from production side, it is found that the

share of intermediate input especially in the secondary and tertiary sectors could

exceed 50%, such as construction (65.08%); electricity, gas and water supply

(64.40%); manufacturing industry (62.63%); and transportation (51.53%).

Whereas, the share of intermediate input in the primary sectors is less than 50%.

The two smallest share of intermediate inputs occurred in mining and quarrying

sector (18.73%) and agriculture, livestock, forestry and fishery sector (26.41%)

(see Table 4.3).

4.2.2. Structure of Income and Expenditure by Institution

Income received by institutions such as households, financial and non-

financial corporations as well as the government is originated from wages and

salaries, operating surplus, capital ownership and transfers. Households as owners

of the labor factor income received all the incomes in the form of wages and

salaries as well as imputed wages and salaries. Labor factor incomes as a

compensation of employees (paid and unpaid) cover gross wages and salaries (in

cash or in kind) as well as imputed wages and salaries. Income from capital comes

from economic activities performed by the respective institution. Other incomes


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come from production factor ownership operated by other institution or other

transfers. Institutional income includes rest of the world income sourced from

production factor ownership abroad or transfers.

Total income received by institutions is subsequently spent for consumption

(final consumption is conducted by households and government) and other

expenditures such as interest payments, dividend and other transfers, for both

domestic or foreign institutions. The difference between total income received by

each institution for expenditure consumption and other expenses represents

institution saving.

Labor factor income including compensation of employees working abroad

amounted to Rp1,487.4 trillion, whereas non-labor factor income as a production

factor, including rent from abroad amounted to Rp1,364.4 trillion (total income of

non-labor factor).

Labor factor income in domestic economy is received mostly by households,

totaling Rp1,484.0 trillion. The remaining is recorded in the income side of rest of

the world account, as foreign labor factor income. Wages and salaries are mostly

received by non-poor households. Non-poor urban households accounted for more

than half of total wages and salaries of Rp923.2 trillion, whereas non-poor

households in rural areas received Rp526.3 trillion. In contrast, poor urban

households received only a minor fraction, more specifically at Rp11.0 trillion,

whereas poor households in rural areas received Rp23.5 trillion.

Meanwhile, allocation of non-labor factor income received by households

was Rp436.0 trillion. The disparities, as for labor factor income, also occurred for

the income allocation of non-labor factor income, precisely between poor and non-

poor households. Non-poor urban households received non-labor factor income of

Rp239.3 trillion, whereas non-poor households in rural areas received Rp176.6

trillion. Poor households in both urban and rural areas received the lowest income

for non-labor factor totaling Rp8.3 trillion and Rp11.8 trillion respectively (see Table


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4.4). It can be observed that the inequality in the ownership of labor and non-labor

factors among households is responsible for such an income disparity.

Table 4.4. Income Allocation by Institution(Billions of Rupiah)

Corporations as producing institutions that utilized production factors, which

are self-owned or owned by other institutions received the largest chunk of

allocation, amounted to Rp802.0 trillion. Most of the non-labor income received by

corporations was received by non-financial corporations totaling Rp738.4 trillion,

whereas financial corporations (excluding the central bank) received Rp63.6

trillion. The central bank received Rp17.0 trillion from non-labor income. Non-laborincome is the primary input for corporations in running their business. Government

only receives income allocation from transfer.

Meanwhile other income, such as investment income (shares, capital and

other non-labor factors) invested either domestic or abroad as well as transfers, is

mostly received by government (Rp655.3 trillion). In comparison, corporations


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received other income totaling Rp202.1 trillion. Most other income received by

corporations is reallocated to other financial corporation (insurance, finance

company, etc) amounted to Rp144.1 trillion, and non-financial corporations

received Rp57.9 trillion.

It is worth noting that other income allocations and transfers received by the

urban non-poor exceeded those received by poor urban households. Households

received transfer amounted to Rp271.3 trillion which mostly received by urban non-

poor households at Rp200.8 trillion. On the other hand, urban poor households

only receive a mere Rp6.5 trillion, representing the smallest share of allocation

among all households. Other incomes, such as transfers, received by poor

households in rural areas were Rp11.4 trillion. Rural non-poor households received

Rp52.7 trillion (see Table 4.4).

Table 4.5. Share of Sources ofIncome by Institution(Percentage)

Income allocation by institution in percentage terms, particularly households,

showed disparities in income structure among households, reflecting transfer


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dependency of the poor. The share of transfer income for poor households was

larger than that received by non-poor households, in both rural and urban areas.

Approximately 25.19% of urban poor household income stemmed from other

income (transfers) and poor rural households was 24.46%.

It is interesting to note that other income of non-bank financial corporations

represented 69.38% of total income, whereas other income of the central bank was

44.64% (see Table 4.5).

Government does not generate operating surplus from its public services,

since it engages in non-market production. Hence, all government revenue

originates from transfer. Such sources include tax, interest income, dividend, inter-

governmental transfers and other transfer, such as fine and duty.

Income of each institution is used for final consumption activities

(particularly for households and government) and other expenditures such as

dividend payment and other transfer, for instance cash transfer (government

transfers to households), tax payments, etc. The difference between income and

expenditure for consumption and other expenses represents the institutions

saving.

Institutions considered as final consumers are households and government.

The largest portion of consumption expenditure is for non-poor urban households

with the consumption amounted to Rp1,111.4 trillion. Whereas the poor urban

spent some Rp23.8 trillion on final consumption, reflecting the smallest expenditure

as compared to other household groups. Consumption expenditure by the

government for services to the general public was recorded at Rp141.0 trillion.

Besides consumption, household income is also spent on transfers (tax

payment, fine, duty, etc) as well as interest payments, etc. The largest other

expenditure was attributed to urban non-poor households, amounting to Rp106.0

trillion. In comparison, rural non-poor households spent much lower at Rp26.7


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trillion. The smallest other expenditure was done by rural poor households and

urban poor households totaling Rp1.1 trillion and Rp1.7 trillion respectively.

Other expenditures by institutions, financial and non-financial, were used to

pay debts, dividends, taxes, and other transfers. The largest portion of those

expenditures was originating from non-financial corporations amounted to Rp432.8

trillion, followed by financial corporations at Rp151.4 trillion, and central bank at

Rp13.7 trillion. Institutional savings (including retained earnings) were generated

by subtracting the expenditure from total income of each institution, which is

elaborated further in subsection 4.2.3.

On the rest of the world side, Indonesias exports reached Rp977.1 trillion in

2005. In the same year, total imports were Rp974.2 trillion, which consisted of an

import value of Rp820.1 trillion, import margin of Rp91.8 trillion and import tax of

Rp62.3 trillion. Therefore, there was a surplus of Rp2.9 trillion or around $303.6

million (see Table 4.1).

4.2.3. Saving Rate

Saving rate is an important variable for the economy as it opens the way forgreater potential investment (physical and financial), which can increase economic

capacity and in turn stimulate economic growth.

In 2005, the saving rate of all institutions reached 20.79%, suggesting that a

large portion of income (79.21 %) is used for consumption. Gross saving primarily

stemmed from the corporate sectors at the amount of Rp489.1 trillion, accounting

more than 50% of economys gross saving. Relative to their income, the saving

rate of corporations stood at 48.71%. Meanwhile, the saving rates of governmentand central bank were recorded at 16.60% and 55.66% respectively.

Households generated saving worth Rp191.8 trillion, reflecting a saving rate

of 8.75%. In the household groups, urban non-poor households had the highest

saving rate (10.70%), whereas rural poor households had the lowest saving rate

(0.37%) (see Table 4.6).


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Table 4.6. Saving Rate by Institution

4.2.4. Saving-Investment Gap

Gross domestic saving in 2005 was recorded at Rp806.8 trillion. On the

other hand, physical investment, as indicated by gross fixed capital formation and

changes in inventory, was only Rp734.9 trillion. As such, there was an excess or

net lending in the amount of Rp71.9 trillion or 8.92% of gross domestic saving. The

figure represents net exports not including import margin and tax. By including

import margin, tax, net current transfers and net factor income, the current account

(CA) was actually in a deficit of Rp82.2 trillion (USD8.4 billion). This figure

contrasts with the CA reported in the Indonesia Balance of Payments (BoP) that

showed a surplus of USD0.3 billion, primarily due to differences in the export and

import calculation methodology. Such conditions indicate that as a whole physical

investment of all institutions in 2005 can be financed by domestic saving. From

different perspective, however, it is revealed that domestic saving was not

optimally disbursed to stimulate the real sectoractivities.


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Table 4.7. Gross Saving and Physical Investment by Institution(Billions of Rupiah)

As regards total gross saving, more than half originated from financial and

non-financial corporations in the amount of Rp489.1 trillion. This saving came from

an increase of retained earnings plus depreciation.

In addition to non-financial corporations, households also recorded relatively

large gross savings amounting to Rp191.8 trillion. The gross savings of households

were in large part contributed by urban non-poor households of Rp145.8 trillion.

Meanwhile, rural non-poor households saved Rp45.6 trillion. This clearly indicates

that nearly all household savings were contributed by non-poor households, both in

rural and urban areas.

Meanwhile, financial corporations (including banks, insurance companies,

finance companies, pension funds and pawn shop) booked gross saving amounted

to Rp56.3 trillion. In the meantime, government and central bank contributed

Rp108.8 trillion and Rp17.1 trillion respectively (see Table 4.7).

Of the total investment, Rp610.7 trillion came from financial and non-

financial corporations, while the government contributed Rp90.2 trillion.


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Several institutions experienced net borrowing as their saving was falling

short of their financing needs for physical investments, those are non-financial

corporations and poor households in rural and urban areas.

Net borrowings of corporations sectors reached Rp121.6 trillion, while poor

households both in urban and rural areas recorded Rp0.4 trillion and Rp0.5 trillion

borrowings respectively. Meanwhile, urban non-poor households experienced net

lending of Rp120.5 trillion. This highlights the role of urban non-poor households as

the largest contributor to the surplus in 2005.

The government and central bank booked domestic savings of Rp18.6trillion and of Rp16.6 trillion respectively. The total surplus for 2005 stood at

Rp71.9 trillion, which was spent for reducing foreign liabilities and acquiring foreign

financial assets.

4.2.5. Financial Analysis Based on Indonesias FSAM 2005

Financial investment by economic agents reached Rp814.1 trillion, whereasthe economy only generated sources of funds amounted to Rp742.2 trillion.

Therefore, the domestic financial sector experienced a deficit of Rp71.9 trillion that

was financed by domestic real sectors net lending (see Table 4.8).


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Table 4.8. Domestic and Rest of The World Financial Accounts(Billions of Rupiah)

Total domestic financial investment, amounting to Rp814.1 trillion, were

primarily in the form of credit (Rp172.5 trillion) consisted of working capital,

investment, consumption, non-bank and trade credits, and followed by time

deposits (Rp148.7 trillion) and shares and equities (Rp121.3 trillion). Based on

institutions, financial investments were dominated by non-financial corporations

(Rp220.2 trillion) (Table 4.10), urban non-poor households (Rp212.5 trillion) (Table

4.9) and banks (Rp196.8 trillion) (Table 4.10).

Non-financial corporations financial investments were largely in the form of

shares and equities (Rp75.5 trillion) and time deposits (Rp34.0 trillion). Similarly

urban non-poor households investments were mostly in the form of shares and

equities and time deposits amounted to Rp49.9 trillion and Rp83.5 trillion

respectively (Table 4.7).


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Meanwhile, banks financial investments mainly in the form of credit and

savings amounted to Rp145.0 trillion and Rp42.4 trillion.

Table 4.9. Financial Account of Households(Billions of Rupiah)

Financial investment by rural and urban non-poor households was mainly in

the form of time deposits (amounting to Rp17.2 trillion and Rp83.5 trillion

respectively) and other long-term securities (Rp15.1 trillion), which were entirely

owned by urban non-poor households (Table 4.9).


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Table 4.10. Financial Account of Other Institutions(Billions of Rupiah)

Financial investment was mostly generated by non-financial corporations

amounted to Rp220.2 trillion, primarily in the form of share and equities and time

deposits (Rp75.5 trillion and Rp34.0 trillion respectively). Meanwhile, banks

financial investment reached Rp196.8 trillion, mostly in the form of credit (Rp145.0

trillion) and savings (Rp42.4 trillion). (see Table 4.10).

4.3. Application of Indonesias FSAM in Economic Analysis

As explained in previous chapters, FSAM is a data system capable of

providing an economic analysis in explaining the impact of a particular monetary or

fiscal policy on the behavior of economic agents. Using FSAM data, more accurate

information can be presented regarding transaction channels, including the

transmission mechanism, to enable more comprehensive macroeconomic analysis.

Analytical tools that typically use FSAM data are multiplier analysis,

Structural Path Analysis (SPA) and Financial Computable General Equilibrium

(FCGE) models. Multiplier analysis is an analytical tool that is used for describing

the impacts of particular economic shocks or policies on the behavior of economic


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agents. While SPA presents various possible linkages that connect one component

to others, followed by one or more changes in other components originating from

economic shocks or policies. Furthermore, direct, indirect and the resulting total

impacts can be measured for every path.

FCGE is a more comprehensive model that can be applied to describe the

behavior of each economic agent if a complex economic phenomenon occurs.

FCGE can also explain structural changes in the real and financial sectors

attributable to some alternatives economic policies (counterfactual analysis).


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CHAPTER V - CONCLUSION

Constructing FSAM represents major stride for statistical work, as it is

expected that with FSAM we can move further in our pursuit of conveying more

comprehensive and integrated information regarding the real and financial sectors.

As experienced in other countries/region that have published FSAM (Euro Area,

China, Cameroon, Turkey and Pakistan), FSAM needs first the availability of SAM

and FoF data and the researchers need to learn further the interactions between

the two data sets. For Indonesia, FSAM 2005 is the first FSAM to be compiled and

it took more than 30 years after the availability of SAM data (first published in

1975) and around 15 years after the first FoF data (first published in 1991). Given

the complexity of data requirements, such construction could only be worked out in

2006-2007 for FSAM 2005. In addition, due to heavy reliance of FSAM on the

availability of I-O data and SAM data which were prepared on a five-yearly basis,

FSAM can only be compiled in every five years.

The process of reconciling savings and investment data with SAM and FoFdata systems has been the most crucial stage in the compilation process of the

FSAM data framework. Methodological differences in compiling SAM and FoF

resulted in saving

fsam05_eng_okt09 (upload3)

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