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Estimating Unpaid Household Activities by Using Socioeconomic and Labor Force Surveys in Indonesia

Estimating Unpaid Household Activities by Using Socioeconomic and Labor Force Surveys in Indonesia

  • Data Source: National Socio-economic Survey (SUSENAS)
  • Data Source: National Labor Force Survey (SAKERNAS)
  • Data Source for Estimating Value Added of Unpaid Household Activities
  • Data Source for Estimating Additional Household GFCF
  • Data Source for Estimating Homemaker
  • Results
  • Limitations and Future Efforts
Languages and translations
English

Estimating Unpaid Household Activities by Using Socioeconomic and Labor Force

Surveys in Indonesia

Genève, April 26, 2023

Wisnu Winardi & Hadi Susanto BPS-Statistics Indonesia

Designed by Freepik

[JUDUL SLIDE SATU BARIS]Background

• Unpaid household activities is one of the prominent topics in the discussion on the possible better method to measure well-being

• The best practice for estimating unpaid household activities is to conduct time use surveys that measure the amount of time spent by each household member

• However, in Indonesia, the method could have adverse implications on ongoing statistical conduct, primarily in the areas of funding, human resources, staff, and respondent burden, resulting in data quality issues

• This research attempts to estimate unpaid household activities using available annual data (socio-economic survey and labor force survey) as an alternative method, pending the possible availability of better data sources

[JUDUL SLIDE SATU BARIS]Data Source: National Socio-economic Survey (SUSENAS)

First survey: 1963 (incidental)

Last survey: 2022 (twice a year: March and September)

Household approach, using sampling area

Sample size (2023):

March : 345.000 households (sufficient for estimation in province level)

September : 75.000 households (capable for estimation in municipal level)

Data collection: direct interview (paper base)

Socio-economic survey collects household and household member characteristics: ● Household: consumption by commodities, income by source of income, housing facilities ● Household member identity: domestic role, marriage status, sex, age, education, health condition,

birth, activities, emplyment status in work, work hours, etc.

[JUDUL SLIDE SATU BARIS]Data Source: National Labor Force Survey (SAKERNAS)

First survey: 1976 (incidental)

Last survey: 2022 (twice a year: February and August)

Household approach, using sampling area

Sample size (2022):

February : 75.000 households (capable for estimation in province level)

August : 300.000 households (capable for estimation in municipal level)

Data collection: direct interview (paper base)

Labor force survey collects household members characteristic: ● role in household, marriage status, sex, age, education and training, activities, status in work, work

hours, economic activity classification, job classification, compensation, etc.

[JUDUL SLIDE SATU BARIS]Data Source for Estimating Value Added of Unpaid Household Activities

SUSENAS questionnaire

VAUHA = homemaker x homemaking cost

Note: VAUHA: Value Added of Unpaid Household Activities

data source National Socio- economic Surveys (SUSENAS)

data source from National Labor Force Survey (SAKERNAS, see slide 8)

Align with market-based estimation suggested in SNA: transactions should be valued at market prices, if no market price is available then value is determined based on the market price of comparable products on the market

[JUDUL SLIDE SATU BARIS]Data Source for Estimating Additional Household GFCF

SUSENAS questionnaire

Household Gross Fixed Capital

Formation (GFCF) due to including

unpaid household activities as part of

the economic activity

data source National Socio- economic Survey (SUSENAS)

[JUDUL SLIDE SATU BARIS]Data Source for Estimating Homemaker

Population 15+

Labor Force Non Labor Force

SchoolingHomemakingEmployedUnempolyed Others SUSENAS questionnaire

Population 15+

Labor Force Non Labor Force

SchoolingHomemakingEmployedUnempolyed Others

data source

[JUDUL SLIDE SATU BARIS]Result: Effect on Unemployment and LF Participation, 2022

Unpaid household activities decreases unemployment rate and labor force participation rate, particularly among women

Note: * Including unpaid household activities as part of the economic activity

Male Female Total

Population 15+ 104.636.251 104.784.132 209.420.383

Unemployment 5.208.623 3.217.308 8.425.931

Employed 82.553.960 52.742.753 135.296.713

Homemaking 3.621.185 37.628.780 41.249.965

Schooling 7.548.454 8.061.085 15.609.539

Others 5.704.029 3.134.206 8.838.235

Unemployment rate (%) 5,93 5,75 5,86

Unemployment rate* (%) 5,70 3,44 4,56

Labor Force Participation Rate (LFPR) (%)

83,87 53,41 68,63

Labor Force Participation Rate (LFPR)* (%)

87,33 89,32 88,33

[JUDUL SLIDE SATU BARIS]Result: Effect on Unemployment Rate at Province Level (%), 2022

0 1 2 3 4 5 6 7 8 9

11 A

ce h

12 S

um ut

13 S

um ba

r 14

R ia

u 15

J am

bi 16

S um

se l

17 B

en gk

ul u

18 L

am pu

ng 19

B ab

el 21

K ep

ri 31

D KI

J ak

ar ta

32 J

ab ar

33 J

at en

g 34

D IY

35 J

at im

36 B

an te

n 51

B al

i 52

N TB

53 N

TT 61

K al

ba r

62 K

al te

ng 63

K al

se l

64 K

al tim

65 K

al ta

ra 71

S ul

ut 72

S ul

te ng

73 S

ul se

l 74

S ul

tra 75

G or

on ta

lo 76

S ul

ba r

81 M

al uk

u 82

M al

ut 91

P ab

ar 94

P ap

ua IN

D O

N ES

IA

Male Unemployment rate Male Unemployment rate*

0

2

4

6

8

10

12

11 A

ce h

12 S

um ut

13 S

um ba

r 14

R ia

u 15

J am

bi 16

S um

se l

17 B

en gk

ul u

18 L

am pu

ng 19

B ab

el 21

K ep

ri 31

D KI

J ak

ar ta

32 J

ab ar

33 J

at en

g 34

D IY

35 J

at im

36 B

an te

n 51

B al

i 52

N TB

53 N

TT 61

K al

ba r

62 K

al te

ng 63

K al

se l

64 K

al tim

65 K

al ta

ra 71

S ul

ut 72

S ul

te ng

73 S

ul se

l 74

S ul

tra 75

G or

on ta

lo 76

S ul

ba r

81 M

al uk

u 82

M al

ut 91

P ab

ar 94

P ap

ua IN

D O

N ES

IA

Female Unemployment rate Female Unemployment rate*

* Including unpaid household activities as part of the economic activity

Unpaid household activities decreases unemployment rate in all provinces, particularly among women. Female unemplyment rate decrease deeper than male

[JUDUL SLIDE SATU BARIS]Result: Effect on LF Participation Rate at Province Level (%), 2022

* Including unpaid household activities as part of the economic activity

72 74 76 78 80 82 84 86 88 90 92

11 A

ce h

12 S

um ut

13 S

um ba

r 14

R ia

u 15

J am

bi 16

S um

se l

17 B

en gk

ul u

18 L

am pu

ng 19

B ab

el 21

K ep

ri 31

D KI

J ak

ar ta

32 J

ab ar

33 J

at en

g 34

D IY

35 J

at im

36 B

an te

n 51

B al

i 52

N TB

53 N

TT 61

K al

ba r

62 K

al te

ng 63

K al

se l

64 K

al tim

65 K

al ta

ra 71

S ul

ut 72

S ul

te ng

73 S

ul se

l 74

S ul

tra 75

G or

on ta

lo 76

S ul

ba r

81 M

al uk

u 82

M al

ut 91

P ab

ar 94

P ap

ua IN

D O

N ES

IA

Male LFPR Male LFPR*

0 10 20 30 40 50 60 70 80 90

100

11 A

ce h

12 S

um ut

13 S

um ba

r 14

R ia

u 15

J am

bi 16

S um

se l

17 B

en gk

ul u

18 L

am pu

ng 19

B ab

el 21

K ep

ri 31

D KI

J ak

ar ta

32 J

ab ar

33 J

at en

g 34

D IY

35 J

at im

36 B

an te

n 51

B al

i 52

N TB

53 N

TT 61

K al

ba r

62 K

al te

ng 63

K al

se l

64 K

al tim

65 K

al ta

ra 71

S ul

ut 72

S ul

te ng

73 S

ul se

l 74

S ul

tra 75

G or

on ta

lo 76

S ul

ba r

81 M

al uk

u 82

M al

ut 91

P ab

ar 94

P ap

ua IN

D O

N ES

IA

Female LFPR Female LFPR*

In all provinces, unpaid domestic work increases the labor force participation rate (LFPR). Female LFPR rises more rapidly than male. Even female LFPR surpasses male LFPR in every province.

[JUDUL SLIDE SATU BARIS]Result: Effect on Household Income and Consumption, 2020**

• Unpaid household activities increase household value added, income, consumption, and GFCF, while keep other transactions remain the same

• In total it increases household final demand

* Including unpaid household activities as part of the economic activity

Household Value Value*

Total Disposable Income 9.658,8 10.319,7

a) Disposable Income 9.658,9 9.658,9

b) Additional disposable income: unpaid household activities

- 660,9

Total Consumption 8.861,4 9.362,5

a) Additional consumption: unpaid household activities

- 660,9

b) Consumption other than durable goods

8.702,6 8.702,6

c) Durable goods part of GFCF 158,8 -

Total GFCF 765,0 923,8

a) GFCF 765,0 765,0

b) Durable goods part of GFCF - 158,8 ** Currently, latest available data for household acconts is 2020 (in trillion Rupiah)

[JUDUL SLIDE SATU BARIS]Result: Effect on Gender Inequality Index, 2022**

** Preliminary figure

Indicator Value Value*

Male Female Male Female

1. Proportion of married or ever married women (15-49 yo) which give birth not in health facilities**

0,098 0,098

2. Proportion of ever-married women (15-49 yo) which first give birth before 20 yo** 0,265 0,265

3. Share of seats in parliament (%) 78,26 21,74 78,26 21,74

4. Population with at least some secondary education (%) 43,34 39,00 43,34 39,00

5. Labor Force Participation Rate (LFPR, %) 83,87 53,41 87,33 89,32

GII 0,382 0,349 * Including unpaid household activities as part of the economic activity

0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55

11 A

ce h

12 S

um ut

13 S

um ba

r 14

R ia

u 15

J am

bi 16

S um

se l

17 B

en gk

ul u

18 L

am pu

ng 19

B ab

el 21

K ep

ri 31

D KI

J ak

ar ta

32 J

ab ar

33 J

at en

g 34

D IY

35 J

at im

36 B

an te

n 51

B al

i 52

N TB

53 N

TT 61

K al

ba r

62 K

al te

ng 63

K al

se l

64 K

al tim

65 K

al ta

ra 71

S ul

ut 72

S ul

te ng

73 S

ul se

l 74

S ul

tra 75

G or

on ta

lo 76

S ul

ba r

81 M

al uk

u 82

M al

ut 91

P ab

ar 94

P ap

ua IN

D O

N ES

IA

GII GII*

Gender Inequality Indices in national and provicial level decrese due to increase of Female LFPR

Proxy indicators for Maternal Mortality Ratio and Adolescent Birth Rate due to the availability annual data in province and

municipal level

[JUDUL SLIDE SATU BARIS]Limitations and Future Efforts

• There is less specificity regarding unpaid household activities in national socioeconomic survey

• Socio-economic survey usually provide underestimation

• Splitting the questions in socio- economic surveys questionnaire to capture the unpaid household activities

• Aligning the estimation procedure within the SUT frameworks to attain a more accurate estimation level

Limitations Future Efforts

Despite its availability and sufficiency to estimate annual unpaid household activities until municipal level, these data source have limitations and need some efforts to improve their quality

[JUDUL SLIDE SATU BARIS]Conclusion

• Estimating unpaid household activities plays important role in explaining welfare. Including it in the national accounts and gender statistic compilation could:

1. Increase household value added, consumpition, and GFCF 2. Increase labor force participation rate, particularly for female. In Indonesia case the increase of female LFPR is higher than male,

event the level of women LFPR to be higher than male 3. Decrease gender inequality index 4. The result of point 2 and 3 show in both national and provincial level

• By considering that ultimate goal of human development is enlarging people choice (including choice to work or homemakig), the resluts above sugest that government has an alternative solution to incerase well- being than encourage women to participate in work market

• Additional resarch could be alocated to find best strategy to improve the role of women in doing unpaid household activities to support the well-being

• The use of socio-economic survey and labor force survey cover most of unpaid household activities coverage and is prospective to be improved

• Cooperation with other ministries and institutions should be conducted to improve the provision of better data source

Thank You www.bps.go.id

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Experimental estimates of Digital Value Added in Indonesian Economy and Potential Use of Decentralized Identities

Experimental estimates of Digital Value Added in Indonesian Economy and Potential Use of Decentralized Identities

Languages and translations
English

Experimental estimates of Digital Value Added in Indonesian Economy

Miftakhul Jannah [email protected] Statistician BPS – Statistics Indonesia

STEPS OF COMPILING DIGITAL VALUE ADDED IN INDONESIA

SUPPLY AND USE TABLE (SUT)

E-COMMERCE STATISTICS, OTHER DATA

RE-ARRANGEMENT

DIGITAL CONCEPT

DIGITAL VALUE ADDED DEFINING SCOPE

AND CLASSIFICATION

DIGITAL ECONOMY CLASSIFICATION

GDP original series.

Analysis by product to

identify full/partial

digital products

ICT goods, four types: (i) Computers and peripheral equipment; (ii) Communication equipment; (iii) Consumer electronic equipment; and (iv) Miscellaneous ICT components and goods.

ICT services, six types: (i) Manufacturing services for ICT equipment; (ii) Business and productivity software and licensing services; (iii) Information technology consultancy and services; (iv) Telecommunications services; (v) Leasing or rental services for ICT equipment; and (vi) Other ICT services.

Priced Cloud computing services: (i) user simply accessing the provider’s applications (Software as a Services, SaaS); (ii) user deploying their own applications onto the providers infrastructure (Platform as a Service, PaaS); and (iii) the userr taking control over operating systems, storage, and deployed applications (Infrastructure as a Service, IaaS).

Computer hardware, Communication equipment, routers

Provision of telecommunicati on networks, Software development and engineering

AWS, Oracle, Azure, Alibaba

Di gi

ta lly

e na

bl ed

in fra

st ru

ct ur

e

Priced Digital intermediary services: service of providing information matching two independent parties via a digital platform in return for an explicit fee, the output of these platforms typically consists of the fees paid by the producer and/or the consumer of the product being intermediated.

The margin collected by Uber, Airbnb, Trivago etc. represent the provision of this product.

D ig

ita lly

-o rd

er ed

tra

ns ac

tio ns

(e

-c om

m er

ce )

Comprises the portion of wholesale and retail margins that involves any product and therefore attributable to e-commerce.

D ig

ita lly

-d el

iv er

ed p

ro du

ct s

Media products: movies, videos, music and other sound recordings, created and delivered (either to intermediaries or final consumers) in digital format, including the associated licensing and broadcasting rights. The fees for distribution and advertising revenue generated from broadcasting are included. Output of

the digital products

SUT INDONESIA 2016 212 Industry (I) x 262 Products (P)

KBLI (Indonesia ISIC)

List of Digital Product

ISIC

CPC SUT

52 (I) x 65 (P) Digital

Product/Industries

SUT 70 (I )x 85 (P)

CONCORDANCE CLASSIFICATION INDONESIA SUT 2016 AND DIGITAL SUT

KBKI (Indonesia CPC)

Source : OECD Guide to Measuring the Information Society 2011, Tables 2.A1.1, 2.A1.2

DIGITAL SUT

Fully Digital

Partially Digital

Non Digital

Notes :

INDONESIAN DIGITAL VALUE ADDED

Industry 2016

Digital (%) Non Digital (%) A. Agriculture, Forestry and Fishing 0.00 100.00 B. Mining and Quarrying 0.00 100.00 C. Manufacturing 2.61 97.39 D. Electricity and Gas 0.00 100.00 E. Water Supply; Sewerage, Waste Management and Remediation Activities 0.00 100.00 F. Construction 0.00 100.00 G. Wholesale and Retail Trade; Repair of Motor Vehicles and Motorcycles 0.27 99.73 H. Transportation and Storage 0.00 100.00 I. Accomodation and Food Service Activities 0.00 100.00 J. Information and Communication 93.14 6.86 K. Financial and Insurance Activities 0.00 100.00 L. Real Estate Activities 0.00 100.00 M/N. Professional, Scientific and Technical Activities; Administrative and Support Service Activities 10.00 90.00 O. Public Administration and Defence; Compulsory Social Security 0.00 100.00 P. Education 0.00 100.00 Q. Human Health and Social Work Activities 0.00 100.00 R,S,T,U. Other Services Activities 0.00 100.00

INDONESIAN DIGITAL VALUE ADDED

0.07 0.26

1.10 1.19 1.59 1.77

2.93 3.04 3.49

3.98 4.27 4.35

5.39 7.45

10.77 13.64 13.98

20.73

0.00 5.00 10.00 15.00 20.00 25.00

E. Water Supply; Sewerage, Waste Management… J. Information and Communication

Q. Human Health and Social Work Activities D. Electricity and Gas

M/N. Professional, Scientific and Technical… R,S,T,U. Other Services Activities

L. Real Estate Activities I. Accomodation and Food Service Activities

P. Education O. Public Administration and Defence;…

Digital K. Financial and Insurance Activities

H. Transportation and Storage B. Mining and Quarrying

F. Construction G. Wholesale and Retail Trade; Repair of Motor…

A. Agriculture, Forestry and Fishing C. Manufacturing

Share of Digital Industry in Indonesian GDP 2016

INDONESIAN DIGITAL VALUE ADDED

0.00% 0.50% 1.00% 1.50% 2.00% 2.50% 3.00% 3.50% 4.00% 4.50%

2013

2014

2015

2016

2017

2018

Digital Industry by Type of Industries

ICT industries E-commerce industries Digital content & media industries

9

1

2

Identify digital economic activity in Indonesia and limited data availability.

Data processing infrastructure has not met adequate standard in dealing with digital economy, which involves massive transactions.

CHALLENGE

Potential Use of Decentralized Identities (DIDs)

to Capture the Dynamic of Decentralized Finance (DeFi)

Hadi Susanto [email protected]

BPS - Statistics Indonesia

Decentralized Finance and the Challenge of Identification

Challenge of Identification

Difficult for authorities to obtain accurate and trustworthy

information regarding financial transactions and user profiles.

Hampering also statistical data collection.

DeFi is a financial technology runs in Web 3.0, which is based on

safe distributed ledgers in a blockchain

environment, much like the ones used by cryptocurrencies.

DeFi advantages over traditional finance:

greater access, cost efficiency,

transparency, and security

Needs

A scheme of identification system to effectively facilitate a secure data collection activities in the

DeFi ecosystem

Identification Challenges in the DeFi Ecosystem

Potential Solution

Decentralized Identities (DIDs) are a new type of identifier that enables

verifiable, decentralized digital identity. A DID refers to

any subject (e.g., a person, organization, thing, data

model, abstract entity, etc.) In a decentralized ecosystem,

a decentralized identifier is more suitable.

Situation

The limitations of traditional identification methods like Know Your

Customer (KYC) and Anti-Money Laundering (AML) in the anonymous and decentralized DeFi

network

Impact

The difficulties authorities face in

monitoring and collecting accurate and trustworthy

data in the absence of centralized authorities

The Potential of Decentralized Identifiers (DIDs) in DeFi

What are they?

a DID is a new type of identifier that allows

for verifiable and decentralized digital

identities.

DIDs

DIDs are designed to be detached from centralized registries and any kind of

identity provider

Without sufficient regulation, the implementation of DIDs will grow divergently. There will be no standard data provision that

will lead to the inability of authorities to collect data in DeFi ecosystem.

Potential

According to study, the global decentralized finance (DeFi) market size was $11.96 billion in

2021 and is projected to reach $232.20 billion by the end of 2030 with a compound annual growth rate (CAGR) of roughly 42.6% between 2022 and

2030. (Mar 7, 2023, https://www.globenewswire.com/)

Features

World Wide Web Consortium (W3C): 10 goals to be achieved by adopting DIDs,

which are decentralization, control, privacy, security, proof-based, discoverability, interoperability,

portability, simplicity, and extensibility.

Providing a more secure, trustworthy, and private form of

identity verification across various DeFi platforms and protocols

Implementation Steps of Standardized and Interoperable DID Protocols in DeFi

Formulating and implementing standardized and interoperable DID protocols across various DeFi platforms to ensure the successful

adoption and incorporation of DIDs in DeFi

Developing collaboration and coordination between regulatory authorities, DeFi developers, and blockchain companies

Emphasizing the potential benefits of such collaboration and standardization in fostering financial inclusion, privacy, and international

cooperation

Conclusion Conclusion:

Several key points of the presentation:

● Awareness on the growing scale of DeFi ● The necessity to implement a proper identifier in DeFi

ecosystem before it is too large to be regulated. ● DIDs has the potential to be an identifier. Initial researches

have been done on DIDs. Yet a common form and mechanism of DIDs have not been agreed.

● Initiatives at the international level to promote DIDs implementation is needed as well as regulations at national level, which comply with the initiatives.

Future Research

Future research would be needed to:

● Identify the impact of DIDs on financial inclusion and services ● Specify and develop the recommended DIDs mechanism and protocol ● Analyze the privacy implications of DIDs in DeFi ● Design the architecture of international cooperation and standardization for the collection and

reporting of DeFi transaction data

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  • Slide Number 8
  • Slide Number 9
  • Potential Use of 
Decentralized Identities (DIDs) 
to Capture the Dynamic of Decentralized Finance (DeFi)
  • Decentralized Finance and the Challenge of Identification
  • Identification Challenges in the DeFi Ecosystem
  • The Potential of Decentralized Identifiers (DIDs) in DeFi
  • Implementation Steps of Standardized and Interoperable DID Protocols in DeFi
  • Conclusion
  • Future Research
  • Slide Number 17

Indonesian Net Domestic Product (NDP) Adjusted for Depletion of Environmental Asset

Languages and translations
English

Economic Commission for Europe Conference of European Statisticians Group of Experts on National Accounts Twenty-second session Geneva, 25-27 April 2023 Item 5 of the provisional agenda Well-being and sustainability

Indonesian Net Domestic Product (NDP) Adjusted for Depletion of Environmental Asset

Prepared by BPS - Statistics Indonesia1

Summary Economic theory and accounting already utilize a tightly defined sustainability

criterion when evaluating the depreciation or consumption of generated capital. This is to make a clear distinction between income and capital to prevent consuming capital base of income generation. By expanding the asset boundary of national accounts to include non- produced natural assets like land/soil, minerals, forests, fish, water, and environmental sinks for pollutants (air, water, land), a broader concept of economic performance sustainability can be defined as the maintenance of produced and natural capital used to produce goods and services. Costing produced and natural capital consumption in the national accounts yields a Depletion-adjusted Net Domestic Product (NDP1). The trend of NDP1 can be considered an indicator of sustained economic growth. This is analogous to gauging economic growth based on the trends of GDP or NDP.

SEEA (System of Environmental-Economic Accounting) is the first international statistical framework that looks at both the economy and the environment. SEEA physical and monetary environmental accounts give us a picture of the uses of natural inputs such as forest, mineral and energy resources in the economy. Further development of monetary environmental accounts is helpful to analyze the depletion impact on national accounts as the result of extracting natural resources. Using the SEEA framework, BPS (Statistics Indonesia) has compiled environmental-economic accounts in which NDP1 is generated. As of 2022, environmental depletion included in BPS' work comprises the total value of extraction of mineral and energy resources and the value of logging less planted timber. This paper will present the rationale for environmental accounting, depletion-adjusted NDP, and its sustainability along with its limitations.

1 Prepared by Ria Arinda.

United Nations ECE/CES/GE.20/2023/14

Economic and Social Council Distr.: General 6 April 2023 English only

ECE/CES/GE.20/2023/14

2

I. Introduction

1. There have been two important indicators of national income over the past 50 years: Gross Domestic Product (GDP) and Net Domestic Product (NDP); with GDP being the most generally utilized. These variables are commonly used in macroeconomic analysis and worldwide comparisons. In addition, they have historically acted as an indicator of a country's economic development and standard of living. Unfortunately, these conventional measures of economic activity are inadequate and deceptive because they fail to account for the role of the environment and its influence on economic activity.

2. First, the NDP aggregate is a representation of the value of goods and services, which is calculated by deducting an allowance known as the consumption of fixed capital. This allowance accounts for the use up of man-made capital, such as machinery and equipment. However, it is important to note that the national accounts do not include an allowance for using environmental assets, which means that they do not provide a sustainable measure of national production.

3. On the other hand, while GDP correctly represents the production of marketed goods and services, it falls short of providing a more comprehensive measure of social welfare. Regarding environmental problems, GDP does not account for environmental degradation and resource depletion. This is a critical problem, particularly in developing countries like Indonesia, which rely heavily on natural resources. If a country clears its forests, depletes its soil fertility, and pollutes its water sources, it will undoubtedly become poorer. However, national income accounts only reflect the market worth of timber, agricultural produce, and industrial output as positive contributions to GDP. This may cause policymakers to view the country's growth in an overly rosy light - at least until the effects of environmental damage become evident, which may take decades in the case of environmental damage.

4. Can we conclude that a country with a higher per capita income is inevitably more prosperous than a comparable nation with a lower per capita national income? Many experts have pointed out that the national income indicators discussed above may provide a misleading picture of economic and human development. Therefore, taking natural capital and environmental quality seriously influences how we estimate national wealth and well- being and the integrated economic and environmental information can come to answer these questions.

5. Because the goal is to account for qualitative and quantitative changes in natural capital, the starting point could be NDP, where the depreciation of real capital has been subtracted from GDP in a similar manner. From this, a (partially) environmentally adjusted net domestic product (EDP) would be the proper measure, taking into consideration natural resource depletion and degradation. However, the compilation of environmentally adjusted NDP in this paper only includes the calculation of depletion of environmental assets.

II. System of Environmental-Economic Accounting (SEEA)

6. SEEA is part of the statistical system and guidelines developed by the world's statistical agencies (UNSC), UN, IMF, World Bank, OECD, and European Commission. It is linked to the System of National Accounts (SNA), which provides a global standard for national accounts. It provides a framework that integrates physical environmental data with monetary data from the SNA to provide a more comprehensive and multipurpose view of the interrelationships between the economy and the environment. It contains internationally agreed concepts, definitions, classifications, accounting rules, and tables for producing internationally comparable statistics and accounts, which are interoperable with the SNA.

7. Two complementary approaches of the SEEA include the SEEA Central Framework (SEEA-CF) and the SEEA Ecosystem Accounts (SEEA-EA). SEEA-CF is a compartmental and partial approach to measuring the environment and natural resources. In the SEEA-CF, there are four types of accounts, namely flow accounts, asset accounts, sequence of economic accounts, and functional accounts. Flow accounts include the measurement of energy, materials, water, and emissions to the environment. Asset accounts include the measurement of mineral and energy, timber, fish / aquatic resources, soil, water, land, and other biological

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resources. The depletion-adjusted economic aggregates are highlighted in a sequence of economic accounts, while the functional accounts contain information on transactions and other economic activities carried out for environmental purposes.

8. On the other hand, SEEA-EA is a systematic framework to measure the contributions of ecosystems to economic activity that is aligned with the National Accounts (UN, 2014a). It includes ecosystem services flows and ecosystem assets. Ecosystem accounting involves expanding SNA production boundaries. This allows the inclusion of a wider range of ecosystem services, such as regulatory services and cultural services, as well as the natural growth of biological assets (such as timber) to measure economic activity. In turn, this can more comprehensively record changes in ecosystem capital (Bordt, 2015). Both SEEA-CF and SEEA- EA can provide information in the physical and monetary unit if supporting data is sufficient.

III. SEEA Implementation in Indonesia

9. Based on the Republic of Indonesia law on Statistics Number 16 of 1997, BPS is the national statistical organization responsible for providing basic statistics, sectoral statistics, and special statistics in Indonesia. In addition, in accordance with Government Regulation No. 46/2017 concerning environmental economic instruments, BPS shall coordinate with the Ministry of National Development Planning (BAPPENAS), the Ministry of Finance (MoF) and relevant ministries that provide data to compile SEEA accounts. Furthermore, in the latest development plan, BAPPENAS has included environmental sustainability, disaster risk management, and climate change mitigation and adaptation as one of the targets in the 7 medium-term development plans (RPJMN) 2020 2024. Related to this, BPS contributes by providing SEEA accounts.

10. BPS has begun to produce a yearly publication called SISNERLING (Integrated System of Environmental-Economics Accounts of Indonesia) since 1990. Through the SISNERLING, BPS aims to implement SEEA accounts, such as asset accounts, flow accounts, and environmental activity accounts.

11. As one of the components of SEEA, asset accounts in physical or monetary units, measure the stock of natural resources and the changes in stock. The compilation of asset accounts in BPS comprises land accounts, timber asset accounts, and mineral and energy asset accounts. The most interesting feature in asset accounts is its ability to estimate of natural resources depletion in physical and monetary units. For non-renewable resources, quantity of depletion is equal to the amount of resources extracted, but for resources that can be renewed, the quantity of depletion take the population, resources, values, growth rate, and improvement of the sustainability rates. The valuation methodology used in the monetary asset accounts adopts Net Present Value (NPV) method which is recommended for valuing the environmental assets.

12. BPS also publishes an annual report specifically dedicated to energy flow accounts, which provides a detailed overview of energy supply and consumption by both economic and environmental units, as well as the corresponding air emissions resulting from energy-related activities. Furthermore, BPS conducts an in-depth study analysis each year to expand its implementation of environmental economic accounting in other areas. The selection of these areas is based on prioritized governmental programs or critical issues that arise during that year. For instance, in 2016 and 2017, BPS conducted an in-depth study on EPEA-EGSS, while in 2018, BPS studied SEEA-AFF. BPS then conducted an in-depth study on sustainable tourism in 2019 and 2020, and ocean accounts in 2021 and 2022. This year, in 2023, BPS will conduct an in-depth study on biodiversity accounts and climate change.

IV. Data source

13. Due to limitations in available data, not all natural resources can be evaluated in monetary terms. As a result, the valuation of monetary asset accounts in BPS is based on a prioritization of natural resources that have a significant impact on the Indonesian economy.

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Specifically, BPS's monetary asset accounts focus on eleven key natural resources, including land, timber, oil, natural gas, coal, gold, silver, copper, tin, nickel, and, bauxite.

14. To compile physical asset accounts for timber in Java, BPS generally rely on forestry data from Perum Perhutani, specifically for teak timber and other types of timber in the region. For physical asset accounts related to timber outside of Java, data from the MoEF's publication on FRA is used. Additionally, to complete the Monetary Asset Account for Timber Resources in Indonesia, we utilize data from BPS-Statistics Indonesia and the MoF.

15. To compile asset accounts for mineral and energy, BPS rely on data from several line ministries in Indonesia, including BPS itself, Ministry of Energy and Mineral Resources (MEMR), and Ministry of Finance. Data from BPS primarily comes from national accounts, such as output and gross value added for each mining industry by type of mineral and energy. BPS also use information from the Input-Output Table (IOT) and the Supply and Use Tables (SUT) which provides detailed information about cost structure of each type of mineral and energy mining industry.

16. The MEMR has the authority in collecting sectoral data related to mineral and energy resources in Indonesia. We use data on reserves, resources, and production of mineral and energy obtained from the MEMR. We also rely on their data to estimate the extraction of mineral resources in both ore and metal content forms. The classification of reserves and resources for each type of mineral and energy resource was adjusted to the UNFC-2009 classification as recommended by the SEEA.

17. Similar to the valuation of timber asset accounts, BPS determine the monetary value of mineral and energy assets by setting a discount rate for each type. BPS use government bond rates obtained from the MoF as discount rate.

18. Lastly, to compile integrated economic-environmental accounts, several sources of data are utilized in the following manner:

a. The Directorate of Expenditure Accounts BPS provides GDP data by expenditure at current prices, which includes macro-level information such as final consumption, capital formation, exports, imports, and depreciation of economic assets.

b. The Indonesian 2016 input-output table is another data source used to determine the structure of product supply and intermediate consumption.

c. The monetary asset accounts for eleven key natural resources dicussed above

V. Methodology

A. Introduction

19. Natural resources that have been compiled in monetary asset accounts in BPS consists of timber, as well as mineral and energy comprised of oil, natural gas, coal, gold, silver, copper, tin, nickel, and bauxite.

B. Timber Asset Account

20. The timber resource asset account is constrained by data limitations hence covers only teak timber of Java, other timber of Java, and other timber outside of Java. The rationale for selecting these commodities is that they make the largest contribution to the value added to the GDP of the Forestry Subcategory. The presentation of asset account for timber resources is therefore presented by teak timber of Java, other timber of Java, and other timber outside of Java.

21. The additions and reductions in stock cannot be presented as detailed as the SEEA asset account standards. In the physical asset accounts (Table 1), additions to stock are detailed according to natural growth and reclassification (reforestation and planting); while reductions in stock are detailed according to removals as well as losses and reclassification

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(natural losses and catastrophic losses). The structure of the monetary asset account for timber resources (Table 2) is almost similar to that of the physical asset account for timber resources. However, the former includes a new element called "revaluation," which serves as a correction factor to account for price fluctuations over a given period. The physical asset account is measured in cubic meters (m3), while the monetary asset account is measured in rupiah.

Table 1 Structure of physical asset account for timber in Indonesia (m3)

Detail

Timber type Total of Timber Resources

Teak Timber of

Java Other Timber

of Java Other Timber Outside Java

(1) (2) (3) (4) (5) Opening stock Additions to stock Natural growth Reclassifications Total additions to stock Reduction in stock Removals Losses and reclassifications Total reductions in stock Closing stock

Table 2 Structure of monetary asset account for timber in Indonesia (Indonesian rupiah)

Detail

Timber type Total of Timber Resources

Teak Timber of

Java Other Timber

of Java Other Timber Outside Java

(1) (2) (3) (4) (5) Opening stock Additions to stock Natural growth Reclassifications Total additions to stock Reduction in stock Removals Losses and reclassifications Total reductions in stock Revaluations Closing stock 22. To calculate the monetary asset account for timber resources, the physical asset account is multiplied by the unit rent (resource rent per unit of resource), which is determined using the Net Present Value (NPV) method. The NPV method involves estimating the value of resources by using their current price as a proxy for their future sales value, and then subtracting the costs of exploitation. Resource rent of timber commodities is obtained using the value of Gross Operating Surplus (GOS), namely by multiplying output by the ratio of GOS to output. GOS ratio is obtained from the 2016 IOT.

23. The formula used is 𝑃𝑃𝑃𝑃 = ∑ 𝐹𝐹𝐹𝐹𝑡𝑡 (1+𝑟𝑟)𝑡𝑡

𝑇𝑇 𝑡𝑡=1 = ∑ 𝑁𝑁𝑡𝑡𝑄𝑄𝑡𝑡

(1+𝑟𝑟)𝑡𝑡 𝑇𝑇 𝑡𝑡=1 . Where PV = The present value

of a natural resource, FVt = Future value of a natural resource, Nt = The value of natural

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resource deducted by exploitation cost at year t, Qt = Exploitation volume at year t, T= year, t = age of natural resources, r = discount rate.

C. Mineral and Energy Asset Account

24. Asset accounts for mineral and energy resources present information on the stock and changes of these resources over time, and are presented both in physical unit and monetary unit. Due to data limitation, the additions and reductions in stock cannot be presented as detailed as the SEEA asset account standards. Physical and monetary asset accounts for mineral and energy are shown in the Table 3 and Table 4 respectively.

25. Mineral and energy resources include proven reserves of oil, natural gas, coal, metallic minerals, and nonmetallic minerals, among others. The United Nations Framework Classification for Fossil Energy and Mineral Reserves and Resources 2009 (UNFC-2009) is the framework used to determine the extent of known deposits. The mineral and energy asset accounts do not include potential mineral deposits because in these deposits, it is not expected that these deposits will be economically viable in the future. Moreover, there is a scarcity of data to assess mining feasibility or the level of confidence in geological knowledge.

26. Like timber resources, mineral and energy resources are categorized as environmental assets that offer provisioning services by being extracted from nature and benefiting humankind. As a result, their market value is determined by estimating their resource rent via the net present value (NPV) approach.

Table 3 Structure of physical asset account for mineral and energy resources in Indonesia

Descriptions

Type of mineral and energy resources

Crude oil (barrels)

Natural gas

(BSCF)

Coal (ton)

Gold (ton)

Silver (ton)

Copper (ton) Tin (ton) Nickel

(ton) Bauxite

(ton)

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Opening stock Extraction Other changes in stock

Closing stock

Table 4 Structure of monetary asset account for mineral and energy resources in Indonesia (Indonesian rupiah)

Descriptions

Type of mineral and energy resources

Crude oil Natural gas Coal Gold Silver Copper Tin Nickel Bauxite

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Opening stock Extraction Other changes in stock

Revaluations Closing stock

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D. Compilation of integrated accounts

27. Further development of monetary asset accounts is used measure environmental depletion, which subsequently can be integrated to the system of national accounts. Due to the limited data available, the calculation of environmentally adjusted national accounts is only carried out up to the generation of NDP1, namely the NDP adjusted for natural resource depletion.

28. In the integrated economic-environmental account (Table 5), the concept of capital formation is expanded into the concept of capital accumulation which also considers capital produced by environment. In this account, mineral, energy, and timber reserves are regarded as natural-made capital. As such, it is essential to calculate the costs that reflect the utilization or reduction of this natural-made capital in economic activities in the form of depletion value. The depletion value for mineral resources is equivalent to the extraction value, whereas for timber resources, the depletion value equals the logging and damage value substracted by the value of planting or addition. The adjusted NDP (NDP1), which takes into account the use of natural assets and the environment, is obtained by subtracting the value of depletion from the conventional NDP in the SNA.

Table 5 Structure of integrated environmental and economic accounts for timber, mineral, and energy commodities

Components

Year Economic Activities Non-produced

Environmental Assets Industries Final

Consumption

Capital Rest of The World

Produced

Assets Non-Produced

Assets (1) (2) (4) (5) (6) (3) (7)

1 Opening stock Capital a. Timber b. Oil c. Natural gas d. Coal e. Gold f. Silver g. Copper h. Tin i. Nickel j. Bauxite 2 Supply 3 Use 4 Depreciation 5 NDP (conventional) 6 Depletion a. Timber b. Oil c. Natural gas d. Coal e. Gold f. Silver g. Copper h. Tin i. Nickel j. Bauxite

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7 Other changes a. Timber b. Oil c. Natural gas d. Coal e. Gold f. Silver g. Copper h. Tin i. Nickel j. Bauxite 8 NDP1 1 9 Revaluations Capital a. Timber b. Oil c. Natural gas d. Coal e. Gold f. Silver g. Copper h. Tin i. Nickel j. Bauxite 10 Closing Stock Capital a. Timber b. Oil c. Natural gas d. Coal e. Gold f. Silver g. Copper h. Tin i. Nickel j. Bauxite

VI. Results (Indonesian depletion-adjusted NDP)

29. Table 6 shows the value of natural resource depletion that impacts the level of NDP and net capital accumulation. Net capital accumulation is a constituent of NDP, valued at Rp2,370,880 billion in 2021, representing 17.15 percent of total NDP. When accounting for the impact of natural resource depletion, the value of net capital accumulation reduces to only Rp2,043,845 billion or 15.14 percent of total NDP 1. The decline in net capital accumulation value is attributed to the deppreciation of fixed capital assets and depletion of the value of natural assets utilized in economic activities namely timber, minerals, and energy resources.

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Table 6 Comparation of conventional NDP and depletion-adjusted NDP (NDP1)

Year Component

NDP NDP1

Value (Billion Rp)

Distribution (%)

Value (Billion Rp)

Distribution (%)

2017

1. Final consumption 9 023 120 80.86 9 023 120 82.47 2. Net capital accumulation 1 999 990 17.92 1 781 980 16.29 3. Net export 135 778 1.22 135 778 1.24 4. Total 11 158 888 100.00 10 940 878 100.00

2018

1. Final consumption 9 793 746 81.35 9 793 746 83.05 2. Net capital accumulation 2 403 579 19.97 2 157 881 18.30 3. Net export -158 599 -1.32 -158 599 -1.34 4. Total 12 038 726 100.00 11 793 028 100.00

2019

1. Final consumption 10 566 547 83.43 10 566 547 85.62 2. Net capital accumulation 2 168 420 17.12 1 844 947 14.95 3. Net export -70 411 -0.56 -70 411 -0.57 4. Total 12 664 556 100.00 12 341 083 100.00

2020*

1. Final consumption 10 575 347 84.64 10 575 347 86.48 2. Net capital accumulation 1 676 913 13.42 1 410 754 11.54 3. Net export 241 952 1.94 241 952 1.98 4. Total 12 494 212 100.00 12 228 053 100.00

2021**

1. Final consumption 10 995 483 79.54 10 995 483 81.46 2. Net capital accumulation 2 370 880 17.15 2 043 845 15.14 3. Net export 458 017 3.31 458 017 3.39 4. Total 13 824 380 100.00 13 497 345 100.00

* Preliminary figures ** Very preliminary figures

30. Table 7 provides a more detailed breakdown of the depreciation of gross fixed capital formation and depletion of natural resources. It is evident from the table that in 2021, the ratio of NDP to GDP (point 4) was approximately 81.46 percent, which means that the consumption of fixed capital as a percentage of Indonesia's GDP was around 18.54 percent. On the other hand, the ratio of NDP1 to GDP (point 5) in 2021 was about 79.53 percent. Moreover, the natural resource depletion percentage in Indonesia in 2021 was 1.93 percent (or 20.47 minus 18.54).

Table 7 Comparation of GDP, NDP, and NDP1 2017 2018 2019 2020* 2021** 1. GDP (Billion Rp) 13 589 826 14 838 756 15.832.657 15.438.018 16.970.789 2. NDP (Billion Rp) 11 158 888 12 038 726 12.664.556 12.494.212 13.824.380 3. NDP1 (Billion Rp) 10 940 878 11 793 028 12.341.083 12.228.053 13.497.345 4. NDP/GDP x 100 (percentage)

82.11 81.13 79,99 80,93 81,46

5. NDP1/GDP x 100 (percentage)

80.51 79.47 77,95 79,21 79,53

6. NDP1/NDP x 100 (percentage)

98.05 97.96 97,45 97,87 97,63

* Preliminary figures ** Very preliminary figures

31. By compiling integrated economic-environmental accounts, the sustainability aspects of natural resources can be analyzed. Table 8 presents a summary of the value of national assets, categorized based on the value of fixed capital assets (produced assets) and the value of natural assets (non-produced assets), obtained from the natural resources accounts

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calculation. Between 2017 and 2021, the contribution of natural assets to the total national assets varied from 14 to 21 percent. However, the monetary value of natural assets is still relatively small since the valuation does not encompass all natural resources.

Table 8 National asset value Year Asset value at closing year

(Billion Rp) Total

(Billion Rp)

Asset value at closing year (percent)

Produced assets

Non- produced

assets

Produced assets

Non- produced

assets 2017 34 987 129 9 431 868 44 418 997 79 21 2018 39 778 340 9 557 955 49 336 295 81 19 2019 44 899 711 9 204 516 54 104 228 83 17 2020* 49 796 761 8 257 793 58 054 554 86 14 2021** 55 024 615 13 204 887 68 229 502 81 19 * Preliminary figures ** Very preliminary figures

32. The sustainable development agenda emphasizes sustainability as a measure, which asserts that the value of total assets per capita or national wealth per capita should not experience negative growth within a given period. The aspect of sustainability assumes that there exists perfect substitution between different types of assets; when the value of one asset decreases, it will be compensated by an increase in the value of other assets. For instance, depletion of oil and gas resources can be compensated by investment in fixed capital such as oil and gas exploration. The value of national wealth per capita and its growth are presented in Table 9.

33. Table 9 presents data indicating that from 2017 to 2021, the value of national wealth exhibited positive growth, as demonstrated by the increase in total national wealth per capita. In 2019 and 2020, the per capita value of natural wealth experienced a contraction in growth. However, the growth rate of fixed capital assets per capita increased at a faster pace, offsetting the decline in natural wealth per capita. Moreover, in 2021, there was a significant increase in national wealth per capita.

Table 9 National asset value per capita

Year The value of asset per capita (Billion Rp) Growth (%)

Produced assets

Non- produced

assets

Produced assets

Non- produced

assets Produced assets

Non- produced

assets 2017 133 594 36 014 169 609 12.89 22.24 14.75 2018 150 098 36 066 186 164 12.35 0.14 9.76 2019 168 219 34 485 202 704 12.07 -4.38 8.88

2020* 184 729 30 634 215 363 9.81 -11.17 6.24 2021** 201 790 48 426 250 216 9.24 58.08 16.18

* Preliminary figures ** Very preliminary figures

34. Table 10 shows the value of depreciation of national assets that indicates the usage of both produced and environmental assets in economic activities. The growth in produced assets, in the form of gross fixed capital formation, fluctuated between -7.08 to 19.42 percent. On the other hand, the use of environmental assets had the lowest growth rate of -17.72 percent in 2020, and the highest growth rate of 31.66 percent in 2019.

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Table 10 Depreciation Value of National Assets in Indonesia

Year

Depreciation value (Billion Rp) Growth (%) Consumption of

fixed capital Depletion of

natural resources Produced assets Non-produced assets

2017 2 430 937 218 010 19.42 12.62 2018 2 800 030 245 697 15.18 12.70 2019 3 168 101 323 473 13.15 31.66

2020* 2 943 805 266 159 -7.08 -17.72 2021** 3 146 409 327 035 6.88 22.87

* Preliminary figures ** Very preliminary figures

VII. Conclusion and Limitation Conclusion

35. Integrated environmental-economic accounting helps to combine economic and environmental accounts and generate adjusted macroeconomic indicators (such as NDP) that accounts for the depletion of natural resources. BPS has compiled integrated enviromental- economic accounts of Indonesia since 1990. In this paper, NDP1 suggests that natural resource depletion in Indonesia is relatively low. However, it is important to interpret this result carefully because not all environmental assets, let alone the valuation of environmental degradation such as pollution and climate change, are accounted for in NDP1.

36. Additionally, damage aspects that were not valued due to the unavailability of appropriate valuation techniques, such as the biodiversity aspects of acidification, can also be included. In future calculations of environmentally adjusted macro aggregates, it is important to also include the transboundary aspects of environmental problems, such as the exports and imports of pollution, which have not been taken into account in this paper.

37. The discount rate used in this paper's NPV calculation is based on government bond rates from the Ministry of Finance, which are more market-oriented and not specifically geared towards environmental sustainability. Therefore, there is a need to explore other suitable discount rates for intergenerational discounting purposes from an environmental sustainability perspective such as non-constant or declining discount rates, weighted average, or shadow capital price approach.

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References

38. Arinda, R. (2021). Investigating the use of social discount rate in the Indonesia Mineral and Gas Asset account [Unpublished mini thesis, Wageningen University and Research]

39. BPS. (2022). Integrated System of Environmental-Economic Accounts of Indonesia.

  • Group of Experts on National Accounts
  • Twenty-second session
  • Indonesian Net Domestic Product (NDP) Adjusted for Depletion of Environmental Asset
    • Prepared by BPS - Statistics Indonesia0F
  • I. Introduction
  • II. System of Environmental-Economic Accounting (SEEA)
  • III. SEEA Implementation in Indonesia
  • IV. Data source
  • V. Methodology
    • A. Introduction
    • B. Timber Asset Account
    • C. Mineral and Energy Asset Account
    • D. Compilation of integrated accounts
  • VI. Results (Indonesian depletion-adjusted NDP)
  • VII. Conclusion and Limitation Conclusion
  • References

Potential Use of Decentralized Identities (DIDs) to Capture the Dynamic of Decentralized Finance (DeFi), Indonesia

Potential Use of Decentralized Identities (DIDs) to Capture the Dynamic of Decentralized Finance (DeFi), Indonesia

Languages and translations
English

Economic Commission for Europe Conference of European Statisticians Group of Experts on National Accounts Twenty-second session Geneva, 25-27 April 2023 Item 4 of the provisional agenda Digitalization

Potential Use of Decentralized Identities (DIDs) to Capture the Dynamic of Decentralized Finance (DeFi)

Prepared by Statistics Indonesia1

Summary

The exponential growth of the Decentralized Finance (DeFi) markets has raised concerns for statistical authorities because often they operate beyond the observation of formal authorities, which respectively could result in significant inaccuracy of economic statistics data. The peer-to-peer transactions inside these platforms, not facilitated by intermediaries make it difficult for authorities to engage in monitoring, identifying and recording the activities. As the main feature of the transactions is their anonymity, cooperating with DeFi platforms is also not a feasible option. The most practical solution is the use of blockchain analytics tools, which require a decentralized ID (DID).

This paper is exploring the possibilities for statistical authorities to use DIDs to ensure correct identification and recording of decentralized financial activities.

1 Prepared by Hadi Susanto.

United Nations ECE/CES/GE.20/2023/11

Economic and Social Council Distr.: General 6 April 2023 English only

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I. Background information about Decentralized Finance and Decentralized Identities

1. Digitalization has led to a significant increase in the volume of data involved and generated. It has also raised concerns regarding data security and privacy. Since the entirety of the modern financial system's operations is conducted digitally, financial data is the primary concern. Traditionally, this data has been managed and stored using centralized systems. A centralized system provides uncomplicated monitoring and management capabilities, such as putting everything on a desk. However, they come with their own risks and issues, such as a single point of failure and vulnerability to security threats.

2. With Decentralized Finance (DeFi), blockchain-based decentralized systems offer an additional option. As depicted in Figure 1, DeFi could facilitate financial transactions without the need for intermediaries such as banks, allowing for greater access, transparency, and security. Decentralized Identities (DIDs), as a proposed protocol in DeFi, will enable individuals to have control over their personal data by permitting them to construct and manage their own identities, thereby decreasing reliance on centralized authorities. Together, DeFi and DIDs provide a decentralized alternative to traditional centralized systems, granting individuals greater control over their data and financial transactions.

Figure 1

Centralized vs Decentralized Financial Activities

(source: https://bap-software.net/en/knowledge/defi-finance/)

3. The World Wide Web Consortium (https://www.w3.org ) is currently developing a standard for DIDs implementation. According to the World Wide Web Consortium, a DID is a new type of identifier that allows for verifiable and decentralized digital identities. A DID can be used to identify an individual, an organization, an object, or even an abstract entity. In contrast to traditional identifiers, DIDs are designed to be detached from centralized registries and any kind of identity provider.

II. Lack of Proper Identification in Statistical Data Collection in DeFi Ecosystem

4. DeFi's escalating prevalence poses a significant challenge for official monitoring and statistics, particularly financial statistics. Since DeFi operates on a decentralized network and without intermediaries, it is exceedingly difficult for authorities to obtain accurate and trustworthy information regarding financial transactions and user profiles. As a characteristic of a decentralized environment, this lack of transparency and centralization would hinder the ability of authorities to monitor financial activities, detect fraud, and ensure compliance with laws and regulations.

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5. In addition, because DeFi is relatively new and rapidly evolving, there are instances of inadequate or inappropriate regulatory frameworks, which could exacerbate these difficulties. Therefore, policymakers and regulators must carefully consider the implications of DeFi and mitigate the risks by developing appropriate measures to resolve these challenges, while also allowing for the continued innovation and expansion of the field.

6. The purpose of this paper is to investigate the potential for DIDs to facilitate statistical data collection in the DeFi ecosystem. This paper will primarily serve as a wake-up call for economic statisticians, given that DeFi is a new area of economic statistics and is still in the process of evolving into the most optimal form of the financial environment. Specifically, economic statisticians will be urged to develop a proper methodology to better capture DeFi as a statistical object.

III. Overview of DeFi and Its Importance

7. Managing DeFi will require appropriate regulatory frameworks to ensure compliance with the law and that it operates within legal boundaries. Due to the decentralized and complex nature of DeFi, regulatory authorities will need to carefully consider the most effective ways to foster innovation and growth while protecting against potential threats such as fraud, tax evasion, money laundering, and financial instability. Managing DeFi can be advantageous for the economy as a whole by fostering greater financial inclusion, efficiency, and stability through a proper equilibrium between innovation and regulation.

8. Emphasis should be placed on the improvement of regulatory matters pertaining to DeFi activities, as this will inevitably increase transparency in DeFi ecosystems. This will result in improved access to transactions and participants for statistical observations.

IV. Importance of Collecting Statistical Data in DeFi

9. It is essential to acknowledge the increasing significance of DeFi platforms in the global financial system. DeFi platforms are gaining popularity as an alternative to traditional financial institutions, and their volume and variety of use are anticipated to increase in the coming years.

10. Given the significance of the aforementioned DeFi platforms, national statistical agencies must covr transactions and participants in these new platforms. This is required to ensure that statistical data accurately reflect the entire scope of a country's financial activity.

11. From the perspective of official statistics, collecting data on DeFi transactions and participants would be beneficial for the following reasons:

• It can enhance the veracity and completeness of financial statistics, ensuring that all financial activity is reflected in official statistics.

• It can provide valuable insight into the utilization and adoption of DeFi platforms in a country. This can aid policymakers and other stakeholders in their decision- making processes by identifying potential threats or opportunities within the DeFi ecosystem and financial system in general.

• Capturing data on DeFi transactions and participants can increase the ecosystem's transparency and accountability. This will help in building trust among users and investors and promote the sustainability of DeFi platform growth and development.

12. The inclusion of DeFi transactions and participants in official statistical data is crucial for ensuring that financial statistics accurately reflect the full scope of financial activity in a country. It can provide valuable insights into the utilization and adoption of DeFi platforms and increase the ecosystem's transparency and accountability.

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V. Limitations of Traditional Identification Methods in DeFi

13. Know Your Customer (KYC) and Anti-Money Laundering (AML) procedures, although effective in current financial systems, will not work in DeFi due to the anonymous and decentralized nature of the network.

14. The first limitation is the difficulty of verifying the true identity of a user, as it is common for users to conceal their identities by using pseudonyms or remaining anonymous. This makes it difficult for DeFi platforms to comply with KYC and AML regulations and consequently increases the likelihood of fraud, money laundering, and other illegal activities.

15. Lastly, the lack of centralized authority in the DeFi space means that identification requirements and user activity monitoring are not enforced. This renders it incapable of ensuring regulatory compliance and holding users accountable for their actions.

16. The limitations of traditional identification methods in the DeFi environment underscore the need for innovative and decentralized solutions that can effectively verify user identities, monitor transactions, and ensure regulatory compliance.

VI. Introduction to DIDs and Their Potential to Resolve Identification Issues in DeFi

17. DIDs are a form of decentralized identity that is gaining prominence in the DeFi ecosystem. DIDs are distinct identifiers anchored to a decentralized system, such as a blockchain, that can be used to authenticate and verify user identity without the need for centralized authorities.

18. There are typically three types of DIDs in the DeFi environment, and they are as follows:

• Public DIDs are accessible to anyone and can be used for multiple purposes, such as authentication, authorization, and digital signatures.

• Private DIDs are only accessible to a limited group of people and are frequently used for more sensitive applications, such as access control and privacy protection.

• Hybrid DIDs, on the other hand, incorporate aspects of both public and private DIDs and are frequently used when both openness and privacy are necessary.

19. DIDs utilize a decentralized identifier registry, which is typically maintained on a decentralized system such as a blockchain. When a user generates a DID, a unique identifier is generated and registered on the decentralized system. When the user interacts with DeFi platforms and protocols, this identifier can be used to authenticate and corroborate the user's identity.

20. Figure 2 illustrates the process by which a user can create and use a DID in a DeFi application. The user first requests access to financial services from the DeFi application, which in turn requests the user to create a DID. The user then registers their DID with a DID registrar, which verifies their personal data. Once the user confirms their DID registration, the DeFi application can get the user's DID and personal information from the registrar. When the user requests to sign a financial transaction, they confirm the transaction with their DID. The DeFi application and DID registrar then verify the transaction with the user's DID, ensuring the transaction is legitimate and secure.

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Figure 2

Mechanism of DIDs Workflow in DeFi

21. One of the primary benefits of DIDs in DeFi is their decentralized nature, which eliminates the need for centralized authorities and decreases the risk of identity theft and other types of fraud. In addition, DIDs are designed to be interoperable, meaning that they can be used across various platforms and protocols, making them a valuable resource for DeFi users, developers, and authorities.

22. DIDs have the potential to solve identification issues on DeFi transactions and participants. DIDs are intended to be a secure, decentralized, and interoperable identity solution compatible with multiple platforms and protocols.

23. DIDs offer a more secure and trustworthy form of identity verification than traditional identification methods. DIDs generate unique, tamper-resistant identifiers that are anchored to a decentralized system, such as a blockchain, using public key cryptography. To pilfer or forge a user's identity, an attacker would need access to the user's private key.

24. A further benefit of DIDs is that they can offer a more private method of identity verification than conventional methods. Because DIDs are decentralized and user-controlled, they enable individuals to maintain control over their personal information and determine when and how it is shared. This is especially crucial in the DeFi space, where users may be reluctant to share sensitive financial data with centralized authorities.

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25. DIDs can also aid in the resolution of the cross-platform data-sharing issue in DeFi. Because DIDs are interoperable, they can be utilized across various DeFi platforms and protocols, allowing users to maintain a consistent and dependable identity across applications. This can reduce the risk of inconsistent data and unreliable statistics and make it simpler for DeFi platforms to comply with regulatory requirements.

26. According to the World Wide Web Consortium, there are 10 goals to be achieved by adopting DIDs. Those goals are decentralization, control, privacy, security, proof-based, discoverability, interoperability, portability, simplicity, and extensibility. Some of the goals are closely related to the improvement of statistical methods in covering DeFi as the new area of financial statistics.

27. Overall, DIDs have significant potential for resolving identification issues among DeFi transactions and participants. DIDs can help improve the overall reliability, security, and transparency of the DeFi ecosystem by offering a more secure, privacy-preserving, and interoperable method of identity verification.

VII. Prior Studies on the Potential of DIDs in DeFi

28. Several research studies have investigated the capability of DIDs to identify transactions and parties in DeFi.

29. Research on decentralized digital identity management was arranged by researchers from the University of Tartu and Tallinn University of Technology, which proposes a blockchain-based solution for DID-based decentralized identity management. The authors say that DIDs can be used to verify and authorize users in the DeFi ecosystem, making transactions more secure and clear.

30. The World Economic Forum has been investigating the potential for DIDs and other decentralized identity solutions to enhance the efficacy and security of financial services, including DeFi. The forum proposes that DIDs can aid in reducing the risk of deception and enhancing the privacy of customers in financial transactions.

31. Several blockchain and DeFi companies are also actively developing and implementing DID solutions for identity management in the DeFi ecosystem.

32. Researchers and industry professionals have acknowledged the potential of DIDs to identify transactions and parties in DeFi, and there is ongoing research and development in this area.

VIII. Summary of the Goals of Implementing DIDs in DeFi

33. Adopting DIDs for capturing statistics in DeFi can offer numerous advantages, including enhanced data quality, transparency, and security. Using DIDs can increase the precision and completeness of DeFi transaction data. DIDs provide a more secure and tamper-resistant form of identity verification, thereby decreasing the likelihood that fraudulent or incomplete data will be recorded.

34. Second, the utilization of DIDs can improve the visibility and traceability of DeFi transactions. DIDs provide a unique identifier for each participant in a transaction, allowing for more precise and granular reporting of DeFi activities.

35. Thirdly, DIDs can enhance the security and confidentiality of DeFi data. DIDs enable users to retain control over their confidential data, thereby reducing the risk of data breaches or misappropriation.

36. Lastly, the adoption of DIDs for statistics collection in DeFi can facilitate international cooperation and standardization in data collection and reporting. DIDs provide a standardized and interoperable form of identity verification, allowing for more accurate and consistent reporting of DeFi activities across jurisdictions.

37. Adopting DIDs for capturing statistics in DeFi can provide several benefits to statistical agencies, including enhanced data integrity, transparency, and security.

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Additionally, it can promote international cooperation and standardization in data collection and reporting, thereby augmenting the comparability and utility of DeFi statistics across jurisdictions.

IX. Recommendations for future research and implementation of DIDs in DeFi

38. The implementation of DIDs in the DeFi ecosystem has the potential to substantially improve the ecosystem's efficiency, transparency, and security. To assure the successful adoption and incorporation of DIDs in DeFi, a number of research and implementation areas should be prioritized.

39. First, standardized and interoperable DID protocols that can be used across various DeFi platforms and protocols are required. This will necessitate collaboration and coordination between regulatory authorities, DeFi developers, and blockchain companies to ensure that DIDs can be seamlessly integrated into the existing DeFi infrastructure.

40. Second, research on the impact of DIDs on financial inclusion and access to DeFi services is required. DIDs have the potential to increase the security and transparency of financial transactions, but they may also present barriers to entry for those who lack access to the required technology or infrastructure.

41. Thirdly, research on the privacy implications of DIDs in DeFi is required. DIDs can provide a more private form of identity verification, but there are concerns that they may also facilitate new forms of unnecessary surveillance and monitoring by governments and other organizations.

42. Lastly, international cooperation and standardization are required for the collection and reporting of DeFi transaction data. This will necessitate collaboration between DeFi actors, regulatory bodies, and statistical agencies to acquire and report DeFi data in a consistent and standardized manner.

43. Overall, the implementation of DIDs in DeFi has the potential to revolutionize the conduct and recording of financial transactions. To ensure that DIDs are incorporated in a manner that fosters financial inclusion, privacy, and international cooperation, however, additional research and collaboration are required.

  • Group of Experts on National Accounts
  • Twenty-second session
  • Potential Use of Decentralized Identities (DIDs) to Capture the Dynamic of Decentralized Finance (DeFi)
    • Prepared by Statistics Indonesia0F
  • I. Background information about Decentralized Finance and Decentralized Identities
  • II. Lack of Proper Identification in Statistical Data Collection in DeFi Ecosystem
  • III. Overview of DeFi and Its Importance
  • IV. Importance of Collecting Statistical Data in DeFi
  • V. Limitations of Traditional Identification Methods in DeFi
  • VI. Introduction to DIDs and Their Potential to Resolve Identification Issues in DeFi
  • VII. Prior Studies on the Potential of DIDs in DeFi
  • VIII. Summary of the Goals of Implementing DIDs in DeFi
  • IX. Recommendations for future research and implementation of DIDs in DeFi

Forest Product Conversion Factors

Forest products conversion factors provides ratios of raw material input to the output of wood-based forest products for 37 countries of the world. Analysts, policymakers, forest practitioners and forest-based manufacturers often have a need for this information for understanding the drivers of efficiency, feasibility and economics of the sector.

Forest Product Conversion Factors

Forest products conversion factors provides ratios of raw material input to the output of wood-based forest products for 37 countries of the world. Analysts, policymakers, forest practitioners and forest-based manufacturers often have a need for this information for understanding the drivers of efficiency, feasibility and economics of the sector.