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A Bidirectional Causality Between Shadow Economy and Economic and Sustainable Development

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Sustainable Finance and Financial Crime

Part of the book series: Sustainable Finance ((SUFI))

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Abstract

The present chapter examines the relation between shadow economy and economic development from a global perspective for 185 countries over the period 2005–2017. Increasing the economic development in many countries was accompanied by a decline of the level of shadow economy. In the same time, the shadow economy seems to have an impact on economic and sustainable development. For capturing the existence of this bidirectional causality, we will test Granger causality along with the panel econometric analysis realized for low-, middle-, and high-income countries. The main empirical findings based on fully modified ordinary least square (FMOLS) and Granger causality tests confirm the significant impact of shadow economy on the economic development. The results of this study can play an important role in the political fight against the shadow economy, even if in some cases the positive impact is confirmed. The government should be aware of the fact that shadow economy will decrease the public revenue, and this will lead in the long run to lower public investments. In this context, the sustainable economic development is affected and all the political efforts for combating the shadow economy must consider all the favoring factors of shadow economy.

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  1. 1.

    https://datahelpdesk.worldbank.org/knowledgebase/articles/906519

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Acknowledgment

This work was supported by a grant of the Romanian Ministry of Education and Research, CNCS - UEFISCDI, project number PN-III-P4-ID-PCE-2020-2174, within PNCDI III.

Author information

Authors and Affiliations

  1. Babes-Bolyai University, Cluj-Napoca, Romania

    Eugenia Ramona Mara, Monica Violeta Achim & Sandra Clement

Authors
  1. Eugenia Ramona Mara
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  2. Monica Violeta Achim
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  3. Sandra Clement
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Corresponding author

Correspondence to Monica Violeta Achim .

Editor information

Editors and Affiliations

  1. École de gestion, Université de Sherbrooke, Sherbrooke, QC, Canada

    Michel Dion

Ethics declarations

The authors declare that she has no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendices

Appendix 1. List of Countries

High-income countries

Middle-income countries

Low-income countries

Andorra

Albania

Afghanistan

Antigua and Barbuda

Algeria

Burkina Faso

Aruba

Angola

Burundi

Australia

Argentina

Central African Republic

Austria

Armenia

Chad

Bahamas

Azerbaijan

Congo Democratic Republic

Bahrain

Bangladesh

Eritrea

Barbados

Belarus

Ethiopia

Belgium

Belize

Gambia

Bermuda

Benin

Guinea

Brunei Darussalam

Bhutan

Guinea-Bissau

Canada

Bolivia

Liberia

Chile

Bosnia and Herzegovina

Madagascar

Croatia

Botswana

Malawi

Cyprus

Brazil

Mali

Czech Republic

Bulgaria

Mozambique

Denmark

Cambodia

Niger

Estonia

Cameroon

Rwanda

Finland

Cape Verde

Samoa

France

China

Sierra Leone

Germany

Colombia

Somalia

Greece

Comoros

South Sudan

Greenland

Congo Republic

Sudan

Hong Kong

Costa Rica

Syria

Hungary

Côte d’Ivoire

Togo

Iceland

Cuba

Uganda

Ireland

Djibouti

Yemen

Israel

Dominica

Italy

Dominican Republic

Japan

Ecuador

Korea

Egypt

Kuwait

El Salvador

Latvia

Equatorial Guinea

Liechtenstein

Eswatini

Lithuania

Fiji

Luxembourg

Gabon

Macao

Georgia

Malta

Ghana

Monaco

Grenada

Netherlands

Guatemala

New Zealand

Guyana

Norway

Haiti

Oman

Honduras

Poland

India

Portugal

Indonesia

Puerto Rico

Iran

Qatar

Iraq

Saudi Arabia

Jamaica

Seychelles

Jordan

Singapore

Kazakhstan

Slovakia

Kenya

Slovenia

Kiribati

South Korea

Kosovo

Spain

Kyrgyzstan

Sweden

Laos

Switzerland

Lebanon

Taiwan

Lesotho

Trinidad and Tobago

Libya

United Arab Emirates

Macedonia

United Kingdom

Malaysia

United States of America

Maldives

Uruguay

Mauritania

Mauritius

Mexico

Moldova

Mongolia

Montenegro

Morocco

Myanmar

Namibia

Nepal

Nicaragua

Nigeria

Pakistan

Panama

Papua New Guinea

Paraguay

Peru

Philippines

Romania

Russia

Saint Lucia

Saint Vincent and the Grenadines

Sao Tome and Principe

Senegal

Serbia

South Africa

Sri Lanka

Suriname

Swaziland

Tajikistan

Tanzania

Thailand

Timor-Leste

Tonga

Tunisia

Turkey

Turkmenistan

Ukraine

Uzbekistan

Vanuatu

Venezuela

Vietnam

Zambia

Zimbabwe

  1. Source: author composition

Appendix 2. Description of Variables

Variable

Specification

Source

Expected sign

Dependent variable

Shadow economy (SE)

Shadow economy (% GDP)

Medina and Schneider (2019)

 

Independent variable

Economic development (LOGGDPCAP)

LOG GDP per capita (constant 2015 US$)

World Bank 2022

https://data.worldbank.org/indicator/NY.GDP.PCAP.KD

–

Sustainable development (HDI)

Human development index varies between 0 and 1.

UNDP, 2022

https://hdr.undp.org/data-center/human-development-index#/indicies/HDI

–

Financial development (FD)

Financial development index

Financial Development – IMF, 2022,

https://data.imf.org/?sk=f8032e80-b36c-43b1-ac26-493c5b1cd33b

–

Control variables

Urban population (URBPOP)

Urban population (% of total population)

Worldbank, 2022: United Nations Population Division. World Urbanization Prospects: 2018 Revision.

https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS

–

Industry (INDUSTRY)

Industry value added (% GDP)

Worldbank, 2022:

https://data.worldbank.org/indicator/NV.IND.TOTL.ZS

–

Trade(TRADE)

Trade (% of GDP)

Worldbank: World Bank national accounts data, and OECD National Accounts data files (2022),

https://data.worldbank.org/indicator/NE.TRD.GNFS.ZS

–

Broad money (BROADMONEY)

Broad money (% of GDP)

Worldbank, 2022,

https://data.worldbank.org/indicator/FM.LBL.BMNY.GD.ZS

+

Index of Money laundering (INDEXMONEY)

Risk of money laundering and terrorist financing (AML)

Basel Institute on Governance, 2022:

https://baselgovernance.org/basel-aml-index

+

POLSTAB

Political Stability and Absence of Violence/Terrorism: Estimate

Worldbank, 2022:

https://databank.worldbank.org/metadataglossary/1181/series/PV.EST

–

Corruption (CPI)

Corruption Perception Index (CPI – ranges from 0 (highly corrupt) to 100 (very clean))

Transparency International, 2022: https://www.transparency.org/en/cpi/2020/index/nzl

 

Inequality (GINI)

Gini index (0 represents perfect equality, while an index of 100 implies perfect inequality)

Worldbank, 2022:

https://data.worldbank.org/indicator/SI.POV.GINI

–

INCOMEINEQUAL

Income inequality (%) – Pre-tax national income

Gini coefficient

World inequality database, 2022:

https://wid.world/data/

–

Out of school

Children out of school (% of primary school age)

Worldbank, 2022:

https://data.worldbank.org/indicator/SE.PRM.UNER.ZS

+

INTERNET

Individuals using the Internet (% of population)

Worldbank, 2022:

https://data.worldbank.org/indicator/IT.NET.USER.ZS

–

TAXBUR

Tax burden

Heritage Foundation, 2022:

https://www.heritage.org/index/explore

+

UNEMPL

Unemployment, total (% of total labor force) (modeled ILO estimate)

Worldbank, 2022:

https://data.worldbank.org/indicator/SL.UEM.TOTL.ZS

+

INFLA

Inflation, consumer prices (annual %)

Worldbank, 2022: https://data.worldbank.org/indicator/FP.CPI.TOTL.ZG

+

  1. Source: Own composition

Appendix 3. Summary Statistics

 

Observations

Mean

Median

Maximum

Minimum

Std. dev.

SE

3618

30.40

30.50

70.50

5.10

12.66

LOGGDPCAP

4727

3.70

3.67

5.26

2.32

0.65

FD

4171

0.31

0.23

1.00

0.02

0.22

INTERNET

4288

26.93

13.98

99.70

0.00

29.40

UNEMPL

4627

8.24

6.42

68.56

0.11

7.05

URBPOPULA

4888

56.30

55.98

100.00

7.21

23.75

INDUSTRY

4330

26.79

25.14

87.80

0.96

12.15

TRADE

4346

86.22

76.19

442.62

0.02

53.22

INFLA

4306

9.98

3.60

4145.11

–18.11

81.34

HDI

4316

0.67

0.70

0.96

0.23

0.17

POLSTAB

4145

–0.06

0.04

1.97

–3.31

1.00

INCOMEINEQUAL

4498

0.57

0.59

0.84

0.34

0.08

OUTOSCHO

2786

8.47

3.03

78.05

0.00

13.09

TAXBUR

3929

73.23

75.10

100.00

10.00

14.51

BROADMONEY

3919

55.18

44.88

452.55

2.86

43.46

INDEXMONEY

1298

5.67

5.61

8.61

1.78

1.23

GINI

1587

37.89

35.90

65.80

23.00

8.81

CPI

3665

43.27

36.00

100.00

4.00

21.33

Appendix 4. Matrix Correlations

An 18 by 18 matrix correlation. The row and column headers are S E, log G D P CAP, trade, income inequal, unemployment, F D, internet, U R B popular, industry, inflation, H D I, polstab, out oscho, tax bur, broad money, index money, gini, and C P I.

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Mara, E.R., Achim, M.V., Clement, S. (2023). A Bidirectional Causality Between Shadow Economy and Economic and Sustainable Development. In: Dion, M. (eds) Sustainable Finance and Financial Crime. Sustainable Finance. Springer, Cham. https://doi.org/10.1007/978-3-031-28752-7_13

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