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High Corruption, Less Bank Efficiency?

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Abstract

This paper investigates the impact of corruption on bank efficiency. Using the stochastic frontier approach, we examine the link between corruption and cost efficiency on a broad sample of commercial banks in 126 countries over the period 2011–2018. We find evidence of a negative effect of increased corruption on bank cost efficiency. Additionally, the results show that the adverse impact of corruption on bank efficiency is observed for banks of all sizes and countries with different levels of economic development. Overall, our findings suggest that anti-corruption policy measures are important for improving efficiency in the banking system.

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Notes

  1. A foreigner who wants to invest in a Russian company, for instance, “must bribe every agency involved in foreign investment, including the foreign investment office, the relevant industrial ministry, the finance ministry, the executive branch of the local government, the legislative branch, the central bank, the state property bureau, and so on” (Shleifer and Vishny, 1993, p. 615).

  2. One such example is the so-called “speed money” which enables bribers to reduce delay and save time cost by hastening the slow-moving public service processes.

  3. According to Transparency International, the 2016 Corruption perception index, for example, is constructed using 13 different data sources from 12 institutions, such as the World Bank, African Development Bank, World Economic Forum, and among others.

  4. The model is estimated using the statistical data analysis software STATA (version 16.1).

    This table presents the summary statistics for variables employed in the study.

  5. In unreported regressions, we also perform estimations with one-year lagged values of the explanatory variables to address endogeneity issues. We obtain similar findings, showing that our results are robust after taking into account endogeneity concerns. The results are available upon request.

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Authors and Affiliations

  1. EM Strasbourg Business School, University of Strasbourg, 61 Avenue de la Forêt Noire, Strasbourg, 67000, France

    Francis Osei-Tutu

Authors
  1. Francis Osei-Tutu
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Correspondence to Francis Osei-Tutu.

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Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

We are grateful to the editor (Nauro F. Campos) and two anonymous referees for valuable comments and useful suggestions. We also thank Laurent Weill and Paul-Olivier Klein for insightful comments on earlier versions of the paper.

Appendices

Appendix 1 Definitions and sources of variables

Variable

Definition and source

Efficiency

Bank cost efficiency scores. Author’s computation.

Corruption–CPI

Corruption perception index. Transformed so that higher values reflect more perceived corruption. Source: Transparency International.

Corruption–WB

Control of corruption index from World Bank. Transformed so that higher values indicate less control of corruption. Source: World Governance Indicators (World Bank).

Bank size

Natural logarithm of total assets. Source: Bank Focus database.

ROAA

Return on average assets. Source: Bank Focus database.

Equity to assets

Ratio of equity to total assets. Source: Bank Focus database.

LOEA

Ratio of loans to other investment assets. Source: Bank Focus database.

Log(GDPPC)

Natural logarithm of gross domestic product per capita. Source. World Development Indicators (World Bank).

Credit/GDP

Domestic credit to the private sector by banks as a share of GDP. Source: World Development Indicators (World Bank).

Inflation

Rate of inflation. Source: World Development Indicators (World Bank).

Concentration

Share of assets that are held by the three largest banks in a country. Source: Global Financial Development Database.

Deposit Insurance

Dummy = 1 if a country has explicit deposit insurance, and 0 otherwise. Source: Demirgüç-Kunt et al. (2015).

Financial freedom

This index scores the financial freedom of a country. Source: Heritage Foundation.

English legal origin

Dummy = 1 if a bank is from a country with English legal origins, and 0 otherwise. Source: La Porta et al. (1999).

Ethno-linguistic fractionalization

This index captures the likelihood that two individuals who are randomly selected within a country do not belong to the same ethno-linguistic group.

  1. This table presents the definitions and sources of data for the variables employed in the study

Appendix 2

Country

Corruption–CPI

Corruption–WB

Efficiency

Country

Corruption–CPI

Corruption–WB

Efficiency

Afghanistan

8.765

1.443

0.721

Finland

1.267

−2.229

0.74

Albania

6.469

0.523

0.75

France

2.978

−1.356

0.731

Algeria

6.597

0.591

0.726

Gabon

6.642

0.763

0.74

Angola

8.074

1.361

0.755

Georgia

4.584

−0.665

0.738

Armenia

6.511

0.519

0.738

Germany

1.999

− 1.846

0.721

Austria

2.612

− 1.525

0.722

Ghana

5.6

0.145

0.731

Azerbaijan

7.14

0.921

0.73

Guatemala

7.138

0.731

0.738

Bahamas

3.307

− 1.191

0.72

Guinea

7.406

1.009

0.739

Bahrain

4.994

− 0.279

0.745

Guyana

6.753

0.518

0.731

Bangladesh

7.375

0.861

0.724

Haiti

8.073

1.237

0.712

Belarus

6.305

0.299

0.731

Hong Kong

2.388

− 1.64

0.669

Belgium

2.432

− 1.579

0.714

Hungary

5.054

− 0.139

0.716

Benin

6.318

0.626

0.747

India

6.192

0.357

0.718

Bhutan

3.435

− 1.279

0.749

Indonesia

6.46

0.435

0.739

Bolivia

6.732

0.658

0.741

Iraq

8.289

1.314

0.709

Bosnia and Herzegovina

6.133

0.4

0.739

Ireland

2.686

− 1.562

0.678

Botswana

3.771

− 0.879

0.741

Italy

5.458

− 0.098

0.726

Brazil

6.089

0.345

0.656

Jamaica

5.95

0.207

0.74

Bulgaria

5.845

0.21

0.741

Japan

2.499

− 1.575

0.712

Burkina Faso

6.077

0.313

0.752

Kazakhstan

7.102

0.796

0.664

Burundi

8.019

1.311

0.743

Kenya

7.363

0.939

0.713

Cambodia

7.921

1.182

0.718

Kyrgyzstan

7.243

1.087

0.715

Cameroon

7.422

1.166

0.712

Lebanon

7.218

0.949

0.723

Cape Verde

4.308

− 0.862

0.74

Lesotho

5.639

− 0.045

0.7

Chad

8.005

1.378

0.737

Libya

8.28

1.422

0.6

Chile

3.058

− 1.294

0.726

Luxembourg

1.803

− 2.09

0.707

China

6.105

0.297

0.727

Madagascar

7.323

0.878

0.734

Colombia

6.336

0.34

0.748

Malaysia

5.102

− 0.236

0.686

Costa Rica

4.495

− 0.647

0.718

Mali

6.843

0.705

0.743

Cote d'Ivoire

6.903

0.599

0.751

Malta

4.394

− 0.781

0.728

Croatia

5.238

− 0.159

0.743

Mauritania

7.103

0.83

0.704

Cyprus

4.052

− 0.912

0.702

Mauritius

4.747

− 0.289

0.725

Czech Republic

4.635

− 0.428

0.702

Mexico

6.917

0.738

0.706

D.R. Congo

7.842

1.317

0.707

Mongolia

6.238

0.471

0.719

Denmark

0.942

− 2.281

0.742

Montenegro

5.652

0.109

0.752

Djibouti

6.726

0.619

0.711

Morocco

6.221

0.278

0.735

Dominican Rep.

6.939

0.791

0.658

Mozambique

7.225

0.731

0.752

Ecuador

6.755

0.643

0.719

Myanmar

7.558

0.771

0.732

Egypt

6.658

0.616

0.734

Nepal

7.071

0.701

0.718

El Salvador

6.397

0.481

0.741

Netherlands

1.623

− 1.993

0.731

Eswatini

6.136

0.349

0.754

New Zealand

1.037

− 2.251

0.707

Country

Corruption–CPI

Corruption–WB

Efficiency

Niger

6.64

0.649

0.743

Nigeria

7.342

1.13

0.725

Macedonia

5.968

0.183

0.745

Norway

1.45

− 2.196

0.726

Oman

5.338

− 0.237

0.749

Pakistan

7.037

0.894

0.71

Panama

6.283

0.439

0.741

Paraguay

7.298

0.86

0.73

Peru

6.357

0.457

0.726

Philippines

6.481

0.475

0.733

Poland

3.91

− 0.677

0.743

Portugal

3.698

− 0.916

0.655

Moldova

6.725

0.806

0.743

Romania

5.43

0.086

0.747

Russian Federation

7.169

0.911

0.716

Rwanda

4.627

− 0.638

0.75

Senegal

5.799

0.079

0.746

Serbia

6.032

0.316

0.739

Seychelles

4.39

− 0.577

0.737

Sierra Leone

6.995

0.84

0.717

Singapore

1.5

− 2.114

0.734

Slovakia

5.172

− 0.203

0.736

South Africa

5.673

0.009

0.726

South Sudan

8.575

1.434

0.607

Spain

4.173

− 0.612

0.667

Sri Lanka

6.275

0.347

0.753

Sweden

1.312

− 2.191

0.726

Switzerland

1.419

− 2.101

0.727

Tajikistan

7.7

1.121

0.789

Thailand

6.363

0.401

0.716

Togo

6.965

0.836

0.755

Tunisia

5.928

0.079

0.711

Turkey

5.632

0.104

0.693

Uganda

7.429

1.051

0.735

Ukraine

7.211

0.941

0.709

United Arab Emirates

3.076

− 1.165

0.733

United Kingdom

2.048

− 1.806

0.72

Tanzania

6.695

0.607

0.751

United States

2.674

− 1.353

0.667

Uruguay

2.816

− 1.317

0.712

Venezuela

8.063

1.324

0.781

Vietnam

6.789

0.479

0.726

Zambia

6.285

0.416

0.746

Zimbabwe

7.867

1.338

0.728

  1. This table presents the means by country for the measures of corruption and bank efficiency

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Osei-Tutu, F. High Corruption, Less Bank Efficiency?. Comp Econ Stud 64, 540–561 (2022). https://doi.org/10.1057/s41294-021-00167-y

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