Measuring the size of the shadow economy in 30 provinces of China over 1995–2016: The MIMIC approach

• Author: Friedrich Schneider,Hailin Chen,Qunli Sun
• DOI: http://doi.org/10.1111/1468-0106.12313
• Date: 01 August 2020
• Published date: 01 August 2020

ORIGINAL MANUSCRIPT

Measuring the size of the shadow economy

in 30 provinces of China over 1995–2016:

The MIMIC approach

Hailin Chen

1

| Friedrich Schneider

2

| Qunli Sun

1

1

China's Income Distribution Research

Center, School of Public Finance and

Taxation, Zhongnan University of

Economics and Law, Wuhan, China

2

Department of Economic, Research

Institute of Banking and Finance, Johannes

Kepler University Linz, Linz, Austria

Correspondence

Qunli Sun and Hailin Chen

China's Income Distribution Research

Center, School of Public Finance and

Taxation, Zhongnan University of

Economics and Law, 182# Nanhu Avenue,

East Lake High-tech Development Zone,

Wuhan 430073, China.

Email: sunqunli108@sina.com (Q.S.) and

Email: znlv1991@163.com(H.C.)

Funding information

China Association for Science and

Technology, Grant/Award Number:

17BJY231

Abstract

Applying the multiple indicators and multiple causes

(MIMIC) approach, the present paper measured the size of

the shadow economies in China's provinces over 1995–2016.

The results show that the average size of the shadow econ-

omy in 30 provinces of China increased from 13.55% in

1995 to 14.39% in 2009, and then decreased to 12.30% in

2016. There are obvious variations in the size of the

shadow economies in different districts of China. The

average size of the shadow economy is lowest in provinces

in the eastern district and highest in the western district. In

addition, the causes and consequences of the shadow

economies in China's provinces have also been analysed

using the MIMIC approach, and the results show that tax

burden, complexity of the tax system, intensity of regula-

tion, unemployment, employment in the agricultural sector

and economic openness have significant positive effects

on China's shadow economy, while the ratio of direct taxes

to indirect taxes, fiscal autonomy and income levels have

significant negative effects on China's shadow economy.

Using the growth rate of energy consumption as the

benchmark indicator, the MIMIC analysis shows that

the shadow economy has significant positive effects on the

development of the official economy and income inequal-

ity while having a significant negative effect on the labour

participation rate.

Received: 13 December 2018 Revised: 28 June 2019 Accepted: 29 August 2019

DOI: 10.1111/1468-0106.12313

Pac Econ Rev. 2020;25:427–453. wileyonlinelibrary.com/journal/paer © 2019 John Wiley & Sons Australia, Ltd 427

1|INTRODUCTION

The shadow economy refers to transactions of goods and services which are intentionally concealed

from market authorities to avoid taxation, social insurance payments, regulations and other adminis-

trative procedures (Schneider, 2014; Schneider & Buehn, 2018). Other terms for the shadow econ-

omy used in different research include the underground economy, the unofficial economy, the

irregular economy and shadow income. Shadow economies exist all over the world. According to a

recent study by Medina and Schneider (2017), the shadow economy in 158 countries accounted for

an average 32.3% of official GDP over 1991 to 2015, and the three largest shadow economies existed

in Georgia, Bolivia and Zimbabwe, where shadow economic activities accounted for 64.9, 62.3 and

60.6% of GDP, respectively; even in Switzerland, the USA and Austria, the 3 countries with the

smallest shadow economies, they accounted for 9.9, 9.4 and 9% of GDP, respectively.

1

This issue

has attracted much attention from researchers and governments for its widespread existence and mul-

tiple consequences. Many empirical research studies have confirmed that shadow economies have

significant effects on the economic system through distorting economic policies and market mecha-

nisms, resulting in unfair competition among firms, and other channels (Hajilee, Stringer, &

Metghalchi, 2017; Schneider & Hametner, 2014; Zaman & Goschin, 2015). Kar and Saha (2012)

found that the shadow economy has increased income inequality in many Asian countries. In addi-

tion, tax evasion and corruption, which can derive from the shadow economy, can threaten public

financial security and reduce government quality (González-Fernández & González-Velasco, 2014).

We can observe the shadow economies all over the world, and China is no exception. Cross-

national research projects have demonstrated that China also has a shadow economy that cannot be

ignored. For example, Schneider, Buehn, and Montenegro (2010) used the multiple indicators and

multiple causes (MIMIC) approach and found that the average size of the shadow economy in China

over 1999–2007 was 12.7%; Alm and Embaye (2013) used the currency demand method, and found

that the average size of the shadow economy was 23.3% in 1990 and 17.7% in 2009; Elgin and

Oztunali (2014) used the DGE approach and found that the average size of the shadow economy

decreased from 34.06% in 1952 to 11.53% in 2009; Medina and Schneider (2017) used the MIMIC

approach and found that the average size of the shadow economy in China over 1991–2015 was

11.2%. However, little attention has been paid to the size of the shadow economy by province in

China, which is crucial for analysing the causes and consequences of China's shadow economy. In

addition, most of the existing related research uses cross-national data, which means that some causes

of the shadow economy in China might be ignored in simulations, and there are also big differences

among the existing results. As the biggest developing country in the world, China has experienced

great political, economic and societal changes, so it is important to measure the size of the shadow

economies in China's provinces precisely and to analyse their causes and consequences.

The existing methods for estimating the size of the shadow economy can be divided into three cat-

egories: the direct method, the indirect method and the modelling method (Medina & Schneider,

2017; Orsi, Raggi, & Turino, 2014; Schneider & Enste, 2000). The direct method is mainly used to

measure the size of the shadow economy by conducting questionnaires, telephone surveys or tax

audits among enterprises and residents. For example, Putnin¸šand Sauka (2015) investigated the

undeclared income of enterprises and employees by using the questionnaire survey method, and

inferred that the average sizes of the shadow economies in Latvia, Lithuania and Estonia were 30.2,

17.1 and 18.9%, respectively, in 2009–2011. The indirect method estimates the size of the shadow

economy mainly through analysing the changes of an economic indicator which is closely related to

the shadow economy, such as currency demand, the cash ratio, the labour participation rate, energy

428 CHEN ET AL.