Dataset for measuring the size of shadow economy in China's 30 provinces from 1995 to 2015

Published: 05-12-2018| Version 1 | DOI: 10.17632/by6srym987.1
Contributor:
Hailin Chen

Description

This dataset contains the data for estimating the size of shadow economy in China’s 30 provinces from 1995 to 2015. To estimate the size of shadow economy using the MIMIC approach, we collected data of casual variables and indicator variables of shadow economy, namely, (1) data on tax burden and tax structure which contains the total tax burden, personal income tax burden, corporate income tax burden, indirect tax burden, HHI index of tax system and the ratio of direct tax burden to indirect tax burden; (2) data on government regulation which contains the size of government and the intensity of labor regulation; (3) data on government decentralization and quality which contains fiscal autonomy and government corruption; (4) data on the labor market which contains unemployment and the employment in agricultural sector; (5) data on economic openness which contains FDI and foreign trade dependence; (6) data on the indicator variables of shadow economy which contain the growth rate of energy consumption, the growth rate of GDP, labor participate rate and income inequality. All these data are collected from different kinds of Statistical Yearbook of China. Specifically, Data on tax burden and tax structure is from Tax Yearbook of China 1995–2015, data on the labor market is from China Labor Statistical Yearbook 1995–2015, data on the growth rate of energy consumption is from China Energy Statistical Yearbook 1995–2015, data on corruption is from Procuratorial Yearbook of China 1995–2015, and other data is from China Statistic Yearbook 1995–2015.

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Steps to reproduce

This dataset is the original data for variables used to measure the size of the shadow economy in China's provinces over 1995-2015. To make the MIMIC regression, people should transfer it to data with a mean value of 0 according to the method used by Dell’Anno & Mourao (2011).