Does Country Risk Promote the Informal Economy? A Cross-National Panel Data Estimation

Abstract

This article investigates whether country risk plays an important role in determining the size of the informal economy. Using annualized panel data for a sample of 131 countries and regions covering 1999–2007, and controlling for a set of control variables, we find that country risk is a robust and significant determinant of the informal economy: a 1% increase in the country risk rating (decrease in the country risk) causes a 0.1% fall in the informal economy, and political risk has the largest effect, followed by economic risk. Moreover, the estimation results provide little evidence in support of an inverted-U relationship between urbanization and the share of the informal sector, which shed new light on the urbanization-the informal economy nexus.

Key Words:

• Country risk
• informal economy
• urbanization
• panel data
• GMM estimator

Jel Classification:

• D81
• O17

Acknowledgements

We thank especially the Editor Prof. Kap-Young Jeong and two anonymous referees for very constructive remarks and suggestions. They make some pertinent comments on the previous version of this article, and also give us some suggestions and hints. Nevertheless, any shortcomings that remain in this research paper are solely our responsibility.

Notes

1 Although much literature is concerned with the relationship between the shadow economy and corruption, the extant empirical evidence on the shadow economy-corruption nexus is mixed. Some argue that the shadow economy and corruption may be substitutes when firms moving to the underground sector reduce rent-seeking opportunities for corrupt officials in the official sector (Dreher et al., 2009). While others contend that corruption and the shadow economy would be complementary when firms operating in the shadow economy would offer bribes to avoid punishment or to secure services from the official sector (Johnson et al., 1997). However, a recent study by Dreher and Schneider (2010) find that the relationship between corruption and the size of the shadow economy is not robust. As the results are depending on the particular corruption measures chosen and the covariates included.

2 This approach builds upon the works of Frey and Weck (1983) and is essentially based on the use of a specific structural equation model, titled the MIMIC approach. This method treats the shadow economy as an unobserved latent variable and essentially consists of two steps: first, one determines the causes and the indicators of the informal economy. Then, given the causes and the indicators and the specified relationship among them through the unobserved latent variable, one runs a structural equation model to estimate the coefficients of the causes and the indicators. See Schneider et al. (2010) for more details and an explanation of superiority of this methodology to others.

3 The semi-parametric model is given bywhere the functional form is unspecified. For more detail about the estimation procedure, please see Baltagi and Li (2002). In our empirical analysis, we use a B-spline regression approach (Desbordes & Verardi, 2012).

4 We classify the sample into developed and developing countries based on the following website: http://www.un.org/en/development/desa/policy/wesp/wesp_current/2014wesp_country_classification.pdf.

Additional information

Funding

This research is partially supported by National Natural Science Foundation of China (No. 71703140), and also supported by Natural Science Foundation of Hunan Province of China (No. 2017JJ3293).