Pollution, shadow economy and corruption: Theory and evidence ☆

TLDR: In this article, the authors study how the shadow economy affects pollution and how this effect depends on corruption levels in public administration, and they use panel data covering the period from 1999 to 2005 in more than 100 countries to test this theoretical prediction.

About: This article is published in Ecological Economics.The article was published on 2012-03-01 and is currently open access. It has received 255 citations till now. The article focuses on the topics: Environmental pollution & Shadow (psychology).

Figures

Table 5. Alternative Corruption Index (CPI)

Table 3. Shadow Economy and CO2 Emissions

Figure 5. Marginal Effects of the Shadow Economy on CO2 Emissions per capita (using S.3.9 in Table 3)

Figure 1. Size of the Shadow Economy (% of GDP) around the World (1999-2005)

Figure 3. Marginal Effects of the Shadow Economy on SO2 Emissions per capita (using S.2.9 in Table 2)

Table 2. Shadow Economy and SO2 Emissions

Frequently asked questions

Q1. What contributions have the authors mentioned in the paper "Pollution, shadow economy and corruption: theory and evidence" ?

The authors study how the shadow economy affects pollution and how this effect depends on corruption levels in public administration. 

Q2. What is the effect of tighter regulation on emissions in the presence of an informal sector?

In the presence of an informal sector, tighter environmental regulation leads to a drop in output in the formal economy and an increase in activity in the shadow economy. 

Q3. What is the advantage of structural equation modeling over traditional regression analysis?

As Chang et al. (2009) note, “the advantage of structural equation modeling over traditional regression analysis is that it explicitly models measurement errors and can estimate parameters with full information maximum likelihood (FIML), which provides consistent and asymptotically efficient estimates”. 

Q4. How is the problem of omitted variables mitigated?

The potential problem of omitted variables, such as historical legacies, culture and geographical characteristics, is mitigated by controlling for country and time-fixed effects. 

Q5. How much of the GDP generated by shadow economies in over 162 countries between 1999 and 2006?

Between 1999 and 2006, the activity of shadow economies generated on average 34.5% of the official GDP in over 162 countries (Schneider et al., 2010). 

Q6. How many deaths do water pollution cause?

According to the World Health Organization (WHO), water pollution is one of the main health risks and leads to approximately 2 million deaths annually. 

Q7. How much does a 1% increase in the shadow economy increase CO2 emissions per capita?

In the baseline model (column S.3.9), a 1% increase in the size of the shadow economy increases CO2 emissions per capita by 1.41% in the absence of corruption. 

Q8. How do Farzin and Bond (2006) show that economic agents can implement their preferences for environmental quality more?

Farzin and Bond (2006), for example, show that economic agents can implement their preferences for environmental quality more effectively in democracies than in autocratic societies. 

Q9. What is the average size of the shadow economy in the developed countries?

From 1999-2006, more than 50% of the overall GDP in Ukraine, Tanzania, Peru, Panama, Guatemala, Georgia and Bolivia originated in the shadow economy (Schneider et al., 2010). 

Q10. What is the main problem with the p.345) approach?

As it is difficult to find time-varying instruments for variables such as corruption and the shadow economy, Verbeek (2004, p.345) suggests an alternative route: “in many cases panel data will provide ‘internal’ instruments for regressors that are subject to measurement error.