Our findings show that the size of the shadow economy in Russia was 45.8% of the GDP in 2017 and slightly decreased to 44.7% of the GDP in 2018. Putting this level into perspective by comparing it to nearby countries), it is similar to the level of shadow economy in countries such as Kyrgyzstan, Kosovo, Ukraine and Romania, but higher than the level seen in the Baltic countries (Estonia, Latvia, and Lithuania). Our findings are largely consistent with other less direct approaches for estimating the size of the shadow economies, such as Schneider (2019). An advantage of our approach is that it is able to provide more detailed information on the components of the shadow economy, which we turn to next.
 

We find that envelope wages and underreporting of business profits stand out as the two largest components of the Russian shadow economy.


Underreporting of salaries or so called ‘envelope wages’ in Russia as a proportion of the true wage accounted for 38.7% on average in 2018, whereas underreporting of the business income (percentage of actual profits) was 33.8%. Underreporting of employees in Russia (percentage of the actual number of employees) is estimated at 28.2% in 2018.

Some companies in Russia, rather than simply concealing part of the income or employees, are completely unregistered and therefore also contribute to the shadow economy. We estimate that such companies make up 6.1% of all enterprises in Russia.

Our findings also suggests that there is very high level of bribery in Russia: the magnitude of bribery (percentage of revenue spent on ‘getting things done’) is found to be 26.4%, whereas percentage of the contract value that firms typically offer as a bribe to secure a contract with the government in Russia is 20.6% in 2018. We also find that more than one-third of companies in Russia pay in bribes more than 25% of the revenue or contract value.

The highest levels of shadow economy are observed in Nizhny Novgorod region, reaching 64% of the GDP, followed by Moscow (47.1%) and Voronezh (41.1%). We also find that the size of the shadow economy in all sectors is close to 40% with somewhat higher levels in the construction and wholesale sectors, controlling for other factors.

Using regression analysis, we find that entrepreneurs that view tax evasion as a tolerated behaviour tend to engage in more informal activity, as do entrepreneurs that are more dissatisfied with the 4 tax system and the government. This result offers some insights into why the size of the shadow economy in Russia is so large – it is at least in part due to relatively high dissatisfaction of entrepreneurs with the business legislation and the government’s tax policy. We also find some evidence that higher perceived detection probabilities and, in particular, more severe penalties for tax evasion reduce the level of tax evasion, suggesting increased penalties and better detection methods as possible policy tools for reducing the size of the shadow economy.

Finally, while firms of all sizes participate in the shadow economy, we find that younger firms tend to do so to a greater extent than older firms. The results support the notion that young firms use tax evasion as a means of being competitive against larger and more established competitors.


Methodology

The Shadow Economy Index draws on methodology developed by Putnins and Sauka (2015) using information from entrepreneurs. It combines business income that has been concealed from authorities, unregistered or hidden employees, and ‘envelope’ wages to estimate the size of the shadow economy as a proportion of GDP.


This research was supported by a Marie Curie Research and Innovation Staff Exchange scheme within the H2020 Programme (grant acronym: SHADOW, no: 778118).