Tag: Estonia

Gender Gaps in Wages and Wealth: Evidence from Estonia

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This policy brief introduces two related papers examining two types of gender gaps in Estonia. First, it presents the work of Vahter and Masso (2019), who study the wage gender gaps in foreign-owned firms and compare this gap with the situation in domestic ones. Then it summarizes a paper of Meriküll, Kukk, and Rõõm (2019), who focus on the wealth gender gaps and highlight the role of entrepreneurship in this gap.

Gender inequality is not only a moral issue. An extensive literature has highlighted the cost of gender inequality in terms of economic (in)efficiency. Most of the academic work has, however, focused on either the US and Western Europe or developing countries. Research focusing on systematic gender disparities in Eastern Europe is rather scarce. Yet, there is much to be learned from this region. The purpose of the FROGEE (Forum for Research on Gender in Eastern Europe) project is to study several issues related to gender inequality in former socialist countries.

This policy brief summarizes two papers presented at the 2nd Baltic Economic Conference at the Stockholm School of Economics in Riga, on June 10-11, where a special session on gender economics was held with the support of the FROGEE project. The event, organized by the Baltic Economic Association (see balticecon.org), gathered more than 85 researchers from the Baltics and all over the world. These two papers focus on Estonia, one of the most successful economies among the transition countries, where however the gender wage gap is among the largest in the European Union.

Firm ownership and gender wage gap

An important source of wage inequality originates in firm-specific pay schemes (see for instance Card et al. 2016). Understanding the characteristics of firms associated with a gender pay gap is thus a necessary step to design relevant policy responses. In a paper entitled “The contribution of multinationals to wage inequality: foreign ownership and the gender pay gap”, Jaan Masso and Priit Vahter, both at the University of Tartu, compare the situation in foreign-owned firms with domestic ones. The fact that foreign-owned firms provide on average higher wages to their employees is well documented. However, the question of whether this premium differs between men and women remains largely overlooked.

A potential channel linking firm ownership and gender wage gap is the transfer of management practices from the home country of the investor to the affiliate. The great majority of FDI in Estonia originates from Finland and Sweden, two countries that regularly top international rankings on gender equality and that have set the fight against gender inequality as a top priority. Observing a lower level of gender wage gap in firms owned by Swedish and Finnish capital would suggest the existence of such a mechanism, even if there is evidence that Scandinavian countries do not stand out in a positive way when it comes to women in the top of the distribution (see for instance Boschini et al., 2018, and Bobilev et al., 2019).

On the other hand, Goldin (2014) has shown that a large part of the gender wage gap in the US can be explained by differences in job “commitment”: firms disproportionately reward workers willing to be available 24/7, more flexible regarding business trips, spending longer hours in the office, etc. Such workers happen to be more often men than women. Multinational firms may require such commitment and flexibility to a larger extent than domestic firms, due for instance to their higher exposure to international competition. This would imply a larger gender pay gap in foreign-owned firms compared to local firms.

To investigate this issue, Masso and Vahter (2019) rely on Estonian administrative data, providing information on the whole universe of workers and of firms in the country between 2006 and 2014. This matched employer-employee dataset allows to track the wage of individuals over the years, but also to compare wages both across and within firms. It thus becomes possible to estimate the gender wage gap at the firm level (controlling for relevant individual-level factors affecting wages, such as age and experience), and then to check whether this measure systematically differs between domestic and foreign-owned firms.

However, simply comparing the gender pay gap between these two types of firms could lead to spurious conclusions. Foreign-owned firms have on average different characteristics than domestic ones: they do not operate in the same sectors, they do not have the same size nor the same productivity. To overcome this issue, the authors rely on a matching method: for each foreign-owned firms, they match a domestic firm with similar (observable) characteristics.

They find that in domestic firms, women are on average paid 19% less than men, even after accounting for many other factors associated with wage. In foreign-owned companies, both men and women are better paid. However, both genders do not benefit from the same premium: men are paid roughly 15% more in foreign-owned firms, whereas the premium for women is only 5.4%. This difference implies an even larger gender wage gap in multinational firms. To illustrate the economic significance of these results, for a man and a woman earning a monthly wage of 1146 euros (the average gross wage in Estonia in 2016), the premium for switching from a domestic to a foreign-owned firm is respectively 171 and 62 euros. Further, they provide some evidence that lower “commitment” is associated with a stronger wage penalty in foreign-owned firms. All in all, these results suggest that there is not necessarily a relationship between a multinational wage policy (especially in its gender wage-gap dimension) and the gender norms prevailing in its country of incorporation.

Gender and wealth gap

The vast majority of academic papers studying gender inequality focuses on the wage gap. But gender inequality can affect other types of economic outcomes, such as labor force participation, unemployment duration, or wealth. The latter is of particular interest since wealth can greatly contribute to empowerment. Merike Kukk, Jaanika Meriküll and Tairi Rõõm, all at the Bank of Estonia, extend the literature with a paper entitled “What explains the Gender Gap in Wealth? Evidence from Administrative Data”. This paper is one of the first to study the gender wealth gap in a post-transition country. The literature on the gender wealth gap is rather scarce because of a lack of suitable data: wealth measures are often computed at the household level, while individual-level data is necessary for such a study.

The main aim of this paper is to depict a precise portrait of this phenomenon in Estonia. In particular, the authors do not simply estimate the overall wealth gap but investigate the magnitude of the gap across the wealth distribution. In other words, is there a difference between the poorest men and the poorest women? Or on the other side of the distribution, are the richest men more wealthy than the richest women?

For this purpose, Kukk, Meriküll and Rõõm combine administrative individual-level data on wealth with survey results. The administrative data are generally considered of much better quality than the other, but they do not provide a lot of additional information on individuals. On the other hand, survey data provide a wealth of information about individual characteristics. Merging allows getting the best of both worlds. Regarding the methodology, the authors use unconditional quantile regression to track gender differences at different deciles of the wealth distribution. They further decompose this “raw” gender gap into two components: the “explained” part, i.e., the part of the gap resulting in differences in characteristics between men and women (demographics, education, etc.), and the “unexplained” part.

This study estimates the raw, unconditional gender wealth gap in Estonia to be 45%, which is of similar magnitude as in Germany. Interestingly, this difference is essentially driven by differences in the top of the distribution: there is a large gap between the richest men and the richest women. This “raw” difference is however explained by a single variable: self-employment, as men are much more likely to have business assets than women. Once controlling for the entrepreneurship status, the wealth difference between the richest Estonians becomes insignificant. This suggests the need to support policies encouraging female entrepreneurship and to remove barriers particularly affecting women. For instance, the literature has previously pointed out that women have less access to external sources of capital than men (e.g., Aidis et al., 2007). Such distortions can ultimately result in a wealth gap at the top of the distribution, as documented by this paper.

In addition, the literature has proposed several mechanisms that could result in gender-specific patterns of wealth accumulation. The simplest channel is through the wage gap, as it can be seen as the accumulation of the wage gap over time (e.g. Blau and Kahn, 2000). The authors thus compare the gender gaps in wealth and income. They uncover a strong wage gap, with men earning significantly more than women starting at the 6th decile: the higher we go in the income distribution, the larger the wage gap. How to reconcile this finding with the absence of a wealth gap conditional on entrepreneurship status? A possible explanation suggested by the authors is that women simply accumulate wealth better than men do.


These two papers illustrate two different mechanisms explaining gender-specific economic outcomes. The larger wage gap observed in multinational companies can be explained by a stronger commitment penalty for women, mostly because of childcare. This asks for two potential policy interventions. First, the development of childcare could facilitate the reduction in the “commitment gap” that disrupts women’s careers. Second, institutions could support a more flexible repartition of childcare responsibilities. Note however that Estonia already has the longest duration of leave at full pay (85 weeks), and that this leave can be freely split between parents. As for the wealth gap at the top of the wealth distribution, it can to a large extent be explained by the entrepreneurship status. This difference could partly be explained by differences in preferences and risk-aversion, which would require long-run policies to be mitigated. But in the short run, there is room for specific policies supporting female entrepreneurship and removing barriers particularly affecting women, such as a tighter credit constraint.


  • Aidis, R., Welter, F., Smallbone, D., & Isakova, N. (2007). Female entrepreneurship in transition economies: the case of Lithuania and Ukraine. Feminist Economics13(2), 157-183.
  • Blau, F. D., & Kahn, L. M. (2000). Gender differences in pay.  Journal of Economic perspectives14(4), 75-99.
  • Bobilev, R., Boschini, A., & Roine, J. (2019). Women in the Top of the Income Distribution: What Can We Learn From LIS-Data?. Italian Economic Journal, 1-45.
  • Boschini, A., Gunnarsson, K., & Roine, J. (2018). Women in Top Incomes: Evidence from Sweden 1974-2013. IZA Discussion Paper No. 10979 .
  • Card, D., Cardoso, A. R., & Kline, P. (2015). Bargaining, sorting, and the gender wage gap: Quantifying the impact of firms on the relative pay of women. The Quarterly Journal of Economics131(2), 633-686.
  • Goldin, C. (2014). A grand gender convergence: Its last chapter. American Economic Review104(4), 1091-1119.
  • Meriküll, J., Kukk, M., & Rõõm, T. (2019). What explains the gender gap in wealth? Evidence from administrative data. Bank of Estonia WP No. 2019-04.
  • Vahter, P., & Masso, J. (2019). The contribution of multinationals to wage inequality: foreign ownership and the gender pay gap. Review of World Economics155(1), 105-148.

Money Laundering: Regulatory or Political Capture?

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Danske Bank has recently been accused of having laundered more than 200 billion Euros through its Estonian branch. The size of the scandal has reinvigorated the discussion over lax enforcement by regulators and poor bank compliance with anti-money laundering laws. In this brief, we concisely review some recent cases of poor regulatory and political behaviour with respect to these matters, focusing in particular on the UK, whose financial system seems to have become a main hub for this type of financial misconduct.

A widespread phenomenon

The size of the recent money laundering scandal at Danske Bank, involving more than 200 billion Euros, has surprised many. Money laundering is a widespread issue in an increasingly complex world where financial transactions are many and instantaneous, while oversight slow and limited (Radu 2016). According to the United Nations Office on Drugs and Crime, an estimated $800 – $2 trillion is laundered every year (United Nations Office on Drugs and Crime). The source of laundered money is often from corruption, crime and drug cartels (as with the HSBC scandal, see below). Attempts to blow the whistle on these illegal transactions have gotten several people killed, especially in Russia (The Daily Beast, October 2018).

Malta’s Pilatus bank recently had its license revoked by the European Central Bank after its chairman was charged with money laundering (Reuters, October 2018). The investigative reporter Daphne Caruana Galizia was killed in a car bomb in October of 2017 in Malta (The Guardian, October 2017). She was leading the Panama Papers investigation into corruption in the country and had accused Pilatus bank of processing corrupt payments (The Guardian, November 2018). In Sweden, some banks have recently been criticized for insufficient actions against money laundering. Experts at the regulator recommended extensive sanctions, but upper management stopped them (Svenska Dagbladet, December 2018). In November, Deutsche Bank’s headquarters in Frankfurt were raided by prosecutors in a money laundering investigation (BBC, November 2018).

Back to Danske Bank. Its Estonian branch was recently accused of having laundered money, amounting to over 200 billion Euros of suspicious transfers (Financial Times, November 2018). In 2011 the Estonian branch accounted for 0.5% of Danske Bank’s assets, while generating 12% of its total profits before taxes. In 2013, 99% of the profits in the branch came from non-residents. Many of the non-resident customers are believed to be from Russia and other ex-soviet states (Forbes, September 2018). The alleged money laundering came to light due to the whistleblower Howard Wilkinson, who headed Danske Bank’s market trading unit in the Baltics from 2007 to 2014. Surprisingly, his anger over these transactions was not primarily aimed at top management in Copenhagen, or failure of rank and file employees to follow protocol in customer acquisition, but against the UK, who he claimed is “the worst of all” when it comes to combating money laundering (Financial Times, November 2018). In fact, the UK institutions seem to have been at the very heart of the scandal (ibid):

“Mr Wilkinson’s emails to Danske executives in 2013 and 2014 highlighted how UK entities were “the preferred vehicle for non-resident clients” at the heart of the scandal.”

In an address to European Union Lawmakers, he said (Reuters, November 2018):

“The role of the United Kingdom is an absolute disgrace. Limited liability partnerships and Scottish liability partnerships have been abused for absolutely years”.

Regulatory or political capture?

The increasingly central role that the UK appears to be playing as a hub for financial crime is perhaps not new or surprising. The UK has indeed come to be widely recognized as one – though certainly not the only – main hub for these illegal transactions (see e.g. Radu 2016, p.15). The UK’s National Crime Agency estimates 93 billion GBP of tainted money is flowing into Britain annually (Financial Times, September 2018).

And according to the classic theory of regulatory capture (Stigler, 1970), it is to be expected that a large, wealthy and highly concentrated sector such as the UK financial industry, will be able to capture regulatory institutions and lead them to act more in its favour than in that of the (national or international) community. However, besides being a concentrated source of special interests, the financial sector also represents a large share of the UK economy. It could be the case, therefore, that the capture goes all the way up to the political system and the government (as in Becker 1983, and Laffont, 1996). So, is it the alleged crime-friendly environment in the UK financial system linked more to problems of regulatory capture, or to deeper political capture?

Already in 2004 there were worrying signs of possibly deep political capture.  At the time, Paul Moore, a senior risk manager at Halifax Bank of Scotland (HBOS), raised concerns about the bank’s risk taking and was subsequently fired by the executive James Crosby. Crosby then proceeded to become Deputy Chairman at the Financial Services Authority (FSA). HBOS then collapsed during the financial crisis of 2008 and merged with Lloyds bank, leading to one of the most concentrated banking systems in the world (the top 5 banks have 85% of the UK banking market). Many took this to substantiate Moore’s claim that the bank had been taking excessive risks. During Prime Minister’s question time in the House of Commons, David Cameron commented on then Prime Minister Gordon Brown’s decision to appoint Crosby to the FSA:

“Sir James Crosby, the man who ran HBOS and whom the Prime Minister singled out to regulate our banks and to advise our Government, has resigned over allegations that he sacked the whistleblower who knew that his bank was taking unacceptable risks.” (cited in Dewing and Russell 2016, p.165)

A suggestive episode directly involving politicians and money laundering is the case of HSBC, with headquarters in London. HSBC avoided criminal prosecution in the US and entered into a deferred prosecution agreement with the DOJ in 2012 (Department of Justice, December 2012). HSBC was found to have violated U.S. Anti-Money Laundering and Sanctions Laws by laundering billions of dollars linked to Mexican drug cartels, groups in Iran and Syria, and groups linked to terrorism. While HSBC apparently had systems to flag suspicious transactions, employees were told to disregard red flags (Garrett 2014, p.201). The case led to a 2016 House Committee report entitled “too big to jail” that was extensively used against the Democrats by the Trump presidential campaign (Committee on Financial Services, 2016).

The report states that on the 10th of September 2012 UK Chancellor George Osborne (the UK’s chief financial minister) wrote a letter to Federal Reserve Chairman Ben Bernanke (with a copy transmitted to then Treasury Secretary Timothy Geithner). In the letter, Chancellor Osborne insinuated that the U.S. was unfairly targeting UK banks by seeking settlements that were higher than comparable settlements with U.S. banks. He also worried about what criminal sanctions against HSBC would imply for financial stability. Criminal charges could also lead to a revoked license, making the bank unable to do business in the US (Financial Times, July 2016). HSBC was eventually ordered to pay a 1.9 billion dollar fine, while another whistleblower claims that the money laundering still went on (Huffington Post, August 2013).

The FSA also appeared much more concerned about criminal sanctions against HSBC than with money laundering for the bloodiest drug cartel in history (estimated to be responsible for several tenths of thousands of murders). In fact, the house committee report states that “The FSA’s Involvement in the U.S. Government’s HSBC Investigations and Enforcement Actions Appears to Have Hampered the U.S. Government’s Investigations and Influenced DOJ’s Decision Not to Prosecute HSBC” (p.24).

Things have not improved more recently. In 2013 the FSA was split up into the Financial Conduct Authority and the Prudential Regulation Authority (FCA & PRA). In 2014 the FCA & PRA came out with a note requested by the British parliament on whether financial incentives for whistleblowers should be introduced in the UK. These financial incentives, or reward programs, are used extensively in the US in tax, procurement, and securities. The FCA & PRA came out strongly against rewards in their seven-page note, yet do not cite a single piece of evidence (PRA and FCA, 2014). Most importantly, the note contains important factual misstatements about available evidence on their effectiveness that were easy to check at the time of the report (Nyreröd & Spagnolo 2017, National Whistleblower Center 2018). Nor was the note amended when one of us repeatedly communicated the mistakes to the agencies. This suggests persistent and deep regulatory capture. Consistent with this interpretation is the sanctioning behavior of UK regulators.

A blatant recent example is the ridiculous fine against CEO of Barclays Bank Jes Staley. He ordered his security team to unveil the identity of an uncomfortable whistleblower, going so far as to request video footage of the person who bought the postage for the letter. Yet, the FCA & PRA decided to just fine him £642 000 – a small fraction of his pay package that year (Reuters, May 2018). When Moore was asked about the fine he replied that “it is a very clear sign to whistleblowers not to bother” (Reuters, April 2018).


Is this regulatory capture, or political capture? The impressive list of consistent cases of regulatory slack and of political complacency suggests both, at least in the case of the UK. But the problem of regulatory capture in the case of financial crimes goes way beyond the somewhat extreme case of the UK. In all jurisdictions financial misbehavior has recently only led to settlements between regulators and the infringing financial institution, with settlement payments way too low to generate (financial stability concerns, and) deterrence effects. Banking regulators appear mainly concerned about banks’ health and profitability, so that large financial institutions have not only become too big to fail, but also too big to jail, and now even too big to fine, at least to the appropriate extent (Spagnolo 2015). All this even though the financial crime has been that actively supporting through money laundering criminal organizations that killed tenths of thousands of innocent people.


Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Entrepreneurship in Latvia and Other Baltic States: Results from the Global Entrepreneurship Monitor

Entrepreneurship in Latvia Policy Brief Image

This policy brief summarises the results and implications of an upcoming Global Entrepreneurship Monitor (GEM) 2012 Latvia Report: a study on the entrepreneurial spirit and the latest trends in entrepreneurial activity in Latvia. The results suggest that Latvia is a rather entrepreneurial country (it rates second out of all EU countries by the share of population in early-stage entrepreneurial activity). GEM also finds that Latvian early-stage entrepreneurial activity is counter-cyclical. Early-stage entrepreneurship and self-employment have been important supports for those who were hit by the crisis in 2008-2009. Latvian entrepreneurs are measured to have strong international orientation and growth ambitions. The majority of them are young and middle-age males; in turn, females and the older age group (55-64) represent an “untapped entrepreneurial resource” potential to be addressed by policymakers.

Baltic Shadow Economies

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This policy brief summarises the results and implications of a recent study of the size and determinants of the shadow economies in Estonia, Latvia, and Lithuania. The results suggest that the shadow economy in Latvia in 2010 is considerably larger than in neighboring Estonia and Lithuania. While the shadow economy as a percentage of GDP in Estonia contracted from 2009 to 2010, it expanded in Latvia and Lithuania. An important driver of shadow activity in the Baltic countries is the entrepreneurs’ dissatisfaction and distrust in the government and the tax system. Involvement in the shadow economy is more pervasive among younger firms and firms in the construction sector. These findings have a number of policy implications, which are discussed at the end of this brief.

Background and Aims

Anecdotal evidence suggests that the shadow economies in the Baltic countries and other emerging Central and Eastern European countries are substantial in size relative to GDP. This is an important issue for these countries because informal production has a number of negative consequences.

First, countries can spiral into a ‘bad equilibrium’: individuals go underground to escape taxes and social welfare contributions, eroding the tax and social security bases, causing increases in tax rates and/or budget deficits, pushing more production underground and ultimately weakening the economic and social basis for collective arrangements. Second, tax evasion can also hamper economic growth by diverting resources from productive uses (producing useful goods and services) to unproductive ones (mechanisms and schemes to conceal income, monitoring of tax compliance, issuance and collection of penalties for non-compliance). Third, informal production can constrain entrepreneurs’ ability to obtain debt or equity financing for productive investment because potential creditors/investors cannot verify the true (concealed) cash flows of the entrepreneur. This can further impede growth. Finally, shadow activities distort official statistics such as GDP, which are important signals to policy makers.

The aim of our study is to measure the size of the shadow economies in Estonia, Latvia, and Lithuania, and to analyse the factors that influence participation in the shadow sector. We use the term ‘shadow economy’ to refer to all legal production of goods and services that is deliberately concealed from public authorities. The study also makes a methodological contribution by developing an index of the size of the shadow economies as a percentage of GDP. It is foreseen that the index will be published regularly.

Although an index invites comparisons, and maybe even ‘competitions’ between countries, the purpose here is not to create a ‘Baltic championship’ on shadow economies. The index should primarily be seen as a tool to promote discussion on the size and role of the shadow economy and to provide a metric which can be used to measure the degree of success in fighting the shadow economy.

Method of Measuring the Shadow Economies

Estimates the size of the shadow economies are derived from surveys of a stratified random sample of entrepreneurs in the three countries (591 in Latvia, 536 in Lithuania and 500 in Estonia). The rationale for this approach is that those most likely to know how much production or income goes unreported, are the entrepreneurs who themselves engage in the misreporting and shadow production.

Survey-based approaches face the risk of underestimating the total size of the shadow economy due to non-response and untruthful response given the sensitive nature of the topic. We minimise this risk by employing a number of surveying and data collection techniques shown in previous studies to be effective in eliciting more truthful responses (e.g., Gerxhani, 2007; Kazemier and van Eck, 1992; Hanousek and Palda, 2004).

These approaches include framing the survey as a study of satisfaction with government policy, gradually introducing the most sensitive questions after less sensitive questions, phrasing misreporting questions indirectly, e.g., asking entrepreneurs about the shadow activity among ‘firms in their industry’ rather than ‘their firm’, and, in the analysis, controlling for factors that correlate with potential untruthful response, such as tolerance towards misreporting. We aggregate entrepreneurs’ responses about misreported business income, unregistered or hidden employees, as well as unreported ‘envelope’ wages to obtain estimates of the shadow economies as a proportion of GDP.

There are three common methods of measuring GDP: the output, expenditure and income approaches. Our index is based on the income approach, which calculates GDP as the sum of gross remuneration of employees (gross personal income) and gross operating income of firms (gross corporate income). Computation of the index proceeds in three steps: (i) estimate the extent of underreporting of employee remuneration and underreporting of firms’ operating income using the survey responses; (ii) estimate each firm’s shadow production proportion as a weighted average of the two underreporting estimates with the weights reflecting the proportions of employee remuneration and firms’ operating income in the composition of GDP; and (iii) calculate a production-weighted average of shadow production across firms. Taking weighted averages of the underreporting measures rather than a simple average is important for the shadow economy index to reflect a proportion of GDP.

Size of the Shadow Economies

Table 1 indicates that the shadow economy as a proportion of GDP is considerably larger in Latvia (38.1%) compared to Estonia (19.4%) and Lithuania (18.8%) in 2010. Only Estonia has managed to marginally decrease the proportional size of its shadow economy from 2009 to 2010 – a statistically significant decrease of 0.8 percentage points. In contrast, the proportional size of the shadow economies in Lithuania and Latvia has increased by an estimated 0.8 and 1.5 percentage points, respectively.

Table 1. Shadow economy index for the Baltic countries


Note: This table reports point estimates and 95% confidence intervals for the size of the shadow economies as a proportion of GDP. The third column reports the change in the relative size of the shadow economies from 2009 to 2010.

Form of Shadow Activity

Figure 1 illustrates the average levels of underreporting (business profits, number of employees and salaries) in each of the countries in 2009 and 2010. The average levels of underreporting in all three areas are in the order of two to three times higher in Latvia compared to Lithuania and Estonia. In Latvia and Lithuania, the degree of underreporting of business profits and salaries (‘envelope’ wages) is approximately twice as large as the underreporting of employees. The exception to this trend is the relatively low amount of underreported business profits in Estonia, likely to be a result of low corporate tax rates. Bribery in Latvia and Lithuania constitutes a similar fraction of firms’ revenue, approximately 10%, whereas in Estonia bribery is less pervasive and constitutes around 6% of firms’ revenue.

Figure 1. Simple averages of underreporting and bribery among Estonian (EE), Lithuanian (LT) and Latvian (LV) firms in 2009 and 2010.


Determinants of Involvement in the Shadow Economy

The literature on tax evasion identifies two main groups of factors that affect the decision to evade taxes and thus participate in the shadow economy. The first set emerges from rational choice models of the decision to evade taxes. In such models individuals or firms weigh up the benefits of evasion in the form of tax savings against the probability of being caught and the penalties that they expect to receive if caught. Therefore the decision to underreport income and participate in the shadow economy is affected by the detection rates, the size and type of penalties, firms’ attitudes towards risk-taking and so on. These factors are likely to differ across countries, regions, sectors of the economy, size and age of firm, and entrepreneurial orientation (innovativeness, risk-taking tendencies, and pro-activeness).

Empirical studies find that the actual amount of tax evasion is considerably lower than predicted by rational choice models based on pure economic self-interest. The difference is often attributed to the second, broader, set of tax evasion determinants – attitudes and social norms. These factors include perceived justice of the tax system, i.e., attitudes about whether the tax burden and administration of the tax system are fair. They also include attitudes about how appropriately taxes are spent and how much firms trust the government. Finally, tax evasion is also influenced by social norms such as ethical values and moral convictions, as well as fear of feelings of guilt and social stigmatisation if caught.

Our study uses regression analysis to identify the factors that are statistically related to firms’ involvement in the shadow economy. The results indicate that the size of the shadow economy is smaller in Estonia and Lithuania relative to Latvia, after controlling for a range of factors.

Tolerance towards tax evasion is positively associated with the firm’s stated level of income/wage underreporting. Satisfaction with the tax system and the government is negatively associated with the firm’s involvement in the shadow economy, i.e. dissatisfied firms engage in more shadow activity, satisfied firms engage in less.

This result is consistent with previous research on tax evasion, and offers an explanation of why the size of the shadow economy is larger in Latvia than in Estonia and Lithuania; namely that Latvian firms engage in more shadow activity because they are more dissatisfied with the tax system and the government as illustrated in Figure 2. Analysing each of the four measures of satisfaction separately we find that shadow activity is most strongly related to dissatisfaction with business legislation, followed by the State Revenue Service, the government’s tax policy, and finally the government’s support for entrepreneurs.

Figure 2. Average satisfaction of firms with the tax system and government in 2010.

Note: These questions use a 5-point scale: 1=“very unsatisfied”; 2=“unsatisfied”; 3=“neither satisfied nor unsatisfied”; 4=“satisfied”; and 5=“very satisfied”. SRS is State Revenue Service.

Another strong determinant of involvement in the shadow economy is firm age, with younger firms engaging in more shadow activity than older firms. This effect dominates relations between firm size and shadow activity. A possible explanation for the relation is that young firms entering a market made up of established competitors use tax evasion as a means of being competitive in their early stages. The regression results also provide some evidence that after controlling for other factors, firms in the construction sector and firms that have a pro-active entrepreneurial orientation tend to engage in more shadow activity.

Policy Implications

First, the relatively large size of the shadow economies in the Baltic countries, and their different expansion/contraction trends, cause significant error in official estimates of GDP and its rates of change, because although statistics bureaus in each of the countries attempt to include some of the shadow production in GDP estimates they do not capture the full extent. Not only is GDP used in key policy ratios such as government deficit to GDP, debt to GDP, but also the rate of change is used as a key indicator of economic performance and therefore guides policy decisions. When the shadow economy is expanding (as in Latvia and Lithuania) official GDP growth rates underestimate true economic growth and when the shadow economy is contracting (as in Estonia) official GDP growth rates overstate true economic growth. At a minimum, policy makers need to be aware of these biases in official statistics, but ideally, statistical bureaus would implement more rigorous methods to estimate and incorporate shadow production in official statistics.

Second, our results suggest that to reduce the size of the shadow economies in the Baltic countries by encouraging voluntary compliance, a key factor that needs to be addressed is the high level of dissatisfaction with the tax system and with the government. Addressing this issue could involve actions such as making tax policy more stable (less frequent changes in procedures and tax rates), and increasing the transparency with which taxes are spent.

Finally, our estimates of the size of the shadow economies suggest that there is significant scope for all three governments to increase their revenues by bringing production ‘out of the shadows’. Investment in programs aimed at reducing the size of the shadow economies could be rather profitable for the Baltic governments, because even a small influence on entrepreneurial behaviour could result in significant revenue increases.


  • Gerxhani, K. (2007) “‘Did you pay your taxes?’ How (not) to conduct tax evasion surveys in transition countries”, Social Indicators Research 80, pp. 555-581.
  • Hanousek, J., and F. Palda (2004) “Quality of government services and the civic duty to pay taxes in the Czech and Slovak Republics, and other transition countries”, Kyklos 57(2), pp.237-252.
  • Kazemier, B., and R. van Eck (1992) “Survey investigations of the hidden economy”, Journal of Economic Psychology 13, pp. 569-587.

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.