Tag: Shadow economy

Estimating Tax Evasion in Europe: Direct vs. Indirect Survey Methods

Posted on November 10, 2025 | Policy Brief

Stack of €50 euro banknotes in partial shadow, symbolizing hidden cash and tax evasion survey methods.

How can societies accurately gauge the share of the workforce engaged in the shadow economy when direct questions inspire selective silence or evasion? This policy brief presents findings from a new cross-country survey experiment combining direct questions and an indirect “list experiment” method, conducted in Latvia, Italy, and Denmark. Results show that, contrary to expectations, indirect methods did not yield higher estimates of undeclared work compared to direct questions. The research reveals that in environments with high tax morale and a substantial shadow economy, both direct and indirect measurements can be biased. Sharing information about prevailing tax norms with respondents can improve survey consistency, informing future tax evasion measurement and anti-evasion policymaking.

Social Desirability Bias in Tax Evasion Surveys

Surveys on tax evasion often provide respondents with multiple response categories beyond simple “yes” or “no.” For example, the survey for Latvia (Kantar, 2024) found that 3% openly acknowledged undeclared income, but refusal (2%) and “hard to say” responses (4%) illustrate additional uncertainty and possible underreporting due to social desirability bias when respondents consciously avoid disclosing disapproved or illegal acts to maintain a positive self-image or avoid perceived censure. This bias is potentially serious in tax compliance research, where both tax morale and fear of consequences can shape reporting behavior.

Indirect questioning techniques, such as list experiments, aim to reduce social desirability bias by allowing individuals to conceal answers within a broader set of innocuous items (Blair et al., 2020). In a typical list experiment, respondents are randomly assigned to receive either a list of non-sensitive items or the same list with an additional sensitive item; by comparing the mean number of items endorsed across groups, researchers estimate the prevalence of the sensitive behavior without requiring explicit disclosure (Blair & Imai, 2012; Glynn, 2013).

Recent empirical work employing list experimental designs has significantly advanced the understanding of tax evasion dynamics across diverse fiscal and cultural contexts. Fergusson, Molina, and Riaño (2019) analyzed VAT evasion among Colombian consumers and found minimal social desirability bias, with list experiments and direct self-reports yielding similar evasion rates (~20%). They attributed this to the normalization of evasion in high-informality regions, where descriptive norms (perceived prevalence of evasion) outweighed injunctive norms, reducing stigma.

This contrasts with Genest-Grégoire et al. (2022), who detected significant bias in Canadian income tax self-reports: list experiments revealed 13.5% income tax evasion (compared to 5.6% in direct questions) and 28.5% consumption tax evasion (compared to 26.2% in direct questions). The study identified stronger stigma around income tax evasion, particularly due to institutional withholding mechanisms that make income tax evasion more difficult compared to consumption taxes. Authors posit that divergent motivational mechanisms underlie these evasion types: income tax noncompliance triggers stronger moral condemnation due to its association with deliberate fraud, while consumption tax evasion is often rationalized as a “victimless” violation of complex regulations.

Hence, high tax morale, while generally associated with greater compliance, also leads individuals to conceal or misrepresent socially undesirable actions more rigorously, which amplifies social desirability bias in survey responses. This effect is particularly pronounced in environments where tax evasion is strongly stigmatized, as respondents may feel increased pressure to align their self-reports with prevailing moral standards, even if those reports do not reflect their true behavior. Conversely, in contexts where evasion is normalized or perceived as widespread, the stigma associated with noncompliance decreases, potentially making individuals less reluctant to report such behavior.  Nevertheless, both direct and indirect measurement techniques may still fail to accurately capture the true prevalence. This is because reduced stigma alone does not eliminate other sources of bias, including cognitive complexity, survey design imperfections, and strategic respondent behavior, such as misinterpreting instructions or using responses to send political or social signals beyond truthful self-disclosure.

Recognizing these persistent methodological challenges, this policy brief presents evidence from a study employing both direct and indirect questions on tax evasion across three European countries with varying levels of tax morale and shadow economy prevalence. By analyzing how social contexts influence reporting behaviors, the brief provides insights into the effectiveness and limitations of these survey approaches in different normative environments.

Approach

The research used a nationally representative sample of 6,915 respondents from Latvia, Italy, and Denmark, utilizing Norstat online panels in the respective countries. It was administered as an online Computer Assisted Web Interview (CAWI) in May 2024. Respondents in the study were randomly assigned to one of two list experiment conditions: half received a 5-item list including the sensitive tax evasion item, while the other half received a 4-item list without the sensitive item (see Figure 1). Importantly, all respondents—regardless of their list group assignment—were asked a direct question about undeclared income at the end of the survey. This design allows comparison between indirect and direct measures within the same individuals, clarifying reporting patterns and social desirability effects.

Figure 1. Indirect question for the control group of the list experimental study

Notes: The 5-item list for the treatment group included additional activity “Received all or part of the income without paying taxes (received money “off the books”)” and asked to indicate max 5 items. The activities were listed in random order for each respondent.

All participants also completed a placebo list experiment, in which both lists – i.e., containing 4 or 5 items – consisted entirely of non-sensitive behaviors (see Figure 2). Correspondingly, everyone was also asked a direct question about the non-sensitive behavior (“Bought a house or apartment (including on credit)”), thereby mimicking the structure of the tax evasion list experiment. This design allowed controlling for possible cognitive errors in filling out a complicated survey task, such as a list survey question, that are unrelated to social desirability bias.

In addition, half of all respondents were primed to information with actual data on how many citizens in their country consider tax evasion unacceptable, sourced from a recent representative survey that was carried out in January 2024. In this pre-survey, just 39% (i.e., minority) found tax evasion wholly unacceptable in Latvia; 59% in Italy, and 53% in Denmark (i.e., majority). The goal of this priming was to test whether informing respondents about local norms affected reporting patterns.

Figure 2. Placebo list of the study

Notes: The 5-item list for the treatment group included additional activity “Bought a house or apartment (including on credit)” and asked to indicate max 5 items. The activities were listed in random order for each respondent.

Key Findings

Results show that indirect list experiment estimates of undeclared work (4.1% overall) did not significantly differ from direct question estimates (7.2%). Hence, respondents did not find the topic sensitive enough to avoid honest answers in either format.

Priming respondents with information about the unacceptability of tax evasion in their country had no statistically significant effect on the direct measure of admitted undeclared income, nor on aggregate estimates from the indirect list experiment, indicating that willingness to disclose undeclared work remained unchanged regardless of norm priming.

Figure 3. Estimates of tax evasion from the list experiment and direct question

Source: Author’s estimate from the survey results.

However, country-level analysis revealed an anomaly in Italy: the list experiment produced an implausible negative estimate, driven by some respondents who marked zero items in the treatment list but later admitted to undeclared work in direct questioning. While this inconsistent response pattern was most prominent in Italy, the country with the highest tax morale (as based on pre-survey), and the largest shadow economy across the three countries (Medina and Schneider, 2018), it has also been recorded in the other two countries. Specifically, the pattern was observed among 11% of respondents who admitted to tax evasion in the direct question in Italy, compared to 5–7% in Latvia and Denmark.

Considering the complexity and unusual formulation of the question for the list experiment, one might attribute this pattern to a respondent’s confusion or cognitive error. However, this explanation is unlikely because of the responses to the placebo list experiment, where all list items and a direct question are non-sensitive. There, the specific response pattern – respondents reporting zero items on the list question while simultaneously admitting to the direct question – is observed substantially less frequently, indicating a low baseline error rate for misunderstanding or inconsistent reporting on non-sensitive items.

The comparison between the sensitive and placebo list experiment results indicates that the anomalous pattern observed in the tax evasion list experiment is unlikely to be due to confusion with the survey format, but rather represents a deliberate, context-specific form of strategic misreporting. One possible reason for this pattern might be that some Italians who admit to tax evasion in the direct question may believe that inflating shadow economy estimates will spur stronger policy reactions or public debate. In this way, their answers to the survey may represent strategic “signal sending.”

Priming respondents with accurate information about societal norms regarding the unacceptability of tax evasion – an approach referred to as vignette priming – consistently reduced the occurrence of this contradictory response pattern. Fewer respondents reported zero items in the list experiment while admitting to evasion in direct questioning, a change observed universally across the three countries.

Two main interpretations of the effects of such vignette priming can be suggested. The first interpretation, related to the strategic motive discussed above, suggests that vignette priming helps align respondents’ understanding of prevailing social norms on tax evasion. This improved awareness discourages deliberate misreporting, thus improving the overall validity and reliability of the survey’s methodology, even if it does not increase overall admissions of tax evasion itself. An alternative explanation is that vignette priming helps respondents better recognize and correctly count items in the list experiment, thereby improving response accuracy and alignment across question formats.

In other words, norm priming fosters more consistent survey responses, whether by reducing the temptation to manipulate results or by increasing recognition and attention among respondents.

Conclusion

Efforts to estimate tax evasion through surveys must strike a balance between the limitations of direct self-reports and the incomplete protection against bias afforded by indirect methods. This study finds that, in the surveyed countries, list experiments do not yield higher or more accurate prevalence estimates than direct questioning. However, particularly in high-morale environments with substantial shadow economies, some respondents may strategically manipulate survey results in hopes of prompting political action.

Norm priming through vignettes enhances experimental integrity and reduces strategic errors, underscoring the importance of accounting for social context in survey designs. For tax policy makers, measurement should always be validated with error diagnostics and social context cues, and survey formats should be adapted for cross-country comparability and public trust.

Acknowledgements

The study is financed by the European Commission’s Marie Sklodowska-Curie Individual Fellowship Action (Grant agreement ID: 101109679).

References

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.

The Shadow Economy in Russia: New Estimates and Comparisons with Nearby Countries

Posted on March 16, 2020 | Policy Brief

Image of a metro elevator going upwards representing shadow economy of Russia

We apply a new method to measure the shadow economy in Russia during the period 2017-2018 and provide evidence on the main factors that influence involvement in the shadow economy. Drawing on a methodology developed by Putnins and Sauka (2015), we estimate that the size of the shadow economy in Russia is 44.7% of GDP in 2018. This is similar to the size of the shadow economy in countries such as Kyrgyzstan, Kosovo, Ukraine, and Romania, but higher than the level of the Baltic countries. Our findings are largely consistent with other less direct approaches for estimating the shadow economy. An advantage of our approach is that it can provide more detailed information on the components of the shadow economy.

Introduction and Approach to Measuring the Shadow Economy

The aim of the Shadow Economy Index, which has now been estimated in a number of countries, is to measure the size of shadow economies and explore the main factors that influence participation in the shadow economy. We use the term “shadow economy” to refer to all legal production of goods and services produced by registered firms that is deliberately concealed from public authorities (OECD, 2002; Schneider, Buehn and Montenegro, 2010).

The Shadow Economy Index draws on a survey-based methodology developed by Putnins and Sauka (2015). It combines estimates of business income that has been concealed from authorities, unregistered employees, and ‘envelope’ wages. The approach exploits the fact that entrepreneurs and business leaders are in a unique position in that they have knowledge about the amount of business income that is concealed from authorities, the number of employees that work for them unofficially, and the extent to which they pay wages informally to avoid taxes.

The challenge for such methods is to elicit maximally truthful responses about these sensitive issues, otherwise, the size of the shadow economy will be underestimated. To address this challenge, we use a number of survey 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). While the full details can be found in Putnins and Sauka (2015), they include confidentiality with respect to the identities of respondents, framing the survey as a study of satisfaction with government policy, phrasing misreporting questions indirectly about “similar firms in the industry” rather than the respondent’s actual firm, gradually introducing the most sensitive questions after less sensitive questions, excluding inconsistent responses, and controlling for factors that correlate with a potential untruthful response such as tolerance towards misreporting.

The Index measures the size of the shadow economy as a percentage of GDP. Computing the Index involves three steps:

  • (i) estimate the degree of underreporting of employee remuneration and underreporting of firms’ operating income using the survey responses;
  • (ii) estimate each firm’s shadow production as a weighted average of its underreported employee remuneration and underreported operating income, with 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.

The survey about shadow activity in Russia from 2017 to 2018 was conducted between February and March 2019. We use random stratified sampling to construct samples that are representative of the population of firms in Russia drawing on the official company register and covering all regions in Russia. In total, 500 phone interviews were conducted with owners, directors, and managers of companies in Russia. We use the same methodology to collect data in other countries, which we compare with Russia, conducting a minimum of 500 interviews in each country.

Size of the Shadow Economy in Russia and Nearby Countries

The estimated size of the shadow economy in Russia is 44.7% of GDP in 2018. Our estimates suggest that the year before, in 2017, the shadow economy was slightly larger with 45.8% of GDP, although the annual change is not statistically significant. For comparison with nearby countries, using the same approach, high levels of shadow economy are also found in Kyrgyzstan (44.5% of GDP in 2018), Kosovo (39.5% of GDP in 2018), Ukraine (38.2% of GDP in 2018), and Romania (33.35% of GDP in 2016), but considerably lower levels are found in the Baltic countries, especially Estonia (16.7% of GDP in 2018). See Table 1 for the full set of estimates.

The estimates using our direct micro-level approach to measuring the shadow economy are largely consistent with other less direct approaches for estimating the size of the shadow economies, such as Schneider (2019). An advantage of the direct micro-level approach is that it is able to provide more detailed information on the components of the shadow economy, which we turn to next.

Components and Determinants of the Shadow Economy in Russia

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 are approximately 38.7% of the true wage on average in 2018, whereas approximately 33.8% of business income (actual profits) are underreported. Unofficial employees in Russia as a percentage of the actual number of employees are estimated 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 suggest that there is a very high level of bribery in Russia: the magnitude of bribery (percentage of revenue spent on ‘getting things done’) is estimated to be 26.4%, whereas the 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 more than 25% of the revenue or contract value in bribes.

We find that the size of the shadow economy in all sectors of the Russian economy 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 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.

Acknowledgments

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

References

  • 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 Palda, F. (2004). Quality of government services and the civic duty to pay taxes in the Czech and Slovak Republics, and other transition countries. Kyklos 57, pp. 237-252.
  • Kazemier, B. & van Eck, R. (1992). Survey investigations of the hidden economy. Journal of Economic Psychology 13, pp. 569-587.
  • Lechmann, E. and D. Nikulin (2017). Shadow Economy Index in Poland. Gdansk University of Technology, Poland: Gdansk.
  • Lysa, O. et al. (2019) Shadow Economy Index in Ukraine. SHADOW: an exploration of the nature of informal economies and shadow practices in the former USSR region. Kyiv International Institute of Sociology, Ukraine: Kyiv.
  • Mustafa, I., Pula J.S., Krasniqi, B., Sauka, A., Berisha, G., Pula, L., Lajqui, S. and Jahja, S. (2019) Analysis of the Shadow Economy in Kosova. Kosova Academy of Sciences and Arts, Kosova: Pristina.
  • OECD, 2002. Measuring the Non-Observed Economy: A Handbook. OECD, Paris, France.
  • Putnins, T.J. and Sauka, A. (2019). Shadow Economy Index for the ‘Baltic Countries 2019-2018. SSE Riga: Riga, Latvia.
  • Putnins, T.J., A. Sauka and A. Davidescu (2020, forthcoming). Shadow Economy Index for Moldova and Romania, 2015-2018. SSE Riga, National Scientific Research Institute for Labour and Social Protection.
  • Putnins, T.J. and Sauka, A. (2015). Measuring the shadow economy using company managers. Journal of Comparative Economics 43, pp. 471-490.
  • SIAR (2019). Shadow Economy Index for Kyrgyzstan. SHADOW: an exploration of the nature of informal economies and shadow practices in the former USSR region. SIAR research and consulting, Kyrgyzstan: Bishkek.
  • Schneider, F. (2019) Calculation of the Size and Development of the Shadow Economy of 35 Mostly OECD Countries up to 2018. Unpublished manuscript.
  • Schneider, F., Buehn, A. and Montenegro, C. (2010). New estimates for the shadow economies all over the world. International Economic Journal 24, pp. 443-461.

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.

Baltic Shadow Economies

Posted on September 26, 2011 | Policy Brief

Policy Brief Image of Two Shadows from Walking Men Representing Shadow Economies

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.

References

  • 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.