1. No category

The Centre for Sustainable Business
at SSE Riga
SHADOW ECONOMY INDEX
for the Baltic countries
2009 – 2012
by Tālis J. Putniņš and Arnis Sauka
About the authors
Dr. Arnis Sauka is Research Fellow at the Stockholm School of
Economics in Riga, and Associate Professor and Academic Pro-rector
of Ventspils University College. He is also Director of the Centre for
Entrepreneurship, Innovation and Regional Development at Ventspils
University College (Latvia). Prior to joining the doctoral program at the
University of Siegen (Germany), Arnis was a visiting PhD candidate at
Jönköping International Business School (Sweden) and University
College London (U.K.). His main research interests are related to tax
evasion, entrepreneurship policies, business strategies, competitiveness
and social contribution of entrepreneurs.
E-mail: [email protected]
Dr. Tālis Putniņš is an Assistant Professor at the Stockholm School of
Economics in Riga, Research Associate at the Baltic International
Centre for Economic Policy Studies (Latvia), and Chancellor's
Postdoctoral Research Fellow at UTS Business School (Sydney,
Australia). His research interests include financial economics, market
microstructure, market manipulation, tax evasion, and partial detection
modelling. Tālis has a Ph.D. from the University of Sydney.
E-mail: [email protected]
2
The Centre for Sustainable Business
at SSE Riga
is powered by
Acknowledgments
We are grateful to the Centre for Sustainable Business at SSE Riga powered by SEB for the
generous financial support that made data collection in 2013 possible, SKDS for data collection
as well as all entrepreneurs who agreed to participate in the interviews.
© Stockholm School of Economics in Riga, May 2013
3
Table of contents
Foreword
5
1. Introduction
6
2. Methods used in constructing the Index
8
3. Shadow Economy Index for the Baltic countries 2009- 2012
11
4. Determinants of shadow activity
19
5. Entrepreneurs’ attitudes regarding shadow activities
34
6. Discussion and conclusions
35
References
38
Appendix 1: Questionnaire form used in 2013
40
Appendix 2: Regression results
45
4
Foreword
This is the third report on the Shadow Economy Index for the Baltic countries. The Report is
published annually and written within the framework of the Centre for Sustainable Business at
the Stockholm School of Economics in Riga, and with the support of SEB.
It is our hope that the Report will contribute to an informed debate on the size of the shadow
economy and its causes in the Baltic countries. As is known from economic theory, the size of
the shadow economy not only affects government tax revenue, it also affects the allocation of an
economy’s resources and thereby its competitiveness. Hence, the Report could also be seen as a
contribution to the ongoing discussion on how to enhance the competitiveness of the three Baltic
economies.
Anders Paalzow
Rector, SSE Riga
5
1. Introduction
The aim of the SSE Riga Shadow Economy Index for the Baltic countries is to measure the size
of the shadow economies in Estonia, Latvia and Lithuania, as well as to 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 that is deliberately concealed from public
authorities.1 The Index is published annually since 2010 to provide policy makers with
information for making justified policy decisions, as well as to foster a deeper understanding of
entrepreneurship processes in the Baltic countries. This report analyses the dynamics of the
shadow economy in Estonia, Latvia and Lithuania during the period 2009-2012. It also provides
evidence on the main factors that influence entrepreneurs’ involvement in the shadow economy.
Measuring the size of shadow economy is a difficult task. Existing methods for measuring the
shadow economy can be divided into the three groups. The first group, ‘macro methods’, makes
use of various macro indicators to assess the size of the shadow economy. This includes,
analysing cash usage in the population (e.g., Gutmann, 1977; Tanzi, 1980), comparing household
expenses (for instance changes in the turnover of supermarkets or consumption of electricity)
against household average income as reported by official statistics (e.g., Kaufmann and
Kaliberda, 1996), and comparing income against expenditure estimates of GDP (e.g., MacAfee,
1980).
The second group, latent variable methods, of which the ‘multiple indicator multiple cause’
(MIMIC) method is an example, view the size of the shadow economy as an unobservable
variable which has both causes and influences (e.g., Frey and Weck-Hannemann, 1984; Giles,
1999; Bajada and Schneider, 2005; and Schneider, Buehn and Montenegro, 2010). This approach
assumes that a particular set of variables determines the size of the shadow economy (‘causes’),
and a different specific set of variables is impacted by shadow activity (‘indicators’). The size of
the shadow economy is inferred from data on the causes and indicators by estimating the
structural model.
As emphasized by OECD handbook “Measuring the Non-observed Economy”, both methods
have limitations in measuring the shadow economy, in particular: (i) it is not clear which parts of
observed or unobserved production these methods include; (ii) these methods draw on very
simplified and often unrealistic assumptions; and (iii) the methods are not very stable since
changes in assumptions can cause significantly different results. The strength of these methods,
however, is that they can be calculated with relatively little cost and time and thus applied to
compare the amount of shadow economy in across many countries.
1
This definition corresponds to what the Organisation for Economic Co-operation and Development (OECD) in
their comprehensive 2002 handbook “Measuring the Non-observed Economy” as well as the System of National
Accounts (SNA 1993) refer to as “underground production”. It is also consistent with definitions employed by other
researchers (e.g., the World Bank study of 162 countries by Schnieder, Buehn and Montenegro (2010)).
6
The third group, ‘direct methods’, draw on direct micro-level observations. These are the most
expensive and time consuming methods, but they manage to overcome many of the limitations
that are typical in macro and MIMIC methods. Direct methods are recommended for situations in
which it is important to define exactly what part of production is being estimated by the method,
and when stability of the measurement is important. The key assumption in these methods is that
the units of observation from which responses are collected, for instance, entrepreneurs, (i) know
and, (ii) are willing to share information on the extent of their involvement in the shadow
economy. Kazemier and van Eck (1992) provide an example of the use of micro-level surveys to
estimate shadow activity. They survey households about home maintenance and repair, including
questions on the use of unreported labour and evasion of tax on construction materials.
Zienkowski (1996) provides an example of using survey methods to estimate the amount of
unreported labour within an economy.
The SSE Riga Shadow Economy Index takes the third approach and is based on annual surveys
of entrepreneurs in the three countries. The reason for adopting this approach is that those most
likely to know how much production/income goes unreported are the entrepreneurs that
themselves engage in the misreporting and shadow production. The Index combines estimates of
misreported business income, unregistered or hidden employees, as well as unreported
“envelope” wages to obtain estimates of the size of the shadow economies as a proportion of
GDP. The method used in this report for estimating the size of the shadow economy requires
fewer assumptions than most existing methods, in particular compared to methods based on
macro indicators. Furthermore, the SSE Riga Shadow Economy Index can be used through time
or across sectors and countries and thus is a useful tool for evaluating the effectiveness of policy
designed to minimise the shadow economy.
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. The SSE
Riga Shadow Economy Index minimises this risk by employing a number of survey and data
collection techniques shown in previous studies to be effective in eliciting more truthful
responses.2 These 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 and, in the analysis, controlling for factors that correlate with
potential untruthful response such as tolerance towards misreporting.
The next section describes how the Index is constructed, starting with the survey and then the
calculations. The third section of this report presents estimates of the Index and analyses the
various forms of shadow activity. Sections 4 and 5 analyse the determinants of entrepreneurs’
involvement in the shadow sector and their attitudes towards shadow activities. Finally, Section
6 discusses the conclusions that we can draw from the results and identifies some policy
implications.
2
For example, Gerxhani (2007), Kazemier and van Eck (1992), and Hanousek and Palda (2004).
7
2. Methods used in constructing the Index
2.1.The survey of entrepreneurs
The SSE Riga Shadow Economy Index is based on an annual survey of company
owners/managers in Estonia, Latvia and Lithuania. The surveys are conducted between March
and April of each year and contain questions about shadow activity during the previous two
years. For example, the survey conducted in March-April 2013 collects information about
shadow activity during 2011 and 2012. The overlap of one year in consecutive survey rounds is
used to validate the consistency of responses.
The sample of surveyed companies is constructed from all active firms in each of the three Baltic
countries contained in the Orbis database maintained by Bureau Van Dijk. For each country, we
form size quintiles (using book value of assets) and take equal sized random samples from each
size quintile. In total a minimum of 500 phone interviews are conducted in each of the three
Baltic countries in each survey round. The 2013 survey collected responses from 500 company
owners/managers in Estonia, 503 in Latvia and 501 in Lithuania. The survey is conducted in
cooperation with SKDS, funded by the Centre for Sustainable Business at SSE Riga and the
Centre for Media Studies at SSE Riga.
The questionnaire form (see Appendix 1) contains four main sections: (i) external influences; (ii)
shadow activity; (iii) company and owner characteristics; and (iv) entrepreneurs’ attitudes. To
increase the response rate and truthfulness of responses the questionnaire begins with nonsensitive questions about satisfaction with the government and tax policy, before moving to more
sensitive questions about shadow activity and deliberate misreporting. This ‘gradual’ approach is
recommended by methodological studies of survey design in the context of tax evasion and the
shadow economy (e.g., Gerxhani, 2007; and Kazemier and van Eck, 1992). Further, the survey
is framed as a study of satisfaction with government policy, rather than a study of tax evasion
and misreporting (similar to Hanousek and Palda, 2004).
In the first survey block, ‘external influences’, respondents are asked to express their satisfaction
with the State Revenue Service, tax policy, business legislation and government support for
entrepreneurs in the respective country. The questions use a five point Likert scale, from “1”
(“very unsatisfied”) to “5” (“very satisfied”). The first section of the questionnaire also includes
two questions related to entrepreneurs’ social norms: entrepreneurs’ tolerance towards tax
evasion and towards bribery. Previous studies argue that entrepreneurs are likely to engage in
more tax evasion when such behaviour is tolerated (Baumol, 1990). The measures of tolerance
serve a second important role as control variables for possible understating of the extent of
shadow activity due to the sensitivity of the topic.
The second section of the questionnaire, ‘informal business’, is constructed based on the
concepts of productive, unproductive and destructive entrepreneurship by Baumol (1990),
8
assessment of ‘deviance’ or ‘departure from norms’ within organisations (e.g., Warren, 2003)
and empirical studies of tax evasion in various settings (e.g., Fairlie, 2002; Aidis and Van Praag,
2007). We assess the amount of shadow activity by asking entrepreneurs to estimate the degree
of underreporting of business income (net profits), underreporting of the number of employees,
underreporting of salaries paid to employees and the percentage of revenues that firms pay in
bribes.
We employ the ‘indirect’ approach for questions about informal business, asking entrepreneurs
about ‘firms in their industry’ rather than ‘their firm’.3 This approach is discussed by Gerxhani
(2007) as a method of obtaining more truthful answers, and is used by Hanousek and Palda
(2004), for example. The study conducted by Sauka (2008) shows that even if asked indirectly
entrepreneurs’ answers can be attributed to the particular respondent or company that the
respondent represents.4 Furthermore, experience from Sauka (2008) suggests that phone
interviews are an appropriate tool to elicit information about tax evasion.5 The second section of
the questionnaire also elicits entrepreneurs’ perceptions of the probability of being caught for
various forms of shadow activity and the severity of penalties if caught deliberately
misreporting.
We use the overlapping years (e.g., answers in both the 2013 survey and 2012 survey about the
level of shadow activity in 2011) to filter out inconsistent responses. This is only possible in
instances where a respondent participates in repeated survey rounds. In particular, our filter
drops responses when the same respondent in two different survey rounds answers the same
shadow activity questions about the same reference year with a difference of +/- 20%. This
filtering helps increase the reliability of survey responses used in calculating the Index.
The third section of the questionnaire asks entrepreneurs about the performance of their
companies (percentage change in net sales profit, sales turnover and employment during the
previous year), the education of the company owner/manager, company age, industry and region.
The fourth section of the questionnaire elicits entrepreneurs’ opinions about why entrepreneurs
evade taxes.
3
Even when asked indirectly, some entrepreneurs choose not to answer sensitive questions about shadow activity.
One way to avoid providing truthful answers to such questions is by simply answering “0” to all of the shadow
activity questions, suggesting that no shadow activity of any kind has taken place during the past two years. We
view it as much more likely that these responses reflect avoidance of sensitive questions than truthful opinions and
therefore treat these cases as non-responses, in order to minimise the downward bias in estimates of shadow activity.
4
Sauka (2008) used the following approach: in the follow up survey (one year after the initial survey), respondents
are ‘reminded’ that in the initial survey they stated that, for example, the degree of involvement in underreporting
business income by ‘their firm’ (not by ‘firms in their industry’ as formulated in the initial survey) was, for example,
23%. Each respondent is then asked whether the degree of underreporting in their companies is the same this year
and if not, to what extent it has changed. The conclusion from using this method is that respondents tend to state the
amount of underreporting in ‘their firm’ when asked about ‘firms in their industry’.
5
Sauka (2008) uses both face-to-face and phone interviews and concludes that willingness to talk about sensitive
issues like tax evasion in Latvia does not differ significantly between the two methods.
9
2.2.Calculation of the Index
The Index measures the size of the shadow economy as a percentage of GDP.6 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 as a weighted average of its
underreported employee remuneration and underreported operating income, 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.
In the first step, underreporting of firm i’s operating income, URiOperatingIncome , is estimated directly
from the corresponding survey question (question 7). Underreporting of employee remuneration,
however, consists of two components: (i) underreporting of salaries, or ‘envelope wages’
(question 11); and (ii) unreported employees (question 9). Combining the two components, firm
i’s total unreported proportion of employee remuneration is:7
URiEmployeeRemuneration  1  (1  URiSalaries)(1  URiEmployees )
In the second step, for each firm we construct a weighted average of underreported personal and
underreported corporate income, producing an estimate of the unreported (shadow) proportion of
the firm’s production (income):
ShadowProportioni  cURiEmployeeRemuneration  (1  c )URiOperatingIncome
where  c is the ratio of employees’ remuneration (Eurostat item D.1) to the sum of employees’
remuneration and gross operating income of firms (Eurostat items B.2g and B.3g). We calculate
 c for each country, c, in each year using data from Eurostat. Taking a weighted average of the
underreporting measures rather than a simple average is important to allow the Shadow
Economy Index to be interpreted as a proportion of GDP.8
6
Two caveats are worth noting: (i) because we do not measure shadow activity in the state (public) sector, our
estimates refer to private sector shadow activity as a percentage of private sector domestic output; and (ii) we do not
attempt to measure the “black economy”, i.e., the illegal goods and services.
7
In deriving the formula we make the simplifying assumption that wages of unreported employees are on average
equal to those of reported employees.
8
For example, suppose in an economy wages sum to 80 and corporate income 20, giving true GDP of 100. Suppose
that wages are underreported by 50% and corporate income by 10% giving an official reported GDP of 40+18=58.
In this example the shadow economy is 42% of true GDP, i.e. (100-58)/100. A weighted average of the two
underreporting proportions accurately estimates the size of the shadow economy: (0.8)(50%)+(1-0.8)(10%)=42%.
However, neither of the two underreporting proportions themselves correctly represent the size of the shadow
economy (50% and 10%), nor does an equal weighted average: (0.5)(50%)+(1-0.5)(10%)=30%.
10
In the third step we take a weighted average of underreported production, ShadowProportioni ,
across firms in country c to arrive at the Shadow Economy Index for that country:
Nc
INDEX cShadowEconomy   wi ShadowProportioni
i 1
The weights, wi , are the relative contribution of each firm to the country’s GDP, which we
approximate by the relative amount of wages paid by the firm. Similar to the second step, the
weighting in this final average is important to allow the Shadow Economy Index to reflect a
proportion of GDP.9
As a final step, we follow the methodology of the World Economic Forum in their Global
Competitiveness Report and apply a weighted moving average of INDEX cShadowEconomy calculated
from the most recent two survey rounds. There are several reasons for doing this, including: (i) it
increases the amount of available information and hence precision of the Index by providing a
larger sample size; and (ii) it makes the results less sensitive to the specific point in time when
the survey is administered. The weighting scheme comprises two overlapping elements: (i) more
weight is given to the more recent survey round as that contains more recent information (past
information is “discounted”); and (ii) more weight is placed on larger sample sizes as they
contain more information.10 Following the approach of the World Economic Forum, for years in
which there are no previous surveys (the 2009 and 2010 results, which are based on the first
survey round conducted in 2011) the Index is simply based on the one survey round.
Consequently, the first two annual Index estimates (2009 and 2010) are more prone to sampling
error than subsequent annual estimates, which benefit from larger samples via the moving
average. To allow comparisons across countries we apply consistent methodology in calculating
the Shadow Economy Index for each of the Baltic countries.
3. Shadow Economy Index for the Baltic countries 2009- 2012
This section reports the levels of the Shadow Economy Index in the Baltic countries during the
past four years. We also separately examine each of the types of shadow activity that make up
the Index, as well as bribery and forms of corruption.
Table 1 reports the size of the shadow economies in Estonia, Latvia and Lithuania as a
percentage of GDP in the years 2009-2012. The change from 2011 to 2012 is not statistically
9
For an example, consider the previous footnote’s example replacing the two sources of income with two firms: a
large one that produces income of 80 and a smaller one that produces income of 20.
10
For details on this procedure see the Global Competitiveness Report 2011-2012 (Box 3, p. 64), which is available
at: http://www3.weforum.org/docs/WEF_GCR_Report_2011-12.pdf
11
significant for Lithuania or Estonia (+1.1% and +0.3 accordingly), i.e., the level of shadow
economy has remained approximately unchanged from 2011 to 2012 in both countries. In Latvia,
however, the size of shadow economy in 2012 contracted by 9.1% of GDP compared to the level
in 2011. This considerable decline in shadow activity follows a similarly notable decrease in the
previous year (2011) of 7.9% of GDP. As a result, the estimates in Table 1 suggest that the size
of the shadow economy in 2012 is no longer considerably higher in Latvia than in Estonia and
Lithuania (21.1% compared to 19.2% and 18.2%, respectively). The differences between the
three countries in 2012 are marginally statistically significant.11
Table 1. SSE Riga Shadow Economy Index for the Baltic countries 2009-2012
This table reports point estimates and 95% confidence intervals for the size of the shadow economies as a proportion
of GDP. The last column reports the change in the relative size of the shadow economy from 2011 to 2012.
Estonia
Latvia
Lithuania
2009
20.2%
2010
19.4%
2011
18.9%
2012
19.2%
2012 - 2011
+0.3%
(18.7%, 21.7%)
(18.0%, 20.8%)
(16.8%, 20.9%)
(16.6%, 21.9%)
(-2.0%, 2.8%)
36.6%
38.1%
30.2%
21.1%
- 9.1%
(34.3%, 38.9%)
(35.9%, 40.3%)
(27.6%, 32.7%)
(18.5%, 23.6%)
(-11.7%, -6.5%)
17.7%
18.8%
17.1%
18.2%
+1.1%
(15.8%, 19.7%)
(16.9%, 20.6%)
(15.2%, 19.0%)
(16.4%, 20.1%)
(-0.7%, 3.0%)
The dynamics of the SSE Riga Shadow Economy Index, shown in Figure 1, are largely
consistent with estimates from other studies that use different estimation methods. This increases
the confidence one can place in the overall conclusions about the Baltic shadow economies. For
example, Schneider (2013) uses an indirect latent variable method and obtains the same
dynamics for Latvia (an increase from 2009 to 2010 and two subsequent years of decreases).
Schneider’s estimates also concur with the dynamics of the Index for Estonia and Lithuania,
other than the statistically insignificant changes from 2011 to 2012. The point estimates,
however, differ in magnitude between the two studies. In contrast to Schneider’s indirect latent
variable method, our approach is able to provide more detailed information on the components of
the shadow economy, which we turn to now.
Figure 2 illustrates the relative size of the components of the shadow economy in each of the
three countries. Envelope wages constitute a large proportion of the shadow economies in all
three countries: they account for 52.3% of the shadow economy in Estonia, 42.9% in Latvia and
39.3% in Lithuania. The next largest component is unreported business income, which makes up
between 28.5% and 42.7% of the shadow economy depending on the country. Finally,
unreported employees makes up the remainder, and accounts for between 17.6% and 19.2% of
the shadow economy in each of the countries.
11
The p-values for independent samples t-tests of the differences in the mean level of shadow economy for the pairs
Latvia-Lithuania and Latvia-Estonia are 0.08 and 0.33, respectively.
12
Figure 1. SSE Riga Shadow Economy Index for the Baltic countries 2009-2012.
Estonia:
Latvia
Lithuania
Figure 2. Components of the shadow economies in each of the Baltic countries, 2012.
13
Figures 3 and 4 illustrate the amount of underreporting of business income (profits). Figure 3
shows the distribution of firms that underreport profits within a given range, whereas Figure 4
shows the dynamics of underreporting profits from 2009 to 2012. Approximately 30.6% of
respondents from Estonia, 14.6% from Lithuania and 14.1% from Latvia state that
underreporting ‘in the industry’ in 2012 is 0%, i.e. that companies report 100% of their actual
profits. The numbers for 2011 where: 21.4% in Estonia, 10.9% in Lithuania and 14.8% in Latvia,
with a similar pattern also in 2010.12 In contrast to 2010 and 2011, however, Latvian companies
no longer fall into higher ranges of profit underreporting, and the gap between the countries has
substantially decreased. Following the trend in Figure 3, we can see from Figure 4 that the
average level of profit underreporting in 2012 is rather similar in Latvia, Lithuania and Estonia
(16.7% in Latvia, 15.7% in Lithuania and 13.0% in Estonia, compared to 26.5%, 16.0% and
9.7% in 2011, respectively). The overall decrease in the underreporting of business profits from
2011 to 2012 among Latvian companies is a substantial 9.8%, whereas during the same period
profit underreporting has increased by +3.3% in Estonia, and remained relatively stable in
Lithuania.
Figure 3. Underreporting of income (profits) in 2012. The vertical axis measures the percentage of each
country’s respondents underreporting within the range given on the horizontal axis.
12
See SSE Riga Shadow Economy Index for the Baltic countries 2009-2010, and 2009-2011.
http://www.sseriga.edu/en/research/projects/shadow-economy-index/
14
Figure 4. Level of underreporting income (percentage of actual profits in 2009-2012).
Figures 5 and 6 illustrate the level of underreporting of the number of employees. Figure 6
suggests that underreporting of employees in Lithuania has increased slightly in 2012 (8.1% in
2012 compared to 7.3% in 2011). In contrast the level of underreporting of employees has
decreased in Estonia and Latvia, to 7.6% and 9.7%, respectively. Similarly to 2011, a relatively
low proportion of respondents claim that underreporting of employees in 2012 represents more
than 50% of employees (Figure 5).
Figure 5. Underreporting of the number of employees in 2012. The vertical axis measures the percentage of
each country’s respondents underreporting within the range given on the horizontal axis.
15
Figure 6. Level of underreporting the number of employees (percentage of the actual number of employees in
2009- 2012).
Figure 8 indicates that in 2012 Latvian companies still pay a higher percentage of salaries in the
form of undisclosed “envelope wages” (26.5% in Latvia, 19.3% in Lithuania and 22.1% in
Estonia). Even though underreporting of salaries still seems to be the main driver of the shadow
economy in Latvia, among the three Baltic countries only Latvia managed to decrease the level
of envelope wages in 2012 compared to 2011. Figure 7 shows that most frequently Latvian
companies underreport 11-30% and 31-50% of actual salaries. In Lithuania and Estonia,
however, more often companies underreport salaries by 1-10% and 11-30%.
Figure 7. Underreporting of salaries in 2012. The vertical axis measures the percentage of each country’s
respondents underreporting within the range given on the horizontal axis.
16
Figure 8. Level of underreporting of salaries (percentage of actual salaries in 2009-2012).
Figure 9 suggests that during 2012 Lithuanian companies paid proportionally more in bribes
(revenues spent on “getting things done”) than Latvian and Estonian companies: 26.3% of
respondents reporting that more than 25% of revenues are paid “to get things done” (in
comparison to 6.2% in Estonia and 8.8.% in Latvia), and a further 24.5% (12.4% in Estonia and
16.3% in Latvia) report paying bribes in the range of 13-25%. This trend can also be observed in
Figure 10, which shows that the percentage of revenue spent on bribes has increased in Lithuania
during the last two years, whereas has been declining or stable in other two Baltic countries.
Figure 9. Bribery in 2012. The vertical axis measures the percentage of each country’s respondents making
unofficial payments ‘to get things done’ within the range given on the horizontal axis.
17
Figure 10. Level of bribery (percentage of revenue spent on payments ‘to get things done’ in 2009-2012).
Finally, Figures 11 and 12 illustrate the percentage of the contract value that firms typically offer
as a bribe to secure a contract with the government. Opposite to the general level of bribery
reported in Figure 10, the level of government bribery has increased in all three Baltic countries
(see Figure 12). Figure 11 illustrates the most frequent size of government bribes (proportion of
the contract value), suggesting that considerable proportion of companies in all Baltic countries
pay more than 10% of the contract value to secure a contract with the government.
Figure 11. Bribing government in 2012. The vertical axis measures the percentage of each country’s
respondents paying bribes within a given range of the contract value to secure contracts with government.
18
Figure 12. Percentage of the contract value paid to government to secure the contract, 2010-2012.
4. Determinants of shadow activity
In this section we examine the factors that influence firms’ decisions to participate in the shadow
economy. We start by reporting the size of the shadow economy by company characteristics
including operating region, sector and firm size. Next, we report descriptive statistics of how the
size of the shadow economies varies with attitudes and perceptions towards tax evasion. Finally,
we use regression analysis to identify the drivers of firms’ involvement in the shadow economy,
while controlling for other factors.
4.1.Company characteristics
Figures 13-15 report the size of the shadow economy by region, in Estonia, Latvia and Lithuania.
As displayed in Figure 13, the size of the shadow economy has decreased in all regions of
Latvia. Similar to 2011, shadow activity seems to be less widespread in Vidzeme (12.4% in
2012) than other regions of Latvia, where the shadow economy ranges from 21.2% to 25.2%. In
Lithuania, the level of shadow activity seems to be lowest in Siaulių, Klaipėdos and Vilnius
regions in 2012. This is in contrast to the findings from 2010 and 2011, where lowest level of
shadow economy was reported in the Panevėžio region. In Estonia, however, the regions with the
lowest level of shadow activity in 2012 are Põlva and Pärnu (13.5% and 15.4%%, respectively),
and the highest level of shadow economy can be observed in Ida-Viru and Valga regions (34.0%
and 29.5%, respectively). The small number of observations in some of the regions in Estonia,
however, create a relatively large margin of error and therefore the Estonian estimates by region
should be interpreted with caution.
19
Figure 13. Size of the shadow economy (% of GDP) by region in Latvia, 2009-2012.
Figure 14. Size of the shadow economy (% of GDP) by region in Lithuania, 2009-2012.
20
13
Figure 15. Size of the shadow economy (% of GDP) by region in Estonia, 2009-2012 .
Figure 16 summarises how the size of the shadow activity varies by sector in 2012. Changes in
the size of the shadow economy across the sectors in 2011 and 2012 are presented in Figures 1719.
Figure 17 suggests that the substantial decrease in the size of the shadow economy in Latvia has
occurred predominantly in retail, services, construction and the category ‘other’, whereas the
level of shadow activity in wholesale has remained at approximately the same level as in 2011
(29.7%). With these changes, wholesale seems to be the sector with the highest level of shadow
activity in Latvia in 2012, which is in contrast to the pattern observed in 2011 and 2010.14 In
Lithuania, however, the level of shadow economy in 2012 is relatively similar in all main sectors
compared to 2011 (range from approximately 18% to 22%), with a slight decrease in the
wholesale sector and slight increase in the service sector (see Figure 18). In Estonia the pattern
of shadow activity across sectors in 2012 is similar to that of 2011, with the exception of the
construction sector, which increased to 33.4% in 2012, up by 10% compared to 2011 (Figure
19).
13
Our sample does not contain valid observations for 2009-2012 on Estonian firms in Hiiu maakond, Viljandi
maakond, Võru maakond, Jõgeva maakond, Lääne-Viru maakond, Rapla maakond and Saare maakond regions.
14
In 2010 the highest proportion of shadow activity in Latvia was in the construction sector (53.6%), followed by
services and retail (41.7% and 40.9%): see SSE Riga Shadow Economy Index for the Baltic countries 2009 and
2010 (http://www.sseriga.edu/en/research/projects/shadow-economy-index/).
21
Figure 16. Size of the shadow economy (% of GDP) by sector, 2012.
Figure 17. Size of the shadow economy (% of GDP) by sector in Latvia, 2011 and 2012.
22
Figure 18. Size of the shadow economy (% of GDP) by sector in Lithuania, 2011 and 2012.
Figure 19. Size of the shadow economy (% of GDP) by sector in Estonia, 2011 and 2012.
Finally, Figure 20 shows the level of shadow activity for firms of different sizes, measured by
the number of employees. The trends for Lithuania and Estonia are similar to what was observed
in 2010. In Latvia, however, in 2012 large companies are proportionally less involved in shadow
activity compared to 2010 and 2011. High levels of underreporting are still common among
smaller Latvian companies (see Figure 21).
23
Figure 20. Size of the shadow economy (% of GDP) by firm size (number of employees), 2012.15
Figure 21. Size of the shadow economy (% of GDP) by firm size (number of employees) in Latvia, 2011 and
2012.
4.2.How attitudes and perceptions affect shadow activity
The literature on entrepreneurship and tax evasion identifies two main groups of factors that
affect the decision to evade taxes and 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
15
Our sample does not contain any valid observations on Estonian firms with more than 50 employees.
24
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.
To measure this first set of factors the survey includes questions about entrepreneurs’
perceptions of the likelihood of being caught for underreporting business profits, number of
employees, salaries, as well as involvement in bribery. We also asked entrepreneurs to evaluate
potential consequences for the firm if it were caught for deliberate misreporting. Figures 22-26
summarise the findings.
The results suggest that entrepreneurs in Estonia perceive the risk of being caught for
misreporting as significantly lower than entrepreneurs in Latvia and Lithuania (see Figures 2225). This is similar to what we observed in 2011. The level of ‘confidence’ of Estonian
entrepreneurs about not being caught, however, has decreased. For example, in 2012, 23.2% of
Estonian entrepreneurs in 2012 believe there is zero likelihood that they would be caught for
underreporting profits, whereas in 2011 the proportion was a staggering 44.4%. Similar changes
have also occurred for the likelihood of being caught for underreporting employees,
underreporting wages and being involved in bribing. Similar to 2011, a relatively high proportion
of both Latvian and Lithuanian entrepreneurs (especially Lithuanian entrepreneurs) estimate the
risk of being caught as very high. Approximately 1/3 of entrepreneurs in Latvia and Lithuania
perceive the probabilities of being caught for underreporting profits, employees and salaries as
being in the range 76%-100% (Figures 22-25).
Both Latvian and Estonian entrepreneurs perceive the potential consequences for being caught as
being more severe than the consequences perceived by Lithuanian entrepreneurs. Approximately
34.6% of Latvian respondents (32.6% in 2011) and 30.8% of Estonian respondents (27.6% in
2011) state that being caught for deliberate underreporting will result in either a serious fine or
closure of the business. The corresponding proportion of Lithuanian respondents is 21.7%
(23.8% in 2011). Furthermore, approximately 1/3 of all respondents from Estonia, Latvia and
Lithuania stated that being caught for underreporting will have only minor or no consequences
(Figure 26).
25
Figure 22. Probability of being caught for underreporting business profits, 2012. Vertical axis measures
percentage of each country’s respondents in each category.
Figure 23. Probability of being caught for underreporting number of employees, 2012. Vertical axis measures
percentage of each country’s respondents in each category.
26
Figure 24. Probability of being caught for underreporting salaries, 2012. Vertical axis measures percentage of
each country’s respondents in each category.
Figure 25. Probability of being caught for making payments to ‘get things done’, 2012. Vertical axis measures
percentage of each country’s respondents in each category.
27
Figure 26. Most likely consequences if caught deliberately underreporting, 2012. Vertical axis measures
percentage of each country’s respondents in each category.
Empirical studies find that the actual amount of tax evasion is considerably lower than predicted
by rational choice models and 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, 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. We measure
firms’ attitudes using four questions about their satisfaction with the State Revenue Service, the
government’s tax policy, business legislation and the government’s support for entrepreneurs
(see Q1-Q4 in Appendix 1).
Figures 27-30 illustrate the distribution of firms’ satisfaction with the State Revenue Service, the
government’s tax policy, business legislation and the government’s support for entrepreneurs, in
each of the Baltic countries in 2012. Figure 31 summarises the degree of satisfaction, reporting
the country means in each of the satisfaction areas, with higher scores indicating more
satisfaction.
Similar to what we observed in 2011 and 2010, across all three countries firms tend to be most
satisfied with the State Revenue Service (SRS). In particular, satisfaction with the SRS has
increased in Latvia: in 2012 as many as 58.4% of Latvian respondents reported that they are
satisfied or very satisfied with SRS, compared to 41.9% in 2010 and 42.7% in 2011.
Nevertheless, 17.9% Latvian companies are unsatisfied or very unsatisfied with the SRS (20.7%
in 2011) (Figure 27).
28
Figure 27. Satisfaction with State Revenue Service, 2012. Vertical axis measures percentage of each country’s
respondents in each category.
Even though some positive progress can be observed compared to 2011, dissatisfaction in Latvia
in 2012 is particularly high with the government’s tax policy. Approximately 60% of
respondents (70% in 2011) are “unsatisfied” or “very unsatisfied” with tax policy (Figure 28).
Furthermore, both Latvian and Lithuanian entrepreneurs seem to be less satisfied with the quality
of business legislation (Figure 29) and with government support (Figure 30), than entrepreneurs
in Estonia. For instance 39.2% of Estonian respondents in 2012 stated that they are unsatisfied or
very unsatisfied with government support to business, compared to 63.1% of respondents from
Latvia and 53.1% from Lithuania.
Figure 28. Satisfaction with the government’s tax policy, 2012. Vertical axis measures percentage of each
country’s respondents in each category.
29
Figure 29. Satisfaction with the quality of business legislation, 2012. Vertical axis measures percentage of
each country’s respondents in each category.
Figure 30. Satisfaction with the government’s support to entrepreneurs, 2012. Vertical axis measures
percentage of each country’s respondents in each category.
30
Figure 31. Average satisfaction of firms with the tax system and government, 2012.
Figures 32 and 33 illustrate the distribution of firms’ stated tolerance of tax evasion and bribery.
Figure 34 summarises the degree of tolerance in each of the countries with higher scores
indicating more tolerance.
The results for 2012 show that both Estonian and Latvian respondents view tax avoidance and
bribery as less tolerated behaviour than their Lithuanian counterparts. Furthermore, as many as
43% of Lithuanian entrepreneurs (compared to 23.2% Latvian entrepreneurs and 25.2% Estonian
entrepreneurs), either “agree” or “completely agree” that tax avoidance is tolerated behaviour in
Lithuania in 2012 (Figure 32). A very similar pattern can be observed for tolerance towards
bribery (Figure 33).
Figure 32. Responses to statement, “tax avoidance is tolerated behaviour”, 2012. Vertical axis measures
percentage of country’s respondents in each category.
31
Figure 33. Responses to the statement, “bribery is tolerated behaviour”, 2012.
percentage of country’s respondents in each category.
Vertical axis measures
Figure 34. Averages of firms’ tolerance of tax evasion and bribery, 2012.
4.3.Multivariate tests of the determinants of shadow activity
We use regression analysis to identify the statistically significant determinants of firms’
involvement in the shadow economy. The regression results are reported in Appendix 2. Model 1
includes most of the possible influential factors and dummy variables for Estonian and
Lithuanian firms (Latvian firms are the base case). Model 2 replaces the country dummy
variables with country*region dummy variables (with Kurzeme, Latvia, as the omitted category).
Model 3 drops statistically insignificant determinants.
32
The country dummy variables suggest that the size of the shadow economy is slightly smaller in
Estonia and Lithuania relative to Latvia after controlling for a range of explanatory factors,
although the coefficients are not statistically significant. Tolerance towards tax evasion is
positively associated with the firm’s stated level of income/wage underreporting, i.e.,
entrepreneurs that view tax evasion as a tolerated behaviour tend to engage in more informal
activity. The measures of tolerance also serve the important role of controlling for possible
understating of the extent of shadow activity (untruthful responses) due to the sensitivity of the
topic.16
The regression coefficients indicate that the effect of perceived detection probabilities and
penalties on the tendency for firms to engage in deliberate misreporting is consistent with the
predictions of rational choice models, i.e., the higher the perceived probability of detection and
the larger the penalties, the lower the amount of tax evasion and misreporting. The effect of
detection probability in particular stands out as being a particularly strong deterrent of shadow
activity. This evidence suggests a possible policy tool for reducing the size of the shadow
economies, namely increasing the probability of detection of misreporting. This could be done
via an increased number of tax audits, whistle-blower schemes that provide incentives to report
information to authorities about non-compliant companies, and investment in tax evasion
detection technology.
The regression results also indicate that a firm’s 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 the descriptive statistics and with previous research on tax evasion, and offers an
explanation of why the size of the shadow economy is slightly 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. Analysing each of the four measures of
satisfaction separately we find that shadow activity is most strongly related to dissatisfaction
with business legislation and the State Revenue Service, followed by the government’s tax
policy and support for entrepreneurs.
Another strong determinant of involvement in the shadow economy is firm size, with smaller
firms engaging in more shadow activity than larger firms, although the descriptive statistics
suggest the relation may be non-monotonic. Firm age, although not statistically significant,
16
For example, consider two firms that underreport income/wages by 40% each, but the first operates in an
environment in which tax evasion is considered highly unethical and is not tolerated, whereas the second operates in
an environment in which tax evasion is relatively tolerated. The first firm might state that its estimate of
underreporting is around 20% (a downward biased response due to the more unethical perception of tax evasion)
whereas the second firm might answer honestly that underreporting is around 40%. This example illustrates that
failure to control for the sensitivity of tax evasion (proxied here by tolerance) can lead to biased comparisons.
33
suggests that younger firms engage in more shadow activity than older firms. A possible
explanation for these two relations is that small, young firms use tax evasion as a means of being
competitive against larger and more established competitors. The sector dummy variables are not
statistically significant but suggest that firms in the construction sector tend to engage in more
shadow activity. There is no evidence of an association between shadow activity and the average
wage paid by a firm or a firm’s change in profits (or employees or turnover).
5. Entrepreneurs’ attitudes regarding shadow activities
In addition to estimating the size of shadow economy and its influential factors, we also elicited
entrepreneurs’ opinions regarding various aspects of the shadow economy in the Baltic countries.
We believe that some of these data might be useful to policy makers, at least as complementary
information.
We asked entrepreneurs a number of questions about the motivation for participating in tax
evasion. Entrepreneurs were presented with various statements, which they were asked to assess
on a 1-7 scale, where ‘1’ represents ‘completely agree’ and ‘7’ represents ‘completely disagree’.
The results are summarised in Figure 35.
Interestingly, even though the size of the shadow economy has decreased considerably in Latvia
compared to 2011, entrepreneurs’ attitudes in 2012 are very similar to those in 2011. Latvian
companies continue to emphasise tax evasion as a possible tool to ensure competitiveness (and
survival) of the firm. For example, in response to the statement “to ensure successful
performance of a company (including survival) it is much more important to have an appropriate
product than to evade taxes”, both Estonian and Lithuanian entrepreneurs reported more towards
1 ‘completely agree’ (1.6 and 1.4 in 2012; 1.6 and 1.7 in 2011), whereas the average response
from Latvian entrepreneurs in 2012 was 3.0 (3.5 in 2011). Also, Latvian entrepreneurs, relative
to Estonian and Lithuanian entrepreneurs, tend to agree with the statement that evading taxes is
necessary to survive (response scores of 3.9 in Latvia, 5.1 Lithuania and 5.2 in Estonia in 2012,
compared to 3.8 in Latvia, 4.8 in Lithuania and 5.0 in Estonia in 2011). Furthermore, Latvian
entrepreneurs are more inclined to link higher levels of tax evasion with lower past performance.
Finally, entrepreneurs from all three Baltic countries seem to agree that performance of their
companies very much depends on the economic situation in the country.
34
Figure 35. Entrepreneurs’ attitudes regarding tax evasion, 2012.
6. Discussion and conclusions
The SSE Riga Shadow Economy Index is estimated annually based on surveys of entrepreneurs
in the Baltic countries using a number of surveying and data collection techniques shown in
previous studies to be effective in eliciting relatively truthful responses. The Index combines
estimates of 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. This report is the third in the series and focusses on the shadow economy estimates for the
year 2012, as well as trends during the years 2009-2012.
One of the key findings of this report is that Latvia has experienced a considerable decrease in
the size of its shadow economy in 2012. This is the second consecutive year in which the Latvian
shadow economy has contracted and as a result it has brought the size of the Latvian shadow
economy as a proportion of GDP to similar levels as neighbouring Estonia and Lithuania. In
2012 the estimated sizes of the Estonian and Lithuanian shadow economies are 19.2% and
18.2% of GDP, respectively, and the Latvian shadow economy is only marginally larger at an
estimated 21.1% of GDP. This is a stark contrast to the situation in 2009, when the size of the
Latvian shadow economy was estimated to be almost twice that of Estonia and Lithuania. The
sizes of the Estonian and Lithuanian shadow economies have not changed significantly in 2012.
35
Interestingly, the dynamics of the shadow economies in the Baltic countries during the period
2009-2012 appear to reflect macroeconomic conditions. For example, in 2009, when the Latvian
shadow economy was estimated to be considerably larger than that of Estonia and Lithuania, the
macroeconomic conditions in Latvia were also considerably more adverse: real GDP was falling
at a rate of 17.7% p.a. compared to 14.1% in Estonia and 14.8% in Lithuania, and unemployment
in Latvia was 17.3% compared to 13.8% in Estonia and 13.7% in Lithuania. In 2012, the shadow
economies are estimated as being more similar in size and the macroeconomic conditions also
display less disparity: real GDP is growing at a rate of 5.6% p.a. in Latvia, 3.2% in Estonia and
3.6% in Lithuania, and unemployment in Latvia is 14.9% compared to 9.8% in Estonia and
13.2% in Lithuania.17 These observations are consistent with the notion that some proportion of
shadow sector activity is a natural response to difficult times in the business environment. Latvia
was hit the hardest in the recent crisis, causing a relatively large amount of shadow activity
during the crisis period, which has subsequently contracted towards more “normal” levels as the
economy recovers. Additionally, deliberate policy efforts aimed at reducing shadow sector
activity in Latvia may have also contributed to the decrease.
The dynamics of the SSE Riga Shadow Economy Index are consistent with estimates from other
studies that use different estimation methods, e.g., Schneider (2013). This increases the
confidence one can place in the overall conclusions about the Baltic shadow economies. An
advantage of our approach is that we are able to provide more detailed information on the
components and determinants of the shadow economy. The micro-level evidence suggests that
the decline in the size of the shadow economy in Latvia has been driven mainly by decreases in
underreporting of business profits, followed by decreasing envelope wages. The Vidzeme region,
and the retail, services and construction sectors have seen the most significant improvements.
Large firms have also experienced a considerable improvement in the amount of misreporting
and tax evasion. Finally, the increasing satisfaction of Latvian entrepreneurs with the State
Revenue Service (SRS) may account for some of the reduction in the level of the Latvian
shadow economy.
The results of our analysis also indicate a number of areas for improvement in each of the Baltic
countries. First, envelope wages continue to make up a large component of the shadow
economies in all three countries: they account for 52.3% of the shadow economy in Estonia,
42.9% in Latvia and 39.3% in Lithuania. The next largest component is unreported business
income. Therefore, targeting these component of the shadow economies, has the potential to
create a significant decrease in the size of the shadow economies overall. Second, dissatisfaction
with government, tax policy, and business legislation is still relatively high across all three
countries. Third, bribery in association with government contracts has increased in all three
countries in 2012.
17
IMF World Economic Outlook 2010, 2012 and 2013.
36
There are also a number of country-specific areas of concern. In Latvia, shadow activity is
particularly high among small companies. Also, Latvian entrepreneurs are still more dissatisfied
with tax policy and the SRS than entrepreneurs in the other two countries, which is likely to
contribute to the marginally larger shadow economy in Latvia. In Estonia, shadow activity is
particularly high in the construction sector. Also, Estonian entrepreneurs perceive the risk of
being caught for misreporting business income, paying envelope wages and paying bribes as
being considerably lower than entrepreneurs in the other two countries. These perceptions are
likely to contribute to shadow activity in Estonia. In Lithuania, shadow activity is particularly
high in the Siaulių region as well as construction and services companies. A recent concern in
Lithuania is the increasing level of general bribery, which has grown from 9.3% of revenue in
2010 to 14% in 2012. In contrast, neither Latvia nor Estonia has had an increase in the level of
general bribery during this period.
This year’s study confirms some of our previous findings regarding what makes Baltic
entrepreneurs more likely to operate in the shadow sector and adds some new ones. Firms that
are dissatisfied with the tax system or the government tend to engage in more shadow activity;
satisfied firms engage in less. This result is consistent with previous research on tax evasion, and
has implications for policy measures to reduce the size of the shadow economy. We also find
that smaller, younger firms engage in proportionally more shadow activity than larger, older
firms, consistent with the anecdotal evidence that tax evasion is used by firms to gain a
competitive edge, and that having an edge is important in competing in an established market.
Finally, the level of tax evasion and deliberate misreporting among Baltic companies is
responsive to the perceived probabilities of being caught and to the expected penalties for being
caught. In particular, companies that perceive the probability of being caught as being higher
tend to engage in less shadow activity.
The findings of this study suggest a number of approaches for policymakers to reduce the size of
the Baltic shadow economies. First, reducing dissatisfaction with the tax system is likely to
decrease the size of the shadow economies. Addressing this issue could involve actions such as
making tax policy more stable (less frequent changes in procedures and tax rates), making taxes
more “fair” from the perspective of businesses and employees, and increasing the transparency
with which taxes are spent. Second, increasing the probability of detection is expected to reduce
shadow activity. This could be achieved via an increased number of tax audits, whistle-blower
schemes that provide incentives to report information to authorities about non-compliant
companies, and investment in tax evasion detection technology.
37
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39
Appendix 1. Questionnaire form used in 2013
ENTREPRENEURS’ SATISFACTION WITH GOVERNMENT POLICY / INFORMAL
ENTREPRENEURSHIP IN THE BALTIC COUNTRIES
My name is ... from the Stockholm School of Economics in Riga. We are conducting a survey
aimed at understanding entrepreneurs’ satisfaction with government policy in Latvia (Lithuania,
Estonia). The main interest of the study is to find out to what extent entrepreneurs are happy with
the government’s activity as well as how government policy influences entrepreneurial
behaviour, including tax avoidance.
I would like to emphasize that we are only interested in your expert opinion and in no way are
we indicating, for instance, that your company is involved in any type of tax avoidance activities.
The interview will last approximately 15 minutes. We guarantee 100% confidentiality as neither
your name nor your company’s name will appear in the data analysis. Data will be analysed
using a computer program without any reference to the data source. If you are interested, we can
also send you the summary of the survey results once the survey is complete.
If respondent hesitates or says ’no’:
This survey is very important to foster the knowledge about the entrepreneurship in Latvia
(Lithuania, Estonia). By participating in this survey you are helping to improve such knowledge.
All your answers will be 100% confidential and no one will be able to track you or your
company. Moreover we are interested in your expert opinion and what you say will be attributed
to the industry or your competitors, not your firm.
40
Questionnaire Form
External influences
1. Please evaluate your satisfaction with the performance of the State Revenue Service in Latvia
(Lithuania, Estonia) with regards to tax administration in 2012.
1
Very
unsatisfied
2
Unsatisfied
3
Neither satisfied
nor unsatisfied
4
Satisfied
5
Very satisfied
2. Please evaluate your satisfaction with the government’s tax policy in Latvia (Lithuania, Estonia)
in 2012.
1
Very
unsatisfied
2
Unsatisfied
3
Neither satisfied
nor unsatisfied
4
Satisfied
5
Very satisfied
3. Please evaluate your satisfaction with the quality of business legislation in Latvia (Lithuania,
Estonia) in 2012.
1
Very
unsatisfied
2
Unsatisfied
3
Neither satisfied
nor unsatisfied
4
Satisfied
5
Very satisfied
4. Please evaluate your satisfaction with the government’s support to entrepreneurs in Latvia
(Lithuania, Estonia) in 2012.
1
Very
unsatisfied
2
Unsatisfied
3
Neither satisfied
nor unsatisfied
4
Satisfied
5
Very satisfied
5. Tax avoidance is tolerated behaviour in Latvia (Lithuania, Estonia).
1
Completely
disagree
2
Disagree
3
Neither agree
nor disagree
4
Agree
5
Completely
agree
6. Bribing is tolerated behaviour in Latvia (Lithuania, Estonia).
1
Completely
disagree
2
Disagree
3
Neither agree
nor disagree
4
Agree
5
Completely
agree
Government policy and amount of informal business
7. Please estimate the approximate degree of underreporting business income by firms in your
industry in 2012:
Firms underreported business income by approximately ____ % in 2012.
8. Please estimate the approximate degree of underreporting business income by firms in your
industry in the previous year (2011):
Firms underreported business income by approximately ____ % in 2011.
41
9. Please estimate the approximate degree of underreporting number of employees by firms in your
industry in 2012:
Firms underreported approximately ____ % of the actual number of employees in 2012.
10. Please estimate the approximate degree of underreporting number of employees by firms in
your industry in the previous year (2011)
Firms underreported approximately ____ % of the actual number of employees in 2011.
11. Please estimate the approximate degree of underreporting salaries paid to employees by
companies in your industry in 2012 (for instance, if in reality an employee receives EUR 400, but
the reported salary is EUR 100, then underreporting is 75%; if EUR 400 and EUR 200, then
underreporting is 50%):
Firms underreported actual salaries by approximately ____ % in 2012.
12. Please estimate the approximate degree of underreporting salaries paid to employees by
companies in your industry in 2011.
Firms underreported actual salaries by approximately ____ % in 2011.
13. On average, approximately what percent of revenue (turnover) did firms in your industry pay
in unofficial payments to ’get things done’ in 2012?
In 2012 firms paid approximately ____ % of their revenue in order to get things done.
14. On average, approximately what percent of revenue (turnover) did firms in your industry pay
in unofficial payments to ’get things done’ in 2011?
In 2011 firms paid approximately ____ % of their revenue in order to get things done.
15. When other firms in your industry do business with the government, approximately how much
of the contract value would firms typically offer in unofficial payments to ’secure’ the contract?
(year 2012)
_____%
16. For a typical company in your industry, what would you say is the approximate probability (0100%) of being caught if the company were to:
(i) underreport its business income? ______%
(ii) underreport its number of employees? _____%
(iii) underreport the amount it pays to employees in salaries? _____%
(iv) make unofficial payments to ‘get things done’? _____%
17. If a company in your industry were caught for deliberate misreporting, what would typically be
the consequence to that company?
Nothing serious
A small fine
1
2
A serious fine that
would affect the
competitiveness of the
company
3
42
A serious fine that
would put the
company at risk of
insolvency
4
The company
would be forced to
cease operations
5
Company
18. What is the approximate percentage change in your net sales profit, sales turnover and total
employment in 2012 compared to 2011?
1 Net sales profit
2. Sales turnover
3 Total employment
Change (increase or
decrease in %) as
compared to 2011.
For example: +20%, 15%, 0 (no change)
19. Do you consider that businesses such as yours contribute to the growth of the Latvian (Estonia,
Lithuanian) economy and society in general?
1
Yes, to a very
large extent
2
Yes, to a large
extent
3
Yes, to some
extent
4
Yes, but very
little
5
No
20. Has your company been involved in sponsorship for social needs (for example, hospitals, social
organisations, sports) during 2012? If so, approximately what percentage of your annual after-tax
profits was spent on sponsorship during 2011?
______ % (0-100 from annual profits after taxes)
21. How many years of business management experience do you have?
_______ years
22. In which year did your company start operation?
Year________
23. What is the main activity (i.e. sector) that your company is engaged in?






Manufacturing
Wholesale
Retail
Services (please specify_______________________________ )
Construction
Other, please specify…………………..
24. What is the highest level of education you have attained?
 Primary school
 Vocational education
 Vocational secondary education
 Secondary school education
 Undergraduate (bachelor’s degree or equivalent)
 Engineering degree
 Master’s degree
 PhD degree
 Other, please specify…………………………
43
25. What was the operating profit of your company in 2012?
EUR __________
26. How many employees are currently employed in your company (full time equivalent, including
you)?
_________ employees
27. Approximately what was the average reported salary in your company in 2012?
____ EUR / month
28. In which region does your company conduct most of its business?
(note: different list of regions for Lithuania and Estonia)





Rīga
Kurzeme
Vidzeme
Zemgale
Latgale
Attitudes
29. Do you agree that (1 = completely agree; 7 = completely disagree):
Firms in your industry tend to evade taxes more if they are
having relatively bad times (for instance, decrease in profits
or turnover in comparison to previous years)
Entrepreneurs in your industry evade taxes because this is
the only way to survive.
Whenever possible, entrepreneurs will try to decrease their
business costs, which also includes evading taxes, regardless
of how their company performs.
Whenever possible, entrepreneurs will try to decrease their
business costs, which also includes evading taxes, regardless
of the nature of the Government’s entrepreneurship policy in
Latvia (state support, tax legislation, etc.)
Entrepreneurs in Latvia trust the Government, and believe
that their tax money is spent appropriately.
Tax evasion is primarily entrepreneurs’ response to what
they believe are incorrect actions by the State with regard to
promoting entrepreneurship.
To ensure successful performance of a company (including
survival) it is much more important to have appropriate
product and business strategies than to evade taxes.
Performance of your company very much depends on the
economic situation in the country: your company performs
considerably better during economic growth, but during
economic recession performance gets considerably worse.
Performance of companies in your industry is very much
influenced by their choice to pay or evade taxes: by evading
taxes firms in your industry considerably increase their
profits.
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1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
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4
5
6
7
Appendix 2. Regression results
Table 2. Determinants of firms’ involvement in shadow activity.
This table reports coefficients from regressions of firms’ unreported proportion of production in 2012 (dependent
variable; see Section 2 for details of calculation) on various determinants of shadow activity, using the pooled
sample of Estonian, Latvian, and Lithuanian firms. D_EE, and D_ LT are dummy variables for Estonian and
Lithuanian firms, respectively (Latvian firms are the omitted category). Tolerance_TaxEvasion is the firm’s
response to Question 5, with higher scores indicating more tolerance. Satisfaction is the first principal component of
the firm’s responses to Questions 1-4, with higher scores indicating higher satisfaction with the country’s tax system
and government. DetectionProbability and PenaltyForDetection measure the firm’s perception of the probability of
being caught for shadow activity and the severity of penalties conditional on being caught (calculated as the first
principal component of responses to Questions 16(i)-16(iv), and the response to Question 17, respectively).
ln(FirmAge) and ln(Employees) are the natural logarithms of the firm’s age in years and its number of employees.
AverageWage is the average monthly salary in EUR paid by the firm. ChangeInProfit is the firm’s percentage
change in net sales profit from 2011 to 2012. D_Wholesale to D_OtherSector are sector dummy variables with
manufacturing as the omitted category. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels.
T-statistics are reported in parentheses.
Model 1
Model 2
Model 3
Intercept
30.502***
24.578***
22.368***
(4.24)
(2.85)
(5.60)
D_EE
-2.103
(-0.68)
D_LT
-3.026
(-0.99)
Tolerance_TaxEvasion
1.781**
1.932**
2.423***
(2.28)
(2.38)
(3.64)
Satisfaction
-1.738*
-2.022*
-1.962**
(-1.78)
(-1.91)
(-2.28)
DetectionProbability
-2.847***
-3.042***
-2.709***
(-2.79)
(-2.81)
(-2.96)
PenaltyForDetection
-0.144
-0.049
(-0.15)
(-0.05)
ln(FirmAge)
-1.976
-0.715
(-0.98)
(-0.33)
ln(Employees)
-1.458**
-1.335*
-1.265**
(-2.18)
(-1.86)
(-2.21)
AverageWage
0.000
-0.001
(-0.27)
(-0.61)
ChangeInProfit
0.014
0.012
(1.05)
(0.85)
D_Wholesale
-1.849
-1.994
-1.608
(-0.68)
(-0.71)
(-0.67)
D_Retail
-0.256
0.012
-1.066
(-0.09)
(0.00)
(-0.45)
D_Services
-1.267
-1.469
-2.321
(-0.50)
(-0.54)
(-1.09)
D_Construction
4.482
4.938
3.424
(1.39)
(1.41)
(1.25)
D_OtherSector
1.760
3.447
-5.035
(0.31)
(0.57)
(-1.23)
Country*Region
dummy variables
R-squared
No
Yes
Yes
8.9%
12.2%
12.6%
45