overview of the unobserved economy, public corruption, and ... econo… · shadow economy). arnis...

Overview of the Unobserved

Economy, Public Corruption, and Money Laundering in Latvia

2017 Report

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About the author

Dr. Arnis Sauka is an Associate Professor, Director of the Centre for Sustainable Business at the Stockholm School of Economics in Riga, Latvia and a board member of the NGO “BASE” (Business Against the Shadow Economy). Arnis has a Ph.D. from the University of Siegen (Germany) and has been a Visiting Scholar at Jönköping International Business School (Sweden) and University College London (U.K.). His academic research findings, which deal with the shadow economy, tax morale, competitiveness, social responsibility, internationalisation of companies, and entrepreneurship policies have been published in a number of peer- reviewed journals and books. Arnis has also been extensively involved in applied research, including studies contracted by the Organisation for Economic Cooperation and Development (OECD), the European Foundation for the Improvement of Living and Working Conditions (Eurofound, an agency of the European Union (EU)), various industry associations, and non-governmental organizations (NGOs). E-mail: [email protected]

Acknowledgments

We are grateful to the Latvian State Revenue Service, the Office for Prevention of Laundering of Proceeds Derived from Criminal Activity (FIU Latvia), Transparency International Latvia / Delna, the Association of Latvian Commercial Banks, and the Latvian Alcohol Industry Association for providing data and other assistance that helped in compiling this report. Any errors are the responsibility of the author.

© Latvian Chamber of Commerce and Industry September 2017

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Executive Summary Considering the importance of linking various forms of the unobserved economy, public corruption, and money laundering, this report aims to summarise recent data on the size of and influential factors behind such phenomena in Latvia. The unobserved economy as well as the amount of public corruption and money laundering may have a more dynamic character in countries such as Latvia (that are still adjusting their institutional framework), compared to more developed market economies in the EU and other parts of the world. For this reason, not only do these phenomena need to be constantly reassessed, it is also strongly recommended that policy initiatives be drawn up using the most recent data. Given the measurement problems for most, if not all, parts of the unobserved economy as well as the lack of reliable measures to assess the magnitude of money laundering and public corruption, it is also not advisable to rely on only one indicator, for instance, when assessing the size of the shadow economy. The report draws on various sources, including recent statistics provided by the Latvian State Revenue Service, the Office for Prevention of Laundering of Proceeds Derived from Criminal Activity (FIU Latvia), Transparency International Latvia / Delna, and the Association of Latvian Commercial Banks as well various industry associations, including the Latvian Alcohol Industry Association. Both statistics and other data (indexes, surveys, etc.) presented by international organisations such as OECD, World Bank, and the European Commission are used in the report. Whenever possible, a cross-country comparison is provided to analyse the situation in Latvia compared to other EU countries and beyond. Furthermore, key activities and results in decreasing the size of the unobserved economy, money laundering, and public corruption are summarised in the report. The report covers the following key topics: (i) the size of the shadow economy and unregistered business in Latvia; (ii) the key components of the shadow economy in Latvia; (iii) the main influential factors behind the shadow economy in Latvia; (iv) the amount of contraband smuggling and other forms of the ‘black’ economy in Latvia; (v) key activities that have been implemented in recent years by institutions and organizations to decrease the shadow economy and contraband smuggling in Latvia and the main results of these activities; (vi) public corruption in Latvia; and (vii) the situation and progress with regard to money laundering in Latvia. According to Schneider (2016) and Putnins and Sauka (2017), the size of the shadow economy in Latvia in 2016 was approximately 20% of GDP. Both studies also show that the shadow economy in other countries in Central and Eastern Europe (CEE) is on approximately the same level, whereas, according to Schneider (2016), the size of the shadow economy in Latvia in 2016 is lower than in neighbouring countries Lithuania and Estonia. Overall, with some exceptions, the size of the shadow economy in comparably more developed European Union countries as well as the United States, Canada, and Japan, according to Schneider, is in the range of 10% of GDP or below. Most importantly, both according to Schneider (2016) and Putnins and Sauka (2017), the size of the shadow economy is decreasing in Latvia, which, arguably, is the result of both the development of the economy in the country and targeted policy activities regarding the shadow economy in Latvia, especially since 2010. Given Latvia’s proximity to Belarus and Russia where alcohol, cigarettes, and oil products are considerably cheaper, it is, however, somewhat challenging to fight the flow of illegal excise goods. Supported by the EU, Latvian authorities are investing considerable resources in strengthening their capacity (including equipment and training), which is also reflected in relatively good results (i.e. the large amount of illegal cigarettes that are confiscated on the Latvian border). As highlighted by the SRS Customs Board, the movement of illegal narcotics across the Latvian border is also still a challenge, especially given the relatively large amount of detected and confiscated illegal narcotics in postal mail, among other things. Yet, even though there are no comparative statistics that compare Latvia with other EU countries1, drawing on the available statistics from the SRS, with high likelihood it can still be 1 According to OECD, CEE (Central and Eastern Europe) countries are Albania, Bulgaria, Croatia, the Czech Republic, Hungary, Poland, Romania, the Slovak Republic, Slovenia, and the three Baltic States: Estonia, Latvia and Lithuania.

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concluded that illegal drugs are not a major problem for Latvia. Ongoing activities initiated by policymakers show that the Latvian government recognises that the unobserved economy (including the shadow economy, unregistered business, and flow of illegal excise goods and illegal drugs) is a complex phenomenon which requires close cooperation between various institutions. Reducing the size of the unobserved economy is defined as one of the key priorities in Latvia. Even though more time is required to judge the results of recently initiated activities to decrease the size of the unobserved economy, it is likely that the shadow economy and the amount of illegal excise goods will continue to decrease in Latvia. Drawing on 2016 data using various sources, however, it can be concluded with relatively high confidence that the size of the shadow economy is not a severe obstacle for the development of entrepreneurship (including attracting foreign investment, operation of the financial sector, etc.) and the overall development of the economy compared to other countries in the CEE region. It can also be concluded that although positive dynamics can be observed for Latvia for the previous three to four years, even more effort is required to strengthen its borders to further decrease the flow of illegal alcohol, cigarettes, and oil products. Available estimates of public corruption in Latvia mainly draw on perceptions of the Latvian population and, given the relatively low reported trust in the government in Latvia, should be interpreted with caution. Regardless of this, according to the Transparency International Corruption Perception Index, which in 2016 ranks Latvia in 44th position (out of 176 countries), Latvia performs better compared to countries such as the Czech Republic, Hungary, Italy, and others. Also, the estimates of the Transparency International Global Corruption Barometer show that corruption is perceived to be among the three main problems by approximately one third of respondents in Latvia. Even though this proportion is not more than in other EU countries (except Germany, Sweden, the UK, etc.), it is far from a good result. Furthermore, the rate of bribery, according to the Transparency International Global Corruption Barometer, though not the highest in the EU, is rather high in Latvia. The main conclusion, however, seems to be that there is not enough reliable data to confirm or deny that public corruption is higher in Latvia compared to other CEE countries as well as at least some other EU countries. Existing data suggests that even though positive progress with regards to perceived levels of corruption can be observed in Latvia, as in many other CEE countries, investors, entrepreneurs, and individuals in Latvia should be aware that public corruption can potentially be an issue. Data provided by relevant institutions, as summarised in the report, also suggest that it is becoming harder to engage in public corruption in Latvia as the institutions involved in fighting it are constantly increasing their capacity and skills. Finally, recent indicators, such as the Basel AML 2017 Index, show that Latvia has achieved impressive progress with regards to reducing the level of the money laundering. As in the case of reducing the shadow economy, this progress has been driven by external pressure from various international organisations. This also includes preparatory steps that were necessary for Latvia to implement for its accession to the OECD in 2016. As a result of the efforts of various institutions and organisations in Latvia and the support of cooperation partners from abroad, Latvia has been removed from the list of jurisdictions of primary concern (with regard to money laundering) for AML. It should be noted, however, that the money laundering situation was far from positive only some three or four years ago. The recent positive dynamics in most of the indicators for Latvia also suggest that it is likely that the shadow economy, unregistered business, and contraband smuggling as well as the amount of money laundering and public corruption will continue to decrease in Latvia. As argued previously in the report, if this is the case, such developments should positively affect the business ecosystem in the country, including the attractiveness of the business climate for investment in Latvia.

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Table of contents

1. Introduction 5

2. Size of the shadow economy and unregistered business in Latvia 7

3. Key components of the shadow economy in Latvia 12

4. Main influential factors behind the shadow economy in Latvia 18

5. Contraband smuggling and other forms of the ‘black’ economy in Latvia:

focus on excise goods and illegal drugs 28

6. Fighting the shadow economy and contraband smuggling of excise

goods and illegal drugs in Latvia 34

7. Public corruption in Latvia 39

8. Money laundering and other types of organised financial crime in Latvia 45

Key conclusions 54

References 57

Appendix 1 61

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I. Introduction It is often argued that informal activities, such as tax evasion, litigation, or rent seeking can also have some positive effects on the development of entrepreneurship, the financial sector, and the economy in general, especially in contexts where formal institutions, such as legal frameworks are weak or not yet properly established (e.g. Warren, 2003, Sauka, 2008). In such environments, corruption, for instance, might help to overcome a huge bureaucracy, making business transactions faster and more efficient. Yet tax evasion and other forms of the informal or shadow economy come with a cost in the longer term as resources are wasted by both firms involved in such activities and governments investing in dealing with tax evaders. As Mickiewicz, Rebmann and Sauka (2017:1) put it: “In the longer term, tax evasion may lead to a vicious circle, where a shrinking tax base falls on fewer businesses that comply fully, in turn pushing even more entrepreneurs into the informal economy. … Under such a scenario, economic development both at the local and at the country level suffers.” One of the challenges for measuring the size of the shadow (informal, grey, underground) economy is the lack of a unified definition of the concept. One way the ‘unobserved economy’ can be classified is presented in Appendix 1. Following this classification, in this report we use the term “shadow economy” to refer to all legal production of goods and services by registered companies that is deliberately concealed from public authorities.2 Key components of the shadow economy, following this definition, are misreported business income, misreported ‘envelope’ wages, and unregistered employees in registered companies. Unreported income of unregistered producers and income from illegal goods/services (in the context of this report – illegal excise goods such as cigarettes, alcohol, and oil products as well as illegal drugs) are two additional major components of the unobserved economy. All forms of the unobserved economy are closely linked with money laundering and public corruption as the two latter are major ‘sources’ of the shadow economy, unregistered business, and contraband smuggling of excise goods. Recognizing this and aiming to reduce the size of the unobserved economy and thus improving the business environment (including the attractiveness of countries for foreign investors), policymakers around the world are taking a wide range of measures that are also targeted at reducing the size of public corruption and money laundering. The unobserved economy (and it various forms), however, cannot always be directly measured, which also makes assessment of policy measures as well as invested funds in fighting the unobserved economy somewhat challenging. Given the measurement problems for most, if not all, parts of the unobserved economy as well as the lack of reliable measures to assess the magnitude of money laundering and public corruption, it is also not advisable to rely on only one indicator, for instance, when assessing the size of the shadow economy. This is especially relevant for policymaking purposes, when it is advisable to use most of the indicators that assess the size of parts of the unobserved economy, money laundering, or public corruption to see the dynamics, and not the absolute amount of certain phenomena. Considering the importance of linking various forms of the unobserved economy, public corruption, and money laundering while taking into consideration the aforementioned challenges, this report aims to summarise recent data on the size of and influential factors behind such phenomena in Latvia. The report draws on statistics and other information provided by the Latvian State Revenue Service, the Office for Prevention of Laundering of Proceeds Derived from Criminal Activity (FIU Latvia), Transparency International Latvia / Delna, the Association of Latvian Commercial Banks, and various industry associations, including the Latvian Alcohol Industry Association and on available official statistics and data (indexes, surveys, etc.) presented by various international organisations. Where data exists, a cross-country comparison is provided to analyse the situation in Latvia compared to other EU countries and 2 The definition corresponds to what the Organisation for Economic Co-operation and Development (OECD) in its 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 similar to definitions employed by other researchers (e.g., the World Bank study of 162 countries by Schneider, Buehn and Montenegro (2010)).

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beyond. Furthermore, key activities and results in decreasing the size of the unobserved economy, money laundering, and public corruption are summarised in the report. The report starts with a section on the size of the shadow economy and unregistered business in Latvia. The key components of the shadow economy in Latvia are identified and analysed in Section 3, while data on the main influential factors behind the shadow economy in Latvia are provided in Section 4. The report proceeds with a presentation of the recent data on the amount of contraband smuggling and other forms of the ‘black’ economy in Latvia with a focus on excise goods and illegal drugs. In Section 6, key activities that have been implemented in recent years by institutions and organizations to decrease the shadow economy and contraband smuggling in Latvia are summarized and the main results of these activities are presented. Public corruption is addressed in Section 7, while Section 8 is devoted to analysing the situation and progress with regard to money laundering in Latvia. Section 9 provides key conclusions. We hope that updated information from most, if not all available sources will make this report useful for entrepreneurs, investors, and policymakers from various countries and industries that are interested in doing business in Latvia or the region. We also hope that this report will serve as guidance for policymakers in Latvia when assessing and further developing mechanisms to fight forms of the unobserved economy, money laundering, and public corruption, thus further increasing the attractiveness of the business climate in Latvia for both local companies and foreign investors.

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II. The size of the shadow economy and unregistered business in Latvia Being a phenomenon that is not directly observable, the size of the shadow economy is difficult to measure. The literature is yet to arrive at a consensus on what the best or most reliable methods are for measuring the shadow economy. According to Schneider, there is “…no ideal or dominating method – all have serious problems and weaknesses” (Schneider, 2014: 41). For this reason, it is risky to rely on shadow economy indicators presented by any single approach. Given the shortcomings of all the methods, for policymaking purposes it is a smarter strategy to compare various indicators, paying particular attention to the dynamics of the shadow economy. Two main approaches to measuring the shadow economy can be distinguished: the direct and indirect approaches (see Schneider and Enste, 2000; OECD, 2002; Slemrod and Webber, 2012; and Buehn and Schneider, 2013, for in-depth reviews). Even though a number of studies using either the direct or indirect approach to measure the shadow economy have been proposed (e.g. Rauch, 1991; Ahumada, 2007; Alm and Embaye, 2013; Feige and Urban, 2008; Helberger and Knepel, 1988; Kazemier and van Eck, 1992; La Porta and Shleifer, 2014; Lackó, 2000; Slemrod, 2007; Slemrod and Webber, 2012 among others, see also Sauka, Schneider and Williams, 2016 for an overview of the methods for measuring the size of the shadow economy), relatively few methods offer estimates across several countries and over time. One such method is the indirect ‘multiple indicator multiple cause’ (MIMIC) approach. The strength of the MIMIC approach is that it measures the shadow economy across a large number of countries over time and is relatively cheap to apply. Yet MIMIC (e.g. Schneider, 2014) has been broadly criticized for the following: (i) the stability of estimates due to often unreliable assumptions; these estimates are likely to overvalue the size of the shadow economy and thus are not appropriate to use for adjusting GDP; (iii) it is not precise about which parts of the unobserved economy it measures; and (iv) the method does not provide estimates of the structure of the shadow economy, which limits its use in policymaking and analysis of the drivers of the shadow activity. Direct methods can overcome the aforementioned shortcomings. An example of such a method is the one developed by Putnins and Sauka (2015). The method draws on surveys of company managers and is based on the notion that company managers that themselves engage in the misreporting and shadow production are most likely to know how much production/income goes unreported. The method estimates the size of the shadow economy as a percentage of GDP, combining estimates of misreported business income, unregistered or hidden employees, and unreported “envelope” wages, exploiting accounting relations in the composition of GDP. The method of Putnins and Sauka (2015) requires fewer assumptions than most existing methods, in particular compared to methods based on macro indicators. Furthermore, the method can be used over time or across sectors and countries and thus is a useful tool for evaluating the effectiveness of policy designed to minimize the shadow economy. The method, however, is relatively expensive and time consuming to apply and, similarly to other direct methods, tends to underestimate the size of the shadow economy. Another challenge is in accurately eliciting information from company managers. To achieve this, Putnins and Sauka (2015) use a number of carefully designed and validated methods in survey design, interview execution, and post-survey data analysis to maximize the accuracy of the estimates. For other approaches to measuring the shadow economy see Putnins and Sauka (2015); Schneider and Buehn (2017). Since both Schneider (2014) and Putnins and Sauka (2015) include Latvia in their shadow economy estimates, the results of recent findings that apply these methods are summarised in Table 1 and Figure 1. Table 1 reports the aggregate size of the shadow economies in Latvia, Lithuania, and Estonia from 2009-2016. The table also includes Poland, for which a comparison is available for 2016.

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Even though these countries – the Baltic states – are often treated as a uniform group, both estimates show that the size of the shadow economy differs in Latvia, Lithuania, and Estonia. This comes as no surprise as, despite the fact that all three countries had a very similar starting point when regaining independence in the early 1990s as a result of the collapse of the Soviet Union, their economic development has proceeded at different speeds and in different trajectories (see Sauka and Chepurenko, 2017). According to Table 1, the size of the shadow economy in Latvia is approximately 20-23% of GDP regardless of whether it is calculated using the methodology of Schneider or the methodology of Putnins and Sauka (2015). Both methodologies also show that the tendency of the shadow economy in Latvia is positive, i.e. the shadow economy is decreasing (see Figure 1). Yet Putnins and Sauka report more dynamic changes in the shadow economy in Latvia, especially during 2009-2012, explaining by the deep financial crisis in Latvia that started in 2008 and the proceeding years of recovery. In contrast, according to Schneider, the reduction of the size of the shadow economy in Latvia has a rather smooth character. Table 1. Size of the shadow economies in Latvia, Estonia, Lithuania, and Poland according to Schneider as well as Putnins and Sauka. Percentage of GDP

Year Estonia Latvia Lithuania Poland

Schneider Putnins and Sauka




2016 25.4 15.4 22.9 20.3 24.9 16.5 23.0 25.0 2015 26.2 14.9 23.6 21.3 25.8 15.0 23.3 24.5 2014 27.1 13.2 24.7 23.5 27.1 12.5 23.5 - 2013 27.6 15.7 25.5 23.8 28.0 15.3 23.8 - 2012 28.2 19.2 26.1 21.1 28.5 18.2 24.4 - 2011 28.6 18.9 26.5 30.2 29.0 17.1 25.0 - 2010 29.3 19.4 27.3 38.1 29.7 18.8 25.4 - 2009 29.6 20.2 27.1 36.6 29.6 17.7 25.9 -

Source: Schneider (2016), Putnins and Sauka (2017) Figure 1. Dynamics of the shadow economies in Latvia (LV), Estonia (EE), and Lithuania (LT) according to Schneider as well as Putnins and Sauka. Percentage of GDP

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Source: Schneider (2016), Putnins and Sauka (2017). The size of the shadow economy is also similar in Poland if both methods are applied – both methods report a slightly higher aggregate shadow economy in Poland as compared to Latvia. Yet, although the dynamics of the shadow economies in Lithuania and Estonia are similar using the two methods, the absolute size of the shadow economies as estimated by Schneider (2016) and Putnins and Sauka (2017) differs significantly in Lithuania and Estonia, i.e. the shadow economy in Latvia, according to this method, is much smaller than in Lithuania and Estonia, two neighbouring countries.3 4

3 Putnins and Sauka (2015: 479) explain several possible reasons for the differences: “First, with indirect methods such as MIMIC it is difficult to define exactly what part of unobserved production is estimated. Therefore, the method applied by Schneider (2013) may effectively measure different components of unobserved production, despite using the same definition of ‘shadow economy’. A second reason why our estimates are lower than those of Schneider (2016) could be the tendency of survey-based methods to underestimate the size of the shadow economy, and/or the tendency for indirect methods to overestimate the size of the shadow economy. Indeed, other papers point out that survey-based methods tend to produce lower estimates than indirect methods (e.g., Feld and Schneider, 2010; Williams, 2013; Buehn and Schneider, 2013); however, there is no consensus as to whether this is due to survey-based methods underestimating the size of the shadow economy, indirect methods producing inflated estimates, or both (Williams, 2013)”. Feld and Schneider (2010, p. 133) suggest that both overestimation and underestimation play a role: “the estimates from the MIMIC approach can be regarded as the upper bound of the shadow economy, and the estimates obtained from the survey approach provide its lower bound.” 4 The size of the shadow economy in Latvia has also been analysed by several older studies. Feige and Urban (2008) estimate the size of the shadow economy during the period of 1990-2001. Using a modified electric consumption approach (MEC) / currency deposit ratio approach (CDM), Feige and Urban estimate the size of the shadow economies in Estonia, Latvia, and Lithuania as 14.3% / 21.6%, 19.4% / 31.6%, and 19.7% / 29.0%, respectively. The corresponding estimates from Putnins and Sauka (2016) for the closest available year (2009) are 20.2%, 36.6%, and 17.7%; the differences are attributable to both the time period covered by the studies and the differences in methodology. A survey-based study, based on approximately 1,000 face-to-face interviews in 27 EU member states during 2007, was conducted by The European Commission’s Special Eurobarometer (No. 284, “Undeclared work in the European Union”). According to Eurobarometer (2007), individuals who admit they receive envelope wages constitute 8% in Estonia, 17% in Latvia, and 11% in Lithuania. For a summary of previous attempts to measure the shadow economy in other countries, see Schneider, F. (2017) Implausible Large Differences in the Sizes of Underground Economies in Highly Developed European Countries? A Comparison of Different Estimation Methods. CESifo Working Papers, No 6522.

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Table 2. Size of the shadow economy of the 28 EU countries from 2003-2016 (as % of GDP)

The shadow economy values for Germany have been adjusted by Schneider (2016) due to a change in the official GDP statistics of the German national accounts.

Source: Schneider (2016) For the sake of comparison with other countries, Table 2 reports Schneider’s (2016) estimates of the size of various European shadow economies using the indirect MIMIC method. The overall conclusion from this table is that, with some exceptions, the shadow economy tends to be relatively larger in post-Soviet countries and countries of Central and Eastern Europe and smaller in more mature market economies, such as Austria, Sweden, or Germany. To summarise, and especially if Schneider’s method (2016) is used as a benchmark, the size of the shadow economy in Latvia does not exemplify a larger threat for the development of the economy, entrepreneurship, or, in particular, the financial sector, including inflow of foreign investment, compared to other countries in Central and Eastern Europe. Correlation between higher economic development and a smaller shadow economy is also confirmed by Table 3, which shows, according to the MIMIC methodology applied by Schneider, that the size of shadow economies tends to be comparably smaller in countries such as the United States (5.6% of GDP in 2016) or Japan (8.5% of GDP in 2016). Table 3. Size of the shadow economy of 5 highly developed non-European countries from 2003-2016 (as % of GDP)

Source: Schneider (2016)

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When it comes to the size of unregistered business (see Appendix 1), according to our best knowledge, there are no comparative studies except Putnins and Sauka (2017), which includes Latvia and reports findings for recent years. Putnins and Sauka (2017) compare the three Baltic countries and, as presented in Table 4, according to these estimates, the proportion of unregistered enterprises in 2016 was largest in Lithuania (8.4% of all enterprises), followed by Estonia (6.1%) and Latvia (5.3%). Table 4. Proportion of unregistered enterprises in the Baltic countries, 2013-2016 This table reports point estimates and 95% confidence intervals for unregistered enterprises as a percentage of all enterprises in Estonia, Latvia, and Lithuania.

Estonia Latvia Lithuania 2016 6.1%

(5.1%, 7.1%) 5.3% (4.1%, 6.5%)

8.4% (7.5%, 9.4%)

2015 5.8% (4.5%, 7.1%)

5.2% (4.1%, 6.3%)

7.3% (6.5%, 8.1%)

2014 6.3% (4.5%, 8.2%)

5.6% (4.5%, 6.7%)

5.2% (4.5%, 6.0%)

2013 7.6% (5.4%, 9.9%)

5.4% (4.2%, 6.6%)

6.2% (5.3%, 7.1%)

Source: Putnins and Sauka (2017)

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III. Key components of the shadow economy in Latvia

This section summarizes available evidence – statistics and studies on determinants of the shadow economy in Latvia. It should be noted that even though a lack of comparative studies exploring determinants of the shadow economy limits comparison of Latvia with most other European Union countries, a number of studies exist that provide insights into the situation in Latvia compared to Lithuania and Estonia. For example, Putnins and Sauka (2017) calculate the relative size of the key components of the shadow economy in each of the three Baltic countries. According to this study, which surveys a representative sample of managers in Latvia, Lithuania, and Estonia, the largest components of the shadow economy in Latvia and Lithuania are unreported business income (42.1% and 38.3%, respectively) and envelope wages (40.2% and 42.4%), followed by unreported or unregistered employees (17.7% and 19.3%). In Estonia, however, the main component of the shadow economy is envelope wages, which accounted for 53.6% of the total shadow economy in 2016. Putnins and Sauka (2017) also report the degree of underreporting of business income, salaries, and number of employees for the period of 2009-2016 in Latvia, Lithuania, and Estonia. As exemplified by Figure 2, since 2009, all three countries have experienced a decline in the proportion of profits that are intentionally concealed from authorities. Latvia, however, is the only Baltic country in which underreporting of business income has decreased since 2014. In Estonia, underreporting of business income in 2016 is estimated as 8.3% (7.5% in 2015), whereas in Lithuania it is 11.7% (10.5% in 2015). Regardless of the decrease, underreporting of business income is still considerably higher in Latvia, at 18.5% in 2016 (19.9% in 2015 and 21.7% in 2014). Putnins and Sauka (2017) also find that approximately 60% of companies in Estonia, as many as 88% of companies in Lithuania, and approximately 80% of companies in Latvia are, on a smaller or larger scale, involved in underreporting of profits. For example, 22.8% of Estonian, 48.0% of Lithuanian, and 27.5% of Latvian companies are underreporting income (percentage of actual profits) in the range of 1-10%, whereas 11-30% are underreporting by 24.8%, 31.5%, and 33.6%, respectively. Furthermore, according to Putnins and Sauka (2017), underreporting of salaries or so-called ‘envelope wages’ as a proportion of true wages is relatively similar in all three Baltic countries (in the range of 15.4%-18.1% of wages; see Figure 3). Furthermore, as presented in Figure 3, after steadily decreasing in the period of 2010-2015, envelope wages in Latvia have increased slightly in 2016 compared to 2015 and now account for 18.1% of wages (17.9% in 2015). Envelope wages in 2016 in all three Baltic countries are at a relatively similar level. Putnins and Sauka (2017) also report that approximately 70% (in Estonia) to 80% (in Latvia and Lithuania) of companies are involved in underreporting salaries, most often underreporting 11%-30% of actual salaries. A similar estimation of the approximate envelope share in the aggregate economy-wide wage bill in Latvia is also calculated by the World Bank (2017) using a different methodology. According to the World Bank (2017), envelope wages accounted for 21.8% in 2014 (most recent data) in Latvia (Putnins and Sauka report 20.3% in the respective year). Also, the World Bank (2017) reports that a high proportion of employees in Latvia receive envelope wages. Yet recent data, showing that approximately 50% of workers in Latvia receive envelope salaries, is available only for 2014.

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Figure 2. Underreporting of business income (percentage of actual profits) from 2009-2016

Source: Putnins and Sauka (2017) Figure 3. Underreporting of salaries (percentage of actual salaries) from 2009-2016

Source: Putnins and Sauka (2017) According to Putnins and Sauka (2017), the third key component of the shadow economy – underreporting of the number of employees – is relatively minor in all three Baltic countries, accounting for 6-7% (Figure 4). Figure 4. Underreporting of the number of employees (percentage of the actual number of employees) from 2009- 2016

Source: Putnins and Sauka (2017)

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Another recent survey exploring key components of the shadow economy in the Baltic countries, Poland, Belarus, and Sweden, was conducted by the Lithuanian Free Market Institute (Zukauskas, 2015).5 Drawing on survey results from the adult population, Zukauskas (2015) calculates both the extent of shadow employment as a proportion of GDP (Table 5) and the extent of unregistered purchases as a proportion of GDP (Table 6) in Latvia, Lithuania, Estonia, Poland, Belarus, and Sweden. Tables 5 and 6 report the situation in 2014, which is the most recent available year. As exemplified by these tables, the situation in Latvia with regard to both indicators is comparably good. Table 5. Extent of shadow employment as a proportion of GDP, 2014

Source: Zukauskas (2015). Table 6. Extent of unregistered purchases as a proportion of GDP, 2014

Source: Zukauskas (2015). More specifically, the results of Zukauskas (2015) show that people in Lithuania reported considerable experience with purchasing goods from legal sellers without a receipt: 63% admitted to doing so, with 5% doing so regularly, 26% doing so up to ten times, and 32% doing so once or twice during the last 12 months. In Latvia, 22% of respondents reported purchasing from legal sellers without a receipt up to 10 times during the previous 12 months, while 35% reported doing so once or twice during the previous 12 5 This study surveys samples of the general population in Lithuania, Latvia, Estonia, Poland, Sweden, and Belarus, aiming to elicit public perceptions as well as involvement in certain shadow economy activities. This survey covers 6,035 respondents (approximately 1,000 in each country) aged 18-75.

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months. The respective figures for other countries are as follows: Belarus, 17% and 32%; Estonia, 10% and 31%; Poland, 15% and 22%; and Sweden, 8% and 13%. No experience of buying from illegal producers was reported by 49% of respondents from Latvia, 48% from Lithuania, 55% from Belarus, 58% from Estonia, 60% from Poland, and 80% from Sweden. All others (except those that did not answer this question – 7-10% from each country) admitted buying from illegal sellers at least once or twice during the previous 12 months. Zukauskas (2015) also calculates the weighted average monthly spending on unregistered purchases in each of the surveyed countries and the results are as follows: Latvia: 57 EUR; Lithuania: 72 EUR; Estonia: 68 EUR; Poland: 80 EUR; Belarus: 59 EUR; and Sweden: 89 EUR. Clothes, food products, auto repair, and cigarettes are the goods most commonly acquired from unregistered purchases in all the surveyed countries. Thirty-six percent of respondents from Latvia, 33% from Poland, 29% from Lithuania and Belarus, 26% from Estonia, and 8% from Sweden admitted that they have friends or relatives who have worked in the shadow labour market (i.e. without labour contracts or receiving part of their wages as envelope wages) during the last 12 months. According to Zukauskas (2015), the weighted average hours spent in shadow employment by friends or relatives per week (for people that are involved in shadow unemployment) is as follows: Latvia: 20 hours per week on average; Poland: 26 hours; Belarus: 23 hours; Estonia: 22 hours; Sweden: 19 hours; and Lithuania: 17 hours. Meanwhile, the weighted average income earned from shadow employment by friends and relatives per month as reported by respondents (Zukauskas, 2015) is as follows: Sweden: 1108 EUR; Estonia: 1022 EUR; Poland: 925 EUR; Belarus: 777 EUR; Lithuania: 489 EUR; and Latvia: only 449 EUR. In all the surveyed countries, most involvement in shadow labour is in the construction and renovation sector. Furthermore, according to Zukauskas (2015 – the most recent data), 58% of the Latvian population agrees that it is justifiable to work under contract yet receive part of one’s salary unofficially, in an ‘envelope’. In Lithuania and Estonia, the corresponding numbers are 43% and 34%, respectively, in Belarus: 51%, Poland: 42%, and Sweden: 28%. Receiving all of one’s salary unofficially (declaring none of one’s income) is more accepted in Latvia (by 42% respondents), Belarus (40%), and Poland (39%) compared to Sweden (30%), Lithuania (24%), and Estonia (23%). Furthermore, according to Zukausakas (2015), as many as 42% of respondents from Latvia justify purchasing a good or service from a legal shop when the buyer knows that the seller is not declaring their payment. The respective numbers for the other countries involved in the survey are as follows: Belarus: 38%; Lithuania: 36%; Estonia: 29%; Poland: 28%; and Sweden: 24%. As exemplified by Figures 5 and 6 above, however, it seems that negative attitudes, i.e. justification of involvement in various aspects of the shadow economy, do not present actual (reported) behaviour, i.e. even though respondents in Latvia seem to justify involvement in shadow economy activities more, they are not, on average, more involved in these activities compared to other surveyed countries. Zukauskas (2015) also reports on respondents’ personal experience of involvement in the shadow labour market: only 3% of respondents from Sweden; 6% from Estonia; 8% from Latvia and Lithuania; and 9% from Poland and Belarus admit such involvement. Also, hours spent per week and income earned from involvement in shadow labour is reported as much less compared to when the question is asked about friends and relatives. In this light, Putnins and Sauka (2015) argue that in cases when sensitive issues such as involvement in shadow economy activities are addressed, it is more appropriate to phrase questions indirectly. Otherwise, there is a very high risk that the study will greatly underestimate actual involvement in, for example, underreporting wages or income – as seems to be the case with part of Zukauskas’s (2015) study. Meanwhile, Sauka (2008) provides some evidence that even when answering questions about someone else, such as a ‘company from your industry’ not ‘your company’, it is very likely that respondents are actually talking about their own case. For this reason, even though they provide cross-country comparisons, the results of Eurobarometer surveys should also be treated with caution, especially parts that ask about respondents’ own experience.

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Namely, these surveys also ask sensitive questions directly and thus, most likely, underestimate the magnitude of the phenomena. A summary of key findings from a recent European Commission Eurobarometer study on undeclared work and a comparison with Zukauskas (2015) is provided in Table 7. Table 7. Summary of the findings of a Eurobarometer study (EC, 2014) and Zukauskas (2015) on envelope wages and involvement in undeclared work: admitting personal involvement vs. knowing about the involvement of friends and relatives.6

(1) Envelope wages

2013 (% of respondents of Eurobarometer

2013 survey answering ‘yes’)

(2) Supply of

undeclared work 2013 – about

yourself (% of respondents of Eurobarometer


(3) Demand for

undeclared work 2013 (% of

respondents of Eurobarometer


(4) Personal

experience of involvement in

the shadow labour market

2014 (% of respondents

answering ‘yes’, Zukauskas’s 2015 study)

(5) Knowing people who underreport income 2013 (% of respondents of Eurobarometer


(6) Having friends or

relatives in the shadow labour market 2014

(% of respondents

answering ‘yes’, Zukauskas’s 2015 study)

Latvia 11% 11% 28% 9% 46% 36% Lithuania 6% 8% 14% 9% 35% 29% Estonia 5% 11% 12% 6% 33% 26% Poland 5% 3% 5% 9% 28% 33% Sweden 1% 7% 16% 3% 39% 8% Belarus n/a n/a n/a 9% n/a 29% Austria 2% 5% 14% n/a 31% n/a Czech Republic

5% 4% 19% n/a 33% n/a

Finland 1% 3% 11% n/a 30% n/a France 1% 5% 9% n/a 38% n/a Germany 1% 2% 7% n/a 27% n/a UK 2% 3% 8% n/a 15% n/a Spain 5% 5% 8% n/a 33% n/a Italy 2% 2% 12% n/a 37% n/a

Source: European Commission (2014). Answers to the question (1) “Sometimes employers prefer to pay all or part of the salary or remuneration (for extra work, overtime hours or part above a legal minimum) in cash and without declaring it to tax or social security authorities. Has your employer paid you any of your income in the last 12 months in this way?” Source: European Commission (2014). Answers to the question (2) “Apart from a regular employment, have you yourself carried out any undeclared paid activities in the last 12 months?” Source: European Commission (2014) Answers to the question (3) “Have you in the last 12 months paid for any goods or services of which you had a good reason to assume that they included undeclared work (e.g. because there was no invoice or VAT receipt)?” Source: Zukauskas (2015) (4) Admitting personal experience of involvement in the shadow labour market. Source: Eurobarometer (2014) Answers to the question (5) “Do you personally know any people who work without declaring their income or part of their income to tax or social security institutions?” Source: Zukauksas (2015) (6) Admitting having friends or relatives in the shadow labour market 2014. A relatively low proportion of employees admit that their employer paid them envelope wages (11% in Latvia, 6% in Lithuania, 5% in Estonia, 5% in Poland, and only 1% in Sweden, Finland, France, or Germany) (see Column 1, Table 7). A relatively small number of respondents also admit that they themselves have carried out any undeclared paid activities in the last 12 months (11% in Latvia and Estonia, 8% in Lithuania, only 3% in Poland, and 1% in countries such as Finland and Germany – 6 Here and further in the report, to save space, only selected countries that were covered by Eurobarometer are included in the tables.

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Column 2, Table 7). Low personal involvement in the shadow labour market is also reported by Zukauskas (see Column 4, Table 7). Yet a comparably large proportion of respondents seem to know people that are involved in undeclared work (see Column 5 and Column 6 in Table 7 for the findings of the Eurobarometer 2013 study and Zukauskas, 2015, accordingly). Finally, in some countries such as Latvia, the Czech Republic, and Sweden, comparably many answered ‘yes’ to the Eurobarometer question, “Have you in the last 12 months paid for any goods or services of which you had a good reason to assume that they included undeclared work? (e.g. because there was no invoice or VAT receipt)” (Column 3, Table 7). Overall, it can be concluded that the findings of various surveys are not easy to explain. For example, is it possible that with the relatively large shadow economy in Poland, as reported by Schneider (2016) (Table 2) and Putnins and Sauka (2017) (Table 1), only 3% of the population has carried out any undeclared paid activity (Column 2, Table 7)? Probably not. It is thus advisable to be very cautious in using studies that include many countries, especially when questions are asked directly and the authors of the study do not take necessary steps to ensure maximum trustworthiness of the answers received. Furthermore, in many cases data across countries may not be directly comparable. There are many potential reasons for this, such as that in some countries a certain activity might be more tolerated and thus people will talk about it more openly even though the activity might not actually be more widespread. Overall, how and why people disclose their opinions is one of the issues we know quite little about, which remains a shortcoming of most indicators using survey data. Yet, such survey methods can be very useful for observing development trends within each individual country. Finally, another indicator / key component of the shadow economy is the so-called ‘tax gap’, which is calculated by the State Revenue Service in Latvia as well as various institutions in European Union countries (and beyond). The tax gap in Latvia is defined as the proportion of the sum of unpaid (both declared and undeclared) taxes within the potential amount of taxes that would be received if all taxpayers had paid all their taxes in the full amount. According to information provided by the Latvian State Revenue Service, the tax gap is calculated for various taxes; however, the value-added tax (VAT) gap is the only calculation available on a cross-country level within the European Union. The most recent data (for 2014) shows that Latvia takes 8th position (i.e. 7 countries in the EU have a larger VAT gap than Latvia). Namely, in 2014, the VAT gap level for Latvia was 23.4%, which is lower than in countries such as Poland (24.1%) and Lithuania (36.8%). The amount of other tax gaps, as calculated by the Latvian State Revenue Service, is provided in Table 8, showing that the general trend for Latvia is that tax gaps are decreasing for all major tax groups. Table 8. Tax gap calculation for major tax groups in Latvia 2013 2014 2015 VAT gap, total, M EUR 485.10 454.57 419.31 VAT gap total, as % of potential VAT amount 22.7% 20.4% 18.4% State Social Insurance Compulsory Contribution gap, as % of potential SSICC amount

23.2%

21.7%

21.0%

Personal income tax gap, total, M EUR 360.04 348.80 352.37 Personal income tax gap as % of potential PIT amount 22.6% 21.1% 20.7%

Source: State Revenue Service of Latvia (SRS, 20177).

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IV. Main influential factors behind the shadow economy in Latvia

An increasing number of studies address the question of what factors influence the size of the shadow economy as well as the size of major components of the shadow economy. Yet most of these studies provide empirical evidence only on a single-country level, thus offering limited cross-country comparison. In this section, we will draw on a framework developed by Mickiewicz, Rebmann and Sauka (2017) to describe key factors influencing the shadow economy in Latvia, providing, where possible, a comparison with other countries. Mickiewicz, Rebmann and Sauka (2017) present a unified framework on how various factors influence – through tax morale – involvement in the shadow economy.78 The study includes arguments from both a rational choice approach and a tax morale approach, distinguishing between (1) the normative perspective (i.e. evaluation of the government), (2) the cultural / cognitive perspective (social identity), and (3) the regulatory / instrumental perspective (effectiveness in applying formal sanctions).

Normative perspective The normative perspective deals with evaluation of the legitimacy and performance of political institutions, the government, and the tax authority. Previous studies show that trust in public officials (e.g., Torgler, 2003 a,b) as well as entrepreneurs’ satisfaction with tax policies and business legislation (e.g. Marien and Hooghe, 2011; Scholz and Lubell, 1998; Torgler, Schaffer and Macintyre, 2010) are among the factors that foster higher tax compliance. Distrust and dissatisfaction are associated with higher levels of shadow activity. Putnins and Sauka (2017) draw on a representative sample of company managers in the three Baltic countries to measure entrepreneurs’ 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. Putnins and Sauka (2017) provide the dynamics of satisfaction for the years 2010-2016 (country averages, measured on a scale of 1-5, where ‘1’ is very low satisfaction and ‘5’ is very high satisfaction) (see Figures 5-8). Following the pattern observed since 2010 across all three Baltic countries, firms in 2016 tend to be more satisfied with the State Revenue Service (SRS) than with the government’s tax policy, business legislation, and the government’s support for entrepreneurs. As illustrated in Figure 5, satisfaction with the SRS in Latvia has decreased in 2016 compared to the period of 2012-2015. Following an increase in 2015, satisfaction with the SRS has also decreased in Lithuania in 2016. In Estonia, satisfaction with the SRS in 2016 remains on a similar level as in 2015. 7 Defined as a moral obligation to pay taxes and “a belief in contributing to society by paying taxes” (Torgler and Schneider, 2009:230.) 8 Data provided by the Latvian State Revenue Service: here and further in the repot referenced as SRS, 2017.

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Figure 5. Satisfaction with the State Revenue Service, 2010-2016 This figure displays country averages over time, measured on a scale of 1-5, where ‘1’ is very low satisfaction and ‘5’ is very high satisfaction.

Source: Putnins and Sauka (2017). Following the trend since 2014, company managers’ satisfaction with tax policy in Latvia has also decreased considerably (2.08 in 2016, compared with 2.43 in 2015) and is the lowest among the Baltic countries (Figure 6). Satisfaction with tax policy has also decreased in Estonia, to 2.57, whereas in Lithuania this indicator has increased slightly, reaching 2.81 in 2016 (Figure 5). In this light, the study also finds that more than 75% of respondents in Latvia stated that they are either very unsatisfied or unsatisfied with tax policy, compared to only 40% in Lithuania and 45% in Estonia. Figure 6. Satisfaction with tax policy, 2010-2016 This figure displays country averages over time, measured on a scale of 1-5, where ‘1’ is very low satisfaction and ‘5’ is very high satisfaction.

Source: Putnins and Sauka (2017). Company managers are more satisfied with business legislation than with tax policy (Figure 7). The average in 2016 is 2.72 in Latvia and 2.84 in Lithuania, with both countries having experienced a decrease in 2016. Satisfaction with business legislation in Estonia has increased slightly in 2016, reaching an average of 3.10. Figure 8 shows that in all three countries, a larger number of firms are dissatisfied with the government’s support for entrepreneurs than are satisfied, somewhat mirroring the general dissatisfaction with tax policy. Compared with previous years, this dissatisfaction with government support in 2015 and 2016 has become particularly strong in Estonia (Figure 30) and Latvia.

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Furthermore, according to Zukauskas (2015), the Latvian population is comparably (to Lithuania, Estonia, and Poland) less satisfied with the amount of labour taxes and thus chooses to receive a bigger part of their salary unofficially. Furthermore, as regards the tax system in Latvia, consistent with the findings of Putnins and Sauka (2017), the findings of Sauka (2011) show that more than 70% of respondents are either very unsatisfied or unsatisfied with the tax system in Latvia. In this light, the majority of respondents in the 2011 study pointed out that taxes should be diversified: 83% of all respondents believe that small companies ought to pay a much smaller (43%) or smaller (40%) amount of taxes, whereas 25% believe that bigger companies could pay slightly more taxes. According to the 2011 survey, entrepreneurs are also very dissatisfied with how the government spends taxpayers’ money, i.e. the results show that as many as 60% of respondents are completely dissatisfied and more than 20% are dissatisfied. Furthermore, only one third of the respondents stated that they trust the government and civil service. Entrepreneurs’ trust in courts, however, is considerably greater: the survey shows that approximately 55% of the respondents trust the courts. This in turn influences the size of the shadow economy in Latvia. A more detailed analysis of the tax system in Latvia is provided by the World Bank (2017). Figure 7. Satisfaction with business legislation, 2010-2016 This figure displays country averages over time, measured on a scale of 1-5, where ‘1’ is very low satisfaction and ‘5’ is very high satisfaction.

Source: Putnins and Sauka (2017). Zukauskas (2015) also reports that one explanation for the relatively high proportion of the shadow economy in Latvia is that, in general, people in Latvia have a low level of satisfaction with the quality (or availability) of services provided by the government and thus often report that they ‘see no reason’ to pay taxes. The findings of respondents’ satisfaction with the government in Latvia from Zukauskas (2015) are presented in Figure 9.

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Figure 8. Satisfaction with government support, 2010-2016 This figure displays country averages over time, measured on a scale of 1-5, where ‘1’ is very low satisfaction and ‘5’ is very high satisfaction.

Source: Putnins and Sauka (2017).

Source: Zukauskas (2015) Latvia. The institutional perspective (North, 1990; Scott, 2014) has been recognized as a useful theoretical framework for analysing the influences of both formal (e.g., laws and regulations) and informal (e.g., attitudes and culture) institutions on involvement in shadow economy activities (Feige, 1997; Van de Mortel, 2002). North emphasises that the incentive structures provided through the institutional environment directly affect outcomes: “If the institutional framework rewards piracy then piratical organizations will come into existence; and if the institutional framework rewards productive activities then organizations and firms will come into existence to engage in productive activities.” (North, 1994: 361). To assess the strength of formal and informal institutions in the Baltic countries, Putnins and Sauka (2017) asked company managers to evaluate whether a range of formal and informal institutions are ‘no obstacle’ (0), ‘a minor obstacle’ (1), ‘a moderate obstacle’ (2), ‘a major obstacle’ (3), or ‘a very severe obstacle’ (4) to the current operations of their company. Figure 10 summarises the results.

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Figure 10. Strength of formal and informal institutions in the Baltic countries

Source: Putnins and Sauka (2017). According to Putnins and Sauka (2017), tax rates seem to be one of the main obstacles for entrepreneurs in Latvia (3.1 of 4, in comparison to 2.6 in Estonia and 2.7 in Lithuania). Entrepreneurs in Latvia are also considerably affected by uncertainty about regulatory policies (2.9 in Latvia, 2.1 in Estonia and 2.4 in Lithuania). Political instability seems to be a major problem in both Lithuania and Latvia and corruption is a major problem in Lithuania. Tax administration is reported as quite a severe obstacle in Latvia as well as in the other Baltic countries. Anti-competitive practices of other competitors seem to be a bigger concern for entrepreneurs in Lithuania and Estonia than in Latvia.

Cultural / Cognitive perspective According to the cultural/cognitive perspective put forward by Scott (2014), involvement in the shadow economy may be driven by social identity, i.e. how entrepreneurs identify themselves within the country in which they pay taxes (Ashforth and Mael, 1989; Hogg et al., 1995). Several studies have found a direct link between stronger social identity, such as ‘belonging to the state’ and higher tax morale, leading to lower involvement in shadow economy activities (Heinemann, 2011; Konrad and Qari, 2012; Martínez-Vázquez and Torgler, 2009). To measure social identity, Putnins and Sauka (2017) asked respondents to evaluate the extent to which they agree/disagree with the following statement: “Being a member of the local community is important to me.” Responses are measured on a scale of 1 to 5, where ‘1’ is ‘completely disagree’ (low social identity) and ‘5’ is ‘completely agree’ (high social identity). The results in Figures 11 and 12 show that community belonging is high in all three Baltic countries, in particular in Latvia, where 43.8% of respondents answered ‘completely agree’ (Figure 12).

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Figure 11. Perceived community belonging, 2016 This figure displays country averages measured from 1-5, where ‘1’ is ‘completely disagree’ (low social identity) and ‘5’ is ‘completely agree’ (high social identity).

Source: Putnins and Sauka (2017). Figure 12. Responses to the statement “Being a member of the local community is important to me”, 2016. Vertical axis measures percentage of each country’s respondents in each category.


Regulatory/Instrumental perspective The rational-choice theory of crime (e.g., Becker, 1968), applied to tax evasion, argues that individuals make decisions about whether or not to evade taxes by weighing the expected benefits of not paying taxes on the one hand against the risk of being caught and the penalties if caught on the other (e.g., Allingham and Sandmo, 1974; Yitzhaki, 1974). A number of recent studies provide some insights into the probability of being caught misreporting and perceptions of the potential consequences if caught – the severity of punishment – using both population surveys and surveys of entrepreneurs. The findings of these studies are presented in Tables 9-11, providing an overview of the situation in Latvia compared to selected EU countries (and Belarus).

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According to a recent Eurobarometer (European Commission, 2014) survey (Table 9), the detection probability when working without declaring income is evaluated as high by 28% of respondents in Latvia. A similar detection probability is also reported in Spain (29%) and Finland (29%), whereas in countries such as Lithuania, Estonia, Austria, and the UK, the detection probability seems to be higher (49%, 44%, 41%, 48%, respectively). The highest perceived likelihood for being imprisoned as a sanction if caught underreporting income is reported by respondents in the UK (14%), Sweden, and France (8%). In Latvia, the respective number is 3%, in Germany it is 6%, while in Estonia and Lithuania it is only 1%. Furthermore, in Latvia more than one third (35%) of respondents expect that there will be both normal tax or social security contributions due, plus a fine, yet approximately the same proportion (37%) think that they will only need to pay normal tax or social security contributions due if caught underreporting. Overall, respondents in Latvia evaluate the likelihood of also paying a fine if caught underreporting as somewhat lower than respondents in most of the other selected countries included in Table 9. Table 9. Eurobarometer (EC, 2014) findings on detection probability and severity of punishment (general population)

(1) Detection probability, 2013

(% of respondents of Eurobarometer 2013 survey

answering ‘high’)

(2) Severity of punishment, 2013

(% of respondents of Eurobarometer 2013 survey answering:) Normal tax or social

security contributions due, plus a fine

Normal tax or social security contributions

due

Prison

Latvia 28% 35% 37% 3% Lithuania 49% 41% 39% 1% Estonia 44% 48% 33% 1% Poland 38% 24% 41% 1% Sweden 22% 70% 17% 8% Austria 41% 67% 15% 5% Czech Republic

24% 60% 21% 7%

Finland 29% 58% 22% 4% France 34% 65% 17% 8% Germany 34% 68% 12% 6% UK 48% 61% 18% 14% Spain 29% 48% 20% 4% Italy 39% 58% 18% 6%

(1) Answer to the question “People who work without declaring income, run the risk that tax or social security institutions find out and issue supplementary tax bills and perhaps fines. How would you describe the risk of being detected in your country?” (2) Answer to the question “What sanction, if any, do you imagine someone would receive if the authorities find out that they receive an income from work which was not declared to tax or social security authorities?” Source: European Commission, (2014). According to Zukauskas’s (2015) study, however, the likelihood of being detected working without a legal job contract or getting at least part of one’s wage as an envelope wage, or of being detected purchasing a good or service from an illegal source that is not registered and does not pay taxes, is quite high, i.e. 39% and 28% of respondents evaluated the likelihood as either very high or high, respectively. Yet Zukauskas (2015) also reports that the likelihood of being detected is slightly lower in Latvia than in most of the other countries included in the survey (Table 10). When it comes to the evaluation of severity of punishment when working without a legal job contract or purchasing a good or service from an illegal source, as in the case of detection, respondents in Latvia evaluate the potential punishment as

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being rather severe. Namely, in the case of envelope wages, severe or quite severe punishment is reported by as many as 45%, which is more than in Poland, Estonia, and Sweden. Meanwhile, in the case of purchasing a good or service from an illegal source, severe or quite severe punishment is expected by 36% respondents from Latvia, which, in turn, is a slightly lower indicator than in most of the other countries that took part in the survey (Figure 13). Finally, more recent data is provided by Putnins and Sauka (2017), who draw on surveys of company managers in the three Baltic countries. The results suggest that entrepreneurs in all three Baltic countries perceive the risk of being caught underreporting income, salaries, and employees as relatively high. For example, as many as 46.5% of respondents in Lithuania and 44.1% of respondents in Latvia report that the probability of being caught underreporting profits is 76-100%. A relatively high number of respondents in Lithuania and Latvia also perceive a very high probability of being caught underreporting employees (52.9% and 47.7%, respectively) (Figure 14). In contrast, in Estonia only approximately 30% respondents report such a high probability of being caught underreporting business profits or number of employees in 2016. In Lithuania 42.1% of respondents, 34.1% in Latvia, and 29.6% in Estonia report that the probability of being caught underreporting salaries is 76-100%. The probability of being caught bribing is perceived to be lower in Latvia, where 34.6% of respondents claim that the likelihood of being caught is 1-10% (Figure 14). Figure 14 also shows that the expected penalties for deliberate misreporting are similar in Latvia and Lithuania, where approximately 40% of respondents (compared to 31.6% in Estonia) expect that the penalty would be a serious fine that would impact competitiveness. In 2016, similar to 2015, almost one third of respondents in Estonia claim that ‘nothing serious’ or only a small fine can be expected. The proportion of responses from entrepreneurs in Latvia and Lithuania within those categories is lower. A similar proportion of respondents in each of the Baltic countries (approximately 20%) perceive the penalty for tax evasion to be so severe that the company will have to cease operations if caught deliberately underreporting. Figure 13. Zukauskas’s (2015) findings on detection probability and severity of punishment (general population) Likelihood of being detected working without Likelihood of being detected purchasing a legal job contract, or getting at least part a good or service from an illegal source that of one’s wage as an envelope wage is not registered and does not pay taxes

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Figure 13 (cont.). Zukauskas’s (2015) findings on detection probability and severity of punishment (general population) Perception of punishment for working Perception of punishment for purchasing without a legal job contract or getting a good or service from an illegal source that at least part of one’s wage as an ‘envelope is not registered and does not pay taxes wage’

Source: Zukauskas (2015). Figure 14. Findings of Putnins and Sauka (2017) on detection probability and severity of punishment (company managers) Vertical axis measures percentage of each country’s respondents in each category Probability of being caught Probability of being caught underreporting underreporting business profits,2016 number of employees, 2016

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Figure 14. (cont.) Findings of Putnins and Sauka (2017) on detection probability and severity of punishment (company managers) Probability of being caught Probability of being caught making underreporting salaries, 2016 payments to ‘get things done’, 2016

Most likely consequences if caught deliberately underreporting, 2016


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V. Contraband smuggling and other forms of the ‘black’ economy in Latvia: focus on excise goods and illegal drugs

This part of the report focuses on providing an overview of the amount of contraband smuggling and/or consumption of excise goods (tobacco, alcohol, and oil products) in Latvia, comparing, where possible, the situation in Latvia with other EU countries. Smuggling activity conducted by criminal organizations in Latvia is mostly related to goods on which excise tax is levied. The biggest market is comprised of illegal trafficking of cigarettes, usually performed by multinational organized groups, including members from Latvia, Lithuania, Estonia, Poland, and other EU countries as well as Russia and Belarus, which is considered one of the biggest hubs for illicit cigarette trafficking in Europe. Recent data on the amount of contraband and counterfeit cigarettes that allows for a comparison of Latvia with other European Union countries is provided by two major sources: the KPMG (2017) Sun project and the Nielsen (2017) empty pack collection study. According to the KPMG (2017) Sun report, Latvia has the highest volume of contraband and counterfeit cigarettes in the European Union. In 2016, contraband and counterfeit cigarettes in Latvia accounted for 22.6% of total cigarette consumption in Latvia. In Estonia and Lithuania, the corresponding numbers are 13.3% and 17% (Figure 15). For comparison, “An estimated 8% to 21% of the approximately 264 billion (in 2014) (Maxwell, 2015) cigarettes consumed in the United States avoid or evade taxes, which equates to $2.95 billion to $6.92 billion in lost local and state revenues annually (National Research Council [NRC] and Institute of Medicine [IOM], 2015)”, (NCCDPHP, 2015: 5).” The high volume of contraband and counterfeit cigarettes in Latvia is explained by the geographical proximity to Belarus and Russia – countries with low-priced cigarettes. As indicated by Nielsen’s (2017) study as well, cities/regions that are closer to borders with Belarus and Russia exhibit a considerably higher proportion of contraband and counterfeit (C&C) cigarettes. KPMG (2017), however, reports positive dynamics for the amount of C&C consumption Latvia, i.e. a decrease of 4.1% in 2015 compared to 2016 (Figure 16). KPMG’s (2017) overall conclusion for the period of 2012-2016 is that a decrease in contraband and counterfeit cigarette volume in the Baltic countries is a result of increased enforcement and controls on the Russian and Belarusian borders. According to KPMG (2017), the majority of contraband cigarettes in Latvia come from Belarus (80.1%, 407 M cigarettes) and Russia (15.1% or 83 M cigarettes). The most popular contraband brands are Premier, NZ, Winston, Bayron, and Fest.

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Figure 15. Contraband and counterfeit (C&C) cigarette consumption as a percentage of total cigarette consumption in the EU, Norway and Switzerland, 2016

Source: KPMG (2017). Figure 16. Dynamics of contraband and counterfeit cigarette consumption in Latvia, 2012-2016

Source: KPMG (2017).

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Nielsen (2017) conducts quarterly ‘empty pack collection’ (EPS) studies aiming to provide an indication on the incidence (NDI) of non-domestic or illicit cigarettes consumed across the European Union. Empty pack collection (randomly, from streets and public bins) in various EU countries is applied as a methodology to gain insight into consumption of non-domestic or illicit cigarettes. In each country, empty pack collection is implemented in major cities, covering a substantial part of the territory. For example, fieldwork for Latvia conducted in April 2017 covered the 25 largest cities and ensured 60.3% coverage of the population. The total sample size collected was 4,900 packs. Table 10 summarises the results of the EPS study of EU countries, Norway, and Switzerland for 2014-2016, showing that the incidence (NDI) of non-domestic cigarettes consumed in Latvia is decreasing, yet still relatively high (i.e. 25% in 2016) compared to other EU countries.9 NDI is, however, even higher in countries such as France (27%), Ireland (30%), the UK (26%), and Norway (43%). Some data allowing for a cross-country comparison also exists with regard to consumption of illegal alcohol in Latvia. The OECD (2016) (various sources) presents government revenue lost from the illicit trade of alcohol (Table 11). As presented in Table 11, the illicit trade in alcohol in 2011 represented a loss of EUR 54 million annually in potential direct tax revenues in Latvia. In addition to this, the extra burden on the social budget is emphasized by Strateicuks, Kaze and Skapars (2012). Data for 2010 on recorded and unrecorded consumption of alcohol across the world is provided by the World Health Organisation in their global status report on alcohol and health from 2014 showing that consumption of illegal alcohol is rather widespread across Europe.10 More recent data on Latvia, however, is presented by a study conducted by the Center for the Prevention and Control of Diseases.11 This study draws on a survey of the general population in Latvia and finds that consumption of unregistered / illegal alcohol in Latvia amounted to 15.6% in 2011 and 10.2% in 2015 (proportion of the total amount of alcohol consumed in Latvia in 2011 and 2015 respectively). The amount of consumption of unregistered / illegal spirits was 38.7% in 2011 and 25.3% in 2015; for wine, the figures are 19.1% in 2011 and 8.1% in 2015. A high proportion of illicit alcohol consumption in Latvia is also reported by a study conducted by the Association of Latvian Spirits Producers and Distributors (Kaze et al., 2011) showing that 56% of regular illicit alcohol consumers have been using such products for over 10 years, 34% plan to purchase them again, and 59% purchase illicit alcohol at least once a month (Kaze et al., 2011). In line with the findings of the Center for the Prevention and Control of Diseases 2011 study, Kaze et al. (2011) also report that of the total alcohol market in Latvia, 32% is illicit, non-commercial alcohol. This is equivalent to approximately 5 million litres per year.

9 EPS study categories: Non-Domestic (Legal)- product that is brought into the market legally by consumers, such as during a cross-border trip (illegal) – C&C (Counterfeit and Contraband, including Illicit Whites). 10 http://apps.who.int/iris/bitstream/10665/112736/1/9789240692763_eng.pdf?ua=1 11 https://spkc.gov.lv/upload/Petijumi%20un%20zinojumi/Atkaribu%20slimibu%20petijumi/atkaribu_izraisoso_vielu_lieto sana_iedz_vidu_2015.pdf (in Latvian)

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Table 10. Nielsen’s (2017) Empty Pack Survey results for 28 EU countries, Norway and Switzerland, 2014-2016

Source: Nielsen (2017) in KPMG (2017).

32

Table 11. Summary of government revenue lost from illicit alcohol trade in selected countries

Source: various sources as compiled by OECD (2016). Overall, these indicators should be treated with caution as the flow of registered and unregistered alcohol across countries is very hard to monitor. The existing data, however, suggests that consumption of unregistered alcohol in Latvia is at a similar level as in most other Central and Eastern European countries. A number of studies also include a cross-country comparison of the population’s perception with regard to excise goods and illegal drugs. For example, the European Commission’s (2016) special Eurobarometer 443 on the public perception of the illicit tobacco trade shows that in Latvia (as well as Sweden, Ireland, and Lithuania), approximately one third of respondents that were included in the study think that black market cigarettes are among the most important sources of revenue for organised crime (Figure 17). In the EU on average, however, as many as 69% respondents said that illegal drugs are one of the most important sources of revenue for organised crime, while 14% indicated black market cigarettes. Figure 17. Most important sources of revenue for organized crime within the EU: % black market cigarettes. Perceptions of the general population (% of respondents)

Source: European Commission (2016). Furthermore, Zukauskas (2015) reports that as many as 21% of respondents in Lithuania and Latvia as well as a high proportion of respondents in Poland and Estonia justify (‘completely’ or ‘rather’) engagement in smuggling, illegal production or sales of cigarettes, alcohol products, and fuel (Figure 18).

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Figure 18. Justification of engagement in smuggling, illegal production or sales of cigarettes, alcohol products and fuel (% of respondents)

Source: Zukauskas (2015). According to our best knowledge and as also confirmed by the Latvian State Revenue Service, there are no further (reliable) data for the amount of illegal excise goods, including oil products. There are also no comparative estimates for Latvia and other EU countries with regard to the amount of illegal drugs in Latvia. According to the study conducted by the Disease Prevention Center of Latvia, however, approximately 5% of respondents admitted in the representative survey of households in Latvia conducted in 2015 that they know at least one place in the neighbourhood where illegal drugs can be bought. In 2011 the same study reported 7%. Overall this study also shows positive dynamics with regard to possibilities of acquiring illegal drugs in Latvia and the study concludes that, in general, access to illegal drugs and the possibility to start using illegal drugs has been gradually decreasing in Latvia since 2003. There are, however, some differences across regions and age groups, with a tendency for younger age groups (15-34) to know more about possibilities to acquire illegal drugs; they also tend to try or use drugs slightly more often than comparably older generations (35-64 years). Access to and use of illegal drugs, according to this study, is also more widespread in the capital city of Riga than in other regions of Latvia. Some further insights on the magnitude of both illegal excise goods and illegal drugs can be obtained from the results of fighting the inflow and consumption of excise good and illegal narcotics in Latvia. This information is provided in the next section of this report.

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VI. Fighting the shadow economy and contraband smuggling of excise goods and

illegal drugs in Latvia

Fighting the shadow economy Reducing the shadow economy in Latvia is among the top priorities of the Latvian government and a number of activities have been implemented in this regard. As highlighted by Putnins and Sauka (2014), at least some of these efforts have had positive results. Namely, Latvia received a substantial bailout package during the recent 2009 crisis from a conglomerate of international organizations, including the European Union and the International Monetary Fund. Assistance, however, was provided on the condition that the Latvian government would commit to stringent austerity measures and a series of reforms, including deliberate policy measures targeting the shadow economy. As a result, back in 2010, the Latvian government set up a high-level working group led by the Ministry of Finance State Secretary and comprising members of various ministries, the Treasury, Latvian Federal Police, the State Revenue Service, and the Corruption Prevention and Combatting Bureau, as well as experts from academia, the Bank of Latvia, and the Central Statistical Bureau. The key outcome from the working group was a package of 66 different policy actions to combat the informal economy. Most of these actions were implemented at different points in time during 2010-2013, with most of the reforms taking effect in 2010 and 2011. Some involved changes to legislation, while others were simply programs or actions undertaken by various ministries and/or government organizations/bureaus working independently or in collaboration. As clearly exemplified by the estimates of the size of the Latvian shadow economy by Putnins and Sauka (2014), these deliberate policy efforts aimed at reducing shadow sector activity were indeed successful. If the estimates of Putnins and Sauka (2014) are considered, Latvia experienced a large decline in the size of its shadow economy from a peak of 38% of GDP in 2010 to a low of 21% in 2012.12 Yet, following the completion of the EU-IMF assistance program in Latvia and the completion of the package of policy actions to combat the informal economy, policy efforts aimed at reducing the Latvian shadow economy subsided substantially. That is, even though the State Revenue Service as well as other involved institutions continued working on reducing the shadow economy, arguably there was a lack of coordinated and targeted policy efforts in fighting the shadow economy, which, according to Putnins and Sauka (2014), may have contributed to the end and reversal of the consecutive contractions in the size of the Latvian shadow economy. A number of leading organisations representing the interests of entrepreneurs in Latvia, including the Latvian Chamber of Commerce and Industry urged the Latvian government to continue targeted and coordinated policy efforts in fighting the shadow economy. As a result, and after a number of attempts, a new plan to reduce the shadow economy (for 2016-2020) was introduced in Latvia in mid-2016. The current plan involves a number of ‘general measures’ related to tax policies and legislation to improve the overall business environment. Yet the plan mostly focuses on rather specific issues in problematic areas, including the construction sector (aiming to introduce a minimum wage in the industry, strengthening the responsibility of the general contractor, etc.), services (targeting specific areas, including security services, the rent market, distribution of illegal audiovisual content, etc.), transport (requirements for taxis, fighting VAT schemes in car sales as well as the car rental business), retail, and wholesale (including better regulation for transactions with payment cards as well as improvement in legislation to decrease possible fraud in trading with excise goods). In most, if not all these areas, the

12 According to Schneider’s estimates, however, in 2012 the size of the shadow economy in Latvia decreased ‘only’ by 1.2% compared to 2010; see Table 2. Schneider also reports a decrease in the shadow economy by approximately 1% in the years after 2012, which is a pattern that was not observed before 2010.

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government closely collaborates with the respective industry associations, which is certainly a good sign. Other aspects covered in the plan to decrease the shadow economy in Latvia for 2016-2020 are related to communication and educational activities regarding the benefits of paying taxes, strengthening regulations related to the application of penalties, and strengthening the capacity of various institutions directly involved in fighting the shadow economy in Latvia. According to information provided by the Ministry of Finance of Latvia, a number of activities included in the plan to reduce the shadow economy have already been implemented. These include improvement of tax supervision for natural persons (the obligation for credit institutions and payment service providers to provide certain information on natural persons if account turnover exceeds EUR 15 000 in the previous year) and the decision on the introduction of reverse VAT for the supply of construction products, metal products, household appliances, and game consoles and extension of this procedure to all construction services. Also, new initiatives have been included in the plan as, according to the Ministry of Finance, the plan is an open document which has to be adjusted regularly to target key challenges in fighting the shadow economy. In addition, the Latvian government is currently implementing a major tax reform which has been developed involving contracted research by the World Bank and expertise from the Bank of Latvia, the Ministry of Finance in Latvia, and major social partners (including the Latvian Chamber of Commerce and Industry, the Employers’ Confederation of Latvia, and various industry associations). Among other things, the tax reform includes targeted policy measures for the labour tax system (aiming to decrease envelope wages) and reform of corporate income tax (aiming to reduce underreporting of income / profits). Both the tax reform and the plan to reduce the shadow economy for 2016-2020 also pay close attention to reducing VAT schemes, through both improved legislation and a detection and penalties system. At this stage, it is too early to evaluate the effectiveness of the recent plan to decrease the shadow economy in Latvia and most activities regarding the tax reform will be implemented starting from January 2018. However, it is very likely that these activities should, in one way or another, have a positive impact on reduction of the shadow economy as well as overall development of the entrepreneurship and investment climate in Latvia. According to information provided by the Latvian State Revenue Service (SRS, 2017), a key priority for a number of years has been fighting VAT schemes as well as decreasing envelope wages in Latvia. The SRS employs both preventive and restrictive methods, following the principle ‘advise first’. Additional payments into the state budget as a result of control measures (both preventive and restrictive) implemented by the SRS are displayed in Table 12. To restrict activities related to substantial violation of tax obligations, the SRS has the right to exclude the company from the register of VAT payers, to suspend the taxpayer’s economic activities, and/or to include company officials in the SRS Risk Register. Restrictive measures implemented by the SRS from 2014-2016 and in the first half of 2017 are summarized in Table 13.

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Table 12. Additional payments (calculated) into the state budget as a result of control measures implemented by the SRS

Num

ber o

f con

trol m

easu

res

Control measures

implemented for legal persons

Contribution to the state budget (calculated) as a

result of control measures

Results of preventive work – corrected amount from

taxpayers as a result of tax control measures

number

Pr

opor

tion

of th

e nu

mbe

r of l

egal

ent

ities

re

gist

ered

in th

e ta

xpay

er re

gist

er (%

)

total (M, EUR)

Pr

opor

tion

of b

udge

t rev

enue

s adm

inis

trate

d by

th

e SR

S (%

)

including VAT

Number of taxpayers

M, EUR

M, EUR

Prop

ortio

n of

the

cont

ribut

ion

to th

e st

ate

budg

et (c

alcu

late

d) a

s a re

sult

of c

ontro

l m

easu

res (

%)

2012. 13 886 7 244 4 188.1 2.9 83.8 44.6 2 657 7.4 2013. 13 902 7 409 3.6 200.3 2.9 106.3 53.0 2 577 5.7 2014. 12 707 6 390 3 191.6 2.7 107.0 55.8 2 277 3.6 2015. 12 495 6 720 3 234.1 3.1 129.0 55.1 2 163 3.4 2016. 11 632 6 321 2.8 291.5 3.6 154.4 53.0 1 609 3.5

Source: SRS (2017). Table 13. Restrictive measures for taxpayers' economic activities

Activity 2014 2015 2016 2017 1st half The number of excluded taxpayers from the VAT payer register on the initiative of the SRS

6 829

6 468

6 362

3 001

Suspended business (number of taxpayers) 10 904 12 544 10 490 2 631 - without warning 1 013 284 475 146 - after warning 9 891 12 260 10 015 2 485 Persons included in the SRS Risk Register (number)

2 440 8 042 1 088 463

The number of addresses listed in the risk address list

6 55 46 14

Source: SRS (2017).

Fighting contraband smuggling of excise goods and illegal drugs in Latvia Key performance indicators of law enforcement authorities in Latvia with regard to fighting contraband smuggling and consumption of illegal excise goods are summarized in Table 14.

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Table 14. Key performance indicators of law enforcement authorities in Latvia for combating contraband smuggling and consumption of excise goods. 2014-2016.

Indicator 2014 2015 2016 Initiated criminal proceedings (number) 859 921 848 Proposed administrative violation protocols (number)

5118 4318 4650

Removed / confiscated cigarettes (M pieces) 130.1 165.42 141.23

Removed / confiscated oil products (thsd. litres) 383.4 240.22 332.84 Removed / confiscated alcohol (thsd. litres) 107.12 137.42 154.87 Eliminated damage (M, EUR) 20.7 26.06 22.68

Source: SRS (2017). The geographical location of Latvia, i.e. its borders with countries such as Belarus and Russia, where cigarettes, alcohol, and oil products are comparably cheap, is one of the key factors determining the relatively large flow of illegal excise goods into (or through) Latvia. For this reason, the key activities of relevant institutions in fighting the phenomenon are, of course, concentrated on the national borders with those countries. As highlighted by the SRS Customs Board, one of the factors that helps in fighting the smuggling of excise goods is modern equipment. For example, customs control points located at the border crossing points with Russia and Belarus are equipped with cargo handling X-ray equipment, vehicle axle weights, platform scales, and fuel detection equipment. Since 2016, all railway customs control points in Latvia have been equipped with railway cargo control X-rays. Eighteen cynologists are working with tobacco search dogs. The SRS Customs Board also implements regular training activities for customs officials. According to a report by the European Commission’s Land Surveillance Task Force, which contains an assessment of the Latvian SRS Custom Board’s performance indicators compared to other EU countries, in both 2015 and 2016 the amount of confiscated cigarettes on the Latvian border was 15 times more than on the border of neighbouring Estonia. Confiscated illegal cigarettes in Latvia also greatly exceed the amounts in countries that do not have EU borders, such as, for example, Hungary. There is no comparative data for other excise goods such as oil products or alcohol. Illicit trafficking of illegal drugs (narcotic substances), however, usually involves the smuggling of illegal drugs and psychotropic substances from Western countries to Russia and the other way around, with ferry lines from Western Europe and the Latvian-Russian border being the key locations where such activity may arise. In 2015, Latvian law enforcement agencies initiated 614 criminal investigations regarding illicit drug trafficking. In 2015, the largest amount of substances seized on one occasion was 1,115 kilos of hashish, whose corresponding market price was EUR 8-12 million. Recent trends indicate that post-Soviet countries in the Baltic region are increasingly becoming hubs for cocaine trafficking as well. As reported by the SRS Customs Board, in April 2015, a shipment from Paraguay containing EUR 6 million worth of cocaine was seized in Riga. Information on the number of reports of criminal acts and the number of convictions for the predicate offences in illicit trafficking of narcotic drugs and psychotropic substances for the years 2013-2016 is displayed in Tables 15 and 16, respectively.

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Table 15. Illicit trafficking of narcotic drugs and psychotropic substances: number of reports of criminal acts to law enforcement agencies Source: FIU Latvia (2017).13 Table 16. Illicit trafficking of narcotic drugs and psychotropic substances: number of convictions for the predicate offences

2013 2014 2015 2016 Cases14 Persons Cases Persons Cases Persons Cases Persons NA 750 NA 731 NA 703 NA 698

Source: FIU Latvia (2017). Information on the amount of illegal narcotics removed by the SRS Customs Board is further presented in Table 17. According to the Board, the largest number of illegal narcotics detention cases, in both 2015 and 2016, involved postal mailings and were identified by airport customs control point officials in co-operation with cynologists. The amount of illegal narcotics found in postal items (marijuana, cocaine, LSD, cannabis, ecstasy, amphetamines) in the consignment was between 0.3 and 800 grams (the weight of drugs containing cannabis is generally higher than that of cocaine). Substances are usually placed in multiple polyethylene packs to make it more difficult for trained dogs to detect. The illegal narcotics are usually imported from countries such as the Netherlands, Germany, France, and other member states of the European Union. As further highlighted by the SRS, one of the main tasks of the SRS in the fight against contraband smuggling is the targeted and effective implementation of control measures that are based on qualitative risk analysis. This is done by maximizing the use of available technical equipment without increasing the administrative burden for law-abiding people and without delaying international trade processes. Table 17. Removed illegal narcotics

2014 2015 2016 Removed illegal narcotics (grams)

307 158.1 1 444 464.43 246 608.15

Source: SRS (2017).

13 Here and further in the report, FIU (2017) refers to information provided by the FIU. 14 It is not possible to detect the number of cases since one case can include conviction for several criminal offences like corruption and environmental crime.

2013 2014 2015 2016 1694 1410 1527 1666

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VII. Public Corruption in Latvia A cross-country comparison of the magnitude of public corruption, defined as “the misuse of public power for private benefits”, that also involves Latvia is provided by Transparency International through two key indicators: the Corruption Perception Index and the Global Corruption Barometer. The Corruption Perception Index (CPI) has been published by Transparency International since 1996; it ranks countries annually according to their perceived levels of corruption in the public sector, as determined by expert assessments and opinion surveys.15 The CPI provides a score for corruption in the public sector using a scale of 0 to 100, where 0 means ‘highly corrupt’ and 100 means ‘no corruption’. Table 18 summarises the scores in the years 2012-2016 in selected countries, ranking Latvia 44th out of 168 countries covered by the survey. The Global Corruption Barometer, which is considered the largest survey in the world tracking public corruption, draws on a survey of the population and asks respondents to evaluate the scale of corruption in their country. Among other things, this study concentrates on answering questions such as ‘How has the level of corruption changed over the past 12 months?’, ‘How corrupt are different powerful groups in your country perceived to be?’, and ‘How well or badly is the government seen to be doing at fighting corruption?’ According to a recent study, in 2016 the bribery rate (% of households who paid a bribe when accessing basic services) for Latvia is estimated to be15% (Figure 19). The highest rates for this indicator are in countries such as Romania (29%), Lithuania (24%), and Hungary (22%), with relatively lower rates for neighbouring Estonia (5%) as well as Poland (7%) and Italy (7%), among others. Table 18. Corruption Perception Index (CPI), 2014-2016, selected countries

2016 rank Country 2016 score

2015 score

2014 score

2013 score

2012 score

1 Denmark 90 91 92 91 90 1 New Zealand 90 88 91 91 90 3 Finland 89 90 89 89 90 8 Netherlands 83 87 83 83 84 9 Canada 82 83 81 81 84 10 Germany 81 81 79 78 79 10 United Kingdom 81 81 78 76 74 18 United States 74 76 74 73 73 22 Estonia 70 70 69 68 64 23 France 69 70 69 71 71 44 Latvia 57 55 55 53 49 47 Czech Republic 55 56 51 48 49 57 Hungary 48 51 54 54 55 60 Cuba 47 47 46 46 48 60 Italy 47 44 43 43 42 75 Bulgaria 41 41 43 41 41 79 Belarus 40 32 31 29 31 79 China 40 37 36 40 39 79 India 40 38 38 36 36 175 South Sudan 11 15 15 14 N/A 176 Somalia 10 8 8 8 8

Source: Transparency International (2017a).

15 It is not possible to detect the number of cases since one case can include conviction for several criminal offences like corruption and environmental crime.

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Figure 19. Rates of bribery: Transparency International Global Corruption Barometer % of households who paid a bribe when accessing basic services, 2016

Source: Transparency International (2017b). Also, perceptions of government action in fighting corruption in Latvia are relatively negative when compared to other EU member states (Figure 20), i.e. as many as 71% of respondents in Latvia think that the government is not doing well with regard to fighting public corruption. This indicator, however, should be interpreted with caution as it most likely correlates directly with overall attitudes towards the government in the respective country, and such attitudes tend to be negative in Latvia. Thus, such perceptions might or might not represent the actual situation regarding government efforts to fight public corruption or provide other services. Figure 20. Perceptions of government action in fighting corruption, EU, 2016 Percentage of respondents who rate their government ‘badly’ at fighting corruption in government.

Source: Transparency International (2017b). In this light, as further reported by Transparency International’s (2017a) Global Corruption Barometer, corruption is perceived to be among the three main problems in Latvia by a comparably smaller proportion of respondents in Latvia (yet still as many as 35%) compared to other EU countries (Figure 22). A somewhat better indicator for Latvia is reported with regard to ‘perception of corruption levels for members of parliament’ (Figure 21), where Latvia is ranked higher than countries such as Spain, France, and the UK.

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Figure 21. Perceptions of corruption levels for Figure 22. Corruption is one of the three biggest members of parliament problems facing the country % of respondents saying ‘most’ or ‘all’ MPs % of respondents who agree are corrupt

Source: Transparency International (2017b). Also, data from Putnins and Sauka (2017), which draws on a representative survey of company managers, provide some cross-country comparison on the level of public corruption in Latvia versus neighbouring countries Estonia and Lithuania. Figure 23 indicates that the magnitude of bribery (percentage of revenue spent by companies on “getting things done” as reported by company managers) has decreased considerably in Lithuania during 2016, to 9.8% of revenue. Bribery in Lithuania nevertheless remains more widespread (a greater percentage of revenue) than in the other Baltic countries. Bribery is also estimated to have decreased in Latvia (from 7.6% in 2015 to 6.5% in 2015), but to have increased slightly in Estonia (from 3.0% in 2015 to 3.6% in 2016).

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Figure 23. Bribery (% of revenue spent on payments ‘to get things done’) from 2009-2016

Source: Putnins and Sauka (2017). Figure 24, however, illustrates trends in the percentage of the contract value that firms typically offer as a bribe to secure a contract with the government. Similar to the general level of bribery, the level of government bribery has decreased considerably in Lithuania (8.1% in 2016 compared to 11.5% in 2015) but remains higher than in Latvia and Estonia. The estimated level of government bribery has also decreased in Latvia (3.9% in 2016 compared to 4.5% in 2015), but increased slightly in Estonia (4.8% in 2016 compared to 4.5% in 2015). Figure 24. Percentage of the contract value paid to government to secure the contract, 2010-2016

Source: Putnins and Sauka (2017). Overall, according to both the Corruption Perception Index and the Global Corruption Barometer as well as the results from Putnins and Sauka (2017), it can be concluded that even though these indicators do not say much about the scale of bribery in monetary terms (e.g. the proportion of the contract, EUR per year, etc.), public corruption seems to be a phenomenon that is relatively widespread in Latvia. This suggests the necessity of either better communication from the government on the results that are achieved in reducing public corruption and/or a better approach in fighting the phenomenon. A number of institutions in Latvia monitor and fight public corruption. Among them are:

• The Corruption Prevention and Combating Bureau – an independent public administration institution, a leading specialised anti-corruption authority in Latvia.

• The Internal Security Bureau, Ministry of the Interior of Latvia, whose responsibility is to detect, prevent, and investigate offences committed by officials and employees of subordinated institutions of the Ministry of the Interior (including the State Police, the State Fire and Rescue Service of Latvia, and the State Border Guard, except the Security Police), thereby raising the

43

trust of society in state administration and improving public safety.16 • The State Revenue Service Internal Security Department, which investigates offences, including

potential corruption on the part of employees of the State Revenue Service and closely cooperates with the Corruption Prevention and Combating Bureau. The Internal Security Department also monitors compliance with the restrictions on the appointment of designated officials in accordance with competence as stated in the law “On the prevention of conflicts of interest in the activities of public officials”. The purpose of this law is to ensure the activities of public officials in the public interest, preventing the influence of any state official, his or her relatives or business partners on the activities of a public official, promoting the transparency of public officials' responsibility to the public, as well as public confidence in the activities of public officials.

• According to the Law on Disciplinary Liability of State Civil Servants, all institutions, in the course of disciplinary proceedings, investigate alleged corrupt actions and violation of the restrictions imposed on officials through the law “On the prevention of conflicts of interest in the activities of state officials” (including disclosure of information, taking bribes, requesting a bribe, making a favourable decision, occupational constraints, etc.).

Even though all the aforementioned institutions compile performance indicators, most of this information has limited public access. Table 19 summarises the results of work with public officials, according to data provided by the Latvian State Revenue Service. Table 19. Data on work with public officials

Indicator 2014 2015 2016 2017 first half

Number of state officials registered by the SRS on the 1st day of the following month of the reporting period

56 944

57 073

57 645

57 782

Recommendations made to public officials (number) 31 186 36 198 26 736 19 080 Informational activities organised for state officials (seminars, discussions, etc.) (number)

45

41

37

27

Accepted declarations from state officials (number) 66 231 66 638 67 244 62 153 Accepted lists of state officials and their amendments (number)

7 958 8 292 8 807 4 591

Inspections conducted at state and municipal institutions (number)

390 429 492 312

Infringements detected (number) 16 10 13 11 Source: SRS (2017). As emphasized by the SRS (2017), according to the law “On the prevention of conflicts of interest in the activities of state officials”, the SRS is obliged to receive lists of state officials submitted by the heads of public authorities, amendments to the list, and declarations of state officials according to the submitted lists and also to check (i) whether the head of a public institution has submitted lists of state officials within the deadline and in accordance with the procedure specified by the Cabinet; (ii) whether the lists of state officials submitted by the head of the public institution have been drawn up correctly and completely; and (iii) whether the declaration of the public official has been submitted and completed in accordance with the prescribed procedure and submitted within the set deadline. The SRS, in accordance with the competence specified in this law, is obliged to inform the public about violations of this law established in the activities of a public official by placing information on the institution’s website. SRS State Official Declarations should be thoroughly examined in the following cases:

16 http://www.idb.gov.lv/en/about_us

44

• if the SRS tax control unit, when carrying out the compliance of the natural person’s income with

expenses, has found inaccurate information in the statement of the state official; • if an application from a legal entity or a natural person or a complaint about allegedly inaccurate

information of a public official has been received in the statement of a public official; • if the letter from the Corruption Prevention and Combating Bureau has been received with a

request to check the information provided by the public official; and • if the application and documents related to the clarification of the declaration by the statutory

deadline have been received by a public official. Considering that any public official is also a natural person – a taxpayer – the SRS, in the course of tax administration and control of natural persons, also uses the information specified in the declarations of state officials (on cash and non-cash provisions, debt, loans granted, investments in shares and securities, transactions performed, and other income – inheritance, donations, etc.). That is, this information is included in the system of risk analysis of individuals and analysed in conjunction with the information already in the possession of the SRS on the person concerned. In case there is indication of risk of potential threat (i.e. not declaring or declaring of property in large amounts), then on the basis of Articles 369 and 370 of the Criminal Procedure Law, the SRS sends the case materials for examination to the SRS Financial Police Department to check whether there are signs of a criminal offense as provided for in Article 219 of the Criminal Law. According to Article 20 of the Law “On the Procedure for the Entry into Force and Application of the Criminal Law”, liability for a major crime committed under the Criminal Law occurs if the total value of the object of the crime at the time of the commission of the offense was not less than fifty times the minimum monthly wage established in the Republic of Latvia (in 2017, the total amount of 50 minimum monthly wages is EUR 19,000). According to information provided by the SRS, the SRS also pays close attention to possible corruption and other types of violations among SRS officials, creating a system for both handling offenses and identifying potential criminal offenses. The Internal Security Division of the Financial Police Department of the SRS operates within the framework of this system; its functions and capacities are being strengthened as of 2016. It is planned that from the year 2018, the Internal Security Division of the SRS Financial Police Department will be reorganized by establishing a separate administration and, in legal terms, allocating to this unit the competence of a separate investigative body.

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VIII. Money Laundering and Other Types of Organised Financial Crime in Latvia17

Money laundering in Latvia before OECD accession in 2016

Before the 2008 financial crisis in Latvia and at least up to 2014, the main sources of illicit financial means involved in money laundering in Latvia have originated from countries of the former Soviet Union (FSU). As of today, there is substantial evidence that laundered funds have originated from systemic corruption and embezzlement in Moldova, the black market in Russia, and various cases of embezzlement in Central Asian countries. For a partial list of incidents and money laundering cases involving Latvian banks as well as their dynamics, see Galeotti (2017) and Stack (2015). Due to the complexity of the phenomenon, it is difficult to measure the exact amount of total funds laundered through / in Latvia. It is estimated that during the period of 2009-2014, at least $13 billion in illicit funds flowed from the FSU through Latvian banks towards the international system and beyond as a result of a vast and complex fraud scheme carried out in Moldova (OCCRP, 2017; The Guardian, 2017). The existence of such a systematic flow of illicit funds was investigated and documented with evidence by investigative journalists at OCCRP. The ownership of some of the involved shell companies was corroborated last year by the so-called Panama Papers leak (ICIJ, 2017). Financial police and prosecutors in Latvia currently believe that the real amount of illicit funds was around $20 billion. According to the OCCRP report, the majority of the transfers were made through one Latvian bank whose license subsequently has been revoked.18 Arguably, the flow of illicit funds from the FSU was made possible by a specific high-risk business model developed by that bank and certain others that specialized in non-resident accounts as well as an absence of proper AML/CFT risk management policies executed in compliance with regulatory requirements. This model included the provision of bank accounts and other “financial logistic services” to non-resident clients from the FSU that were incorporated as shell companies in offshore or onshore jurisdictions. In such a way, a vast number of accounts were set up in Latvian non-resident banks. Shell companies in offshore jurisdictions are routinely used by major American companies for captive insurance programs, by global transportation companies for each of their ships, by companies that trade with countries with certain currency controls, and by others for a host of other legitimate purposes. Such a business model among Latvian non-resident banks has been acknowledged by Latvian financial regulators (FKTK, 2012) and it is apparent on the banks’ websites, which typically advertise the extent of a bank’s correspondent banking network, the speed of opening an account and the speed of implementing a wire transaction as well as accompanying costs. According to official statistics compiled by the Financial and Capital Market Commission of Latvia (FKTK), in 2011 the vast majority of non-resident deposits were owned by companies registered in offshore and onshore secrecy havens (in the British Virgin Islands, United States, UK, Cyprus, Panama, New Zealand) (Figure 25). The data were later confirmed by the IMF, according to which in 2013 approximately 80-90% of Latvian banks’ non-resident depositors were from FSU states and the vast majority of these (around 90%) owned their deposits via legal entities incorporated in jurisdictions outside the FSU. Since it is well known that offshore shell companies can be used by their owners for illegitimate purposes such as tax evasion and transferring the proceeds of fraud, such companies present higher risk so that regulations require more extensive due diligence to ensure that declared beneficial owners are real and that transactions are continuously monitored.

17 This section is co-authored with Jānis Volberts, Transparency International Latvia / Delna. 18 “The Russian Laundromat Exposed,” Organized Crime and Corruption Reporting Project, March 20, 2017.

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Latvia’s Law on the Prevention of Laundering of Proceeds Derived from Criminal Activity was enacted in December 1997 and became effective in June 1998. Adopting the new AML law EU Directives 2005/60/EC and 2006/70/EC into national legislation, the new Law on the Prevention of Laundering the Proceeds from Criminal Activity (Money Laundering) and of Terrorist Financing (the AML law) was adopted by the Latvian Parliament in 2008. The new law also addressed the deficiencies identified by the IMF in the Detailed Assessment Report on Anti-Money Laundering and Combating the Financing of Terrorism published in June 2007. Two years before the first version of the current Latvian anti-money laundering law went into effect, the US Financial Intelligence Unit, stated that it is precisely “the combination of shell companies with correspondent banking” that has allowed FSU business to wire money abroad effectively and anonymously (Fincen, 2006:12). Warnings about the amount and also the sources of non-residents’ deposits were also issued back in 2012 by various international organisations such as the IMF (e.g. see http://www.reuters.com/article/latvia-banks-imf/imf-warns-latvia-about-non-resident-use-ofbank-deposits-idUSL5E8MQ7US20121126 or https://en.rebaltica.lv/2016/02/u-s-pressures-latvia-to-clean-up-its-non-resident-banks/). This suggests that some of the Latvian banks violated regulations by serving shell companies with insufficient information on beneficial owners. Figure 25. % of non-resident deposits in Latvian banks (2011)

Source: Official statistics, Latvia. Also, the US Department of State’s International Narcotics Control Strategy Report (INCSR) (2016) states that non-resident deposits pose a substantial risk in that money obtained from corruption and other crimes committed outside of Latvia can be laundered inside the country. The INCSR (2016), however, in many cases makes very strong conclusions drawing on somewhat outdated data, which, as stated in the next section, might be problematic in the case of countries such as Latvia. Furthermore, the Deloitte (2016) research report clearly demonstrates that non-resident deposits do not present an unacceptable risk when appropriate anti-money launderingcontrols are in place. The next subsection addresses this issue, presenting an overview of the key results achieved in decreasing money laundering in Latvia in recent years.

Measures implemented to fight money laundering schemes in Latvia and results achieved: the OECD accession stage and after

Even though money laundering is still an important issue for Latvia and remains a big challenge in many neighbouring countries (and beyond), recent data shows that Latvia has achieved remarkable success in fighting the phenomenon and improving its financial ecosystem. As exemplified by Putnins and Sauka (2017), the shadow economy in countries such as Latvia, which is still undergoing major changes in an institutional framework, is also a more dynamic phenomenon compared to more developed market economies, such as Sweden or Germany, where the shadow economy is rather static. For this reason alone, the situation in the shadow economy and, arguably, money laundering as well, needs to be

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constantly reassessed. Given the abovementioned influential factors on money laundering in Latvia, such as the country’s accession to the OECD and strong signals from various international organizations, it is thus not reasonable to rely on data that is even three or four years old, as major changes might have taken place during this relatively short period. The most recent available data with regard to the money laundering situation in Latvia is briefly summarized below. Both international criticism, which very negatively influenced the reputation of the Latvian banking system, and preparatory steps that Latvia had to take prior to joining the OECD in 2016, resulted in major efforts on the part of various authorities in Latvia, which, in cooperation with the respective authorities worldwide, have implemented a number of activities to fight money laundering. As a result, some remarkable progress has been made. For example, according to the Basel AML Index, which measures countries’ risk of money laundering and terrorist financing based on publicly available sources, Latvia is among the top 10 improvers, with an overall score of 4.44 in 2017, compared to 4.91 in 2016. According to the Basel AML 2017 Index (Figure 26), the risk of money laundering and terrorist financing in Latvia is among the lowest of the 146 countries included in the study – lower than Poland, France, the UK, the US, Canada, Japan, Italy, and many other countries. Figure 26. Basel AML 2017 Index Scores and Rankings From highest to lowest risk – a higher score represents higher risk

Source: Basel AML Index 2017 report.19 19 https://index.baselgovernance.org/sites/index/documents/Basel_AML_Index_Report_2017.pdf

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As stated by the Basel AML Index 2017 report (p. 4), “Positive changes for the majority of other countries that improved their ranking in the 2017 Basel AML Index were driven by the recent updates of US INCR data. For instance, Australia, France, Germany, Greece, Israel, Latvia, Luxembourg and Taiwan (China), were excluded this year from the list of jurisdictions of primary concern for AML.” Improvement in the recent Basel AML Index can be explained at least partly by significant steps to reform the system that were taken by Latvian authorities and actors as a result of OECD accession (and pre-accession activities) as well as international criticism. Arguably, the most significant reforms in Latvia have taken place on the preventive enforcement level. Since the beginning of 2016, the Financial and Capital Market Commission of Latvia (FKTK) has set up the Compliance Control Division and increased its personnel as well as the number and frequency of onsite visits to banks. A number of new and renewed AML/CTF regulations were introduced and an independent US review of Latvian banks (coordinated by the FKTK) was carried out. Notably, starting from 2015, fines for financial institutions for non-compliance were increased substantially. While up until 2014 the maximum amount of a sanction was EUR 140,000, in 2015-2016 it was increased to 10% of annual income and at present is capped at 10% of annual turnover. Personal liability for banking officers was also increased, and as of 2016 persons responsible for the compliance of the bank with AML regulations can be fined up to EUR 5 million if they violate the law. As a result of this new regulatory environment, between 2014 and 2016, seven Latvian banks were fined for a total amount of more than EUR 8 million and the license of one Latvian bank (Trasta Komercbanka, involved in the Global Laundromat scandal) was withdrawn. Over the last year, international clients’ deposits have decreased by 26.3%. The head of the FKTK has claimed that this significant decrease is a direct result of increased regulatory pressure, as risky capital has been taken away from Latvian banks. Changes with regard to the impact (and structure) of non-resident deposits in Latvia have also been reported (see a summary of recent developments in OECD, 2016a). More specifically, the non-resident banking model also came under special scrutiny with Latvia’s accession to the OECD. Both during the preparation stage and after accession to the OECD, institutions in Latvia have taken certain steps to fight money laundering, including money laundering through non-resident banks. Non-resident deposits held by Latvian banks in Latvia continue to decrease; according to information from the Financial and Capital Market Commission of Latvia, non-resident deposits held by Latvian banks in the first half of 2017 dropped by 8.7% or EUR 800.165 million in the same period in 2016 to EUR 8.351 billion at the end of June 2017. Non-resident deposits accounted for 41.1% of total bank deposits in Latvia at the end of June 2017 (42.8% at the end of June 2016). The amount of non-resident personal deposits in Latvian banks decreased by 5.9% or EUR 120.539 million in the first half of this year to EUR 1.933 billion, and non-resident corporate deposits decreased to 10.9% or EUR 724.346 million to EUR 5.923 billion. As highlighted in the 2016 report by the Office for Prevention of Laundering of Proceeds Derived from Criminal Activity (FIU Latvia), of all the known typologies of legalization, in 2016, the most significant ones are related to tax abuse (VAT schemes) and fraudulent money laundering from abroad in the Republic of Latvia (so-called ‘phishing’schemes).2021 One way to fight VAT and ‘phishing’ schemes is to improve reporting on unusual transactions, which has been one of the focus areas of FIU Latvia in recent years and, among other things, has involved a number of training activities for FIU personnel as well as activities targeted at informing (and involving in reporting) a wider audience. Some examples of the most frequently reported unusual transactions in 2016, as compiled by FIU Latvia, include: (i) transactions which exceed 40 000 EUR (3212 reported transactions); (ii) a customer selling or buying currency exceeding 8000 EUR or the equivalent (2810 reported transactions); and (iii) cash transactions exceeding 60 000 EUR (except salaries, pensions and social benefits, credits, interbank transfers) (2235 20 http://www.kd.gov.lv/index.php/en/ 21 Phishing is the attempt by criminal organizations to obtain sensitive information such as usernames, passwords, and credit card details from unaware individuals by disguising themselves as trustworthy entities in an electronic communication.

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reported transactions). The following key signs of suspicion have been reasons for reporting in 2016: (i) the origin of the funds used in the transaction was not clear (3963); (ii) the money was debited from the account right after it was credited (3065), and (iii) the case does not have an obvious legal intent (2079). Table 20 and 21. Suspicious transaction reports (STR) 2015 and 2016, Latvia

2015 Reporting entity TOTAL

STRs22 Breakdown of STRs

ML FT Other criminal offences Attempted

transactions Evasion of

Taxes Fraud

Banks 17047 14300 3 537 2207 69 Insurance sector 4 4 0 0 0 0 Securities sector 0 0 0 0 0 0 Investment firms 0 0 0 0 0 0 Currency exchange 314 269 0 0 45 0 E-money service providers 17 17 0 0 0 0 Other financial institutions 129 87 1 41 0 2 2016

Reporting entity TOTAL STRs23

Breakdown of STRs

ML FT Other criminal offences Attempted

transactions Evasion of

Taxes Fraud

Banks 17936 14663 15 1689 1569 176 Insurance sector 0 0 0 0 0 0 Securities sector 0 0 0 0 0 0 Investment firms 2 2 0 0 0 0 Currency exchange 494 476 0 0 18 7 E-money service providers 28 21 0 0 7 1 Other financial institutions 215 208 0 6 1 1

Source: FIU Latvia (2017). Table 20 and 21 provide information on the effectiveness of the suspicious transaction reporting regime in the years 2015 and 2016. The tables include information on the number of suspicious transaction reports received by the FIU from reporting entities and a breakdown of disclosures about suspicious transactions. More detailed information with regard to these indicators is provided in the recent Moneyval report on Latvia (forthcoming, 2017). According to FIU Latvia, in total 15 money laundering convictions (the number of convicted persons) took place in Latvia in 2013, while for 2014, 2015, and 2016 the numbers are 10, 17 and 9, respectively. Of these convictions, 11 (in 2013), 7 (in 2014), 9 (in 2015), and 8 (in 2016) were for fiscal predicate offences.24 Furthermore, as exemplified by Figure 27, which shows the dynamics of orders issued by FIU Latvia and the amount of frozen funds from 2011-2016, FIU Latvia, in cooperation with other authorities in Latvia and abroad, has achieved significant results in fighting money laundering in Latvia. Further information on FIU actions and provisional measures is summarised in Table 22. According to 22 The number of all transactions and relations, including additional information to the reports. 23 The number of all transactions and relations, including additional information to the reports. 24 Defined as relating to offences in connection with taxes, duties, customs, and exchange.

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information provided by the FIU, as a result of cases dealing with illicit assets, a total of EUR 136.784 million was frozen during 2014-2016, which is a considerable achievement for Latvia, especially given the relatively small size of the country. Figure 27. Orders issued by FIU Latvia and the amount of frozen funds (EUR) Frozen funds: bars; orders issued: line

Source: FIU Latvia (2017). Data summarising some of the progress made through enforcement mechanisms in fighting money laundering in Latvia is presented in Table 23. This data presents statistics for the years 2015- 2016 and has been provided by the Ministry of Justice of Latvia in the context of an inquiry concerning the Panama Papers conducted by the European Parliament’s PANA Committee. Meanwhile, data on the criminal proceeding initiated by the Financial Police Department of the State Revenue Service in Latvia as well as further key performance indicators of the department is provided in Table 24. Altogether, the information provided by FIU Latvia and the information displayed in Table 23 and Table 24 shows the high level of activity of various institutions in fighting money laundering as well as tax evasion and fraud in Latvia. These tables also clearly present the progress of such efforts in recent years, which has arguably contributed to money laundering risk reduction in Latvia as well, as exemplified by the Basel AML 2017 Index.

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Table 22. FIU actions and provisional measures

Year Number of postponement

orders issued by the FIU to suspend

transactions/block accounts

Number of cases where the FIU

order was followed by a preliminary

investigation and a freezing order was

issued

Number of cases where the FIU order was

followed by a preliminary

investigation and a seizure order was

issued25

Number of cases where a prosecution/indictment

was initiated26

Convictions and confiscation

Cases Amount (in EUR)

Cases Amount (in EUR)

2013 126 17 8 1 312 039 1 0 0

2014 403 34 18 2 106 966 9 1 2 410

2015 243 32 21 25 398 308 11 4 201 247

2016 252 36 49 50 316 682.16

14 3 182 275

Source: FIU Latvia (2017). Table 23. Progress through enforcement mechanisms in fighting money laundering in Latvia

Source: Ministry of Justice of the Republic of Latvia (2017).

25 A court decision on criminally acquired property has been made and the funds have been seized. 26 Criminal cases sent to the court.

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Table 24. Key performance indicators of the Financial Police Department of the State Revenue Service in Latvia

Indicator 2014 2015 2016 2017, 1st half

1. Criminal proceedings initiated (number), including: 217 157 186 177

1.1. Tax evasion 129 101 135 76 1.2. Money laundering 15 8 7 9 1.3. Fraud 20 8 15 10 1.4.

Unlawful actions of officials and employees of the State Revenue Service of Latvia (taking bribes; mediation in bribery; disclosure of undisclosed information)

1

4

3

0

2. Criminal proceedings sent for commencement of criminal prosecution (number), including:

137 123 82 39

2.1 Tax evasion 67 79 58 25

2.2 Money Laundering 18 13 5 3

2.3 Fraud

29 13 6 5

2.4 Unlawful actions of officials and employees of the State Revenue Service of Latvia (taking bribes; mediation in bribery; disclosure of undisclosed information; misuse of service)

1 2 1 1

3. The amount of losses incurred by the state in the criminal proceedings sent for commencement of criminal prosecution (thousand EUR)

20 131.29 25 647.32 13 333.18 2 948.63

4. Decisions taken on initiation of proceedings for the application of measures of forced enforcement to legal persons (number)

51 52 31 27

5. Persons prosecuted (number) 231 244 145 65

6. Operations of organized criminal groups discovered and ceased (number)

9 9 9 4

7. Property that has been recognized as a criminal offense and transferred to the state budget (thousand EUR)

1 077.12 435.97 952.89 3 836.35

Source: SRS (2017).

Some further challenges A number of other activities besides those mentioned in previous sections of this report have been implemented to fight money laundering and other financial crime in Latvia. For instance, since 2012, as a member of the steering committee, Latvia has been part of the German-led ISEC project “Strengthening cross-border operational cooperation in the fight against Mobile Organized Crime Groups (MOCGs) from the Baltic Sea Region, including Russian-speaking MOCGs”. So far, the project has resulted in the dismantling of more than 100 MOCGs. A total number of 575 arrest warrants were issued. The damage caused by the MOGCs targeted by this project exceeded EUR 65 million. These MOCGs mainly carried out organized property crime, drug trafficking, document counterfeiting, and money laundering. Besides the ISEC project, Latvian authorities take part in a series of multilateral and bilateral activities against organized crime and in Europol programs. Moreover, they participate in the task force made up of Baltic Sea region countries in the fight against organized crime. “The Baltic Sea Task Force on organized crime in the Baltic Sea Region” (BSTF) is a platform where all the EU member states from the region (plus Iceland, Norway, and Russia) as well as Europol, Interpol, and the EU Commission participate. BTSF supports the participating countries, their governments, and enforcement authorities in delivering a coordinated overview and initiation of activities to meet both operational and political needs in

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preventing and combating organized crime in the Baltic Sea region. Yet a number of further challenges still exist for Latvia in this regard. One of them is the constant development of methods to fight organized criminal groups involved in money laundering, fraud, and tax evasion. Namely, as recognized by the SRS Financial Police Department in Latvia, the prior experience of the department in discovering and ceasing the activities of organized criminal groups suggests that criminal groups are constantly inventing new methods to commit financial crime affecting government revenues. This includes investing more financial resources to hide traces of financial crime (by using the latest IT developments and the most advanced technical solutions). Furthermore, a large number of companies are involved in so-called business chains, including companies established inside and outside the European Union. Complex cash flow schemes, using credit institutions from Latvia, the EU, the CIS, and elsewhere are also exploited. In addition, the attraction of high quality lawyers and financial experts by organized criminal groups makes discovering such activities more challenging and also time consuming. Another challenge seems to be the very large amount and proportion of cash transactions, represented by the amount of cash payments in the retail sector as well as the amount of cash paid into ATMs in Latvia. Namely, according to estimates provided by the Association of Latvian Commercial Banks, which draws on an analysis of official statistics, the total turnover in retail in 2016 was EUR 8 billion. Of this amount, approximately 64% of payments in the retail sector were made in cash. Also, a large amount of cash is paid into ATMs in Latvia; according to official statistics in 2014, EUR 2.38 billion was paid into ATMs in Latvia, while in 2015 and 2016 the amount was EUR 2.59 and EUR 2.67 billion, respectively. Approximately half of this amount is paid into ATMs by legal persons and another half by natural persons, yet the origin of this cash is not yet known and certainly requires in-depth investigation. To conclude, in a public discussion organized by the Association of Latvian Commercial Banks (LKA), which took place in June 2017, all relevant stakeholders in the fight against money laundering discussed further challenges and declared upcoming relevant measures to enhance the transparency and efficiency of the Latvian AML system.27 The most prominent challenges identified are the need for more collaboration and coordination between stakeholders in the anti-money laundering system, the need for more accurate information on beneficial owners, and better quality STRs forwarded to the FIU by financial institutions. The discussion focused on the definition of ‘politically exposed person’ (PEP) as well as on the need to provide a comprehensive list of domestic and foreign PEPs, eventually extending it to foreign EU nationals. Among the most relevant measures on the agenda are harsh sanctions for failure to provide adequate information on beneficial owners and a collaborative platform among AML actors. Furthermore, participants of the aforementioned public discussion recognized the potential benefits of public access to beneficial owner information, and they also committed to make it possible in the near future. A full account of the outcome of the discussion and specific measures can be found on the website of the LKA.

27 http://lka.org.lv/en/about_us/

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Key Conclusions Considering the importance of linking various forms of the unobserved economy, public corruption, and money laundering, this report uses recent available data (including official statistics, surveys, indexes, etc.) to provide information on the magnitude and influential factors behind each of these phenomena in Latvia. Where data exists, a cross-country comparison is provided. Furthermore, key activities and results in fighting the unobserved economy, public corruption, and money laundering in Latvia as well as the main results achieved by various institutions are summarised in the report. The key conclusions are as follows. According to both Schneider (2016) and Putnins and Sauka (2017), which are the only studies that provide recent estimates on the size of the shadow economy in Latvia compared to other countries (in the EU and, in the case of Schneider, 2016, beyond), the size of the shadow economy in Latvia in 2016 was approximately 20% of GDP. Both studies also show that the shadow economy in other countries of Central and Eastern Europe is on approximately the same level, whereas, according to Schneider (2016), the size of the shadow economy in Latvia in 2016 is actually lower than in neighbouring countries Lithuania and Estonia (22.9% vs. 24.9% and 25.4%, respectively) as well as other CEE countries (Bulgaria: 30.2%, Croatia: 27.1%, Poland: 23%). Overall, with some exceptions (Spain: 17.9% of GDP in 2016; Italy: 20.2%, Belgium: 16.1%), the size of the shadow economy in comparably more developed European Union countries as well as the US, Canada, and Japan, according to Schneider, is in the range of 10% of GDP or below. Most importantly, both according to Schneider (2016) and Putnins and Sauka (2017), the size of the shadow economy is decreasing in Latvia, which, arguably, is the result of both the development of the economy in the country and targeted policy activities regarding the shadow economy in Latvia, especially since 2010. Policymakers, the State Revenue Service of Latvia, and other institutions involved in fighting the shadow economy, though sometimes motivated by criticism from various international organizations and authorities, seem to recognize the necessity to constantly invest in fighting the phenomena, by using restrictive methods as well as providing a ‘carrot’. A recently initiated plan to decrease the shadow economy in Latvia (for 2016-2020) as well as tax reforms should be mentioned among the main activities implemented in this regard. These activities target key challenges – drivers of the shadow economy in Latvia, including envelope wages, underreported income, and VAT schemes. Increasing trust in the government, which directly influences the tax morale of entrepreneurs and also the general population (see Mickiewicz, Rebmann and Sauka, 2017), is another challenge policymakers in Latvia and other former Soviet countries are trying to address through ongoing policy measures. Rooted in the previous regime, such attitudes are not likely to change quickly in Latvia and other CEE countries; indeed, they have persisted to some extent even 25 years after the collapse of the Soviet Union. Given Latvia’s proximity to Belarus and Russia, where alcohol, cigarettes, and oil products are considerably cheaper, it is, however, somewhat more problematic to fight the flow of illegal excise goods. Supported by the EU, Latvian authorities are investing considerable resources in strengthening their capacity (including equipment and training), which is also reflected in relatively good results (i.e. the large amount of illegal cigarettes that are confiscated on the Latvian border). As highlighted by the SRS Customs Board, movement of illegal narcotics across the Latvian border is also still a challenge, especially given the relatively large amount of detected and confiscated illegal narcotics in postal mail, among other things. Yet, even though there are no comparative statistics that compare Latvia with other EU countries, drawing on the available statistics from the SRS, with high likelihood, it can still be concluded that illegal drugs are certainly not a major problem for Latvia.

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Most importantly, ongoing activities initiated by policymakers show that the Latvian government recognises that the unobserved economy (including the shadow economy, unregistered business, and flow of illegal excise goods and illegal drugs) is a complex phenomenon which requires close cooperation between various institutions. Reducing the size of the unobserved economy (including the shadow economy, unregistered business, and flow of illegal excise goods) is defined as one of the key priorities in Latvia. Even though more time is required to judge the results of recently initiated activities to decrease the size of the unobserved economy, it is likely that the shadow economy and the amount of illegal excise goods will continue to decrease in Latvia. Drawing on 2016 data using various sources, however, it can be concluded with relatively high confidence that the size of the shadow economy is not a severe obstacle for the development of entrepreneurship (including attraction of foreign investment, operation of the financial sector, etc.) and the overall development of the economy compared to other countries in the CEE region. It can also be concluded that although positive dynamics can be observed for Latvia for the previous 3-4 years, even more effort is required to strengthen its borders to further decrease the flow of illegal alcohol, cigarettes, and oil products. As emphasized earlier in this report, available estimates of public corruption in Latvia mainly draw on perceptions of the Latvian population and, given the relatively low reported trust in the government in Latvia, should be interpreted with caution. Regardless of this, according to the Transparency International Corruption Perception Index, which in 2016 ranks Latvia in 44th position (out of 176 countries), Latvia performs better compared to countries such as the Czech Republic, Hungary, Italy, and others. Also, the estimates of the Transparency International Global Corruption Barometer show that corruption is perceived to be among the three main problems by approximately one third of respondents in Latvia. Even though this is not more than in other EU countries (except Germany, Sweden, the UK, etc.), it is far from a good result. Furthermore, the rate of bribery, according to the Transparency International Global Corruption Barometer, though not the highest in the EU, is rather high in Latvia. Overall, however, these figures might or might not reflect reality and, mostly because they draw on survey data, might be somewhat misleading with regard to placing very different countries (such as Belarus, Sweden, Greece, and Latvia) in a single ranking. This is, however, a shortcoming for most similar studies and similarly as for the size of the shadow economy, it is advisable to use such indicators to see the progress of changing attitudes, rather than to judge the actual magnitude of public corruption in a specific country relative to other countries. In this light, as demonstrated by the Transparency International Corruption Perception Index, Latvia has achieved considerable progress since 2012. Progress over the past 3-4 years in decreasing public corruption is also demonstrated by Putnins and Sauka (2017). The main conclusion, however, seems to be that there is not enough reliable data to confirm or deny that public corruption is higher in Latvia compared to other CEE countries as well as at least some other EU countries. Existing data, however, suggests that even though positive progress with regard to perceived levels of corruption can be observed in Latvia, as in many other CEE countries, investors, entrepreneurs, and individuals in Latvia should be aware that public corruption can potentially be an issue. Data provided by relevant institutions, as summarised in the report, also suggest that it is becoming harder to engage in public corruption in Latvia as the institutions involved in fighting it are constantly increasing their capacity and skills. Finally, recent indicators, such as the Basel AML 2017 Index, show that Latvia has achieved impressive progress with regards to reducing the level of money laundering. As in the case of reducing the shadow economy, this progress has been driven by external pressure from various international organisations. This also includes preparatory steps that were necessary for Latvia to implement for its accession to the OECD in 2016. As a result of the efforts of various institutions and organisations in Latvia and the support of cooperation partners from abroad, Latvia has been removed from the list of jurisdictions of primary concern (with regard to money laundering) for AML. It should be noted, however, that the money laundering situation was far from positive only some three or four years ago.

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In this light, it should be highlighted that the unobserved economy as well as the amount of public corruption and money laundering may have a more dynamic character in countries such as Latvia (that are still adjusting their institutional framework) compared to more developed market economies in the EU and other parts of the world. For this reason, not only do these phenomena need to be constantly reassessed, but it is also strongly recommended that policy initiatives be drawn up using the most recent data which, as exemplified in the cases of money laundering and the shadow economy, are different in 2016 compared to 2011 and before. The recent positive dynamics in most of the indicators for Latvia also suggest that it is likely that the shadow economy, unregistered business, and contraband smuggling as well as the amount of money laundering and public corruption will continue to decrease in Latvia. As argued previously in the report, if this is the case, such developments should positively affect the business ecosystem in the country, including the attractiveness of the business climate for investment in Latvia.

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References

1. Ahumada, H. et al. (2007) The monetary method and the size of the shadow economy: a critical assessment. Review of Income and Wealth 53 (2), 363–371.

2. Allingham, M. and A. Sandmo (1974) Income tax evasion: A theoretical analysis. Journal of Public Economics, 3(2), 323–338.

3. Alm, J., and A. Embaye (2013) Using dynamic panel methods to estimate shadow economies around the world, 1984–2006. Public Finance Review, 41(5), 510–543.

4. Ashforth, B. and F. Mael (1989) Social identity theory and the organization. The Academy of Management Review, 14(1), 20–39.

5. Becker, G. (1968) Crime and punishment: An economic approach. Journal of Political Economy, 76(2), 169–217.

6. Buehn, A. and F. Schneider (2013) Estimating the size of the shadow economy: methods, problems and open questions. (Unpublished manuscript).

7. Deloitte (2016). Risk management and financial sector reputation. Research report. Deloitte. 8. European Commission (2016) Special Eurobarometer 443. Public perception of illicit tobacco

trade. 9. European Commission (2014) Special Eurobarometer 402: Undeclared work in the European

Union. European Commission. 10. Feige, E. and I. Urban (2008) Measuring underground (unobserved, non-observed, unrecorded)

economies in transition countries: Can we trust GDP? Journal of Comparative Economics 36, 287-306.

11. Feige, E. (1997) Underground activity and institutional change: productive, protective, and predatory behavior in transition economies.’ in Tilly, C.; Nelson, J.; Walker, L. (Eds.), Transforming communist political economies, Washington DC: National Academy Press, pp. 21-34.

12. Financial Crimes Enforcement Network (Fincen) (2006) ‘The role of domestic shell companies in financial crime and money laundering’.

13. FKTK (2012) Financial and Capital Market Commission (FCMC/FKTK): Non-resident banking business in Latvia, Benefits and Risks, available at: fktk.lv/en/publications/press_releases/2012-11-26_nonresident_banking_busi/

14. The Guardian (2017) Global Laundromat, 2017. 15. Galeotti, M. (2017) ‘Crimintern: How the Kremlin Uses Russia’s Criminal Networks in Europe’,

European Council on Foreign Relations (www.ecfr.eu). 16. Heinemann, F. (2011) Economic crisis and morale. European Journal of Law and Economics,

32(1), 35–49. 17. Helberger, C. and H. Knepel (1988) How big is the shadow economy? A re-analysis of the

unobserved-variable approach of B.S. Frey and H. Weck-Hannemann. European Economic Review 32(4), 965-976.

18. Hogg, M., D. Terry and K. White (1995) A tale of two theories: A critical comparison of identity theory with social identity theory. Social Psychology Quarterly, 58(4), 255–269INCSR (2016)

19. 2016 International Narcotics Control Strategy Report. https://www.state.gov/j/inl/rls/nrcrpt/2016/

58

20. The International Consortium of Investigative Journalists (ICIJ) (2017) ‘The Panama Papers’, 2017.

21. Kaze, V., A. Strateicuks, R. Skapars (2011) Consumer Values and Consumption Patterns Driving Latvian Strong Alcoholic Beverages Market. Proceedings of International Conference ‘Current Issues in Management of Business and Society Development’, Riga, LU: 2011, 331-341.

22. Kazemier, B. and R. van Eck (1992) Survey investigations of the hidden economy. Journal of Economic Psychology 13, 569-587.

23. Konrad, K. and S. Qari (2012) The last refuge of a scoundrel? Patriotism and tax compliance. Economica, 79(315), 516–533.

24. KPMG (2017) Project SUN. A study of the illicit cigarette market in the European Union, Norway and Switzerland. 2016 results.

25. Lackó, M. (2000) Hidden economy – An unknown quantity? Comparative analysis of hidden economies in transition economies, 1989-95. Economics of Transition 8(1), 117-149.

26. La Porta, R. and A. Shleifer (2014) Informality and Development. Journal of Economic Perspectives 28(3): 109-126.

27. Marien, S. and Hooghe, M. (2011) Does political trust matter? An empirical investigat ion in the relation between political trust and support for law compliance. European Journal of Political Research, 50(2), 267–291.

28. Martınez-Vazquez, J., & Torgler, B. (2009) The evolution of tax morale in modern Spain.

29. Journal of Economic Issues, 43(1), 1–28.

30. Mickiewicz, T., A. Rebmann and A. Sauka (2017) To Pay or not to Pay? Business Owners’ Tax Morale: Testing a Neo-Institutional Framework in a Transition Environment. Journal of Business Ethics. DOI: 10.1007/s10551-017-3623-

31. NCCDPHP (2015) Preventing and Reducing Illicit Tobacco Trade in the United States. National Center for Chronic Disease Prevention and Health Promotion. https://www.cdc.gov/tobacco/stateandcommunity/pdfs/illicit-trade-report-121815-508tagged.pdf

32. Nielsen (2017) Nielsen Empty Pack Surveys, 2014-2016.

33. North, D. (1990) Institutions, institutional change and economic performance. New York: Cambridge University Press.

34. Organized Crime and Corruption Reporting Project (OCCRP) (2017) ‘The Russian Laundromat Exposed’, 2017.

35. OECD (2016) OECD reviews of risk management policies. Illicit Trade. Converging Criminal Networks. OECD.

36. OECD (2016a) Latvia: review of the financial system. OECD. https://www.oecd.org/finance/Latvia-financial-markets-2016.pdf

37. OECD (2002) Measuring the Non-Observed Economy: A Handbook. OECD, Paris, France

38. Putnins, T. and A. Sauka (2017) Shadow Economy Index of Baltic Countries 2009-2016.SSE Riga.

39. Putnins, T. and A. Sauka (2015) Measuring the Shadow Economy Using Company Managers.

59

Journal of Comparative Economics, 43 (2015), 471-490.

40. Putnins, T. and A. Sauka (2014) Shadow Economy Index of Baltic Countries 2009-2013. SSE Riga.

41. Rauch, J. (1991) Modeling the Informal Sector Formally. Journal of Development Economics 35(1): 33–47.

42. Sauka, A. (2008) Productive, Unproductive and Destructive Entrepreneurship: A Theoretical and Empirical Exploration. Frankfurt am Main: Peter Lang GmbH.

43. Sauka, A. and A. Chepurenko (2017) (Eds.), Entrepreneurship in Transition Economies. Diversity, Trends, and Perspectives. Springer.

44. Sauka, A., F. Schneider and C. Williams (2016) (Eds.), Entrepreneurship and the Shadow Economy. Edward Elgar.

45. Schneider F. (2014) TheShadow Economy and Shadow Labor Force: A Survey of Recent Developments. Mimeo.

46. Schneider, F. (2016) Size and Development of the Shadow Economy of 31 European and 5 other OECD Countries from 2003 to 2016: Different Developments. Unpublished manuscript.

47. Schneider, F. and A. Buehn (2017) Estimating a Shadow Economy: Results, Methods, Problems, and Open Questions. Open Economics 2017; 1: 1–29.

48. Schneider, F., A. Buehn and C. Montenegro (2010). New estimates for the shadow economies all over the world. International Economic Journal 24 (4), 443–461.

49. Schneider, F. and D. Enste, (2000) Shadow economies: size, causes, and consequences. Journal of Economic Literature 38 (1), 77–114.

50. Scholz, J. and M. Lubell, M. (1998) Trust and taxpaying: Testing the heuristic approach to collective action. American Journal of Political Science, 42(2), 398–471.

51. Scott, R. (2014) Institutions and organizations (4th ed.). Thousand Oaks: Sage.

52. Slemrod, J. (2007). Cheating ourselves: The economics of tax evasion. Journal of Economic Perspectives 21(1), 25-48.

53. Slemrod, J. and C. Webber (2012) Evidence of the invisible: Toward a credibility revolution in the empirical analysis of tax evasion in the informal economy. International Tax and Public Finance, 19 (1), 25-53.

54. Stack, G. (2015a) Baltic shells: on the mechanics of trade-based money-laundering in the former Soviet space, Journal of Money Laundering Control, vol.18 Issue: 1, pp. 81-98.

55. Stack, G. (2015b) Shell companies, Latvian-type correspondent banking, money laundering and illicit financial flows from Russia and the Former Soviet Union, Journal of Money Laundering Control, vol. 18 Issue: 4, pp. 496-512.

56. Strateicuks, A., V. Kaze and R. Skapars (2012) Impact of Changes in Excise Tax Rate for Strong Alcohol on Consumption and State Revenues in Latvia. World Academy of Science, Engineering and Technology International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 6: 4, 2012.

57. Torgler, B. (2003a) Tax morale in transition countries. Post-Communist Economies, 15(3), 357–

60

381.

58. Torgler, B. (2003b) Does culture matter? Tax morale in an East-West-German comparison.

59. FinanzArchiv Public Finance Analysis, 59(4), 504–528.Torgler, B., M. Schaffner and A. Macintyre (2010) Tax compliance, tax morale, and governance quality. In J. Alm, J. Martinez- Vazquez, & B. Torgler (Eds.), Developing alternative frameworks for explaining tax compliance (pp. 141–173). London: Routledge.

60. Torgler, B. and F. Schneider (2009) The impact of tax morale and institutional quality on the shadow economy. Journal of Economic Psychology, 30(2), 228–245.

61. Transparency International (2017a) Corruption Perceptions Index 2016. https://www.transparency.org/news/feature/corruption_perceptions_index_2016

62. Transparency International (2017b) People and Corruption: Europe and Central Asia. Global Corruption Barometer. https://www.transparency.org/whatwedo/publication/people_and_corruption_europe_and_central_as ia_2016

63. Van de Mortel, E. (2002) An Institutional Approach to Transition Processes, Hants, UK: Ashgate.

64. Warren, D. (2003) Constructive and destructive deviance in organizations. Academy of Management Review 28 (4), 622–631.

65. World Bank (2017) Latvia Tax Review, http://www.fm.gov.lv/files/files/2017-05 12_10_53_48_Latvia%20Tax%20Review.pdf

66. Yitzhaki, S. (1974) A note on income tax evasion: A theoretical analysis. Journal of Public Economics, 3(2), 201–202.

67. Zukauskas, V. (2015) (Ed.), Shadow Economies in the Baltic Region, Lithuanian Free Market Institute.

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6. Income that is REPORTED and fully OBSERVED income

1. Income from all economic production (theoretical GDP)

2. Income from production of LEGAL goods/services

3. Income from production of ILLEGAL goods/services

Appendix 1. Observed and unobserved (or non-observed) components of GDP

(Putnins and Sauka, 2015)

OBSERVED ECONOMY

NON-OBSERVED ECONOMY

Notes on some of components 1-7 follow. Income refers to both business income and employee income. Illegal production (3) includes production of goods/services that are illegal regardless of who produces them (e.g., narcotics, prostitution) and production of goods that themselves are legal but the production is illegal because it is carried out by an unauthorized producer (e.g., unlicensed surgeons, unlicensed production of alcohol). Goods/services that are produced legally (2) can still involve breaches of the law at the registration or reporting stage (e.g., intentional underreporting of profit to evade taxes). Most of the income generated from producing legal goods is reported by registered firms and therefore fully captured in official GDP (6). However, some proportion of income is intentionally hidden from authorities either by not registering the enterprise (5) or by misreporting wages or company earnings (7). Following other studies, we refer to the latter (7) as the ‘shadow economy’, and use the term ‘non-observed’ economy in a broader sense referring to illegal goods/services, activities of unregistered enterprises, and the shadow economy.

SHADOW ECONOMY UNREGISTERED ENTERPRISE

4. Income of REGISTERED

producers

5. Income of UNREGISTERED

producers

7. Income that is NOT REPORTED

overview of the unobserved economy, public corruption, and ... econo… · shadow economy). arnis...

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