Tag: Tax Evasion

Minimum Wage Spike and Income Underreporting

Posted on January 16, 2023 | Policy Brief

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The labor markets of many transition countries are characterized by two features: a spike at the minimum wage level in the wage distribution and widespread use of so-called envelope wages, i.e. non-declared cash payments in addition to the official wage. In this brief, we present a body of suggestive evidence showing that tax evaders are overrepresented among minimum wage earners in Latvia. 

Introduction

Labor markets in many transition and post-transition countries are characterized by the prevalence of payroll tax evasion in the form of envelope wages, i.e. non-declared cash in addition to the official wage (see for instance Putnins and Sauka (2015) for Latvia, Paulus (2015) and Kukk and Staehr (2014) for Estonia and  Bíró et al. (2022) and Elek et al. (2012) for Hungary).

Another defining characteristic of these transition economies is a very large peak at exactly the minimum wage in the wage distribution. To explain this phenomenon, Tonin (2011) argues that the mass of individuals at the minimum wage level is composed to a large extent of workers receiving envelope wages, where employers and employees collude and agree on reporting only the minimum wage to minimize tax liabilities while remaining under the radar of the tax authorities. In such a setup, the minimum wage policy becomes an enforcement tool for the fiscal administration, as it pushes non-compliant firms to convert part of the envelope wage into an official wage so that it reaches the new minimum wage.

However, only scarce concrete evidence shows that payroll tax evaders are overrepresented among minimum wage earners. Considering the regular minimum wage hikes in the region (e.g., a 95 percent increase in Latvia in 2010-2022 and a planned increase by another 24 percent in 2023), understanding the interaction between minimum wage policy and labor tax evasion is crucial.

In this brief, we present a body of suggestive evidence highlighting the prevalence of wage underreporting at exactly the minimum wage level in Latvia.

Data and Methodology

We use Latvian administrative employer-employee data for 2011 to 2015, covering the full Latvian employed population at a monthly rate. To identify tax evasion, we rely on the comparison between small and large firms. The literature studying tax evasion provides considerable evidence showing that small firms tend to evade more taxes than large firms. Kleven et al. (2016) provide a theoretical foundation for this result, showing that collusive evasion is more difficult to sustain in firms with more employees. Empirically, this effect has been documented in many countries (see for instance Putnins and Sauka (2015), Gavoille and Zasova (2021), and Benkovskis and Fadejeva (2022) for the results on Latvia, Bíró et al. (2022) for Hungary, Paulus (2015) for Estonia, and Kumler et al. (2020) for Mexico).

In this brief, we use a very broad definition for firm size categories and divide firms into firms employing 30 or fewer employees as small and firms with more than 30 employees as large. With such a crude definition, it is inevitable that firms below and above the threshold are highly heterogeneous, implying that some firms below the threshold are tax-compliant, while some firms above the threshold are tax-evading. For our purposes though, it is sufficient to assume that the share of evading employees in small firms is larger than that in the sample of large firms.

Results

We begin by plotting the distribution of wages in the private sector. Figure 1 plots monthly wages in the range of 0–1000 Euros in 2011. The right most dashed vertical line in the figure marks the minimum wage (284.57 Euros per month in 2011) and the left most dashed line marks 50 percent of the minimum wage. There are clear spikes at the minimum wage (and at half of the minimum wage). The minimum level wage spike in small firms (top graph) is much more pronounced than in large firms (bottom graph), which is consistent with the idea that the spike is driven by income underreporting.

Figure 1. Gross wage distribution in the private sector in small (< 30 empl.) and large (> 30 empl.) firms in 2011.

Note: Micro enterprises are excluded. Vertical lines depict the minimum wage (284.57 Euro) and half of the minimum wage (142.29 Euro) in 2011. Source: Authors’ calculations.

This explanation implies that employers and employees choose to declare employment and underdeclare earnings instead of staying completely informal, which is consistent with the available evidence. Staying completely informal involves much higher risks of detection if authorities perform regular inspections of workplaces, and in many Central European countries with prevalent income underreporting, completely informal employment is not very common (OECD, 2008). In Latvia, firms have to register employees in the electronic system of the State Revenue Service before they start to work, hence the probability that an unofficially employed person is detected during a workplace inspection is very high (State Labor Inspectorate, 2010). Existing empirical evidence on Latvia also suggests that income underreporting is much more widespread than completely informal employment, which is estimated at only 2–3.5 percent (European Commission, 2014; Hazans, 2012). Hence, we interpret the spikes as indicative of tax evaders bunching at the minimum wage.

Wage Growth Among Minimum Wage Earners

Wages are expected to grow with tenure, but if minimum wage earners receive part of their income in cash, their reported wage can remain unchanged even after years of employment within a firm (as any increase would arguably go through the non-declared cash). To examine if this is the case, we exploit a period when there were no changes in the Latvian minimum wage (January 2011–December 2013). We select employees who were employed by the same firm in all months of 2011–2013, assign them to wage bins according to their wage in 2011, and in each wage bin calculate the share of workers whose wage in 2013 was the same as in 2011. We assign workers to 10-Euro bins, with the exception of minimum wage earners, whom we assign to a bin of 1 Euro.

As evident from Figure 2 minimum wage earners clearly stand out from other employees. In small firms, almost 45 percent of employees earning the minimum wage in 2011 had the same reported wage in 2013. There is also a spike at the minimum wage in large firms (28 percent), but it is less pronounced than in small firms.

Figure 2. Proportion of continuously employed workers facing no wage growth between 2011 and 2013, by wage bins, in small (< 30 empl.) and large (> 30 empl.) firms.

Note: Micro enterprises and public sector firms are excluded. Source: Authors’ calculations.

An alternative explanation for the large share of minimum wage earners who experience no wage growth could be that, for many of them, the minimum wage is binding. To rule this out, we perform the same calculations on a sample of young employees (24 or younger in 2011). Workers in the early stages of their careers tend to have higher returns to experience and tenure; thus, young workers are less likely to have no wage growth after three years of employment with the same firm. Figure 3 plots the results for young workers. In large firms, the spike at the minimum wage is more than twice as small as for the full sample of workers (12 percent vs. 28 percent), but in small firms it remains very high (33 percent).

Figure 3. Proportion of continuously employed young workers (aged 24 or less in 2011) facing no wage growth between 2011 and 2013, by wage bins, in small (< 30 empl.) and large (> 30 empl.) firms.

Note: Micro enterprises and public sector firms are excluded. Source: Authors’ calculations.

Conclusion

This brief documents highly prevalent tax evasion among minimum wage earners in Latvia. In such a context, the minimum wage is a powerful fiscal instrument as a higher minimum wage pushes non-compliant firms to disclose a larger share of their employees’ true earnings. In addition, wage underreporting among minimum wage earners can act as a shock absorber and cushion the negative employment effects of a minimum wage hike in countries where a large share of workers officially receive the minimum wage.

These upsides however come at a cost. The results presented in this brief by no means imply that all minimum wage earners are tax evaders; a notable share of employees receiving the minimum wage on paper do honestly earn only the minimum wage. In our paper (Gavoille and Zasova, 2022), we show that the flip side of the positive fiscal effect of a minimum wage hike is job losses among genuine low-wage earners and closures of tax-compliant firms that are affected by the hikes.

Acknowledgement

This brief is based on a recent article published in the Journal of Comparative Economics (Gavoille and Zasova, 2022). The authors gratefully acknowledge funding from LZP FLPP research grant No.LZP-2018/2-0067 InTEL (Institutions and Tax Enforcement in Latvia).

References

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

Detecting Labor Tax Evasion Using Administrative Data and Machine-Learning Techniques

Posted on May 17, 2022 | Policy Brief

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Labor tax evasion is a major policy issue that is especially salient in transition and post-transition countries. In this brief, we use firm-level administrative data, tax authorities’ audit data and machine learning techniques to detect firms likely to be involved in labor tax evasion in Latvia. First, we show that this approach could complement tax authorities’ regular practices, increasing audit success rate by up to 35%. Second, we estimate that about 30% of firms operating in Latvia between 2013 and 2020 are likely to underreport the wage of (some of) their employees, with a slightly negative trend.

Introduction

Tax evasion is a major policy issue that is especially salient in transition and post-transition countries. In particular, “envelop wage”, i.e., an unofficial part of the wage paid in cash, is a widespread phenomenon in Eastern Europe (European Commission, 2020). Putnins and Sauka (2021) estimate that the share of unreported wages in Latvia amounts to more than 20%. Fighting labor tax evasion is a key objective of tax authorities, which face two main challenges. The first is to make the best use of their resources. Audits are costly, so the choice of firms to audit is crucial. The second challenge is to track the evolution of the prevalence of labor tax evasion. For this purpose, most of the existing literature relies on survey data.

In our forthcoming paper (Gavoille and Zasova, 2022), we propose a novel methodology aiming at detecting tax-evading firms, using administrative firm-level data, tax authorities’ audit data and machine learning techniques.

This study provides two main contributions. First, this approach can help tax authorities to decide which firms to audit. Our results indicate that the audit success rate could increase by up to 20 percentage points, resulting in a 35% increase. Second, our methodology allows us to estimate the share of firms likely to be involved in labor tax evasion. To our knowledge, this paper is the first to provide such estimates, which are however of primary importance in guiding anti-tax evasion policy. We estimate that over the 2013-2020 period, about 30% of firms operating in Latvia are underreporting (at least some of) their workers’ wages.

Methodology

The general idea of our approach is to train an algorithm to classify firms as either compliant or tax-evading based on observed firm characteristics. Tax evasion, like any financial manipulation, results in artifacts in the balance sheet. These artifacts may be invisible to the human eye, but machine learning algorithms can detect these systematic patterns. Such methods have been applied to corporate fraud detection (see for instance Cecchini et al. 2010, Ravisankar et al. 2011, West and Bhattacharya 2016).

The machine learning approach requires a subsample of firms for which we know the “true” firm behavior (i.e., tax-evading or compliant) in order to train the algorithm. For this purpose, we propose to use a dataset on tax audits provided by the Latvian State Revenue Service (SRS), which contains information about all personal income tax (PIT) and social security contributions (SSC) audits carried out by SRS during the period 2013-2020, including the outcome of the audit. The dataset also contains a set of firm characteristics and financial indicators, covering both audited and non-audited firms operating in Latvia (e.g., turnover, assets, profit). Assuming that auditors are highly likely to detect misconduct (e.g., wage underreporting) if present, audit outcomes provide information about a firm’s tax compliance. Firms sanctioned with a penalty for, say, personal income tax fraud are involved in tax evasion, whereas audited-but-not-sanctioned firms can be assumed compliant. The algorithm learns how to disentangle the two types of firms based on the information contained in their balance sheets. Practically, we randomly split the sample of audited firms into two parts, the training and the testing subsamples. In short, we use the former to train the algorithm, and then evaluate its performance on the latter, i.e., on data that has not been used during the training stage. If showing satisfying performance on the training sample, we can then apply it to the whole universe of firms and obtain an estimate of the share of tax-evading firms.

In this study, we successively implement four algorithms that differ in the way they learn from the data: (1) Random Forest, (2) Gradient Boosting, (3) Neural Networks, and (4) Logit (for a review of machine learning methods, see Athey and Imbens, 2019). These four data mining techniques have previously been used in the literature on corporate fraud detection (see Ravisankar et al. 2011 for a survey). Each of these four algorithms has specific strengths and weaknesses, motivating the implementation and comparison of several approaches.

Results

Predictive Performance

Table 1 provides the out-of-sample performance of the four different algorithms. In other words, it shows how precise the algorithm is at classifying firms based on data that has not been included during the training stage. Accuracy is the percentage of firms correctly classified (i.e., the model prediction is consistent with the observed audit’s outcome). In our sample, about 44% of audited firms are required to pay extra personal income tax and social security contributions. This implies that a naive approach predicting all firms to be evading would be 44% accurate. Similarly, a classification predicting all firms to be tax compliant would be correct in 56% of the cases. This latter number can be used as a benchmark to evaluate the performance of the algorithms. ROC-AUC (standing for Area Under the Curve – Receiver Operating Characteristics) is another widespread classification performance measure. It provides a measure of separability, i.e., how well is the model able to distinguish between the two types. This measure is bounded between 0 and 1, the closer to 1 the better the performance. A score above 0.8 can be considered largely satisfying.

Table 1. Performance measures

Source: authors’ calculations

Random Forest is the algorithm providing the best out-of-sample performance, with more than 75% of the observations in the testing set correctly classified. Random Forest is also the best performing model according to the ROC-AUC measure, with performance slightly better than Gradient Boosting.

Our results imply that a naive benchmark prediction is outperformed by almost 20 percentage points by Random Forest and Gradient Boosting in terms of accuracy. It is important to emphasize that this improvement in performance is achieved using a relatively limited set of firm-level observable characteristics that we obtained from SRS (which is limited compared to what SRS has access to), and that mainly come from firms’ balance sheets. This highlights the potential gain of using data-driven approaches for the selection of firms to audit in addition to the regular practices used by the fiscal authorities. It also suggests a promising path for further improvements, as in addition to this set of readily available information the SRS is likely to possess more detailed limited-access firm-level data.

Share of Tax-Evading Firms Over Time and Across NACE Sectors

We can now apply these algorithms to the whole universe of firms (i.e., to classify non-audited firms). Figure 1 shows the share of firms classified as tax-evading over the years 2014 to 2019 for our two preferred algorithms – Gradient Boosting and Random Forest. Random Forest (the best performing algorithm) predicts that 30-35% of firms are involved in tax evasion, Gradient Boosting predicts a slightly higher share (around 40%). Both algorithms, especially Random Forest, suggest a slight reduction in the share of tax-evading firms since 2014.

Figure 1. Share of tax-evading firms over time

Source: authors’ calculations

The identified reduction, however, does not necessarily imply that the overall share of unreported wages has declined. In fact, existing survey-based evidence (Putnins and Sauka, 2021) indicate that the size of the shadow economy as a share of GDP remained roughly constant over the 2013-2019 period, and that there was no reduction in the contribution of the “envelope wages”. With our method, we are estimating the share of firms likely to be involved in labor tax evasion. Unlike the survey approach, our methodology does not allow the measurement of tax-evasion intensity. In other words, the share of non-tax compliant firms may have decreased, but the size of the envelope may have increased in firms involved in this scheme.

Next, we disaggregate the share of tax-evading firms by the NACE sector. Figure 2 displays the results obtained with Random Forest, our best performing algorithm.

Figure 2. Share of tax-evading firms by NACE, based on Random Forest

Source: authors’ calculations

First, the sector where tax evasion is the most prevalent is the accommodation/food industry, where the predicted share of tax-evading firms is 70-80%. Second, our results indicate that the overall decrease in the share of firms likely to evade is not uniform. It is mostly driven by the accommodation/food and manufacturing sectors. Other sectors remain nearly flat. This highlights the fact that labor tax evasion varies both in levels and in changes across sectors.

Conclusion

We show that machine learning techniques can be successfully applied to administrative firm-level data to detect firms that are likely to be involved in (labor) tax evasion. Machine learning techniques can be used to improve the selection of firms to audit in order to maximize the probability to detect tax-evading firms, in addition to the regular practices already used by SRS. Our preferred algorithms – Random Forest and Gradient Boosting – outperform the naive benchmark classification by almost 20 percentage points, which is a substantial improvement. Once implemented, the use of these tools can improve the audit effectiveness at virtually no extra cost.

Our findings also suggest a promising path for further improvements in the application of such methods. The improvement in predictive power achieved by our proposed algorithm is attained by using a limited set of variables readily available from the firms’ balance sheets. Given that SRS is likely to have access to more detailed firm-level information that cannot be provided to third parties, there is clear room for improving the performance of the algorithms by using such limited-access data.

Acknowledgement: The authors gratefully acknowledge funding from the Latvian State Research Programme “Reducing the Shadow Economy to Ensure Sustainable Development of the Latvian State”, Project “Researching the Shadow Economy in Latvia (RE:SHADE)”; project No VPP-FM-2020/1-0005.

References

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

Paradise Leaked: An Analysis of Offshore Data Leaks

Posted on January 31, 2022 | Policy Brief

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In recent years, there have been several high-profile leaks of documents related to the offshore financial industry, such as the Pandora Papers released last year. Some of the data contained in the leaked documents have now been made public. In this brief, we discuss the advantages and pitfalls of using these data for economic analysis. We show that despite some caveats, there are patterns in these data that can shed light on a secretive industry. For instance, the number of offshore entities linked to a country increases significantly when that country experiences a change in political leadership. By contrast, financial sanctions on a given country result in a reduction in the number of established offshore entities. In the immediate aftermath of the financial crisis, many countries signed bilateral treaties with tax havens in order to promote transparency. Our analysis of the leaked data shows that the overwhelming majority of offshore entities are not governed by these treaties.

“… that I may see and tell of things invisible to mortal sight.”

John Milton, Paradise Lost

Offshore Tax Haven Leaks

Zucman (2013) estimates that household wealth held in offshore tax havens is equivalent to 10% of world GDP. While there are many legitimate reasons for wealthy individuals to use offshore financial services, the secrecy surrounding offshore holdings has also enabled tax evasion and money laundering. The international community has launched several initiatives trying to increase the transparency of offshore wealth holdings. Over the past decade, several large collections of documents from offshore financial service providers have been leaked to the media: Pandora Papers (2021), Paradise Papers (2017/2018), Bahamas Leaks (2016), Panama Papers (2016), and Offshore Leaks (2013). Investigative journalists have used information from the leaks to expose many instances of secretive financial dealings linked to political leaders. Examples from FREE network countries include: the connections between a close ally of Belarussian President Alexander Lukashenko and a gold mining venture in Zimbabwe, the offshore business holdings of past and present Ukrainian presidents and their respective allies, and the wealth of Russian President Vladimir Putin’s close associates and childhood friends (see, for instance, Cosic 2021, Mylovanov and Mylovanova 2016).

The International Consortium of Investigative Journalists (ICIJ) has made public information on more than 800,000 offshore entities that are part of the offshore data leaks (see ICIJ Offshore Leaks database). The data contain information on the names of companies or people who set up offshore entities, their country of origin, the offshore jurisdiction, and the dates of incorporation and deactivation for offshore entities.

What Can We Learn from the Data?

Despite the wealth of information that this database contains, there has been relatively little academic research using the offshore leaks data. Two notable exceptions are Alstadsæter, Johannesen and Zucman (2019), and Londoño-Vélez and Ávila-Mahecha (2021), who link information from the Panama Papers to administrative records from Scandinavia and Columbia, respectively. They find that tax evasion is concentrated among the richest households. Guriev, Melnikov and Zhuravskaya (2021) use the revelation of the Panama Papers to study its effect on perceptions of corruption.

There are several challenges to using the offshore leaks data for systematic data analyses. First, there are both legitimate and illegal uses of offshore financial services, and without further information, it is not possible to distinguish between them. Second, as this information is obtained through leaks at specific offshore services providers, the data are unlikely to be representative of overall offshore financial activity. Third, there is no information on financial transactions, and we do not know the amounts of money involved in the offshore entities. Finally, more sophisticated offshore structures may make it impossible to deduce the ultimate owner of each entity and its country of origin. Especially for the second and third reasons, economists have tended to focus on balance of payments statistics and cross-border bank deposit data when estimating flows to offshore accounts. For example, Andersen, Johannesen, Lassen and Paltseva (2017) show how the oil wealth of countries with weak institutions is diverted into secret offshore accounts. Becker (2019) investigates recent trends in Russian capital flows and shows that a significant share of Russian money flows to Western European banks. See also Nyreröd and Spagnolo (2018, 2021) for discussions of the role of European banks in recent money laundering scandals.

With these caveats in mind, Figure 1 shows the correlation between the number of offshore entities in the data (on the y-axis) and the offshore wealth holdings of each country’s households (on the x-axis) as estimated by Alstadsæter, Johannesen and Zucman (2018). While the chart shows a positive correlation of 0.56 between these two measures, it also illustrates that the number of leaked entities may be a poor proxy for the stock of offshore wealth. Countries with a significant fraction of offshore wealth in European tax havens are underrepresented in the leaks (e.g., France, Germany, and Italy) while the UK, Russia, and Latvia account for a disproportionate share of leaked offshore entities.

Figure 1. Number of offshore entities and estimated offshore wealth

Source: ICIJ Offshore Leaks database, Alstadsæter, Johannesen and Zucman (2018) and authors’ calculations.

Timing of Offshore Entity Creation

While the number of overall leaked entities per country might not be a perfect measure of the amount of offshore wealth, we find that there are systematic patterns in the timing of the creation of offshore entities. In particular, more offshore entities are created when individuals face political uncertainty in their own countries and fewer offshore entities are created by individuals from countries under financial sanctions.

Elections and Change of Leadership

Figure 2 shows the average number of newly incorporated offshore entities linked to a given country (on the y-axis), depending on that country’s political situation. Panel A shows no clear pattern of offshore entities being created by companies or individuals around the time of elections. Elections are often predictable and frequently result in the reelection of the incumbent government. In contrast, Panel B shows a clear increase in the number of offshore entities linked to a country around the time when that country experiences a change in the de facto political leader. Around four months before there is a change in political leadership, the average number of entities created per country per month almost doubles. Offshore entity creation falls back to normal levels typically around half a year following the transition of power. This pattern suggests that wealth leaves countries at times of political uncertainty and is consistent with the findings of Andersen, Johannesen, Lassen and Paltseva (2017) and Earle, Shpak, Shirikov and Gehlbach (2021).

Figure 2. Offshore entity creation and national political situation

Panel a. Elections

Panel b. Change of political power

Source: ICIJ Offshore Leaks database, The Rulers, Elections, and Irregular Governance (REIGN) Dataset and authors’ calculations. A change of power is defined as a change in the de-facto political leader (e.g., due to the incumbent losing an election or the collapse of a coalition government).

International Sanctions

Figure 3 shows the impact of sanctions from the United Nations, European Union, and the United States on the average number of offshore entities linked to a given country (on the y-axis). Panel A shows that when a country is subject to financial sanctions, the number of linked offshore entities created falls to around 10 per year from an average of 25 before the introduction of sanctions. The impact of sanctions can already be seen in the year before the start of the sanctions, which could reflect measurement and reporting errors or anticipation of the sanctions. In contrast, Panel B shows that trade sanctions that are not accompanied by financial sanctions have no significant impact on offshore activities. These charts suggest that financial sanctions may have some impact on how much capital can be moved from countries under sanctions to offshore accounts.

Figure 3. Offshore entity creation and international sanctions

Panel a. Financial sanctions

Panel b. Trade (without financial) sanctions

Source: ICIJ Offshore Leaks database, Global Sanctions Data Base and authors’ calculations.

Promoting Transparency

After the Financial Crisis in 2009, G20 countries compelled offshore tax havens to sign bilateral treaties to allow for the exchange of banking information under the threat of economic sanctions. More than 300 treaties were signed by tax havens that year. The effectiveness of this policy has been debated. For instance, Johannesen and Zucman (2014) show that the treaties lead to a relocation of bank deposits from compliant to less compliant offshore tax havens.

The G20 crackdown required each tax haven to sign at least 12 bilateral treaties. Relative to a comprehensive multilateral agreement, this policy had two limitations. Firstly, it leaves room for the diversion of funds identified by Johannesen and Zucman (2014). Secondly, tax havens were able to choose freely among potential partner countries – regardless of the underlying financial flows. Figure 4 shows that only a small fraction of the entities in the offshore leak database have a country of origin that signed a treaty with the tax haven in which they were incorporated. In addition, the small share of entities that will be subject to treaties suggests that havens did not always sign treaties with the most important counterparts. While the leaked entities may not be representative of offshore finance as a whole, this picture appears inconsistent with the OECD’s claim that “the era of bank secrecy is over” (OECD 2011)

Figure 4. Entity creation by treaty status

Source: ICIJ Offshore Leaks database, treaty events from Johannesen and Zucman (2014) and authors’ calculations.

Conclusion

A series of leaks over the past decade have exposed over 40 million documents related to the secretive offshore financial industry. Information related to over 800,000 offshore financial entities has been made public by the ICIJ. While a few high-profile cases received significant media coverage and gave rise to further investigations, the vast majority of references to networks of individuals, trusts, and shell corporations are difficult to decipher. This brief argues that, collectively, these leaked documents can be informative. They can be used to analyze the reasons for moving money offshore (such as domestic political uncertainty) as well as the constraints individuals face when doing so (such as international sanctions or bilateral treaties on bank secrecy).

In an effort to further increase transparency, 102 jurisdictions committed to a new standard for the automatic exchange of certain financial account information between tax authorities from 2019. Until such reforms are successful, leaks by whistleblowers are likely to remain a valuable source of information on the offshore financial industry.

References

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

From Russia with Love?

Posted on December 20, 2021 | Policy Brief

Russia Moscow City representing money laundering

Some recently discovered money laundering schemes have funnelled large amounts of illegal money from former soviet states through European banks. This note briefly describes the evolution of the Anti-Money Laundering (AML) regime for financial institutions, the introduction of which was concurrent with the post-soviet transition and the connected illegal flows of funds. It discusses the effectiveness of the current AML regime – and its ability to detect and seize illegal funds. The brief also highlights some of its deficiencies as well as lack of compliance with its prescriptions. It proceeds to stress that after judging the current framework insufficient, the US recently introduced whistleblower rewards for AML-infringements. Europe might want to follow their lead if it really aims at limiting money laundering.

Introduction

In recent years significant deficiencies in Anti-Money Laundering (AML) compliance have been discovered in some European banks (Spagnolo and Nyreröd, 2021). A notable example is the Danske Bank case that emerged in 2018.   Some have called it the largest money-laundering scandal in history: it is estimated that about $230 billion in suspicious funds went through its Estonian branch between 2007 and 2015.

In several of these cases, the sources of a large fraction of the illicit assets were Russia or other former Soviet states (Shaffer and Cassella, 2020).

Prior to the Danske revelations, several schemes have been uncovered that were aimed at laundering illicit money from former soviet states into the western financial system.

In a classic example going back to 2006, about $230 million were stolen in fraudulent tax refunds perpetrated by officials in Russia and then laundered through Moldova, Latvia and then UK shell companies and banks (Browder, 2009). Famously, the tax lawyer Sergei Magnitsky investigated the theft and testified against the fraudsters and was later put in detention for the same tax theft he was investigating. About a year after he was arrested, Magnitsky passed away after allegedly being tortured and denied medical care. This tragic episode gave rise to the Magnitsky Act, which prohibits persons believed to be involved in the theft to enter the US and access its financial system.

Another famous (and partly related) case is the so-called Russian Laundromat (then Global Laundromat), a scheme estimated to have funneled over $70 billion of illegal money out of Russia, through Latvia, Moldova, and then the UK (Tofilat and Negruta, 2019).

Indeed, Russia is widely considered the country with the largest estimated amount of ‘dark’ money hidden abroad, both as a percentage of GDP and in absolute terms (estimated around $1 trillion by Novokmet et al., 2017).

However, the origin of money laundered in the transition region is not limited to Russia. For example, it is estimated that between 2012 and 2014, about $2.9 billion from Azerbaijan were illegally laundered through UK shell companies and then European banks.

Funds from all these schemes appear to have been transacted through Danske bank (Bruun and Hjejle 2018: 33), Swedbank (Clifford Chance 2020: 123), and other European banks.

This evidence warrants some reflection on the effectiveness of the AML framework, particularly in Europe.

The Current AML Regime

The development of the global AML framework has been largely concurrent with the transition from communism and the connected illegal flows of funds.

The Financial Action Taskforce (FATF) was formed in 1989, after an initiative by the G7. FATF’s mission is to develop policies to combat money laundering and blacklist countries that do not comply. The FATF issued its first recommendations in 1999 and continually updates them, most recently in FATF (2021).

These recommendations set out essential measures that countries should have in place to identify money laundering risks, including regulation on preventive measures for the financial and other sectors, powers and responsibilities for competent authorities, coordination of their actions, and the facilitation of international cooperation (FATF 2021: 7).

AML regulation requires financial institutions to know their customers and engage in due diligence to reduce the risk that they onboard criminals seeking to launder money. Information about suspicious transactions and activities should be forwarded to a national financial intelligence unit, usually the financial police. National Financial Services Authorities (FSAs) are usually responsible for enforcing compliance with AML rules – the “preventive” side of money laundering regulation. The “repressive” criminal law or “enforcement” side of the fight against money laundering is usually enforced by the national financial police (Reuter and Truman 2004, Svedberg Helgesson and Mörth 2018).

There are certainly valid questions to be raised regarding the effectiveness of the current AML framework. While the World Bank estimates that between 2 and 5% of global GDP is laundered annually, it is also estimated that less than 1% of the proceeds of crime laundered via the financial system are currently seized by regulators and law enforcement agencies (UNODC 2011: 7).

At the same time, the framework is quite costly to comply with. There have been six EU Directives related to AML. All require legal implementation and impose new demands on banks and other covered institutions. FATF also requires that its members frequently carry out National Risk Assessments, and countries are also subject to Membership Evaluation Reports which imposes additional costs. Compliance costs for banks are estimated in the billions of dollars (Spagnolo and Nyreröd, 2021), and a whole industry surrounding “AML Compliance” has emerged. Part of these costs, not only monetary ones, end up transferred to bank customers.

From a more rigorous policy evaluation point of view, the AML regime is also problematic. There is a remarkable lack of data for assessing the effectiveness of the framework relative to its objectives (see e.g., Halliday et al. 2014, Levi 2018, Levi et al. 2018, Pol 2018, 2020).

Bank’s Failures

A lack of compliance with this preventative framework has been widespread.  In Sweden, for example, most large banks have been fined for various degrees of AML deficiencies. Similarly, many banks in other European countries received fines from local and US regulators (in the order of billions of dollars) for failing to comply with this framework, including HSBC, Credit Suisse, Deutsche Bank (multiple times), BNP Paribas, MagNet Bank, and Barclays Bank. Since 2016, the US has issued AML-related fines on eight occasions to banks with headquarters in European countries for an aggregate amount of $1.7 billion (mean $217 million fine; data from violationtracker.org).

In the case studies we discuss in Spagnolo and Nyreröd (2021), most forms of internal controls failed to some extent. Whereas external whistleblowing was rare or non-existent, internal whistleblowers did not manage to rectify the problems either.

Simultaneously, there were often clear red flags that should have alerted board members and executives. At Danske Bank group, for example, returns on allocated capital in the non-resident portfolio at their Estonian branch, where a substantial part of the money laundering occurred, hit 402% in 2013, compared with the 6.9% average for the whole group, a clear red flag (Schwartzkopff, 2018).

Supervisor’s Failures

The extensiveness of AML non-compliance cannot only be traced to negligent banks – it also has to do with the ineffectiveness of the enforcement of AML rules by supervising authorities.

In the cases reviewed in Spagnolo and Nyreröd (2021), supervisors appeared by and large aware of at least part of the AML deficiencies. Oftentimes, banks were given warnings by regulators, yet continued to violate the same rules.

For example, both the Danish FSA and the Estonian FSA seem to have had some knowledge of the AML deficiencies at Danske Bank’s subsidiary already in 2007, with little consequences.

Coordination between regulators has also been poor. The Danish FSA argues that the primary AML oversight responsibility for the Estonian branch should be the local FSA (Finanstilsynet, 2019), while the Estonian FSA retorts that European rules are not as clear and that the Danish FSA at least has some responsibility to oversee the branches of Danske Group (Finantsinspektsioon, 2019).

On September 24, 2018, the European Banking Authority (EBA) opened an investigation to assess whether the Danish and Estonian FSAs have violated any European laws. On April 16, 2019, it voted to reject an internal draft into supervisory failings that allegedly identified several shortcomings in how Danish and Estonian authorities supervised Danske bank. (Brunsden 2019). The EBA supervisory board’s decision to close the investigation without adopting any findings drew criticism from a range of senior policymakers and spurred calls for its reform. The EBA has also been criticized for its reluctance to pass judgment on its members (Bjerregaard and Kirchmaier 2019: 38).

Conclusion

The limited regulatory enforcement and compliance with the current AML system are likely to only marginally increase the cost of money laundering for criminals. Policymakers should thus wonder whether the current system is delivering value for money. There could be different ways to improve it. Increased fines for non-compliance may for example induce covered entities to comply with the AML framework to a greater extent.

Moving forward, the inconsistent enforcement of AML rules has led experts and policymakers to suggest centralizing some supervision and enforcement of AML regulation at the EU level (Kirschenbaum and Véron 2018, 2020; Unger 2020; JPP 2019; EC 2020, p.8), and improving information sharing between supervisors.

We believe these measures may not be sufficient for facilitating compliance with AML, while imposing substantial enforcing costs.

One way to increase AML compliance at a relatively low cost could be introducing whistleblower reward programs, as done in the US early this year (Nyreröd and Spagnolo, 2021). These programs offer substantial monetary rewards, often in the order millions of dollars, for information on non-compliance, and have proven extremely effective in combating fraud against the government, tax evasion, and securities fraud. While national EU supervisors may not have sufficient resources or competence to manage such programs, centralized actors such as the European Commission appear able to do so. If we see more centralized supervision, together with increased resources and competence, a well-designed and properly implemented whistleblower reward program may become a highly effective way to fight money laundering in the EU.

References

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  • Browder, W (2009). “Hermitage Capital, the Russian State and the Case of Sergei Magnitsky.” REP Edited Transcript, Chatham House.
  • Bruun and Hjejle (2018). “Report on the Non-Resident Portfolio at Danske Bank’s Estonian Branch.” Danske Bank.
  • Brunsden, J. (2019). “EBA faces calls to reform after dropping Danske Bank probe.” Financial Times, April.
  • Clifford Chance (2020). “Report of Investigation on Swedbank AB (publ).” Swedbank.
  • EC (2020). “Communication from the Commission on an Action Plan for a Comprehensive Union Policy on Preventing Money Laundering and Terrorist Financing.” 7.5.2020 C(2020) 2800 final.
  • FATF (2021). “International Standards on Combating Money Laundering and the Financing of Terrorism & Proliferation: The FATF Recommendations.”
  • Finanstilsynet (2019). “Report on the Danish FSA’s Supervision of Danske Bank as Regards the Estonia Case.” Danish Financial Services Authority.
  • Finantsinspektsioon (2019). “Response to the Report on the Danish FSA’s Supervision of Danske Bank.” Estonian Financial Services Authority.
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  • Kirschenbaum, J., and N. Véron (2020). “A European Anti-Money Laundering Supervisor: From Vision to Legislation.” Peterson Institute for International Economics, January.
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  • Schwartzkopff, F (2018). “Danske’s 402% Return Should Have Raised Red Flag, FSA Says.” Bloomberg, May.
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Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Foreign-Owned Firms and Labor Tax Evasion in Latvia

Posted on November 29, 2021 | Policy Brief

20211128 Foreign-Owned Firms Image 01

It is well-documented that foreign-owned firms often pay higher wages than domestic firms. This phenomenon is usually explained by foreign firms being more productive. In this brief, we discuss another mechanism that drives the wage premium for employees of foreign-owned firms. By comparing income and expenditures of households led by employees of foreign-owned firms, domestic firms and public enterprises in Latvia, we show that employees of foreign-owned firms receive less undeclared cash payments than employees of domestic firms.

Introduction

A vast economic literature documents a wage premium for employees of foreign-owned firms (e.g., Heyman et al., 2007; Hijzen et al., 2013). This can result from self-selection of foreign firms in highly productive sectors (Guadalupe et al., 2012) or from a productivity increase (Harding and Javorcik, 2012). In a recent paper (Gavoille and Zasova, 2021), we provide evidence of a third driver: foreign-owned firms are more (labor) tax compliant than domestic firms.

Envelope wage, i.e., an unreported cash-in-hand complement to the official wage, is a widespread phenomenon in transition and post-transition countries (e.g., Gorodnichenko et al., 2009 in Russia, Putninš and Sauka, 2015 in the Baltic States, Tonin, 2011 in Hungary). Employees are officially registered, but the income reported to tax authorities is only a fraction of the true income, the difference being paid in cash. If domestic firms are more likely to underreport wages than foreign-owned ones, the documented wage premium for employees of foreign-owned firms is overestimated.

Methodology and data

To compare the prevalence of income underreporting in foreign and domestic firms, we use an approach similar to Pissarides and Weber (1989). This approach is based on two main assumptions. First, even though households participating in an expenditure survey can have incentives to misreport their expenditures, they accurately report their expenditure on food.

The second assumption is that if all households would fully report their income, similar households would report a similar share of spending on food. If, however, a group of households is likely to underreport income, their fraction of income spent on food will systematically be higher than that of tax-compliant households. Using the propensity to food consumption of a group of households that cannot evade payroll tax as a benchmark, we can identify groups of tax-evading households by comparing their food consumption with the reference group.

In this brief, we mainly focus on three household groups: households where the head is an (1) employee of a foreign-owned firm (reference group), (2) employee of a public sector enterprise, and (3) employee of a domestic firm. We introduce public sector employees as an additional comparison group, since they cannot collude with employers to underreport wages. Hence, our approach allows us to test whether households in the third group are more likely to receive undeclared payment than households in the first group, and additionally test if our reference group is systematically different from public sector employees.

We estimate Engel curve-type relationships for food consumption for different types of households, i.e., we estimate how households’ food consumption varies with income depending on employment of the main breadwinner (employed in a foreign-owned firm, public sector enterprise, domestic firm or self-employed), controlling for various household characteristics (number of adults, size of household, place of residence, level of education of the main breadwinner, and other).

Our data comes from three sources. First, we use the 2020 round of the Latvian Household Budget Survey (HBS), which provides information on household consumption, income and characteristics in 2019. Second, we use an administrative matched employer-employee dataset providing information on reported wages for the whole population of employees in Latvia. We match the second database with HBS using (anonymized) individual IDs contained in both datasets. Finally, we use (anonymized) firm IDs contained in the second database to merge it with a third data source, which provides detailed information on firms’ foreign-ownership status.

Results

For simplicity, in the rest of the brief we denote “household where the head is an employee of a foreign-owned firm” as simply “foreign-owned households”. A similar simplification applies to other household groups.

Comparing domestic and foreign-owned households, domestic households spend a higher share of their income on food. Figure 1 plots a non-parametric Engel curve for the two groups. The two curves exhibit fairly similar behavior, but the Engel curve for domestic households always lies above the one for foreign-owned households: for a given income, domestic households always spend a larger fraction on food than foreign-owned ones.

Our model estimations provide two main results. First, we find that the net wage premium for employees of foreign firms is 13-35%, depending on the sample and the source of data on income. Second, we show that domestic households are more likely to underreport income than foreign-owned households. On average, domestic firm households are estimated to conceal 26% more income than foreign-owned ones. At the same time, public sector households do not exhibit a significantly different food consumption pattern than foreign-owned firm households. Assuming that public sector households cannot evade, foreign-owned firm households hence do not underreport. The estimated share of concealed income is even larger (about 40%) if we restrict our sample to households where the head is aged below 50 years and is full-time employed.

Figure 1. Engel curve

Source: authors’ calculations. Note: We follow Hurst et al. (2014). We regress (administrative) wage and food consumption separately on demographic controls to condition out these factors. We recenter the residuals at the unconditional averages for each group and use these residuals to estimate the Engel curve with a cubic spline.

Conclusions

In a context of widespread labor tax evasion, the observed wage premium for employees of foreign-owned firms can be driven by payroll tax compliance. How much of the wage premium can underreporting explain? Our results for Latvia suggest a net wage premium of 13% to 35% for the group of foreign-owned households. This roughly corresponds to the magnitude of the underreporting factor, indicating that nearly all of the wage premium can be explained by labor tax evasion. Even though the precise underreporting point estimates should be cautiously interpreted, and this 1-to-1 relation is anecdotal, this nevertheless highlights the potential importance of envelope wages in explaining the wage premium of employees of foreign-owned firms when labor tax evasion is prevalent.

Acknowledgement: This brief is based on a recent article published in Economics Letters (Gavoille and Zasova, 2021). The authors gratefully acknowledge funding from LZP FLPP research grant No.LZP-2018/2-0067 InTEL (Institutions and Tax Enforcement in Latvia).

References

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