Tag: hidden wages

Employment and Envelope Wages During the Covid-19 Crisis in Latvia

Posted on November 13, 2023 | Policy Brief

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The Covid-19 pandemic created one of the most substantial negative exogenous shocks in decades, forcing firms to rapidly adapt. This brief examines an adjustment mechanism that played a significant role in Latvia, and potentially in other countries in Eastern and Central Europe. Specifically, we focus on the role of envelope wages as a buffer for absorbing the shock. Our analysis demonstrates that this form of tax evasion indeed acted as a cushion during the Covid-19 pandemic. Our results indicate that, in the short run, tax-evading firms experienced smaller employment losses in response to the Covid-19 shock compared to compliant firms.

Introduction

The Covid-19 pandemic generated one of the largest negative, exogenous shocks in decades. To absorb this shock, firms had to swiftly adapt. Prior literature has demonstrated that firms responded by reducing employment and investment (Lastauskas, 2022; Fernández-Cerezo et al., 2023; Buchheim et al., 2020). In this brief, we discuss another margin of adjustment – potentially important for many countries in the region. We focus on the role of envelope wages as a buffer for negative shock absorption.

Envelope wages is a widespread form tax evasion, in which, for employees that are formally registered, a portion of their salary (often at the minimum wage level) is reported to tax authorities, while the remaining ‘envelope’ portion is paid unofficially. The prevalence of this phenomenon has been extensively documented in Eastern and Central Europe (see Kukk and Staehr (2014) and Paulus (2015) for Estonia, Gorodnichenko et al. (2009) for Russia, Putniņš and Sauka (2015) for the Baltic States, Tonin (2011) and Bíró et al. (2022) for Hungary).

In addition to the evident objective of reducing tax obligations, a primary incentive for firms to employ this evasion scheme is the extra flexibility it provides. The unreported portion of wages operates outside of the legal framework, offering firms a means of adaptation in the face of production restrictions, supply chain disruptions, and overall substantial uncertainty caused by the Covid-19 pandemic. In this brief, we argue that firms utilizing envelope wages reduced their employment less than compliant firms during the pandemic in Latvia.

Identifying Firms That Pay Envelope Wages

We identify firms that paid (at least partly) their employees in cash before the pandemic using a rich combination of Latvian administrative and survey data and the methodology proposed by Gavoille and Zasova (2021).

The idea is as follows: We use a subsample of firms for which we can assume that we know whether they pay envelope wages and, using this subsample, train an algorithm that is capable of distinguishing compliant and evading firms based on their observed characteristics and reported financials.

Following Gavoille and Zasova (2021), we use firms owned by Nordic investors as a subsample of tax-compliant firms. To obtain a subsample of non-compliant firms, we combine data on administrative (i.e., reported) wages with several rounds of Labor Force Survey data in order to spot employees who are paid suspiciously little given their personal characteristics (education, experience, etc). Firms employing these employees form the subsample of evading firms. Using these samples of compliant and evading firms, we train a Random Forest algorithm to classify firms according to their type. We then use the algorithm to classify the universe of firms used in this study. Table 1 shows the classification results.

Table 1. Classification results: share of tax-evading firms and employees.

Source: Authors’ calculations.

We find that almost 40 percent of firms (employing about 20 percent of employees) underreport at least some of their workers’ wages. The cross-sectoral heterogeneity is consistent with survey evidence: the construction and transport sectors are the sectors with the highest prevalence of envelope payments. Comparing the share of tax-evading firms with the share of workers working within these firms also indicates that on average, tax-evading firms are smaller than tax-compliant ones. This is yet again in accordance with survey evidence.

Employment Response During Covid-19

Figure 1. Average firm-level change in employment during the Covid-19 pandemic.

Note: This figure shows the average change in employment between January 2020 and any subsequent month, weighted by firm size (average turnover 2017-2019).
Source: Authors’ calculations.

The Covid-19 crisis had a severe impact on Latvia. The government declared a state of emergency as early as March 13, 2020, which entailed significant restrictions on gatherings and on-site work, leading to a six-fold increase in the proportion of remote workers within a matter of months.

During the second wave, in Autumn 2021, Latvia had the highest ranking in the world in terms of new daily positive cases per capita. A substantial number of firms were directly affected by the pandemic (see Figure 1).

We study firm-level employment response at a monthly frequency in compliant and tax-evading firms, from January 2020 to December 2021. Our empirical approach is in the spirit of Machin et al. (2003) and Harasztosi and Lindner (2019), who study the effect of minimum wage shocks. In essence, this approach consists of a series of cross-section regressions, where the dependent variable is the percentage change in employment in a firm between a reference period (set to January 2020) and any subsequent month until December 2021. Our key interest is the difference in cumulative employment response between tax-compliant and evading firms, controlling for a set of (pre-pandemic) firm characteristics, such as the firm’s age, average profitability, average export share, and average labor share over the 2017-2019 period.

The Aggregate Effect

Figure 2 shows the estimated coefficients that measure the difference between employment effects in compliant and tax-evading firms, aggregate for all sectors. Period 0 denotes our reference period, i.e., January 2020, while the estimated coefficients in other periods show the cumulated difference between tax compliant and tax-evading firms in the respective period relative to January 2020 (e.g., the estimated coefficient in period 10 shows the cumulated differential employment response in October 2020 vis-à-vis January 2020).

We document a noticeable difference in the employment response between the two types of firms starting in April 2020. The positive coefficient associated with evading firms indicates that the change in employment growth was not as negative in evading firms as in compliant firms (see Figure 2). Labor tax-evading firms exhibit, on average, a less sensitive employment response than tax-compliant firms. In March 2021, the point estimates are about 0.025, implying that compared to March 2020, tax-evading firms contracted, on average, 2.5 percentage points less than compliant ones. This difference however fades over time and turns insignificant (at the 95 percent level) about halfway through 2021.

Figure 2. Evasion and total employment.

Note: This figure shows the cumulative difference between employment effects in compliant and tax-evading firms, aggregate for all sectors, by month, with respect to January 2020 (reference period).
Source: Authors’ calculations.

Differences by Sector

Figure 3 displays the estimated difference in employment response, disaggregating the sample by sector. We show the results for two sectors: trade and transportation. These two sectors exhibited the most significant differences in employment response between evading and non-evading firms.

For trade, evading firms have been able to maintain employment losses at approximately 5 percentage points less than compliant firms (see Figure 3(a)). This is consistent with the envelope wage margin mechanism. Contrary to the aggregate results, the difference in employment response does not fade over time. This suggests that this margin is not a shock absorber only in the very short run.

The decrease of the evader effect at the aggregate level is caused by negative point estimates of the evasion indicator in the transportation sector, starting in the first quarter of 2021 (see Figure 3(b)). In this sector, evading firms have on average experienced a larger employment decline in 2021 than compliant firms.

Figure 3. Employment effect – by sector.

Note: These figures show the cumulative difference between employment effects in compliant and tax-evading firms, disaggregated by sectors. Source: Authors’ calculations.

The outcome in the transportation sector is likely influenced by the taxi market. There were two major changes in 2021 that particularly affected taxi drivers receiving a portion of their remuneration through envelopes. Firstly, amendments to State Revenues Service’s (SRS) regulations made it more difficult to underreport the number of taxi trips, as each ride was now automatically recorded in the SRS system through taxi apps. Secondly, commencing in July, legal amendments mandated a minimum social security tax, which had to be paid based on at least the minimum wage. Given that many taxi drivers work part-time, and that those associated with evading firms tend to underreport their rides, this new requirement was more binding for evading firms. Additionally, there was a significant shift of taxi drivers to the food delivery sector, where demand for driver services surged during the pandemic.

Conclusion

Our results indicate that employment losses in response to the Covid-19 shock were smaller in tax-evading firms than in compliant firms in the short run. We also demonstrate that by the end of 2021, the discrepancy between the two types of firms had disappeared. This can be explained by significant heterogeneity in employment responses across sectors.

These findings contribute to our understanding of the pandemic’s impact on the size of the informal sector. Despite tax-evading firms generally having more restricted access to finance, the added flexibility provided by unreported wages may have increased their resilience to the negative shock.

Acknowledgement

This brief is based on a forthcoming working paper COVID-19 Crisis, Employment, and the Envelope Wage Margin. The authors gratefully acknowledge funding from EEA and Norway, grant project “Micro-level responses to socio-economic challenges in face of global uncertainties” (Grant No. S-BMT-21-8 (LT08-2-LMT-K-01-073)).

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. 

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.

Avoiding Corruption and Tax Evasion in Belarus’ Construction Sector

Posted on November 20, 2017 | Policy Brief, Policy Papers

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This brief summarizes our research on the problem of corruption and tax evasion in the construction sector in Belarus. We conducted a survey of construction companies, asking them to estimate the extent of different dimensions of tax evasion and corruption within the sector. The results show the most problematic directions in the sphere. Based on international experiences, we develop recommendations of how to reduce corruption and tax evasion in construction of Belarus.

Shadow economy and the construction sector

The problem of a shadow economy is real for many countries in the world. Many countries try to minimize the level of this illegal activity. But it is very difficult to liquidate tax evasion or envelope wages fully.

In Belarus there is a lot of discussion about corruption and tax evasion limitation. The country ranked 79th in the Corruption Perception Index 2016. The situation in Belarus is much better then in Russia or Kazakhstan, but worse than in Sweden, Finland and Switzerland.

There is lack of systematically updated knowledge about the situation with corruption and tax evasion in the different economic spheres of Belarus. At the same time, there are sectors, which are more prone to develop a shadow economy. One of them is the construction sector. Multilevel chains of relations between contractors and subcontractors, numerous suppliers, and complicated procedures for facility acceptance create possibilities for illegal schemes.

Construction plays an important role in national production. In 2016, the construction sector corresponded to more than 6% of Belarusian GDP. In 2014, this indicator was above 10%. The decline can be explained by a reduction of preferential lending in housing construction and a recession in the economy. Despite the reduction in the share of GDP, around 8% of the total labor force works in construction. More than 90% of the legal entities in the sphere are presented by privately owned enterprises [8].

Taking into account the importance of construction it is necessary to emphasize that reducing the size of the shadow economy could create a better business environment, reduce companies’ expenditures for resolving issues in informal ways, and increase budgetary revenues.

In this brief we present a short summary of our research “Problems of corruption and tax evasion in construction sector in Belarus”, which is forthcoming in the International Journal Entrepreneurship and Sustainability Issues. The project was made in the framework of the project “Corporate engagement in fighting corruption and tax evasion”, financed by the Nordic Council of Ministries.

Method

In order to understand the main issues and challenges in construction sector, we surveyed 50 Belarusian construction companies. We took 20 companies from Minsk and its surrounding region, and 6 organizations from each Belarusian region (Brest, Grodno, Vitebsk, Gomel, and Mogilev). The survey was based on the method used in Putnins and Sauka (2016). This method includes a questionnaire, which helps understanding the actual situation with the shadow economy in the sector. The questions of the survey were divided into three parts.

The first part included neutral questions about economic characteristics of the company, such as number of employees, profit level, the year of establishment, wage levels, and form of ownership.

The second part include more sensitive questions, but which can help us understanding the most problematic issues concerning to corruption and tax evasion. These questions concern such indicators as the level of underreported business income, the degree of underreported number of employees, the percentage of revenue that firms pay in unofficial payments to ‘get things done’, and main barriers to business development. In order to make the answers easier for participants, all the questions deal with the situation in the sector as a whole, and not the company in particular.

The third part of questions concerns the situation in public procurement, and includes the perception of main problems in the sphere.

Survey results

The first part of the survey shows that there has been a decline in many of the economic indicators during the last two years. This may be one factor stimulating the sector’s development of informal activities. Indeed the results of the second part of survey demonstrate that level of shadow economy has significant dimensions. More then 60% of the respondents agree that some firms in the sector received hidden income. More than 50% of the interviewed companies believe that some organizations in the construction sector hire part of their employees unofficially. Wages in “envelopes” is also a problem for the construction companies.

Unregistered firms are a big threat to having a well-developed construction sector. More than 60% of the interviewed companies agree with the existence of unregistered companies. Such non-official organizations create unfair competition in the sector and decrease the level of budget revenues. Many of the unregistered companies work in the sphere of home improvements and renovations.

Figure 1. Estimation of the approximate level of hidden salaries (“wages in the envelopes”) in construction industry

Notes: X-axis is the percentage of respondents that agree with the statement. Source: Results of the survey

The survey results allow us to conclude that the state budget loses part of its corporate income taxes, taxes on wages and social contributions due to the existence of hidden incomes, wages in envelopes, and unregistered companies and employees.

The last, but not the least, question in the second part of the survey was about main obstacles and barriers for operating in the construction sphere. Most of the respondents underlined three groups of barriers. One of them is the administrative challenge, including high level of taxation, inconsequent business legislation, and attitude of the government towards business in general. The second barrier includes economic problems such as lack of funds for business investments, payment behavior of clients, low product or service demand from customers, low access to credits, and inflation. The third group of problems in the construction sector is related to the shadow economy. A large part of the enterprises experiences a problem of high competition from illegal business and corruption. At the same time, a positive thing is that the majority of respondents does not consider crime and racketeering as a threat for the sector.

Figure 2. Estimation of approximate share of unregistered firms production in the total output in construction industry

Notes: The X-axis is the percentage of respondents that agree with the statement. Source: Results of the survey

In the third part of the survey, companies were asked about their participation in public procurement tenders. About 42% of all respondents did not have this experience over the past two years. One of the questions was about competition among construction companies. About 40% of all respondents underlined that they have lost at least one public tender because of unfair competition. Given that only 58% of the companies participated in tenders, we can conclude that unfair competition is a widespread problem for the majority of public procurement auction participants. Imperfect legislation is another problem for the companies. 46% of all respondents believe that the quality of legislation in the sphere is unsatisfactory. Only 12% of the companies did not see any problems in the national legislation.

At the end of the interview, companies were asked to list three main problems in the sphere of public procurement. The answers are shown in Figure 3.

Figure 3. Main problems that companies face when participating in public procurement tenders

Notes: The X-axis is the percentage of respondents that agree with the statement. Source: Results of the survey

The most common answer was corruption. Unfair competition and nepotism were also quite common problems in the public procurement sphere. Among administrative barriers, companies emphasized the complexity of documentation preparation and imperfect legislation. Important economic problems were inflation and unequal conditions for public and private enterprises.

International experiences and recommendations in fighting corruption and tax evasion in the construction sector

Corruption and tax evasion can be stimulated by different factors. One of the main preconditions of the shadow economy in the Belarusian construction sector is inconsistent and frequently changing legislation. For example, public procurements are regulated by the Presidential Decree (Ukaz) on procurement of goods (works, services) in construction. However, this regulation document expires at the end of 2018. Before 2017, such operations were regulated by several legislative acts. Developing understandable and sustainable legislation, which creates clear rules for participants of the market, is very important to increase transparency and openness of the market [11; 12; 13; 15; 18].

Another problem concerns the relations of contractors and sub-contractors. In many cases negotiations between parties are closed and non-transparent. So, it is very difficult to estimate the effectiveness of costs and proper use of funds.

Modern E-Government system adoption can support increased transparency between contractors and sub-contractors, as well as improve the quality of state services. One of the directions in this sphere is the transition towards full electronic document management. [3; 4; 6].

Another risk is related to public procurement procedure. Direct communications between public tender participants and organizers create possibilities for unfair competition. There is substantial international evidence showing that full digitalization of the process would improve the transparency of the public procurement procedure [3; 4; 21]. For example, good reference points for implementation of such digitalization can be the Georgian or Ukrainian experiences of electronic tenders. These two countries have relatively similar institutional environment and heritage as Belarus.

The problem of tax evasion is often related with payments in cash. Such transactions are less transparent and visible for authorities. According to national legislation operations between legal entities should be in cashless form. But there are exceptions to the rule [20]. In this regards the level of tax evasion would be decreased if payments in cash will be minimized.

Another concern is the efficiency of the public procurement procedures. During public procurement auctions in construction, price plays the most important role. The share of “Bid Price” criterion in total volume of all criteria can be up to 50%. The project with the lowest price has the best chance to win the tender. This is not always reasonable. Moreover, some companies hire disabled people that allow them to obtain preferential treatment in the public procurement procedure – for example, apply special correction indicators to the final price. In many cases it is better to install more expensive but high efficiency (more qualitative or ecological) equipment instead of buying cheap but low quality ones. Of course, even in EU legislation, the cost or price of projects is a very important criterion. But then it is often defined as a price-quality ratio. In this regards, the quality of the project can be estimated from the environmental, qualitative or social side [12; 19].

One more issue according to survey results is the problem of unregistered labor force in construction. It can be partly resolved by ID card implementation for all workers and employers in construction sector. In Finland, for example, all workers in construction must have such cards during workdays. Tax authorities can check the availability of the cards at any time [17].

Conclusion

Our survey of Belarusian construction companies confirmed wide exposure of the sector to tax evasion and corruption. The majority of the respondents agreed that some companies hire unregistered workers, pay wages in envelopes, or have hidden income. The most common answer to the main problems in the public procurement sphere was corruption. Based on international experience and national peculiarities, it is advisable to propose the following measures to reduce corruption and tax evasion in construction sector:

  1. Adoption of sustainable legislation.
  2. E-Government system development.
  3. Modernization of the electronic tender system to require no direct contacts between organizers and tender participants.
  4. Reduction of the possibilities of making payments in cash.
  5. Implementation of a price-quality ratio as one of the main criteria for choosing the winner of tenders.
  6. Introduction of ID cards for all employees and employers in the construction sector.

These and other measures are likely to significantly improve the business environment in the construction sector.

References

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