Tag: Employment

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

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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.


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.


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.


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)).


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. 

Did Russian Migration to Russia Affect the Labor Market?

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As a result of the collapse of the Soviet Union, five million Russian and Russian-speaking people repatriated to Russia during 1990-2002. I use this natural experiment to study the effect of a large migration wave on the employment and wages of the local population. Taking into account the non-random choice of location by migrants within Russia, I find a negative effect of the inflows of immigrants on the local population’s employment but not on wages. The initial negative effects on employment are particularly large for local men, but they disappear after about ten years from the peak of the migration wave.

The effect of migration on the labor market of the host country is a long-standing question within economic literature and in public debate. In many cases, researchers try to estimate this effect using the data on large and unanticipated migration movements. The most famous study of this kind is probably Card (1990). Another case is the Russian migration to Russia resulting from the collapse of the Soviet Union. According to the 2002 Russian Census, 5.2 million of the people living in Russia in 2002 resided outside the country in 1989. That is, 3.6 percent of the 2002 population immigrated to Russia after 1989. Almost all of them (94.4 percent) immigrated from the former Soviet republics, most notably Kazakhstan, Ukraine and Uzbekistan.

The existing literature on migration flows in the former Soviet Union (fSU) since its collapse has emphasized the socio-political factors of migration. Locher (2002) finds that ethnic sorting was a major determinant of migration among the fSU countries, with the countries’ stage of transition and wealth level playing a minor role. Yerofeeva (1999) shows that ethnic repatriation was one of the main reasons behind migration from northern and eastern Kazakhstan.

In Lazareva (2015), I study two sides of the labor market effects of the immigration from fSU countries to Russia. The first side is the process of assimilation of migrants in the Russian labor market. The second side is the effect that inflows of immigrants had on the labor market position of the local population in Russia. Data used for estimation span a long period of time, which allows for tracing dynamic long-term effects of the influx of immigrants. This is the first comprehensive study of the labor market effects of one of the largest migration waves in Europe in recent history.


In order to estimate the effects of the inflow of immigrants on the employment and wages of the local population, I exploit variation in the share of immigrants across Russian regions. According to the Census in 2002, migrants were quite dispersed over Russia’s vast territory; their share in population varied from 0.42% in the Tyva region to 8.5% in the Kaliningrad region. A relatively large share of migrants is observed along the border to fSU countries as well as in the oil-rich regions of Western Siberia.

A major problem when using regional variation to estimate labor market effects is that the migrants’ choice of region may be affected by the condition of that region’s labor market. Naturally, migrants tend to choose locations with higher wages and more employment opportunities. If this is the case, the estimates of the labor market effects will be biased.

However, the immigrants’ choice of location was not completely unconstrained due to the costs of migration related to the distance and access to information. Given these constraints, there is a relative crowding of immigrants in the regions of Russia that are closer to the border with fSU countries. Hence, I use the variation in the share of migrants across regions, which depend on the geographical distance from the source countries. In other words, I obtain the estimates from the comparison of regions that are similar in all their characteristics except for the distance to the border with fSU.

Data and Results

I use panel data on households from the Russian Longitudinal Monitoring Survey for the period 1995-2009. In the 2009 survey, the respondents were asked since what year they live in the Russian Federation. I define as immigrants, people at the age of 18 and above who moved to Russia after 1989. Note that the RLMS sample, which consists of people residing in the same dwelling units in each round, is unlikely to include illegal migrants or temporary (seasonal) labor migrants. Rather these are mainly people who settled in Russia permanently at some point during the 1990s and 2000s.

In the RLMS sample, 3.6 percent of the respondents moved to Russia after 1989. This is consistent with the national-level statistical data on immigration flows. A majority of the immigrants arrived to Russia in the early and mid-1990s. Immigration peaked in 1994 when almost 1.2 million people moved to Russia. After that, immigration steeply declined; during the 2000s, the registered level of immigration was at about 200,000 people per year.

A majority of the immigrants (71.7%) in the RLMS sample are of Russian ethnicity, and there is a slightly higher share of males. Importantly, migrants are not significantly different from the locals in terms of their education levels. The statistics on marital status show that a higher share of migrants compared to locals have families and children. Apparently, family migration was a large part of this migration wave.

Using the methodology described above, I obtain an insignificant effect of the share of immigrants on the wages of the local population over the period of 1995-2009. The effect of immigrant share on the unemployment of the local population is also insignificant. In contrast, estimates for the labor force participation (LFP) show a significant negative effect of immigration on the LFP of the local population. The size of the effect is non-negligible: a one-percentage point increase in the share of immigrants in a region reduces the probability for a local person to be in the labor force by 0.6 percentage points. Thus, over the whole period of 1995-2009, Russian immigration is estimated to have had some displacement effect, but only in terms of the labor force participation of the local population.

Since the inflow of immigrants was mostly concentrated in the first half of 1990s, I estimate my model for three sub-periods: 1995-2000, 2001-2004, and 2005-2009. The results for the wages remain insignificant in all sub-periods. Immigration is shown to increase the unemployment among locals in the first half of 2000s, but this effect dissipated in the second half of 2000s. The effect of immigration on the labor force participation is negative and highly significant for the late 1990s, still negative and significant but smaller in magnitude in the early 2000s, and disappears in the late 2000s. This analysis suggests that the immigration wave had a quite significant displacement effect for the local population in terms of unemployment and labor force participation, but not in terms of wages. This effect slowly declined and had disappeared by the second half of 2000s. My results also suggest that the negative labor market effects were more significant for men than for women.


The results of this study have implications for the debate on the effect of immigration on local labor markets, in particular on wages and employment opportunities for the native population. The majority of existing studies find only minor negative effects of migration on the labor market position of locals. My results suggest that immigrants who are close substitutes to the local labor force, due to the common language and similar education, have more significant effects on the labor market outcomes of the local population.

The finding that displacement effects in Russia dissipated quite slowly may be related to the very low migration rates of the local population in Russia throughout the transition. In order to reduce negative labor market effects of large influxes of immigrants, policy measures are needed that improve labor mobility across regions. These may include moving or housing subsidies, retraining programs and policies ensuring equal access to jobs and public services for internal migrants across the regions of Russia.


  • Card, David, 1990, The Impact of the Mariel Boatlift on the Miami Labor Market, Industrial and Labor Relations Review, Vol. 43, No. 2, pp. 245-257.
  • Lazareva O. Russian Migrants to Russia: Assimilation and Local Labor Market Effects //IZA Journal of Migration. 2015. No. 4:20
  • Locher, Lilo, 2002, “Migration in the Soviet Successor States,” Applied Economics Quarterly, 48 (1), 2002, 67-84
  • Yerofeyeva, Irina, 1999, “Regional aspects of Slavic migration from Kazakhstan on the basis of examples from North Kazakhstan and East Kazakhstan provinces”. In: Vyatkin, Anatoly, Kosmarskaya, Natalya, Panarin, Sergei (Eds.), V Dvizhenii Dobrovoljnom i Vynuzhdennom [In Motion—Voluntary and Forced]. Natalis, Moscow, pp. 154–179