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.
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.
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.
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.
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)).
- Bíró, A., Prinz, D. and Sándor, L. (2022). The minimum wage, informal pay, and tax enforcement. Journal of Public Economics, 215, 104728.
- Buchheim, L., Dovern, J., Krolage, C. and Link, S. (2020). Firm-level Expectations and Behavior in Response to the COVID-19 Crisis. CESifo Working Paper No. 8304
- Fernández-Cerezo, A., González, B., Izquierdo Peinado, M. and Moral-Benito, E. (2023). Firm-level heterogeneity in the impact of the COVID-19 pandemic. Applied Economics 55(42), 4946-4974.
- Gavoille, N. and Zasova, A. (2021). What we pay in the shadows: Labor tax evasion, minimum wage hike and employment. SSE Riga/BICEPS Research paper No.6.
- Gorodnichenko, Y., Martinez-Vazquez, J. and Sabirianova Peter, K. (2009). Myth and reality of flat tax reform: Micro estimates of tax evasion response and welfare effects in Russia. Journal of Political Economy 117 (3), 504-554.
- Harasztosi, P. and Lindner, A. (2019). Who pays for the minimum wage? American Economic Review, 109, 2693–2727.
- Kukk, M. and Staehr, K. (2014). Income underreporting by households with business income: evidence from Estonia. Post-Communist Economies, 26(2), 257-276.
- Lastauskas, P. (2022). Lockdown, employment adjustment, and financial frictions. Small Business Economics 58(2), 919-942.
- Machin, S., Manning, A. and Rahman, L. (2003). Where the minimum wage bites hard: Introduction of minimum wages to a low wage sector. Journal of the European Economic Association, 1, 154–180.
- Paulus, A. (2015). Tax Evasion and Measurement Error: an Econometric Analysis of Survey Data Linked with Tax Records. ISER Working Paper Series 2015-10.
- Putniņš, T. and Sauka, A. (2015). Measuring the shadow economy using company managers. Journal of Comparative Economics, 43(2), 471-490.
- Tonin, M. (2011). Minimum wage and tax evasion: Theory and evidence. Journal of Public Economics, 95(11-12), 1635-1651.
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.
This policy brief is based on an empirical examination of the early-stage migration of Ukrainian war asylum seekers to Latvia in 2022, following the Russian invasion. The study highlights the urgent nature of their displacement and identifies the pivotal role of kinship in Latvia in the decision-making. Three categories of refugees emerge based on kinship ties, employment opportunities, and cultural affinity. The study also reveals the substantial influence of the pre-existing Ukrainian diaspora and underlines the significance of network effects in refugees’ location decisions. Contrary to previous studies, refugees didn’t necessarily settle for the first country available. The research underscores the strategy of seeking support from personal networks in acute displacement scenarios, which appears to be the most influential factor for the choice of location in the decision-making process.
Ukrainian Displaced People in Latvia
The Russian invasion of Ukraine in 2022 triggered a geopolitical upheaval in Europe and resulted in a mass exodus that had not been witnessed since World War II. With the war showing no signs of cessation, return for many of these displaced people appears difficult in the near future. Latvia, although not a bordering country, have become a haven for 36 000 Ukrainian refugees.
This brief seeks insight into Ukrainian displaced people’s preference for Latvia, using interviews conducted in March 2022, a month after the war began. With no common border between Ukraine and Latvia these refugees had to transit through other countries, making the question about the choice of Latvia as their ultimate destination particularly relevant.
Unlike during the migration crisis in 2015 and during the recent influx of Syrians and other groups, the Ukrainian refugees found themselves being welcomed with open arms, belying Latvia’s typically guarded stance towards immigrants. This unexpected warmth is influenced by a multifaceted kinship rooted in historical connections from the Soviet era, a pre-existing Ukrainian diaspora in Latvia, labor migration, and shared cultural elements.
These factors can also play a role in Ukrainian refugees’ choice of Latvia as their ultimate destination. The study underlying this policy brief seeks to explore these facets and unravel the reasons behind the Ukrainian refugees’ choice to seek safety in Latvia.
Two aspects are crucial in the analysis of migration decisions: the factors that influence refugees’ choice of destination and the process underlying this decision.
Traditional assumptions surrounding asylum-seeker migration, as emphasized by Böcker and Havinga (1997), suggest that when people are forced to flee, their primary focus is safety – not destination. However, more nuanced perspectives have evolved in recent studies (see Robinson and Sergott, 2002; Brekke and Aarset, 2009). They highlight the calculated and adaptable nature of refugee destination choices throughout the asylum-seeking migration journey, demonstrating that circumstances and journey stage significantly influence destination choices.
Research indicates that host country policies and economic conditions can both enhance and limit refugee flows (Czaika and de Haas, 2017; Ortega and Peri, 2013; Brekke and Aarset, 2009; Diop-Christensen and Diop, 2021; Kang, 2021; Suzuki,2020; Collyer, 2005). However, another line of research emphasizes that policy and economic factors are secondary to networks, cultural affinity, language, and perceptions in determining destination choices (Robinson and Sergott, 2002). Factors such as social networks (Koser and Pinkerton, 2002; Tucker 2018), kinship (Havinga and Böcker, 1999; Neumayer, 2005; Mallett and Hagen-Zanker, 2018), financial resources (Mallett and Hagen-Zanker, 2018), geography (Neumayer, 2005; Kang, 2021), destination country image (Benzer and Zetter, 2014), culture (Suzuki, 2020), and colonial links (Havinga and Böcker, 1999) have been established to be significant at various stages of migration. Economic and education opportunities are also found to have a marginal influence on destination decision-making compared to the possibility of resolving statelessness (Tucker, 2018).
These varying determinants of destination may also be contingent on the refugee journey stage. Policies may not dominate in acute cases of forced migration (Diop-Christensen and Diop, 2021). For individuals with time to prepare for migration, a cost-benefit analysis often informs their decisions. In contrast, those in urgent circumstances, such as during the Russian invasion of Ukraine, may have to take immediate refuge and put less emphasis on benefits and policies (Robinson and Sergott, 2002). Destination determinants differ by both origin and destination countries (Havinga and Böcker, 1999, Tucker, 2018, Gilbert and Koser, 2006). Thus, research on underexplored regions and countries is valuable for a comprehensive understanding of migration patterns.
Migration, voluntary or forced, involves intricate decision-making. As Mallett and Hagen-Zanker (2018) aptly state, the dynamic experiences ‘on the road’ shape refugees’ journey and destination choices. Robinson and Sergott (2002) and Brekke and Aarset, 2009 have pioneered models for asylum seekers’ decision-making, suggesting that factors such as networks, language, cultural affinity, and perceptions evolve across different stages of the asylum journey. Others, like Gonsalves (1992) and Shultz et al. (2020), have constructed models delineating stages of refugee passage and displacement, highlighting the changing needs and preferences of refugees.
While existing literature mainly focuses on the later stages of forced migration journeys, limited empirical evidence exists on the migration moves during acute displacement. Additionally, further understanding on migration induced by the war on Ukraine is needed. There is also incomplete coverage of asylum seeker and refugee topics in the Baltic countries, making such research particularly relevant. To address these gaps, this brief aims to provide qualitative findings on the decision-making and experiences of Ukrainian displaced people in Latvia.
Understanding the Decision
The research underlying this brief explored the reasons behind Ukrainian displaced people’s choice of Latvia as their migration destination during the early part of the invasion. The study is based on 34 semi-structured, in-depth interviews with displaced people conducted in March 2022. The dataset is part of a larger study that includes continuous interviews to understand Ukrainian displaced people’s lives, plans and needs in Latvia.
From the interviews, it was apparent that the predominant factor in respondents’ decision-making was the presence of kin or acquaintances in Latvia.
All but one participant had some connection to Latvia, whether through distant relatives, friends, or professional contacts. The one participant without such connections arrived from Russia and not from Ukraine, working on a contract. A minority of our participants considered staying in Ukraine. One example is Lidiia, who initially planned to move near Lviv, but redirected to Riga during the journey.
“She found a family that would host us, 100 km from Lviv… We agreed, but then our friends… called us on the way, we were leaving Kyiv under bombardment. Our train was delayed because of the air alarm. When we just arrived there, a shell exploded above the railway station… And on the way, friends from Riga called us and invited us: ‘Come, everyone will help here’. Therefore, everything changed while we were on the train, we decided everything“ (Lidiia).
Proximity of kin was not the primary concern for the interviewees; the mere fact that they had a relative in Latvia appeared more influential in their narratives. Indeed, the majority of participants had distant rather than close kin, though a few had close family in Latvia (grandparents, parents, common-law husband, and sister). As Olena explained, the presence of even distant relatives influenced her choice: “there are distant relatives, very distant… That’s why we came” (Olena). However, ties in Latvia were not the only determinants as many of the participants also had family connections in other parts of Europe.
The speed of decision-making was also striking – most decisions to migrate were not a matter of long-term planning but a reaction to the sudden crisis, often influenced by incoming offers of assistance. Nataliia remembered: “My mother said, ‘You have to leave because everything is so fatally bad. Take the children and leave.’ And literally overnight I packed up, bought the tickets. But first I went to Poland, to my brother” (Nataliia).
Maryana ended up choosing her destination only after leaving home. “At first, we thought to go to Poland, but it is completely crowded, and then we called to whoever we could. There are no relatives in other countries. No, there are relatives in other cities, but these are Luhansk, Donetsk, we are from Slobozhanska Ukraine, so all our relatives are from the side where very heavy fighting is going on now“ (Maryana). Such testimonies illuminate how, owing to the immediacy of the situation, the eventual destination of some displaced Ukrainians was not predetermined but evolved during their respective journeys.
From the interviews with the participants who knew someone in Latvia, one can identify three groups based on the main factor that determined their decision.
Network, First of All
For respondents who did not have family in Latvia, friends, acquaintances, and professional contacts in Latvia acted as anchors. Like family members, such acquaintances often reached out, offering assistance and lodging as soon as they heard the news of the war. The influx of supportive communication from Latvian acquaintances influenced the decision for many participants.
Olha decided to flee with her friend, who had a distant cousin residing in Latvia. Upon the onset of the conflict, the cousin reached out and urged them to come to Latvia. As Olha recalls: “As soon as she heard that there was a bombing in Kharkiv, she said, ‘Come’. My friend, with whom I came, Lesya, does not have a car, so she immediately told me… let’s run away’” (Olha).
Lidiia received an invitation from a Latvian friend she had met through her church, even as she was already in the process of fleeing Ukraine. Similarly, Andrii, who was vacationing abroad at the time of the war’s outbreak, remembered: “On the 25th our best friend wrote to us that, ‘There is housing, come here’ and we began to negotiate with the embassy to fly here” (Andrii).
Even in the absence of explicit messages, displaced individuals recalled having friends and family in Latvia and chose to make their way to Riga. Olena, like Lidiia, initially set off without a clear destination in mind. It wasn’t until she reached the border that she decided to head to Latvia: “Just at the border that you decided where to go?” (Olena).
Existing friendships and ongoing communication also influenced some people’s choice to opt for Latvia. Olha (2) was encouraged by her daughter to relocate to Riga due to her daughter’s friendships with Latvians that she had formed at a camp in Estonia: “Friends appeared, with whom she was in close contact for six months. That’s why for her there was no choice at all ‘Where?’. She immediately said: ‘To Riga’” (Olha (2)).
Opportunities and Realities
The turning point for many respondents was their arrival in Poland as, initially, Latvia was not the principal or only choice of destination. These respondents emphasized that, besides having friends and relatives in Latvia, they also contemplated where they might find better opportunities. Their narratives provide a contrasting perspective of Poland and Latvia. While traversing Poland, their general impression was that the country was already ‘overfilled’, which in turn kindled the notion that Latvia might harbor more possibilities. For this group of displaced individuals, the importance of employment prospects was paramount.
Nataliia took the decision to head for Latvia, choosing to stay with remote kin there rather than with her sibling in Poland, as she believed Poland lacked opportunities for her. In Myroslava’s case, a friend helped secure a job in Latvia: “We didn’t choose Latvia for any particular reason – better or worse, we didn’t care. We needed somewhere to stay, somewhere to work in order to live. Well, that’s why when a job turned up through acquaintances, they said that a person was needed here, we immediately gathered. Could not be found in Poland. In Poland, there was simply no work, no housing” (Myroslava).
Bohdan, too, mentioned the crowdedness and the high cost of living in Poland, hence deciding to move further north to Latvia: “We didn’t have a specific plan because we weren’t at all sure we would succeed. In general, my wife benefits from going to Poland, she works for an IT company operating in Poland. And we thought about getting there at first, but when we got to Poland, everything was already full. There were such expensive options, $1600 a month, we were shocked” (Bohdan).
Anastasiia echoed similar concerns: “We arrived in Warsaw, reunited there and tried to stay in Warsaw and look for a place, but there are a lot of people there, and there is no place to live, very… food, maybe cheaper than in Latvia, but there is no place to live… no place to work. And I would like to work somehow… not to be dependent” (Anastasiia).
These stories illuminate another stratum of decision-making, that beyond familial ties, participants also considered the opportunities available at their chosen destination. They accumulate information on their journey and recalibrate their destination accordingly.
Cultural Kinship, Language, Diaspora
Not all participants had prior personal experience with Latvia, even if they had relatives there. A lot of their understanding about the country stemmed from stories they’d heard or news they’d come across. This third group of participants decided on Latvia not only because they knew someone in the country, but also because they saw value in shared language, culture, and history.
Political and cultural connections played a significant role in their choice. Being able to communicate in Russian and Ukrainian in Latvia was a crucial factor, as it was associated with a smoother integration process and increased job opportunities. Nadiia, who traveled to Latvia via Poland and Budapest, elaborated on this: “And I was in Latvia and here there is an opportunity to communicate in Ukrainian, in Russian” (Nadiia).
The possibility of being accepted and integrated into the local community was also mentioned as a decision-driver. Oksana shared that her father, who had previously worked in Riga, advised her to go to Latvia: “you guys, probably go to Riga, well, because you will be accepted there, accommodated” (Oksana).
Nonetheless, choosing Latvia because of the possibility to communicate in Russian does not come without complications. Nataliia B., for instance, found the topic of language stirring up strong emotions and confessed that she doesn’t wish to speak Russian anymore: “I had such a psychological reaction – I didn’t speak Ukrainian for many years, and when all these events began, I read, I remember well how I woke up in the morning and began to speak Ukrainian. My thoughts have become Ukrainian” (Nataliia B.).
Moreover, having knowledge of the Ukrainian diaspora in the country also proved an important factor. “I also found out that there is a Ukrainian diaspora in Latvia of about 50 000 people, as I heard in the Latvian news. And this also encouraged me, I realised that I could find help from my compatriots” (Nadiia). This observation underlines the role of cultural kinship in the decision-making process regarding destination, and it can indeed be seen as a decisive factor. As the diaspora expands with the influx of more displaced people, this rationale for choosing Latvia may become increasingly common.
The study underlying this brief provided empirical insight into the initial phases of Ukrainian war asylum seekers’ journey to Latvia in 2022, enhancing our understanding of the factors that influenced the choice of Latvia over other destinations.
Ukrainians fleeing the early stage of the 2022 Russian invasion were compelled to make swift and difficult decisions due to the pressing crisis. Leaving behind their familiar lives, properties, and dear ones – often the very individuals facilitating their exodus for safety reasons – was a harrowing reality. The support from kin and acquaintances in Latvia was crucial in endorsing their decision to seek refuge in the country.
Three groups emerged among the Ukrainian refugees in Latvia, all connected by personal relationships to some degree. The factors influencing their migration ranged from the presence of kin and considerations of employment prospects, to shared language, culture, and history. The fact that the initial outreach usually originated from the Latvian side underscores the profound solidarity and active support provided by Latvians to their Ukrainian counterparts. This likely also played a significant role in the refugees’ decisions. The pre-existing Ukrainian diaspora in Latvia, estimated at around 50 000 before the invasion, also significantly influenced the choice of Latvia as a refuge.
Financially-related factors such as seeking benefits were largely absent from the narratives, likely due to the geographic proximity, relatively low costs, and the urgent nature of the displacement. The most significant determinant in choosing Latvia as the destination appeared to be the network effect, contrasting with Robinson and Sergott (2002) findings that acute asylum seekers often settle for the first country available.
Given the emergency nature of the displacement, no unambiguous pattern in the location decision could be established. The narrative varied considerably among respondents with decisions often being made, or altered, on the fly. However, in most cases, personal relationships played a primary role in shaping the choices among Ukrainian refugees in Latvia.
For policy-makers planning and responding to acute migration crises, the study highlights the importance of mapping and understanding multifaceted kinships, as well as culture and history. The mapping can be used to plan support and allocate resources to give displaced people an opportunity of a place where they feel welcomed and connected, with hopes of greater integration.
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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.
The Covid-19 pandemic generated a massive and sudden shift towards teleworking. Survey evidence suggests that remote work will stick in the post-pandemic period. The effects of remote work on workers’ productivity are however not well understood, some workers gaining in productivity whereas others experience the opposite. How can this large heterogeneity in workers productivity following the switch to teleworking be explained? In this brief, we discuss the importance of personality traits. We document strong links between personality, productivity, and willingness to work from home in the post-pandemic period. Our results suggest that a one-size-fits-all policy regarding remote work is unlikely to maximize firms’ productivity.
The Covid-19 pandemic triggered a large and sudden exogenous shift towards working from home (WFH). Within a few months in Spring 2020, the share of remote workers increased from 8.2 percent to 35.2 percent in the US (Bick et al., 2020), and from 5 percent to more than 30 percent in the EU (Sostero et al., 2020). Surveys of business leaders suggest that WFH will stick in the post-pandemic period (e.g., Bartik et al., 2020).
The prevalence of teleworking will ultimately depend on its impact on workers’ productivity and well-being. This impact however remains ambiguous, some studies reporting an overall positive impact, some studies a negative one. Overall, the balance of these pros and cons can vary greatly across individuals. The existing literature emphasizes the importance of gender and occupation for workers’ productivity under WFH arrangements, but a large share of this heterogeneity remains unexplained.
In a recent paper (Gavoille and Hazans, 2022) we investigate the link between personality traits and workers’ productivity when working from home. Importance of non-cognitive skills, in particular personality traits, for individual labor market outcomes is well documented in the literature (e.g., Heckman et al., 2006; Heckman and Kautz, 2012). In the context of WFH, soft skills such as conscientiousness or emotional stability, are good candidates for explaining heterogeneity in relative productivity at the individual employee level.
The Latvian context provides an ideal setup for studying the effect of teleworking on productivity. First, Latvia has a large but unexploited potential for teleworking. Dingel and Neiman (2021) estimate that 35 percent of Latvian jobs could be done remotely, which is about the EU average. However, prior to the pandemic only 3 percent of the workforce was working remotely – one of the smallest figures in the EU. Second, the Latvian government declared a state of emergency in March 2020, which introduced compulsory WFH for all private and public sector employees, except for cases where on-site work is indispensable due to the nature of the work. This led to a six-fold increase in the share of remote workers within a couple of months. This stringent policy constitutes a massive exogenous shock in the worker-level adoption of WFH, well suited for studying.
To study the link between personality traits, teleworking, and productivity, we designed an original survey, implemented in May and June 2021 in Latvia. The target population was the set of employees who experienced work from home (only or mostly) during the pandemic. To reach this population, we used various channels: national news portals, social media (Facebook and Twitter) and radio advertisement. More than 2000 respondents participated in the survey, from which we obtained more than 1700 fully completed questionnaires.
Productivity and Remote Work
In addition to the standard individual characteristics such as age and the likes, we first collect information about respondents’ perception of their own relative productivity at the office and at home. More specifically, we ask “Where are you more productive?”. The five possible answers are “In office”, “In office (slightly)”, “No difference”, “At home (slightly)” and “At home” (plus a sixth answer: “Difficult to tell”). Table 1 provides a description of the answers. Roughly one third of the respondents reports a higher productivity at home, another third a higher productivity at the office, and one third do not report much of a difference. This measure of productivity is self-assessed, as it is the case with virtually any “Covid-19-era” paper on productivity. Note however that our question is not about absolute productivity as such, but relative productivity of teleworking in comparison with productivity at the office, which is arguably easier to self-assess.
Second, we ask “Talking about the job you worked at mostly remotely, and taking into account all difficulties and advantages, what would you choose post-pandemic: working from home or in office for the same remuneration (if you had the choice)?” The five possible answers are “Only from home”, “Mostly from home”, “Indifferent”, “Mostly in office”, “Only in office” (and a sixth option: “Difficult to tell”). The main aim of this question is to study who would like to keep working remotely in the post-pandemic period, irrespective of productivity concerns. Notably, the answers are much different than from the productivity question (see Table 1), which suggests the latter does not reflect preferences.
Finally, we ask respondents about the post-pandemic monthly wage premium required by the respondent to accept i) working at the office for individuals preferring to work from home; ii) working from home for individuals preferring to work at the office. Median values of these premia for workers with different preferences are reported in Table 1 (panel C). These values appear to be economically meaningful both in absolute terms and relative to the median net monthly wage in Latvia (which was 740 euro in 2021), reinforcing the reliability of the survey.
Table 1. Outcome variables
Source: reproduced from Gavoille and Hazans (2022).
Measuring Personality Traits
The survey contains a section aiming at evaluating the personality of the respondent through the lens of the so-called Five Factor Model of Personality. The psychometrics literature offers several standardized questionnaires allowing to build a measure for each of these five factors – Openness to Experience, Agreeableness, Extraversion, Emotional Stability and Conscientiousness. We rely on the Ten-Item-Personality-Inventory (TIPI) measure (Gosling et al., 2003). This test is composed by only ten questions, making it convenient for surveys, and it has been widely used, including in economics. As simple as this approach seems, the performance of this test has been shown to be only slightly below those with more sophisticated questionnaires, and to provide measures highly correlated with the existing alternative measures of personality traits.
Overall, the results indicate that personality traits do matter for productivity at home vs. at the office. The personality trait most strongly related to all three outcome variables is Conscientiousness. Controlling for a battery of other factors, individuals with a higher level of conscientiousness are reporting a higher productivity when working from home as well as a higher willingness to keep working from home after the pandemic. This link is not only statistically significant but also economically meaningful: an individual with a level of conscientiousness in the 75th percentile is 8.4 percentage points more likely to report a higher productivity from home than a similar individual in the 25th percentile. Considering that the sample average is 31 percent, this difference is substantial.
Previous studies documented a positive correlation between Conscientiousness and key labor market outcomes such as wage, employment status and supervisor evaluation. A usual concern of employers is a possible negative selection of workers in teleworking. Observing that highly conscientious workers are more willing to work from home, where they are more productive, suggests that firms do not need to exert a very strict control on employees choosing to telework.
Openness to Experience shows a similar positive relationship with productivity. Extraversion on the other hand is only weakly negatively related to productivity. The relationship between this trait and willingness to work from home is however much stronger. These findings are intuitive: workers with a high Openness to Experience are more likely to cope easily with the important changes associated with switching to WFH. On the other hand, extravert individuals may find it more difficult to remain physically isolated from colleagues.
The literature studying the relationship between WFH and productivity suggests a conditional effect based on gender. In parallel, the literature investigating the role of personality traits on labor market outcomes also documents gender-specific patterns. As our work builds on these two strands of literature, we provide a heterogeneity analysis of the personality traits/productivity relationship conditional on gender.
When disaggregating the analysis by gender, it appears that the relationship between personality traits and productivity is stronger for women than for men. Conscientiousness and (to a smaller extent) Openness to Experience have a strong positive relationship with relative productivity of teleworking for women, while Extraversion and Agreeableness feature economically meaningful negative relationships. Noteworthy, the effects of Agreeableness and Openness to Experience do not concern the probability to be more productive at the office but only the willingness to work from home after the pandemic. For men, only Conscientiousness is significant, with a much smaller magnitude than for women.
We document that personality traits matter for changes in productivity when switching to a WFH regime. In particular, individuals with high levels of Conscientiousness are much more likely to report a better productivity from home than from the office. Additionally, Openness to Experience and Extraversion also do play a role.
Taken together, these results suggest that a one-size-fits-all policy is unlikely to maximize neither firms’ productivity nor workers’ satisfaction. It also highlights that when estimating firm-level ability in switching to remote work, characteristics of individual workers should be considered. In particular, employers practicing remote work should invest in socialization measures to compensate the negative effect of teleworking on the wellbeing of more extravert workers. Finally, several surveys (e.g., Barrero et al., 2021) document that more than a third of workers in the US would start looking for a new job allowing (some) work from home if their current employer would impose a strict in-office policy. Our results support this finding but also indicate that the opposite also holds: some workers would strongly oppose to remaining in a WFH setup after the pandemic. Personality traits are important determinants of the value attached to working from home.
This research is funded by Iceland, Liechtenstein and Norway through the EEA Grants. Project Title: The Economic Integration of the Nordic-Baltic Region through Labour, Innovation, Investments and Trade (LIFT). Project contract with the Research Council of Lithuania (LMTLT) No is S-BMT-21-7 (LT08-2-LMT-K-01-070).
- Barrero, J. M., Bloom, N. and Steven, D. (2021). Why working from home will stick, NBER Working Paper 28731.
- Bartik, A., Cullen, Z., Glaeser, E., Luca, M. and Stanton, C. (2020). What jobs are being done at home during the COVID-19 crisis? Evidence from firm-level surveys, NBER Working Paper 27422.
- Bick, A. and Blandin, A. (2021). Real-time labor market estimates during the 2020 coronavirus outbreak.
- Dingel, J. and Neiman, B. (2021). How many jobs can be done at home?, Journal of Public Economics, 189, 104235.
- Gavoille, N. and Hazans, M. (2022). Personality traits, remote work and productivity, IZA Discussion Paper 15486.
- Gosling, S., Rentfrow, P. and Swann, W. (2003). A very brief measure of the Big-Five personality domains. Journal of Research in personality, 37(6), pp. 504-528.
- Heckman, J., Stixrud, J. and Urzua, S. (2006). The effects of cognitive and noncognitive abilities on labor market outcomes and social behavior, Journal of Labor economics, 24(3), pp. 411-482.
- Heckman, J. and Tim Kautz. (2012). Hard evidence on soft skills. Labour Economics, 19(4), pp. 451-464.
- Sostero, M., Milasi, S., Hurley, J., Fernandez-Macias, H. and Bisello, M. (2020). Teleworkability and the COVID-19 crisis: a new digital divide?, JRC Working Papers Series on Labour, Education and Technology, No. 2020/05.
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.
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.
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.
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.
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
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
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
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.
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.
- Athey, Susan, and Guido Imbens. 2019. “Machine Learning Methods That Economists Should Know About.” Annual Review of Economics 11: 685–725.
- Cecchini, Mark, and Haldun Aytug, and Gary J. Koehler, and Praveen Pathak, 2010. “Detecting management fraud in public companies“. Management Science 56, 1146-1160.
- European Commission, 2020. “Undeclared Work in the European Union. Special Eurobarometer 498” (Report)
- Gavoille, Nicolas and Anna Zasova, 2022. “Estimating labor tax evasion using tax audits and machine learning”, SSE Riga/BICEPS Research papers, forthcoming.
- Putnins, Talis, and Arnis Sauka, 2021. “Shadow Economy Index for the Baltic Countries 2009–2020” (Report), SSE Riga
- Ravisankar, Pediredla, and Vadlamani Ravi, and Gundumalla Raghava Rao, and Indranil Bose, 2011. “Detection of financial statement fraud and feature selection using data mining techniques“. Decision Support Systems, 50(2), 491-500.
- West, Jarrod, and Maumita Bhattacharya, 2016. “Intelligent financial fraud detection: a comprehensive review“. Computers & security, 57, 47-66
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.
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.
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
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).
- Gavoille, Nicolas; and Anna Zasova, 2021. “Foreign ownership and labor tax evasion: Evidence from Latvia”, Economics Letters, 207, 110030.
- Gorodnichenko, Yuriy; and Jorge Martinez‐Vazquez; and Klara Sabirianova Peter, 2009. “Myth and Reality of Flat Tax Reform: Micro Estimates of Tax Evasion Response and Welfare Effects in Russia“, Journal of Political Economy, 117 (3), pages 504-554.
- Guadalupe, Maria; and Olga Kuzmina; and Catherine Thomas, 2012. “Innovation and Foreign Ownership“, American Economic Review, 102 (7), pages 3594-3627.
- Harding, Torfinn; and Beata S. Javorcik, 2012. “Foreign Direct Investment and Export Upgrading“, The Review of Economics and Statistics, 94 (4), pages 964–980.
- Heyman, Fredrik; and Fredrik Sjöholm; and Patrik Gustavsson Tingvall, 2007. “Is there really a foreign ownership wage premium? Evidence from matched employer–employee data“, Journal of International Economics, 73 (2), pages 355-376.
- Hijzen, Alexander; and Pedro S. Martins; and Thorsten Schank; and Richard Upward, 2013. “Foreign-owned firms around the world: A comparative analysis of wages and employment at the micro-level“, European Economic Review, 60, pages 170-188.
- Hurst, Erik; and Geng Li; and Benjamin Pugsley, 2014. “Are Household Surveys Like Tax Forms? Evidence from Income Underreporting of the Self-Employed“, The Review of Economics and Statistics, 96 (1), pages 19–33.
- Pissarides, Christopher A.; and Guglielmo Weber, 1989. “An expenditure-based estimate of Britain’s black economy“, Journal of Public Economics, Volume 39 (1), pages 17-32
- Putninš, Tālis J.; and Arnis Sauka, 2015. “Measuring the shadow economy using company managers“, Journal of Comparative Economics, 43 (2), pages 471–490.
- Tonin, Mirco, 2011. “Minimum wage and tax evasion: Theory and evidence“, Journal of Public Economics, 95 (11–12), pages 1635-1651.
This brief summarizes the results of an annual study on the development of the investment climate in Latvia from the viewpoint of key foreign investors – companies that have made the decision to invest in the country and have been operating here for a considerable time period. The study was initiated in 2015 and aims to assess investors’ evaluation of the government policy initiatives to improve the investment climate in Latvia. It also aims to provide an in-depth exploration of the main challenges for and concerns of the foreign investors, both by identifying problems and offering solutions. The study draws on a survey/ mini case studies of the key foreign investors in Latvia. Our findings suggest that in recent years, some progress has been achieved on a number of dimensions that are crucial for the competitiveness of the investment climate in Latvia, such as the political efforts by the government of Latvia to improve the investment climate, the overall attitude to foreign investors, and labour efficiency. At the same time, foreign investors see little, if any, improvement with regards to other key areas, such as the availability of labour, the quality of education, the court system, corruption and the shadow economy.
The study on the development of the investment climate in Latvia from the viewpoint of key foreign investors in Latvia was first launched in 2015 by the Foreign Investors’ Council in Latvia (FICIL) in cooperation with the Stockholm School of Economics in Riga (SSE Riga). This study aims to foster evidence-based policy decisions and promote a favourable investment climate in Latvia by:
- (i) Assessing how foreign investors evaluate the government’s efforts and current policy initiatives aimed towards improving the investment climate in Latvia, and
- (ii) Providing an in-depth exploration of the main challenges and concerns for the foreign investors, both by identifying problems and offering solutions.
The study draws on a survey/mini case studies of the key foreign investors in Latvia. The first 2015 wave of the survey covered 28 key foreign investors in Latvia. Our panel has gradually expanded over time, reaching 47 participating companies in 2019. From September to early November 2019, we interviewed 47 senior executives representing companies that are key investors in Latvia. Altogether, these companies (including their subsidiaries) contribute to 23% of Latvia’s total tax revenue from foreign investors, 9% of the total profit and employ 11% of the total workforce employed by foreign investors in Latvia, where by foreign investors we mean companies with above a 145 000 EUR turnover and 50% foreign capital (data form Lursoft, 2018).
All interviews were conducted by FICIL board members. The guidelines for the interviews consist of the following key parts:
- (i) Assessment of whether, according to foreign investors, the investment attractiveness of Latvia has improved during the past 12 months;
- (ii) Assessment of the work of Latvian policy-makers in improving the investment climate during 2019;
- (iii) Evaluation of progress in the major areas of concern identified by foreign investors in Latvia in 2015, including demography, access to labour, level of education and science, quality of business legislation, quality of the tax system, support from the government and communication with policy-makers, unethical or illegal behaviour on the part of entrepreneurs, unfair competition, uncertainty, the court system and the healthcare system in Latvia.
Furthermore, in the 2019 study we included questions related to some of the key issues discussed between foreign investors and policymakers during 2019, including the tax system, the stability of the financial sector and the quality of higher education and science in Latvia.
Investment Attractiveness of Latvia: Key Concerns of Foreign Investors in Latvia
The results of the 2019 study suggest that, even though the assessment of foreign investors with regards to the investment attractiveness of Latvia and the work of policy-makers to improve the investment climate in Latvia is still at the average level, it shows some positive tendencies. Namely, on a scale from 1 to 5, where ‘1’ means that there are no improvements at all, ‘3’ some positive improvements and ‘5’ significant improvements, the development of the investment climate in 2019 was evaluated as ‘2.6’ (‘2.5’ in 2018 and 2017). Furthermore, when asked to score the policy-makers’ efforts to improve the investment climate in Latvia, using a scale of 1-5, where ‘1’ and ‘2’ were fail and ‘5’ was excellent, investors responded with an average of ‘2.9’ in both the 2017 and 2018 studies, whereas in 2019, the score improved to ‘3.1’.
Foreign investors were also asked to evaluate whether there has been any progress within the key areas of concern as identified in 2015. The results of the most recent study suggest that the demographic situation, which in the long term reflects both the availability of labour and market size, is still among the key challenges for the foreign investors. Namely, on the scale from 1-5 (where an indicator value of 1 means that Latvia is not competitive and 5 means that Latvia is very competitive in this dimension), investors assessed the demographic situation of Latvia with only ‘1.5’ in 2019. Furthermore, as many as 35 (out of 47) foreign investors stated that they had not seen any progress in this area over the past 12 months. This lack of progress is, perhaps, not very surprising as demographic changes may take substantial time.
Another two key areas where investors would like to see more progress are the quality of education and science and the availability of labour. On a 5-point scale, the quality of education and science was evaluated with ‘2.7’ in 2019 (‘3.0’ in 2018, ‘3.1’ in 2017) and 30 out of the 47 investors interviewed have seen no progress in the development of education and science in Latvia over the past 12 months. The availability of labour was evaluated with ‘2.8’ in 2019 (‘2.7’ in 2018 and 2017); investors scored the availability of blue-collar labour with ‘2.4’ in 2019 (‘2.3’ in 2018, ‘2.5’ in 2017) and the availability of labour at management level with ‘3.1’ (‘3.0’ in 2018, ‘2.9’ in 2017). The majority, i.e. 39 of 47 investors have also seen no progress with regards to the access to labour during the past 12 months. In this context, however, it should be emphasised that the efficiency of labour is increasing in Latvia, according to foreign investors: in 2018, it was assessed with ‘2.9’, yet, in 2019, investors evaluated the efficiency of labour in Latvia with ‘3.4’ out of ‘5’.
The quality of health and social security as well as the quality of business legislation are yet another two indicators of the competitiveness of the investment climate in Latvia that have been evaluated around the average level of ‘3’. Further, 33 of 47 investors have seen no progress with regards to improvement of the healthcare system in Latvia over the past 12 months.
While the overall standard of living is evaluated rather positively at ‘3.8’ in 2019, there is still not much improvement in this indicator as compared to the previous three years. One encouraging result of the 2019 study is that according to foreign investors, the attitude towards foreign investors is gradually improving in Latvia: from ‘3.2’ and ‘3.1’ in 2016 and 2017 to ‘3.6’ in 2018 and reaching ‘3.7’ in 2019.
The foreign investors in Latvia who took part in the 2019 study also expressed an expert opinion with regards to whether there has been any progress during the previous 12 months in the other areas of concern. In this light, the perception of uncertainty should be highlighted. As many as 25 (out of 47 investors) have seen no progress in this area, 16 have seen partial progress and 6 stated that there has been progress in reducing uncertainty. The court system of Latvia is another area where many foreign investors have seen no progress, i.e. 22 said ‘no progress’, 23: ‘partial progress’ and only 1 that there has been progress in the development of the court system in Latvia.
Specific Issues: Tax System, Stability of the Financial System and Quality of Higher Education and Science
In the 2019 study, we also initiated an in-depth exploration related to three key issues of concern extensively discussed between foreign investors and Latvia’s government during the FICIL High Council 2019 spring meeting, and throughout the year 2019 in general. These are: (i) the tax system, (ii) the stability of the financial system, and (iii) the quality of higher education and science. Foreign investors were asked to comment on the current situation and progress over the past years, as well as to provide suggestions to the policymakers in order to improve the situation in the particular area.
(i) Tax system:
The most recent tax reform was implemented in 2018, and the newly elected government has announced that the next reform will take place in 2021. Therefore, this year we asked investors to evaluate the results of the previous tax reform in Latvia. We also asked investors to comment on whether the recent tax reform has brought any benefits to their company and the overall economy of Latvia. On average, foreign investors scored the results of the previous tax reform in Latvia with ‘3.1’, i.e. slightly above the average.
Overall, at least one part of the foreign investors who took part in the 2019 studies highlighted that the previous tax reform was a step ‘in the right direction’. In particular, the zero-rate on reinvested profit was highlighted by a large number of investors as a very positive improvement. In some cases, investors also praised the progressivity of labour tax rates. However, a number of foreign investors highlighted that the tax system has actually become more complex after the reform. Investors also expressed suggestions for further steps to improve the tax system in Latvia, and these are as follows:
Avoid uncertainty. Stability and predictability of the tax system is what the majority of the foreign investors wish to see. In essence, this means fewer changes to the tax system.
Simplify and explain. Investors highlight that paying taxes should be a “simple task” and easy to understand. According to the viewpoints of foreign investors, there is also the potential for improvement with regards to how the responsible organisations, such as the State Revenue Service, communicate changes in the tax system to the private sector.
(Continue) the shift from taxing labour to consumption. Some of the investors that took part in the 2019 studies see that the process has been initiated by the previous tax reform and recommend continuing in this direction.
(ii) Stability of the financial sector in Latvia.
On average, foreign investors evaluated the progress with regards to the effectiveness of combating economic and financial crime with 3.2, i.e. above average. We then asked foreign investors whether they have felt any negative effects on their companies with regards to the situations in the financial sector over the past 2 years. We received some positive opinions, yet the negative ones prevailed. Namely, foreign investors highlighted the reputation risks of Latvia that often impact upon the operation of their companies and create challenges when working with foreign banks.
(iii) Quality of university education and science in Latvia.
Here, foreign investors were asked to reflect upon whether they were aware of any activities that policymakers carried out during the past year to improve the situation. On a positive note, a number of investors mentioned the recent development of the University of Latvia and Riga Technical University’s campuses. Some investors also highlighted that the reform to change the governance model of higher education institutions, initiated by the Ministry of Education and Science, was a good step towards improving the quality of higher education and science in Latvia. However, we also received a number of negative opinions, such as “Nothing has been accomplished, just talking”.
When asked “What changes would you suggest to improve the quality of education and science in Latvia and why? How would this help the business environment, e.g. companies such as yours?”, foreign investors emphasised the following:
Higher education (and science) is too local, fragmented and outdated. In essence, investors pointed out that there are simply too many higher education institutions in Latvia, that they work with outdated methods and are afraid (with no good reason) to open up internationally – also by attracting top quality foreign staff.
Change the governance of higher education institutions in Latvia is another strong request from foreign investors in Latvia. Many investors believe that changes in the financing model should also follow.
Improved connection between education and science and the world of business was yet another important aspect which was highlighted during the 2019 interviews, and also strongly emphasised in the previous studies.
Further Investment Plans and Message to the Prime Minister
When asked whether they plan to increase their investments in Latvia, as many as 64% of the investors interviewed answered with ‘yes’ (in the 2018 study, 55% interviewed answered with ‘yes’), 25% said ‘no’ (35% in the 2018 study) and 11% answered that ‘it depends on the circumstances’ (10% in the 2018 study) or that they have not yet decided.
Finally, we invited foreign investors to send a message to the Prime Minister of Latvia: one paragraph on what should be done to improve the business climate in Latvia, from the viewpoint of a foreign investor. These messages closely parallel the other findings of the 2019 study, stressing a number of key concerns that foreign investors are still facing in Latvia: the situation with regards to demography, quality of education and science, availability of labour, challenges with corruption and the shadow economy as well as needs for improvements in the health care sector amongst others.
The findings of the 2019 study on the view of the key foreign investors of the investment climate in Latvia suggest that in recent years, some progress has been achieved on a number of dimensions, such as political effort to improve the investment climate, attitude towards foreign investors, and labour efficiency. At the same time, foreign investors see little, if any, improvement with regards to other key areas, such as the availability of labour, the quality of education, the court system, corruption and the shadow economy.
Our findings highlight the need to continue policy-makers’ efforts to improve the investment climate in Latvia and provide policymakers with better grounds for making informed policy decisions with respect to the entrepreneurship climate in Latvia. We also hope that our study will further facilitate constructive communication between foreign investors and the government of Latvia.
- Lursoft (2018). Official company statistics of Latvia, 2018.
- FICIL Sentiment Index (2019), https://www.sseriga.edu/centres/csb/sentiment-index
In August 2017, the Latvian parliament adopted a major tax reform package that will come into force in January 2018. This reform was a long-awaited step from the Latvian authorities to make the personal income tax more progressive. Some of the elements of the adopted reform, e.g. the changes in the basic tax allowance are estimated to help reducing the tax wedge on low wages and help addressing the problem of high income inequality. At the same time, the way the newly introduced progressive tax rate is designed will effectively lead to a reduction in the tax burden on labor and will hardly introduce any progressivity to the system.
In recent years, reducing income inequality has become one of the top priorities of the Latvian government. Income inequality in Latvia is higher than in most other EU and OECD countries, and the need to address this issue has been repeatedly emphasized by the Latvian officials, the European Commission, the World Bank and OECD.
The main reason for high income-inequality is a low degree of income redistribution ensured by the tax-benefit system. The personal income tax (PIT) has been flat since the mid-nineties. While the non-taxable income allowance introduces some progressivity to the system, the Latvian tax system is characterized by a very high tax burden on low wages, compared to other EU and OECD countries.
Since the beginning of 2017, the government has worked on an extensive tax reform package that was passed in the parliament in August and will become effective as of January 2018.
Two years ago, we wrote about the tax reform of 2016. In this brief, we estimate the effect of the 2018 reform on the tax burden on labour and income inequality. We will only consider changes in direct taxes on personal income – the changes in enterprise income tax and excise tax are outside the scope of our analysis. Parts of our estimations are done using the tax-benefit microsimulation model EUROMOD (for more details about the EUROMOD modelling approach, see Sutherland and Figari, 2013) and EU-SILC 2015 data.
Tax reform 2018
We focus our analysis on four elements of the reform that are expected to affect income inequality and that are described below. In our simulations, however, we take into account all changes in the PIT rules.
First, the flat PIT rate of 23% will be replaced by a progressive rate with three brackets: 20% (applied to annual income not exceeding 20,000 EUR), 23% (for annual income above 20,000 EUR and below 55,000 EUR) and 31.4% (applied to income exceeding 55,000 EUR per year).
Second, the maximum possible PIT allowance will be increased and the structure of the PIT allowance will be made more progressive. Latvia has a differentiated allowance since 2016, which means that individuals with lower incomes are eligible for a higher tax allowance. Figure 1 shows the changes in the non-taxable allowance that will be introduced by the reform. Another important change is that the differentiated allowance will be applied to the taxable income in the course of the year. The current system foresees that, during a calendar year, all wages are taxed applying the lowest possible allowance (60 EUR per month in 2017), but workers eligible for a higher allowance have to claim the overpaid tax in the beginning of the next year.
Figure 1. Basic PIT allowance before (2017) and after (2018-2020) the reform, EUR
Third, the rate of social insurance contributions will be increased by 1 percentage point. Social insurance contributions are capped and the cap will be increased from 48,600 EUR per year to 55,000 EUR per year, i.e. to the same income threshold that divides the top PIT bracket.
Finally, the reform will modify the solidarity tax – a tax, which was introduced in Latvia in 2016 and which is paid by top income earners. When this tax was initially introduced, one of its objectives was to eliminate the regressivity from the tax system caused by the cap on social insurance contributions. Hence, the rate of the solidarity tax was set at the same level as the rate of social insurance contributions and was effectively replacing social insurance contributions above the cap. The reform foresees that part of the revenues from the solidarity tax (10.5 percentage points) will be used to finance the top PIT rate. This element of the reform implies that after January 2018 those falling into the top PIT bracket will, in fact, not face a higher PIT rate than those falling into the second income bracket – the introduction of the top rate will be offset by the restructuring of the solidarity tax.
There are four main findings. First, the reform will reduce the tax wedge on labor income, whereas the tax wedge on low wages will remain high by international standards. Second, most of the PIT taxable income earners (93.5%) will fall into the bottom income bracket. Hence the reform will effectively reduce the tax burden, while the effect on progressivity is very limited. Third, the (small) increase in tax progressivity is ensured mainly by changes in the tax allowance, while the effect of changes in the tax rate on progressivity is negligible: Even those few PIT payers that fall into the top tax bracket will not experience any increase in the tax burden due to a compensating change in the solidarity tax. Finally, it is mainly the households in the middle of the income distribution that will gain from the reform.
Effect on tax wedge
We start with a simple comparison of the average labor tax wedge in Latvia and other OECD countries for different wage levels before and after the reform. The tax wedge measures the share of total labor costs that is taxed away in the form of taxes or social contributions payable on employees’ income.
Table 1. Average tax wedge for single wage earners without dependents in Latvia and other OECD countries, before and after the reform
67% of average worker’s wage
100% of average worker’s wage
167% of average worker’s wage
|OECD average in 2016, % (a)||32.3||36.0||40.4|
|Latvia 2016, % (a)||41.8||42.6||43.3|
|Latvia’s rank in 2016* (a)||6||11||16|
|Latvia 2018, % (b)||39.4||42.3||42.6|
|Latvia 2019, % (b)||39.1||42.1||42.6|
|Latvia 2020, %(b)||39.0||41.9||42.8|
Source: (a) OECD and (b) authors’ calculations. Note: * Ranking across 35 OECD countries. Higher ranking implies higher tax wedge relative to other countries.
Table 1 shows that the tax wedge on low wages (67% of an average worker’s wage) in Latvia is pretty high. In 2016, it was the 6th highest across OECD countries, while the tax wedge on high incomes (167% of the wage) is much closer to the OECD average.
While the reform will slightly reduce the tax wedge for low wage earners (from 41.8% to 39.0% in 2020), it will still remain high by OECD standards. Despite an increase in PIT rate for high-income earners, the reform will also lower the tax wedge for those who earn 167% of the average wage. Why? The explanation comes from the income thresholds for the tax brackets. The income of those earning 167% of the average wage is estimated to fully fall into the first tax bracket in 2018–2019 and only slightly exceed the income bracket for the second PIT rate by 2020. This means that most of the incomes of people earning 167% of the average wage will be taxed at the rate of 20%, which is lower than the current flat rate of 23%. Moreover, in 2020, only a small share of their income will be taxed at 23% – the same rate that these individuals would have had faced in the absence of the reform. Hence, we observe a reduction in the tax wedge for high-income earners.
Generally, only a very small share of taxpayers will fall into the middle and the top income brackets. According to our estimations, as many as 93.5% of all PIT taxable income earners will fall into the lowest income bracket, and only about 6.5% will fall into the second income bracket and about 0.5% will face the top PIT rate.
Apart from the progressive PIT schedule, the reform envisages important changes in the solidarity tax. As explained above, part of the revenues from the solidarity tax will be used to finance the top PIT rate. Therefore, even those (very few) taxpayers whose income will exceed the threshold for the top PIT rate, will not experience any increase in the tax burden because of the compensating change in the solidarity tax. Therefore, the reform will effectively reduce the tax burden on labour with very little effect on progressivity.
While lowering the tax burden is generally welcome, the motivation for applying the top rate to such a small group of taxpayers is not clear. For example, in their recent in-depth analysis of the Latvian tax system, the World Bank (World Bank, 2016) came up with a tax reform proposal that envisaged a considerably lower threshold for the top PIT rate, which, according to our estimations, would cover about 12% of the taxpayers. Given the limited budget resources and an especially high tax wedge on low wages, a more targeted reduction in the tax burden would be preferable. Similar concerns about insufficient reduction in the tax burden on low-income earners are expressed in the latest OECD economic survey of Latvia (OECD, 2017).
Effect on income distribution
Below we present the results from the tax-benefit microsimulation model EUROMOD. Figure 2 shows the simulated change in equivalized disposable income by income deciles compared to the baseline “no-reform” scenario in 2018-2020.
Figure 2. Change in equivalized disposable income by income deciles caused by the reform compared to “no-reform” scenario, %
The first thing to note is that these are mainly households in the middle of the income distribution who will gain from the reform – their income will increase due to both the increase in non-taxable allowance and the introduction of the progressive rate.
The gain in the bottom of the income distribution is smaller for several reasons. First, the proportion of non-employed individuals (unemployed and non-active) is larger in the bottom deciles. Second, individuals with low wages are less likely to gain from the reduction in the tax rate and the increase in the basic allowance, since they might already have most of their income untaxed due to the currently effective basic allowance. The same applies to pensioners who have a higher basic allowance than the employed individuals and who are mainly concentrated in the bottom of income distribution.
Our results suggest that the wealthiest households will also see their incomes grow as a result of the reform (by about 1% in 10th decile). The growth is ensured by the fact that annual income below 20,000 EUR will be taxed at a reduced rate of 20%, and, taking into account that even in the top decile only about half of the individuals get income from employment that exceeds 20,000 EUR per year, the gain from the tax reduction is considerable even in the top decile. A reduction in the tax allowance for high-income earners will have a negative effect on wealthy individuals’ income, but this will be more than compensated by the above positive effect of the change in the tax rate. Hence, the net effect on the incomes in the top deciles is estimated to be positive.
Finally, Table 2 summarizes the effect of the reform on the income distribution, measured by the Gini coefficient on equivalized disposable income. On the whole, the reform is estimated to slightly reduce income inequality – in 2020, the Gini coefficient is expected to be 0.6 points lower than it would have been in the absence of the reform. This reduction is mainly driven by the changes in the non-taxable allowance, while the three PIT rates are estimated to have an increasing impact on income inequality.
Table 2. Gini coefficient on equivalized disposable income in the reform and “no-reform” scenario
Source: authors’ calculations using EUROMOD-LV model
The 2018 tax reform was a long-awaited step from the Latvian authorities on the way to a more progressive tax system. The planned changes in the basic tax allowance are estimated to help reducing the tax wedge on low wages and help addressing the problem of high income-inequality.
At the same time, the second major aspect of the reform, the introduction of a progressive PIT rate, raises more questions than answers. The progressive rate, the way it is designed, will effectively lead to an across-the-board reduction of the tax burden on labor and will hardly help to reach the proclaimed objective of taxing incomes progressively. Given the limited budgetary resources and given that taxes on low wages will remain high compared to other countries even after the reform, a more targeted reduction of the taxes on low-income earners would have been a more preferred option.
- OECD, 2017. “OECD Economic Surveys: Latvia 2017”, OECD Publishing, Paris. http://dx.doi.org/10.1787/eco_surveys-lva-2017-en
- Sutherland, H. and Figari, F., 2013. “EUROMOD: the European Union tax-benefit microsimulation model”, International Journal of Microsimulation, 1(6), 4-26.
- World Bank, 2016. “Latvia Tax Review”, available at http://fm.gov.lv/files/nodoklupolitika/Latvia%20Tax%20Review%20Draft%20231216%20D.pdf
Competition is a good thing not only because of lower prices and larger variety. Higher competition in the domestic market also shifts necessary labour and capital resources from less productive domestic-oriented firms to export-oriented productivity champions. Such firms will make better use of production factors and generate larger output. Thus, simply increasing the level of competition in the domestic market can boost the aggregate productivity of a country.
The aggregate productivity of a country can be boosted even without changing the productivity of individual enterprises. This can be achieved by improving the allocation of resources – the redistribution of labour and capital towards more productive firms. These firms will make better use of production factors and generate larger output. But how can one affect the allocation of resources? Economic theory says that allocation depends on the productivity of individual firms: more productive enterprises attract more labour and capital. However, there exists another factor behind allocation: distortions.
Distortions affect the allocation of resources
A model developed by Hsieh and Klenow (2009) – one of the most popular frameworks to study the allocation of resources – has a very important and realistic feature: it acknowledges that firms are not treated equally. Some firms may face lower supply of banking loans ending with higher capital costs. Other firms could confront with trade unions and higher wages. Tax rates may also differ across firms. These are all examples of distortions. Firms facing larger distortions are forced to underuse respective production factor, while firms that enjoy more favourable conditions tend to overuse capital and labour, generating more output.
While it is virtually impossible to imagine an economy without any distortions (the one where all firms face the same taxes, costs of labour, capital etc.), not all distortions damage the allocation of resources. Only distortions to productive firms create misallocation of resources by shifting labour and capital towards unproductive firms. Thus, removal of such distortions can improve the efficiency of allocation and raise the aggregate output of the country.
According to Hsieh and Klenow (2009) the distortions faced by every individual firm can be quantified from the balance sheets and profit/loss data. For example, observing lower-than-usual ratio of capital to intermediate inputs (comparing with other enterprises in a narrowly defined industry) indicates a capital distortion, possibly related with limited access to banking loans. Similarly, lower-than-usual share of wages in total production costs implies high labour distortions. Finally, the size of the distortion can be detected as a case of abnormally low share of intermediate inputs in total output, and signals about the restrictions to total output (e.g. due to higher taxes for large enterprises).
Misallocation of resources is small in Latvia
In my recent research (see Benkovskis, 2015), I use anonymised firm-level dataset for 2007–2013 and apply the Hsieh and Klenow (2009) model to study the allocation of resources in Latvia – a unique example of a small and open economy facing extreme structural shifts during the financial crisis. According to my estimates, the negative contribution of misallocation to aggregate productivity was close to 27% in 2013 (see Figure 1). In other words, it suggests that actual aggregate productivity could be boosted by 27% if all distortions were removed!
This may seem large but in fact 27% is a comparatively low figure. Hsieh and Klenow (2009) argue that full liberalisation would boost aggregate manufacturing productivity by 86–115% in China, 100–128% in India, and 30–43% in the US. Dias et al. (2015) show that removing distortions would lead to a 30% gain in output of Portugal in 2011. Thus, misallocation of resources is relatively small in Latvia. Even more important: the misallocation of resources decreased after the crisis in Latvia (contrary to the case of Portugal), adding more than 10 percentage points to aggregate productivity growth between 2010 and 2013.
Figure 1. Contribution from misallocation of resources to aggregate total factor productivity, %
Source: Benkovskis (2015). Note: shows the contribution of misallocation comparing with the counterfactual case of no distortions.
The finding that allocation of resources improved after the crisis is interesting per se, but uncovering the reasons behind the improvement is even more important. Figure 1 provides a decomposition, which shows that labour distortions are minor in Latvia due to high flexibility of labour market (in line with recent findings by Braukša and Fadejeva, 2016). The capital distortions, while being minor in 2007–2008, increased afterwards, pointing to some credit supply constraints faced by the highly productive enterprises after the financial crisis. However, by far largest contribution comes from the misallocation of intermediate inputs – the turnover of the most productive firms face some constraints. And it was the ease of constraints to turnover for the most productive firms that determined the improvements in aggregate productivity since 2010.
The level of competition matters for misallocation
My research stresses the importance of the competition level on the market, since higher competition serves as a natural constraint for the firm to increase its turnover. What if the most productive Latvia’s firms systematically come up against higher competition? I found that indeed this is the case. First, recent results by Fadejeva and Krasnopjorovs (2015) show that Latvia’s domestic market has lower competition level comparing with external markets. Second, it is widely acknowledged that exporters tend to be more productive comparing with domestically oriented firms (see e.g. Bertou et al., 2015, who report positive export premiums for EU countries, while Benkovskis and Tkačevs, 2015, find higher productivity of exporters in Latvia). Thus, Latvia’s productive export-oriented firms are subject to higher competition and cannot enlarge their turnover as easy as other entities. This shifts labour and capital towards small and less productive firms working solely on domestic market, creating the misallocation of resources.
The domestic competition factor can also explain the improving allocation of resources after 2010. The study by Fadejeva and Krasnopojorovs (2015) reveals that the competition gap between domestic and foreign markets narrowed after the financial crisis (see Table 1). Namely, life was too easy on the local Latvia’s market during the boom time, allowing unproductive firms to survive and drain away resources from more productive firms. But conditions became tougher after the crisis (although the competition level still remained lower than abroad). We can view this as a “cleansing effect of the crisis”: some of the least productive domestic oriented firms went bankrupt (or decreased their turnover), freeing the necessary capital and labour resources for productive exporters.
Table 1: Change in the competitive pressure on main product in domestic and foreign markets compared to the situation before 2008, %
|Domestic market||Foreign market|
|Does not apply||2.8||2.8||9.4||13.1|
Source: Fadejeva and Krasnopjorovs (2015), Table A.102. Notes: based on the sample of 557 Latvia’s firms; results are weighted to represent firm population.
This research has an important policy conclusions applicable to any country that seeks to increase aggregate productivity. The competition level in the domestic market is important not only for consumers, who enjoy lower prices and higher variety. Higher competition in the domestic market also shifts necessary resources from less productive domestic-oriented firms to export-oriented productivity champions.
- Benkovskis, Konstantins; 2015. “Misallocation of resources in Latvia: did anything change during the crisis?”, Latvijas Banka Working Paper No.5/2015.
- Benkovskis, Konstantins; and Olegs Tkacevs, 2015. “Everything you always wanted to know about Latvia’s service exporters (but were afraid to ask)”, Latvijas Banka Working Paper No.6/2015.
- Berthou, Antoine; Emmanuel Dhyne; Matteo Bugamelli; Ana-Maria Cazacu; Calin-Vlad Demian; Peter Harasztosi; Tibor Lalinsky; Jaanika Meriküll ; Filippo Oropallo; and Ana Cristina Soares, 2015. “Assessing European Firms’ Exports and Productivity Distributions: The CompNet Trade Module”, ECB Working Paper, No. 1788.
- Braukša, Ieva; and Ludmila Fadejeva, 2016. “Internal labour market mobility in 2005–2014 in Latvia: the micro data approach”, Baltic Journal of Economics, 16(2), 152–174.
- Dias, Daniel A.; Carlos Robalo Marques; and Christine Richmond, 2015. “Misallocation and Productivity in the Lead Up to the Eurozone Crisis“, International Finance Discussion Papers 1146.
- Fadejeva, Ludmila; and Olegs Krasnopjorovs, 2015. “Labour Market Adjustment during 2008–2013 in Latvia: Firm Level Evidence”, Latvijas Banka Working Paper, No. 2/2015.
- Hsieh, Chang-Tai; and Peter J. Klenow, 2009. “Misallocation and manufacturing TFP in China and India“, The Quarterly Journal of Economics, 124(4), 1403–1448.
Today policy makers in developing and middle-income countries face tremendous challenges in combating various forms of tax evasion. Increasingly it is proposed to tie social security benefits to the reported income and in this way increase tax compliance incentives. We use administrative data from Latvia to study generous childcare benefits, which depend on the reported wages in the pre-childbirth period. Our analysis reveals pronounced wage growth shortly before the childbirth, which we rationalize by the legalization of previously undeclared wages. Obtained results show that the wage growth is temporary and lasts only until the end of the period, which is taken into account when calculating parental benefits.
Today policy makers around the world are increasingly preoccupied with reducing various forms of tax evasion. To provide tax compliance incentives it is often proposed to tie social security benefits to declared wages. For example, Kumler et al. (2013) show that a reform tying future pension benefits to the payroll tax in Mexico increased tax payments after the reform. Similarly, Cruces and Bergolo (2013) and Bergolo and Cruces (2014) demonstrate that a reform tying health care insurance of children to the reported earnings of parents increased “legal” labor supply in Uruguay.
On the other hand, Kreiner et al. (2016) document inter-temporal wage shifting in Denmark to enjoy significantly lower marginal tax rates. In light of the results by Kreiner et al. (2016), it is possible that employees and employers collude to increase the wage during the period, which is taken into account when calculating social security benefits. If the wage increase is temporary then the result of tying social security benefits to wages might be a net loss to the government finances. Hence, the question of whether tying social security benefits to reported wages is a solution to the problem of payroll tax evasion is still open.
We demonstrate that tying social security benefits to the declared wages can backfire to the extent that it can lead to the excessive payments of social security benefits, while doing almost nothing to reduce payroll tax evasion, in this way producing net fiscal loss to government finances. More specifically, we show that if the contribution period that determines the size of the benefit is relatively short and social security benefits are generous, then by colluding, employees and employers can temporally increase the legal wage to extract generous benefits afterwards. This result can have implications for the design of social benefit systems in many countries, where relatively short contribution periods ensure generous long-lived benefits afterwards.
Institutional background and methodology
We illustrate this phenomenon by studying the childcare benefit in Latvia, which in 2005-2008 depended on parents’ declared wage in the pre-childbirth period. This system, introduced in 2005, replaced a universal (very modest in size) childcare benefit. The new rules foresaw that one of the parents could receive a benefit that was equivalent to the parent’s previous net wage until the child became one year old. The average wage that determined the size of the benefit was calculated over the 12-months period that ended three months before the childbirth (hereinafter – benefit qualification period) and therefore included 5 months of pregnancy. Initially the benefit was not compatible with employment but as of March 2007 it became possible to simultaneously work full-time and receive the benefit.
Presumably, the 2005 reform created incentives to report higher earnings before the childbirth, because of the generosity of the new benefit and because the benefit qualification period included pregnancy, i.e., the period when the mother knows if/when she will be eligible for the benefit. To uncover the effects of the incentives to report more income, we use administrative data on declared monthly wages and use three sources of identifying variation in a difference in differences setup.
First, we compare wage growth during pregnancy with wage growth of women who did not become pregnant. The identifying assumption is that, in the absence of pregnancy, the wages of women who became pregnant would follow the same trend as the wages of other women. Under this assumption, any difference in the wage growth can be interpreted as a legalization of previously undeclared wages. However, this assumption may not hold because pregnancy is not randomly assigned across women: women can anticipate a wage increase (e.g. anticipate a promotion) and adjust the decision to have a child. Therefore, we use a second source of identifying variation by comparing wage growth during pregnancy for women employed in the private sector with wage growth for women employed in the public sector, where tax evasion is presumably absent. Assuming that promotion anticipation effects in the private and the public sector are identical, this difference in wage growth can be interpreted as the growth of wages resulting from wage legalization.
Our previous assumption might be violated if promotions in the public sector can be easier to predict (which means that anticipation effects in the private and the public sectors are not necessarily identical). To address this challenge, we use a third source of identifying variation coming from the 2005 reform, which tied the childcare benefit to the previous earnings. Since this reform increased incentives to disclose higher earnings during pregnancy, the difference in wage growth in the private sector versus public sector should not be observed before the reform.
Estimations are based on a matched employee – employer administrative dataset, which covers monthly-declared earnings of all employed workers in Latvia from 1996 to 2010.
There are three main findings. First, wage growth during the first five months of the pregnancy in the private sector is always higher than that in the public sector. If we use this observation to obtain an estimate of the wage growth due to the legalization of previously undeclared wages, we find, depending on the regression specification, that it varies between 5 and 7 percent.
Second, this effect is mainly driven by the time period after the reform of 2005 (see Figure 1). Thus, if we use the time period before the reform of 2005 only to difference out permanent differences in the anticipation effects between public and private sector, our preferred regression specifications provide us with an estimate that varies from 5 to 6 percent.
Figure 1. Difference-in-difference-in-difference estimate by year, %
Note: difference in difference in differences estimate for a given year is calculated by first comparing wages of pregnant women with those of not pregnant before and during first five months of the pregnancy. Then this estimate is compared between public and private sectors. Everything is compared with respect to one year before the reform announcement – 2003.
The final finding shows that the sharp jump in the wage growth in private sector versus the public sector starts to appear exactly in the first month of the pregnancy (see Figure 2). It is important to note that we do not see any differential wage growth between the public and the private sector before the date of conception, indicating that potential anticipation effects are limited.
Figure 2. Difference-in-difference-in-difference-in-differences estimate by pregnancy month, %
Note: difference in difference in difference in differences estimate for a given month is calculated by first comparing wages of pregnant women with those of not pregnant in a given month with respect to one month before the date of conception. Then this estimate is compared between public and private sectors and finally previously calculated difference is contrasted before and after the reform tying parental benefits to reported wages.
Due to the fact that many women do not return to the same employer after childbirth, it is problematic to make inferences about the wage a woman receives once she returns to the labor market. To overcome this challenge we use the same social security data for men for the time period covering January 2007 until August 2010.
As explained previously, starting in March 2007 the childcare benefit became compatible with full time employment. The outcome of this reform was that many men started to receive the benefit, while continuing to work. This allows us to perform the previous analysis for the sample of men.
Results presented in the Figure 3 show that similarly as in the sample of women we see a sharp increase in the wage during the qualification period. Additionally, we see a slowdown in the wage growth once the qualification period ends. It is important to mention that displayed coefficients describe the difference between public and private sector in the change in wages between men whose partners became pregnant and those who did not with respect to the reference period (here one month before the conception date). We also record a sharp growth in wages in the public sector in the months following the childbirth. On the contrary, wages in the private sector stay the same, hence the large difference in the months following the childbirth.
Figure 3. Difference-in-difference-in-differences estimate for men by month of partner’s pregnancy, %
Note: difference in difference in differences estimate for a given month is calculated by first comparing wages of men whose partner became pregnant with those men whose partner did not become pregnant with respect to one month before the date of conception. Then this estimate is compared between public and private sectors
Drawing on the example of the childcare benefit in Latvia, we show that declared wages sharply increase during the period that is taken into account when calculating social security benefits. This wage growth is temporary and does not continue once the benefit qualification period is over. We interpret this phenomenon as the legalization of previously undeclared wages: this temporary legalization of earnings is possible, because the benefit qualification period is relatively short (12 months), and includes 5 months of pregnancy, which makes the average wage during the qualification period relatively easy to affect. Such setting creates bad incentives – an employee and an employer can collude to increase the average wage that determines the size of the benefit.
Additionally, our research casts doubts on policies tying parental benefits to declared earnings with an aim to reduce opportunity costs of high earners and increase their fertility. Researchers analyzing such policies should be very cautious when interpreting their results because the effect that they capture might not come from high earning women, but rather from women who manage to increase their income during pregnancy. Absent monthly data, it might be challenging to disentangle the two.
Many countries implement earnings-dependent benefits. Our results show that even very well designed social security benefits can and will be abused if people are given wrong incentives. Thus to achieve the best outcomes policy makers when deciding whether to tie social security benefits to declared earnings should take into account side effects described in this brief.
- Bergolo, Marcelo & Guillermo Cruces, 2014. “Work and tax evasion incentive effects of social insurance programs,” Journal of Public Economics, Elsevier, vol. 117(C), pages 211-228.
- Cruces, Guillermo & Marcelo Bergolo, 2013. “Informality and Contributory and Non-Contributory Programmes. Recent Reforms of the Social-Protection System in Uruguay,” Development Policy Review, 31, issue 5, p. 531-551.
- Kleven, Henrik Jacobsen & Claus Thustrup Kreiner & Emmanuel Saez, 2016. “Why Can Modern Governments Tax So Much? An Agency Model of Firms as Fiscal Intermediaries,” Economica 83, no. 330 (April 1, 2016): 219–46. doi:10.1111/ecca.1218Kreiner, Claus Thustrup & Søren
- Kreiner, Claus Thustrup & Søren Leth-Pedersen & Peer Ebbesen Skov, 2016. “Tax Reforms and Intertemporal Shifting of Wage Income: Evidence from Danish Monthly Payroll Records,” American Economic Journal: Economic Policy, 8(3):233–257, August 2016.
- Kumler, Todd & Eric Verhoogen & Judith A. Frías, 2013. “Enlisting Employees in Improving Payroll-Tax Compliance: Evidence from Mexico,” NBER Working Papers 19385, National Bureau of Economic Research, Inc.
The 2016 budget includes measures aimed at increasing the progressivity of the Latvian income tax system. In this brief we report some exercise on the impact of these measures using the Latvian EUROMOD tax-benefit microsimulation model. We show that by their design, the reforms are aimed at a reduction in income inequality and an increase in the progressivity of the tax system. However, there are risks that the behavioural response of the tax payers will subvert the intended impact of the reforms.
Ever since it was introduced in 1994 the Latvian personal income tax has been applied at a flat rate, albeit varying over time, mitigated only by a small untaxed personal allowance. Partly as a result of this, the Latvian tax-benefit system redistributes less original income than most other EU countries. Is this all about to change? The 2016 budget currently being debated in the Parliament contains two proposals aimed at introducing more progressivity in the personal income tax. These are the introduction of a “solidarity tax” aimed at high earners and the introduction of an earnings differentiated non-taxable allowance. The stated aims of these measures are to reduce inequality and help low wage-earners.
Description of the Reforms
The solidarity tax foresees that income above 48,600 EUR per year will be taxed at a rate of 10.5% (employee’s part), plus 23.59% (employer’s part). The new tax will affect a very small share of wage earners. According to Finance ministry’s estimate, this tax will affect 4.7 thousand persons, whose income in 2015 exceeded this threshold, or 0.59% of all employed individuals (Finance Ministry, 2015).
Differentiated Non-Taxable Personal Allowance
The differentiated non-taxable personal allowance will be introduced gradually between 2016 and 2020. The basic idea is to make the allowance dependent on income: individuals receiving income below a certain threshold are eligible for the maximum possible allowance, then the allowance gradually declines with income until it is zero. The system will be introduced gradually in the sense that the minimum allowance will not reach zero until 2020 – it will be gradually reduced from 85 EUR in 2016 to 0 EUR in 2020.
The way the system will be implemented foresees that during a fiscal year, all individuals will be taxed applying the minimum non-taxable allowance (e.g., 85 EUR in 2016). At the beginning of the next year, people eligible for a higher tax allowance will have the opportunity to apply for a tax refund, by making an income declaration, and to get the overpaid tax back.
Simulations of Reforms: Inequality
Below we present simulation results from EUROMOD, which is an EU-wide tax-benefit microsimulation model (for more details see Jara and Leventi, 2014). The results show the first-round effect of the simulated policies, i.e., they show the pure effect of the proposed reforms abstracting from any behavioural responses that these reforms might induce. We simulate the effect of five reform scenarios: two scenarios of differentiated non-taxable allowance (one scenario reflects the system that is planned to be introduced in 2016, the second scenario represents the system that is planned to be introduced in 2020), one scenario that simulates introduction of the solidarity tax, and two scenarios that combine the solidarity tax with the new non-taxable allowances. We compare these reforms with the baseline system, which describes the tax-benefit rules that are in place in 2015.
It is important to note that we assume in the simulations that everyone who is eligible for a tax refund under the new non-taxable allowance rules does in fact apply for the refund, which means that we estimate the maximum possible effect from the introduction of the higher tax allowances.
Table 1 summarizes the effect of the proposed reforms on income inequality as measured by the Gini coefficient. All the proposed reforms reduce income inequality, but the solidarity tax achieves higher equality by reducing incomes in the top decile. The non-taxable allowance mainly affects people in the middle of the income distribution, as the bottom deciles contain proportionally fewer employed individuals, while in the top deciles the allowance, which is set in absolute terms, makes a smaller share of the income – hence, a weaker effect. Pensioners, who mainly belong to the lower deciles of the income distribution, do not gain from a higher allowance, because of a special taxation regime for pensions that already provides for a higher personal allowance. All major benefits (unemployment benefit, social assistance, child-related benefits) are not subject to personal income tax, hence benefit recipients also do not gain from the proposed changes (see Figure 1).
Table 1. Gini Coefficient Associated with the Reforms
|Baseline||ST*||2016 allowance||2020 allowance||ST + 2016 allowance||ST + 2020 allowance|
Source: authors’ calculations using EUROMOD
Note: ST – solidarity tax
Figure 1. Deviation of Equivalised Disposable Income from the Baseline Scenario, %
Figure 1 also shows that the losers from the solidarity tax are in the highest decile, though it should be borne in mind that enterprises are also losers because they now have to pay part of the solidarity tax. The solidarity tax generates no direct gainers.
Impact on Progressivity
The progressivity of a tax or system is typically measured by the Kakwani index. The Kakwani index (Kakwani, 1977) can vary between −1 and 1 and the larger the index, the more progressive is the tax. A positive index indicates that the tax is progressive and a negative index indicates it is regressive. Table 2 shows the calculated Kakwani index for all major direct taxes (which include personal income tax, social contributions and the newly introduced solidarity tax) and separately for personal income tax (PIT) for each of the postulated scenarios. The results suggest that all of the proposed reforms increase the progressivity of the tax system.
Table 2. The Kakwani Index for the Six Scenarios
|Baseline||ST*||2016 allowance||2020 allowance||ST + 2016 allowance||ST + 2020 allowance|
|All income taxes*||0.034||0.040||0.048||0.058||0.054||0.064|
Source: authors’ calculations using EUROMOD
Note: ST – solidarity tax; income taxes include personal income tax, social contributions and the newly introduced solidarity tax
Qualifications and Risks
The above results capture the so-called first round impact of the tax changes. In practice people will react to the changed incentives by changing behaviour and thereby changing the impacts. For example, the higher net reward for working in low wage jobs may increase the supply of workers willing to work in such jobs thereby possibly having a bigger positive effect on the incomes of low income households than implied by the simulations.
Perhaps more significant is the potential effect of the solidarity tax on the behaviour of high earners and of the enterprises that employ them. This effect is captured by the concept of the elasticity of taxable income – defined as the change in taxable income in response to a change in the marginal tax rate. The taxable income elasticity concept takes into account all the behavioural aspects of the taxpayer in response to a change in the tax rate. As well as labour supply responses it includes other responses e.g. switching the form in which income is received as well as simple tax evasion (Saez et al., 2012). It is the switching of the form in which income is received, away from wage income towards other less-taxed forms of income that can be expected here. Thus according to an internal Latvian Employers Confederation employer survey, if the solidarity tax is implemented one third of employers will consider using legal tax optimization tools such as dividends or the microenterprise tax to avoid paying the tax. Here, employers are important as well as employees, because employers will pay the larger share of the tax. If this happens on a significant scale (high elasticity of taxable income) then the intention of the solidarity tax will be subverted.
There are also risks with the differentiated personal allowance. If the burden of annual reporting of income is too high then many may simply not do it and suffer the loss of income or find a way of recouping through shadow earnings.
The Latvian authorities should be applauded for grasping the nettle of progressive taxation but perhaps only with one hand for the way they have chosen to do it. Thus, the solidarity tax creates an incentive for both employers and employees to find ways of avoiding it and find they surely will. A tax accountant once said of the 80% supertax applied to high earnings in pre-Thatcher UK that it was a ‘voluntary tax’. This is also the likely fate of Latvia’s solidarity tax.
The differentiated personal allowance will clearly benefit low earners, if they claim it. In fact it will also benefit people earning well over the average wage. But will the low earners claim? Very few people in Latvia have ever filed an income declaration and we fear that many low earners will not do so now.
Thus at the top end progressivity is likely to be largely avoided and at the bottom end may not be fully claimed.
- Finance Ministry (2015). “Solidaritātes nodokli maksās tikai personas ar algu virs 48 600 eiro gadā,” available at http://www.fm.gov.lv/lv/aktualitates/jaunumi/nodokli/51253-solidaritates-nodokli-maksas-tikai-personas-ar-algu-virs-48-600-eiro-gada
- Kakwani, Nanak C. (1977). “Measurement of Tax Progressivity: An International Comparison”. Economic Journal 87 (345): 71–80
- Jara, X. and Leventi, C. (2014). “Baseline results from the EU27 EUROMOD (2009-2013),” EUROMOD Working Papers EM18/14, EUROMOD at the Institute for Social and Economic Research.
- Saez, E., J. Slemrod, and S. H. Giertz, (2012). “The Elasticity of Taxable Income with Respect to Marginal Tax Rates: A Critical Review.” Journal of Economic Literature, 50(1): 3-50