Tag: Unemployment

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

Posted on November 13, 2023 | Policy Brief

20231113 Envelope Wages Tax Evasion Image 01

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

Introduction

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

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

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

Identifying Firms That Pay Envelope Wages

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

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

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

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

Source: Authors’ calculations.

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

Employment Response During Covid-19

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

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

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

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

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

The Aggregate Effect

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

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

Figure 2. Evasion and total employment.

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

Differences by Sector

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

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

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

Figure 3. Employment effect – by sector.

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

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

Conclusion

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

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

Acknowledgement

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

References

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

The Cost of Climate Change Policy: The Case of Coal Miners

Posted on October 10, 2022 | Policy Brief

20221010 The Cost of Climate Change Image 01

The phasing out of coal is considered a key component of the upcoming energy transition. While environmentally appealing, this measure will have a devastating effect on those working in the coal industry. Using the dissolution of the UK coal industry under Margret Thatcher as a natural experiment, we estimate the long run costs of being displaced as a coal miner. We find that within the first year of displacement, earnings fall by 80-90 percent, relative to the earnings of a carefully matched blue-collar manufacturing worker, while the wages of miners who find alternative employment fall by 40 percent. The losses are persistent and remain significant fifteen years after displacement. Our results are considerably above the estimates provided by other studies in the job displacement literature and may serve as a guide for policy makers when aiming for a just energy transition.

The Coal Mining Industry and Global Warming

According to the recent IPCC report, limiting global warming to 2 degrees Celsius requires a near complete and rapid elimination of coal in the global use of energy. Such a drastic measure is bound to have devastating effects on anybody economically linked to and dependent on the coal industry. Our back-of-the-envelope calculation suggests that the closure of the currently 2300 active industrial coal mines would translate into more than 5 million displaced coal miners. In Figure 1 we plot the spatial distribution of coal mines, indicating the locations of the upcoming displacements globally.

Figure 1. Location of industrial coal mines. The seven biggest producers and exporters of coal are marked in green.

Source: SNL Energy Data Set produced by S&P Global.

In a new paper (Rud et al., 2022), we estimate the average loss in the earnings of coal miners who have been displaced following one of the most notorious labor disputes of the 20th century: the dissolution of the coal sector in the UK. When Margaret Thatcher came into power many of the mines were unprofitable (Glyn, 1988). Considering the mines to be ripe for closures, the UK government publicly announced the closure of 20 mines in 1984. After additional information on further closures reached the press, the Union of Miners called for a general strike. The strike lasted for nearly a year and ended with a devastating defeat of the miners. From 1985 and onwards, the closure of mines proceeded at such an incredible pace that the dissolution of the UK coal industry is considered the most rapid in the history of the developed world (Beatty and Fothergill, 1996). As shown in Figure 2, the closures resulted in an equally rapid displacement of miners, from 250 000 employed miners in 1975 to less than 50 000 by 1995.

Figure 2. Coal Mining Employment in the UK 1975-2005

Note: The number on employed miners is collected from National Coal Board (1970-1993) and used in Aragon et al., (2018). The percent of employment shown on the right axis was calculated from the New Earnings Survey, the main data source used in this paper.

The Effects of UK Coal Mine Closures on Miners

At the heart of our empirical analysis is the New Earnings Survey, a longitudinal dataset covering 1 percent of the UK population since 1975. For the period 1979-1995 (marked in gray in Figure 2), among the 25-55 years old and those who were employed by the same mine for at least two consecutive years, we identify 2152 miners who experienced a final separation from a mine. In our baseline specification, these miners are matched to a single manufacturing worker using a large array of observables such as age, gender, hours worked, pre-separation employment and earnings, geographical administrative unit (county), as well as whether their respective wage was determined in a collective agreement. By the nature of the exercise we are unable to match on industry and instead match on detailed occupational information. A variety of other matching procedures suggest our results are robust.

In Figure 3 we plot the estimated differences in the evolution of earnings and wages for four years before, and fifteen years after displacement. The coefficients are estimated conditional on time and individual fixed effects. Due to the normalization of the dependent variable, the estimates should be interpreted as the percentage change relative to pre-displacement values. In Panel A of Figure 3 we show that hourly wages and weekly earnings conditional on employment drop by around 40 percent in the year after displacement and recover only slowly. It should be noted that the losses in earnings conditional on employment are not driven by changes in hours since the two series are close to identical.

In Panel B of Figure 3 we show the effect on earnings taking into account the losses of those who have not been successful in finding alternative employment in another industry. To get to these results we need to make some assumptions since the New Earnings Survey neither includes earnings information on the self-employed, nor on those who are active in the informal sector. Many other studies in the job displacement literature share similar data limitations, so we follow their approach in dealing with these. On the one hand, we assume zero individual earnings for periods without any observed labor earnings in the data, as assumed by Schmieder et al. (2022) and Bertheau et al. (2022). This assumption does not appear too strong since there is some evidence suggesting that ignoring the self-employed only marginally affects the results (Upward and Wright, 2017; Bertheau et al., 2022). On the other hand, we complement our results with an approach inspired by Jacobson (1993) where we keep only individuals who experience positive earnings within four years after displacement. The latter approach provides a more conservative estimate of displacement costs by assuming zero earnings only for individuals who eventually return to work.

Figure 3. The hourly wage and earnings conditional on employment (Panel A), and overall earnings costs of final displacement from a mine (Panel B).

Note: We plot the coefficients of the estimated panel data model with time and individual fixed effects and distributed leads and lags. ”Earnings: come back” refers to the treatment group where we only include those who have positive earnings at some point four years after job loss, and impute periods without employment as zeros. ”Earnings: all zeros” refers to the treatment in which we replace the earnings of any miner with a zero if the miner is not observed for any year, without restrictions.

Interpreting all periods of missing information as zeros, we find the initial losses to be around 90 percent of pre-displacement earnings within the first year after separation, while the more conservative estimates are only slightly lower at around 80 percent in the short run. In the long run, the losses are persistent and remain significantly depressed even fifteen years after displacement. Over the fifteen years after displacement these numbers amount to the miners losing on average between 4 to 6 times of their pre-displacement earnings. This implies that miners only receive 40-60 percent of the present discounted counterfactual earnings.

Our estimates are considerably above those provided by studies in the job displacement literature that focus on mass layoffs. Couch and Placzek (2010), for instance, report initial losses to amount to about 25-55 percent, while Schmeider et al. (2022) find initial earnings losses to be around 30-40 percent. Davis and Wachter (2012) estimate the long-run effects based on US data and find the present discounted earnings losses to be on average 1,7 times the workers’ pre-displacement earnings.

The large estimated individual costs to the displaced miners are likely due to a combination of at least two reasons. First, the complete collapse of the sector forces displaced miners to reallocate and search for another job in other industries, and likely other occupations. Since coal mining is a highly specialized occupation, this greatly reduces miners’ ability to transfer the accumulated human capital to another activity (Beatty and Fothergill, 1996; Samuel, 2016). Second, most coal miners are employed in remote and rural areas where mining is often the main employer, something which remains an issue for current miners around the world (see Figure 1). This feature reduces local economies’ capacity to absorb displaced miners after a mine closure and, due to the need to relocate, greatly increases workers’ job searching costs.

Conclusion

While it is important to globally transition away from the excessive use of fossil fuels, we should keep in mind the devastating effects such transition will end up having on some groups. And while coal miners are particularly vulnerable to the upcoming energy transition, the ramifications do not stop there. Individuals employed in industries linked to the coal industry are likely to also be affected by its dissolution. Moreover, individuals employed in industries providing local services, such as retail stores, restaurants and pubs are likely to experience a significant drop in demand. Thus, the impact of coal mine closures on coal dependent communities typically goes far beyond the displacement of miners (Aragon et al., 2018). The closure of mines will lead to spikes in local unemployment, often unregistered (“hidden”), as well as an exodus of the population. Estimating and accounting for these effects is important if we aim to provide a just energy transition for all.

Attempts have been made to foster economic recovery of affected communities. Regeneration policies have included re-training of local workers, support of small and medium-sized businesses, and investments in local infrastructure, among others. However, their success has been limited and former mining communities remain among the poorest in the UK (Beatty et al., 2007). Preparing a set of policies which will have the capacity to reduce the costs of the transition, as not to repeat the devastating experience of UK coal miners and their communities, is an important task ahead of current policy makers.

References

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

Inequality in the Pandemic: Evidence from Sweden

Posted on March 22, 2021 | Policy Brief

Condominium houses near the water representing inequality during the covid19 pandemic

Most reports on the labor-market effects of the first wave of COVID-19 have pointed to women, low-skilled workers and other vulnerable groups being more affected. Research on the topic shows a more mixed picture. We contribute to this discussion. Using monthly official unemployment data in Sweden we find that across wage levels, occupations with lower salaries display higher increases in unemployment, and low-wage occupations are also more difficult to do from home. The job loss probability is also higher in sectors with a higher concentration of workers born outside of the EU and those aged below 30. But we find no evidence of a gender unequal impact in Sweden. Overall, our results point to higher effects for low-wage groups but small gender differences overall.

Introduction

The ongoing Covid-19 pandemic has affected the health of millions of people worldwide. But it has also had an enormous impact on economic and living conditions through government policies aimed at containing the spread of the infection. While, at the onset of the pandemic, government officials, mainstream media, and even celebrities labeled COVID-19 “the great equalizer” (Mein, 2020), the reality has proven quite different, with the most vulnerable groups of the population appearing to be the most harmed by both the health and the economic crises (see, for instance, The World Economic Forum, Joseph Stiglitz in this IMF article, and The World Bank). In this brief, we focus on one specific economic impact of the pandemic, namely its effect on unemployment status, and we study the extent to which this impact has been unequal across different groups of the Swedish society. Our analysis uses administrative data and segments the population by wage, gender, age, and foreign-born status.

Covid-19 and Inequality in the Labor Market

An extensive review of the emerging literature on the effect of the pandemic on different kinds of inequality is beyond the scope of this brief. However, a number of studies are especially relevant to put our analysis in context, as they are focused on the unequal labor market impacts of the crisis and study real-time data. Based on these studies, a number of patterns emerge. First, the effect of the pandemic on the increased probability of job loss appears stronger for low-skilled workers, as proxied by education level (see e.g., Adam-Prassl et al., 2020, Gaudecker et al.  2020, Casarico and Lattanzio 2020). Gaudecker et al. (2020) also observe that in the Netherlands the negative education gradient has been mitigated by the government identifying some sectors of the economy as essential since some of these sectors are characterized by a high concentration of low-educated workers. Second, the evidence of unequal gender impacts on the probability of job loss is mixed. While survey information from the UK and the US reveals that labor market outcomes for women have more severely deteriorated during the crisis (Adams-Prassl et al., 2020), there is no evidence of unequal impacts by gender in Germany (Adams-Pras et al., 2020) and Italy (Casarico and Lattanzio, 2020). Other papers confirm that the effect on labor-market outcomes by gender varies across contexts (see, e.g.,  Hupkau and Petrongolo, 2020).

Analysis of Labor Market Data From Sweden

Our analysis of the Swedish labor market provides a valuable contribution to the existing findings for a number of reasons. First, despite rising inequality over the past decades, Sweden is characterized by relatively low income inequality (e.g. OECD, 2019), high participation of women in the labor market, and high level of society inclusiveness (e.g. Gottfries, 2019, OECD 2016) among OECD countries. Second, unlike the majority of countries worldwide, throughout the pandemic, Sweden has not adopted stay-at-home orders that would have separated sectors of the economy between “essential” and “non-essential”. As a result, sectors that were typically shut down in other countries, for instance, the hospitality industry, were not ordered to close during the first wave of the pandemic and have then only faced partial limitations during the second wave. Importantly, schools below the secondary level were never closed. Third, as we will describe in more detail below, the availability of administrative information on unemployment claims on a monthly basis allows studying the “real-time” development of unemployment throughout the pandemic for the universe of employees in the Swedish labor market.

Data

We use data from the registry of unemployed individuals kept by the Swedish Public Employment Service (Arbetsförmedlingen), the government agency responsible for the functioning of the Swedish labor market. The incentives for laid-off individuals to register with the Employment Service are high since the registration is directly connected to the right to claim various (relatively generous) unemployment benefits. As such, the data arguably includes a large share of employees who lost their job over the period studied. Based on the high incentives to register as unemployed, we also assume that the probability to register does not differ the segments of the population that we consider. The data does not include some self-employed who for various reasons choose not to register, but this group is not believed to be significant. Also, furloughed workers do not count as unemployed. This group was significant, especially in the very early stages of the pandemic, but still small relative to all unemployed. As of July 2020, they represented 13% of the total pool of unemployed individuals in Sweden (Swedish Agency for Economic and Regional Growth, 2021).

The population-wide coverage is the main advantage of our data vis-à-vis the survey information used in many recent studies of the labor market throughout the pandemic (other studies using administrative data are Casarico and Lattanzi, 2020, studying the Italian labor market, and Forsythe et al., 2020, who analyze the US case).

We consider everyone registered as unemployed/seeking employment each month from January 2019 to July 2020. The data is grouped by 4-digit occupational classification (there are about 440 occupations at this level) and each occupational group is further broken down by sex, age, and foreign-born status (specifically, Sweden born, foreign EU born, and foreign non-EU born.) We then merge this data with information on the average wage by occupational group and gender in 2019, as reported by Medlingsinstitutet and publicly available at Statistics Sweden. This measure, although not being at the individual level, allows us to develop a relatively precise proxy of wages by occupation that we use to rank unemployment by wage deciles.

Evidence

With the data described above, we build the following measure of the change in job-loss probability (JLP) between February and July 2020, adjusted for seasonality:

where u is the number of workers in 4-digit occupational sector who registered as unemployed in a month over the average number of employed in the same sector in 2017 and 2018 (data available at Statistics Sweden). Put it simply, ΔJLP is a sector-level indicator of the change in job loss probability due to the pandemic; it measures the change in chances of job loss between February and July 2020, i.e. between five months after the start of the pandemic and the month before its onset, as compared to the equivalent change the year before. We thus account for seasonal factors by differencing out the job loss probability during the same months of 2019, when the pandemic was neither occurring nor anticipated. Below we use ΔJLP to show differences in the impact of the pandemic on the chances of job loss for different groups of the Swedish society.

Job loss probability by wage deciles. We leverage information on sector-level average wages and the number of employees to partition occupational sectors into (approximate) wage deciles. The purpose of such a partition is to rank sectors as being typically “low-” or “high-” wage within the Swedish context. As we document in Figure 1, the pandemic has increased the probability of job loss across all sectors of the economy; however, this increase in percentage points is higher the lower is the average sector wage, with the category of least-paid workers being the most likely to lose their job. This category includes occupations such as home-based personal care and related workers, cleaners and helpers in offices, hotels and other establishments, or restaurant and kitchen helpers. Considering that the pre-pandemic probability of becoming unemployed was already largest for this group (19.7% compared to the average 6% in 2019), the existing inequality in the labor market has been exacerbated by the Covid-19 crisis. In our regression analysis that is available by request, we also find that accounting for an index of the share of tasks that can be performed from home, defined at 2-digit occupational level, does not explain away the negative and significant relationship between wages and job loss probability. Although, we confirm previous evidence that the probability of losing jobs is lower among occupations that can be performed from home. The substantial contraction in economic activity in some sectors of the economy seems to be the driver of the unequal distribution of job losses.

 Figure 1. Change in job loss probability by wage decile between February and July

Source: Author’s own calculation, for data sources see Data Section.

Job loss probability by gender. Figure 1 also documents that, even though the change in job loss probability is higher in sectors dominated by women, the likelihood of men losing jobs has increased more in these sectors. As a result, in the regression analysis we find that there is no significant association between the share of women in a sector and the sector-level change in job loss probability.

Job loss probability by foreign status and age. We find that workers who are born outside of EU countries are significantly more likely to transition into unemployment during the pandemic (see Figure 2). The difference is striking. Based on our indicator, considering male workers the pandemic has raised the probability of job loss by roughly 7 p.p. more for non-EU citizens as compared to non-Swedish EU citizens, and by 9 p.p. more compared to Swedish citizens. These differences are only slightly smaller for women. Another group particularly affected is that of workers in the age group below 30 (result available upon request). Such patterns are due to foreign-born and younger workers being more concentrated in those low-wage sectors that also appear, based on our analysis, to be more impacted by the pandemic in terms of job loss probability

Figure 2. Change in job loss probability by foreign status between February and July 2020

Source: Author’s own calculation, for data sources see Data section

Conclusion

Our analysis of administrative monthly data on the number of workers who register as unemployed in Sweden confirms previous evidence that the Covid-19 crisis has not been “the great equalizer”. While the pandemic has increased the probability of losing jobs across all sectors, the most affected in Sweden are those workers in occupations where the lowest wages were paid before the pandemic. Considering other demographic characteristics, vulnerable groups that were most impacted by the crisis are workers born outside of the EU and workers aged below 30. However, we do not find evidence of a gender-unequal impact of the pandemic in terms of the probability of job loss. There may of course be many other aspects to the issue along gender lines. For example, on one hand, there might be gender-unequal effects that we cannot observe in our data, for instance in the number of hours worked, temporary unemployment, and level of stress due to increased childcare responsibility. On the other hand, since schools in Sweden stayed open throughout the pandemic, the concerns related to increased childcare responsibility, which have led to identifying mothers as most vulnerable in other countries, do not necessarily apply to the Swedish context.

Sweden has adopted a number of measures to shield workers from the worst effects of the pandemic. As the country plans the recovery, special attention should be devoted to the opportunities for re-employment for the most vulnerable groups. Absent such focus, the economy emerging from the crisis might be less inclusive and equal than it has been before the pandemic, with important consequences for many societal outcomes that are generally linked to labor market inclusiveness.

References

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

Laissez-faire Covid-19: Economic Consequences in Belarus

Posted on March 15, 2021 | Policy Brief

20210301 Addressing the COVID-19 Pandemic FREE Network Policy Brief Image 04

Despite its traditional paternalistic role, the Belarusian government chose minimal reaction to the Covid-19 pandemic. No meaningful economic or social measures were taken in response to the pandemic. We explore a unique dataset to document how major Covid-related shocks affected the earnings of Belarusians in 2020. We utilize the differential timing and sectoral effects of the shocks to identify the impact of Covid-19 on individual socioeconomic outcomes. Not surprisingly, we find that Covid-related shocks increase the probability of an income reduction. This effect is most pronounced for those employed in the private sector. In the absence of a social security net, vulnerable groups had to cope with the economic consequences of the pandemic on their own.

Introduction

Belarus had its first official case of Covid-19 registered on February 27 and its first death on March 31. At first, the increase in newly registered cases was slower than in most other countries, but at the beginning of April Belarus started to catch up. The peak of the first wave was recorded on May 18 with 943 new daily cases. According to the official statistics, the second wave started in September 2020 and was much more severe than the first one, reaching 1,890 new daily cases by the end of December.

Belarusian authorities did not undertake any substantial interventions, such as lockdowns, to fight the spread of the pandemic.  Nevertheless, there were several other key mechanisms through which Covid-19 affected the Belarusian economy. The population’s reaction to the risks of contamination led to a substantial fall in mobility that resulted in decreased sales in retail and services requiring physical interaction. For example, sales in the restaurant industry decreased by 20% in 2020. Lockdowns in major international trade partners such as Russia have led to a decrease in demand for Belarusian exports of goods and transportation services. In the face of these economic challenges, the government focused its attention on supporting full employment and production in state-owned enterprises while ignoring the rest of the economy.

In this brief, we present evidence of the economic effects of Covid-19 in Belarus. We employ a unique dataset on socioeconomic outcomes collected by BEROC to study how individuals are affected by Covid-related shocks in mobility and exports. In order to isolate the effects of these shocks on the well-being of Belarusians, we exploit their timing and sectoral differences.

Measuring Covid-related Shocks

Figure 1 depicts changes in the Yandex self-isolation index which measures the use of Yandex services, including Yandex traffic monitoring and customer mobility compared to the average pre-pandemic day (Yandex DataLens, 2021). Individual everyday mobility started to decline in mid-March, and as the first wave of the pandemic gained momentum, mobility reached its lowest point at the end of April. It started to decline again in November-December 2020 following the second wave.

Figure 1. Yandex self-isolation index in Belarus, 2020

Source: Yandex. The average value during 24 Feb-8 March 2020 set to 100. Seven-day rolling average.

Belarus is a small and open economy with Russia as its main trading partner. The lockdown in Russia that lasted from the end of March until mid-May along with the spring lockdowns in Europe caused a major contraction in external demand for Belarusian goods. Figure 2 shows total physical exports and non-energy physical exports in 2020. The largest difference between total and non-energy exports can be observed in January, February, and March during which Russia and Belarus had an oil-supply dispute. To focus on the effects of the pandemic we use non-energy physical exports to approximate Covid-related exogenous shocks to the economy.

Figure 2. Physical export indices, Belarus

Source: Belstat. December 2019=100.

Income Dynamics

To measure the impact of Covid-19 on Belarusian society, BEROC, in cooperation with the marketing and opinion research company SATIO, conducted a series of online surveys representative of the urban population of Belarus (Covidonomics, 2021). The five waves of the 2020 survey were carried out on April 17-22, May 8-11, June 8-15, September 11-16, and November 25-30.

Respondents were asked about recent changes to their income, and also to specify the reasons for income reduction (if this was the case), including depreciation of the ruble, salary cut, furlough, etc.  Figure 3 depicts the percentage of individuals who reported an income reduction in the previous month for reasons other than currency depreciation by sector of employment. The income reductions peaked in April-June, with the situation relatively stabilizing by September.

Figure 3. Income dynamics by sector

Percentage of respondents reporting income reductions in the previous month for reasons other than currency depreciation, Source: BEROC/SATIO data

The fact that the share of respondents reporting termination peaked at 2.9% in May indicates that firms did not use employment reduction to adapt to the pandemic environment. A big share of respondents employed in the service sector reported domestic demand contraction (fewer orders/clients) as a key factor for their income reduction. The industries that took the hardest hit were hospitality-retail and transportation. In early spring, manufacturing appeared to be one of the most affected industries. However, as exports started to recover in June, the share of manufacturing workers that reported an income reduction decreased significantly, becoming one of the lowest across industries.

Identifying the Effects of Covid-19 Shocks

In this section, we estimate the probability of facing a reduction in individual income as well as the likelihood of being furloughed due to the Covid-19 pandemic.

In 2020, the Belarusian economy suffered due to the oil-supply dispute with Russia, the Covid-19 pandemic, and the national political crisis. To isolate the effects of Covid-19 from those driven by the oil dispute and the political crisis, we add interactions between Covid-related shocks and dummies indicating industries affected by those shocks. This implies three interactions with different binary indicators: exports and manufacturing, exports and transportation, and mobility and hospitality/retail.

To estimate these effects, we use a fixed-effects probit regression controlling for sector of employment, education, age, and gender.

Table 1. Probability of income reduction and furlough

Source: Own estimates from BEROC/Satio data. Controls include age, sector of employment, and education level.

Table 1 shows that individuals employed in the hospitality and retail industry face higher risks of an income reduction due to decreased mobility caused by self-isolation behavior. A 10-percentage-point increase in the self-isolation index is associated with a 1.3 percentage point increase in the probability of income reduction for those employed in the retail and hospitality industry. The interaction term between exports and the manufacturing dummy also appears to be statistically significant for various specifications. A 10-percentage-point decline in physical volumes of exports is associated with a 8.6 percentage point increase in the probability of income reduction for manufacturing workers.

Notably, the private sector employment coefficient shows strong statistical significance which highlights the choice of the authorities to support SOEs, with little to no support for the private sector. Being employed in the private sector increases the probability of facing an income reduction by 7.9 percentage points.

The Gender Dimension

Despite concerns that women experience larger economic losses due to consequences of the pandemic (Dang and Nguyen, 2021; Alon et al., 2020b), we do not find a statistically significant effect of gender in our sample.  In particular, our results offer no evidence of women being more likely to experience an income reduction during the pandemic, similar to findings in Germany (Adams-Prassl et al. 2020c).

While job losses were uncommon during the Covid-19 crisis in Belarus, being furloughed was one of the most common reasons for an income reduction (11.3% of respondents reported being furloughed in May). We also investigate the separate channels through which individuals lose income due to the Covid-related shocks. Notably, the only channel of income reduction that is more prevalent among women than men is through furlough. This finding is consistent with Adams-Prasslet al. (2020a) who argue that this discrepancy can be explained by gender differences in childcare responsibilities.

Conclusion

Belarus is close to unique in having almost no government response to the Covid-19 pandemic. Despite the absence of lockdowns and other restrictions, the Belarusian economy has experienced several Covid-associated shocks. Due to the economy’s openness to trade, it was seriously affected by export contractions. Belarusians have voluntarily reduced their mobility to minimize health risks which has affected the hospitality and retail industry.

We utilize the differential timing and sectoral impact of Covid-related shocks to estimate the pandemic’s effect on the socioeconomic outcomes of individuals. By using a unique dataset, we find evidence that the pandemic increased the likelihood of income reductions for Belarusians, mainly due to the effects of decreased mobility and fall in exports. We also find that those employed in the private sector were more likely to suffer from negative shocks, reflecting the policy choice of the Belarusian government to only provide economic support to the state sector. Finally, we show that, while women are as likely as men to see their income reduced, they are significantly more likely to be furloughed.

Many Belarusians saw their well-being deteriorating as a result of the Covid-19 pandemic. In the absence of unemployment benefits and other social protection mechanisms (Umapathi, 2020), those economically affected had to bear the cost of the shocks on their own.

References

  • Adams-Prassl, A., Boneva, T., Golin, M., and Rauh, C. (2020a). Furloughing. Fiscal Studies, 41(3):591–622.
  • Adams-Prassl, A., Boneva, T., Golin, M., and Rauh, C. (2020b).  Inequality in the impact of the coronavirus shock:  Evidence from real time surveys. Journal of Public Economics, 189:104245.
  • Covidonomics project (2020). BEROC and Satio. http://covideconomy.by/
  • Dang, H.-A. H. and Nguyen, C. V. (2021). Gender inequality during the Covid-19 pandemic: Income, expenditure, savings, and job loss. World Development, 140:105296.
  • Umapathi, N. (2020). Social protection system in Belarus:  perspective. Bankovskiy Vestnik, (3):75–80.  (in Russian).
  • Yandex (2021) Yandex DataLens, https://datalens.yandex.ru/

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.

Unemployment in Transition and Its Long-Term Consequences

Posted on December 3, 2018 | Policy Brief

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We examine the relationship between the experience of unemployment in the early years of the socio-economic transition in Poland and a number of wellbeing measures about two decades later. The analysis takes advantage of the rich content of data from the Survey of Health, Ageing and Retirement in Europe (SHARE) by matching retrospective information on labour market experiences with outcomes observed in the survey after year 2006. While there is a strong correlation between unemployment and general wellbeing measures such as life satisfaction, depression and subjective assessment of material conditions, the relationship cannot be interpreted as causal. On the other hand, we find that unemployment in the early years of the transition has strong, negative, long-term consequences for income and house ownership. The analysis sheds light on the implications of unemployment and on the nature of job losses in the follow-up of the Polish ‘shock-therapy’.

Introduction

Next year, the countries of Central and Eastern Europe will celebrate the 30th anniversary of the political breakthrough and the beginning of a major socio-economic transformation which followed. In the Polish case, the ‘shock therapy’ approach to the reform process implemented by the Mazowiecki government, though not without faults, has generally been viewed as the origin of the country’s economic success story. Afterwards, Poland experienced nearly three decades of uninterrupted economic growth and the Polish GDP returned to its pre-reform level already in 1995.

However, discussions of negative implications of the reform package still fuel the academic discourse as well as the political debate. While the majority of the population managed to avoid significant economic difficulties, many families experienced the painful hardship of the transition period in the form of job losses, poverty and exclusion. Given the scale of the socio-economic change, surprisingly little is known about the long-term consequences of individual experiences at that time. In particular, it is unclear if the negative outcomes observed many years after the reforms started can be causally linked to individual experiences in the early 1990s.

This lack of evidence is not unique for Poland and is largely due to unavailability of good individual-level data spanning the time before and after the collapse of communism. Since the transition cannot be lived through again, we shall never know how socio-economic conditions would have looked like under numerous alternative reform scenarios. However, as we show in a recent paper (Myck & Oczkowska, 2018), much can be learnt from the combination of contemporary and retrospective information on the nature of labour market histories during the transition and their relationship to outcomes recorded many years later.

The analysis presented in Myck and Oczkowska (2018) relies on the treatment of the systemic changes in the early 1990s as a major exogenous shock and on differentiating between reasons behind individual experiences of unemployment. We demonstrate that the observed strong correlation between unemployment in the initial years of the transition and a number of subjective wellbeing measures in later life is endogenous, and may reflect unobservable individual characteristics. It seems plausible to argue that these characteristics were the reasons behind the recorded job losses once the economy was liberalised and firms could fire their least productive employees.

Work histories in the SHARE dataset

The analysis is based on individual-level data from the Polish part of the Survey of Health, Ageing and Retirement in Europe (SHARE). SHARE is a multidisciplinary biennial panel survey focusing on individuals aged 50 years and over. Since the start of the project in year 2004 seven waves of data have been collected, and the survey was conducted in Poland in waves 2, 3, 4, 6 and 7. While the standard waves of the survey focus on contemporary conditions of respondents such as health, economic conditions, labour market activity and social networks, in wave 3 (the so-called SHARE-Life), participants were asked about their life histories including their family history, mobility and labour market experiences. The detailed labour market histories recorded in SHARE-Life allow us to identify transition-related job losses, which can be matched with current information on several measures of material conditions and wellbeing for the same individuals.

In Figure 1 we present labour market profiles since 1988 of those in the sample who were working prior to the start of the reform process.

Figure 1. Labour market status 1988 – 2008 conditional on working in 1988 in Poland

Source: Myck and Oczkowska, 2018.

The figure shows that along with rapidly increasing unemployment rates, the degree of inactivity of the Polish population grew substantially in the two decades following the transition. This data confirms that in the follow-up of the ‘shock-therapy’ reforms many individuals faced unemployment, while others, especially among older groups of employees, used several other labour market exit options, such as retirement or disability.

Analysing long-term consequences of economic shocks

To examine the role of unemployment experiences in the initial years of the transition for outcomes observed a few decades later, we use data from waves 2, 3 and 4 of the SHARE study. The analysis focuses on two groups of later-life outcomes – objective measures of material conditions such as household income, real assets and house ownership, and subjective indicators of wellbeing such as life satisfaction, depression or reporting difficulties in making ends meet.

We are able to control for an extensive set of individual characteristics which are usually unobservable to the researcher, through a complex set of background variables available in SHARE. These include respondents’ childhood conditions, parental background as well as health and labour market experience prior to 1988. With regard to the experience of unemployment we differentiate the instances of unemployment between the initial (1989-1991) and later (1992-1995) period of the transition to examine the potential differential implications of the rapid pace of the reforms in the early 1990s. Most importantly though, the data allows us to distinguish between different reasons behind job losses and we can separately examine the relationship with plant/office closures and other reasons for unemployment. Following other examples in the literature (Farber, 2011; Jacobson et al., 1993), we argue that plant closures can be treated as reasons for exogenous job separations. This in turn allows us on the one hand, to give a causal interpretation to the estimated coefficients, and on the other, to interpret those on other reasons for unemployment in the light of the causal relations.

Effects of unemployment experience on later-life outcomes

We find that experiencing unemployment due to plant/office closure between 1989 and 1991 is associated with almost a 30 percent lower level of household income and a lower probability of house ownership of about 10 percentage points (pp) some two decades afterwards. There is also a strong relationship between unemployment in the early years of the transition and wellbeing measures two decades later – individuals who experienced unemployment in the first three years of the transition have a 14 pp. higher likelihood of reporting great difficulties in making ends meet, a 10 pp. lower probability of  high life satisfaction and a 11 pp. higher likelihood of depression. However, since these relations do not hold for unemployment due to plant closures, they cannot be treated as causal. The results are therefore most likely driven by unobserved factors which simultaneously determine the lower level of outcomes two decades after the ‘shock-therapy’ reforms, and the likelihood of experiencing unemployment in the early 1990s.

Conclusion

In this policy brief we outline recent results on long-term implications of labour market developments in the early years of the economic transition in Poland. The analysis is based on a combination of contemporary and retrospective data from the SHARE survey, and focuses on the associations between the experience of unemployment in the initial years of the transition in Poland and a number of outcomes measured about two decades later. Using plant/office closures as exogenous sources of job separations during the early 1990s, we find a strong and statistically significant, negative, long-term effect on income and home ownership, which can be treated as causal.

We also find strong negative associations between unemployment for other reasons than plant / office closures and a number of subjective measures of wellbeing. This relationship however, does not hold for the exogenous reasons for job losses, which suggests an important role of unobservable factors that lead to unemployment and at the same time are responsible for the lower level of outcomes in later life. This is consistent with the labour market reality of central planning characterised by labour hoarding and maintaining employment regardless of workers’ productivity. When the economic reality changed in 1989, the least productive individuals were the first to be fired, and as our analysis shows, these are also the individuals with lower subjective levels of wellbeing two decades later. We confirm thus that those who lost their jobs in the early 1990s have lower measures of the subjective wellbeing outcomes, although the latter cannot be identified as specific consequences of unemployment in the first years of transition.

References

Acknowledgement

The authors gratefully acknowledge the support of the Polish National Science Centre through project no. 2015/17/B/HS4/01018. For the full list of acknowledgements see Myck and Oczkowska (2018).

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 Market Adaptation of Internally Displaced People: The Ukrainian Experience

Posted on October 29, 2018 | Policy Brief

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This brief is based on research that investigates the probability of employment among displaced and non-displaced households in a region bordering territory with an ongoing military conflict in Eastern Ukraine.  According to the results, internally displaced persons (IDP) are more educated, younger and more active in their job search than locals. Nevertheless, displaced individuals, particularly males, have experienced heavy discrimination. After controlling for personal characteristics, the structure of the household, location, non-labour incomes and endogeneity of displacement, IDP males are 17% less likely to be formally employed two years after resettlement than locals.

Internally displaced persons in Ukraine

In 2014, 23 years after independence, Ukraine suddenly found itself among the top-ten of countries with the largest internally displaced population. During the period 2014–2016, 1.8 million persons registered as internally displaced. Potentially, about 1 million more reallocated to Russia and about 100,000 to other countries nearby, where they sought refugee or labour migrant status (Smal, 2016).

The Ministry of Social Policy of Ukraine (MSPU) has regularly published very general reports on displaced persons. According to these reports, at the end of February 2016, the internally displaced persons in Ukraine included 22,000 individuals from Crimea and over 1.7 million citizens from Eastern Ukraine. These are mostly individuals who registered as IDPs to qualify for financial assistance from the state and some non-monetary benefits. Among them, 60% are retired people, 23.1% are individuals of working age, 12.8% are children and 4.1% are people with disabilities (Smal and Poznyak, 2017). In fact, the MSPU registers not only displaced persons but also those who de facto live in the occupied territories and occasionally travel to territories controlled by the Ukrainian authorities to receive their pension or social benefits (so called ‘pension tourism’). On the other hand, some IDPs did not register either to avoid bureaucracy or because they were unable to prove their status due to lack of documents. Recent publications that are based on surveys portray a more balanced distribution: 15% are retired people, 58% are individuals of working age, 27% are children and 13% are people with disabilities (IOM and the Ukrainian Centre for Social Reforms, 2018).

Only limited information is available about IDPs’ labour market activity. According to the State Employment Service (SES), between March 2014 and January 2016, only 64,300 IDPs or 3.75% referred to the SES for assistance (Smal and Poznyak, 2017). On the one hand, this figure reflects the relatively low reliance of displaced Ukrainians on the SES services in their job search. On the other hand, the geographical variation in the share of SES applicants suggests that Ukraine’s IDPs who moved further from the war zone and their homes were more active in trying to find a job.

Data

Our primary data were collected in June–August 2016 by REACH and provided by the Ukraine Food Security Cluster (UFSC) as a part of the needs assessment in Luhansk and Donetsk oblasts of Ukraine – two regions that were directly affected by the conflict. These two regions have hosted roughly 53% of all IDPs in Ukraine (Smal and Poznyak, 2017). We argue that households that did not move far from the place of conflict are most likely to be driven by conflict only, while long-distance movers may combine economic and forced displacement motives.

The data set offers information on 2500 households interviewed in 233 locations and is statistically representative of the average household in each oblast. It includes respondents currently living in their pre-conflict settlements (non-displaced, NDs) and respondents who report a different place of residence before the conflict (IDPs). The IDP group comprises individuals with registered and unregistered status and from both sides of the current contact line. The non-IDP group includes only households living on the territory controlled by the Ukrainian Government that did not move after the conflict had started.

Our sample covers 1,135 displaced households that came from 131 settlements. Most of the reallocations took place in early summer 2014 with the military escalation of the conflict in Eastern Ukraine. Thus, the average duration of displacement up to the moment of the interview was 637 days (or 21 months). This is a sufficiently long period for adaptation and job search. However, there is enough variation in this indicator – some families left as early as March–April 2014, while others were displaced in June 2016, just a few days before the interviews started.

Results

Simple comparison shows that heads of displaced households are on average almost four years younger than those of non-displaced households (Table 1). In terms of education, displaced households are found to be more educated than non-displaced households, as there are significantly more IDP household heads with tertiary education and significantly fewer individuals with only primary, secondary or vocational degrees. In particular, 37% of IDP household heads hold a university degree compared with 22% of household heads among the local population. This seems to suggest positive displacement selection. IDPs are slightly more likely to be headed by females and unmarried persons, although these differences are statistically insignificant. Displaced households include more children aged under five (0.35 vs. 0.22 children per non-displaced household) and 6 to 17 years (0.42 vs. 0.34, respectively) and fewer members aged over 60 years (0.58 vs 0.66, respectively). There is no difference in the number of working-age adults or disabled individuals per household among IDPs and non-IDPs. The average household size is statistically similar for the groups (2.74 vs. 2.65 persons per IDP and non-IDP household, respectively).

Table 1. Selected descriptive statistics

Internally displaced households Non- displaced households
Household head employed 0.43*** 0.48***
Household head characteristics
Age (years) 48.10*** 52.85***
Male 0.49 0.52
Education
vocational 0.42*** 0.49***
university 0.37*** 0.22***
Household characteristics
Size (persons) 2.74 2.65
Number of children 0-5 0.35*** 0.21***
Number of children 6-17 0.42*** 0.34***
Number of members 60+ 0.58** 0.66**
IDP payments 0.50*** 0***
Humanitarian assistance 0.78*** 0.28***

There are further differences in the types of economic activity and occupations among IDPs and non-IDPs. Prior to the conflict, displaced respondents were more likely (than non-displaced persons) to be employed as managers or professionals and less likely to hold positions as factory or skilled agricultural workers. This result also speaks in favor of a positive displacement selection story.

As expected, the conflict has had a negative effect on human capital in the government controlled areas of Donetsk and Luhansk regions. We observe some deskilling at the time of the interviews, which is especially pronounced for IDPs. In particular, the share of managers among the IDPs had reduced from 12% to 5% and that of technicians from 15% to 12%, while the proportion of service and sales employees had increased from 10% to 13%, that of factory workers from 11% to 15% and that of skilled agricultural workers from 2% to 6%.

Considering the economic activity in the current location, we can note that on average the heads of displaced households are 5% less likely to be employed than those of non-displaced households (43% vs. 48%, respectively). In both groups, a large share of respondents report difficulties in their job search, but IDPs are 13% more likely to experience this problem. They report changing their pre-conflict occupation three times more often than non-IDPs (37% vs. 11%).

Government and non-government assistance may also drive the differences in employment. Economic theory states that individuals are less likely to work if they have some backup in the form of non-labour earnings. Financial support and humanitarian assistance are widely used to smooth a displacement shock. At the same time, improperly designed assistance schemes may reduce the stimulus to search for a job.

IDPs are 9% less likely to include earnings in their household’s top three main sources of income than the non-displaced population (46% vs. 55%, respectively), meaning that they rely more on various social payments and pensions. In addition, displaced households may be slightly more reluctant to search for a job due to displacement assistance from the government (received by 50% of IDPs compared with 0% for non-IDP households), although the amounts are quite modest. According to the existing legislation, IDPs can receive regular monthly state payments and one-time state payments. Regular monthly payments can be received by any IDP and cannot exceed UAH 3,000 (~$111) for an ordinary household, UAH 3,400 for a household with disabled people and UAH 5,000 (~$185) for a household with more than 2 children. Eligibility and the size of the one-time payment are determined by the local government. In the data set, 95% of IDPs receive less than UAH 3,000 while the 2016 average monthly wage was UAH 6,000 in Donetsk and UAH 4,600 in Luhansk regions.

In addition, IDPs are three times more likely to receive humanitarian assistance (78% vs. 28% among displaced and non-displaced persons, respectively). This support includes mostly food and winterisation items but also cash (26% among displaced vs. 12% among non-displaced assistance receivers). On the other hand, to cover reallocation and adaptation costs, some IDPs use their financial reserves, and as a result they are by 10 p.p. more likely to report no or already depleted savings. This may increase their stimulus to engage in a more active job search.

After taking into account the observed and unobserved differences between the groups as well as controlling for the location fixed effect, we find that the difference in the probability of employment between displaced and non-displaced persons increases from a casually observed slit of 5% to a chasm of 17.3%. This result suggests that IDPs are [negatively] discriminated despite being younger, more educated, skilled and more ‘able’ in the labour market. Specifically, 7 out of 17 p.p. (41% of the gap) are due to the variation in observed household head characteristics and family composition, while unobserved displacement-related features (such as attitude towards change, activism, mental and physical ability to reallocate) account for 5 p.p. (29%) of the gap. Controlling for particularities of a current location does not substantially affect the estimated differences.

Figure 1. Main results

We re-estimate these regressions using an employment indicator that includes both formal and informal employment (as defined by the respondents), accounting for occasional and irregular employment, including subsistence agricultural work. Since informal work is more common among IDPs, this definition of employment leads to a reduction in the average casually observed gap from 5% to 3%. However, after controlling for all the factors, we obtain the same result – a 17.8% difference between displaced and non-displaced households.

Conclusion

Policy makers and international donors should not be misled by the seemingly comparable probability of employment among IDPs and non-IDPs based on simple statistics. The average 0–5% difference in unconditional employment rates conceals the actual 17% gap in the likelihood of having a job. The contribution of unobserved displacement-related factors in hiding the true gap is large, especially for males seeking formal employment. Without adjusting for it, we would underestimate the real difference in employment probability by one-third to one-half.
Our study produces firm evidence that displaced individuals in Ukraine, particularly males, have been discriminated against in terms of employment. Our results further suggest that male heads of displaced households experience more discrimination in the formal labour market, while the situation is the opposite for females, who are more likely to face unequal treatment in the informal sector. Policy makers and volunteers should take this difference into account in the adaptation of male- and female-headed households.

Humanitarian assistance to displaced individuals was found to have no negative effect on their employment, which suggests that it is provided in an effective manner. Thus, this tool can be used to mitigate the discrimination.

References

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

Meeting Qualification Mismatch with Vocational Training

Posted on October 20, 2014 | Policy Brief

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While in an ideal world the qualification preferences of job seekers and employers would coincide, in reality this is often not the case. Besides informational asymmetries (job seekers not knowing which qualifications are demanded by employers) the reason is that employers may be in need of qualifications that are not considered attractive by the job seekers. In the country of Georgia, we want to address this problem through a “recommendation system” which will suggest vocational training to job seekers. There are two main problems to be tackled in this project: (1) How can we decide what would be the most useful qualification for a given job seeker, and (2) how can we incentivize the job seekers to follow our recommendations? This policy brief discusses our approach to this problem.

Introduction

Qualification mismatches are common in many labor markets around the world (see for example, Ghignoni and Verashchagina (2014) for Europe, McGuinness and Sloane (2011) for the UK, and Béduwé and Giret (2011) for France). It is well known that qualification mismatch is a relevant problem also in the country of Georgia, as was shown in various studies (see ISET (2012) and The World Bank (2013)).

The ISET Policy Institute (ISET-PI) was commissioned by the World Bank to assist the Social Service Agency (SSA) of Georgia, an agency of the Ministry of Labor, Health, and Social Affairs, in developing a system which will recommend vocational training to job seekers with the aim to reduce the qualification mismatch in Georgia.

Job Seekers’ Preferences Matter

Vocational training addresses the needs of two different groups. It is demanded by job seekers, who want to improve their human capital in a way that matches their preferences and, in the optimal case, maximizes their chances to get back into employment. At the same time, vocational training also addresses the needs of employers, whose businesses may face shortages in qualified personnel.

It is not enough to only include employers in the analysis if one wants to effectively fight the qualification mismatch. If one does not consider job seeker’s preferences, it may happen that people prefer to not participate in the vocational training system at all. Even if one can effectively incentivize job seekers to attend training programs, as is the case in Germany for example, where the refusal to participate in training is sanctioned by a reduction of unemployment benefits (cf. Neubäumer (2012)), it is likely that involuntary training will be less effective. Therefore, it is problematic that most studies which analyze the demand for qualifications in the job market, for example for the European Union (Lettmayr and Nehls (2012)), New Zealand (Earle (2008)), and Australia (Shah (2010)), exclusively focus on employers and neglect the preferences of the people who are to be trained. In Georgia, we will do it differently.

Why Would Job Seekers Follow Our Recommendations?

The objective of the recommendation system we develop is to maximize the impact the training has on the employment chances of the job seeker. Arguably, this is also the primary goal for most job seekers, as they often state that they want to receive training in an “employable” profession. Therefore, if the purpose of the recommendation system is communicated properly, and if it is transparent and trustworthy, the job seekers may want to voluntarily follow its advice.

Recommendation System vs. Matching Algorithm

One can think of two different ways of advising job seekers in their training choices: recommendation systems and matching algorithms.

Recommendation systems make suggestions to job seekers separately. These kinds of systems are ubiquitous on the Internet. For example, Amazon.com proposes books to its customers based on their purchasing history. In a similar way, a recommendation system for vocational training would suggest vocational training programs to job seekers based on relevant data about their characteristics and the job market situation. Yet its major shortcoming is that a recommendation system will not take into account what other job seekers do and what recommendations were given to them.

For that reason, in a recommendation system, it can happen that the number of people recommended to choose a certain program is larger than that program’s capacity (because the advice comes as a ranking, this does not cause the system to be useless, as the job seeker may then choose the program which is highest in the ranking and which has free places).

Likewise, if many job seekers follow the advice of the recommendation system, oversupply and undersupply of certain qualifications in the job market is not ruled out. This is again due to the fact that recommendations are made separately. If there is a huge demand for, say, plumbers, and many people receive the advice to receive training in plumbing, this may subsequently cause an oversupply of plumbers.

In contrast, a matching algorithm aims at an overall optimum for the whole group of job seekers. Genuine matching algorithms do not make separate recommendations, but propose a globally optimal assignment. In Western countries they are used, for example, to match interns to hospitals, students to universities, and kidneys to dialysis patients. Matching theory is one of the most successfully applied subfields of game theory, acknowledged through the award of the Economics Nobel Prize of 2012 to matching theorist Alvin E. Roth. The standard survey of matching theory is Roth and Sotomayor (1990).

In a matching algorithm, the abovementioned problems of a recommendation system would not occur (up to statistical uncertainty), because the matching algorithm would take into account how the suggestions made by the system affect the demand for a program. It would aim to keep the number of people, likely to choose a program, to remain below its capacity.

While a matching algorithm is more ambitious, it also has disadvantages compared to a simple recommendation system. First of all, the data requirements are higher, as the capacities of programs have to be taken into account. More importantly, in a matching algorithm the recommendations will be generated in a way that is not transparent to the job seeker (though it is possible to give some general explanations). This may reduce acceptance and willingness to participate. The recommendation system, on the other hand, can work in a relatively transparent way. Finally, a recommendation system can be adjusted and changed on an ongoing basis by Social Service Agency personnel without the help of external experts. Given its complexity, this is hardly possible with a matching algorithm.

Therefore, it was decided that the simpler option of a recommendation system is to be pursued. Later, the system may be upgraded to a full-blown matching algorithm.

The Technical Aspects of How Recommendations are made

Consider the situation of a job seeker looking for vocational training. Through the envisioned system, they will receive a recommendation of which qualification to pick in the vocational training system of the SSA.

The pieces of information used for making this recommendation are personal characteristics of the job seeker (like age, gender, preferences, skills, and other information obtained through the website worknet.ge which is operated by the SSA) and the current and future economic situation in different sectors. To this end, we will use value added tax data that can be decomposed into 45 sectors and updated on a monthly basis. For forecasts, we will draw on the Business Confidence Index of ISET, which allows decomposition into 5 sectors.

Given the information about the job seeker and the economic environment in different sectors, we will answer the question: “How many months do we expect the job seeker to be unemployed in the year after the training if the training was in qualification X?” Here, X can be whatever is offered in the vocational training system at the location of the job seeker, for example welder, mechanic, accountant, or IT expert. Alternatively, we could answer the question: “What is the salary we expect the job seeker to have in the year after the training if the training was in qualification X?”

The recommendation made to the job seeker will be: “Choose the training in field X if somebody with your personal characteristics, given the economic situation and outlook, has the lowest expected number of unemployed months (or the highest salary) in X in the year after training in X was received.” This recommendation is likely to be accepted by the job seeker if also the job seeker wants to maximize their employment chances (or maximize salary).

The forecast can be made using econometric regression analysis. Let i be a job seeker and xi be the number of months unemployed in the year after training was received. Then we have for each qualification one estimation equation

FPB_Oct20_fig1where alpha is the intercept and the betas are the coefficients for different personal and economic characteristics. When the alpha and beta coefficients are known, then one can enter the specific data for a job seeker and forecast how long it would take him to find a job if training would be received in a particular field.

For estimating the coefficients, no recommendations will be made for some time (like 3 months) after the system is launched and only information will be collected. The SSA or a specialized survey agency will call the job seekers every month after they received training and ask whether they found employment. Job seekers who received training through the SSA will be obliged to answer this question truthfully. Information about the characteristics of the job seeker is known through their participation in the worknet.ge system, which is a requirement for anybody who wants to receive vocational training through the SSA.

When the recommendation phase starts, further data will be collected. Errors in the estimation of the coefficients will be corrected “automatically” through the feedback (in terms of job market performance of the trainees) that the system gets on an ongoing basis. To increase this effect, the database used for the estimation of the coefficients will be “rolling”, i.e. people who recently received training will be added while those who received training a longer time ago (e.g. one year or more) will be removed from the database.

Conclusion

In Georgia, ISET will design and implement a recommendation system for vocational training, addressing the qualification mismatch in the labor market. As in many other areas, Georgia is willing to go for innovative policy solutions making use of advanced economic methods, very much in line with the country’s reputation as one of the top reformers in the world.

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References

  • Béduwé, Catherine and Giret, Jean-Francois (2011): “Mismatch of vocational graduates: What penalty on French labour market?”, Journal of Vocational Behavior 78, pp. 68-79
  • Earle, David (2008): “Advanced trade, technical and professional qualifications: Matching supply to demand”, New Zealand Government Ministry of Education, Auckland.
  • Ghignoni, Emanuela and Verashchagina, Alina (2014): “Educational qualifications mismatch in Europe. Is it demand or supply driven?”, Journal of Comparative Economics, in press
  • ISET (2012): “National Competitiveness Report for Georgia”, Tbilisi.
  • Lettmayr, Christian F. and Nehls, Hermann (2012): “Skills supply and demand in Europe: Methodological framework”, CEDEFOP Working Paper No. 25
  • McGuinnes, Seamus and Sloane, Peter J. (2011): “Labour market mismatch among UK graduates: An analysis using REFLEX data”, Economics of Education Review 30, pp. 139-145
  • Neubäumer, Renate (2012): “Bringing the unemployed back to work in Germany: training programs or wage subsidies?”, International Journal of Manpower 33, pp. 159 – 177
  • Roth, Alvin E. and Sotomayor, Marilda (1990): “Two-Sided Matching: A Study in Game-Theoretic Modeling and Analysis”, Econometric Society
  • Shah, Chandra (2010): “Demand for qualifications and the future labour market in Australia 2010 to 2025”, Center for the Economics of Education and Training Working Paper, Monash University
  • The World Bank (2013): “Georgia: Skills Mismatch and Unemployment Labor Market Challenges”, World Bank Report No. 72824-GE

The relationship between education and labor market opportunities: the case of Ukraine

Posted on March 10, 2014 | Policy Brief

Education and labour market policy brief image

Author: Hanna Vakhitova, KSE and Tom Coupe, KSE

This brief is based on a research project that analyses the extent to which the educational system in Ukraine contributes to better local employment opportunities, hence diminishing the outflows. According to the results, additional year of education increases the chance of finding a job by 2-3%. However, the effect of education on wages is small, especially when compared to other transition countries (1-5% wage premium for a year of education). In addition, while in 8 out of 10 countries education has zero or positive impact on the probability of starting a business, this impact is negative and significant in Ukraine. *)

Property Rights and Internal Migration

Posted on April 16, 2012 | Policy Brief

20160125 FREE Network Policy Brief Featured Image

Authors: Paul Castañeda Dower and Andrei Markevich, CEFIR.

Russia currently faces an important policy challenge related to relatively high levels of regional inequality. Regional imbalances that persist, especially in unemployment, reflect inefficiency and may lead to political instability. National capital and labor markets should work to correct these imbalances. This policy brief focuses on the labor market. In particular, why internal migration is relatively low in Russia, and suggests a new direction of policies to increase the mobility of the Russian workforce.

Interregional differences in income and unemployment remain high in Russia relative to the US and Europe (Andrienko and Guriev 2004). Figure 1 shows the change in unemployment for Russia’s regions between 1992 and 2007 plotted against the level of unemployment in 1992. We calculate the change in unemployment using 2007 since the global financial crises led to a different type of convergence, a widespread increase in unemployment. The absence of a downward slopping trend demonstrates that convergence across regions is not taking place.

Internal migration could solve regional imbalances in unemployment by matching unemployed individuals from areas with high unemployment to job vacancies in areas with more employment opportunities. In the US, for example, Blanchard and Katz (1990) show that regional economies adjust to region-specific shocks mainly through internal migration. However, disparities persist in Russia, in part, because of the lack of internal migration, which is relatively low compared to the US and Europe (Andrienko and Guriev 2004). It is not surprising then that a recent report by the World Bank (World Bank 2010) claims that Russians should be moving more within the country than they currently are, considering the economic costs and benefits of migration. The remainder of this policy brief discusses the connection between property rights and internal mobility in order to understand why the Russian labor market allows such high levels of regional disparities.

To address this issue, we look to the past since there is evidence from the late Tsarist period linking property rights to internal migration that has modern day policy implications. For most of Russia’s history, labor mobility has been restricted and controlled. Serfdom limited peasants’ mobility for centuries; restrictions survived after emancipation under the Russian repartition commune regime. The Soviet propiska system introduced in 1932 heavily regulated internal migration till the very end of the USSR and there are remnants of this propiska system even today. However, the extensive state control over internal mobility was not always the case. In the late Russian Empire, internal mobility was relatively unrestricted by the state and internal migration worked to correct regional imbalances (Markevich and Mikhaillova 2012). This historical period offers a good opportunity to investigate the economic causes of labor mobility in Russia without the veil of legal and political restrictions.

Figure 2 shows a startling pattern in the migration flows from the European provinces to the Asian part of the empire during this period. The sparsely populated regions of Siberia and Northern Kazakhstan that had abundant virgin land were attractive destinations for Russian peasants. We propose that an important factor in understanding the explanandum is the Stolypin agrarian reform, the timing of which is exhibited by the vertical dotted line in Figure 2. The annual number of migrating households was about 15,000 before the reform but dramatically increased to a level of 40,000 households per year after the reform. We argue that the reform increased migration flows largely because it improved the liquidity of peasants’ assets, providing greatly needed funds to finance migration.

The Stolypin titling reform can be thought of as a quasi-natural experiment through which one can judge the importance of financial constraints. For our purposes, the reform’s impact on liquidity is limited to forty-one European provinces (guberniya) where at least five percent of the rural population resided in repartition (peredel’naya) communes. The remaining nine European provinces, where few, if any, peasants were members of repartition communes, constitute the control group. The reform gave households the right to exit from repartition communes and convert their communal allotment to individual ownership of land recognized by a land title. The conversion to individual ownership improved the liquidity of land and made migration more attractive since migration no longer entailed losing one’s allotment and households could more easily sell their land allotments to finance migration.

Using a panel dataset of regional migration to the Asian part of the empire, we apply a difference-in-differences analysis using the distinction between treatment and control groups mentioned above. Our results indicate that 160,000 of the 441,000 households that migrated after the reform can be attributed to the reform. In other words, the relaxing of land liquidity constraints explains at least 18.1% of all post-reform Europe-Asia migration in the late Russian Empire. To understand how large of an impact the reform had, we make a back of the envelope calculation that yields an estimate of 0.12 percentage points of GDP growth per year or about 5% share of total economic growth during this period (Chernina et al 2012).

This historical evidence of the relative importance of liquidity of land for internal migration translates well into the contemporary policy discourse. After consulting both qualitative and quantitative studies on internal migration in Russia, Andrienko and Guriev (2005) conclude that “the most important barrier to migration is the underdevelopment of financial and real estate markets.” Figure 3 shows the relationship between growth of unemployment in a region and the share of privatization of residences using an added variable plot. Here, we condition the relationship on GDP per capita in 2000 and include federal district fixed effects in order to more closely isolate the liquidity effect of privatization. We use as base year 2000 instead of 1992 as in figure 1 because not all regions had initiated privatization until as late as the mid to late 90’s. While this correlation is not strong and is merely suggestive of an underlying relationship between private ownership and mobility, the graph illustrates that those regions with greater levels of privatization in 2000 subsequently experienced greater declines in unemployment during 2000-2007.

In summary, the ability of property rights to affect the financing of migration as well as the role that property rights play in the opportunity cost of migration calls for policymakers to include the issue of property rights when considering barriers to internal mobility. These findings fit well within the new economics of migration literature that criticizes and widens the previous narrow focus on wage differentials. In transition countries, these findings also point towards the importance of how privatization occurred. Different ways of organizing private ownership lead to different transaction costs incurred in buying and selling residential property. For example, in some former Soviet Republics, the privatization of individually owned apartments often did not fully specify property rights concerning the ownership of the apartment building and the internal structures that support the individual apartments. These ambiguities increase transaction costs and reduce the liquidity of the asset. Policies concerning internal mobility should therefore pay closer attention to the liquidity of Russians’ assets and how to improve it.

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References

  • Andrienko, Y., Guriev, S. (2004). “Determinants of Interregional Labor Mobility in Russia.” Economics of Transition 12(1).
  • Andrienko, Y., Guriev, S. (2005). “Understanding Migration in Russia.” CEFIR Policy Paper Series 23.
  • Blanchard, O. and Katz, L. (1992) “Regional Evolutions”, Brooking Papers on Economic Activity, 1.
  • Chernina E., Castañeda Dower P., and Markevich, A. (2012) “Property Rights, Land Liquidity and Internal Migration” NES Working Paper.
  • Markevich, A. and Mikhailova, T. (2012). “Economic Geography of Russia” in The Handbook of Russian Economy. Oxford University Press, eds. Alexeev, M. and Weber, S.