3. Drivers of economic inactivity in Polish regions

A host of factors can drive economic inactivity, ranging from childcare to discouragement from job search. This chapter delves into the drivers of economic inactivity in Poland. Section 3.2 provides an overview of potential causes of economic inactivity across demographic groups. Section 3.3 explores regional level factors behind inactivity differences across Poland. Section 3.4, meanwhile, analyses economic inactivity in light of broader megatrends that accentuate labour market inequalities.

An inclusive labour market provides access and equal opportunities to all groups. However, in many OECD countries, labour market inequalities have been widening, with persistent difficulties to participate fully in the labour market for some groups and significant disparities in pay, working conditions and career prospects. Breaking down individual circumstances for inactivity can provide useful insights for policymakers to identify target groups for future local labour market activation strategies.

Compared to the average across other European countries, the economic inactivity rate is higher in Poland among youth who are not in education or training, older workers, women, men and migrants. The inactivity rate is highest among youth who do not currently pursue education or training. 57% of those aged 15 to 30 who do not currently pursue education or training are economically inactive, a rate 10 percentage points above the average across the rest of Europe. Older workers are the second group that is characterised by a high economic inactivity rate in Poland. One in two individuals aged 55 to 65 do not participate in the labour market, while this rate is much lower in other European countries (Figure 3.1).

Women, with or without children, are the third group with the highest inactivity rates. In addition to cultural reasons for low female participation, women face challenges to labour market participation, especially when they have young children. Interestingly, women without children participate even less in the labour force than women with children, while such a difference does not exist in other European countries. Both men and migrants in Poland have similar inactivity rates. In these groups, one in three do not participate in the labour market. While the inactivity rate of migrants is closer to the one observed in other European countries, men in Poland clearly participate less in the labour market compared to men in other European countries.

Human capital is a crucial element in understanding the labour force participation differences within societies. Individuals with higher years of formal education have a higher probability of participating in the labour market, earning higher wages, and working longer in life. Being in education for longer helps people gain a wider range of skills that make them more adaptable and successful in a rapidly changing labour market. Across the OECD, the economic inactivity rate is 24 percentage points higher for people with low education levels (i.e. below upper secondary education) in comparison to those having attained tertiary education (Figure 3.2).

Labour market participation in Poland is highly uneven across populations with different skill levels. Nine out of ten individuals who have high levels of education are active in the labour market. In contrast, one in two individuals with low levels of education do not participate in the labour market. As observed in the numbers corresponding to the rest of the European countries, it is common that individuals with low education levels are less likely to participating in the active labour force than those with higher education levels. However, in the case of Poland, this gap is strikingly large for workers with lower levels of education, indicating that some factors are preventing them from taking part in the labour market.

People can be economically inactive for a number of different reasons. These reasons can differ from place to place, but also from different demographic groups. Like in other European countries, students and retirees constitute the largest shares of working-age population economically inactive (Figure 3.3). It is reasonable to expect that students are well placed to enter the labour market they are qualifying for and that early retirees have most likely been able to leave the labour market because they are financially able to do so. Still, it is noteworthy that the share of retired individuals among Poland's inactive population is roughly 17% higher than the rest of Europe.

Reasons for inactivity differ significantly between men and women. Retirement is the main reason for inactivity for both genders and corresponds roughly half of those who are inactive (Figure 3.4). While education and training remain the second most important reason for both genders, it is a more important factor for men than women. For instance, while 1 in 4 men do not participate in the labour market due to participation in education and training, the share is only 1 in 6 for women. The difference between genders can be driven by many factors. For example, culturally, men might be competitive in the labour market, leading them to to invest in education for longer years, to achieve higher levels of degrees to unlock their access to higher positions. However, the difference might also be driven by women who do not participate in education as they do not expect to participate in the labour market.

Women are more likely to remain economically inactive to take care of their families. Family responsibilities such as taking care of the household, looking after the children, the chronically ill or older members of the family are more likely to fall on women than on men. In fact, 1 in 4 inactive women cites family duties as the reason behind their economic inactivity, while this is the case only for 1 in 20 inactive men. This difference highlights the importance of a cultural factor in the family roles where women bear the responsibility of family responsibility and stay home. The provision of free childcare and elderly care services remains an important policy response for reducing the share of women unable to participate in the labour market due to family care duties.

Across the OECD, part-time work, including those workers who usually work less than 30 hours per week at their main or only job, has been increasing in recent decades. An increase in part-time work can be considered a positive development as it allows an increase in labour force participation of women or people with disabilities or allows a better work-life balance. Part-time employment can also be an issue if workers are forced to work part-time as they could not find a full-time job, or they would like to work more hours. Under such circumstances, involuntary part-time employment could be an indicator of low job quality.

The share of part-time work has increased in Poland over the past decades, yet remains well below the rest of European countries. In 2019, around 7 percent of Poland's active workers worked part-time as an employee or self-employed, while the rate was 21.5 percent in rest of Europe. The prevalence of part-time varies significantly across different groups. For instance, while only 4 percent of male workers are employed part-time, the rate is more than double for all other groups (Figure 3.5).

The share of part-time work is highest among women. In most OECD countries, women are more inclined to work part-time to balance family responsibilities. However, this part-time employment can be partially involuntary due to lack of childcare or healthcare facilities. In fact, among male workers in Poland, only 1 percent report working part-time due to reasons such as looking after children or incapacitated adults in the household, while this share stands at 13 percent among female workers (Figure 3.6). The large gap is likely driven by a set of complex factors, including social norms and gender roles in Poland, which puts the responsibility of caring for family members on women. However, a part of this gap could also be driven by a lack of access to care services that prevents women from contributing to the labour market with their full potential. Experience from other OECD countries further indicates that involuntary part-time work increases during economic downturns (OECD, 2019[1]). Thus, the less favourable labour market conditions due to the COVID-19 pandemic could have increased involuntary part-time work, exacerbating involuntary part-time work among female workers.

Part of the inactive population could return to the labour market if the right conditions were provided and be a critical source of labour supply. Various factors could be leading individuals to inactivity. While some individuals are inactive by choice (e.g. to spend more time with their children) or due to their personal situation (e.g., severe disability limiting physical movement), others remain inactive as they believe there are no jobs available or they are forced to stay home to take care of family due to a lack of childcare or elderly care services. Identifying individuals who remain inactive due to their circumstances and would be willing to work if the necessary conditions were provided is crucial, as putting the right conditions in place could lead to significant economic gains.

Factoring this group of people into an analysis of unemployment rates across OECD countries and regions reveals pockets of “hidden” unemployment. “Hidden” unemployment is not an official unemployment rate but serves as a useful concept to quantify the share of the economically inactive that could be activated. It thus provides a metric of the extent to which policy can reduce economic inactivity. A detailed description of how “hidden” unemployment is calculated is provided in Box 3.1.

In 2019, the share of the economically inactive who were willing to work (i.e., the “hidden” unemployment) was on average 11 percent across European countries, almost twice as high as the unemployment rate of 6 percent (Figure 3.7). The gap between the “hidden” unemployment and the traditional measure of unemployment varies across countries. In countries such as Germany, Hungary, Ireland or Romania, the unemployment rates adjusted for “hidden” unemployment is more than two times as high as the unemployment rate. In others such as Sweden or Finland, the gap is much smaller.

In Poland, taking into account “hidden” unemployment, such a rate would be 5.7 percent which was 75 percent above the standard unemployment rate of 3.3 percent. However, one can see fairly large variations in the difference between these two rates across Polish regions (Figure 3.8). The smallest difference is observed in Podlaskie, where the hidden unemployment rate is 30 percent higher than the unemployment rate. In contrast, in Lesser Poland or Warmian-Masuria, the hidden unemployment rate is more than twice the unemployment rate. While the gap is a consequence of a complex set of factors, it indicates a disadvantage in some Polish regions in utilising their employment potential. This suggests that while the Polish regions had relatively low unemployment rates, the rate could be much higher if people who are not in employment but could potentially work were to be accounted for. If Polish regions provided the right conditions, they could likely increase their labour supply and benefit from a potential that remains untapped for the time-being.

One of the striking features of the Polish economic inactivity rates is its decline over the past decade. Economic inactivity in Poland peaked at 37% in 2007 and then declined to 29.4% in 2019. However, this steady improvement on the national level masks region-specific trends that can be linked to local historical, economic and geographical characteristics. This section explores the regional dimension of economic inactivity in more detail. It illustrates the regional factors that shaped these trends using the examples of Lower Silesia and Podkarpacia.

The share of the economically inactive population differs across Polish regions. In 2019, economic inactivity varied by over 13 percentage points between regions. In Warmian-Masuria, economic inactivity reached over 34%, while it was slightly above 21% in Warsaw. In 2019, economic inactivity fell below the OECD average in Pomerania, Greater Poland and Lower Silesia, while it remained above the average across all Polish regions.

Regional economic inactivity rates converged over the past two decades in Poland. Regions with historically high economic inactivity rates saw their economic inactivity rates declining the most on average. Thus, on average, regions with historically high economic inactivity rates managed to tap into their economically inactive population relatively more, likely due to higher potential at the margin of the economically inactive when inactivity is high. Figure 3.9 shows that these convergence dynamics are able to explain almost half of the fall in economic inactivity across Poland when excluding the Warsaw capital city and the surrounding Mazowiecki region.

However, some regions outperformed and others fell below the expected convergence across regions. Lower Silesia outperformed compared to the trend and dropped to being one of the regions with the the lowest economic inactivity rates in Poland. Lower Silesia, where economic inactivity stood at 34.4% in 2000 managed to add 6.9% of its working age population to the labour force. On the other hand, Podkarpacia, where economic inactivity stood at 33.6% in 2000 still had an economic inactivity rate of 32.8% in 2019.

Trends in economic inactivity across Polish regions are closely tied to trends in regional GDP. All regions in Poland saw their per capita production rising sharply over the past two decades. Even in the least economically developed region of Poland, the Warmian-Masurian voivodeship, GDP per capita more than doubled from 2000 to 2019, rising from EUR 3 900 to EUR 9 500 at current market prices. However, other regions such as the Warsaw capital region where economic inactivity is the lowest in Poland, experienced a rise from EUR 10 600 to EUR 30 500 in GDP per capita at current market prices, exacerbating regional inequalities and providing a potential explanation for differing trends in economic inactivity across Polish regions. In fact, over the past two decades, changes in GDP per capita are strongly associated with differences in regional economic inactivity trends, even when excluding the Warsaw capital region as shown in Figure 3.10.

Short-term trends in economic inactivity also vary across Polish regions. Between 2018 and 2019, two-thirds of regions saw the share of economically inactive people decrease, following the national average and generally continuing the long-term convergence dynamic (Figure 3.11). For example, in some regions with high economic inactivity, such as Silesia and Swietokrzyskie, the share of the economically inactive population decreased from 32.7% to 32.4% and from 32.5% to 31.6%, respectively. Conversely, in one-third of the regions, the share of the economically inactive population increased. In some regions, these short-term fluctuations oppose long-term positive trends. For example, in Warsaw capital city and Lodzkie, economic inactivity rose from 19.5% to 21.1% and from 26.7% to 28.2%, respectively, between 2018 and 2019.

In Poland, population-level regional differences may explain some of the short-term fluctuations in economic acitivity, but do not appear to be a large factor explaining long-term trends. In other OECD countries, higher regional economic inactivity has been tied to the greater presence of economically inactive groups such as retirees, students or stay-at-home parents (Barr, Magrini and Meghnagi, 2019[2]). In Poland, regions containing student centres include the Warsaw capital region, Kraków in Lesser Poland or Wrocław in Lower Silesia. Between 2018 and 2019 economic inactivity increased in Warsaw capital city and Lodzkie, home to major urban centres. Rises in the student population or other trends related to urban employment may help explain this short-term trend. However, Warsaw capital city, Kraków and Wrocław, contain smaller overall shares of economically inactive people relative to other Polish regions: 21.1%, 30.1% and 27.5%, respectively.

Geography and better access to the EU market provide possible explanations for the differing economic trajectories of Lower Silesia and Podkarpacia. Following Poland’s accession to the EU in 2004, both the Warsaw capital region and the Polish regions bordering the EU benefitted from relatively large increases in foreign capital investments (Ambroziak, 2019[5]). The foreign capital stock in regions bordering Germany increased by up to 400% per capita (in West Pomerania). Lower Silesia, a region bordering both Germany and the Czech Republic, with its student and economic hub of the Wroclaw metropolitan region, experienced a rise in its foreign capital stock of around 150%. Most of the foreign capital originated from Germany, but the region also attracted foreign capital from Italy, Switzerland and the USA. In total, 8.6% of Poland’s total foreign capital stock was invested in Lower Silesia in 2017. On the other hand, the per capita foreign capital stock remained virtually unchanged at very low levels in Podkarpacia, Podlaskie and the Lublin Province between 2005 and 2017, regions located at the external border of the EU (Ambroziak, 2019[5]).

The lack of foreign investment in the East of Poland translates into the lack of large enterprises in these regions. Figure 3.12 shows that the share of local employment in SMEs in total employment is strongly associated with regional GDP per capita across Polish regions, which in turn is linked to economic inactivity. For example, in the regions of Lower Silesia, Silesia and Greater Poland, the share of employment in SMEs stood at 66%, 65% and 63% respectively. In Podkarpackie, Podlaskie and Lublin, the most Eastern regions of Poland, these shares stood at 76%, 79% and 75%.

Local level support to SMEs could therefore benefit lagging regions in particular. Technical assistance and mentoring to small businesses at the local level could build on existing local business centres and contact points for EU funds (OECD, 2020[6]). A pilot project to identify local strengths and weaknesses in attracting entrepreneurs with the objective to build “business services centres” in some medium-sized cities (Radom, Tarnów, Elbląg and Chełm) was completed in 2019 (Ministerstwo Rozwoju, 2019[7]). The main recommendations from the pilot include

  • better cooperation of cities with local universities and secondary schools to improve the local human capital base

  • work on the image of the city, its cultural offer and leisure activities to attract young workers;

  • incentivize local developers to build modern office space in collaboration with industries cities are trying to attract.

In addition, a broader strategy of internationalising SMEs could further boost employment and income opportunities in lagging regions. The high economic growth in Poland following its transition to a free market economy can partly be attributed to an increasing internationalisation of firms. However, Poland’s SMEs have not integrated into global value chains the same way as larger companies. One of the key reasons is the low productivity of SMEs in Poland compared to other OECD countries (OECD, 2020[6]). Boosting productivity of small companies in particular could therefore increase their capacity to export, which could improve local income opportunities and encourage labour force participation in lagging regions. Box 3.2 summarizes the key policy levers the OECD recommends in its 2020 OECD Economic Surveys on Poland.

The rapid decline of the large agricultural sector in Poland may still weigh on economic inactivity today. Employment in agriculture declined strongly all across Poland over the past three decades. Overall, the share of employment in the agricultural sector in overall employment declined from 22.6% in 1995 to 9.1% in 2019. In absolute terms, the agricultural sector lost around 1.8 million jobs. With employment in manufacturing only slightly increasing over time, new employment in the wake of high economic growth rates was mostly created in the service sector, which gained around 2.9 million jobs over the 1995 to 2019 period.

The Eastern regions of Poland bore the brunt of the decline in agricultural activity, providing an additional potential reason for their relatively unfavourable trends in economic inactivity. Apart from the geographical disadvantage, the historical economic sector composition in the Eastern parts of Poland may act as an additional obstacle to faster economic growth and may make these regions less attractive for foreign investment. Figure 3.13 shows the high correlation between the share of agricultural employment in total employment in 1995 and subsequent trends in economic inactivity from 2001 to 2019 for all Polish regions outside the Warsaw capital region. The voivodeships located at the Eastern border, Podkarpacia, Podlaskie and Lublin Province, as well as Swietokrzyskie. had large agricultural sectors, with 34%, 42%, 45% and 42% of the total employed workforce employed in agriculture in 1995. Their ineconomic inactivity rates showed no or very small improvements over the past two decades. On the other hand, regions such as Lower Silesia had a relatively less prominent agricultural sector (12% in total employment in 1995) and experienced a much faster decline in economic inactivity between 2000 and 2019.

Similar historical legacy costs have been found in other countries. For example, OECD research has found that local economic structure has also shaped the form and degree of economic inactivity across regions in the United Kingdom Box 3.3.

Agricultural employment may exhibit further distortions that are difficult to capture in Labour Force Surveys. Some farm owners employ family members that add little to the productivity of the farm and could be more productively utilised elsewhere (OECD, 2018[4]). This underutilisation of labour is linked to economic inactivity in two ways. First, it may lead to an underestimation of economic inactivity if those employed on family farms report themselves as working in labour force surveys. Second, the lack of work contracts in within-family farm employment leads to similar issues around potential old-age poverty the economically inactive face.

The underutilisation of skills in agriculture persists across Polish regions (OECD, 2018[4]). However, the magnitude of the problem has declined due to the decline in total employment in agriculture. In the regions of Kuyavian-Pomerania, Lublin Province, Lesser Poland, Podkarpacia, Podlaskie and Swietokrzyskie, where the share of agriculture was high historically, the underutilization of labour in agriculture is likely to be strongest (KOŁODZIEJCZAK, 2020[8]).

Regional differences in the size of the informal economy in Poland are unlikely to explain geographical differences in economic inactivity rates. In Poland, around 3% of workers have no written contract at all according to Labour Force Statistics (OECD, 2020[6]). However, while the geographical concentration of informal work coincides with larger economic inactivity rates, these phenomena are unlikely to be linked causally. Rather, they are likely to have the common explanation of fewer attractive locally available income opportunities (see also Box 3.4). Thus, while stricter labour law enforcement remains important, such measures are unlikely to have large effects on regional labour force participation.

Regions across Poland face a relatively high risk of jobs changing or dissapearing due to automation compared to the average across OECD countries. In the median OECD region, the share of jobs at a high or significant risk of automation is 47.2% (Figure 3.14). According to the OECD, jobs at high risk of automation are like to dissapear completely as over 70% of tasks associated with the job may be replaced by technology, while those at significant risk have between 50% and 70% of their tasks vulnerable to replacement (Nedelkoska and Quintini, 2018[11]). In Poland, all regions but Warsaw capital city, where 40% of jobs are at some risk of automation, face a risk higher than 48%.

The COVID-19 pandemic could accelerate automation within firms. Early evidence suggests companies are digitalising and automating the way they produce and deliver services in response to social distancing requirements and tighter margins (Pissarides, 2020[12]). Evidence from the aftermath of the 2008 financial crisis in the United States also shows those areas facing the steepest downturns in employment tended to see firms increase their capital stock and change their skill requirements away from routine occupations (Hershbein and Kahn, 2017[13]). This research posits firms make labour-saving cost changes to their production during crises in the face of tigther marings. Taken together, this process can contribute to “jobless” recoveries in which employment does not recover fully.

The risk of automation varies significantly across regions. In Mazowieckie, Lublin Province and Swietokrzyskie, the OECD estimates 58%, 56% and 56% of jobs to be at significant or high risk of automation respectively, the highest shares in the country (Figure 3.14). In Mazowieckie, 23% of jobs are at high risk of automation, the highest proportion in Poland. In Silesia, Lesser Poland and Pomerania, meanwhile, 49%, 50% and 50% of jobs respectively are at high or significant risk, the lowest shares in Poland after Warsaw capital city. Employment in these regions may be particularly susceptible to automation due to the presence of sectors containing vulnerable occupations, such as construction and manufacturing, major employers in the region. Especially in Swietokrzyskie and Mazowieckie, the share of regional GDP in industry and construction or industry exceeds the Polish national average (European Commission, 2021[14]).

In these regions, those workers who are displaced by changes may face economic inactivity when unemployed spells lengthen, weighing on their economic and social wellbeing. OECD research in the United States has shown that a large share of workers displaced by structural changes have remained economically inactive. In particular, a share of US workers who have been displaced due to import competition have suffered durable losses in income and struggle to find work (OECD, 2019[15]). Many of the specific skills they held for work in export industries may not be reconvertable in local labour markets, weighing on their capacity to find jobs of equivalent quality. In the same way, automation could supress or reshape jobs in Poland’s export-oriented industrial sectors as the effects of the pandemic persist, displacing workers with firm-specific skills durably. Here, an agreement between worker representatives, government and business representatives could ensure a fair and productive transition to a 4.0 industry. Poland can turn to international examples on how to lean on social dialogue to anticipate the effects of automation in industry (Box 3.5).

Since 2000, the Polish labour market has undergone job polarisation. Job polarisation is a process in which the relative shares of high and low-skill jobs grow, while those of middle-skill jobs fall. Job polarisation is a process of change in the occupational structure of an economy. In Poland between 2000 and 2018, the relative share of middle-skills jobs has decreased by 9.8 percentage points, that of high-skill jobs has increased by 10.3 percentage points and that of low-skill jobs has decreased by 0.5 percentage points (Figure 3.15). In total, Poland gained 262 400 low-skills jobs, shed 666 000 middle-skill jobs and gained 2 213 700 high-skill jobs. This a sign the economy is upskilling its jobs. The quality of jobs, however, may not be keeping pace with upskilling as the incidence of non-standard work contracts increased in Poland over this period (Lewandowski, Góra and Lis, 2017[17]).

Job polarisation also shapes economic inactivity by determining labour demand. After the 2008 financial crisis, research has shown that job polarisation, by decreasing the demand for middle-skill occupations in Europe, weighing on the labour market participation of men with lower levels of education (Verdugo and Allègre, 2017[18]). The same research suggests polarisation accelerated following the great recession across Europe. There is a risk of durable labour market exclusion for those with lower levels of education as the occupational structure of the Polish labour market evolves after COVID-19. As the pandemic reshapes demand for Polish exports durably, many workers in trade-related sectors may see their jobs supressed or automated. Although a large-scale wage subsidy policy is maintaining jobs and incomes throughout Poland since 2020, these will likely be lifted progressively across sectors as firms face a redefined demand for their products. Those workers will require social and educational assitance to ensure the pandemic does not discourage and exclude them from participating on the labour market.

Job polarisation in Poland has also unfolded differently across regions. The polarisation pattern has been most noted in the Warsaw capital region, Mazowieckie and Silesia, where middle-skill jobs have decreased by relative shares of 14.1 and 13.3 percentage points, representing a decrease of 162 500 and an increase of 26300 jobs respectively (Figure 3.15). In these regions, the share of low-skill jobs have also decreased by 2.7 and 3.3 percentage points respectively, but low-skill jobs increased in absolute numbers by 9000 and 47300 jobs respectively. Contrary to this pattern, regions such as Lublin Province and Podkarpacia have seen their shares of low-skill jobs increase by 4.5 and 2.1 percentage points respectively. Although predictions remain speculative as the COVID-19 pandemic continues, it is possible those regions that have faced steeper levels of middle and low-skill jobs loss may see lower skill workers at higher risk of falling into economic inactivity as the crisis may accelerate the polarisation pattern.

Polish voivodeships track changes in labour demand within their annual Occupational Barometer. An expert panel gets together annually to fill in questionnaires that asks questions on occupation-specific labour demand. The surveys are then used to forecast within-occuption labour demand. Local labour offices also utilize the survey data to design appropriate training for the unemployed. An alternative approach to the Occupational Barometer is the more skill-oriented “Abilitic2Perform” method applied in Wallonia, Belgium (see Box 3.6).

Poland, like other OECD countries, will experience both employment gains and losses due to the transition to net zero greenhouse gas emissions. To become a net-zero emissions economy by 2050, the primary EU target year as envisioned by the Paris agreement, Poland will have to transform its energy system gradually. Jobs will be at risk in industries such as coal and other mining, manufacturing, transport and chemical and plastic production.

Some regions in Poland face a relatively higher risk of job loss due to the net-zero transition. Figure 3.16 shows the employment share in industries at risk in Polish TL-2 regions compared to the OECD average. Most Polish regions only face moderate risks of employment loss. For all regions but Silesia, the share of employment in industries at risk of job loss is below 4.6%, around the OECD average. However, in Silesia, due to its heavy dependence on mining of coal and lignite, the share of employment in sectors at risk stands at 6.7% (Figure 3.16).

Place-based policies can help regions such as Silesia to manage the transition to mitigate the risk of employment loss resulting in longer lasting economic inactivity. To avoid a rise in economic inactivity, regions facing job losses due to the net-zero transition can help smoothing that transition through smart local active labour market policies that target those who are likely at risk of job loss. An example of such a policy developed in the Belgian region Flanders is presented in Box 3.7.

There will also be employment gains due to the net-zero transition but their geography is less certain. The largest employment gains are likely to occur in renewable power production and the recycling of material. Other gains are likely to come from electric vehicle production and the service sector (OECD, 2021[20]). Since the renewable energy sector is relatively more employment-intensive than the fossil fuel industry, there may be net gains from the transition (European Commission, 2018[22]). However, it is unclear where these gains will occur geographically (OECD, 2021[20]).

“Green skills” could be integrated into Poland’s Occupational Barometer surveys. Poland currently lacks a long-term labour market demand forecast. Such long-term forecasting could be integrated into the existing Occupational Barometer surveys. The on-going matching of the Polish Classification of Occupations and Specialisations to the European Skills, Competences, Qualifications and Occupations Taxonomy (ESCO) and the International Standard Classification of Occupations (ISCO-08) will ensure international comparability of these forecasts.


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[18] Verdugo, G. and G. Allègre (2017), Labour Force Participation and Job Polarization: Evidence from Europe during the Great Recession, https://www.ofce.sciences-po.fr/pdf-semofce/Labor-Force-Participation-and-Job-Polarization.pdf.

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