JOBS WORKING PAPER Issue No. 69 Towards a Just Coal Transition Labor Market Challenges and People’s Perspectives from Lower Silesia Luc Christiaensen, Céline Ferré Tomasz Gajderowicz and Sylwia Wrona TOWARDS A JUST COAL TRANSITION LABOR MARKET CHALLENGES AND PEOPLE’S PERSPECTIVES FROM LOWER SILESIA Luc Christiaensen Céline Ferré Tomasz Gajderowicz Sylwia Wrona © 2022 International Bank for Reconstruction and Development / The World Bank. 1818 H Street NW, Washington, DC 20433, USA. Telephone: 202-473-1000; Internet: www.worldbank.org. Some rights reserved This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. 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All queries on rights and licenses should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; e-mail: pubrights@worldbank.org TOWARDS A JUST COAL TRANSITION Labor market challenges and people’s perspectives from Lower Silesia 1 Towards a Just Coal Transition Labor Market Challenges and People’s Perspectives from Lower Silesia Luc Christiaensen, Céline Ferré, Tomasz Gajderowicz, and Sylwia Wrona.1 Background papers, valuable data analysis, and comments were provided by Jan Frankowski, Joanna Mazurkiewicz, Jacub Sokołowski and Piotr Lewandowski , 2 Maciej Jakubowski,3 and Elizabeth Ruppert Bulmer4. September 2022 1 Corresponding authors: Luc Christiaensen: lchristiaensen@worldbank.org, Céline Ferré: cferre@worldbank.org, Tomasz Gajderowicz: tgajderowic@wne.uw.pl.edu. 2 Institute of Structural Research (IBS). 3 University of Warsaw. 4 Jobs Group, World Bank 2 Acronyms AI Artificial Intelligence ALMP Active Labor Market Program BGT Burning Glass Technologies BKL Human Capital Balance (Bilans Kapitału Ludziego) CHP Combined Heat and Power DCE Discrete Choice Experiment EC European Commission EGD European Green Deal EU European Union GVA Gross Value Added GDP Gross Domestic Product GHG Greenhouse Gas GUS Statistics Poland (Główny Urząd Statystyczny) IBS Instytut Badań Strukturalnych (Institute of Structural Research) IEA International Energy Agency ILO International Labor Organization IO Input-Output IRENA International Renewable Energy Agency ISCO International Standard Classification of Occupations JTF Just Transition Fund JTM Just Transition Mechanism NDC Nationally Determined Contribution NUTS Nomenclature des Unités Territoriales Statistiques (Nomenclature of Territorial Units for Statistics) OECD Organisation for Economic Cooperation and Development OTJ On-the-job PES Public Employment Services PLN Polish Złoty RES Renewable Energy Sources TA Technical Assistance TJTP Territorial Just Transition Plan WTP Willingness to Pay 3 Executive Summary Coal-related jobs are at the forefront of the disruption brought about by the transition toward a low-carbon economy. Some displaced workers may be able to transition easily to new job opportunities, whereas many others may not. The resulting disruption to jobs and livelihoods may exacerbate the already challenging labor market environment in remote regions and traditional sectors that have not kept pace with broader economic modernization trends. A series of recent World Bank studies zoomed in on the labor and skills challenges brought about by the transition out of coal in three Polish regions: Wielkopolska, Silesia, and Lower Silesia.5 This paper examines the labor market challenges regarding the just coal transition in Lower Silesia and the affected people’s perspectives and opportunities. Particular attention goes to analyzing spatial, expectation, and skill-related mismatches between current and possible future jobs and their bearing on the viability of job transitions (including through reskilling). Adjudication is facilitated through a specially developed excel- based “viable-job-transition-pathway” tool. A data-driven and people-centered approach is illustrated to explore viable job transitions for the affected coal-related workforce. Administrative labor market and occupation description data from Poland are combined with specific job preference information of the affected workforce. The latter is obtained through newly developed discrete choice experiments and random parameter logistic regressions. Task similarity across occupations is detected through the application of artificial intelligence text mining algorithms. Together the findings help policymakers assess the viability of different job transition pathways in the current labor markets as well as the job-generating potential and their accessibility to the affected coal-related workforce of mine repurposing activities and economic diversification strategies. The empirical application is to Lower Silesia, but the approach can also be applied to address similar questions in other coal or single employer-dominated labor markets, in Poland and beyond. Lower Silesia is the region currently least advanced in the transition out of coal. As Europe’s largest coal producer, Poland is today at the forefront of the European coal transition. While having reduced coal mining substantially in the 1990s, within Poland, Lower Silesia is currently the region least advanced in the transition out of coal. Historically, Lower Silesia was the only Polish region extracting both hard coal and lignite, but most mines closed during the 1990s. In 2022, only one lignite extraction site and one coal-fired power plants are still active, concentrated in the powiat of Zgorzelecki, in the region of Turoszów, at the border with Germany and Czechia. No closure is anticipated in the nearby future. Yet historical lessons from mine closures highlight the importance of timely planning. 5 The work has been supported by a grant from the Directorate-General for Energy of the European Union. 4 The affected coal-related workforce in Lower Silesia is heavily concentrated in one conglomerate and lives concentrated around the mines, calling for a geographic, or local economic development, rather than a sectoral approach. About 3,500 workers are employed in the Turów mine and power plant, making up 64 percent of the coal value chain workforce. Another estimated 1,800 coal-related jobs are in the Turów subsidiaries providing support services for mining extraction and power plants. The Turów mines, power plants and subsidiaries are all part of one vertically integrated structure, PGE Capital Group. Only an estimated additional 230 coal-related are generated in Lower Silesia by subcontractors to Turów, i.e. outside PGE. In sum, coal-related employment in Lower Silesia is heavily concentrated in one conglomerate (much like in Wielkopolska). Equally important is the geographic concentration. The Zgorzelecki powiat is particularly vulnerable, with an estimated 11 percent of employees working in mining extraction, 6 percent in the power plant, and an additional 5 to 8 percent in associated companies. At the same time, the labor market of the Zgorzelecki powiat lags already behind, in an otherwise thriving region. Average gross salary (PLN 59 thousand/year) is 86 percent of the regional average (which outperforms the national average). Inactivity rates are high (62 percent of the working-age population, compared to 47 percent for the rest of the region). Unsurprisingly, about one in three respondents in the heavily affected municipalities (Bogatynia and Zgorzelec) believe that the coal phase out will have a significant impact on their lives. Moreover, these are relatively poorer communities to begin with, with over 30 percent of respondents in the two municipalities reporting an average monthly net income per person below PLN 2,100, which is below the poverty line. Residents from the municipalities of Bogatynia and Zgorzelec have lower foundational and communication skills and better technical and artistic abilities than other Poles and other Lower Silesian workers. These patterns are similar for lower and higher educated workers. The broad pattern of lower foundational and better technical skills compared to regional and national counterparts also holds across gender and age. With transversal skills universally found to facilitate job transitions, these findings underscore the need for a geographic or local economic development approach to brokering a just coal transition, rather than a sectoral approach, to include all workers in the more affected communities. Coal-related workers in Lower Silesia display an aversion to commuting; they also put a premium on continued use of their competencies and job security. The DCE results show that reducing the travel time to work by one hour (each way) is valued at PLN 749, equivalent, on average, to 20% of a resident’s net monthly salary. Men are more sensitive to shorter commuting times, along with lower educated and older workers. Similarly, municipality residents prefer not to relocate. Surprisingly, however, their aversion to relocation is less than their aversion to commuting, plausibly linked to Lower Silesia’s longer standing tradit ion of working in Germany and the Czech Republic compared with Wielkopolska and Silesia, where the aversion to relocation was much larger and much larger than the aversion to commuting. Another valued job attribute is consistency with one’s educational spe cialization, especially 5 for women, older workers, and more educated individuals. They are ready to sacrifice on average PLN 808 to find a job related to their education, even if it requires initial training, and PLN 556 to have a job corresponding to their education and skills. Employment security is also valued, but workers are ready to accept lower job entry conditions given good prospects for wage progression. The most attractive sector of activity for municipality residents is renewable energy (RE). Moreover, respondents are willing to forego PLN 425 to avoid work in mining, a sharp contrast with the findings in Silesia where coal workers preferred working in the mines above work in any other sectors (apart from RE, but only for the higher educated workers). Finally, most workers would agree to substitute a sizeable part of salary for certain fringe benefits. They are willing to pay PLN 749 to enjoy flexible working time, PLN 707 to have private medical care, PLN 653 to attend certified professional courses, and PLN 585 for childcare. The latter contrasts sharply with the mining workforce in Silesia, for whom access to childcare services did not add any value. Women also value those benefits more than men; while lower educated respondents display a stronger preference towards certified professional courses, private medical care, and childcare services than more educated respondents. Machine learning and administrative job descriptions are further leveraged to determine skills (mis)matches across jobs and to identify viable transition pathways for the coal- affected workforce. The job matching tool used big data techniques to identify positions requiring tasks and skills most similar to the position held by the worker at risk of dismissal, narrowing down options to occupations with demand surplus, as identified by the local labor market barometer annually published by the local labor offices and to wage offerings sufficient to overcome mobility aversion if the job offers are only available outside the affected municipality or region. Viable transition pathways are available in the current local labor market for blue-collar workers (70 to 80 percent of coal-related workers), but the numbers may not be sufficient to absorb everyone; tertiary-educated specialists may need more reskilling given the demand for high specialization. Interestingly, most viable trajectories do also not point to retraining and upskilling in renewable energies, the focus of much of the regional diversification efforts, or in digital jobs, often considered the jobs of the future. Key challenges going forward will be to provide adequate opportunities for affected workers, especially non-mine workers in the most affected municipalities. This segment of the population deserves special attention: their numbers are non-negligible, they are less skilled, they operate in heavily affected and already lagging local economies, and they are not covered by the social agreements covering mine workers. The “viable-job-matching tool” can be developed further through ground-truthing with case workers in local labor offices and as a planning tool to guide re-purposing plans and economic diversification that leverage the available skills and account for the workforce’s key job attribute preferences. 6 7 Table of Contents Acronyms .................................................................................................................................... 3 Executive Summary...................................................................................................................... 4 1 Introduction ...................................................................................................................... 11 2 Coal in Poland – A declining sector with peculiar employment features ............................... 18 2.1 Decarbonization adds impetus to Poland’s longstanding decline of coal production ............................ 18 2.2 Concomitantly, employment in the mining sector has fallen sharply to 92,600 jobs ............................. 19 2.3 Recent estimates put the number of coal-related jobs between 145 and 218 thousand ....................... 20 2.4 Large and increasing wage premia in coal-related jobs, especially for the lower skilled ....................... 23 3 Coal-related jobs in Lower Silesia are limited and concentrated in two municipalities .......... 25 3.1 Concentration in the lagging district of Zgorzelecki ............................................................................... 25 3.2 Excess demand for higher skilled workers in the region and substantial skills mismatch ...................... 27 3.3 Dominant position of Turów in Zgorzelec and Bogatynia ...................................................................... 30 3.4 Very few jobs in the coal value-chain in Lower Silesia are outside PGE ................................................. 31 3.5 Direct and indirect coal-related jobs are especially important to Zgorzelecki powiat ........................... 32 4 The type of jobs coal-related workers can and would like to do........................................... 34 4.1 Local understanding of coal-related workers’ skills and job preferences is needed ............................... 34 4.2 Affected municipality residents lack foundational, but have good technical skills ................................ 36 4.3 Job attributes coal-related workers value .............................................................................................. 42 5 The identification of viable job transition pathways for coal-related workers ...................... 47 5.1 Ingredients needed to assess the viability of individual job and labor force transitions ........................ 47 5.2 Towards a “viable-job-matching” decision tool tailored to the Polish labor market ............................. 49 5.3 Five transition pathways relevant to the mining and energy sector ...................................................... 51 5.4 Demonstrated proof of concept, but further validation and development needed. .............................. 57 6 Conclusions ....................................................................................................................... 57 Bibliography .............................................................................................................................. 61 Annex 1: Methodology to estimate the indirect impact of mines closure ..................................... 64 Indirect jobs are those contracted out by mining conglomerates.................................................................... 64 Annex 2: Skills and preference survey questionnaires ................................................................. 67 Survey questionnaire........................................................................................................................................ 67 8 Annex 3: Skills and preference survey methodology.................................................................... 77 DCE methodology............................................................................................................................................. 77 Sampling and reweighting ............................................................................................................................... 79 Econometric model .......................................................................................................................................... 80 Annex 4: The viable job matching tool - methodological considerations ...................................... 83 Tables Table 1: Previous estimates of indirect impact of mines closures........................................................ 65 Table 2: JRC (2018) Estimates ............................................................................................................... 66 Table 3: Job attributes – DCE design 1 .................................................................................................. 77 Table 4: Job attributes – DCE design 2 .................................................................................................. 78 Table 5: Identification of similar professions with the designed tool .................................................. 84 Figures Figure 1: Most of coal employment in Europe is concentrated in Poland ........................................... 13 Figure 2: Employment in the mining sector was divided by 4.5 between 1989 and 2019 ................... 19 Figure 3: Polish workforce directly and indirectly employed in mining and power generation .......... 21 Figure 4: Employees in coal-related professions enjoy wage premia of 50 percent or more .............. 24 Figure 5: Mining is concentrated around the Turów extraction site and power plant at the border with Germany and Czechia ........................................................................................................................... 26 Figure 6: Employment is skewed towards industry and construction.................................................. 26 Figure 7: Zgorzelecki powiat is a lagging district within a very buoyant region ................................... 27 Figure 8: over 4 in 10 job offers are in services and retail .................................................................... 28 Figure 9: Most job offers in Lower Silesia are for lower skilled individuals .......................................... 29 Figure 10: Low skilled blue-collar characterized by overeducation, technicians by undereducation .. 30 Figure 11: 2 in 5 coal-related jobs are in the mine and power plant; 9 in 10 jobs are provided by Turów (including subsidiaries) ......................................................................................................................... 31 Figure 12: Residents of Bogatynia and Zgorzelec assess most of their skills below the national and regional averages, except for technical and artistic abilities ................................................................ 37 Figure 13: The skills deficit in all dimensions, but technical and creative abilities, remains when holding education constant ............................................................................................................................... 38 Figure 14: Both male and female residents have lower foundational, but better technical skills than others in the region or Poland .............................................................................................................. 41 9 Figure 15: Example of a choice card ..................................................................................................... 43 Figure 16: Residents' preferences – DCE 1 ........................................................................................... 45 Figure 17: Employees' preferences – DCE 2 ......................................................................................... 46 Figure 18: Examples of transition pathways for an energy engineer ................................................... 52 Figure 19: Examples of transition pathways for a lignite mining technician ........................................ 54 Figure 20: Examples of transition pathways for a truck mechanic ....................................................... 55 Figure 21: Examples of transition pathways for an electrician ............................................................. 56 Figure 22: Examples of transition pathways for an electrical machine fitter ....................................... 57 Boxes Box 1: Five lessons from past mine closure in Poland and the U.S. ..................................................... 17 Box 2: Estimating the number of indirect coal-related jobs in Poland ................................................. 22 Box 3: Turów subsidiaries and subcontractors ..................................................................................... 33 10 1 Introduction Following the Paris Agreement (2015)6, the European Union (EU) adopted “climate neutrality by 2050”, while also committing to leave no one behind. The aim is to reduce net greenhouse gas (GHG) emissions in the EU to zero by 2050, while also ensuring no one place or person is left behind. This ambition has been converted into law in 2021 through the adoption of the European Climate Law. The Law also sets the intermediate target of reducing GHG emissions by 55 percent by 2030 (compared to 1990 levels). To achieve the intermediate target, a set of concomitant legislative proposals and policy initiatives has further been advanced (the “Fit for 55” package). They include, among others, a reduction of the GHG emission caps of power plants through a modification of the Emission Trading System.7 To alleviate the socio-economic impacts of the transition in the most affected, mostly carbon- intensive regions, the EU further provides targeted support to help mobilize 100 billion euro in these regions over the 2021-2027 period. These efforts are governed under the Just Transition Mechanism (JTM).8 With carbon-neutral energy production at the forefront of reducing GHG emissions, accelerating the coal-phase out is a key area of policy action. The shift to climate neutrality requires a shift to carbon neutral energy production as well as the transformation of production and consumption patterns towards greater energy efficiency and lower resource use.9 With carbon dioxide the primary contributor to GHG emissions10, the shift away from fossil fuels (coal, petrol, gas) is at the forefront of the transition. Coal is the most carbon-rich energy source11, making the accelerated phase-out of coal a primary area of policy action. Over the past decades, coal consumption in the EU has come down substantially already (from 1,100 Mt in 1990 to 437 Mt in 2021).12 Yet, solid fossil fuels (i.e. coal and coal derived 6 https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement 7 There are also many other measures such as a reduction of Member States’ emission reduction targets, reversing the declining trend of carbon removals through land-use change and reforestation, and increasing efficiency of energy use in transport (including in shipping and aviation), buildings, and industry through a combination of investments, CO2 emission standards, energy taxation, and carbon border adjustment mechanisms. 8 https://ec.europa.eu/info/strategy/priorities-2019-2024/european-green-deal/finance-and-green-deal/just- transition-mechanism_en 9 Beyond energy production, other heavily affected sectors include energy-intensive industries (such as steel, chemicals, plastics), agriculture, waste management and transport. 10 In 2019, CO2 accounted for 81.6 percent of EU GHG emissions. 11 The amount of CO2 emitted per million British thermal units (Btu) for coal, petrol and gas are respectively 229/215 (anthracite or hard coal/lignite), 161/157 (diesel/gasoline), and 139/117 (propane/natural gas) pounds (https://www.americangeosciences.org/critical-issues/faq/how-much-carbon-dioxide-produced-when- different-fuels-are-burned). 12 In 2021, total (hard and brown) coal consumption in the EU stood at 437 Mt (of which 160 Mt hard coal and 277 Mt brown coal). This is down from 1,100 Mt in 1990 (of which 400 Mt hard coal and 700 Mt brown coal). Most coal use is for power production (49 percent of hard coal and 92 percent of brown coal in 2020). 11 solids)13 still account for 12.6 percent of the EU’s 2020 electricity generation (Eurostat, 2022)14, but makes up 62 percent of electricity and heating CO2 emissions (EIA, 2019)15. Coal regions and workers are thus particularly exposed to the energy transition, exacerbated by their economies’ strong dependence on coal and the oft better labor contracts enjoyed by coal mine workers. Coal mines and their associated activities (including power generation) tend to be spatially concentrated, around the extraction sites. They are often the main employer in the local economy, providing financially attractive and secure employment for many semi-skilled workers, especially those working in the mines. With few attractive alternatives present in the oft-little diversified and lagging local economies, coal mine closures tend to heavily affect their workers and communities. The high reservation wage and historical and cultural attachment to mining further impede labor mobility (across sectors and space). Labor unions often successfully manage to negotiate transition packages to somewhat mitigate the social costs of mine closures. Even so, the negotiated packages typically do not (or only partially) apply to workers in the supporting industries or the non- coal-related workers in the local economies, leaving many exposed. They also don’t compensate for the broader economic loss. As a result, across the world, transitions out of coal have been fraught with major social challenges, especially in coal producing regions (World Bank, 2018a; Lobao et al., 2021; Ruppert Bulmer et al., 2021). In recognition of this challenge, a specific financial instrument has been created by the EU under the JTM, the Just Transition Fund (JTF), to mitigate the social and employment effects of the coal transition in carbon-intensive regions. For the EU’s coal phase out and its broader climate transition, a successful transition out of coal in Poland is particularly important. After Germany, Poland is the EU’s second largest coal producer and consumer.16 It further employs about half of Europe’s coal related labor force (Figure 1). What happens with coal production, coal workers and coal regions in Poland is a first order matter for brokering a just coal and energy transition in Europe. The transition out of coal in Poland has started long time ago. But coal remains of strategic importance to the Polish economy. In 2020, over 40 percent of the country’s total energy supply (TES) and 70 percent of its electricity generation come from coal and lignite (IEA, 2022), the highest rate in Europe. Coal also continues to employ about 88,000 people directly in the mines, down from about 444,000 in 1989 (415,000 in hard coal; 29,000 in lignite). Europe’s commitment 13 Solid fossil fuels cover various types of coal (such as hard and brown coal) as well as solid products derived from coal (such as coke which is used in the steel industry). 14 Eurostat consulted on 13 May 2022 (online data code NRG_BAL_PEH) (https://ec.europa.eu/eurostat/databrowser/view/NRG_BAL_PEH__custom_2712428/default/table?lang=en) 15 Solid fuel is defined as coal, peat and oil shale (EIA, 2019). 16 Total (hard and brown) coal consumption in Poland amounts to 117.6 Mt in 2021 or 26.9 percent of the EU’s total (hard and brown) coal consumption (Eurostat, 2022). Most of it is for power generation and heating. Total (hard and brown) coal consumption in Germany is 153.8 Mt in 2021 (36.8 Mt hard coal (all imported) and 127 Mt brown coal). https://ec.europa.eu/eurostat/statisticsexplained/index.php?title=Coal_production_and_consumption_statisti cs 12 to stop its fossil fuel imports from Russia following Russia’s invasion of Ukraine will slow down Poland’s coal phase-out in the near future to ensure energy security in Europe,17 but Poland remains committed to a complete coal mine closure by 2049. Figure 1: Most of coal employment in Europe is concentrated in Poland 160 Employment (thousand workers) 155 140 120 100 80 60 40 20 27 22 18 16 15 4 3 3 3 2 0 Poland Romania Czechia Serbia Bulgaria Germany Greece Slovakia Slovenia North Spain Macedonia Note: employment in hard coal and lignite, as registered under sector B05 of NACE Rev.2 classification. Source: Eurostat (2019), table lfsa_egan22d. In 2021, coal was still extracted in six regions in Poland : hard coal in Silesia, Lesser Poland, and Lublin; and lignite in Greater Poland (i.e., Wielkopolska), Lower Silesia, and Łódź. Most of the extraction sites are located in the Upper Silesian basin, but only about half of the coal seams are economically viable. In addition to hard coal, Poland exploits several lignite deposits: three sites, exclusively surface mines, are currently operating, in Łódź, Lower Silesia, and Wielkopolska. In contrast to Poland’s hard coal mines, which operate at an average working depth of 600 meters, the lignite mines consist almost entirely of mine-mouth power plants. This report, which focuses on Lower Silesia, is part of a larger set of regional labor market studies in coal transition regions in Poland. They cover the three regions most advanced in their coal phase-out and economic diversification process: Wielkopolska, Silesia, and Lower Silesia. Together, they account for 86 percent of all Polish coal mining employment, the preponderance in Silesia. Each region also qualifies for JTF support. The studies are funded by the European Commission. 17 Before Russia’s invasion of Ukraine on February 24, 2022, Poland’s coal phase -out was on an accelerating path, given the high cost of extraction and the challenge for Polish coal-fired power plants to provide electricity within politically acceptable price margins. 13 Among the three regions, Lower Silesia is the region least advanced in the transition out of coal. Mining-related activities are concentrated around Turów opencast mine, which fuels the nearby Turów Power Station, both part of Polska Grupa Energetyczna (PGE) Group. Mining activities are planned to continue until 2044, when the coal reserves are expected to be depleted. In 2019, lignite burned at Turów’s power plant produced 5.5m tons of CO2, making it the fifth largest source of GHG emissions in Poland (it was also the eight least efficient power station in the EU in 200718). Turów coal mine has a significant impact on surrounding areas’ ground and surface waters: in February 2021, the Czech Republic sued Poland over the mine at the European Court of Justice, arguing that the environmental impact from the mine is severely affecting their quality of life, and threatens the survival of several villages close to the border by causing their wells to dry up. PGE Group is one of the biggest employers in the region and the largest power producing company in Poland, consisting of entities engaged in lignite extraction, power generation and distribution, and electricity trading. The report presents a people’s perspectives on the transition, giving voice to those affected; it assesses their skills and prospects for future jobs within the context of their local labor markets. It begins by evaluating the number of workers (directly and indirectly) affected by the mine closures within the broader context of the local and regional labor markets within which they operate. It subsequently maps out their skills and job attribute preferences and aspirations. It categorizes the task content of all jobs within Poland and combines this with the information on the affected workers’ skills and job attribute preferences to identify viable job transition pathways for affected workers within the current local and broader regional/national labor market. To inform these different areas of inquiry, the study uses administrative and secondary labor force data from national labor force surveys and local labor market barometers from the public employment offices. To assess the skills and reveal the job attribute preferences of affected workers, the study further conducted surveys and discrete choice experiments with representative samples of the two most affected surrounding communities, Zgorzelec and Bogatynia. The findings provide an important piece of the “just coal transition” policy puzzle in Poland, but abstract from labor demand related issues. Among the lessons learned from past mine closures (Box 1) are the importance of timely analysis and broad consultation as well as the need for a comprehensive approach, addressing both labor supply and demand side issues. This report abstracts from the employment opportunities new investors and employers should provide (including through repurposing of the mining lands and assets), i.e. the labor demand side of the challenge, including the investments and policy reforms needed to attract them. As such it produces only part of the information needed. Particularly, it provides policymakers and potential investors with an in-depth characterization of the local labor pool, in terms of numbers and demographics, their job attribute preferences and aspirations, as 18 WWF, 2007. “Dirty Thirty: Ranking of the most polluting power stations in Europe.” https://wwfeu.awsassets.panda.org/downloads/european_dirty_thirty_may_2007.pdf 14 well as their skill profile (cognitive/non-cognitive; routine/non-routine). This helps investors and policymakers with investment decisions and the design, targeting and content of their active labor market programs (ALMPs). The matching tool developed for the report, identifying the viable job transition pathways within the current labor market for those affected, can further be readily adapted to identify skill and aspiration gaps with future job opportunities as the mines and their lands get repurposed and the local economies get restructured. Overall, the report finds that the impact of the transition will be concentrated on a few municipalities, highly dependent on coal extraction, where all workers display a strong preference for continuity and stability, and where the most at-risk individuals will be non- mine workers. In Lower Silesia, a limited number of workers will be directly and indirectly affected by the transition: 4,500 workers are still employed by Turów, and an additional 230 workers are employed by Turów subcontractors in 2020. But, coal-related employment is spatially concentrated around the mines, in four municipalities where Turów is the dominant employer. Those municipalities display sluggish labor markets, even though they are embedded in a dynamic regional economy (Lower Silesia). Non-mine workers in affected municipalities deserve special attention, especially as they are less skilled, are not covered by social agreements, and face limited transition prospects as they operate in heavily affected and already lagging local economies. Residents of the two most highly-affected municipalities in Lower Silesia want to remain in municipalities where they live; work in similar positions/sector of activity; and they value job security. However, they are willing to give up much less for these job characteristics than respondents in Wielkopolska and Silesia, indicating less reluctance to change. The job-matching tool developed for this report proposes viable transition pathways and reskilling options under current local labor market conditions, but the number of potential positions available may not be sufficient to absorb all workers who may lose their job in the future. Future work should complement and link the information in this report with the development of a regional economic and diversification strategy, an in-depth analysis of the local investment climate to reorient coal-related businesses and attract new domestic and foreign investment, and an assessment of the number and type of jobs the regional strategy it is likely to generate (locally in the mining communities, and/or elsewhere in the region or country). While new employment opportunities should come from all sectors of the economy, the energy sector can also still be a catalyst for regional development, including in Lower Silesia.19 Overall, the key labor market challenge is to create a more supportive environment for firms to thrive and create employment and to provide adequate opportunities for workers to build skills relevant to alternative, more competitive sectors of work. 19 At the global level, new jobs in transition-related technologies and sectors are expected to outweigh job losses in fossil fuels and nuclear energy (IRENA, 2021). 15 The report proceeds as follows. Section 2 elaborates on the extent and nature of coal production and employment in Poland by way of background This is followed by an analysis of the local labor market features and performance in Lower Silesia and the current (and historical) role of coal related activities within it (Section 3). The skills and job attribute preferences and aspirations of Lower Silesia’s affected workers are reviewed in Section 4. Based on a decomposition of all jobs in Poland, including those related to coal, into their composite tasks, viable job transition pathways for different types of affected workers within the current labor market are discussed in Section 5. The application also takes workers’ job attribute preferences and skill profiles into account. The section further illustrates how the job matching tool can be used when information about future jobs and associated skill needs becomes available, for labor market planning and ALMP programming (such as the nature and design of reskilling programs) as well as for individual case counseling of affected workers. Section 6 concludes. 16 Box 1: Five lessons from past mine closure in Poland and the U.S. First, timing and pace are key: transition takes a long time and preparation should start early. When many workers, businesses and communities are implicated, fundamental change to an industry cannot happen quickly, even with the best advance planning and post-closure transition policies in place. The timing and speed of transition are subject to political economy dynamics. Uncertainty around commodity prices makes it difficult for communities to transition because prices affect both willingness and capacity to diversify toward other industries. Where actors are public (e.g., Poland), governments have the power to act quickly but risk the future support of the electorate. Where actors are private but unions are strong and/or regulatory authority is weak or captured by private interests (e.g., the U.S.), boom and bust cycles can be exacerbated, which could create obstacles to both the design and implementation of effective transition policies. Finally, severe social dislocation in a context of fragile local economic viability may also pass a point of no return. The risk is higher where long-term dependence on coal has delayed acceptance of transition. Second, transition requires a comprehensive approach with complementary initiatives, policies and incentives to sway the many actors along the coal value chain, including those with vested interests like utility monopolies and manufacturers of mining equipment. Labor demand (attracting firms) and labor supply (e.g. reskilling) challenges must be addressed simultaneously, with inappropriate or delayed repurposing of the mining lands and assets often leaving many missed opportunities. Third, economic diversification is essential and requires help from both local and higher-level government with respect to planning and financial resources. Advance planning, investment in infrastructure, addressing environmental degradation and attracting private investment are key ingredients of economic diversification, requiring significant local and regional institutional capacity and coordination, including for repurposing of the mining lands and assets. Fourth, transition support packages need to be inclusive, to help workers who are directly or indirectly affected by mine closure to find new jobs. Addressing worker transition challenges effectively requires a solid understanding of the scope and nature of the potential impacts of transition. Policymakers need to understand the ways in which a future transition away from coal may affect the livelihoods of both coal and non-coal workers and their surrounding communities, in order to implement policies and programs for managing transition effectively. Transition assistance programs targeting formal mine workers fall short of meeting the needs of informal or subcontracted workers in and around the mines. Even large mine operators employ a significant share of their workforce on temporary and/or informal contracts. Informal coal sector workers are at greater risk than their formal counterparts and less equipped to weather income shocks. Packages can include jobs search support, reskilling, counseling, entrepreneurship training, and/or access to financial resources. Fifth, the advantages of inducing voluntary job separations through generous compensation packages are offset by the risk of inflicting long-term damage on local economies. High reservation wages dampen local labor demand and economic recovery through diversification, and this can undermine public fiscal health and ultimately diminish local institutions and social capital. Too- generous severance packages can induce labor force exit, which can accelerate a coal community’s decline. Source: Ruppert Bulmer et al. (2021). 17 2 Coal in Poland – A declining sector with peculiar employment features 2.1 Decarbonization adds impetus to Poland’s longstanding decline of coal production Poland’s coal production has more than halved since the 1990s, driven by reduced economic viability. Over the past three decades total coal production in Poland has more than halved, from 229 Mt in 1990 to 108 Mt in 2020. Most of the reduction happened in hard coal (from 147 Mt in 1990 to 55Mt in 2020). This is consistent with the broad downward trend in coal production in the Europe Union. In fact, Poland is by now virtually the only hard coal producer left in the EU (producing 96% of all EU hard coal production; the Czech Republic produces the remainder). Hard coal reserves in Poland are managed through a few state- owned conglomerates. Hard coal mostly consists of steam coal for electricity generation and heating (72 percent).20 It is deep-mined and fully mechanized with over 90 percent of coal produced by longwall systems (Euracoal, 2020). Nonetheless, only half of the Polish coal seams are still considered economically viable (at least until the recent price hike following Russia’s invasion of Ukraine on Feb 24, 2022).21 Poland further exploits several lignite deposits, which consist almost entirely of mine-mouth power plants. Productivity is around 6,800 tons per employee, which is much lower than the levels observed in Germany (the largest EU lignite producer) and Greece (EURACOAL, 2020). Finally, state-owned mines or units of integrated mines (whether hard coal or lignite) that are unprofitable and slated for phase-out are transferred to the public entity Spółka Restrukturyzacii Kopalń (SRK) to manage their eventual closure. In 2021, there were 12 mines or units of mines managed by the restructuring company (SRK, 2021). In the May 2021 “social agreement” between the government and mining trade unions, Poland presented a decarbonization plan that lays out the timetable for hard coal mines closure.22 Following the European Green Deal and in line with the “social agreement”, the coal sector in Poland will gradually reduce its output capacities and close its mines, over the next three decades, with the last mines closing in 2049. In the energy sector, the country wants to eventually replace its coal-fired power generation capacity with increased production from renewable energy, including a significant portion from offshore wind, coupled with nuclear energy, to stabilize output in an increasingly RES-dependent power grid. Poland is facing a historic challenge to transition quickly from an energy model based on coal extraction – in place for the past 70 years – to a zero-emission future. This is further 20 The rest mostly consists of coking coal. 21 As a result, until recently, part of Poland’s hard coal consumption was imported (in 2020, 10.6 Mt out of the 65.6 Mt of hard coal consumed in Poland was imported, much of it from Russia). Brown coal or lignite is little traded. 22 https://www.gov.pl/web/aktywa-panstwowe/umowa-spoleczna. 18 compounded by the February 2022 invasion of Ukraine by Russia, which undermines the potential of natural gas as transition fuel.23 2.2 Concomitantly, employment in the mining sector has fallen sharply to 92,600 jobs Since 1989, as coal production declined, employment in the coal mining sector 24 has fallen by 80 percent. Over the past three decades, Poland’s coal mining sector lost approximately 300,000 jobs, down to 92,600 in 2019 (Figure 2). Most of the reduction in employment happened in the first decade, during Poland’s transition from a centrally planned model to a market economy (1989-2002). Coal extraction now represents only 1 percent of total employment in Poland. But it still represents 5 percent for the largest extracting region (Silesia),25 and it can represent up to half of employment at the local level, rendering coal- intensive municipalities particularly vulnerable, as discussed further below. Figure 2: Employment in the mining sector was divided by 4.5 between 1989 and 2019 500,000 28,393 450,000 415,740 Total number of workers 400,000 350,000 300,000 250,000 200,000 9,301 150,000 83,300 100,000 50,000 0 1989 1994 1999 2004 2009 2014 2019 Hard coal Lignite Note: Totals exclude employment in coal processing. Source: INSTRAT, 2021. The sharp reduction in coal mining employment was the result of hard coal mines closure in the early 1990s. Between 1990 and 1995, employment reduction was achieved almost entirely through a hiring freeze. The remaining cuts were achieved through a combination of hiring freezes, expanded access to early retirement, and – beginning in 1998 – voluntary exits 23 Russia has traditionally been an important supplier of gas for Poland, which now needs to be sourced from elsewhere. Ongoing investments to expand natural-gas-fueled power generation are continued, but the extension of the lifetime of some coal-fueled blocks is considered as alternative for constructing new natural- gas powered power plants. 24 Note that “coal mining sector” refers to hard coal and lignite mining activities. 25 Eurostat, 2020. Indicator: lfsa_egan22d. 19 facilitated by a generous Mining Social Package comprising cash benefits (severance pay and allowances), activation instruments (training, scholarships, retraining contracts, support for starting a business) and instruments aimed at employers (contribution refunds, reimbursement of employment costs). By 2002, coal mine jobs had fallen by nearly two-thirds to 164,000. This was followed by a slower pace of contraction, largely due to attrition (Baran et al. 2020). Whereas lignite extraction operated with a relatively stable workforce of around 24,000 until then, it began to decline steadily from 2002 onward, falling below 10,000 by 2015 (see Figure 2). Recent years have seen some limited new hiring, for example during the 2008 economic and financial crisis, and more recently during the 2021 energy crisis. 2.3 Recent estimates put the number of coal-related jobs between 145 and 218 thousand At the end of 2020, the number of workers directly employed in coal mines is estimated at 87,600. Two groups of coal-related employment are considered, those directly employed in the mines by the mining companies and mining conglomerates, and those deriving their jobs and incomes from coal-related activities. The number of direct coal jobs can be obtained from the mines with relative precision and is estimated at 87,600 at the end of 2020 (80,000 in hard coal; 7,600 in lignite extraction) (INSTRAT, 2021). But each coal mining job also generates additional jobs in the coal value chain, i.e. in the firms and businesses supplying goods and services to the operations of the mines. Coal mines further provide the inputs for coal-fired power stations. Finally, much of the demand for locally produced goods and services in the local communities (and the related jobs) originates in the salary mass of the coal and coal- related workers (the so-called induced effects). The latter three employment effects (linked to the coal value chain, coal-fired power stations and the induced effects) are much harder to estimate. Appropriate disaggregated data is hard to come by and studies use different methods and different sectoral and geographic coverage. A wide-ranging range of estimates exists. The results are reviewed briefly here, under the broad umbrella of indirect effects (as opposed to the direct effects). Recent studies using input-output tables estimate the total number of jobs indirectly linked to Poland’s coal mining sector between 57,000 to 130,000 (Figure 3; Box 2). It is common to calculate the number of indirect jobs related to a sector using input-output (IO) tables. IO tables are a set of national (or regional) economic accounts disaggregated by sector. They are a snapshot of flows of products and services in the economy for a single year: they identify and disaggregate all of the monetary flows between industries (inter-industry expenditure flows), between consumers and industries, and between industries and suppliers of factors in the economy. The results suggest that every coal mining job generates between 0.6 and 1.4 additional jobs, or an additional 57,000 to 130,000 jobs indirectly linked to coal mining. This puts the estimated total number of coal-related jobs in Poland today between 144,600, and 217,600 (Figure 3). 20 Figure 3: Polish workforce directly and indirectly employed in mining and power generation 158 Direct - Hard coal extraction Direct - Lignite extraction Direct - Other coal and lignite (beyond extraction) 130 Power plants (PP) Upper-bound 112 70 Upper-bound 110 Lower-bound 88 96 Lower-bound 8 (including Inter-regional subcontractors) 57 Recipients 80 49 Suppliers (including intermediaries = Intra-regional coal-fired PP, heating and coking plants) 13 Recipients Recipients (power plants) (power plants) Coal Power Kiewra Alves Dias Frankowski and Ingram Mining plants et al (2019) et al (2018) Mazurkiewicz et al (2020) DIRECT JOBS (2020) Mining and Power Plants INDIRECT JOBS Note: Each square represents 1,000 jobs. All estimates exclude induced jobs. “Hard coal extraction” and “Lignite extraction” estimates are reported by INSTRAT (2021) and refer to administrative data shared by mining conglomerates. However, Eurostat reports a larger number of individuals employed in “coal and lignite” (table lfsa_egan22d), namely 158 thousand: “Other coal and lignite (beyond extraction)” refer to the differential between the two estimates. Source: Alves Dias et al (2018), Eurostat (lfsa_egan22d, 2019), IBS (2020), Ingram (2020), INSTRAT (2021), Kiewra et al (2019). Nonetheless, these studies only provide orders of magnitudes at best, and should be treated as such. Estimating the indirect impact of mines closure on employment through IO models is limited by the simplified assumptions of the methodology. IO models rely on complicated yet simplified systems of equations, and the emphasis on the product side of the economy doesn’t explain why inputs and outputs follow a specific economic pattern. First, the assumption of linear equations relating to one industry’s output may not be realistic: since the factors are largely indivisible, it is not always necessary to increase the inputs 21 proportionally to increase the output. Second, IO models do not allow substitution between factors, and in the long-run, replacement potential between inputs may be relatively high. Box 2: Estimating the number of indirect coal-related jobs in Poland Using IO tables from 2015, Kiewra et al (2019) and Alves Dias et al (2018) estimate that between 57 and 88 thousand persons respectively are indirectly linked to coal mining in Poland.26 The estimations rely on the use of IO tables and multipliers from the EU Joint Research Center, originally developed for predicting the effects of a change in the final demand in one sector on other related sectors (Thissen and Mandras, 2017). The reasons for the difference in their estimates are not immediately clear. Both Alves Dias et al (2018) and Kiewra et al (2019) use the IO tables from 2015 and they both consider employment by coal recipients or users (particularly coal-fueled power plants). To calculate the number of indirect jobs generated in the coal supply chain, they both assume that the ratio of mining-related value-added to mining-value added corresponds to the ratio of mining- related jobs to mining jobs. Kiewra et al apply this ratio to the 2017 number of mining jobs; the base year of mining employment used by Alves et al (2018) is not clear. In terms of reporting, Alves Dias et al report separately also the intra- and inter-regional impacts, respectively 49 and 39 thousand jobs, though both effects should be included by Kiewra as the estimates are national. Frankowski and Mazurkiewicz (2020) further refine Kiewra et al’s work, estimating that Poland’s coal mining sector generates between 96 and 112 thousand indirect jobs, 41 thousand of which are generated among suppliers. As in Kiewra et al, indirect jobs are calculated as the share of value added transferred to the mining industry in the total value added generated in a given sector, multiplied by the number of employees in that sector. The paper differs in that employment data was retrieved from Eurostat data for 2018, and that employment among other recipients than coal-fired power plants (in manufacturing, gas, steam, and air conditioning (respectively NACE Rev.2 C and D) were added, proportionally to the share of coal in each sector. Finally, Ingram et al (2020) provide a slightly larger range of estimates, from 110 to 130 thousand, using a specific survey of 207 companies.27 The specificity of the study is to consider the timeline of mines closure: under an optimistic scenario, 26,667 jobs would be indirectly affected by 2030, half of what is estimated under a plausible scenario (50,580). The different results, along with estimates of the workforce directly employed in the mining sector and in the power plants, are illustrated in Figure 3. Third, IO models require huge amounts of data, which may not be accurate or up-to-date, and are rarely disaggregated below the national level: IO tables are only available every five years in Poland (the latest year of information published by GUS is 201528). As a result, while providing some benchmarks, the results of IO models can only provide orders of magnitudes at best, and should be treated as such, as inputs into a broader debate. Below the report argues that the spatial concentration of coal mines and coal mining related activities in certain 26 These estimates include power generation, equipment supplies, services, and R&D. 27 Unspecified sampling framework. 28 https://stat.gov.pl/en/topics/national-accounts/annual-national-accounts/input-output-table-at-basic- prices-in-2015,5,3.html 22 municipalities is likely the more important finding, indicating that the effects of coal mine closures are hard felt locally. Additional bottom up estimates of the indirect effect of the mine closure on employment in the coal value chain of Lower Silesia are further presented in section 3. In addition to national-level estimates, Alves Dias et al (2018) estimate the indirect impact of the transition at NUTS-II level29: a total of 3,045 jobs are indirectly linked to coal mining in Lower Silesia, 1,698 of which through intra-regional and 1,347 through inter-regional linkages. This report adds to this using a simple bottom-up approach: data on companies contracted out by Turów through public tenders was obtained through official reporting, data requests and data scrapping, whose total workforce was estimated using administrative data (section 3.4). Final estimates of the workforce indirectly affected by the mines closure were disaggregated between those working on sites located in Lower Silesia, and those working on sites outside of Lower Silesia. The impact of the transition on households living in the municipalities most affected by mines closure is further refined in section 3.5. 2.4 Large and increasing wage premia in coal-related jobs, especially for the lower skilled Wage premia of 50 percent or more are not uncommon for employees in coal-related professions, especially for lower-skilled workers and in state-owned mines or mining conglomerates. Hourly wages in coal-related occupations tend to be substantially higher than wages in other sectors, both on average and when comparing within the same occupational categories (Figure 4).30 If coal-related workers were to occupy a similar occupation in a sector other than mining or energy production, their average gross salary would be 20 to 40 percent lower. Put differently, most workers earn at least 50 percent more than those in similar occupations outside the mining sector. The wage premium earned by coal-related workers is particularly high for the lower skilled occupations (craft workers, plant operators and especially elementary occupations) and in public mines and mining conglomerates, where the average sectoral wage gap across professions was PLN 3,065 in 2018 compared to 1,339 in private mines.31 When controlling for demographic characteristics such as gender, age, and years of education, in addition to occupation, miners’ wage premia are even higher. The logarithm of the earnings regressed by applying the Mincer’s Equation (1974) , which 29 The NUTS classification (Nomenclature of territorial units for statistics) is a hierarchical system for dividing up the economic territory of the EU for the purpose of the collection, development and harmonization of European regional statistics. NUTS-I refers to major socio-economic regions, NUTS-II to basic regions for the application of regional policies, and NUTS-III to small regions for specific diagnoses (https://ec.europa.eu/eurostat/web/nuts/background). 30 61 occupations from the International Standard Classification of Occupations (ISCO-08), which are directly affected by the Just Transition, were identified. These occupations, which are directly related to mines and coal- fueled power plants, are listed in different sectors of activity: management of power plants and mines (4 professions), mining in general (24 professions), quarry mining (11 professions), energy production (15 professions), and mining Infrastructure (7 professions). 31 In high-skilled professions in private-owned mines, the premium does not exceed 20% on average, while in the public sector it is similar to this for low-skilled workers 23 regresses earnings on the worker’s educational attainment, including additional variables indicating sociodemographic (age, sex) and controlling for working time indicates that miners earn 50 to 60% more than other workers with similar education and work experience. Figure 4: Employees in coal-related professions enjoy wage premia of 50 percent or more Managers Professionals Technicians 0.30 0.15 0.20 0.10 0.20 0.10 0.05 0.10 0.00 0.00 0.00 0 5,000 10,000 15,000 20,000 0 5,000 10,000 15,000 20,000 0 5,000 10,000 15,000 20,000 Craft workers Plant operators Elementary occupations 0.40 0.40 0.40 0.30 0.30 0.30 0.20 0.20 0.20 0.10 0.10 0.10 0.00 0.00 0.00 0 5,000 10,000 15,000 20,000 0 5,000 10,000 15,000 20,000 0 2,500 5,000 7,500 10,000 Mining and quarrying All other sectors Note: Average gross wages. Average monthly earnings were calculated as total salaries and benefits for 2018 divided by 12 and number of hours worked. Source: Author’s computations using Structure of Earnings Data (BDL, access: 1.11.2021). Between 2004 and 2018, the wage32 gap vis-à-vis other sectors generally widened further. The average salaries of mining engineers, technicians and miners’ more than doubled over that period: from PLN 4,617 to PLN 8,926 for engineers, from PLN 3,927 to PLN 8,032 for technicians, and from PLN 3,112 to PLN 6,700 for miners. While in 2004 a mining engineer earned PLN 1,563 more than an engineer in another sector, in 2018, the wage gap widened to PLN 4,495. Similar increases were observed for technicians and miners. On the other hand, average salaries of other types of occupations in the mining sector grew at a slower pace, more closely aligned with national wage trends (GUS, 2020).33 These results are consistent with the World Bank (2018) study which finds that coal wages are not only high but have been 32 Average gross monthly earnings were calculated as total salaries and benefits for 2018 divided by 12. 33 The average wage for the Polish economy grew by 100.26 percent between 2004 and 2018 (in nominal terms). 24 rising faster over time, even when comparing coal and non-coal workers with the same demographic characteristics, including educational attainment.34 Finally, the types of occupations, contract terms, compensation and working conditions can vary widely between coal mine workers, and across the coal value chain. Employees of mining conglomerates benefit from the most protective contracts and social packages (in case of dismissal), negotiated by often powerful unions. They enjoy good working conditions (high wages, open-ended contracts, sometimes guaranteed employment within the conglomerate), generous health and unemployment insurance, early retirement eligibility, and pensions indexed to their high wages. They often also benefit from subsidized access to energy (free coal or subsidized heating). Within mining conglomerates, underground miners (hard coal) receive more generous compensation than surface miners (lignite). On the other hand, employees of supporting industries within the coal value chain (suppliers and service providers) tend to have less secure contracts, lower wages, and less protective social insurance than those directly employed by the mining conglomerates, even though their working conditions are on average likely still better than those outside mining and quarrying (Figure 4).35 3 Coal-related jobs in Lower Silesia are limited and concentrated in two municipalities 3.1 Concentration in the lagging district of Zgorzelecki In 2022, only one extraction site and one coal-fired power plants are still active in Lower Silesia. Historically, Lower Silesia was the only Polish region extracting both hard coal and lignite, but most mines closed over the 1990s. Between 1990 and 1996, three mines in the Wałbrzych region were shut down, dismissing over 16,000 workers. The last hard coal mine in Nowa Ruda was closed in 2000. Since then, Lower Silesia has been producing lignite only. Lower Silesia’s coal mining activity is concentrated in the region of Turoszów, at the border with Germany and Czechia. The area is situated in the powiat of Zgorzelecki (Figure 5), in the western part of Lower Silesia and covers an area of 839 km2 (less than 5 percent of the region). In 2020, 88,000 people lived in Zgorzelecki powiat, which constituted 3 percent of the population of the region. Three in four inhabitants live in one of five cities, namely Zgorzelec, Bogatynia, Zawidów, Pieńsk and Węgliniec. The powiat concentrates about 2 percent of all 34 Different factors likely underpin such wage differences. They may simply be a compensation for the health hazards and drudgery of coal-related employment. Given high capital intensity of the mining sector, they may also reflect higher labor productivity. Finally, through effective unionization coal-related workers may have more effectively leveraged their bargaining power, especially the lower-skilled workers in deep mining who are also most exposed to health hazards and drudgery. 35 Given definitional issues, the exact wage premium by occupation between those indirectly employed in coal and those employed in other sectors is hard to discern from the Structure of Earnings Data which does not clearly distinguish between those directly employed in mining and those indirectly employed. 25 business entities registered in the region. Most of them operate in construction, retail trade and transport (IBS, 2021). Figure 5: Mining is concentrated around the Turów extraction site and power plant at the border with Germany and Czechia Source: IBS, 2021. Lower Silesia relies heavily on the industrial and construction industry (Figure 6). One third of the region’s working population is employed in the industry sector, while services and agriculture employ 58 and 10 percent of the population respectively (GUS, 2020). Reliance on industry and construction is similar in Zgorzelecki powiat, where they represent 58 and 9 percent respectively. Figure 6: Employment is skewed towards industry and construction Lower Silesia Zgorzelecki powiat 10% 9% 33% 32% 32% 33% 2% 3% 22% 24% Agriculture, forestry, fishing Industry and construction Rest Trade, repair of motor vehicles, transportation and storage, accommodation and catering, ICT Financial and insurance activities, real estate activities Source: GUS, 2020. 26 The labor market of the Zgorzelecki powiat lags behind the regional averages. At PLN 59 thousand per year, the average gross salary is at 86 percent of the regional average (which outperforms the national average). Inactivity rates are high (62 percent of the working-age population, compared to 47 percent for the rest of the region). The powiat currently has a relatively favorable labor market situation with unemployment rates of 6 percent in 2020, i.e. higher than the average for Lower Silesia, but lower than the average unemployment rate for Poland (respectively 5.6 and 6.2 percent) (GUS, 2020). This situation may reflect the proximity with the German and Czech borders, and a possibility for Polish workers to work abroad. Figure 7: Zgorzelecki powiat is a lagging district within a very buoyant region PL LS GDP PER CAPITA 55 60 65 70 (‘000 PLN per year) ZG PL LS AV. GROSS SALARY 55 60 65 70 (‘000 PLN per year) EMPLOYMENT IN LS ZG PL 40 45 50 55 TRADITIONAL SECTORS (Share, %) PL LS ZG INACTIVITY RATE 40 50 60 70 (Share, %) LS ZG PL REGISTERED 5 6 7 UNEMPLOYMENT (Share, %) LS PL POVERTY RATE 0 5 10 (Share, %) ZG = Zgorzelecki Powiat LS = Lower Silesia PL = Poland Source: GUS (2020) excluding GDP per capita (GUS, 2019) 3.2 Excess demand for higher skilled workers in the region and substantial skills mismatch Due to data limitations, the analysis is carried out for the region of Lower Silesia. 27 3.2.1 Labor demand is concentrated in the tertiary sector and lower-skilled occupations In Lower Silesia, job offers registered with the Public Employment Services (PES)36 are concentrated in tertiary activities (Figure 8). Business administration and support activities, retail, accommodation and other services represent over 1 in 2 job offers. Scientific activities, education, healthcare and social assistance represent another 8 percent. Traditional sectors of activity represent a third of all PES job offers, with respectively 22, 7 and 5 percent for industrial processing, construction and transportation/warehouse. Figure 8: over 4 in 10 job offers are in services and retail Industrial processing Scientific activities Construction Education Transport/warehouse Healthcare and social assistance Business/admin services Retail Public admin, defense Accommodation Other Other services Note: Job vacancies listed by the Public Employment Services (PES). Source: Lower Silesia Labor Office, 2019. “Information about the situation on the labor market in Lower Silesia.” Job vacancies in Lower Silesia are concentrated among professions requiring lower skills (Figure 9Error! Reference source not found.). For example, most online job offers in Lower Silesia target low-skilled blue collars, i.e. “workers doing simple jobs,” and machine and device operators and assemblers (respectively 35 and 19 percent of all vacancies); and low-skilled white-collar vacancies represent 21 percent of all job offers. On the other hand, only 13 percent of vacancies target high-skilled blue-collars, and 12 percent high-skilled white-collars (Lower Silesia Regional Labor Office, 2019). Employers demand soft skills that are in line with tasks performed. “Planning and organizational skills” is the most demanded skill in most job vacancies. In addition, “Psychophysical and psychomotor efficiency” is the most demanded qualification in vacancies targeting low-skilled workers. On the other hand, communication and digital skills were most 36 Job offers submitted by employers to PES are still a popular recruitment channel in Poland. Past studies show that PES service 1/3 of the labor market, though the network of private employment agencies and internet platforms is expanding, with the latter are extensively used by Silesian employers. The analysis of selected job databases a few years ago shows that the largest number of job offers came from companies from the Mazowieckie voivodeship (16.9%), followed by the Silesia voivodeship (12.4%) (IPISS, 2018). According to the entrepreneurs’ declarations, almost 47% of them use the services of the Employment Office to seek employees, 43% post advertisements on recruitment portals, and slightly over 15% use the services of private employment agencies, recruitment agencies, headhunting companies (BKL, 2019). 28 demanded for office work, and vacancies targeting other high-skilled white-collars demanded communication, planning and teamwork.37 Demand exceeds supply in all professions. Lower Silesia’s labor market is tight, with job creation far exceeding job destruction: throughout 2019, 52 thousand new jobs were created, and 23 thousand jobs were liquidated (GUS, 2020). At the time of BKL in survey in 2019, 18 percent of employers were looking for workers (BKL, 2019), and according to the occupational barometer,38 employers report difficulty filling vacancies in most occupations. Figure 9: Most job offers in Lower Silesia are for lower skilled individuals Workers doing simple jobs 35% LOW-SKILLED BLUE COLLARS Machine and device operators and assemblers 19% HIGH-SKILLED Industrial workers BLUE COLLARS and craftsmen 13% Salesman and 2% service workers 11% LOW-SKILLED WHITE COLLARS Office workers 10% Technicians and associate professions 7% HIGH-SKILLED WHITE COLLARS Specialists 4% Source: Lower Silesia Regional Labor Office, 2019. “Labor Market in the Lower Silesian Region in 2019.” 3.2.2 Skills mismatch is significant, especially among the lower skilled and technicians In Lower Silesia, skills mismatches occur mainly among low-skilled blue collars (overeducated) and technicians (undereducated) (Figure 10). Beyond professionals, whose educational attainments largely match the requirements of their jobs (only 12 percent are undereducated), there are important skills mismatches across the different occupations. Technicians and associate professionals are often undereducated (52 percent), suggesting 37 Lower Silesia Regional Labor Office, 2019. “Labor Market in Lower Silesia in 2019.” 38 Lower Silesia Regional Labor Office, 2019. “Monitoring of Professions in Deficit and Surplus in the Lower Silesian Voivodeship in 2019.” Professions in deficit, surplus and balance are identified by a group of experts, which gathers every year. As such, it is not a quantitative comparison of the number of registered jobseekers and registered vacancies. 29 that the sector fails to attract employees with the right education and employs lower educated workers instead. Skilled agricultural, forestry and fishery workers, service and sales workers, and craft and related trade workers also display undereducation, but to a lesser extent. On the other hand, low-skilled blue collars are overwhelmingly overeducated, suggesting that low-skilled blue collars are facing limited work prospects and accept positions below their qualifications. To a lesser extent, clerical support workers, and service and sales workers, also experience overeducation. The situation of Lower Silesia reflects a general situation in Poland, where skill mismatch amounts to 65 percent. Figure 10: Low skilled blue-collar characterized by overeducation, technicians by undereducation 100% 12% 31% 80% 48% 57% 69% 74% 60% 78% 88% 86% 88% 40% 69% 52% 40% 14% 2% 20% 22% 24% 4% 12% 17% 10% 0% 3% Managers Professionals Technicians Clerical Service Skilled Craft Plant Elementarty and Support and Agricultural and and Occupations associate Workers Sales Forestry Related Machine professionals Workers and Trades Opperators Fishery Workers and Workers Assemblers High skilled white collar workers Low skilled white collar High skilled blue collar Low skilled blue collar workers workers wokrers Undereducated Overeducated Rightly Educated Note: Data for Lower Silesia. The correspondence between occupations and skills is using the ILO nomenclature. High-skilled white collars are expected to have tertiary education (ISCED 5-8), low- skilled white collars and high-skilled blue collars are expected to have more than upper-secondary non- tertiary education (ISCED 3-4), and low-skilled blue collars are expected to have up to upper-secondary education (ISCED 0-2). Source: Author’s computations using Structure of earnings data (BDL, access: 1.11.2021). 3.3 Dominant position of Turów in Zgorzelec and Bogatynia Turów is the last remaining mine and coal-fired power plant left in Lower Silesia. The mine and power plant are owned and operated by the largest lignite conglomerate: PGE Górnictwo i Energetyka Konwencjonalna (PGE GiEK). PGE GiEK owns 2 lignite mines (providing almost 90 percent of domestic lignite production in Poland), 2 lignite-fueled power plants, and 3 hard coal-fired power plants. 30 Turów lignite complex is the dominant employer in the municipalities of Zgorzelec and Bogatynia. It is the largest employer in the powiat of Zgorzelecki powiat: in 2020, it employed 3,500 people between the extraction site and the power plant. Over 95 percent of Turów employees lived in Zgorzelecki powiat: 80 percent live in two municipalities: Bogatynia and Zgorzelec. Turów employs mainly older men with secondary education. The average age of Turów employees is relatively high: in 2020, almost 72 percent of mine workers and 70 percent of power plant employees exceeded the age of 45. Most of them will reach retirement age in the current decade. The vast majority of Turów workers are men: women make up less than 15 percent of the workforce in the mine and less than 20 percent in the power plant. (IBS, 2021). Three fourth of the mine employees and almost two thirds of power plant workers have at most upper-secondary education: respectively 39 and 36 percent of the mine employees graduated from secondary and vocational education – as compared to 50 and 10 percent in the power plant (IBS, 2021). Employees of the coal mining sector enjoy high wages, which are substantially higher than counterparts in the other lignite-extracting region of Wielkopolska (BDL, access: 1.12.2021). Mining engineers and technicians respectively earn PLN 6,899 and PL 7,324 in Lower Silesia, which is about PLN 309 and 1,994 higher than in Wielkopolska, but PLN 1,474 and PLN 57 lower than in Silesia (characterized by hard-coal mining). 3.4 Very few jobs in the coal value-chain in Lower Silesia are outside PGE More than nine in ten coal-related jobs in Lower Silesia are within PGE Capital Group. Unlike in other regions of Poland, PGE’s vertically integrated structure means that most coal-related activities are carried out within the PGE Capital Group, including both direct mining activities and indirect coal value chain activities. In fact, Turów mine workers make up 64 percent of the coal value-chain workforce (Figure 11). Figure 11: 2 in 5 coal-related jobs are in the mine and power plant; 9 in 10 jobs are provided by Turów (including subsidiaries) 1,774 Turów mine and power plant Turów subsidiaries 3,536 447 Indirect (lower bound) 227 Indirect (upper bound) Note: Each square represents 200 jobs. Source: IBS, 2021. The remaining 1,774 employees work in one of seven subsidiaries operating within PGE Capital Group (see Box 3). They provide support services for mining extraction and power 31 plants, mostly in repair and maintenance, specialized equipment, and cleaning and waste management. All indicated subsidiaries operate within the PGE Capital Group, which owns 100 percent of the shares. The activities of the subsidiaries are strongly limited to services provided almost exclusively to the Capital Group, resulting in a very high level of dependency on the mining conglomerate. The majority of workers in the subsidiaries are older men, with low levels of education. Large companies, located outside of the region, dominate the pool of the Turów complex subcontractors. Forty percent of all suppliers employ more than 500 workers, and medium- sized companies and micro-enterprises represent 20 percent each.39 Enterprises providing services for the Turów mine and power plant are mainly located outside of Lower Silesia. Only three small companies working for the mine (Jarex-Trans, Engram, Vektor) have headquarters in the region (and only the last one – the company providing transport services is from Zgorzelecki powiat) with very limited share of revenues stemming from the mining complex (4 percent). An estimated 230 workers are employed in manufacturing, trade and supply of equipment and goods for lignite production, professional engineering and technical services. Twenty companies cooperating with Turów mining complex were identified, on the basis of internal data shared by PGE. The coal extraction site represented 35 percent of total tenders, and the power plant the remaining 65 percent. A total of 670 workers are employed in those 20 subcontracting firms: correcting that figure by the share of each company’s total revenues derived from tenders with Turów mining complex and narrowing down to the branches located in Lower Silesia only, 230 workers are employed in the coal value chain. Subcontractors of the extraction site are more dependent than those providing services for the power plant: respectively 9 and 1.5 percent of total annual revenues of these firms are derived from contracts with Turów mining complex. 3.5 Direct and indirect coal-related jobs are especially important to Zgorzelecki powiat The economy of Zgorzelecki powiat is quite vulnerable to the transition out of coal, given the high concentration of the economy and employment in the coal value chain. Turów complex and associated companies employ almost a quarter of employees in small, medium and large companies (hiring 10 and more people) in Zgorzelecki powiat. IBS (2021) estimates that within Zgorzelecki powiat, 11 percent of employees worked in mining extraction, 6 percent in power plant, and an additional 5 to 8 percent in associated companies. Most of these workplaces are directly linked to Turów complex and located in one city, namely Bogatynia. The scale of employment in subcontracting companies is marginal, however, the calculations did not include all economic activities supporting lignite and power production. In particular, due to business confidentiality, it is not possible to capture contracts that are 39 For the remaining 20% of contractors, we were not able to determine details. 32 not subject to public offer procedures as well as transactions between associated companies.40 About one in three respondents in the heavily affected municipalities (Bogatynia and Zgorzelec) (see section 4) believe that the coal phase out will have a significant impact on their lives. 75 percent of respondents reported that up to 20 percent of their household income is dependent on the functioning of a hard coal mine or an energy company. Thirty percent expect this process to have a significant impact on their lives; another third of the population believes that the Just Transition process will have a moderate impact on their lives. More broadly, these are relatively poorer communities to begin with, with over 30 percent of respondents in the two municipalities reporting an average monthly net income per person below PLN 2,100.41 Box 3: Turów subsidiaries and subcontractors The largest companies providing services to Turów mine and power plant are vertically integrated within the PGE capital group supply chain, especially for repair and maintenance services, transport, investment supervision and organization of social facilities for employees. The companies working for Turów mine and power plant are: - RAMB limited liability company. The company is based in Bełchatów (Łódzkie) and specializes in works accompanying opencast mining, particularly in anti-corrosion protection, execution and assembly of steel structures, and general construction and electrical works. In 2020, the share of revenues derived from the Turów complex accounted for 21 percent. The company employed 1330 people in 2020, mostly prime-age experienced men, with less than upper-secondary education. Men accounted for 93 percent of total employment; average age was 42 years; the average total length of service was 19 years (including 9.5 years in the company; and 39 percent of workers had secondary education, 29 percent vocational education, and 21 percent higher education. - ELTUR-SERWIS limited liability company. The company is based in Bogatynia and specializes in the provision of repair services and the construction of technological and industrial installations in power plants. The company employed 718 people nationwide, including 172 workers in Bogatynia, mostly older men: 85 percent of employees were men; 38 percent were older than 50; equally distributed across vocational, secondary and tertiary education. - Bestgum limited liability company. The company is based in Rogowiec (Łódzkie) and specializes in construction and modernization of the conveyors used in opencast mining and waste recycling plants, runner production and vulcanizing services. Bestgum derives 95 percent of its revenues from PGE. The company employed 895 people in 2020, including 80 workers in Bogatynia. 40 I.e. EPORE based in Bogatynia is a company dependent from PGE GiEK, providing landfill services and other services for Turów lignite mine. At the end of 2020, EPORE employed 409 people. 41 Taking 2/3 of median per capita income as the poverty line would put the 2020 poverty line in Poland at 2,245 PLN. 33 - Eltur-Trans limited liability company. The company is located in Bogatynia, and belongs to the Betrans company based in Bełchatów. It provides transport services for people and goods (including the operation of railway sidings), maintenance and repair of vehicles, and specialized construction works. At the end of 2020, the branch employed 71 workers, mostly older experienced men with less than upper-secondary education: men accounted for 89 percent of total employment; average age was 52 years; average length of service was 22 years; and respectively 42 and 28 percent had vocational and secondary education. - Megaserwis limited liability. The company provides cleaning and order maintenance services in industrial facilities, offices and urban areas, and catering services addressed to mines, power plants and other energy plants and industrial companies. At the end of 2020, the company employed 1,024 people, mostly older women with less than upper-secondary education: women accounted for 76 percent of total employment; 38 percent were workers over 50 years; and respectively 30 and 43 percent had vocational and secondary education. - ELBIS limited liability company. The company, based in Rogowiec (Łódzkie) specializes in the management of investment projects, the construction of new power units, environmental protection installations and the modernization of existing energy units. At the end of 2020, the company employed 70 people, mostly older educated men: men accounted for 73 percent of total employment; average age was 48 years; average length of service was 6 years; and 97 percent graduated from tertiary education. - EPORE limited liability. The company, based in Bogatynia, specializes in the collection and transportation of combustion by-products, operation of milling and ash removal system. Due to the diversification of business activity, in 2020, over half of total revenues were generated outside PGE. The company is based in Bogatynia. In 2020, the company employed 409 people. Source: IBS, 2022. 4 The type of jobs coal-related workers can and would like to do 4.1 Local understanding of coal-related workers’ skills and job preferences is needed The coal-related workforce displays little mobility traditionally. Mining activities are by nature location-bound, with coal-based industries (coal-fired power stations, coking and steel industries) typically also establishing in the vicinity of the mines, given the bulkiness and cost to transport coal. This has often generated a strong attachment of coal-related workers to the local community and low geographical mobility (the Ruhr valley in Germany, the Nord- Pas-de-Calais basin in France, the Upper-Silesian basin in Poland). Beyond limited geographic mobility, employees of the mining sector usually also display little cross-sectoral mobility. Many spend their entire career working for one employer, the mining conglomerate, which typically also dominates the local labor market. Coal mining jobs often involve semi-skilled production and machine operation occupations, which have limited immediate transferability to other sectors. As they are usually better paid than others holding similar jobs in other sectors, employees in the mining sectors also have high wage expectations (section 3.3.4). As a result, even employees with skills that are easily transferable (e.g. electricians, drivers) have limited incentive to look for work elsewhere. These social and economic forces driving the 34 limited geographic and intersectoral mobility of coal-related workers are compounded by a deeply-felt cultural identity, rooted in mining.42 Reasons for this could be traced to the communities’ historical contribution to their nations’ early industrialization and energy security, the emergence of social rights and workers’ movements around mining-related activities, and strong group solidarity and work ethics in general. Does limited mobility of the coal-related workforce across space and sectors still hold today? Low worker mobility limits the pool of potential job opportunities, exacerbating the challenge of a just coal transition. This holds especially when the surrounding districts or regions are economically vibrant, as in many regions in Poland (especially in Lower Silesia, but to a lesser extent also in Silesia and Wielkopolska). Yet, whether workers in Lower Silesia value mine related work and job opportunities locally to the same extent as has been historically observed in Poland and elsewhere, is an empirical question. Mining has mechanized and automatized over the past decades. Along with it, the skill profile of its workers, and possibly also the link of their identity to mining and the mining community, may have changed.43 Given the different health hazards involved, the mining related identity may also be stronger among deep-coal mining communities (as in Silesia) than among lignite workers (as in Wielkopolska and Lower Silesia). To inform policymakers and investors regarding the potential and desirability of different mine repurposing activities and economic diversification strategies, the skills profile and job aspirations of the directly and indirectly affected workforce Lower Silesia must be better understood. This also helps develop tailored training and job transition programs and policies for the affected workers and informs other labor market transitions affecting Lower Silesia, such as the ongoing digitization of the economy and workplace and the transition to more environmentally sustainable economic activities. A new skills and worker job preference survey among Lower Silesia’s coal-related workers helps shed light. In February 2022, a skills and job preference survey was conducted among a random sample of inhabitants aged 18-65 of two municipalities identified as being most at- risk to coal phase-out: Bogatynia and Zgorzelec. In total, four hundred interviews were conducted among the municipalities' working-age population (148 in Bogatynia, 252 in Zgorzelec). Households and workers in these municipalities stand to be indirectly affected given the heavy dependence of their local economies and local public finance on mining activities. Annex 3 provides detailed information about the survey sampling frames and the sampling as well as the sample selection bias corrections that were applied after the survey. Non-response, and particularly selectivity in non-response, is inherent to any survey. This can undermine the accuracy and representativity of the findings derived from the recorded responses. Given additional information from other sources on key variables for both the 42 Carley, et al., 2018, Mayer, 2018, Robertson (2006). 43 Findings from the UK, however, suggest that the new technologies in mining that have intensified workplace monitoring and surveillance, have not changed mine workers’ unique sense of identity as coal miners, given their continuing role as autonomous workers that mediate the impact of technology on their working practices (Allsop and Calveley, 2009). 35 respondents and non-respondents, the effects of non-random non-response on the representativity of the findings can be mitigated through appropriate sample bias corrections. The survey consisted of two parts: a skills and employment questionnaire and a Discrete Choice Experiment (DCE). It measured a range of transversal hard and soft skills and identified how respondents value different job characteristics, such as job location, job security, job compensation, through a discrete choice experiment (DCE). The questionnaire also included detailed questions to determine the respondents’ employment status and socio-economic situation to identify potential differences in skills and job attribute preferences across different socio-economic groups. The questionnaires were each time field tested and fine- tuned accordingly. They are in Annex 2. 4.2 Affected municipality residents lack foundational, but have good technical skills 4.2.1 Skills measurement and scales The focus is on transversal skills. ILO (2004) shows that individuals are most employable when “they have broad-based education and training, basic and portable high-level skills, including teamwork, problem solving, information and communications technology (ICT) and communication and language skills. This combination of skills enables them to adapt to changes in the world of work.” The better equipped the affected workforce in Lower Silesia is with these “transversal” skills, the easier the transition to other jobs will be. The skills questionnaire thus focuses on core transversal skills that are not exclusively related to any particular sector or occupation. The analysis is comparative. Rather than assessing the skill profile of the coal-related workers in absolute terms, the report compares them with the rest of the labor force in Poland to assess their comparative (dis)advantage vis-à-vis other workers. Information on universal competencies among workers in Poland has been annually collected through the Polish Human Capital Balance (Bilans Kapitału Ludziego, BKL) survey. It is the largest cross-sectional panel study monitoring the Polish labor market from 2010 to 2022. Self-assessment scales are used, going from 1 (very poor) to 5 (very developed), which will serve as a benchmark for our analysis. Self-skills assessments are easier and less expensive to administer than test-based skill measurement, though the findings on whether they are a good proxy for test-based skill measurement are mixed. By taking a comparative perspective, lingering doubts about the accuracy of self-assessed skills are mitigated.44 The transversal skills of the coal-related 44 Research by Allen & Van Der Velden (2005), Brown et al. (2015) and Davis et al. (2006) shows mixed conclusions as to whether self-skills assessments can be a good proxy for test-based skill measurement The comparison of the skill profiles among different groups holds, however, as long as potential biases in the self- assessments are unrelated to the group defining features. The report thus mainly operates on the differences in self-assessment, treating them as a relative measure and refraining from treating them as indicators of competencies in absolute terms. 36 workforce are thus assessed by comparison to those of the broader regional and national labor pool, using the same questions and similar survey settings as in the BKL survey. Following skill domains were adopted. They were also queried in the BKL survey and relate to: self-organization and initiative taking, communication with other people, organizing and conducting office work, managerial abilities and organization of other people’s work, fluency in Polish language (oral and written), ability to search for and analyze information and draw conclusions, capacity to maintain assemble and repair technical devices, ability to perform simple calculations, willingness to travel frequently, knowledge of specialized computer programs, ability to code and create websites, artistic and creative abilities, and physical strength. 4.2.2 Lower foundational, but better technical and artistic skills Residents from the municipalities of Bogatynia and Zgorzelec assess most of their skills below those of other Poles and Lower Silesian workers, the opposite holds only for technical and artistic abilities (Figure 12). When looking at foundational and communication skills, municipality residents display lower scores than most Poles and Lower Silesian workers. On the other hand, municipality residents have much better technical skills related to assembly and repair of technical devices than most Poles and Lower Silesia workers (+0.9). 45 Figure 12: Residents of Bogatynia and Zgorzelec assess most of their skills below the national and regional averages, except for technical and artistic abilities Poland Lower Silesia Municipalities resistance to stress 5 self-organization of work and showing ART artistic and creative abilities initiative 4 physical fitness coordinating the work of others ORGANIZATION 3 2 fluent use of the Polish language in 1 groupwork speech and writing 0 easily establish contacts with FOUNDATIONAL performing simple calculations colleagues or clients COMMUNICATION using a computer willingness to frequent travels searching and analysing information assembly and repair of technical and drawing conclusions devices knowledge of specialised programs, the ability to write programs or create websites TECHNICAL 45 The difference is statistically significant. 37 Source: World Bank Skills and Preference Survey (2021), BKL (2017). Holding educational level constant, both, lowly and highly educated residents of Bogatynia and Zgorzelec display lower foundational skills than most Poles and Lower Silesian workers (Figure 13). Employees with at least tertiary education have slightly higher managerial, organizational and technical skills. They also have the largest advantage for assembly and repair technical devices (+1.64) and artistic and creative abilities (+0.8), and they feel more resistant to stress and rate their physical condition above the national average.46 Figure 13: The skills deficit in all dimensions, but technical and creative abilities, remains FOUNDATIONAL when holding education constant Poland Lower Silesia Municipalities At least tertiary education resistance to stress 5 self-organization of work and ART artistic and creative abilities showing initiative 4 physical fitness coordinating the work of others ORGANIZATION 3 2 fluent use of the Polish language 1 groupwork in speech and writing 0 FOUNDATIONAL easily establish contacts with performing simple calculations colleagues or clients COMMUNICATION using a computer willingness to frequent travels searching and analysing assembly and repair of technical information and drawing devices conclusions knowledge of specialised programs, the ability to write programs or create websites TECHNICAL 46 The differences are statistically significant. 38 At most upper-secondary education Poland Lower Silesia Municipalities resistance to stress 5 self-organization of work and ART artistic and creative abilities showing initiative 4 physical fitness coordinating the work of others ORGANIZATION 3 2 fluent use of the Polish language in 1 groupwork speech and writing 0 easily establish contacts with performing simple calculations FOUNDATIONAL colleagues or clients COMMUNICATION using a computer willingness to frequent travels searching and analysing assembly and repair of technical information and drawing devices conclusions knowledge of specialised programs, the ability to write programs or create websites TECHNICAL Source: World Bank Skills and Preference Survey (2021), BKL (2017). A similar trend appears for employees with at-most upper-secondary education: they record lower skills than Poles and Lower Silesian with similar educational attainment in most dimensions, but display a slight advantage for technical skills, including assembly and repair of technical devices and advanced digital abilities. In addition, within municipalities, residents with at least tertiary education display higher skills than the lower educated; the largest skill gap (+0.9) is visible for managing other people's work.47 The broad pattern of lower foundational and better technical skills holds across gender and age. Both male and female residents have lower foundational, but better technical skills than their regional and national counterparts. Similarly, older respondents (45 years old and above) usually fare worse than their counterparts at national and regional level, the only exceptions being technical, managerial, and artistic skills (compared to regional averages), and also computer use and physical fitness (compared to national averages). Younger respondents (below 45 years of age) have an advantage in artistic and creative abilities and feel more confident in the assembly and repair of technical devices.48 47 The difference is statistically significant. 48 The differences are statistically significant. 39 4.2.3 Overall willingness to reskill to get a new job, especially for hard skills Most residents feel that their skills are suited to their jobs. This holds for 68 percent of the residents in the municipalities; 27 percent feel that they have the skills to handle more demanding duties. 5 percent of women and 5 percent of men indicate that they need further training to do their current job well. For one third of the residents, the tasks performed in their current job are inconsistent with their education and skills, particularly among younger workers (aged 45 or less). The majority of unemployed residents would be willing to reskill or acquire new competencies when looking for another job. 65 percent of municipality respondents would be interested in upskilling in hard skills, and 70 percent would like to upskill in soft skills (assertiveness, communication, etc.) and job search methods. Language courses were the most sought-after training, suggesting that workers are looking to more nationally or globally competitive sectors as a potential source of good-quality jobs. Workers also underlined their willingness to participate in training focused on digital skills – the field where the respondents rate their abilities especially low. 40 Figure 14: Both male and female residents have lower foundational, but better technical skills than others in the region or Poland Poland Lower Silesia Municipalities resistance to stress 5 self-organization of work and ART artistic and creative abilities showing initiative Males 4 ORGANIZATION physical fitness coordinating the work of others 3 2 fluent use of the Polish language in 1 groupwork speech and writing FOUNDATIONAL 0 easily establish contacts with performing simple calculations colleagues or clients COMMUNICATION using a computer willingness to frequent travels searching and analysing information assembly and repair of technical and drawing conclusions devices knowledge of specialised programs, the ability to write programs or create websites TECHNICAL Poland Lower Silesia Municipalities Females resistance to stress 5 self-organization of work and ART artistic and creative abilities showing initiative 4 physical fitness coordinating the work of others 3 ORGANIZATION 2 fluent use of the Polish language in 1 groupwork speech and writing 0 FOUNDATIONAL easily establish contacts with performing simple calculations colleagues or clients COMMUNICATION using a computer willingness to frequent travels searching and analysing information assembly and repair of technical and drawing conclusions devices knowledge of specialised programs, the ability to write programs or create websites TECHNICAL Source: World Bank Skills and Preference Survey (2021), BKL (2017). 41 4.3 Job attributes coal-related workers value 4.3.1 The valuation of job attributes requires comparing job packages To properly assess people’s valuation of different jobs and job attributes, they need to be confronted with a clear (discrete) choice between different job packages with clearly defined attributes. Oftentimes, respondents do not or cannot know what future situation they prefer, either because they don't have an answer, or because they do not have enough information to make an informed decision. For example, one may be willing, or not, to move regions for a job, but the answer will depend on many other characteristics of the job offer such as the position, the wage, the type of contract, the benefits, the availability of childcare etc. Hence, instead of asking separately direct questions, such as one’s willingness to move regions, or to change sectors, or to accept a pay cut, the DCE methodology reveals these preferences by asking the participant to choose several times, between two hypothetical options, each of which includes a significant set of characteristics, or attributes. This methodology follows a selection process of probabilistic nature, as described by McFadden (1974). The choice of an alternative that seems more attractive to respondents is determined by a utility that depends on individuals’ observed characteristics and unobserved idiosyncrasies. This process allows modeling respondents’ decision processes and extracting information about preferences (individual utility function parameters) based on pre- composed successive choices in a controlled environment. The DCE was administered to residents of the two municipalities most affected by the mines closure (Figure 15). The design consisted of six choice sets, each of them offering two different job descriptions to choose from. These included detailed job characteristics, or attributes: alignment with educational specialization, type of contract, monthly wage, wage increase over the first two years of work, benefit levels, commuting time. The attributes and their levels were based on the literature review and preliminary qualitative work, including focus groups with local stakeholders (including social partners in the municipalities surveyed). To prevent cognitive overload, the choice experiment was divided into two designs (DCE 1 and DCE 2) with seven attributes each. The designs had two common, overlapping attributes (net salary and form of employment). See Annex 3 for more detail on the choice cards and their attributes and the underlying econometric procedures applied to estimate the value attached to the different job attributes. 42 Figure 15: Example of a choice card Source: World Bank Preference Survey, 2021. 4.3.2 Municipality residents strongly prefer permanent employment close to home DCE queries about workers’ disposition towards job alternatives among the residents of residents of Bogatynia and Zgorzelec indicate employment close to home as an important attribute when choosing a job, and even more so among men, lower educated and older workers. Reducing the travel time to work by one hour (each way) is valued at PLN 749 overall. This means that respondents would agree to sacrifice on average PLN 749 of their monthly salary if their work commute is reduced by one hour each way - which is equivalent, on average, to 20% of a resident’s net monthly salary. Men are more sensitive to shorter commuting times, along with lower educated and older workers. Similarly, municipality residents prefer not to relocate. Surprisingly, however, their aversion to relocation is less than their aversion to commuting. They evaluate the financial compensation justifying moving abroad for work at PLN 674 per month and moving to another region within Poland at PLN 537. Females display higher WTP not to relocate (respectively PLN 378 and 385 more than men for moving abroad and elsewhere in Poland respectively). Older workers, and those with higher education are also less willing to relocate than younger and less educated workers. Overall, residents of Bogatynia and Zgorzelec prefer a job nearby, with jobs further away implying a sizeable welfare loss. At the same time, their aversion to mobility is not as large as among workers in Silesia and Wielkopolska, for whom the welfare loss of an hour commute exceeds 1000 PLN, while the welfare equivalent compensation needed to take up a job abroad is estimated between 2500 (Silesia) and 5,000 (Wielkopolska). The lesser aversion to work abroad among workers in these two municipalities compared with their peers more inland is consistent with the longer standing tradition of workers in Lower Silesia to work in the bordering Czech Republic and Germany. Another valued job attribute when choosing a new job is consistency with one’s educational specialization, especially for women, older workers, and more educated individuals. They are ready to sacrifice on average PLN 808 to find a job related to their education, even if it requires initial training, and PLN 556 to have a job corresponding to their education and skills. 43 Women are less likely to accept job offers requiring retraining; the same holds true for older workers. More educated respondents value jobs corresponding to their skills and education more than lower educated ones; however, the reverse relationship is visible for work related to education (requiring initial training). Employment security is a third valued factor when choosing a job, but workers are ready to accept lower job entry conditions if the good prospects for wage progression. Respondents’ preference goes to public sector contracts before the private sector and self-employment. Interestingly, the most attractive form of contract is with NGOs, which is valued at PLN 378. The value attached to job security is corroborated by the preference for fixed-term contracts over temporary ones, for which they would be willing to forego PLN 216. In addition, residents do not want to undertake a dangerous job (they would forego PLN 657 to avoid it), especially women and older respondents. Municipality residents also value wage progression, valuing the prospect of a 40 percent earnings growth at PLN 471. The most attractive sector of activity for municipality residents is renewable energy (RE). Respondents are willing to forego PLN 425 to avoid work in mining, a sharp contrast with the findings in Silesia where coal workers preferred working in the mines above work in any other sectors (apart from RE, but only for the higher educated workers). The preference for RE is especially strong for men (WTP 652), older respondents (PLN 554), and those with higher education (PLN 479). The least preferred employment sectors are agriculture and construction: for these sectors, respondents would request compensations close to PLN 234 and PLN 367 respectively. Most of the workers would agree to substitute a sizeable part of salary for certain fringe benefits. They are willing to pay PLN 749 to enjoy flexible working time, PLN 707 to have private medical care, PLN 653 to attend certified professional courses, and PLN 585 for childcare. The latter contrasts sharply with the mining workforce in Silesia, for whom access to childcare services did not add any value. Women also value those benefits more than men; while lower educated respondents display a stronger preference towards certified professional courses, private medical care, and childcare services than more educated respondents. 44 Figure 16: Residents' preferences – DCE 1 -1200 -800 -400 0 400 800 1200 commuti work per ng (one time to Travel way) hour -736 Base: Work education with Compliance of inconsisent Work related to your education (requires initial training) 808 with your work education and skills (requires complete Job corresponds to your skills and education 556 retraining) Base: Employment Apprenticeship / internship 140 contract for an indefinite period Contract Temporary (contract of mandate) -22 Fixed-term employment 216 Change of financial Base: No change 40% increase over initial salary 471 conditions 20% increase over initial salary -39 Base: No benefits Mobile phone or computer 152 Childcare services 585 Private medical care 707 Benefits Transport provided to and from work by bus 259 Certified professional courses 653 Flexible working time 749 Note: a darker color means a significance at 10 percent. Source: World Bank Skills and Preference Survey, 2021. 45 Figure 17: Employees' preferences – DCE 2 -1000 -500 0 500 Company size 1 Base: Poland (no need to Poland (need to change the place of residence) -537 Location change the place of residence Abroad (need to change the place of residence) -674 conditions Base: Not Working burdensome Dangerous -657 Base: Self- employed Public sector 96 Sector Private sector 11 NGO 378 Base: Mining Construction -267 Industrial processing 290 Type of industrial activity Renewable energy (photovoltaics, wind farms) 425 Agriculture -232 Transport services, repairs, communication 376 Other services (gastronomy, tourism and recreation, etc.) 174 Administration (office work) 361 Note: a darker color means a significance at 10 percent. Source: World Bank Skills and Preference Survey, 2021. 46 These findings give important voice to the affected workforce and help steer repurposing plans and investments to generate attractive employment and develop viable job transition paths. Understanding the aversion towards relocation and especially commuting, the need for utilizing existing competencies, and the overall willingness to retrain, allow to better identify and generate viable transition pathways compatible with the affected workforce’s skills and job-related preferences and steer economic diversification (including through repurposing of mining lands and stranded assets). 5 The identification of viable job transition pathways for coal-related workers 5.1 Ingredients needed to assess the viability of individual job and labor force transitions The (local) availability of jobs, workers’ job preferences, and the extent of skills’ (mis)match, determine the viability of job transitions for Lower Silesia’s coal-related workforce. Section 4 shows that most workers in the municipality have lower foundational, but better technical skills as other workers in the region. All workers put a sizeable value on being able to work nearby, with little commuting, in positions that match their skills and provide wage progression. But they are open to re/up-skilling. Assessing the extent of the coal transition challenge for laborers thus requires assessing the spatial, expectations and skills mismatch, i.e. 1) assessing the availability of alternative job opportunities and their location, 2) determining their attributes (salary, contract type, sector, benefits, …), and 3) identifying the skills required and the workers’ ability and attitude towards re/up -skilling. Trade-offs across these dimensions further complicate the assessment. Most jobs, whether currently available or only available in the future, are unlikely to score equally well on all three dimensions (local availability, job preferences, skills (mis)match). Systematically mapping out the trade-offs will be equally important to assess the individual and overall labor transition challenge. A job may be available, for example, but only in another region, or only under a self-employed as opposed to a salaried contractual arrangement. The better a job scores on each of the three dimensions, i.e. the smaller the spatial, expectations and skills mismatch, the more viable is the transition. At the aggregate or regional planning level, the more well matching jobs that there are already available or will be generated through appropriate repurposing of the mines or economic diversification, or that could be filled through well designed and well targeted re/up-skilling programs, the smoother will be the overall labor transition. Yet, as highlighted in Box 1 and illustrated in detail for the Appalachia region in the United States, in the past, the labor transition has proven to be an uphill battle for many mining communities.49 49 In Appalachia, a longstanding coal mining region in the United States, only 4 out of the 420 coal counties covered by the Appalachian Regional Commission managed a successful socio-economic transition out of coal (Labao et al., 2021). 47 Seemingly intuitive job transition pathways often prove unsuccessful. Job transitions considered by stakeholders are regularly restricted to different occupations within the same sectors of activity (e.g. reskilling a belt maintenance technician into a photovoltaic installer), or similar occupations in another sector of activity (e.g. offering a school bus position to a dismissed shuttle car operator). In Wielkopolska, ZE PAK’s recent experience of reskilling workers within the energy sector from brown to green jobs has shown limited take-up, however, mostly due to the reluctance to commute, the limited ability of older displaced workers to retrain in a new occupation, and limited career prospects in the medium-to-long term. Such experience is not uncommon. As Michael Bloomberg said at the 2014 Bloomberg Energy Summit: "You’re not going to teach a coal miner to code. Mark Zuckerberg says you teach them [people] to code and everything will be great. I don’t know how to break it to you . . . but no.” 50 Obtaining timely and systematic information on the spatial, expectation and skills mismatch of alternative jobs and adjudicating potential trade-offs is challenging. Hiring intentions of employers captured through national employer surveys are frequently used to assess labor demand trends across occupations, sectors and locations (the number and types of jobs). They provide a powerful source to assess employment opportunities in the nearby future (typically the next 6-12 months), complementing the job vacancies published by public and private employment services. Yet not everywhere are employer surveys implemented on a regular basis, and when administered, the results may overlook employment opportunities provided by micro and small enterprises, they may not be representative at local/district level, or they are not publicly available. Employer surveys also give little guidance regarding future job emanating from mine repurposing or regional diversification strategies, either directly, but also indirectly in the supply chains, or through consumption linkages in the broader community. This further complicates proper tailoring of re/up skilling programs to the needs of the local labor market, while generic re/upskilling programs (unlinked to particular job opportunities) often prove to be rather ineffective (European Commission, 2012). Second, to assess job preferences regarding different job attributes, DCEs can be used, but they are typically not widely or systematically applied. The potential of the DCE technique to assess job-related preferences/expectations has been illustrated in the context of coal- related workers in section 4. How these insights can then be used to help delineate the set of viable job alternatives is elaborated further in this section. Such information is, however, typically not readily available to policymakers, while the more widely used direct questions about attitudes towards different job attributes are deficient. As a result, worker preferences regarding job attributes are often insufficiently accounted for, neither in the planning of re/upskilling programs, nor in the development of repurposing activities or regional 50 https://gigaom.com/2014/04/09/michael-bloomberg-you-cant-teach-a-coal-miner-to-code/ 48 diversification strategies, burdening their chances of success as illustrated by the limited job placements following ZE PAK’s renewable energy retraining program (Box 4). Thirdly, and most challenging, is to assess the extent of skills (mis)match across jobs. It has also attracted a great deal of attention among policymakers. A pre-condition for a viable job transition is the worker’s capacity to execute the tasks required (with or without re/upskilling). While this may be easier to assess on a case-by-case basis, from a planning perspective, it is also important to know how the skills of the workforce at risk of dismissal, match with the skills needed in currently available or future alternatives. To assess this requires having a similar skills metric that can be used to both describe the skills profile of the current labor force and the skills needed to perform the currently available or future jobs. This is data and computationally intensive. New artificial intelligence (AI) text mining algorithms are now starting to be used to do so. WEF (2018) and OECD (2021), for example, used online job postings collected by Burning Glass Technologies (BGT) and artificial intelligence (AI) text mining algorithms to analyze similarities between pairs of occupations, respectively in the US (WEF, 2018), and in EU-27 Member States, Australia, Canada, the United Kingdom, the United States, and New Zealand (OECD, 2021). Using online job postings has the advantage to use real time data and the most up-to-date vacancies description (tasks and skills requirements, location, contract type, salary, etc.), but it may skew the analysis towards higher skilled occupations and sectors, both of which are more likely to use online job postings. 5.2 Towards a “viable-job-matching” decision tool tailored to the Polish labor market Publicly available Polish and location specific data on occupations and new jobs, DCE information on job preferences for coal-related workers and big data techniques are used to assess the extent of viable job transitions for the coal-related work force as present in the current labor market. Particularly, the section develops and illustrates how a new job matching decision tool anchored in location specific data can help individual case workers, mining conglomerates, public employment services (PES) and local authorities prioritize their actions, time and investments, including to develop tailored re/upskilling programs. It maps out job transitions opportunities for workers at-risk of losing their jobs in the mining and energy sectors, but the methodology can be applied to anyone looking for another job, looking to upskill and/or improve their wage prospects and job satisfaction. The method can also be readily adapted to different contexts and demand projections, including those of new investors or related to repurposing activities. In particular, to identify viable job transitions, the following three constraints were considered: (i) the amount of reskilling due to the transition between two occupations was minimized, (ii) only occupations in growing demand on the local labor market were considered, and finally (iii) potential candidates were further screened using the results of the preference survey described in the previous section. 49 First, to assess the skills (mis)match across positions, the task-similarity between all professions was first assessed. Job descriptions (including those held by workers at risk through the coal transition) were extracted from the Polish Labor and Labor Offices Job Postings Office using the Selenium web scraper. This resulted in a list of 2.7 thousand key occupations. Occupations were then compared one by one based on their task content, using Latent Semantic Indexing (LSI), and a (task) similarity score (between 0 and 1) was constructed for each pair of occupations (yielding a 2.7k by 2.7k similarity score matrix). A similarity score close to zero signifies two occupations with very little overlap in terms of tasks demanded; a similarity score close to one signifies two occupations with large overlap in terms of tasks to perform. A career switch between two occupations with a low similarity score is likely to require substantial reskilling, or may be even impossible, while a career switch between two positions with a high similarity score may require limited retraining and may also be much more feasible, at least from a technical capacity point of view. The similarity score index thus allows to identify occupations which are very similar in task content to those currently conducted by the coal-related workforce, suggesting a high probability of a good match, provided that they meet the expectations of the workers and that they are available on the (local) labor market. For robustness and refinement, an additional metric to assess skill similarity across occupations was introduced. In particular, the types of tasks required by each position were divided into five groups: non-routine manual (NRM), non-routine interactive (NRI), routine manual (RM), non-routine manual (NRM), routine cognitive (RC). This classification builds on the measure of routine and non-routine task content developed by Mihaylov and Tijdens (2019), who provide a measure of the Routine and Non-Routine Task Content of 427 Four- Digit ISCO-08 Occupations. Each task of occupation is assigned to one of the 5 skill groups (NRM, NRI, RM, NRM, RC). The indexes are created by dividing the number of tasks belonging to a given category by the number of all tasks performed in a given occupation. Then, the occupation is classified to the skill group, which account for the largest share of tasks in that occupation. The results are reported at the 4-digit level and were integrated in the dashboard, defining the top similar professions across those five dimensions. In what follows below, the job with the highest similarity score in each of the 5 skill groupings will be featured and its viability discussed based on its availability in the local market and the wage such a job earns on average (as reported in Labor Force Survey, 2018). Second, job transitions were identified for typical lignite workers in Lower Silesia and restricted to positions with excess demand on the local labor market. Poland does not administer an employer survey on a regular and highly disaggregated basis that could be used to locally investigate labor demand. Yet, it annually produces a more qualitative assessment of labor demand through the so-called occupational barometers. These are developed by the 50 powiat labor offices and used here to identify jobs with excess demand,51 in order to restrict job transitions to occupations in growing demand, or with high potential (Górna – Kubacka, Komasa and Rybak, 2020).52 Finally, the viability of the options is checked against the value assigned to different job attributes as revealed through the DCE for the two most heavily affected lignite communities in Lower Silesia. The DCE results of Section 4 were used to further scrutinize the opportunities short-listed by the similarity scores and the occupational barometers. For instance, lignite workers are averse to commuting and also moving regions/abroad, albeit to a lesser extent. The subset of job offers that implied moving to another part of the country were scrutinized: if the wage increase was below lignite employees’ willingness to pay, the option was discarded. In the next steps of the tool development, these procedures will be automatized. For more detail on the method and application, see Annex 4, which also provides screenshots of the data presented in the designed online tool used to develop the different pathways. 5.3 Five transition pathways relevant to the mining and energy sector Five positions typically found in the mining and power sector were selected, five associated transition pathways identified, and their viability discussed. Using the methodology described in Section 5.2, five potential transition pathways are each time identified for each position, one for each of the 5 skills grouping discussed (non-routine manual, non-routine interactive, routine manual, non-routine manual, routine cognitive) and implying different reskilling efforts needed. Within each skill group, the occupation with the highest task similarity score was retained to capture the most similar job. The potential wage differential associated with the career change, and the availability of those jobs locally are also discussed. Most trajectories imply reskilling and some involve pay cuts, in the short-term, but wages should increase in the longer-term given that all occupations are in growing demand. Most trajectories do not point to retraining and upskilling in renewable energies, the focus of much of the regional diversification efforts, or in digital jobs, often considered the jobs of the future. In fact, the transition from coal-based energy production to renewable energy can often be complicated by the complexity of retraining and upskilling (can dismissed workers be reskilled, and are they willing to?), fundamental differences between workplace characteristics (is the nature of the work aligned with displaced workers’ expectations and preferences), and/or lack of such opportunities on the local labor market (especially in the 51 Occupational barometers of occupations in surplus, balance or deficit, produced by local Labor Offices, were used here to identify occupations in excess demand. The occupational barometers represent a qualitative consensus view of local labor market stakeholders, including local labor offices, the industry and their chambers of commerce, and civil society (labor unions, academics, etc.). The methodology can be adapted to use other sources of labor market forecasting. 52 https://barometrzawodow.pl/#wielkopolskie 51 case of dismissed workers with low geographic mobility). The transition from a mechanic of opencast mining machinery and equipment to fitter of renewable energy devices displays a score of 0.54, which is relatively low. Similarly, the transition from a mining engineer – lignite mining to an engineer of equipment and renewable energy systems displays an even lower similarity score of 0.36, suggesting limited overlap of skills (at least as captured by task similarity). Finally, the transition from a technician of opencast mining and equipment to a technician of renewable energy systems displays a similarity score of 0.26, suggesting that this option would not utilize most of the skills and experience acquired by the displaced workers. 5.3.1 High-skilled white-collar: energy engineer Due to the high degree of specialization of an energy engineer, most transition pathways display low similarity scores, and lower salaries (Figure 18). An energy engineer mainly performs non-routine analytical activities, and rarely executes non-routine interactive tasks. The profession’s average salary is high, and equal to PLN 4,696 per month (BDL, access: 1.12.2021). Three out of five proposed transitions offer a lower salary than an energy engineer’s average monthly salary. Figure 18: Examples of transition pathways for an energy engineer 52 The position with most task overlap is a mechatronics technician, for which the similarity score is 0.6. individuals willing to move to this profession will need to accept a lower salary (- PLN 3,744). In this non-routine manual profession, equipment maintenance using modern techniques are especially valuable. This transition pathway was however disregarded as it implies a monthly pay decrease of PLN 953, and a decrease in position prestige from engineer to technician. Ship mechanic technician and chemical reactor controller display reasonably similar task- similarity scores (respectively 0.42 and 0.36), but neither of them is in demand in Lower Silesia. In the case of ship mechanic technician, the results of the preference survey show that the positive wage differential (+PLN 7,640 per month) would be large enough for a dismissed engineer to move to the Polish coast, but the position was discarded as it implies a demotion from engineer to technician. The transition to computer network operator was also discarded as it is associated with a lower monthly pay (-PLN 1,078). The most reasonable transition pathway appears to be a telecommunication engineer. Reskilling is moderate, with a similarity score of 0.40. The energy engineer will need to be able to conduct research, supervise technical modernization of works, introduce digital technology, and implement new solutions, measure and test equipment. The switch to telecommunication engineer is associated with a monthly wage increase of PLN 2,167, or almost a 50 percent increase. 5.3.2 High-skilled blue-collar: lignite mining technician Due to the large wage premium in the mining sector, most transition pathways for a lignite mining technician imply a large salary decrease (Figure 19). A lignite mining technician mainly performs non-routine analytical activities, and rarely executes non-routine cognitive tasks. The profession’s average salary is high, and equal to PLN 5,596 per month (BDL, access: 1.12.2021). All of the proposed transitions offer at least a 50 percent wage decrease, which mostly reflects the high wage premium associated with being a skilled technician in the mining sector. The position with most task overlap is a papermaking technician, for which the similarity score is 0.71. It is also the transition that implies the lowest wage drop, with an average monthly salary of PLN 3,558. In this non-routine manual profession, the worker is supervising and conducting various technological processes related to wood and pulp processing. 53 Figure 19: Examples of transition pathways for a lignite mining technician 5.3.3 Low-skilled blue-collar: truck mechanic All transition pathways for a truck mechanic are available locally, and involve a slight wage increase and limited reskilling (Figure 20). Truck mechanics mainly perform non-routine manual activities, and rarely execute non-routine analytical tasks. The profession’s average salary is equal to PLN 2,643 per month (BDL, access: 1.12.2021). All proposed transitions offer an equivalent or higher salary. The most reasonable transition pathways are tractor mechanic and fitter of internal combustion engine. The position with most skills overlap is a tractor mechanic, for which the similarity score is 0.99, and offers the same salary. With a similarity score of 0.85, one other position requires limited retraining (fitter of internal combustion engine), with a slight wage increase (PLN 2,995). The other three position require more reskilling (similarity scores between 0.41 and 0.56), with slight wage increases. 54 Figure 20: Examples of transition pathways for a truck mechanic 5.3.4 High-skilled blue-collar: electrician Transition pathways for electricians involve very limited reskilling, and limited wage changes (Figure 21). Electricians mainly perform non-routine manual activities, and rarely execute routine cognitive and non-routine analytical tasks. The profession’s average salary is equal to PLN 2,889 per month (BDL, access: 1.12.2021). All proposed transitions offer salaries within a PLN 500 range difference. The position with most skills overlap is electromechanical engineer. This position has a similarity score of 0.95 and offers a slightly higher salary. In the new position, the engineer will have to install, assemble, repair electrical devices using electric installation and locksmith tools, power tools and basic electrical meters. 55 Figure 21: Examples of transition pathways for an electrician 5.3.5 Low-skilled blue-collar: electrical machine fitter Transition pathways for an electrical machine fitter involve limited reskilling, and no wage changes (Figure 22). Electrical machine fitters mainly perform non-routine manual activities, and rarely execute routine cognitive and non-routine analytical tasks. The profession’s average salary is equal to PLN 2,625 per month (BDL, access: 1.12.2021). All proposed transitions offer similar salaries. The position with most skills overlap is electrical engineer, for which the similarity score is 0.83, and offers a slightly higher salary (PLN 3,129). 56 Figure 22: Examples of transition pathways for an electrical machine fitter 5.4 Demonstrated proof of concept, but further validation and development needed. This section has demonstrated the power of data-driven approaches for finding solutions to job disruptions more broadly, including job transition pathways and reskilling opportunities that might not be immediately apparent. To be fully operational, however, further ground truthing and data manipulations will be needed, including to better inform the adjudication of trade-offs and to operationalize it as a planning tool. This section has focused instead on showing proof concept and illustrating the tool with a series of examples of individually viable transition paths as observed in the current labor markets and given the preferences of the labor force in two heavily affected municipalities in Lower Silesia. The underlying findings are also available as an online dashboard for further manipulation. 6 Conclusions Climate change is forcing a transition to carbon neutrality putting many jobs at risk, in, and especially around the mines, calling for a geographic, or local economic development, rather than a sectoral approach. As Europe’s largest coal producer, Poland is today at the forefront of the European coal transition. Workers in fossil fuel sectors and carbon intensive industries, and the towns53 and regions where they are concentrated, will be at the forefront 53Especially Zgorzelec and Bogatynia, which display already weak labor market indicators, and are highly vulnerable to changes in PGE’s labor demand. 57 of the transition, and the hardest hit. Within Poland, Lower Silesia is the region least advanced in the transition out of coal, but Europe’s Green Deal and contentions with the Czech Republic over its mines, call for timely planning of mine closures and viable job transitions. The energy sector can also be a catalyst for regional development and employment, but the path to it is not necessarily automatic. At the global level, new jobs in transition-related technologies and sectors are expected to outweigh job losses in fossil fuels and nuclear energy (IRENA, 2021).54 Conversion into wind or solar parks, for example, could provide re- employment opportunities for some coal workers after an adjustment of skills, since electrical and mechanical skills, experience of working under difficult conditions and sophisticated safety experience are in demand in the wind and solar energy industries. Similarly, the re-use of closed mines for geothermal energy or hydropower applications could also provide jobs and socioeconomic benefits to post-mining communities. However, the distributional impact of the transition may not guarantee successful job transitions across all skill sets: medium- skilled occupations display the highest opportunities for retraining and upskilling measures for the green transition, but low- and high-skilled workers are likely to find less options and opportunities (ILO, 2018). The success of these transitions within the mining and energy sectors are also subject to the jobs-related aspirations of the dismissed workforce and their willingness to participate in retraining and upskilling programs. Combining large and representative datasets, econometric techniques, and machine learning can help develop well-tailored, realistic and acceptable retraining and reskilling programs. Firstly, the discrete choice experiment (DCE) showed how voice can be given to displaced workers, whose preferences are typically ignored, partly because it is challenging to capture them adequately. The vast majority of workers in the communities affected by the transition want to take up other work and reskill as needed, especially if the jobs are within a short commuting distance. They reveal sizeable aversion to commuting (but are less averse to relocation compared to other regions in Poland). The DCE results also showed how workers’ relatively high reservation wages could be addressed by offering jobs with a set of desired non-pecuniary benefits such as job security, a clearly defined career development path (with prospects of earnings increase) and jobs compatible in skills and education, and employment sector. Secondly, leveraging machine learning and online job descriptions allowed to identify plausible transition pathways for workers affected by the transition. Incorporating the DCE results further helped to narrow down available and acceptable options that not only imply good matches in terms of skill and task similarity but that are also in excess demand in the 58 local labor market and within the range of desired job attributes. Oftentimes, job transition pathways considered by stakeholders are unrealistic. The possibility of a transition from brown jobs to green occupations, for example, is often treated overly optimistically in the transformation process, but trajectories to retrain and upskill the dismissed workforce to renewable energies jobs display low similarity scores, suggesting that displaced workers would not utilize most of the skills and experience acquired until then. Most viable transition pathways identified for affected occupations are in fact in mechanics, with easier transitions for non-sector-specific workers, and more difficult transitions for higher-skilled specialized employees. More broadly, the methodologies and techniques presented above could be used to assist public officials in designing effective labor market transition plans during factory closure in single employer dominated labor markets. First, the discrete choice experiment (DCE) ran among miners and other workers in the mining towns will be essential to design tailored coal transition policies that leave no one behind.  In addition, the findings can be used to inform other labor market transitions affecting the coal mine region, such as the ongoing digitization of the economy and workplace and the transition to more environmentally sustainable economic activities.  Doing so requires further ground truthing and operationalization of the tools. The DCE and job matching tool illustrated here in the context of the just coal transition are as such just an example of the type of methodology that could be used in other mass lay-offs in other areas with single employer dominance. Second, the identification of viable job transition pathways demonstrates the potential power of data-driven approaches for finding solutions to job disruptions, including reskilling opportunities that might not be immediately apparent. Job opportunities were mapped out for workers at-risk of losing their jobs in the mining and energy sectors, but the methodology can be applied to anyone looking to upskill and improve their wage prospects and job satisfaction: the methodology is not limited to the geography and sector presented here, and can be feasibly adapted to different contexts and demand projections: instead of matching displaced workers with current opportunities as identified by local Labor Offices, labor market forecast, development strategies, or investors’ interest in moving to the region (including Special Economic Zones) can be fitted in the job transition pathway tool developed. Despite potential delays in the closure of Poland’s coal mines in the short run, in view of the Russian invasion of Ukraine, the diagnostic and methodology developed for this report remain valid. The transition away from coal is as relevant as ever. With Poland already importing a fair amount of coal from Russia, the Russian invasion is likely to accelerate the development of carbon neutral energy sources in the medium run, while securing energy supplies with the use of coal in the near-term.55 In the short-run, the European Commission 55 https://www.iea.org/news/how-europe-can-cut-natural-gas-imports-from-russia-significantly-within-a-year 59 proposed to reduce Poland’s dependence on natural gas and coal imports from Russia, extending the lifetime of coal-fueled blocks, while accelerating the rollout of renewable energy sources.56 The changes in energy policy, as well as the influx of Ukrainian refugees may have an impact on an already tight labor market.57 The results of the skills and preference survey are expected to be unchanged in a short timeframe (5 years): lower foundational skills of workers in the affected municipalities, reluctance to commute or relocate, preference for job security, willingness to switch sectors if employees can keep deploying their skills, willingness to take up lower paying job when there are credible wage growth prospects, etc. Finally, the matching tool developed to identify optimal transition pathways can be adapted to include new occupations that may arise in the near future, as well as changing labor demand (to identify occupations in growing demand). 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World Economic Forum (WEF), 2018. “Towards a Reskilling Revolution - A Future of Jobs for All,” World Economic Forum, Geneva. 63 Annex 1: Methodology to estimate the indirect impact of mines closure A simple bottom-up approach was followed to estimate the indirect impact of the mines closure. Indirect jobs are those contracted out by mining conglomerates Indirect jobs are defined as positions contracted out by mining conglomerates. These include (i) jobs provided by companies “associated” with mining conglomerates, providing material and analytical support, and often vertically integrated within the mining conglomerate; as well as (ii) jobs in companies that are directly subcontracted by mining conglomerates, often providing good and services. On a case-by-case basis, jobs were carefully mapped to the mining conglomerate (direct employment), subsidiaries (companies “associated” with the conglomerates), or subcontractors (entities which won public tenders, even when they were subsidiaries). . 64 Table 1: Previous estimates of indirect impact of mines closures Study Authors Year Methodology and assumptions Estimations Joint Alves Dias 2018 I-O (Eurostat)58. The estimation of Poland: 48,746 (intra- Research et al. indirect employment in the coal sector regional). 87,760 (inter- Centre relied on the use of input-output regional) (JRC) tables and multipliers developed by Upper Silesia: 22,106 (intra- the EU Joint Research Center, regional), 34,536 (inter- originally, for predicting the impacts regional) of a change in the final demand of one Lower Silesia: 1,698 (intra- sector on other related sectors regional) 3,045 (inter- (Thissen and Mandras, 2017). Indirect regional) employment was estimated by Wielkopolska: 3,447 (intra- applying the same multipliers to the regional); 8,090 (inter- number of coal direct jobs. The indices regional) used, besides extending the supply- note: Inter-regional figures chain coverage to all sectors that include Intra-regional effect might be impacted by changes in coal and, therefore, differences mining and coal power plants are due to the inter- activities, are assessed at intra- regional trade between regional level, and also consider spill- NUTS 2 regions. over effects at inter-regional level. Institute Kiewra, 2019 Input-Output (Central Statistical 56,700 – suppliers for Szpor, Office, base year 2015) 13,000 – recipients (coal- Structura Witajewsk Indirect jobs are calculated as the fired power plants) l i-Baltvilks share of value added transferred to Research the mining industry in the total value (IBS) added generated in a given section multiplied by the number of employees in this sector (according to data for 2017) Frankowsk 2020 Input-Output (Central Statistical 96,000 – 112,000 i, Office; base year 2015) subcontractors 41,000- Mazurkie Indirect jobs are calculated as the 56,700 wicz share of value added transferred to intermediaries 55,300 (coal- the mining industry in the total value fired power plants, heating added generated in a given section and coking plants) multiplied by the number of employees in this sector (according to Eurostat data for 2018) + additions of employment in Section C and D, proportionally to the share of coal in a given sector Universit Ingram 2020 Survey (207 companies; sampling The total number of indirect y of et.al. frame not specified) jobs: 110,000 – 130,000 Economi Number of affected indirect jobs (until 2030): 58 The share of added value associated with mining to the mining added value is the same as the share of mining- related jobs to the number of mining jobs. 65 cs in Optimistic scenario: 26,667 Katowice Plausible scenario: 50,580 Pessimistic scenario: 75,876 Table 2: JRC (2018) Estimates Direct jobs impacted by coal PP Indirect Jobs Direct retirement Impact Inter-regional Intra-regional 2025 2030 (total) Poland 112,500 48,746 87,760 mines 99,500 2,077 5,276 power plants 13,000 Silesia 82,459 22,106 34,536 mines 79,548 315 1,558 power plants 2,911 Lower Silesia 1,926 1,698 3,045 mines 1,108 480 102 power plants 818 Wielkopolska 3,400 3,447 8,090 mines 2,079 748 321 power plants 1,306 Source and methodology: JRC (2018), using EURACOAL database. 66 Annex 2: Skills and preference survey questionnaires The World Bank Skills and Preference Survey consisted of two parts: a skills and employment questionnaire and a Discrete Choice Experiment (DCE). The questionnaire included detailed questions to determine the respondents’ employment status and socio - economic situation. Questions were grouped into thematic blocks, consisting of (i) general information, (ii) employment characteristics, (iii) job search, and (iv) self-assessment of competencies. For the municipalities, the blocks consisted of (i) general information, (ii) professional situation, and (iii) self-assessment of competencies. Survey questionnaire 67 Good morning! You are about to start the survey on work and professional preferences, carried out at the request of the World Bank Group in order to support the transformation of the labor market related to the Just Transition process. All Any of your statements will be kept in complete confidence and will only be used anonymously, along with other people’s responses. We are counting on your honest answers. MODULE G: GENERAL INFORMATION G1 Please enter the year of birth |__I__I__I__| G2 What is your gender? 1. Woman 2. Man G3 What is your marital status? 1. Married 2. Divorced or separated 3. Widowed 4. Single G4 Do you have children? 1. Yes 2. No G5 Please indicate your level of education 1. Primary 2. Vocational 3. Secondary (high school or technical college) 4. Post-secondary 5. BA/BSc/Engineering degree 6. Master’s degree 7. Tertiary education with at least a Ph.D. degree G6 How many people are in your household, …................................................................................. including you? G7 How many household members, apart from you, work in the sector dependent ………………………………………………………………………………. on the fuel and energy sector? G8 Please indicate what% of household 1. -0%-20% income depends on the activity of a hard 2. 21%-40% coal mine or an energy company 3. 41%-60% 4. 61%-80% 5. 81%-100% To what extent can the Just Transition 1. At all process negatively impact your life? G9 2. A bit 3. Moderately 4. Very 5. Incredibly G10 How many people in your household have income (including yourself)? 68 1. 0-600 zł G11 W jakim przedziale mieści się miesięczny 2. 601–900 zł średni dochód netto (na rękę) na osobę w 3. 901–1200 zł Pan(i) gospodarstwie domowym? 4. 1201–1500 zł 5. 1501–1800 zł 6. 1801–2100 zł 7. 2101–2400 zł 8. 2401–2700 zł 9. 2701–3000 zł 10. 3001–3300 zł 11. Powyżej 3300 zł MODULE N: PROFESSIONAL SITUATION N1 What is your total work experience in …............................................................................... years (consider all your employers)? N2 Please indicate the number of all your past employers (i.e., places where the ….................................................................................. employment period was at least 3 months) N3 Last week, did you do any work that 1.      Yes-> go to P1 brought you income or helped with a 2.      No business that your relative owns? N4 Last week, did you do any paid work for 1.      Yes-> go to P1 at least 1 hour? 2.      No N5 During the analyzed week, did you have a 1.      Yes-> go to N5a, and next to P1 job but did not perform it temporarily? 2.      No-> go to N6 N5a What was the reason you did not 1. Disease perform work in the last week? 2. Annual leave 3. Maternity leave 4. Parental leave 5. Business interruption 6. Difficult weather conditions 7. Working time system 8. Studying, improving qualifications 9. Unpaid leave 10. Other reasons, what…? N6 Are you looking for a job? 1.   Yes -> go to module Q (Unemployed) 2.   No -> go to module RR (Professionally inactive) MODULE P: EMPLOYED We would now like to ask about your main workplace. We consider the main place of work to be the place where you spend the most time doing your work. If you work the same number of hours in two or more places, consider the main one where you earn more. P1 1.      I__I__I__I hours 2.      I do not know 69 How many hours have you worked in the last seven calendar days in your main place 3.      Refusal to answer of work? P2 How many hours do you usually work per 1.      I__I__I__I hours week at your main place of work? 2.      I do not know 3.      Refusal to answer P3 What is your employment status at your 1.      I am self-employed ->go P4 current principal place of work? 2.      I am employed under a contract of employment- > go to P5 3.      I am an employee on the basis of a civil law contract (contract of mandate/contract for specific work)-> go to P6 4.      I am an employed person without a written contract of employment-> go to P7 5.      I am an employed person without a written contract of employment -> go to P6 6.      Other, what? -> go to P8 P4 In the last seven days, did you hire any 1.      Yes-> go to P6 employees? 2.      No-> go to P6 P5 Your main job is: 1.      permanent, for an indefinite period 2.      temporary P6 The institution/company that is your main 1.      public company/institution place of work is a 2.      private company/institution 3. NGO P7 How many people work in an 1.      From 1 to 10 people institution/company which is your main 2.      From 11 to 49 people place of work? 3.      From 50 to 100 people 4.      From 101 to 250 people 5.      From 251 or more 6.      I do not know 7.      Refusal to answer P8 What work did you do in the last seven calendar days? What is the name of your profession or occupation?   P9 What is the name of your work position?   P10 What do you do in your job? What are your main responsibilities?   P11 Do you supervise or manage the work of 1.      Yes other people? 2.      No P12 Indicate how much was your net earnings 1.      up to PLN 600 (in hand) earned in the previous month in 2.      PLN 601 – 800 your main place of work? 3.      PLN 801 – 1000 4.      PLN 1001 – 1200 5.      PLN 1201 – 1500 6.      PLN 1501 – 1800 70 7.      PLN 1801 – 2100 8.      PLN 2101 – 2500 9.      PLN 2501 – 3000 10.   PLN 3001 – 4000 11.   PLN 4001 – 5000 12.   PLN 5001 – 6000 13. PLN 6001 – 7000 14. PLN 7001 – 8000 15.   PLN 8001 – 9000 16. PLN 9001 – 10 000 17.   Over PLN 10 000  18. Refusal to answer P13 Agriculture, forestry, hunting and fishing Mining and quarrying Industrial processing Production and supply of electricity, gas, steam, hot water and air for air conditioning systems Water supply sewerage, waste management and remediation activities Construction  Wholesale and retail trade; repair of motor vehicles, including motorcycles Transport and warehouse management Activities related to accommodation and catering What sector of the economy are you services employed in? Information and communication   Financial and insurance activities Activities related to servicing the real estate market Professional, scientific and technical activities Business administration and support activities Public administration and national defense; compulsory social security Education Health care and social assistance Cultural, entertainment and recreational activities Other service activities Households with employees; households producing goods and providing services for their own needs Organizations and extraterritorial teams P14 In which month and year did you start 1.      I__I__I month working at your main workplace? 2. I do not know/ I do not remember 1.      I__I__I__I__I year 2.      I do not know/ I do not remember P15 1. Tertiary education with Ph.D. degree 71 2. M.Sc. degree 3. BA/BSc degree 4. Engineering In your opinion, what level of education 5. Post-secondary is the most appropriate for the work you 6. Secondary general education do in your main place of work? 7. Secondary vocational 8. Lower than secondary 9. The level of education does not matter P16 In your opinion, which field of study is 1. Field of study that I am studying/graduated the most appropriate for your current from main job? 2. Related to the field of study, I am studying/graduated from 3. Different field of study from the one I am studying/graduated from 4. The field of study does not matter 5. I don’t know, I cannot define P17 Which of the following best describes 1. I need further training to do my job well. your skills in relation to the current job in 2. My current skills are well suited to my your place of work? responsibilities/duties. 3. I have the skills to handle more demanding duties. P18 Referring to your competencies related to the job you currently perform, please indicate which of the following two statements better describes the nature of your current job. It does not It requires require many any additional additional competences competences 1 2 3 4 5 P19 In the last 12 months, have you 1. Yes considered changing your job? 2. No P20 Have you recently been looking for a job 1. Yes / participated in the recruitment for the new job? 2. No 1. I strongly disagree To what extent do you agree with the 2. I tend to disagree P21 statement: I feel burned out / burned out 3. I have no opinion professionally? 4. I tend to disagree 5. I strongly agree 1. Negatively How do you think how the Just Transition 2. Rather negatively P22 will affect the performance of the 3. It will not affect the exercise of this profession profession you are currently working in? 4. Rather positively 5. Positively MODULE U: UNEMPLOYED 72 Q1 How long have you been looking for a job? 1.      |__I__I__| (Please enter the number of months) Q2 Are you currently registered as 1.      Yes unemployed at the Labor Office? 2.      No Q3 Are you looking for a job due to…. : 1.      Loss of job 2.      Resigning from work 3.      Willingness to return to work after a break 4.      Willingness to take up the first job in life Q4 Do you know institutions or organizations 1. Yes, that support job search? Please provide examples …......................................... 2. No If yes, write what institutions or Q4a ………………………………………………………………………………… organizations you know Q5 When looking for a job, would you be 1. Definitely not willing to learn a new job/reskill? 2. Rather not 3. It's hard to say 4. Rather yes 5. Definitely yes Q6 How do you assess your chances of 1. Very good finding a job outside the fuel and energy 2. Good sector? 3. Moderate 4. Bad 5. Very bad 6. I do not know Q7 Which of the following factors may be an 1. Childcare obstacle for you in the process of looking 2. Taking care of another family member for a job? (Many answers are possible) 3. Taking care of the house 4. Taking care of a farm 5. Poor health 6. Age 7. Lack of appropriate certificates and authorizations 8. Level of education 9. Insufficient experience 10. Study or training 11. There are no job offers in the area 12. Lack of appropriate contacts, Nie wymaga Wymaga wielu acquaintances Q8 Please rate how prepared you are (on a scale from 1 to 5, where 1 means żadnych very bad and 5 very innych, dodatkowych dodatkowych well) to: kompetencji kompetencji a) Search for and select job offers 1 Nie wymaga 2 3 4 Wymaga 5 wielu żadnych     innych,  dodatkowych dodatkowych kompetencji kompetencji b) Prepare application documents Nie wymaga 1 2 3 4 Wymaga5 wielu żadnych innych,  dodatkowych     dodatkowych kompetencji kompetencji 1 2 3 4 5      73 c) Participate in interviews Q9 Would you like to undertake training in the area of: a) Job search 1. Yes 2. No b) Soft skills (such as assertiveness, 1. Yes communication, etc.) 2. No c) Hard qualifications 1. Yes 2. No d) Other (what kind?) ….................................................................................. Q10 Training in which area do you find the 1. Job search most useful for you? 2. Soft skills (such as assertiveness, communication, etc.) 3. Hard qualifications 4. Other If other, please indicate what training you Q10a find most useful for you. If it was possible to finance any training Q11 from state funds, what training would you like to undertake? Q12 Which form of employment are you 1. Only part-time job interested in? 2. Depending on the offer, a part-time or full-time job 3. Only full-time job 1. Yes Would you consider taking up Q13 2. Depends on the job offer employment in Turek? 3. No Q14 What net monthly salary (in hand) for a 1. PLN 1201–1500 full-time job would be satisfactory for 2. PLN 1501–1800 you / would you agree to accept? 3. PLN 1801–2100 4. PLN 2101–2500 5. PLN 2501–3000 6. PLN 3001–4000 7. PLN 4001–5000 8. PLN 5001–6000 9. PLN 6001–7000 10. PLN 7001–8000 11. PLN 8001–9000 12. PLN 9001–10 000 13. above PLN 10 000 14. Not applicable Q15 Have you ever considered the possibility 1.Yes of starting your own business? 2.No -> go to Module K 74 Q15a If yes, what kind of business did you consider establishing? …............................................................................. MODULE K: COMPETENCIES Different types of work require different skills and abilities. It is often the case that our capabilities are relatively high in one or two areas, while in others, they are much lower. Below is a list of the various skills. For each of them, I will ask you to assess the level of your own skills in this respect on a 5-point scale, where 1 is low, 2 is basic, 3 - medium, 4 - high, and 5 - very high. How do you rate your skill level? Competencies Very low basic average high high self-organization of work and showing initiative 1 (Planning and timely implementation of activities at 1 2 3 4 5 work, effectiveness in achieving the goal) 1.1 independent decision making 1 2 3 4 5 1.2 entrepreneurship and showing initiative 1 2 3 4 5 creativity 1.3 1 2 3 4 5 (Being innovative, coming up with new solutions) 1.4 resistance to stress 1 2 3 4 5 1.5 timely implementation of planned activities 1 2 3 4 5 contacts with other people, both with co-workers as 2 1 2 3 4 5 well as clients and subordinates. 2.1 cooperation in a group 1 2 3 4 5 2.2 easy contact with colleagues or clients 1 2 3 4 5 2.3 being communicative and communicating clearly 1 2 3 4 5 2.4 solving conflicts 1 2 3 4 5 3 organizing and conducting office work 1 2 3 4 5 managerial abilities and organization of work of 4 1 2 3 4 5 others 4.1 coordinating the work of other employees 1 2 3 4 5 disciplining other employees - bringing them to 4.2 1 2 3 4 5 order. 5 availability 1 2 3 4 5 5.1 willingness to travel frequently 1 2 3 4 5 5.1 willingness to work flexible hours 1 2 3 4 5 fluent use of the Polish language in speech and 6 writing (linguistic correctness, rich vocabulary, ease 1 2 3 4 5 of expression). searching and analyzing information and drawing 7 1 2 3 4 5 conclusions 7.1 quick summarizing a large amount of text 1 2 3 4 5 7.2 logical thinking, fact analysis 1 2 3 4 5 7.3 constantly learning new things 1 2 3 4 5 75 maintenance, assembly and repair of technical 8 1 2 3 4 5 devices 9 performing calculations 1 2 3 4 5 9.1 performing simple calculations 1 2 3 4 5 9.2 performing advanced mathematical calculations 1 2 3 4 5 10 computer operation and use of the Internet 1 2 3 4 5 10.1 basic knowledge of the MS Office type package 1 2 3 4 5 knowledge of specialized programs, the ability to 10.2 1 2 3 4 5 write programs or create websites 11 artistic and creative abilities 1 2 3 4 5 12 physical fitness 1 2 3 4 5 MODULE D: JOB CHARACTERISTICS Finally, we would like to ask about your job characteristics. Please indicate which answer best Work inconsistent with your education and skills D1 describes the compliance of your work Work related to your education with your education. Work matches your skills and education Flexible working time Certified professional courses Please indicate which of the following Transport provided to and from work by bus non-wage benefits you receive in your Private medical care D2 current workplace. (Many answers are Childcare services (kindergartens close to the possible) workplace, financed by the employer) Mobile phone or computer I am not getting any of the listed non-wage benefits Up to 15 min Please indicate which time of the 15 min – 30 min D3 following is the closest to your 30 min – 1 hour commuting time to work (one way). 1 hour – 1.5 hour 1.5 hour – 2 hours 76 Annex 3: Skills and preference survey methodology DCE methodology To analyze preferences towards the workplace, a Discrete Choice Experiment (DCE) was applied. The concept of discrete choice methods is derived from the theory of economic value (Lancaster, 1966) and random utility theory (McFadden, 1974; McFadden, 2001). The utility refers to the level of satisfaction a person achieves with a particular consumption structure – here the consumed good is defined as the workplace. In the discrete choice method, respondents select an alternative that seems to be more attractive to them or decide not to make a choice. This selection process is of probabilistic nature (McFadden, 1974). The choice is determined by a utility that depends on the observed characteristics (attributes) and unobserved idiosyncrasies and for individuals. This process allows modeling worker's decision processes and extracting information about preferences (individual utility function parameters) based on pre-composed successive choices in a controlled environment. The design of choice situations (composition of attributes) is based on an effective Bayesian method that allows obtaining the most accurate estimates with minimal respondents' choices (utility balance). The designing process is done using the simulation with obtained priors from the pilot study. Over the study, the design is optimized several times to minimize D-error (a scaled measure of the determinant of the Fisher Information Matrix, which summarizes how good a design is at extracting information about preferences). The choice of attributes and levels was based on the preliminary qualitative work and literature review. Prior pilot-testing enabled researchers to refine the attribute levels and their wording. To avoid cognitive overload resulting from too many attributes, two DCE designs with two overlapping attributes were prepared. To estimate the monetary equivalents of a change in job components a monetary attribute (monthly net salary) was included. Table 3: Job attributes – DCE design 1 Attribute Levels of attributes Net monthly salary • PLN 3000 • PLN 4000 • PLN 5000 • PLN 6000 • PLN 7000 • PLN 8000 Contract • Labor code based (permanent) • Fixed-term employment • Temporary (contract of mandate - regulated by the Civil Code, not the Labor Code) 77 • Apprenticeship / internship • Change in financial conditions within • No change the next 2 years from starting work • 20% increase over initial salary • 40% increase over initial salary Non-wage benefits • No • Flexible working time • Certified professional courses • Transport provided to and from work by bus • Private medical care • Childcare services (kindergartens close to the workplace, financed by the employer) • Mobile phone or computer Compliance of work with education • Job incompatible with skills and education (requires complete reskilling) • Related workplace (requires entry training) • Job corresponds to your skills and education Travel time to work (one way) • 0 (remote work) • 15 min • 30 min • 1 hour • 1.5 hour2 hours Table 4: Job attributes – DCE design 2 Attribute Levels of attributes Net monthly salary • PLN 3000 • PLN 4000 • PLN 5000 • PLN 6000 • PLN 7000 • PLN 8000 Contract • Self-employed • Public • Private • Non-governmental organization • Company size • Employing 10 people • Employing 100 people • Employing 250 people • Employing 500 people 78 Industry sector • Mining • Construction • Industrial processing • Renewable energy (photovoltaics, wind farms) • Agriculture • Transport services, repairs, communication • Other services (gastronomy, tourism and recreation, etc.) • Administration (office work) Compliance of work with education • Job incompatible with skills and education (requires complete reskilling) • Related workplace (requires entry training) • Job corresponds to your skills and education Working conditions • Not burdensome • Burdensome (physically strenuous or dangerous) Job location • Poland (no need to change the place of residence) • Poland (need to change the place of residence) • Abroad (need to change the place of residence) Sampling and reweighting The survey was conducted between January 10th and March 1st, 2022, as an in-person door- to-door survey on households in the two municipalities most affected by the transition out of coal, namely Zgorzelec and Bogatynia. The sample of 400 respondents was selected using a random walk method around locations chosen (based on the random draws from the address database). If more than one household was found at the same address, the household would be randomly selected. If the household refuses to participate, the adjacent one (the following address) was selected as a replacement. The interviewers used a (scripted) online survey tool prepared and shared by the World Bank team. The interviewers visited each sampled household, equipped with tablets or laptops to conduct the survey (CAPI data collection). They explained how to navigate the online survey tool and remained to provide technical help and collect the laptops once the survey is finished. The respondents themselves filled the survey on average in 25 minutes. The output data (responses) were assessed based on the plausibility (statistical properties) and to what extent the results correspond to the targeted population structure. 79 The sample had a quota nature. The characteristics of the population that were taken into account in compiling the sample were: • The respondent's place of residence (Bogatynia/Zgorzelec) • Gender of the respondent (male/female) • The respondent's age (5 categories): 18-24 years old, 25-34 years old, 35-44 years old, 45-54 years old, 55-65 years old • Education (4 categories): primary, basic vocational, secondary, higher • The proportions of the sample in the targeted quotas corresponded well to the proportions of the population according to the latest published data from the Statistics Poland available at the Local Data Bank at www.stat.gov.pl.The relations between the targeted structure (Statistics Poland) and the achieved sample were presented in the tables below. As the quotas were insignificantly different from the targeted configuration, we have not conducted further weighting procedures. Gender distribution Gender Targeted structure Achieved Sample Male 50.25% 50% Female 49.75% 50% Age distribution Gender Targeted structure Achieved Sample 18-24 10.25% 10.66% 25-34 19.50% 19% 35-44 24.75% 25% 45-54 20.00% 21% 54-65 25.50% 25% Highest level of education achieved Gender Targeted structure Achieved Sample Primary 15.25% 15% Vocational 30.50% 30% Secondary 39.50% 41% Higher 14.75% 14% Econometric model The choice is determined by a utility which depends on the observed characteristics (attributes) and unobserved idiosyncrasies and for individual, i , resulting from choosing an alternative, j , in the situation, t , can be expressed as: U ijt = Xijt β + eijt . (1) 80 Where Xijt , corresponds to observed job attributes with the vector of parameters, β , and eijt , refers to the stochastic component, standing for the unobservable factors. Assuming that the stochastic component ( eijt ) follows an independent and identical extreme value (type I) distribution, the probability of, i, respondent selecting an alternative, j, from set of ,J, alternatives takes the form of conditional logistic regression: exp ( Xijt β ) P ( j |J ) = , (2)  exp ( Xikt β ) J k =1 which, depending on the selection and levels of attributes, enables to derive the maximum likelihood estimator of the utility function. As the respondents want to maximize their utility while making a choice, this probability represents an optimization problem. The designated formula 3 is known as the multinomial logit model (MNL). Train (2003) proved that choice behavior logit models have several limitations. Namely, MNL express the preference variation that relates to observed characteristics of the decision- maker - this is so-called in literature the systematic taste variation but not random taste variation. The second limitation is that logit cannot handle situations where unobserved factors are internally correlated over time. One of the ways to relax these restrictions is to allow for preference heterogeneity and, possibly, correlations between the alternatives and choices across time. This can be done by including the individual-specific parameters, βi , which leads to more general models. (McFadden and Train, 2000). To account for these limitations in addition to the MNL the data will be analyzed with a random parameter logistic regression called as mixed logit model (RPL or MIXL). Mixed logit is expressed as the integrals of standard logit probabilities over a density of parameters, where is and individual and is an alternative. e  n xni Pni =   xnj  (  b, ) d , (3) j n e Here in the random parameter model we will use the panel specification formulated by Revelt and Train (1998). In the baseline assumption all non-price coefficients will be assumed to follow a normal distribution but the coefficients related to income will be assumed to follow a log-normal distribution. In this case, assuming lognormal distribution for the income coefficient is 81 plausible from a behavioral perspective and the model accountability. Assuming log-normal distribution restricts respondents to have positive income sensitivity, in addition, this assumption guarantees that the resulting distributions of WTP are useful and meaningful i.e. have finite moments (Daly et al., 2012). Given above it is convenient to introduce modification of the basic utility function defined above which is to use the so-called estimation in WTP space (Train and Weeks, 2005): Uijt =  ( pijt + Yijt b  ) + eijt =  ( pijt + Yijt β ) + eijt . (4) where is the stochastic component, is the time attribute, and the vector of parameters is a vector of implicit prices for the non-monetary attributes . In the survey, respondents were asked to choose between two hypothetical alternative job offers described by several attributes or decide not to make a choice. This selection process is of probabilistic nature (McFadden, 1974). The choice of an alternative that seems to be more attractive to respondents is determined by a utility that depends on individuals’ observed characteristics and unobserved idiosyncrasies. This process allows modeling workers’ decision processes and extracting information about preferences (individual utility function parameters) based on pre-composed successive choices in a controlled environment. 82 Annex 4: The viable job matching tool - methodological considerations The extent of skills’ (mis)match, the availability of jobs, and workers’ preferences determine the viability of transiting to future jobs. Whether the path to an alternative job for a worker is viable depends on 1) whether the worker has the capacity to do the job, 3) whether the job attributes (salary, benefits, contractual arrangements, etc.) live up to the expectations of the worker (and compared to the value and compensation received when not working), and 3) whether the job is available locally or not. Most jobs are unlikely to score equally well on all three dimensions. The job may be available, for example, but only in another region, or only under a self-employed as opposed to a salaried contractual arrangement. The better a job scores on each of these dimensions (matching skills, attribute preferences, and local availability), the more viable is the transition. In particular, in identifying matches, the following three constraints were considered : (i) the amount of reskilling due to the transition between two occupations was minimized, (ii) only occupations in growing demand on the local labor market were considered, and finally (iii) potential candidates were further screened using the results of the preference survey described in the previous section. The technical feasibility of an alternative job in terms of skills was first assessed based on task-similarity between professions (as a proxy for skills) using artificial intelligence (AI) text mining algorithms. Job descriptions were extracted from the Polish Labor Office and Labor Offices Job Postings using Selenium web scraper and resulting in a list of 2,700 key occupations. Occupations were then compared one by one, using Latent Semantic Indexing (LSI), and a similarity score (between 0 and 1) was constructed for each pair of occupations. Latent semantic indexing (LSI) is an indexing and retrieval method that uses a mathematical technique called singular value decomposition (SVD) to identify patterns in the relationships between the terms and concepts contained in an unstructured collection of text. LSI is based on the principle that words that are used in the same contexts tend to have similar meanings. A key feature of LSI is its ability to extract the conceptual content of a body of texts by establishing associations between those terms that occur in similar contexts. Applying LSI model with 100 topics (number established using trial and errors approach), we were able to describe each job/occupation in our corpus as a mixture of topics. Using standard distance metrics, the similarity of all pairs of jobs/occupations was calculated – in other words similarity of vectors containing information about topics mixtures. We used cosine distance to create a 2.7k/2.7k (rows/columns) similarity scores matrix. The score could take values bounded between 0 and 1, going from least to most similar. To complement and refine the task-based pairing and ranking of jobs, the types of tasks required by each position were further divided into five groups: non-routine manual, non- routine interactive, routine manual, non-routine manual, routine cognitive. This classification builds on the measure of routine and non-routine task content developed by Mihaylov and Tijdens (2019), who provided a measure of the Routine and Non-Routine Task Content of 427 83 Four-Digit ISCO-08 Occupations. The indexes are created by dividing the number of tasks in a given category by the number of all tasks performed in a given occupation. The results are reported at the 4-digit level and were integrated in the dashboard, defining the top similar professions in each of five categories of occupations. Table 5: Identification of similar professions with the designed tool Second, job transitions were restricted to positions with excess demand on the local labor market. The occupational barometers developed by the powiat labor offices were used to identify jobs with excess demand, in order to restrict job transitions to occupations in growing demand, or with high potential. Finally, using the results of the preference survey it can further be determined whether jobs fall within the realm of the job attribute preferences expressed by the coal-related workers. For instance, energy-sector workers display a strong aversion to moving regions. Job offers that are good matches, but imply moving to another part of the country, would thus be discarded if the wage increase was below the expressed welfare loss from taking up job in another location. The option was then discarded from the viable set. The proposed method of identifying possible transition paths and similar professions can be used as a tool supporting the activities of labor offices (e.g., in providing career consultations). Its ease of use allows employees to make a preliminary overview of possible career paths 84 themselves and allows employers to identify employees who may be placed in newly created positions, e.g., as part of creating new economic zones. 85 Most Recent Jobs Working Papers: 68. A Tale of Two Countries: Labor Market Profiles of Youth in Urban and Rural Cameroon. (2022) Ioana Botea and Mitja Del Bono 67. Improving Smallholders’ Jobs Through Agribusiness Linkages : Findings of the Mozambique Agricultural Aggregator Pilot (MAAP). (2022) Michael Baxter,Christopher Brian Delgado, Jose Romero and David Ian Walker. 66. Rural Employment in Africa: Trends and Challenges. (2022) Luc Christiaensen and Miet Maertens 65. 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Determinantes Del Crecimiento De La Demanda Laboral Y De La Productividad Del Sector Privado (Spanish) (2021) Mariana Vijil Click here for full Jobs Paper Series Address: 1776 G St, NW, Washington, DC 20006 Website: http://www.worldbank.org/en/topic/jobsanddevelopment Twitter: @WBG_Jobs Blog: https://blogs.worldbank.org/jobs/