{"id":6171,"date":"2026-07-02T19:15:35","date_gmt":"2026-07-02T19:15:35","guid":{"rendered":"https:\/\/ceo.com.pl\/en\/?p=6171"},"modified":"2026-07-02T19:20:40","modified_gmt":"2026-07-02T19:20:40","slug":"polands-workforce-by-profession-top-jobs-ageing-workforce-and-ai-risk-48778","status":"publish","type":"post","link":"https:\/\/ceo.com.pl\/en\/polands-workforce-by-profession-top-jobs-ageing-workforce-and-ai-risk-48778\/","title":{"rendered":"Poland\u2019s Workforce by Profession: Top Jobs, Ageing Workforce and AI Risk"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Statistics Poland has published the first dataset of this level of detail on the occupations people actually perform in Poland\u2014not the professions they trained for, but those reported in social-insurance records. Rather than relying on the once-in-a-decade Population Census, the data draw on administrative registers, making it possible to examine 443 four-digit occupational codes by gender, age and place of residence. The picture that emerges is of a labour market deeply divided by gender, ageing in healthcare, reliant on Ukrainian workers in transport and increasingly exposed to automation in office work.<\/p>\n\n\n\n<div class=\"guszaw26-wrap\">\n<style>\n.guszaw26-wrap { --gz-navy:#131F49; --gz-amber:#e67a2d; --gz-bg:#f7f8fb; --gz-border:#e2e5ee; font-family:-apple-system,BlinkMacSystemFont,\"Segoe UI\",Roboto,Arial,sans-serif; color:#232733; line-height:1.65; max-width:980px; margin:0 auto; }\n.guszaw26-wrap p { margin:0 0 16px; font-size:16px; }\n.guszaw26-wrap h2.guszaw26-title { color:var(--gz-navy); font-size:28px; font-weight:800; line-height:1.25; margin:0 0 6px; }\n.guszaw26-kicker { display:inline-block; background:var(--gz-amber); color:#fff !important; font-size:12px; font-weight:700; letter-spacing:.06em; text-transform:uppercase; padding:4px 10px; border-radius:4px; margin-bottom:12px; }\n.guszaw26-dateline { font-size:13px; color:#6b7280; border-bottom:1px solid var(--gz-border); padding-bottom:14px; margin-bottom:22px; }\n.guszaw26-wrap h3 { color:var(--gz-navy); font-size:22px; font-weight:700; margin:36px 0 14px; padding-bottom:8px; border-bottom:3px solid var(--gz-amber); }\n.guszaw26-wrap h4 { color:var(--gz-navy); font-size:16.5px; font-weight:700; margin:20px 0 8px; }\n.guszaw26-lead { font-size:18px; color:#333c50; font-weight:500; }\n\n.guszaw26-cards { display:grid; grid-template-columns:repeat(4,1fr); gap:14px; margin:24px 0 30px; }\n.guszaw26-card { background:var(--gz-bg); border:1px solid var(--gz-border); border-radius:10px; padding:16px; }\n.guszaw26-card .guszaw26-card-label { font-size:12px; color:#6b7280; text-transform:uppercase; letter-spacing:.03em; margin-bottom:6px; }\n.guszaw26-card .guszaw26-card-value { font-size:21px; font-weight:800; color:var(--gz-navy); line-height:1.15; }\n.guszaw26-card .guszaw26-card-sub { font-size:12.5px; color:#6b7280; margin-top:4px; }\n@media (max-width:820px){ .guszaw26-cards{ grid-template-columns:repeat(2,1fr); } }\n@media (max-width:520px){ .guszaw26-cards{ grid-template-columns:1fr; } }\n\n.guszaw26-chartbox { background:#fff; border:1px solid var(--gz-border); border-radius:10px; padding:18px; margin:20px 0 28px; }\n.guszaw26-chartbox canvas { max-height:380px; }\n.guszaw26-chart-title { font-size:14px; font-weight:700; color:var(--gz-navy); margin-bottom:12px; }\n\n.guszaw26-callout { background:var(--gz-navy); border-radius:10px; padding:18px 20px; margin:24px 0; }\n.guszaw26-callout p, .guszaw26-callout strong { color:#ffffff !important; }\n.guszaw26-callout p { margin:0 0 8px; font-size:15px; }\n.guszaw26-callout p:last-child { margin-bottom:0; }\n\n.guszaw26-two-col { display:grid; grid-template-columns:1fr 1fr; gap:22px; }\n@media (max-width:760px){ .guszaw26-two-col{ grid-template-columns:1fr; } }\n\n.guszaw26-table { width:100%; border-collapse:collapse; margin:18px 0 26px; font-size:14.5px; }\n.guszaw26-table th { background:var(--gz-navy); color:#fff; text-align:left; padding:10px 12px; font-weight:600; }\n.guszaw26-table td { padding:9px 12px; border-bottom:1px solid var(--gz-border); }\n.guszaw26-table tr:nth-child(even) td { background:#f7f8fb; }\n\n.guszaw26-source { font-size:12.5px; color:#8a8f9c; margin-top:6px; }\n<\/style>\n\n<span class=\"guszaw26-kicker\">Labour market<\/span>\n<div class=\"guszaw26-dateline\">Data: Statistics Poland, \u201cOccupational diversity in Poland\u2019s labour market\u201d (experimental study, published July 2026), data as at 31 December 2024.<\/div>\n\n<div class=\"guszaw26-cards\">\n  <div class=\"guszaw26-card\">\n    <div class=\"guszaw26-card-label\">People employed in the national economy<\/div>\n    <div class=\"guszaw26-card-value\">14.1 million<\/div>\n    <div class=\"guszaw26-card-sub\">as at 31 December 2024, excluding individual farming<\/div>\n  <\/div>\n  <div class=\"guszaw26-card\">\n    <div class=\"guszaw26-card-label\">Four most common occupations<\/div>\n    <div class=\"guszaw26-card-value\">13.5%<\/div>\n    <div class=\"guszaw26-card-sub\">share of shop assistants, truck drivers, warehouse workers and teachers<\/div>\n  <\/div>\n  <div class=\"guszaw26-card\">\n    <div class=\"guszaw26-card-label\">Foreign workers \u2014 Ukrainian citizens<\/div>\n    <div class=\"guszaw26-card-value\">67.7%<\/div>\n    <div class=\"guszaw26-card-sub\">share of all employed foreign nationals<\/div>\n  <\/div>\n  <div class=\"guszaw26-card\">\n    <div class=\"guszaw26-card-label\">Occupations partly exposed to AI<\/div>\n    <div class=\"guszaw26-card-value\">4.5 million people<\/div>\n    <div class=\"guszaw26-card-sub\">according to NASK-PIB and ILO analysis; 640,000 in the highest-risk group<\/div>\n  <\/div>\n<\/div>\n\n<h3>Four occupations that underpin the labour market<\/h3>\n<p>The most common occupation in Poland is shop assistant or cashier, accounting for 5.3% of all workers; the average worker is 40 years old and 83.7% are women. They are followed by truck drivers (3.0%; average age 44; only 1.3% women), warehouse workers (2.6%; age 39; 23.8% women) and primary-school teachers (2.6%; age 47; 86.8% women). Together, these four occupations\u2014representing retail, transport and education\u2014make up 13.5% of total employment in the country, even though the occupational classification covers as many as 443 distinct categories active in the labour market.<\/p>\n\n<table class=\"guszaw26-table\">\n  <thead><tr><th>Occupation<\/th><th>Share of employment<\/th><th>Average age<\/th><th>Women\u2019s share<\/th><\/tr><\/thead>\n  <tbody>\n    <tr><td>Shop assistants and cashiers<\/td><td>5.3%<\/td><td>40 years<\/td><td>83.7%<\/td><\/tr>\n    <tr><td>Truck drivers<\/td><td>3.0%<\/td><td>44 years<\/td><td>1.3%<\/td><\/tr>\n    <tr><td>Warehouse workers and related occupations<\/td><td>2.6%<\/td><td>39 years<\/td><td>23.8%<\/td><\/tr>\n    <tr><td>Primary-school teachers<\/td><td>2.6%<\/td><td>47 years<\/td><td>86.8%<\/td><\/tr>\n    <tr><td>Office and hotel cleaners and helpers<\/td><td>1.7%<\/td><td>49 years<\/td><td>91.2%<\/td><\/tr>\n    <tr><td>Accountants<\/td><td>1.5%<\/td><td>44 years<\/td><td>92.1%<\/td><\/tr>\n  <\/tbody>\n<\/table>\n\n<h3>A labour market split in two<\/h3>\n<p>The dataset confirms strong gender-based occupational segregation. Occupations that are almost entirely female include midwives, early-childhood education specialists and dental assistants; women account for more than 99% of workers in each of these groups. At the other end are male-dominated occupations\u2014miners, bricklayers, agricultural-machinery operators and roofers\u2014where men account for almost 100% of workers. Statistics Poland notes that single-gender concentration is stronger in male-dominated occupations: as many as 19 occupations exceed the 98% male threshold, while fewer categories meet the similarly high 95% threshold for women.<\/p>\n\n<div class=\"guszaw26-chartbox\">\n  <div class=\"guszaw26-chart-title\">Most female- and male-dominated occupations (share of the respective gender, %)<\/div>\n  <canvas id=\"guszaw26-chart-gender\"><\/canvas>\n<\/div>\n\n<div class=\"guszaw26-callout\">\n<p><strong>Rare exceptions to the pattern.<\/strong> Only a handful of occupations have a relatively balanced gender split close to 50\/50, including shop owners, bakers and pastry chefs, domestic-service workers, head chefs and university teachers. These are exceptions in a labour market where the vast majority of occupations are strongly female- or male-dominated.<\/p>\n<\/div>\n\n<h3>Ageing specialist doctors<\/h3>\n<p>The highest average age in the entire economy is recorded among specialist doctors\u201456 years\u2014and specialist dentists\u201455 years. This is around 20 years higher than the average age of doctors without a specialisation, which Statistics Poland attributes to the longer route to obtaining specialist status. More than 66% of specialist doctors are over 50, with the largest share in the 60-and-over group. A similar, though slightly less pronounced, pattern applies to specialist nurses, nearly 60% of whom are over 50.<\/p>\n\n<div class=\"guszaw26-chartbox\">\n  <div class=\"guszaw26-chart-title\">Occupations with the highest and lowest average worker age<\/div>\n  <canvas id=\"guszaw26-chart-wiek\"><\/canvas>\n<\/div>\n\n<div class=\"guszaw26-callout\">\n<p><strong>A warning of a generational gap in healthcare.<\/strong> The high concentration of workers aged over 50 in specialised medical occupations may mean a clustering of departures from the profession\u2014a wave of retirements\u2014within a relatively short period. Statistics Poland directly links this to the fact that doctors have for years appeared on the list of shortage occupations in most Polish counties, according to the Occupational Barometer.<\/p>\n<\/div>\n\n<p>At the opposite end are occupations with the youngest demographic profile: fast-food preparation workers as well as athletes and jockeys average 29 years of age, while models and flight attendants average 31. Application programmers and other IT specialists also have a young profile, at 34\u201336 years, reflecting the relatively recent and dynamic development of this sector in Poland.<\/p>\n\n<h3>Foreign workers: transport for men, retail and cleaning for women<\/h3>\n<p>Ukrainian citizens clearly dominate among foreign nationals working in Poland, accounting for 67.7%, followed by Belarusians (13.0%), Indians (1.8%), Georgians (1.4%), and Vietnamese, Russians and Turks (1.1% each). The most common occupation among foreign workers overall is truck driver (12.6%)\u2014an occupation that is itself listed as a shortage occupation in Poland by the Occupational Barometer. Among the four largest male nationalities\u2014Ukrainians, Belarusians, Indians and Georgians\u2014truck driver is the number-one occupation in every group, although with markedly different intensity: as many as 45.3% of Belarusians employed in Poland work in this occupation, compared with 16.9% of Ukrainians.<\/p>\n\n<div class=\"guszaw26-chartbox\">\n  <div class=\"guszaw26-chart-title\">Nationality structure of foreign workers in Poland<\/div>\n  <canvas id=\"guszaw26-chart-cudzoziemcy\"><\/canvas>\n<\/div>\n\n<p>Women account for 37% of all foreign nationals working in Poland and are most often employed as shop assistants, warehouse workers and cleaners, at around 5% each. The structure differs markedly by nationality, however: while Ukrainian women most often work in warehouses, shops and cleaning, IT occupations predominate among Belarusian and Indian women\u2014application programmers and computer-systems analysts, with an average age of 32\u201333 years respectively.<\/p>\n\n<h3>Who will be most affected by artificial intelligence<\/h3>\n<p>For the first time, the publication links occupational data with two independent analyses of exposure to automation: a report by NASK-PIB and the International Labour Organization (ILO), and a report by the Polish Economic Institute (PIE). According to the NASK-PIB and ILO analysis, more than 4.5 million people working in Poland are in occupations that could be partly automated by generative AI, while more than 640,000 are in the highest-risk group, where artificial intelligence could take over a substantial share of everyday tasks. Women account for as much as 77.2% of this latter group, whose average age is just under 40.<\/p>\n\n<div class=\"guszaw26-chartbox\">\n  <div class=\"guszaw26-chart-title\">Scale of exposure to automation and AI (million people)<\/div>\n  <canvas id=\"guszaw26-chart-ai\"><\/canvas>\n<\/div>\n\n<p>Independent estimates based on the PIE methodology indicate that around 3 million people work in occupations ranked among the 20 most exposed to the impact of artificial intelligence, with an average age of 41 and a 57.8% share of women. The most exposed are occupations based on repetitive information processing; among secretaries, women account for 98.8% of the at-risk group. Geographically, occupations threatened by automation are concentrated in large urban areas with developed service sectors\u2014where business-process outsourcing centres, call centres and accounting offices operate\u2014and are least prevalent in rural and peripheral regions.<\/p>\n\n<h3>Civil-law contracts: from teenagers to retirees<\/h3>\n<p>In addition to employees on standard employment contracts, Statistics Poland also analysed 1.4 million people working solely under contracts of mandate and similar civil-law arrangements. Cleaners and helpers (6.0%) and workers performing elementary jobs in industry (3.7%) are most frequently employed on this basis, together accounting for nearly 10% of this population. Age variation in this group is exceptionally wide: public officials and specialist doctors are the oldest, averaging 65 years, while athletes, jockeys and product demonstrators are the youngest, averaging 27. Flexible work arrangements in catering, customer service and transport favour younger workers, who combine work with study or gain their first professional experience.<\/p>\n\n<h3>The geography of occupations<\/h3>\n<p>For the first time, the experimental study enabled Statistics Poland to map the distribution of selected occupational groups at municipal level using the location quotient. IT occupations are concentrated almost exclusively in major cities and their suburban zones\u2014Warsaw, Krak\u00f3w, Wroc\u0142aw, Pozna\u0144, the Tri-City and the Katowice conurbation. Transport, warehousing and logistics are arranged along the main transport corridors\u2014the A2 and A4 motorways\u2014with the strongest concentration in the \u0141\u00f3d\u017a and Mazowieckie voivodeships. Construction is decentralised, with local clusters in peripheral regions and smaller centres. Tourism and hospitality are the most evenly distributed, with visible clusters on the coast, in Masuria and in the mountains; however, an end-of-December data snapshot shows mountain municipalities ahead of coastal ones, reflecting the seasonality of tourist traffic.<\/p>\n\n<p class=\"guszaw26-source\">Data source: Statistics Poland, \u201cOccupational diversity in Poland\u2019s labour market\u201d (Experimental Studies, July 2026), the Statistics Poland Department of Methodology and Survey Quality in cooperation with the Statistical Office in Bydgoszcz. Data on exposure to automation and AI: NASK-PIB and ILO, \u201cGenerative Artificial Intelligence and the Polish Labour Market\u201d (2025), and the Polish Economic Institute, \u201cAI in the Polish Labour Market\u201d (2024). Authors\u2019 own analysis based on Statistics Poland data. The data are based on administrative registers (Social Insurance Institution records and registers of regulated professions) and the 2021 National Population and Housing Census; the occupation performed was identified for 85.9% of workers in the national economy and 78.3% of people working under contracts of mandate, with remaining gaps filled using hot-deck imputation.<\/p>\n\n<script src=\"https:\/\/cdnjs.cloudflare.com\/ajax\/libs\/Chart.js\/4.4.1\/chart.umd.min.js\" nowprocket data-cfasync=\"false\"><\/script>\n<script nowprocket data-cfasync=\"false\">\n(function () {\n  var attempts = 0;\n  var maxAttempts = 100;\n  var poll = setInterval(function () {\n    attempts++;\n    if (typeof Chart !== 'undefined') {\n      clearInterval(poll);\n      initGuszaw26Charts();\n    } else if (attempts >= maxAttempts) {\n      clearInterval(poll);\n    }\n  }, 100);\n\n  function initGuszaw26Charts() {\n    var navy = '#131F49';\n    var amber = '#e67a2d';\n    var pink = '#c2185b';\n\n    \/\/ Chart 1: gender-dominated occupations\n    try {\n      var c1 = document.getElementById('guszaw26-chart-gender');\n      if (c1) {\n        new Chart(c1.getContext('2d'), {\n          type: 'bar',\n          data: {\n            labels: ['Midwives', 'Early-childhood education specialists', 'Dental assistants', 'Secretaries', '', 'Miners', 'Bricklayers', 'Agricultural-machinery operators', 'Roofers'],\n            datasets: [{\n              label: 'Share of the respective gender (%)',\n              data: [99.7, 99.2, 99.1, 98.8, null, 99.6, 99.5, 99.4, 99.3],\n              backgroundColor: [pink, pink, pink, pink, 'transparent', navy, navy, navy, navy],\n              borderRadius: 5\n            }]\n          },\n          options: {\n            indexAxis: 'y',\n            responsive: true,\n            plugins: {\n              legend: { display: false },\n              tooltip: { callbacks: { label: function (ctx) { return ctx.parsed.x ? 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Rather than relying on the once-in-a-decade Population Census, the data draw on administrative registers, making it possible to examine 443 four-digit occupational codes by [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":6174,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","jetpack_publicize_message":"Who really works in Poland?\n\nNew workforce data paints a detailed picture of employment across the country. Shop assistants and cashiers are the largest occupational group, followed by truck drivers, warehouse workers and primary-school teachers. Together, these four professions account for 13.5% of all jobs.\n\nThe figures also reveal deeper challenges: strong gender divides between occupations, an ageing generation of medical specialists, Poland\u2019s reliance on foreign workers in transport and logistics, and rising exposure to AI-driven automation in office-based roles.\n\nMore than 4.5 million people work in occupations that could be partly automated by generative AI, while healthcare may soon face a growing retirement gap among specialist doctors and nurses.\n\nRead the full analysis of Poland\u2019s changing labour market.","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[21,15,7],"tags":[2974,129,2877,2690,75,70,73,37,4716,2722,3529,64,74,133,2822,71],"class_list":["post-6171","post","type-post","status-publish","format-standard","has-post-thumbnail","category-careers","category-economy","category-reports-analysis","tag-artificial-intelligence","tag-bydgoszcz","tag-catering","tag-gap","tag-katowice","tag-krakow","tag-lodz","tag-machinery","tag-nask","tag-outsourcing","tag-pie","tag-poland","tag-poznan","tag-tri-city","tag-wave","tag-wroclaw"],"jetpack_publicize_connections":[],"_links":{"self":[{"href":"https:\/\/ceo.com.pl\/en\/wp-json\/wp\/v2\/posts\/6171","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ceo.com.pl\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ceo.com.pl\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ceo.com.pl\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ceo.com.pl\/en\/wp-json\/wp\/v2\/comments?post=6171"}],"version-history":[{"count":1,"href":"https:\/\/ceo.com.pl\/en\/wp-json\/wp\/v2\/posts\/6171\/revisions"}],"predecessor-version":[{"id":6175,"href":"https:\/\/ceo.com.pl\/en\/wp-json\/wp\/v2\/posts\/6171\/revisions\/6175"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ceo.com.pl\/en\/wp-json\/wp\/v2\/media\/6174"}],"wp:attachment":[{"href":"https:\/\/ceo.com.pl\/en\/wp-json\/wp\/v2\/media?parent=6171"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ceo.com.pl\/en\/wp-json\/wp\/v2\/categories?post=6171"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ceo.com.pl\/en\/wp-json\/wp\/v2\/tags?post=6171"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}