Integration of high-skilled migrants, and the gender dimension

International migration profoundly influences local labour market dynamics, industrial specialisation, and long-term economic development [11]. The effective economic integration of migrant workers depends on their engagement in key aspects of the host country, including labour market participation, as noted by Ager and Strang [4]. Within this process, the recognition of qualifications, licensing requirements, and the broader economic and policy context are critical determinants that shape integration outcomes [20]. Access to opportunities and sectoral conditions are crucial, as the industries employing migrants strongly influence their economic outcomes and vulnerability to exploitation or deskilling [21].

Importantly, economic migrants and refugees often experience different labour market trajectories due to variations in legal status, migration motives, and expected length of stay [22]. Both groups can face significant barriers to entry, frequently relying on temporary, low-paid, or lower-skilled jobs as an initial step into employment [21,23]. Refugee women often experience a “double disadvantage”, which can be further exacerbated by factors such as race, ethnicity, age, disability, sexual orientation, or socioeconomic status, resulting in a “triple disadvantage” [18]. How these layered disadvantages translate into labour-market outcomes is highly context-dependent, shaped by host-country institutions (e.g., credential recognition, cultural proximity, and local labour-market structures), which was visible during Europe’s post-2015 refugee inflow [24]. In Turkey, for example, Syrian refugee men exhibit the smallest employment gaps in manufacturing, while refugee women fare relatively better in agriculture, underscoring how cultural proximity, pre-existing networks, and a large informal sector can channel integration pathways differently from EU settings [25]. Comparative evidence across OECD countries likewise shows substantial heterogeneity in refugee–migrant labour-market gaps over time and across host contexts, reflecting differences in institutions and recognition regimes [26].

Scholars have long studied skilled migration [17,27,28], but definitions of “highly skilled migrant” vary, including formal education, professional experience, and industry-specific expertise [29]. While tertiary education is a common proxy for skill, a substantial portion of highly skilled migrants derive their expertise from work experience rather than formal credentials. Focusing solely on academic qualifications may therefore underestimate the economic potential of skilled migrants, particularly in countries like Poland where many Ukrainian women bring valuable industry experience despite facing formal credentialing barriers [19].

Since the 1980s, migration has become increasingly feminised, with women representing a growing share of skilled migrants [30], however, highly skilled migrant women remain underrepresented in research [31]. Migration studies have traditionally focused on male-dominated sectors, leaving gender-specific challenges in professional and managerial roles underexplored [17]. Notably, many highly skilled migrant women experience overqualification and deskilling, highlighting the need for policies that address the intersection of gender and migrant status [32]. Evidence also shows that the realised utilisation of skills is shaped by institutional, organisational, and social arrangements, such as licensing requirements in regulated professions, recognition of qualifications, care-system design, exposure to discrimination, and the gendered composition of sectors [cf. 33–36]. Recognising these challenges is crucial, as integrating a gender perspective into migration policies not only promotes equality but also enhances economic outcomes [37]. However, gender-blind approaches often obscure these dynamics, limiting effective policy responses [38]. Recognising and addressing these barriers is crucial for designing effective policies that not only support refugee women’s employment but also enable host economies to capitalise on their skills.

Skilled migrants, innovation, and economic growth

Economic growth relies on the continuous development of human capital, knowledge transfer, and technological innovation, all of which are shaped by the integration of a skilled labour force [39,40]. In this context, highly skilled migrants contribute significantly to productivity and industrial dynamism in host economies by introducing new skills, fostering knowledge diffusion, and increasing workforce complementarities [41–43]. Both high- and low-skilled migrants contribute significantly to the host labour market by filling roles that natives may be unqualified for or reluctant to take [1]. Importantly, interactions between skilled migrants and local workers amplify productivity and knowledge dissemination in host countries [6]. Consequently, the influx of skilled workers enhances firm and sector performance by leveraging complementarities with existing capabilities, ultimately boosting productivity [44].

Skilled labour mobility is a key driver of knowledge transfer, leading to productivity improvements [45] and the diffusion of knowledge across regions and sectors [46]. Migrants with industry-specific skills play a crucial role in the emergence of new industries, and their influx supports regional skill enhancement and diversification [47]. Industry-specific knowledge and managerial skills significantly impact firm performance by fostering innovation, competitiveness, and effective problem solving [48]. Such expertise drives productivity growth, especially among high-technology firms [49]. Management team diversity in educational and sectoral backgrounds enhances firms’ performance by fostering knowledge spillovers and driving targeted innovation strategies, resulting in a more innovative product portfolio [50,51].

The debate over whether economic diversity or specialisation drives industrial transformation remains central to migration and labour market research. Specialisation-based arguments suggest that firms benefit from clustering around existing industries, as geographic proximity enhances knowledge sharing and productivity growth [12]. From this perspective, skilled migrants can accelerate technological advances in specialised industries by introducing complementary skills and managerial capabilities [46], aligning with M1 in our framework. Conversely, diversity-based perspectives emphasise inter-industry linkages as drivers of innovation, whereby skilled migrants’ diverse knowledge cross-pollinates sectors, consistent with M2 in our framework. Taken together, the classic accounts associated with Marshall [12] (specialisation) and Jacobs [14] (diversity) provide a concise conceptual bridge between within-path upgrading and related diversification, clarifying how migrant expertise can both reinforce established industries and generate spillovers into technologically or organisationally related activities.

Ukrainian migration, gender dynamics, and market integration in Poland

In Ukraine’s pre-war economy, men dominated high-level managerial and directorial roles [52]. Nevertheless, the 2017–2020 period saw progress in women’s representation in business leadership [53]. In recent decades, rising emigration driven by political instability, economic challenges, and cultural shifts, has caused brain drain and skill mismatches in the Ukrainian workforce [54,55]. Importantly, these emigration patterns were not uniform across destinations. In parts of Southern Europe (e.g., Italy), labour migration from Ukraine was highly feminised, driven by sectoral demand in domestic and care work. While many of the women engaged in these activities did not have tertiary qualifications, a considerable share were skilled or highly educated professionals whose qualifications were under-recognised and thus channelled into low-paid, gendered labour niches [56]. This situation illustrates Kofman’s [17] observation on the invisibility of skilled female migrants and Bircan and Yılmaz’s [38] critique of gender-blind migration theories that overlook structural constraints.

By contrast, migration from Ukraine to Poland until 2022 was predominantly male, shaped by structural demand in temporary, blue-collar sectors such as construction, agriculture and transport [57]. Ukrainian women in Poland were more often employed in long-term roles in domestic services, care work, and certain agricultural jobs, with their share growing gradually over time [58]. These contrasting patterns highlight how gendered migration flows are closely tied to the sectoral composition of labour markets in different destination countries.

In recent years, however, a growing presence of Ukrainian migrants in hospitality, retail, and entrepreneurship indicates a broader shift in Poland’s migration landscape. The outbreak of war in 2022 profoundly reshaped previous migration patterns. The Ukrainian refugee inflow was predominantly female, with 80% of arrivals being women aged 30–44 [2], and included a significant share of highly educated professionals – over 50% held higher education degrees and had diverse professional backgrounds [59]. Despite their qualifications, many encountered significant barriers to labour market integration, including language obstacles, restrictions on accessing regulated professions, and challenges in obtaining long-term residency [2], leading many to accept jobs below their skill level [60]. In the case of Ukrainian refugee women, multiple structural barriers can intersect to hinder effective labour-market integration. Aigner et al. [19] show that even women with substantial professional experience face significant deskilling risks when such barriers remain unaddressed. These findings reinforce earlier evidence on underemployment and “brain waste” among highly skilled migrants [61]. In Poland, caregiving responsibilities and limited access to childcare services present additional challenges for refugee women with children [62], further constraining their ability to re-enter professional roles and advance in the labour market. Beyond these individual and structural barriers, broader social and cultural integration conditions, such as workplace inclusivity, access to affordable and adequate housing, and the availability of reliable childcare, are also critical to sustaining long-term labour market participation. Comparative evidence [19] from Austria and Poland, shows that the absence of such enabling conditions can exacerbate deskilling risks for highly skilled refugee women, underscoring the need for integration strategies that address both economic and social dimensions.

These varied experiences underscore the complexities of labour market integration of Ukrainian women refugees in Poland [63,64], emphasising the need for targeted policies that effectively harness refugees’ skills and enhance their contributions to host economies. Incorporating a gender-sensitive perspective into these policies would address both immediate barriers to employment and the long-term underutilisation of refugee women’s human capital, while also unlocking opportunities to match their skills with industries in which Poland is already specialised and to leverage their expertise for diversification into related industries.

Synthesising these strands of literature, our framework builds on the premise that the inflow of highly skilled Ukrainian women may interact with host-industry structures through two canonical pathways: matching and complementarities within existing specialisations (M1), and relatedness-based entry into adjacent sectors (M2). However, the realised utilisation of these skills is contingent upon gendered sectoral constraints (M3), which can systematically dampen the returns to M1 and M2 even where sectoral fit or capability proximity is high. We focus analytically on M1 and M2 as the measurable industry pathways commonly operationalised in the specialisation/diversity literature [12–14], while recognising that gendered sectoral constraints (M3) fundamentally shape how, and to what extent, these pathways translate into actual labour-market outcomes.

Data

This study employs firm-level data from Bureau van Dijk’s Orbis global database [65], a comprehensive database that provides coverage for over 108 million firms worldwide (including more than 20 million are situated across Europe, with approximately 2.5 million employing 10 or more individuals), offering key variables such as industry classification, revenue, employment size, and gender composition in managerial roles, geographic location, among others. The database allows for cross-country comparisons in firms’ specialisation patterns, making it particularly suitable for studying sectoral and labour market structures. Orbis database has been extensively validated in the literature as a reliable dataset for analysing firm-level labour market characteristics. Studies comparing Orbis with official statistics confirm that it captures a significant portion of formal sector employment. Farole et al. [66] found that Orbis covers approximately 73% of total firms in Spain, Poland, Italy, and Romania, with national-level representation ranging from 58% to 89%. Zalas and Drążkowski [67] utilised Orbis database to examine labour share evolution in Poland, demonstrating its applicability in employment analysis.

To align with the research objectives, we collected information on firms’ industries (categorised by NAICS4) and their operating locations (classified by NUTS1), employment levels, and managerial gender composition. We focused on European countries that, up to the cutoff date in June 2023 and according to the UNHCR [68], had a recorded Ukrainian refugee population of 50,000 or more. The 20 selected countries, listed in descending order of refugee intake, were Germany, Poland, the Czech Republic, the United Kingdom, Spain, Italy, Bulgaria, Romania, Moldova, Slovakia, the Netherlands, Austria, Ireland, Lithuania, France, Belgium, Switzerland, Portugal, Finland, and Hungary. We also included Ukraine; however, because of the absence of a NUTS1 classification in Ukraine, firms were grouped by their respective Oblasts into three general areas: West, Central/North, and East/South.

Following the methodology of Kalemli-Ozcan et al. [69], to ensure data quality and representativeness, we applied a structured data-cleaning process by removing firms with incomplete or inconsistent records, and duplicate entries at the NUTS1 regional level. Consistent with studies by Farole et al. [66], Bajgar et al. [70], Bussolo et al. [71], and others, we addressed the issue of underrepresentation of small firms by selecting firms that reported at least 10 employees, location (NUTS1), industry (NAICS4), and managerial gender composition for at least one year during 2017–2021. The multi-year requirement reduces volatility from one-off firm records and ensures comparability across countries.

The final dataset comprises 1,515,295 firms employing approximately 145 million workers across the selected European economies. Within this dataset, 8.3 million managers were identified, of whom 2.2 million were women (26.6%). In line with OECD/ISCO conventions, we operationalise “highly skilled” as managerial roles. In our data, pre-war Ukrainian women’s managerial presence by industry serves as a proxy for industry-specific expertise likely to be transferable to Poland.

A comparison with official national statistics for Poland and Ukraine, before the war commenced, confirms the strong representativeness of the Orbis database for firms with 10 or more employees. According to the European Commission [72], Ukraine had approximately 64,668 enterprises classified as small (10–49 employees), medium (50–249 employees), and large (250 + employees), employing 4.68 million workers. The Orbis dataset for Ukraine includes 74,886 firms, closely aligning with these figures, while capturing a slightly larger workforce of 5.7 million employees. Similarly, in Poland, official statistics indicate that there were 67,679 enterprises with at least 10 employees, employing 5.6 million workers [73]. The Orbis dataset includes 52,316 such firms with a total workforce of 5.6 million employees. These small discrepancies may be attributed to variations in classification methodologies, updates in firm registries over time, COVID-related distortions, and the pre-war cutoff period of the Orbis dataset (2017–2021), which may capture structural labour market shifts not reflected in earlier national reports. Furthermore, the Orbis data for Ukraine includes approximately 79 thousand workers in managerial positions, out of which approximately 19 thousands are women; while for Poland, it includes approximately 186 thousands workers in managerial positions, of which 54 thousands were occupied by women before the war.

Network construction

We constructed a network that encapsulated the statistical relationships – or ‘relatedness’ – of industries across various regions in the 20 European countries that have hosted the largest populations of Ukrainian refugees, as well as in Ukraine itself. This network is referred to as the ‘benchmark industry network’, which serves as a structural reference point against which Poland’s and Ukraine’s industrial profiles can be compared. Network construction draws upon the conceptual frameworks and methodologies within the economic complexity literature [74,75]. It also adheres to the principle of ‘relatedness’ [15], applying it to the level of industry-regional employment. This network design allows for a comparative examination of how Poland and Ukraine are positioned and specialised in distinct industries compared to the benchmark industry network. By embedding Ukrainian and Polish data into a pan-European benchmark, one can identify industries where skills brought by Ukrainian women managers could be most effectively matched to Poland’s existing specialisations.

Influential research introduced the ‘Product Space’ concept, measuring product relatedness through a proximity indicator based on co-exportation frequencies between countries [76]. This concept was later extended to the industry-regional level, suggesting that regions are more likely to diversify into industries that are technologically or skill-related to their existing ones [16,77]. Such findings indicate that regional capabilities, including skills, play a crucial role in developing new industries. This is particularly relevant for refugee integration, as it implies that skills carried by newcomers can facilitate diversification into related industries, even if these industries are not yet well established or specialised in the host economy.

Furthermore, geographic clustering of firms is driven by three Marshallian mechanisms: the sharing of goods, labour, and ideas, each playing a substantial role in influencing co-location patterns [78]. These mechanism underpin our interpretation of “relatedness” not only as a technological or input-output relationship but also as a human capital and knowledge-spillover relationship.

We analysed the collocation patterns of employment across various NUTS1 regions in selected European economies to quantify industry interconnections. An industry is likely to benefit not only by direct shocks -in the form of knowledge or other – to itself but also by shocks benefiting other co-located industries [79] or industries in locations geographically closer [80,81]. This has significant implications for migration studies; a positive shock in the form of new knowledge introduced into a specific industry in Poland could have cascading benefits. This could enhance not only the output of that particular industry but also that of other interconnected industries.

In this context, a measure of geographic specialisation: the Location Quotient [LQ], offers a nuanced comprehension of industries that possess a high degree of specialisation in specific regions.

where ‘X’ is employment, ‘i’ represents the industry, ‘l’ the location under analysis and ‘L’ the number of total locations.

This measure aids in evaluating an industry’s relative significance in a particular regional or national economy relative to its importance across all regions and countries under study. An LQ value exceeding one indicates that a region or country exhibits a specialised pattern in that industry.

The industry network quantifies the likelihood that two industries will co-locate in a NUTS 1 region within the selected European host countries, conditional on both exhibiting and LQ > 1. Higher probability values signify closer interindustry proximity. These proximity values capture diverse forms of interlinkages, which can manifest in various channels such as supply chain dependencies or other knowledge-sharing arrangements.

Industry co-location was initially driven by value chain partnerships, where firms benefited from proximity to suppliers and customers. Recent evidence, however, highlights a shift towards skill sharing as the primary factor influencing co-location, especially within the service sector and knowledge-intensive business services. These evolving dynamics suggest that future industry locations will increasingly depend on the availability and mobility of specialised skills [82].

In summary, we employed network analysis to discern inter-industry proximities within host countries, as defined by our benchmark industry network. A nuanced understanding of these interrelationships is essential to assess how Ukrainian women’s managerial skills can most effectively benefit the economies of host countries. Identifying the main industries, especially those that align with the expertise brought into the country by highly skilled Ukrainian refugees, could facilitate the formulation of strategies to bolster existing industries at regional and national levels.

The benchmark industry network

The benchmark industry network delineates the collocation probability between various pairs of industries across major European nations hosting Ukrainian refugees. This network offers valuable insights into the relative presence or absence of specific industries in each country included in our study. In this study, we use it to contrast the industrial structures of Ukraine and Poland, thereby establishing a reference framework for assessing how refugee women’s skills can be matched to existing specialisations or leveraged for related diversification (Fig 1).

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Fig 1. The benchmark industry network.

Source: Authors’ elaboration using Orbis global database. Note: In the network representation, nodes symbolise individual industries, and edges denote the proximity or relatedness between pairs of industries in countries hosting the largest number of Ukrainian refugees. Square nodes (104) represent industries where the presence of women managers exceeds the average of the host countries (26.6%), whereas triangular nodes (184) denote industries below that threshold (lower representation of women managers). For graphical clarity, the network displays only 1,364 strongest connections (edges) for 288 industries (nodes), while ensuring to have a connected network (minimum spanning tree using Kruskal’s algorithm) and sparse visualisation without leaving aside any essential industry connections (industries with proximities higher than 0.5).

https://doi.org/10.1371/journal.pone.0338655.g001

Within the network, the nodes symbolise the 288 NAICS4 industries in which firms from host economies operated before the onset of the war (out of the 310 NAICS industries under analysis). Conversely, the edges of the network – lines connecting the industries – represent the measured distances between industries, calculated as the inverse probability of co-location within the same geographical area. This measure serves as an index of inter-industry relatedness, which is central for identifying both high-absorption industries (M1) and potential pathways for related diversification (M2).

Comparative analysis of industrial landscapes in Ukraine and Poland

Poland and Ukraine boast a considerable array of industries, totalling 258 each. However, the proportion of industries achieving notable specialisation (LQ > 1) differs; Ukraine has 142 such industries compared with Poland’s 198. Additionally, the prevalence of women in managerial roles within these industries surpasses the average in host countries, with Ukraine and Poland having 89 and 103 industries, respectively, featuring women management.

Upon closer examination of the specific industries in which women hold managerial roles in Poland and Ukraine, both countries exhibited solid women leadership in the education and healthcare sectors. However, distinct patterns have emerged in other industries. Poland has a significant women managerial presence in fashion-related industries, particularly in apparel manufacturing, aligning with its robust textile and clothing sector. Conversely, Ukraine has demonstrated a notable women managerial presence in its food production industry. These variations underscore the unique industrial landscape shaped by women managers in each country. Poland also tends to lean towards personal care and aesthetic services, such as florists and personal care stores, whereas Ukraine exhibits a more robust women representation in business support services. From the M1 and M2 perspective, these overlaps and divergences point to concrete opportunities that can be activated through targeted matching policies, especially where Ukraine’s women’s expertise is adjacent to or embedded within Poland’s existing industrial structure.

The network shown in Fig 2A delineates Ukraine’s industrial landscape. By intersecting industries with a pronounced presence of women managers in Ukraine (89) with those achieving significant specialisation (142), we identified 32 industries (nodes represented by solid squares) in Ukraine as particularly noteworthy. This intersection reflects the unique patterns of women-led industry specialisation in Ukraine compared to the host country’s industry network. Ukrainian firms have demonstrated remarkable performances in these industries, allowing women managerial teams to accumulate extensive knowledge and expertise. Consequently, our analysis focused on revitalising the Polish economy by integrating highly skilled Ukrainian migrant women into these 32 identified industries (Table 1). These industries serve as anchor points for cross-border skill matching and can act as conduits for transferring managerial know-how, further strengthening Poland’s competitive position in related industries.

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Table 1. Number of industries in Ukraine and Poland: Specialisation patterns and women management participation.

https://doi.org/10.1371/journal.pone.0338655.t001

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Fig 2. Comparative industry specialisation of Ukraine and Poland in the benchmark industry network.

A. Industry landscape for Ukraine. B. Industry landscape for Poland. Source: Authors’ elaboration. Source: Authors’ elaboration. Note: In the network diagrams (Figs 2A and 2B), nodes represent industries and edges show the connections between industries in countries hosting the largest numbers of Ukrainian refugees. The node size shows the share of women managers in Ukraine and Poland's industries. The square nodes indicate industries with more women managers than the host countries' average, whereas the triangular nodes indicate fewer women managers. Empty nodes are industries absent in Ukraine and Poland. Solid nodes denote specialised industries in the country, whereas outlined nodes are less specialised. The node labels identify the economic sector.

https://doi.org/10.1371/journal.pone.0338655.g002

We also examined Poland’s industrial landscape, as depicted in the network of Fig 2B. Our analysis identifies 198 industries in which Poland exhibits a relative degree of specialisation compared with other host economies. We observed 60 comparatively underdeveloped industries against the same international benchmarks. Furthermore, in 103 of existing industries, the proportion of women managers exceeded the average in host countries, and 72 of these industries demonstrated a discernible degree of specialisation. From the lens of M2, the existence of these underdeveloped but potentially related industries offers Poland an opening to diversify via relatedness, especially when Ukrainian women bring experience in industries that Poland has yet to fully exploit.

The decision tree in Fig 3 classifies the 32 industries into two categories: (i) industries where Poland already has a degree of specialisation, further divided by whether women’s participation in managerial teams is high or low; and (ii) industries where Poland does not have such specialisation, identified as diversification opportunities. The first category corresponds to M1, while the second aligns with M2.

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Fig 3. Decision tree for categorising industries with the potential to be shaped by market competitiveness and diversification.

https://doi.org/10.1371/journal.pone.0338655.g003

To evaluate the potential opportunities for Poland, particularly in the context of the 32 industries identified as having a high level of women managerial participation and exhibiting patterns of specialisation in Ukraine relative to the host economies, we adopted a decision tree to categorise these industries based on their potential influence on Polish markets (Fig 3).

Initially, the 32 industries were examined to determine whether they show a degree of specialisation in Poland above that of other host economies. If yes, industries were further divided based on women participation in managerial roles in Poland. If women participation is above that of host economies, the industries were categorised under ‘Market competitiveness in women-led industries’ (17 industries). Otherwise, they were categorised as ‘Market competitiveness in male-led industries’ (7 industries). Industries that did not show a higher degree of specialisation in Poland compared to other host economies were classified as industries with ‘Diversification opportunities’ (8 industries).

This study highlights that 24 industries in Poland, 17 Women-led (Table 2) and 7 Men-led (Table 3), including food manufacturing, textiles, business services, wholesale, real estate, and healthcare, could benefit significantly from the integration of highly skilled Ukrainian women, enhancing market competitiveness via M1. In this scenario, the alignment between Poland’s existing industrial strengths and the skills profile of Ukrainian women creates complementarities that can raise productivity, innovation and firm performance in already specialised industries. This dynamic could shift the focus from traditional migrant-reliant sectors, such as agriculture, construction, and domestic services, as well as the growing gig economy, to areas with higher labour productivity.

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Table 2. Women-led industries in Poland with the potential to be shaped by market competitiveness (M1).

https://doi.org/10.1371/journal.pone.0338655.t002

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Table 3. Men-led Industries in Poland with the Potential to be shaped by market competitiveness (M1).

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Table 2 and 3 delineate specific industries poised for growth through increased market competitiveness, contingent upon the successful integration of this proficient group of Ukrainian professionals into the Polish industry landscape.

Specifically, industries in Table 3 represent areas where Ukrainian women had pre-war specialisation in Ukraine but are currently male-led in Poland, suggesting both opportunities for skill transfer and the presence of gender-related barriers.

Seven industries demonstrate a paucity of women managerial participation (Table 3) in Poland, indicating a gender divide that highly skilled Ukrainian women could potentially bridge, which challenges the migration literature’s focus on gender-typical sectors and urges further exploration into women’s experiences in male-dominated fields. It could also suggests a potential double barrier for highly skilled Ukrainian women: entering male-dominated sectors where their expertise could be complementary, yet facing entrenched gender imbalances that limit access to decision-making roles (M3: Gendered Constraints). Addressing these barriers will be crucial to ensure that the competitive potential of these industries is fully realised.

Migration literature has largely concentrated on women entering traditionally female-dominated sectors, often neglecting those who move into male-dominated fields. This gap highlights the need to explore whether migrant women in male-biased industries face similar challenges to their counterparts in female-dominated industries and to examine how these experiences might vary across different national contexts [33].

Economic diversification involves expanding into new economic activities, typically branching into areas closely aligned with existing capabilities [85]. This strategy is vital for economies seeking to grow into new industries effectively. Our analysis highlights eight industries in Poland that present strong opportunities for diversification and could greatly benefit from the specialized skills and experience of Ukrainian women (M2 of Diversification via Relatedness). While they were established in Ukraine and feature a higher women managerial presence, these industries are less prevalent in Poland. In this second scenario, relatedness allows Poland to expand into new industries by leveraging the know-how and managerial experience that Ukrainian women bring, thereby reducing the risks and cost of diversification. Thus, the integration of Ukrainian women may spearhead Poland’s economic expansion into these new areas, as shown in Table 4.

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Table 4. Industries with the potential for diversification (M2).

https://doi.org/10.1371/journal.pone.0338655.t004

In sum, the 24 industries where Poland already has a competitive specialisation – 17 women-led and 7 men-led – represent M1, where integration can rapidly strengthen existing market advantages. The 8 diversification opportunities illustrate M2, where Ukrainian women’s expertise could help Poland expand into new but related sectors. The gender disparities observed in the men-led group highlight that even within M1, barriers to women’s entry into managerial roles may persist, warranting targeted integration strategies.

Taken together, the results point to three key findings. First, there is a strong potential for skill matching between Ukrainian refugees and Poland’s existing industrial strengths, but this is uneven across sectors. Second, there are clear opportunities for related diversification, where refugee expertise could extend Poland’s industrial specialisation into adjacent activities. Third, gendered sectoral patterns shape both matching and diversification outcomes, potentially constraining the full utilisation of skilled refugee women’s capabilities. These findings provide a direct empirical bridge to the mechanisms in our analytical framework, highlighting where targeted interventions – such as credential recognition, sector-specific mentorship programmes for women, and diversification incentives – can maximise both economic and integration outcomes.

By linking network analysis with a gender-disaggregated lens, this study moves beyond descriptive mapping to identify actionable pathways where policy can directly influence integration outcomes.

Data limitations

While we used data on industries where Ukrainian highly skilled women managers were employed prior to the onset of the war, precise figures on how many of them left the country and their current locations remain uncertain. Some opted for internal migration within Ukraine, whereas others chose various European Union (EU)/non-EU destinations. A substantial portion of them settled in Poland, with a significant number intending to stay permanently [87].

While precise data on the geographical distribution and industry-specific expertise of highly skilled Ukrainian women in Poland is unavailable, this study leverages robust pre-war datasets to deliver actionable insights. By focusing on industries where these women held managerial roles prior to the war, the analysis identifies strategic opportunities to bridge gaps in Poland’s industrial landscape. This approach ensures a nuanced understanding of potential labour market impacts, highlights industries primed to benefit from the integration of skilled migrant women and provides valuable, actionable insights.

The methodology we used in this study highlights the importance of proactive industrial policy interventions to maximise the economic contributions of refugees. Our analysis illustrates how a labour supply shock – specifically, the influx of highly skilled Ukrainian women – can be transformed into an opportunity to strengthen existing capabilities or drive diversification into new industries. This strategic framework provides policymakers with a data-driven tool to develop proactive, evidence-based approaches that enhance both short-term competitiveness and long-term resilience in Poland’s economy. Instead of allowing refugee employment to be determined by ad hoc outcomes, policymakers can strategically direct talent into sectors where it delivers the greatest marginal benefit for both refugees and the host economy.