1. Introduction
The military conflict that broke out in Ukraine in February 2022 generated a massive flow of refugees to neighboring states, including Romania. In the face of this humanitarian crisis, the Romanian authorities, in cooperation with European institutions and international organizations, responded by adopting measures aimed at facilitating the integration of refugees, including their access to the labor market. Romania offered temporary or long-term settlement for tens of thousands of refugees. In this context, the integration of refugees has become a priority for public policies, and their participation in the labor market is an essential indicator of the success of the integration process.
The rapid implementation of the European Directive on Temporary Protection allowed Ukrainian refugees to benefit from a favorable legal framework, which eliminated the need to obtain a work permit. This gave the refugees the right to directly engage in employment, to participate in vocational training programs, and to access public counseling services offered through the local offices of the National Employment Agency. However, the integration of the Ukrainian refugees into the workforce proved to be a complex challenge, as it was hampered by linguistic, institutional, social and cultural barriers.
This study aims to analyze the experiences, perceptions and difficulties encountered by Ukrainian refugees in integrating into the labor market in Romania. The study is based on a questionnaire completed by a sample of 399 refugees, selected in such a way as to reflect the demographic and geographical diversity of the refugee community in Romania. Through this survey, relevant data were collected regarding the occupational status of the respondents, their level of education, knowledge of the Romanian language, the fields of activity accessed, and the obstacles felt in the employment process.
The goal of this study is to examine not only the participation rates in the labor market, but also the structural and institutional barriers that influence access to a stable job that is appropriate to the professional training. Equally, the study aims to identify good practices and specific needs in order to formulate public policy recommendations, designed to support the integration of refugees in the Romanian labor market.
Although the literature on the integration of refugees into the labor market is relatively rich, including numerous quantitative studies conducted in countries such as Germany, Poland or the Czech Republic, in many countries, such as Romania, empirical research on this topic still remains limited. Most of the existing analyses have a descriptive or normative character, and investigations based on direct quantitative data collected from refugees are rare and fragmentary. The lack of consolidated applied research on the refugee phenomenon in Romania reflects both the relatively new nature of this issue in the local context, as well as the methodological difficulties associated with accessing and surveying this population. Therefore, the present study aims to contribute to reducing this gap. This investigation contributes to understanding the processes of refugee integration from an empirical perspective, providing insights for decision-makers, non-governmental organizations, employers and other actors involved in the management of forced mobility. Moreover, the study is part of the broader effort of documentation and critical reflection on how Romania responds to the challenges of contemporary migration and the socio-economic potential it implies.
2. Literature Review
The participation of Ukrainian refugees in the labor market across host countries is a complex phenomenon best understood through the lens of key theoretical models that move beyond simple correlation. This review synthesizes the existing literature by focusing on how three dominant frameworks—Human Capital Theory, Segmented Assimilation Theory, and Gendered Migration Theory—explain the variability and challenges of integration. While Human Capital Theory predicts success based on refugees’ high levels of education and skills, empirical evidence reveals a paradox of underemployment where these assets are often devalued by labor market structures and persistent discrimination. We argue that the success or failure of integration is not uniform but segmented, determined less by individual effort and more by the specific context of reception—the calibrated public policies (such as “Work-First” versus “Human Capital” models) and structural constraints (such as the lack of childcare and prevalence of informal labor) that dictate the segment of the economy into which refugees are channeled.
Human Capital, Credentials, and the Paradox of Underemployment. Empirical research strongly supports the Human Capital Theory with respect to linguistic competence. The Human Capital Theory (
Becker 1964) posits that an individual’s value in the labor market is determined by their stock of productive qualities, such as education, training, language proficiency, and work experience. This theory predicts that refugees with high levels of pre-migration education and relevant professional experience should achieve rapid and successful labor market integration. Conversely, barriers that prevent the effective transfer or utilization of this human capital—such as language deficiencies or non-recognition of foreign credentials—are directly predicted to result in lower wages and underemployment.
A few studies studies conducted in Poland (
Strzelecki 2024) and Germany (
Kamyshnykova 2024;
Londar et al. 2024) showed that proficiency in the official language of the host country predicts easier access to jobs. However, findings from other studies conducted in the Czech Republic and Poland demonstrate a skills mismatch paradox that challenges the theory’s simple predictive power (
Motruk 2024;
Aigner et al. 2025). Highly educated Ukrainian refugees frequently work in low-skilled sectors, suggesting that their pre-migration human capital is being discounted or devalued due to the limited recognition of foreign credentials (
Aigner et al. 2025). This de-linking of education from professional outcomes is a key mechanism of Segmented Assimilation, steering highly skilled refugees toward the low-wage segment of the labor market.
Policy Models, Discrimination and Segmented Assimilation. The existing literature also demonstrates that there are variations in integration outcomes across European nations, and these differences often are due to differences in the type of public policy adopted by the host country toward the integration of Ukrainian refugees. These differences can be explained by the Segmented Assimilation theory (
Portes and Zhou 2025) when one analyzes the prevalent policy models observed in host countries. This theory rejects the traditional linear model of immigrant assimilation, arguing instead that newcomers may integrate into different sectors of the receiving society. Integration is “segmented,” meaning that some groups may assimilate into the mainstream middle class, while others may integrate into marginalized, low-wage, or “underclass” sectors. The specific segment into which a refugee integrates is strongly influenced by their individual human capital, the reception context (public policy), and the structural openness of the local labor market. This theory is particularly useful for explaining why high-skilled individuals often end up in precarious, low-status jobs.
The “Work-First” model in Poland prioritizes immediate economic participation over human capital acquisition. This policy led to rapid, but precarious integration into the low-wage segment of the work force (e.g., agriculture, services), offering limited long-term career prospects (
Gromadzki and Lewandowski 2022). In contrast, the Human Capital (Germany) model prioritizes investment in language and credential recognition. While yielding slower initial integration, this policy aims for assimilation into the higher-skilled, professional segment, promising more sustainable benefits (
Kamyshnykova 2024). Furthermore, the literature points to the dual role of financial assistance—where inadequate job-finding support can reduce motivation (
Polents and Dvořáková 2024)—requires policies to be carefully calibrated to ensure that welfare dependency does not inadvertently lead to integration into a non-working segment.
In addition to the role of policies, ethnic discrimination can also be a barrier, as audit experiments in Poland have shown (
Londar et al. 2024). This discrimination reduces employment opportunities regardless of a refugee’s human capital, thus acting as a mechanism of segmented assimilation by blocking access to the formal, higher-status labor market. When employers use ethnicity as a proxy for risk or lack of proficiency, high-skilled refugees are pushed into low-wage segments simply because the door to their preferred segment is closed. Secondly, this vulnerability is aggravated by the high prevalence of informal labor (
Slany et al. 2024), particularly in high-demand sectors like services. While informality offers the immediate benefit of quick employment, it comes at the cost of basic labor rights and social protection. This not only places refugees at heightened risk of exploitation (
Slany et al. 2024) but also traps them in cycles of low-wage work, hindering upward mobility. Collectively, discrimination and informal labor define the “context of reception” for many refugees. They can dismantle the potential benefits of both human capital and well-intentioned policy, resulting in a precarious integration where economic activity is present but lacks stability, security, and long-term prospects. This outcome critically undermines the goal of sustainable economic self-sufficiency.
Gender Constraints and Structural Barriers. The existing literature also points to gender differences in workforce participation, with women immigrants experiencing lower participation rates. The observed differences in labor market participation are explained by the Gendered Migration theory. This theory posits that migration and labor market experiences are fundamentally shaped by gender roles, relations, and power structures both in the country of origin and the host society (
Boyd and Grieco 2023). This framework highlights how structural factors—such as the lack of state-supported childcare and the perpetuation of traditional caregiving norms—disproportionately constrain the participation of refugee women in the labor market. It views employment gaps not as a failure of individual skills, but as a result of policy designs and social structures that embed gender inequality. Women, particularly those with childcare responsibilities, face significant structural obstacles (
Stan et al. 2023). The absence of accessible, flexible childcare services forces many refugee women to choose between work and family duties, which leads to lower employment rates (
Postepska and Voloshyna 2025). This is not a failure of individual skills, but a consequence of policy and social norms that reinforce traditional caregiving roles. Policies supporting childcare in Poland (
Kochaniak et al. 2024a,
2024b) are effective precisely because they alleviate the structural gender constraints highlighted by the theory.
3. Methods
3.1. Instrumentation
For the purpose of this study, the researchers adapted the questionnaires used by
Sydorov and Kovalska (
2022) and
Brücker et al. (
2023) in their studies of Ukranian refugees. Additionally, questions from the survey developed by
Pedziwiatr et al. (
2022) for refugees in Poland were adapted for the Romanian context. The questionnaire included the following sections: (a) demographics, (b) the refugee’s migration experience, (c) living situation in the host country, (d) plans for future, and (e) the importance of faith/religion. The adapted instrument was translated from Romanian into Ukrainian using a back-translation protocol. The translated questionnaire was independently reviewed by a bilingual expert (fluent in both Romanian and Ukrainian) who was not involved in the original study design. This expert compared the back-translated text to the original to ensure conceptual and linguistic equivalence and mitigate potential bias from cultural or idiom misunderstandings. Prior to its full deployment via Qualtrics, the survey was piloted by a group of 10 refugees to assess flow, clarity, and completion time. This process ensured that the adapted questionnaire was understandable to the target population.
To carry out this research, the questionnaire was sent to Ukrainian refugees settled in Romania through the organization World Vision Romania. This organization, active in supporting vulnerable people, provided logistical and institutional support in disseminating the research instrument to Ukrainian refugees. In order to facilitate collaboration, an explanatory letter addressed to the organization was attached to the questionnaire, in which the purpose of the research, the voluntary and anonymous nature of participation, as well as the social and scientific relevance of the study were presented. The support provided by World Vision Romania was essential for reaching the target group and for obtaining a significant number of responses from Ukrainian refugees.
Data were collected via non-probability convenience sampling facilitated by World Vision Romania. This pragmatic sampling strategy was necessitated by rapid displacement, the absence of a complete national register for beneficiaries, and the mobility of the population studied. We acknowledge that this sampling procedure introduces selection bias; the resulting sample is likely over-represented by individuals actively seeking aid and engaged with NGO services such as World Vision Romania, potentially leading to different labor market profiles compared to those refugees integrated without formal assistance. Furthermore, the sample is strongly skewed toward women (90%), which, while a faithful reflection of the demography of this displacement due to Ukrainian martial law, limits the generalizability of our findings to the experience of male refugees. Therefore, the results are interpreted primarily through a gendered lens.
3.2. Description of the Sample
A convenience sample was used for this study. The questionnaire was distributed to Ukrainian refuges living in Romania via Qualtrics during the period 19 July 2024–15 March 2025. The survey was sent to 800 refugees. In total, 399 valid answers were obtained, which represents a 50% response rate. Only one person in each household was allowed to participate in the survey.
As
Table 1 shows, the survey sample consisted primarily of female respondents (90%). The age distribution shows a concentration in the 31–50 age range, with 45.4% aged 31–40 and 33.6% aged 41–50. A significant majority of respondents speak Ukrainian (72.4%) and Russian (59.6%), while fewer speak English (42.6%) or Romanian (10.5%).
In terms of education, study participants are highly educated, with 50.9% holding a master’s degree and 26.8% a bachelor’s degree. A vast majority identify as Eastern Orthodox (92%). Most participants are married (66.2%), followed by divorced (12.8%) and single (12%). Most respondents have children, with 45.9% having two children and 42.9% having one. Furthermore, 89.5% of the participants have minor children.
3.3. Data Analysis
First, survey data were analyzed using descriptive statistics (i.e., percentages) to describe factors that are expected to affect participation in the labor market. These factors include most of the demographic variables identified in
Table 1. Next, the researchers conducted a bivariate analysis using Chi-Square statistics to determine whether differences in workforce participation between various demographic groups were statistically significant. Third, a binary logistic regression model was developed to identify significant factors that affect the probability that a refuge will find a job.
4. Results
4.1. Descriptive Statistics
Table 2 presents frequency distributions for questions related to current workforce participation and plans for future. Only 21.7% of the respondents were able to find work and about a quarter stated that they could not work due to childcare responsibilities. Overall, 30.7% would like to have a job in the same field that they had in Ukraine and half are interested in becoming fluent in Romanian so that they can improve their prospects for employment.
Additionally, we explored the perceived challenges that Ukrainian refugees experience in finding employment in Romania.
Table 3 indicates that about 42% consider that the refugee status affected their job search. By far, lack of Romanian language proficiency was the most important obstacle to finding employment. Childcare-related obstacles were reported by only 16.8% of the participants.
4.2. Bivariate Analysis
Pearson Chi-Square statistics were employed to examine differences in workforce participation between various demographic groups.
Table 4 indicates a significant effect for the following groups: refugees who know the Romanian language are two times more likely to find a job (χ2 (1) = 10.34,
p < 0.001); refugees who are singles are also two times more likely than married refugees to be employed (χ2 (1) = 11.75,
p < 0.001); refugees that are younger than 30 and those in their 50s are more likely than the other age groups to have a job (χ2 (4) = 10.09,
p < 0.05). The difference by gender was marginally significant (χ2 (1) = 3.67,
p = 0.055), with men being more likely to be employed than women (34.3% vs. 21.7%) to be employed. Number of children or having minor children in the household were not found to be significant. Additionally, the ability to speak English did not increase the probability of being employed.
4.3. Logistic Regression Results
Based on the results of the bivariate analysis, we developed a binary logistic regression model to estimate the probability of being employed (see
Table 5). The model includes controls for demographic and background variables such as gender, marital status, age, number of children, having minor children, as well as human capital characteristics such as level of education and spoken foreign languages.
To evaluate how much of the variance in the dependent variable can be explained by the model, we used Cox and Snell R Square and Nagelkerke R Square. The independent variables introduced in the model explain from 8.1% (Cox and Snell R Square) to 12.4% (Nagelkerke R Square) of the variance in the probability to be employed. The relatively modest share of variance is likely due to omitted variables such as social networks, discrimination, and nuances in legal status. Including these factors would likely provide additional explanatory power to the model.
The VIF values for all predictors are well below the commonly accepted threshold of 5, indicating that multicollinearity is not a significant concern for the stability of the parameter estimates. We also conducted a robustness check by comparing the full model to a reduced model containing only the significant predictors. The results confirmed that the significance and direction (Odds Ratio) of our core findings (i.e., the strong predictive power of Romanian language proficiency and gender) remain stable across these alternative specifications.
The model indicates that men have 2.56 times higher odds of workforce participation compared to women. The odds of workforce participation decrease by approximately 62.7% (1 − 0.37 = 0.63) for married respondents, compared to single respondents. Additionally, refugees who speak Romanian have 3.25 times higher odds of workforce participation compared to those who do not speak Romanian, controlling for other factors. This is the variable with the highest impact on workforce participation in the model. At the same time, English language proficiency (p = 0.418), Highest Level of Education (p = 0.560), Age (p = 0.477), Has Minor Children (p = 0.302), and Number of Children (p = 0.187) were not found to be statistically significant predictors of workforce participation in this model.
The non-significant findings for educational attainment and English proficiency were unexpected given much of the empirical evidence from the human capital literature. They do not indicate an absence of relationship, but rather they point to the barriers and challenges faced by this population. Specifically, these results suggest the presence of credential deflation and underemployment among Ukrainian refugees, where foreign educational qualifications and high levels of proficiency in English are not sufficiently recognized or valued by the hiring managers.
To sum up, the factors that were associated with increased odds of workforce participation in this model are Gender (with men having notably higher odds) and Romanian language fluency. Being married is significantly associated with lower odds of workforce participation. Other factors like English language proficiency, education level, age, number of children, and having minor children were not found to be statistically significant predictors.
5. Discussion
Our findings reveal a slow process of labor market integration for Ukrainian refugees in Romania, characterized by a low employment rate (≈21.7%). This rate is consistent with observations in other host countries where despite initial policy support like the Temporary Protection Directive, effective integration remains sluggish (
Kamyshnykova 2024;
Bešić et al. 2023).
Our results strongly support the fundamental premise of the Human Capital Theory (
Becker 1964) by confirming that Romanian language proficiency is the single most powerful predictor of employment, increasing the odds of working by over three times. This is reinforced by the finding that nearly two-thirds (66.1%) of respondents cite the language barrier as the primary obstacle, and half express a strong need for language training (
Strzelecki 2024;
Londar et al. 2024). However, the analysis also points to findings that cannot be explained by the theory. Contrary to Human Capital tenets, educational level and English language proficiency did not emerge as a statistically significant predictor of employment. This non-significance is highly consequential, pointing directly to the phenomenon of skills mismatch and credential deflation reported across Europe (
Jirka et al. 2023;
Aigner et al. 2025). The lack of diploma recognition means that highly educated refugees could be channeled into low-skilled roles where their existing human capital provides no competitive advantage. Furthermore, the low predictive power of English proficiency suggests that the majority of jobs available in the Romanian labor market are concentrated in sectors where local language skills are far more critical than international fluency. This outcome underscores that while the Ukrainian refugees possess high human capital, its effective utilization is systematically blocked by the structure of the host economy.
The survey results can also be explained by the Segmented Assimilation Theory (
Portes and Zhou 2025), which highlights how structural barriers steer refugees into marginalized segments of the labor market. The aspirational profile—where 31% seek professional parity with the jobs they held in Ukraine while approximately 13% aim for entrepreneurship—indicates a strong desire for upward mobility. Yet, the non-significance of educational level, coupled with the low employment rate, demonstrates that integration is currently occurring into the low-wage segment. Although only 2% reported discrimination, the lack of Romanian language proficiency pushes high-skilled refugees into lower skill jobs. This vulnerability is also reflected in the unexpected significance of age, where employed individuals are concentrated at the younger (under 30) and older (50–60) extremes. This atypical finding may reflect employer openness to filling entry-level and short-term positions with these groups, or a self-selection among older migrants with clear employment intentions, but it is not indicative of broad, sustainable integration pathways. Furthermore, being married significantly reduces the odds of employment by over 60%. Interpreted through this lens, this result reflects the increased domestic and caregiving burden placed upon refugee women, making them less available for employment. The vast majority of married women in the sample had minor children at home to care for.
Finally, another important finding is the pronounced gender disparity, with men having over 2.5 times higher odds of employment than women. This result is a clear illustration of the Gendered Migration theory (
Boyd and Grieco 2023), which emphasizes how integration is shaped by structural gender inequalities. In this context, refugee women face a dual burden: navigating the challenges of labor market integration while simultaneously managing the bulk of unpaid care work. The absence of readily available, affordable, and flexible childcare options—particularly those that accommodate irregular shift work or vocational training schedules—acts as a powerful deterrent to active labor market participation among women, creating a clear barrier to the initial job search and to sustained employment once a job is secured.
This structural constraint is confirmed by the data: while the number of children in the household was not statistically significant, the fact that 24.8% of respondents cited childcare responsibilities as the reason they cannot work—and that this was the second most cited obstacle overall—highlights the need for childcare support. The absence of accessible, flexible childcare acts as the critical mediating factor (
Postepska and Voloshyna 2025), confirming that it is the lack of public support services (
Stan et al. 2023) that forces refugee women to manage the domestic burden, thus severely limiting their ability to engage with the labor market. These results unequivocally indicate the need for integrated public policies that shift from mere administrative access (Temporary Protection Directive) to active structural support, especially in the area of accessible childcare. Future interventions must prioritize not only language acquisition and formal recognition of professional credentials but, critically, the development of accessible childcare infrastructure to alleviate the domestic burden on refugee women. Without these interventions, the significant human capital of Ukrainian refugees will continue to be wasted, leading to a delayed and precarious integration.
6. Limitations and Suggestions for Future Studies
Although this study contributes to our understanding of the factors influencing the participation of Ukrainian refugees in the Romanian labor market, there are several limitations that must be considered when interpreting the results. First, the study is based on a sample obtained through non-random methods, through a humanitarian organization (World Vision Romania), which may introduce a selection bias. Respondents who accessed and completed the questionnaire may be more active, better informed, or more open to integration than the general refugee population. This limits the generalizability of the conclusions.
Second, the response rate of 50% introduces the possibility of nonresponse bias. Given that data collection was facilitated through a convenience sample in partnership with an NGO, those refugees who chose to complete the survey may systematically differ from nonrespondents in several key ways. Specifically, the observed 50% is likely composed of individuals who (a) are more engaged with aid institutions and thus more accessible to NGO-led outreach efforts, (b) have higher civic engagement or trust in formal processes (like academic surveys), and (c) possess more free time or are less actively employed, as employed individuals may have lacked the time to complete the survey. This potential self-selection bias suggests our findings may over-represent the challenges of integration (e.g., lower employment rates or greater perceived barriers) or under-represent the most successfully integrated refugees who have achieved independence and severed ties with aid networks. While we cannot statistically eliminate this bias, we acknowledge its presence and urge cautious generalization of our findings, particularly those related to the estimated level of participation in the workforce.
Third, the research design is cross-sectional, since we captured refugees’ perceptions and behaviors at a fixed point in time. This does not allow for the identification of causal relationships between variables or the tracking of the dynamics of professional integration over time. Also, some key variables (such as discrimination, health status, detailed legal status, or type of informal support received) were not included in the quantitative model, which may omit important factors in the complex understanding of integration. Another limitation is related to the self-reported measurement of variables, which may be influenced by subjective perceptions or socially desirable response tendencies, especially in the case of questions about professional aspirations or perceived obstacles.
In terms of future research directions, the current study opens several promising avenues. First, it would be useful to conduct longitudinal studies that track the evolution of refugee labor market participation over time, correlated with language learning, qualification recognition, and legislative developments. Qualitative analyses -using interviews or focus groups- could also provide a deeper understanding of subjective experiences, obstacles, and individual strategies related to workforce integration. In addition, conducting a comparative study that analyzes the integration of Ukrainian refugees in several European regions or countries would allow us to better understand the contextual impact of local public policies on participation in labor markets. Last but not least, future research should pay more attention to the gender dimension, exploring in more detail how refugee women simultaneously negotiate family pressures, cultural constraints, and access to work, in an institutional framework that is often not sensitive to these particularities.
7. Conclusions
The present study explored factors shaping the participation of Ukrainian refugees in the Romanian labor market and provided a nuanced perspective on the interaction between individual characteristics, structural barriers, and institutional context. Although legal access to work has been facilitated by European measures such as the Temporary Protection Directive, the reality of employment remains marked by significant difficulties, as reflected in the low employment rate identified among Ukrainian respondents. Professional integration appears to be a slow process, with multiple obstacles, but also with considerable potential for targeted interventions.
The results highlight that gender, marital status and knowledge of the Romanian language have an important influence on the probability of employment. Men and unmarried people are more likely to actively participate in the labor market, while mastery of the Romanian language emerges as the most important condition favorable to employment, reinforcing the findings of the existing literature that underlines the fundamental role of language skills in integration processes. In contrast, the level of education, age or knowledge of the English language did not have a significant influence, suggesting that refugees face an underutilization of human capital and difficulties related to the recognition of professional qualifications, a phenomenon already well documented in other host countries in Europe.
The questionnaire also provided valuable insights into the aspirations of refugees. Many of the participants expressed a desire to engage in the professions they previously held in Ukraine, and almost half stated that they wanted to pursue training or education courses, in particular to learn the Romanian language. These data indicate a real willingness for integration in the host society, but which remains conditional on adequate institutional support, access to public services and policies sensitive to the specific needs of refugees—especially women and those with childcare responsibilities.
Overall, the results of this research indicate that professional integration is not only a matter of access to work, but also a social process, reflecting the way in which the host society valorizes the human potential of those fleeing conflict. Therefore, it is essential that public policies go beyond the exclusively economic rationale and include dimensions of psychosocial, educational and community support. Only through an integrated approach can a framework of sustainable inclusion be built, which responds to both the aspirations of refugees and the needs of the host nation.
Author Contributions
Conceptualization, D.T., K.-A.A. and I.C.P.; methodology, D.T. and K.-A.A.; software, D.T. and K.-A.A.; validation D.T., K.-A.A. and I.C.P.; formal analysis, D.T., K.-A.A. and I.C.P.; investigation, D.T., K.-A.A. and I.C.P.; resources, D.T., K.-A.A. and I.C.P.; data curation, D.T., K.-A.A. and I.C.P.; writing—original draft preparation, D.T., K.-A.A. and I.C.P.; writing—review and editing, D.T. and K.-A.A.; visualization, D.T., K.-A.A. and I.C.P.; supervision, D.T., K.-A.A. and I.C.P.; project administration, D.T., K.-A.A. and I.C.P.; funding acquisition, K.-A.A. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of Ovidius University of Constanța (Committee for Bio-Ethics, Constanța, Romania; approval code 8970 on 4 August 2025). This research received no external funding.
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors on request.
Conflicts of Interest
The authors declare no conflict of interest.
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Table 1.
Characteristics of survey participants (N = 399).
Table 1.
Characteristics of survey participants (N = 399).
| N | % |
---|
Gender | | |
Female | 359 | 90.00% |
Male | 40 | 10.00% |
Age | | |
30 or Younger | 28 | 7.00% |
31–40 | 181 | 45.40% |
41–50 | 134 | 33.60% |
51–60 | 21 | 5.30% |
60 or Older | 35 | 8.80% |
Languages Spoken | | |
Ukrainian | 289 | 72.40% |
Russian | 238 | 59.60% |
English | 170 | 42.60% |
Romanian | 42 | 10.50% |
Highest Level of Education | | |
Basic general secondary education | 21 | 5.30% |
High School Diploma | 56 | 14.00% |
Bachelor’s | 107 | 26.80% |
Master’s | 203 | 50.90% |
Doctorate | 12 | 3.00% |
Religion | | |
Orthodox | 367 | 92.00% |
Catholic | 2 | 0.50% |
Other | 11 | 2.80% |
None | 19 | 4.80% |
Marital Status | | |
Married | 264 | 66.20% |
Single | 48 | 12.00% |
Divorced | 51 | 12.80% |
Widower/Widow | 19 | 4.80% |
Civil Partnership | 17 | 4.30% |
Number of Children | | |
One | 171 | 42.90% |
Two | 183 | 45.90% |
Three or more | 45 | 11.50% |
Has Minor Children | | |
No | 42 | 10.50% |
Yes | 357 | 89.50% |
Table 2.
Current employment status and plans for future.
Table 2.
Current employment status and plans for future.
| N | % |
---|
Were you able to find work? | | |
Yes | 73 | 21.7 |
No | 264 | 88.3 |
Total | 309 | 100 |
What kind of work are you hoping to find in the host country? | | |
A job in the profession that I had in Ukraine | 93 | 30.7 |
Any job offered to me | 28 | 9.2 |
I cannot work because I have minor children | 75 | 24.8 |
Vocational | 11 | 3.6 |
I want to become an entrepreneur | 39 | 12.9 |
Other | 57 | 18.8 |
Total | 303 | 100 |
Are there any skills or qualifications you would like to acquire to improve your employment prospects? | | |
Romanian language | 151 | 48.7 |
English language | 83 | 26.8 |
IT skills | 29 | 9.4 |
Vocational conversion to a qualification for vocational occupations | 20 | 6.5 |
Other | 27 | 8.7 |
Total | 310 | 100 |
Do you have plans for further education or vocational training? | | |
Yes | 148 | 47.9 |
No | 74 | 23.9 |
Maybe | 87 | 28.2 |
Total | 309 | 100 |
Table 3.
Perceived challenges to employment.
Table 3.
Perceived challenges to employment.
| N | % |
---|
How much has refugee status affected your job search? | | |
Very much | 66 | 19.6 |
Significantly | 75 | 22.3 |
Neutral | 130 | 38.6 |
Almost not at all | 41 | 12.2 |
Not at all | 25 | 7.4 |
Total | 337 | 100 |
What obstacles do you see in finding employment? |
Language | 201 | 66.1 |
Culture | 4 | 1.3 |
Financial | 26 | 8.6 |
Discrimination | 6 | 2 |
Childcare | 51 | 16.8 |
Other | 16 | 5.3 |
Total | 304 | 100 |
Table 4.
Differences in workforce participation between various demographic groups.
Table 4.
Differences in workforce participation between various demographic groups.
| No | Yes | | Chi-Square Results |
---|
| No | % | N | % | N | |
---|
Gender | | | | | | |
Female | 241 | 79.80% | 61 | 20.20% | 302 | |
Male | 23 | 65.70% | 12 | 34.30% | 35 | χ2 (1) = 3.67, p = 0.055 |
Total | 264 | 78.30% | 73 | 21.70% | 337 | |
Number of Children | | | | | | |
One Child | 114 | 78.10% | 32 | 21.90% | 146 | |
Two Children | 122 | 78.70% | 33 | 21.30% | 155 | χ2 (2) = 0.03, p = 0.988 |
Three or more | 28 | 77.80% | 8 | 22.20% | 36 | |
Total | 264 | 78.30% | 73 | 21.70% | 337 | |
Has Minor Children | | | | | | |
No | 29 | 82.90% | 6 | 17.10% | 35 | |
Yes | 235 | 77.80% | 67 | 22.20% | 302 | χ2 (1) = 0.47, p = 0.493 |
Total | 264 | 78.30% | 73 | 21.70% | 337 | |
Speaks Romanian | | | | | | |
No | 244 | 80.80% | 58 | 19.20% | 302 | |
Yes | 20 | 57.10% | 15 | 42.90% | 35 | χ2 (1) = 10.34, p < 0.001 |
Total | 264 | 78.30% | 73 | 21.70% | 337 | |
Speaks English | | | | | | |
No | 150 | 80.60% | 36 | 19.40% | 186 | |
Yes | 114 | 75.50% | 37 | 24.50% | 151 | χ2 (1) = 1.30, p = 0.254 |
Total | 264 | 78.30% | 73 | 21.70% | 337 | |
Marital Status | | | | | | |
Single | 65 | 66.30% | 33 | 33.70% | 98 | |
Married | 199 | 83.30% | 40 | 16.70% | 239 | χ2 (1) = 11.75, p < 0.001 |
Total | 264 | 78.30% | 73 | 21.70% | 337 | |
Highest Level of Education | | | | | |
High School or lower | 42 | 72.40% | 16 | 27.60% | 58 | |
Bachelor’s Degree | 68 | 79.10% | 18 | 20.90% | 86 | χ2 (2) = 1.47, p = 0.480 |
Graduate Degree | 154 | 79.80% | 39 | 20.20% | 193 | |
Total | 264 | 78.30% | 73 | 21.70% | 337 | |
Age | | | | | | |
30 or Younger | 18 | 72.00% | 7 | 28.00% | 25 | |
31–40 | 127 | 82.50% | 27 | 17.50% | 154 | |
41–50 | 81 | 74.30% | 28 | 25.70% | 109 | χ2 (4) = 10.09, p < 0.05 |
51–60 | 11 | 57.90% | 8 | 42.10% | 19 | |
60 or Older | 27 | 90.00% | 3 | 10.00% | 30 | |
Total | 264 | 78.30% | 73 | 21.70% | 337 | |
Table 5.
Logistic regression results for workforce participation.
Table 5.
Logistic regression results for workforce participation.
Variable | B | S.E. | Wald | p | Exp(B) |
---|
Gender | 0.942 | 0.410 | 5.277 | 0.022 | 2.564 |
Marital Status | −0.985 | 0.295 | 11.18 | <0.001 | 0.373 |
Languages Spoken: Romanian | 1.179 | 0.393 | 8.981 | 0.003 | 3.251 |
Languages Spoken: English | 0.246 | 0.303 | 0.656 | 0.418 | 1.279 |
Highest Level of Education | −0.11 | 0.188 | 0.340 | 0.560 | 0.896 |
Age | 0.109 | 0.154 | 0.506 | 0.477 | 1.115 |
Has Minor Children | 0.563 | 0.546 | 1.064 | 0.302 | 1.756 |
Number of Children | 0.151 | 0.115 | 1.741 | 0.187 | 1.163 |
Constant | −1.836 | 0.952 | 3.716 | 0.054 | 0.159 |
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