Publications of the Ministry of Economic A�airs and Employment Integration • 2025:6 A Comparative Analysis of the Reception of Immigrants into Finnish Working Life in 2016 and 2024 A Comparative Analysis of the Reception of Immigrants into Finnish Working Life in 2016 and 2024 Akhlaq Ahmad Ministry of Economic Affairs and Employment of Finland Helsinki 2025 Publications of the Ministry of Economic Affairs and Employment 2025:6 Ministry of Economic Affairs and Employment of Finland This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited. ISBN pdf: 978-952-327-785-4 ISSN pdf: 1797-3562 Layout: Government Administration Department, Publications Helsinki 2025 Finland Publication distribution Institutional Repository for the Government of Finland Valto julkaisut.valtioneuvosto.fi https://julkaisut.valtioneuvosto.fi/ Description sheet 27 January 2025 A Comparative Analysis of the Reception of Immigrants into Finnish Working Life in 2016 and 2024 Publications of the Ministry of Economic Affairs and Employment 2025:6 Subject Integration Publisher Ministry of Economic Affairs and Employment of Finland Author(s) Akhlaq Ahmad Language English Pages 64 Abstract The report examines the receptiveness of working life in Finland towards individuals of foreign backgrounds. The research data consists of 10,000 fictitious job applications submitted to publicly advertised positions by equally qualified applicants of Finnish, English, Russian, Iraqi and Somali backgrounds. The experiment tested job vacancies in the restaurant and catering, retail trade, cleaning, clerical and customer service sectors. A previous study conducted in 2016 (Ahmad 2020) identified significant ethnic disparities in hiring practices, which persisted in 2024. However, these disparities have notably decreased, particularly for applicants of Iraqi and Somali backgrounds. In 2016, Somali applicants had to submit 3.94 and Iraqi applicants 2.91 times as many applications as equally qualified Finnish candidates to receive an interview invitation. By 2024, these figures had dropped to 2.48 and 2.06. Conversely, applicants of Russian background experienced a slight decline in callback rates. These findings suggest a shift towards greater inclusivity in the Finnish labour market, potentially driven by evolving hiring policies, growing acceptance of diversity and/or increased labour demand in the sectors studied. Despite this progress, Finnish applicants continue to enjoy a clear advantage over equally qualified second-generation immigrant candidates in the Finnish labour marke. Keywords integration of migrants, immigrants, immigrations, work, access to employment, workplaces ISBN PDF 978-952-327-785-4 ISSN PDF 1797-3562 URN address https://urn.fi/URN:ISBN:978-952-327-785-4 https:// Kuvailulehti 27.1.2025 Vertaileva analyysi maahanmuuttajien vastaanotosta suomalaisessa työelämässä vuosina 2016 ja 2024 Työ- ja elinkeinoministeriön julkaisuja 2025:6 Teema Kotoutuminen Julkaisija Työ- ja elinkeinoministeriö Tekijä/t Akhlaq Ahmad Kieli Englanti Sivumäärä 64 Tiivistelmä Raportissa tarkastellaan suomalaisen työelämän vastaanottavuutta ulkomaalaistaustaisia henkilöitä kohtaan. Tutkimusaineistona käytettiin 10 000 kuvitteellista työhakemusta, joita keskenään yhtä pätevät suomalais-, englantilais-, venäläis-, irakilais- ja somalialaistaustaiset hakijat jättivät julkisesti ilmoitettuihin työtehtäviin. Kokeilun kohteena oli avoimia työpaikkoja ravitsemis- ja ruokahuoltoalalla, vähittäiskaupassa sekä siivous-, toimistotyö- ja asiakaspalvelualoilla. Edellisessä tutkimuksessa vuodelta 2016 (Ahmad 2020) havaittiin merkittäviä eroja työhönotossa etnisyyksien välillä. Eroja havaittiin edelleen vuonna 2024. Erot ovat kuitenkin vähentyneet etenkin irakilais- ja somalialaistaustaisten hakijoiden kohdalla. Vuonna 2016 somalitaustaiset hakijat joutuivat jättämään 3,94 kertaa ja irakilaistaustaiset 2,91 kertaa enemmän hakemuksia saadakseen kutsun työpaikkahaastatteluun verrattuna yhtä päteviin suomalaisiin hakijoihin. Nämä luvut olivat laskeneet 2,48:aan ja 2,06:een vuoteen 2024 mennessä. Toisaalta venäläistaustaisten hakijoiden saamat haastattelukutsut vähenivät hieman. Nämä tulokset viittaavat siihen, että suomalaisista työmarkkinoista on tulossa osallistavampia, mikä voi johtua muuttuvista menettelytavoista työhönotossa, lisääntyneestä hyväksynnästä monimuotoisuutta kohtaan ja/tai työvoiman kysynnän kasvusta tutkituilla aloilla. Näistä edistysaskelista huolimatta suomalaisilla hakijoilla on edelleen selvä etulyöntiasema verrattuna yhtä päteviin toisen polven maahanmuuttajahakijoihin Suomen työmarkkinoilla. Asiasanat kotoutuminen (maahanmuuttajat), maahanmuuttajat, maahanmuutto, työ, työllistyminen, työpaikat ISBN PDF 978-952-327-785-4 ISSN PDF 1797-3562 Julkaisun osoite https://urn.fi/URN:ISBN:978-952-327-785-4 https:// Presentationsblad 27.1.2025 En komparativ analys av hur invandrare mottogs på den finska arbetsmarknaden 2016 respektive 2024 Arbets- och näringsministeriets publikationer 2025:6 Tema Integration Utgivare Arbets- och näringsministeriet Författare Akhlaq Ahmad Språk Engelska Sidantal 64 Referat I rapporten undersöks den finska arbetsmarknadens mottaglighet för personer med utländsk bakgrund. Undersökningens forskningsdata utgörs av 10 000 fiktiva jobbansökningar som skickats i anslutning till offentligt utlysta tjänster från kandidater med finsk, engelsk, rysk, irakisk och somalisk bakgrund, med likvärdiga meriter. Experimentet gjordes på lediga tjänster i sektorerna restaurang och catering, detaljhandel, städning, kontorsarbete och kundservice. I en tidigare studie från 2016 (Ahmad 2020) identifierades kraftigt bristande etnisk jämlikhet i rekryteringen, vilken kvarstod 2024. Denna bristande jämlikhet har dock märkbart minskat, framför allt för kandidater med irakisk och somalisk bakgrund. År 2016 behövde somaliska sökande skicka 3,94 gånger fler, och irakiska sökande 2,91 gånger fler, ansökningar än finska kandidater med likvärdiga meriter för att kallas till en intervju. År 2024 hade dessa siffror sjunkit till 2,48 respektive 2,06. Det omvända förhållandet gällde för sökande med rysk bakgrund, för vilka svarsfrekvensen minskade något. Dessa upptäckter tyder på ett skifte mot förbättrad inkludering på den finska arbetsmarknaden, möjligen tack vare utvecklade rekryteringspolicyer, en stigande vilja till inkludering och/eller en ökad efterfrågan på arbetskraft i de undersökta sektorerna. Trots denna utveckling åtnjuter finska sökande fortsatt en klar fördel på den finska arbetsmarknaden jämfört med andragenerationsinvandrare med likvärdiga meriter. Nyckelord integration, invandring, invandrare, arbete, sysselsättning, arbetstillfällen, ISBN PDF 978-952-327-785-4 ISSN PDF 1797-3562 URN-adress https://urn.fi/URN:ISBN:978-952-327-785-4 https:// Contents 1 Introduction.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.1 The Finnish context.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.2 Why is There Discrimination?.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.3 Methods for Studying Discrimination in Work Life. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2 Experiment Design And Methods of Analyses.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.1 Selecting Ethnically Distinguishable Names. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.2 Creating Fictitious Job Applications.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.3 Occupations Under Focus.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.4 Submitting Application and Classifying Employer Responses.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.4.1 Methods Employed in Statistical Analyses.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3 Results.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.1 Employer Responses by Name.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.2 Employer Responses by Gender.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.3 Employer Responses by Economic Sector.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.4 Employer Responses by Occupations.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.5 Employer Responses by Job-skill Level.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.6 Employer Responses by Firm Size. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.7 Employer Responses by City of Job Location. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4 Discussion.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 5 Concluding Remarks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 6 References.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 7 Publications of the Ministry of Economic Affairs and Employment 2025:6 1 Introduction This report analyses and compares the reception of immigrants into Finnish working life in 2016 and 2024, examining how their access to the labour market has evolved over these two periods. Drawing on two large-scale correspondence studies, it offers concrete insights into whether the trends observed towards immigrant recruitment in 2016 have remained stable, improved or declined by 2024.1 By exploring the connections between ethnicity, gender, economic sectors, occupations and other relevant factors, the report aims to present a detailed picture of both the progress made and the persistent challenges faced by ethnic minority groups in integrating into the Finnish labour market. Monitoring changes in these trends is essential, as successful economic integration is a fundamental prerequisite for broader societal incorporation. This is particularly relevant in contemporary Western societies, where work plays a critical role in shaping individual identity and providing pathways for social and economic mobility. A failure to achieve labour- market integration can, therefore, severely hamper overall societal inclusion. Barriers to immigrants’ access to the labour market pose challenges on two key levels. First, they can act as a disincentive for further investment in human capital if immigrants perceive that such investment does not yield sufficient rewards. Secondly, these barriers may push immigrants to rely more heavily on ethnic networks, potentially leading to employment in occupations of low socio-economic status, where chances for occupational mobility are often limited. In Finland, where an ageing workforce and demand for skilled labour make immigrant integration increasingly important, addressing these issues will be crucial for ensuring long- term economic and social integration. 1 A correspondence study here refers to a field experiment for studying labour-market discrimination. In this approach, pairs of fictitious job applicants submit substantively equivalent CVs and job letters to publicly advertised vacancies. These pairs are identical in all respects except for the basis of discrimination, namely ethnicity. Applicants’ ethnic background, along with their mother tongue, is signalled in the CVs through carefully selected popular names that strongly indicate their ethnic affiliation. 8 Publications of the Ministry of Economic Affairs and Employment 2025:6 This report is especially significant in that it highlights the labour-market receptiveness of of second-generation immigrants in Finland, focusing on five groups having both European and non-European backgrounds. The success or failure of the children of immigrants who are brought up and educated in the new country serves as the ultimate benchmark of their long-term integration. In this regard, the findings of this report offer valuable insights into whether the substantial disadvantages observed eight years ago concerning the hiring of young second-generation immigrant job seekers who were raised and educated entirely in Finland, have diminished over time or whether the ethnic hierarchies prevalent in the job market in 2016 continue to reward them differentially based on their origin . Additionally, the results of this report are also valuable for assessing the receptiveness of the labour market to skilled migrant workers more generally. From a policy perspective, the findings are particularly relevant when viewed in the context of the Programme of Prime Minister Orpo’s Government, which explicitly aims to strengthen the reception capacity of working life for individuals with partial work ability, older individuals, those with lower education levels as well as immigrants. To effectively implement this government programme, it is imperative to have solid, up-to-date empirical data on the reception of jobseekers with immigrant backgrounds in the labour market. This report makes a timely contribution in this regard. Furthermore, the findings also provide important insights for evaluating the situation after the Ministry of Economic Affairs and Employment’s Working Life Diversity Programme (2021–2023), which aimed to improve recruitment practices and better integrate the skills of immigrants into workplaces. The report contributes to understanding how the recruitment scenario has developed since the implementation of this programme. In summary, this report seeks to provide empirical knowledge of the labour-market receptiveness of immigrants for relevant labour authorities, while also fostering awareness among the general public of the opportunities available and the challenges faced by second-generation immigrants in linking with the world of work in Finland. 1.1 The Finnish context The economic incorporation of immigrants is widely recognised as a key indicator of their successful integration into host societies, thus making equitable access to the labour market a growing policy priority. However, despite the presence of several anti-discrimination laws across many Western countries, numerous 9 Publications of the Ministry of Economic Affairs and Employment 2025:6 studies reveal that immigrants continue to face substantial barriers in entering the labour market. They frequently encounter ethnic penalties in several important areas, including limited occupational mobility, lower remuneration, and insecure employment contracts, alongside lower levels of job satisfaction and commitment (e.g., Triana et al. 2010), as well as more mental and physical health issues (e.g., Paradies et al. 2015). These challenges have been reported to persist even among immigrants with local education and work experience (e.g., Zschirnt and Ruedin 2016; Weichselbaumer 2015; Ahmad 2020). Non-European immigrants and visible minorities, in particular, experience the greatest disadvantages in recruitment and career progression. In Finland, labour-market outcomes for immigrants reflect the broader patterns of disadvantage seen across other European countries. Although immigrant employment has improved over time, considerable disparities persist compared to the native Finnish population. For instance, in 2024, according to the Employment Bulletin, the unemployment rate for foreign job seekers was 17.8%, while it stood at 7.2% for Finnish citizens. Immigrants of Asian or African origin tend to experience higher unemployment rates than their European counterparts, with those from African and Middle Eastern countries encountering the most significant barriers to employment. In 2022, unemployment rates for certain groups examined in this report, such as immigrants of Russian and former Soviet Union, Somali, and Iraqi origin, were 17%, 31%, and 38% respectively. Immigrant workers in Finland are also disproportionately employed in temporary and part-time positions, often with less favourable working conditions compared to native Finns (Ojala et al. 2024; Saukkonen 2017 ). These occupations are typically more vulnerable to job instability, particularly during economic downturns. Furthermore, immigrants are more likely to be employed in positions that do not match their qualifications, a trend less common among native Finns (Myrskylä and Pyykkönen 2014). As regards sectoral distribution, immigrants, notably those from non-Western countries, are predominantly employed in the retail trade and service sectors (Sutela 2015). The health care sector has also seen an increase in immigrant workers in recent years, likely due to both labour shortages and relatively lower skill requirements in some jobs in these areas. In 2013, immigrants from Iraq, Russia, and Somalia residing in the Helsinki Capital Region were primarily employed in hospitality, health and social services, retail trade and administrative support services (Saukkonen 2022). To explain the challenges immigrants face in the Finnish labour market, previous studies on their economic mobility have identified several key factors that influence both the availability and quality of employment opportunities. Among the most 10 Publications of the Ministry of Economic Affairs and Employment 2025:6 frequently cited factors are insufficient proficiency in the Finnish language, a lack of Finnish education and work experience, limited Finland-specific cultural competence and informal capital, ethnically homogeneous social networks, and the non-recognition of foreign qualifications (e.g., Koivunen, Ylöstalo and Otonkorpi- Lehtoranta 2015; Nieminen 2014; TEM 2012; Huttunen and Kupari 2007; Pehkonen 2006; Ahmad 2005; Forsander 2002). Immigrants who arrived in Finland through family reunification, on humanitarian grounds, or during periods of economic recession are also considered to be at a disadvantage (e.g., Eronen et al. 2014). However, notwithstanding the indispensable role of human-capital attributes, the labour-market underperformance of immigrants is not solely the result of deficiency in these credentials. Rather, it stems from the interplay of several factors on both the supply and demand sides of the labour market. A failure to grasp this interplay hinders a full understanding of the issue. This report, among others, aims to shed light on one such factor impeding immigrants’ reception into the labour market, namely discrimination at the recruitment stage. To explore why immigrants’ labour- market performance lags behind that of their native counterparts, it is essential to study not only those who are employed or unemployed, but also the decisions of those who hold the power to grant access to job vacancies. Employer aversion towards immigrant candidates not only limits the employment opportunities available to immigrants but also affects the overall diversity and inclusiveness of the workforce. Especially, when skilled applicants are overlooked based on their background, both the individual and society suffer economically and socially. The under-utilisation of immigrant skills can lead to long-term unemployment, lower job satisfaction and slower incorporation into the host country’s labour market, and ultimately meagre multidimensional integration. Addressing discriminatory practices is therefore crucial in ensuring equal opportunities for all and harnessing the full potential of a diverse labour force. It would therefore be useful to first briefly examine the concept of discrimination and its possible causes in recruitment before proceeding to discuss the methodology and research procedure used in the studies on which this report is based. 1.2 Why is There Discrimination? Discrimination refers to the unjust or prejudicial treatment of an individual or group based on characteristics such as race, ethnicity, national origin, gender, age or other distinguishing factors including religion, disability or sexual orientation. It may manifest in both subtle and overt forms, arising from conscious or unconscious biases, but in all instances, it subjects the targeted individual or group to a subordinate or disadvantaged position. In the labour market, discrimination can 11 Publications of the Ministry of Economic Affairs and Employment 2025:6 present itself along multiple dimensions, ranging from the exclusion of certain job applicants during the recruitment process to disparities in wages, adverse workplace treatment, biased job performance evaluations, limited access to training and career development opportunities, diminished job security and disproportionate contract terminations. Although defining discrimination is often easier, identifying its underlying motivations and causes can prove to be a far more complex task. This complexity arises from a wide array of factors, from overt prejudice to more intricate systemic issues such as institutional or historical practices, which may sometimes interrelate. Consequently, explanations for the existence of discrimination vary across theoretical frameworks, reflecting the multifaceted nature of the phenomenon. In sociology, explanations for discrimination are often grounded in conflict theory (Tilly 1998; Tomaskovic-Devy 1993), which views discrimination as an effort by the dominant group to protect and maintain its privileged access to scarce resources, such as jobs, by excluding members of the minority groups. The members of the dominant group endeavour to sustain a system of inequality to preserve their exclusive privileges (Reskin 2000). Social psychological explanations draw on social cognitive theory to explain the labour-market marginalisation of immigrant job seekers. This theoretical perspective argues that people tend to automatically categorise others into in-groups and out-groups (e.g., Fiske et al. 1999). These automatic categorisations, however, can have a dual effect. On the one hand, they may help people to process the vast amount of incoming information in a complex world (Fiske 1998). On the other hand, social categorisations can potentially create biases in how we perceive and evaluate others, fostering ‘us-and-them’ attitudes. In economics, neoclassical models of discrimination primarily focus on either subjective prejudice or perceived deficiencies in personal qualifications, often distinguished as pure and statistical discrimination. These models, being simpler than other theoretical explanations, analyse discrimination using the same tools employed for other forms of labour-market behaviour (Shulman 1996). The basic assumption is that, over time, market competition will naturally reduce discrimination (as in Becker 1957). In the pure discrimination model, recruiters, co-workers, or clients from the majority group exhibit a “taste” for discrimination and are willing to pay a premium to avoid certain socio-demographic groups (see Kirschenman and Neckerman 1991; Cain 1986). This model suggests that minority job seekers may still face discrimination despite having the necessary qualifications. In contrast, in the statistical discrimination model, it is not prejudice that encourages employers to discriminate against immigrant workers, but, rather, it is imperfect information about these workers’ true productivity for which they discriminate such workers. When faced with uncertain situations, employers are 12 Publications of the Ministry of Economic Affairs and Employment 2025:6 claimed to rely on their stereotypes or generalisations and they may use race, skin colour or group membership as a proxy for aspects of productivity that are relatively expensive or impossible to measure (Kirschenman and Neckerman 1991; Arrow 1973; Phelps 1972). Thus, in this model, individuals are treated based on perceived traits of their group (Shulman 1996). An alternative explanation of discrimination in economics is offered by the political economy framework, which challenges the assumptions of neoclassical models. This perspective places greater emphasis on how political factors influence economic outcomes. Rather than focusing on individual motives, it highlights the role of institutional structures in generating socioeconomic inequality. According to this approach, institutional discrimination occurs when certain social groups, such as immigrants and minorities, are systematically denied equitable access to resources due to the implicit or explicit rules, practices, structures, or operating policies of social institutions, organisations or governments (see, e.g., Sampson 2008). In brief, it is the normal functioning of society that leads to unequal outcomes and the denial of equal rights and opportunities for some groups. Emerging from the classic work on emotional labour, a more recent theoretical framework, known as aesthetic labour, shifts attention to another form of labour- market discrimination. It proposes that the hireability of job applicants in interactive service roles can be influenced by their physical appearance, attributes and other corporeal dispositions (see Nickson et al. 2001; Warhurst et al. 2000). Employers tend to favour workers who not only look good and sound right but also, in their view, would better appeal to their clients’ sensibilities (Nickson et al. 2012). These embodied attributes are then further developed, mobilised, and commodified by organisations. Some studies indicate that even the accents of immigrant jobseekers can lead to discrimination. For instance, a study by Timming (2017) found that while applicants with British-accented English received the most favourable responses in telephone-based job interviews, managers actively discriminated against those with Chinese, Mexican and Indian accents in jobs involving face-to-face contact with customers. Apart from these theoretical explanations, literature on ethnic hierarchies and social distance suggests that while ethnic out-groups may generally face discrimination, some groups experience either lower or higher levels of social acceptance or rejection from the mainstream society. Research in this area has shown that in-groups tend to exhibit specific preferences for contact with members of out- groups, who are ranked as more or less desirable social partners (see Hagendoorn 1995). It is argued that there is often a social consensus regarding the rank assigned to certain groups, shared by most in-group members, regardless of whether or not 13 Publications of the Ministry of Economic Affairs and Employment 2025:6 they hold ethnocentric or racist views (e.g., Verkuyten and Kinket 2000). In other words, out-group preferences persist independently of the broader acceptance or rejection of particular out-groups. As Hagendoorn and Hraba (1987) highlight in the context of the Netherlands, ethnic hierarchy largely reflects the social representation of the status hierarchy of out-groups. Numerous studies across various countries support the thesis of ethnic hierarchies, including Snellman and Ekehammar (2005) in Sweden, Verkuyten and Kinket (2000) and Hagendoorn and Hraba (1989) in the Netherlands, and Bobo and Zubrinsky (1996) in the USA. However, only a limited number of studies have examined the existence of ethnic hierarchies in the labour market, such as Weichselbaumer (2017), Andriessen et al. (2012), and Baert et al. (2017). A recent correspondence study by Vernby and Dancygier (2019) in Sweden clearly demonstrates that ethnic hierarchies significantly restrict immigrants’ employment opportunities, showing that the likelihood of receiving a callback declines substantially with increasing socio-cultural and ethnic distance from the mainstream group. Since the 1990s, several surveys have been conducted in Finland to examine the attitudes of the mainstream population towards various immigrant groups (e.g., Jaakkola 1999, 2005, 2009). These surveys revealed that immigrants from European countries, such as Britain and Norway, consistently ranked at the top, while those from Iraq, Somalia, and other non-Western countries were placed at the lower end of the ethnic preference ladder. These hierarchies were also reflected in Finns’ views on preferred neighbours and desirable marriage partners. Although the ethnic hierarchy remained largely intact, the results of these surveys also indicated that attitudes towards immigrants had become less negative over the years. While no recent comprehensive data on ethnic hierarchies is available since the last survey in 2009, observations from media discussions and other sources suggest that these hierarchies have not changed significantly. The aim of this report is not to specifically test the thesis of social distance and ethnic hierarchies, but it provides an opportunity to observe the extent to which the ethnic hierarchies observed in previous surveys are also reflected in the labour market, leading to unequal employment opportunities for otherwise equal job applicants. 1.3 Methods for Studying Discrimination in Work Life The discussion above has aimed to present the various reasons proposed by different conceptual paradigms for why discrimination exists, and why, even in the face of numerous legislative and policy interventions, this recalcitrant phenomenon persists. While these theoretical explanations help us understand discrimination 14 Publications of the Ministry of Economic Affairs and Employment 2025:6 from various perspectives, measuring labour-market discrimination itself presents challenges, particularly in distinguishing how much it is due to employers’ discriminatory practices versus how much it potentially stems from immigrants’ insufficient personal qualifications. To do so, researchers in the field have turned to a variety of methods. A commonly used technique involves comparing wages and occupational mismatches between minority and majority workers, adjusting for attributes related to human capital. Yet, this approach has its drawbacks: if human capital is not precisely measured or if average human capital differs between minority and mainstream populations, any unmeasured human capital effect may mistakenly be attributed to discrimination (Gaddis 2015). Further issues with this method include challenges in measuring cumulative discrimination and potential sample-selection bias (see, for example, Lucas 2008; Jones and Kelley 1984). Another method involves qualitative approaches, such as interviews or surveys, to explore minority experiences of differential treatment during job searches. While valuable, this approach is not without its limitations either; subjective element may cause minority workers to overstate or understate how they are treated by employers (Ahmad 2005). Similarly, interviewing employers to understand barriers that immigrants face in recruitment has also limitations, as employers might respond with socially desirable answers rather than their true beliefs. As pointed out by Pager and Quillian (2005), there exists a notable gap between employers’ stated intentions and actual practices. Given the limitations of the above mentioned methods, field experiments have become the preferred approach for studying discrimination in recruitment. Particularly, correspondence methodology, which is employed in the studies described in this report, has been the most prominent approach to measure discrimination based on ethnicity. In this method, researchers create pairs of fictitious applicants with identical qualifications – such as age, education, work experience, vocational diplomas, and other productivity-related characteristics – who apply for publicly advertised job openings. These applicants differ only in their names, which are carefully selected to signal ethnicity in their CVs. By controlling all other variables, the correspondence method offers a highly effective way to objectively measure discrimination (Pager and Shepherd 2008). This approach has been widely adopted in many countries, including the Netherlands (Thijssen, Coenders and Lancee 2021), Sweden (Granberg, Andersson and Ahmed 2020), Switzerland (Zschirnt 2019), Belgium (Baert et al. 2017), Norway (Midtbøen 2015), 15 Publications of the Ministry of Economic Affairs and Employment 2025:6 Germany (Kaas and Manger 2012), Ireland (McGinnity and Lunn 2011), Australia (Booth and Leigh 2010), Greece (Drydakis and Vlassis 2010), and Great Britain (Wood et al. 2009). In Finland, only a limited number of studies have previously employed field experiment techniques to capture evidence of discrimination in recruitment practices. The earliest study by Ahmad (2002) used the participant-observation method; however, it did not fully qualify as a situation testing study, as a control group was applied in only a subset of cases. The subsequent study by Larja et al. (2012) utilised the situation testing method to explore labour-market discrimination, focusing exclusively on one ethnic group, Russians. Given the increasingly diverse nature of the Finnish labour market, limiting the study to a single group may restrict the generalisability of these findings to other immigrant populations, especially considering the well-established distinction between European and non-European immigrants, who may be viewed differently within European societies. This distinction is particularly relevant in Finland, where multiple surveys (e.g., Jaakkola 1999, 2005) reveal that European immigrants tend to rank higher in the ethnic hierarchy than non-European immigrants, who are often perceived as competitors for jobs or, in some cases, as a burden on social welfare. A more recent study by Ahmad (2020), which included five immigrant groups from both European and non-European origins, was the first to employ correspondence methodology to investigate discriminatory practices in hiring procedures in Finland. 16 Publications of the Ministry of Economic Affairs and Employment 2025:6 2 Experiment Design And Methods of Analyses The findings of this report are based on two field experiments using correspondence methodology, conducted in 2016 and 2024. The aim is to examine and compare whether, and in what ways, the reception of second-generation immigrants in the Finnish labour market has changed over the intervening eight years. Since both studies involved the same immigrant groups, economic sectors, and equivalent sample sizes, they offer a valuable opportunity to assess changes in the recruitment trends of these groups over time. The experiments focused on four immigrant groups – English, Iraqi, Russian, and Somali – with the Finnish group acting as the reference. This approach allowed for a more comprehensive and representative analysis of the labour-market situation for immigrants in Finland. There are several reasons behind this choice of groups: 1. Diversity in Finland’s Labour Market: First, the Finnish labour market has become increasingly diverse, with immigrants originating from a wide range of countries and backgrounds. Focusing on only one or two groups, as previous studies have often done, limits the ability to generalise findings across different immigrant communities. Including five groups instead allows for a much broader capture of immigrant experiences and challenges faced by immigrants. 2. Comparison Between European and Non-European Immigrants: Secondly, there is a well-known distinction between European and non-European immigrants in Finland, with European immigrants often perceived more favourably in the public imagination. Including both European and non-European groups allows the field experiments to explore potential differences in how these groups are treated in the labour market, providing a deeper understanding of discrimination based on ethnicity and origin. 3. Generalisation of Findings: Third, studying multiple groups enables broader generalisation across the immigrant population, rather than restricting conclusions to the experiences of a single ethnic group. This approach supports drawing more robust conclusions about the nature of immigrants’ labour-market conditions in Finland. 17 Publications of the Ministry of Economic Affairs and Employment 2025:6 4. Reflecting Finland’s Immigrant Composition: Finally, since Finland’s immigrant population is drawn from diverse regions, focusing on just one group would not accurately reflect the true composition of the workforce. Including five groups ensures that the study better mirrors the actual diversity of the immigrant population. In summary, the inclusion of five groups allows the study to provide a more complete picture of the labour-market situation and account for the varying experiences of different immigrant communities in Finland. Data for each field experiment included in this report was collected by responding to 1000 job openings advertised on the website of the Finnish national employment service (te-palvelut.fi). Five fictitious job candidates of Finnish, English, Iraqi, Russian and Somali origin submitted applications that were substantively identical in terms of education, work experience and other qualifications to each of these 1000 positions. In total, 5000 applications were sent to various firms located in different cities across Finland, including Helsinki, Espoo, Tampere, Vantaa, Turku, Oulu, Lahti, Kuopio, Jyväskylä and Pori. Half of the jobs were answered using male names and the other half using female names across all five groups, but men and women did not apply for the same positions. When taken together, a total of 10,000 job applications were submitted in the two field experiments. The extensive data gathered ensured that the results were robust and not the outcome of some spurious effects. 2.1 Selecting Ethnically Distinguishable Names In correspondence experiments, the first key step is selecting names that reflect ethnic backgrounds effectively. For this purpose, an initial set of names was first selected from various websites listing the most common and popular names for English, Iraqi, Russian and Somali individuals. A final list of names was prepared after consulting immigrants from the respective groups to ensure the names were typical of their backgrounds. The name of the applicant appeared in large font at the top of the CV to make the ethnicity more visible to the recruiter. To further emphasise ethnicity, the candidates’ mother tongues – such as Arabic, English, Russian and Somali – were explicitly stated in the CV and job letter. Finnish names, in contrast, are distinctive enough that recruiters would have no difficulty identifying a Finnish applicant. Both first and surnames were used. 18 Publications of the Ministry of Economic Affairs and Employment 2025:6 2.2 Creating Fictitious Job Applications The job application consisted of a cover letter and a CV. Five letters, different in style but equivalent in content, were constructed for each of the five sectors included in these experiments. These letters consistently conveyed the impression that the applicants were motivated, ambitious and flexible persons. It was apparent from the style and grammar of these letters that the immigrant applicants had excellent proficiency in Finnish. The order of assigning letters to the candidates was altered each time they applied to a new vacancy to avoid the risk of detection. All ethnic applicants stated that they had received their entire education, vocational training and work experience in Finland, ensuring that employers would not discriminate based on foreign qualifications or experience. The job letters also contained a brief explanation of why the applicant was suited for the position, emphasising personal attributes such as teamwork, work ethic, communication skills and flexibility. In job applications, the applicants were seeking work with their recent job having ended between 1-3 months ago. The age of the applicants ranged from 24 to 28 years. When they answered the same vacancy, minor variations in age (between five months and one-and-a-half years) were introduced to avoid suspicion2. The rationale behind selecting this particular age group was two-fold. First, this choice aligns with the focus of the research on second-generation immigrants. Additionally, selecting this age bracket is relevant, as Finland’s immigrant population predominantly consists of younger individuals. Secondly, the aim was to avoid the potential confounding effect of age discrimination on callback rates, as employers may be more inclined to hire younger workers over older ones (see, e.g., Neumark, Burn and Button 2017; Koivunen, Ylöstalo and Otonkorpi-Lehtoranta 2015). The CVs were created using a CV generator software. Each time the CV generator created five stylistically different but equivalent CVs in terms of education, work experience and vocational diplomas. To avoid risk of exposure, the font type, font size and layout of the CVs and the order in which the various sections appeared differed. The applicants’ hobbies were also different, but they were rather typical 2 Cross-tabulations and chi-square tests of independence were conducted to assess whether slight differences in age, the style of the application letter, the format of the CV or the order of submission influenced the likelihood of receiving a callback from employers. The results showed that these variations had no statistically significant effect on the probability of securing an interview offer. 19 Publications of the Ministry of Economic Affairs and Employment 2025:6 and did not stand out. The applicant addresses selected in the experiment were located in areas with similar socio-economic characteristics. Only personal details such as names, phone numbers and email addresses remained constant throughout the experiment. The CVs provided a detailed account of the applicants’ credentials, including their education, previous job history, proficiency in Finnish and English, computer skills and the names of institutions where they obtained their qualifications. The CVs also provided information on age, gender, postal address, phone number, email address and mother tongue. A particular effort was made to ensure that the fictitious job applicants had suitable and sufficient experience relevant to the job applied for, and their CVs stood out. In both correspondence experiments, the response rate was high, standing at approximately 50%. This reflects the high quality of both the CVs and the job letters. For any job, all candidates had the same amount of work experience, although there were slight differences in the length of individual jobs included in the CVs. All immigrant applicants were mentioned to have received their entire education and work experience in Finland, indicating they were either born in Finland or had arrived at a very young age. In brief, the only feature essentially distinguishing the five applicants was their name and mother tongue. 2.3 Occupations Under Focus The two experiments tested job vacancies across five occupational sectors: y restaurant and catering y retail trade y cleaning y clerical y customer service The jobs ranged from unskilled to medium-skilled and skilled positions, including: y cooks y waiters y kitchen staff y café workers y shop assistants y office clerks y receptionists y cashiers y cleaners y miscellaneous restaurant staff 20 Publications of the Ministry of Economic Affairs and Employment 2025:6 Both male and female applicants applied for jobs in the restaurant, catering, retail, and cleaning sectors, while only female candidates applied for positions in the clerical and customer service sectors. This approach aimed to avoid gender bias in the selection process, as these jobs in Finland, like in many other countries, are typically female-dominated. Table 1 provides a description of characteristics of different jobs and firms included in the two experiments conducted in 2016 and 2024. A high proportion of the job openings tested were in the restaurant, catering, and retail trade sectors, accounting for the majority of vacancies. This higher concentration reflected the availability of jobs during the data collection period. Efforts were made to submit applications on the same day the jobs were advertised. It was done to demonstrate a strong interest and avoid applying to positions that had already been filled. Additionally, the study examined two types of jobs: those requiring face-to-face interaction with customers and those that did not. This distinction was included to test whether customer-facing roles introduced additional biases in hiring based on perceived customer preferences. All applications were sent to private firms, ranging from small and medium-sized enterprises to large companies. 21 Publications of the Ministry of Economic Affairs and Employment 2025:6 Table 1.  Firm-related and job-related descriptive statistics Category 2016 N 2016 % 2024 N 2024 % Firm-related characteristics Firm location Helsinki region 575 57.5 323 32.3 Outside Helsinki Region 425 42.5 677 67.7 Firm size Micro 582 58.2 479 47.9 Small 278 27.8 288 28.8 Medium 115 11.5 111 11.1 Large 25 2.5 122 12.2 Employer gender Male 463 46.3 502 50.2 Female 537 53.7 498 49.8 Job-related characteristics Job sector Restaurant & catering 591 59.1 676 67.6 Retail trade 215 21.5 122 12.2 Cleaning 127 12.7 130 13.0 Clerical 55 5.5 39 3.9 Customer service 12 1.2 33 3.3 Job duration 1-3 months 144 14.4 56 5.6 3-6 months 148 14.8 81 8.1 6-12 months 65 6.5 91 9.1 22 Publications of the Ministry of Economic Affairs and Employment 2025:6 Category 2016 N 2016 % 2024 N 2024 % Permanent 643 64.3 772 77.2 Job type Part-time 368 36.8 382 38.2 Full-time 632 63.2 618 61.8 Required advertised experience Less than 1 year 171 17.1 160 16.0 1-5 years 446 44.6 217 21.7 Not mentioned 179 17.9 321 32.1 Not mentioned but required 100 10.0 205 20.5 Not required but plus 104 10.4 97 9.7 Job-skill level Low 271 27.1 337 33.7 Medium 431 43.1 375 37.5 High 298 29.8 288 28.8 Job requires Vocational diploma 366 36.4 175 17.5 No vocational diploma 634 63.4 825 82.5 Job involves Visual contact with customers 436 43.6 550 55.0 No visual contact with customers 564 56.4 450 45.0 Job requires Fluent Finnish 639 63.9 265 26.5 No fluent Finnish 361 36.1 735 73.5 23 Publications of the Ministry of Economic Affairs and Employment 2025:6 Jobs tested were geographically spread all over Finland including smaller and major cities. The rationale behind sending applications to both smaller and major cities was to capture a more comprehensive and representative picture of the labour market across different geographical areas. Recruitment practices and job market conditions can vary significantly between large urban centres and smaller towns. Including both types of cities allows the study to assess whether location influences the likelihood of receiving a job interview offer, providing insights into how regional factors – such as economic activity, local labour demand, and employer attitudes – may affect the employment prospects of immigrant applicants. This broader geographical scope, therefore, ensures the findings are not skewed by urban or rural biases and reflect the diversity of the Finnish job market. Moreover, including a broader range of cities also grants the opportunity to examine if the size of the proportionof population of foreign origin in a given city exerts any impact on the chances of receiving employment opportunities. 2.4 Submitting Application and Classifying Employer Responses All job applications were submitted via email, which is the standard practice in Finland for responding to job vacancies. For each job, five applications were sent within a period of four hours to avoid any disadvantages in selection due to submission timing. The order of submitting applications was altered in each job to prevent any applicant from being favoured based on when their application was received. For example, if the first applicant submitted was Finnish in one case, the first applicant in the next case might be Somali or Russian. This strategy ensured that all applicants had an equal chance of being considered. Employers could contact the applicants through email or phone. Voicemail boxes were set up for each applicant to manage phone responses. Calls were not answered directly; instead, the employer was requested to leave a message. Employer responses were classified into two main categories: positive and negative. A response was categorised as positive when the employer invited the applicant for an interview. A negative response was recorded when the applicant was formally rejected or received no response at all. Also, automated emails acknowledging the receipt of applications were recorded as negative outcomes. The issue of whether a negative response or a lack of response from an employer constitutes equal treatment or non-observation has been a subject of discussion in experimental studies (e.g., Riach and Rich 2002). One might argue that, despite the negative outcome, it represents equal treatment if an employer either responds 24 Publications of the Ministry of Economic Affairs and Employment 2025:6 to or ignores both candidates, as it indicates that both applications have been evaluated. However, it is also possible that notifying a candidate of rejection is merely a formality, given that another candidate may have already been selected. Therefore, the specific approach of categorising responses as positive or negative, as employed in this study, serves as a useful methodological choice. 2.4.1 Methods Employed in Statistical Analyses This study employs logistic regression models, along with binary and chi-square tests, to evaluate the statistical significance of differences in callback rates among the five groups examined. Specifically, binary and chi-square tests assess statistical significance in Panel A of Table 3, and Tables 4, 5, 6, 7 and 8, while logistic regression models are used in Table 2 and Panel B of Table 3. The chi-square test is a non-parametric statistical test used to determine whether there is a significant association between two categorical variables and compares the observed counts in each category with the expected counts, assuming no association between the variables. Logistic regression models, on the other hand, estimate the probability of a binary outcome (e.g., callback or no callback) as a function of multiple independent variables. This technique provides coefficients that quantify how the odds of the outcome change with each one-unit change in an independent variable, holding other factors constant. Logistic regression is well-suited for causal- type analysis, as it extends beyond simple descriptive insights, which is central to this study. By assigning specific values to the independent variables in the model, we obtain an estimated odds ratio for the dependent variable – for example, the odds of callbacks received versus callbacks not received. Comparing odds across values of an independent variable gives us an odds ratio, capturing how the odds shift due to changes in that variable. The logistic regression coefficients offer an adjusted estimate of this change, accounting for random variation and the influence of other variables. The change indicated by each coefficient is presented relative to a reference category. In this analysis, the Finnish group serves as the reference; a larger coefficient for another group signals that its members receive more callbacks relative to the Finnish group. Statistical significance – the probability of observing the odds ratios estimated by the model if the null hypothesis were true – is evaluated using a t-statistic, which is approximately normally distributed for large sample sizes. The data consists of responses to five applications submitted for each job. It is possible that each employer may exhibit a slightly different response pattern compared to others. This variation could result in intra-employer correlations, which 25 Publications of the Ministry of Economic Affairs and Employment 2025:6 would violate the fundamental assumption of case independence in regression analysis. Such correlations could impact the accuracy of standard error estimates, and consequently, the significance tests. To address the potential inaccuracies caused by this phenomenon, known as the clustering effect, a modified version of logistic regression was applied. In SPSS, this can be managed in several ways. For this analysis, a complex sample design was employed. This involved defining a complex analysis plan, where each case was assigned a weight of 1 and a clustering variable was introduced. The clustering was based on a variable identifying responses by each employer, treating them as clusters. SPSS then performed the analysis on the weighted data, accounting for the potential clustering effect3. 3 The clustering effect was small, however, and in some tests conducted by comparing an ordinary logistic regression model and a complex samples logistic regression model, the minor differences were completely irrelevant to the interpretation of results. 26 Publications of the Ministry of Economic Affairs and Employment 2025:6 3 Results 3.1 Employer Responses by Name We now turn to examine whether there have been any changes in employers’ recruitment practices towards hiring second-generation immigrants under focus between 2016 and 2024, and whether applicants’ name, rather than their human capital, still plays a decisive role in determining who gets invited to a job interview. Table 2 summarises and compares the main results of two field experiments, conducted eight years apart, providing employer responses to 10,000 job applications sent by five equally qualified candidates who differed only in their name. In the table, ni represents the number of job interview offers received by each ‘i’ group when N applications are submitted. The callback rate column shows the percentage of callbacks out of all applications for each applicant group, calculated as 100 × ni/N. The other columns show the outputs of two logistic regression analyses, where the null hypotheses assumed that there is no significant difference in callback rates between the Finnish and immigrant applicants. Putting it differently, the odds ratios for receiving callbacks are identical across all groups and that the name does not influence the odds of receiving a callback. In other words, for each immigrant group, where the subscripts I and F refer to immigrant and Finnish groups, respectively, and c and nc denote callback received and no callback received. 27 Publications of the Ministry of Economic Affairs and Employment 2025:6 Table 2.  A Comparison of callback rates by applicant name in 2016 and 2024, and a logistic regression model with main effects Category Callbacks (ni) N CR, % (ni/N) B S.E. t-statistic df Sig. Design effect Exp(B) Panel A: Data 2016 Intercept -0.478 0.066 -7.247 9999.000 0.000 1.545 0.620 Somali 99 1000 9.9 -1.771 0.126 -14.022 9999.000 0.000 1.514 0.170 Iraqi 134 1000 13.4 -1.431 0.115 -12.437 9999.000 0.000 1.513 0.239 Russian 228 1000 22.8 -0.775 0.101 -7.664 9999.000 0.000 1.533 0.461 English 269 1000 26.9 -0.531 0.098 -5.413 9999.000 0.000 1.546 0.588 Finnish (ref.) 390 1000 39.0 Total 1120 5000 Panel B: 2024 Intercept -0.409 0.074 -5.530 9999.000 0.000 0.657 0.664 Somali 168 1000 16.8 -1.239 0.122 -10.155 9999.000 0.000 0.645 0.290 Iraqi 202 1000 20.2 -1.049 0.117 -8.934 9999.000 0.000 0.645 0.350 Russian 221 1000 22.1 -0.912 0.115 -7.964 9999.000 0.000 0.645 0.402 English 303 1000 26.9 -0.549 0.109 -5.053 9999.000 0.000 0.644 0.578 Finnish (ref.) 416 1000 41.6 Total 1310 5000 28 Publications of the Ministry of Economic Affairs and Employment 2025:6 As Table 2 shows, the null hypotheses are rejected. The table reflects both the relative stability and certain shifts in the reception of different immigrant groups in the labour market between 2016 and 2024. First, it is easy to observe that there continues to be a significant disparity in callback rates based on names, as all immigrant groups lag considerably behind the Finnish group across the two periods. In 2016, while the Finnish applicants received the highest callback rate at 39.0%, the candidates with an English, Russian and Iraqi name were invited to an interview in 26.9%, 22.8% and 13.4% of the cases respectively. Applicants having a Somali name bore the brunt of employer aversion, with the lowest callback rate of 9.9%. The logistic regression Exp(B) coefficients in Panel A highlight these disparities, indicating the odds ratios associated with each predictor. For instance, Somali applicants had an odds ratio of 0.170, showing that they had only 17.0% of the odds of receiving a callback compared to their Finnish counterparts. The situation of candidates with an Iraqi name was not too different from Somali applicants. They had an odds ratio of only 0.239, meaning their odds of receiving a callback were just 23.9% of those with a Finnish name. In the first experimental study, the impact of an applicant’s name on entering the Finnish labour market was, therefore, highly significant (p = 0.000). This means that there is less than a 0.1% chance that the observed difference in callback rates between Finnish and immigrant-named applicants could occur by random chance if there were truly no discrimination. To what extent have these disparities in accessing the world of work changed over the past eight years? Panel B in Table 2 sheds light on the situation for immigrants in 2024. Overall, the crucial role of an applicant’s name remains as influential as it was in 2016, with a statistical significance at p = 0.000. However, although they remain the most disadvantaged among the groups studied, some notable progress can be seen for Somali and Iraqi immigrants. Their callback rates have increased from 9.9% and 13.4% to 16.8% and 20.2% respectively. These changes are statistically significant at p = 0.002 and p = 0.020 for Somali and Iraqi applicants respectively. For applicants with an English and Russian name, the labour-market position of the former group remains stagnant with a callback rate of 26.9%, while for the latter, it has slightly declined. Considering the highly negative political climate surrounding the Ukrainian conflict and its consequences for everything Russian, this slight decline is not unexpected. Meanwhile, applicants with a Finnish name have slightly strengthened their position compared to 2016. It would also be useful to explain the scope of opportunities and the extent of disadvantage for different immigrant groups using odds ratios, which indicate how likely it is for individuals of various backgrounds to receive a callback compared to the reference group (in this case, Finnish applicants). In 2016, Somali applicants’ odds of 29 Publications of the Ministry of Economic Affairs and Employment 2025:6 receiving a callback were 83.0% less compared to Finnish candidates. Although still far less likely than Finnish applicants to receive a callback, in 2024, they had improved odds, being only 71.0% less compared to Finnish counterparts. The disadvantage for applicants with an Iraqi name has similarly decreased: Their odds of receiving a callback were76% less in 2016, whereas by 2024, they were 65.0% less compared to Finnish applicants. For candidates with a Russian name, their relative chances worsened slightly over time, from being 54.0% less in 2016 to 60% less in 2024. For applicants with an English name, their position has remained stable across both time periods, showing a moderate disadvantage compared to Finnish candidates. In contrast, Finnish applicants continue to enjoy the highest callback rate, which has increased slightly to 41.6% from the previous figure of 39.0% in 2016. To summarise, the above empirical observations indicate a modestly shifting landscape in recruitment practices within the Finnish labour market. While certain immigrant groups, namely Somali and Iraqi applicants, have increased their access to work opportunities, all non-Finnish groups continue to experience discrimination in recruitment procedures. This evolving yet persistently unequal treatment underscores the ongoing challenges faced by second-generation immigrants in Finland, where name and background still significantly influence recruitment outcomes. 3.2 Employer Responses by Gender Next, we explore whether gender plays any significant role in securing employment opportunities. Analysing men and women separately is essential, as gender can intersect with ethnicity and other social identities to produce unique experiences and challenges in the labour market. Men and women often confront distinct expectations, barriers and biases that affect their career trajectories in different ways. For instance, while immigrant males may encounter certain stereotypes that limit their opportunities, immigrant women might face compounded biases, not only as immigrants but also due to gendered expectations regarding work. Thus, examining men and women separately enables to capture these nuanced differences, providing a clearer picture of how gendered dynamics impact discrimination and opportunities in the labour market. This also allows for more targeted and effective policy recommendations that address the specific needs of both men and women, contributing ultimately to a more equitable and inclusive labour market. To what extent immigrant men and women job seekers differ in their opportunities, Table 3 sheds light on this by splitting the results by gender in Panel A. The null hypothesis posits that there is no significant change in callback rates between 2016 30 Publications of the Ministry of Economic Affairs and Employment 2025:6 and 2024 for each applicant group based on name and gender. The chi-square tests of significance (given in asterisks after ethnicity) show that the null hypothesis for some groups can be rejected. The most significant improvements can be observed among Somali and Iraqi applicants. Somali male applicants experienced a more than twofold increase in callback rates, rising from 6.8% in 2016 to 15.8% in 2024. This change, marked by a p-value of 0.000, suggests a highly significant shift, indicating improved opportunities for Somali men over time. Somali female applicants also saw considerable improvement, with callback rates increasing from 13.0% in 2016 to 17.8% in 2024, a change significant at p = 0.01. Similarly, Iraqi male applicants experienced considerable gains, with callback rates rising from 9.2% in 2016 to 17.8%, which is also highly significant (p = 0.000). Iraqi female applicants followed a similar trend, with callback rates improving from 17.6% to 22.6%, significant at p = 0.01. These observations strongly suggest a positive shift in the labour-market reception of Somali and Iraqi applicants, especially among men, who experienced the most pronounced gains. In contrast, the labour-market standing of Russian applicants, both male and female, remained stable. They showed minimal change between 2016 and 2024, with no statistically significant improvement. For English male and female applicants, their situation also did not demonstrate any noticeable change, though slight improvement can be seen. In comparison, Finnish applicants, who already held the highest callback rates in 2016, saw modest increases that did not reach statistical significance. Finnish male applicants’ callback rates rose slightly from 33.8% in 2016 to 37.4% in 2024, while Finnish female applicants saw a small increase from 44.2% to 45.8%. These changes, though positive, indicate continuity rather than any substantial shift, reflecting the already established advantage Finnish applicants hold in the labour market. In panel A the significances of the changes between 2016 and 2024 are tested separately for male, female and the total data. The changes for male applicants seem to be somewhat different from the changes in the female group. To test whether these differences are statistically significant, a logistic regression model was fitted. It appears that the differences are not significant, however, since for the interaction term, p=0.488. Panel B of Table 3 reports the results of logistic regression, in which the null hypothesis assumed that the likelihood of receiving a callback was independent of ethnicity, gender or year (2016 vs. 2024). Thus, it offers another way to identify the persistent or shifting impacts of these factors on recruitment practices in Finland. As we can see, the hypothesis that callback rates did not significantly differ between 2016 and 2024 is rejected based on the significant increase in callbacks for the 2024 dataset. 31 Publications of the Ministry of Economic Affairs and Employment 2025:6 Table 3.  Callback rates and logistic regression analysis by applicant gender across 2016 and 2024. Panel A Gender 2016 Callbacks (ni) 2016 N 2016 Callback rate, % (ni/N) 2024 Callbacks (ni) 2024 N 2024 Callback rate, % (ni/N) Male applicants Somali*** 34 500 6.8 79 500 15.8 Iraqi*** 46 500 9.2 89 500 17.8 Russian 96 500 19.2 100 500 20.0 English 123 500 24.6 132 500 26.4 Finnish 169 500 33.8 187 500 37.4 Total 468 2500 587 2500 Female applicants Somali* 65 500 13.0 89 500 17.8 Iraqi* 88 500 17.6 113 500 22.6 Russian 132 500 26.4 121 500 24.2 English 146 500 29.2 171 500 34.2 Finnish 221 500 44.2 229 500 45.8 Total 652 2500 723 2500 32 Publications of the Ministry of Economic Affairs and Employment 2025:6 Panel B Gender B t-statistic df Sig. Design Effect Exp(B) (Intercept) -0.222 -3.377 9999 0.001 1.29 0.801 Dataset 2024 0.205 2.897 9999 0.004 0.502 1.227 Dataset 2016 (ref.) 0.000 Male -0.496 -7.028 9999 0.000 1.505 0.609 Female (ref.) 0.000 Somali -1.627 -17.296 9999 0.000 1.236 0.196 Iraqi -1.336 -15.163 9999 0.000 1.256 0.263 Russian -0.82 -10.017 9999 0.000 1.311 0.441 English -0.542 -6.872 9999 0.000 1.316 0.581 Finnish (ref.) 0.000 Dataset 2024 *Male 0.07 0.693 9999 0.488 0.693 1.073 Dataset 2024 *Female (ref.) 0.000 Dataset 2016 *Male (ref.) 0.000 Dataset 2016 *Female (ref.) 0.000 Note: In panel A, the asterisks after ethnicity give the statistical significance of change between 2016 and 2024. Chi-square tests were used. *: p<0.05, **: p<0.01, ***: p<0.001 Dependent Variable: Response to the application submitted (reference category = No callback). Model: (Intercept), Datasets, Gender, Ethnicity, Datasets*Gender. Additionally, the hypothesis that male and female applicants would have equal chances of receiving callbacks is also rejected, as male applicants consistently had lower callback rates than female applicants. However, the interaction term between gender and the 2024 dataset was not significant. This means that the increased callback rates in 2024 did not differentially affect male and female applicants. The regression analysis reveals significant differences in callback rates across years, gender, and ethnicity. In 2024, job applicants had 22.7% higher odds of receiving callbacks compared to 2016 (p = 0.004), indicating an overall improvement. However, male applicants had a consistently lower chance of getting invited to an interview than female applicants, with an odds ratio of 0.609, reflecting a 39.1% 33 Publications of the Ministry of Economic Affairs and Employment 2025:6 lower likelihood for men (p = 0.000). The interaction between gender and dataset year (2024*Male) was not significant (p = 0.488), which shows that the general improvement in 2024 callback rates did not specifically favour male applicants. Ethnic differences were also pronounced: Somali applicants had 80.4% lower odds of receiving a callback (p = 0.001), Iraqi applicants had 73.7% lower odds (p = 0.001) Russians had 55.9% lower odds (p = 0.001), and English applicants had 41.9% lower odds (p = 0.001) compared to Finnish applicants. In brief, although the 2024 dataset generally increased callback rates, gender and ethnic disparities persisted, with male applicants and certain ethnic groups (particularly Somali and Iraqi) facing notable disadvantages in gaining access to the labour market compared to their Finnish counterparts. Drawing on the above observations, the findings suggest that the double burden hypothesis (DBH) – which posits that female immigrant job seekers would face compounded disadvantages in the labour market due to their gender and immigrant status (see, e.g., Berdahl and Moore 2006) – is not supported in this context. Instead, the results align more closely with the subordinate male target hypothesis (SMTH), which proposes that immigrant men may face greater labour-market disadvantages as they are often perceived as more socially and economically threatening than immigrant women. The data reveal that male applicants, particularly those from Somali and Iraqi backgrounds, consistently received fewer callbacks than female applicants, even with overall improvements in callback rates by 2024. This pattern highlights specific biases in the hiring process that affect immigrant men differently than immigrant women. Although immigrant women are reported to have limited employment opportunities in the Finnish labour market due to factors such as family responsibilities, inadequate education and cultural expectations (e.g., Chang and Holm 2017; Liebig and Widmaier 2009), the findings suggest that immigrant women who have obtained locally gained qualifications and possess host-language proficiency may achieve employment chances on par with immigrant men. However, they may still face disadvantages when competing with Finnish female applicants. The extent of this disadvantage will likely depend on the extent of their differences from the majority population in terms of race, culture and religion. 34 Publications of the Ministry of Economic Affairs and Employment 2025:6 3.3 Employer Responses by Economic Sector This section examines if certain sectors are more open or resistant to the reception of immigrants than others by using the chi-square tests of significance. In Table 4, the null hypothesis is that there is no significant difference in callback rates between 2016 and 2024 across different economic sectors – namely, restaurant and catering, retail trade, cleaning, clerical and customer service – for the groups under consideration. The table clearly indicates an improvement in employers’ responsiveness to Somali applicants in some sectors over the observed period. They experienced significant increases in callback rates within restaurant and catering from 11.5% in 2016 to 18.9% in 2024 (p = 0.000) and retail trade from 5.6% to 13.1% (p = 0.016), thus allowing for the rejection of null hypothesis. However, callback rates in other sectors, such as cleaning, clerical, and customer service showed no significant change, with a notable decline in customer service callback rates, which could suggest sector-specific hiring preferences. Iraqi applicants also gained greater access to the restaurant and catering sector: their callback rate rose from 16.2% in 2016 to 22.9% in 2024 (p = 0.003). Also in retail trade, they increased their callback rate from 6.5% to 13.9% (p = 0.023), signaling potential improvements in employer attitudes towards Iraqi applicants in this sector. In comparison, the cleaning sector showed no statistically significant changes for Iraqi applicants, while the customer service sector experienced a significant decline in callback rates. 35 Publications of the Ministry of Economic Affairs and Employment 2025:6 Table 4.  A comparison of callback rates (CR) differentiated by economic sectors across 2016 and 2024 with chi-square tests. Country Job Sector 2016 CR (%) 2016 N1 2024 CR (%) 2024 N χ2 df p-value Somali Restaurant & catering 11.5 591 18.9 676 13.308 1 0.000 Retail trade 5.6 215 13.1 122 5.798 1 0.016 Cleaning 11.0 127 14.6 130 0.741 1 0.389 Clerical 5.5 55 7.7 39 0.191 1 0.662 Customer service 16.7 12 6.1 33 1.222 1 0.269 Iraqi Restaurant & catering 16.2 591 22.9 676 8.871 1 0.003 Retail trade 6.5 215 13.9 122 5.134 1 0.023 Cleaning 13.4 127 18.5 130 1.234 1 0.267 Clerical 5.5 55 10.3 39 0.763 1 0.382 Customer service 33.3 12 6.1 33 5.664 1 0.017 Russian Restaurant & catering 26.7 591 24.1 676 1.146 1 0.284 Retail trade 12.6 215 16.4 122 0.954 1 0.329 Cleaning 28.3 127 23.8 130 0.675 1 0.411 Clerical 7.3 55 10.3 39 0.261 1 0.610 Customer service 25.0 12 9.1 33 1.927 1 0.165 English Restaurant & catering 31.5 591 34.5 676 1.278 1 0.258 Retail trade 19.5 215 25.4 122 1.583 1 0.208 Cleaning 26.8 127 23.8 130 0.291 1 0.590 Clerical 9.1 55 12.8 39 0.334 1 0.563 Customer service 16.7 12 9.1 33 0.511 1 0.475 Finnish Restaurant & catering 45.9 591 46.7 676 0.101 1 0.751 Retail trade 27.4 215 28.7 122 0.060 1 0.806 Cleaning 38.6 127 40.0 130 0.054 1 0.816 Clerical 14.5 55 17.9 39 0.197 1 0.657 Customer service 25.0 12 18.2 33 0.256 1 0.613 Note: The p-values indicate the statistical significance of changes between 2016 and 2024. 1N includes both individuals who received a callback and those who did not. Thus, N provides the total from which the percentages are calculated. 36 Publications of the Ministry of Economic Affairs and Employment 2025:6 For European immigrant groups, Russian applicants did not experience statistically significant changes in callback rates across any sector, suggesting stable employer attitudes in these industries. For example, the restaurant and catering sector saw a small, statistically insignificant decrease from 26.7% in 2016 to 24.1% in 2024. Similarly, the retail trade sector had a minor increase from 12.6% to 16.4%, while sectors like cleaning, clerical and customer service also showed no meaningful changes, as indicated by p-values above 0.05. For English applicants, callback rates similarly did not change significantly across sectors. In restaurant and catering, the callback rate increased slightly from 31.5% to 34.5%, and in retail trade, from 19.5% to 25.4%, both of which are non-significant. Likewise, cleaning, clerical, and customer service sectors showed minimal changes, maintaining stable callback rates that may suggest consistent employer attitudes towards their recruitment in these areas. As regards the Finnish group, they displayed consistently high callback rates across all sectors, with no statistically significant shifts from 2016 to 2024. This consistency across these sectors, alongside the consistently high callback rates, may reflect a preferential bias for Finnish applicants in the job market. In conclusion, the findings from Table 4 are indicative of the fact that some sectors are becoming more open to certain immigrant groups, notably Somali and Iraqi applicants, while other sectors remain resistant to change. Significant increases in callback rates for Somali and Iraqi candidates in restaurant and catering, and retail trade suggest possible shifts in employer recruitment patterns or attitudes toward these groups. In contrast, cleaning, clerical, and customer service show little to no change across most groups. For Russian, English and Finnish candidates, the lack of significant change in callback rates across all sectors suggests stability in employer hiring practices, which may indicate a consistent demand or possibly a bias that favours certain groups, especially in the case of Finnish candidates who maintain high callback rates throughout. 3.4 Employer Responses by Occupations After analysing the findings by sector, it would also be essential to explore whether callback rates vary with respect to individual occupations. Examining callback rates by occupations separately is important for several reasons: 1. Loss of Specificity: When we consider the sector as a whole, it essentially averages the callback rates for all occupations within that sector. This results in a loss of detail and can mask the variation between different occupations within the sector. In other words, it can obscure significant differences between individual occupations, which might 37 Publications of the Ministry of Economic Affairs and Employment 2025:6 lead to an underestimation or overestimation of discrimination or bias in certain jobs. For instance, discrimination may be higher in managerial roles than in lower-skilled positions, and combining these would dilute the effect. 2. Neglecting Occupation-specific Biases: Sector-wide analysis might fail to capture occupation-specific biases that occur in certain jobs but not others. It is quite possible that different jobs in the same sector can be subject to different recruitment practices or employer expectations. For example, as it has often been reported, employers might discriminate more against immigrant applicants in jobs that require face-to-face contact with customers (e.g., waitstaff or customer-service representatives) than for back-end roles (e.g., dishwashers or cooks). These biases would not be exposed if the entire sector is analysed without breaking it down by occupation. 3. Effect of Sample Size Variation: When different occupations within a sector have varying sample sizes, those with larger sample sizes will have more influence on the overall sector-wide analysis, which could potentially skew the results. For instance, if the data includes more chef positions than waiter positions, the results for chefs will dominate the sector-wide callback rates. This could result in imprecise conclusions if there is significant discrimination in waitstaff positions that are overshadowed by less discriminatory hiring practices for chefs. 4. Interaction Effects Between Occupation and Ethnicity: It is essential to understand how ethnicity can have impact on callback rates for specific roles. Members of certain ethnic groups might confront greater employer aversion in specific occupations, such as front-office jobs compared to back-end roles lacking interaction with customers. In sector-wide analysis, these interaction effects between ethnicity and the specific occupation can potentially be missed. For example, Somali applicants might face more discrimination when applying for waiter positions compared to chef positions, but this detail could be lost when considering the sector as a whole. 5. Statistical Power: Analysing the sector as a whole might increase the statistical power of analysis, especially if some occupations have small sample sizes. By aggregating data across occupations, it is possible to detect statistically significant differences, which may not otherwise be detectable when studying individual occupations due to smaller sample sizes. However, this increased power comes at the expense of sensitivity to occupation-specific trends. In other words, a statistically significant finding at the sector level might not reflect meaningful differences across all occupations. 38 Publications of the Ministry of Economic Affairs and Employment 2025:6 6. Misleading Generalisations: Finally, when we examine the sector as a whole, we risk overgeneralising the findings to all occupations in that sector, despite the fact that the level of discrimination or bias might vary by job type. Sector-wide conclusions, thus, can lead to misleading generalizations or impressions, which might prevent the identification of key areas namely specific occupations that need attention in terms of anti-discrimination policies or targeted interventions. Returning to the matter under consideration, Table 5 differentiates the callback rates by various occupations using the chi-square tests. The p-values in the table indicate the statistical significance of changes between 2016 and 2024. The null hypothesis in these tests posits that there is no significant difference in callback rates for each occupation across the two time periods for different groups. As the table shows, for Somali applicants, significant changes can be observed in the callback rates for cooks and cashiers. The callback rate for cooks rose notably from 12.3% in 2016 to 23.1% in 2024, a significant increase of 10.8% (p = 0.002), rejecting the null hypothesis. This may reflect an increasing openness toward Somali candidates in skilled positions, potentially driven by a reduction in occupation- specific bias and/or evolving labour demands. The most striking increase is for cashiers’ positions, where their callback rate jumped from 2.0% in 2016 to 33.3% in 2024, an impressive change of 31.3% (p = 0.000). The null hypothesis can also be rejected in this case. Table 5 also reveals similar significant improvements for the Iraqi candidates in the roles of cook and cashier. Callback rates for cooks increased from 12.7% to 26.0% over the eight-year period, marking a 13.3% rise (p = 0.001). Their callback rate in cashier positions also rose markedly from 2.0% to 27.8%, an increase of 25.8% (p = 0.001), leading to the rejection of the null hypothesis. This upward trend reflects a positive change in the perception or demand for Iraqi candidates in these occupations, who may have previously faced more barriers due to differential treatment in these positions. The Russian applicants saw significant improvement only in the cashier occupations, where callback rates increased from 8.2% in 2016 to 27.8% in 2024, reflecting a substantial 19.6% rise (p = 0.037) and resulting in the rejection of the null hypothesis. While other occupations did not show significant changes in their callback rates, this marked increase for cashiers suggests a reduced bias or increased acceptance in roles that involve customer interactions or transactional tasks. 39 Publications of the Ministry of Economic Affairs and Employment 2025:6 Table 5.  A comparison of callback rates differentiated by various occupations across 2016 and 2024 with chi-square tests. Country, job sector 2016 CR (%) 2016 N1 2024 CR (%) 2024 N χ2 df p-value Somali Restaurant staff 8.2 243 13.5 215 3.301 1 0.069 Waiters 13.1 206 18.9 180 2.414 1 0.120 Cooks 12.3 220 23.1 281 9.706 1 0.002 Cleaners 11.0 127 14.6 130 0.741 1 0.389 Salespersons 5.7 87 9.6 104 0.980 1 0.322 Office clerks/receptionists service/ receptionists 7.4 68 6.9 72 0.009 1 0.925 Cashiers 2.0 49 33.3 18 13.778 1 0.000 Iraqi Restaurant staff 13.6 243 17.7 215 1.460 1 0.227 Waiters 20.4 206 24.4 180 0.913 1 0.339 Cooks 12.7 220 26.0 281 13.462 1 0.000 Cleaners 13.4 127 18.5 130 1.234 1 0.267 Salespersons 6.9 87 11.5 104 1.196 1 0.274 Office clerks/receptionists service/ receptionists 10.3 68 8.3 72 0.160 1 0.690 Cashiers 2.0 49 27.8 18 10.695 1 0.001 Russian Restaurant staff 20.2 243 18.6 215 0.177 1 0.674 Waiters 29.1 206 26.1 180 0.436 1 0.509 Cooks 27.7 220 27.0 281 0.029 1 0.865 Cleaners 28.3 127 23.8 130 0.675 1 0.411 Salespersons 12.6 87 14.4 104 0.128 1 0.721 Office clerks/receptionists service/ receptionists 10.3 68 9.7 72 0.013 1 0.910 Cashiers 8.2 49 27.8 18 4.355 1 0.037 40 Publications of the Ministry of Economic Affairs and Employment 2025:6 Country, job sector 2016 CR (%) 2016 N1 2024 CR (%) 2024 N χ2 df p-value English Restaurant staff 22.2 243 33.0 215 6.706 1 0.010 Waiters 39.3 206 32.2 180 2.100 1 0.147 Cooks 30.0 220 37.0 281 2.705 1 0.100 Cleaners 26.8 127 23.8 130 0.291 1 0.590 Salespersons 24.1 87 23.1 104 0.030 1 0.863 Office clerks/receptionists service/ receptionists 10.3 68 11.1 72 0.024 1 0.876 Cashiers 12.2 49 38.9 18 5.976 1 0.015 Finnish Restaurant staff 36.6 243 41.9 215 1.313 1 0.252 Waiters 51.5 206 45.6 180 1.339 1 0.247 Cooks 44.5 220 50.9 281 1.989 1 0.158 Cleaners 38.6 127 40.0 130 0.054 1 0.816 Salespersons 34.5 87 25.0 104 2.056 1 0.152 Office clerks/receptionists service/ receptionists 16.2 68 18.1 72 0.087 1 0.768 Cashiers 14.3 49 55.6 18 11.841 1 0.001 Note: The p-values show the statistical significance of changes between 2016 and 2024. 1N includes both individuals who received a callback and those who did not. Thus, N provides the total from which the percentages are calculated. 41 Publications of the Ministry of Economic Affairs and Employment 2025:6 For the English applicants, the findings reveal significant changes in both restaurant staff and cashier positions. Callback rates for restaurant staff rose from 22.2% to 33.0%, an increase of 10.8% (p = 0.010), whereas callback rates in cashier occupations rose from 12.2% to 38.9%, marking an impressive 26.7% increase (p = 0.015). Thus, the null hypothesis was rejected for both these occupations. Lastly, for the Finnish candidates, the most notable change was also in cashier positions, where callback rates increased from 14.3% in 2016 to 55.6% in 2024, a significant jump of 41.3% (p = 0.001). For other occupations, no significant changes in callback rates were observed between 2016 and 2024. In sum, the above analysis across all groups highlights substantial changes within the cashier roles, where applicants from all backgrounds saw significant increases in callback rates. This trend may suggest an industry-wide shift toward more inclusive hiring practices for entry-level, customer-facing roles that may have previously been affected by employers’ discriminatory practices. Furthermore, the increased callbacks for cook and restaurant staff roles among Somali, Iraqi and English applicants imply a possible reduction in occupation-specific biases, particularly in roles requiring specialised skills. This pattern may also reflect broader growing sentiment in society and mass media about equality issues, coupled with evolving labour-market demands, which collectively appear to necessitate more equitable recruitment practices across diverse occupations for immigrants. 3.5 Employer Responses by Job-skill Level Next, we consider how the five groups under consideration have fared in the Finnish labour market with respect to skills required in a certain job. Table 6 provides a comparative analysis of callback rates for different applicant groups in 2016 and 2024, differentiated by job-skill levels (low, medium and high). The table also reports the results of the chi-square tests with corresponding p-values. These statistical tests are used to assess whether the differences in callback rates between ethnic groups are significant across the two years and job skill levels. The null hypothesis for this analysis posits that there is no significant difference in callback rates between 2016 and 2024 for these groups across job-skill levels, and any observed variations in callback rates are due to chance rather than any systematic changes in the hiring process over time. As earlier, a p-value less than 0.05 indicates a statistically significant difference in callback rates between the two years for a given job-skill level and ethnic group. 42 Publications of the Ministry of Economic Affairs and Employment 2025:6 As Table 6 reflects, Somali applicants experienced significant changes in both the medium and high job-skill levels. In the medium skill level, the callback rate increased from 9.0% in 2016 to 18.4% in 2024, yielding a p = 0.000, indicating a substantial improvement. At the high-skill level, the callback rate rose from 11.1% to 18.8%, also statistically significant (p = 0.009). In both these cases, the null hypothesis was rejected. In contrast, the low skill level did not indicate a significant change in callback rates, with an increase from 10.0% to 13.4% (p = 0.198), suggesting that improvements in callback rates for Somali applicants were more pronounced at higher skill levels. Iraqi applicants, in comparison, saw statistically significant increases across all job-skill levels. For low-skill jobs, the callback rate increased from 12.5% in 2016 to 17.2% in 2024 (p = 0.000), marking a notable improvement. In jobs requiring medium-skill level, they observed an increase from 14.4% to 22.4%, reflected in a p-value of 0.003. Finally, at the high-skill level, the callback rate rose from 12.8% to 20.8% (p = 0.009). These increases across all skill levels suggest a comprehensive improvement in callback rates for Iraqi applicants over time. Russian applicants showed no statistically significant changes across any job-skill levels. In fact, for low-skill jobs, they experienced a decrease from 24.4% in 2016 to 18.4% in 2024, which approached significance but did not reach it (p = 0.073). Medium and high-skill levels showed minimal differences: medium skill callback rates slightly increased from 20.9% to 23.2% (p = 0.428), and high skill rates rose marginally from 24.2% to 25.0% (p = 0.814). The absence of significant change for Russian applicants may indicate stable perceptions of employability for this group across job-skill levels over the period. 43 Publications of the Ministry of Economic Affairs and Employment 2025:6 Table 6.  A comparison of callback rates of different ethnic groups differentiated by job-skill level across 2016 and 2024. Country Job-skill level 2016 CR (%) 2016 N1 2024 CR (%) 2024 N χ2 df p-value Somali Low 10.0 271 13.4 337 1.654 1 0.198 Medium 9.0 431 18.4 375 15.111 1 0.000 High 11.1 298 18.8 288 6.826 1 0.009 Iraqi Low 12.5 271 17.2 337 2.545 1 0.000 Medium 14.4 431 22.4 375 8.684 1 0.003 High 12.8 298 20.8 288 6.868 1 0.009 Russian Low 24.4 271 18.4 337 3.207 1 0.073 Medium 20.9 431 23.2 375 0.629 1 0.428 High 24.2 298 25.0 288 0.056 1 0.814 English Low 23.6 271 24.6 337 0.084 1 0.772 Medium 29.5 431 31.5 375 0.379 1 0.538 High 26.2 298 35.4 288 5.878 1 0.015 Finnish Low 34.7 271 37.7 337 0.584 1 0.445 Medium 41.1 431 39.2 375 0.291 1 0.590 High 39.9 298 49.3 288 5.208 1 0.022 Note: The p-values show the statistical significance of changes occurred between 2016 and 2024. 1N includes both individuals who received a callback and those who did not. Thus, N provides the total from which the percentages are calculated. 44 Publications of the Ministry of Economic Affairs and Employment 2025:6 Table 6 further highlights the situation for English applicants, who held the most advantageous position in both time periods among all the immigrant applicant groups. For them, significant changes can be observed only at the high-skill level, where callback rates increased from 26.2% in 2016 to 35.4% in 2024 (p = 0.015). Low and medium-skill levels exhibited negligible changes, suggesting that the most significant improvement in callback rates for this group occurred primarily in high- skill jobs. Likewise, Finnish applicants also experienced a statistically significant increase exclusively at the high-skill level, with callback rates rising from 39.9% in 2016 to 49.3% in 2024 (p = 0.022). They also did not exhibit any significant changes for low-skill and medium-skill occupations. In conclusion, the findings reveal distinct trends in callback rates for the five groups under focus between 2016 and 2024. Somali and Iraqi applicants saw significant improvements across several skill levels, highlighting more openness towards these groups in accessing high-skill positions. For Russian applicants, callback rates remained relatively stable, with no significant changes across any skill levels. English and Finnish applicants saw improvements only at high-skill levels, possibly because it is already easier for them to access the lower and medium ends of the job-skill spectrum. 3.6 Employer Responses by Firm Size We now examine whether there have been any significant changes in employment opportunities for immigrants by firm size across the two periods of observation. Before proceeding to the results, however, it would be useful to first briefly discuss why studying callback rates by firm size may offer several benefits. First, examining callback rates by firm size can help in understanding recruitment practices by firm type. For example, large and small firms often have different recruitment processes, resources and biases. Exploring callback rates across firm sizes offers the opportunity to uncover trends specific to each type, highlighting how recruitment standards may differ between large corporations and smaller enterprises. Especially, it aids in identifying barriers to employment for immigrant groups. For certain groups, for instance, smaller firms may be more accessible due to potentially less formal recruitment procedures. In contrast, larger firms might have established diversity policies that influence their callback rates. Thus, studying these differences can reveal which types of firms may unintentionally generate barriers or offer better chances for immigrant applicants from different backgrounds. 45 Publications of the Ministry of Economic Affairs and Employment 2025:6 Secondly, identifying disparities in callback rates across firm sizes allows for targeted interventions and promote more fair practices for immigrant applicants. In addition, knowing which firm sizes are more receptive to immigrant applicants based on callbacks can inform and change their job-search strategies. Immigrant job seekers might choose to focus their efforts on firm sizes where callback rates are higher, thereby improving their chances of obtaining both job interview offers and the jobs themselves. 46 Publications of the Ministry of Economic Affairs and Employment 2025:6 Table 7.  A comparison of callback rates (CR) differentiated by firm size across 2016 and 2024 with chi-square tests. Country 2016 CR (%) 2016 N1 2024 CR (%) 2024 N χ2 df p-value Somali Micro 10.5 582 15.2 479 5.393 1 0.020 Small 6.5 278 16.7 288 14.264 1 0.000 Medium 14.8 115 22.5 111 2.236 1 0.135 Large 12.0 25 18.0 122 0.535 1 0.465 Iraqi Micro 13.6 582 20.5 479 8.962 1 0.003 Small 11.5 278 17.4 288 3.908 1 0.048 Medium 15.7 115 25.2 111 3.193 1 0.074 Large 20.0 25 21.3 122 0.021 1 0.884 Russian Micro 22.2 582 19.2 479 1.394 1 0.238 Small 20.5 278 24.3 288 1.175 1 0.278 Medium 30.4 115 26.1 111 0.517 1 0.472 Large 28.0 25 24.6 122 0.128 1 0.720 English Micro 25.4 582 28.4 479 1.177 1 0.278 Small 27.0 278 33.3 288 2.709 1 0.100 Medium 31.3 115 35.1 111 0.374 1 0.541 Large 40.0 25 26.2 122 1.928 1 0.165 Finnish Micro 39.9 582 41.3 479 0.237 1 0.627 Small 38.5 278 44.1 288 1.835 1 0.176 Medium 34.8 115 42.3 111 1.363 1 0.243 Large 44.0 25 36.1 122 0.558 1 0.455 Note: The p-values indicate the statistical significance of changes between 2016 and 2024. 1N includes both individuals who received a callback and those who did not. Thus, N provides the total from which the percentages are calculated. 47 Publications of the Ministry of Economic Affairs and Employment 2025:6 Table 7 provides the results of chi-square tests for different firm sizes (micro, small, medium and large firms) and applicant groups4. The null hypothesis for each test is that there is no association between the firm’s size and applicant background over time and the observed differences in callback rates across firm sizes between 2016 and 2024 can be attributed to chance. As can be observed, applicants of Somali background experienced significant increases in callback rates in both micro and small firms. In micro firms, the callback rate rose from 10.5% in 2016 to 15.2% in 2024, with a p-value of 0.020, indicating a statistically significant improvement and rejecting the null hypothesis. The increase was even more pronounced in small firms, with callback rates jumping from 6.5% to 16.7% (p = 0.000). However, medium and large firms did not show significant changes, with medium firms showing an increase from 14.8% to 22.5% (p = 0.135) and large firms from 12.0% to 18.0% (p = 0.465). Iraqi applicants showed significant improvements in micro and small firms with a noteworthy rise as well. In micro firms, the callback rate increased from 13.6% in 2016 to 20.5% in 2024 (p = 0.003) Small firms also showed a statistically significant increase, rising from 11.5% to 17.4% (p = 0.048). While medium firms showed an increase from 15.7% to 25.2%, this change was not statistically significant (p = 0.074). Callback rates in large firms remained stable, changing minimally from 20.0% to 21.3% (p = 0.884). Thus, improvements for Iraqi applicants were primarily concentrated in smaller firms. In comparison, there were no significant changes across any firm size for Russian applicants. In micro firms, the callback rate slightly decreased from 22.2% in 2016 to 19.2% in 2024. Small firms saw a minor increase and medium and large firms also showed marginal changes without statistical significance. The callback rates for medium firms dropped from 30.4% to 26.1% and for large firms from 28.0% to 24.6%. These observations suggest a stable trend in callback rates for Russian applicants across all firm sizes. English applicants did not exhibit any significant