313 © 2025 Conscientia Beam. All Rights Reserved. Control of corruption and education: Determinants of migration in the European Union Larysa Sysoyeva1+ Maria Jesus Martinez-Usarralde2 1,2Department of Comparative Education and Education History, Faculty of Philosophy and Education Sciences, University of Valencia, Spain. 1Email: larysa.sysoyeva@uv.es 2Email: m.jesus.martinez@uv.es (+ Corresponding author) ABSTRACT Article History Received: 2 December 2024 Revised: 19 January 2025 Accepted: 31 January 2025 Published: 26 February 2025 Keywords Corruption Education European union Migration Migration policy Tertiary education. The article substantiates the existence of the correlation between migration and key factors such as corruption control and the level of tertiary education. The objective is to analyze how these factors influence the migration flows within the European Union (EU). The methodological basis is the regression model using data from the 27 EU countries. The results of the analysis confirmed the impact of the level of higher education and corruption control on immigration rates. The developed model identified the trend towards lower immigration rates in countries with higher levels of corruption and lower levels of tertiary education. This trend is reflected in the decreasing number of immigrants in Greece, Lithuania, Slovakia, and Slovenia. On the other hand, countries with low corruption and access to quality education are generally more attractive to migrants. However, stricter migration policies and higher administrative barriers in countries such as Finland and France result in lower immigration rates despite their high levels of tertiary education. The study conducted highlighted the importance of promoting practical aspects of migration policy. Policies focused on improving education access and combating corruption could influence the politics of migration within the EU, potentially attracting immigrants in alignment with the country's interests. Contribution/Originality: This study examines the impact of education quality and corruption on migration in EU countries emphasizing the importance of implementing migration policies that align with national interests to enhance a country’s attractiveness to migrants. It also showed that countries with high levels of tertiary education and low levels of corruption are better positioned to attract highly qualified specialists, investors and students. 1. INTRODUCTION 1.1. Background and Problem Statement The complexity of the phenomenon of corruption in recent years along with changes in European Union migration policies following the 2015 refugee crisis, the coronavirus pandemic, and the Russian invasion of Ukraine, has raised crucial questions about the impact of corruption on access to education, its quality and equity (Hallak & Poisson, 2005) and social progress (Bernini, Bossavie, Garrote-Sánchez, & Makovec, 2023; Chapman & Lindner, 2016). In this context, European policies evidence a dual approach to migration (Brady, 2024). On the one hand, the migration of highly qualified people is promoted as a driver of economic development as reflected in the directive on the entry and residence of third-country nationals for highly qualified people (European Union, 2021). On the other hand, the increase in the migratory flow in various European Union countries has generated a growing complexity in the social and demographic structures. The reinforcement of security systems within the EU through Humanities and Social Sciences Letters 2025 Vol. 13, No. 1, pp. 313-323 ISSN(e): 2312-4318 ISSN(p): 2312-5659 DOI: 10.18488/73.v13i1.4107 © 2025 Conscientia Beam. All Rights Reserved. https://orcid.org/0000-0002-2254-7379 https://orcid.org/0000-0001-6777-3399 mailto:larysa.sysoyeva@uv.es mailto:m.jesus.martinez@uv.es https://www.doi.org/10.18488/73.v13i1.4107 Humanities and Social Sciences Letters, 2025, 13(1): 313-323 314 © 2025 Conscientia Beam. All Rights Reserved. increased border control and more restrictive measures linked to irregular migration (European Commission, 2024a) may lead to a restriction on the human rights of migrants, including the right to education (Neubauer, 2023). This situation in which the rights of migrants are violated increases the risk of corruption which can aggravate the barriers they face to access equitable and quality education. Although EU member countries are committed to the principles that guide European law, including equality, justice, and education (European Union, 2016) the current situation shows that the application of these principles is compromised. This occurs when policies are implemented that restrict the rights of migrants as exemplified by the recent deportation of migrants from Italy to Albania (Domínguez, 2024). Although supranational educational policies generally do not impose legal obligations on countries, they have a significant influence on the development of national education systems such as the Bologna Plan and the Erasmus Programs which have driven a profound transformation in higher education (Álvarez-López & Matarranz, 2020; Valle & Pedró, 2021). These challenges encompass several aspects, including the urgent need for more holistic and relevant educational tools. These tools should effectively address the current challenges linked to migratory movements and facilitate the right to education for all regardless of ethnicity and culture in EU society (Neubauer, 2020). In this context, the education of migrants is fundamental for their integration and social inclusion (European Commission, 2020). Furthermore, Terrón-Caro, Cárdenas-Rodríguez, Luna, and Delgado (2018) argue that access to education, permanence and quality in the education system constitutes an essential axis for social development (p. 792). This research analyzes how the quality of education and the level of corruption control influence the choice of migration destination within the European Union. These factors act as socioeconomic determinants in migrants' decision-making processes. To achieve this, this study will develop a multifactorial regression model to evaluate how these factors influence migration flows. This paper is structured as follows: After an introduction contextualizing the socio-political reality of migration in the EU, a review of the existing literature on the corruption paradigm in general and its specific manifestation in the educational sphere is presented. Subsequently, the methodology employed is detailed including the type of empirical analysis, specifying the multiple regression and statistical tools that allow identification of the correlation between the number of migrants in the country and determinants such as corruption and the level of tertiary education in EU countries. The results obtained are then presented and discussed. Finally, the conclusions of the research as well as its limitations are presented. 2. LITERATURE REVIEW 2.1. Corruption and Education and Its Consequences in Social Development Regarding political corruption, this has long been considered a significant factor in legitimacy understood as the ability to justify power (Beetham, 1991) by those in charge of institutions who have attributed the ability to make binding decisions for collectives, mainly state populations (Manow, 2003; Wolf, 2014). However, research has attributed varying degrees of importance to the impact that corruption has had on the formulation and implementation of educational policies specifically in relation to economic and social issues. In the 1960s, some authors i.e., Huntington (1968) and Leff (1964) agreed that corruption in the context of modernization had a positive impact on economic growth and socio-political stability. This was especially evident when the companies were owned by foreign individuals. Leff (1964) argues that graft can have beneficial effects and can provide the direct incentive necessary to mobilize the bureaucracy for more energetic action on behalf of the entrepreneurs (p.10). In other words, lack of government support for economic activity or innovation could result in a reluctance to actively support the economy. It was argued pragmatically that corrupt government officialdom might be more inclined to innovate than their non-corrupt counterparts (Huntington, 1968). Humanities and Social Sciences Letters, 2025, 13(1): 313-323 315 © 2025 Conscientia Beam. All Rights Reserved. However, more recent theorists have challenged this assessment arguing that its implicit support for the toleration of corruption is irresponsible (Jansen & Priddat, 2005) as is its imposition of a strict separation between the public and private spheres (Theobald, 1990). According to Shleifer and Vishny (1993) the corruption entails costs due to the distortions it generates especially because of the need for secrecy. These distortions derived from corruption can discourage productive investments and hinder economic growth. Jimenez- Sanchez (2023) argues in his study that the lack of transparency in government functioning increases opportunities for corruption and hinders control over the use of public resources. This is particularly relevant in education where opacity can conceal corrupt practices like fund diversion and preferential contracting. Similarly, De Graaf (2007) notes that corruption can manifest as an individual act motivated by economic or emotional factors rather than solely as a result of group or institutional dynamics. The definition of “corruption” in the education sector is broad and includes the systematic use of the public sector for the benefit of the private sector which affects the availability, quality, and equity of education (Kirya, 2019). In countries with high levels of corruption, less is invested in education due to fewer opportunities to obtain bribes in this sector. Corruption in education can manifest itself through low salaries for teachers and civil servants as well as a lack of accountability. In contemporary society, there has been considerable interest in the phenomenon of corruption in education (Hallak & Poisson, 2007; Wysmułek, 2024). Research has explored educational corruption using data from various countries which provides a broader and comparative perspective on this phenomenon at an international level because of its significant and negative implications for education. Heyneman (2004) argues that a corrupt education system has devastating consequences for education as it destabilizes national cohesion. It is a serious systemic flaw that compromises the concept of the public good. Furthermore, corruption in education has emerged as a global challenge that poses a direct threat to education quality, equity, and social inclusion. Corruption fuels inequality and social exclusion creating a breeding ground for social problems such as insecurity while conflicts in the struggle for scarce resources aggravated by institutional corruption can polarize groups and exacerbate social divisions (Alcaide Zugaza & Larrú Ramos, 2007). According to Megías, Gouvêa Maciel, De Sousa, and Jiménez Sánchez (2024) “resource gearing” that benefits some people may entail a loss for another part of the citizenry which increases the perception of social injustice. According to Saengchai, Sawasdee, and Siriattakul (2020) corruption in education refers to the abuse of authority for individual and material purposes which negatively impacts trust in the education system. This study presented results describing the impact of corruption on educational quality in Asian countries over a 26-year period. Corruption in education is thus presented as a multifaceted phenomenon that negatively affects individual and social development. This manifests in various forms including pecuniary and non-pecuniary corruption (Sabic-El- Rayess & Heyneman, 2020). Pecuniary corruption involves the exchange of money for favors or undue benefits, such as the payment of bribes for higher grades or admission to a prestigious educational institution (Heyneman, 2004). In contrast, non-pecuniary corruption refers to the exchange of favors between individuals or groups without the direct involvement of money. This may include nepotism or the influencing and manipulation of selection processes to benefit certain individuals or groups (Osipian, 2009). The consequences of educational corruption are wide-ranging and damaging. This directly affects students, who may experience diminished learning opportunities and professional development (Sayfullooh, Rusdinal, Hadiyanto, Olivia, & Marhadi, 2023). Moreover, the perception of corruption in educational institutions can lead to mistrust, discouragement, and a lack of motivation among students increasing the risk of them dropping out of the education system. Transparency International (2017) warns that corruption in education can lead to apathy and disinterest in civil society regarding educational decision-making thereby making it difficult to implement reforms and improve the quality of education. Humanities and Social Sciences Letters, 2025, 13(1): 313-323 316 © 2025 Conscientia Beam. All Rights Reserved. Data from the special global corruption barometer (Transparency International, 2013) on the perception of corruption in institutions including the education sector also provide a clear picture of the problem in certain European countries. It is noteworthy that their analysis shows a particularly strong perception of educational corruption in countries such as Denmark, Finland, and Spain with average scores of 2.0, 2.1, and 2.1, respectively (on a scale from 1 to 5, where 1 means “not at all corrupt” and 5 means “extremely corrupt”). Conversely, countries like Bulgaria (3.4), Croatia (3.5), Moldova (3.7), and Ukraine (4.0) have faced significant challenges with educational corruption. However, it is evident that data from a single year is not sufficient to draw conclusions. In many countries, corruption in education is considerably lower than in sectors such as the legal system, health care or police forces. Moreover, the recent research on perceptions of corruption in Spain (Megías et al., 2024) reveals a shocking interpretation among the subjects surveyed despite the fact that Spanish society tends to accept a greater flexibility of ethical principles to solve problems (almost 15% argue that “Yes” it makes sense to ignore ethical principles to solve problems ), a significant segment of the population strongly opposes this practice. Furthermore, Villoria and Jiménez (2012) argue that “certain social beliefs” about corruption may lead to a greater tolerance of corruption. In their study, they assert that the normalization of corrupt practices has eroded society's ability to demand transparency. Therefore, this discrepancy suggests the existence of a contemporary society that is diverse in its attitudes towards corruption where education in ethics and integrity emerges as a crucial factor in fostering a more critical and demanding citizenry in terms of transparency and favoritism practices. This idea is echoed by authors such as Parker, Spires, Farook, and Mean (2008) who argue that a more educated citizenry is more sensitive to ethical issues. According to Varvarigos (2023) one of the main endogenous factors in the development of corruption is the lack of social education defined as “inadequate attitudes or behaviors”. This tends to affect more underdeveloped countries where citizens are not aware of or do not want to become aware of the misconduct of their governments or business. Hallak and Poisson (2007) and Stephens (2019) indicate that corruption has become a threat to the quality of education at all levels from primary to higher education. This issue affects everyone involved in the educational process, including teachers, administrative staff, families, students and society at large. However, in research, we will focus on the quality of tertiary education as one of the factors to be analyzed. Tertiary education is defined as the post-secondary stage where the formative process of young people is consolidated (Colorado, 2017). In Spain, for example, the tertiary or higher education system includes university education regulated by the Organic Law (2023) of the University System (LOSU) as well as higher vocational education and training together with higher artistic and sports education. 3. METHODOLOGY 3.1. Instrument Adaptation This study investigates the relationship between migration and tertiary educational attainment and the control of corruption. A regression analysis was conducted using data from 27 EU countries spanning the period 2002- 2021. Data from 2022 were excluded to maintain analytical integrity. This exclusion is accounted for by the significant impact of Ukrainian refugee movements on migration patterns, particularly within Europe. The data of Eurostat (2024a) indicates a 3 million increase in migrants to EU countries in 2022 (448.8 million) compared to 2021 (445.8 million) largely due to displacement resulting from the Russian aggression in Ukraine. Additionally, data from Luxembourg and Malta were omitted. Their small populations (slightly over 0.5 million) render migration figures highly susceptible to fluctuations potentially introducing bias into the analysis. Consequently, this study utilizes a 20-year period to provide a more reliable assessment of long-term migration trends. Humanities and Social Sciences Letters, 2025, 13(1): 313-323 317 © 2025 Conscientia Beam. All Rights Reserved. This analysis employed the number of immigrants per 1,000 population as the dependent variable. This variable was calculated by dividing the annual number of immigrants by the population as of January 1st of the subsequent year from Eurostat data. The first independent variable, tertiary educational attainment was sourced from Eurostat. In accordance with the International Standard Classification of Education (UNESCO, 2012) of Eurostat (2024b) tertiary education encompasses levels 5-8. This indicator is measured as the percentage of the population aged 25-34 inclusive of both sexes. The next indicator is the Control of Corruption Index, a component of the Worldwide Governance Indicators (WGI) of the World Bank measuring perceptions of the extent to which public power is exercised for private gain. This index captures perceptions of corruption within public institutions encompassing factors such as bribery, political corruption, and the effectiveness of anti-corruption initiatives. The index ranges from -2.5, reflecting weak control of corruption, to +2.5 indicating strong control. A multiple linear regression model is employed using the statistical software Statgraphics Centurion to determine how variables significantly influence the indicator “the immigrants per 1000 population”. 𝑌 = 𝑏0 + 𝑏1𝑥1 + 𝑏2𝑥2 + ⋯ + 𝑏𝑛𝑥𝑛 (1) It is assumed that a multiple linear regression model is expressed as follows (Statgraphics Centurion 18, 2017, p. 277): Equation 1 presents the multiple linear regression model that is employed to determine how the independent variables (e.g., x1, x2,…,xn) influence the dependent variable Y. 4. RESULTS 4.1. Multiple Regression Model Development Practical calculations were performed in the applied software Statgraphics using the multiple variable analysis function. A multiple linear regression analysis was conducted to examine the relationship between immigration rates (measured as the number of immigrants per 1000 population) and two independent variables: tertiary educational attainment and control of corruption. Equation 2 argues that immigration rates increase with higher levels of tertiary educational attainment and stronger control of corruption. 𝐼𝑚𝑚𝑖𝑔𝑟𝑎𝑛𝑡𝑠 𝑝𝑒𝑟 1000 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 = −2.1675 + 0.2568 ∗ (𝑇𝑒𝑟𝑡𝑖𝑎𝑟𝑦 𝑒𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝑎𝑡𝑡𝑎𝑖𝑛𝑚𝑒𝑛𝑡) + 1.1370 ∗ (𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑜𝑓 𝐶𝑜𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛) (2) The results are presented in Table 1. This table presents the results of fitting multiple regression models to describe the relationship between the immigrants per 1000 population and two predictor variables. Models have been fit containing all combinations based on the number of observations i.e., 476 in this analysis. We used the criterion of the highest adjusted R-squared to evaluate the quality of the model. The model explained 33.5% of the variance in immigration rates (R² = 0.3350). Table 1. Results of multiple regression modeling. Regression statistics Value Multiple R 0.5788 R square 0.3350 Adjusted R square 0.3321 Standard error 4.4946 The ANOVA results presented in Table 2 indicated that the model was statistically significant, F >> Fcritical value (2, 473) or 119.11>>7.0, p < .001. The model comprising tertiary educational attainment and control of corruption as predictors accounted for a significant proportion of the variance in immigration rates as evidenced by the substantial mean square for the model (2406. 32) compared to the residual mean square (20. 20). Humanities and Social Sciences Letters, 2025, 13(1): 313-323 318 © 2025 Conscientia Beam. All Rights Reserved. Table 2. Analysis of variance. Source Sum of squares Df Mean square F-ratio p- value Model 4812.64 2 2406.32 119.11 < 0.001 Residual 9555.34 473 20.20 - - Total (Corr.) 14367.98 475 - - - Since the p-value in the ANOVA Table 3 is less than 0.05, there is an indication of possible serial correlation at the 95.0% confidence level. In addition, the statistical significance of the model is confirmed by the criterion, the level of the p-value (see Table 3), R-squared statistics, and the Durbin-Watson test. Table 3. Statistical features of model parameters. Parameters Estimate Standard error T statistic p-value Constant -2.167 0.712 -3.043 0.002 Tertiary educational attainment 0.257 0.021 12.363 < 0.001 Control of corruption 1.137 0.286 3.979 < 0.001 Table 3 presents the results of a regression analysis examining the impact of two independent variables, tertiary educational attainment and the control of corruption on the immigration rates (measured as immigrants per 1,000 population). The analysis reveals that both factors have a statistically significant positive effect on immigration. For every one-unit (in percentage) increase in tertiary educational attainment, the immigrant rate increases by 0.2568 per 1,000 population. Table 4. Means of variables. Country Country code Mean Number of observations 01.01.22 Immigrants per 1000 population Tertiary educational attainment Control of corruption Population Austria AUT 12.04 29.44 1.62 18 8, 978, 929 Belgium BEL 11.87 43.58 1.45 18 11, 617, 623 Bulgaria BGR 3.74 31.32 -0.26 11 6, 482, 484 Cyprus CYP 20.74 50.84 0.91 20 904, 705 Czechia CZE 5.72 24.29 0.44 20 10, 516, 707 Germany DEU 9.39 27.62 1.81 20 83, 237, 124 Denmark DNK 10.61 40.83 2.31 20 5, 873, 420 Spain ESP 11.83 41.49 0.97 20 47, 486, 843 Estonia EST 5.81 36.90 1.15 20 1, 331, 796 Finland FIN 5.17 39.65 2.25 20 5, 548, 241 France FRA 5.04 44.44 1.35 16 67, 957, 053 Greece GRC 6.90 33.81 0.08 20 10, 459, 782 Croatia HRV 4.34 26.42 0.09 19 3, 862, 305 Hungary HUN 4.57 26.39 0.32 20 9, 610, 403 Ireland IRL 17.83 49.47 1.58 20 5, 154, 277 Italy ITA 5.98 21.71 0.28 20 59, 030, 133 Lithuania LTU 7.55 46.36 0.42 20 2, 805, 998 Luxembourg LUX Data was not used. 645, 397 Latvia LVA 4.05 33.68 0.34 20 1, 875, 757 Malta MLT Data was not used. 520, 174 Netherlands NLD 8.60 41.29 1.99 20 17, 590, 672 Poland POL 3.65 35.82 0.51 20 36, 889, 761 Portugal PRT 3.80 28.21 0.96 20 10, 421, 117 Romania ROU 7.94 23.53 -0.23 14 19, 042, 455 Slovakia SVK 1.10 34.64 0.86 20 5, 434, 712 Slovenia SVN 9.64 34.64 0.86 20 2, 107, 180 Sweden SWE 10.65 42.63 2.16 20 10, 452, 326 Total EU 8.02 35.36 0.97 476 445, 837, 374 Source: Eurostat (2024a). Humanities and Social Sciences Letters, 2025, 13(1): 313-323 319 © 2025 Conscientia Beam. All Rights Reserved. This effect is observed while holding control of corruption constant. Similarly, a one-unit increase in control of corruption indicating stronger control leads to a 1.1370 increase in the immigrant rate, holding tertiary educational attainment constant (the higher the value of control of corruption, the better the control of corruption, the maximum is +2.5). These findings are statistically significant as evidenced by the p-values for both coefficients, indicating a strong relationship between these factors and immigration rates. Table 4 shows the average indicators of the three variables by EU countries. In the case of some countries such as Bulgaria and Romania, the indicators were not available for all the years of the period analyzed. The following main results are obtained: First, countries such as Bulgaria, Croatia, Hungary, Latvia, Poland, Portugal, and Slovakia have the lowest levels of immigrants per 1.000 inhabitants with fewer than 5 immigrants per 1.000 inhabitants. In contrast, the highest levels of immigration are found in Austria, Belgium, Cyprus, Denmark, Spain, Ireland, and Sweden with more than 10 per 1.000. Second, the countries with the lowest levels of tertiary education are the Czech Republic, Croatia, Hungary, Italy, and Romania while the highest levels are observed in countries such as Belgium, Cyprus, Denmark, Spain, France, Ireland, Lithuania, the Netherlands, and Sweden. Third, the Control of Corruption Index shows very low levels in Bulgaria and Romania as well as in countries such as the Czech Republic, Greece, Croatia, Hungary, Italy, Lithuania, and Latvia which exhibit rather moderate levels of control of corruption (below 0.5). On the other hand, the best situation is observed in countries such as Austria, Germany, Denmark, Finland, Ireland, the Netherlands and Sweden. Thus, the model confirms a real decrease in the number of immigrants in Greece, Lithuania, Slovakia and Slovenia. The high level of education and the low level of corruption in the country generally enhance its overall attractiveness. A country with a developed economy, a highly skilled labor force and a low level of corruption is able to attract highly qualified specialists as well as investors, entrepreneurs, and students. These factors serve as indicators of a country where immigrants can find a high degree of legal protection, access to resources, transparent and fair labor conditions, and opportunities for development and growth. However, some countries require particular analysis. Specifically, Finland and France exhibit low numbers of immigrants per 1.000 inhabitants despite having a high level of tertiary education and effective control of corruption. These data can be explained by the presence of stricter immigration regulations and administrative barriers although they do not prevent the arrival of immigrants or reduce the growth rate of immigration. On the other hand, Cyprus shows higher levels of immigration than the modeled values. In this country, the average number of immigrants per 1.000 inhabitants is 20.74 (the highest average) with an average tertiary educational attainment of 50.84% and an average control of corruption at a low level, measured at +0.91. Explanations for this could include the climate attracting seasonal and foreign immigrants as well as attractive conditions for investors and immigrant workers. The country's tourism and service sector results in high seasonality that requires foreign labor. 4.2. Discussion This research has delved into the impact of two determinants such as the level of education and the level of control of the corruption in the country and how these variables can increase the country's attractiveness for migrant populations. Morrice, Shan, and Sprung (2017) point out that policies in the country and their inclusion strategies often obscure a deficit discourse through which migrants are interpreted as important agents for social inclusion with education as the basis. However, according to the Statistics on Migration to Europe (European Commission, 2024b) education was the main factor for only 4% of migrant respondents. Factors such as family (34%), job search (21%), and refugee reasons (15%) have been the primary motivations for migration. Higher levels of education may indicate a country's greater capacity to attract talent and offer opportunities, which could explain its relationship with immigration. This idea aligns with the view of Luxán Serrano, Imaz Bengoetxea, Bereziartua Etxeberria, and Lauzurika Arrondo (2014) who emphasize the role of higher education in Humanities and Social Sciences Letters, 2025, 13(1): 313-323 320 © 2025 Conscientia Beam. All Rights Reserved. forming social influence groups and promoting social inclusion for all. Additionally, another finding of our study is consistent with previous research by Hadjar and Gross (2016) who examine the relationship between macro characteristics of educational systems and individual-level inequality arguing that corruption in the educational system affects the perceived quality of schools and social inequalities leading to a poor perception of the educational system in general among stakeholders including migrants. Moreover, the results indicate migrants' preference for countries with low levels of corruption. Arif (2022) and Höckel, Santos Silva, and Stöhr (2018) argue that reforms not only reduce the magnitude of corruption in a country but also make it easier for migrants, thus highlighting the need to focus efforts on fostering demand for high-quality educational attainment. 5. CONCLUSION The main objective declares that both factors in synergy specifically a high level of education and a low level of corruption enhance a country's attractiveness. A nation with a developed economy, a highly skilled workforce and a low level of corruption is able to attract highly qualified specialists as well as investors, entrepreneurs, and students. Consequently, these factors serve as indicators of a country where immigrants can find a high degree of legal protection, access to resources, transparent and fair working conditions and opportunities for development and growth. Marshall and Bottomore (1998) note that education can also act as a mechanism for the reproduction of social inequalities while being a citizenship right and a state function. They further emphasize that segregation in access to education and the different "influences" received within educational settings deepen social stratification. In other words, equal opportunities will only be effective when there is a systemic policy aimed at addressing both economic and educational inequalities. You (2015) points out that inequality and corruption create a vicious cycle, inequality fosters an environment where corruption thrives, while corruption exacerbates social inequality. This complex challenge extends to the realm of education. 6. POLICY SUGGESTIONS This study resulted in a positive correlation with the level of education suggesting that migrants with a high level of education are attracted to countries that value their skills and provide opportunities for professional growth and advancement. Access to European education known for its high quality is among the reasons cited by migrant populations who seek educational opportunities not only for themselves but also for their children (Terrón-Caro et al., 2018). Additionally, poor educational quality in the country of origin can hinder prospects for migrants whose goal is often to secure jobs with competitive salaries. It is important to emphasize that data on corruption within educational settings, particularly in the university sector, remains limited compared to annually published data on corruption control (Transparency International, 2013). Second, the positive relationship between migration flows and corruption highlights the fundamental role of good governance and transparency (Mauro, 1998). Controlling corruption also has a positive effect which is logical since countries with lower levels of corruption attract more immigrants. Corruption acts as a deterrent to immigration by creating instability, discouraging investment, and impeding a nation's economic growth. According to Ferrarons (2021) to achieve transparency in the fight against corruption in Spain, “it is important to foster internal ethical culture through institutional integrity strategies as a collective mandate directed at all levels...” (p.179). He argues that the positive impact of citizens' influence on policymaking and the feedback from their participation is achieved through transparency. Therefore, it is essential for migrants, like any other social group, to feel that they have equal access to both the educational system and the programs designed to include them which may not always allow them to fully integrate into society or foster a sense of belonging. Thus, policies aimed at improving educational systems and strengthening anti-corruption efforts can serve as effective tools for attracting immigrants in alignment with the country's interests. Humanities and Social Sciences Letters, 2025, 13(1): 313-323 321 © 2025 Conscientia Beam. All Rights Reserved. 7. LIMITATIONS AND IMPLICATIONS As for limitations, while the resulting model suggests that both education level and control of corruption determine migrants' choice of a country to live in where the university education level is an indicator of a more highly educated population it is itself a sign of a developed economy and a highly skilled labor force that increases a country's attractiveness to migrants. Other factors such as family and the labor market were not considered in our research due to the different scientific focus of the study. To conclude, the findings of this research suggest the need to broaden the focus for future studies. In this context, it is important to emphasize that the level of migration may also be related to historical connections with other countries and the presence of pre-existing migrant communities that contribute to the influx of new migrants through connections and social support. In this scenario, it is essential to consider regional particularities (such as the discrepancy between northern and southern EU countries). Funding: This study received no specific financial support. Institutional Review Board Statement: Not applicable. Transparency: The authors declare that the manuscript is honest, truthful and transparent, that no important aspects of the study have been omitted and that all deviations from the planned study have been made clear. This study followed all rules of writing ethics. Competing Interests: The authors declare that they have no competing interests. Authors’ Contributions: Both authors contributed equally to the conception and design of the study. Both authors have read and agreed to the published version of the manuscript. REFERENCES Alcaide Zugaza, L., & Larrú Ramos, J. (2007). Corruption, development aid, poverty and human development. Boletín Económico De ICE, 1(2917), 37-58. Álvarez-López, G., & Matarranz, M. (2020). Quality and assessment as global trends in educational policy: A comparative study of national assessment agencies in compulsory education in Europe. Revista Complutense de Educación, 31(1), 83-93. Arif, I. (2022). Educational attainment, corruption, and migration: An empirical analysis from a gravity model. Economic Modelling, 110(C), 1-14. https://doi.org/10.1016/j.econmod.2022.105802 Beetham, D. (1991). The legitimation of power. London: Red Globe Press. Bernini, A., Bossavie, L., Garrote-Sánchez, D., & Makovec, M. (2023). Corruption as a push and pull factor of migration flows: Evidence from European countries. Policy Research Working Paper Series 10566, The World Bank. Brady, H. (2024). Breaking taboos: EU asylum and migration policy since 2020 international centre for migration policy development (ICMPD). Retrieved from https://www.icmpd.org/blog/2024/breaking-taboos-eu-asylum-and-migration-policy- since-2020 Chapman, D. W., & Lindner, S. (2016). Degrees of integrity: The threat of corruption in higher education. Studies in Higher Education, 41(2), 247-268. https://doi.org/10.1080/03075079.2014.927854 Colorado, I. C. T. (2017). Issues to be addressed in tertiary education. Rutas de Formación: Prácticas y Experiencias, 3, 30-35. https://doi.org/10.24236/24631388.n3.2016.631 De Graaf, G. (2007). Causes of corruption: Towards a contextual theory of corruption. Public Administration Quarterly, 31(1), 39- 86. Domínguez, Í. (2024). Italy activates a second deportation of migrants to Albania but only manages to transfer eight. EL PAIS. Retrieved from https://elpais.com/internacional/2024-11-06/italia-activa-una-segunda-deportacion-de-migrantes-a-albania- pero-solo-consigue-trasladar-a-ocho.html European Commission. (2020). Action plan on integration and inclusion 2021-2027. Retrieved from https://eur-lex.europa.eu/legal- content/EN/TXT/PDF/?uri=CELEX:52020DC0758 European Commission. (2024a). Pact on migration and asylum a common EU system to manage migration. Retrieved from https://home-affairs.ec.europa.eu/policies/migration-and-asylum/pact-migration-and-asylum_en https://doi.org/10.1016/j.econmod.2022.105802 https://www.icmpd.org/blog/2024/breaking-taboos-eu-asylum-and-migration-policy-since-2020 https://www.icmpd.org/blog/2024/breaking-taboos-eu-asylum-and-migration-policy-since-2020 https://doi.org/10.1080/03075079.2014.927854 https://doi.org/10.24236/24631388.n3.2016.631 https://elpais.com/internacional/2024-11-06/italia-activa-una-segunda-deportacion-de-migrantes-a-albania-pero-solo-consigue-trasladar-a-ocho.html https://elpais.com/internacional/2024-11-06/italia-activa-una-segunda-deportacion-de-migrantes-a-albania-pero-solo-consigue-trasladar-a-ocho.html https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52020DC0758 https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52020DC0758 https://home-affairs.ec.europa.eu/policies/migration-and-asylum/pact-migration-and-asylum_en Humanities and Social Sciences Letters, 2025, 13(1): 313-323 322 © 2025 Conscientia Beam. All Rights Reserved. European Commission. (2024b). Reasons to stay in Europe. Retrieved from https://commission.europa.eu/strategy-and- policy/priorities-2019-2024/promoting-our-european-way-life/statistics-migration-europe_en European Union. (2016). Charter of fundamental rights of the European union. Retrieved from https://eur-lex.europa.eu/legal- content/EN/TXT/?uri=celex%3A12016P%2FTXT European Union. (2021). Directive EU 2021/1883 of the European parliament on the conditions of entry and residence of third-country nationals for the purpose of highly qualified employment. Retrieved from https://eur-lex.europa.eu/legal- content/EN/TXT/?uri=CELEX%3A32021L1883 Eurostat. (2024a). Population and social conditions Retrieved from https://ec.europa.eu/eurostat/web/main/data/database Eurostat. (2024b). Population by educational attainment level, sex and age. Retrieved from https://ec.europa.eu/eurostat/databrowser/view/EDAT_LFSE_03/default/table?lang=en Ferrarons, M. F. (2021). Ordinances, interest groups and regulatory quality. Anuario del Buen Gobierno y de la Calidad de la Regulación: ABGCR(1), 153-182. Hadjar, A., & Gross, C. (2016). Education systems and inequalities international comparisons. Bristol: University of Bristol. Hallak, J., & Poisson, M. (2005). Ethics and corruption in education: An overview. Journal of Education for International Development, 1(1), 1-3. Hallak, J., & Poisson, M. (2007). Corrupt schools, corrupt universities: What can be done? Paris: International Institute for Education Planning. Heyneman, S. P. (2004). Education and corruption. International Journal of Educational Development, 24(6), 637-648. Höckel, L. S., Santos Silva, M., & Stöhr, T. (2018). Can parental migration reduce petty corruption in education? The World Bank Economic Review, 32(1), 109-126. https://doi.org/10.1596/32167 Huntington, S. (1968). Political order in changing societies. London: Yale University Press. Jansen, S. A., & Priddat, B. P. (2005). Corruption – unenlightened capitalism. Multidisciplinary perspectives on the function and consequences of corruption. Wiesbaden: VS-Verlag. Jimenez-Sanchez, F. (2023). Crisis and corruption in Spain: Improving the quality of governance to fight corruption. Siyasal: Journal of Political Sciences, 32(1), 1-14. https://doi.org/10.26650/siyasal.2023.32.1249638 Kirya, M. (2019). Education sector corruption: How to assess it and ways to address it. Bergen Norway: 4 Anti-Corruption Resource Centre. Leff, N. H. (1964). Economic development through bureaucratic corruption. American Behavioral Scientist, 8(3), 8-14. https://doi.org/10.1177/000276426400800303 Luxán Serrano, M., Imaz Bengoetxea, J., Bereziartua Etxeberria, G., & Lauzurika Arrondo, A. (2014). University and social transformation. Journal of Sociology of Education-RASE, 7(3), 673-688. Manow, P. (2003). Political corruption as a subject of political science. In H. H. Arnim, Corruption. Networks in politics, public offices and business. Munich: Droemer Knaur. Marshall, T., & Bottomore, T. (1998). Citizenship and social class. Madrid: Alianza Editorial. Mauro, P. (1998). Corruption and the composition of government expenditure. Journal of Public Economics, 69(2), 263-279. https://doi.org/10.1016/s0047-2727(98)00025-5 Megías, A., Gouvêa Maciel, G., De Sousa, L., & Jiménez Sánchez, F. (2024). Comparative perceptions of corruption in Spain and Portugal research report. Murcia: Valencia y Lisboa. Morrice, L., Shan, H., & Sprung , A. (2017). Migration, adult education and learning. Studies in the Education of Adults,, 49(2), 129- 135. Neubauer, A. (2020). The right to education of refugee and asylum-seeking minors from a human rights-based approach: Difficulties, purpose and educational intervention. Revista Española de Educación Comparada, 35, 70-91. Neubauer, A. (2023). Migrant children’s right to education: Education policy stages of the European union. Education Policy Analysis Archives 31, 1-22. Organic Law. (2023). Organic law. Retrieved from https://www.boe.es/eli/es/lo/2023/03/22/2/con https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/promoting-our-european-way-life/statistics-migration-europe_en https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/promoting-our-european-way-life/statistics-migration-europe_en https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A12016P%2FTXT https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A12016P%2FTXT https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32021L1883 https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32021L1883 https://ec.europa.eu/eurostat/web/main/data/database https://ec.europa.eu/eurostat/databrowser/view/EDAT_LFSE_03/default/table?lang=en https://doi.org/10.1596/32167 https://doi.org/10.26650/siyasal.2023.32.1249638 https://doi.org/10.1177/000276426400800303 https://doi.org/10.1016/s0047-2727(98)00025-5 https://www.boe.es/eli/es/lo/2023/03/22/2/con Humanities and Social Sciences Letters, 2025, 13(1): 313-323 323 © 2025 Conscientia Beam. All Rights Reserved. Osipian, A. L. (2009). Corruption hierarchies in higher education in the former Soviet Bloc. International Journal of Educational Development, 29(3), 321-330. https://doi.org/10.1016/j.ijedudev.2008.08.006 Parker, S., Spires, P., Farook, F., & Mean, M. (2008). State of trust: How to build better relationships between councils and the public. London: Demos. Sabic-El-Rayess, A., & Heyneman, S. (2020). Education and corruption. Oxford Research Encyclopedia of Education. Retrieved from https://oxfordre.com/education/view/10.1093/acrefore/9780190264093.001.0001/acrefore-9780190264093-e-1637 Saengchai, S., Sawasdee, A., & Siriattakul, P. (2020). Are corruption, demographic pressure and brain drain damaging the quality of education? Evidence from Asia. Journal of Security and Sustainability Issues, 9, 122-132. Sayfullooh, I. A., Rusdinal, R., Hadiyanto, H., Olivia, N., & Marhadi, A. (2023). The unveiling the faces of corruption in education: A systematic review of various forms of corruption across countries and educational levels. Acta Paedagogica Vilnensia, 51, 47-62. Shleifer, A., & Vishny, R. (1993). Corruption. Quarterly Journal of Economics, 108(3), 599-617. Stephens, J. M. (2019). Natural and normal, but unethical and evitable: The epidemic of academic dishonesty and how we end it. Change: The Magazine of Higher Learning, 51(4), 8-17. https://doi.org/10.1080/00091383.2019.1618140 Terrón-Caro, T., Cárdenas-Rodríguez, R., Luna, T. E. C., & Delgado, B. V. (2018). Education and migrant women in transit on the northern border of Mexico. Education as a variable and future expectation in the migration process. Revista Complutense de Educación, 29(3), 791-805. Theobald, R. (1990). Corruption, development, and underdevelopment. Durham: Duke University Press. Transparency International. (2013). Global corruption barometer. Retrieved from https://images.transparencycdn.org/images/GCB_Citizens_voices_FINAL.pdf Transparency International. (2017). Global corruption barometer: People and corruption: Citizens’ Voices from Around the World. Berlin, Germany: Transparency International. UNESCO. (2012). International standard classification of education: ISCED 2011. Montréal: UNESCO. Valle, J. M., & Pedró, F. (2021). Supranational education and higher education: Keys to global transformation for complex societies. Revista Española de Educación Comparada, 37, 10-25. https://doi.org/10.5944/reec.37.2021.29228 Varvarigos, D. (2023). Cultural persistence in corruption, economic growth, and the environment. Journal of Economic Dynamics and Control, 147, 104590. https://doi.org/10.1016/j.jedc.2022.104590 Villoria, M., & Jiménez, F. (2012). Corruption in Spain (2004-2010): Data, perception and effects. Revista Española de Investigaciones Sociológicas (REIS), 138(1), 109-134. https://doi.org/10.5477/cis/reis.138.109 Wolf, S. (2014). Corruption, anti-corruption policy and public administration. (S. V. Wiesbaden, Ed.). Wiesbaden: Springer. Wysmułek, I. (2024). Corruption in the public schools of Europe: A cross-national multilevel analysis of education system characteristics. International Journal of Comparative Sociology, 65(1), 10-38. https://doi.org/10.1177/00207152221096841 You, J.-S. (2015). Democracy, inequality and corruption. Cambridge: Cambridge University Press. Views and opinions expressed in this article are the views and opinions of the author(s), Humanities and Social Sciences Letters shall not be responsible or answerable for any loss, damage or liability etc. caused in relation to/arising out of the use of the content. https://doi.org/10.1016/j.ijedudev.2008.08.006 https://oxfordre.com/education/view/10.1093/acrefore/9780190264093.001.0001/acrefore-9780190264093-e-1637 https://doi.org/10.1080/00091383.2019.1618140 https://images.transparencycdn.org/images/GCB_Citizens_voices_FINAL.pdf https://doi.org/10.5944/reec.37.2021.29228 https://doi.org/10.1016/j.jedc.2022.104590 https://doi.org/10.5477/cis/reis.138.109 https://doi.org/10.1177/00207152221096841