The Influence of Gender and Educational Attainment Differences on International Migrants’ Occupational Status in OECD Countries

Abstract

The purpose of the study is to show two essential elements of the occupational status of international migrants in OECD countries. The study extends the current research by focusing on how the educational attainment of international migrants and the gender dimension affect migrants’ occupational status. To achieve the aim of the study, a quantitative research approach was followed. Ordinary least square regression analysis was used to emphasize the relationship between educational attainment and gender differences, and occupational status. The databases were taken from the OECD DIOC 2015/2016. It has been found that high-level educational attainment matches with representative vacancies, and female migrants tend to have those occupations more than male migrants in OECD countries. On the other hand, the study’s limitations included a lack of data on testing the exact occupational status of migrants in OECD countries, as well as educational attainment levels that were not specifically divided into each level of education in the databases.

1. Introduction

International migration has become an issue of great importance over the last few decades, in national and international markets and economies. Regardless of the geographical, economic, and social obstacles, numerous individuals and groups have travelled to several other regions, pursuing better lives for themselves, as well as for their families (Sobotka 2009). In recent times, many people reside beyond their country of origin. In total, they are reported to number more than 280.6 million, or up to 3.6% of the world’s population (Migration Data Portal 2021).

In several nations, migration is a critical aspect of social and economic life, while the demographic characteristics of migration vary considerably; this is to the extent that migration has become a very significant motivating force in these aspects, and it is reconstructing the community, both socioeconomically and demographically (Anderson 2016).

Much research has concentrated on post-migration homogeneity in host societies, by contrasting migrants with locals and/or other migrant communities in receiving countries. However, the distinctions in receiving communities may not even be very relevant for migrants themselves, since individuals move to enhance their life prospects compared to those provided to them in their home country (Barner et al. 2014).

The reality that migration occurs because of variations in the host countries’ geographical locations and economic conditions is already well known. In addition, high labour demands from certain receiving countries, low incomes, poor schemes of social security, high unemployment rates, and many different push factors from the country of origin, create a possible influx of labour migration to destination countries (Constant and Massey 2002).

Raising new questions about labour migration is important and includes issues such as the occupational status of the migrant, which indicates the migrants place on the career ladder, and evaluates the degree of success and the performance of occupational attainment when concentrating on the hierarchical framework of careers (Ueno and Krause 2020). In this article, we try to identify the effect of both educational attainment and gender differences on occupational status. The research has been limited to OECD countries. However, it should be bear in mind that OECD countries have various types of migration, which differ from other countries. These types differences may influence the different occupational statuses among the migrants in countries too.

Thus, the purpose of this article is to address the following research questions:

• Does educational attainment affect the occupational status of international migrants?
• Is there a gender difference in terms of occupational status in OECD countries?

2. Literature Review

2.1. Occupational Status of Migrants

International migration has become an issue of great importance over the last few decades, in national and international markets and economies, and there has been a crucial increase in labour migration between regional labour markets (Van Ham et al. 2012). This is particularly because of people’s desire for better employment and because migration is an essential instrument in accessing this. Constant and Massey (2002) indicate that, especially in advanced economies, occupations that are socially and geographically highly favoured by migrants in global markets are available. These jobs are mainly in the service sector, i.e., white-collar, professional, and managerial occupations. However, they are increasingly concentrated in core economic regions (Udor 2019). For this reason, the number of employment seekers from abroad amounts to hundreds of thousands each year (Chamaratana et al. 2017). This trend has a profound impact on the nature of skilled immigrants, leading to the development of different forms and frameworks of the phenomenon, in that the demand for labour drives the increase in the pressure for skilled migration from developing to advanced countries (Pagano 2018).

In the economic literature, the human capital theory has been widely used to explain international migration mobility, the components of obtaining an occupation, and labour market integration, in migration studies (Máté et al. 2017a). According to the human capital approach, migrants educational, personal, and professional experience from their country of origin enhances their career opportunities and places them in the labour market, by obtaining an occupational status in destination countries (Tharmaseelan et al. 2010). In other words, highly educated migrants are more likely to secure high-income or high-status occupations. On the other hand, few studies have critically evaluated the ethical background and the migrant’s health influence on obtaining the occupational status of migrants in destination countries (Schenker 2010). However, the human capital theory, also in the context of identifying the interaction between the ethics, national background, and migrants health, plays an essential role in the employment status of migrants (Korpi and Clark 2017). Concerning this, the relative immigrant impact of human capital on the labor market outcomes is assumed to increase with the relative impact of ground-based attributes, such as gender, ethnicity, religion, and health affiliation.

Additionally, the human capital model of migration emphasizes the reason behind the individual’s decision to migrate and what the migrant expects to gain from the destination country (Korpi and Clark 2017). On the other hand, this model also considers migration as a potential investment, where individuals have a chance to maximize socio-economic returns and gain high wages by this geographical move (Wilson 1985; and Flippen 2014). However, migrants commonly choose to change their location, from developing countries to developed countries. Thus, host societies perceive migrants to have a lower-quality education and expect their labor experience to be hard to transfer, due to a lack of knowledge of the new environment (Kanas and Tubergen 2009). Therefore, native employees are preferred in the labor market, and employers are reluctant to recognize migrants as potential workers in their labor sector. Employers may therefore ignore the trainability of their human capital abilities, such as career-related skills, language, and other aspects of their occupational status, increasing, over time, in the receiving country (Bolíbar 2020). On the other hand, migrants think that their labor market skills are usually conceptualized around the following three elements: first, the fact that their human capital skills are not perfect and have mainly been acquired in their country of origin; second, the selectivity factors from the destination country; and lastly, their economic gain in the destination country (Chiswick and Miller 2008).

Kossoudji and Cobb-Clark (2000) and Flippen (2014) have pointed out that, for natives as well as for migrants, in order to obtain an occupational status, occupational attainment is a necessary key factor, and migration is a factor in gaining a higher occupational status. On the other hand, much research has found that newly arriving migrants tend to remain in poverty, face unemployment, and struggle from lower employment positions and lower wage levels (Nakhaie and Kazemipur 2013). From this perspective, the importance of the regional background of migrants is an essential component, because regional differences flow from geographical and social mobility, and regional economic characteristics signify opportunities in the sector (Chernina 2020; De Haas 2021). However, the regional background of migrants also affects the occupation status they can obtain in destination countries. In this study, we shed light on the occupational status of migrants from different regions.

2.2. The Relationship between the Educational Attainment Background of Migrants and Their Occupational Status

In this section, we investigate the importance of educational attainment in occupational status. Educational attainment is considered to be a part of the human capital model, leading to individuals gaining better outcomes from migration, and it varies, depending on several factors (Williams 2009). Mostly, educational attainment depends on the country of origin, and the average educational attainments lag far behind those in advanced countries (Feliciano and Lanuza 2017). On the other hand, occupational status is defined as a powerful characteristic of a person, depending on social and economic variables, of which educational attainment is one (Hunt 2012). According to another definition of occupational status, workers who have the same average wages and levels of education have the same occupational achievement score; higher salaries and scarcer jobs give higher occupational status scores than jobs that are risky, common, or have low salaries (Bohon 2005).

There are many dimensions to obtaining an occupation in the destination country as a migrant; education is crucial for occupational status attainment (Requena 1991). In this regard, Chiswick and Miller (2009) mentioned that an individual’s education level is a very important component, in terms of where this individual applies for the occupational position. Moreover, occupational status depends on the different levels of schooling for workers in a specific profession, compared to the receiving country as a whole (Dajnoki et al. 2017).

Andersen and van de Werfhorst (2010) shed light on the impact of schooling on career results, which varies, due to the features of different education systems. Nations that are marked by heavily stratified educational programs, with an excellent occupational direction, seem to strongly connect education and profession, compared to nations with low professional and technical training.

The developed nations are much more likely to commit more funds to education than less-developed nations (Bumpus et al. 2020). In this regard, Marks (2005) mentioned that the differences between migrants’ and host communities’ educational systems might negatively affect labor market opportunities and the chances of getting the job or occupation of choice. In other words, the lower educational background of the migrants affects the work outcomes of occupational status and occupational prestige in the destination countries.

On the other hand, the educational background of migrants may not affect their occupational status. For instance, in certain service-based or post-industrial economies, general information is more valuable than professional and technical training. Therefore, a significant component of professional and technical training is not needed (Andersen and van de Werfhorst 2010). Also, already being fluent in the destination country’s language provides access to higher-status jobs (which require language skills) for migrants (Spörlein and Van Tubergen 2014). Furthermore, whilst the meaning of a ‘highly qualified immigrant’ may seem to be clear, it is inadequately defined. Some academic literature refers to this group based on educational standards, namely, those immigrants with higher educational qualifications (i.e., at least with a BSc degree) or beyond. Various scholars have also identified the need to consider wages and past careers (Pagano 2018). In this article, we claim that the educational level of migrants influences their occupational status. They can obtain a job in the destination country based on their level of education. Parities created from DIOC indicators and occupations, conceptualized from the International Standard Classification of Occupations (ISCO-08), are used as predictors to answer the first research question.

2.3. The Gender Dimension in the Occupational Status of Migrants

Early international migration research neglected the participation of women in the labor market of destination countries, and their contribution to the household decision-making process of migration. Thus, the role of gender is accompanied by a male bias in research. Based on those studies, males migrate primarily for labor, and females join them later as dependents, for example, as a wife (Raghuram 2004). However, Kofman and Raghuram (2006) emphasized three different phases of gender differences in the labor market in destination countries, as follows: first, women’s contribution to the migration process; second, the role that is taken in decision making; and third, women’s self-directed migration. Despite all these aspects, due to the lack of data, female migration is still not a matter of importance in the labor market of host communities. Moreover, the economic and administrative dependency of female migrants impacts their economic participation in destination countries (Sarihasan 2017).

In the last few decades, the lower probability of migrant women being employed has contributed to fears regarding the rising numbers of unemployed and inefficient immigrants (Author et al. 2016). However, female migrants face additional obstacles when entering the job sector in the destination countries, for instance, because of gender prejudice, family obligations, and the inability to obtain the requisite training (Iredale 2005). As a result, the sectors that are most affected by migrant influxes are those occupied by male migrants, such as construction, agriculture, etc. Female migrants mostly occupy the public and service sectors. Moreover, this is also caused by the fact that less attention is paid to female migrants in the labor market (Pagano 2018). Occupational incongruity is a major issue, since it relates to substantial socio-economic costs for workers, corporations, and economic conditions in the country (Chiswick and Miller 2009).

Migrants coming from third-world nations are the most affected, usually facing financial and social integration issues in the destination nations. In terms of employees, the lower returns to skill sets have lasting impacts on their health and social and economic conditions, resulting in lower work satisfaction, stress, and job insecurity. For companies, the lower returns to abilities are related to reduced worker motivation and performance, and much more significant revenue levels, causing greater costs for testing, hiring, and training. Given the economic conditions in various nations, inadequacies in matching jobs to workers cause greater joblessness and pressure on societal well-being schemes. The societal costs of inequality are perhaps even much higher (Zorlu 2016). Additionally, it is vital to understand the causes and repercussions of global immigration in a gender context, as the social interactions that are associated with gender form the migration experience of migrants, regardless of whether they are female or male (Máté et al. 2018). It is essential to design plans and policies to respond to the needs of females who move to destination countries (Papp et al. 2019).

Despite the variety of theoretical perspectives used to interpret the continued international migration, there is very limited research on how international migration invariably creates global networks, by obtaining an occupational status in destination countries. This is an essential point in international migration, because networks are very influential conduits of capital and knowledge (Máté et al. 2017b). Additionally, migrants can generate powerful development effects in destination countries, derived from skilled talent, which comes with the strong educational attainment background of migrants. Moreover, migrants create a significant demand for goods and services. This also helps to boost economic growth in destination countries (De Haas 2007). These innovations will be possible if migrants gain an occupational status in destination countries, based on their educational attainments and regardless of their gender. Most of the destination countries require high-skilled migrants; however, employee training and the organizational opportunities of migrants are also essential, to enhance their skills and show their abilities in the employment positions they obtain in the receiving societies. These points should also be taken into consideration in the migration studies literature.

3. Methodology

To achieve the objective of the research study, a quantitative research approach was followed. Furthermore, the research was boosted by the positivist philosophical paradigm, as it primarily made use of numerical data, which was obtained from a secondary database and interpreted through various statistical methods (Remenyi et al. 2000). The DIOC databases of migration are based on data from population censuses of OECD countries for the 2000 census round, and they are updated every five years.

This data gathered estimations of the annual immigration influx from 34 OECD member states and more than 200 host countries. For this study, the last updated OECD International Migration Database 2015/2016 (DIOC) was used. The data also include information about the migrant’s gender, place of birth, age, educational attainment, duration of stay, labor market status, and occupational status. This study specifically focuses on the occupational differences in regional migration.

To achieve the goal of the research, the Occ_1d variable is used. The occupations are documented according to the International Labour Organization’s 2008 revision of the International Standard Classification of Occupations (ISCO-08). ISCO-08 is structured as follows: 1 = managers (MNGR), 2 = professionals ($PRFSN)$, 3 = technicians and associate professionals ($TNAP)$, 4 = clerical support workers $\left(CSW\right)$, 5 = services and sales workers (SSW), 6 = skilled agricultural, forestry and fishery workers (SAFFW), 7 = craft and related trades workers (CRTW), 8 = plant and machine operators and assemblers (PMOA), and 9 = elementary occupations (EO).

Linear regression defines the association between one or more factor variable(s) and one result variable, and is generally used during modelling and extrapolative analysis. Linear regression is also known as ordinary least squares (OLS) regression multiple regression (Uyanık and Güler 2013). In this study, binary logistic regression demonstrates the gender differences in occupational status in OECD countries. This regression model was created by Cox (1958). The model aims to explore the most appropriate and sensible way to describe the correlation between independent variables and the result (Bhandari 2004), as well as to explore the most logical and suitable model to distinguish the correlation between the independent and dependent variables (Hosmer and Lemeshow 2000). Furthermore, these models are comprehensively used in research studies to define the effect of various demographic and socioeconomic aspects on the likelihood of occurrence, to balance the variables.

Model Specification

In this study, two different models were employed to test the hypotheses and avoid the dummy trap; elementary occupations were selected as a control variable. A dummy trap is identified as a position in which the independent variables are multi-collinear, or a situation in which two or more variables are strongly correlated, i.e., one variable may be estimated from the other. Another independent variable is educational attainment (EDU), based on the International Standard Classification of Education (ISCED), where 1 = low, 2 = medium, 3 = high level of education. To measure educational attainment and the relationship with occupations, Equation (1) is employed in the analysis.

$$\begin{gathered} \left(EDU\right)={\beta }_0+{\beta }_{1}DMNG{R}_i+{\beta }_{2}DPRFS{N}_i+{\beta }_{3}DTNA{P}_i+{\beta }_{4}DCS{W}_i+{\beta }_{5}DSS{W}_i+{\beta }_{6}DSAFF{W}_i \\ +{\beta }_{7}DCRT{W}_i+{\beta }_{8}DPMO{A}_i+{\beta }_{9}F.Bor{n}_i+{ϵ}_i \end{gathered}$$

(1)

Another estimation in the study is a demonstration of gender differences in the occupational status of migrants. To achieve this, Equation (2) is employed, to analyze the gender differences.

$$\begin{gathered} \left(Gender\right)={\beta }_0+{\beta }_{1}DMNG{R}_i+{\beta }_{2}DPRFS{N}_i+{\beta }_{3}DTNA{P}_i+{\beta }_{4}DCS{W}_i+{\beta }_{5}DSS{W}_i+{\beta }_{6}DSAFF{W}_i \\ +{\beta }_{7}DCRT{W}_i{\beta }_{8}DPMO{A}_i+{\beta }_{9}F.Bor{n}_i+{ϵ}_i \end{gathered}$$

(2)

4. Results

First, descriptive statistics is run, to demonstrate the mean, standard deviation, and min and max number of variables (see

Table 1. Descriptive statistic of variables.

Variables	Mean	Std. Dev	Min.	Max
Educational Attainment (Edu)	2.04	0.803	1	3
Gender	1.48	0.499	1	2
Foreign-Born (F.born)	0.87	0.338	0	1
Managers (MNGR)	0.09	0.292	0	1
Professionals (PRFSN)	0.10	0.302	0	1
Technicians and Associate Professionals (TNAP)	0.10	0.304	0	1
Clerical Support Workers (CSW)	0.10	0.294	0	1
Services and Sales Workers (SSW)	0.11	0.307	0	1
Skilled Agricultural, Forestry and Fishery Workers (SAFFN)	0.07	0.259	0	1
Craft and Related Trades Workers (CRTW)	0.09	0.289	0	1
Plant and Machine Operators and Assemblers (PMOA)	0.09	0.280	0	1
Elementary Occupations (EO)	0.09	0.292	0	1

Source: Authors’ own compilation (2021).

).

Table 2. Pearson correlation matrix of examined variables.

	1	2	3	4	5	6	7	8	9	10	11	12
Gender	1000
Edu	0.009	1000
F-Born	−0.017 **	0.018 **	1000
MNGR	0.004	0.020 ***	0.002	1000
PRFSN	0.009	0.037 ***	0.011	−0.109 ***	1000
TNAP	0.008	0.013	0.013 *	−0.109 ***	−0.104 ***	1000
CSW	0.018 ***	0.002	0.005	−0.105 ***	−0.101 ***	−0.101 ***	1000
SSW	0.014 **	−0.013	0.014 **	−0.111 ***	−0.115 ***	−0.116 ***	−0.112 ***	1000
SAFFW	−0.012	−0.016 **	−0.026 ***	−0.090 ***	−0.094 ***	−0.095 ***	−0.091 ***	−0.096 ***	1000
CRTW	−0.014 **	−0.020 ***	−0.002	−0.103***	−0.107 ***	−0.108 ***	−0.104 ***	−0.109 ***	−0.089 ***	1000
PMOA	−0.019 ***	−0.022 ***	−0.008	−0.099 ***	−0.103 ***	−0.104 ***	−0.100 ***	−0.105 ***	−0.086 ***	−0.098 ***	1000
EO	0.004	0.020 ***	0.002	1000 ***	−0.109 ***	−0.109 ***	−0.105 ***	−0.111 ***	−0.090 ***	−0.103 ***	−0.099 ***	1000

Source: author’s own compilation (2021). Note: significance *** at 1 percent. ** at 5 percent. * at 10 percent. The p-values without an index mean that the coefficient is not significant, even at 10 percent. Numbers in the first row represent the following: 1 = gender, 2 = edu, 3 = f.born, 4 = MNGR, 5 = PRFSN, 6 = TNAP, 7 = CSW, 8 = SSW, 9 = SAFFW, 10 = CRTW, 11 = PMOA, 12 = EO.

shows the results of the Pearson correlation matrix between the variables. One of the study aims is to demonstrate the relationship between the occupational status of migrants and their educational attainment. The correlation shows that for the occupations of managers and other professionals, there is a significant and positive correlation with educational attainment. It can be deduced, from this outcome, that for migrants, a high educational level is required for these positions. However, clerical support workers, plant and machine operators and assemblers, forestry and fishery workers, skilled agricultural workers, and elementary occupations are negatively, but significantly, correlated with educational attainment.

Another aim was to demonstrate the gender differences among migrants, in terms of occupational status. Services and sales workers, and clerical support workers, are positively and significantly correlated with gender variables, while craft and related trades workers, and plant and machine operators and assemblers are negatively, but significantly, correlated. To show nativity status differences, the concept of foreign-born was taken into consideration. Technicians, associate professionals, and services and sales workers have positive and significant correlations, but skilled agricultural, forestry and fishery workers are negatively, but significantly, correlated with the foreign-born variable. On the other hand, Table 2 also shows the result of the correlation between occupational status. Based on the outcome, professionals’ occupation positions were negatively, but positively, correlated with managers’ occupation status. The technicians and associate professionals occupation was negatively, but significantly, correlated with the managers and professionals occupation. On the other hand, the clerical support workers occupation was also negatively, but significantly correlated with the managers, professionals, technicians, and associate professionals occupations. In the case of the other occupations, such as services and sales workers, skilled agricultural, forestry, and fishery workers, craft and related trades workers, and plant and machine operators and assemblers are negatively, but significantly, correlated with each other. However, in the case of the elementary occupation, it is positively and significantly correlated with the managers’ occupational status, but negatively and positively correlated with other occupations.

Table 3. Results of the OLS regression for Equations (1) and (2).

Independent Variables	Model 1	Model 2	Model 3	Model 4
Constant	1.999 ***	1.999 ***	1.493 ***	1.478 ***
	92.456	92.456	118.303	378.673
DMNGR	0.051 ***	0.019 ***	0.13
	2.031	2.282	0.906
DPRFSN	0.088 ***	0.035 ***	0.021
	3.589	4.268	1.421
DTNAP	0.032		0.019
	1.304		1.312
DCSW	0.009		0.033 **	0.017 **
	0.359		2.227	2.139
DSSW	−0.023		0.028 **	0.014 **
	(−0.960)		1.920	1.751
DSAFFW	−0.035		−0.015
	(−1.287)		(−0.890)
DCRTW	−0.041		−0.014
	(−1.636)		(−0.937)
DPMOA	−0.048 **	−0.020 **	−0.021
	(−1.870)	(−2.511)	(−1.374)
F.born	0.041 **	0.018 **	−0.026 ***	−0.017 ***
	(2.052)	2.167	(−2.266)	(−2.146)
Observations	17,355
Adj. R2	0.003	0.003	0.003	0.002
F-test (max)	4.719 ***	6.139 ***	2.704 ***	4.885 ***
VIF (max)	1.274	1.001	1.259	1.000

Source: Authors’ own compilation (2021). Notes: Heteroscedasticity robust t-statistics are in parentheses. Letters in the upper index refer to significance: ***: significance at 1 per cent, **: 5 per cent. The p-values without an index mean that the coefficient is not significant, even at the 10 per cent level.

demonstrates the result of the relationship between educational attainment and the occupational status of migrants in OECD countries. Model 1 is based on the Equation (1), where educational attainment is the dependent variable, and the occupational statuses (managers, professionals, technicians and associate professionals, clerical support workers, services and sales workers, skilled agricultural, forestry and fishery workers, craft and related trades workers, and plant and machine operators and assemblers) are the independent variables. Model 2 only shows the results of the significant variables. Because of the large number of observations (17355), the adjusted R-squared is relatively low, according to the results of the OLS regression. The F-test of the model is 4.719, and it is significant at p < 0.01, which demonstrates that our model fits perfectly with the data. The multi-collinearity is examined by a variance inflation factor (VIF). The maximum VIF for each regression coefficient ranges between a high of 1.274 and a low of 1.001. Hair (2011) pointed out that the VIF test is acceptable at a level of less than 10. Based on this, our model does not have a multi-collinearity issue.

The constant of the models is significant at the p < 0.01 level. The first result is the estimation for managers. This occupation group includes senior officials and legislators, administrative and commercial managers, retail, production and specialized services managers in hospitality, chief executives, and other service managers. Based on the outcome, there is a positive and significant relationship with educational attainment at p < 0.01. This result indicates that, for migrants, a high educational level is required for these positions. In other words, increasing the educational level also gives migrants the opportunity to obtain a managerial occupation.

The second estimation of results is for professionals. This occupational level includes health professionals, engineering and science professionals, information and communications technology professionals, teaching professionals, business and administration professionals, and legal, social, and cultural professionals. The outcomes show that educational attainment and professional occupation have a significant and positive relationship at the p < 0.01 level. Based on this estimation, migrants with a high educational level are much more likely to have professions at this level.

The third significant result is related to assemblers, plant, and machine operators. According to the International Labour Organization, this occupational category comprises assemblers, stationary plant and machine operators, and mobile plant operators and drivers. The result of the linear regression shows that educational attainment has a negative, but significant, relationship with plant and machine operators and assemblers at the p < 0.05 level. This result indicates that migrants with lower educational attainment tend to have occupations at this level.

This study also aims to specify the differences between the educational levels of foreign-born and native-born individuals in OECD countries. Foreign-born and educational attainment has a significant and positive relationship at the p < 0.05 level. Therefore, foreign-born migrants tend to be more educated than native-born migrants in OECD nations.

Model 3 represents the full model for gender differences and model 4 presents the significant variables in model 3. The dependent variables is gender, and dummy occupational statuses (professionals, managers, clerical support workers, technicians and associate professionals, services and sales workers, plant and machine operators and assemblers, forestry and fishery workers, craft and related trades workers, foreign-born, and educational attainment) are the independent variables. According to the omnibus test (F-Test), the model significantly explains the data, and the VIF test shows that this model also does not have a multi-collinearity issue. The results of the empirical testing show that male migrants tend to have more occupations in clerical support workers, and services and sales workers, than female migrants.

The last estimation of the results proves that there are also gender differences in the nativity status of migrants in OECD countries. Foreign-born and dummy male variables have a significant positive relationship, while dummy female variables have significant and negative relationships (Sarihasan 2016). This result indicates that the number of foreign-born male migrants tends to be higher than that of foreign-born female migrants in OECD countries.

5. Discussion and Implications of the Study

The study aims to signify the relationship between educational attainment and migrants’ occupational status in OECD countries. A large body of migration research has examined the educational selectivity of international migrants and the role of educational attainment on occupational status in destination countries (Villarreal 2016). It has been found, in this study, that high-level occupation for example, managerial and professional level occupations require a high level of educational attainment. As a result, migrants with sufficient education from their home country can find employment in these occupations. However, Chevalier (2003) emphasized the mismatch between migrants’ educational level and their occupation in destination countries. Thus, this study implies that a migrants educational attainment is clearly determined by destination countries, and should be considered in the labor market. The relationship between educational attainment and occupational status will lead the migrant workers working more effectively in their current occupation. As well as this, more lenient labor market legislation and active labor market training can enhance the chance of obtaining an occupation in destination countries. Sometimes, even if the migrant is sufficiently qualified to obtain a job, a native can be preferred instead. Besides this, the individual’s source country’s occupational status declines directly after moving to the destination country. If the individual improves their language skills, schooling, and experience that he or she brought from the destination country—which is an essential determinant of success in the host country’s labor market—this will enhance an increase in occupational mobility (Rooth and Ekberg 2006). Furthermore, Spörlein and van Tubergen (2014) pointed out that speaking the destination country’s language is an important element in acquiring a comparative advantage over those migrants who do not have fluency in the spoken language, as part of their occupational attainment.

This study also implies that destination countries provide some opportunities for migrants to improve their skills. Language skills, in particular, are also essential for the integration process in a new society, because insufficient language ability makes the adaptation harder in receiving countries as well as in the labor market.

Second, there are gender differences among male and female immigrants in OECD countries. Surprisingly, the outcomes of the analyses show that female migrants tend to hold higher-level occupations than male migrants in OECD countries. For example, as managers, professionals, technicians and associate professionals, clerical support workers, and services and sales workers. Male migrants are mostly plant and machine operators and assemblers. This outcome indicates that there is a substantial improvement in female migrants’ occupational status in OECD countries. Previously, it has been indicated, in many research studies, that female migrants are mostly inactive and take a role in secondary sectors (Villarreal 2016; Sarihasan 2017; Bilecen et al. 2019). This new result for female migrants can also contribute to a theoretical approach to gender differences in migration. Thus, this study also implies that giving more opportunities to female migrants in destination countries can enhance their role in labor markets. Additionally, female migrants still face more discrimination than male migrants in destination countries (Al-Dalahmeh and Dajnoki 2021). From this perspective, this study suggests that policy makers should give implications for reducing skill gaps, by motivating female migrants to gain more of an education and take more roles in the labor market.

6. Limitations of the Study

Similarly, to every other piece of research, this study also has some limitations; the first of which is that, unfortunately, the lack of data represents a limitation in testing the exact occupational status of migrants in OECD countries. It would be advantageous to compare migrants’ occupational statuses from the country of origin with their new occupations in OECD countries, after arrival. In other words, this research does not offer clear proof that the patterns of the occupational attainment of immigrants are significantly different between the source country and the host country. However, the database that was used does not provide information about the country of origin in this regard. Moreover, there is also one limitation regarding educational attainment. It was not possible to show the exact educational level of migrants, because this is not divided specifically into each level of education in databases. Additionally, another limitation comes from illegal migration (Sarihasan 2016). For example, illegal international migrants from Africa and Western Asian countries to Western European OECD countries, do not have legal documents, because they enter countries illegally. This situation causes a significant issue of identifying their country of origin, educational background, gender, occupational status, and ethnicity. Due to that, there is also a lack of information, which causes insufficient data to identify issues related to their occupational status. Lastly, migrant health and ethnicity may also affect the occupational status in destination countries. However, DIOC data on international migration do not provide information about it, and it was another limitation of the study to show that these components also influence obtaining an occupation in receiving societies.

7. Conclusions

This paper argues that the educational attainment background of migrants, and gender differences, influence the occupational status of international migration in OECD countries. First, for a more meaningful understanding of the topic, the occupational status of migrants, educational attainment, and gender difference’s roles conceptualize within given sets of perceived opportunities in the literature review.

In order to answer the research questions, data are empirically tested, by using the OLS regression method. The analysis outcomes demonstrated that a high level of educational attainment affects migrants’ chances of obtaining high-level occupations in OECD countries. For instance, managers and professional-level occupations have a significant and positive relationship with educational attainment. On the other hand, jobs at lower levels, such as plant and machine operators and assemblers, do not require the same educational background. Migrants with deficient educational levels tend to work in such occupations in OECD countries.

Secondly, another goal of the research was to explore the gender differences in occupational status in OECD countries. The results confirmed that in OECD countries, female migrants mainly obtain higher-level occupations than male migrants. On the other hand, male migrants’ occupational levels are typically in the plant and machine operators and assemblers category.

Additionally, as underpinned in the literature review of the study, the nativity status of migrants is another component that influences occupational status in destination countries. Based on the findings, foreign-born migrants are more educated than male migrants, and the number of foreign-born male migrants tends to be higher than that of foreign-born female migrants in OECD countries.

Finally, various theoretical perspectives have been used to interpret the continued economic and occupational disparities of migration. However, despite its importance, there are very limited studies on how the educational attainment backgrounds of migrants, and gender differences, influence their occupational status in destination countries. This study paints a very consistent picture of relatively high-level occupations, such as managers and professionals, which require a high-level educational background of migrants. Moreover, gender discrimination is a crucial topic in migration studies. This research was good evidence that the perspective on male migrants taking a more significant role in the labor market in destination countries is changing over time, and as much as male migrants, female migrants are also starting to achieve high positions, with sufficient educational attainment, in host societies.