The shortage of skilled labor and the global competition for highly qualified employees has challenged Dutch companies to develop strategies to attract Highly Skilled Migrants (HSMs). This paper presents a study exploring how well-being is experienced by HSMs living in the Eindhoven region, a critical Dutch Tech Hub. Our population includes highly skilled women and men who moved to Eindhoven for work or to follow their partner trajectory. By analyzing data according to these four groups, we detect significant differences among HSMs. Given the exploratory nature of this work, we use a qualitative method based on semi-structured interviews. Our findings show that gender plays a crucial role in experienced well-being for almost every dimension analyzed. Using an intersectional approach, we challenge previous models of well-being, and we detect different factors that influence the respondents’ well-being when intersecting with gender. Those factors are migratory status, the reason to migrate, parenthood, and origin (EU/non-EU). When all the factors intersect, participants’ well-being decreases in several areas: career, financial satisfaction, subjective well-being, and social relationships. Significant gender differences are also found in migration strategies. Finally, we contribute to debates about skilled migration and well-being by including an intersectional perspective.

The visual portrayal of social groups in media reinforces stereotypes and narratives, potentially leading to discriminatory actions and policies. That is particularly true for underrepresented or stigmatized groups such as migrants and is a phenomenon that varies per country. Therefore, studying the representation of migrants requires analyzing considerable amounts of visual data from different locations. This work addresses that challenge with an interdisciplinary approach characterizing the visual portrayal of migrants using Deep Learning techniques and analyzing results through the lenses of migration and gender studies. Images associated with migrants found on the internet through a search engine and from ten countries are processed to quantify and analyze the demographic and emotional information of the people portrayed. An intersectional approach is employed regarding gender, age, physical features, and emotions. The general group “migrants” is compared with the specific groups “refugees” and “expats”. Results suggest that portrayals predominantly focus on asylum seekers and associate them with poverty and risks for host societies. Moreover, the demographics in the portrayals do not match the official statistics. For expats, an over-representation of “white” and an under-representation of “asian” faces were found, while for migrants and refugees, depictions align with the demographics of low-skilled migrants. Furthermore, results evidence the power struggle underlying the “expat vs. migrant” dichotomy and its inherent colonial nature. The emotions displayed are predominantly negative and align with emotional and gender stereotypes literature. Positive emotions are more associated with women than men, and with expats than refugees and migrants. Previous results regarding the under-representation of migrant women in media are confirmed. Also, women are portrayed as younger than men, and expat women are the youngest. Children appear more in pictures associated with refugees and migrants than with expats. Likewise, migrants are often depicted as crowds, but when that is not the case, migrant and refugee women appear in larger groups than men. A higher proportion of images associated with expats do not contain people. All these effects, however, differ per location. Finally, we suggest future directions and analyze possible limitations of automatic visual content analysis using existing Deep Learning models.