Abstract

This paper describes the employability of immigration from different countries in South and Central America to Mexico and the sociodemographic, socio-occupational, and territorial heterogeneity. The primary data source of information is the 2023 National Survey of Occupation and Employment (ENOE). The quantitative technique used to analyze the data is logistical regression models.
The results demonstrate an increase in the volume of immigration from South and Central America to Mexico, differentiated patterns of employability for the birth country and region, and emerging demographic changes in the recent arrival of immigrants that detonate a significant heterogeneity. Although the concentration of the immigrants from Guatemala, Honduras, and El Salvador is, in relative terms, on no modern subsectors of the tertiary activities, there is a certain grade of specialization according to the country of origin: 1) more concentration of the Guatemalans and Hondurans in the agriculture; 2) and the Salvadorans in the tertiary sector (mainly in no modern subsectors).
Conversely, South American immigrants in Mexico are concentrated in the modern subsector of tertiary activities. The specialization according to the country of origin is Colombians, Cubans, and Venezuelans in modern subsectors, such as professional services.
The adjustment of the logistical regression models for the population and differentiated group reaffirms the importance of the local labor markets for increasing the possibility of getting a formal job, as well as the branches of economic activity, for instance, residing in the northern part of the country, in the first place, or the center, in second place. In addition, if these regions have an incidence of manufacturing industry or modern services, improve the opportunity to get a better job.
This paper aims to shed light to the extensive field of immigration and provide findings that help to understand its dynamic better in the frame of the principal two of the universal values: leave no one behind.