Employability, skills and training in Portugal (1988‐2000): evidence from official data

Abstract

PurposeThe purpose of this paper is to analyse the determinants of employability in Portugal.Design/methodology/approachCalculations were made using official data for 1991, 1994 and 1997 on education, skills and tenure, and on 1990, 1991, 1996 and 1997 regarding training.FindingsHigh skills, high levels of education and high levels of tenure seem to generate employability in Portugal. However, it was also found in Portugal that low skills, low education and low levels of tenure also generate employability, indicating that Portugal has a very dual labour market. Furthermore, in what concerns training, the public sector and the private sector seem to be complementary: when the investments are publicly financed they generate more employability, while privately funded investments tend to be a way of substituting untrained workers with trained ones.Research limitations/implicationsThe concept of employability used is linked to the growth of employment by sub‐sector of activity. This is a considerable limitation, but it was not possible to obtain better data, given that the study uses published data. A more detailed analysis is under way, using unpublished company‐based data, provided by the public authorities.Practical implicationsFirst, the Portuguese authorities should try to help the establishment in the country of companies that might extract returns from the skilled, educated and experienced workers that exist. Those actions would improve the employability of the HR possessors. Second, the Portuguese authorities should continue to support the investments in education and in training. That support would generate a form of Welfare State in which the public presence is considerable, far away from the liberal guidelines and ideas that tend to marginalize the public actions in the social domain.Originality/valueThe paper tries to apply the concept of employability to a country, and this is seldom the case in HRD studies, due mainly to lack of data.