Purpose -The purpose of this paper is to investigate which sectors are more vulnerable to human capital depreciation, with an emphasis on potential differences in skills and in ICT intensities. Design/methodology/approach -The authors estimate an extended Mincerian earnings equation based on Neuman and Weiss's (1995) model using the EU-KLEMS international database for 15 sectors for the period from 1980 to 2005. The authors also test structural ruptures in earnings and human capital depreciation in the labor market per decade controlling by technological intensity. Findings -Human capital depreciation ranges from 1 to 6 percent. It is mainly significant in skill-intensive sectors regardless of the sector's technological intensity. The analysis of structural breaks shows that human capital value indeed changed from decade to decade. It even appreciated in low skill-intensive sectors in the 1980s and in the high skill-intensive during the 1990s. Appreciation though, was mainly skill-biased. Research limitations/implications -Information about on-the-job-training and non-cognitive skills that can also affect human capital depreciation are not included due to lack of data. Practical implications -To prevent human capital from depreciating in particular sectors and periods educational systems should provide the tools for ongoing lifelong learning at all skills levels. Education is subject to dynamic effects that should be addressed to increase the potential benefits of technological change. Originality/value -First, instead of using cross-section analysis which is considered to be a pitfall in studying the depreciation of knowledge, the authors observe its dynamic on a longitudinal basis. Second, the international macro-sectoral approach goes beyond limited micro-sectoral analysis in certain countries.

The unique identifiers of the teachers and schools in each contract allowed us to link the administrative information with the information from the Population and Housing Census on the characteristics of the districts where the schools were located. The database built contained all the contracts of the Ministry of Public Education in 2018, 37,281 contracts offered to 19,622 teachers who were proprietary, interim, or novice and desired to enter the system.The Ministry of Public Education offers contracts to teachers for a specified number of lessons up to a maximum of 40 lessons per week (full-time position). From the contract base, we excluded those contracts that were offered for less than half that number of lessons (20 lessons) per school. Short-hour contracts are often rejected by teachers and are "unattractive" because they are forced to work in more than one school and to accept more than one contract to complete their salary (DIUE, 2018; Lentini, 2019). In the decision that teachers make to accept more than one contract, factors that cannot be observed through the model (for example, proximity to two schools where the same teacher works) can also have a significant impact.The exclusion of contracts of less than 20 lessons meant that the base remained with 14,960 observations. The second exclusion we made was that of contracts that were already assigned to teachers in permanent positions, to keep only those that were offered to temporary or novice teachers. Therefore, the final base of contracts that we selected, once those with less than 20 lessons and the permanent ones have been eliminated, was made up of 6,070 contracts.