In this sense, it is necessary to draw attention to the fact that the non-standard employment statistics presented below are not homogeneous among countries. In countries where both forms of nonstandard employment were identified, we define non-standard employment as those occupations that satisfy at least one of the conditions, that is, either corresponds to a part-time occupation or a temporary job. In countries where temporary employment was not identified in the data, our nonstandard employment category will coincide with part-time employment. Note that in either case, as other non-standard employment modalities are not identified, the indicators presented in this paper indicate a lower level with respect to the true dimension of the phenomenon.

The only aspect addressed that required the use of additional information was the analysis linked to the profile of tasks that are developed in the framework of non-standard jobs. To carry out this analysis, the information available in the O*NET (Occupational Information Network) database was used in conjunction with the Household surveys. This database provides information referring to the content of tasks of the occupations. Since 2003, O*NET data have been collected in the United States for approximately 1,000 occupations based on the Standard Occupational Classification (SOC), and it has been updated periodically from then until 2014.5 Following Acemoglu and Autor (2011), Hardy et al. (2015) and Apella and Zunino (2017), five measures of content or intensity of main tasks performed by workers are constructed: non-routine cognitive analytical and interpersonal, routine cognitive and manual and non-routine manual. The definition of the type of task performed by the worker is associated with the risk of automation and therefore its implication in terms of earned wage. While routine, and especially manual, are susceptible of automation, those non-routine tasks, especially cognitive tasks (both analytical and interpersonal) not only are not exposed to the risk of automation but also could be complemented by automation, increasing the productivity of the workers.

# 4. NSE trends in Latin America and the Caribbean and Europe and Central Asia

As was mentioned in the previous section, the available data sources limit the statistical analysis presented below to two types of NSE: temporary employment and part-time employment. At the same time, it was not possible in all cases to identify the employees whose employment relationship is temporary. In this sense, it is necessary to take into consideration that all indicators of prevalence of NSE and its profile will be limited to a subset of this kind of workers.

## 4.1 Latin America and the Caribbean

This section focuses on the Latin America and the Caribbean region, where a set of 9 countries, that we consider representing the different realities of the region in an exhaustive way, was analyzed. Specifically, the analysis was conducted for Argentina, Brazil, Bolivia, Chile, El Salvador, Mexico, Peru, Dominican Republic and Uruguay.

> 5O*NET is the successor of DOT (Dictionary of Occupational Titles) which is no longer updated. O*NET was launched in 1998 on the basis of the BLS Occupational Employment Statistics codes. In 2003, it was changed to SOC which implies that the consistent measures of task content are calculated from 2003.

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