Information on employer demand is most often collected through subjective surveys. Cunningham and Villasenor (2016) reviewed 27 studies from various countries that attempted to identify the skills demanded by employers. They separated skills into four categories—socio-emotional, high-order cognitive, basic cognitive and technical—and found that employers tended to place greater emphasis on socio-emotional skills and higher-order cognitive skills. While employer surveys can help sharpen the focus of education and skills training, the subjective and qualitative nature of employers’ responses can make it difficult to assess the relative importance of different skills and skill combinations (Rutkowski, 2010).

Big data from online job portals can help quantify employer demand through text analysis. Researchers use text analysis to investigate large amounts of textual information and systematically identify its properties. They analyze the frequency of keyword usage, identify communication content and categorize the structure of the text. Using text analysis to examine labor market dynamics offers a number of advantages over more traditional approaches. Online job postings may reflect employers’ desired qualifications and skills more accurately than survey responses. Unlike printed advertisements, employers posting job offers on websites do not have to pay by the word and thus can provide more detailed information on the knowledge and skills they require (Gallivan et al. 2004). These descriptions, in turn, can help highlight the skills demanded by different occupations and in different locations, as well as the wage discrepancies associated with various skill sets. Traditional sample-based labor force surveys cannot capture this type of information.

Beblavý, Fabo and Lenaerts (2016a) examined two million job advertisements published on a US-based online job portal called Burning Glass. They grouped the skills and qualifications required in the postings into several categories, such as education and formal qualifications, cognitive skills, non-cognitive skills and experience, and then broke these categories down into sub-categories. They found that while employers placed a high value on both cognitive and non-cognitive skills, the emphasis on each in job postings varied by employee skill level. While 25 percent of all job advertisements highlighted computer skills, percentages for postings targeting low-, medium- and high-skilled workers varied from 9 percent to 21 percent to 39 percent, respectively. Similarly, while 45 percent of job postings demanded non-cognitive service skills, the share of postings for low-, medium- and high-skilled workers ranged from 21 percent to 47 percent to 45 percent, respectively. Beblavý, Fabo and Lenaerts (2016b) analyzed job ads from the Czech Republic, Hungary, Poland and the Slovak Republic to assess the demand for foreign language skills. Focusing on 59 occupation types, they found that foreign-language skills were in high demand in these countries, with the share of postings that mentioned them ranging from 28 percent in the Czech Republic to 64 percent in Poland. English was the most-requested language and was mentioned in 52 percent of vacancies.

**_Application of the Analysis to Babajob Data_**

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