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Article summary:
1. This article discusses the importance of depicting differences in returns to skills in labor economics and how online job postings can provide timely insights into the current needs of job markets.
2. The article presents two major challenges when analyzing job advertisement data: interval-valued response and high-dimensionality of predictor vectors.
3. The article proposes new statistical procedures to address these challenges, which are applicable for various job databases including both online and newspaper job advertisements.
Article analysis:
The article is generally reliable and trustworthy, as it provides a comprehensive overview of the importance of depicting differences in returns to skills in labor economics, as well as the two major challenges when analyzing job advertisement data. The proposed statistical procedures are also presented in detail, with references to existing works on interval censoring that pave a way to construct a dependence measure between the interval-valued posted salary and a potential skill.
However, there are some points that could be further explored or discussed more thoroughly. For example, while the article mentions that online job postings provide an informative way for labor economists and statisticians to investigate the differences among returns to skills, it does not discuss any potential biases or limitations associated with this method (e.g., selection bias). Additionally, while the article mentions that many empirical studies have been conducted to estimate the returns to skills, it does not provide any evidence or examples of such studies. Furthermore, while the article discusses how online job postings can provide timely insights into current needs of job markets compared with conventional methods such as ORS used by labor economists, it does not explore any counterarguments or alternative perspectives on this point (e.g., potential drawbacks associated with relying solely on online job postings).
In conclusion, overall this article is reliable and trustworthy but could benefit from further exploration of potential biases or limitations associated with its methods as well as providing evidence for its claims and exploring counterarguments or alternative perspectives on its points.