Digital Requirements and Advertised Salaries in the Educational Labor Market of Kazakhstan: An Nlp-Based Analysis of Job Vacancy Data

This paper examines whether digital skill requirements in educational job postings are associated with higher advertised salaries in the labor markets of Almaty and Astana, Kazakhstan, and whether the observed salary gap reflects genuine skill valuation or employer heterogeneity. Using 794 vacancies collected via the HH.kz API, we construct an analytical sample of 755 observations after data cleaning. A seven-category dictionary-based skill extraction pipeline is implemented and its integral lexical consistency is evaluated against a TF-IDF+Logistic Regression baseline (5-fold CV: F1=0.755, AUC=0.928). Vacancies specifying at least one digital requirement carry a median advertised salary that is 18.2% higher than non-digital postings. OLS with HC3- robust standard errors and occupational and city controls yields a coefficient of 0.200 (SE = 0.039, p< 0.001, approximately +22.2%). Adding an employer-type proxy reduces this estimate to +15.2% (p < 0.001). In the Almaty subsample, the effect is no longer statistically significant (p = 0.075). Part of the observed premium reflects a measurement problem. Higher-paying employers are also more likely to specify digital requirements, inflating the estimated association. The design does not identify firm effects and supports only an associational interpretation. Quantile regression shows that the salary-digital gradient increases from +15.0% at Q25 to +44.4% at Q90. This pattern is consistent with labor market segmentation rather than a uniform skill premium.