# Enhancing the Demand for Labour survey by including skills from online job advertisements using model-assisted calibration

## TL;DR

This paper enhances the Demand for Labour survey by integrating online job ad skills data using model-assisted calibration, improving skill demand estimates and reducing bias compared to traditional methods.

## Contribution

It introduces a novel data integration approach combining calibration with LASSO to correct online data biases without unit-level survey data.

## Key findings

- LASSO-assisted calibration reduces bias and standard errors.
- Online data overestimates interpersonal and managerial skills.
- Under-representation of certain occupations affects skill estimates.

## Abstract

In the article we describe an enhancement to the Demand for Labour (DL) survey conducted by Statistics Poland, which involves the inclusion of skills obtained from online job advertisements. The main goal is to provide estimates of the demand for skills (competences), which is missing in the DL survey. To achieve this, we apply a data integration approach combining traditional calibration with the LASSO-assisted approach to correct representation error in the online data. Faced with the lack of access to unit-level data from the DL survey, we use estimated population totals and propose a~bootstrap approach that accounts for the uncertainty of totals reported by Statistics Poland. We show that the calibration estimator assisted with LASSO outperforms traditional calibration in terms of standard errors and reduces representation bias in skills observed in online job ads. Our empirical results show that online data significantly overestimate interpersonal, managerial and self-organization skills while underestimating technical and physical skills. This is mainly due to the under-representation of occupations categorised as Craft and Related Trades Workers and Plant and Machine Operators and Assemblers.