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All Journal JDE (Journal of Developing Economies) PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Rani Nooraeni
Politeknik Statistika STIS, Jakarta, Indonesia
Computer Science & IT

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Uncovering Indonesia’s Hidden Unemployment Through Google Trends: A Nowcasting-Oriented Mixed-Frequency Modeling Syfriza Davies Raihannabil; Achmad Maulana Andi Wicaksono; Rani Nooraeni
Journal of Developing Economies Vol. 11 No. 1 (2026)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jde.v11i1.76834

Abstract

Objective: The unemployment rate reported in official statistics does not fully capture labor market conditions as it overlooks hidden unemployment. These individuals, who appear to be employed, are not optimally engaged in economic activities. This study aims to nowcast hidden unemployment in Indonesia using high-frequency big data from Google Trends, thereby addressing the limitations of official statistics and providing more adaptive labor market indicators within the broader context of employment dynamics and policy evaluation. The research is empirical in nature. Design/Methods/Approach: The study employs time-series data, combining official statistics from the National Labor Force Survey (SAKERNAS) with search query data from Google Trends. Three econometric models — MIDAS, U-MIDAS, and BMF VAR — are applied to assess their performance in nowcasting hidden unemployment. The analysis is divided into pre-pandemic and combined periods to evaluate the model’s sensitivity to structural shocks, such as the COVID-19 pandemic. Findings: The results indicate that the MIDAS model outperforms the alternatives, with the lowest forecast errors (∆RMSE = 0.3538; ∆MAPE = 0.9028%) and the highest stability in capturing hidden dynamics of unemployment. Using the best-performing model, predictions for the first semester of 2025 indicate that hidden unemployment will reach 33.14 percent, reflecting persistent vulnerabilities in the labor market structure. Originality/Value: The study contributes to labor market research by integrating high-frequency big data with econometric nowcasting methods to estimate hidden unemployment, a phenomenon often overlooked in official statistics. This approach introduces a novel application of real-time indicators to enhance the timeliness and relevance of employment monitoring in emerging economies. Practical/Policy implication: The findings underscore the importance of adaptive employment policies that address hidden unemployment as a structural issue. By providing early indicators, this study offers policymakers timely insights to design responsive interventions, reduce labor market inefficiencies, and mitigate the risks of increasing employment disparities.
Job Competency Extraction in Information and Technology Sector Using K-Means and Non-Negative Matrix Factorization (NMF) Algorithms Alfitra Rifa Geandra; Amir Mumtaz Siregar; Rani Nooraeni
Joint International Conference 2026: The 2nd Indonesia Forum on Labour and Productivity (IFLP) & The 4th International Conference on Data Science and Official Statistics (ICDSOS) Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.684

Abstract

The advancement of information technology has led to a surge in online job vacancy data, which contains valuable information about the skill demands in the digital labor market. This study aims to extract job competency in the information and technology sector using a combination of KMeans clustering and Non-Negative Matrix Factorization (NMF). A total of 350 job postings were collected from the Kalibrr platform and processed through web scraping, text preprocessing, and feature representation using TF-IDF. The clustering results indicate that the optimal configuration consists of 10 clusters, as evaluated using the Silhouette Score and Davies-Bouldin Index. Each cluster represents a specific job topic, such as backend development, data science, QA automation, cybersecurity, and digital marketing. The results offer a structured overview of digital skill demands and can be utilized by educational institutions, training providers, and labor policy makers. However, the dataset’s limited size, reliance on a single job platform, and the use of traditional machine learning techniques may not capture all semantic variations and complexities present in the broader job market. Consequently, future work should involve larger and more diverse datasets as well as advanced deep learning text representation approaches to enhance the robustness and generalizability of the results. 
Co-Authors Achmad Maulana Andi Wicaksono Alfitra Rifa Geandra Amir Mumtaz Siregar Syfriza Davies Raihannabil