A Web-Based Approach to Measure Skill Mismatches and Skills Profiles for a Developing Country : : The Case of Colombia.

• Author: Cárdenas Rubio, Jeisson Arley.
• Place / Publishing House: Bogota : : Universidad Colegio Mayor de Nuestra Senora del Rosario,, 2020.; ©2020.
• Year of Publication: 2020
• Language: English
• Physical Description: 1 online resource (406 pages)

Several interdisciplinary studies highlight imperfect information as a possible explanation of skill mismatches, which in turn has implications for unemployment and informality rates. Despite information failures and their consequences, countries like Colombia (where informality and unemployment rates are high) lack a proper labour market information system to identify skill mismatches and employer skill requirements. One reason for this absence is the cost of collecting labour market data. Recently, the potential use of online job portals as a source of labour market information has gained the attention of researchers and policymakers, since these portals can provide quick and relatively low-cost data collection. As such, these portals could be of use for Colombia. However, debates continue about the efficacy of this use, particularly concerning the robustness of the collected data. This book implements a novel mixed-methods approach (such as web scraping, text mining, machine learning, etc.) to investigate to what extent a web-based model of skill mismatches can be developed for Colombia. The main contribution of this book is demonstrating that, with the proper techniques, job portals can be a robust source of labour market information. In doing so, it also contributes to current knowledge by developing a conceptual and methodological approach to identify skills, occupations, and skill mismatches using online job advertisements, which would otherwise be too complex to be collected and analysed via other means. By applying this novel methodology, this study provides new empirical data on the extent and nature of skill mismatches in Colombia for a considerable set of non-agricultural occupations in the urban and formal economy. Moreover, this information can be used as a complement to household surveys to monitor potential skill shortages. Thus, the findings are useful for policymakers, statisticians, and education and training providers, among others.

Table of Contents

Intro -- Half-Title Page -- Title Page -- Copyright Page -- Author -- Contents -- List of Figures -- List of Tables -- Acronyms and Abbreviations -- 1. Introduction -- 2. The Labour Market and Skill Mismatches -- 2.1. Introduction -- 2.2. Basic definitions -- 2.2.1. Labour supply -- 2.2.2. Labour demand -- 2.2.3. Informal economy -- 2.2.4. Skills -- 2.3. How the labour market works under perfect competition -- 2.3.1. Labour demand -- 2.3.2. Labour supply -- 2.3.3. Market equilibrium -- 2.4. Market imperfections and segmentation -- 2.4.1. Segmentation -- 2.4.2. Imperfect market information -- 2.5. Conclusion -- 3. The Colombian Context -- 3.1. Introduction -- 3.2. The characteristics of the Colombian labour market -- 3.2.1. Labour supply -- 3.2.2. Labour demand -- 3.3. Skill mismatches in Colombia -- 3.4. An international example of skill mismatch measures -- 3.5. Lack of accurate information to develop well-orientated public policies -- 3.6. Conclusion -- 4. The Information Problem: Big Data as a Solution for Labour Market Analysis -- 4.1. Introduction -- 4.2. A definition of Big Data -- 4.3. Big Data on the labour market -- 4.3.1. Labour supply -- 4.3.2. Labour demand -- 4.4. Potential uses of information from job portals to tackle skill shortages -- 4.4.1. Estimating vacancy levels -- 4.4.2. Identifying skills and other job requirements -- 4.4.3. Recognising new occupations or skills -- 4.4.4. Updating occupation classifications -- 4.5. Big Data limitations and caveats -- 4.5.1. Data quality -- 4.5.2. Job postings are not necessarily real jobs -- 4.5.3. Data representativeness -- 4.5.4. Limited internet penetration rates -- 4.5.5. Data privacy -- 4.6. Big Data in the Colombian context -- 4.7. Conclusion -- 5. Methodology -- 5.1. Introduction -- 5.2. Measurement of the labour demand: Job vacancies.

5.3. Selecting the most important vacancy websites in the country -- 5.4. Web scraping -- 5.5. The organisation and homogenisation of information -- 5.5.1. Education, experience, localisation, among other job characteristics -- 5.5.2. Wages -- 5.5.3. Company classification -- 5.6. Conclusion -- 6. Extracting More Value from Job Vacancy Information (Methodology Part 2) -- 6.1. Introduction -- 6.2. Identifying skills -- 6.3. Identifying new or specific skills -- 6.4. Classifying vacancies into occupations -- 6.4.1. Manual coding -- 6.4.2. Cleaning -- 6.4.3. Cascot -- 6.4.4. Revisiting manual coding (again) -- 6.4.5. Adaptation of Cascot according to Colombian occupational titles -- 6.4.6. The English version of Cascot -- 6.4.7. Machine learning -- 6.5. Deduplication -- 6.6. Imputing missing values -- 6.6.1. Imputing educational requirements -- 6.6.2. Imputing the wage variable -- 6.7. Vacancy data structure -- 6.8. Conclusion -- 7. Descriptive Analysis of the Vacancy Database -- 7.1. Introduction -- 7.2. Vacancy database composition -- 7.3. Geographical distribution of vacancies and number of jobs -- 7.4. Labour demand for skills -- 7.4.1. Educational requirements -- 7.4.2. Occupational structure -- 7.4.3. New or specific job titles -- 7.4.4. The most in-demand skills (ESCO classifications) -- 7.4.5. New or specific skills demanded in the Colombian labour market -- 7.4.6. Experience requirements -- 7.5. Demand by sector -- 7.6. Trends in the labour demand -- 7.7. Wages -- 7.8. Other characteristics of the vacancy database -- 7.9. Conclusion -- 8. Internal and External Validity of the Vacancy Database -- 8.1. Introduction -- 8.2. Internal validity -- 8.2.1. Wage distribution by groups -- 8.2.2. Vacancy distribution by groups -- 8.3. External validity -- 8.3.1. Data representativeness: Vacancy versus household survey information.

8.3.2. Time series comparison -- 8.4. Conclusion -- 9. Possible Uses of Labour Demand and Supply Information to Reduce Skill Mismatches -- 9.1. Introduction -- 9.2. Labour market description -- 9.2.1. Colombian labour force distribution by occupational groups -- 9.2.2. Unemployment and informality rates -- 9.2.3. Trends in the labour market -- 9.3. Measuring possible skill mismatches (macro-indicators) -- 9.3.1. Beveridge curve (indicators of imbalance) -- 9.3.2. Volume-based indicators: Employment, unemployment, and vacancy growth -- 9.3.3. Price-based indicators: Wages -- 9.3.4. Thresholds -- 9.3.5. Skill shortages in the Colombian labour market -- 9.4. Detailed information about occupations and skill matching -- 9.4.1. Skills -- 9.4.2. Skill trends -- 9.5. Conclusions -- 10. Conclusions and Implications -- 10.1. Introduction -- 10.2. Conceptual contributions -- 10.3. Contributions to methodology -- 10.4. Empirical contributions -- 10.5. Implications for practice and policy -- 10.5.1. For national statistics offices -- 10.5.2. For policymakers -- 10.5.3. For education and training providers -- 10.5.4. For career advisers -- 10.6. Limitations -- 10.7. Further research -- 10.7.1. Improving machine learning and text mining algorithms -- 10.7.2. New job titles and potential new occupations -- 10.7.3. International comparison -- 10.8. Conclusions -- References -- Appendix -- Appendix A: Examples of Job Portal Structures -- Appendix B: Text Mining -- Appendix C: Detailed Process Description for the Classification of Companies -- C.1. Manual coding -- C.2. Word-based matching methods ("Fuzzy merge") -- C.3. A return to manual coding -- Appendix D: Machine Learning Algorithms -- Appendix E: Support Vector Machine (SVM) -- Appendix F: SVM Using Job Titles -- Appendix G: Nearest Neighbour Algorithm Using Job Titles -- Appendix H: Additional Tables.

Back Cover.