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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