Work-in-Data’s activities are organised around four pillars that together support a global, harmonised understanding of work, time use, and labour markets.
1. Data Harmonisation and Dataset Development
We build and maintain harmonised micro-datasets that combine household, labour force, and time-use surveys from around the world. Our activities include:
- Developing a comprehensive repository of all household and labour surveys
- Cleaning and standardising surveys using the Work-in-Data codebook
- Producing two harmonised micro-datasets for global comparison
- Harmonized World Labor Force Survey (HWLFS), a harmonized micro-dataset of thousands of surveys from over 100 countries.
- Harmonized World Time Use Survey (HWTUS), a harmonized micro-dataset of around 250 time use surveys from over 50 countries.
2. Academic Research
The HWLFS and HWTUS serve as the foundation for frontier research. So far, they have been used to analyse:
- The Gender Division of Work
- Human Capital and Labor Markets
A full list of research papers based on these datasets is [here].
3. Policy Indicators and Measurement Tools
We translate harmonised data into policy-relevant measures and tools that support evidence-based decision-making. Policy relevant activities are:
- Indicators : [Global Gender Distortions Index]
- Data dashboards : [Gender Growth Gap]
- COVID Policy simulators : [Sectoral lockdown policies]
4. Teaching and Capacity-Building
Work-in-Data integrates teaching and research. Through the “Macro Development” course, bachelor and master students learn data cleaning, dataset harmonisation, simple regression analysis, and how to link empirical work with macroeconomic models.
Students contribute directly to:
- Cleaning and processing survey data
- Building harmonised datasets
- Applying empirical tools in a macro-development context
This hands-on approach trains the next generation of macro / labour / development economists and fosters the development of work-in-data.