big data for labour market...
TRANSCRIPT
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Opportunities and risks of machine learning
Ekkehard Ernst Research Department
International Labour Organization
Big Data for labour market interventions
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What’s new?
Today's business needs information that is:
1. Real-time
2. Location-precise
3. Individual-specific
4. Robust
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What is Big Data?
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How is it being used?
• Combine different data sources: • Official data, open source, social media and proprietary data
• Multi-dimensional analysis of data: • Combine individual characteristics with aggregate information (regional, national)
• Analyse multi-faceted social outcomes (e.g. social network dynamics, mobility patterns)
• Use sophisticated statistical tools to detect relationships: • “Machine learning”: detect statistical relations automatically
• Clustering to detect similarities between groups (consumers, employees, firms, etc.)
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Example: Social unrest
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Example: Communication
People connect through language
Most (social media) information only available as text
Text analysis for predictions
People’s communi-cation contains information about expected future(s)
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What can policy makers learn from it?
More precise targeting of policies
Source: Foresight Alliance. 2016. The futures of work. (Rockefeller Foundation, New York)
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What can policy makers learn from it?
Forecasting and manpower planning tools
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What are the risks for labour?
Business risks shifted to crowd workers and contingent workforce
Source: JP Morgan, 2016. Paychecks, paydays and the online platform economy.
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What are the risks for labour?
Business risks are increasingly shifted to labour
Temporary contracts and “zero-hour” contracts
HR analytics to optimize production stream
Burden of adjustment falls entirely on labour
Source: Manpower, 2015. Contingent workforce index.
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What needs to be done?
• Enforcement of labour standards, including for contingent workforce • Avoid zones of “non-droit”, especially for temporary workers
• Business risk must remain with businesses
• Use Big Data more intensively for policy purposes • Detect labour market shifts and talent shortages early on
• Use it also for labour inspection: Where is enforcement particularly needed?
• Target policies (geographically) for more efficient implementation of labour market policies
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In conclusion
• Big data allows to combine and exploit a wealth of information from different sources; but data ownership remains an issue
• Currently, private users have advantage in making use of this data for HR and business planning strategies; generate significant revenues
• Some of these efforts have created benefits for both employees and companies, others have shifted risks to more vulnerable groups
• Potential benefits in policy making and implementation remain underexploited
• Policy makers need to monitor Big Data HR-strategies to avoid undercutting of labour standards