As a part of the project ‘”Building resiliency through greater adaptability to long-term challenges” LEED is conducting a series of expert webinars to explore the conceptual and practical dimensions of the notion of ‘local economic resilience’. These 1-hour webinars are an opportunity to gatherpolicy experts, academics and local practitioners for a short and in-depth discussion followed by a question and answers session. . The first two webinars “Understanding resilience” were held in early December 2015 and focused on how to define and measure economic resilience, particularly in the context of local labour markets.

1. Measuring labour market
resilience in a comparative
perspective
Prof. dr. Menno Fenger
Erasmus University Rotterdam
INSPIRES project manager

2. Labour market resilience
• Absorption capacity of the labour market
• “Resist, withstand or recover from external
challenges in order to maintain or improve
its pre-shock state”
• Growing attention for resilience of labour
markets (OECD, EC)

3. Double comparative perspective
• In INSPIRES: focus on vulnerable groups
• Between countries
• Between vulnerable groups: youth,
migrants, older workers, disabled workers
• Resilience and economic growth

4. Netherlands
Sweden Estonia
GreeceGreece

5. Potential explanations
• Economic growth: Okun’s law (i.e. Okun’s rule of
thumb)
• Demographic conditions (change in population,
characteristics of population…)
• Economic conditions (structure of economy,
openness…)
• Institutional characteristics: welfare state, social
dialogue ….

6. Challenge
1. Show differences between different
groups within countries
2. Show differences between similar groups
in different countries
3. Make differences comparable
4. Explain

7. Basic idea
• Try to model unemployment rates (for total
population and vulnerable groups)
• If models fit: unexplained variance is a
proxy for ‘effective policies’
• A model that is used for determining social
assistance (WWB) budget in NL

8. Regression analysis
• Level of explained variance about 30%
• All models significant at 0.01
• Models need to be improved
• But institutions/policies matter

9. Outcomes
• Predictions based on regression model
• Sum of actual developments and predicted
developments as an indicator
• Strong differences between countries and
between groups
• For total population and for vulnerable
groups

10. Example
• Belgium 2010 total unemployment -3.24
– Meaning: on the base of the changes in GDP
2009 we would have expected unemployment
in Belgium to be 3.24% higher

11. Example
2010 Total
unemployment
Youth Older
workers
Migrants
Belgium -3.24 -4.05 -3.17 -3.24
Italy 0.9 7.97 `-1.86 -2.89
Germany -0,32 -7,23 0,99 -1.40

12. Regression results
Country Total
2007
Total
2010
Youth
2007
Youth
2010
Older
2007
Older
2010
Migrants
2007
Migrants
2010
Belgium -0,21 -3.24 2,38 -4,05 -1,39 -3,17 -0,19 -3,24
Netherlands -0,48 -0,64 -0,98 -0,63 -0,85 -1,53 -4,51 -5,72
Switzerland -0,62 -2,77 1,69 -4,50 -0,62 -2,58 1,23 -1,45
Italy -0,65 0,9 2,34 7,97 -2,01 -1,86 -2,83 -2,89
Slovenia -1,68 -2,15 -6,36 -8,17 -1,01 -2,19 1,00 0,28
Sweden 0,40 1,59 5,44 7,40 -0,32 0,77 2,29 6,47
UK 1,00 -2,26 3,54 -1,56 0,10 -2,68 0,73 -4,08
Greece 1,19 1,48 4,54 4,32 0,03 0,44 -1,18 2,54
Spain 0,60 6,60 -3,67 10,37 0,25 7,40 -0,35 8,89
Germany 2,52 -0,32 -1,67 -7,23 4,78 0,99 4,00 -1,40
Hungary 0,28 -0,59 1,95 0,42 -0,98 -1,65 -1,33 -7,93

13. Lessons for measuring resilience
• Relation between unemployment and GDP not
significant but varies (as expected)
• Regression analysis as ‘proven technology’?
• Even used in budget allocation at the local level
in the Netherlands
• Resilience is not similar to employment
performance
• More effort needed in refining models