eduworks-network.eu  

facebook.com/eduworksnetwork@EduworksNetwork

This project has been funded with support from the European Commission.This communication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be

made of the information contained therein.

Pablo de PedrazaAIAS,

Amsterdam Institute for Advanced Labour Studies,

University of AmsterdamAmsterdam, June 2016

Labor market matching, economic cycle and online vacancies

Labor market matching, economic cycle and online vacancies

1.- About the research process: Improve and study the matching process in the labour market

2.- Data generation process & data quality

3.- Research approach (Examples): 3.1.- One country starting with traditional data

Dutch Matching Function and the Great Recession

3.2.- Combine and compare with web data Vacancy data & economic cycle (CBS vs web vacancies)

1.- About the research project

More and more online activities, Data Revolution, also in the matching process between Labour Supply & Labour Demand 

BUT methodological issues are still under discussion

Networking : Academic point of view to the Institutional discussion on Web data (World Bank, JRC, Eurostat, ECB…)

Methodological perspectives: Web base data collection methods for scientific research (DATA QUALITY).

Macroeconomic  perspectives:  Matching Function and the Beveridge Curve, Unemployment and Vacancies matching process. Building block un Equilibrium Unemployment Theories.

1. Labour Demand (LD) 2. Labour supply (LS)

Macroeconomics of the matching processEmployment, Unemployment, …

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1.- Main goal: Improve the study the matching process between supply and demand of labour using web data

2.- Data generation process (non-scientific) & data quality (Scientific research)

3.- Research approach (examples):

3.1.- One country starting with traditional data: “Dutch Matching function and the Greta Recession”

3.2.- Combine and compare with web data

2. Data generation & data quality

Data generation  as a by-product of internet activities, Ex. Looking for a job/looking for a workers. 

Data collection Ex. Data crawling (text kernel) Ex. Web surveys (wage indicator)

Data analyses and statistics 

Data transformation/curation  Ex. Semantic analyses Ex. Weights to balance  

Scientific 

Macroeconmics

Microeconomics

Behavioral sciences

Matching learning techniques

(…)

Practical 

Ex. Matchmaking services

 

 

Political decisions 

Data quality evaluation 

 

Reference samples from statistical Institutes  

Textkernel has made vacancy data crawled from the web available for the project.

- Conducting semantic analysis of vacancy’s texts: skills, sector, education…

- Weighting techniques

Comparing  CBS (probabilistic) and web vacancy data & conclusions we can obtain from them

3. Research Approach1.- Main goal: Improve and study the matching process between supply and demand of labour

2.- Data generation process & data quality

3.- Research approach (Examples):

3.1.- One country starting with traditional data Dutch Matching Function and the Great Recession

3.2.- Combine and compare with web data Vacancy data & economic cycle (CBS vs web vacancies)

3.1- Dutch Matching Function and the Great Recession 

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3.1- Dutch Matching Function and the Great Recession

Misspecification of

Labour supply- Matching efficiency increase is driven by short term employed job seekers.

-Counter-cyclical elasticities to short term employees + Pro-cyclical elasticities to the stock of unemployed = combination of growing unemployment with increase matching efficiency

- Elasticities to the stock of unemployed are not constant across unemployed stocks: New entrants.

Labour Demand- Growing unemployment + active employed = reducing search friction for employers.

- Flow of new vacancies rather than the stock

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We need better measures of both sides of the albour market

Research Approach1.- Main goal: Improve and study the matching process between supply and demand of labour

2.- Data generation process & data quality

3.- Research approach (Examples): 3.1.- One country starting with traditional data

Dutch Matching Function and the Great Recession

3.2.- Combine and compare with web data Labor demand: Vacancy data & economic cycle (CBS vs web

vacancies)

2. Data generation & data quality

Data generation  as a by-product of internet activities, Ex. Looking for a job/looking for a workers. 

Data collection Ex. Data crawling (text kernel) Ex. Web surveys (wage indicator)

Data analyses and statistics 

Data transformation/curation  Ex. Semantic analyses Ex. Weights to balance  

Scientific 

Macroeconmics

Microeconomics

Behavioral sciences

Matching learning techniques

(…)

Practical 

Ex. Matchmaking services

 

 

Political decisions 

Data quality evaluation 

 

Reference samples from statistical Institutes  

Textkernel has made vacancy data crawled from the web available for the project.

- Conducting semantic analysis of vacancy’s texts: skills, sector, education…

- Weighting techniques

Comparing CBS and web vacancy data & conclusions we can obtain from them

DO THEY REFLECT THE SAME ECONOMIC REALITY?

2. Data generation & data quality 2.3.- Web vacancy Data validation

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_cons 131156.8 35013.77 3.75 0.001 59434.34 202879.3 time 3396.148 623.344 5.45 0.000 2119.285 4673.01 total_vnodup Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 5.0374e+10 29 1.7370e+09 Root MSE = 29551 Adj R-squared = 0.4973 Residual 2.4452e+10 28 873283493 R-squared = 0.5146 Model 2.5922e+10 1 2.5922e+10 Prob > F = 0.0000 F( 1, 28) = 29.68 Source SS df MS Number of obs = 30

. reg total_vnodup time if yearq<20143 & year>20064

_cons 442845.7 32043.15 13.82 0.000 377208.3 508483.2 time -4362.266 570.4586 -7.65 0.000 -5530.797 -3193.734 Vnewt Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 6.3247e+10 29 2.1809e+09 Root MSE = 27044 Adj R-squared = 0.6646 Residual 2.0479e+10 28 731388134 R-squared = 0.6762 Model 4.2768e+10 1 4.2768e+10 Prob > F = 0.0000 F( 1, 28) = 58.48 Source SS df MS Number of obs = 30

. reg Vnewt time if yearq<20143 & year>20064

2. Data generation & data quality 2.3.- Web vacancy Data validation

Table 1.- Total number of vacancies

Table.2.- De-trended

Table 3.- De-trended and Smooth MA(1,1,1)

Table 4.- No time trend and Smooth MA(1,1,1)

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- SO FAR: After removing noise from signals both series are not very different

- EXPLORING:

- by sector and regions (Not all sectors follow the same pattern)

- relationship of the time trends with:- Internet penetration. ICT enterprise survey - Non response

- compare the cyclical behaviour of both data sources with some economic climate indexes.

2. Data generation & data quality 6/19 where the activity is a bit below but is catching up and follow similar evolutionB Mining & quarryingC ManufacturingF Construction G Wholesales, retail trade & repair motorH Transport & storageO Public Administration & Social security

9/19 where activity level is very similar and following evolutionD Electricity, gas, steam supply J Information and communicationK Financial InstitutionsL Renting and buying of real stateM Consultancy research & other specialized services P Education Q Health & social workR Culture, sports & recreationS Other services

1/19 sector where do not capture the whole activity but same evolutionI Accommodation and food

1/19 similar level but differences in the up and downE water sup

2/19 Cases where there are big differences N renting & leasing A Agriculture

2. Data generation & data quality 6/19 where the activity is a bit below but is catching up and follow similar evolutionB Mining & quarryingC ManufacturingF Construction G Wholesales, retail trade & repair motorH Transport & storageO Public Administration & Social security  

9/19 where activity level is very similar and following evolutionD Electricity, gas, steam supply J Information and communicationK Financial InstitutionsL Renting and buying of real stateM Consultancy research & other specialized services P Education Q Health & social workR Culture, sports & recreationS Other services

1/19 sector where do not capture the whole activity but same evolutionI Accommodation and food

1/19 similar level but differences in the up and downE water sup

2/19 Cases where there are big differences N renting & leasing A Agriculture

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1997q1 2001q3 2006q1 2010q3 2015q1date3q

(sum) number (sum) Vnewt(sum) Vendt

2. Data generation & data quality 6/19 where the activity is a bit below but is catching up and follow similar evolutionB Mining & quarryingC ManufacturingF Construction G Wholesales, retail trade & repair motorH Transport & storageO Public Administration & Social security

9/19 where activity level is very similar and following evolutionD Electricity, gas, steam supply J Information and communicationK Financial InstitutionsL Renting and buying of real stateM Consultancy research & other specialized services  P Education Q Health & social workR Culture, sports & recreationS Other services

1/19 sector where do not capture the whole activity but same evolutionI Accommodation and food

1/19 similar level but differences in the up and downE water sup

2/19 Cases where there are big differences N renting & leasing A Agriculture

2. Data generation & data quality 6/19 where the activity is a bit below but is catching up and follow similar evolutionB Mining & quarryingC ManufacturingF Construction G Wholesales, retail trade & repair motorH Transport & storageO Public Administration & Social security

9/19 where activity level is very similar and following evolutionD Electricity, gas, steam supply J Information and communicationK Financial InstitutionsL Renting and buying of real stateM Consultancy research & other specialized services P Education Q Health & social workR Culture, sports & recreationS Other services

1/19 sector where do not capture the whole activity but same evolutionI Accommodation and food 

1/19 similar level but differences in the up and downE water sup

2/19 Cases where there are big differences N renting & leasing A Agriculture

2. Data generation & data quality 6/19 where the activity is a bit below but is catching up and follow similar evolutionB Mining & quarryingC ManufacturingF Construction G Wholesales, retail trade & repair motorH Transport & storageO Public Administration & Social security

9/19 where activity level is very similar and following evolutionD Electricity, gas, steam supply J Information and communicationK Financial InstitutionsL Renting and buying of real stateM Consultancy research & other specialized services P Education Q Health & social workR Culture, sports & recreationS Other services

1/19 sector where do not capture the whole activity but same evolutionI Accommodation and food

1/19 similar level but differences in the up and downE water sup

2/19 Cases where there are big differences N renting & leasing A Agriculture

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GENERAL CONCLUSIONS

- Traditional matching function fails during the Great Recession (misspecification). Better measures of job seekers (Supply side) are needed.

-Web data: Labour Demand: seem to have a lot of potential for Macro and micro research (The first quality test is quite positive)

  eduworks-network.eu  

facebook.com/eduworksnetwork@EduworksNetwork

This project has been funded with support from the European Commission.This communication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be

made of the information contained therein.

Pablo de PedrazaAIAS,

Amsterdam Institute for Advanced Labour Studies,

University of AmsterdamAmsterdam, May 2016

Happy birthdayand thanks