Pablo de Pedraza's presentation at Textkernel's Conference Intelligent Machines and the Future of Recruitment on 2 June 2016. The number of job openings, or vacancies, is an important indicator of the state of the economy and the labour market. They are extensively used by institutions and in academic papers to calculate the Beveridge Curve or estimate the matching function, center pieces of macroeconomic models studying labor markets. Vacancies can be measured using administrative registers, surveys to employers, advertisements in printed press or using online advertising. This presentation is divided into two sections. In the first one we study the Dutch Beveridge curve and the matching function using the number of vacancies inferred from a survey to employers conducted by the Dutch Central Bureau of Statistics (CBS) from 1997 until the end of 2014. We obtain conclusion about matching process before and after the Great Recession. In the second section we compare number of vacancies inferred from CBS vacancy data with the number of vacancies posted online. According to CBS data, the number of vacancies increases during positive shocks and goes down during negative ones. We can observe the number of web vacancies posted online from 2006 until today and compare them with CBS data during a complete economic cycle. Results show a positive time trend in the number of online vacancies and negative time trend in the number of vacancies inferred from a survey. We show that both series reflect very similar economic reality once we account for both trends. We settle our future research lines focusing on exploring the sources behind both trends and how they compare across sectors.

1. 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 Pedraza
AIAS,
Amsterdam Institute for Advanced
Labour Studies,
University of Amsterdam
Amsterdam, June 2016
Labor market matching, economic cycle and
online vacancies

2. 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)

3. 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 process
Employment, Unemployment, …

 11  ttt LDLSH
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

4. 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

5. 3. Research Approach
1.- 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)

6. 3.1- Dutch Matching Function and the Great Recession
-.4-.2
0
.2.4
logresidual
2002Q2 2004Q4 2007Q2 2009Q4 2012Q2 2013Q4

 tttt VUH 
USA
-Long Term Unemployment
NL
-Assumption failure, misspecification?
- Study the residual……

7. 3.1- Dutch Matching Function and the Great Recession
-5000
0
5000
10000
0 2004Q4 2009Q4 2013Q4

 11  ttt LDLSH
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21
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and
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Where
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8. 3.1- Dutch Matching Function and the Great Recession
-5000
0
5000
10000
0 2004Q4 2009Q4 2013Q4
MF0 MF5.1
-5000
0
5000
10000
0 2004Q4 2009Q4 2013Q4
MF0 MF5.2
-5000
0
5000
10000
0 2004Q4 2009Q4 2013Q4
MF0 MF5.3
-5000
0
5000
10000
0 2004Q4 2009Q4 2013Q4
MF0 MF5.4
-5000
0
5000
10000
0 2004Q4 2009Q4 2013Q4
MF0 MF5.5

 tttt VUH  
 11  ttt LDLSH

9. 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

 tttt VUH  
 11  ttt LDLSH
We need better measures of both sides of the albour market

10. Research Approach
1.- 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)

11. 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?

12. 2. Data generation & data quality
2.3.- Web vacancy Data validation
0
100000200000300000400000
19950 20000 20050 20100 20150
yearq
(sum) Vnewt total_vnodup
(sum) Vendt (sum) Vcancelt
(sum) Vocc
_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

13. 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)
0 20 40 60 80
time
New V New V web
-40000-20000
0
200004000060000
Residuals
40 50 60 70
time
Residuals Residuals
0
2000040000
40 50 60 70
time
New V detrend & smooth MA(1 1 1) Web detrend & smooth MA(1 1 1)
-40000-20000
0
2000040000
40 50 60 70
time
New V detrend & smooth MA(2,1,2) New V detrend & smooth MA(2,1,2)
- 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.

14. 2. Data generation & data quality
6/19 where the activity is a bit below but is catching up and follow similar evolution
B Mining & quarrying
C Manufacturing
F Construction
G Wholesales, retail trade & repair motor
H Transport & storage
O Public Administration & Social security
9/19 where activity level is very similar and following evolution
D Electricity, gas, steam supply
J Information and communication
K Financial Institutions
L Renting and buying of real state
M Consultancy research & other specialized services
P Education
Q Health & social work
R Culture, sports & recreation
S Other services
1/19 sector where do not capture the whole activity but same evolution
I Accommodation and food
1/19 similar level but differences in the up and down
E water sup
2/19 Cases where there are big differences
N renting & leasing
A Agriculture

15. 2. Data generation & data quality
6/19 where the activity is a bit below but is catching up and follow similar evolution
B Mining & quarrying
C Manufacturing
F Construction
G Wholesales, retail trade & repair motor
H Transport & storage
O Public Administration & Social security
9/19 where activity level is very similar and following evolution
D Electricity, gas, steam supply
J Information and communication
K Financial Institutions
L Renting and buying of real state
M Consultancy research & other specialized services
P Education
Q Health & social work
R Culture, sports & recreation
S Other services
1/19 sector where do not capture the whole activity but same evolution
I Accommodation and food
1/19 similar level but differences in the up and down
E water sup
2/19 Cases where there are big differences
N renting & leasing
A Agriculture
0
100002000030000
CManufacturing
1997q1 2001q3 2006q1 2010q3 2015q1
date3q
(sum) number (sum) Vnewt
(sum) Vendt

16. 2. Data generation & data quality
6/19 where the activity is a bit below but is catching up and follow similar evolution
B Mining & quarrying
C Manufacturing
F Construction
G Wholesales, retail trade & repair motor
H Transport & storage
O Public Administration & Social security
9/19 where activity level is very similar and following evolution
D Electricity, gas, steam supply
J Information and communication
K Financial Institutions
L Renting and buying of real state
M Consultancy research & other specialized services
P Education
Q Health & social work
R Culture, sports & recreation
S Other services
1/19 sector where do not capture the whole activity but same evolution
I Accommodation and food
1/19 similar level but differences in the up and down
E water sup
2/19 Cases where there are big differences
N renting & leasing
A Agriculture

17. 2. Data generation & data quality
6/19 where the activity is a bit below but is catching up and follow similar evolution
B Mining & quarrying
C Manufacturing
F Construction
G Wholesales, retail trade & repair motor
H Transport & storage
O Public Administration & Social security
9/19 where activity level is very similar and following evolution
D Electricity, gas, steam supply
J Information and communication
K Financial Institutions
L Renting and buying of real state
M Consultancy research & other specialized services
P Education
Q Health & social work
R Culture, sports & recreation
S Other services
1/19 sector where do not capture the whole activity but same evolution
I Accommodation and food
1/19 similar level but differences in the up and down
E water sup
2/19 Cases where there are big differences
N renting & leasing
A Agriculture

18. 2. Data generation & data quality
6/19 where the activity is a bit below but is catching up and follow similar evolution
B Mining & quarrying
C Manufacturing
F Construction
G Wholesales, retail trade & repair motor
H Transport & storage
O Public Administration & Social security
9/19 where activity level is very similar and following evolution
D Electricity, gas, steam supply
J Information and communication
K Financial Institutions
L Renting and buying of real state
M Consultancy research & other specialized services
P Education
Q Health & social work
R Culture, sports & recreation
S Other services
1/19 sector where do not capture the whole activity but same evolution
I Accommodation and food
1/19 similar level but differences in the up and down
E water sup
2/19 Cases where there are big differences
N renting & leasing
A Agriculture
0
500
100015002000
Ewatersup
1997q1 2001q3 2006q1 2010q3 2015q1
date3q
(sum) number (sum) Vnewt
(sum) Vendt

19. 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)

20. 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 Pedraza
AIAS,
Amsterdam Institute for Advanced
Labour Studies,
University of Amsterdam
Amsterdam, May 2016
Happy birthday
and thanks