Transcript No Slide Title

Structural Unemployment in Croatia
How Important is the Occupational Mismatch?
Iva Tomić
The Institute of Economics, Zagreb & Faculty of Economics, University of Ljubljana


2009).
Aim
 To what extent can the existing level of
unemployment (jn Croatia) be attributed
to structural (occupational) mismatch or
by how much would unemployment fall
were structural balance to be achieved?
1.
2.
3.
4.
5.
6.
7.
8.
9.
Legislators, senior officials and managers;
Professionals;
Technicians and associate professionals;
Clerks;
Service and shop and market sales workers;
Skilled agricultural and fishery workers;
Craft and related trades workers;
Plant and machine operators and assemblers;
Elementary occupations.
NLS
α
β
ξ
time trend
unben
2
May-11
Sep-10
Jan-10
May-09
Sep-08
Jan-08
May-07
Sep-06
Jan-06
May-05
Sep-04
Jan-04
α
β
log M t  const.   j  j log k j ,t 1   logU t 1   logVt 


   t

ξ
time trend
Results
Table 1. Estimation results for the aggregate function
α
β
ξ
time trend
unben
constant
monthly
dummies
CRS
R2
restricted estimation
TSNLS_1
TSNLS_2
1.089***
1.312***
1.007***
0.815***
0.849***
0.811***
-5.879
-6.551
-5.627
-16.298
-12.629
-14.713
0.250***
0.409***
0.266***
0.185
0.151
0.189
-3.320
-2.843
-3.435
(---)
(---)
(---)
-0.089
0.211
-0.090
-1.180
-1.739
-1.217
(-0.170)
(0.656)
(-0.191)
(-1.210)
(-1.240)
(-1.479)
0.003***
0.003***
0.003***
0.003***
0.003***
0.003***
-6.029
-4.303
-5.984
-6.210
-5.268
-5.948
-1.241***
-0.713
-1.175*** -1.382***
-1.355***
-1.355***
(-3.637)
(-1.563)
(-4.854)
(-4.095)
(-4.293)
-8.288***
-11.441***
-8.503*** -5.094***
-5.171***
-5.039**
(-3.830)
(-4.730)
(-3.712)
(-9.817)
(-7.713)
(-8.644)
YES
YES
YES
YES
YES
YES
2.329 (YES) 7.662*** (NO)
2.473 (YES)
-
-
-
0.913
0.911
0.910
0.911
0.913
0.907
(-3.341)
Jun-11
Oct-10
Feb-10
Jun-09
Oct-08
Feb-08
Jun-07
Oct-06
Feb-06
Jun-05
Oct-04
Feb-04
Jun-11
Oct-10
Feb-10
Oct-08
Feb-08
Jun-09
TSNLS
white-collar
occupations
NLS
highly-skilled whitecollar occupations
TSNLS
NLS
1.053***
1.408***
0.790***
1.084***
-5.377
-6.329
-5.691
-5.514
-6.004
-5.307
-14.388
-8.138
-15.107
-11.499
-12.007
-9.359
0.105*
0.308
0.194***
0.441*
0.222***
0.704***
0.111
0.125
0.134
0.085
0.218
0.255
-1.688
-1.579
-2.791
-1.875
-2.740
-2.977
(---)
(---)
(---)
(---)
(---)
(---)
1.802***
1.962***
1.740***
1.900***
1.592***
1.699***
-5.779
-9.465
-14.237
-22.241
-6.323
-12.682
0.007***
0.007***
0.007***
0.008***
0.006***
0.005***
-8.990
-7.803
-8.342
-8.196
-8.172
-4.980
-2.181***
-1.764***
-1.725***
-1.154*
-1.957***
-0.692
(-7.913)
(-4.805)
(-6.651)
(-1.958)
(-5.771)
(-0.968)
-6.761*** -13.195***
0.889*** 0.875***
TSNLS
1.840*** 1.839***
-8.113
-6.632
0.007*** 0.007***
-9.950
-8.956
-2.173*** -2.048***
(-7.772)
(-6.868)
0.866*** 0.915***
skilled
white-collar
occupations
NLS
TSNLS
1.050***
1.582*** 1.620***
-16.527
-14.903
0.007*** 0.007***
-10.057
-9.430
-1.835*** -1.886***
(-7.707)
0.782*** 0.745***
1.581*** 1.556***
-6.517
-6.466
0.006*** 0.006***
-8.239
-8.080
-1.964*** -1.714***
(-7.385)
(-6.214)
(-5.155)
-7.445*** -7.217***
-7.030*** -7.375***
-6.620***
-6.087
(-14.194) (-10.961)
(-8.174)
(-6.949)
(-2.856)
(-3.446)
(-3.829)
(-3.695)
(-3.425)
(-3.646)
(-11.354)
(-8.013)
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
0.040
1.512
1.500
3.656*
0.005
3.833*
-
-
-
-
-
-
0.837
0.821
0.916
0.906
0.829
0.774
0.839
0.839
0.916
0.915
0.831
0.831
Note: * p < 0.1; ** p < 0.05; *** p < 0.01. t-statistics is in parentheses. unben – natural logarithm of the share of the
users of the unemployment benefits in total unemployment. NLS – non-linear least squares. TSNLS_1 – tw o-stage nonlinear least squares w ith endogenous variables: unemployment, vacancies and the share of the users of unemployment
benefits and instruments: lagged endogenous variables plus log of monthly index of construction w orks; log of monthly
share of the average net in the average gross w age and log of the spread betw een interest rates on short-term loans
for enterprises and interest rates on foreign currency deposits for enterprises. TSNLS_2 – the same as TSNLS_1
except that instruments are also first-differenced endogenous variables. CRS - constant returns to scale-show s the Fstatistics of Wald test of coefficient restrictions, w here null hypothesis is equal to α+β=1.
Source: Author’s calculation based on CES data.
24th annual EALE Conference, Bonn – Germany, September 20-22, 2012
semi-skilled bluecollar occupations
TSNLS
2
NLS
TSNLS
restricted estimation
lower-skilled bluecollar occupations
NLS
TSNLS
blue-collar
occupations
NLS
semi-skilled bluecollar occupations
TSNLS
NLS
lower-skilled bluecollar occupations
TSNLS
NLS
TSNLS
1.229***
1.326***
1.036***
1.289***
0.937***
1.092***
0.826***
0.902***
0.825***
0.829***
0.716***
0.665***
-6.771
-7.308
-6.045
-10.051
-2.919
-3.682
-9.956
-5.784
-25.319
-19.245
-6.015
-3.887
0.278***
0.463**
0.264***
0.521***
0.301**
0.820***
0.174
0.098
0.175
0.171
0.284
0.335
-2.862
-2.596
-3.152
-3.588
-2.513
-2.712
(---)
(---)
(---)
(---)
(---)
(---)
1.078***
1.217***
0.841
1.264***
1.172***
1.480***
1.343**
1.083
0.127
0.120
1.186***
1.216***
-3.644
-4.476
-1.642
-5.210
-5.093
-8.515
-2.380
-1.119
(0.357)
(0.316)
-3.925
-3.938
0.003***
0.002**
0.004***
0.003*** 6.50E-05
-0.001
0.002**
0.002
0.004***
0.004***
-0.0002
-0.001
-3.276
-2.137
-5.892
-4.182
(0.063)
(-0.901)
-2.485
-1.652
-6.246
-5.980
(-0.338)
(-0.683)
-1.009**
-0.399
-1.257***
-0.503
-0.582
0.887
-1.291***
-1.385***
-1.450***
-1.349***
-0.620
-0.304
(-2.391)
(-0.730)
(-3.317)
(-1.121)
(-1.179)
(0.840)
(-3.574)
(-2.894)
(-4.689)
(-4.299)
(-1.297)
(-0.527)
-6.775* -11.445***
-5.272***
-5.602***
-4.393***
-4.253***
-4.076***
-3.416***
-10.694*** -12.871***
-7.915*** -12.720***
(-4.690)
(-6.074)
(-2.693)
(-4.795)
(-1.848)
(-2.963)
(-7.896)
(-5.710)
(-6.375)
(-5.542)
(-4.525)
-2.930
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
5.329**
12.785***
1.555
10.589***
0.476
3.982**
-
-
-
-
-
-
0.926
0.921
0.926
0.917
0.914
0.874
0.920
0.917
0.926
0.925
0.915
0.923
Note: * p < 0.1; ** p < 0.05; *** p < 0.01. t-statistics is in parentheses. unben – natural logarithm of the share of the users of the unemployment benefits in total unemployment. NLS –
non-linear least squares. TSNLS – tw o-stage non-linear least squares w ith endogenous variables: unemployment, vacancies and the share of the users of unemployment benefits
and instruments: lagged endogenous variables plus log of monthly index of construction w orks; log of monthly share of the average net in the average gross w age and log of the
spread betw een interest rates on short-term loans for enterprises and interest rates on foreign currency deposits for enterprises. CRS - constant returns to scale-show s the Fstatistics of Wald test of coefficient restrictions, w here null hypothesis is equal to α+β=1.
Source: Author’s calculation based on CES data.
Summary of the results
 the impact of occupational mismatch on
the matching process is insignificant on
the aggregate level;

the importance of mismatch on the level of U
depends on the distribution of both U and V over
submarkets (occupations), but also on the size of
the particular submarket.
NLS
NLS
skilled
white-collar
occupations
NLS
TSNLS
0.863***
blue-collar
occupations
R
TSNLS_2
TSNLS
restricted estimation
unrestricted estimation
 Besides the aggregate function, the
study estimates the disaggregated
matching functions based on the
grouping of (similar) occupations;
 Matching functions explicitly incorporate
mismatch index (based on Dur, 1999)
for different submarkets (occupations).
TSNLS_1
highly-skilled whitecollar occupations
-6.999*** -11.216*** -10.125*** -16.530***
constant
CRS
unrestricted estimation
Jun-07
white-collar
occupations
monthly dummies
Vi ,t

 Vt
Oct-06
unrestricted estimation
NLS
 U i ,t 1 

 1   log i 
 U t 1 
Feb-06
Table 2a. Estimation results forthe white-collar occupations
Table 2b. Estimation results for the blue-collar occupations
U_bc/U
V_bc/V
12 per. Mov. Avg. (V_bc/V)

mm*u
Note: mm – mismatch index.
Source: Author’s calculation based on CES data.
R
Methodology
NLS
Jun-05
0.00
Oct-04
0.00
CRS
May-11
Sep-10
Jan-10
May-09
Sep-08
Jan-08
0.20
constant
 Monthly data from CES in the period from
January 2004 until December 2011:
To be able to detect the existence of
mismatch in the labour market, all
variables are divided according to the 9
broad occupational groups:
0.01
unben
Data

0.40
monthly dummies
Note: U_wc/U = the share of unemployed in the white-collar segment (submarket) in total unemployment; V_wc/V =
proportion of vacancies in white-collar submarket in the total number of vacancies (same applies to blue-collar occupations).
Source: Author’s calculation based on CES data.

1. the number of registered unemployed
persons (U),
2. the number of reported vacancies (V), and
3. the number of employed persons from the
Service registry (M).
May-07
U_wc/U
V_wc/V
12 per. Mov. Avg. (V_wc/V)
Jan-04
mismatch is the result of significant changes
during the 1990ies in the structure of product
markets, which have led to changes in the
structure of labour demand (Obadić, 2004);
low mobility across different occupations,
industries and locations (Boeri, 2000);
skill shortages as a key impediment to faster
labour reallocation and convergence to the
EU-15 employment structures (Brixiova et al.,
Blue-collar workers
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
0.60
Feb-04
Figure 1. Share of unemployment and vacancies in total unemployment
(vacancies) by white- and blue-collar classification
 In (most) transition countries:

occupations represents separate submarkets in the
overall labour market.
White-collar workers
0.80
mm
 Occupational imbalance (mismatch) is
measured relative to the existing
aggregate levels of unemployment and
vacancies in the economy;
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
0.05
0.02
a. semi-skilled blue-collar occupations (5-7);
b. lower-skilled blue-collar occupations (8-9);
Sep-06

1.00
0.03
2. blue-collar occupations (5-9)

0.06
0.04
a. highly-skilled white-collar occupations (1-2);
b. skilled white-collar occupations (3-4);
Jan-06
inadequate education and training or
insufficient geographical and occupational
labour mobility.

1. white-collar occupations (1-4):
May-05
 In order to completely utilize the stock of
human capital in the population it is
essential to match individuals’ educationspecific skills with the occupational job
characteristics (Nordin et al., 2010).
 Both the efficiency of the matching
process and mismatch may be important
determinants
of
the
level
of
unemployment in the economy (Dur, 1999).
 Labour market mismatch (structural
imbalance):
 These 9 occupations are grouped into 2
main categories:
Sep-04
Background
Figure 2. Share of total unemployment attributed to occupational mismatch (left)
and unemployment attributed to occupational mismatch as a percentage of the
labour force (right)
however, it affects (negatively) the matching
process when labour market is examined
through its submarkets;
 share of the unemployment benefits users
in total unemployment has negative impact
on the matching process, while time trend
affects it positively;
 in most of the cases the hypothesis of
CRS cannot be rejected.
 the portion of total unemployment that can
be attributed to occupational mismatch is
estimated to be only up to 6%, which
evidently cannot explain high and
persistent unemployment in Croatia;

in different submarkets this fraction is even
smaller (except for the white-collars).
Contact
The Institute of Economics, Zagreb
Trg J. F. Kennedyja 7
10000 Zagreb, Croatia
Ph: +385-1-2362-244
Email: [email protected]
Web: http://www.eizg.hr