The Analysis and Forecasting of Unemployment in Croatia
Professor Ante Rozga, Ph.D., University of Split
Ekonomski fakultet Split, Matice Hrvatske 31, 21000 Split, Croatia
E-mail: rozga@efst.hr
Ana Secer, BSc. Econ
Metula, Put sv. Lovre 55c, 21000 Split, Croatia
E-mail: ana.secer@st.t-com.hr
Josip Arneric, MSc. Econ., University of Split
Ekonomski fakultet Split, Matice Hrvatske 31, 21000 Split, Croatia
E-mail: josip.arneric@efst.hr
Abstract
Many factors influence trends in unemployment in Croatia. After proclaiming independence
in 1991 Croatia had to battle the war for independence with devastating consequences
particularly on unemployment which soared. During and after the war government decided to
privatize social-owned companies which on the other side pushed once again unemployment
upwards since in socialist economy productivity was much lower than in capitalist one.
Foreign and domestic private owners of privatized companies tried to minimize the cost of
labor force and they dismissed many workers.
The consequence of introducing market-oriented economy in Croatia has been ever increasing
percentage of women among unemployed persons which is now 61.6 % (in 1991 it was
52.2%), compared with 51.5% in the total population. Also, the number of unemployment
benefit recipients has fallen dramatically because welfare state in market-oriented economy is
not important as in socialist one. The educational background of unemployed persons has
been also changed due to huge turmoil in Croatian economy.
When analyzing trends and forecasting the unemployment in Croatia we must take into
account its seasonal character because Croatian economy relies very much on tourism and
related industries with very strong seasonal variations. This could be helpful for unemployed
persons to find a job for several months per year but it is not good for economy as a whole to
rely too much on seasonal industries. Also, calendar variations are also very important. We
tried several methods for seasonal adjustment and forecasting time series in order to find the
most suitable one. Traditional methods of X-11-ARIMA from Statistics Canada and X-12ARIMA from Census Bureau were confronted to model-based method TRAMO/SEATS. The
newest method called X-13-ARIMA-SEATS tried to overcome some problems of both
methods and to integrate it. Also, we used structural time series modeling by Harvey and
Durbin to see different approach to modeling of time series. TRAMO/SEATS proved to be
better when analyzing particular Croatian unemployment series compared with very popular
empirically based methods such as X-11-ARIMA and X-12-ARIMA. Structural time series
model could not be compared directly with traditional seasonal adjustment methods because
of different assumptions, but gave very good results in adjustment and forecasting.
Keywords: unemployment, seasonal variations, seasonal adjustment, trend.
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1. The analysis of historical data
We have analyzed number of unemployed persons from September 2000 until September
2007. The peak was in March 2002 with 415 352 unemployed persons. The number has fallen
from 359 921 at the beginning of the period observed to 246 191 at the end of time interval,
which is the fall of 31.6%. The number of persons seeking first job for has fallen by 47.6%
and the number of unemployment benefit recipients has also fallen by 17.1%. The latest
figure is due to fact that redundancy jobs have effected mostly older employees with the long
lasting unemployment benefits. Regarding professional attainment the things have also
changed. In the year 2000 the share of unskilled persons was 18.9%, while in the year 2007 it
was only 7.3%. The share of unemployed persons with tertiary education was 7.3% in 2000
compared with 7.4% which is almost the same, but the share of tertiary education among
employees has been improved.
Figure 1. Unemployed persons
Unemploy ed
420000
400000
380000
360000
340000
320000
300000
280000
260000
date
240000
Sij2001
Sij2002
Sij2003
Sij2004
Sij2005
Sij2006
Sij2007
2. The analysis of seasonal variations
Since the unemployment is affected by seasonal variations we have used two programs for
seasonal adjustment and decomposition of time series. The first is very popular one, namely
X-12-ARIMA developed by Census Bureau in US. The second method which has been
slightly introducing as an official method in some European countries is TRAMO/SEATS
method developed in Banco de España. TRAMO/SEATS is model-based method. Scientists
from Census bureau are developing X-13-ARIMA-SEATS which is to encompass some
disadvantages of X-12-ARIMA combined with TRAMO/SEATS.
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Figure 2. Final seasonal factors
Final seasonal f actors - Model 1 (Tramo-Seats)
Final seasonal f actors - Model 2 (X-12-Arima)
1.08
1.06
1.04
1.02
1
0.98
0.96
0.94
date
0.92
Sij2001
Sij2002
Sij2003
Sij2004
Sij2005
Sij2006
Sij2007
As we can see from the figure above there is no big difference between two methods
regarding final seasonal factor. Both methods detected one additive outlier (August 2002) and
one temporary change (November 2001).
Comparing the quality of seasonal adjustment between two methods, we have used seasonal
adjustment quality index. The SA quality index is calculated using all diagnostic statistics that
are also applied in usual quality check. Quality index vary from 0 (the optimal value) to
infinity (the worst value). If the value exceeds the standard base value of 10 at least one test
statistic must have been significant. In our example, SA quality index for X-12-Arima was
2.67 and for TRAMO/SEATS 1.492, thus giving the advantage to TRAMO/SEATS method
regarding the quality of seasonal adjustment.
It is clearly seen that seasonal factors are getting higher over time, which means the greater
seasonality in more recent years. This is the consequence of booming in tourism, trading,
travel and leisure industry which are labor intensive industries and as a such giving significant
seasonal variations on unemployment. It is not so good for labor market since tourist season
in Croatia is rather short, but still improving and enlarging.
When analyzing irregular factors we could see more differences between two methods.
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Figure 3. Final irregular factors
Final irregular f actors - Model 1 (Tramo-Seats)
Final irregular f actors - Model 2 (X-12-Arima)
1.012
1.008
1.004
1
0.996
0.992
0.988
0.984
date
0.98
Sij2001
Sij2002
Sij2003
Sij2004
Sij2005
Sij2006
Sij2007
We can conclude that X-12-ARIMA produces higher irregularities, particularly at the
beginning and at the end of time series.
Figure 4. Final trend in unemployment in Croatia
Final trend - Model 1 (Tramo-Seats)
Final trend - Model 2 (X-12-Arima)
405000
390000
375000
360000
345000
330000
315000
300000
285000
270000
255000
date
240000
Sij2001
Sij2002
Sij2003
Sij2004
Sij2005
Sij2006
Sij2007
Unemployment has been falling since 2002 and the trend is like that, showing downward
tendency.
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Figure 5. ACF of residuals
Lower conf idence limit f or ACF of residuals - Model 2 (X-12-Arima)
Upper conf idence limit f or ACF of residuals - Model 2 (X-12-Arima)
ACF of residuals - Model 2 (X-12-Arima)
ACF of Tramo residuals - Model 1 (Tramo-Seats)
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
lag
-1
1
12
24
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3. Forecasting of unemployment in Croatia
Using both methods we tried to forecast the number of unemployed persons in Croatia for the
next twelve months.
Figure 6. Forecasts of unemployment in Croatia
Lower conf idence limit f or Forecasts - Model 2 (X-12-Arima)
Upper conf idence limit f or Forecasts - Model 2 (X-12-Arima)
Forecast of orig. uncorr. series - Model 2 (X-12-Arima)
Lower conf idence limit f or Forecasts - Model 1 (Tramo-Seats)
Upper conf idence limit f or Forecasts - Model 1 (Tramo-Seats)
Forecast of orig. uncorr. series (with Tramo model) - Model 1 (Tramo-Seats)
320000
300000
280000
260000
240000
220000
200000
180000
160000
140000
date
120000
Sij2006
Sij2007
Sij2008
Two methods are quite similar when forecasting trend in unemployment.
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Figure 7. Forecasts of seasonal factors
Forecast of f inal seasonal f actors - Model 1 (Tramo-Seats)
Forecast of f inal seasonal f actors - Model 2 (X-12-Arima)
1.08
1.06
1.04
1.02
1
0.98
0.96
0.94
date
0.92
Sij2006
Sij2007
Sij2008
Sij2009
Forecasts of final seasonal factors for the next twelve months show their slightly rising as it
was the tendency over the historic time series. The differences between two seasonal
adjustment methods were not very large.
4. Conclusion remarks
After the outbreak of the war in Croatia the unemployment in Croatia soared, pushed by
destruction of industries as well as the economic transition from socialist type economy to
market oriented one. When the war finished and the economy has been consolidated the
number of unemployed persons has been ever falling. Booming of service industries pushed
by direct foreign investments contributed very much for that fall. We must also include
demographic changes with the births dramatically falling.
Regarding methods for seasonal adjustments we have concluded that method
TRAMO/SEATS is slightly better when focusing on seasonal adjustment quality index. The
other differences regarding trend and forecasts were not so big.
References
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