Development of the Finnish labour market

Annual Meeting of the International Forecasting Network

HelsinkiMay 9/10 2011

Ilkka Nio

Finland has got rid of the global recession • In 2009 the GDP decreased in 2009 by 7.2 % but quite rapid growth started

from the second half of 2010. Growth for the year 2010 was 3.1 per cent. All demand items are contributing positively to growth, with exports as the strongest driver.

• Earnings increased rapidly in 2009 relative to the cyclical environment keeping up the positive expectations and private consumption, which started grow during the last quarter of 2009. In 2010 it grew by 2 %. However, many facts indicate only modest growth of consumption. The new wage settlements will likely be rather moderate after the recession. Purchasing power will be eroded by rising interest rates and the acceleration of inflation.

• Negative risks are associated with the public sector indebtedness and financial imbalances and the slow pace of employment growth. Finland's strong public finances deteriorated sharply during the economic crisis, although the deficit is still under the 3 % threshold.

• Economic growth forecast for 2011-2013 is not high enough that the deficit in state finances would turn into a surplus during the economic recovery, but without additional measures, they will continue to remain in deficit in 2015.

• All in all, the Finnish economy is expected to grow by 4 % in 2011. Without Nokia´s production problem the GDP would increase even more. The growth is expected to continue moderately ( 2-3 %) during the next years.

GDP growth in 1997-2011 (I), monthly figures

-15

-10

-5

0

5

10

15

Change in demand 1991-2010 (IV), quarterly figures

-30

-20

-10

0

10

20

30

Export Consumtion Investments

Supply and demand of labour

• Alongside the growth of the economy the demand for labour has started to strengthen slowly.

• Young peolple were mainly influenced by the impacts of the recession, while the aged labour force kept their jobs and stayed in the labour market. It seems that working careers are still being prolonged.

• Even then the changing population age structure will begin to affect the supply of labour. The ageing of the population will soon begin to constrain the labour market.

• The number of hours worked by persons employed fell during the recession twice as much as the number of people in employment: the number of hours worked dropped by 6 % and the number of persons employed only by 3 %.

• Employment is expected to grow slowly. In particular the financial problems of the public sector will limit employment opportunities. The productivity will be raised by rationalizing the use of current labour force. Employment will start to grow faster after the labour input and production are rebalanced.

Labour force and employed persons in 1988-2011 (III)

1900

2000

2100

2200

2300

2400

2500

2600

2700

2800

2900

'88 '90 '92 '94 '96 '98 '00 '02 '04 '06 '08 '10

Labour force

Employed

Thousand persons

Rapidly changing age structure

Age 2010 2020 Change  

      Persons %

     

15-24 659 800 605 800 -54 100 -8.2

25-49 1 727 100 1 730 700 3 700 0.2

50-64 1 160 600 1 072 000 -88 600 -7.6

65-74 506 700 719 000 212 300 41.9

     

Total 4 054 200 4 127 500 73 300 1.8

Expectation of time spent in the labour market at age 50 in 1990 - 2010

5

6

7

8

9

10

11

'90 '92 '94 '96 '98 '00 '02 '04 '06 '08 '10

In labour force

In employment

Expection, years

Lengthening of working careers by two years untill 2020

0

20

40

60

80

100

Age

Per c

ent

In labour force 2010 In labour force 2020

  Change 2010-2020

Age Persons %

   

15-24 -24 000 -7.3

25-49 2 000 0.1

50-64 33 000 4.1

65-74 49 000 121.9

   

Total 60 000 2.2

* participation rates for ages 15-49

on the level of 2010

Unemployment situation

• The deterioration of labour market situation during the crisis was not as bad as predicted. Companies were keen to retain their skilled staff, offering them shorter working hours and layoffs over redundancies. (Labour hoarding)

• Part of the reason behind the recent decreasing unemployment is the considerable increase in the use of active labour market policy measures.

• The structural composition of unemployment has been aggravated. The long-term unemployment and structural unemployment has increased, slowing down the decrease of total unemployment.

• The unemployment rate ( 8.4 % in 2010) will decrease annually by not more than one percentage point > to 7,5 per cent in 2011 and on to 6,5 per cent in 2012.

Unemployment rates according to the LFS and JSR in 1989 - 2011 (III)

0

5

10

15

20

25

'89 '90 '91 '92 '93 '94 '95 '96 '97 '98 '99 '00 '01 '02 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12

LFS JSR

%

Youth unemployment according to the jobseekers register and Labour Force Survey 1991 – 2011 (III)

0

20

40

60

80

100

120

140

160

180

200

'91 '93 '95 '97 '99 '01 '03 '05 '07 '09 '11

JSR LFS

Thousand persons

Hysteresis or persistence in Unemployment ?

• There is considerable evidence ( micro and macro ) that hysteresis ( or persistence) of unemployment is an important factor in the Finnish labour market.

• Strong negative duration dependence means rapidly declining chances of becoming employed when the duration of unemployment spell lengthens.

• Persistent high unemployment can lead to an increase in the long-term natural rate of unemployment ( NAIRU) determined in the labour market. Different country can follow different time path in achieving the equilibrium.

• The effects of shocks are difficult to assess and forecast. Hysteresis is a lagging variable among explanatory variables of unemployment.

• Overcoming the problem of hysteresis has been the major policy issue in Finland. For this purpose a specific indicator was created in 2003 to measure the core of structural unemployment. In March it accounted for 144 000 ( 5,5 % ).

• Considerable increase in the use of ALMP measure ( in March 4,1 %)

Unemployed persons seeking work (1) and unfilled vacancies at the employment service (2) . Original monthly figures and seasonally adjusted figures (S)

0

50

100

150

200

250

300

350

400

450

500

550

'61 '63 '65 '67 '69 '71 '73 '75 '77 '79 '81 '83 '85 '87 '89 '91 '93 '95 '97 '99 '01 '03 '05 '07 '09 '11

Thousand persons

(1)

(S)

(2)

Average duration of incomplete unemployment ( stock) is a lagging cyclical indicator

Average terminated duration ( flow)

0

5

10

15

20

25

30

0 5 10 15 20 25

Unemployment rate % ( JSR)

Wee

ks

1991 I

2011 II2008 VI

Average incomplete duration ( stock)

0

10

20

30

40

50

60

0 5 10 15 20 25

Unemployment rate (JSR)

Wee

ks

1991 I

2009 IX

2008 VI

Structural unemployment in 2004 – 2011 (III)

0

20 000

40 000

60 000

80 000

100 000

120 000

140 000

160 000

180 000

200 000

2004 2005 2006 2007 2008 2009 2010 2011

Recurrent participation labour policy measures

Unemployed after participation in active labour policy measures

Recurrent unemployment

Long-term unemployment

0

5

10

15

20

25

0

1

2

3

4

5

6

Unemployment rate (JSR) In ALMP-measure, per cent of labour force

Unemployed and persons placed on ALMP-measures, per cent of labour force 1988-2011( III)

Both quantitative and qualitative methods are used in order to get maximum information from the available data

Big macroeconomic models do not provide robust information to forecast the turning points and cyclical changes in employment. >> Need for short term forecasts.

Qualitative approach • Collecting qualitative information from regions• Comparing the forecasts of different authors • Herd instinct among forecasters > The forecasts do not often differ much from each others

Time Series Analysis ( STAMP, ARIMA)• Monthly follow up of time series from the economy and the labour market • Specification of cyclical, seasonal and irregular variations in order to search turning points

Experimental quantitative approach• Often simple models are useful to help understanding the relationships between the

economy and the labour market.• Searching leading indicators• It is difficult to interprete the variables regarding expectations

Whole country 8.4 %

11,0 % and more

9,0 % - 10,9 %

7,0 % - 8,9 %

Less than 7,0 %

Lappi

Etelä-Pohjanmaa

SatakuntaPirkan-maa

Häme

Varsinais-Suomi

Kaakkois-Suomi

Uusimaa

Keski-Suomi

Kainuu

Pohjois-Pohjanmaa

Pohjois-Savo Pohjois-

Karjala

Etelä-Savo

Pohjanmaa

6,48,1

10,69,0

9,78,8

9,9 7,9

10,06,7 8,2

12,5

9,010,2

11,3

Source: Statistics Finland

Unemployment rate by regions2010

Expectations, one year ahead by regions

Lappi

Etelä-Pohjanmaa

SatakuntaPirkan-maa

Häme

Varsinais-Suomi

Kaakkois-Suomi

Uusimaa

Keski-Suomi

Kainuu

Pohjois-Pohjanmaa

Pohjois-Savo

Pohjois-Karjala

Etelä-Savo

Little decline

Very little decline

No change

Pohjanmaa

Source: MEE

Spring 2011

STAMP Structural Time Series Analyser, Modeller and Predictor• Follow up of estimated local trends every month in order to understand the behaviour of the time series

and the fit of the forecasts.

• Labour market figures are dominated by stochastic trend component without explanatory variables forecasting possible only in the very short run

• The aim of structural time series modelling is the specification of various components: cyclical, seasonal and irregular. The components are considered as stochastic unobserved components which are assessed by looking at the behaviour of the series throughout the whole sample rather than at the end of the period.

• Comparing the structural time series models with ARIMA models, the essential difference is that the trend or the unit root component is modelled together with the stationary part of time series. There are two parts to the trend: Stochastic level which is the actual value of the trend and stochastic slope.

• Kalman filter forecasts the continuation of the series and also gives, as a side product, the estimates for

the unobserved components. The forecast function of STAMP estimation is a straight line with upward or downward slope.

• Including explanatory variables in a structural time series model - as stochastic variables - results in a mixture of time series and classical regression.

• The combination of unobserved stochastic components with explanatory variables opens in principle many possibilities for dynamic modelling. However, in practice the choice of potential explaining variables is rather limited.

• The predictive power of forecasts can be strengthened by means of leading economic indicators such as

business expectations or business assessment using data on order books, various confidence indexes, stock market index, etc

Slope of unemployment rate 1988-2011(III), STAMP

-0,3-0,2-0,1

00,10,20,30,40,50,60,7

1988-1

1989-3

1990-5

1991-7

1992-9

1993-1

1

1995-1

1996-3

1997-5

1998-7

1999-9

2000-1

1

2002-1

2003-3

2004-5

2005-7

2006-9

2007-1

1

2009-1

2010-3

JSR LFS

Consumers´ confidence ( saldo) and change in employment ( slope), 1000 persons, monthly figures ( 2011 III)

-20

-15

-10

-5

0

5

10

15

20

25

-12

-10

-8

-6

-4

-2

0

2

4

6

8

10

Consumers´ confidence Slope of employment

Consumers´ confidence ( saldo) and change in unemployment ( slope 1000 persons), monthly figures until 2011 (III)

-20-15-10-505

10152025

M:1

995/

10

M:1

996/

05

M:1

996/

12

M:1

997/

07

M:1

998/

02

M:1

998/

09

M:1

999/

04

M:2

000/

05

M:2

000/

12

M:2

001/

07

M:2

002/

02

M:2

002/

09

M:2

003/

04

M:2

003/

11

M:2

004/

06

M:2

005/

01

M:2

005/

08

M:2

006/

03

M:2

006/

10

M:2

007/

05

M:2

007/

12

M:2

008/

07

M:2

009/

02

M:2

009/

09

M:2

010/

04

M:2

010/

11

-6-4-20246810

Consumers´ confidence Slope of unempl. job seekers

Cross-correlation of consumers´ confidence and slope of unemployment, monthly figures

Quantitative experimental approach

• Purpose is to find handy and simple models which could be precise, convincing and clear enough, so as to be interesting for decision-makers

• Starting with simple models which contain just a small number of equations and variables formulated on a priori considerations, in order to combine the theoretical considerations with the empirical observations.

• Different calculations are carried out by adding explanatory variables in various combinations and taking advantage of background economic forecasts for output and various demand items, which have a lead over the employment data. Changing their values, the path of the forecasts can be examined under different scenarios.

• Due to intercorrelated variables, we need to compromise with the scientific needs regarding

explanatory ability and accuracy of the estimates ( when certain that the same pattern of multicollinearity of dependent variables will continue ).

• However, there is no standard business cycle and thus the stability of the estimates and their sensitivity to the changes in the sample period can vary.

GDP growth and change in employment 1977 - 2010

-200

-150

-100

-50

0

50

100

-6 -4 -2 0 2 4 6 8

GDP (t)=0,5*((GDP (t)+GDP(t-1))

Thousand persons

1977 - 1993

1994 - 2010

2009

2010

Model: Dependent variable: Quarterly change in employmentExplanatory variables: change in demand items, lag 2 quartersMethod: Least squares, R-squared 0,69

Demand item

Unstandardized Coefficients

Standardized Coefficients

t Sig.

Collinearity Statistics

B Std. Error Beta Tolerance VIF

(Constant) -9,743 5,206   -1,871 ,066    

Export(-2) 1,445 ,313 ,365 4,616 ,000 ,783 1,276

Consumption(-2) 6,956 2,131 ,321 3,264 ,002 ,508 1,970

Investments(-2) 1,554 ,495 ,331 3,140 ,003 ,442 2,262

Fit of employment model, 1000 persons, demand items as explanatory variables

1900

2000

2100

2200

2300

2400

2500

2600

Actual Fitted

Residual of the employment model