Sectoral and Regional disturbances to industrial output in Finland Some new empirical evidence on economic fluctuations and adjustment mechanisms in Finland FIRST DRAFT

I. Cadoreta A. Kangasharjub C. Tavérac

April 2005 Abstract This paper examines the sources of disturbances to output in the Finland regions and analyses labour market adjustments mechanisms.

Key words: Error component model; Labour market; France; Regions; Shock adjustment.

1. Introduction Many recent papers focus on regional adjustment in the United-States, in European countries and in Europe. Blanchard and Katz (1992), for instance, examine U.S. state-level data on employment, wages, and unemployment. They show that a negative shock on employment in a given state produces relatively little real wage response and that the labour market regains equilibrium as the excess labour moves to a new location within the Unites States. The implication is that in the United States interregional labour mobility is the major equilibrating force in the economy and employment bears the brunt of regional adjustment. In Europe, by contrast, the main equilibrating mechanism over the short run appears to be changes in the labour force participation rate (Decressin and Fatás, 1995). Pekkala and Kangasharju (2002 a, 2002 b) analyse regional labour market adjustment in the Finnish provinces and show that region-specific labour demand shocks adjust mainly via participation, whereas total shocks are adjusted by unemployment. Moreover, migration is

a CREM, Rennes, France (E.Mail : isablelle.cadoret@ univ-rennes1.fr) b VATT, Helsinki, Finland (E.Mail: aki.kangasharju@ vatt.fi) c CREM, Rennes, France (E.Mail : christophe.tavera@ univ-rennes1.fr)

1 more important in the region-specific case where, after a few years, it acquires a large role in the adjustment process.

However, while there are several reasons to focus on regions rather than countries to evaluate labour market adjustment mechanisms following employment shocks, analysing both regional diversification of industries and the source of disturbances to fluctuations in the growth rate of industrial production in regions seems to be a prerequisite for the understanding of regional labour market adjustment.

W hen the degree of regional specialisation is high, a negative shock to a given industry amounts to a regional shock hitting the region where this industry is highly concentrated. In case of a negative shock on this region (or on the industry which is highly concentrated in this region) , inter-regional migration flows may be small because of the lack of labour availability in the same kind of industry outside the region. At the opposite, if regions are less specialised, separating industry-specific shocks from regional-specific shocks may become important. In case of an industry-specific disturbance, inter-regional labour mobility is expected to be small because job opportunities in the same industry but in other regions are limited while job opportunities in other industries of the same region are not. Alternatively, interregional migration should have a larger role in adjusting regional-specific shocks1. As a consequence, region-specific shocks may trigger different adjustment mechanisms than industrial or national shocks: one would, for instance, expect substantially more migration between regions in response to region-specific shocks, than in response to industry-specific shocks.

This paper is concerned with industrial diversification and the nature of, adjustment to, underlying disturbances in Finland. Our first objective is to investigates the source of disturbances to fluctuations in the growth rate of industrial production in Finland regions over the past decade by isolating changes in

1 It is important to note that the previous remarks cannot be directly related to ”real business cycle models‘ which suggest that a large fraction of fluctuations in aggregate output may result from technology shocks. The main reason is that if technology in a particular industry differs substantially across regions but does not differ much across industries within a region, then disturbances to technology could induce region-specific rather than industry-specific shocks in output growth.

2 output that are due to region-specific disturbances from changes in output that are associated with industry-specific disturbances. Industry-specific shocks are defined as changes in output growth that are unique to an industry but common to every Finland region in the sample; region-specific shocks are defined as changes in output growth that are unique to a particular region but shared by all industries in that region. An error-component model originally specified by Stockman (1988) is used to determine what fraction of the variations in output growth can be attributed to industry-specific shocks and what fraction can be attributed to nation-specific shocks.

Our second objective is to analyse the nature of labour market adjustment to these industrial and regional disturbances. A cross-sectional regression without time dimension is used to evaluate the relative importance of regional and industrial factors in labour market adjustments. This decomposition permits to identify the importance of labour factor mobility.

Section 2 of the paper presents the econometric methodology. Section 3 reports the empirical results and Section 4 concludes.

2. Econometric methodology This section presents the statistical model that we employ to identify three sources of disturbances : regional shocks that affect all industries within a given region; industrial shocks that affect industries across all regions and aggregate shocks that affect all regions and all industries simultaneously.

The econometric model used in this paper and initially proposed by Stockman (1988) and Costello (1993), is given by:

∆ ln( yi,r,t ) = ƒαi,t .Di,t + ƒ βr,t .Dr,t + ƒγ t .Dt + εi,r,t (1) i=1,3,I r=1,3,R t=1,3,T t=1,3,T t=1,3,T

where ∆ ln( yi,r,t ) represents the first difference of the natural logarithm of output in industry i, region r, and period t; Di,t = 1 for industry i in period t and zero otherwise, Dr,t = 1 for

3 region r in period t and zero otherwise, Dt = 1 for period t and zero otherwise, and εi,r,t is an error term, assumed to be an i.i.d variable2.

As our primary interest is in identifying the sources of observed output fluctuations rather than their propagation, no additional dynamic term is included in our specification. However, tests for dynamic interactions across the identified disturbances may be performed. At the opposite, previous papers, such as Norbin-Schlagenhauf (1996) and Pekkala-Kangasharju (2002a, 2002b) have used richer dynamic models aimed at identifying innovations to output (or employment) growth.

The model represented by Eq. (1) is not identified since a sum over all i of the αi,t coefficients would be equal to the time-specific dummy variable γ t and the same is true of the region dummies βr,t , if summed over all r. As a consequence it is necessary to make some normalisations to make the estimation feasible. One approach to deal with this problem is to eliminate one industry and one region from the set of dummies. Assuming that region R and industry I are eliminated, these constraints can be written as :

DI ,t = 0, ∀t = 1,3,T (2) and DR,t = 0, ∀t = 1,3,T (3)

These normalisation give the estimated coefficients a simple interpretation. The series

αi = (αi,1 ,αi,2 ,3,αi,T ) , represents the shock to industry i relative to the shock to the industry that was excluded from the estimation. Similarly, the series βr = ( βr,1 ,βr,2 ,3,βr,T ) represents the shock to region r relative to the excluded region. Lastly, the series

γ = (γ 1 ,γ 2 ,3,γ T ) represents the sum of the aggregate disturbance plus the shocks to the excluded industry and excluded region.

In order to construct series that represent the underlying disturbances of industry i or region r, we make the assumption that industrial and regional shocks represent deviations from an

2 ∆ln(yirt) is measured as the deviation from the mean growth rate of the series as a whole in industry i, region r, at time t, thereby controlling for individual fixed effect. To control for differences in cyclical sensitivities across industries within each region the growth rate of every sector is divided by the standard deviation of every sector of the base country.

4 underlying aggregate disturbance. The aggregate impact of these deviations thus sums to zero in each period t. This assumption leads to the following constraints:

ƒαi,t = 0, ∀t = 1,3T or α I ,t = −(α1,t + 3 + α I −1,t ), ∀t = 1,3,T (4) i =1,3,I and

ƒ βr,t = 0, ∀t = 1,3T or β R,t = −( β1,t + 3 + β R−1,t ), ∀t = 1,3,T (5) r =1,3,R

The series of sectoral and regional shocks are then calculated as :

αi = (αi,1 ,αi,2 ,3,αi,T ) for i = 1,3,I with α I ,t calculated from equation (4), and βr = ( βr,1 ,βr,2 ,3,βr,T ) for r = 1,3,R with β R,t calculated from equation (5).

The series of aggregate disturbances is finally calculated as the value γ t minus the implied shocks to the industry and region excluded from the estimated set of dummy variables :

φt = γ t −α I ,t − β R,t , t = 1,3,T (6)

An F-test of the joint significance of the remaining αi,t coefficients represents a valid test of the importance of industry-specific shocks in the regression, as does a similar test of the joint significance of the βr,t coefficients. Note that as the industry-specific and region-specific dummy variables are orthogonal by construction, the explanatory power of these variables can also be calculated from the reduction in the R2 statistic caused by excluding them from the original regression. Any variation that is explained by the regression but that is not specifically attributable to either set of dummy variables can be attributed to the aggregate disturbances. The choice of the omitted industry and region does not affect tests of the significance and explanatory power of the industrial or regional effects.

After decomposing the sources of disturbances to output in French regions, we also consider the nature of labour market adjustments to these disturbances, using the following cross- sectional regression:

∗ xi,r = ƒαi .Di + ƒ βr .Dr + εi,r (7) i=1,3,I r=1,3,R

5 ∗ where xi,r stands for average values of output, output per worker, and employment over

∗ several years alternatively. More precisely, xi,r is the across-period average value of variable x for industry i in region r and equation (7) is used to calculate the relative importance of regional and industrial factors in labour market adjustments. As there is no time dimension, it is not possible to identify an aggregate disturbance. The analysis is therefore limited to the relative importance of regional and industrial factors in medium-term adjustment. Largely industry-specific productivity trends indicate a relatively high level of labour market integration, as such integration is needed to reduce productivity differentials across regions. Decomposing long-run employment growth into region- and industry-specific factors helps us to identify whether labour or capital is the main channel of factor mobility.

3. Empirical results The model (1) was estimated with data on indexes of real output (value added) in 18 industrial classifications. These are Agriculture (Agr), Forestry (Forest), Fishing (Fish), Food industry (Food), W ood industry (W ood), Metal industry (Metal), Other manufacturing industries (Oth- man), Construction (Const), W holesale and retail trade (W ho/Ret), Hotel and restaurants (Hot/Rest), Transportation and communication (Tr/Com), Finance (Fin), Business services (Bu-Ser), Housing ownership and renting (Own), Education (Edu), Government and Social security (Gov), Health and social services (Heal/Soc), Other society services (Oth-Ser), Household services (Hou-Ser). Data are annual and cover the period 1975-2002 for 21 Finland regions : Uusimaa, Varsinais Suomi, Kanta-Hame, Päijät-Häme, Kymenlaakso, Etelä-Karjala, Itä-Uusimaa, Satakunta, Pirkanmaa, Keski-Suomi, Etelä-Pohjanmaa, Pohjanmaa, Etelä-Savo, Pohjois-Savo, Pohjois-Karjala, Kainuu, Keski-Pohjanmaa, Pohjois-Pohjanmaa, Lappi, and Ahvenanmaa.

Preliminary analysis of regional and industrial characteristics Table 1 reports some comparative statistics across the data set. It shows the average share of total output produced by each industry within the region, as well as the mean and the coefficient of variation of these industry output shares.

6 The average share of total output produced by each industry within the region clearly shows that Household services and Fishing have the smallest share of output while the largest shares of output are observed for service industries such as Transportation and communication, and Health and social services.

The mean values illustrate the composition of output across different industries. Taken as a whole, the regional output produced by a given industry are rather similar across regions. This is especially true for service sector such as Housing ownership and renting, Health and social services, Construction, Hotel and restaurants, Government and Social security, Education, W holesale and retail trade, and Finance. Noticeable exceptions are the large output shares associated with Transport and Communication in Ahvenanmaa, W ood industry in Kymenlaakso, Etelä-Karjala, and Keski-Suomi (to e lesser extent), and Other manufacturing industries in Itä-Uusimaa.

The coefficient of variation is a measure of the degree of regional specialisation of an industry. The larger the variation in the composition of output across regions, the larger the coefficients of variation. The largest coefficients of variations are associated with primary industries such as Fishing (which is concentrated in Ahvenanmaa), W ood (mainly concentrated in Kymenlaakso, Etelä- Karjala, and Keski-Suomi) and Forestry (which is concentrated in Etelä-Savo, Pohjois- Karjala, Kainuu, and Lappi). At the opposite, Housing ownership and renting, Health and social services, Construction, Hotel and restaurants, Government and Social security, Education, W holesale and retail trade, and Finance are evenly distributed across Finland regions and exhibit the lowest coefficients of variation. The large value of the coefficient of variation calculated for the whole country is mainly attributable to the large share of Finland output produced by Uusimaa (close to 15%). The across regions coefficient of variation is divided by two when calculated by excluding this region).

Taken a whole, the relative specialisation of Finland regions does not exhibits any clear pattern so that industry-specific disturbance are likely to have broadly similar effects across these regions. Hence, in case there are significantly different economic impact from

7 underlying disturbances, the main reasons for explaining those differences have to be found in the nature of the disturbances themselves. The next section focus on this point.

8 Table 1. Industry output shares

Agr For Fish Food W ood Metal Oth- Const W ho/ Hot/ Tr/ Fin Bu-Ser Own Edu Gov Heal/ Oth- Hou- Total man Ret Rest Com Soc Ser Ser Finland regions Uusimaa 0.33 0.26 0.06 1.52 3.36 3.36 4.26 6.50 16.42 1.67 10.71 6.03 13.13 8.19 6.41 4.44 8.18 5.13 0.05 15.12 Varsinais Suomi 3.32 1.04 0.16 2.88 1.55 14.36 8.70 7.66 9.13 1.32 7.66 3.34 5.52 8.33 8.33 5.13 8.93 2.52 0.11 4.37 Kanta-Hame 3.11 2.54 0.08 3.11 6.14 8.24 9.57 7.86 7.43 1.44 7.48 2.95 4.43 8.90 7.55 5.46 9.32 4.27 0.12 1.43 Päijät-Häme 2.15 2.27 0.09 2.72 7.52 8.42 12.30 7.59 9.08 1.67 6.79 2.74 5.69 8.74 4.36 5.27 9.20 3.28 0.12 1.70 Kymenlaakso 2.06 1.69 0.11 1.39 21.13 4.32 6.78 7.12 7.55 1.23 13.21 2.31 3.38 7.16 6.21 4.16 7.51 2.58 0.10 1.99 Etelä-Karjala 2.07 2.81 0.09 1.64 25.54 3.75 6.42 6.73 7.36 1.46 8.37 2.38 3.26 7.61 4.93 4.72 7.98 2.78 0.09 1.42 Itä-Uusimaa 2.96 1.64 0.15 1.37 6.22 6.36 26.56 8.90 6.92 1.19 6.93 2.37 3.60 8.84 3.04 4.44 6.13 2.30 0.08 0.83 Satakunta 3.33 1.80 0.11 2.24 8.92 12.22 10.12 8.39 7.60 1.14 8.11 2.83 3.93 8.39 4.69 4.91 8.51 2.64 0.13 2.24 Pirkanmaa 1.54 1.94 0.07 1.33 9.63 11.19 9.29 7.11 9.25 1.51 6.76 3.02 6.18 8.63 4.46 5.49 9.02 3.50 0.08 4.01 Keski-Suomi 1.89 4.75 0.13 1.03 13.24 9.57 4.81 8.06 7.20 1.48 6.67 2.46 4.66 8.53 5.90 6.95 9.51 3.05 0.12 2.27 Etelä_Pohjanmaa 7.85 3.68 0.08 3.09 2.44 6.54 6.21 11.65 10.72 1.07 6.41 3.55 2.56 9.51 4.46 6.47 10.68 2.83 0.21 1.49 Pohjanmaa 8.14 2.09 0.14 2.19 8.10 11.93 6.68 8.12 7.79 1.05 8.07 2.93 3.49 7.78 4.60 5.70 8.48 2.68 0.04 1.70 Etelä-Savo 3.89 8.80 0.16 1.09 5.08 4.14 4.69 9.22 6.84 1.79 9.31 3.07 3.29 9.24 6.36 6.99 12.31 3.44 0.26 1.31 Pohjois-Savo 3.62 5.02 0.14 1.72 7.01 5.22 6.84 8.61 8.87 1.42 7.05 3.34 4.23 8.78 6.19 6.89 11.71 3.16 0.18 2.11 Pohjois-Karjala 3.49 7.00 0.15 1.08 6.35 4.10 6.37 9.34 7.35 1.34 10.27 2.68 3.53 8.75 6.85 7.44 10.74 3.00 0.17 1.39 Kainuu 2.79 8.85 0.15 1.04 9.38 2.71 4.92 9.43 6.64 1.54 7.88 2.67 2.93 8.52 8.62 7.73 10.52 3.54 0.15 0.74 Keski-Pohjanmaa 7.84 3.36 0.11 2.22 1.84 7.34 10.12 10.26 9.26 1.17 8.78 3.11 2.91 8.54 4.82 6.34 8.91 2.94 0.14 0.57 Pohjois-Pohjanmaa 2.97 2.84 0.13 1.13 3.53 12.94 6.45 9.97 8.41 1.33 8.24 2.55 4.89 8.10 5.18 7.50 10.91 2.78 0.16 2.98 Lappi 1.84 6.10 0.13 1.16 10.15 5.76 4.72 9.66 6.65 2.12 8.73 2.46 3.77 7.58 8.23 7.74 9.97 3.12 0.11 1.76 Ahvenanmaa 3.11 1.31 1.01 2.32 1.43 1.01 2.41 6.57 7.23 1.77 32.26 5.96 2.00 6.86 6.42 4.80 9.88 3.61 0.05 0.32

Mean 3.41 3.49 0.16 1.81 7.93 7.17 7.91 8.44 8.39 1.44 9.48 3.14 4.37 8.35 5.88 5.93 9.42 3.16 0.12 2.49 Coeff. of variation 0.61 0.70 1.22 0.39 0.76 0.52 0.62 0.16 0.25 0.19 0.58 0.32 0.52 0.08 0.25 0.20 0.15 0.20 0.43 1.26 Notes : Industry output shares are in percent. The column labelled —Total“ indicates the average share of each region in total Finland output. The final two rows show the means and the coefficients of variation for industry output shares in each group.

9 Sources of disturbances Table 2 reports the overall explanatory power of equation (1) and the importance of industry- specific, region-specific, and aggregate disturbances in this total. Dummies for the Agriculture industry and Uusimaa region are excluded from the regression to avoid collinearity. The significance and explanatory power of the industrial and regional effects are robust to the choice of the omitted industry and region. An F-statistic, for testing the null hypothesis that all the αi,t terms (β j,t terms, respectively) are zero is used as a valid test of the importance of industry-specific shocks (regional-specific shocks, respectively).

Table 2 Decomposition of output fluctuations (aggregate results) Estimation period 1975 - 2002 1975 - 1988 1989 - 2002 Total R square 0.30 0.21 0.30

Contribution of Aggregate shocks 20.00% 14.29% 20.00%

Regional shocks 13.33% 26.19% 10.22% F (P-value) 0.99 (0.51) 1.27 (0.00) 0.77 (0.99)

Industrial shocks 66.67% 63.81% 66.87% F (P-value) 5.60 (0.00) 3.40 (0.00) 5.65 (0.00)

As shown in the second column of Table 2, equation (1) explains 30 percent of the variation in desegregated Finland output growth over the period 1975-2002. The industry disturbance is the most important factor, explaining 67% of the total R2, while the aggregate and regional dummy variables explain 20% and 13% of the overall R2, respectively.

Moreover, the F-statistic for testing the null that all of the αi,t coefficients or all of the βr,t coefficients indicates that region-specific disturbances are close to zero so that variations in desegregated Finland output seem to be mainly driven by aggregate and industry-specific disturbances. As one industry (Agriculture) has been eliminated to identify the model, the joint significance of the industry effects indicated that the Finland industries jointly experienced industry-specific shocks, common to regions, that differed from industry-specific shocks in Agriculture. To determine whether the obtained results are influenced by the choice of the omitted industry and region, equation (1) was also estimated when region effects are normalised on Varsinais Suomi and industry effects are normalised on Other manufacturing industries. Estimated

10 results are not reported in this paper but reveal that the choice of the omitted industry and region does not affect the explanatory power of the industrial or regional effects. The fractions of variations explained are quite robust with respect to the choice of excluded industry and region. Differences between region effects within Finland regions are just as important as between a given Finland region and the other regions of Finland. In order to evaluate the time deformation of the relative contribution of aggregate, industry- specific and region-specific disturbances, model (1) is also estimated over two consecutive sub-periods: 1975 œ 1988 and 1989-2002. Estimated results are reported in the third and fourth columns of table 2. W hile the global goodness of fit marginally increases over the second sub-period, the ratio of industrial and regional disturbances to overall R2 also shows that industry-specific shocks still appears as the most important factor in both sub-samples. Moreover, the relative contribution of regional shocks clearly decreases over time while aggregate dummies simultaneously become more important. Finally, while F-statistics have marginal significance level for both region-specific and industry-specific shocks over the first sub-period, the null hypothesis that the relative contribution is zero is not rejected by the data in the second sub- period.

Taken as a whole, empirical results in Table 2 indicate that the major part of the variation in desegregated Finland output growth is dominated by aggregate shocks and industrial disturbances, common to regions. This result may be explained by the fact the Finland labour, goods and financial markets are highly integrated so that business cycles are mainly influenced by industry disturbances that affect all regions homogeneously because of a rapid and diffusion of shocks within each sector.

The limited influence of regional disturbances may also reveal that regional-specific exogenous policies turn out to be relatively unimportant despite the growing degree of decentralizations and the redistribution of power and responsibilities to local levels of government over the past decades. The ratios of regional revenue to total government revenue have progressively increased in Finland as in several OECD countries (Cerniglia, 2003) over the last few decades, as decentralisation and reallocation of taxing and expenditures powers to various levels of local government were more and more regarded as a strategy to enhance economic growth and welfare.

11 Note that it is possible that industry-specific effects may appear as dominant even if the only disturbances are those to technology. Clearly, this would occur if regional policies and regional shocks exhibited greater differences across sectors for a given region than across regions for a given sector. Then region disturbances would be more industry-specific than region-specific. In this paper, we make the assumption that interpreting the data in terms of a model in which regions shocks and regional policies differs across regions but is common to industries, appear as a reasonable approximation in the sample under study.

Table 3a and 3b reports the overall R2 and the decomposition between the different factors for each region (Table 3a) and for each industry (Table 3b). These results are calculated using the estimated coefficients from the full regression but limiting each calculation to only those observations that involve a given industry or a given region. Both tables show estimated results for the full period (1975-2002) and for previously defined sub-periods (1975-1988 and 1989-2002).

Table 3a shows that industrial disturbances are the most important factors in all Finland regions. The smallest contribution of industrial factors and the largest contribution of regional factors are obtained for Ahvenanmaa, Kainuu, Etelä-Pohjanmaa, and Itä-Uusimaa. Note that the first three regions in the list are specialised in primary products (Fishing in Ahvenanmaa, Forestry in Kainuu and W ood in Etelä-Pohjanmaa) while the fourth region is the most specialised in manufacturing industries. At the opposite, the highest contribution of industrial shocks (and the lowest influence of regional disturbances) is observed in Koko-maa. Lastly, the conjoint contributions of aggregate, industrial and regional shocks to regional output is very similar in several regions. This seems to be the case for Uusimaa, Varsinais Suomi, Kanta-Hame, Pirkanmaa, Pohjanmaa, and Pohjois-Karjala.

W e examine whether the patters that exist over the full 1975-2002 period can also be identified over somewhat shorter periods by repeating the analysis for two sub-periods, 1975- 1988 and 1989-2002.The regressions over shorter time periods show that the relative fraction of R-square attributable to region-specific disturbances fall somewhat, while the fraction attributable to aggregate shocks rise somewhat from the first to the second sub-period.

12 Moreover, while the relative contribution of industrial shocks to regional output movements is globally unchanged for a large group of regions, it clearly increases in the second sub-period for several regions such as Itä-ussimaa, Etelä-Savo, Kainuu, Kski-Pohjanmaa, and Ahvenanmaa. Etelä-Savo, Kainuu, Kski-Pohjanmaa, and Ahvenanmaa are relatively specialised in primary products while Itä-ussimaa is highly specialised in Other manufacturing industries. The higher relative importance of industrial disturbances in these regions may be explained by the fact that regional output variations was mainly influenced by local economics conditions in the first sub-period but market integration has progressively resulted in a rising contribution of industrial disturbances. Finally, the model performs relatively better in the second-sub-period, since the average R-square rise from 31% to 39% from the first to the second sub-period.

The decomposition between the different factors for each industry is reported in Table 3b and indicates that sectoral disturbances are important in many industries over the full time period and over both retained sub-periods. This result may indicate that primary products, manufactured goods and services are more and more easily traded across regions so that the influence of industrial factors may logically dominate the influence of regional factors. It also reveals the rather high degree of integration of markets in Finland. The most important contributions of regional and aggregate factors are thus generally obtained for sectors such as Construction, Transport, and Education since product of these industries are not easily (or not at all) traded geographically. The case of Other manufacturing industries (which is non intuitive on the ground of the previous justification of the influence of industrial shocks) can be explained by the fact that this industry is partly concentrated in Itä-Uusimaa so that regional influences may be partly increased in this model. Lastly, the case of the Food industry may be linked to the potentially large influence of macro-economic demand on the level of activity of this sector so that the cumulated effect of regional and aggregate shocks may be as important as pure sectoral shocks. Regressions over shorter time periods show that the relative significantly diminishes in the second sub-period for all sectors. On average, regional shocks account for shocks account for only 12% of the total R-square over the 1989-2002 sub-period while about 30% of the R- square was explained by regional effects over the 1975-1988 sub-period. For several sectors , this reduction of the relative contribution of regional shocks over time goes in hand with an equivalent increase of the relative contribution of industrial shocks. This is typically the case for (Agriculture, Construction, Finance, Education, Other Services). For

13 other sectors, the reduction of the relative contribution of regional shocks is either compensated by an increase of regional shock contribution (Fishing, Food, Transport and Communication, Metal, Health, Housing ownership) or by an increase of both regional and aggregate shock contributions Forestry, W holesale and retail trade, Hotel and restaurants, Government social security).

14 Table 3a. Decomposition of output fluctuations (region results)

Total R2 and factor contributions : 1975-2002 Total R2 and factor contributions : 1975-1988 Total R2 and factor contributions : 1989 - 2002 Total R2 Aggregate Industry Region Total R2 Aggregate Industry Region Total R2 Aggregate Industry Region Uusimaa 0.80 20.30% 68.30% 11.40% 0.81 13.92% 71.35% 14.73% 0.85 20.76% 68.88% 10.36% Varsinais Suomi 0.52 20.34% 68.43% 11.23% 0.31 13.01% 66.68% 20.31% 0.63 21.28% 70.62% 8.09% Kanta-Hame 0.37 20.47% 68.89% 10.64% 0.30 12.95% 66.40% 20.65% 0.42 21.69% 71.99% 6.32% Päijät_Häme 0.41 21.08% 70.92% 8.01% 0.47 13.59% 69.64% 16.78% 0.36 22.18% 73.59% 4.23% Kymenlaakso 0.33 20.11% 67.66% 12.23% 0.22 12.50% 64.06% 23.45% 0.32 20.44% 67.78% 11.78% Etelä-Karjala 0.36 19.81% 66.65% 13.54% 0.24 12.01% 61.57% 26.41% 0.38 20.53% 68.13% 11.34% Itä-Uusimaa 0.21 17.96% 60.44% 21.59% 0.19 9.32% 47.78% 42.90% 0.20 19.24% 63.83% 16.93% Satakunta 0.42 21.24% 71.47% 7.28% 0.22 13.84% 70.93% 15.23% 0.47 21.38% 70.95% 7.67% Pirkanmaa 0.49 20.62% 69.36% 10.02% 0.48 14.30% 73.27% 12.43% 0.51 21.28% 70.60% 8.13% Keski-Suomi 0.26 20.90% 70.33% 8.77% 0.17 14.41% 73.85% 11.75% 0.25 21.34% 70.82% 7.83% Etelä-Pohjanmaa 0.29 17.75% 59.72% 22.53% 0.20 8.11% 41.59% 50.30% 0.32 20.77% 68.91% 10.32% Pohjanmaa 0.21 20.55% 69.13% 10.33% 0.21 13.09% 67.09% 19.82% 0.16 20.99% 69.65% 9.36% Etelä-Savo 0.27 20.62% 69.37% 10.01% 0.20 13.41% 68.72% 17.87% 0.30 22.07% 73.23% 4.70% Pohjois-Savo 0.42 21.20% 71.32% 7.48% 0.24 14.24% 72.99% 12.77% 0.52 22.25% 73.82% 3.94% Pohjois-Karjala 0.29 20.47% 68.87% 10.67% 0.27 14.22% 72.89% 12.88% 0.29 20.89% 69.31% 9.81% Kainuu 0.24 17.65% 59.39% 22.96% 0.21 9.58% 49.09% 41.34% 0.23 18.62% 61.79% 19.59% Keski-Pohjanmaa 0.18 19.57% 65.84% 14.59% 0.16 10.06% 51.55% 38.39% 0.13 21.05% 69.86% 9.09% Pohjois-Pohjanmaa 0.30 21.46% 72.19% 6.35% 0.17 13.48% 69.10% 17.42% 0.35 22.31% 74.02% 3.68% Lappi 0.27 18.80% 63.27% 17.93% 0.16 12.49% 64.02% 23.49% 0.27 19.47% 64.59% 15.94% Ahvenanmaa 0.14 16.50% 54.75% 28.75% 0.12 8.68% 44.52% 46.80% 0.13 15.71% 53.80% 30.50%

Mean 0,38 20,00% 67,25% 12,75% 0,31 12,53% 64,22% 23,25% 0,39 20,82% 69,15% 10,03%

Table 3b. Decomposition of output fluctuations (industry results)

15 Total R2 and factor contributions : 1975-2002 Total R2 and factor contributions : 1975-1988 Total R2 and factor contributions : 1989-2002 Total R2 Aggregate Industry Region Total R2 Aggregate Industry Region Total R2 Aggregate Industry Region Agriculture 0.56 17.95% 70.08% 11.97% 0.95 15.01% 53.10% 31.89% 0.48 15.18% 77.07% 7.75% Forestry 0.67 16.59% 72.35% 11.06% 0.87 12.10% 62.19% 25.71% 0.58 18.88% 71.49% 9.63% Fishing 0.38 23.20% 61.34% 15.46% 0.96 12.41% 61.22% 26.37% 0.28 24.66% 62.75% 12.59% Food industry 0.11 28.12% 53.13% 18.75% 0.11 12.99% 59.43% 27.59% 0.12 32.07% 51.56% 16.37% W ood industry 0.15 18.46% 69.23% 12.31% 0.15 4.98% 84.44% 10.58% 0.14 23.50% 64.51% 11.99% Metal 0.20 16.06% 73.23% 10.71% 0.10 6.78% 78.82% 14.40% 0.34 17.51% 73.55% 8.94% Other manufacturing 0.16 42.19% 29.68% 28.12% 0.13 22.44% 29.89% 47.67% 0.15 42.81% 35.34% 21.84% Construction 0.40 31.16% 48.07% 20.77% 0.50 23.89% 25.35% 50.76% 0.45 18.80% 71.60% 9.59% W holesale - Retail trade 0.52 30.47% 49.22% 20.31% 0.78 19.82% 38.07% 42.11% 0.48 30.39% 54.10% 15.51% Hotel - Restaurants 0.58 21.29% 64.52% 14.19% 0.45 14.56% 54.51% 30.93% 0.62 23.43% 64.61% 11.96% Transport - Communication 0.17 35.25% 41.26% 23.49% 0.23 19.19% 40.03% 40.78% 0.21 39.92% 39.70% 20.37% Finance 0.92 11.78% 80.36% 7.86% 1.34 11.41% 64.36% 24.23% 0.89 10.56% 84.05% 5.39% Business services 0.16 24.46% 59.23% 16.31% 0.10 17.12% 46.52% 36.36% 0.18 25.96% 60.79% 13.25% Housing Ownership-Renting 0.49 19.79% 67.02% 13.19% 0.68 8.82% 72.44% 18.74% 0.41 18.49% 72.08% 9.43% Government-Social security 0.24 11.38% 81.04% 7.58% 0.29 12.07% 62.29% 25.64% 0.17 10.45% 84.22% 5.33% Education 0.17 31.64% 47.27% 21.09% 0.51 21.94% 31.45% 46.61% 0.10 31.23% 52.84% 15.93% Health-Social Services 0.67 25.61% 57.33% 17.07% 0.61 15.31% 52.16% 32.53% 0.55 35.99% 45.65% 18.36% Other society services 0.42 10.53% 82.44% 7.02% 0.19 9.45% 70.48% 20.07% 0.43 13.07% 80.26% 6.67% Household services 0.73 19.75% 67.23% 13.02% 1.10 10.45% 67.36% 22.20% 0.56 32.92% 51.83% 15.25%

Mean 0.41 22.93% 61.79% 15.28% 0.53 14.25% 55.48% 30.27% 0.38 24.52% 63.05% 12.43%

16 In order to go further with the analysis of the aggregate disturbance contribution, Table 4 shows the correlation between the aggregate disturbance and the disturbances for individual regions (Panel A) and for individual industries (Panel B).

Table 4. Correlation with aggregate disturbance (1975-2002) A. Correlation between aggregate and regional disturbances Regions Correlation Regions Correlation Uusimaa -0.18 (0.93) Etelä-Pohjanmaa 0.31 (1.64) Varsinais Suomi -0.28 (1.49) Pohjanmaa -0.17 (0.91) Kanta-Hame 0.19 (0.97) Etelä-Savo 0.06 (0.29) Päijät_Häme 0.07 (0.36) Pohjois-Savo 0.03 (0.18) Kymenlaakso 0.06 (0.32) Pohjois-Karjala 0.01 (0.03) Etelä-Karjala 0.18 (0.91) Kainuu 0.03 (0.17) Itä-Uusimaa -0.01 (0.04) Keski-Pohjanmaa 0.08 (0.39) Satakunta -0.33 (1.78) Pohjois-Pohjanmaa 0.29 (1.53) Pirkanmaa -0.22 (1.16) Lappi 0.17 (0.88° Keski-Suomi -0.22 (1.17) Ahvenanmaa -0.13 (0.69)

B. Correlation between aggregate and industrial disturbances Industry Correlation Industry Correlation) Agriculture -0.16 (0.85) Transport - Communication 0.05 (0.23) Forestry 0.05 (0.23) Finance 0.06 (0.28) Fishing -0.65 (4.35) Business services 0.40 (2.24) Food industry -0.67 (4.66) Housing Ownership-Renting -0.84 (8.00) W ood industry 0.32 (1.74) Government-Social security 0.26 (1.39) Metal 0.42 (2.35) Education -0.08 (0.40) Other manufacturing -0.38 (2.07) Health-Social Services 0.13 (0.66) Construction -0.21 (1.08) Other society services 0.52 (3.14) W holesale - Retail trade 0.37 (2.03) Household services -0.51 (3.03) Hotel - Restaurants 0,12 (0.62)

Note : absolute values of t-values are in parentheses.

The correlation between the regional disturbances and the aggregate are generally small and non significant or only marginally significant at the 10% confidence level. The correlations between the industrial disturbances and the aggregate show that the disturbances for Metal industry, W holesale and retail trade, Business services and Other society services are significantly positively correlated with the aggregate disturbance, indicating that the cyclical effects of aggregate shocks may be amplified in these three industries. By contrast, the disturbance associated with Fishing, Food, Other manufacturing industry, Housing ownership and renting, and Household services are negatively correlated with the aggregate. This negative correlation may reflect the dampening of aggregate fluctuations by these industries and/or the countercyclical output of these industries.

17 Finally, Figure 1 presents scatter-plot diagrams graphing the relationship between the contribution of the aggregate disturbance contribution (evaluated as the ratio of aggregate shock contribution to totalR2 ) and the relative contribution of industrial and regional disturbances (evaluated as the ratio of industrial contribution to regional contribution). The relationships is evaluated separately for regional output (Figure 1a) and industrial output (Figure 1b). each figure shows the relations ship obtained for both 1975-1989 and 1989-2002 time periods.

Figure 1a : Decomposition of Regional output

80

n 70 o i n t o u i t

b 60 i u r t b i n r 50 t o n c

o

y 1989-2002 c

r 40

t l s a u n 30 d o i n I g

1975-1988 e :

20 R o

i t o t a 10 R 0 5 7 9 11 13 15 17 19 21 23 25 Contribution of aggregate shocks to total R-square

Figure 1b : Decomposition of Industrial output

18,00

o t

16,00 n o n i 14,00 t o i u t b u i 12,00 r b t i r n t

o 10,00 n c o 1989-2002 c y

r 8,00 l t a s n u 6,00 o d i n g I

e

: 4,00 R o i

t 1989-2002

a 2,00 R 0,00 0 5 10 15 20 25 30 35 40 45 Contribution of Aggregate shocks to total R-square

18 Figure 1a shows a clear positive correlation between the ratio of industrial to regional disturbances across regions. Logically, Koko-maa, with high levels of both ratios, is located far from the remaining regions on the graph, but seems to fit the relationship quite well. Globally, this graph shows that the larger the ratio of industrial to regional disturbances, the larger the sensitivity of regional output to aggregate shocks. Similarly, we can say that regions with industrial disturbances dominated by regional disturbances, are more isolated from aggregate shocks than regions where industrial disturbances contribute more and regional disturbance contribute less. This may reveal the fact that Finland region are characterised by a low degree of regional specialisation. However it also reveals that powerful regional policies may lead to a lowering of the correlation between regional output growth and aggregate exogenous disturbances. As can be seen on Figure 1a, the curve moves to the right of the scatter plot in the more recent sub-period. This displacement without slope variation clearly indicates that the relative contribution of aggregate shocks to regional output fluctuations has tended to rise from the first to the second sub-period, as already observed in tables 3a.

This result suggest some comments for the debated issue of decentralisation. Further steps in the decentralisation process will give more responsibilities and more expenditure power to regional and regional governments in fields such as investment in infrastructures or tax competition. If this movements leads to an increase in the relative contribution of regional to industrial disturbances in Finland regions, the sensitivity of regional output growth to aggregate disturbance will be simultaneously reduced. At the opposite, for a given contribution of regional disturbances, a greater degree of good and financial market integration may lead to a larger contribution of industrial disturbances in explaining region output growth and, thus, to a larger correlation between regional growth and aggregate disturbances.

Turning to the case of industrial output, Figure 1b shows that there is a negative link between the contribution of aggregate shocks to variations in industrial output and the ratio of industrial to regional disturbances. Aggregate disturbances are important in industries where industrial shocks contribute less than regional shocks to output variations. There again, the displacement of the curve to the right of the diagram reveals the higher sensibility of industrial output fluctuations to aggregate shocks over the more recent sub-period.

19 In order to examine whether aggregate, ragional and industrial disturbances have the same effects on private and public sector output, equation (1) is re-estimated by including only private industries and only public sector industries, alternatively. Corresponding results are reported in Table 5 (a complete and detailed presentation of results estimated for each region and each industry is given in Appendix A (Tables A1 and A2)).

Table 5 Decomposition of output fluctuations (1975-2002)) Total R2 and factor contributions Public sector Private sector Total R square 0.38 0.29

Contribution of Aggregate shocks 23.16% 19.66%

Regional shocks 23.68% 14.83% F (P-value) 1.11 (0.04) 1.03 (0.33)

Industrial shocks 49.68% 63.44% F (P-value) 6.04 (0.00) 5.14 (0.00)

The influence of industrial shocks is clearly larger in the private sector than in the public sector. At the opposite the relative fraction of output variance attributable to regional and aggregate shocks is higher in the public sector than in the private sector. This higher contribution of regional and aggregate shocks in public sector output variations may be an indirect indicator of the fact that output decisions of the public sector are partly influenced by influence welfare considerations such as local and regional employment or countercyclical output (social services output increase when aggregate output is low and unemployment is high) .

Labor market adjustment The previous analysis gave some indications on the nature of disturbance to desegregated output growth. It is also important to try to evaluate the nature of the economic adjustment mechanism that take place following such disturbances. In this paper, we focus on the degree of integration and nature of adjustment of labour market in France by considering the determinants of long-term trends in output, employment and productivity. These trends are decomposed into sectoral and regional components by estimating equation

(7) for each of the following variables: the rate of growth of output (∆ln(yi,r)) , the logarithm

20 of the level of labour productivity (ln(qi,r)= ln(yi,r/ni,r) ), the growth rate of labour productivity

( ∆ln(qi,r)= ln(yi,r/ni,r) ), and the growth rate of employment (∆ln(ni,r)). If labour markets are highly integrated across regions, implying an absence of wage differentials, the levels of productivity should be independent of regional effects (assuming that the same technology is used in given industry across all regions). By contrast, if productivity trends are primarily regional, this would imply a low level of labour market integration. The relative importance of regional and industrial disturbances in employment trends, on the other hand, indicates the degree to which labour markets equilibrate through firms moving to regions of excess labour supply (region-specific effects) or labour moving to expanding industries (industry-specific effects). Hence, productivity regressions measure the integration of labour markets, while employment regressions measure how the labour market adjustment that does occur is achieved.

The underlying econometric approach is similar to that used to examine disturbances, except that the time dimension is excluded. The sample averages for each of the relevant variables were calculated for each sector and region. Fore each of these variables, equation (7) was then estimated over the full sample 1975-2002. Empirical results are presented in Table 6.

Table 6. Long-term adjustments Total Industry Region Growth rate of output : ∆ln(yi,r) Full period 1975-2002 0.64 0.58 0.06 32.58 (0.00) 2.87 (0.00)

Logarithm of output per worker : ln(qi,r) Full period 1975-2002 0.95 0.95 0.00 392.41 (0.00) 0.90 (0.59)

Growth rate of output per worker : ∆ln(qi,r) Full period 1975-2002 0.951 0.95 0.00 96.32 (0.00) 1.63 (0.04)

Growth rate of employment : ∆ln(ni,r) Full period 1975-2002 0.77 0.73 0.04 63.22 (0.00) 3.02 (0.00) Notes: The first figure shown in the each cell of the columns labelled —Industry“ and —Regions“ is the explanatory power of the industrial and regional effects, respectively. Under this figure is indicated the value of the F-tests of the significance of this estimated explanatory power. The associated P-value is in parentheses.

21 The full regression explains over 65 percent of the variation in average rates of output growth over the full period. Nine-tenths of the explanatory power comes from the industrial dummies and one-tenth from the regional dummies. The performance of an industry within a region appears much more closely related to the overall performance of that industry rather than to the performance of that region. There again, this result indicates that the influence of regional policy shocks on decentralised Finland regional output growth is rather limited despite the decentralisation process that has already taken place over the past decades in Finland.

The results for both the level of productivity and the growth rate of productivity indicate that the contribution of regional-specific factors is zero (and non significant at the 5% confidence level), and, hence, that Finland local labour markets are highly integrated. This result is unchanged when the sample period is divided into two five-year sub-periods. Note that this conclusion is conditional upon the time span retained (thirteen years and twenty seven years) for the econometric estimation, and cannot be interpreted as if Finland local labour markets were perfectly integrated over shorter time spans such as a quarter or a year. The employment regression indicates a larger and significant, although still subsidiary, role for regional factors. Only five percent of the total explanatory power in the regression comes from the regional dummy variables. This implies that economic adjustment occur mainly through movements of labor to with expanding industries. Only a limited part of economic adjustment occurs through regional shocks incorporating movements of expanding industries to regions with excess labor and regional policies. This large contribution of the regional dummies to long-term trends in employment is more or less similar with results found for the United States where the majority of economic adjustments occur through interregional labor migrations (Blanchard-Katz, 1992).

These result are not altered when repeating the analysis for the sub-periods 1975-1988 and 1989-2002, excepted in the case of employment where the relative contribution of regional factor slightly decreases in the sub-sample 1996-2001.

22 4. Conclusion This paper analyses the effects the relative importance of different types of shocks to decentralised Finland output growth and also the subsequent labour market adjustment mechanisms. The major part of changes in regional industrial production growth rates can be attributed to industry-specific disturbances that are common across Finland regions. These may result from disturbances to technology or preferences for different type of goods. Analysing the origins of output growth rate for each region separately does not change this conclusion since industrial disturbances are also the most important factor in all Finland regions. The small contribution of regional disturbances indicates that regional-specific exogenous policies turn out to be relatively unimportant despite the growing degree of decentralizations and the redistribution of power and responsibilities to local levels of government over the past two decades. Examining each industry separately reveals that sectoral disturbances are also important in many industries, but disturbances explain a significant part of the variance in several industries. Regressions for long term employment, output and labour productivity produce results consistent with a high degree of inter-regional labour mobility.

23 References

Bayoumi, T., Prasad, E., 1997. Currency unions, economic fluctuations and adjustment: some new empirical evidence. IMF Staff Papers 44, 36-58. Bean, C.R., 1994. European unemployment: a survey. Journal of Economic Literature, 32, 573-619. Cerniglia, F., 2003. Decentralization in the public sector: quantitative aspects in federal and unitary countries. Journal of Policy Modelling 25, 749-776. Costello, D.M., 1993. A cross-country, cross-industry comparison of productivity growth. Journal of Political Economy 101(2), 207-222. Decressin, J., Fatás, A., 1995. Regional labor market dynamics in Europe. European Economic Review 39, 1627-1655. Marimon, R., Zilibotti, F., 1998. ”Actual‘ versus ”virtual‘ employment in Europe. Is Spain different ?. European Economic Review 42, 123-153. Norbin, S., Schlagenhauf, D., 1996. The role of international factors in the business cycle: a multi-country study. Journal of International Economics 40, 85-104. Pekkala, S., Kangasharju, A., 2002a. Regional labour market adjustment: are positive and negative shocks different ? Labour 16(2), 267-286. Pekkala, S., Kangasharju, A., 2002b. Regional labor markets in Finland: adjustment to total versus region-specific shocks. Papers in Regional Science 81, 329-342. Ramos, R., Clar, M., Suriñach, J., 2003. National versus sectoral shocks: new evidence for the manufacturing sector in European countries. Economics Letters 78, 241-245. Stockman, A., 1988. Sectoral and aggregate disturbances to industrial output in seven European countries. Journal of Monetary Economics 21, 387-409.

24 Appendix A : Decomposition of output fluctuations for each region and each sector Table A1. Decomposition of output fluctuations (region results) Total R2 and factor contributions : public sector Total R2 and factor contributions : private sector Total R2 Aggregate Industry Region Total R2 Aggregate Industry Region Uusimaa 0.95 27.50% 58.96% 13.53% 0.74 21.14% 67.93% 10.93% Varsinais Suomi 0.43 27.26% 58.44% 14.30% 0.54 21.17% 68.04% 10.79% Kanta-Hame 0.55 25.80% 55.30% 18.90% 0.37 21.24% 68.24% 10.52% Päijät_Häme 0.36 24.26% 52.01% 23.73% 0.35 19.70% 63.30% 17.00% Kymenlaakso 0.42 28.05% 60.13% 11.82% 0.35 21.10% 67.81% 11.09% Etelä-Karjala 0.39 25.35% 54.36% 20.29% 0.37 20.38% 65.50% 14.12% Itä-Uusimaa 0.21 17.42% 37.34% 45.24% 0.21 16.94% 54.43% 28.63% Satakunta 0.59 26.80% 57.45% 15.76% 0.40 22.04% 70.81% 7.15% Pirkanmaa 0.68 28.18% 60.41% 11.41% 0.51 21.36% 68.65% 9.99% Keski-Suomi 0.43 19.96% 42.79% 37.26% 0.31 22.74% 73.08% 4.18% Etelä-Pohjanmaa 0.40 26.25% 56.29% 17.46% 0.31 17.66% 56.75% 25.59% Pohjanmaa 0.52 27.13% 58.16% 14.71% 0.13 19.38% 62.26% 18.36% Etelä-Savo 0.46 22.19% 47.58% 30.23% 0.36 21.56% 69.27% 9.18% Pohjois-Savo 0.49 26.14% 56.03% 17.83% 0.45 22.49% 72.28% 5.22% Pohjois-Karjala 0.44 26.81% 57.48% 15.71% 0.26 20.53% 65.98% 13.49% Kainuu 0.35 21.44% 45.96% 32.60% 0.22 18.29% 58.78% 22.93% Keski-Pohjanmaa 0.20 25.02% 53.64% 21.34% 0.27 20.44% 65.68% 13.88% Pohjois-Pohjanmaa 0.40 25.53% 54.73% 19.74% 0.29 21.62% 69.48% 8.89% Lappi 0.45 28.16% 60.38% 11.46% 0.24 19.28% 61.97% 18.75% Ahvenanmaa 0.27 9.43% 18.98% 71.59% 0.13 15.02% 47.71% 37.27%

Mean 0.45 24.43 52.32 23.25 0.34 20.20 64.90 14.90

Table A2. Decomposition of output fluctuations (industry results) Total R2 and factor contributions : public sector Total R2 and factor contributions : private sector Total R2 Aggregate Industry Region Total R2 Aggregate Industry Region Agriculture 0.56 16.46% 71.06% 12.48% Forestry 0.65 20.36% 62.46% 0.17 0.68 17.08% 69.97% 12.95% Fishing 0.38 21.10% 62.90% 16.00% Food industry 0.11 27.34% 51.93% 20.73% W ood industry 0.15 17.82% 68.68% 13.51% Metal 0.23 31.51% 44.60% 23.89% Other manufacturing 0.16 39.33% 30.85% 29.82% Construction 0.41 14.35% 68.93% 0.17 0.41 27.44% 51.76% 20.80% W holesale - Retail trade 0.53 27.48% 51.69% 20.83% Hotel œ Restaurants 0.60 19.23% 66.20% 14.58% Transport - Communication 0.40 31.23% 32.38% 0.36 0.35 28.31% 50.23% 21.46% Finance 0.93 11.18% 80.35% 8.47% Business services 0.16 21.95% 52.48% 0.26 0.20 23.40% 58.85% 17.74% Housing Ownership-Renting 0.50 17.97% 68.42% 13.62% Government-Social security 0.59 38.72% 16.18% 0.45 Education 0.30 21.51% 53.43% 0.25 0.11 20.09% 64.69% 15.23% Health-Social Services 0.98 37.30% 19.25% 0.43 0.28 17.97% 68.42% 13.62% Other society services 0.69 21.74% 52.95% 0.25 0.27 12.75% 77.58% 9.67% Household services 0.73 21.97% 62.68% 15.36%

Mean 0.53 25.90% 44.76% 0.29 0.40 22.13% 61.16% 16.71%

25