monitoring social inclusion in some european regions

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DOI: 10.1177/09589287030134002

2003 13: 335Journal of European Social Policy Kitty StewartMonitoring Social Inclusion in Europe's Regions

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Monitoring social inclusion in Europes regionsKitty Stewart *, London School of Economics, UK

Article

Journal of European Social Policy 0958-9287 (200311)13:4 Copyright 2003 SAGE Publications, London, ThousandOaks and New Delhi, Vol 13 (4): 335356; 036947

Summary The European Council recentlyadopted a list of target indicators for EUmember states in the fields of poverty andsocial exclusion, with likely implications forsocial policies across Europe. The targetschosen are largely national average figures.This paper argues that if regional disparitiesare to be taken seriously, all key indicatorsshould instead be measured at regional as wellas at national level. Using a variety of datasources, it examines regional indicators of poverty, unemployment, education and health,and shows, first, that the degree of dispersiondepends on the indicator under scrutiny; andsecond, that it is not always the same regionswithin each country which do best and worstin each case. It follows that a single dispersionmeasure will only be misleading, while thechoice of disparity in regional unemploymentrates in particular has additional problemswhich are also discussed. Finally, the paperdraws attention to the limits of currentlyavailable data at regional level, in light ofthe fact that one key aspect of the Lisbon2000 European Council summit conclusionswas a commitment to the collection of betterdata on poverty and social exclusion in theEU.

Key words EU, exclusion, regionaldisparities, well-being

Rsum Le Conseil europen a adopt rcem-ment une liste dindicateurs dits dobjectifspour les Etats membres de lUnion europennedans les domaines de la pauvret et de lexclu-sion sociale ce qui devrait avoir des implica-tions pour les politiques sociales en Europe.Les objectifs choisis reposent largement sur desdonnes nationales moyennes. Cet article argu-mente que si les disparits rgionales doiventtre prises srieusement, tous les indicateurscls devraient ce moment-l tre mesurs tantau niveau national quau niveau rgional. Sebasant sur diffrentes sources de donnes, lar-ticle examine les indicateurs rgionaux de pau-vret, de chmage, dducation et de sant. Ilmontre que le degr de dispersion dpend delindicateur examin et ensuite quil ne sagitpas toujours des mmes rgions lintrieur dechaque pays qui ont les meilleures et les piresperformances pour chaque cas. Il en suitquune mesure unique de dispersion conduirait des erreurs. De plus, le choix dutiliser lestaux de chmage rgionaux conduit dautresproblmes qui sont galement abords.Finalement cet article attire lattention sur leslimites des donnes actuellement disponiblesau niveau rgional en rappelant que les conclu-sions du Conseil europen de Lisbonne (2000)sengageaient rassembler de meilleuresdonnes sur la pauvret et lexclusion socialedans lUnion europenne.

* Author to whom correspondence should be sent: Kitty Stewart, ESRC Research Centre for Analysis of Social Exclusion (CASE), London School of Economics, Houghton Street, London WC2A 2AE, UK.[email: [email protected]]

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altogether in the EU, including 12 in the UK(North West, South East etc.), 8 in France, 11in Italy, 16 in Germany and 7 in Spain. 2

Regional populations average nearly 5m, and6 EU countries consist of just one NUTS level-1 region each (Portugal, Sweden, Finland,Denmark, Ireland and Luxembourg). Clearlythis begs enormous questions about the degreeof intra-regional disparity: how differentwould things look using NUTS level-2 orNUTS level-3 regions, for example? However,problems with the robust measurement of many indicators even at NUTS level-1 make itclear that holding out for a greater level of detail is simply unrealistic. And while less

than perfect, disparities at NUTS level-1 lenda wealth of additional colour to a picturebased purely on national averages.

Indicators

There is an enormous literature exploring thecase for measuring poverty and social exclu-sion on a number of dimensions, rather thanjust through income poverty rates: see forexample Sen, 1992; Erikson, 1993; Nolan andWhelan, 1996; Burchardt et al., 2002. Forreasons of space, we do not rehearse thesearguments here, but take the range of targetindicators chosen by the Commission as ourstarting point.

Following the Atkinson recommendations,the social exclusion indicators agreed atLaeken were divided into three levels of prior-ity. The ten primary indicators are intendedas lead or headline indicators covering thebroad fields considered most important tosocial exclusion; while eight secondary indi-

cators were chosen to support the lead indica-tors and describe other dimensions of theproblem. 3 Both levels are to be commonlydefined and used by member states in the nextround of National Action Plans. In addition, itis envisaged that member states will them-selves choose a third level of indicator to high-light specific areas of interest and add detail tothe primary and secondary indicators. These

last will not be harmonized at EU level. Thispaper focuses exclusively on the primary level.

Table 1 presents the primary indicatorsapproved in Laeken, and beside them theclosest equivalent indicators available atregional level. Identical indicators can befound in several areas, and near equivalentsare used in others. Only three indicators aremissing completely (though not all are dis-cussed in this paper). Small regional samplesizes in available household surveys for manycountries make the poverty persistence andmedian poverty gap measures problematic.These problems are illustrated below in thediscussion of results for the poverty head-

count, which makes lesser demands on thedata. The third indicator missing entirely isthe jobless household rate: this could in prin-ciple be calculated from Eurostat LFS rawdata (and sample sizes should not be aproblem here), but these figures are not cur-rently publicly available, while access to theraw data is restricted.

Small sample sizes are also responsible forthe choice of the decile ratio over the quintileshare ratio (not discussed in this paper; seeStewart, 2002); and for the use of the

Luxembourg Income Study (LIS) in place of the European Community Household Panel(ECHP) for the calculation of both income-based measures. Finally, while self-assessedpoor health can be calculated by region fromthe ECHP, it is not presented here: as a subjec-tive indicator it is rather different in nature tothe other measures and there is insufficientspace in this paper to do it justice (see Stewart,2002 for further discussion).

Measuring disparity the coefficientof variation

How should regional disparities in these indi-cators be measured? The coefficient of varia-tion appears attractive in its offer to sum updisparity in a single, mean-independent num-ber. But, as Atkinson et al. point out, there isa problem with comparing the extent of

Monitoring social inclusion in Europes regions 339

Journal of European Social Policy 2003 13 (4)

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340 Stewart


Table 1 Primary indicators and regional equivalentsNational indicator Data source Proposed Proposed data

regional indicator sourcePoverty and inequality1. Poverty headcount (share of individuals in Eurostat ECHP a. Poverty Luxembourghouseholds with income below 60% national headcount Income Studyequivalized median income) a b

2. Quintile share ratio (S80/S20) Eurostat ECHP b. Decile ratio c LuxembourgIncome Study

3. Poverty persistence (share below 60% Eurostat ECHPmedian income in at least two out ofthree years) b

4. Median poverty gap b Eurostat ECHP

Regional cohesion5. Coefficient of variation of unemployment Eurostat c. CV of Eurostatrates at NUTS level-2 LFS unemployment LFS

rates

Unemployment6. Long-term unemployment rate b Eurostat d. Long-term Eurostat

LFS unemployment LFSrate

7. Persons living in jobless households EurostatLFS

Education8. Low educational attainment (% of 18 Eurostat e. Share of 17- Eurostat24-year-olds without ISCED3 qualifications, LFS year-olds not in LFSand not in education or training) b education or training

Health9. Life expectancy at birth b Eurostat f. Standardized Eurostat

Demography mortality ratio DemographyStatistics Statistics

10. Self-assessed poor health by income level Eurostat g. Self-assessed Eurostat(ratio of share in bottom and top income ECHP poor health c ECHPgroups assessing their health as bad orvery bad) b

Notesa The poverty headcount is also to be provided with breakdown by employment status, household typeand tenure status.b These indicators are to be provided for the whole population and with gender breakdown.c The inequality measures and the self-assessed health measure are not presented in this paper becauseof space constraints: for further discussion, including an explanation of why the decile ratio isfavoured over the quintile share ratio, see Stewart (2002).Source : For national indicators: Social Protection Committee (2001).



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disparity (using any measure) across countrieswith differing numbers and sizes of regions(Atkinson et al., 2002: 767). Taking NUTSlevel-1 regions as the unit of measurementsolves the size problem to some degree, as thebasic concept of the region is at least the samein each country: it would clearly be misleadingto compare UK regions with French com-

munes , for example. But it remains true thatdispersion will tend to be less in a countrywith three regions than in a country with 30.

A second problem arises if the disparitymeasure is to be examined in isolation fromthe national average (as is implied by theCommission list the coefficient of variationof regional unemployment rates is included as



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a primary indicator, but the national averageunemployment rate is not). Taken on its own,a CV could be very misleading: a MemberState that has a 10 percent unemployment rateacross all regions might score well on consis-tency, but this can hardly be a performance tobe commended (Atkinson et al., 2002: 77).What we want to know is whether a nationalrate of 5 percent disguises a regional low of 1percent and a high of 10 percent.

In the case of unemployment in particular,this issue has an additional twist whichAtkinson et al. (2002) overlook. Regional un-employment disparities have a strong cyclicalelement: during a boom, employment tends to

take off faster in areas which had lower unem-ployment to begin with, leaving other regionsfurther behind. Thus a low coefficient of vari-ation of regional unemployment rates couldsimply reflect recession. Figure 1 illustrates,showing national average unemployment andthe NUTS level-2 coefficient of variation (withregional population weights) over the lastdecade for three countries. (Unemployment isthe only indicator which can be well andeasily measured at NUTS level-2.) In bothSweden and the UK, there is a strong negative

correlation between the level of unemploy-ment and the level of regional disparity. 4Belgium, Spain, Greece, Portugal and Finlandwould have made equally good examples allhave negative correlations of over 0.50. Thecase of France shows that this relationshipdoes not hold everywhere: the rise in overallunemployment during the 1990s has notaffected regional disparities. However, changesin unemployment have been small in France,and regional disparities were low to beginwith.

In summary, then, the coefficient of vari-ation (or indeed any summary disparitymeasure) is likely to be a misleading indicator:(a) in comparisons across countries of differ-ent sizes; (b) in all cross-country comparisonsif examined out of the context of the nationalaverage; and (c) in tracking disparity evenwithin a single country if changes in thenational average are not taken into account.

This is not to say that such disparity measuresare of no use, just that they need to be usedwith care and alongside an indicator of national average performance. In the follow-ing sections, we use maps and graphs ratherthan a single dispersion measure to allow usto reflect both the national average rankingand the regional dispersion around theaverage, while also taking into account thenumber of regions within a country. We returnto compare coefficients of variation at the endof the paper, aiming to avoid the dangers out-lined.

Poverty and inequalityFigure 2 presents poverty rates by NUTS level-1 region for the mid-1990s, using the mostrecent wave of data available for each countryfrom the Luxembourg Income Study. 5/6 Thepoverty rate is defined as the share of individ-uals living in households with equivalizedincome below 60 percent of the nationalmedian. Relative poverty lines are now usedas standard across Europe, in keeping with the1994 EC definition of the poor as those with

resources . . . so limited as to exclude themfrom the minimum acceptable way of life inthe member state in which they live (Eurostat,1997: 3). Sixty percent of the median isbecoming increasingly adopted as the norm,and is the level recommended in the Laekenindicators (although poverty rates measuredusing a number of other thresholds are to betracked as secondary indicators).

The figure is drawn with cats whiskersshowing the 95 percent confidence intervalsfor each poverty rate. 7/8 These remind us that

in many cases the size of a regional sample isjust too small for us to be certain, or nearcertain, that the poverty rate in a given regionis really higher or lower than that in its neigh-bour. In the UK case, for example, we can beat least 95 percent confident that the povertyrate in the South East is lower than that inNorthern Ireland, the North East, Scotland,Yorkshire, the North West and the West

342 Stewart




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Midlands; but the poverty rate in the EastMidlands cannot be said with confidence to

differ from that in any other UK region.Confidence intervals are much narrower forsome countries (such as France and Spain)than for others, because regional sample sizesfor those countries are larger. This is illus-trated in Table 4 which gives sample sizes forthe LIS datasets alongside those for theEuropean Community Household Panel, analternative potential data source. Regions withthe widest confidence intervals such asNorthern Ireland, Brussels, Mecklenburg andRheinland-Pfalz are those where sample

sizes are smallest (each of these samples con-tains less than 300 households). 9Bearing the whiskers in mind, there are still

clear differences in poverty rates which arestatistically significant. In Italy, the NorthSouth divide stands out, with poverty in Sicilyin particular higher than in any other region.As many as one in two Sicilians live onincomes below half the Italian median. Italy

also shows the greatest regional dispersion:poverty rates in the North are among the low-

est in Europe, dropping below 10 percent inEmilia-Romagna and Lombardy. (It is strikingjust how few of Europes regions have povertyrates below this 10 percent level, which is thenational rate in Sweden and Finland.)

Spanish regions display a very clear EastWest division: along with Madrid, the regionsof the North and North East have povertyrates less than half of those in the Centre,South and Canaries, with the North Westfalling in the middle. Belgium also showssignificant regional disparity, with poverty in

the Flemish-speaking area several percentagepoints lower than in Brussels or Wallonia.This contrasts with the clustering of regions inboth Austria and the Netherlands.

Perhaps the most surprising results are forGermany: former East and West Germanregions are found at both ends of the Germanpoverty rankings. In the East, poverty rangesfrom around 10 percent in Saxony-Anhalt to



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Figure 2 Regional poverty rates (19945) using a national poverty standard



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around 20 percent in Mecklenburg; and in the

West from 10 percent in the North to 22percent in Rheinland-Pfalz. These results areclearly not very robust, as standard errors forthe German sample are large. But it is unlikelythat the story is completely inaccurate (there isonly a small overlap in the whiskers for Saxony-Anhalt and Mecklenburg, for example). Theresults may reflect the ongoing impact of thesocialist inheritance in parts of East Germany.

Figure 3 shows the geography of povertyacross the EU as a whole, and a clear patternemerges. The highest poverty rates are found

in Northern England and Scotland, SouthernItaly and South-west Spain the areas furthestfrom Europes geographical centre. The lowestpoverty regions fall in a vertical line runningdown from the Netherlands and Luxembourgthrough parts of Germany into Eastern Franceand Northern Italy.

With the exception of one or two EastGerman regions, this pattern is very similar to

the one we get if we map average income

across Europe (see Hills, 1995; Stewart,2002). This is hardly surprising: regions withlow average income are very likely to have ahigher proportion of people living below anygiven share of national median income. But itdoes lead us to the question of whether anational poverty line is the right standard touse. Recent debate has focused on whether acommon EU poverty line should replace indi-vidual national lines (see e.g. Atkinson, 1998;de Vos and Zaidi, 1998). Here the question isthe opposite one: in talking about poverty at

regional level, ought we to move down from anational poverty line to individual regionallines? If average incomes in a region are lower,will this not reflect the level of resourcesneeded to participate in that community?

There are two arguments for a regionalpoverty line. First, the cost of living variesacross regions, so the resources needed toachieve a given standard of living will differ

344 Stewart


Figure 3 Poverty rates in EU regions (19941995), share of population living in householdsbelow 60 percent national median income



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Unemployment

We have discussed unemployment briefly inthe context of the coefficient of variation. Butit is in fact the long-term unemployment ratewhich is included in the final list of primaryindicators, not the rate of unemploymentitself. This is very straightforward to measureat regional level because of the size of the Euro-pean Union Labour Force Survey: as noted,unemployment data are robust at NUTS level-2, but NUTS level-1 is used here for consis-tency with the rest of the analysis.

Figure 6 presents long-term unemploymentrates the percentage of the labour force whohave been out of work for more than 12months. Italy displays the highest degree of regional disparity by a long way: parts of Northern Italy have long-term unemploymentrates among the lowest in the EU, while thevery highest EU rates are found in SouthernItaly. In Campania one in five members of the

work force has been unemployed for morethan a year compared with less than 2 percentin Emilia-Romagna. Spain has the highestnational unemployment and long-termunemployment rates of any member state, butlong-term unemployment even in the worst-performing Spanish region, the South, is wellbelow that in Campania and Sicily.

In most countries, the regional distributionof long-term unemployment shows many simi-larities to the poverty distribution. In Italy, asnoted, long-term unemployment shows a clearNorthSouth divide; while unemployment andpoverty patterns are very much alike in bothFrance and UK. But for Germany we see a

strong EastWest divide on unemployment, andthis was not the case for poverty. In Spain rela-tively high long-term unemployment is found inthe three high-poverty regions the South, theCentre and the Canaries but Madrid and theNorth West do much worse on long-termunemployment than on the poverty headcount.



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Education

The share of 18 to 24-year-olds withoutISCED3 (upper secondary) qualifications andnot in education or training was adopted atLaeken as the main education indicator.Atkinson et al. (2002: 1659) argue the casefor increased attention to direct measures of literacy and numeracy as well, but they alsopoint to unresolved comparability problemswith existing data sources, while noting thateducational attainment (as measured throughqualifications) is a more important determi-nant of earnings than literacy or numeracymeasures.

Given the decision to focus on aqualification-based indicator, several factorsact in favour of ISCED3 as the cut-off. First,while the range of qualifications covered bythe ISCED3 umbrella is enormous, in most EUcountries it is not compulsory to completeupper secondary education, so all those withISCED3 qualifications will have made achoice to continue in education. 10 Second,Steedman and McIntosh (2001) find large dif-ferences in employment probabilities acrosscountries between individuals with no qualifi-cations higher than ISCED2 and those withISCED3. They conclude that for adults,ISCED2 [i.e. non-completion of ISCED3] is areliable measure of attainment across EUcountries (p. 580). ISCED3 also appears to bethe best choice across countries if we aretrying to assess proxy literacy (see OECD,2000: Figure 2.4).

At regional level, however, ISCED attain-ment levels for the 1824 age group are notcurrently publicly available. Data from theEuropean Union Labour Force Survey are

published by region for 2559-year-olds, butnot for the younger group. What are available although not for all countries are currentparticipation rates in education at differentages. This paper therefore uses participationat 17 as a proxy for upper-secondary attain-ment.

Figure 7 presents regional participationrates at 17 for those countries for which data

are available, plotted against ISCED3 attain-ment levels for the older population. The ideais that the latter figures give us a picture of theregions past educational record, providingcontext for the current picture (although of course migration rates will affect the resultsfor the older group). For all regions outsideEast Germany, the data-points in the figurefall below the 45 diagonal, indicating that theshare of 17-year-olds in education in the mid-1990s was higher than the share of adultswho had completed upper secondary. This isas expected given steady increases acrossEurope in staying-on rates. Improvementsacross the board have in turn resulted in

falling dispersion in participation across theEU. The areas of Europe performing best Germany, the Netherlands, France, Swedenand Finland are now pushing up against theceiling of full enrolment, with participationabove 90 percent, and this has allowed weakerperformers, such as the UK and Spanishregions, to catch up. Participation among 17-year-olds ranges from 60 to 100 percentacross regions with available data, comparedto ISCED3 shares ranging from 30 to 95percent.

However, dispersion has not fallen withineach individual country. In particular, in theUK, the distribution has been stretched, as theexpansion of education in some regions notably Scotland and Northern Ireland hasoutstripped that in others. While the share of 2559-year-olds with ISCED3 qualificationsfalls between 50 and 60 percent in all UKregions, participation among 17-year-oldsranges from 60 to 83 percent. This leaves theUK with the largest disparities of the threelarger countries in the figure, while the four

regions with the lowest participation rates of all countries shown are also found in the UK(Yorkshire and Humberside, Wales, theNorth, and the East Midlands). Qualificationsamong the working-age population are lowerin every Spanish region than in any other withavailable data, as shown by the distributionacross the vertical axis in Figure 7, but thehorizontal axis shows that participation rates

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at 17 are higher even in the South of Spain

than in these four UK regions.

Health

This section looks at the standardized mortal-ity ratio (SMR). The national mortality indi-cator chosen by the Commission is lifeexpectancy at birth, but this is not easily avail-able at regional level. The SMR, which can becalculated from Eurostat raw mortality data,provides a reasonable proxy: at national level,

the correlation between the SMR and lifeexpectancy at birth is 0.85 for EU countries.Figure 8 plots the SMR against the poverty

headcount data shown in Figure 2 for the fourlargest countries with available data (GermanSMR data are incomplete), with regionsordered left to right by the poverty data foreach country. 11 Regional disparity is highest inthe UK and lowest in Italy. However, what is

striking is the fact that, for each country, only

the extremes of the distribution are well cor-related. In the middle of the distributionsregional poverty and mortality rates do notappear to be related. For the UK, the SMRdata do suggest a NorthSouth divide, and thejagged SMR line could be driven by inaccurateordering for poverty (confidence intervals arelarge for the UK poverty data, as already dis-cussed). But for other countries, the correlationin the middle is weak at best. In particular,Italian mortality data show little evidence of aNorthSouth health divide, in contrast to indi-

cators for Italy in every other domain: mortal-ity is high in Sicily and Campania, but as lowin the South and in Sardinia as in Lombardyand the North East. In Spain, the Centralregion, with one of the highest poverty rates inEurope outside Southern Italy, also showsamong the lowest European rates for SMR.And in France, with the exception of Ile deFrance and the Nord at the extremes, regional



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poverty rates and regional mortality appear tobe negatively correlated.

Summary and conclusions

The causes of the regional differences pre-sented in each of the figures above are worthyof much greater consideration. Some of theresults in particular, such as the absence of aNorthSouth divide in Italy on health and inthe UK on education, might not have beenpredicted and call for future investigation.However, there is no space to do these issues

justice here, and the current paper had a moremodest aim. Having argued that regional in-equality in exclusion indicators is importantas it reflects regional inequality in opportuni-ties, it set out to examine how far differentexclusion indicators tell us different storiesabout the pattern of regional disparity in EUcountries. We sum up these patterns here.

Table 2 presents CVs for the seven regional

primary indicators. Countries have beenarranged into three groups so that we only

compare CVs across countries with a similarnumber of regions. In each group, the highestCV for each indicator is highlighted. This doesnot imply anything about overall performance the CV for the poverty headcount is lower inthe UK than in either Germany or France, butpoverty rates in most UK regions are higherthan in most German or French regions. But itdoes show us that a high coefficient of varia-tion on one indicator (such as unemployment)does not necessarily tell us anything about thelevel of dispersion in another. 12

In the first group, Belgium shows the high-est regional disparity on several indicators,including unemployment, but low disparity onregional inequality and poverty measuredagainst a regional standard. The Netherlandsis in just the opposite situation, while Greecedoes badly on unemployment and the SMR.Only Austria shows strong regional cohesionin all areas.

350 Stewart


0

10

20

30

40

50

60

p o v e r

t y

80

90

100

110

120

130

140

h e a l

t h

poverty rate SMR

Spain France Italy UK

Figure 8 Standardized Mortality Rate (1998) and LIS regional poverty headcounts (19945)



18/23

In the second group, France and Spain runneck and neck on regional unemployment, in-equality and the SMR. But disparities arehigher in Spain for one of the remaining indi-cators national standard poverty and higherin France for two others poverty against aregional standard and long-term unemploy-ment. Finally, in the third group, Italy showsthe greatest disparity on both unemploymentmeasures and on national standard poverty;but the UK and Germany each have thehighest (or joint highest) CV on two or threeindicators out of the seven.

So high disparities in some indicators ordomains need not mean high disparities in

others. But how far do these differences justreflect a stretching-out or compressing of asingle distribution, and how far is there a re-shuffling of regional positions? Do disparitiesreflect multidimensional regional divides with-in countries or do rankings change from indi-cator to indicator? Table 3 shows coefficientsof correlation between regional unemploymentin 1997 and the other regional indicators forthe five largest EU countries. Unemploymentis only consistently associated with long-termunemployment, poverty measured against a

national standard and (for three countries outof five) the SMR. There is no significant corre-lation between unemployment and partic-ipation in education at 17 in any of the fivecountries; and almost none between unem-ployment and regional standard poverty orthe decile ratio. Indeed, for Germany boththese indicators show a statistically significantnegative correlation, with lower inequalityfound in regions with higher unemployment.This is, of course, likely to be explained by theEast German effect, with the greater equality

inherited from the communist regime sittingalongside high rates of unemployment sincereunification in the former East Germanregions.

While Germany may be seen as a specialcase, the general point still stands: knowingthat a given region has a high rate of unem-ployment tells us nothing about how thatregion ranks on a number of other dimensions



T a

b l e 2 C o e

f f i c i e n t s o f v a r

i a t i o n

O b s e r

O b s e r v a

t i o n s

v a t i o n s

B e l g i u m

B e l g i u m

A u s

t r i a

A u s

t r i a

G r

G r e e c e e e c e

N e t

h e r l a n

d s

N e t

h e r l a n

d s

S p a i n

S p a i n

F r a n c e

F r a n c e

I t a l y

I t a l y

U K U K

G e r

G e r m a n y

m a n y

3 3

3 3

4 4

4 4

7 7

8 8

1 1 1 1

1 2 1 2

1 6 1 6

P o v e r

P o v e r t y

( n a t .

s t d )

t y ( n a t .

s t d )

0 . 3

1

0 . 3

1

0 . 0 4 0 . 0 4

0 . 1 0

0 . 1 0

0 . 4

1

0 . 4

1

0 . 2 9 0 . 2 9

0 . 6

4

0 . 6

4

0 . 2 1

0 . 2 1

0 . 2 8 0 . 2 8

P o v e r

P o v e r t y

( r

t y ( r e g .

s t d )

e g .

s t d ) 0

. 0 6

0 . 0

6

0 . 1 0 0 . 1 0

0 . 2

5

0 . 2

5

0 . 1 1 0 . 1 1

0 . 1 4

0 . 1 4

0 . 1 9 0 . 1 9

0 . 1 4

0 . 1 4

0 . 2

9

0 . 2

9

D e c

i l e r a t i o

D e c

i l e r a t i o

0 . 0

5

0 . 0

5

0 . 0 7 0 . 0 7

0 . 0

9

0 . 0

9

0 . 0

8

0 . 0

8

0 . 0 9

0 . 0 9

0 . 2

0

0 . 2

0

0 . 1 2

0 . 1 2

0 . 2

0

0 . 2

0

U n e m p l o y m e n t

U n e m p l o y m e n t

0 . 3

5

0 . 3

5

0 . 1 5 0 . 1 5

0 . 3

3

0 . 3

3

0 . 1 6

0 . 1 6

0 . 2

2

0 . 2

2

0 . 2 3

0 . 2 3

0 . 5

7

0 . 5

7

0 . 2 1

0 . 2 1

0 . 4 2 0 . 4 2

L L T U T U

0 . 4

0

0 . 4

0

0 . 3 8 0 . 3 8

0 . 3 7 0 . 3 7

0 . 3 0

0 . 3 0

0 . 1 7 0 . 1 7

0 . 2 7

0 . 2 7

0 . 7

1

0 . 7

1

0 . 4 4

0 . 4 4

0 . 4 5 0 . 4 5

P a r

P a r t i c i p a t

i o n a t

1 7

t i c i p a t i o n a t

1 7

0 . 0 2 0 . 0 2

0 . 0 9 0 . 0 9

0 . 1

0

0 . 1

0

0 . 0 4 0 . 0 4

S M R

S M R

0 . 0

8

0 . 0

8

0 . 0 4 0 . 0 4

0 . 1

0

0 . 1

0

0 . 0 2

0 . 0 2

0 . 0

9

0 . 0

9

0 . 0 9

0 . 0 9

0 . 0 6 0 . 0 6

0 . 0

9

0 . 0

9

0 . 0

8

0 . 0

8

N o t e :

F i g u r e s

i n b o l d a r e

f o r

h i g h e s t C

V i n g r o u p .



19/23

of regional deprivation. High unemploymentregions in the EU are not the Europeanregions with the poorest record on educa-tional attainment, nor are they the areas withthe highest levels of internal inequality. WithinGermany, Italy and the UK, the regionalunemployment rate is a reliable indicator of aregions health, but this is not the case forSpain or France (and is far from true whenlooking at all EU regions together). Thus a fallin the rate of unemployment in a Spanishregion might be expected to have an impacton the regions performance on long-termunemployment and on national standardpoverty, but not to affect how it stands with

regard to internal inequality, educationalattainment or average health.

In sum, it should be clear that the coeffi-cient of variation of regional unemploymentrates is not a sensible or adequate way of measuring regional disparities more widely(quite aside from the specific problemsattached to this measure which were exploredearlier in the paper). This paper has illustratedthat the extent of regional disparity dependson the indicator examined, and that it is noteven the same regions within each country

which do best and worst in each case. If theCommission is serious about measuring reg-ional disparity in exclusion and the inclusionof even one disparity measure as a primaryindicator suggests that it does attach impor-tance to geographical inequality then itneeds to rethink its strategy. It must givegreater consideration to the recommendationthat regional disparities in all primary indica-tors are followed (Atkinson et al., 2002). Asingle indicator will not do all the work.

Finally, a word about data. The paper had

the additional aim of exploring just how farkey social indicators could in fact be measuredat regional level using existing data sources.We found that many of the primary indicatorschosen at the Laeken Summit can be approxi-mated at regional level using currently avail-able data, but that indicators derived fromhousehold survey data are not very robuststatistically for all countries. Analysis of

352 Stewart


T a

b l e 3

C o r r e

l a t i o n c o e

f f i c i e n t s

f o r u n e m p l o y m e n t 1

9 9 7 a n d

r e g i o n a l e x c l u s

i o n

i n d i c a t o r s

P o v e r t y r a

t e

P o v e r t y r a

t e

D e c

i l e r a

t i o

L T U

1 7 - y

e a r - o l

d

S M R

( n a t i o n a

l s t a n d a r

d )

( r e g

i o n a

l s t a n d a r

d )

p a r t

i c i p a t

i o n r a

t e

F r a n c e

0 . 7

3

0 . 1 2

0 . 2 0

0 . 9

9

0 . 3 9

G e r m a n y

0 . 2 8

0 . 6

6

0 . 7

7

0 . 9

8

0 . 3

1

0 . 9

4

I t a l y

0 . 9

1

0 . 5 8

0 . 8

9

0 . 9

9

0 . 7

5

S p a i n

0 . 7

9

0 . 3 6

0 . 4 8

0 . 8

7

0 . 7

3

0 . 6 5

U K

0 . 7

9

0 . 2

8

0 . 0

1

0 . 7

6

0 . 0 8

0 . 6

3

A l l E U r e g i o n s

0 . 4

3

0 . 0 3

0 . 1 4

0 . 9

4

0 . 0

3

0 . 0 1

T o t a

l r e g

i o n s

6 1

6 1

6 1

6 7

3 7

6 1

N o t e s : F

i g u r e s

i n b o l d r e p r e s e n t c o r r e

l a t i o n s i g n

i f i c a n t a t

5 % l e

v e l a n d o f s i g n e x p e c t e d . F

i g u r e s

i n i t a l i c s r e p r e s e n t c o r r e

l a t i o n s i g n

i f i c a n t a t

5 % l e

v e l b u t n o t o

f e x p e c t e d s i g n . F

o r p u r p o s e s o f p r e s e n t a t i o n , h

i g h u n e m p l o y m e n t r e g

i o n s a r e

e x p e c t e

d t o

h a v e

h i g h e r p o v e r t y ,

h i g h e r

i n e q u a

l i t y ,

l o w e r e d u c a t i o n a l a t t a

i n m e n t , a n

d w o r s e

h e a l t h .



20/23



BelgiumVlaams Gewest flan 2,747 1,303Wallonne wall 1,551 1,468Bruxelles brux 230 435

SpainNoroeste noroe 2,535 916Noreste norest 3,184 977Comunidad de Madrid madrid 761 554Centro centro 5,677 976Este este 3,769 1,398Sur sur 4,406 1,033Canarias canar 770 391

FranceIle de France ile 2,139 938Bassin Parisien bass 2,000 1,082Nord Pas-de-Calais nord 715 374Est est 1,095 528Ouest ouest 1,543 859Sud-Ouest sudou 1,260 663Centre-Est centr 1,183 662Mediterranee med 1,354 692

GermanyBerlin berl 280Schleswig Holstein schl 132

Hamburg ham 77Niedersachsen nied 462Bremen brem 50Nordrhein-Westfalen nord 1,105Hessen hess 431Rheinland-Pfalz rhein 297Baden-Wurttemberg baden 743Bayern byern 742Mecklenburg- meck 170

VorpommernBrandenburg brand 260Sachsen-Anhalt sanh 303Thuringen thur 292

Sachsen sach 485ItalyNord Ovest novest 1,046 723Lombardia lomb 824 817Nord Est nest 1,009 930Emilia-Romagna emil 725 398Centro cent 1,248 785Lazio laz 411 502Abruzzo-Molise abru 396 395

Campania camp 705 596Sud sud 905 872Sicilia sic 556 549Sardegna sard 295 399

UKNorth East neast 405 268North West nwest 594 374Yorkshire & york 722 332

HumbersideEast Midlands emid 491 273West Midlands wmid 621 324East east 282 121London lond 694South East seast 1,274 1,029South West swest 635 350Wales wales 339 190Scotland scot 604 325Northern Ireland nirel 133 165

NetherlandsNoord-Nederland noord 516Oost-Nederland oost 804West-Nederland west 1,977Zuid-Nederland zuid 872

Austria

Ostosterreich osto 6,913 1,439Sudosterreich sudo 3,880 736Westosterreich westo 8,455 1,037

GreeceVoreia Ellada 1,526Kentriki Ellada 1,151Attiki 1,356Nisia Aigaiou, Kriti 597

Luxembourg lux 1,813 890

Ireland irel 3,292 890

Denmark den 12,829 2,920

Sweden swed 16,256

Finland fin 9,261 4,139

Portugal 4,490

Table 4 Sample sizes for NUTS level-1 regions in LIS and ECHP (households)Abbrev. LIS w4 ECHP w3 Abbrev. LIS w4 ECHP w3



21/23

Luxembourg Income Study data suggests thatto calculate a poverty headcount measure with95 percent confidence intervals of two to threepercentage points on either side, we wouldneed responses from between 1,000 and 1,500households in each region. A confidence inter-val of just one to two percentage points wouldrequire at least 2,000 household responses perregion.

In practice, household surveys included inLIS fulfil the former criterion for most NUTSlevel-1 regions in many EU member states, butdo not do so for the UK, Germany or most of Italy. The second and tighter criterion is metonly by the datasets submitted for Spain and

Austria (and those for countries which form asingle NUTS level-1 region). When we turn tothe ECHP, which has been specifically desig-ned for comparative research into living stan-dards and poverty in the EU, the situation ismuch worse. In total, only 11 NUTS level-1regions (as well as Sweden, Denmark andPortugal) have more than 1,000 householdresponses. 13 The shortfall is much greater insome countries than in others: for Greece,regional samples average 1,150 households;for Spain 890; for France 725 and for Italy

630. In the UK, only one region has a sampleof more than 400 households.

The ECHP has now been discontinued; infuture, national poverty and social inclusionindicators will be drawn from a new survey,the EU-SILC. The EU-SILC should representan improvement on the ECHP in a number of respects, but there do not appear to be plansto increase regional sample sizes. Whether thisis due to oversight, or whether it conveys animplicit message about the priority the EUcurrently assigns to regional issues, it may

well come to be seen as a rare but wastedopportunity to allow the serious monitoringof regional differences in poverty and socialexclusion.

Acknowledgements

I would like to thank STICERD for thefunding which made this research possible. I

am also very grateful to John Hills, TonyAtkinson, Tania Burchardt, Ian Gough andtwo anonymous referees for very helpful com-ments on earlier versions of this paper.

Notes

1 This paper draws on the analysis in Stewart(2002).

2 NUTS is the EU acronym for Nomenclature of Territorial Units for Statistics. NUTS level-0refers to the member state.

3 In practice there are to be many more than 10primary indicators because breakdowns arerequested by gender and various household char-acteristics. The Atkinson et al. proposals hadenvisaged a rather different ratio of primary tosecondary indicators, intending there to be a fewheadline figures backed by a much largernumber of secondary indicators, including thebreakdowns (Atkinson et al., 2002).

4 Because of missing data, coefficients of variationfor Sweden are calculated using six regionsbetween 1990 and 1996 and eight regions in1997 and 1998. For the UK, 29 regions are usedbetween 1987 and 1994 (excluding London,Wales and Scotland) and 37 regions between1995 and 1998. In both cases, the inclusion of the missing regions makes only a negligible dif-ference to the results. For France, 22 regions areused in all years.

5 Unfortunately this is not always as recent as onewould hope: data for the Netherlands and Irelandare for 1987; Spain 1990; Denmark 1992; France,Luxembourg and Germany 1994; Italy, Austria,Finland, Sweden and the UK 1995; Belgium 1996.Portugal and Greece are not yet members of LIS.

6 Incomes have been equivalized using the squareroot of the number of household members, fol-lowing Atkinson et al. (1995). All zero incomeshave been dropped as in most of the LIS data-sets it is impossible to distinguish betweengenuine zero incomes and missing values: seeAtkinson et al. (1995).

7 Confidence intervals have been estimated usingbootstrapping techniques; see Efron andTibshirani (1993).

8 However, these confidence intervals are rathermore restrictive than they need to be. We wantto be 95 percent confident that average incomein Region A is higher than that in Region B.Requiring that the whiskers do not overlap rep-resents the stricter condition of 95 percent confi-dence that Region As average income is higherthan a value x and, independently, 95 percentconfidence that Region Bs average is lower thanx. Calculating joint significance tests is unwieldy

354 Stewart




22/23

as it involves separate calculations for each pairof figures, so the more restrictive results are pre-sented here.

9 In two cases, regions have been merged so as toincrease sample size and produce more robustestimates. The Germany city-state of Hamburghas been combined with the surrounding regionof Schleswig-Holstein; and Bremen has beencombined with Lower Saxony. In both instancesthe cities are richer than the surrounding regions,but are arguably still reasonably similar. In othercases, however, neighbouring regions are too dis-tinctive for merges to be sensible. Little would begained, for example, from combining Brusselswith either Flanders or Wallonia, or NorthernIreland with Scotland.

10 In Belgium and Germany it is compulsory tocontinue at least part time in education until the

age of 18 (European Commission, 2000).11 Standardization has been carried out by theauthor using the indirect method (see Armitageand Berry, 1994: 437), with deaths and popula-tions grouped as follows: 014, 1524, 2534,3544, 4554, 5564, 6574, 7584, 85 plus.(Both deaths and population data are taken fromEurostat New Cronos Database.) Deaths arestandardized to the 1998 EU death rates,although German data are incomplete and notincluded, while 1996 data are used for Italy and1997 data for Austria and France.

12 The technique of highlighting the country withthe highest coefficient of variation in each groupis used because CVs for different indicators

cannot usefully be compared directly with oneanother, despite possessing the quality of meanindependence. It is not informative that the CVfor French SMR is 0.09 compared to 0.23 forunemployment because the magnitude of normaldispersion in each is so different (a 10 percentincrease in the SMR would be a very substantialchange, while an increase in unemployment from,say, 10 to 11 percent would be nothing morethan expected during an economic downturn).

13 These include two regions in Belgium, Spain andAustria, one each in the UK and France andthree in Greece.

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monitoring social inclusion in some european regions

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