Poverty, Welfare Problems and Social Exclusion

INTERNATIONAL

J O U R NA L O F

SOCIAL WELFARE

ISSN 1369-6866

© 2007 The Author(s)Journal compilation © 2008 Blackwell Publishing Ltd and the International Journal of Social Welfare.Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA

15

DOI: 10.1111/j.1468-2397.2007.00503.x

Int J Soc Welfare 2008:

17

: 15–25

Halleröd B, Larsson D. Poverty, welfare problems and socialexclusionInt J Soc Welfare 2008: 17: 15–25 © 2007 The Author(s),Journal compilation © 2008 Blackwell Publishing Ltd and theInternational Journal of Social Welfare.

This article investigates whether, and to what degree, povertyis linked to other types of welfare problems and, in largerperspective, whether the situation can be understood in termsof social exclusion. Two different measures of poverty –income poverty and deprivation poverty – and 17 indicatorsof welfare problems were used in the analysis. It was shownthat income poverty was rather weakly related to other typesof welfare problems, i.e. the most commonly used measure ofpoverty seems to discriminate a section of the population thatdoes not suffer from the kinds of problems we usually assumethat poverty causes. Deprivation poverty, identifying thosewho most often had to forgo consumption of goods andservices, did correlate strongly with other types of welfareproblems. Hence, people living under poor conditions dosuffer from welfare problems even though this section of thepopulation is not always captured by income povertymeasures. The final analysis showed that the types of welfareproblems that were most likely to cluster were deprivationpoverty, economic precariousness, unemployment, psychologicalstrain and health problems. Whether these types ofaccumulated welfare problems, from a theoretical perspective,can be seen as indicators of social exclusion is more doubtful.

Björn Halleröd, Daniel Larsson

Umeå University, Sweden

Blackwell Publishing LtdOxford, UKIJSWInternational Journal of Social Welfare1369-6866© 2007 The Author(s), Journal compilation © Blackwell Publishing Ltd and the International Journal of Social WelfarexxxOriginal Article

Poverty, welfare problems and social exclusionHalleröd & Larsson

Poverty, welfare problems and social exclusion

Key words: poverty, social exclusion, welfare, deprivation, socialpolicy, marginalisation

Björn Halleröd, Department of Sociology, Umeå University,SwedenE-mail: [email protected]

Accepted for publication December 8, 2006

The Amsterdam treaty places the fight against povertyand social exclusion at the centre of the EuropeanUnion’s (EU) social agenda. But what is the relationshipbetween poverty and social exclusion? Can we andshould we distinguish these concepts from each other,or is the label just a tautology? Is the fight againstpoverty one thing and the fight against social exclusionanother, or do they essentially constitute a single battle?To answer these questions, we need to conceptuallydistinguish poverty from its causes and consequencesand empirically investigate whether, and how, povertyis linked to other types of welfare problems and, in theend, whether this situation can be understood in termsof social exclusion.

In this article, we will study the consequences ofpoverty, focusing mainly on how poverty is related to arange of other welfare problems such as unemployment,health, psychological distress, victimisation etc. Thepurpose is also to analyse whether, and how, differentwelfare problems are related to not only poverty butalso to each other. In a wider perspective, the analysisis linked to the discussion about social exclusion, aphenomenon often understood as accumulated welfareproblems, i.e. a situation in which a single individual is

suffering from several different welfare problems at thesame time (cf. Gallie, Paugam & Jacobs, 2003; Hills,Le Grand & Pichud, 2002).

The article is organised in the following way: thefollowing section deals with the theoretical definition ofpoverty and how to distinguish poverty from other typesof welfare problems and social exclusion. Thereafter,measurements of poverty and welfare problems arediscussed. The empirical analyses are presented in thepenultimate section, followed by the discussion andconclusions.

Poverty and social exclusion

Why is it important to identify the poor and to takeaction against poverty? The commonsensical answer isthat poor people suffer from malnutrition, lack ofshelter, ill health, exclusion from an ordinary lifestylein society etc., and that such a situation is unacceptable.In a way, one could say that this broad picture ofpoverty is correct because if the poor do not suffer froma wide range of problems, why should we bother aboutpoverty? The problem from an analytic perspective isthat inclusion of almost every unwanted condition in

Halleröd & Larsson

© 2007 The Author(s)

16

Journal compilation © 2008 Blackwell Publishing Ltd and the International Journal of Social Welfare

the theoretical definition of poverty makes it impossibleto analyse the causes and consequences of poverty. Italso becomes more or less unfeasible to distinguishpoverty from other concepts such as social exclusion.We therefore need a theoretical definition of povertythat focuses on the inability to make ends meet. Thatis,

the poor are those who, due to insufficient access toeconomic resources, have an unacceptably low levelof consumption of goods and services

. The importantconsequence of this kind of definition is that, forexample, malnutrition

per se

is not poverty; it is causedby poverty only if it is lack of economic resources thatmakes it impossible for a person to acquire food. Thefact that malnutrition is most often a poverty problemdoes not mean that it is always a poverty problem.Consider, for example, anorexia, a condition in whichmalnutrition is not caused by poverty. Similarly, badhealth is, in many cases, unrelated to poverty. It is onlya consequence of poverty if it is caused by an inabilityto buy adequate food, provide for shelter or pay forhealthcare. The relationship between poverty and othertypes of welfare problems is further complicated by thefact that welfare problems often cause poverty. Thereis plenty of evidence to show that unemployment isproblematic even if the unemployed person is protectedfrom poverty (cf. Nordenmark, 1999; Strandh, 2000).But it is, of course, also reasonable to argue thatunemployment often causes poverty. It is also easyto see that health problems can cause poverty bypreventing people from earning a living. Disentanglingcauses and effects at the individual level is, to put itmildly, empirically complicated, and the process isprobably best understood as either a ‘vicious circle’leading to an accumulation of welfare problems intoa situation of social exclusion, or a ‘good circle’ out ofsocial exclusion. An important step in facilitating ourunderstanding of these ‘circles’ is to discover whether,and how, poverty and other welfare problems areinterlinked with each other at one point in time. Thismeans that we must first distinguish different types ofwelfare problems and then empirically check whetherthey form a cluster of accumulated welfare problems.In this perspective, poverty should be seen as just onewelfare problem conceptualised as lack of economicresources.

There are a number of studies showing that welfareproblems do cluster (Bask, 2005; Erikson & Tåhlin,1987; Fløtten, 2005; Halleröd, 1991; Halleröd &Heikkilä, 1999; Tham 1994). Recently, Bradshaw andFinch (2003) examined the degree to which threedifferent measures of poverty were related to differentaspects of social exclusion in Britain. In line withearlier findings (cf. Berthoud, Bryan & Bardasi, 2004;Halleröd 1991, 1995, 2000; Kangas & Ritakallio, 1998),they found that the overlap between different povertymeasures was fairly limited, i.e. different measures

tended to identify different individuals as poor and thatdifferent measures of poverty were distributeddifferently in the population. The unique contributionof Bradshaw and Finch is that they also demonstratedthat different measures of poverty relate differently todifferent indicators of welfare problems. Thecontribution of this article is that it takes previousanalyses of the relationship between welfare problemsa step further and studies in detail whether welfareproblems are related and, if so, what kind of welfareproblems cluster and how such a cluster is related topoverty. Lastly, we will approach the issue of whetheran empirically observed cluster of welfare problems canbe reasonably understood in terms of social exclusion.

Three hypothetical outcomes regarding the relationshipbetween poverty and other types of welfare problemscan be put forward:

1. Poverty is not related to other welfare problems, aresult implying that poverty in today’s Sweden,because it apparently does not have any welfareconsequences, is more or less a quasi-problem.

2. We find a cluster of related welfare problems, but thecluster is unrelated to poverty. The conclusion in thiscase would be that accumulation of welfare problemsis an empirical fact clearly distinguished from povertyand that poverty is still to be viewed as a quasi-problem.

3. Poverty is related to a range of welfare problems.Poverty, thus, can be viewed as a serious problemand keeping people out of poverty would appear tobe an important goal for social policy. This outcomealso makes it reasonable to view the fight againstpoverty and the fight against social exclusion as asingle battle.

Data and measurements

The data are from Statistics Sweden’s annual Survey ofLiving Conditions from 1998 (ULF98) and are basedon face-to-face interviews with a random sample of theSwedish population aged 16–84 years. The randomsample contains 7,400 individuals, and the totalworking sample amounts to 5,732, which givesa response rate of 77.5 per cent. The working sampleis limited to respondents aged 20–74 years, which givesa working sample consisting of 4,941 cases. ULF98contains a large number of welfare indicators and aunique set of questions regarding consumption of goodsand services that facilitates a so-called consensualmeasurement of poverty introduced by Mack andLansley back in the early 1980s.

Poverty measures: operationalisation

We will employ two measurements of poverty: incomepoverty and deprivation poverty. Income poverty is


© 2007 The Author(s)Journal compilation © 2008 Blackwell Publishing Ltd and the International Journal of Social Welfare

17

measured in accordance with the conventional EUmeasurement of relative poverty, i.e. those who live ina household with an equivalent disposable income thatis below 60 per cent of the median household incomeare defined as poor. Income data are gathered from theincome register and represent the households’ disposableafter-tax income from all registered sources. Thedisposable income is adjusted to household size usingan equivalence scale developed by the SwedishNational Board of Health and Welfare (StatisticsSweden [SCB], 2003).

1

Deprivation poverty is measured using a weighteddeprivation index (WDI) (Halleröd, Gordon, Larsson &Ritakallio, 2006). The index measures inability to

consume goods and services in accordance with thegeneral lifestyle in the society, and is a developedversion of the consensual measurement of povertyintroduced by Mack and Lansley (1985). Informationfor the measurement of poverty comes from a list of36 consumption items, see Table 1, and for each itemrespondents have been asked whether they have theitem (or engage in the activity). If the answer is ‘No’,respondents are asked whether this is because they can-not afford the item/activity or whether they are simplynot interested in having/engaging in it. Respondentsreceive a score on the deprivation index for every itemthey cannot afford. In order to strengthen the connectionbetween the deprivation index and the ordinarylifestyle, the index score is weighted in relation to thefraction of the population that has the item in question.For example, 84 per cent of adults in Sweden state thatthey have a yearly dental examination. Therefore, notbeing able to afford such an examination results in a

1

The equivalence scale gives a weighting of 1.16 to the firstadult, 0.76 to the second adult and 0.96 to any additionaladults (18 years or older). Children between 11 and 17 yearsare given weightings of 0.76, 4–10 years, 0.66, and childrenbelow 4 years are given a weighting of 0.56.

Table 1. Proportion of the population that have, do not want and cannot afford consumption items.

Consumption item Have Do not want

Would like to have but cannot afford

Not relevant

Dishwasher 53.5 35.4 8.5 2.2Washing machine 74.8 15.9 7.1 1.9Freezer 98.2 0.6 0.8 0.1Microwave oven 77.4 17.3 4.3 0.9Vacuum cleaner 99.3 0.2 0.3 0.1Telephone 98.4 0.6 0.7 0.2Mobile telephone 65.8 26.8 5.9 1.2TV 98.6 1.0 0.3 0.1VCR 83.6 11.7 3.9 0.6Stereo equipment 89.2 7.9 2.2 0.5Computer (PC or Mac) 58.0 25.9 13.0 2.7Daily newspaper 76.6 14.2 8.4 0.7First-hand contract or self-owned accommodation 97.0 0.7 1.5 0.7Modern dwelling (bath/shower, WC, central heating, oven and refrigerator) 98.6 0.5 0.6 0.2Balcony or garden 92.6 2.0 2.8 2.4Not more than two persons per bedroom 96.0 1.0 1.9 1.0Comprehensive home insurance 96.8 0.6 1.3 0.6Car 84.5 7.1 7.3 1.0Driving licence 86.0 5.5 5.9 2.4Public transport for one’s needs 73.6 17.1 1.0 8.1Clothes that to some degree correspond with fashion 87.6 7.6 3.8 0.4A ‘best outfit’ for special occasions 91.9 3.8 3.8 0.2Buying new clothes, not second-hand 92.2 2.2 5.1 0.2A haircut every third month 69.3 21.4 6.7 2.4A hot meal each day 97.3 1.8 0.6 0.2A special meal once a week 72.9 18.5 7.5 0.7Celebrations on special occasions 87.0 7.5 4.4 0.8Presents for friends and family at least once a year 97.2 1.6 1.0 0.1Friends/family for a meal once a month 40.5 41.9 11.9 4.6One-week holiday away from home once a year (not with friends or relatives) 63.5 13.7 19.7 2.6Access to a summer cottage one week once a year 43.2 36.3 13.0 7.1A night out once a fortnight 27.5 51.3 17.8 3.0Go to the cinema, theatre or a concert once a month 22.7 54.6 17.6 4.5Dental examination once a year 84.1 7.1 6.9 1.5Medical treatment and medicine If necessary 96.8 1.0 1.6 0.4Private pension insurance 45.8 21.6 19.0 10.6Save at least 500 Swedish Crowns every month 53.7 9.6 34.6 1.5

Halleröd & Larsson


18


score of 0.841 on the deprivation index. However, theseweights are only average. It is likely that consumptionpatterns are different in different sections of the population.Calculation of the index takes into account two suchdifferences (for a more detailed discussion see Halleröd1995; Halleröd et al., 2006). First, it is acknowledgedthat consumption patterns differ across age groups.Second, it is also recognised that families with childrenhave needs that families without children do not have.Construction of the deprivation index implies that:

a) People who cannot afford consumption items/activitiesthat most people have/engage in are suffering frommore deprivation than are people who cannot affordthings/activities that very few people have/engage in.

b) An elderly person is not suffering from deprivationif he/she cannot afford things/activities that onlyyoung people have/engage in, and vice versa.

c) A person who lives in a household without childrendoes not score on the index if he/she lacks thingsthat, practically, only households with children have.

Formally, the WDI is calculated in the following way:

I =

Σγ

i

(P =

α

+

β

i

χ

1

+

β

i

χ

2

)

where

γ

i

tells us whether individual i wants but cannotafford item

γ

, P is the weight for item

γ

, estimated asthe probability for individual i to have

γ

given

χ

1

(his/her age), and

χ

2

(whether or not she/he is living in ahousehold with children). As is the case for mostdeprivation of this type, the reliability is very high, with aCronbach alpha score of 0.86. As can be seen in Table 1,10.4 per cent of the population is counted as incomepoor. We use this figure to define a likewise arbitrarypoverty line for the deprivation index. Counting thosewith a deprivation score above 4.36 as poor will resultin a corresponding poverty figure of 10.4. In the bestof all worlds, the same 10.4 per cent is defined as poorby both measures. However, we do not live in such aworld, and only 2 per cent are actually income poor anddeprivation poor at the same time. Thus, there are goodreasons for using both measures in the following analysis.

Welfare problems: indicators of social exclusion

To analyse how different welfare problems relate toeach other, in addition to the poverty measures, 17indicators of different welfare problems will be used.These indicators range from aspects that tap into areasoften discussed in relation to social exclusion, such asneighbourhood characteristics, unemployment and lackof political involvement, but more individual characteristicssuch as health conditions, loneliness and psychologicaldistress are also included.

Lack of political activity is measured in the followingway. Respondents are asked whether they have ‘at anytime tried to do something about any deficiency or

inaccuracy in your municipality’, and whether they inthat case had: contacted any civil servant or otherrepresentatives, written a letter to the press or an articlein a newspaper, signed any appeal, participated in anydemonstration or done anything else to express theirview? In addition to this, a more general question wasalso posed: ‘Considering political issues overall, notonly here in the municipality, have you at any time:written a letter to the press or an article in a newspaper,signed any appeal or participated in any demonstration’.Those who answered ‘No’ to all eight questions aredefined as politically inactive. This indicator issupplemented with a measure that identifies those whodid not vote in the latest general election.

We do not have access to information about housingenvironment as such, i.e. we lack information related tospecific areas. However, we do have information fromthe respondents that, at least to some degree, can beseen as indicators of area features at the same time asthey also indicate individual welfare problems. Toindicate what we will call ‘socially disorganised areas’,a concept originally used by Shaw and McKay (1969),we use a set of variables that indicate whether therespondent is living in a suburban area in a larger townor city in Sweden. The indicator shows that the respondentis living in a town with at least 90,000 inhabitants, thatthe accommodation is rented and located in a block offlats with at least three floors and that the respondentstates that damage to housing facilities is common.If all four of these criteria are met, the housing area isdefined as socially disorganised (Nilsson & Estrada,2004). An additional housing variable measures whetherthe respondent thinks that his/her dwelling is too small,adequate or too big. The answer ‘too small’ is used asan, admittedly subjective, indicator of crowded housing.

We also measure victimisation and concerns aboutcrime. One variable measures whether the respondenthas been exposed to any type of theft or propertydamage during the previous 12 months. A second variablemeasures whether the respondent, during the previous12 months, has been exposed to violence or threats ofviolence. A third variable captures whether the respondent,again during the past 12 months, has refrained fromgoing out during the evening due to fears of beingassaulted in any way or whether the respondent isworried about theft and damage to his/her own dwelling.

Health, psychological strain and, to some degree,health behaviour are measured using six differentindicators. Health problems are measured via a questionthat asks whether the respondent suffers from any long-standing illness or handicap that negatively impacts onhis/her ability to work or perform daily activities. Addi-tional indicators of health condition and psychologicaldistress are based on questions about occurrence, overthe previous two weeks, of sleeping problems, recurrentheadaches or migraine and anxiety, worry or anguish.



19

Lastly, we also include two important health predictors:smoking and obesity. Smokers are those who smokeevery day, and people are defined as obese if their bodymass index exceeds 30.

Loneliness is measured using a subjective indicator.Respondents are confronted with five statements andare asked to pick the one that best describes theirsituation. The statements are:

1) I basically never feel lonely.2) Sure, sometimes I feel lonely, but I don’t consider it

to be a problem.3) Sometimes I feel lonely and I wish that I could see

other people more often.4) I very often feel lonely.5) I almost always feel lonely.

People are considered to be lonely if they agree withstatement 3, 4 or 5.

Human capital is indicated by education, and avariable is created that discriminates those who haveprimary compulsory education as their highest level ofeducation. Economic vulnerability is a condition that,in one sense, is almost synonymous with poverty. Wewill argue, however, that lack of a cash margin in thehousehold economy, measured as the inability to raise14,000 SEK (Swedish Crowns) within a week, is anadditional indicator of economic vulnerability that may ormay not coincide with poverty. We will, lastly, measureunemployment. The measure we have chosen capturesthose who, during the previous five years, have beenunemployed for a total period of at least 12 months.

The distribution of welfare problem indicators is shownin Table 2. The most common problems are political

inactivity and long-standing illness. Seven of theindicators are experienced by less than 10 per cent ofthe population, whereas nine of the welfare problemsare experienced by between 10 and 40 per cent of thepopulation.

Results

Table 3 shows the proportion among income poor,deprivation poor and non-poor that experience any ofthe 17 additional welfare problems. Eleven of the17 welfare problems are significantly more commonamong the income poor than among the non-poor, whileall 17 welfare problems are significantly more commonamong the deprivation poor than among the non-poor.In 14 cases, there is also a significant difference betweenthe income poor and the deprivation poor. In all thesecases, incidences of welfare problems are higher amongthe deprivation poor. Thus, poverty seems to be closelyrelated to other types of welfare problems, somethingthat is emphasised when we move from the commonlyused income measure to a deprivation-based measure ofpoverty.

Estimations of the bivariate association betweendifferent welfare problems are displayed in Table 4. Tomore clearly see the pattern of associations between thevariables, in Figure 1 we have used the coefficients fromTable 4 as input values to a multi-dimensional scalinganalysis, estimating Euclidian distances in order toconstruct two-dimensional visualisations of the results.The fit of this two-dimensional model is not particularlygood; the so-called stress value is 0.17, indicating thatthe data have a structure that is too complex to be

Table 2. Basic statistics on poverty and social exclusion indicators.

N Per cent

Income poor 510 10.4Deprivation poor 490 10.4Income poor and deprivation poor 93 2.0Not poor 3,837 81.3Did not vote 490 10.6Politically inactive 2,046 41.8Victimisation: crime 1,475 30.1Victimisation: violence 333 6.9Worried about crime 451 9.3Disorganised area 297 6.2Crowded housing 725 15.0Health problem 1,978 40.4Headache 742 15.3Anxiety 872 18.0Sleep 961 19.8Obesity 406 8.5Smoking 1,026 21.1Loneliness 480 9.9Low education 1,057 21.6Lack of cash margin 755 15.6Unemployed 466 9.7

Table 3. Prevalence of welfare problems among income poor, deprivation poor and non-poor (per cent).

Income poor

Deprivation poor

Not poor

Did not vote 18.5*** 20.6*** 8.5Politically inactive 49.3*** 48.2** 40.5Victimisation: crime 39.3*** 38.6*** 28.4Victimisation: violence 8.9* 15.1*** 5.6Worried by crime 7.4 19.0*** 8.2Disorganised area 9.0*** 17.1*** 4.6Crowded housing 18.9** 26.6*** 13.5Health problem 34.4* 49.4*** 39.1Headache 14.7 28.0*** 14.1Anxiety 22.6*** 40.1*** 14.7Sleeplessness 18.3 36.7*** 17.8Obesity 7.1 14.2*** 8.1Smoking 23.5 35.9*** 19.1Loneliness 12.3*** 27.9*** 7.6Low education 20.2 26.0** 20.8Lack of cash margin 28.3*** 60.5*** 9.1Unemployed 18.4*** 28.9*** 7.0

* Indicates significance in relation to the non-poor: * p < 0.05; ** p < 0.001; *** p < 0.0001. Bold figures indicate significant difference (p < 0.001) between income poor and deprivation poor.

Halleröd & Larsson


20


represented by two dimensions. The map neverthelessgives an informative first overview of how welfareproblems are related to each other. Looking first at thepoverty measures, we see that income poverty is placedfar out to the left of the map. Income poverty is, in fact,one of the variables used here that has the weakest totalrelationship to other kinds of welfare problems, and itis also one of the variables that are significantly relatedto the fewest of the other variables (see Table 4). In thisanalysis, economic poverty shares this peripheralposition with lack of political involvement, crowdedhousing, exposure to property crime and obesity. Forthese four indicators, we can find convincing argumentsas to why they are only weakly related to other typesof welfare problems. The way we measure lack ofpolitical involvement discriminates everyone who hasnot made an active effort to act in a political matter.Thus, people might score on this indicator because theyare content with political decisions, not because theylack a political voice. Crowded housing is moreaffected by household composition than by socialexclusion; it is mainly households with children thatreport lack of housing space. Exposure to propertycrime is a function not only of the vulnerability relatedto social exclusion, but also of the extent to whichpeople have properties that are worth stealing (Larsson,2006). Obesity was included as an indicator of healthhazards and is also an indicator that relates strongly tolong-standing health problems, but, as the resultsindicate, obesity is only loosely associated with otherwelfare problems. Therefore, we have few reasons to be

concerned about the weak correlation between thesefour indicators and other welfare problems. It is, ofcourse, more disturbing that the most commonly usedpoverty measure discriminates a section of thepopulation that is only marginally connected to otherwelfare problems.

Poverty measured in terms of deprivation is placedalmost exactly in the middle of the map. Furtherinvestigation of Table 4 shows that deprivation povertyis the only variable to be positively and significantlycorrelated with all the other variables, and the correla-tions are generally high. Around deprivation poverty wesee a cluster of welfare problems that are stronglyinterlinked and also closely connected to deprivationpoverty. There is a cluster of welfare problems that tellsus that deprivation poverty often coincides with long-standing health problems, anxiety and sleeping pro-blems and, also, loneliness. Deprivation poverty, to amuch higher degree than economic poverty, is alsoassociated with lack of an economic buffer. Living in asocially disorganised area is also connected to depriva-tion poverty, as are concerns about being subjected tocrime. The connection between unemployment anddeprivation is also relatively strong.

The analysis presented in Table 4 and Figure 1 notonly tells us that deprivation poverty seems to be moreclosely connected to other types of welfare problemsthan is income poverty, but also gives us a picture ofthe kinds of problems that most likely make up the corecauses and consequences of poverty, i.e. inability toconsume in accordance with the ordinary lifestyle,

Figure 1. Relationship between income poverty and welfare problems. Multi-dimensional scaling based on Kendall tau_b estimates.



21

unemployment, living in disorganised areas, sufferingfrom social and psychological strain and also, to somedegree, health problems.

Latent class analysis

The analysis above clearly shows that some types ofwelfare problems are more closely interlinked thanothers. It also reveals that the data are too complex tobe properly represented by two dimensions. To find outwhether there are distinct clusters of welfare problems,a series of latent class analyses was conducted. Theanalysis identifies clusters that group together personswho share common characteristics. The model classifiescases into clusters based on membership probabilities.It offers a wide range of indicators of model fit and alsoa decomposed measure, making it possible to evaluatethe contribution of single indicators. In the first setof analyses, models were specified that included all19 welfare indicators (including the poverty measures).The number of clusters was allowed to vary from 1 to10 in order to determine a stage at which an additionalincrease in the number of clusters did not lead to asubstantial improvement of the model fit. Theimprovement of the model is indicated by the chi-square-based likelihood value (L

2

). The L

2

value forone cluster model tells us how much total residualvariance there is left to explain in the model, variancethat theoretically can be explained by additionalclusters. These analyses showed that when the one-cluster model was relaxed to a three-cluster model, theL

2

value decreased by 20 per cent, from 12,431 for theone-cluster model to 9,887 for the three-cluster model.Thereafter, the L

2

value decreased only marginally foreach extra cluster added to the model. However, theoverall performance of the three-cluster model wasquite poor, as indicated by the very modest decrease inL

2

value. This was due to the fact that several of themanifest welfare problem variables were only weaklyassociated with the three latent clusters at the same timeas these welfare problems were only weakly related toeach other, i.e. they did not form separate clusters whenthe model was relaxed to allow more than three clusters.To find a simpler model containing only variables thatwere more closely related to each other, we reduced thenumber of variables in the model. Looking at theresiduals for each variable and the Entrypo-R

2

indicating to what degree the latent cluster variablepredicted the manifest variable outcome, we started todelete from the model the manifest variables that weremost weakly associated with the three clusters,beginning with the one with the lowest R

2

value (whichhappened to be income poverty). After the removal ofa variable, we checked the model fit, as indicated by theBIC variable (the L

2

value adjusted for model complexity),calculated as BIC

L

2

= L

2

– In(

N

)DF, making sure that

Tabl

e 4.

Biv

aria

te a

ssoc

iatio

ns b

etw

een

wel

fare

pro

blem

indi

cato

rs: K

enda

ll ta

u_b.

Inco

me

Poor

Depr

ivat

ion

poor

Did

not

vote

Polit

ical

in

activ

e Vi

ctim

isat

ion:

cr

ime

Vict

imis

atio

n:

viol

ence

Wor

ried

by

crim

eDi

sorg

anis

ed

area

Crow

ded

hous

ing

Heal

th

prob

lem

Head

ache

Anxi

ety

Slee

ples

snes

sOb

esity

Smok

ing

Lone

lines

sLo

w

educ

atio

nLa

ck o

f ca

sh m

argi

n

Depr

ivat

ion

poor

0.10

**Di

d no

t vo

te0.

10**

0.11

**Po

litic

ally

inac

tive

0.06

**0.

05**

0.11

**Vi

ctim

isat

ion:

crim

e0.

07**

0.06

**0.

01

−

0.08

**Vi

ctim

isat

ion:

vio

lenc

e0.

03*

0.11

**0.

06**

−

0.07

**0.

11**

Wor

ried

by c

rime

−

0.02

0.12

**0.

04**

0.06

**0.

06**

0.04

**Di

sorg

anis

ed a

rea

0.04

**0.

16**

0.04

**0.

000.

13**

0.07

**0.

11**

Crow

ded

hous

ing

0.04

**0.

12**

0.05

**

−

0.03

*0.

08**

0.05

**0.

000.

09**

Heal

th p

robl

em

−

0.05

**0.

07**

−

0.00

−

0.02

−

0.02

0.01

0.12

**0.

05**

−

0.01

Head

ache

−

0.01

0.13

**0.

06**

−

0.04

**0.

04**

0.06

**0.

06**

0.07

**0.

05**

0.15

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xiet

y0.

04**

0.20

**0.

09**

0.01

0.05

**0.

11**

0.16

**0.

09**

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**0.

17**

0.17

**Sl

eepl

essn

ess

−

0.01

0.15

**0.

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0.01

0.02

0.09

**0.

13**

0.10

**0.

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22


the removal actually improved the model. In the end,we ended up with eight manifest measures of welfareproblems that were all relatively strongly associatedwith the three clusters: long-standing health problems,headaches, anxiety, sleeping problems, loneliness, lackof cash margin, unemployment and deprivation poverty.

For these eight variables, a series of analyses wasconducted in which the number of clusters was allowedto vary between 1 and 5. Allowing for only two clustersresulted in a considerable improvement of the model.The L

2

value decreased from 2,445 to 704. Allowingfor an additional third cluster decreased the L

2

value to369. A fourth cluster only marginally improved themodel, at the same time as making a substantialinterpretation of the clusters increasingly difficult. Thefirst row of Table 5 shows the size of the three clustersestimated from the modal values. Here we can see that71 per cent of the population ended up in the non-problematic group, i.e. they have low probabilities ofscoring on any of the eight welfare problems. Almost20 per cent belong to cluster 2. The risk of belongingto this cluster is basically determined by the health-related indicators, while unemployment, lack of cashmargin and deprivation poverty played a minor role.The third cluster was the smallest, and around 10 percent of the population was estimated to belong to thiscluster. The difference between cluster 2 and cluster 3is that cluster 3 combines high probability of beingunemployed and having economic problems with highprobabilities of having health impairments, anxiety,sleeping problems and headaches. Hence, cluster 3groups together people with a high probability ofscoring on all eight welfare problems.

In a final analysis, we used a multinominal logisticregression model to estimate the risk for people indifferent sections of the population to end up in cluster2 or cluster 3. The independent variables are: equivalentdisposable income, age (both these variables aresupplemented with a quadratic term) and socio-economic class as indicated by Statistic Sweden’s

socioeconomic code, which in turn closely resemblesthe well-known EGP schema (Erikson & Goldthorpe,1993). Seven different classes were distinguished (seeTable 6). People currently not employed or self-employedwere classified according to their previous labour marketposition or, as the second option, according to theirspouse’s labour market position. If neither of theseoptions were available, the categorisation ‘unclassified’was used. Also included are household type and gender.The purpose of this analysis was not to make athorough search for the most important determinates ofaccumulated welfare problems or to test a particulartheoretical model. It was simply a test of the degree towhich a number of important stratification variables arerelated to the cluster we obtained and therefore alsohelps us to interpret the clusters in a meaningful way.

Looking first at cluster 2, there is no relationshipbetween income and cluster probability. The probabilityincreases with age, but is only weakly related to socialclass. There is a slight risk increase among those wholive in households without children and among singleparents compared with couples with children. Lastly, itis revealed that the odds ratio is significantly lower formen. The analysis confirms that cluster 2 is basically ahealth cluster that is more or less unrelated to economiccircumstances. Cluster 3 is different. Here we see aclose connection to income: the higher the income, thelower the cluster probability. The significant estimatefor the quadratic term also tells us that the risk ofbelonging to cluster 3 decreases with increasing speedas income increases. This is, of course, not a surprise,as deprivation poverty and lack of cash margin arestrongly related to the cluster. However, the result isinteresting when seen against the above resultsregarding income poverty, as it basically indicates thataccumulation of welfare problems is a low income

Table 5. Latent class analysis: cluster size, classification probabilities and model fit.

Cluster 1 Cluster 2 Cluster 3

Health problem 0.30 0.68 0.51Headache 0.07 0.37 0.30Anxiety 0.05 0.49 0.42Sleeplessness 0.06 0.57 0.36Loneliness 0.04 0.18 0.29Lack of cash margin 0.06 0.15 0.72Unemployment 0.06 0.07 0.35Deprivation poverty 0.02 0.05 0.75

Cluster size (per cent of population) 71 19 10Df 229L2 369BIC(L2) −1566

Table 6. Multinominal logistic regression analysis. Odds-ratio estimates for cluster belongingness.

Cluster 2Odds ratio

Cluster 3Odds ratio

Equivalent disposable household income 0.996 0.717***Quadratic term 1.000 0.981***Age 1.016*** 0.984**Quadratic term 1.000 0.998***

Class: Higher white collar as ref groupUnskilled blue collar 1.521** 5.258***Skilled blue collar 1.239 4.004***Lower white collar 1.141 3.155**Middle white collar 0.874 1.814Self-employed 1.055 0.805Others 1.393 4.824***

Household: Cohabiting with child(ren) as ref group Cohabiting without children 1.264* 1.354Single adult without children 1.567*** 3.639***Single adult with children 1.673* 5.158***

Men (women ref group) 0.493*** 0.639***



23

problem, but not so much an income poverty problem.Relating this outcome to earlier findings about therelationship between income distribution anddeprivation, the most likely explanation is that theprevalence of income measurement errors is largest atthe bottom end of the income distribution. Therefore,we find among the income poor a rather large fractionnot suffering from deprivation with regard toconsumption of goods and services and, likewise, notparticularly exposed to other types of welfare problems(Halleröd, 1997; Halleröd et al., 2006). To furtherunderpin this conclusion, in Figure 2 the probabilityscore for cluster 3 is plotted against disposable income.The estimates are derived from a logistic regressionmodel that includes disposable income and thequadratic term of disposable income. Here we can seethat the probability is highest among people living in alow-income household and that the probability peaksjust below the poverty line. However, among thoseliving in households with extremely low incomes, theprobability risk decreases. Because it is unlikely that theextremely poor are better off than people living inhouseholds closer to the poverty line, it is difficult toimagine that the result is caused by anything other thanmeasurement problems. A recent comparative analysisindicates that this problem is not exclusive to Sweden,as the same phenomenon has been seen in two otherstudied countries: Finland and Great Britain (Hallerödet al., 2006).

The risk of belonging to cluster 3 is highest amongyoung people and decreases with age with increasingspeed, which is shown by the negative coefficient forthe quadratic age term. There are large differencesbetween different classes, and the odds ratio forunskilled workers is five times greater than that forhigher white-collar workers. The same is true of peopleliving in single adult households with children. Also,

single adults without children have a substantiallyhigher risk of falling into cluster 3. The risk forinclusion in cluster 3 is also lower for men than forwomen. Thus, the analysis supports the interpretationthat clusters 2 and 3 are essentially different.

Conclusions and discussion

The questions addressed here concern the relationshipbetween poverty and a wide range of welfare problemsand, in a wider perspective, the relationship betweenpoverty and social exclusion. The analyses wereconducted in several steps. Our data allowed us to useboth an income-based measure of poverty definingthose with an income below 60 per cent of the medianincome as poor, and a deprivation-based measuredefining those who most often have to forgoconsumption of goods and services as poor. In additionto these two poverty measures, we also distinguished 17indicators of welfare problems that covered integrationin the political process, neighbourhood conditions,housing conditions, health impairments, anxiety andpsychological distress, health hazards, socialintegration, educational marginalisation, unemploymentand economic vulnerability. In the first step of theanalysis, the interrelationships between poverty and all17 welfare problems were estimated. The analysisshowed that poverty measured as deprivation wassignificantly related to each of the other 17 welfareproblems. However, the income poverty measure wasrelated to only 11 of the welfare problems, and theserelationships were generally comparably weak. In fact,the analysis revealed that income poverty was one ofthe most peripheral of all welfare problems. Thus,knowing that someone has an income below 60 per centof the median income does not tell us with any certaintythat she/he actually suffers from welfare problems, and

Figure 2. Estimated probability of belonging to cluster 3 by household equivalent income.

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we can certainly not claim that fighting income poverty,as we are able to identify it, will also contributesignificantly to the fight against other forms of welfareproblems. These results are in line with much of theprevious research in the area (Berthoud et al., 2004;Bradshaw & Finch, 2003; Callan, Nolan & Whelan,1993; Halleröd, 1995; van den Bosch, 2001). However,we cannot draw the conclusion that poverty in today’sSweden is a trivial problem. Those who were identifiedas deprivation poor, i.e. those most unable to consumegoods and services in accordance with the generallifestyle in contemporary Sweden, suffered greatly froman accumulation of welfare problems.

What our analysis shows is that the traditional wayof measuring poverty as low income is problematicbecause of a series of unsolved measurement problemsthat are most likely to affect the lower tail of the incomedistribution. To estimate poverty correctly based onincome data, we of course need correct data on people’saccess to economic resources from the start. We can berelatively certain that income data are problematic fromthis point of view. Income from the black economy,income in kind, savings, non-monetary resources etc.,cloud the connection between income data andconsumption (Behrendt, 2002; Halleröd, 2000). Otherproblems concern the difficulties in identifying thecorrect household unit and the fact that equivalencescales adjust for household size in a rather crude way.Time is another important factor. A short poverty spellmight be mitigated by the use of savings, and theacquisition of clothes, furniture and other kinds ofseldom-consumed items can be postponed (Berthoudet al., 2004; Breen & Moisio, 2004; Gordon, 2005;Layte & Whelan, 2003). The assumption of equalsharing within the household has been increasinglyquestioned during recent decades (Nyman, 2002; Pahl,1989; Vogler & Pahl, 1994).

In the next step of the analysis, latent class analysiswas used to distinguish a set of closely related welfareproblems. In the end, a three-cluster model was derived,based on eight welfare problems. These were long-standing health problems, recurring headaches, anxiety,sleeping problems, loneliness, unemployment, lack ofcash margin and poverty measured as deprivation. Acluster that comprises about 10 per cent of thepopulation scored high on all eight of these variables.Thus, when people in today’s Sweden suffer from arange of welfare problems, these are the types ofproblems most likely to affect them. The resultspresented here are based on cross-sectional data anddo not prove any causality between welfare problems.Thus, we cannot know whether poverty causes healthproblems or whether health problems cause poverty. Arealistic guess is that causality goes in both directions.In our opinion, further investigation of the causalitybetween welfare problems is of great importance to social

policy discussions. However, the analysis does give anindication of what it is like to be poor in Sweden today.

The results presented here indicate that fightingpoverty and social exclusion largely constitutes thesame battle if we accept two basic assumptions. Thefirst is that we need to measure poverty in a moreaccurate way than is usually the case, i.e. we cannot relyon available income data only. Second, the approach tosocial exclusion that we have applied is largely datadriven. Based on our analysis, we can argue that thesocially excluded in today’s Sweden are poor andunemployed and that they also experience healthproblems, loneliness and psychological distress. Thesocially excluded, thus, are excluded from the labourmarket and from ordinary consumption of goods andservices, and in addition they suffer from moreindividual problems such as ill health and psychologicaldistress. However, aspects that are often looked upon ascentral in the debate on social exclusion – such asexclusion from the political arena and education system,and spatial segregation – seem to be only weakly relatedto each other and to the core problems identified in thisstudy, making the relation between poverty and socialexclusion less straightforward than is usually assumed.Our hope is that the findings presented here can serveas fuel not only for continued empirical analysis ofsocial exclusion, but also for the theoretical debate onthe concept of social exclusion.

Regarding further empirical studies, we see twoimportant issues. First, the kind of analysis conductedhere needs to be extended to include other countries.The EU-SILC (Survey of Income and Living Con-ditions) database will hopefully provide the informationnecessary for such an analysis. Second, we need tounderstand the social mechanisms that link poverty anddifferent kinds of welfare, i.e. we need to understandthe process of social exclusion and, of course, also theprocesses that lead to social inclusion. This is, on alllevels, a challenging endeavour for social science.

References

Bask M (2005). Welfare problems and social exclusion amongimmigrants in Sweden.

European Sociological Review

21:73–89.

Behrendt C (2002).

At the Margins of the Welfare State

.Aldershot, Ashgate.

Berthoud R, Bryan M, Bardasi E (2004).

The dynamics ofdeprivation: the relationship between income and materialdeprivation over time

. Report No. 219, Department for Workand Pensions, London.

Bradshaw J, Finch N (2003). Overlaps in Dimensions of Poverty.

Journal of Social Policy

32: 513–525.Breen R, Moisio P (2004). Poverty Dynamics Corrected for

Measurement Errors.

Journal of Economic Inequality

2: 171–191.

Callan T, Nolan B, Whelan CT (1993). Resources, Deprivationand the Measurement of Poverty.

Journal of Social Policy

22: 141–172.



25

Erikson R, Goldthorpe JH (1993).

The Constant Flux; A Studyof Class Mobility in Industrial Societies

. Oxford, ClarendonPress.

Erikson R, Tåhlin M (1987). Coexistence of Welfare Problems.In: Erikson R, Åberg R, eds.

Welfare in Transition

. Oxford,Clarendon Press.

Fløtten T (2005).

Poverty and Social Exclusion – Two Sides ofthe Same Coin? A Comparative Study of Norway andEstonia

. Oslo, Fafo.Gallie D, Paugam S, Jacobs S (2003). Unemployment, poverty

and social exclusion: Is there a vicious circle of socialexclusion?

European Societies

5: 1–32.Gordon D (2005). The Concept and Measurement of Poverty.

In: Pantazis C, Levitas R, eds.

Poverty and Social Exclusionin Britain: The Millennium Survey

. Bristol, The Policy Press.Halleröd B (1991).

Den svenska fattigdomen

[The SwedishPoverty]. Lund, Arkiv.

Halleröd B (1995). The Truly Poor: Indirect and DirectMeasurement of Consensual Poverty in Sweden.

Journal ofEuropean Social Policy

5: 111–129.Halleröd B (1997). Fattigdom och materiell deprivation: En

empirisk analys [Poverty and Deprivation: An EmpiricalAnalysis]. In: Vogel J, Häll L, eds.

Välfärd och ojämlikhet i20-års perspektiv 1975–1995

. Örebro, StatistiskaCentralbyrån.

Halleröd B (2000). Socialbidragstagande och fattigdom [SocialAssistance and Poverty]. In: Puide A, ed.

Socialbidrag iforskning och i praktik.

Göteborg, Gothia.Halleröd B, Gordon D, Larsson D, Ritakallio V-M (2006).

Relative Deprivation – A Comparative Analysis of Britain,Finland and Sweden.

Journal of European Social Policy

16:328–345.

Halleröd B, Heikkilä M (1999). Poverty and Social Exclusion inthe Nordic Countries. In: Kautto M, Fritzell J, Hvinden B,Kvist J, Uusitalo H, eds.

Nordic Social Policy. London,Routledge.

Hills J, Le Grand J, Piachud D (2002). Understanding socialexclusion. Oxford, Oxford University Press.

Kangas O, Ritakallio V-M (1998). Different Methods – DifferentResults? Approaches to Multidimensional Poverty. In:Andreß HJ, ed. Empirical Poverty Research in a ComparativePerspective. Aldershot, Ashgate.

Larsson D (2006). Exposure to crime as a consequence ofpoverty. Umeå, Umeå University, Department of Sociology.

Layte R, Whelan CT (2003). Moving in and out of poverty –The impact of welfare regimes on poverty dynamics in theEU. European Societies 5: 167–191.

Mack J, Lansley S (1985). Poor Britain. London, George Allen& Unwin Ltd.

Nilsson A, Estrada F (2004). Exposure to threatening and violentbehaviour among single mothers – the significance oflifestyle, neighbourhood and welfare situation. BritishJournal of Criminology 44: 168–187.

Nordenmark M (1999). Unemployment, Employment Commitmentand Well-Being. Umeå, Umeå University, Department ofSociology.

Nyman C (2002). Mine, yours or ours. Umeå, Umeå University,Department of Sociology.

Pahl J (1989). Money and Marriage. London, MacmillanEducation Ltd.

SCB (2003). Inkomstfördelningsundersökningen [IncomeDistribution Survey]. Örebro, Statistics Sweden.

Shaw CR, McKay HD (1969). Juvenile delinquency and urbanareas; a study of rates of delinquency in relation todifferential characteristics of local communities in Americancities. Chicago, University of Chicago Press.

Strandh M (2000). Varying Unemployment Experience? TheEconomy and Mental Well-Being. Umeå, Department ofSociology, Umeå University.

Tham H (1994). Ökar marginaliseringen i Sverige? [IsMarginalization in Sweden Increasing?]. In: Fritzell J,Lundberg O, eds. Vardagens villkor [Conditions of daily life].Stockholm, Brombergs.

van den Bosch K (2001). Identifying the Poor. Aldershot, Ashgate.Vogler C, Pahl J (1994). Money, Power and Inequality within

Marriage. The Sociological Review 42: 263–288.

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