ANALYSING INTERGENERATIONAL INFLUENCES ON INCOME POVERTY AND ECONOMIC VULNERABILITY WITH EU-SILC

The primary goal of inter-generational mobility research has always been to explain how and why social origins influence peoples’ life chances. This has naturally placed family attributes at centre stage. The key role of such influences relative to, for example, neighbourhood factors, has been confirmed by recent research. Thus, Solon et al. (2000) used the cluster sampling design of the Panel Study of Income Dynamics to estimate both sibling and neighbourhood correlations of years of schooling, and found correlations for the former of around 0.5 whereas their estimates for the latter were as low as 0.1. Raaum et al. (2003) used Norwegian census data and also concluded that neighbourhood correlations are small compared to sibling correlations, for both education and long-run earnings. Without reviewing the wide range of studies involved (on which see, for example, Esping-Andersen 2004 a, b; D'Addio 2007), for present purposes the key point is that they suggest that causal mechanisms related to the family are critical in relation to intergenerational mobility.

A major theme of recent research has been the identification of early childhood conditions as fundamental for subsequent outcomes (Carnerio and Heckman 2003; Waldfogel 2006). Over the past decade research relating to the role of social institutions has been characterised by an increased emphasis on welfare state effects. Differences in welfare redistribution are well documented but the extent to which they influence intergenerational transmission of outcomes is less well understood (Nolan et al.2011). Recent research focuses attention on the manner in which welfare state arrangements may shape early childhood experience through availability of, for example, high-quality preschool programmes (Esping-Andersen 2009).

Over the past decades, substantial advances have been made in understanding how differences in welfare state institutions underpin significant variation across countries in poverty levels and trends (OECD 2009). However, while there is substantial evidence that poverty is inherited across generations (Duncan et al.1998; Corak 2001; Blanden and Gibbons 2006), the extensive literature does not provide a ready basis for assessing the processes involved in intergeneration transmission.

Against this background the European Union Statistics of Income and Living Conditions (EU-SILC) 2005 wave appears to offer an outstanding opportunity to explore such issues since it includes a special module on inter-generational transmission of poverty. In addition to the standard data relating to income and material deprivation, information relating to parental background and childhood circumstances was collected for all household members or selected respondents aged over 24 and less than 66 at the end of the income reference period.¹ In principle, the module provides an unprecedented opportunity to apply a welfare regime perspective to a comparative European analysis of the relationship between current poverty and social exclusion outcomes and parental characteristics and childhood economic circumstances.

In this paper, we seek to exploit the potential of this data by examining the relationship of parental characteristics to measures of income poverty and an indicator of economic vulnerability understood in multidimensional terms. However, in so doing it is necessary to alert readers to significant limitations relating to the data that make up the EU-SILC intergenerational module.

Since 2004, the EU-SILC survey has been the reference source for statistics on income and living conditions, and common indicators for social inclusion in the EU. In 2005 the survey was extended to include 25 Member States plus Norway and Iceland. For the purpose of this analysis we use the User Database of the EU-SILC 2005 wave and our analysis is conducted at the individual level. The data set covers 26 countries with Malta not included. The sample sizes range from 6744 cases in Iceland to 47,311 cases in Italy constituting a total sample size of 419,043 individuals. The reference period in relation to the intergenerational module is when the interviewee was a young teenager, between the ages of 12 and 16.

We have found it necessary to exclude a range of countries from our analysis because of either intractable problems in relation to missing values or lack of comparability in relation to the measurement of key variable such as parental education. Further information is available from the authors.

The ‘class schema’ employed in this paper is of a very aggregated and crude nature. The variables available for the parents’ generation involve a level of detail that is well below the level for successful implementation of the European Socio-economic Classification or Erikson-Goldthorpe-Portocarero procedures. The occupational variable is a two-digit ISCO-88 and we can derive a ‘rough’ four category social-class variable with the following classification:²

The key independent variables in our analysis are parents’ social class and ‘childhood economic circumstances’. The latter is constructed from the answers to a question relating to whether there were financial problems in the household when the interviewee was a young teenager. Possible answers ranged from ‘never’ to ‘most of the time’ and we distinguish those who answered ‘most of the time’ and ‘often’ from all others.³ Our key independent variables are the conventional at risk of poverty (ARP) measures using the ‘Modified OECD equivalence scale’ and a measure of current ‘economic vulnerability’. The latter is derived from a latent-class analysis that identifies two clusters with sharply contrasting multidimensional profiles in relation to being ‘at risk of poverty’, being above a threshold on a measure of consumption deprivation that corresponds to the cut off point for the 60% of median income ARP line (Whelan and Maître 2010).

Overall our assessment would be that cross-national analysis involving the EU intergenerational module must be interpreted with great care. This does not preclude the possibility of productive analysis involving particular countries and specific variables. However, even here one would ideally wish to be able to compare the results deriving from the EU-SILC combined data sets with those deriving from national data sets. Schneider and Müeller (2009) and Vallet (2009) in their discussions provide an illustration of what could be achieved.

In what follows, we have sought to limit the effect of the data difficulties referred to above by excluding countries with particular problems in relation to missing data and by adopting dominance procedures in relation to both social class and education (Erikson 1984) by using the information relating to the available partner where it is missing for the other partner. Thus where information is available for both partners we opt for the individual with the superior occupation or educational qualifications but where information is recorded for only one person that determines the parents’ status or defines childhood economic circumstances. We have restricted our analysis to a set of ten countries where the missing value levels seem tolerable. The exception involves the UK which has been included in order to ensure that we retain more than one country in the liberal welfare regime cluster despite an unduly high level of missing values in relation to parents’ occupation.

Our analysis of the impact of parental social class and childhood economic circumstances on poverty and economic vulnerability will include the following countries: Denmark, Finland, Austria, France, United Kingdom, Ireland, Italy, Spain, Estonia and Slovakia.

In line with our concern with the manner in which welfares states mediate the impact of intergenerational transmission we have allocated countries to the following welfare regimes.

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  Denmark and Finland constitute examples of the social democratic regime which assigns the welfare state a substantial redistributive role. A high level of employment flexibility is combined with high security in the form of generous social welfare and unemployment benefits to guarantee adequate economic resources independently of market or familial reliance.

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  Austria and France provide examples of the corporatist regime which involves less emphasis on redistribution and views welfare primarily as a mediator of group-based mutual aid and risk pooling, with rights to benefits depending on being already inserted in the labour market. Relatively strict employment protection legislation (EP) policies are aimed at protecting established inside workers.

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  The UK and Ireland are treated as members of the liberal regime which acknowledges the primacy of the market and confines the state to a residual welfare role, social benefits typically being subject to a means test and targeted on those failing in the market. These countries exhibit levels of flexibility coupled with limited measures to actively sustain employment.⁴

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  Italy and Spain are members of the southern European regime which is distinguished by the crucial role of family support systems. Labour market policies are poorly developed and selective. The benefit system is uneven and minimalist in nature and lacks a guaranteed minimum income provision.

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  Alber et al. (2007) and Juhász (2006) note the difficulties involved in categorising the welfare regimes of post-socialist countries, although low levels of spending on social protection and weakness of social rights are common. Bukodi and Róbert (2007) observe that there has been a general increase in employment flexibility with most transition countries displaying a level of labour market flexibility significantly less than the UK but significantly greater than in southern European countries. They distinguish two clusters. The corporatist post-socialist regime comprises the central European countries, with mostly transfer oriented labour market measures and a moderate degree of employment protection. Slovakia is located in this cluster.

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  The post-socialist liberal cluster comprises the Baltic countries which are characterised by a more flexible labour market, with employers unwilling to abide by legal regulation of the market, and an absence of policies aimed at sustaining employment. Estonia is included in this group.

In Table 1 we show the relationship between parental social class and being income poor, where the threshold is set at 60% of equivalised income; being deprived in terms of enforced lack of 3+ items on a seven-item consumption deprivation index⁵ ; and being economically stressed which involves a contrast between households that are experiencing difficulty or great difficulty in making ends meet and all others and parental social class. Four class categories are distinguished in relation to parents in the EU-SILC intergenerational module. These comprise the ‘higher non-manual’, the ‘lower non-manual’, the ‘skilled manual’ and ‘elementary occupations’. From Table 1 we can see that the impact of parents’ class is relatively weak in Social Democratic countries. In Denmark no systematic pattern emerges while in Finland a gradual increase from 6% to 12% is observed as one move from the higher non-manual class to elementary occupations. In relation to the corporatist countries, France displays a rather similar profile to Finland while for Austria the contrast is between the elementary occupations with a poverty rate of 15.4% and the remaining categories where the figure ranges between 8% and 10%. For the liberal countries fairly clear patterns of class differentiation emerge. For the UK we observe almost a doubling of the rate across class categories from 8.1% to 15.8%. For Ireland the absolute levels are higher but the differential is somewhat less sharp with the corresponding figures being 12.4% and 19.8%. Class differentials are more accentuated for the Southern European countries, although very little differentiation is observed within the non-manual stratum. For Italy we see that the poverty rate increases from 9.2% to 25.1% as one descends the class hierarchy. The corresponding figures for Spain are 11.3% and 20.6%. The pattern for Slovakia is not dissimilar to that found for the earlier corporatist examples with the poverty rate ranging from a low of 9.6% to a high of 14.8%. For Estonia, which constitutes an example of the post-socialist liberal cluster, a rather sharper pattern of class differentiation is observed with the poverty rate rising steadily from 9.9% for the higher non-manual class to 22.6% for the elementary occupations group.

For consumption deprivation, with the exception of Denmark, we observe a more uniform and sharper pattern of variation by parental social class. At the higher end of the spectrum we observe disparity ratios of between 2.9 and 2.6 for Estonia, Italy and Ireland for the comparison involving the highest and lowest parental social classes. Excluding Denmark, the figures for the remaining countries range between 1.9 and 1.4.

For economic stress we again observe a weak pattern of differentiation for both the social democratic and corporatist countries. However, with the exception of the UK, we observe significant class differentials for all the remaining countries. For Slovakia, Ireland, Spain, Italy and Estonia the rate of economic stress approximately doubles as one moves from the highest to the lowest social class of origin.

In Table 2 we show the impact of childhood economic circumstances for income. In every case income poverty is higher for those who had difficult childhood circumstances. However, in most cases the effects are modest. In Denmark and Finland the number poor rises from 8% to 10% and in Austria and France from 9/10 to 13%. The UK is similar to the foregoing countries but with higher levels observed for both groups as reflected in poverty rates of 13.5% and 16.4%. For Ireland, on the other hand, the impact of childhood economic circumstances is more dramatic with the poverty rate more than doubling from 12.9% to 26.8%. Among the Southern European countries, Italy resembles Ireland with rates of 12.2% and 20.4% while Spain occupies an intermediate position. Among the post-socialist countries Estonia resembles Spain while by far the weakest impact is observed for Slovakia.

For consumption deprivation, apart from Slovakia, we observe clear effects of childhood economic circumstances for each country. By far the highest disparity ration of 3.4 is observed for Ireland. The weak effects of 1.5 and 1.6, respectively, are associated with the UK and Estonia; consistent with the scale of intergenerational change observed in these societies. With the exception of Slovakia, the values for the remaining countries lie in the range 1.7 to 2.4.

For economic stress the impact of childhood economic circumstances is substantially greater than for income poverty. With the exception of Slovakia which represents, something of a deviant case, in very case difficult childhood economic circumstances are associated with higher current levels of economic stress.

Overall we can see that intergenerational factors tend to have their weakest influence on income poverty in social democratic countries and their greatest consequences for members of liberal and Southern European welfare regimes. The disparity ranges from 2.1 in Ireland to 1.3 in Estonia.

At this point we wish to extend our analysis by incorporating our three outcome indicators into a multidimensional approach. A number of related debates have focused attention on the limitations of relative poverty measures based solely on a national income. The first relates to the relative merits of uni-dimensional approaches focusing on income poverty versus approaches that attempt to capture the multidimensional nature of social exclusion (Nolan and Whelan 2007). The second relates to increasing concern that the enlargement of the European Union has exacerbated the limitations of focusing on income poverty measures, defined in purely national terms. This approach is seen to produce results that are counterintuitive and at odds with our knowledge of variation across the EU in terms of objective living conditions and subjective feelings of deprivation (Fahey 2007). The final issue relates to whether social-class differentials in poverty and social exclusion continue to play an important role and the extent to which the answers to this question are influenced by the choice of dependent variables (Beck 2007; Goldthorpe 2007; Whelan and Maître 2008b).

Such considerations have led authors such as Fahey (2007), to argue for the development of an EU-wide poverty line alongside national measures. However, recent efforts in this direction suggest that, while the latter may fail to capture cross-national or welfare regimes differences, conversely the former have difficulty in appropriately capturing socio-economic differences.⁶ If we are to seek alternatives or complements to conventional income poverty measures, it would seem desirable to develop indicators that can capture adequately both between country/welfare regime variation in social exclusion and within country/regime socio-economic variation. Making use of latent-class analysis procedures we develop a multidimensional approach to the measurement of social exclusion. In particular, we focus on identifying individuals that we characterise as ‘economically vulnerable’.⁷

In applying latent-class analysis, each of our indicators is taken as an imperfect measure of economic vulnerability. Our income poverty variable has four categories distinguishing between those below 50% of median income, between 50–60% and 60–70% and above 70%. Our results will be reported in terms of the conditional probabilities of being below each of the three median income lines. Our deprivation outcome reports the conditional probability of experiencing an enforced lack of 3+ items on a seven-item consumption deprivation index.⁸ Finally the economic stress variable involves a dichotomy between those in households that are experiencing difficulty or great difficulty in making ends meet and all others.

Our objective is to identify groups who are vulnerable to economic exclusion in being distinctive in their risk of falling below a critical resource levels, being exposed to consumption deprivation and experiencing subjective economic stress. Following Chambers (1989), we can define vulnerability as not necessarily involving current deprivation but rather insecurity and exposure to risk and shock. It can be seen as implicitly involving a multidimensional and dynamic perspective that is consistent with the notion of social exclusion as a process rather than simply an outcome.

As Moisio (2004) notes, implicit in the notion of multidimensional measurement of exclusion is the assumption that there is no one ‘true’ indicator of the underlying concept. Instead, we have a sample of indicators that tap different aspects of a complex phenomenon. We need a measurement model that enables us to understand the manner in which our indicators are related to the underlying concept. In this paper we make use of latent-class modelling to achieve this objective. The basic idea is long established and very simple (Lazarsfeld and Henry 1968).⁹ The associations between a set of categorical variables, regarded as indicators of an unobserved typology, are presumed to be accounted for by membership of a small number of latent classes. Latent-class analysis assumes that each individual is a member of only one of N latent classes and that, conditional on latent-class membership, the manifest variables are mutually independent of each others. Conditional independence is a version of the familiar idea that the correlation between two variables may be a result of their common dependence on a third variable. The logic is identical but explanatory variable is unobserved and must be identified statistically.

In Table 3, we display the results for model fit, size of the vulnerable class and conditional probabilities. Given large sample sizes, any particularly parsimonious model is unlikely to fit the data. Nevertheless, the latent-class model does remarkably well across all six welfare regimes in accounting for the patterns of association between the income, consumption deprivation and economic stress indicators. The size of the G² for the independence model provides one benchmark against which to assess the fit of the latent-class model. The value ranges from 6.20 in Austria to 179.9 in Italy. One useful indicator of goodness of fit is the reduction in the G² for the independence model. This ranges from 98.1% in Estonia to 99.7% in Austria. The index of dissimilarity or the proportion of cases misclassified goes from a high of 0.033 in Estonia to a low of 0.005 in Austria with the figure for seven of the 10 countries being below 0.020.

A systematic pattern of variation in the size of the vulnerable class is observed across welfare regimes. The lowest level of 11.1% is observed in Denmark while the figure for the other member of this regime Finland reaches 15.1%. For the corporatist regimes the figures are 11.2% and 18.3% for Austria and France, respectively. For the liberal regime the figure goes from 18.5% in UK to 23.9% in Ireland. A similar pattern is observed for the Southern European countries where the figure goes from 23.8% in Spain to 24.8% in Italy. The figure rises to 28.2 for Slovakia. A lower figure of 24.9% is found for Estonia which earlier work has shown to occupy a particularly favourable position within the post-socialist liberal cluster. On average we find that the Social Democratic countries occupy the most favourable situation while the post-socialist countries are at the other extreme.

Focusing on the multidimensional patterns differentiating the vulnerable and non-vulnerable we find that the discriminatory power of income poverty is relatively similar across countries. The conditional probability of income poverty at the 50% line, given that one is the non-vulnerable class, ranges from 0.028 in Finland to 0.084 in Spain and in 7 of the 10 cases it is at or below 0.05. Among the vulnerable class the poverty rate goes from 0.153 in Denmark to 0.342 in Italy. While income poverty systematically distinguishes between the vulnerable and non-vulnerable classes with the differential ranging from three to seven to one, as will become apparent, it is the least potent of the elements making up the vulnerability profile.

For the non-vulnerable class, variation across countries in levels of economic stress is modest with the figure running from 0.012 in Estonia to 0.173 in Italy. For the vulnerable class stress levels run from 0.456 in Finland to 0.870 in Italy.

While substantial patterns of differentiation are observed in relation to economic stress, the most powerful discriminating factor in relation to economic vulnerability is consumption deprivation. Among the non-vulnerable class, with the exception of the post-socialist countries, deprivation levels are close to zero with the highest conditional probability of 0.020 being reported for the UK. Among the vulnerable the lowest conditional probability of 0.562 is observed for Spain, it rises to 0.871 and 0.908 for Estonia and Slovakia, respectively.

In Table 4 we set out the relationship between parental social class and economic vulnerability. As with income poverty, for Denmark we find no systematic relationship with vulnerability being equally distributed across parental-class categories. In contrast for Finland where vulnerability rates for the non-manual classes are approximately 9%, they then rise for the manual classes and peak at 14.1% for the elementary occupations group. A comparable pattern is found for Austria. Similarly for France the vulnerability rate rises steadily from 12.6% for the higher non-manual class to 21.5% for the elementary occupations class. Turning to the liberal countries, we find a similar pattern for the UK with respective levels of 10.5% and 16.1%. However, once again the class gradient is rather sharper for Ireland with the level of vulnerability rising from 10.2% to 21.9% as one descends the class hierarchy. Similar, if somewhat sharper, class profiles are observed for the southern European countries. For Italy the level of vulnerability rises gradually from 12.2% to 32.8% while the corresponding figures for Spain are 9.1% and 25.4%. The post-socialist countries patterns are similar to those for the southern European countries with the Estonian figure rising from a low of 10.7% to 26.5% and the corresponding figures for Slovakia being 13.9% and 30%.

In Table 5 we look at the impact of childhood economic circumstances on economic vulnerability. In every case vulnerability levels are higher for those whose families experienced severe financial problems in their childhood ‘often to most of the time’ compared to those who responded ‘never to occasionally’. This is true even in Denmark, where effects up this point have been muted, with the respective figures being 6.6% and 15.4%. For Finland the corresponding figures are 9.9% and 16.3%. For Austria the gap is slightly wider with the relevant figures being 8.0% and 14.3%. For France a sharper pattern of differentiation is observed with the level of vulnerability rising from 13.5% to 23.5%. For the Liberal countries the contrast is sharpest for the UK with respective figures of 12.5% and 18.7%. The impact of childhood economic circumstances is greater in Ireland with the vulnerability level rising from 11.3% to 37.7%. Differentials are slightly less sharp for Italy and Spain with the corresponding figures being approximately 14% and 31%. A further moderation of difference is found for Estonia with vulnerability levels of 15.7% and 24.6% and Slovakia with rates of 18.7% and 26.9%.

In Table 6 we report the odds ratios from a set of logistic regressions at the combined impact of parental social class and childhood economic circumstances on income poverty. For social class we find that net effects are relatively weak in the social democratic and corporatist countries where the odds ratios for elementary occupations ranges from 0.479 in Denmark to 1.644 in Slovakia. They are strongest in the Liberal and Southern European countries (excluding Ireland) where it goes from 2.0 in Spain to 2.9 in Italy. The impact in Ireland is somewhat weaker than we might have anticipated which is related to the fact that the net odds ratio for economic circumstances in childhood in Ireland at 2.1 is higher than for any other country.

In Table 7 we look at the corresponding results relating to economic vulnerability. The strongest impact of parental social class is found in the Southern European and post-socialist countries with odds ratio for the contrast between elementary occupations and higher non-manual ranging from 2.4 in Slovakia to 2.8 in Spain. The lowest values are observed for the Social Democratic countries with the respective values for Denmark and Finland being 0.5 and 1.3. For the remaining countries the values lies between 1.5 and 1.6.

The net impact of childhood economic circumstances is generally higher than in the case of income poverty. The impact is particularly high in Ireland with an odds ratio of 3.9 the next highest values are observed in the Southern European countries and Denmark with values between 2.5 and 2.9. By far the weakest effect is observed in Finland.

In Table 8 we look at the cumulative impact of elementary occupation level of parental social class and the family having experienced severe financial problems most of the time or often in childhood, relative to those with professional and managerial origins whose families experienced financial stress rarely or never. We do so for both income poverty and economic vulnerability. Focusing first on income poverty, we find that the largest cumulative impact is observed for Italy where the odds ratio reaches 4.6. For Austria, Ireland, the UK, Spain and Estonia the value ranges between 2.5 and 3.5. Denmark is the only case where the value does not exceed one. Controlling for current social class produces only modest reductions in these ratios.

Turning our attention to economic vulnerability, we can see that the cumulative impact of parental social class and childhood is generally sharper than in the case of income poverty. By far the highest odds ratios are observed for Ireland and the Southern European countries where the value ranges from 6.3 in Ireland to 7.0 in Spain. The weakest effects are found in the Social Democratic countries and Slovakia where the odds go from 1.5 to 1.7. The UK and France follow with values of 2.3 and 2.9. For Austria and Estonia the figure rises to just below 4.0.

As we have shown, the EU-SILC Intergenerational Module appears to offer an unprecedented opportunity to conduct a comparative analysis of the relationship between current poverty and social exclusion outcomes and parental characteristics and childhood economic circumstances. However, as our analysis reveals, serious problems relating to the scale of missing values and major reservations about the comparability of key variables means that the results of any such analysis must be treated with considerable caution.

We have endeavoured to overcome data difficulties by maximising the use of information for both parents and generally restricting our analysis to countries where such problems are least severe. Even so the situation remains less than satisfactory and our finding must continue to be treated with a considerable degree of circumspection.

The main focus of our analysis was on the manner in which welfare regimes mediate the impact of parental social class and childhood economic circumstances on poverty and economic vulnerability. Employing a four category social-class schema we found that intergenerational factors tended to have their weakest influence on income poverty in social democratic countries and their greatest consequences in liberal and southern European welfare regimes.

Our analysis was extended to incorporate a multidimensional perspective by focusing on economic vulnerability. A systematic pattern of variation in the size of the economically vulnerable class was observed by welfare regime with, on average, the socials democratic countries occupying the most favourable position with the post-socialist regimes at the other extreme and intermediate variation being modest. Income poverty is the least discriminatory dimension in relation to economic vulnerability while the sharpest variation is associated with consumption deprivation.

The pattern of variation for vulnerability in relation to both parents’ social class and childhood economic circumstances is generally sharper than in the case of income poverty. The weakest differentiation is again found in the social democratic regime. Patterns of differentiation are sharper for the corporatist, liberal and southern European welfare regimes. For the post-socialist regimes clear absolute differences are observed across social classes and, unlike the situation in relation to income poverty, vulnerability levels for all social classes are higher than for the remaining welfare regimes. Economic vulnerability levels are also significantly higher in every welfare regime for those who experienced difficult economic circumstances in childhood.

Our analysis was extended in order to consider the joint impact of parents’ class and childhood economic circumstances on income poverty and economic vulnerability. Focusing on net odds ratios we found that the impact of parental social class on income poverty was weak in the social democratic and corporatist countries and strongest for the liberal and southern European countries. For economic vulnerability the net impact of social class is generally higher. This is also true in relation to economic circumstances.

Focusing on the cumulative impact of social class and economic circumstances in childhood we find that in relation to income poverty we observe odds ranging between 4.5 and 2.5 for a number of countries with Denmark being the only case where the value does not exceed one. For economic vulnerability the cumulative impact is much sharper. The lowest values are again observed in the social democratic countries with Ireland and some of the southern European countries being at the other end of the spectrum.

Overall, by attempting to minimise the missing values problems and focusing on a restricted set of variables and countries, we have been able to reveal fairly systematic variation across welfare regimes in the strength of intergenerational influences. This is particularly so in relation to economic vulnerability. However, this should not conceal the real need for a substantial improvement in the quality of data available relating to the comparative impact of intergenerational influences on poverty and social exclusion across European countries.

The authors would like to acknowledge the comments of two anonymous referees. They would also like to acknowledge funding from funding under the Economic Change, Quality of Life Social Cohesion (EQUALSOC) Network and the Growing Inequalities Impacts (GINI) projects funded by the European Commission(DG Research) as part of the Sixth and Seventh Framework programmes.

Christopher T. Whelan is Professor of Sociology and Senior Fellow at the Geary Institute, in University College Dublin. He is a former Chair of the Standing Committee for the Social Sciences of the European Science Foundation and of the Governing Council of the EU Economic Change, Quality of Life and Social Cohesion (EQUALSOC) Network of Excellence. He is currently Chair of the European Consortium of Sociological Research. His research interests include the causes and consequences of poverty and inequality, quality of life and social mobility and inequality of opportunity. He has published on these topics in leading international academic.

Brian Nolan has been Professor of Public Policy in UCD's School of Applied Social Science since 2007, and is currently Principal of the College of Human Sciences. He was previously at the Economic and Social Research Institute and the Central Bank of Ireland. He has a BA in Economics and History from UCD, an MA in Economics from McMaster University (Ontario), and a doctorate in economics from the London School of Economics and Political Science. His main areas of research are poverty, income inequality, the economics of social policy, and health economics. Recent publications include studies on social inclusion in the EU, top income shares, child poverty, deprivation and multiple disadvantage, tax/welfare reform, and the distributional impact of the economic crisis.

Bertrand Maître is a Research Officer at the Economic and Social Research Institute, Dublin. His main research interests focus on multidimensional approaches to poverty, social exclusion and quality of life. Working on these issues throughout various projects, he has gained extensive experience in the use of a wide range of large European (ECHP, EU-SILC, European Quality of Life Survey) and Irish data sets. He has published articles on these issues in various international journals: The European Sociological Review, The Journal of European Social Policy and Social Indicators Research.