2. Building a Material Deprivation Index in a Multinational Context: Lessons from the EU Experience

For an examination of the different approaches to “material deprivation”, see Dickes (1989) and Fusco (2007).

For more information on EU-SILC, see: http://​epp.​eurostat.​ec.​europa.​eu/​portal/​page/​portal/​microdata/​eu_​silc. For a comprehensive presentation of the two EU material deprivation indicators and of all the other commonly agreed indicators used at EU level in the context of the Social OMC (for monitoring progress towards the agreed EU objectives in the field of social inclusion as well as pensions and healthcare and long-term care), see: http://​ec.​europa.​eu/​social/​main.​jsp?​catId=​756&​langId=​en. In June 2010, EU Heads of State and Government adopted a social inclusion target as part of the new “Europe 2020” Strategy on smart, sustainable and inclusive growth: to lift at least 20 million people in the EU from the risk of poverty and exclusion by 2020. One of the three indicators used to monitor progress towards this target is the EU indicator of “severe material deprivation”, which is the standard EU indicator of material deprivation (MD) presented above but with a threshold set at 4 rather than 3 lacks out of 9. This EU target has increased considerably the importance of MD indicators at EU level. The 2009 wave of EU-SILC included a thematic module on MD with several items specifically focused on the situation of children. Guio, Gordon and Marlier (2012) have analysed these data and proposed an analytical framework for developing robust EU MD indicators for the whole population as well as for children. This work will feed into the revision of the core EU-SILC MD items and indicators, which will take place in 2015 as part of the mid-term review of Europe 2020. (For a thorough discussion of the social challenges linked to the new Europe 2020 Strategy, see the various contributions included in Marlier, Natali and Van Dam (2010)).

Marlier et al. (2007) discuss the two forms of aggregation in detail. They stress the various technical and political issues raised by composite indicators and conclude by stating that even though composite indicators, like the HDI, undoubtedly can play a valuable role in certain contexts, they should not be employed for monitoring policies. See also Atkinson (2003).

For a thorough overview of the Social OMC and the indicators needed in this context, see inter alia Atkinson et al. (2002); Frazer and Marlier (2008); Marlier et al. (2007, 2012).

Two other domains could have been included: financial access to healthcare and local environment. Items of access to healthcare available in EU-SILC refer to (self-reported) unmet needs in medical/dental examination. These items could have been used as material deprivation indicators. However, for the monitoring of the Social OMC objectives, the EU considered it important to use them separately, and in particular to develop a specific indicator on access to healthcare based on the question on unmet need for medical care. As to the items related to the local environment, an important reason why they have been excluded from the EU aggregate is that they tend to reflect a rural/urban divide rather than actual deprivation. It is important to stress that the EU measures of deprivation do not include subjective items on the feeling of poverty (e.g. items such as “difficulty in making ends meet”…). See Whelan et al. (2008) and Bossert et al. (2009) for other analysis of deprivation using EU-SILC.

In EU-SILC, questions regarding durable goods rely on this Mack and Lansley’s format and enable distinguishing between “lack of items” (due to choice) and “enforced lack of items” (people would like to possess/access the items but cannot afford them). Only this latter group is considered as reflecting “deprivation”, in order to exclude lifestyle preferences from the concept of deprivation.

The Eurobarometer was conducted on behalf of the European Commission with a view to informing the preparation of a thematic module on material deprivation that was included in the 2009 wave of EU-SILC (see Guio, Gordon and Marlier 2012).

Each country, whether small or large, receives the same importance in the EU-27 averages; these averages are thus not computed on the basis of population weighted national results (contrary to standard practice). For calculating the EU-27 averages, national samples have been reweighted so as to achieve a sample size of 1,000 for each country.For a list of the official EU countries’ abbreviations, see Annex 4, Table A4.1.

Detailed tables can be obtained upon request.

Age is measured with four dummy variables (16–24; 25–39; 40–54; 55+), occupational status is measured with three dummies (employed, self-employed, not working), subjective financial poverty with three dummies (how is your income compared to what is necessary to make ends meets—higher, more or less the same, lower), household type is a combination of the number of adults (15+) and the number of children (below 15). Detailed tables, for each country, can be obtained upon request (see above).

We applied an ordered probit regression to tackle the ordinal nature of each item. As already mentioned, in the previous consensus surveys individuals could only choose between two answer categories: “necessary” and “desirable but not necessary”. In the Eurobarometer, additional modalities were included allowing to better approach the range of views of interviewees.

In a previous analysis of the Eurobarometer data, Dickes et al. (2010) assess the (in)variance of the structure of the perception of social needs between countries on the basis of an extension of the Multi-Dimensional Scaling (MDS) method. They show that there is a high level of congruence between the 27 national patterns. An important consequence of their analysis is that it tends to support the approach which consists of measuring deprivation on the basis of a same set of (validated) items across all the Member States.

The analysis was conducted using SAS, proc CALIS. The matrix of tetrachoric correlations was used as the input for the CFA as it fits better with the binary nature of the items used. Oblique rotation was applied, implying the hypothesis that the dimensions are correlated. For a use of CFA in the deprivation literature, see also Whelan et al. (2001), Eurostat (2002), Jensen et al. (2002), Carle et al. (2009), Dekkers (2008).

The items are those presented in Sect. 2.3. The item “lack of space” that was considered as relevant to study deprivation in Sect. 2.3 has been discarded due to a lack of homogeneity with the other items.

As mentioned above, the nine items concern the incapacity to afford: to face unexpected expenses; one week annual holiday away from home; to pay for arrears (mortgage or rent, utility bills or hire purchase instalments); a meal with meat, chicken or fish every second day; to keep home adequately warm; to have a washing machine; to have a colour TV; to have a telephone; to have a personal car. As shown in Sect. 2.3 above, all these items satisfactorily meet both the “social consensus” criterion and the “homogeneity of preferences” criterion.

For Stata users—mdepriv—is a useful user written command which allows computing synthetic scores of multiple deprivation similar to that presented in this chapter (see Pi Alperin and Van Kerm 2009). Several alternative weighting rules are available.

When normalised to sum to one, the weight of each item in an equal weighting framework is 1/m. If normalisation is a common practice that allows comparing indices composed of different numbers of items, it is possible to attribute a weight of 1 to each item so that the weights sum to m. In that case, we can talk of a counting approach (see Atkinson 2003).

In case of redundancy, it has been proposed to introduce in the weighting scheme a correlation component (see Betti and Verma 2000).

In the case of the deprivation rate, the choice between a threshold of 2+ or 3+ enforced lacks is arbitrary and can be influenced by different considerations. First and most important, a threshold of 3 + items allows focusing on more severe deprivation and limiting the impact of potential measurement errors and misclassification. Moreover, it offers the advantage of leading to percentages which, in most EU countries, are closer to the value of the poverty rate; this makes it easier to compare the two figures.

Let us take two countries A and B. In country A, the proportion of individuals possessing a car is 90 % and that of individuals having a jacuzzi is 10 %. In country B, these proportions are 45 and 5 %. The normalised weight for each item in the two countries will be the same (car: 90/100 = 45/50 = 0.9; Jacuzzi: 10/100 = 5/50 = 0.1).

See Decancq and Lugo (2012) for a survey of other options to weight the items, including regression-based methods and statistical weights. See also Haisken-DeNew and Sinning (2010) proposal to weight components by their subjective contribution to an overall measure of life satisfaction.

For a similar approach, see: Desai and Shah (1988), Tsakloglou and Papadopoulos (2002), Whelan et al. (2001), Willits (2006).

Guio (2009) analyses different functional forms of weights, e.g. logarithmic of exponential transformation of the proportion of non-deprived.

Brandolini (2008) provides the example of an analysis he carried out with D’Alessio showing that in 1995 the share of Italians deprived in terms of health and education could be estimated at 19.5 and 8.6 % respectively, which would lead to education receiving a weight more than twice higher than that of health which could be seen as “a matter of disagreement”.

See also Boarini and Mira d’Ercole (2006) and Brandolini (2008) for a similar argument.

In view of the difficulty to define a deprivation threshold on a weighted indicator, we have opted here for the presentation of the mean weighted indices rather than the deprivation rates.