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Perceptions of Society’s Necessary Standard of Living: Are Perceptions Determined by What People Have, or Do They Reflect a Social Consensus?


Analyses of material deprivation usually use lists of goods and activities to assess an individual’s possessions and to compare it with the society’s standard of living. If the number of possessions falls below a certain threshold, the individual is assumed to be materially deprived. Also state-guaranteed minimum income payments are often based on a basket of goods, which are assumed to represent a minimal living standard in the respective society. However, this approach rests on the assumption that a social consensus exists about what constitutes society’s necessary standard of living, which has never been tested in a theoretically and methodologically sound way. Our paper provides a model of the main determinants of standard of living perceptions in the public, develops a measurement model for a survey of necessary items, and tests whether these perceptions are expressions of a consensual normative standard or reflections of the respondent’s idiosyncratic individual living situation. We have used two waves of the GESIS Panel in 2016 to survey the current opinions about the necessary standard of living in the German population. Estimating cross-lagged auto-correlated structural equation models, we find that necessity evaluations are quite homogeneous across social groups but are influenced by individuals’ possessions, which vary within society and hence, challenge the social consensus assumption. Moreover, instead of using only the most necessary items, survey instruments should include both necessary and less necessary items to reflect the whole distribution of possible standard of living perceptions. Our results further suggest that analyses using single items should be avoided due to possible measurement errors.

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  1. 1.

    Cited from the MIS website at (downloaded on September 5th, 2019); emphasis added.

  2. 2.

    Note that the minimum income standards approach tries to capture this interactive process by a sequence of discussions in (focus) groups from the general public; of course, at the expense of being laborious and time-consuming.

  3. 3.

    Numerous other items have been collected in the 2009, 2014, 2015, and 2018 ad hoc modules of the EU-Statistics on Income and Living Conditions data collection, but have not been included in the official EU measure of material deprivation (for further details see

  4. 4.

    Similar patterns of “vertical” and “horizontal” respondent characteristics can be seen in the descriptive breakdowns of necessity evaluations from the Breadline Britain and UK Poverty and Social Exclusion surveys.

  5. 5.

    For example, it has been found that poor people often do not mention the true reason (financial deprivation) for missing items in their households. Instead, they often declare other reasons for missing items, a phenomenon also known as “adaptive” preferences (Halleröd 2006).

  6. 6.

    Note that this is a hypothesis about the test–retest reliability of single items and does not contradict our assumption about the stability of the underlying theoretical constructs (see H1).

  7. 7.

    Latent variables are also useful when comparing surveys with partially different item lists (Andreß 2008).

  8. 8.

    Data file: ZA5665—standard edition, version 24.0.0, doi:10.4232/1.13001 ( The syntax file used for data preparation and analysis is available on request.

  9. 9.

    The population statistics are based on population updates of the 2011 census data (; downloaded on October 5th, 2018). We do not control for the two-stage survey design of the GESIS Panel because it is based on population registers and selection probabilities proportional to the size of the selected communities; hence with respect to the design, it is a self-weighted sample.

  10. 10.

    We estimated the measurement models with Stata’s gsem command (version 15.1). gsem by default applies an equation-by-equation deletion of missing values. This means that missing observations on, e.g., necessity item \(N_{i1}\) would not be included in an equation where \(N_{i1}\) is part of, but in an equation where \(N_{i1}\) is not part of. This allows us to use the largest possible sample size overall.

  11. 11.

    A measurement model with identical discrimination parameters for all items is a restricted version of model (1). Likelihood ratio tests showed that this restriction leads to a significant decrease in model fit for both latent variables at both time points.

  12. 12.

    The computations were again done with Stata’s gsem command. For the generation of count variables, we treated missing values as “not necessary or desirable” regarding necessities and as “not have” for possessions.

  13. 13.

    In Germany, the Panel Study Labor Market and Social Security (PASS) provides the latest representative data on SSOL perceptions for Germany. Information on necessities (and possessions) was collected in 2006/2007 and again in 2015 using a list of twenty-six items in both waves. Only the 2006/2007 data were publicly available at the time of our update. PASS is a representative panel study conducted yearly by the Institute for Employment Research (IAB) of the German Federal Employment Agency (BA).

  14. 14.

    The update study encompassed 1436 participants. It was conducted using the Socio-Scientific Panel (SoSci Panel)—an online panel based on voluntary participation (

  15. 15.

    Note, however, that the wording is slightly different for some items.

  16. 16.

    From a descriptive point of view, seventy per cent of all respondents find sufficient winter clothing absolutely necessary, while only five per cent say the same for eating out in a restaurant (see Table 3 in the Appendix).

  17. 17.

    Unfortunately, these reliability estimates are impaired by the fact that – due to the self-administered survey design of the GESIS Panel—it is not guaranteed that the same individual answered the questionnaire at both time points. But this instability of respondents should equally affect the stability of both necessities and possessions and hence not bias the stability comparison between necessities and possessions.

  18. 18.

    Actually, a random sample of 790 respondents from the analysis sample was used for this analysis because using the full analysis sample would imply more than five million comparisons.

  19. 19.

    Results available on request.


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Correspondence to Tamara Gutfleisch.

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See Table 3.

Table 3 Items used in the GESIS Online Panel

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Gutfleisch, T., Andreß, H. Perceptions of Society’s Necessary Standard of Living: Are Perceptions Determined by What People Have, or Do They Reflect a Social Consensus?. Soc Indic Res (2020).

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  • Necessary standard of living
  • Social consensus
  • Material deprivation
  • Measurement quality
  • Structural equation modeling