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Empirical Economics

Erschienen in:

06.07.2019

The individual (mis-)perception of wage inequality: measurement, correlates and implications

verfasst von: Andreas Kuhn

Erschienen in: Empirical Economics | Ausgabe 5/2020

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Abstract

This paper presents a simple conceptual framework specifically tailored to measure individual perceptions of wage inequality. Using internationally comparable survey data, the empirical part of the paper documents that there is huge variation in inequality perceptions both across and within countries as well as survey-years. Focusing on the association between aggregate-level inequality measures and individuals’ subjective perception of wage inequality, it turns out that there are both a high correlation between the two measures and a considerable amount of misperception of the prevailing level of inequality. The final part of the analysis shows that subjective inequality perceptions appear to be more important, in a statistical sense, in explaining variation in individual-level attitudes toward social inequality than objective measures of inequality. This underlines the conceptual and practical importance of distinguishing between subjective perceptions of inequality and the true level of inequality.

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Such as the perception of inequality of opportunity (Brunori 2017), tax rates (Gemmell et al. 2004), the perception of corruption (Olken 2009), or individuals’ self-assessment of how their own well-being would change as a result of various life events (Odermatt and Stutzer 2018). One persistent and well-known finding relates to individuals’ (mis-)perception of probabilistic events (e.g., Dohmen et al. 2009).

Consistent with this line of reasoning, evidence is accumulating on behavioral and attitudinal spillovers from inequality from a diversity of contexts and based on either experimental (e.g., Clark et al. 2010; Card et al. 2012; Kuziemko et al. 2015) or non-experimental data (e.g., Clark et al. 2010; Cornelissen et al. 2011; Dube et al. 2019; Kuhn 2019; Pfeifer 2015). Moreover, a related literature, focusing on the effect of inequality on subjective measures of satisfaction or happiness, generally finds that higher inequality is associated with less satisfaction and/or lower happiness (e.g., Senik 2005; Verme 2011). Clark and D’Ambrosio (2015) provide a comprehensive survey of both experimental and survey evidence on individuals’ attitudes to income inequality.

Previous applications of the framework include a comparison of inequality perceptions and redistributive preferences between former East and West Germany (Kuhn 2013) as well as individuals’ perceptions of executive compensation (Kuhn 2017).

Moreover, considering the results from Eriksson and Simpson (2012) and Chambers et al. (2014) on the relevance of the measurement framework, the fact that the framework proposed here does not rely on individual estimates of relative frequencies may be another advantage.

The same data, or parts thereof, were used with a similar purpose by several previous studies (e.g., Jasso 1999; Niehues 2014; Osberg and Smeeding 2006), but all of these studies used different frameworks to measure individuals’ inequality perceptions. See also Knell and Stix (2017), who compare different conceptualizations of inequality perceptions.

The data are available to researchers from the GESIS data archive (http://www.gesis.org). More information about the ISSP is available from the organization’s website (http://www.issp.org). Note that the cumulation file contains a harmonized list of variables, but that it does not cover all of the countries taking part in the separate waves of the survey.

Moreover, while most respondents were asked to estimate earnings before taxes, respondents in a few realizations of the survey were asked to estimate wages after taxes. This further complicates any simple comparison between the two measures. In the empirical analysis below, however, the inclusion of country and survey-year fixed effects will largely eliminate this issue. Another issue is that the questions in the ISSP module implicitly refer to full-time workers only, while objective inequality measures cover both full- and part-time workers.

Kuhn (2013) provides kind of a validity check of the framework, showing that the framework is able to capture plausible differentials in inequality perceptions between (former) East and West Germany, consistent with evidence from other, independent sources of data (Alesina and Fuchs-Schündeln 2007).

However, because the Gini coefficient constructed from grouped data ignores within-group inequality, it tends to underestimate the true level of inequality (e.g., Fuller 1979; Ogwang 2003).

The two population shares are treated as fixed parameters, even though it is easy to imagine that individuals have different (and potentially biased) perceptions of these quantities as well; see Evans and Kelley (2004), for example. This contrasts with other frameworks used in the literature which rely on individuals’ estimates of relative group sizes (e.g., Engelhardt and Wagener 2014; Gimpelson and Treisman 2018; Niehues 2014). As mentioned in Introduction, the findings from Eriksson and Simpson (2012) and Chambers et al. (2014) suggest that the extent of inequality individuals perceive might differ, depending on whether the measurement is based on individuals’ estimates of wages and/or relative group sizes.

In principle, and in contrast to the Gini coefficient describing the objective distribution of wages, the subjective Gini coefficient given by Eq. (4) can take on negative values because some individuals may believe that \({\overline{y}}^{\mathrm{bottom}}> {\overline{y}}\), which would imply that the perceived wage share of the bottom group is larger than their actual population share. (That is, \(q_{i}^{\mathrm{bottom}}\) can take on any value between zero and one.) Empirically, as shown in Table 1, this is true for a small fraction of the overall sample (less than 0.4% of the overall sample).

Estimated group shares do not change much over time, and thus, the results would hardly change if I only were to allow the population shares to vary across countries (but not over time within a given country). There are, however, substantial differences in \(f_{\mathrm{bottom}}\) across countries.

While it is possible to include a full set of country \(\times \) survey-year fixed effects, note that the fixed effects will fully pick up any potential effect of aggregate-level inequality on inequality perceptions (i.e., it is not possible to estimate both a full set of fixed effects and the effect of any aggregate-level variable). Nonetheless, estimating such a specification is useful as a robustness check, as discussed in Sect. 5.4 below.

“Online Appendix Table B.5” reports estimates using alternative measures of individual-level inequality perceptions in place of the baseline subjective Gini coefficient.

The exact wording of the corresponding items is as follows: (i) “Income differences in (respondent’s country) are too large” (five possible answer categories, ranging from “strongly agree” to “strongly disagree”), (ii) “Government should reduce income differences” (with the same possible answers as in (i)), and (iii) “Should people with high incomes pay more taxes” (five possible answers, ranging from “much larger share” to “much smaller share”).

As a simple robustness check, I also estimated similar regressions using binary variables (indicating a respondent’s agreement with the underlying survey item) as dependent variables, yielding qualitatively identical results. Moreover, estimation by ordered probit also yields qualitatively identical results. These additional estimates are shown in “Appendix” Table 7.

Mean inequality perceptions are only marginally influenced by individual inequality perceptions, but one might argue that a respondent’s perception of wage inequality is influenced by the perceptions of people around him or her (for example his or her colleagues at work). One potential issue with this instrument is that there might be a direct (positive) effect on individuals’ attitudes, which would bias the 2SLS estimates (upward). On the other hand, the results from “Online Appendix Table B.3” suggest that regional differences (within countries) in inequality perceptions do not appear to be especially relevant, conditional on country and survey-year fixed effects.

I also estimated the models using both instruments at the same time and based on different estimation methods (see “Appendix” Table 9). Overall, the resulting point estimates are close to the 2SLS estimates from Table 5. Moreover, I cannot reject the null hypothesis that the overidentifying restrictions are valid in two out of three cases, supporting the credibility of the 2SLS estimates.

For each of the three outcomes of Table 4, I also estimated a regression specification including a full set of country \(\times \) survey-year fixed effects (see, again, “Appendix” Table 8). In each case, the estimates turn out very similar to those from Table 4, further strengthening the case that attitudes toward inequality are primarily driven by the perception of inequality, rather than by the true level of inequality.

Moreover, the finding of such a close association between inequality perceptions and aggregate-level inequality, using entirely independent sources of data, shows that simple survey items are sometimes surprisingly powerful indicators of even complex economic phenomena.

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Titel: The individual (mis-)perception of wage inequality: measurement, correlates and implications
verfasst von: Andreas Kuhn
Publikationsdatum: 06.07.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Empirical Economics / Ausgabe 5/2020
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-019-01722-4

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