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Social Choice and Welfare

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09.08.2016 | Original Paper

Accounting for the spouse when measuring inequality of opportunity

verfasst von: Andreas Peichl, Martin Ungerer

Erschienen in: Social Choice and Welfare | Ausgabe 3/2016

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Abstract

The existing literature on inequality of opportunity (IOp) has not addressed the question of how the circumstances and choices of spouses in a couple should be treated. By omitting information relevant to the spouse in IOp estimations, the implicit assumption has been full responsibility for the spouse’s income, effort and circumstance variables. In this paper, we discuss whether or not the spouse’s characteristics should be treated as responsibility factors. Using German micro data, we analyze empirically, how IOp estimates are affected when a spouse’s circumstance or effort variables are included in the analysis. We find that including spousal variables can increase IOp measures by more than 20 (35) percent for gross (net) earnings. The less responsibility assumed for the partner’s variables, the higher the IOp estimate.

Vorheriger Artikel A necessary and sufficient condition for weak Maskin monotonicity in an allocation problem with indivisible goods

Nächster Artikel Efficiency and strategy-proofness in object assignment problems with multi-demand preferences

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See, e.g., Ramos and Van de gaer (2016), Roemer and Trannoy (2016) or Ferreira and Peragine (2015) for recent surveys.

In contrast, studies on inequality within couples look at inequality of outcomes rather than IOp. For instance, Lise and Seitz (2011) show that standard measures of inequality in terms of consumption are underestimated by about 50 percent, if one neglects intra-household inequality. For a survey, see e.g., Browning et al. (2013).

This is even more problematic when estimating IOp for societies where marriages are arranged (e.g., by parents) and where partners themselves have a limited say in who they marry.

The notation closely follows Niehues and Peichl (2014).

As is common in the majority of EOp literature, we do not explicitly take into account the role of luck in our estimations. Hence, we (implicitly) assume that luck belongs to the sphere of individual responsibility and in our deterministic model, the individual is held responsible for any random component that may affect his income and that cannot be attributed to the observed circumstances. The same is true for potential measurement errors in the earnings data. See Lefranc et al. (2009) for the extension of the EOp framework in order that it explicitly take luck into account.

In the (empirical) EOp literature, two different approaches have been used to estimate IOp (Fleurbaey and Peragine 2013): ex-ante vs. ex-post. The former partitions the population into types, i.e. groups of individuals endowed with the same set of circumstances, and IOp is measured as inequality between types. In the latter case, individuals are classified into responsibility groups (tranches) of individuals at the same effort level and inequality within tranches is investigated.

In addition, one might not be fully aware of a spouse’s full set of circumstances due to asymmetric information when committing to a relationship.

In empirical estimations of EOp, it is impossible to observe all characteristics that constitute an individual’s circumstances (e.g. innate talent or ability). Hence, existing estimates of IOp are only lower bound estimates of the true share of unfair inequalities due to circumstances (Ferreira and Gignoux 2011). Exceptions are Bourguignon et al. (2007) who simulate the magnitude of omitted variable bias to estimate bounds around the true effect of observed circumstances on income inequality and Niehues and Peichl (2014) who suggest an upper bound estimator.

In contrast, non-parametric methods avoid the arbitrary choice of a functional form on the relationship between outcome, circumstances and effort (e.g. Lefranc et al. (2008), Ferreira and Gignoux (2011) or Aaberge et al. (2011)). However, this approach has the drawback that considering more than one circumstance variable is difficult due to practical reasons in the presence of small cell sizes which is usually the case in survey data. Access to large-scale administrative panel data with information on circumstances (family background), which is not available in Germany, would allow to estimate IOp also non-parametrically.

We use the log of incomes since estimation in logs is common in labor economics as log-normal is typically a very good fit for (right-skewed) earnings data. Nevertheless, we have also estimated the models in levels (as robustness checks; not reported) and did not find systematic differences.

Note that for this specification we cannot estimate Eq. (6).

A detailed overview of the SOEP is provided by Haisken De-New and Frick (2005) and Wagner et al. (2007). Issues concerning sampling and weighting methods or the imputation of information in case of item or unit non-response is well documented by the SOEP Service Group.

The eleven groups are public administration and social security, which serves as the benchmark, fishery and agriculture, energy, chemicals and steel, engineering, manufacturing, construction, wholesale and trade, transport, financial industry, service, education, and health service.

Note that these measures do not fulfill the axiom of path-independent decomposability and hence the results should be interpreted with caution.

Note that we abstract from potential behavioral responses (such as labor supply) when facing a new partner with different characteristics (Pestel 2016). Furthermore, we only compare IOp measures in gross earnings as we would have to re-calculate the total tax burden of the new randomly matched couples in order to also analyze IOp in net earnings.

The literature on assortative mating shows an increase in assortative mating in education over time that also reflects in earnings (Schwartz and Mare 2005). A detailed discussion on the importance of assortative mating is conducted in 4.5.

The case of responsibility for spouse’s circumstances is implemented by controlling for spouse’s effort and income variables in Eq. (7).

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Titel: Accounting for the spouse when measuring inequality of opportunity
verfasst von: Andreas Peichl
Martin Ungerer
Publikationsdatum: 09.08.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Social Choice and Welfare / Ausgabe 3/2016
Print ISSN: 0176-1714
Elektronische ISSN: 1432-217X
DOI: https://doi.org/10.1007/s00355-016-0985-9

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