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.
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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).
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 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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Andreas Peichl and Martin Ungerer are grateful to two anonymous referees, Marc Fleurbaey (the editor), Rolf Aaberge, Paul Hufe, Vito Peragine, Dirk Neumann, Nico Pestel, Sydni Pierce, John Roemer, Arna Vardardottir and seminar and conference participants at the Canazei Winter School, ECINEQ Luxemburg, in Mannheim and London for many valuable comments and suggestions.
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See Table 2.
A.2 Descriptive statistics
A.3 Inequality of opportunity levels
The lower bound level of inequality of opportunity (IOL) is estimated by computing the MLD of the predicted values from the earnings equation. We do this for specifications Eqs. (4) and (6)–(8) using gross and net annual earnings. In addition, we distinguish between male and female individuals. The results for gross and net earnings are shown in Fig. 9. The black line displays the baseline case from Eq. (4). The red line corresponds to the model including spouse’s circumstance variables shown by Eq. (6). The case of the model from Eq. (7), including both the spouse’s circumstance and effort variables, is displayed by the blue line. Finally, the full specification including circumstances, effort and income variables of the partner corresponds to the yellow graph (Eq. (8)).
For the baseline case, the inequality of opportunity level is lower for net earnings than for gross earnings. Aside from this, the figures for IOL in net earnings are slightly higher. Regardless of the specification, IOL shows a slightly increasing trend, with a jump between 2006 and 2007. In terms of gross income, controlling for the spouse’s income (case of responsibility for spouse’s circumstances and effort, displayed by the red graph) does not increase IOL. Using net earnings instead, we find a significant increase in explanatory power and hence also in IOL when controlling for the income of the partner. This indicates that it is an effect of the German tax system, as is already visible from the earnings regression. The effect, however, declines over time, visualized on the graph by the narrowing gap between the red and black lines. This may be due to the changing relation of spouses’ incomes. We indeed find a negative, but in absolute terms decreasing, correlation between partners’ incomes.Footnote 16
The case of responsibility for spouse’s circumstances represented by the blue graph shows mostly identical results for gross and net earnings.Footnote 17 Both graphs show an increased IOL compared to the previous cases. Finally, the case of no responsibility, displayed by the yellow graphs, shows a similar development compared to the previous case. However, since 2005, the circumstance variables of the partner increase in explanatory power.
In order to further disentangle the development of IOL, we also consider male and female individuals separately. The results are shown in Fig. 10 for gross and net earnings as well as for male and female sub-samples. In general, we find that the personal information of the partner has more explanatory power for women than for men, thereby increasing our IOL measures in the different scenarios. Male IOL in gross and net earnings is fairly constant until 2008, with a sharp increase thereafter. For the female sub-sample, we find greater fluctuations over time and also higher IOL for all specifications except the baseline case. In contrast to the development of male IOL, the inequality of opportunity level for women decreases after 2008. When comparing male and female sub-samples in terms of net earnings, the case of responsibility for spouse’s circumstance (red graph) and effort variables yields interesting results. Controlling for spouse’s earnings only slightly increases IOL for men, while there is a significantly larger effect for women. However, following a peak in 1997, the divergence between the black and red graph diminishes over time. This again indicates an effect of the income tax splitting as well as changing correlation in spouses’ earnings.
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Peichl, A., Ungerer, M. Accounting for the spouse when measuring inequality of opportunity. Soc Choice Welf 47, 607–631 (2016). https://doi.org/10.1007/s00355-016-0985-9
- Labor Supply
- Assortative Mating
- Baseline Case
- Inequality Measure
- Annual Earning