Lower and upper bound estimates of inequality of opportunity for emerging economies

Abstract

Equality of opportunity is an important normative ideal of distributive justice. In spite of its wide acceptance and economic relevance, standard estimation approaches suffer from data limitations that can lead to both downward and upward biased estimates of inequality of opportunity. These shortcomings may be particularly pronounced for emerging economies in which comprehensive household survey data of sufficient sample size is often unavailable. In this paper, we assess the extent of upward and downward bias in inequality of opportunity estimates for a set of twelve emerging economies. Our findings suggest strongly downward biased estimates of inequality of opportunity in these countries. To the contrary, there is little scope for upward bias. By bounding inequality of opportunity from above, we address recent critiques that worry about the prevalence of downward biased estimates and the ensuing possibility to downplay the normative significance of inequality.

Is Capital Income Unfairer than Labor Income: Evidence from Inequality of Opportunity in China

“…This may lead to unobserved bias in the results. Second, as mentioned by Ferreira and Gignoux (2011) and Hufe et al (2021), this result only reports the lower bound estimates of IOp given by available circumstantial data, which is yet conclusive given that omitted circumstance variables and other unobservable factors may contribute differently. Also, the lack of appreciation/depreciation in assets over time, as mentioned in section 2.3, may underestimate capital income and undermines the results.…”

“…While literature in IOp developed rapidly in various aspects over the last two decades, including the compensation principle (ex-ante vs. ex-post), reward principle (liberal vs. utilitarian), and measurement methodology (direct, indirect, stochastic dominance, and norm-based), etc. (Devooght, 2008;Lefranc et al, 2008;Ferreira & Gignoux, 2011;Roemer, 2012;Fleurbaey & Peragine, 2012;Ramos & Van de gaer, 2015;Brunori et al, 2018;Hufe et al, 2021), there has yet to focus on the differences among income sources, such as capital and labor income, to the best of our knowledge. We believe such distinguish is not only critical in income compositional inequality, as suggested by recent literature (Ranaldi, 2021;Ranaldi & Milanović, 2022), but also is essential in the estimation of IOp.…”

“…Although region at birth could be viewed as a circumstance variable that is beyond one's control (Golley et al, 2019), using the region of residency as a proxy variable is not appropriate since migration could also be under one's control 30 . Furthermore, literature also adopts migration as an effort variable as it is at least partially under individual control (Bourguignon et al, 2007;Hufe et al, 2021). Since the region at birth is not available in CHFS data, we believe the region of residency is more appropriate in the heterogeneity analysis 31 .…”

Upper and Lower Bound Estimates of Inequality of Opportunity: A Cross‐National Comparison for Europe

“…For each individual we predict a fixed effect, which is then used as our measure. This approach was proposed by Niehues and Peichl (2014) and has been used for the UK (Flatscher, 2020) as well as for a number of middle‐income countries (Hufe et al ., 2022). The main benefit of this approach is that it does not require data on circumstances, only repeated waves for each individual.…”

“…Because the fixed effect is assumed to capture all (or at least, most) circumstances but also time invariant efforts, this approach results in “upper bound” estimates of IOp. While not as widespread as the lower bound approach, a few articles have provided upper bound estimates for the UK and some middle‐income countries (Flatscher, 2020; Hufe et al ., 2022).…”