Correction to: Horizontal Inequality and Data Challenges

Abstract

Inequality and social exclusion receive considerable contemporary policy attention. In the field of international development, inequality-both vertical (between individuals and households) and horizontal (between groups)-is a core concern in the 2030 Agenda for Sustainable Development. Despite considerable attention to horizontal inequality in both research and policy, there are notable gaps and weaknesses in our empirical knowledge about how it manifests within and across countries and over time. This has implications not only for the rigour with which we can build and test theories in this area, but also for informing policy, monitoring trends, and evaluating the impact of interventions. This article probes what more can be learned from existing survey and census data to address empirical gaps. It argues that key methodological, conceptual, and-in particular-political issues pose persistent challenges for such survey and census data on topics relating to 'ethnicity' broadly defined. These challenges imply not only real limits in the so-called data revolution for sustainable development, but also risks to 'evidence-based' policy making in this area when it relies too heavily on quantitative data. This article serves also as the introductory and framing paper for this special issue.

Inequality between identity groups and social unrest

“…ethnic, regional, religious, cultural) [24][25][26][27][28]. Current efforts to measure horizontal inequality [9,[29][30][31][32][33] have not yet led to a consensus measure like those established for vertical inequality, such as income Gini coefficient [4] and income share held by the top 1% [34].…”

Horizontal inequality, COVID-19, and lockdown readiness: Evidence from India

“…https://www.ifs.org.uk/inequality/expert-comment/we-may-be-in-this-together-but-that-doesnt-mean-we-are-in-this-equally (accessed November 2020).2 It is worth being clear that our view is not that such data should be necessarily compiled and made public in order for such analysis to be conducted; in some contexts, there are good political reasons for caution, and the financial costs and practicalities of compiling such data also require consideration(Canelas and Gisselquist 2019).…”