Staff at the World Bank are cognizant of these issues, and efforts have been made to address them. A recent report on poverty in Sub-Saharan Africa (Beegle et al., 2019) used a color-coded system (red to green) to indicate the authors’ assessment of how reliable different surveys in the continent were. Acknowledging such differences, and the existence of serious limitations, is an important first step. But the quality issues are substantial and seeking to address them will require much greater investment. Progress here is not constrained by awareness or incompetence. Indeed, the cross-departmental team currently in charge of the Bank’s ongoing global poverty monitoring exercise now produces a Global Poverty Monitoring Technical Note series that carefully and transparently documents the main methodological details, adjustments and limitations involved in the analysis. These notes are available online and are a great source of information on the ‘nuts and bolts’ of poverty and inequality measurement at the Bank. Instead, the binding constraint on further progress is largely a matter of resources: at present the Bank simply does not have enough trained staff to attempt this task, given the amounts of data that are received and curated every year.

A second area where the Bank has had some leadership – and to which the Fund has recently contributed as well – is the area of measuring inequality of opportunity. I have argued elsewhere that inequality of opportunity is the “bad cholesterol” of inequality: it is the part of observed inequality in outcomes which is most ethically objectionable and it is also likely to be the most detrimental to growth and efficiency (because those deprived of opportunities fail to be as productive as they might otherwise have been). Because inequality of opportunity largely reflects the extent to which predetermined circumstances shape life outcomes, it is very closely related to the concept of intergenerational mobility (or lack thereof). Family background characteristics (captured by parental income, but possibly also by parental education, occupation, locational choices, etc.) are among the most important circumstances shaping people’s life chances and outcomes – including in education and income.

Unfortunately, reliable data on income, wealth, or other living standard indicators for two or more linked generations – that is, where it is well-measured for both parents and their adult children in representative samples (or administrative databases) – are still rare in most developing countries. Given their frequent interactions with national statistical offices, the Bank and the Fund can play an important role in pushing for more and better data collection on family background in poor countries. Beyond documenting the extent of intergenerational persistence or inequality of opportunity more broadly, there is much work to be done in disentangling the causal pathways for that persistence, unpacking genetics from the nurture effect of families, those effects from that of communities and neighborhoods, and those in turn from those of schools, including their teachers and peers.

Third is the intersection between persistent inequality and climate change. Much interesting work has already been done – at the World Bank and elsewhere – using model-based simulations to examine what might happen to food prices, occupational and migration choices, incomes, etc., given changes in temperature, rainfall patterns, and sea levels. There is also work on the actual effects of the climate change that has already occurred including, for example, on the effects of high temperatures on learning (Goodman et al. 2019). But I imagine that more of this kind of work should be possible as, unfortunately, the effects of a warming planet and large changes in the climate and in the frequency of natural disasters take place. Specific causal attribution of any particular event to the broad process of climate change is both notoriously difficult and not particularly necessary. So long as we understand that, on average, certain events are becoming more likely and how, on average, their costs and consequences are distributed, we will have made much progress.

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