It’s about six months since we triggered a good wonk-tastic discussion here on Duncan’s blog on how to measure inequality. We proposed a new indicator and called it ‘the Palma’ after Chilean economist Gabriel Palma, on whose work it was based. We suggested the Palma would complement, or perhaps even replace the (in our view) less useful Gini index. Here we bring things up to date with a look at inequality in the post-2015 debate, and present some further findings on the relative merits of Gini and Palma, based on our new paper.
First, post-2015 and all that.
Last week the Center for Global Development held an event in Washington DC to discuss the best income inequality measures for post-2015, with both a technical panel (video) comparing alternative measures, including the median, the Palma, the Commitment to Equity indicator and a multidimensional approach.
There was also a ‘user’ panel (video) with wonks from the IADB, IMF, Oxfam, UNICEF and the World Bank, discussing the policy need and the scope for implementation. While panelists and other participants did not agree on the idea of a post-2015 inequality goal or target (surprise, surprise), there was near unanimity on the importance of measuring income inequality, and doing so better than we do now.
That consensus, however, seems to be lacking in the intergovernmental discussion on post-2015. There appears to be broad support for a focus on economic inequality from Latin American representatives, but more patchy support elsewhere. At the CGD event, Nancy Birdsall highlighted that, to the extent that governments reflect elite interests, there may not (yet) be a political consensus; but that there is instead a “people’s consensus”, with inequality riding high on the agenda of popular concern in countries at all income levels.
Whether this can be translated into more effective political support is unclear, though Oxfam and Save the Children are working on building that political consensus.
But back to feeding the wonks (and remember skimming the next section will save you from having to read the actual paper).
Our new paper, released to coincide with the event, makes three main points (we’re told always make three points – never more, lest people’s heads explode …).
First, inequality measures for policy frameworks such as post-2015 must be considered on the basis of policy criteria, not only the common technical criteria – in other words, we need to think about whether measures are useful for policy processes, not only whether they exhibit certain mathematical properties. A technically perfect measure which is unintelligible to most people (or which requires significant explanation) is highly unlikely to form the basis for policy-maker accountability. So we propose five policy axioms (bear with us):
- That the value judgments of using this indicator are sufficiently explicit.
- That it is clear what signal is being given to policymakers on the preferred direction of change of inequality (improving or worsening).
- That it is clear to a public (ie non-technical) audience what has changed and what it means.
- That the policy response is sufficiently clear to policy-makers (meaning how policies do or do not influence the indicator).
- That it is possible to capture horizontal (e.g. gender and ethno-linguistic group) as well as vertical inequality in the indicator.
So, what? We think the Palma, which is the income share of the top 10% divided by the income share of the poorest 40% , is much more meaningful to policy makers and otherwise normal people, than the Gini and Theil measures, which are both relatively obscure statistical constructs.
Second, income inequality is in ‘the tails’ (the rich and the poor). That is, most of the difference when you compare countries, or the same country over time, is in what happens at the top and bottom of society rather than in the middle. For example, the middle 50% of the population have about half of national income in both Honduras and Morocco; but the poorest 40% in Morocco have more than twice the share of national income as in Honduras, and the top 10% correspondingly less (see figure below).
But the Gini is overly sensitive to the middle of the distribution, so it is not well equipped to address this type of inequality – while the Palma is designed to do just that.
Third – and this is a bit technical – a criticism of the Palma: that it relies only on two points of the distribution, and so ignores too much information about inequality. We show that in regression analysis (bear with us), the same two points of the distribution (i.e. the income shares of the top 10% and bottom 40%) can perfectly explain the Gini – so that in practice it contains no more information than does the Palma.
The difference is that while the Palma is the simple ratio of the two, the Gini has the following form:
Gini = (0.581 * income share of top 10%) – (1.195 * income share of bottom 40%) + 0.419
Hardly intuitive to the person in the street, eh?
In practice, the Ginis that are used in much analysis contain no more information than the Palma, but the Palma is transparent about this, and we think intuitively clear – take what the rich get and divide it by what the poor get.
As we said when presenting the Palma last week, we’re not trying to completely replace the Gini, just saying that the Palma has something to add that is understandable to more people.
Our view is that multiple measures should be used to monitor income inequality for policy purposes – including not only the Palma but also, for example, the median income; and if you must, the Gini.
But if one measure alone is to be used: please don’t let it be the Gini.