From the tables it looks as though the top 10%’s share of income in Kenya declined from about 50% to about 40% over the 20 years, where the bottom 40%’s share increased from about 10% to about 13% (sorry, can’t see the exact figures).

Let’s assume Kenya’s national income was 100 in 1990 and grew at 3% compound for the next 20 years (recent growth has been faster, of course), it would now be 180. The top guys’ share would have gone up from 50 to 72 (+22), whereas the bottom guys’ share would have gone up from 10 to 23 (+13). Big deal, one might say, in the context. I guess this speaks to the issue that the paper raised about the difficulty of determining what an acceptable level of inequality is, or, for that matter, what we should consider to be an acceptable rate at which inequality declines.

In particular, because they are both generated from the same data (income distributions), it would not be difficult to track them side by side. Then one could determine which one is more interesting to a given topic at hand using statistical analysis to form a theory about which type of inequality is most pertinent.

Although the normative assumptions about the Gini coefficient are important to consider, we might also consider whether it is of practical importance if it gives us information about where the Palma is going. If the Gini coefficient is more sensitive to changes in the middle, then we might ask what these changes indicate, and what this can tell us about other issues, rather than dismissing this as less useful than a different type of predictive power.

Although your results for the Palma and MDGs are really interesting, I think that your note that “Given the findings of section 3, it follows that similar results are likely for the Gini
also.” is more critical than the parentheses imply. The question seems less either/or and more, which does a better job predicting what?

It might be that different inequality distributions have different importance in different situations, just like we measure both blood pressure and heart rate, and these are different in men and women.

Congratulations on this excellent paper and thanks for your work. I hope that the development community can take a balanced approach to putting another tool in the toolkit of poverty analysis and reduction.

1. Ha! Not sure; Adam Wagstaff points out that the leaked documents are only drafts and can change, so I’m hoping that means the Palma might yet feature.
blogs.worldbank.org/developmenttalk/should-inequality-be-reflected-in-the-new-international-development-goals

2. I like it – although the data challenges are significant. It’s probably worth mentioning that some defenders of the Gini have criticised the Palma precisely because the data on the top end of the distribution is bad. (As if it somehow speaks in favour of the Gini that it is insensitive to a badly measured bit of the distribution.)

There isn’t the same stability of the omitted bit of the distribution as the Palma seems to have, which makes it less attractive. But on the other had, we could call it ‘extreme Palma’, or Palmax…

1. did the World Bank in setting it’s strategic goal on the growth of the bottom 40% go for la palma and then lose their nerve before they got to the top 10%. what gives? how do you interpret that?

2. I wonder if the top 10% is actually a good enough selection. After all, in the US, it’s hardly the growth of the top 10% that’s driving inequality. Even the top 1% is probably too expansive. the big driver of inequality is the extraordinary, outrageous growth in the top 0.01%. NOt sure how applicable that is in other countries/contexts. But politically, it’s important. If new taxes and other confiscations are necessary, we want to do good and effective targeting. And, not trivially, we don’t want to make unnecessary enemies. So, how about a “super Palma” = income share of 1%/40%? How different would that be? Would it have any analytical or other value?

What about opportunity? It’s a bit more sketchy, but doesn’t it trump inequality, justice-wise?

Take a busker and a banker – no reason they shouldn’t have lots of inequalities, right? If they both went to Eton, and had a similar enough menu of life choices – good; if the busker wants to be a banker (or vice-versa), not so good.

The problem comes when you start to wish your past menu choices were different, but can’t retread (path dependence). For me, for example, dropping maths means I’m unlikely to get an Economics PhD. Some people are never given the opportunity to be musical (a much harsher deprivation, in my view).

Chris, we are certainly pursuing the result. Watch this space…

Catarina, as we discuss a little in the paper, part of our thinking comes from exploring the set of group inequality measures that seem likely to be part of the post-2015 framework (e.g. on gender and ethnolinguistic group), and seeing some value in having a comparable measure for economic inequality. There’s a little discussion of that in the synthesis report of the post-2015 inequalities consultation report too – but I know your sector has done a good deal of thinking about measurement issues already, in fact I’ve drawn on a bit of it for a DHS-based paper that’s in the works.

Max, no reason not to use this for wealth – am also trying that in the aforementioned DHS bit of work. Though of course we need much better data on wealth, it makes the income distribution data look quite good which is no mean feat.

As to when we can start fighting inequality, not just measuring and talking about it: our hope is that the intuitive simplicity of the Palma can make this whole area resonate much more readily than the Gini with citizens and policymakers alike. Bring it on!

and also when can we start fighting inequality and not just measuring and talking about it?