The inaugural Tax Justice Research Bulletin

January 2015. Over at the Tax Justice Network, we’ve just launched the inaugural Tax Justice Research Bulletin, the first of a monthly series dedicated to tracking the latest developments in policy-relevant research on national and international taxation.

This issue looks at a new paper Henrekson Stenkula 2015 fig3using the longest series of tax data that exist for any one country (challenges to this very welcome!), and an article on property taxation in Africa. The Spotlight section focuses on inequality and redistribution – including an important study from UN-DESA, Joe Stiglitz’s take on Piketty, and answers to that question you’ve been quietly pondering: just how much could you tax the 1%?

It’s a work in progress so any comments on the format, content etc, or suggestions for future research to include, would be most welcome.


Inequality: How much to tax the 1%?

Republished from the Tax Justice Research Bulletin – find it all there, with added blues. 

UN-DESA’s Pierre Kohler has produced a really useful and broad – yet far from shallow – overview, ‘Redistributive Policies for Sustainable Development: Looking at the Role of Assets and Equity’. Part of the basis is figure 3 on the left, which shows the extent to Inequality and the 1%which redistribution has remained relatively static in the facing of rising market inequality – leaving final inequality to mirror that rise. But Kohler’s real focus is on the distinction between stock inequality (in e.g. land and capital), and flow inequality (in derived income streams). The paper draws on the work of Piketty and related researchers, and the main distributional databases, to establish the base from which a relatively comprehensive analysis of main policy areas is then constructed. Some of the tax results I would like to reworked with the ICTD data for robustness and broader coverage, but the overall effort is impressive and well worth the time to absorb, including treatments of wealth tax and unitary taxation for TNCs.

The paper also goes beyond the increasingly criticised Gini measure of inequality, Inequality and the 1%making me happy with references to the Palma ratio and also covering some of the literature on the top 1%. The latter’s correlation with top marginal tax rates, and the absence of correlation between those rates and growth, is striking. Indeed, it begs the question, how highly could the top 1% be taxed without negative economic effects? A life-cycle model published last year concluded that “significant welfare gains [arise] from increasing top marginal labor income tax rates above 80%… and that these gains outweigh the macroeconomic costs” (Kindermann & Krueger, 2014: 19).

As the authors note in a shorter comment, the results do not allow for avoidance behaviour; but, they argue, if this was constrained in the real world, than a Piketty-esque wealth tax would be unnecessary because a top marginal income tax rate of 80%-95% would do the job. Of course, Piketty’s own paper (with Saez and Stantcheva) does allow for avoidance, and uses detailed empirical work on elasticities to find that the revenue-maximising top tax rate for plausible scenarios ranges between 62% (full tax avoidance scenario, where any e.g. policy-led reductions in avoidance change the elasticities and raise the optimal tax rate) and 83%.

Finally, Joe Stiglitz has taken on Piketty from a progressive perspective, arguing that the latter’s analysis of growing wealth concentration fails to capture a major part of the dynamic: not increases in capital but rather rises in the value of existing assets urban land, driven by factors outside the owners’ control (i.e. rents). [I have a hard copy of the paper from December’s fantastic Columbia conference, and it is referenced in interviews – but I haven’t found a published version online yet; will link when I do.]

$17 trillion: ActionAid counting the gender (employment) gap

AAid gender employment fig1

Here are four big bullets from ActionAid UK’s new report, ‘Close the gap!’:

  • Women earn 15% less than men on average. If women’s wage were raised to the level of men’s wages in all developing countries, with all else held equal, women would earn $2 trillion more.
  • Women’s participation is 37% lower than men’s. Raising women’s participation to equal that of men, all else held equal, would see women earn $6 trillion more.
  • Addressing both the wage gap and participation gap simultaneously in this way would see women earn $9 trillion more.
  • Extending the analysis to rich countries generates a global total gap of nearly $17 trillion.

Congratulations are due – it’s an enormously important issue and these are striking findings, so I hope it gets serious attention. [Disclosure: I commented on an early draft of the quantitative analysis.]

How good are the numbers? (Uncounting ahoy)

The methodology is fairly straightforward, and clearly set out in the report. If there’s a weakness, and there is, it’s in the data. ActionAid are commendably straightforward about this too:

Pay gaps and ratio of male to average wage taken from ILO data. There are many missing values. We fill the pay gaps using regional medians…

Inevitably given the extent of missing values, some of the extrapolations of pay gaps verge on the heroic. I’d judge the methodology to be reasonable in the data context, but make no mistake – the data context is shocking. Meanwhile,

Labour share data are taken from a [2012] working paper

I’ve no reason at all to doubt the quality of these data, but how can it be that there is no better source than these multi-year averages calculated by a single IDPM researcher a couple of years ago? The report quite rightly highlights the gender implications of the failure to count unpaid work, and to this can be added the pretty desperate state of counting of paid work.

Normally I would insert some blather here about post-2015 and reasons to be cheerful, but ba’ hairs I’m having a bad day. Talk to me about the data revolution when you’ve decided who’ll be first up against the wall. It seems we’re really talking about incremental data reforms. Either way, serious improvements in gender disaggregation are urgently needed.

Some progress will certainly come via the Sustainable Development Goals, but let’s not kid ourselves. The Open Working Group SDG proposal includes:

8.5 by 2030 achieve full and productive employment and decent work for all women and men, including for young people and persons with disabilities, and equal pay for work of equal value

That should do it, right? Maybe. Remember the current failure of counting is the end-product of 15 years of the Millennium Development Goals – which included this more prominent target:

Target 1.B: Achieve full and productive employment and decent work for all, including women and young people

Still, I suppose the MDGs didn’t include a data revolution so this time is bound to be different. Right?

Three points of caution

  • Presentation. One issue to mention is the possibility that the number take a life of its own, as these numbers can, and ends up being presented as the cost of sex discrimination in employment. It’s not this – because there’s no reason to think that $17 trillion of extra employment will suddenly come into being if the world was fairer, so a good part of this would likely come instead from reduced male employment earnings. The $17 trillion reflects the estimated scale, in currency terms, of the sex gap in employment in developing countries.
  • Economics above the rest? While it makes sense in advocacy terms to go for a big number (that may be why you’re reading this post, for example), it’s unfortunate if it adds to the sense that only economic arguments matter. As the report makes clear elsewhere (in the bits that won’t make any headlines), the deeper dignity and empowerment dimensions are much more important and complex.
  • Inequality reinforced? A related point is that the nature of the calculation reinforces a different aspect of inequality. Consider two economies of the same size with the same gender participation gap but where the average wage in one is twice as big as in the other. The methodology will value the gap in the first as also being twice as big as the other. Now that seems unhelpful, on the face of it, if we would broadly think that the two economies and their respective gaps are of equal importance. In fact, we might think that the gap in the lower-income country is more important, since it is more likely to imply poverty for those on the wrong end.

To give a sense of how important this potential problem is within the overall calculation, compare the developing country and advanced country totals. In particular, note that the wage gap in rich countries is nearly twice that in developing countries. We certainly shouldn’t downplay the sale of the problem in rich countries, and I’m glad ActionAid have made the analysis global rather than giving the impression it’s only a developing world problem. But at the same time, measuring in dollar terms may overstate the relative importance of the problem in high-income settings; when the human costs in lower-income contexts may be equal or greater.

AAid gender employment table1

Welcome to Uncounted

Film trailer voiceover voice:

Imagine a world of such structural inequality that even the questions of who and what get counted are decided by power.

A world in which the ‘unpeople’ at the bottom go uncounted, and so too does the ‘unmoney’ of those at the very top. Where the unpeople are denied a political voice. Public services. Opportunities. And the unmoney escapes taxation, regulation and criminal investigation, allowing corruption and inequality to flourish out of sight.

This is the world we live in. This is Uncounted.

What links a samizdat publication in turn-of-the-century Khartoum, the liquidation of a Scottish football club, a Burmese mobilisation for the US census, the Swiss role in supporting apartheid, a campaign around UK learning disabilities and Thomas Piketty’s proposal for a global financial registry? The common thread is the relationship between power, inequality and being uncounted – a relationship that demands we pay much more attention to who and what are counted and not.

We may pride ourselves on being the generation of open data, of big data, of transparency and accountability, but the truth is less palatable. We are the generation of the uncounted – and we barely know it.

Counting is fundamentally political. Decisions about what and who to count not only reflect unequal power, they are also a major driver of inequalities. Our failure to acknowledge and challenge these automatic tendencies means that we unthinkingly facilitate them.

There are two major elements to the uncounted: that which is uncounted because of a lack of power, and that which is uncounted because of an excess of power. In addition, the category of that which is only counted in private has its own power dynamics. Policy implications vary according to the context and the type of uncounted – but there are some very clear channels if we decide – as we surely must – to address the problem head-on.

This site will include semi-regular blogging (that may eventually result in a book) on these issues and others, along with related publications and data as they appear. There’s also a particular space for the Palma: a measure of inequality, developed with Andy Sumner on the basis of Gabriel Palma‘s analysis of the income distribution.

[NB. This post will also live at ‘About Uncounted‘, for ease of location.]