Competition is good for you, part 294

My post on how competition has been improving productivity and lowering prices in both Australia (retailing in general) and New Zealand (electricity) didn't go down well with everyone. One commenter on Twitter said that it was all very well for companies to try to become more efficient to cope with increased competition, but "In their desperation for competitiveness, where do the retailers push employee wages? Down. Migrants & casualisation".

As it happens, there hasn't been a lot of research on the distributional effects of greater competition: a big survey last year done by European Commission staff, 'Ex-post economic evaluation of competition policy enforcement: A review of the literature' found (p29) that

When a lack of competition raises prices and reduces the quality of products, it causes damages to all consumers, including the poorest people. In this context, it could be interesting to analyse the distributional effects of market power. Existing evidence seems to suggest that an increase in competition is particularly beneficial for low-income people. However, the literature in this area remains in its infancy and there are a number of topics deserving further research.

But as luck would have it, along comes some new research, 'Competition policy and inclusive growth', which has had a crack at looking at the distributional impact of increasing competition (through the various effects of  the European Union's policies against anti-competitive mergers and cartels). Their bottom line is that "Interventions have important redistributive effects that benefit the poorest in society", and here are some of the key numbers. The model captures the eventual economy-wide effect of a 'mark-up' shock (a setback to producers' profit margins from competition enforcement) on different groups in society.

You can see that there are more jobs, and higher wages, for rich and poor alike, but poorer households' consumer spending goes up a good deal more than rich households' (because poorer households of necessity save less). And the rich unambiguously lose through the reduced profitability of the companies they, as the shareholding class, own.

I wouldn't necessarily go mad about this: these are early days for this kind of research, and the type of DSGE model used, while the bee's knees in modern modelling circles, can be a finicky hothouse contraption. But the results are exactly what you'd have expected: rolling back anti-competitive market power is good for consumers, and for poorer consumers more than richer. I'd draw an analogy with the producer market power created by protectionism: the poorer are disproportionately affected by the higher prices of the things that are typically 'protected' most (food, clothes, shoes). And it stands to reason that the poorer will be worst hit by any anti-consumer development: they had the least choice to begin with, whereas the rich have more options.

In any event we should know a bit more in the near future: this work was part of a conference the World Bank ran last year on 'Promoting Effective Competition Policies for Shared Prosperity and Inclusive Growth', and there's apparently a conference volume on its way.

Finally a hat-tip to the Vox website, the policy portal of the Centre for Economic Policy Research, which published these results. It's a terrific compendium of timely, wide-ranging, practical research, with something to say (in readable, short format) on all the important issues of the day. Highly recommended.

Gini needs friends

I've been away at the NZ Association of Economists' annual shindig - full conference programme here, with links to abstracts and to quite a few of the full papers, including my own one on why our Commerce Commission should have the right, and obligation, to carry out market studies - and it's been the usual interesting mix.

This afternoon I went to the session on 'Income and Inequality' - yes, I know, it's very trendy these post-Piketty days, but I went all the same - and I learned something that maybe I should have known before, but didn't. Here it is.

A conventional way of looking at income distribution is to calculate the 'Gini coefficient' (0 if everyone earns the same, 1 if one person earns it all). And the session presented by Treasury's Christopher Ball - a reprise of the recent Treasury Working Paper written by him and Victoria's John Creedy - showed us how inequality measured by the Gini coefficient has behaved over the past 30 years, as shown below. The 'market' line shows the distribution of pre-tax incomes, the 'disposable' line shows the distribution of post-tax post-transfer-payment incomes, and the 'consumption' line shows the distribution of consumer spending.

This chart has been all over the blogosphere already - mostly because people have wanted to point out that, contrary to what the fuss about Piketty might have led you to believe, income inequality peaked some 20 years ago and has either stabilised or dropped since - so I won't belabour it much further. My only comment would be that I suspect the low level of inequality in 1984 was somewhat artificial and somewhat undesirable, in the sense that the wage freeze and fixed wage relativities of late Muldoonery were gummaging up the efficient workings of the labour market: pay rates were not able to move to reflect supply and demand for different occupations. But in any event, there you have it: inequality rose mid '80s to mid '90s, then steadied or maybe declined.

You knew that. I knew that. But what I certainly didn't appreciate was how misleading a Gini coefficient can be, looked at in isolation, and I learned that from a very interesting paper presented by Athene Laws (of Motu Economic and Public Policy Research) and co-written by Athene, Victoria's Norman Gemmell, and the ubiquitous John Creedy.

Athene's big point,which I've taken from the abstract of her paper - was that

In answering distributional questions that are important for many economic phenomena, researchers and analysts should not solely examine cross-sectional aspects to the neglect of income dynamics and mobility across time. 

In other words, the Gini coefficient is based on a cross-section of income in a single year. But what if, one year, I start off  working as a wage slave, the next year I make pots of money from a book or an app, the year after I make nothing when my second book or app goes phut, the year after that I'm back working for someone else. One year I'll have been right up the wealthy end of the income distribution, the next year right down towards the poor end.

On average, I may have done reasonably okay over time. And if everybody else has been experiencing the same thing, they'll have done reasonably well, too. Over time, we may all end up much the same, which means when you look at our incomes over a longer time-frame, the Gini coefficient could well turn out quite low, even if the Gini snapshot of any individual year still shows quite a wide disparity of earnings. And Athene's data (based on access to an anonymised sample of IRD tax returns) showed precisely that pattern: there is income mobility over time, and the longer the timeframe you use to look at people's earnings, the less the actual income inequality.

So that's what I learned: never trust a Gini coefficient on its own. It may or may not be telling you something interesting, but at a minimum it needs to be read alongside what's happening to mobility. That doesn't mean that you can wave a magic mobility wand and all inequality concerns are wizarded away: unfortunately, there seems to be evidence that in some places equality of opportunity is diminishing, and the gateways to those good years at the top of the income spectrum are getting narrower (eg as the kids of the already well-to-do get a bigger share of entry to the better universities). But it does mean that you need a bigger picture of what's going on than Gini alone can tell you.