SciBlogs

Pollution taxes? Eric Crampton Jul 02

The Environmental Defence Society wants a shift to taxing pollution. It sounds fine in principle, but there may be a few problems in practice.For some things, like GHG emissions, where the negative effect is global rather than localised, it’s both simp…

Don’t you try overturning my anecdotes with data. Eric Crampton Jul 01

Few things are more depressing than a Stuff.co.nz comment thread, even on a good day. But this one was a doozy.Michael Daly reported on the Treasury paper showing, if anything, declines in income inequality in NZ since the early 2000s and, for expendit…

Treasury and the Reserve Bank have words Matt Nolan Jun 30

I am not blogging at the moment – and I’m incredibly sorry about that.  I won’t really be back until I can commit to being back properly – which won’t be until I’ve completed a lot of modeling work related to income inequality in New Zealand.  I am not back today to talk about any […]

Inequality crisis? Eric Crampton Jun 29

Consumption inequality in New Zealand is down. Not just down on the mid-1990s post-reform peak, but also down relative to 1984.

That’s the conclusion out of new work by Ball and Creedy at the Treasury. I cover it over at The Initiative’s Sandpit blog. But here’s the key figure.

Gini Inequality and Tax Changes 1984 to 2013

The dashed “Market” line traces Gini inequality in market earnings over the period – that’s before taxes and transfers. That series rose from the late 1980s through about 1994, then levelled off before easing back to early 1990s levels.

The solid “Disposable” line tracks Gini inequality in disposable incomes – that’s after tax and transfer. This measure rose from 1988 through to about 1994 then was basically flat. Note that the spike at 2001, and again around 2010, coincide with tax changes that encouraged income shifting from one year to another, generating the hump.

The dashed “Consumption” line is the one that’s particularly interesting. It measures inequality in real consumption. That measure rose a bit from the late 80s, plateaued through the mid-90s, and has eased off since then. Current inequality in consumption is lower than it was before the 80s reforms.

I doubt that data will have much effect on media frenzies around inequality. But at least you and I know better.

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Minimums and medians Eric Crampton Jun 27

It’s a bit predictable. Whenever there’s a hike in the NZ minimum wage, the unions point to Doucouliagos’s metastudy that minimum wages in the US have had small (if any) employment effects and to Dube’s work also showing little effect in the US. I point to that American minimum wages are less than 40% of the median wage and that New Zealand’s minimum wage is around 65% of the median. When the minimum wage is more binding, you should expect bigger employment effects.

And so I was interested to read this:

The University of Massachusetts economist Arindrijat Dube, a strong proponent of minimum wage policies, suggests that states and cities should use a threshold of about half of the median wage when setting wage floors. By Bernstein’s estimate, the L.A. minimum wage in 2020 will be about 60-65 percent of the city’s median wage—a full 10 to 15 percentage points above Dube’s recommended threshold. This is uncharted territory, which may be one of the reasons that some L.A. unions asked for an exemption to the $15 minimum. In a city like St. Louis, where both the median wage and the cost of living are lower by comparison, the $15 minimum is even riskier.

I’ve typically said that I get nervous about the employment effects where minimum wages are 45-50% of the median. And here we have Dube suggesting half the median.

I’ll have to remember to pull this out the next time the unions here cite Dube at me when I caution against minimum wages that are 65% of the median.

As reminder:


Note that that’s as fraction of the mean; the mean is higher than the median.

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Racial bias in mortgages? Eric Crampton Jun 26

Simon Collins at the Herald asked me for comment on a paper alleging racial bias in mortgage lending; his story’s now up.

The paper is available here. It shows, using ordered logit regression, that people who self-identify as being more easily identified as Maori are less likely to own their own home, correcting for income and a few other variables. The paper’s empirics say absolutely nothing about mortgages or banks. But the study nevertheless concludes:

“To sum it up in one sentence: results from a large national probability sample of Māori indicate that the more Māori you look, the less ‘mortgage worthy’ you are.”

Here are a few alternative hypotheses:

  • The empirics correct for current employment and current income but not past employment and past income. If Māori employment histories are more varied than non-Māori, and if this also follows the “is identified as Māori ” indicator, Māori will have less accumulated wealth at any given level of income, and this is not controlled in the study.
  • If those who look more Māori are given preference in state housing, then home ownership would also be attenuated.
  • If parental resources are negatively correlated with looking more Māori , then that also affects ability to put together a deposit on a house. Note too the potential influence of holding household wealth under Māori land tenure.
I also think they’ve an error in how they described the magnitude of the effect. Remember that this is an ordered logit regression. So you can’t just take the point estimate and multiply it by the number of interval steps to get an accumulated effect; you have to ask your stats package to give you a predicted value at the different values of the category. At page 11, it really looks like they linearised from the point estimate:

Some readers may be wondering how large this effect is in practical terms. One way to think about it is like this: when statistically adjusting for numerous other demographics, such as differences in income, region of residence, and education, a Māori person with a score of 5.55 on our Perceived Appearance measure of Māori identity would be twice as likely to not own their home relative to someone with a score of 1 in Perceived Appearance. This is a statistically significant association, which in our view represents a large and extremely important difference in the rate of home ownership based solely on merely appearing more Māori.

They have an odds ratio of 0.82, which ought to mean that a step change increase in perceived appearance score from the mean score reduces likelihood of owning a home by 18%. I don’t think that means that if you go 5.55 steps in the other direction (1/0.18) from the mean score doubles your likelihood of home ownership, except under some pretty strong assumptions. But it’s been a little while since I’ve played around in ordered logit.

Here’s the bit where Collins quoted me – entirely fairly:

However Dr Eric Crampton of the NZ Initiative think-tank said there could be many other explanations for this besides racial bias. For example, people who looked more Maori might have parents who did not have freehold properties to use as collateral for loans, a factor that was not surveyed.
“Banks would be throwing money away if they decided to not lend to somebody simply based on looks,” he said.
Mortgage brokers Bruce Patten in Auckland and Karen Essex-Mooney in Blenheim both said they had never seen a mortgage application turned down because the borrowers were Maori. They said many borrowers now applied online and never actually met the lenders.
New Zealand Bankers’ Association chief executive Kirk Hope said racial stereotyping was not in the banks’ or their customers interests especially within such a competitive part of the banking sector.
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IUDs and teen pregnancy Eric Crampton Jun 25

Better access to IUDs at subsidised family planning clinics reduces teen birth rates, says a new NBER working paper by Jason Lindo and Analisa Packham.From their abstract:Despite a near-continuous decline over the past 20 years, the teen birth rate in …

Times Tables Eric Crampton Jun 24

Me at The Press on the Initiative’s numeracy report:

The Numeracy Project, as it was called, had some really great elements. Rather than simply rote learning facts, students would also build their understanding of why six by nine is 54 and alternative strategies for figuring it out. Those alternative strategies can really help when trying to multiply larger figures beyond the twelve-by-twelve that those my age had to memorise.

But something went wrong in the implementation – or at least in some schools. There is a lot of school-by-school variation in New Zealand – and this is a good thing. But some schools took the Numeracy Project a bit too literally. Rather than complementing the times tables with the additional strategies, they threw out the rote learning part.

Patterson’s report suggests this too great a lean against rote learning lies behind some of New Zealand’s recent poor maths scores. Kids who have to spend time working out six by nine use up mental capacity that then is not available to take the next steps. Those who memorised it can move quickly to the next step in applying their answer.

Far from a call to abandon modern teaching practices in favour of rote learning, Patterson’s report argued simply that the pendulum has swung too far. We need both rote learning and understanding. The report also recommended measures to help parents ensure that their kids’ teachers are ready to really apply the more modern mathematics teaching methods which require greater teacher numeracy than teaching simple rote memorisation.

While the Initiative’s Twitter stream filled with the usual attempts to pigeon-hole our recommendations into the Kiwi Twitteratti’s ideological view of the world, our email inbox filled up with supportive messages from teachers, university lecturers, and maths tutors who agree that New Zealand kids really deserve better.

Read the whole thing…

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Effective Altruism Eric Crampton Jun 23

I’ll be chairing a discussion with Peter Singer in Christchurch in September. If you’re anywhere in the neighbourhood, by which I mean within a 3-hour flight, you should attend.

I have never loved and hated and been changed by a book as much as Singer’s Practical Ethics. I threw it across the room more often than any other. Actually, I think it’s the only book I’ve ever hurled against the wall. But his arguments are almost impossible to resist.

The morning that I got the call from the Christchurch Festival inviting me to this, I’d walked in to work with Eleanor, then aged 4. On the way, that morning, I’d explained trolley problems to her – as you do with your four year old. She proved a very strict utilitarian. She then went on to propose ever differing bundles of who might be on which rail lines and whether you’d pull the switch – she was basically running hypothetical choice experiments to find out my marginal willingness to pay across options. Most of the options involved kitties of varying cuteness against family members, so it was all pretty easy for me. Then I got the call asking to come in to talk with Peter Singer. It was a great day.

I’ll be discussing Singer’s latest work on effective altruism. I’m really looking forward to it. Hit the link at the top to register and get tickets.

ON EFFECTIVE ALTRUISM

Peter Singer 4
How can we do the most good? Peter Singer, often described as the world’s most influential living philosopher, presents a challenging new movement in the search for an ethical life. Effective altruism requires a rigorously unsentimental view of charitable giving, urging that a substantial proportion of our money or time should be donated to the organisations that will do the most good with those resources, rather than to those that tug the heartstrings. Chaired by Eric Crampton.
Peter Singer is the author of more than 20 books, including the groundbreaking work on ethics, Animal LiberationThe Ethics of What We EatThe Life You Can Save, and his latest, The Most Good You Can Do. He teaches philosophy at Princeton and Melbourne Universities.
Eric Crampton is Head of Research with The New Zealand Initiative in Wellington and Adjunct Senior Fellow with the Department of Economics and Finance at the University of Canterbury. He blogs at Offsetting Behaviour.

Another fun bit: the Christchurch festival folks invited me, in part, because I’d blogged on the ridiculousness of charity races some time ago.

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Is 400 the new 300 in ODIs? Seamus Hogan Jun 22

The current ODI series between England and NZ has been quite extraordinary. England has scored more than 300 in every game, but has lost twice. Yesterday morning, New Zealand’s total of 349 was not only chased down by England, it was chased down with ease, with England losing only three wickets and having 6 overs to spare. 

My sense from my Twitter feed is that the conventional wisdom is as follows:
  1. The rule changes dating from October 2012 (that saw two new balls in each innings and a reduction in the number of fielders allowed outside the circle in non-powerplays) allowed for larger scores, as the outfield gaps and still-hard balls allow batsmen to score at will in the final overs.
  2. These rule changes coincided with new batting skills honed in 20-20 competitions like the IPL.
  3.  New Zealand has been leading the way in showcasing an aggressive approach to cricket; England  prior to now has continued to play with an outdated conservative style, but has now belatedly accepted the new approach, in which “400 is the new 300”.

There is probably much right with this version of events, but I’m not fully convinced. Here are some raw numbers. Since the October 2012 rule changes prior to the current series between England and New Zealand, there were 177 ODI games played involving two teams from the top 8 (defined as the test-playing nations excluding Zimbabwe and Bangladesh), excluding games with a Duckworth-Lewis-Stern reduction in overs. Of those, 56 (or 31%) saw the team batting first score 300 or more. This is certainly a higher rate than we would have seen in past eras, but not as high as conventional wisdom seems to be suggesting. Moreover, getting to 300 still made the team batting first the overwhelming favourite. Of those 56 games with a first-innings score in excess of 300, the team batting first won 48 (86%). More pertinently, of the 18 games where the first innings score only just got to 300 (defined as a score between 300 and 310), the team batting first won 14, which is still a %77 success rate. If 400 is the new 300, it really should be easier to chase down 300 than these data suggest.


I wrote last year about how, once you adjust for mismatches between teams and where the game has been played, there wasn’t much evidence in the data for a general trend towards increasing first-innings scores. Taking all games from the start of the English 2002 season through to the end of the World Cup, controlling for team ability, home-field advantage, and the ground being used, first innings scores since Oct 2012 are only 12 runs higher on average than in the 10 years before Oct 2012. (For data geeks, I describe the exact model at the bottom.)

The following graph illustrates the lack of a trend. The small red dots are the difference between the first-innings score and a prediction based on the team batting, the team bowling, which team (if any) was playing at home, the ground at which the game was played, and allowing for a 12-run premium for the current rules. The solid red dots are a 25-game smoothed moving average, to take out some of the random variation and make any trends clearer. Although there appears to be a bit of an upward trend over the period since October 2012, scores by the end of this period were still only 28 runs higher than in the 2002-2012 period, suggesting that 328 is the new 300!



But now look at the four blue dots. These are the out-of-sample prediction errors for the first four ODIs between England and NZ in the current series. These predictions take into account England’s and New Zealand’s recent (since Oct 2012) batting and bowling form, England’s home-field advantage, and how high scores typically are at those four grounds. The prediction, actual score, and prediction error are as follows:


Ground

Predicted Score

Actual Score

Prediction Error

Edgbaston

235

408

173

The Oval

256

398

142

The Rose Bowl

269

302

33

Trent Bridge

232

349

117

Chester-le-Street

225 (Eng) 228 (NZ)

?

?


The point here is that rather than there having been a world-wide trend in the past few years that England have only now come to grips with; the current series has been extraordinary in every respect even in comparison to recent history. So what is going on? I can think of four hypotheses:
  1. I have made a massive coding error in my database.
  2. There has been a structural break in conditions: The four English groundsmen have produced very different pitches than in the past, ones much more favourable to high scores.
  3. There has been a structural break in team quality: Both NZ and England have better batting and/or worse bowling in this series than they had in the recent past.
  4. These four games have been black-swan events; and things will return to normal soon.
  5. There has been a strategic mindset shift in both New Zealand and England.

When things look extraordinary, coding errors are always a good bet, and I wouldn’t rule this out, but the raw predictions don’t look too far out from my own intuition, so I  don’t think this is the problem here.


I can’t comment on whether conditions were very different from usual in the four games so far, but I haven’t seen any commentary from England suggesting that the groundsmen have been producing untypical pitches, so I suspect hypothesis 2 is not the right one.


There is probably some truth to hypothesis 3. I don’t think the batting is too much different, but the bowling is quite possibly weaker. New Zealand have lost Vettori, Anderson and Milne from their World Cup bowling line-up, and have had Southee and Boult together for only one of the four matches. England have rested Anderson and Broad. Even so, I would put my money on the final two hypotheses explaining most of the data. 


The idea that teams are not aggressive enough when batting is something Scott Brooker and I have been saying for a long time. Back when he was writing his thesis, Scott experimented with what an average team would be able to achieve if they applied the optimal level of aggression. Based on the strike rates and dismissal rates that we can observe batsmen having in different game situations (e.g. conservative batting in the middle overs versus aggression in the death overs), he constructed a set of frontiers describing the trade-off between risk and return for typical batsmen in positions #1-#11, and then simulated optimal behaviour. He found that scores could be roughly 30 runs higher if batting teams were more aggressive, but that there would be more variance in scores and a higher probability of not batting out the overs. This was based on data from before 2007. It is likely that under the new rules, the value of extra aggression is even higher. 

What I think we are seeing in the current series is two teams who keep pushing the boundaries of this approach, forcing the other team to react in kind, and so all kinds of previously unrealised potential that has existed for a while is now being revealed. Contrary to the conventional wisdom, I don’t think this has been New Zealand’s approach before now. Rather, I think they have emphasised retaining wickets during the middle overs in preparation for an all-out assault in the final 10. New Zealand’s famous aggression in the World Cup was mostly seen in its approach to bowling, putting an emphasis on wicket taking rather than containment.


If I am right, that there has been a mind-set change for both teams in the current series, I am mindful that Scott’s conclusion was that the additional 30 runs on average would come alongside a big increase in variance. This brings me back to the black-swan hypothesis. We have seen scores more than 100 runs in excess of what recent form would have predicted on average. It is likely that in each game, there was a degree of luck. Batsmen got away with taking risks on these occasions, but we could just as easily have seen scores that were quite low. Even allowing for the fact that dropped catches are more likely when batsmen are hitting the ball hard, the catching in the current series does seem to have been below par. Realistically, 370 might be the new 300, but equally 180 the new 200.

Method:


My prediction model was based on a database of all non-rain-affected ODIs involving the top-8 countries since May 2002, using only games played on grounds where there were at least 10 matches played (but also including Chester le Street, as that is the venue for the final ODI between England and NZ).


The model was an OLS regression of first innings score on a dummy variables for the batting-team country, dummy variables for the bowling-team country, a dummy variable for each of the 53 grounds, a dummy variable for when the batting team was playing at home, and for when the bowling team was playing at home. Finally, I added a dummy variable for matches played since Oct 2012, and more dummy variables for this recent-era interacted with the batting and bowling team dummies. 

These interaction teams mean that only post-Oct-2012 data is used to determine the effect of team ability on scores; the only reason for pooling the data with the pre-Oct-2012 era is to provide enough data to estimate ground effects. Essentially, the model is assuming that the relative impact on scores of being at a particular ground and the relative impact of home-field advantage has not changed from before the Oct 2012 to after.


In the 25-game  moving average shown in the chart above, the data is broken around the structural break of Oct 2012, so that the smoothed line before the break is not influenced by games played after the break and vice versa. 

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