Posts filed under Politics (148)

August 1, 2015

NZ electoral demographics

Two more visualisations:

Kieran Healy has graphs of the male:female ratio by age for each electorate. Here are the four with the highest female proportion,  rather dramatically starting in the late teen years.



Andrew Chen has a lovely interactive scatterplot of vote for each party against demographic characteristics. For example (via Harkanwal Singh),  number of votes for NZ First vs median age



July 15, 2015

Bogus poll story, again

From the Herald

[] has surveyed its users and found 36 per cent of people spoken to bought property in New Zealand for investment.

34 per cent bought for immigration, 18 per cent for education and 7 per cent lifestyle – a total of 59 per cent.

There’s no methodology listed, and this is really unlikely to be anything other than a convenience sample, not representative even of users of this one particular website.

As a summary of foreign real-estate investment in Auckland, these numbers are more bogus than the original leak, though at least without the toxic rhetoric.

July 11, 2015

What’s in a name?

The Herald was, unsurprisingly, unable to resist the temptation of leaked data on house purchases in Auckland.  The basic points are:

  • Data on the names of buyers for one agency, representing 45% fo the market, for three months
  • Based on the names, an estimate that nearly 40% of the buyers were of Chinese ethnicity
  • This is more than the proportion of people of Chinese ethnicity in Auckland
  • Oh Noes! Foreign speculators! (or Oh Noes! Foreign investors!)

So, how much of this is supported by the various data?

First, the surnames.  This should be accurate for overall proportions of Chinese vs non-Chinese ethnicity if it was done carefully. The vast majority of people called, say, “Smith” will not be Chinese; the vast majority of people called, say, “Xu” will be Chinese; people called “Lee” will split in some fairly predictable proportion.  The same is probably true for, say, South Asian names, but Māori vs non-Māori would be less reliable.

So, we have fairly good evidence that people of Chinese ancestry are over-represented as buyers from this particular agency, compared to the Auckland population.

Second: the representativeness of the agency. It would not be at all surprising if migrants, especially those whose first language isn’t English, used real estate agents more than people born in NZ. It also wouldn’t be surprising if they were more likely to use some agencies than others. However, the claim is that these data represent 45% of home sales. If that’s true, people with Chinese names are over-represented compared to the Auckland population no matter how unrepresentative this agency is. Even if every Chinese buyer used this agency, the proportion among all buyers would still be more than 20%.

So, there is fairly good evidence that people of Chinese ethnicity are buying houses in Auckland at a higher rate than their proportion of the population.

The Labour claim extends this by saying that many of the buyers must be foreign. The data say nothing one way or the other about this, and it’s not obvious that it’s true. More precisely, since the existence of foreign investors is not really in doubt, it’s not obvious how far it’s true. The simple numbers don’t imply much, because relatively few people are housing buyers: for example, house buyers named “Wang” in the data set are less than 4% of Auckland residents named “Wang.” There are at least three other competing explanations, and probably more.

First, recent migrants are more likely to buy houses. I bought a house three years ago. I hadn’t previously bought one in Auckland. I bought it because I had moved to Auckland and I wanted somewhere to live. Consistent with this explanation, people with Korean and Indian names, while not over-represented to the same extent are also more likely to be buying than selling houses, by about the same ratio as Chinese.

Second, it could be that (some subset of) Chinese New Zealanders prefer real estate as an investment to, say, stocks (to an even greater extent than Aucklanders in general).  Third, it could easily be that (some subset of) Chinese New Zealanders have a higher savings rate than other New Zealanders, and so have more money to invest in houses.

Personally, I’d guess that all these explanations are true: that Chinese New Zealanders (on average) buy both homes and investment properties more than other New Zealanders, and that there are foreign property investors of Chinese ethnicity. But that’s a guess: these data don’t tell us — as the Herald explicitly points out.

One of the repeated points I  make on StatsChat is that you need to distinguish between what you measured and what you wanted to measure.  Using ‘Chinese’ as a surrogate for ‘foreign’ will capture many New Zealanders and miss out on many foreigners.

The misclassifications aren’t just unavoidable bad luck, either. If you have a measure of ‘foreign real estate ownership’ that includes my next-door neighbours and excludes James Cameron, you’re doing it wrong, and in a way that has a long and reprehensible political history.

But on top of that, if there is substantial foreign investment and if it is driving up prices, that’s only because of the artificial restrictions on the supply of Auckland houses. If Auckland could get its consent and zoning right, so that more money meant more homes, foreign investment wouldn’t be a problem for people trying to find somewhere to live. That’s a real problem, and it’s one that lies within the power of governments to solve.

June 23, 2015

Refugee numbers

Brent Edwards on Radio NZ’s Checkpoint has done a good job of fact-checking claims about refugee numbers in New Zealand.  Amnesty NZ tweeted this summary table


If you want the original sources for the numbers, the Immigration Department Refugee Statistics page is here (and Google finds it easily).

The ‘Asylum’ numbers are in the Refugee and Protection Status Statistics Pack, the “Approved” column of the first table. The ‘Family reunification’ numbers are in the Refugee Family Support Category Statistics Pack in the ‘Residence Visas Granted’ section of the first table. The ‘Quota’ numbers are in the Refugee Quota Settlement Statistics Pack, in the right-hand margin of the first table.

Update: @DoingOurBitNZ pointed me to the appeals process, which admits about 50 more refugees per year: 53 in 2013/4; 57 in 2012/3; 63 in 2011/2; 27 in 2010/11.


May 22, 2015

Budget viz

Aaron Schiff has collected visualisations of the overall NZ 2015 budget

A useful one that no-one’s done yet would be something showing how the $25 benefit increase works out with other benefits being considered as income — either in terms of the distribution of net benefit increases or in terms of effective marginal tax rate.

May 21, 2015

Fake data in important political-science experiment

Last year, a research paper came out in Science demonstrating an astonishingly successful strategy for gaining support for marriage equality: a short, face-to-face personal conversation with a gay person affected by the issue. As the abstract of the paper said

Can a single conversation change minds on divisive social issues, such as same-sex marriage? A randomized placebo-controlled trial assessed whether gay (n = 22) or straight (n = 19) messengers were effective at encouraging voters (n = 972) to support same-sex marriage and whether attitude change persisted and spread to others in voters’ social networks. The results, measured by an unrelated panel survey, show that both gay and straight canvassers produced large effects initially, but only gay canvassers’ effects persisted in 3-week, 6-week, and 9-month follow-ups. We also find strong evidence of within-household transmission of opinion change, but only in the wake of conversations with gay canvassers. Contact with gay canvassers further caused substantial change in the ratings of gay men and lesbians more generally. These large, persistent, and contagious effects were confirmed by a follow-up experiment. Contact with minorities coupled with discussion of issues pertinent to them is capable of producing a cascade of opinion change.

Today, the research paper is going away again. It looks as though the study wasn’t actually done. The conversations were done: the radio program “This American Life” gave a moving report on them. The survey of the effect, apparently not so much. The firm who were supposed to have done the survey deny it, the organisations supposed to have funded it deny it, the raw data were ‘accidentally deleted’.

This was all brought to light by a group of graduate students who wanted to do a similar experiment themselves. When they looked at the reported data, it looked strange in a lot of ways (PDF). It was of better quality than you’d expect: good response rates, very similar measurements across two cities,  extremely good before-after consistency in the control group. Further investigation showed before-after changes fitting astonishingly well to a Normal distribution, even for an attitude measurement that started off with a huge spike at exactly 50 out of 100. They contacted the senior author on the paper, an eminent and respectable political scientist. He agreed it looked strange, and on further investigation asked for the paper to be retracted. The other author, Michael LaCour, is still denying any fraud and says he plans to present a comprehensive response.

Fake data that matters outside the world of scholarship is more familiar in medicine. A faked clinical trial by Werner Bezwoda led many women to be subjected to ineffective, extremely-high-dose chemotherapy. Scott Reuben invented all the best supporting data for a new approach to pain management; a review paper in the aftermath was titled “Perioperative analgesia: what do we still know?”  Michael LaCour’s contribution, as Kieran Healy describes, is that his approach to reducing prejudice has been used in the Ireland marriage equality campaign. Their referendum is on Friday.

April 14, 2015

Northland school lunch numbers

Last week’s Stat of the Week nomination for the Northern Advocate didn’t, we thought point out anything particularly egregious. However, it did provoke me to read the story — I’d previously only  seen the headline 22% statistic on Twitter.  The story starts

Northland is in “crisis” as 22 per cent of students from schools surveyed turn up without any or very little lunch, according to the Te Tai Tokerau Principals Association.

‘Surveyed’ is presumably a gesture in the direction of the non-response problem: it’s based on information from about 1/3 of schools, which is made clear in the story. And it’s not as if the number actually matters: the Te Tai Tokerau Principals Association basically says it would still be a crisis if the truth was three times lower (ie, if there were no cases in schools that didn’t respond), and the Government isn’t interested in the survey.

More evidence that number doesn’t matter is that no-one seems to have done simple arithmetic. Later in the story we read

The schools surveyed had a total of 7352 students. Of those, 1092 students needed extra food when they came to school, he said.

If you divide 1092 by 7352 you don’t get 22%. You get 15%.  There isn’t enough detail to be sure what happened, but a plausible explanation is that 22% is the simple average of the proportions in the schools that responded, ignoring the varying numbers of students at each school.

The other interesting aspect of this survey (again, if anyone cared) is that we know a lot about schools and so it’s possible to do a lot to reduce non-response bias.  For a start, we know the decile for every school, which you’d expect to be related to food provision and potentially to response. We know location (urban/rural, which district). We know which are State Integrated vs State schools, and which are Kaupapa Māori. We know the number of students, statistics about ethnicity. Lots of stuff.

As a simple illustration, here’s how you might use decile and district information.  In the Far North district there are (using Wikipedia because it’s easy) 72 schools.  That’s 22 in decile one, 23 in decile two, 16 in decile three, and 11 in deciles four and higher.  If you get responses from 11 of the decile-one schools and only 4 of the decile-three schools, you need to give each student in those decile-one schools a weight of 22/11=2 and each student in the decile-three schools a weight of 16/4=4. To the extent that decile predicts shortage of food you will increase the precision of your estimate, and to the extent that decile also predicts responding to the survey you will reduce the bias.

This basic approach is common in opinion polls. It’s the reason, for example, that the Green Party’s younger, mobile-phone-using support isn’t massively underestimated in election polls. In opinion polls, the main limit on this reweighting technique is the limited amount of individual information for the whole population. In surveys of schools there’s a huge amount of information available, and the limit is sample size.

March 31, 2015

Polling in the West Island: cheap or good?

New South Wales has just voted, and the new electorate created where I lived in Sydney 20 years ago is being won by the Greens, who got 46.4% of the primary vote and currently 59.7% on preferences. The ABC News background about the electorate says

In 2-party preferred terms this is a safe Labor seat with a margin of 13.7%, but in a two-candidate contest would be a marginal Green seat versus Labor. The estimated first preference votes based on the 2011 election are Green 35.5%, Labor 30.4%, Liberal 21.0%, Independent 9.1, the estimated Green margin after preferences being 4.4% versus Labor.

There was definitely a change since 2011 in this area, so how did the polls do? Political polling is a bit harder with preferential voting when there are only two relevant parties, but much harder when there are more than two.

Well, the reason for mentioning this is a piece in the Australian saying that the swing to the Greens caught Labor by surprise because they’d used cheap polls for electorate-specific prediction

“We just can’t poll these places accurately at low cost,” a Labor strategist said. “It’s too hard. The figures skew towards older voters on landlines and miss younger voters who travel around and use mobile phones.”

The company blamed in the story is ReachTEL. They report that they had the most accurate overall results, but their published poll from 19 March for Newtown is definitely off a bit, giving the Greens 33.3% support.

(via Peter Green on Twitter)


March 25, 2015

Foreign drivers, yet again

From the Stuff front page


Now, no-one (maybe even literally no-one) is denying that foreign drivers are at higher risk on average. It’s just that some of us feel exaggerating the problem is unhelpful. The quoted sentence is true only if “the tourist season” is defined, a bit unconventionally, to mean “February”, and probably not even then.

When you click through to the story (from the ChCh Press), the first thing you see is this:


Notice how the graph appears to contradicts itself: the proportion of serious crashes contributed to by a foreign driver ranges from just over 3% in some months to just under 7% at the peak.  Obviously, 7% is an overstatement of the actual problem, and if you read sufficiently carefully, the graphs says so.  The average is actually 4.3%

The other number headlined here is 1%: cars rented by tourists as a fraction of all vehicles.  This is probably an underestimate, as the story itself admits (well, it doesn’t admit the direction of the bias). But the overall bias isn’t what’s most relevant here, if you look at how the calculation is done.

Visitor surveys show that about 1 million people visited Canterbury in 2013.

About 12.6 per cent of all tourists in 2013 drove rental cars, according to government visitor surveys. That means about 126,000 of those 1 million Canterbury visitors drove rental cars. About 10 per cent of international visitors come to New Zealand in January, which means there were about 12,600 tourists in rental cars on Canterbury roads in January.

This was then compared to the 500,000 vehicles on the Canterbury roads in 2013 – figures provided by the Ministry of Transport.

The rental cars aren’t actually counted, they are treated as a constant fraction of visitors. If visitors in summer are more likely to drive long distances, which seems plausible, the denominator will be relatively underestimated in summer and overestimated in winter, giving an exaggerated seasonal variation in risk.

That is, the explanation for more crashes involving foreign drivers in summer could be because summer tourists stay longer or drive more, rather than because summer tourists are intrinsically worse drivers than winter tourists.

All in all, “nine times higher” is a clear overstatement, even if you think crashes in February are somehow more worth preventing than crashes in other months.

Banning all foreign drivers from the roads every February would have prevented 106 fatal or serious injury crashes over the period 2006-2013, just over half a percent of the total.  Reducing foreign driver risk by 14%  over the whole year would have prevented 109 crashes. Reducing everyone’s risk by 0.6%  would have prevented about 107 crashes. Restricting attention to February, like restricting attention to foreign drivers, only makes sense to the extent that it’s easier or less expensive to reduce some people’s risk enormously than to reduce everyone’s risk a tiny amount.


Actually doing something about the problem requires numbers that say what the problem actually is, and strategies, with costs and benefits attached. How many tens of millions of dollars worth of tourists would go elsewhere if they weren’t allowed to drive in New Zealand? Is there a simple, quick test would separate safe from dangerous foreign drivers, that rental companies could administer? How could we show it works? Does the fact that rental companies are willing to discriminate against young drivers but not foreign drivers mean there’s something wrong with anti-discrimination law, or do they just have a better grip on the risks? Could things like rumble strips and median barriers help more for the same cost? How about more police presence?

From 2006 to 2013 NZ averaged about 6 crashes per day causing serious or fatal injury. On average, about one every four days involved a foreign driver. Both these numbers are too high.


March 17, 2015

Bonus problems

If you hadn’t seen this graph yet, you probably would have soon.


The claim “Wall Street bonus were double the earnings of all full-time minimum wage workers in 2014″ was made by the Institute for Policy Studies (which is where I got the graph) and fact-checked by the Upshot blog at the New York Times, so you’d expect it to be true, or at least true-ish. It probably isn’t, because the claim being checked was missing an important word and is using an unfortunate definition of another word. One of the first hints of a problem is the number of minimum wage workers: about a million, or about 2/3 of one percent of the labour force.  Given the usual narrative about the US and minimum-wage jobs, you’d expect this fraction to be higher.

The missing word is “federal”. The Bureau of Labor Statistics reports data on people paid at or below the federal minimum wage of $7.25/hour, but 29 states have higher minimum wages so their minimum-wage workers aren’t counted in this analysis. In most of these states the minimum is still under $8/hr. As a result, the proportion of hourly workers earning no more than federal minimum wage ranges from 1.2% in Oregon to 7.2% in Tennessee (PDF).  The full report — and even the report infographic — say “federal minimum wage”, but the graph above doesn’t, and neither does the graph from Mother Jones magazine (it even omits the numbers of people)

On top of those getting state minimum wage we’re still short quite a lot of people, because “full-time” is defined by 35 or more hours per week at your principal job.  If you have multiple part-time jobs, even if you work 60 or 80 hours a week, you are counted as part-time and not included in the graph.

Matt Levine writes:

There are about 167,800 people getting the bonuses, and about 1.03 million getting full-time minimum wage, which means that ballpark Wall Street bonuses are 12 times minimum wage. If the average bonus is half of total comp, a ratio I just made up, then that means that “Wall Street” pays, on average, 24 times minimum wage, or like $174 an hour, pre-tax. This is obviously not very scientific but that number seems plausible.

That’s slightly less scientific than the graph, but as he says, is plausible. In fact, it’s not as bad as I would have guessed.

What’s particularly upsetting is that you don’t need to exaggerate or use sloppy figures on this topic. It’s not even that controversial. Lots of people, even technocratic pro-growth economists, will tell you the US minimum wage is too low.  Lots of people will argue that Wall St extracts more money from the economy than it provides in actual value, with much better arguments than this.

By now you might think to check carefully that the original bar chart is at least drawn correctly.  It’s not. The blue bar is more than half the height of the red bar, not less than half.