Ban Uber?

The NBR's Jason Walls asked me for comment on Minister Bridges's warnings of a potential ban on Uber. His article is here ($).

I have a hard time seeing what consumers get out of Uber drivers getting a P endorsement over and above what Uber is already doing - and especially where Uber drivers and passengers are GPS tracked at every point along the journey. People who want a P-endorsed driver can hire a taxi that provides P-endorsed drivers. If the P-endorsement is so gosh-darned important, why aren't the incumbent cab operators advertising heavily about how their drivers have it? If customers really wanted that, then existing operators would have no need to worry about the competition.

While I think it would be best to ease back the taxicab regs across the board, it isn't nuts to see the app tracking as being a pretty decent substitute for existing safety regs that apply to standard taxis. And especially where customers can choose.

Hot takes and inequality data

Me at The NBR on some ... oddities ... in the latest HES wealth data. A snippet (ungated):

The HES tables sort households into quintiles by net worth. The first has the 20% of households with the lowest household net worth; the fifth quintile has the top 20%. The tables list the assets and liabilities held by households in each quintile. 

Households with less than $39,500 in net assets make up the bottom quintile. Collectively, those households own owner-occupied houses worth $3.99 billion, or $219,000 for the median household that owns the house they live in. The figure feels a bit low: are there really that many houses out there that are worth less than $220,000? But part-ownership of those houses could help make sense of it if parents putting down the deposit on the kids’ house gives them a stake in the ownership.

More puzzling was that, against that $3.99 billion in housing assets owned by the least wealthy 20%, were $4.899 billion in loans against owner-occupied residences. The last time I checked, banks needed to comply with 80% LVR ratios, not 123% ones. But even if there were no LVR considerations, what bank would lend multiples of the value of a house to the people in the least wealthy cohort? And so it was time to check the footnotes. 

The notes tell us that assets held in a business or trust are not counted, unless they are mentioned. 

It seems implausible that the least wealthy 20% of the population have put their houses into trusts but it could be a consideration in some cases. 

If a family owns houses in a trust, and the 20-something living in it has just graduated from university and has taken over the mortgage, the mortgage liability could show up in the accounts but not the asset. But that story seems to fall apart when we look back to the median tables rather than the totals. Among those first quartile households reporting ownership of a house, the median house value is $219,000. But the median debt on owner-occupied housing, among those with mortgages in first quartile households is $240,000. 

It does not make sense that the debt on the median house owned with debt is greater than the value of the median owned house in that quartile, unless the median home recently purchased by those in the bottom quartile is of substantially better quality than those that have been long owned by those in the bottom quartile. More plausible are problems caused by out-of-date valuations. Statistics New Zealand tells us that the valuations used in the HES are from government valuations which can be up to three years old. GVs are well behind actual house prices. 

If you have just purchased a house, the liability ledger will accurately reflect the value of your mortgage but your house could be undervalued by 20-30% – or more if you bought in the right place in Auckland. 

These measurement issues then mean that wealth held by the bottom quartile is probably strongly understated – as are the housing assets held by all other quartiles. It matters a lot more in the bottom quartile – it winds up having about a billion dollars more in total household liabilities than it has in total household assets. Counting education loans on the liabilities side of the ledger while not counting the value of the human capital it embodies also makes for problems. These loans loom large on the liabilities side for households in the first quartile but are mostly held by younger people with strong future earnings potential and good future upward mobility – not the cohort typically worried about in discussions of inequality. 

Green green grass of home

Looks like migrants are less happy when things back home turn greener. From Akay, Bargain and Zimmermann:

This paper examines whether the subjective well-being of migrants is responsive to fluctuations in macroeconomic conditions in their country of origin. Using the German SocioEconomic Panel for the years 1984 to 2009 and macroeconomic variables for 24 countries of origin, we exploit country-year variation for identification of the effect and panel data to control for migrants' observed and unobserved characteristics. We find strong evidence that migrants' well-being responds negatively to an increase in the GDP of their home country. That is, migrants seem to regard home countries as natural comparators, which grounds the idea of relative deprivation underlying the decision to migrate. The effect declines with years-since-migration and with the degree of assimilation in Germany.

So if you want happier migrants, target immigration towards countries you expect to decline?

Update: the link to the paper is here. Sorry.

Beat the baseline

It turns out that America's Earned Income Tax Credit, the basic model underlying later things like New Zealand's Working for Families, improves children's school achievement.

Brookings's Grover Whitehurst explains things here. America's spending billions on pre-K programmes, but it looks like just giving parents money through EITC is more effective.

I have compared the effects of direct income transfers to low-income families (such as the earned-income tax credit, or EITC) with programs designed to increase school readiness (universal preschool and Head Start). It turns out that putting money directly into the pockets of low-income parents, as many other countries do, produces substantially larger gains in children’s school achievement per dollar of expenditure than does a year of preschool or participation in Head Start. The results throw water on the conventional wisdom.

The results show that while the EITC isn’t specifically designed to boost academic achievement, it does so anyway — and not just for younger kids. The EITC is also a bargain compared with the programs specifically designed to help poor kids academically.

Specifically, each of four evaluations of U.S. family income support programs found substantially larger test score increases per $1,000 of public expenditure than resulted from programs specifically aimed at improving educational outcomes by focusing on school readiness. In particular, neither pre-K nor Head Start provided the same amount of improvement as the family support programs did. Other studies of the EITC also show impacts on even later outcomes — such as college enrollment and earned income.

The current annual federal expenditure on the EITC is about $65 billion. During the 2013 tax year, the average EITC was $3,074 for a family with children. In contrast, Head Start runs about $8,000 per child. Boston’s and the District’s pre-K programs run more than $16,000 per student. Spending less (EITC) is actually more effective than spending more (Head Start, universal pre-K). It’s a win-win.

Former senator Daniel Patrick Moynihan likened government bureaucracies dispensing social services to the poor as “feeding the sparrows by feeding the horses.” The school readiness option feeds the horses. Perhaps it is time to rethink our paradigm for supporting poor families. Let’s give them what they desperately need — more money — and let them decide how to spend it on the early care and education of their children.

Straight cash transfers should be the baseline against which other 'helping people' programmes would be assessed.

Unlocking school data

You should subscribe to The National Business Review. Then you'd be able to read my columns there when they're printed, rather than waiting for them to show up here.

Here's an ungated version of my piece on unlocking school data. The Auditor General's report on Maori education found things very similar to what The Initiative has found more generally: poor use of data throughout the sector, and variable performance.

A snippet:

If you found out your local school had a 60% NCEA level three pass rate, would you know whether you should congratulate the principal or demand a sacking?

Students come to school from different starting points. If one school teaches kids who never saw a book before showing up at school and another school teaches kids whose parents mostly teach at the local university, it would be daft to expect both schools to deliver similar outcomes. 

Praising or damning schools for their performance relative to fixed national benchmarks then is just a little silly. A 60% pass rate could be a failure or a triumph.

At the same time, differences in student outcomes at similar schools can be vast. 

The Auditor General’s report on Maori education, released this month, makes for sobering reading. 

Among small decile 1 primary schools, the percentage of Maori students meeting or exceeding the bar on National Standards ranges from just over 20% to just under 90%. Comparing small decile 2 secondary schools, the percentage of Maori students at or above average NCEA level 2 results ranged from just under 40% to about 95%. 

Students’ backgrounds matter for educational outcomes. But if decile were destiny and student backgrounds were all that mattered, there would not be yawning gaps in performance among broadly similar schools. 

The best small decile 2 secondary school would not have a 55 percentage point NCEA achievement lead on the worst performing small decile 2 secondary school if all that mattered were the mix of incoming students. And the worst performing small decile 1 primary school would not be more than 60 percentage points behind the best performing small decile 1 primary school on National Standards.

[From Auditor General's report, Figure 7, page 24.]

The Auditor General’s report focuses on outcomes for Maori students but the problem is much broader. 

Tertiary crystal balls

Two weekends ago, I contributed to a panel session run at the Tertiary Education Union's conference. My notes are copied below, cross-posted from The Sandpit. Interestingly, a few days later, Labour proposed something that sounds close to what I here suggested; I hope that they're planning on using the available data appropriately.

Notes for address to the Tertiary Education Union’s conference, Voices from Tertiary Education, 23 July 2016.

Dr Eric Crampton, Head of Research, The New Zealand Initiative

Check against delivery.

I’d like to thank the TEU for inviting me onto today’s panel. I’m sometimes a token free-market diversity addendum to these kinds of panels, but the TEU has done a great job in having a really diverse set of commenters on our panel’s assigned topic, “A look at the educational needs of society and the economy”. I thank the organisers for that.

I’ll start with a bit of background: I served as lecturer and senior lecturer in the Economics Department at the University of Canterbury from 2003 through 2014 before moving to Wellington to serve as Head of Research with The New Zealand Initiative, a public policy think-tank. I still keep a foot in the lecture theatre though: I taught Public Finance for Victoria University’s School of Government this past semester.

I’ll take perhaps a deviant tack on the topic and say that it is impossible for any of us here to be able to say anything terribly specific about what society, or business, will need in 20 years’ time. We have a pretty good sense of where there are current job shortages: in which sectors businesses are crying out for qualified workers, and in which sectors qualified staff are a dime a dozen. But I just do not think it is possible for any of us to be able to look out a couple of decades and say whether the country will be facing shortages of biologists, computer scientists, or critical literature specialists.

So what we really need is a system that is robust to not really knowing what the future holds. We’ve never really known what the future will hold, and it’s pretty clichéd to say that the pace of change is accelerating, but it at least doesn’t seem to be getting easier to say what things will look like in 20 years.

But what we are getting better at is data analytics. I don’t think we can use those to project what’s going to be needed in 20 years’ time, but we can do a much better job of making education and outcome data available to students and their families, so that each of them can make their best forecast of where they might fit in tomorrow’s economy.

What do I mean? Right now, MBIE does a great job in putting out employment forecasts showing which jobs currently look like they’re hot prospects: where employment and salary expectations are strong. But does it help a kid who’s barely making NCEA ‘Achieved’ in easy standards to find out that there are strong employment prospects for civil engineers? Not so much. And what about a smart kid in a poor school where there’s been little history of sending students through to tertiary – how can that kid know that astrophysics should be an option for her?

I’ll tell you a little story about where I went to high school. It was a small town in southern Manitoba, 200 kids from kindergarten through to Grade 12. Sometime around the eleventh grade we all did career testing on computers. I did pretty well on all of the skills they tested – I think my worst one was in mechanical and spatial reasoning, so engineering would have been a bad fit. But I did particularly well in the clerical speed and accuracy test. Why? I’d had a Commodore 64 since I was in the second grade and knew how to type. And so our guidance counsellor, who went through the results with me afterwards, asked if I’d considered a career as a clerk. Maybe I should have, but probably not.

Data’s gotten a lot better since. Right now, it is entirely within the wit of the Ministry of Education to produce student-specific reports telling each and every student in the country what outcomes have been for students who, from where they currently are, tried different paths. They could tell every child that, of 1000 students who are very similar to them in terms of grades and courses taken and family background, the 200 who went on to get a trades certificate had these kinds of outcomes, the ones who tried different university degrees had these other kinds of outcomes, and those who went directly into the workforce had still this other set of outcomes.

All of that data sits within the Statistics New Zealand’s Integrated Data Infrastructure, which links up all of the back end data that the government has about all of us. It is entirely possible to link NCEA scores for students from 2004 onwards to their training choices, training outcomes, and ultimate employment. Now past performance is never a guarantee of future success, but finding out more about what students like you have been able to achieve is a lot better than being told that, because you can type well, you should be a clerk when you really should be thinking about where to get your doctorate.

With that kind of information, students could start making much better informed choices about their education and training options. And even better if universities and training institutes started actually publishing data on outcomes – not just for graduates, but also for those who enrolled and dropped out. If attrition rates are high, advertising employment and salary outcomes for the ones who make it is a bit of false advertising.

That helps students make more informed choices. But the range of options they can choose across depends critically on the quality of instruction they’ve had prior to tertiary. The most generous student loan and bursary scheme in the world will not help a poor kid who has been trapped in a school where the maths teachers do not understand calculus and where expectations are low.

And so I will make a perhaps controversial pitch to this audience: tertiary reform has to start by redirecting some resources from the tertiary sector back into primary and secondary schools. And this isn’t just to help the kids stuck in failing schools and to let the sector boost pay by enough to attract more highly skilled people into teaching in the first place.

It’s also because we should be humble about picking winners when we cannot see the future. Having an exceptionally strong primary and secondary sector, combined with really good data helping students make choices across tertiary options – even if those students then have to pay a bit more for it out of their own pockets – is policy that remains fit for purpose even when circumstances change. Students who come out of high school literate, numerate, and able to reason their way through complex problems have the base they need for any kind of tertiary study, or to change focus mid-career in a changing world.

Rising uncertainty about the future makes a strong case for building generalist skills at an early level, getting each child up to that child’s full potential by the time the kid hits 18, rather than focusing on developing specialised skills for the ones lucky enough to have had the training to let them into university. Better data analytics helping each student have realistic expectations about the options available to them and, I think, would help encourage more students to see the real potential available in vocational training rather than assuming that getting a C average in an Arts degree is any kind of path to prosperity. But, the nice thing is that, if I’m wrong, the data would show students that too and let them make the choices that are right for them – rather than having experts push them into STEM, or whatever else.

Refocusing resources towards better data analytics and towards strengthening primary and secondary schooling is not only better for business in helping ensure a skilled, flexible and adaptable workforce, it’s also better for society. Remember that, compared to decile 1 and 2 schools, more than twice as many kids from decile 9 and 10 schools make it into tertiary in the first place. Our current policy of spending over $600 million dollars per year on subsidies through the interest-free student loan programme does a great job of subsidising access to tertiary education for the kids who would have gone to university regardless of 0% loans, and nothing to improve prospects for those shut out of university by poorly performing primary and secondary schools. That needs to change.

Thank you.

ENDS

Mark McGuire has Storified the conference; the feature image for this post on the front page is stolen shamelessly from his twitter feed.

The Initiative’s report on failing schools is here.

Resuming our regularly scheduled service

I'm back! But I've never been far away. Just writing elsewhere without time to pop things up here as I should have.

Subscribers to our Insights newsletter will have caught my piece there on the lunacy of alcohol special licences in Nelson, and on the Auckland Unitary Plan.

I also there gave a teaser for the Wellington semi-final of The Initiative's coming debate series. The debating teams from New Zealand's universities square off in these annual meets, sponsored by the Friedlander Foundation and organised by The Initiative. The Wellington semi-final will have the teams debate the moot "This house would reduce household inequality by banning those with university degrees from marrying each other." Matt Nolan and I will serve as panellists on this one.

Plus a couple pieces in the NBR and assorted other media commentary; they'll be getting their own posts soon.

It has been a busy month. We've lost one of our excellent research assistants, Khyaati Acharya, who's moved to the other side of the Tasman; we'll be releasing her report taking a decade-on retrospective look at the zero percent student loan policy in about a fortnight though. I've spent a bit of time helping to get that report ready for publication. Jason Krupp launched his report with Bryce Wilkinson on local government in Switzerland, the Netherlands, the Manchester Accord, and Montreal's amalgamation and de-amalgamation. And I've (barely) started in on some work with InternetNZ looking at the state of regulation and the digital economy.

We've also added two more researchers.

Randall Bess joined us to take a look at the state of recreational fishing; his first draft is now completed and I'm looking forward to the September launch.

And Rachel Webb has joined us from Canterbury. Rachel completed her doctorate there under Andrea Menclova and was teaching third year labour econometrics before I stole her away. She will get to have all of the fun in Stata that I haven't time for. Her first task is looking at the data on immigration for a report we hope to have out early next year.

Busy fun times.

Other places you might have caught me recently:

• Quoted at Stuff, on the intergenerational wars (millenials right to be pissed off about housing; tuition fees, not so much)
• Again at Stuff, on housing again
• At Atlas, on our report on heritage regulations
• Interest's Top 10