Australia: blokey from the get-go

It’s Raining Men! Hallelujah?
Pauline Grosjean and Rose Khattar

We document the implications of missing women in the short and long run. We exploit a natural historical experiment, which sent large numbers of male convicts and far fewer female convicts to Australia in the 18th and 19th century. In areas with higher gender imbalance, women historically married more, worked less, and were less likely to occupy high-rank occupations. Today, people living in those areas have more conservative attitudes towards women working and women are still less likely to have high-ranking occupations. We document the role of vertical cultural transmission and of homogamy in the marriage market in sustaining cultural persistence. Conservative gender norms may have been beneficial historically, but are no longer necessarily so. Historical gender imbalance is associated with an aggregate income loss estimated at $800 per year, per person. Our results are robust to a wide array of geographic, historical and present-day controls, including migration and state fixed effects, and to instrumenting the overall sex ratio by the sex ratio among convicts.

Keywords: Culture, gender roles, sex ratio, natural experiment, Australia
JEL: I31 N37 J16

Open Data and the G20

OpenGovData Venn

From a recent column for the AFR. The report can be downloaded here.

Earlier this year our Treasurer, Joe Hockey, led the G20 Finance Ministers to pledge lifting GDP by 2 percent over ‘business as usual’ over the next five years. It’s a big win for the Treasurer, but how can it be delivered? There aren’t many easy options for reform on that scale that don’t create swathes of losers around whom the media then swarm, thus amplifying the inevitable campaigns against change.

But one opportunity is sitting under our noses. In a knowledge economy, data is the new infrastructure. The more open it is, the more it can be reused repurposed. The more it attracts value adding as business and civil society find clever new ways of making it ever more useful. Most data Google Maps delivers has existed for decades. But government open data policies – and Google – convey open data seamlessly to your mobile as you search out your target.

That’s why, Australia’s Government implemented the recommendations of the 2009 Government 2.0 Taskforce which I chaired. But in Australia as elsewhere, high-level commitments have achieved less than they could have if they’d been seamlessly translated down to the delivery coalface as Google has with geospatial data.

Omidyar Network today releases a Lateral Economics report that estimates that a more vigorous open data commitment could grow Australia’s economy by around $16 billion per year. That’s half Joe Hockey’s G20 growth target.

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Clever new piece of work on what drove the industrial revolution

Human Capital and Industrialization: Evidence from the Age of
Enlightenment

by Mara P. Squicciarini, Nico Voigtlaender – #20219 (DAE EFG)

Abstract:

While human capital is a strong predictor of economic development today, its importance for the Industrial Revolution is typically assessed as minor. To resolve this puzzling contrast, we differentiate average human capital (worker skills) from upper tail knowledge both theoretically and empirically. We build a simple spatial model, where worker skills raise the local productivity in a given technology, while scientific knowledge enables local entrepreneurs to keep up with a rapidly advancing technological frontier. The model predicts that the local presence of knowledge elites is unimportant in the pre-industrial era, but drives growth thereafter; worker skills, in contrast, are not crucial for growth. To measure the historical presence of knowledge elites, we use city-level subscriptions to the famous Encyclopedie in mid-18th century France. We show that subscriber density is a strong predictor of city growth after 1750, but not before the onset of French industrialization. Alternative measures of development confirm this pattern: soldier height and industrial activity are strongly associated with subscriber density after, but not before, 1750. Literacy, on the other hand, does not predict growth. Finally, by joining data on British patents with a large French firm survey from 1837, we provide evidence for the mechanism: upper tail knowledge raised the productivity in innovative industrial technology.

Is the struggle for equality of opportunity over?

Equality of opportunity was one of the big themes of Gough Whitlam’s 1969 and 1972 campaigns. His 1972 policy speech promised "a new drive for equality of opportunities" through reforms to education, health and urban planning. He argued that opportunity depends on the kind of investments only government can make. In his 1985 book The Whitlam Government 1972–1975 he drew on Abraham Lincoln for support:

There is, of course, nothing novel in this idea of action by governments to promote community welfare. Before he became President, Abraham Lincoln wrote: "The legitimate object of government is to do for the people what needs to be done but which they cannot, by individual effort do at all, or do so well for themselves" (p 3).

Today we’re more likely to associate this Lincoln quote with Tony Abbott or his government’s Commission of Audit. But Whitlam argued that there were important things people could not achieve on their own. Equality of opportunity was one of them. According to Whitlam, for many Australians the doors to opportunity begin closing in early childhood. In the pre-school years "inequality is rivetted on a child for a lifetime", he said. He argued that "Education should be the great instrument for the promotion of equality" but "Under the Liberals it has become a weapon for perpetuating inequality and promoting privilege." According to Whitlam, only government can make sure every Australian has access to a quality education all the way from pre-school to university.

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How to lie with statistics: the case of female hurricanes.

I just came across an article in PNAS (the Proceedings of the National Academy of Sciences) with the catchy title ‘Female Hurricanes are deadlier than male hurricanes’. It is doing the rounds in the international media, with the explicit conclusion that our society suffers from gender bias because it does not sufficiently urge precautions when a hurricane gets a female name. Intrigued, and skeptic from the outset, I made the effort of looking up the article and take a closer look at the statistical analysis. I can safely say that the editor and the referees were asleep for this one as they let through a real shocker. The gist of the story is that female hurricanes are no deadlier than male ones. Below, I pick the statistics of this paper apart.

The authors support their pretty strong claims mainly on the basis of historical analyses of the death toll of 96 hurricanes in the US since 1950 and partially on the basis of hypotheticals asked of 109 respondents to an online survey. Let’s leave the hypotheticals aside, since the respondents for that one are neither representative nor facing a real situation, and look at the actual evidence on female versus male hurricanes.

One problem is that the hurricanes before 1979 were all given female names as the naming conventions changed after 1978 so that we got alternating names. Since hurricanes have become less deadly as people have become better at surviving them over time, this artificially makes the death toll of the female ones larger than the male ones. In their ‘statistical analyses’ the authors do not, however, control adequately for this, except in end-notes where they reveal most of their results become insignificant when they split the sample in a before and after period. For the combined data though, the raw correlation between the masculinity in the names and the death toll is of the same order as the raw correlation between the number of years ago that the hurricane was (ie, 0.1). Hence the effects of gender and years are indeed likely to come from the same underlying improvement in safety over time.

Using the data of the authors, I calculate that the average hurricane before 1979 killed 27 people, whilst the average one after 1978 killed 16, with the female ones killing 17 per hurricane and the male ones killing 15.3 ones per hurricane, a very small and completely insignificant difference. In fact, if I count ‘Frances’ as a male hurricane instead of a female one, because its ‘masculinity index’ is smack in the middle between male and female, then male and female hurricanes after 1978 are exactly equally deadly with an average death toll of 16.

It gets worse. Even without taking account of the fact that the male hurricanes are new ones, the authors do not in fact find an unequivocal effect at all. They run 2 different specifications that allow for the naming of the hurricanes and in neither do they actually find an effect unequivocally in the ‘right direction’ (their Table $3).

In their first, simple specification, the authors allow for effects of the severity of a hurricane in the form of the minimum air pressure (the lower, the more severe the hurricane) and the economic damage (the higher, the more severe the hurricane). Conditional on those two, they find an insignificant effect of the naming of the hurricanes!

Undeterred and seemingly hell-bent to get a strong result, the authors then add two interaction terms between the masculinity of the name of the hurricane and both the economic damage and the air pressure. The interaction term with the economic damage goes the way the authors want, ie hurricanes with both more economic damage and more feminine names have higher death tolls than hurricanes with less damage and male names. That is what their media release is based on, and their main text makes a ‘prediction graph’ out of that interaction term.

What is completely undiscussed in the main text of the article however is that the interaction with the minimum air pressure goes the opposite way: the lower the air pressure, the lower the death toll from a more feminine-named hurricane! So if the authors had made a ‘prediction graph’ showing the predicted death toll for more feminine hurricanes when the hurricanes had lower or higher air pressures, they would have shown that the worse the hurricane, the lower the death toll if the hurricane had a female name!

The editors and the referee were thus completely asleep for this pretty blatant act of deception-by-statistics. Continue reading

Do people know what’s good for them – or their children. (Hint: Not always)

Human Capital Effects of Anti-Poverty Programs: Evidence from a Randomized Housing Voucher Lottery by Brian Jacob, Max Kapustin, Jens Ludwig – #20164 (CH ED HE PE)

Abstract:

Whether government transfer programs increase the human capital of low-income children is a question of first-order policy importance. Such policies might help poor children if their parents are credit constrained, and so under-invest in their human capital. But it is also possible that whatever causes parents to have low incomes might also directly influence children’s development, in which case transfer programs need not improve poor children’s long-term life chances. While several recent influential studies suggest anti-poverty programs have larger human capital effects per dollar spent than do even the best educational interventions, identification is a challenge because most transfer programs are entitlements. We overcome that problem by studying the effects on children of a generous transfer program that is heavily rationed–means-tested housing assistance. We take advantage of a randomized housing voucher lottery in Chicago in 1997, for which 82,607 people applied, and use administrative data on schooling, arrests, and health to track children’s outcomes over 14 years. We focus on families living in unsubsidized private housing at baseline, for whom voucher receipt generates large changes in both housing and non-housing consumption. Estimated effects are mostly statistically insignificant and always much smaller than those from recent studies of cash transfers, and are smaller on a per dollar basis than the best educational interventions.

 

What are the likely consequences of HECS fee liberalisation?

The Australian government education minister Christopher Pyne has made his wishes clear for the tertiary education sector: he is following the wishes of the GO8 Vice-Chancellors and wants to remove the caps from the HECS fees asked of domestic students. This seems to fit in a vision of greater competition for the sector, and hence the fee deregulation is accompanied by changes to the accreditation regime to make it easier for competitors to come in. There seems to be no appetite for changes in university governance, nor any attempt to remove the benefit that existing inner-city universities have in the sense that they don’t have to pay for their premises whilst newcomers would have to pay rent for their premises.

Let us presume that the minister gets his way, either this time round or next time round, and that we will hence see HECS fee deregulation for domestic students with easier rules for accreditation, and no significant changes otherwise to the current environment. Following on from the efforts of Rabee Tourkey and Rohan Pitchford on this blog, what are the likely consequences?

First off, we can look at the consequences of a similar fee deregulation in the UK in 2010: at least a doubling of fees at the top and a proliferation of discounters at the bottom.

Should we expect these things to occur here too? One should expect both elements to occur here even more strongly because in our case there are less alternatives for students and hence a greater potential to increase fees and have chaos as the bottom: the main differences between us and the UK in this market is that the UK is more densely populated and that students thus have many more universities close by them, allowing for far more real competition than will be the case here. Thus, given that distances between cities are so large in Australia, one should expect the price hike to be well above double in this country. So from Hecs fees of around 10,000 AUS per year, I expect we would go to 30,000 AUS per year at the GO8s, pretty much within a year or two following fee deregulation.

At the bottom, it will be chaos, just like in the UK. You will get all sorts of scams and desperate ploys, essentially because the government is writing a blank cheque via the HECS scheme. Many will come to collect. Continue reading