Everyone knows - and should have known - that the numbers from the National Household Survey (NHS) would be dodgy. Statistics Canada has always claimed that the NHS numbers would be useful for many purposes, and this line has been swallowed by many. After all, Statistics Canada has a deserved reputation for professionalism, and their work deserved the benefit of the doubt.
No longer. It took Frances Woolley only a couple of hours to come across some seriously wonky numbers from the very first NHS release. Moreover, StatsCan isn't planning to release the technical documentation for the NHS until 2014. (In the ordinary course of things, methodology is presented before results.) And now there's today's last-minute delay in the NHS income numbers.
Chief Statistician Wayne Smith was telling us a few weeks ago
It’s irresponsible to try and dissuade Canadians from using what is an extraordinary rich and powerful database. To make them nervous about that is I think irresponsible.
It's entirely reasonable to be skeptical about numbers generated by an untried and untested methodology in the absence of technical documentation. Many people - me included - were prepared to give StatsCan the benefit of the doubt. They no longer deserve it.
In an old post, Chris Auld attacked the "zombie argument" that healthy lifestyles substantially decrease demand on the health care system. As he put it:
All people -- and I do not mean to shock anyone -- die some time, even including people who live very healthy lifestyles. Preventing someone from dying of a smoking-related illness only means that they will die of a non-smoking related illness. The effect of smoking on lifecycle health care costs is the difference between costs which are incurred if the person smokes and the costs which would be incurred if the person doesn't smoke. Whether improvements in lifestyle increase or decrease lifetime health care costs depends in a complicated manner on how a healthy lifestyle affects length of life and health care costs at any given age. Whether smoking or other unhealthy behaviors increase or decrease health care costs is an empirical question.
One answer to that empirical question is given below the fold:
We know that inflation targeting failed. But we don't know why it failed.
One theory is that inflation targeting failed because inflation targeting made the Phillips Curve flatter. It made the Phillips Curve so very flat that keeping inflation close to target wasn't enough to keep output and employment close to potential. Inflation targeting contained the seeds of its own destruction. It destroyed the usefulness of inflation as a signal of whether money should be tightened or loosened.
We want to test that theory.
The trouble is, it is hard to distinguish econometrically between: inflation targeting made the Phillips Curve really flatter, and; inflation targeting made the Phillips Curve look flatter.
Note: I have re-written this post in response to comments from biostatistician Thomas Lumley below.
It made headlines around the world: Facebook ‘likes’ can reveal users’ politics, sexual orientation, IQ. According to Michal Kosinski, the lead researcher, information on "gender, race, political views, religion, sexual orientation, personality, IQ and so on," can be extracted from the knowledge that a person likes Lady Gaga or Harley-Davidsons.
The study noted that many of the most predictive "likes" weren't obvious ones. For example, fewer than five per cent of users labelled as "gay" were connected with gay groups such as the "No H8 campaign." Instead, likes such as "Britney Spears" and "Desperate Housewives" were "moderately indicative of being gay."
Meanwhile, the "likes" most correlated with high intelligence were thunderstorms, The Colbert Report, science and curly fries...
A followup to my previous post on university retention and males.
Assume boys and girls are identical, except: there's something in the water at high schools that causes boys to do worse than girls; and there's something in the water at universities that causes boys to do worse than girls.
Suppose you had a data set for all university students, that told you for each student i: that student's performance at university Ui; that student's performance at high school Hi; and that student's sex Si (Si=1 for male, Si=0 for female).
Suppose you ran a multiple regression of Ui on Hi and Si:
Ui = a + bHi + cSi + ei
What would you expect to find?
Every other macro blogger seems to be taking a crack at this question. I like what they have to say. But I have a much simpler theory.
Let's suppose you wanted to design an experiment to test the effects of monetary and fiscal policy. And suppose you had the power to do whatever you wanted, and couldn't care less about getting clearance from the Research Ethics Board. What experiment would you design?
The Google search engine continues to demonstrate its use as a tool for predicting human behaviour and activity based on the frequency of topics searched by its users. Google data has been used to track flu activity and even fertility behaviour. A Banca D’Italia November 2012 working paper by Francesco D’Amuri and Juri Marcucci is the latest installment on using the predictive power of Google searches and applies it to forecast US unemployment. As the authors note, such an approach has already been examined for unemployment in Germany, Italy and Israel.
The better the Bank of Canada is doing its job, the more it will appear that the Bank of Canada should be broken in two. The worse the ECB does its job, the more it will appear that the Eurozone is an Optimal Currency Area. If central banks were even slightly more competent than random number generators, then the existing set of currency areas would probably appear worse than an equal number of currency areas chosen at random.
Canada's 2005 National Graduates Survey asked respondents the following question: "Compared to the rest of your graduating class in your field(s) of study, did you rank academically in the top 10? Below the top 10% but in the top 25%..." The responses are shown below:
Google Trends is a quick and popular way to assess the importance of ideas, events and trends by looking at the results of people’s web searches. In fact, as is well known, it has been used to study flu activity based on searches for flu related terms. And, right here on WCI, it has been used to study marital discord and the holiday spirit. Indeed, it can also provide economic information and is apparently even becoming a way to supplement economic forecasting tools. If the searches for something are trending up, it is suggestive that it is growing in importance as an economic force.
Well, after my last post I finally got around to calculating another measure of the birth rate – births per capita. Total births are a useful aggregate but the measure does not adjust for population size. There are other fertility measures out there such as births per woman aged 15 to 44 years but per capita births is a nice intuitive measure that adjusts for population size and can be readily compared to other numbers – such as per capita income. Moreover, it was easy to calculate.
The May GDP number came out today, so it's time for my quarterly exercise in trying to come up with a preliminary estimate for quarterly GDP growth a month before Statistics Canada makes its first announcement. As regular readers will no doubt be weary of being reminded, the estimate is based on the GDP numbers for the first two months of the quarter along with the information in the LFS release for the third month. The most recent exercise is here.
The number I get for the annualised growth rate for GDP in the first quarter of 2012 is 1.6%.
I know I'm right in saying that Milton Friedman's thermostat is an important idea that all economists ought to be aware of. And I'm pretty sure I'm right in asserting that almost all economists are unaware of this important idea. Am I wrong? Are you aware of this idea? Maybe under some other name??
Google seems to tell me I'm right. I'm the first link, which is really pathetic for such an important idea; the second is Friedman himself (pdf); and most of the rest on the first page are other bloggers, mostly Market Monetarists. But this idea has got nothing (in particular) to do with Monetarism. Compare that to what Google comes up with for another of Milton Friedman's important ideas. Scholarly articles, and its own Wikipedia page.
And every few days I come across an economist saying something that he would not have said if he were aware of Milton Friedman's thermostat. Today it was Casey Mulligan, but the class "economists who seem to be unaware of Milton Friedman's thermostat because they say something they would not say if they were aware of it" seems to me to cover lots of very different economists.
This really bugs me.
Econometricians spend their lives trying coming up with new and better estimation techniques. Some of the ideas are excellent but impractical ("Just find a suitable instrumental variable"), and some complicate matters for minimal benefit (some argue that using logit or probit rather ordinary least squares estimation falls into this category).
So when I stumble across some well-intentioned econometric advice, one of first questions I ask myself is: does it matter?
The February GDP number came out yesterday, so it's time for my quarterly exercise in trying to come up with a preliminary estimate for quarterly GDP growth a month before Statistics Canada makes its first announcement. As regular readers will no doubt be weary of being reminded, the estimate is based on the GDP numbers for the first two months of the quarter along with the information in the LFS release for the third month. The most recent exercise is here.
The number I get for the annualised growth rate for GDP in the first quarter of 2012 is 1.6%.
My first job after finishing my undergraduate degree in economics involved using Lotus 1-2-3 - the first "killer app" spreadsheet program - to create graphs. I'd never been taught to use a spreadsheet, but I worked it out.
Fast forward a couple of decades. Spreadsheets are ubiquitious in the workplace. When a new research assistant joins the Bank of Canada, their first job - like mine - is crunching data with a spreadsheet. Yet, at some universities, it is possible to graduate with an economics degree and never learn how to use Excel.
The data portal contains all of Statistics Canada's CANSIM data, as well as data from the Department of Finance, Health Canada, Environment Canada, Transport Canada, Citizen and Immigration Canada, and so on.
To try out the data portal, I gave it a couple of tests.