I hope not. In the 1990’s, Macroeconomic Advisors GDP forecasts performed worse than random walk forecasts (basically guessing next year’s GDP will be the same as last year’s) in 6 out of 9 years (Fed WP, Anderson 1998). No sense paying someone to give you worse answers than a quick glance on a government website would give.
Brad Delong says we should believe these people’s forecasts because they get paid to make them. MA’s performance, on the other hand, suggest people pay them for services other than their GDP point estimates.
… every time I run into a statement like this: “But all interesting models involve unrealistic simplifications, which is why they must be tested against data.”
We can know things a priori, but we can’t know how important those things are a priori. A model can tell us how a particular mechanism operates, but it can’t tell us how important that mechanism is. Effect size can only be determined by looking at the data.
Here’s the density of temperature changes over centuries. I used these data and calculate the average change in temperature per century.
Last century’s temperature increase of 0.8 degrees C was an outlier (but not an extreme outlier). About 95% of temperature changes were slower than last century’s temperature change. If the climate models are correct and the world sees a 2.5 degree increase, this would be an extreme outlier. Only about 1 in a three or four hundred centuries sees that dramatic of temperature changes.
This, of course, doesn’t guarantee catastrophe, but it suggests we should at least insure ourselves against the possibility of catastrophe.
The Economist marvels that the percentage of households that have had someone in the house lose their job last year is greater than the unemployment rate. They say the first number, 24%, shows “a lot of people directly affected by recession”. But is this true?
Let me start by giving the obvious disclaimer: the recession has been hard on people. Yessir. Nothing I say below should suggest otherwise.
Outside of recessions and booms, 1.3% of employed workers become unemployed every month (look at this neat graph). This means employed workers have about an 15% chance of being “affected” by unemployment in a year. There’s something like 1.22 workers per household so a household has about an 18% chance of being “affected” by unemployment in a year (I have to assume these proportions are the same whether or not the worker is the primary earner in the house). This is in “normal” times.
Survey error notwithstanding, it appears this recession is associated with a 6% increase in the number of households “affected” by unemployment. I don’t know if that’s a big number or not.
I do know that the separation rate is down in this recession (hire rates are down further, though).
I also know that lay-offs rates have only slightly increased in the last year or so.
The unemployment rate is high because the job finding rate is low not because the separation rate is high.
- 29 more minutes than employed people looking for jobs
- 54 more minutes sleeping
- 29 more minutes socializing
- 52 more minutes watching TV or movies
- 15 more minutes playing sports or on the computer
- 0 more minutes volunteering
- 47 more minutes doing housework
That’s not the question Will Wilkinson asked, but until more disaggregate data becomes available we won’t know the impact of the recession on inequality. What’s more, we only have data at the State level through 2008 so we don’t even see the brunt of the badness. Here’s State income levels in 2007 plotted against income growth over the next year:
There’s a statistically insignificant and small negative relationship between initial income level and growth rate.
Beware the ecological fallacy (in reverse).
Kaldor published some stylized facts about growth in the sixties and growth theorist went about explaining them. They’re done now. Professors Jones and Romer came up with a new set of growth facts (h/t Kling). I will spend my career hearing theories that explain these new facts. Yeah!
Two UC Davis econ profs are cited.
Also, in that paper is my original dissertation idea:
The interaction between institutions and idea flows is easy to illustrate in familiar contexts. For example, until 1996, opponents successfully used the local permit process to keep Wal-Mart from building stores or distribution centers in Vermont. This kept powerful logistics ideas like cross-docking that Wal-Mart pioneered from being used to raise productivity in retailing in the state. Such nonrival ideas must have been at least partly excludable. This is why Wal-Mart was willing to spend resources developing them and why competitors were not able to copy them. All this fits comfortably in the default model of endogenous discovery of ideas as partially excludable nonrival goods.
Looking at the macro (state-level) data, I couldn’t find the relationship suggested in the bolded section. There’s great data on Walmart’s spread out of Arkansas after its founding. If anybody’s interested in this stuff, I can pass on citations, etc.
I’m what you might call a political newb so this might be an obvious question: why is Obama talking about redistributionist policies like credit cards and health care when the real problem in America, on this front at least, is Black poverty? What political costs would he pay that outweigh the large benefits of fixing this problem? Wouldn’t these costs would be relatively small for him? Or is it not about politics at all…is the problem just intractable?
Being an economist, I just assume redistributionist policy is easy. Maybe I’m wrong.
Mike asks for median wage-age profiles. I don’t know how to do quantile regressions for panel data, but I have a second best for him. Here’s wage-age profiles with the top 90th percentile wage earners removed:
And here’s the wage-age profile with both the bottom 10th percentile and the top 90th percentile removed: