Evans and Honkapohja’s work on learning in macro is important. Watch this to get a sense for what they’re doing:
But learning dynamics are not well understood empirically. Prof. Evans describes the dynamics under adaptive learning. Under rational expectations the economy would just pop to the good equilibrium. Under other learnings schemes the dynamics would be different. How do we know which dynamics describe the actual economy? We’d need to have empirical tests of the different learning regimes. We don’t have these tests.
In the same vein, Prof. Evans is just speculating about where the economy is right now in the phase diagram. He suggest we’re close to to the deflationary zone where things go to hell if the Fed raises interest rates. In fact, there’s no a priori way to know where we’re at in the diagram — we don’t have good measures of expectations.
(ht Thoma. BTW, Thoma says that Evan’s model shows increasing interest rates “increases rather than decreases the chance of a deflationary spiral”. They do no such thing. The little arrows on the phase diagram aren’t invariant to policy, see the paper figure 1. Suppose you’re close to the deflationary zone. Where you were before the policy may be in the deflationary zone after the policy, but your location after the policy is determined, in part, by the policy and it doesn’t have to be in the deflationary zone. Anyway, this probability could be computed as simulated comparative static, but Evan’s didn’t do this in his paper.)
Here’s the per capita GDP for the G8 countries (relative to the USA):
You can take two lessons from this chart.
1) Japan hasn’t had bad monetary policy relative to, say, the continental European countries
2) Monetary policy just doesn’t matter in the long-run
Also, the more you stare at these real GDP per capita charts, the more you think the 1980s in Japan was an outlier (aka bubble).
“The unemployment resulting from wage rigidity and job rationing is sometimes called structural unemployment.” — Mankiw’s intermediate text
“Frictional unemployment [w.a. where the only other kind of unemployment they mention is cyclical unemployment] is the unemployment that exists when the economy is at full employment.” — Dornbusch, Fischer and Startz
“The part of unemployment associated with the institutional features of an economy, including hiring and firing costs and the structure of the unemployment compensation system.” — Jones’ intermediate text
There may be confusion about this term because there seems to be such a multitude of definitions (wikipedia’s definition talks about skill mismatch). I like Jones’ definition the best, but the point I want to make is that the lefty bloggers are reading more into these definitions than necessary.
There’s no reason to think the relationships between “wage rigidity”, “full employment”, “institutions” or “skill mismatch” and unemployment are constant over the business cycle. You can make good arguments that the mechanisms producing these relationships are responsive to overall conditions in the economy (I won’t). And… and!… estimates of structural unemployment suggest that it is time-varying: higher during recessions and lower during booms (anyone have a good cite for an estimate of time-varying NAIRU?).
When the Fed folks say structural unemployment is high right now (e.g. Altig or Kocherlakota), they’re not saying its permanently high. They’re not trying to pull an inception making us feel like permanent high unemployment is ok. They’re observing momentarily high structural unemployment that fits in a pattern consistent with historic experience.
Yes, in general our rationale might lead to a greater investment in bonds then the ‘80/20 rule’ calls for.
I’d suggest that the 80/20 rule (and other ‘rules of thumb’ like it) carry hidden dangers to investors, despite how neatly they may organize things in our minds. The danger stems from the inability of such over-simplified rules to account for the human factor:
The rational for an 80/20 portfolio is that it is destined to fluctuate widely with the market, yet it is also predicted to provide good returns in the long run. Therefore it suggests that investing in 80% stocks makes sense for the long term. Notice that this rational works only if we really keep our money intact for the duration of the investment. But how many of us really do?
Unfortunately, in reality, investors have many external and internal reasons to break an investment when the market goes sour. For example, many investors ‘broke’ their 80% stock investment earlier then planned for because they were scared of losing it all. Other investors just had an emergency (like losing their job) and needed the money sooner then expected – smack in the middle of the crisis. In both cases, these investors missed the general tendency of the markets to bounce back and provide positive returns for the long run. If either of these investors understood the risks involved in breaking the investment sooner, they might have not chosen the 80/20 portfolio to begin with.
My second gripe with the 80/20 rule is that it reflects the tendency to consider all bonds as safe and all stocks as having the same level of risk. In reality this is not the case. I’d suggest that other types of mixes are also viable – even for long term investments – depending on the specific bonds and stocks that are being used.
Back to Plantly – our diversification rational is to create an investment plan that aims towards your chosen target return while reducing its risk as much as possible. And yes, sometimes this calls for a greater investment in bonds as you’ve noticed – but this only works with the right kind of bonds, when mixed with the right kinds of stocks.
Investors want to have investments that pay off when their marginal utility is
low high. This is a bitch to model and measure (poor, poor economists that *have* to spend their careers obsessing about this problem), but its a pretty easy thing to believe.
Here’s all the Jolts data (minus job openings):
These are not seasonally adjusted so that’s why you get the inverted-U shape every year for the hiring and quits series and the inclined saw shape for firings. Still, you can see something happened the last two or three years. Firings (the pink line) spiked in early 2009 but several months before that quits (blue) and hirings (green) declined by about 20 to 30% each. Here’s a close-up of the most recent period:
A couple things that strike me about these graphs:
1. Firings had a one month spike in January 2009 (a month with a high number of firings to begin with) but the series has stayed about where it usually is (maybe 10% higher if you ignore the recent dip). The story of this recession isn’t of people losing their jobs.
2. Looking at the dramatic decrease in hirings, the story of the recession is that people aren’t finding new jobs.
3. However, because there’s much, much fewer people quitting their jobs the job market isn’t as tight as it could be.
One of the things I do in my dissertation is treat changing jobs as an investment decision. When a worker quits their old job and looks for a new one (maybe not in that order), they are forgoing human capital that made them productive at their old job and they’re investing in new human capital to make them productive in their new job. One possibility — given the JOLTS evidence and thinking of job switching as investment activity — is that like employers who are holding back on new invests (and thus holding back on hiring), workers are holding off on making new investments as well.
I’m not sure about the implications for aggregate demand, but I’m pretty sure this is bad for growth prospects.
One of the embarrassing dirty little secrets of economics is that there is no such thing as economic theory properly so-called. There is simply no set of foundational bedrock principles on which one can base calculations that illuminate situations in the real world. Biologists know that every cell runs off instructions for protein synthesis encoded in its DNA. Chemists start with what the Heisenberg and Pauli principles plus the three-dimensionality of space tell us about stable electron configurations. Physicists start with the four fundamental forces of nature. Economists have none of that. The “economic principles” underpinning their theories are a fraud–not bedrock truths but mere knobs twiddled and tunes so that th right conclusions come out of the analysis.
What are the “right” conclusions? It depends on what type of economist you are, for [there] are two types. One type chooses, for non-economic and non-scientific reasons, a political stance and a political set of allies, and twiddles and tunes their assumptions until they come out with conclusions that please their allies and their stance. The other type takes the carcass of history, throws it into the pot, turns up the heat, and boils it down, hoping that the bones and the skeleton that emerge will teach lessons and suggest principles that will be useful to voters, bureaucrats, and politicians as they try to guide our civilization as it slouches toward utopia.
There’s a third type of economist that cooks a pot of history to discover interesting patterns for the sake of discovering interesting patterns. Ironically, given the existence of the first type of economist, you should only listen to the third type for policy advice.
A few weeks ago, Mike was asking me about the long term unemployed. I’m running some regressions using CPS data so I have the data he asked about just hanging out on my desktop. Public service:
The unemployed during this recession ((I looked at 2008 and 2009 data for folks that worked one week or more)) are different than those that don’t become unemployed. They’re younger, less educated, more likely to be male and less likely to be married.
Now, the long-term unemployed (greater than 25 weeks unemployed) are different from the short-term unemployed. They are even less educated, less likely to be white, less likely to be married and slightly older.
The never unemployed average 40.1 years old, the short-term unemployed 35.5 and the long-term unemployed are 36. Contrary to Tyler Cowen, unemployed workers above 40 with a college degree are not much more likely to be long-term unemployed (they’re 24% of the 40+ long-term sample compared to 21.9% of the 40+ short-term unemployed sample). Relative to all college educated workers, college educated workers older than 40 are just more likely to be unemployed.
Ok. I can’t wait anymore. Someday this paper (co-written with my advisor) will show up here. The paper reviews trends in immigration in California and summarizes the findings of one of Peri’s working papers. In that more technical paper, Peri found a clever way to control for all three confounding effects I’ve discussed before that can contaminate estimates of the effects of immigration on native employment outcomes. Consistent with most of the literature, he finds no negative effects of immigration in this respect.
Also, here’s Peri talking about immigration: