For every dollar of short-term debt, pay the government (say) 10 cents. I don’t know the exact number either, but a wrong tax rate does a lot less damage than a wrong quanti[t]ative restriction.
Instead of telling banks what their ratio of debt to equity should be, let them choose that ratio, based on a tax that offsets the implicit subsidy to debt that comes from bailouts. Makes sense.
It is called The Up Side of Down: Why Failing Well Is the Key to Success. I am still digesting it. Meanwhile, you can read this article on why writers procrastinate, which is taken from the book.
they seem to be paralyzed by the prospect of writing something that isn’t very good.
There are plenty of newspaper columnists who clearly do not suffer from such paralysis.
We have now returned to an economy where those who leave their jobs are more likely to have done by quitting voluntarily than by being laid off or discharged involuntarily
Read the whole thing. One interpretation of the data is that the labor market has now divided. There are active workers, who are exhibiting fairly typical patterns of working, quitting, and being laid off. And there are inactive workers, who have either officially or unofficially left the labor force. If this interpretation is correct (and I am not confident that it is), then we may be close to full employment.
From Technology Review.
Though genome editing with CRISPR is just a little over a year old, it is already reinventing genetic research. In particular, it gives scientists the ability to quickly and simultaneously make multiple genetic changes to a cell. Many human illnesses, including heart disease, diabetes, and assorted neurological conditions, are affected by numerous variants in both disease genes and normal genes. Teasing out this complexity with animal models has been a slow and tedious process. “For many questions in biology, we want to know how different genes interact, and for this we need to introduce mutations into multiple genes,” says Rudolf Jaenisch, a biologist at the Whitehead Institute in Cambridge Massachusetts. But, says Jaenisch, using conventional tools to create a mouse with a single mutation can take up to a year. If a scientist wants an animal with multiple mutations, the genetic changes must be made sequentially, and the timeline for one experiment can extend into years. In contrast, Jaenisch and his colleagues, including MIT researcher Feng Zhang (a 2013 member of our list of 35 innovators under 35), reported last spring that CRISPR had allowed them to create a strain of mice with multiple mutations in three weeks.
The IGM forum tried to ask economists to take sides in the end-of-innovation debate by asking if they agreed with
Future innovations worldwide will not be transformational enough to promote sustained per-capita economic growth rates in the U.S. and western Europe over the next century as high as those over the past 150 years.
The most popular answer was “uncertain,” and the next most popular answer was “disagree.” I would note that Tyler Cowen has consistently said that he is bullish on innovation longer term.
The Krugosphere is hostile to the macroeconomics of the University of Minnesota. I understand that. Krugman has used the term “Dark Age Macroeconomics” to describe what took place between the late 1970s and today. I understand that, too.
But what happened in Minnesota could have stayed in Minnesota. Instead, Stan Fischer and Olivier Blanchard gave MIT’s blessing to DSGE models and vector autoregressions. To me, those two are the real villains.
Had Fischer taken his cues from, say, Clower and Leijonhufvud, rather than from Sidrauski and his ilk, macroeconomists might have spent the last 30 years working on interesting issues and gaining some better understanding of the economy. Instead, they spent the last thirty years diddling with fancy unverifiable equations and pouring a few globs of macro data into the VAR immersion blender.
CBO estimates that federal debt held by the public will equal 74 percent of GDP at the end of this year and 79 percent in 2024 s. 4 (the end of the current 10-year projection period). Such large and growing federal debt could have serious negative consequences, including restraining economic growth in the long term, giving policymakers less flexibility to respond to unexpected challenges, and eventually increasing the risk of a fiscal crisis (in which investors would demand high interest rates to buy the government’s debt).
Some possibilities:
1. The CBO are Koch-funded austerians.
2. Just like the reduction in hours worked that the CBO forecasts for Obamacare, eventually increasing the risk of a fiscal crisis is actually a good thing.
3. More government debt gives the Fed more debt to buy, which in turn makes the stock market happy.
Two pointers from Reihan Salam.
America doesn’t have a jobless recovery; it has a hireless recovery. Don’t confuse them. After all, you first have to get hired to have a job. Organizations may be desperate to grow, but they overwhelmingly lack the desire to hire. Fewer people are working longer, harder and (presumably) smarter hours. So many firms have proven so productive even after several rounds of layoffs, that serious economists wonder if, in fact, large slices of the workforce actually offer ZMP — Zero Marginal Productivity — to their enterprise. In other words, the Great Recession reveals many employees not just to be worth less but economically worthless. Ouch.
For most organizations, people are a means and medium to an end. They’re not hiring employees, they’re hiring value creation.
One point I am starting to harp on is that many workers are not concurrently productive. That is, the work they do helps the firm be more productive in the future. That means that when firms think about hiring they have a lot of discretion (we can meet the demand for widgets today without adding new people) and they face a lot of uncertainty (will these social media marketers really deliver us new customers?).
Schrage again:
What’s structurally changed is not the job but why people get hired. In other words, is hiring someone really essential to getting the job done? Just as important, as we look at employment costs, risks and uncertainties over the next five years, is hiring someone the most cost-effective way to get the job done?
[in the year 2030] Firms also are not interested in paying for training, so most people now go through a 10-year unpaid internship while simultaneously attending school online and engaging other pursuits on a more or less random schedule.
Don’t dream of doing a mooc on your own. You need video and IT help. Most of all, you need pedagogical help, people who keep up with the fast-evolving art of how to successfully port classes on moocs. I had that help at the University of Chicago, and it saved me from horrible beginner blunders. Example: I wanted to tape my live classes. No, Emily, who was in charge of my class, insisted that we do it months ahead of time in 5-8 minute segments.
In fact, it takes considerable time and effort to come up with an effective, compelling short video. A typical lecture is way too long and way too boring to translate into the online world. One of the leading MOOC suppliers offered a statistics course in which the professor opened up with a 20-minute lecture on histograms. I have to assume that the course was a total failure. As Cochrane puts it,
no question about it, the deadly boring hour and a half lecture in a hall with 100 people by a mediocre professor teaching utterly standard material is just dead, RIP. And universities and classes which offer nothing more to their campus students will indeed be pressed.
One of Cochrane’s main points is that online education really underscores the fixed cost in lesson preparation. Consider that it probably takes much more work to create an effective online lesson than it does to put a lesson in the form of a textbook. Yet anyone who has ever written a textbook can tell you that it is difficult and painstaking, so imagine what it would take to do an entire online course as well as you possibly could.
I believe that it is unlikely that any one person can create an entire course as a MOOC using today’s tools and make anywhere close to the best use of the online medium. Perhaps the tools will get much better. But meanwhile, I would recommend that would-be online instructors focus on producing really good lessons, as opposed to entire courses.
Suppose you can produce ten high-quality lessons of 8 minutes or less. This may take hours and hours of planning, scripting, editing, and so on. It will not cover an entire course. But if you then combine it with other lessons that are available on line, you can cobble together a high-caliber course. That is one scenario for how online education might develop over the next few years.
In a comment on this post, Noah Smith commended to me the work of George-Marios Angeletos of MIT. Unfortunately, Angeletos is fond of vector autoregressions (VARs), which I detest.
I got my start in macro working on structural macroeconometric models. I saw them close up, and I am keenly aware of the problems with them. Hence, I wrote Macroeconometrics: The Science of Hubris.
However, I will give the old-fashioned macroeconometricians credit for at least worrying about the details of the data they are using. If there are structural factors that are changing over time, such as trend productivity growth or labor force participation, the macroeconometrician will keep track of these trends. If there are special factors that change quarterly patterns, such as the “cash-for-clunkers” program that shifted automobile purchases around, the macroeconometrician will take these into account.
The VAR crowd cheerfully ignores all the details in macro data. The economist with a computer program that will churn out VARs is like a 25-year-old with a new immersion blender. He does not want to spend time cooking carefully-selected ingredients. He just wants to throw whatever is in the pantry into the blender to make a smoothie or soup. (Note that I am being unfair to people with immersion blenders. I am not being unfair to people who use VARs.)
The VAR appeared because economists became convinced that structural macroeconometric models are subject to the Lucas Critique, which says that as monetary policy attempts to manipulate demand, people will adjust their expectations. My reaction to this is
(a) the Lucas critique is a minor theoretical curiosity. There are much worse problems with macroeconometrics in practice.
(b) How the heck does running a VAR exempt you from the Lucas Critique? A VAR is no less subject to breakdown than is a structural model.
The macroeconometric project that I first worked with is doomed to fail. Implicitly, you are trying to make 1988 Q1 identical to 2006 Q3 except for the one causal factor with which you are concerned. This cannot be done. There is too much Manzian causal density.
The VAR just takes this doomed macroeconometric project and cavalierly ignores details. It is not an improvement over the macroeconometrics that I learned in the 1970s. On the contrary, it is inferior. And if the big names in modern macro all use it, that does not say that there is something right about VAR. It says that there is something wrong with all the big names in modern macro. On this point, Robert Solow and I still agree.