Tyler Cowen's Blog, page 95

Assorted links

1. Scott Sumner on demand- and supply-side stagnation.  I also would stress the risk premium as a transmission mechanism linking the two curves.

2. Austin Frakt reviews Google’s Inbox.

3. What the world eats.

4. When DNA is the matchmaker (dubious, in my view).

5. How candy conquered Halloween.

6. The 86-square foot Paris apartment.

Is recovery always slow after a financial crisis?

That has been the received wisdom, but it is now challenged by a new paper (pdf) by Christina and David Romer:

This paper revisits the aftermath of financial crises in advanced countries in the decades before the Great Recession. We construct a new series on financial distress in 24 OECD countries for the period 1967-2007. The series is based on narrative assessments of the health of countries’ financial systems that were made in real time; and it classifies financial distress on a relatively fine scale, rather than treating it as a 0-1 variable. We find little support for the conventional wisdom that the output declines following financial crises are uniformly large and long-lasting. Rather, the declines are highly variable, on average only moderate, and often temporary. One important driver of the variation in outcomes across crises appears to be the severity and persistence of the financial distress itself when distress is particularly extreme or continues for an extended period, the aftermath of a crisis is worse.

There is Justin Lahart coverage here, including a contrast with Reinhart and Rogoff.

Was there mismatch unemployment during the Great Recession?

I remember this question being debated extensively circa 2009-2011, and those who said there was a (limited) role for mismatch unemployment were mocked pretty mercilessly.  Well, Sahin, Song, Topa, and Violante have a piece in the new American Economic Review entitled “Mismatch Unemployment.”  (You can find various versions here.)  It’s pretty thorough and state of the art.  Their conclusion:? “…mismatch, across industries and three-digit occupations, explains at most one-third of the total observed increase in the unemployment rate.”  The people thrown out of work could not be matched as well as the unemployed workers of the past.

Much of the matching problem was for skilled workers, college graduates, and in the Western part of the country.  Geographical mismatch unemployment did not appear to be significant.  Now, “at most one-third” is not the main problem, but it is not small beans either.  That’s a lot of people out of work because of matching problems.

Again, the Great Recession arose from a confluence of supply and demand problems.

State capacity and military conflict

There is a new paper (pdf) by Nicola Gennaioli and Hans-Joachim Voth, forthcoming in The Review of Economic Studies:

Powerful, centralized states controlling a large share of national income only begin to appear in Europe after 1500. We build a model that explains their emergence in response to the increasing importance of money for military success. When fiscal resources are not crucial for winning wars, the threat of external conflict stifles state building. As finance becomes critical, internally cohesive states invest in state capacity while divided states rationally drop out of the competition, causing divergence. We emphasize the role of the “Military Revolution”, a sequence of technological innovations that transformed armed conflict. Using data from 374 battles, we investigate empirically both the importance of money for military success and patterns of state building in early modern Europe. The evidence is consistent with the predictions of our model.

The pointer is from Mark Koyama.

Michigan markets in everything department of why not?

Oakley, Mi. is barely a town at 300 people, only one streetlight and, until recently, one police officer. The one cop was good at his job, reports Vocativ’s M.L. Nestel, until he was forced to step down after getting caught stalking a teenage girl.

In 2008, new chief Robert Reznick made some changes: he hired 12 full-time officers and started an enormous volunteer officer program which allowed lawyers, doctors and football players (from other towns) to work toward upholding the law.

One qualifies for this prestigious program simply by paying $1,200 to the police department. In return, you’ll get a uniform, bullet-proof vest and gun. For an additional donation, you’ll get a police badge and the right to carry your gun basically anywhere in the state, including stadiums, bars and daycares.

There is more here, via Larry Rothfield.

Assorted links

1. RBC theory won’t die soon.

2. Beware of fake priests in cemeteries.

3. Scott Alexander on whether polyamory is boring.

4. Average is Over, Sydney beach bum edition.  And Bolivia legalizes child labor for ten year olds.

5. Not your grandfather’s pot.

6. A Larry Summers summary post on secular stagnation.

7. My 2013 New York Times column on how to fight pandemics.

Sentences to ponder

Here is Jody Lanard and Peter M. Sandman on the risks of an Ebola pandemic in the developing world:

The two of us are far less worried about sparks landing in Chicago or London than in Mumbai or Karachi.

Do read the whole thing, via Andrea Castillo.

Everyone in development economics should read this paper

It is by Eva Vivalt and is called “How Much Can We Generalize from Impact Evaluations?” (pdf).  The abstract is here:

Impact evaluations aim to predict the future, but they are rooted in particular con-

Impact evaluations aim to predict the future, but they are rooted in particular contexts and results may not generalize across settings. I founded an organization to systematically collect and synthesize impact evaluations results ona wide variety of interventions in development. these data allow me to answer this and other questions across a wide variety of interventions. I examine whether results predict each other whether variance in results can be explained by program characteristics, such as who is implementing them, where they are being implemented, the scale of the program, and what methods are used.  I find that when regressing an estimate on the hierarchical Bayesian meta-analysis result formed from all other studies on the same intervention-outcome combination, the result is significant with a coefficient  of 0.6-0.7, though the R-squared is very low.  The program implementer is the main source of heterogeneity in results, with government-implemented programs faring worse than and being poorly predicted by the smaller studies typically implemented by academic/NGO research teams, even controlling for sample size.  I then turn to examine specification searching and publication bias, issues which could affect generalizability and are also important for research credibility.  I demonstrate that these biases are quite small; nevertheless, to address them, I discuss a mathematical correction that could be applied before showing that randomized control trials (RCTs) are less prone to this type of bias and exploiting them as a robustness check.

Eva is on the job market from Berkeley this year, her home page is here.  Here is her paper “Peacekeepers Help, Governments Hinder” (pdf).  Here is her extended bio.

Impact evaluations aim to predict the future, but they are rooted in particular con-
texts and results may not generalize across settings. I founded an organization to
systematically collect and synthesize impact evaluation results on a wide variety of in-
terventions in development. These data allow me to answer this and other questions
across a wide variety of interventions. I examine whether results predict each other and
whether variance in results can be explained by program characteristics, such as who is
implementing them, where they are being implemented, the scale of the program, and
what methods are used. I �nd that when regressing an estimate on the hierarchical
Bayesian meta-analysis result formed from all other studies on the same intervention-
outcome combination, the result is signi�cant with a coe�cient of 0.6-0.7, though the
R
2
is very low. The program implementer is the main source of heterogeneity in results,
with government-implemented programs faring worse than and being poorly predicted
by the smaller studies typically implemented by academic/NGO research teams, even
controlling for sample size. I then turn to examine speci�cation searching and publica-
tion bias, issues which could a�ect generalizability and are also important for research
credibility. I demonstrate that these biases are quite small; nevertheless, to address
them, I discuss a mathematical correction that could be applied before showing that
randomized controlled trials (RCTs) are less prone to this type of bias and exploiting
Impact evaluations aim to predict the future, but they are rooted in particular con-
texts and results may not generalize across settings. I founded an organization to
systematically collect and synthesize impact evaluation results on a wide variety of in-
terventions in development. These data allow me to answer this and other questions
across a wide variety of interventions. I examine whether results predict each other and
whether variance in results can be explained by program characteristics, such as who is
implementing them, where they are being implemented, the scale of the program, and
what methods are used. I �nd that when regressing an estimate on the hierarchical
Bayesian meta-analysis result formed from all other studies on the same intervention-
outcome combination, the result is signi�cant with a coe�cient of 0.6-0.7, though the
R
2
is very low. The program implementer is the main source of heterogeneity in results,
with government-implemented programs faring worse than and being poorly predicted
by the smaller studies typically implemented by academic/NGO research teams, even
controlling for sample size. I then turn to examine speci�cation searching and publica-
tion bias, issues which could a�ect generalizability and are also important for research
credibility. I demonstrate that these biases are quite small; nevertheless, to address
them, I discuss a mathematical correction that could be applied before showing that
randomized controlled trials (RCTs) are less prone to this type of bias and exploiting

 

The East 25 Years After Communism

That is the new Foreign Affairs piece by Andrei Shleifer and Daniel Treisman, and they argue that matters have gone strikingly well and are relatively normal.  Here is one excerpt:

Newspapers overflowed with accounts of soaring mortality amid the stress of transition. On average, however, life expectancy rose from 69 years in 1990 to 73 years in 2012. The speed of improvement was two thirds faster than in the communist 1980s. Russia’s life expectancy today, at 70.5, is higher than it has ever been. Infant mortality, already low, fell faster in percentage terms than in any other world region.

Eastern Europe is infamous for unhealthy binge drinking. However, average alcohol consumption fell between 1990 and 2010 from 7.9 to 7.6 liters of pure alcohol a year per resident aged over 14. There were exceptions — drinking rose in Russia and the Baltic states but even in Russia recorded consumption in 2010, 11.1 liters, was lower than that in Germany, France, Ireland, or Austria. (Of course, more drinking might escape the statisticians in the Slavic region.) Smoking among adult males was high – 42 percent on average but about the same as in Asia. In short almost all statistics suggest a dramatic improvement in the quality of life.

In short, almost all statistics suggest a dramatic improvement in the quality since 1989 for citizens of the average postcommunist country — an improvement that rivals and often exceeds those in other parts of the world.

You will note that the published version in Foreign Affairs has slightly different wording and organization.

Good sentences

To develop an intuition for our main result, note that the equilibrium private saving behavior must be resistant to rare mutants.

That is from the new Robson and Szentes AER paper, “Biology and Social Discounting,” which argues that the nature of sexual reproduction causes private discount rates to rise above social ones.