The latest on growth accounting

This the early days of the Solow growth model, we are accustomed to the decomposition of GDP growth into the contributions of capital, labor and the "Solow residual," sometimes interpreted as technology or total factor productivity. Roughly, each contributed a third, and this distribution has changed relatively little once measurements or models have been refined, for example in adding other forms of capital, such a human capital or public capital.

This is revisited by Robert Tamura, Gerald Dwyer, John Devereux and Scott Baier (with an impressive data appendix) who reassemble data from 168 countries and are especially carefully in constructing new estimates for human capital. Instead of adding years of schooling and possibly years of experience, they use a human capital production function that depends also on the parents' human capital and the world's frontier. In the end, the residual accounts for much less than previously, at most a third in some extreme specification, a tenth in the other extreme. The rest is split quite evenly between physical and human capital. What this means, is that technology, institutions and whatever else you want to throw in the Solow residual accounts for much less than we previously thought in per capita output growth and in differences of output per capita across countries.

Paul Ryan's Early Aspirations

"As a young man, Ryan held numerous amusing summer jobs, including a stint as an Oscar Mayer salesman in which he drove a Wienermobile. He envisioned eventually going to the University of Chicago for an advanced degree in economics and becoming an economist/academic, but he says he 'kept getting lured into politics.'"

Source.

China's biggest threat: its men

Many people in the Western world are afraid of China as a new economic superpower. To a large extend, this is because of a mistaken belief that the world economy is a zero-sum game, and any progress in China is to the detriment of currently rich economies. Of course, rich economies mostly benefit from China's growth, as it makes some goods cheaper and opens new markets. And the richer countries are, the less they will want to get into destructive wars, if this is what you are worried about.

If there is a threat, it is rather from within China. First, there is a huge number of undocumented internal migrants who do not have access to social services. Second, as Jane Golley and Rod Tyers describe, there is a time-bomb resulting from an imbalance in the sex ratio. The surplus of men leads families to save too much in order to compete for scarce women to marry their sons to. The imbalance is so strong that regular immigration or human trafficking are not sufficient. But the most striking menace comes from the discouraged single and low-skilled men, who could amount to a quarter of all men of reproductive age by 2030, who are prime candidates for a criminal life. And reverting such trends is going to be very difficult.

Voluntary pollution restrictions do not work

The literature on international environmental agreements has established that when such agreement are only of the self-enforcing kind (not imposed by a supranational entity), they cannot exceed three participants. That is certainly disappointing, as we would need much more than that to get significant impact. This literature, however, looked at these countries in a vacuum, in particular the only interaction they would have is through pollution. Now it turns out that in reality they also trade with each other, and trade policy is also available as an instrument.

This is how Thomas Eichner and Rüdiger Pethig expand the extant literature. The addition of trade allows more countries to participate in a self-enforcing agreement. But this comes at the cost of an agreement with significantly less bite. These are interesting results, but I have a hard time finding intuition for this, and the authors are not of much help. Consider this to be an appeal for clarification.

An Interview with John List

A nice discussion of his research using field experiments.

Mathematics, Econometrics and the top economist's career outcomes

Some people have been complaining about the increasing mathematization of econonmics and how it leads to a disconnect of economics with real life (which I suppose is void of mathematics). I would argue that this is actually a good thing, first because it forces you to have rigorous arguments, second because you often need quantitative answers to questions that result in qualitatively ambiguous outcomes, and third because it simply allows us to look at more complex problems. But, has mathematization actually increased?

Miguel Espinosa, Carlos Rondon and Mauricio Romero look at the publications of top economists and look at how many equations or econometric outputs per article they produced. The analysis for the last century shows a gradual increase of mathematization throughout, except for the number of equations which went through a serious recession in the 1980's. And of course, econometrics started only seriously in the 1950's. It also appears that professional success as measured by prestigious prizes is certainly linked to the use of mathematics, but not the econometric kind.

The Two Labor Market Surveys

If you go to the recent release from the BLS, you can find these two sentences a few paragraphs apart:

Total employment rose by 873,000 in September.

Total nonfarm payroll employment increased by 114,000 in September.

To a layman, this may seem confusing.  The first statement suggests a robust labor market, the second a more lackluster one.  What is going on?

The issue is that there are two surveys.  The first estimate of employment comes from the survey of households; the second is from the survey of establishments.  I thought readers might like to hear what my favorite intermediate macro textbook says about this issue.  Here is an excerpt:

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Because the BLS conducts two surveys of labor-market conditions, it produces two measures of total employment. From the household survey, it obtains an estimate of the number of people who say they are working. From the establishment survey, it obtains an estimate of the number of workers firms have on their payrolls.

One might expect these two measures of employment to be identical, but that is not the case. Although they are positively correlated, the two measures can diverge, especially over short periods of time. A particularly large divergence occurred in the early 2000s, as the economy recovered from the recession of 2001. From November 2001 to August 2003, the establishment survey showed a decline in employment of 1.0 million, while the household survey showed an increase of 1.4 million. Some commentators said the economy was experiencing a “jobless recovery,” but this description applied only to the establishment data, not to the household data.

Why might these two measures of employment diverge? Part of the explanation is that the surveys measure different things. For example, a person who runs his or her own business is self-employed. The household survey counts that person as working, whereas the establishment survey does not because that person does not show up on any firm’s payroll. As another example, a person who holds two jobs is counted as one employed person in the household survey but is counted twice in the establishment survey because that person would show up on the payroll of two firms.

Another part of the explanation for the divergence is that surveys are imperfect. For example, when new firms start up, it may take some time before those firms are included in the establishment survey. The BLS tries to estimate employment at start-ups, but the model it uses to produce these estimates is one possible source of error. A different problem arises from how the household survey extrapolates employment among the surveyed households to the entire population. If the BLS uses incorrect estimates of the size of the population, these errors will be reflected in its estimates of household employment. One possible source of incorrect population estimates is changes in the rate of immigration, both legal and illegal.

In the end, the divergence between the household and establishment surveys from 2001 to 2003 remains a mystery. Some economists believe that the establishment survey is the more accurate one because it has a larger sample. Yet one recent study suggests that the best measure of employment is an average of the two surveys. [George Perry, “Gauging Employment: Is the Professional Wisdom Wrong?,” Brookings Papers on Economic Activity (2005): 2.]

More important than the specifics of these surveys or this particular episode when they diverged is the broader lesson: all economic statistics are imperfect. Although they contain valuable information about what is happening in the economy, each one should be interpreted with a healthy dose of caution and a bit of skepticism.