Category Archives: Growth Causes and Consequences

Measuring Output of the GDP Factory

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Martin Feldstein writes,

More generally, as Triplett and Bosworth (2004) note, the official data imply that productivity in the health industry, as measured by the ratio of output to the number of employee hours involved in production, declined year after year between 1987 and 2001. They conclude (p. 265) that such a decline in true productivity is unlikely, but that officially measured productivity declines because “the traditional price index procedures for handling product and service improvements do not work for most medical improvements.” More recent data show that health sector productivity has continued to decline since 2001.

When you think of a factory producing, say, slate shingles, measurement of output is pretty straightforward. That makes measurement of productivity pretty straightforward.

Now introduce a second good, iron nails. These are produced in a different factory, but the idea of aggregation is to treat the economy as if it were a single GDP factory producing shingles plus nails. Of course, if you measure output as shingles plus nails, you will get strange results. If the economy produces 1 less shingle and 101 more nails, the total goes up by 100. But you do not know if the value of what was produced went up at all.

To obtain a more reasonable measure of total output, the statisticians take a weighted average of nail production and shingle production, where the weights are based on relative prices. If one shingle sells for $1.01 and one nail costs $.01, then producing 1 less shingle and 101 more nails results in no change to total output.

But using relative prices this does not completely solve the problem. Relative prices can change. Then you have to decide whether to base the weights on last year’s prices, this year’s prices, or some combination of the two.

But choosing a relative-price base year does not completely solve the problem, either. Relative prices can change because of quality change. Suppose that this year the shingle maker produces shingles that are more durable than the shingles produced last year, but charges the same price. Because quality has gone up, the relative price has gone down. Will the statisticians capture this?

Think of what you are trying to accomplish with these sorts of measurements. You might start by asking for any particular product how many hours a low-skilled worker would have to work in order to obtain that product. Brad DeLong once calculated that five hundred years ago it would take someone about three days in order to obtain the equivalent of one bag of flour. For today’s low-skilled workers in the U.S., this would take only a matter of minutes.

But then, how do you take a weighted average over many products? What do you do about quality change? How do you value new products?

Next, you have to note that people with more skills have higher wages, which reduces the number of hours that they must work to obtain the same goods. As the skill mix of the population changes, how do we want that to affect our measure of the productivity of the GDP factory?

In my view, the attempt to treat the economy as a GDP factory is bound to be very far from precise. It always amazes me when economists take seriously a concept like “the change in the trend rate of productivity.” The level of productivity is a very imprecise measure, for the reasons sketched above. When you measure the growth rate of productivity, you necessarily boost the noise to signal ratio. Then, when you measure the change in the rate of productivity growth rate of productivity, you once again boost that noise to signal ratio, to the point where you are pretty close to talking nonsense.

Broadberry and Wallis: A Suggested Interpretation

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The Economist reports,

Stephen Broadberry of Oxford University and John Wallis of the University of Maryland have taken data for 18 countries in Europe and the New World, some from as far back as the 13th century. To their surprise, they found that growth during years of economic expansion has fallen in the recent era—from 3.88% between 1820 and 1870 to 3.06% since 1950—even though average growth across all years in those two periods increased from 1.4% to 2.55%.

Instead, shorter and shallower slumps led to rising long-term growth. Output fell in a third of years between 1820 and 1870 but in only 12% of those since 1950. The rate of decline per recession year has fallen too, from 3% to 1.2%.

Tyler Cowen inspired me to find the article.

Set up two random-number generators, each producing a normal distribution. Give generator M a mean of 2.55 and a standard deviation of 1.5, and give generator H a mean of 1.4 with a standard deviation of 3. Take about 100 draws from those two random-number generators, and then separate the results into positive and negative numbers.

Presumably, the negative numbers from generator H will be more numerous and have a more negative average than those from M. The positive numbers from H, although fewer, could turn out to be larger on average than those from M. That is, by truncating the results from H at zero, the larger standard deviation might lead to a higher average for H than for M. If it does not, then tweak the difference in standard deviations between the two generators a bit more.

In other words, you can replicate the Broadberry-Wallis results without the nature of booms or recessions having any causal role. If modern economic growth, M, is higher with a lower annual standard deviation than historical economic growth, H, then you would observe these sorts of results if you arbitrarily select 0 as your dividing point between booms and slumps.

[UPDATE: a reader writes,

1. With the parameters you suggested, the conditional expectation for H is 2.99, whereas the conditional expectation for M is 2.7.

2. To reproduce their results with the Gaussian model, we’d need to have a standard deviation of about 4.16 for (H), and a standard deviation of about 2.2 for situation (M).

I would add that as you go back in history, much of output is agricultural, and subject to annual variation in weather. So variance might well have been higher for that reason.]

The New Economy in France

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Christopher Caldwell writes,

Guilluy doubts that anyplace exists in France’s new economy for working people as we’ve traditionally understood them. Paris offers the most striking case. As it has prospered, the City of Light has stratified, resembling, in this regard, London or American cities such as New York and San Francisco. It’s a place for millionaires, immigrants, tourists, and the young, with no room for the median Frenchman. Paris now drives out the people once thought of as synonymous with the city.

…As a new bourgeoisie has taken over the private housing stock, poor foreigners have taken over the public—which thus serves the metropolitan rich as a kind of taxpayer-subsidized servants’ quarters. Public-housing inhabitants are almost never ethnically French; the prevailing culture there nowadays is often heavily, intimidatingly Muslim.

Pointer from Glenn Reynnolds. Read the whole thing. This is one of those articles that one cannot excerpt enough.

State Capacity

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Noel D. Johnson and Mark Koyama write,

The origins of modern economic growth are to be found in the expansion of market exchange and trade that gave rise to a more sophisticated and complex division of labor that rewarded innovation and to the cultural and potentially non-economic factors that helped spur innovation (Howes, 2016; McCloskey, 2016; Mokyr, 2016). The importance of the rise of high capacity states to this story is that these states helped to provide the institutional conditions that either enabled growth and innovation to take place or at least prevented their destruction through warfare or rent-seeking

we suggest that the long-run relationship between culture, social capital, identity, and state capacity has only just begun to have been studied and awaits much future research.

Pointer from Tyler Cowen.

The authors say that state capacity consists of the capacity to enforce laws and the capacity to collect taxes in order to provide public goods.

Making India More Legible

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Via John Mauldin, Raoul Pal says,

India, pre-2009, had a massive problem for a developing economy: nearly half of its people did not have any form of identification. If you were born outside of a hospital or without any government services, which is common in India, you don’t get a birth certificate. Without a birth certificate, you can’t get the basic infrastructure of modern life: a bank account, driving license, insurance or a loan. You operate outside the official sector and the opportunities available to others are not available to you. It almost guarantees a perpetuation of poverty and it also guarantees a low tax take for India, thus it holds Indian growth back too.

…But in 2009, India did something that no one else in the world at the time had done before; they launched a project called Aadhaar which was a technological solution to the problem, creating a biometric database based on a 12-digit digital identity, authenticated by finger prints and retina scans.

Read the whole essay. He goes on to claim that this greater legibility (to use James Scott’s term) will greatly increase India’s efficiency and economic growth.

You may know that about ten years ago economist Hernando de Soto drew attention to the idea that legibility of property ownership would do wonders for capital formation in underdeveloped countries. It is plausible that human legibility is even more important.

War, State Capacity, and Economic Growth

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Jared Rubin looks at the literature which says that European states fought many wars, that this required them to add “state capacity,” and this in turn produced economic growth. He concludes,

while the war argument has many merits, it needs to be complemented by other arguments for “why the West got rich.” Specifically, we need to understand i) why Europe was so fractionalized in the first place, and ii) why northwestern Europe pulled ahead first. As I noted at the beginning, I think that combining the war argument with ones that look at other aspects of political institutions (especially legitimacy!) and certain aspects of culture paints a more complete story.

Pointer from Mark Thoma. Read Rubin’s whole post. A few quick points.

1. For libertarians, the idea that state capacity is important for economic growth is hard to swallow. But it may be correct. Remember your North, Weingast, and Wallis.

2. I am looking forward to Rubin’s discussion of “certain aspects of culture.” As you know, I take the view that mental-cultural factors are under-emphasized in most of the disciplines that study human behavior, including economics.

Household Production, Continued

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The insightful Handle writes,

free YouTube videos combined with cheap and quick home delivery of tools and parts have made my own home, computer, and auto repairs much more worth my time than trying to arrange for an experienced professional.

I get it that having a YouTube video that tells me how to fix my toilet can lower the time it takes me to do it myself. But the Internet also makes it easier for me to find a cheap handyman. Overall, I think that my propensity to spend time fixing things myself has gone down rather than up over the past decade. Not that it was very high to begin with.

Another commenter writes,

I think Prof. Kling misunderstood Prof. Cowen’s point. Less household production as share of GDP is not necessarily a bad thing and the best number may very well be 0%.

However, household production is generally not included in the GDP figures, even though it arguably should be. If actual GDP, including household production, used to be 37% higher than measured GDP, but now is only 20% higher than measured GDP, then the growth in actual GDP over the period has been even lower than the pretty dismal numbers we are observing.

Hence, this statistic supports Prof. Cowen’s hobby horse of the Great Stagnation, regardless of how one feels the ideal percentage of GDP household production ought to be. I think he is right on this point.

In a follow-up, the commenter writes,

My neighbor and I live in identical houses and we are equally messy. Initially, we both clean our own houses and nothing is added to measured GDP.

Then we decide to pay each other $100/week to clean each other’s house. Suddenly, measured GDP is $10k/year higher than it used to be. But economically nothing has changed. This suggests that we ought to include our cleaning labors in GDP regardless of whether we clean our own houses or each other’s.

I think this is misleading. I prefer to look at it this way:

1. Suppose you are willing to pay me $100 for 5 hours of cleaning services. Then that puts a value on my time of $20 an hour.

2. Now, suppose that I decide to spend 5 hours cleaning my own house. You want to say that I have produced $100 of output.

3. I would say instead that the 5 hours I spend cleaning my own house is a waste of time!

Maybe if you assume that the most valuable work I can do is cleaning houses, then you are sort of right. But if I am a surgeon, then you are pretty much wrong. And I claim that as an economy gets more efficient at using specialization, you become less and less right and more and more wrong.

In terms of comparing well-being now with well-being 50 years ago, suppose that most of the reduction in housework is due to the prevalence of permanent-press clothes rather than having to iron them. Suppose that our entire (market-based) GDP consists of shirt production. It would be really nice if the GDP calculation subtracted the need for ironing from the cost of today’s shirts. But it could only show up as a quality adjustment that I suspect is too sophisticated to be captured in the statistics.

Suppose that actual shirt production remains the same as it was 50 years ago. Then measured GDP might be the same, also (it could be a little higher, if the statisticians pick up some of the quality adjustment). But the alternative concept, of GDP + household production, has gone down from 50 years ago, because we have stopped ironing. To me, that makes GDP + household production a stupid concept. It has things exactly backwards.

If you are going to add anything household-related in GDP, it ought to be leisure, not housework. If you show me that leisure + GDP is not growing very much, then I would count that as an argument for a Great Stagnation. But I am not the least bit persuaded by a measure of GDP + housework.

Asymmetric Intolerance

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In the United States, the average number of automobile accidents per year is 5.25 million (source). The average number of fatalities per year is 30,000 to 35,000. (source).

How many accidents are we willing to tolerate involving self-driving cars before we stop trying to restrict their usage? Pretty much zero, right?

Let’s call this “asymmetric intolerance.” We accept a phenomenon that is highly flawed (human-driven cars) while we refuse to tolerate a phenomenon if it has any flaws at all (self-driven cars).

If asymmetric intolerance had been a policy principle 125 years ago, might we not still be transporting ourselves in horse and buggy?

Some further thoughts:

1. Maybe this is in line with the issue of resistance to change that is a theme of Tyler Cowen’s latest book.

2. Is an obsession with terrorism an example of asymmetric intolerance?

3. I have a relative in California who buys “organic toaster pastries” (non-GMO, of course). In other words, Pop-Tarts that have been blessed as all natural. Isn’t that an example of asymmetric intolerance?

4. Where else do we observe dramatic examples of asymmetric intolerance? Or is this the only example that comes to mind?

Labor’s “share” in a Garett Jones World

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Timothy Taylor looks at an article on the secular decline in labor’s share of income, and he concludes

These explanations all have some plausibility, but it isn’t clear to me that, taken together, they adequately explain the fall of more than four percentage points in labor share in the decade or so from the early 2000s (roughly 61%) to the years right after the Great Recession (just above 56%). The labor share does show some sign of rebounding in the last couple of year, and it will be interesting to see whether that turns out to be true bounce-back or a damp squib.

“Labor’s share” is one of those macro-Marxist concepts that I distrust. It ignores the heterogeneity of labor. Some workers have few skills. Others have highly marketable skills. It ignores heterogeneity of capital. But perhaps even more important, it ignores the fact that most of us are Garett Jones workers, who do not produce output but instead produce organizational capital.

As an example of a firm with a high labor “share,” consider a 1990s dotcom, which has lots of dreams but little revenue. For many of the dotcom darlings, labor’s share was way over 100 percent, and hence they went bust. Those that survived are now living off the organizational capital that they developed back in the day, which could make for a low labor share today.

In some (many?) firms, the labor share is arbitrary. For example, my guess is that as of now the “labor share” at Google is low, because the organizational capital that it built up during its first decade of existence is very valuable relative to the necessary labor input to keep it running. But Google has a lot of leeway. The more it invests today in organizational capital (research into driverless cars and such), the higher will be its (current) labor’s share. The more it just sticks to its existing business and trims workers in the research areas, the lower will be its labor’s share.