I put this essay up on Medium. I like the concept of Medium. I would like to be able to reach some people who are on the left. Everyone seems to love the juvenile, anti-capitalist rants that people put up. I thought I would put up some different ideas. Based on statistics, hardly anyone who goes to the site seems to want to read what I write. If that continues to be the case, then I will just stick to this blog.
Disaggregating the economy/New Commanding Heights watch
A chart from Jeff Desjardins shows the largest employer in each state. The results: Wal-mart is the largest in 22 states. A health care network is the largest in 12 states. A university system is the largest in 11 states. That leaves 5 states with “other,” only one of which is a manufacturing firm (Boeing in the state of Washington).
I think that this reflects a Great Consolidation in retail and in health care. Mom-and-pop stores and small medical practices have been wiped out. That means you want to be really careful about interpreting statistics that seem to say that Americans aren’t starting new businesses the way that they used to. The opportunities are not what they used to be.
Disaggregating the polity: frontier culture
[UPDATE: clarifying definitions. In the paper below, the frontier is by definition very sparsely settled. Also, “Greater Appalachia” as Woodard uses the term describes the Scots-Irish who gradually spread westward, not simply people born in what we now call Appalachia]
Samuel Bazziy, Martin Fiszbeinz, and Mesay Gebresilasse write,
In our simple conceptual framework, the significance of the frontier can be explained by three factors. First, frontier locations attracted individualists able to thrive in harsh conditions. Second, the frontier experience, characterized by isolation and low population density, further promoted the development of self-reliance. At the same time, favorable prospects for upward mobility through effort nurtured hostility to redistribution. Finally, frontier populations affected local culture at a critical juncture, thus leaving a lasting imprint.
Pointer from Tyler Cowen.
My immediate reaction is to interpret this using Colin Woodard’s 11-nations model, in which he divides the U.S. into cultural sub-nations. The nation most likely to seek out the frontier would be Greater Appalachia. The other migratory nations that settled the west were Yankeedom, which was very community-oriented and would have avoided the frontier, and Midlands, which also preferred to live in towns or farming communities, rather than in isolated frontier settlements. The political and cultural description that Bazziy and co-authors give to frontier-influenced populations does seem to fit the Greater Appalachia Jacksonian model.
John Ioannidis on Economics
Self-recommending. But here is an excerpt.
Most empirical data do not come from experiments but from non-experimental sources such as surveys and routinely collected information. Along with Chris Doucouliagos and Tom Stanley, my research center examined 6,700 empirical studies encompassing 159 topics. We found that there is probably substantial bias in much of this literature. For example, the value of a statistical life, which measures how much people are willing to pay to reduce their risk of death, appears to have been exaggerated by a factor of eight. On average, the strength of the results may have been exaggerated by a factor of two. In a third of the studies, by a factor of four.
But overall, he is more upbeat than I am on economics as a science.
Pointer from Mark Thoma.
Work becomes optional
participation has changed along an understudied margin of labor supply. I find that “in-and-outs”—men who temporarily leave the labor force—represent a growing fraction of prime age men across multiple data sources and are responsible for roughly one third of the decline in the participation rate since 1977. In-and-outs take short, infrequent breaks out of the labor force in between jobs, but they are otherwise continuously attached to the labor force. Leading explanations for the growing share of permanent labor force dropouts, such as disability, do not apply to in-and-outs. Instead, reduced-form evidence and a structural model of household labor supply both indicate that the rise of in-and-outs reflects a shift in labor supply, largely due to the increasing earnings of men’s partners and the growth of men living with their parents.
Pointer from Tyler Cowen. My thoughts:
1. When we think of labor force participation declining, we think of, say, John Smith, deciding to never work again. What this paper is saying is that the statistics reflect something different. One month Smith takes a break, then next month he gets a job and Tom Jones takes a break.
2. I think we have always had a large number of workers who are not fully employed year round. That is, there have always been a lot of workers who take breaks between jobs. This is common in construction work, for example.
3. I don’t know if this matters for the phenomenon at hand, but we used to have inventory recessions. In those cases, workers would be out of a job for a while, but they would still be in the labor force, because they were waiting to be recalled by the firm that had laid them off.
4. It seems to me that this is an important paper. Re-read the last sentence in the quoted excerpt.
Disaggregating the economy using big data
When do you suppose that the following sentences were written:
Should we worry about a computerized creation that plays to our unconscious? How vulnerable are we to these increasingly refined sales pitches?
They come from Michael J. Weiss, on p.25 of his book The Clustering of America. It is a mostly-favorable treatment of the use of big data to sort American zip codes into socioeconomic clusters, to help businesses make better use of direct-mail marketing and local advertising. The data also were used by political organizations to target efforts to get out the vote, solicit donations, and tailor messages.
The book appeared almost thirty years ago, in 1988. I read it when it first came out, and I recently ordered it so that I could read it again. I also ordered a follow-up book that Weiss wrote in 2000, called Our Clustered World. I will have more to say about the two books when I have finished. I am interested in what they contribute to the project of disaggregating the economy, meaning treating the U.S. as a collection of diverse economies that trade with one another.
One side note: In the late 1990s, when I was running my commercial web site providing information to people who were relocating, we contacted a company that had a similar cluster analysis, in order to enable users to search for particular types of towns. For example, you could select a place where you lived (or wish you lived) near Baltimore and then look for the three most similar towns near, say, Los Angeles. The application would take the socioeconomic cluster that you started with and match you with a part of Los Angeles that had a similar socioeconomic cluster.
The company provided us with their data on a couple of CD’s, and for us, loading it and putting up a front-end that could do the searches the way we wanted was a technical project. Probably the biggest challenge was creating a way to search by town name as well as by zip code.
Shortly after the application went live on the web, I received a very angry note from a Civil Rights organization. The data for each socioeconomic cluster included the two or three consumer items that were purchased much more in that cluster than in other clusters. Our application spat out that information, along with the other data about location. It turned out that one cluster’s unusually strong consumer propensities included fast food fried chicken. Someone evidently had done a search that caused this cluster description to appear and contacted the The Civil Rights group about it. The note that they sent us accused us of stereotyping the location as African-American, so that we were promoting segregation and redlining.
Of course, the company was not using racial stereotyping to speculate on consumer propensities. All of the consumer propensities that the company identified were data driven. If this was a stereotype, it evidently had a basis in reality.
We decided that it was appropriate to edit out that particular example, and just leave in the consumer propensities that did not have any racial connotations. As I recall, we looked in the cluster descriptions for other examples of consumer propensities that might have ethnic connotations, but we did not see any.
David Brooks on the siege mentality
The siege mentality starts with a sense of collective victimhood. It’s not just that our group has opponents. The whole “culture” or the whole world is irredeemably hostile.
As Handle points out, Brooks seems to be siding with Yuval Levin in what I call his debate with Victor Davis Hanson. My thoughts:
1. It is possible for both sides to believe that they under siege. Palestinians can believe that the Israelis want all their land. Israelis can believe that the Palestinians want to drive Jews out of Israel. Each side can point to evidence that seems convincing.
2. But that does not preclude the possibility that one side really is under siege and the other side really does wish for the other side to either adopt the “correct” religion or be annihilated. My father, whose family escaped Cossack pogroms in Russia and who had relatives murdered in the Holocaust, used to say that it is not always wrong to believe that there are people out to get you.
3. I am still reading Colin Woodard, who describes the Puritan mindset as a belief that they are the chosen people and everyone else should be like them. If you suppose, as does Woodard, that progressives are the descendants of the Puritans, then they are quite capable of being intolerant. Woodard says that the Puritans saw it as their mission to reform the sinners who were all around them. Puritanism has a propensity both to feel under siege (by the sinners) and to make others feel as though they are under siege.
Disaggregating the economy: product scanner data
David Argente, Munseob Lee, and Sara Moreira write,
In this paper, we exploit detailed product- and firm-level data to study the sources of innovation and the patterns of productivity growth in the consumer goods sector over the period 2006Q3–2014Q2. Using a dataset that contains information on the products of each firm and the characteristics of each product, we document new facts on product reallocation. First, we find that an important component of reallocation of products happens within the boundaries of the firm. Second, the largest changes in product quality come from new firms launching new varieties and from small firms expanding to other product lines. Third, we document that product reallocation within firms is procyclical. Fourth, we find that within-firm product reallocation is larger in high productive firms and firms that invest more in R&D. Finally, we quantify how important how product reallocation affects firm-level productivity growth and and innovation as reflected by changes in their total factor productivity.
NOTE: I am quoting a version that is labeled VERY PRELIMINARY AND INCOMPLETE. The published version is gated. Pointer to the published version from Tyler Cowen.
Apparently, during the recession, firms reduced the pace at which they added new products and retired existing products. I interpret this as a slowdown in investment.
As a first approximation, this is not supportive of my view of a recession as a breakdown of existing patterns of specialization and trade. One would expect to see an increase in the retirement of existing products if my view were correct.
One way to rescue my view would be to say that firms respond to a deterioration in the sustainability of existing patterns of specialization and trade by reducing their investment in creating new patterns. This seems like a counterproductive response, except that it does conserve cash in the short run.
Disaggregating the Polity: Colin Woodard
He wrote a book called American Nations, which I just read for the first time. He offers a model of America as having a culture that can be thought of as eleven different nations, each dominant in particular geographic regions. It seems to me that it is a book that someone should have pressed me to read before. I will be recommending it often in the future, I am sure.
Woodard sees a centuries-long struggle for power between the nation he calls Yankeedom (New England) and the two nations that he calls Tidewater and Deep South. His antipathy toward the latter shows through, especially in the final chapters of the book.
More recently, he has some essays that I am checking out. In this essay, he claims that the urban-rural divide is simplistic and wrong, and that his 11-nations model works better.
In five of the regional cultures that together comprise about 51 percent of the U.S. population, rural and urban counties always voted for the same presidential candidate, be it the “blue wave” election of 2008, the Trumpist storm of 2016, or the more ambiguous contest in between. In Greater Appalachia, the Deep South, Far West, and New France, rural and urban voters in aggregate supported Republican candidates in all three elections, whether they lived in the mountain hollers, wealthy suburbs, or big urban centers. In El Norte, both types of counties always voted Democratic, be they composed principally of empty desert or booming cityscapes.
…The stark urban-rural divide in the country is to be found almost exclusively in the Midlands, where it has a disproportionate effect on the Electoral College, as that region straddles several historic swing states: Pennsylvania, Ohio, Iowa, and Missouri among them.
I am curious to delve into his 11-nations model and to consider each nation in economic terms. Are there likely differences in what they import and export? Differences in wealth? etc. Here is a first pass, using nine of his nations (omitting New France, which is mostly in Canada, and First Nation, which is locations with a lot of Native Americans). The table offers my impressions of the leading industries in the various regions.
| Nation (Woodard’s name) | Typical Cities | Major industries |
|---|---|---|
| Yankeedom | Boston, Madison | Higher education, high tech, health care |
| New Netherland | New York City, Greenwich Ct. | Financial services, entertainment, international trade |
| Midlands | Philadelphia, Peoria | Agriculture, manufacturing |
| Tidewater | McLean, Newport News | Federal government, military |
| Greater Appalachia | Wheeling, Muskogee | Extractive (mining, forestry, etc.) |
| Deep South | Charleston, Mobile | Agriculture, manufacturing |
| El Norte | El Paso, Tijuana | Extractive, retirement services |
| Left Coast | San Francisco, Portland, Ore. | High tech, international trade |
| Far West | Bozeman, Rapid City | Extractive, tourism |
I am wildly guessing about the industries for El Norte. I think he wants to limit it to the southwestern U.S. (plus northern Mexico), and he wants to exclude southern Florida.
I am not sure where Los Angeles fits in his scheme. It must be an amalgam of some sort. Some of New Netherland, with its ethnic diversity, ambition, and glamour. Some of El Norte, with its Hispanic population. Perhaps an element that is Far West, where there is dependence on government investment combined with resentment of government.
Any other criticisms or suggested modifications to my industry guesses are welcome.
Disaggregating the economy: Yelp data
Edward L. Glaeser, Hyunjin Kim, and Michael Luca write,
Our results highlight the potential for using Yelp data to complement CBP by nowcasting – in other words, by shedding light on recent changes in the local economy that have not yet appeared in official statistics due to long reporting lags. A second potential use of crowd-sourced data is to measure the economy at a more granular level than can be done in public facing government statistics. For example, it has the potential to shed light on variation in economic growth within a metropolitan area. In Section V, we turn to New York City to see how Yelp does at measuring the micro-geography of a municipality. Yelp does seem capable of tracking the evolution of neighborhoods even below the ZIP code level.
CBP = County Business Patterns, a government statistical publication.
I am interested in the potential to be able to use new data sources to decompose the U.S. economy into regions. These might be actual regions, like the Mid-Atlantic or virtual regions, like the major metros on the two coasts.