Category Archives: disaggregating the economy

Consumers’ Surplus and well-being

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Another paper from the AEA session on measuring well-being. The abstract of the paper by Erik Brynjolfsson, Felix Eggers, and Avinash Gannamaneni says,

In principle, changes in consumer surplus (compensating expenditure) provide a superior measure of changes in consumer well-being than GDP and metrics derived from it, like productivity, especially for digital goods. In practice, consumer surplus has been difficult to measure. We demonstrate the potential of massively scalable online Single Binary Discrete Choice experiments for addressing this issue. These experiments provide a measure of consumers’ willingness to accept compensation for losing access to various digital goods and thereby estimate the changes in consumer surplus from these goods. Drawing on several hundred thousand online experiments, our results indicate that digital goods have created substantial gains in well-being which are largely missed by conventional measure of GDP and productivity, and suggest that our approach can be scaled up to a broader set of goods and services. Two limitations of our methods are that they are much less precise than changes in GDP and they suffer from hypothetical bias. We show how much of an improvement in precision can be achieved with larger sample sizes and demographic controls and we document the direction and magnitude of hypothetical bias by conducting incentive compatible experiments with a smaller group of subjects. By periodically querying a large, representative sample of goods and services, including those which are not priced in existing markets, changes in consumer surplus and other new measures of well-being derived from these online choice experiments have the potential for providing cost-effective supplements to existing national income and product accounts.

A Social Progress Index

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At this year’s AEA meetings, there apparently was an interesting session on measuring well-being.

One paper, by Daniel Fehder, Scott Stern, and Michael E. Porter, says

we describe the construction of a synthetic measure of non-economic performance, the Social Progress Index (SPI). Building on a wide range of prior literature, it incorporates more than 50 indicators into 12 components that are then aggregated into three primary dimensions of non-economic societal performance: Basic Human Needs, Foundations of Wellbeing, and Opportunity.

…
overall social progress is decomposed into three distinct dimensions, Basic Human Needs (“Does a country provide for its people’s most essential needs?), Foundations of Well-Being (“Are the building blocks in place for individuals and communities to enhance and sustain wellbeing?), and Opportunity (“Is there opportunity for all individuals to reach their full potential?”). Whereas Basic Human Needs centers on non-economic conditions that a society provides (e.g., achieving a low child mortality rate and a high level of sanitation, shelter, and personal safety), Foundations of Wellbeing focuses on whether a society offers individual an opportunity to invest in themselves and their communities to advance their wellbeing (e.g., allowing individuals to achieve a basic level of education, gain access to information, and maintain strong lifelong health and local environmental quality). Finally, Opportunity focuses on those components of social progress that concern the ability of individuals to achieve their own personal objectives, including their degree of personal rights and freedom in the context of an inclusive and educated society.

I like the idea of diversifying the portfolio of economic and social indicators. Recall my recent essay proposing to measure occupational satisfaction.

The clustering of the world

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Razib Khan writes,

Serbia has a much stronger affinity with Russia, Croatia is in Catholic Europe, while Slovenia seems more like Northern European nations than Croatia.

You have to go read the whole thing. He discusses a cultural map of the world, based on two scales: traditional values vs. secular/relational values; and survival values vs. self-expression values.

He then goes on to discuss the Peter Turchin, et al paper that I’ve seen referenced on several blogs. It’s the paper that develops an index of social complexity. I think of it as something like an IQ measure that operates at a cultural level.

Are locational wage differentials also productivity differentials?

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I think that an argument about this arose in the comments on this post. Let me provide a framework for discussion.

Suppose that we observe that zip code X has higher average wages for waiters than zip code Y. Can we infer that waiters in X are more productive than waiters in Y? Can we infer that removing barriers to mobility so that waiters can move more easily from Y to X will raise real GDP?

I think that we need to know more about why waiters are paid more in X.

a. It could be that, working with a given level of capital, the same waiter can serve more customers per hour in X than in Y. Maybe restaurants in zip code X are better managed. Or maybe restaurants in zip code Y do not get enough customers.

b. It could be that the cost of living is higher in X than in Y. Waiters serve the same number of customers per hour in each, but if you raised wages in Y all the waiters would move there to get a higher real income. There has to be a wage differential to compensate for the cost of living differential.

If (a) is true, then removing mobility barriers would raise real GDP in the restaurant industry. But if (b) is true, then removing mobility barriers would not raise real GDP in the restaurant industry.

Suppose that the mobility barrier is a housing market restriction in X. Then getting rid of the housing restriction might raise social welfare by making the housing market function more efficiently. But there is no additional benefit from waiter productivity. What would happen if you got rid of the housing market restriction is that the wages of restaurant workers in X and Y would equalize. As waiters move from Y to X, the wage differential would go away. The new wage would be somewhere between the old wage in X and the old wage in Y.

Note that in case (b), restaurants might use more capital in X than in Y, because the cost of labor is higher (because the cost of living is higher). That would enable waiters in X to serve more customers per hour than in Y, but this is not a pure productivity differential. If you remove the mobility restriction, then eventually the capital intensity of restaurants in X and Y will be equalized.

What if the main difference between zip code X and zip code Y is that quality of life is better in zip code X? In that case, other things equal, cash wages ought to be lower in zip code X. Of course, other things are unlikely to be equal. Housing supply is probably not perfectly elastic, so some of the quality-of-life differential should be eaten up by housing costs. And of course, quality of life means different things to people with different tastes, and that accounts for some (much?) of location choice.

If I might try to coin a phrase of opprobrium, I believe that economists who equate locational wage differentials to productivity differentials are guilty of casual neoclassicism. They should be required to read James Buchanan’s Cost and Choice and take an exam afterward.

Disaggregating the economy: California

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Carson Bruno wrote,

between 2009 and 2014, the Silicon Valley metro areas – a region that accounts for just 1/5th of the state’s population – accounted for 50% of California’s private industry real GDP growth.

Steve Baldwin adds,

a far more accurate assessment of [California’s] economy, [Richard] Rider writes, would be per capita GDP as compared to the rest of the country. After adjusting the GDP figures to account for the cost of living (COL), the Golden State ends up with a paltry 37th place ranking within the U.S.A., with a $45,696 per capital GDP. Even rustbelt states, such as Michigan and Ohio, have a higher adjusted per capita GDP.

There are parts of California where adjusting for the cost of living makes incomes lower than they might otherwise appear to be. Other parts of California have low incomes, but this is alleviated slightly by a lower cost of living.

It is difficult to think of California as a homogeneous economy with a single GDP factory. It is even more problematic to try to look at the entire United States through that lens.

Disaggregating the economy: clusters ten years later

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A dozen years after coming out with The Clustering of America, Michael Weiss published The Clustered World, in 2000. This incorporated census data from 1990, which moved the analysis 10 years forward, but still leaves it well out of date as of 2017.

There was a movement to outlying locales that Weiss described as “repopulating rural America,” which struck me as a questionable description. I wonder if instead it represented metro areas spreading out into “edge cities.”

There was a rise in the Hispanic population, and Weiss claimed that this population was showing signs of wanting to stick together, rather than to assimilate into the rest of the country. He also saw an increase in isolation of the African-American population, which is the opposite of what one would have extrapolated based on prior trends toward integration.

Weiss used a survey of journalists to find that they lived predominantly in a few clusters toward the upper end of the income and status scale. It was already clear to him that they would have difficulty relating to people in middle-class and poor clusters.

Writing in 2000 and looking ahead, Weiss foresaw a continued increase in growth in far suburbs. Also, he made the straightforward projection that the Baby Boom generation would be headed toward a lifestyle characterized by retirement. With aging in general, he expected to see new clusters emerging with the age 55-75 bloc, as well as a cluster of people over 85 and ensconced in assisted living facilities.

He thought that there would be a distinctive Asian cluster, but my impression is that this has not developed. If I am correct about that, then the explanation is pretty simple. The “Asian” category is too broad, encompassing mainland Chinese, Taiwanese, Japanese, Koreans, Vietnamese, Indians, Pakistanis, and so on. These disparate nationalities do not all naturally congregate with one another. Instead, one is likely to find them dissolved into the rest of the U.S. population.

I would be curious to see what clusters would show up today. I assume that Charles Murray’s Coming Apart story means that we would see clusters that differ dramatically by marital status. We would see households with two married adults in relatively large numbers in affluent zip codes, and we would see single parents prevalent in poor zip codes.

I speculate that we would see a decline in the share of employment in the for-profit sector and an increase in employment in non-profits. This is based in part on the growth of the New Commanding Heights of education and health care. Also, I am guessing that the super-rich are inclined to fund non-profits, so that the size of that sector goes up as more wealth accrues to the super-rich. I do not know how significant a trend this is, or how well it can be measured.

I speculate that we would see an increase in the urban-rural divide. That is, compare average incomes in zip codes where most households are within, say, 25 miles of a major city (one of the top 20, say) with zip codes where most households are more than 50 miles from a major city.

I speculate that differences in average levels of education across zip codes now are even more predictive of income differences than they were twenty years ago.

I speculate that we would see little progress in integration of African-Americans and Hispanics. They would continue to appear in their own distinctive clusters more often than mixed in with clusters of non-Hispanic whites.

Disaggregating the economy: levels of digital skills

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Mark Muro writes,

the wage premium for digital skills (highlighted above) has nearly doubled since 2002. That means that the mean pay of workers in higher-level digital occupations (about $73,000) now more than doubles the $30,000 wage of low-digital workers.

…women—despite having slightly higher mean digital scores—remain heavily under-represented in highly digital computer and engineering occupations (but over-represented in office admin, education, and health occupations). Likewise, blacks are overrepresented in medium-digital occupations like office support and health care support, while Hispanics are greatly underrepresented in highly digital tech positions and overrepresented in low-digital domains such as farming, construction, and building and grounds maintenance.

… the digital rich among metro areas appear to be getting even richer, and now—as a consequence—are pulling away from the rest of metros on basic measures of prosperity.

Turning to the full report by Muro and co-authors, I find

In 2002, 56 percent of the jobs studied required low amounts of digital skills. Nearly 40 percent of jobs required medium digital skills and just 5 percent required high digital skills.

A lot has changed. By 2016, the share of jobs requiring high digital skills had jumped to 23 percent. The share requiring medium digital skills rose to 48 percent. And in a huge shift, the share of jobs requiring low digital skills fell from 56 to 30 percent.

Revive this research

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I just finished re-reading The Clustering of America, by Michael J. Weiss. It is a narrative description of forty different socioeconomic clusters, derived primarily from census data, mixed with some market information. It was published in 1988.

1. Somebody should keep this project going. Weiss published an updated version in 2000, which I will read next. But there would be much to be gained by providing a narrative that describes socioeconomic clusters based on the most recent data available (2010 if you take the most recent census, but my guess is that you could get reasonable estimates for 2015). It would be valuable to look at the cluster evolution over time.

2. The company with the data, Claritas, still exists. Elsewhere, of course, there is much more data, at companies like Google and Amazon. I suppose it is easier for those firms to merge census data in with their own proprietary data than it is for someone outside of those firms to start with the Claritas data and attempt to acquire and integrate the information from the web giants.

3. Weiss offered a brief section, called “clusters of the future.” One of them, which he called Gentrification Chic, seems spot on. Other predictions are not as easy to defend.

Stanford University reports that twenty occupations will account for 35 percent of the new jobs in the ’80s, but only two of them, elementary-school teaching and accounting, require college degrees. The AFL-CIO predicts that by 1990 the nation will be home to 500,000 “surplus college graduates” with outmoded skills. The end of the decade may bring about a cluster for the postindustrial age: New-Collar Condos. In city condo and townhouse developments, singles and couples will work in service-industry professions, as paralegals, computer programmers and medical assistants.

Before you snort at the prediction of “surplus college graduates,” consider why that prediction appears to be wrong. Yes, if you compare incomes of people with college degrees with those lacking a college degree, the gap is wide and has gotten wider. However, (a) it may nonetheless be true that a lot of college graduates are doing jobs that use skills not acquired in college; (b) some of the “college premium” represents returns to signaling or licensing requirements; (c) the ordinary BA has not done so well in the market. Graduate degrees and STEM undergraduate degrees account for much of the college premium.

Weiss does not appear to have obtained an advanced degree. However, The Clustering of America is a classic work of social science.

Disaggregating the economy/New Commanding Heights watch

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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

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[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.