Monday, March 25, 2019

Time to Abolish "Statistical Significance"?

The idea of "statistical significance" has been a basic concept in introductory statistics courses for decades. If you spend any time looking at quantitative research, you will often see in tables of results that certain numbers are marked with an asterisk or some other symbol to show that they are "statistically significant."

For the uninitiated, "statistical significance" is a way of summarizing whether a certain statistical result is likely to have happened by chance, or not. For example, if I flip a coin 10 times and get six heads and four tails, this could easily happen by chance even with a fair and evenly balanced coin. But if I flip a coin 10 times and get 10 heads, this is extremely unlikely to happen by chance. Or if I flip a coin 10,000 times, with a result of 6,000 heads and 4,000 tails (essentially, repeating the 10-flip coin experiment 1,000 times), I can be quite confident that the coin is not a fair one. A common rule of thumb has been that if the probability of an outcome occurring by chance is 5% or less--in the jargon, has a p-value of 5% or less--then the result is statistically significant. However, it's also pretty common to see studies that report a range of other p-values like 1% or 10%.

Given the omnipresence of "statistical significance" in pedagogy and the research literature, it was interesting last year when the American Statistical Association made an official statement "ASA Statement on Statistical Significance and P-Values" (discussed here) which includes comments like: "Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold. ... A p-value, or statistical significance, does not measure the size of an effect or the importance of a result. ... By itself, a p-value does not provide a good measure of evidence regarding a model or hypothesis."

Now, the ASA has followed up with a special supplemental issue of its journal The American Statistician on the theme "Statistical Inference in the 21st Century: A World Beyond p < 0.05" (January 2019).  The issue has a useful overview essay, "Moving to a World Beyond “p < 0.05.” by Ronald L. Wasserstein, Allen L. Schirm, and  Nicole A. Lazar. They write:
We conclude, based on our review of the articles in this special issue and the broader literature, that it is time to stop using the term “statistically significant” entirely. Nor should variants such as “significantly different,” “p < 0.05,” and “nonsignificant” survive, whether expressed in words, by asterisks in a table, or in some other way. Regardless of whether it was ever useful, a declaration of “statistical significance” has today become meaningless. ... In sum, `statistically significant'—don’t say it and don’t use it.
The special issue is then packed with 43 essays from a wide array of experts and fields on the general theme of  "if we eliminate the language of statistical significance, what comes next?"

To understand the arguments here, it's perhaps useful to have a brief and partial review of some main reasons why the emphasis on "statistical significance" can be so misleading: namely, it can lead one to dismiss useful and true connections; it can lead one to draw false implications; and it can cause researchers to play around with their results. A few words on each of these.

The question of whether a result is "statistically significant" is related to the size of the sample. As noted above, 6 out of 10 heads can easily happen by chance, but 6,000 out of 10,000 heads is extraordinarily unlikely to happen by chance.  So say that you do an study which finds an effect which is fairly large in size, but where the sample size isn't large enough for it to be statistically significant by a standard test. In practical terms, it would be foolish to ignore to ignore this large result; instead, you should presumably start trying to find ways to run the test with a much larger sample size. But in academic terms, the study you just did may be unpublishable: after all, a  lot of journals will tend to decide against publishing a study with negative results--a study that doesn't that doesn't fine a statistically significant effect

Knowing that journals are looking to publish "statistically significant" results, researchers will be tempted to look for ways to jigger their results. Studies in economics, for example, aren't about simple probability examples like flipping coins. Instead, one might be looking at Census data on households that can be divided up in roughly a jillion ways: not just the basic categories like age, income, wealth, education, health, occupation, ethnicity, geography, urban/rural, during recession or not, and others, but also various interactions of these factors looking at two or three or more at a time. Then, researchers make choices about whether to assume that connections between these variables should be thought of a linear relationship, curved relationships (curving up or down), relationships are are U-shaped or inverted-U, and others. Now add in all the different time periods and events and places and before-and-after legislation that can be considered. For this fairly basic data, one is quickly looking at thousands or tens of thousands of possible connections relationships.

Remember that the idea of statistical significance relates to  whether something has a 5% probability or less of happening by chance. To put that another way, it's whether something would have happened only one time out of 20 by chance. So if a researcher takes the same basic data and looks at thousands of possible equations, there will be dozens of equations that look like they had a 5% probability of not happening by chance. When there are thousands of researchers acting in this way, there will be a steady stream of hundreds of result every month that appear to be "statistically significant," but are just a result of the general situation that if you try enough

A classic statement of this issue arises in Edward Leamer's 1983 article, "Taking the Con out of Econometrics" (American Economic Review, March 1983, pp. 31-43). Leamer wrote:
The econometric art as it is practiced at the computer terminal involves fitting many, perhaps thousands, of statistical models. One or several that the researcher finds pleasing are selected for re- porting purposes. This searching for a model is often well intentioned, but there can be no doubt that such a specification search in-validates the traditional theories of inference. ... [I]n fact, all the concepts of traditional theory, utterly lose their meaning by the time an applied researcher pulls from the bramble of computer output the one thorn of a model he likes best, the one he chooses to portray as a rose. The consuming public is hardly fooled by this chicanery. The econometrician's shabby art is humorously and disparagingly labelled "data mining," "fishing," "grubbing," "number crunching." A joke evokes the Inquisition: "If you torture the data long enough, Nature will confess" ... This is a sad and decidedly unscientific state of affairs we find ourselves in. Hardly anyone takes data analyses seriously. Or perhaps more accurately, hardly anyone takes anyone else's data analyses seriously."
Economists and other social scientists have become much more aware of these issues over the decades, but Leamer was still writing in 2010 ("Tantalus on the Road to Asymptopia," Journal of Economic Perspectives, 24: 2, pp. 31-46):
Since I wrote my “con in econometrics” challenge much progress has been made in economic theory and in econometric theory and in experimental design, but there has been little progress technically or procedurally on this subject of sensitivity analyses in econometrics. Most authors still support their conclusions with the results implied by several models, and they leave the rest of us wondering how hard they had to work to find their favorite outcomes ... It’s like a court of law in which we hear only the experts on the plaintiff’s side, but are wise enough to know that there are abundant for the defense. 
Taken together, these issues suggest that a lot of the findings in social science research shouldn't be believed with too much firmness. The results might be true. They might be a result of a researcher pulling out "from the bramble of computer output the one thorn of a model he likes best, the one he chooses to portray as a rose." And given the realities of real-world research, it seems goofy to say that a result with, say, only a 4.8% probability of happening by chance is "significant," while if the result had a 5.2% probability of happening by chance it is "not significant." Uncertainty is a continuum, not a black-and-white difference.


So let's accept the that the "statistical significance" label has some severe problems, as Wasserstein, Schirm, and Lazar write: 
[A] label of statistical significance does not mean or imply that an association or effect is highly probable, real, true, or important. Nor does a label of statistical nonsignificance lead to the association or effect being improbable, absent, false, or unimportant. Yet the dichotomization into “significant” and “not significant” is taken as an imprimatur of authority on these characteristics. In a world without bright lines, on the other hand, it becomes untenable to assert dramatic differences in interpretation from inconsequential differences in estimates. As Gelman and Stern (2006) famously observed, the difference between “significant” and “not significant” is not itself statistically significant.
But as they recognize, criticizing is the easy part. What is to be done instead? And here, the argument fragments substantially. Did I mention that there were 43 different responses in this issue of the American Statistician?

Some of the recommendations are more a matter of temperament than of specific statistical tests. As Wasserstein, Schirm, and Lazar emphasize, many of the authors offer advice that can be summarized in about seven words: "Accept uncertainty. Be thoughtful, open, and modest.” This is good advice! But a researcher struggling to get a paper published might be forgiven for feeling that it lacks specificity.

Other recommendations focus on the editorial process used by academic journals, which establish some of the incentives here. One interesting suggestion is that when a research journal is deciding whether to publish a paper, the reviewer should only see a description of what the researcher did--without seeing the actual empirical findings. After all, if the study was worth doing, then it's worthy of being published, right? Such an approach would mean that authors had no incentive to tweak their results. A method already used by some journals is "pre-publication registration," where the researcher lays out beforehand, in a published paper, exactly what is going to be done. Then afterwards, no one can accuse that researcher of tweaking the methods to obtain specific results.

Other authors agree with turning away from "statistical significance," but in favor of their own preferred tools for analysis: Bayesian approaches, "second-generation p-values," "false positive risk,"
"statistical decision theory," "confidence index," and many more. With many alteratative examples along these lines, the researcher trying to figure out how to proceed can again be forgiven for desiring little more definitive guidance.

Wasserstein, Schirm, and Lazar also asked some of the authors whether there might be specific situations where a p-value threshold made sense. They write:
"Authors identified four general instances. Some allowed that, while p-value thresholds should not be used for inference, they might still be useful for applications such as industrial quality control, in which a highly automated decision rule is needed and the costs of erroneous decisions can be carefully weighed when specifying the threshold. Other authors suggested that such dichotomized use of p-values was acceptable in model-fitting and variable selection strategies, again as automated tools, this time for sorting through large numbers of potential models or variables. Still others pointed out that p-values with very low thresholds are used in fields such as physics, genomics, and imaging as a filter for massive numbers of tests. The fourth instance can be described as “confirmatory setting[s] where the study design and statistical analysis plan are specified prior to data collection, and then adhered to during and after it” ...  Wellek (2017) says at present it is essential in these settings. “[B]inary decision making is indispensable in medicine and related fields,” he says. “[A] radical rejection of the classical principles of statistical inference…is of virtually no help as long as no conclusively substantiated alternative can be offered.”
The deeper point here is that there are situation where a researcher or a policy-maker or an economic needs to make a yes-or-no decision. When doing quality control, is it meeting the standard or not? when the Food and Drug Administration is evaluating a new drug, does it  approve the drug or not? When a researcher in genetics is dealing with a database that has thousands of genes, there's a need to focus on a subset of those genes, which means making yes-or-no decisions on which genes to include a certain analysis. 

Yes, the scientific spirit should "Accept uncertainty. Be thoughtful, open, and modest.” But real life isn't a philosophy contest. Sometimes, decisions need to be made. If you don't have a statistical rule, then the alternative decision rule becomes human judgment--which has plenty of cognitive, group-based, and political biases of its own.

My own sense is that "statistical significance" would be a  very poor master, but that doesn't mean it's a useless servant. Yes, it would foolish and potentially counterproductive to give excessive weight to "statistical significance." But the clarity of conventions and rule, when their limitations are recognized and acknowledges, can still be useful. I was struck by a comment in the essay by Steven N. Goodman:
P-values are part of a rule-based structure that serves as a bulwark against claims of expertise untethered from empirical support. It can be changed, but we must respect the reason why the statistical procedures are there in the first place ... So what is it that we really want? The ASA statement says it; we want good scientific practice. We want to measure not just the signal properly but its uncertainty, the twin goals of statistics. We want to make knowledge claims that match the strength of the evidence. Will we get that by getting rid of P−values? Will eliminating P−values improve experimental design? Would it improve measurement? Would it help align the scientific question with those analyses? Will it eliminate bright line thinking? If we were able to get rid of P-values, are we sure that unintended consequences wouldn’t make things worse? In my idealized world, the answer is yes, and many statisticians believe that. But in the real world, I am less sure.

Friday, March 22, 2019

What Did Gutenberg's Printing Press Actually Change?

There's an old slogan for journalists: "If your mother says she loves you, check it out." The point is not to be  too quick to accept what you think you already know.

In a similar spirit, I of course know that the introduction of a printing press with moveable type by to Europe in 1439 by Johannes Gutenberg is often called one of the most important inventions in world history. However, I'm grateful that Jeremiah Dittmar and Skipper Seabold have been checking it out. They have written "Gutenberg’s moving type propelled Europe towards the scientific revolution," for the LSE Business Review (March 19, 2019). It's a nice accessible version of the main findings from their  research paper, "New Media and Competition: Printing and Europe'sTransformation after Gutenberg" (Centre for Economic Perfomance Discussion Paper No 1600 January 2019). They write:

"Printing was not only a new technology: it also introduced new forms of competition into European society. Most directly, printing was one of the first industries in which production was organised by for-profit capitalist firms. These firms incurred large fixed costs and competed in highly concentrated local markets. Equally fundamentally – and reflecting this industrial organisation – printing transformed competition in the ‘market for ideas’. Famously, printing was at the heart of the Protestant Reformation, which breached the religious monopoly of the Catholic Church. But printing’s influence on competition among ideas and producers of ideas also propelled Europe towards the scientific revolution.While Gutenberg’s press is widely believed to be one of the most important technologies in history, there is very little evidence on how printing influenced the price of books, labour markets and the production of knowledge – and no research has considered how the economics of printing influenced the use of the technology."


Dittmar and Seabold aim to provide some of this evidence. For example, here's their data on how the price of 200 pages changed over time, measured in terms of daily wages. (Notice that the left-hand axis is a logarithmic graph.) The price of a book went from weeks of daily wages to much less than one day of daily wages. 



They write: "Following the introduction of printing, book prices fell steadily. The raw price of books fell by 2.4 per cent a year for over a hundred years after Gutenberg. Taking account of differences in content and the physical characteristics of books, such as formatting, illustrations and the use of multiple ink colours, prices fell by 1.7 per cent a year. ... [I]n places where there was an increase in competition among printers, prices fell swiftly and dramatically. We find that when an additional printing firm entered a given city market, book prices there fell by 25%. The price declines associated with shifting from monopoly to having multiple firms in a market was even larger. Price competition drove printers to compete on non-price dimensions, notably on product differentiation. This had implications for the spread of ideas."

Another part of this change was that books were produced for ordinary people in the language they spoke, not just in Latin. Another part was that wages for professors at universities rose relative to the average worker, and the curriculum of universities shifted toward the scientific subjects of the time like "anatomy, astronomy, medicine and natural philosophy," rather than theology and law.
The ability to print books affected religious debates as well, like the spread of Protestant ideas after Martin Luther circulated his 95 theses criticizing the Catholic Church in 1517.

Printing also affected the spread of technology and business.
Previous economic research has studied the extensive margin of technology diffusion, comparing the development of cities that did and did not have printing in the late 1400s ...  Printing provided a new channel for the diffusion of knowledge about business practices. The first mathematics texts printed in Europe were ‘commercial arithmetics’, which provided instruction for merchants. With printing, a business education literature emerged that lowered the costs of knowledge for merchants. The key innovations involved applied mathematics, accounting techniques and cashless payments systems.
The evidence on printing suggests that, indeed, these ideas were associated with significant differences in local economic dynamism and reflected the industrial structure of printing itself. Where competition in the specialist business education press increased, these books became suddenly more widely available and in the historical record, we observe more people making notable achievements in broadly bourgeois careers.
It is impossible to avoid wondering if economic historians in 50 or 100 years will be looking back on the spread of internet technology, and how it affected patterns of technology diffusion, human capital, and social beliefs--and how differing levels of competition in the market may affect these outcomes. 

Thursday, March 21, 2019

The Remarkable Renaissance in US Fossil Fuel Production

M. King Hubbert was a big-name geologist who worked much of his career for Shell oil. Back in the 1970s, when OPEC taught the US that the price of oil was set in global markets, discussions of US energy production often began with the "Hubbert curve," based on a 1956 paper in which Hubbert predicted with considerable accuracy that US oil production would peak around 1970. The  2019 Economic Report of the President devotes a chapter to energy policy, and offers a reminder what happened with Hubbert's curve.

The red line shows Hubbert's predicted oil production curve from 1956. The blue line shows actual US oil production in the lower 48 states. At the time of Hubbert's death in 1989, his forecast looked spot-on. Even by around 2010, his forecast looked pretty good. But for those of us who had built up a habit since the 1970s of looking at US oil production relative to Hubbert's prediction, the last decade has been a dramatic shock. .

Indeed, domestic US oil production now outstrips that of the previous world leaders: Saudi Arabia and Russia.

The surge in US fossil fuel production is about natural gas as well as oil. Here's a figure which combines output of all US fossil fuel production, measured by its energy content. You can see that it's (very) roughly constant from the 1980s up through about 2010, and then everything changes.



Many Americans are ambivalent about fossil fuel production. We demonstrate our affection for it by driving cars, riding in airplanes, and consuming products that are shipped over US transportation networks and produced with fossil fuels (for many of us, including electricity). People who live in  parts of the country that are active in fossil fuel production often like the jobs and the positive effects on the local economy. On the other side, many of us worry both about environmental costs of energy production and use, and how they might be reduced.

Big picture, the US economy has been using less energy to produce each $1 of GDP over time, as have other high-income economies like those of western Europe.
My guess is that the higher energy consumption per unit of output in the US economy is partly because the US is a big and sprawling country, so transportation costs are higher, but also that many European countries impose considerably higher taxes on energy use than the US, which tends to hold down consumption.

The US could certainly set a better example for other countries in making efforts to reduce carbon emissions. But that said, it's also worth noting that US emissions of carbon dioxide have been essentially flat for the last quarter-century. More broadly, North America is 18% of global carbon emissions, Europe is 12%, and the Asia-Pacific region is 48%.  Attempts to address global carbon emissions that don't have a heavy emphasis on the Asia Pacific region are missing the bulk of the problem.

Overall, it seems to me that the sudden growth  of the US energy sector has been a positive force. No, it doesn't mean that the US is exempt from global price movements in energy prices. As the US economy has started to ramp up energy exports, it will continue to be clear that energy prices are set in global markets. But the sharp drop in energy imports has helped to keep the US trade deficit lower than it would otherwise have been. The growing energy sector has been a source of US jobs and output. The shift from coal to natural gas as a source of energy has helped to hold down US carbon dioxide emissions. Moreover, domestically-produced US energy is happening in a country which has, by world standards, relatively tight environmental rules on such activities.

Wednesday, March 20, 2019

Wealth, Consumption, and Income: Patterns Since 1950

Many of us who watch the economy are slaves to what's changing in the relatively short-term, but it can be useful to anchor oneself in patterns over longer periods. Here's a graph from the 2019 Economic Report of the President which relates wealth and consumption to levels of disposable income over time.
The red line shows that total wealth has typically equal to about six years of total personal income in the US economy: a little lower in the 1970s, and  a little higher in recent years at the peak of the dot-com boom in the late 1990s, the housing boom around 2006, and the present.

The blue line shows that total consumption is typically equal to about .9 of total personal income, although it was up to about .95 before the Great Recession, and still looks a shade higher than was typical from the 1950s through the 1980s.

Total stock market wealth and total housing wealth were each typically roughly equal to disposable income from the 1950s up through the mid-1990s, although stock market wealth was higher in the 1960s and housing wealth was higher in the 1980s. Housing wealth is now at about that same long-run average, roughly equal to disposable income. However, stock market wealth has been nudging up toward being twice as high as total disposable income in the late 1990s, round 2007, and at present .

A figure like this one runs some danger of exaggerating the stability of the economy. Even small  movements in these lines over a year or a few years represent big changes for many households.

What jumps out at me is the rise in long-term stock market wealth relative to income since the late 1990s. That's what is driving total wealth above its long-run average. And it's probably part of what what is causing consumption levels relative to income to be higher as well. That relatively higher level of stock market wealth is propping up a lot of retirement accounts for both current and future retirees--including my own.

Reentry from Out of the Labor Market

Each year, the White House Council of Economic Advisers published the Economic Report of the President, which can be thought of as a loyalist's view of the current economic situation. For example, if you are interested in a rock-ribbed defense of the Tax Cuts and Jobs Act passed in December 2017 or of the deregulatory policies of the Trump administration looks like, then Chapters 1 and 2 of the 2019 report are for you. Of course, some people will read these chapters with the intention of citing the evidence in support of the Trump administration, while others will be planning to use the chapters for intellectual target practice. The report will prove useful for both purposes.

Here, I'll focus on some pieces of the 2019 Economic Report of the President that focus more on underlying economic patterns, rather than on policy advocacy.  For example, some interesting patterns have emerged in what it means to be "out of the labor market."

Economists have an ongoing problem when looking at unemployment. Some people don't have a job and are actively looking for one. They are counted as "unemployed." Some people don't have a job and aren't looking for one. They are not included in the officially "unemployed," but instead are "out of the labor force." In some cases, those who are not looking for a job are really not looking--like someone who has firmly entered retirement. But in other cases, some of those not looking for a job might still take one, if a job was on offer.

This issue came up a lot in the years after the Great Recession. The official unemployment rate topped out in October 2009 at 10%. But as the unemployment rate gradually declined, the "labor force participation" rate also fell--which means that the share of Americans who were out of the labor force and not looking for a job was rising.You can see this pattern in the blue line below.
There were some natural reasons for the labor force participation rate to start declining after about 2010. In particular, the leading edge of the "baby boom" generation, which started in 1945, turned 65 in 2010, so it had long been expected that labor force participation rates would start falling with their retirements.

Notice that the fall in labor force participation rates levelled off late in 2013. Lower unemployment rates since that time cannot be due to declining labor force participation. Or an alternative way to look at the labor market is to focus on employment-to-population--that is, just ignore the issue of whether those who lack jobs are looking for work (and thus "in the labor force") or not looking for work (and thus "out of the labor force"). At about the same time in 2013 when the drop in the labor force participation rate leveled out, the red line shows that the employment-to-population ratio started rising.

What especially interesting is that many of those taking jobs in the last few years were not being counted as among the "unemployed." Instead, they were in that category of "out of the labor force"--that is, without a job but not looking for work. However, as jobs became more available, they have proved willing to take jobs. Here's a graph showing the share of adults starting work who were previously "out of the labor force" rather than officially "unemployed."
A couple of things are striking about this figure.

1) Going back more than 25 years, it's consistently true that more than half of those starting work were not counted as "unemployed," but instead were "out of the labor force." In other words, the number of officially "unemployed" is not a great measure of the number of people actually willing to work, if a suitable job is available.

2) The ratio is at its  highest level since the start of this data in 1990. Presumably this is because when the official unemployment rate is so low (4% or less since March 2018), firms that want to hire are needing to go after those who the official labor market statistics treated as "not in the labor force."

Tuesday, March 19, 2019

Alternatives to the Four-Year College Track for Everyone Else

As the US goes through one of its periodic paroxysms over how the small minority of high school graduates who attend a selective four-year undergraduate college is chosen, it's worth taking a moment to remember everyone else.  US high schools graduate about 3.6 million students each year. That's a big group, with a wide array of abilities, preparation, and interests. For a substantial number of them, high school was not an especially rewarding academic experience, and no matter what they are told by their college-educated teachers and college-educated counselors, the idea of signing up for a few more years of academic courses is not very enticing.

Oren Cass has written a short essay about this group, "How the Other Half Learns: Reorienting an Education System That Fails Most Students" (Manhattan Institute, August 2018). Here are a couple  of points that caught my eye. 

One study looked back at the students who graduated from high school in 2003, and how the education system had treated them six years later. The data is obviously a few years old, but the overall patterns don't seem to have changed much. For example, about 70% of high school grads enrolled in college in 2003, and about 70% did so in 2016, too.  

Of the 70% who started off to college in 2003, about two-thirds went to a four-year school and one-third to a two years school. Six years later, by 2009, fewer than half of those who started off to college in 2003 had a degree. 

Cass calculates the proportions this way: 
Consider a cohort of 100 students arriving in the ninth grade:
  • Of the 100, 18 of them won’t graduate on time from high school 
  • Of the 82 who do graduate, 25 won’t enroll in higher education
  • Of the 57 who do enroll, 29 won’t earn even an associate’s degree after six years
  • Of the 28 who do graduate, 12 will land in jobs that do not require a degree 
  • Only 16 will successfully navigate the high school to college to career pipeline—the current aim of the education system.
Of course, these problems are fairly well-known. I've written here about "The Problem of College Completion Rates" (June 29, 2018), and about "Some Proposals for Improving Work, Wages and Skills for Americans" (February 19, 2019) like a dramatic expansion of community colleges. But my sense is that the issue here runs deeper. Cass offers one way of thinking about alternatives. 

Of course, this alternative track does require students to make some choices in about 11th grade, but frankly, that doesn't worry me too much. By 10th grade, a lot of students have a fairly good grip on where they stand academically. And if a student chooses a career and technical education track, but decides after a couple of years to give college a try, that's of course just fine. 

The bigger hurdle, it seems to me, is that the alternative vision requires a group of employers who are willing to restructure their organizations in a way that will enable a steady stream of 18-20 year-olds to enter a subsidized work program every year. Moreover, these employers need to treat these young workers not just as an unskilled pair of hands, but to think about what kinds of training and certification these young workers should be attaining during this time. Most US employers are not used to thinking of themselves in these roles. 

But it seems to me that a substantial share of the 3.6 million high school graduates each year might prefer something like Cass's "alternative pathway" to a four-year college degree. As the figure illustrates, society would not be investing less in these young adults--it would just be investing differently. Like a lot of people who ended up working in academia, I went off to college eager and enthusiastic about doing things like reading Adam Smith and Plato, and writing papers about topics like the international law ramifications of the UK-Icelandic cod fishery disputes of the 1970s. But I have noticed over time that not everyone shares my enthusiasms. The US needs alternative paths to good jobs at good wages that don't just involve telling all of the 3.6 million high school graduates  they need to keep going to school.  

Monday, March 18, 2019

Paul Cheshire: "Cities are the Most Welfare Enhancing Human Innovation In History"

Hites Ahir interviews Paul Cheshire in the March 2019 issue of his Housing Watch Newsletter (interview here, full newsletter here). Here are a few of Cheshire's comments that caught my eye:

The Economic Gains from Cities
"There are many types of agglomeration economies in consumption and we really know very little about them still but my assessment is that cities are the most welfare enhancing human innovation in history: they empowered the division of labour, the invention of money, trade and technical inventions like the wheel – let alone government, the arts or culture."
Why Land is Regaining Importance in Economic Analysis
"Classical economists devoted far more effort to trying to understand the returns to land than they did to labour or capital: it was both the most important asset and the most important factor of production. When Adam Smith was writing only about 12 percent of Europe’s population lived in cities and even in the most industrialised country, Britain, the value of agricultural land was about 3 times that of annual GDP. But as the value of other assets increased, interest in land diminished so that by about 1970 really only agricultural economists and a few urban economists were interested in it: and they did not talk to each other. But by 2010 residential property, mostly the land on which houses sat, was worth three times as much as British GDP. By the end of 2013 houses accounted for 61 percent of the UK’s net worth: up from 49 percent 20 years ago. Land, now urban land, is valuable, so there is renewed interest."
Urban Policy Often Misses the Problems of the Modern City
"Luckily cities are so resilient because urban policy is generally so bad! ... Policy has been dominated by physical and design ways of thinking: great for building those fantastic innovations of the 19th Century – sewers or water supply. But not useful for facilitating urban growth and offsetting for the costs of city size. We know cities keep on getting more productive the bigger they are but some costs – the price of space, congestion, for example – also increase with city size. So urban policy should offset for those costs. Instead it mainly increases them. Popular policies of densification and containment restrict the supply of space, increasing its price as cities grow so we forego socially valuable agglomeration economies. Another popular policy – height restrictions – reduces gains from ‘vertical’ agglomeration economies."