Month: March 2019

Teachers of intro economics, as well as industrial organization classes, are often on the lookout for recent examples of market shares that can be used for talking about the extent to which certain markets are concentrated or competitive. The W3 Counter offers a monthly breakdown of market shares for browsers and platforms. For February 2019, here\’s a figure for internet browser market share:

Here\’s a figure showing patterns over time, and thus showing the substantial rise of Chrome and the mild rise of Firefox, and the  corresponding falls of Internet Explorer/Edge  and Safari. Depending on whether you are a glass half-full or half-empty person, you will have a tendency to see this either as proof that Google\’s Chrome has a worrisome level of market dominance, or as proof that even seemingly dominant browser market shares can fall in a fairly short time.

Finally, here\’s a table with the market share of various platforms in February 2019, but it needs to be read with care, since it lists multiple versions of Windows, Android, and ioS/Mac.

In the latest installment of its \”Global Perspectives\” series of conversations, Robert S. Kaplan of the Dallas Fed, discussed national and global economic issues with Greg Mankiw on March 7, 2019. The full 50 minutes of video is available here.

For a quick sample, here\’s what Mankiw had do say on what economists don’t understand about politicians, and vice versa:

I don’t think economists fully understand the set of constraints that politicians operate under, probably because we have tenure, so we can say whatever we want. The politicians don’t. They constantly have to get approval by the voters, and the voters have different views of economic issues than economists do. So the politicians are sort of stuck between the voters they have to appeal to and the economists who are giving them advice. I think understanding the difficult constraints that politicians operate under would be useful.

In terms of what politicians don’t understand about economists, I think they often turn to (economists) for the wrong set of questions. My mentor, [Princeton University economist] Alan Blinder, coined what he calls Murphy’s Law of economic policy, which says that economists have the most influence where they know the least, and they have the least influence where they know the most.

Politicians are constantly asking us, ‘What’s going to happen next year?’ But we are really bad at forecasting. I understand why people need forecasting, as part of the policy process, but we’re really bad at it, and we’re probably not going to be good any time soon. On the other the hand, there are certain problems where we kind of understand the answer. We understand that rent control is not a particularly good way to run a housing market. We understand that if you want to deal with climate change, you probably want to put a price on carbon. If you have a city that suffers from congestion, we can solve that with congestion pricing. 

A common goal for principles of economics courses is to teach students to \”think like economists.\” I\’ve always been a little skeptical of that  high-sounding goal. It seems like a lot to accomplish in a semester or two. I\’m reminded of an essay written by Deirdre McCloskey back in 1992, which argued that while undergraduates can be taught about economics, thinking like an economist is a much larger step that will only in very rare cases happen in the principles class. Here\’s McCloskey (\”Other Things Equal: The Natural,\” Eastern Economic Journal, Spring 1992):

\”Bower thinks that we can teach economics to undergraduates. I disagree. I have concluded reluctantly, after ruminating on it for a long me, that we can\’t. We can teach about economics, which is a good thing. The undergraduate program in English literature teaches about literature, not how to do it. No one complains, or should. The undergraduate program in art history teaches about painting, not how to do it. I claim the case of economics is similar. Majoring in economics can teach about economics, but not how to do it….

As an empirical scientist I have to conclude from this and other experiences that thinking like an economist is too difficult to be a realistic goal for teaching. I have taught economics, man and boy, for nearly a century, and I tell you that it is the rare, gifted graduate student who learns to think like an economist while still in one of our courses, and it takes a genius undergraduate (Sandy Grossman, say, who was an undergraduate when I came to Chicago in 1968). Most of the economists who catch on  do so long after graduate school, while teaching classes or advising governments: that\’s when I learned to think like an economist, and I wonder if your experience is not the same. 

\”Let me sharpen the thought. I think economics, like philosophy, cannot be taught to nineteen-year olds. It is an old man\’s field. Nineteen-year olds are, most of them, romantics, capable of memorizing and emoting, but not capable of thinking coldly in the cost-and-benefit way. Look for example at how irrational they are a few years later when getting advice on post-graduate study. A nineteen-year old has intimations of immortality, comes directly from a socialized economy (called a family), and has no feel on his pulse for those tragedies of adult life that economists call scarcity and choice. You can teach a nineteen-year old all the math he can grasp, all the history he can read, all the Latin he can stand. But you cannot teach him a philosophical subject. For that he has to be, say twenty-five, or better, forty-five. …

In practical terms, the standard principles of economics course is a long march through a bunch of conceptual ideas: opportunity cost, supply and demand, perfect and imperfect competitions, comparative advantage and international trade, externalities and public goods, unemployment and inflation, monetary and fiscal policy, and more. The immediate concern of most students is to master those immediate tools–what McCloskey calls learning \”about\” economics. But I do think that in the process of learning \”about,\” many principles students get a meaningful feeling for a the broader subject and mindset. In the introduction to my own principles textbook, I write:

There’s an old joke that economics is the science of taking what is obvious about human behavior and making it incomprehensible. Actually, in my experience, the process works in the other direction. Many students spend the opening weeks of an introductory economics course feeling as if the material is difficult, even impossible, but by the middle and the end of the class, what seemed so difficult early in the term has become obvious and straightforward. As a course in introductory economics focuses on one lesson after another and one chapter after another, it’s easy to get tunnel vision. But when you raise your eyes at the end of class, it can be quite astonishing to look back and see how far you have come. As students apply the terms and models they have learned to a series of real and hypothetical examples, they often find to their surprise that they have also imbibed a considerable amount about economic thinking and the real-world economy. Learning always has an aspect of the miraculous.

Thus, I agree with McCloskey that truly \”thinking like an economist\” is a very rare outcome in a principles course, and unless you are comfortable as a teacher with setting a goal that involves near-universal failure, it\’s not a useful goal for instructors. But it also seems true to me that the series of topics in a conventional principles of economics course, and how they build on each other, does for many students combine to form a comprehensible narrative by the end of the class. The students are not thinking like economists. But they have some respect and understanding for how economist think.

During the 1990s, a social and legal expectation arose in the United States that single mothers would usually be in the workforce, even when their children were young. In turn, this immediately raised a question of how child care would be provided. The 2019 Economic Report of the President. from the White House Council of Economic Advisers,  offers some useful graphs and analysis of this subject.

Here\’s are some patterns in the labor force for \”prime-age\” women between the ages of 25 and 54, broken down by single and married, and children or not. Back in the early 1980s, for example, single women with no children (dark blue line) were far more likely to be in the labor force than other women in this age group, and less than half of the married women with children under the age of six (green line) were in the labor force.

But by about 2000, the share of single prime-age women with no children in the labor force has declined, and had roughly converged with labor force participation rates of the other groups shown–except for the labor force participation rates of married women with children under six, which rose but remained noticeably lower. The report notes: \”These married mothers of young children who are out of the labor force are evenly distributed across the educational spectrum, although on average they have somewhat less education than married mothers of young children as a whole.\”

The two especially big jumps in the figure are for labor force participation of single women with children, with the red line referring to single women with children under the age of six and the gray line referring to single women with children over the age of six. Back around 1990, single and married women with children over the age of six were in the labor force at about the same rate, and single and married women with children under the age of six were in the labor force at about the same rate. But after President Clinton signed the welfare reform legislation in 1996 (formally, the
Personal Responsibility and Work Opportunity Reconciliation Act of 1996), work requirements increased for single mothers receiving government assistance.

If single mothers with lower levels of incomes are expected to work–especially mothers with children of pre-school age–then child care becomes of obvious importance. But as the next figure shows, the cost of child care is often a sizeable percentage of the median hourly wage in a given state. And of course, by definition, half of those paid with an hourly wage earn less than the median.

Mothers who are mainly working to cover child care costs face some obvious disincentives. The report cites various pieces of research that lower child care costs tend to increase the labor force participation of women. For example, a 2017 \”review of the literature on the effects of child care costs on maternal labor supply … concludes that a 10 percent decrease in costs increases employment among mothers by about 0.5 to 2.5 percent.\”

So what steps might government take to make child care more accessible to households with low incomes? Logically, the two possibilities are finding ways to reduce the costs, or providing additional buying power to those households.

When it comes to reducing costs, one place to look is at the variation in state-level requirements for child care facilities. It\’s often politically easy to ramp up the strictness of such requirements; after all, passing requirements for child care facilities doesn\’t make the state spend any money, and who can object to keeping children safe? But when state regulations raise the costs of providing a service, the buyers of that service end up paying the higher costs. The report points out some variations across states by staffing requirements.

\”For 11-month-old children, minimum staff-to-child ratios ranged from 1:3 in Kansas to 1:6 in Arkansas, Georgia, Louisiana, Nevada, and New Mexico in 2014. For 35-month-old children, they ranged from 1:4 in the District of Columbia to 1:12 in Louisiana. For 59-month-old children, they ranged from 1:7 in New York and North Dakota to 1:15 in Florida, Georgia, North Carolina, and Texas. Assuming an average hourly wage of $15 for staff members (inclusive of benefits and payroll taxes paid by the employer), the minimum cost for staff per child per hour would range from $2.50 in the most lenient State to $5 in the most stringent State for 11-month-old children, from $1.25 to $3.75 for 35-month old-children, and from $1.00 to $2.14 for 59 month-old children.\”

Here\’s a figure illustrating the theme.

Solar geoengineering refers to putting stuff in the atmosphere that would have the effect of counteracting greenhouse gases. Yes, there would be risks in undertaking geoengineering. However, those who argue that substantial dangers of climate change are fairly near-term must be willing to consider potentially unpleasant answers. Even if the risks of geoengineering are too substantial right now, given the present state of climate change, if the world as a whole doesn\’t move forward with steps to hold down emissions of greenhouse gases, then perhaps the risks of geoengineering will look more acceptable in a decade or two? 

• Both unchecked climate change and any potential deployment of solar geoengineering (SG) are governed by processes that are currently unknowable; i.e., either is afflicted with ignorance. 
• Risk, uncertainty, and ignorance are often greeted with the precautionary principle: “do not proceed.” Such inertia helps politicians and bureaucrats avoid blame. However, the future of the planet is too important a consequence to leave to knee-jerk caution and strategic blame avoidance. Rational decision requires the equal weighting of errors of commission and omission. 
• Significant temperature increase, at least to the 2°C level, is almost certainly in our planet’s future. This makes research on SG a prudent priority, with experimentation to follow, barring red-light findings. …

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 seems foolish 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 with its small sample size may be unpublishable: after all, a  lot of journals will tend to decide against publishing a study that doesn\’t find a statistically significant effect–because it feels as if such a study isn\’t pointing out any new connection or insight.

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 look at a very large number of equations one at a time, some of them will seem to mean something. It\’s a little like flipping a coin 10,000 times, but then focusing only on the few stretches where the coin came up heads five times in a row–and drawing conclusions based on that one small portion of the overall results.

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:

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.\”

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. .

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.

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.