Author: conversableeconomist

About 40 years ago, when I was a junior on the high school debate team, we argued for the abolition of the money bail system. Like many positions taken by high school juniors in debate tournaments, our arguments were sweeping and simplistic. But we were correct in recognizing that there are real problems with money bail.

As one example, 14 elected prosecutors wrote a joint letter to New York state lawmakers on March 6, 2019.  The prosecutors  wrote:

We support ending money bail because safety, not wealth, should be the defining feature of the pretrial justice system. Three out of every four people in New York cannot afford to pay the bail amount that the judge sets at their arraignment. That means many people are jailed simply because they are too poor to purchase their freedom. … The only people who should be detained pretrial are those who a judge finds pose a specific, clear and credible threat to the physical safety of the community, or who are a risk of intentionally evading their court dates. Jails across New York frequently are over-capacity, and they are filled with people who do not need to be there. … Research shows that people who spend even a short period in jail, as opposed to being released pretrial, are more likely to commit a future crime. This makes sense. Jail is traumatizing. Jobs are lost. Families can’t pay rent. For reasons big and small, people who are away from their family, their job, and their community become more vulnerable and less stable.

Patrick Liu, Ryan Nunn, and Jay Shambaugh provide a useful backgrounder on this subject in \”The Economics of Bail and Pretrial Detention,\” written for the Hamilton Project at the Brookings Institution (December 2018). Will Dobbie and  Crystal Yang have now offered \”Proposals for Improving the U.S. Pretrial System,\” written as a Hamilton Project Policy (March 2019).  Here\’s an overview comment from the conclusion of the Liu, Nunn, and Shambaugh paper:

\”Bail has been a growing part of the criminal justice system. Nonfinancial release has been shrinking, and more and more defendants are using commercial bonds as a way to secure their release while awaiting trial. Bail can make it more likely that defendants will reappear in court, and as such reduce costs for the criminal justice system. There are, however, extensive costs. Beyond the direct costs of posting the bail, either from paying a fee or having to liquidate assets, widespread use of bail has meant that many people are incarcerated because they are unable to post bail.

\”Nearly half a million people are in jail at any given time without having been convicted of a crime. The overwhelming majority of these people are eligible to be released—that is, a judge has deemed that they are safe to be released—but are unable to raise the funds for their release. The impact of monetary bail falls disproportionately on those who are low-income, cannot post bail out of liquid assets, and thus often remain in jail for extended periods. Furthermore, as a growing body of literature has shown, the assignment of financial bail increases the likelihood of conviction due to guilty pleas, and the costs—to both individuals and society— from extra convictions can be quite high.\”

Let\’s spell some of this out more explicitly.

Nearly half a million people are incarcerated on any given day without having been convicted of a crime. Add it all up, and over 10 million people during a given year year are locked up without being convicted of anything. Roughly one-quarter of all inmates in state and local jails have not been convicted. Here\’s a figure from Liu, Nunn, and Shambaugh:

In the last few decades, the use of money bail has been rising. As Dobbie and Yang write (figures and references omitted):

The high rate of pretrial detention in the United States in recent years is largely due to the increasing use of monetary or cash bail—release conditional on a financial payment—and the corresponding decreasing use of release on recognizance (ROR), a form of release conditional only on one’s promise to return to the court. The share of defendants assigned monetary bail exceeded 40 percent in 2009 in the set of 40 populous U.S. counties where detailed data are available, an 11 percentage point–increase from 1990. The fraction of defendants released on their own recognizance decreased by about 13 percentage points over the same period in these counties, with only 14 percent of defendants being released with no conditions in 2009. The widespread use of monetary bail directly leads to high pretrial detention rates in most jurisdictions because many defendants are unable or unwilling to pay even relatively small monetary bail amounts. In New York City, for example, an estimated 46 percent of all misdemeanor defendants and 30 percent of all felony defendants were detained prior to trial in 2013 because they were unable or unwilling to post bail set at $500 or less.

The time that accused people spend in pretrial detention can be significant. Liu, Nunn, and Shambaugh write:

[T]the amount of time that a person is detained if they are unable to afford bail is substantial, ranging from 50 to 200 days, depending on the felony offense. The pretrial detention period is also growing …  From 1990 to 2009, the median duration of pretrial detention increased for every offense, ranging from an increase of 34 percent for burglary to 104 percent for rape.  … Even for durations that are relatively short—for example, 54 days for those accused of a driving-related felony—pretrial detention represents a nearly two-month period during which individuals are separated from their families and financial hardships are exacerbated. Moreover, the typical wait until trial is much longer in some places than others (e.g., 200 days in one sample of Pennsylvania counties).

Dobbie and Yang also point out that when it comes to international comparisons, the US locks accused people at a much higher rate before trial than other countries. This figure shows the number of people detained pre-trial per 100,000 population. The US is the tallest bar on the far right.

Sorting out the costs and benefits of different levels of pretrial detention isn\’t easy. The direct costs of holding people in jails and prisons is straightforward. But how many of those accused people would not have appeared before the court? If they did not appear for court, how would the cost of finding them have compared to the cost of locking them up for days–and in some cases for weeks or even months? What are the additional costs of being locked up in terms of loss of employment opportunities, or stresses on families? How many of those would have committed crimes if not detained? (And how comfortable are we as a society with locking people up not because they have been convicted of a crime, but because we suspect they might commit a crime in the future?) When thinking about conditions of pretrial release, judges are supposed to take all of this account: for example, the presumption that an accused person is innocent, the risk of the person not showing up for trial and the costs of finding them, the risk of the person committing another crime if they are not detained, the person\’s social ties to the community, the person\’s economic ability to put up a monetary bond.

To figure out the effects of different methods of pretrial detention, a social scientist might ideally like to take a large pool of people accused of crimes and conduct a randomized experiment, in which some randomly get offered differing levels of money bail, some are released on their own recognizance, and we see what happens. While it would be grossly inappropriate for the justice system to plan to operate in this way, it turns out that this randomized experiment is being conducted by reality.

Decisions about whether to offer bail, or at what level, are not made consistently across the judicial system. When there are multiple judges in a given court, some will tend to be tougher in granting bail and some will be easier, so whether defendants like it or not, they are living in a randomized experiment depending on the judge to whom they are randomly assigned. In addition, the evidence show that even the same judge will treat accused people with seemingly identical characteristics in the same way, which adds another element of randomness. Thus, research in this area can start by setting aside those who are essentially always granted bail or essentially never granted bail, and instead focus on those with seemingly identical characteristics who are more-or-less randomly granted bail in some cases but not in others.

Dobbie and Yang have been among the leading researchers in this area, and they describe the results of this research in their paper.  For example:

Those who are detailed pretrial are more likely to be found guilty, mainly because those who are detailed pretrial are more likely to take a plea bargain–which may include credit for time already served. Pretrial detention clearly reduces the risk of pretrial flight and pretrial crime, but at least in some studies, greater exposure to jail time before the trial is associated with a rise in posttrial crime. Defendants who because of the randomness ins the system are released pretrial, rather than being held pretrial, are more likely to have income and to be employed 2-4 years later.  In some jurisdictions, the randomness in the process of granting bail takes the form of racial disparities.

For those of us who aren\’t ready to take the plunge and eliminate the money bail system altogether, what might we do to move in the direction of reducing the use of money bail and rationalizing the system? Dobbie and Yang offer some proposals based on the existing research.

Some are pretty simple. When defendants are released on their own recognizance before trial, set up a system of text message or emails to remind them of their court date. For low-risk crimes, make greater use of writing citations, rather than arresting people, and when people are arrested, lean toward releasing them on their own recognizance. For those defendants where a higher degree of monitoring seems appropriate, make greater use of electronic or personal monitoring.

Some more complex proposals involve machine learning. It\’s now possible to plug the data on the characteristics of those who get bail, or are released on their recognizance, into a computer algorithm, which can look for patterns in those who are more or less likely to flee before trial, or more or less likely to commit crimes. The feedback based on studies can then be turned over to judges, so they have a systematic sense of  how they ruled in past similar cases, and how they compare with how other judges have ruled in similar cases. It\’s easy to feel queasy about this approach. Are we going to let the result of computer number-crunching play a substantial role in whether people are granted bail? But computer number-crunching may have greater clarity and consistency in its decisions than at least some judges, and could help produce results that both let more people out before trial while also leading to lower pretrial flight risks and crime. Pilot tests along these lines in jurisdictions willing to give it a try seem warranted.

\”The Federal Government is the Nation’s largest employer, and its footprint is global. The total workforce comprises approximately 2.1 million non-postal civilian workers and 1.4 million active duty military, as well as approximately one million military reserve personnel serving throughout the country and the world. The postal workforce includes an additional 500,000 employees. Approximately 85 percent of the Federal workforce, or 1.7 million people, live outside of the Washington, D.C., metropolitan area. Notably, an even larger “indirect” workforce carries out much of the work paid for by Federal funds. This includes Federal contractors and State, local, and nonprofit employees whose jobs are funded by Federal contracts, grants and transfer payments.\”

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.\”