I enjoyed the Dracula mini-series on Netflix–it’s smart, stylish and a fresh take. Also, at just three episodes, it’s satisfying without requiring a huge time investment.
Dracula has some pointed commentary on contemporary mores, including economics. After sleeping for a hundred years he finds himself in an ordinary home and speaks to the owner, Kathleen:
D: You’re clearly very wealthy.
D: Yes. Well, look at all this stuff. All this food. The moving picture box. And that thing outside, Bob calls it um, a car. And this treasure trove is your house!
K: It’s a dump.
D: Kathleen, I’ve been a nobleman for 400 years. I’ve lived in castes and palaces among the richest people of any age. Never….never! Have I stood in greater luxury than surrounds me now. This is a chamber of marvels. There isn’t a king, or queen or emperor that I have ever known or eaten who would step into this room and ever agree to leave it again.
I knew the future would bring wonders. I did not know it would make them ordinary.
The Mughals of Northern India are famous for their tombs, Humayun’s tomb in Delhi, Jahangir’s Tomb in Lahore and, of course, the Taj Mahal. Why so many tombs? Culture surely has something to do with it, although conservative Muslims tend to frown on tombs and ancestor worship as interference with the communication between man and God. Incentives are another reason.
Under the Mansabdari system which governed the nobility, the Mughal Emperor didn’t give perpetual grants of land. On death, all land that had been granted to the noble reverted back to the Emperor, effectively a 100% estate tax. In other words, land titling for the Mughal nobility was not hereditary. Since land could not be handed down to the next generation, there was very little incentive for the Mughal nobility to build palaces or the kind of ancestral homes that are common in Europe. The one exception to the rule, however, was for tombs. Tombs would not revert back to the Emperor. Hence the many Mughal tombs
Here is some lovely jali (stone lattice) work in Barber’s tomb in the Humayan tomb complex.
The Aga Khan Development Network has done some great restoration work on Isa Khan’s tomb, again in the Humayun’s tomb complex. Here’s the ceiling and another piece of jali work.
Private schools in India teach a remarkable 30-40% of the population, especially among the urban poor. (See my 2013 paper, Private Education in India: A Novel Test of Cream Skimming for more.) But private schools have come under increasing pressure in recent years from government regulation.
Inspired by projects like Doing Business the Center for Civil Society in India did a detailed examination of what it takes to open a private school in Delhi. This excellent video describes the results:
The US offers a limited number of H1-B visas annually, these are temporary 3-6 year visas that allow firms to hire high-skill workers. In many years, the demand exceeds the supply which is capped at 85,000 and in these years USCIS randomly selects which visas to approve. The random selection is key to a new NBER paper by Dimmock, Huang and Weisbenner. What’s the effect on a firm of getting lucky and wining the lottery?
We find that a firm’s win rate in the H-1B visa lottery is strongly related to the firm’s outcomes over the following three years. Relative to ex ante similar firms that also applied for H-1B visas, firms with higher win rates in the lottery are more likely to receive additional external funding and have an IPO or be acquired. Firms with higher win rates also become more likely to secure funding from high-reputation VCs, and receive more patents and more patent citations. Overall, the results show that access to skilled foreign workers has a strong positive effect on firm-level measures of success.
Overall, getting (approximately) one extra high-skilled worker causes a 23% increase in the probability of a successful IPO within five years (a 1.5 percentage point increase in the baseline probability of 6.6%). That’s a huge effect. Remember, these startups have access to a labor pool of 160 million workers. For most firms, the next best worker can’t be appreciably different than the first-best worker. But for the 2000 or so tech-startups the authors examine, the difference between the world’s best and the US best is huge. Put differently on some margins the US is starved for talent.
Of course, if we play our cards right the world’s best can be the US best.
Among experts it’s well understood that “big data” doesn’t solve problems of bias. But how much should one trust an estimate from a big but possibly biased data set compared to a much smaller random sample? In Statistical paradises and paradoxes in big data, Xiao-Li Meng provides some answers which are shocking, even to experts.
Meng gives the following example. Suppose you want to estimate who will win the 2016 US Presidential election. You ask 2.3 million potential voters whether they are likely to vote for Trump or not. The sample is in all ways demographically representative of the US voting population but potential Trump voters are a tiny bit less likely to answer the question, just .001 less likely to answer (note they don’t lie, they just don’t answer).
You also have a random sample of voters where here random doesn’t simply mean chosen at random (the 2.3 million are also chosen at random) but random in the sense that Trump voters are as likely to answer as are other voters. Your random sample is of size n.
How big does n have to be for you to prefer (in the sense of having a smaller mean squared error) the random sample to the 2.3 million “big data” sample? Stop. Take a guess….
The answer is…here. Which is to say that your 2.3 million “big data” sample is no better than a random sample of that number minus 1!
On the one hand, this illustrates the tremendous value of a random sample but it also shows how difficult it is in the social sciences to produce a truly random sample.
Meng goes on to show that the mathematics of random sampling fool us because it seems to deliver so much from so little. The logic of random sampling implies that you only need a small sample to learn a lot about a big population and if the population is much bigger you only need a slightly larger sample. For example, you only need a slightly larger random sample to learn about the Chinese population than about the US population. When the sample is biased, however, then not only do you need a much larger sample you need it to large relative to the total population. A sample of 2.3 million sounds big but it isn’t big relative to the US population which is what matters in the presence of bias.
A more positive way of thinking about this, at least for economists, is that what is truly valuable about big data is that there are many more opportunities to find random “natural experiments” within the data. If we have a sample of 2.3 million, for example, we can throw out huge amounts of data using an instrumental variable and still have a much better estimate than from a simple OLS regression.
A sad day for me. He was a big influence on my life growing up in Toronto and I’d always hope to meet “the professor.” Here is Red Barchetta one of Rush’s great liberty songs.
Addendum: Rolling Stone on Peart.
Kathleen Kingsbury of the NYTimes editorial page is proudly announcing that instead of following their historic practice of talking with the candidates off-the-record and then announcing an endorsement they will be utterly “transparent.”
On Jan. 19, the @nytimes editorial board will publish our choice for the Democratic nomination for president. It won’t be the first time we’ve endorsed a candidate — we’ve been doing that since 1860 — but we aim to make it our most transparent endorsement process to date. Historically, endorsement interviews are off-the-record — meaning nothing said leaves the room, other than the board’s final judgement.
[But now,] in a first for @nytopinion, all presidential candidate interviews will be on the record and filmed. Next week, we’ll be publishing the full, annotated transcripts online.
What an awful idea, sure to neuter whatever influence the NYTimes might once have had.
Here’s the problem. Under the off-the-record system a candidate could sit down with some smart people and say things like “look, I know tariffs won’t help but the WTO will knock them down anyway and I need to appeal to my base.” Or, “taxes on billionaires won’t raise enough to fund everything I want but to raise taxes on the middle class we need the middle class to know that everyone is going to pay their fair share.” Or “Our troops are demoralized and the plan isn’t working.” If everything is recorded, none of this can happen.
Indeed, what possible value-added can the NYTimes make with a “transparent,” “public” process? Everything that will be said, has been said.
In contrast, a non-transparent, off-the-record process can reveal new information because less transparent can be more honest. The off-the-record system isn’t a guarantee of useful information, as the NYTimes has its biases and the off-the-record system only works because it is coarse, but coarse systems can reveal more information.
The demand for transparency seems so innocuous. Who could be against greater transparency? But transparency is inimical to privacy. And we care about privacy in part, because we can be more honest and truthful in private than in public. A credible off-the-record system leaks a bit of honesty into the public domain and thus improves information overall. Too much transparency, in contrast, makes the world more opaque.
Farmers are getting billions of dollars in bailout money to compensate for the trade war with China. If big banks or big business were being bailed out there would be an uproar but big farmer bailouts seem immune to opposition as Dan Charles points out in this piece from NPR:
…a few weeks later, the USDA announced another $16 billion in trade-related aid to farmers. It came on top of the previous year’s $12 billion package, for a grand total of $28 billion in two years. About $19 billion of that money had been paid out by the end of 2019, and the rest will be paid in 2020.
…it’s an enormous amount of money, more than the final cost of bailing out the auto industry during the financial crisis of 2008. The auto industry bailout was fiercely debated in Congress. Yet the USDA created this new program out of thin air; it decided that an old law authorizing a USDA program called the Commodity Credit Corp. already gave it the authority to spend this money.
“What’s unique about this is, [it] didn’t go through Congress,” Glauber says. Some people have raised questions about whether using the Commodity Credit Corp. for this new purpose is legal.
This is a telling example of how politics works–the process rather than the fundamental question determines much of the outcome. In this case, since the spending was not authorized by Congress there was no debate. No debate in Congress meant no opportunity for soundbites, no debate in the media and thus no debate among the public. The battle for attention was lost before it was begun. On the plus side there was no opportunity for grandstanding in Congress either and the money was approved and spent quickly.
The Arthashastra, the science of wealth and politics, is one of the world’s oldest treatises on political economy. Written by Kautilya, legendary advisor to the Indian King Chandragupta Maurya (reign: 321–298 BCE), the Arthashastra has often been compared to Machiavelli’s The Prince and has been a touchstone in Indian political economy for well over a thousand years.
Vijay Kelkar and Ajay Shah, two long-time advisors to the Indian government, have written the new Arthashastra, In Service of the Republic: The Art and Science of Economy Policy. In Service doesn’t go into great detail on current policies in India (Joshi’s Long Road is the best recent overview), it instead distills timeless wisdom on the making of political economy.
When faced with a potential government intervention, it is useful to ask three key questions. Is there a market failure? Does the proposed intervention address the identified market failure? Do we have the ability to implement the proposed intervention?
Public policy failures are born of: (1) The information constraint; (2) The knowledge constraint; (3) the resource constraint; (4) The administrative constraint; and (5) The voter rationality constraint. These five problems interact, and jointly generate government failure, of both kinds; pursuing the wrong objectives and failing on the objectives that have been established.
A government organization that is riven with corruption is not one which was unlucky to get a lot of corrupt people. It is one where the rules of the game facilitate corruption.
The competitive market process should force the exit of low-productivity firms. This does not happen when the low-productivity firms violate laws–e.g. a low productivity firm may emit pollution, while the high-productivity firm incurs the higher costs associated with the pollution control required in law….When enforcement capabilities, of laws or of taxes, are improved…production will shift from low-productivity firms to high-productivity firms. This reallocation will yield GDP growth, in and of itself.
There are two pillars of intervention in banking in India. On one hand, the state regulates banking. In addition, the Indian state produces banking services through the ownership of bank….There are conflicts between these two [pillars]. Regulation by the state may be indulgent towards its own entities….this calls for strong separation between the two pillars.
Kelkar and Shah are especially concerned with policy making in the Indian context of low state-capacity:
A policy pathway that is very successful in (say) Australia may not work in India as it is being placed in a very different setting. Envisioning how a given policy initiative will work in India requires deep knowledge of the local context.
If the fine for driving through a red light is Rs 10,000, there will be pervasive corruption. Jobs in the highway police will be sought after; large bribes will be paid to obtain these jobs. There will be an institutional collapse of the highway police. It is better to first start with a fine of Rs 100, and build state capacity.
(On that theme see also my paper with Rajagopalan, Premature Imitation.)
In Service to the Republic is the book that every policy maker and future policy maker should be given while being told, “before you do anything, read this!”
Addendum: I will be in India next week and after a visit to Agra and Hampi, I will be giving some talks at Ramaiah University in Bangalore and later in the month at the Indian School of Public Policy.
As Tyler argued last week one of the most common analytical inaccuracies on Twitter is to blame the Fed for being too conservative with monetary policy over the last few years. I see this problem on both the left and the right. One of the ways the argument goes is as follows::
This month’s unemployment rate is lower than last month’s unemployment rate. Thus, we could not have been at full employment last month.
Monetary policy should be less conservative. If only we had been more aggressive earlier, we could have reached where we are sooner and made millions of people better off.
All of this is wrong. To begin, full employment does not mean the lowest possible unemployment rate. We are at full employment when we are at the natural rate of unemployment and as Milton Friedman wrote:
The ‘natural rate of unemployment’….is the level that would be ground out by the Walrasian system of general equilibrium equations, provided there is imbedded in them the actual structural characteristics of the labor and commodity markets, including market imperfections, stochastic variability in demands and supplies, the cost of gathering information about job vacancies and labor availabilities, the costs of mobility, and so on.
The natural rate can change over time, even in a sustained direction, as the structural characteristics of the economy change, as demand, supply, demographics, information and so forth change. Change does not mean disequilibrium. When the production of apples is bigger this year than last year we don’t jump to the conclusion that last year the apple market was out of equilibrium. Similarly, the fact that unemployment was lower this year than last year does not mean that we weren’t at full employment last year.
The point of Friedman’s 1968 piece was that monetary policy can’t do much to influence the natural or full employment rate. Thus, the second half of the argument also doesn’t follow. In other words, it doesn’t follow from the fact that unemployment is declining that monetary policy last year could have achieved this year’s unemployment rate last year. My children are taller this year than last year but that doesn’t mean I could have accelerated their growth by feeding them more last year.
Monetary policy can make a big difference in arresting a negative spiral of declining spending leading to declining income leading to declining spending….Keynes was right. Scott Sumner was also right to call for more aggressive monetary policy in 2008-2010. But that was a disequilibrium event, now long over. When children are starving, you can get them to grow faster by feeding them more, but don’t try using that rule in normal times. Today we are in normal times. The economy has been growing steadily for over a decade. We are not in a downward spiral and wages and prices are not stuck at 2008 levels. In fact, since the end of the recession a large majority of workers are in new jobs! Indeed, a good chunk of the labor force has retired since 2008 to be replaced by entirely new workers. Nothing sticky there.
Standard macro models do not imply that monetary policy can always lower unemployment. (I can’t believe I have to write that in 2020 but the great forgetting is well upon us). Indeed, the standard models, as Tyler discussed, are all about testing and deepening our understanding of the Friedman list, most notably “the cost of gathering information about job vacancies and labor availabilities.” Bottom line is that nobody ever said that we had to like the Walrasian equilibrium but like it or not, monetary policy can’t do much to change it.
Germany’s closing of nuclear power stations after Fukishima cost billions of dollars and killed thousands of people due to more air pollution. Here’s Stephen Jarvis, Olivier Deschenes and Akshaya Jha on The Private and External Costs of Germany’s Nuclear Phase-Out:
Following the Fukashima disaster in 2011, German authorities made the unprecedented decision to: (1) immediately shut down almost half of the country’s nuclear power plants and (2) shut down all of the remaining nuclear power plants by 2022. We quantify the full extent of the economic and environmental costs of this decision. Our analysis indicates that the phase-out of nuclear power comes with an annual cost to Germany of roughly$12 billion per year. Over 70% of this cost is due to the 1,100 excess deaths per year resulting from the local air pollution emitted by the coal-fired power plants operating in place of the shutdown nuclear plants. Our estimated costs of the nuclear phase-out far exceed the right-tail estimates of the benefits from the phase-out due to reductions in nuclear accident risk and waste disposal costs.
Moreover, we find that the phase-out resulted in substantial increases in the electricity prices paid by consumers. One might thus expect German citizens to strongly oppose the phase-out policy both because of the air pollution costs and increases in electricity prices imposed upon them as a result of the policy. On the contrary, the nuclear phase-out still has widespread support, with more than 81% in favor of it in a 2015 survey.
If even the Germans are against nuclear and are also turning against wind power the options for dealing with climate change are shrinking.
Hat tip: Erik Brynjolfsson.
Rather than fading away, solitary imprisonment, a form of torture in my view, has become more common:
Criminal Justice Policy Review: Solitary confinement is a harsh form of custody involving isolation from the general prison population and highly restricted access to visitation and programs. Using detailed prison records covering three decades of confinement practices in Kansas, we find solitary confinement is a normal event during imprisonment. Long stays in solitary confinement were rare in the late 1980s with no detectable racial disparities, but a sharp increase in capacity after a new prison opening began an era of long-term isolation most heavily affecting Black young adults. A decomposition analysis indicates that increases in the length of stay in solitary confinement almost entirely explain growth in the proportion of people held in solitary confinement. Our results provide new evidence of increasingly harsh prison conditions and disparities that unfolded during the prison boom.
Hat tip: Kevin Lewis.
In Why Online Education Works I wrote:
The future of online education is adaptive assessment, not for testing, but for learning. Incorrect answers are not random but betray specific assumptions and patterns of thought. Analysis of answers, therefore, can be used to guide students to exactly that lecture that needs to be reviewed and understood to achieve mastery of the material. Computer-adaptive testing will thus become computer-adaptive learning.
Computer-adaptive learning will be as if every student has their own professor on demand—much more personalized than one professor teaching 500 students or even 50 students. In his novel Diamond Age, science fiction author Neal Stephenson describes a Young Lady’s Illustrated Primer, an interactive book that can answer a learner’s questions with specific information and also teach young children with allegories tuned to the child’s environment and experience. In short, something like an iPad combining Siri, Watson, and the gaming technology behind an online world like Skyrim. Surprisingly, the computer will make learning less standardized and robotic.
In other words, the adaptive textbook will read you as you read it. The NYTimes has a good piece discussing recent advances in this area including Bakpax which reads student handwriting and grades answers. Furthermore:
Today, learning algorithms uncover patterns in large pools of data about how students have performed on material in the past and optimize teaching strategies accordingly. They adapt to the student’s performance as the student interacts with the system.
…Studies show that these systems can raise student performance well beyond the level of conventional classes and even beyond the level achieved by students who receive instruction from human tutors. A.I. tutors perform better, in part, because a computer is more patient and often more insightful.
…Still more transformational applications are being developed that could revolutionize education altogether. Acuitus, a Silicon Valley start-up, has drawn on lessons learned over the past 50 years in education — cognitive psychology, social psychology, computer science, linguistics and artificial intelligence — to create a digital tutor that it claims can train experts in months rather than years.
Acuitus’s system was originally funded by the Defense Department’s Defense Advanced Research Projects Agency for training Navy information technology specialists. John Newkirk, the company’s co-founder and chief executive, said Acuitus focused on teaching concepts and understanding.
The company has taught nearly 1,000 students with its course on information technology and is in the prototype stage for a system that will teach algebra. Dr. Newkirk said the underlying A.I. technology was content-agnostic and could be used to teach the full range of STEM subjects.
Dr. Newkirk likens A.I.-powered education today to the Wright brothers’ early exhibition flights — proof that it can be done, but far from what it will be a decade or two from now.
I find windmills beautiful but many people disagree, even in environmentally conscious Germany.
Bloomberg:…it’s getting harder to get permission to erect the turbine towers. Local regulations are getting stricter. Bavaria decided back in 2014 that the distance between a wind turbine and the nearest housing must be 10 times the height of the mast, which, given the density of dwellings, makes it hard to find a spot anywhere. Wind energy development is practically stalled in the state now. Brandenburg, the state surrounding Berlin, passed a law this year demanding that wind-farm operators pay 10,000 euros ($11,100) per turbine each year to communities within 3 kilometers of the windmills.
…local opponents of the wind farms often go to court to stall new developments or even have existing towers dismantled. According to the wind-industry lobby BWE, 325 turbine installations with a total capacity of more than 1 gigawatt (some 2% of the country’s total installed capacity) are tied up in litigation. The irony is that the litigants are often just as “green” as the wind-energy proponents — one is the large conservation organization NABU, which says it’s not against wind energy as such but merely demands that installations are planned with preserving nature in mind. Almost half of the complaints are meant to protect various bird and bat species; others claim the turbines make too much noise or emit too much low-frequency infrasound. Regardless of the validity of such claims, projects get tied up in the courts even after jumping through the many hoops necessary to get a permit.
Another reason for local resistance to the wind farms is a form of Nimbyism: People hate the way the wind towers change landscapes. There’s even a German word for it, Verspargelung, roughly translated aspollution with giant asparagus sticks.
As I wrote earlier, more and more the sphere of individual action shrinks and that of collective action grows and, as a result, nothing can get done because there are so many veto players in the system. We have locked ourselves into an innovation prisoner’s dilemma where each player can say no and as a result we are all worse off.
The Lancet Commission on Pollution and Health, an authoritative review with well-over a dozen distinguished co-authors, is unusually forthright on the effect of pollution, most especially lead, on IQ. I think some of their numbers, especially in paragraph three, are too large but the direction is certainly correct.
Neurotoxic pollutants can reduce productivity by impairing children’s cognitive development. It is well documented that exposures to lead and other metals (eg, mercury and arsenic) reduce cognitive function, as measured by loss of IQ.168
Loss of cognitive function directly affects success at school and labour force participation and indirectly affects lifetime earnings. In the USA, millions of children were exposed to excessive concentrations of lead as the result of the widespread use of leaded gasoline from the 1920s until about 1980. At peak use in the 1970s, annual consumption of tetraethyl lead in gasoline was nearly 100 000 tonnes.
It has been estimated that the resulting epidemic of subclinical lead poisoning could have reduced the number of children with truly superior intelligence (IQ scores higher than 130 points) by more than 50% and, concurrently, caused a more than 50% increase in the number of children with IQ scores less than 70 (figure 14).265 Children with reduced cognitive function due to lead did poorly in school, required special education and other remedial programmes, and could not contribute fully to society when they became adults.
Grosse and colleagues 46 found that each IQ point lost to neurotoxic pollution results in a decrease in mean lifetime earnings of 1·76%. Salkever and colleagues 266 who extended this analysis to include the effects of IQ on schooling, found that a decrease in IQ of one percentage point lowers mean lifetime earnings by 2·38%. Studies from the 2000s using data from the USA 267,268 support earlier findings but suggest a detrimental effect on earnings of 1·1% per IQ point.269 The link between lead exposure and reduced IQ 46, 168 suggests that, in the USA, a 1 μg/dL increase in blood lead concentration decreases mean lifetime earnings by about 0·5%. A 2015 study in Chile 270 that followed up children who were exposed to lead at contaminated sites suggests much greater effects. A 2016 analysis by Muennig 271 argues that the economic losses that result from early-life exposure to lead include not only the costs resulting from cognitive impairment but also costs that result from the subsequent increased use of the social welfare services by these lead-exposed children, and their increased likelihood of incarceration.