The Nobel Prize goes to Abhijit Banerjee, Esther Duflo and Michael Kremer (links to home pages) for field experiments in development economics. Esther Duflo was a John Bates Clark Medal winner, a MacArthur “genius” award winner, and is now the second woman to win the economics Nobel and by far the youngest person to ever win the economics Nobel (Arrow was the previous youngest winner!). Duflo and Banerjee are married so these are also the first spouses to win the economics Nobel although not the first spouses to win Nobel prizes–there was even one member of a Nobel prize winning spouse-couple who won the Nobel prize in economics. Can you name the spouses?
Michael Kremer wrote two of my favorite papers ever. The first is Patent Buyouts which you can find in my book Entrepreneurial Economics: Bright Ideas from the Dismal Science. The idea of a patent buyout is for the government to buy a patent and rip it up, opening the idea to the public domain. How much should the government pay? To decide this they can hold an auction. Anyone can bid in the auction but the winner receives the patent only say 10% of the time–the other 90% of the time the patent is bought by the government at the market price. The value of this procedure is that 90% of the time we get all the incentive properties of the patent without any of the monopoly costs. Thus, we eliminate the innovation tradeoff. Indeed, the government can even top the market price up by say 15% in order to increase the incentive to innovate. You might think the patent buyout idea is unrealistic. But in fact, Kremer went on to pioneer an important version of the idea, the Advance Market Commitment for Vaccines which was used to guarantee a market for the pneumococcal vaccine which has now been given to some 143 million children. Bill Gates was involved with governments in supporting the project.
My second Kremer paper is Population Growth and Technological Change: One Million B.C. to 1990. An economist examining one million years of the economy! I like to say that there are two views of humanity, people are stomachs or people are brains. In the people are stomachs view, more people means more eaters, more takers, less for everyone else. In the people are brains view, more people means more brains, more ideas, more for everyone else. The people are brains view is my view and Paul Romer’s view (ideas are nonrivalrous). Kremer tests the two views. He shows that over the long run economic growth increased with population growth. People are brains.
The work for which the Nobel was given is for field experiments in development economics. Kremer began this area of research with randomized trials of educational policies in Kenya. Duflo and Banerjee then deepened and broadened the use of field experiments and in 2003 established the Poverty Action Lab which has been the nexus for field experiments in development economics carried on by hundreds of researchers around the world.
Much has been learned in field experiments about what does and also doesn’t work. In Incentives Work, Dufflo, Hanna and Ryan created a successful program to monitor and reduce teacher absenteeism in India, a problem that Michael Kremer had shown in Missing in Action was very serious with some 30% of teachers not showing up on a typical day. But when they tried to institute a similar program for nurses in Putting a Band-Aid on A Corpse the program was soon undermined by local politicians and “Eighteen months after its inception, the program had become completely ineffective.” Similarly, Banerjee, Duflo, Glennerster and Kinnan find that Microfinance is ok but no miracle (sorry fellow laureate Muhammad Yunus). A frustrating lesson has been the context dependent nature of results and the difficult of finding external validity. (Lant Pritchett in a critique of the “randomistas” argues that real development is based on macro-policy rather than micro-experiment. See also Bill Easterly on the success of the Washington Consensus.)
Duflo, Kremer and Robinson study How High Are Rates of Return to Fertilizer? Evidence from Field Experiments in Kenya. This is an especially interest piece of research because they find that rates of return are very high but that farmers don’t use much fertilizer. Why not? The reasons seem to have much more to do with behavioral biases than rationality. Some interventions help:
Our findings suggest that simple interventions that affect neither the cost of, nor the payoff to, fertilizer can substantially increase fertilizer use. In particular, offering farmers the option to buy fertilizer (at the full market price, but with free delivery) immediately after the harvest leads to an increase of at least 33 percent in the proportion of farmers using fertilizer, an effect comparable to that of a 50 percent reduction in the price of fertilizer (in contrast, there is no impact on fertilizer adoption of offering free delivery at the time fertilizer is actually needed for top dressing). This finding seems inconsistent with the idea that low adoption is due to low returns or credit constraints, and suggests there may be a role for non–fully rational behavior in explaining production decisions.
This is reminiscent of people in developed countries who don’t adjust their retirement savings rates to take advantage of employer matches. (A connection to Thaler’s work).
Duflo and Banerjee have conducted many of their field experiments in India and have looked at not just conventional questions of development economics but also at politics. In 1993, India introduced a constitutional rule that said that each state had to reserve a third of all positions as chair of village councils for women. In a series of papers, Duflo studies this natural experiment which involved randomization of villages with women chairs. In Women as Policy Makers (with Chattopadhyay) she finds that female politicians change the allocation of resources towards infrastructure of relevance to women. In Powerful Women (Beaman et al.) she finds that having once had a female village leader increases the prospects of future female leaders, i.e. exposure reduces bias.
Before Banerjee became a randomistas he was a theorist. His A Simple Model of Herd Behavior is also a favorite. The essence of the model can be explained in a simple example (from the paper). Suppose there are two restaurants A and B. The prior probability is that A is slightly more likely to be a better restaurant than B but in fact B is the better restaurant. People arrive at the restaurants in sequence and as they do they get a signal of which restaurant is better and they also see what choice the person in front of them made. Suppose the first person in line gets a signal that the better restaurant is A (contrary to fact). They choose A. The second person then gets a signal that the better restaurant is B. The second person in line also sees that the first person chose A, so they now know one signal is for A and one is for B and the prior is A so the weight of the evidence is for A—the second person also chooses restaurant A. The next person in line also gets the B signal but for the same reasons they also choose A. In fact, everyone chooses A even if 99 out of 100 signals are B. We get a herd. The sequential information structure means that the information is wasted. Thus, how information is distributed can make a huge difference to what happens. A lot of lessons here for tweeting and Facebook!
Banerjee is also the author of some original and key pieces on Indian economic history, most notably History, Institutions, and Economic Performance: The Legacy of Colonial Land Tenure Systems in India (with Iyer).
Before last year’s Nobel announcement Tyler wrote:
I’ve never once gotten it right, at least not for exact timing, so my apologies to anyone I pick (sorry Bill Baumol!). Nonetheless this year I am in for Esther Duflo and Abihijit Banerjee, possibly with Michael Kremer, for randomized control trials in development economics.
As Tyler predicted he was wrong and also right. Thus, this years win is well-timed and well-deserved. Congratulations to all.
An amazing result:
Many people have claimed that sleep has helped them solve a difficult problem, but empirical support for this assertion remains tentative. The current experiment tested whether manipulating information processing during sleep impacts problem incubation and solving. In memory studies, delivering learning-associated sound cues during sleep can reactivate memories. We therefore predicted that reactivating previously unsolved problems could help people solve them. In the evening, we presented 57 participants with puzzles, each arbitrarily associated with a different sound. While participants slept overnight, half of the sounds associated with the puzzles they had not solved were surreptitiously presented. The next morning, participants solved 31.7% of cued puzzles, compared with 20.5% of uncued puzzles (a 55% improvement). Moreover, cued-puzzle solving correlated with cued-puzzle memory. Overall, these results demonstrate that cuing puzzle information during sleep can facilitate solving, thus supporting sleep’s role in problem incubation and establishing a new technique to advance understanding of problem solving and sleep cognition.
Hat tip: Kevin Lewis.
Time spent on social media has been blamed for increased suicides and depression, just as were other new technologies and pastimes such as phones and Dungeons and Dragons.
… but is social media the real culprit? Or are we engaged in a moral panic, perhaps not understanding the root of the problem? One major limitation of the current literature is that the vast majority of research on SNSs and mental health are cross sectional and cannot speak to developmental change over time or direction of effects. Additionally, research to date rely on traditional regression techniques that model between-person relations among variables. These techniques ignore individual processes that are vital to our understanding of the true relationship between these variables. Thus, the aim of the current study is to test a causal model of the associations between time spent using social media and mental health (anxiety and depression), using both between and within subjects analyses, over an 8-year-period of time, encompassing the transition between adolescence and emerging adulthood.
That’s from an impressive, 8-year long study. It’s not a random experiment but this is the most credible research on the question I have read to date.
Of course, this raises the question of why mental health is down and fragility is up among the young. One answer is that the evidence on mental fragility is flimsy, which is true in general, but the data on suicides is reasonably good and suicides among youth have increased a lot since 2000. I’m not sure of the answers but although social media fit the time trend I now down weight that explanation.
Hat tip: The awesome Rolf Degen.
USA Today: Nearly three years after city voters approved a $1.2 billion construction program over 10 years, the city has yet to see the first building completed. Average per-apartment costs have zoomed more than $100,000 past prior predictions, the study by city Controller Ron Galperin finds.
…At an average cost of $531,373 per unit – with many apartments costing more than $600,000 each – building costs of many of the homeless units will exceed the median sale price of a market-rate condominium.
…Prices rose dramatically because of higher-than-expected costs for items other than actual construction, such as consultants and financing. Those items comprise up to 40% of the cost of a project, the study found. By contrast, land acquisition costs averaged only 11% of the total costs.
Based on a recent audit of the program.
It’s absurd for a government to be building houses, a task for which it is manifestly unsuited. What the government should be doing is easing restrictions on building, improving public transportation which increases the supply of effective housing and dealing with any shortfalls by using housing vouchers.
People born between 1963 and 1965 are less likely to drive a car to work, are more likely to commute using public transit and are even less likely to own a car than people born just before or after those years. Why? It’s a great puzzle. Give it a guess.
Severen and van Benthem have a compelling answer:
An individual’s initial experiences with a common good, such as gasoline, can shape their behavior for decades. We first show that the 1979 oil crisis had a persistent negative effect on the likelihood that individuals that came of driving age during this time drove to work in the year 2000 (i.e., in their mid 30s). The effect is stronger for those with lower incomes and those in cities. Combining data on many cohorts, we then show that large increases in gasoline prices between the ages of 15 and 18 significantly reduce both (i) the likelihood of driving a private automobile to work and (ii) total annual vehicle miles traveled later in life, while also increasing public transit use. Differences in driver license age requirements generate additional variation in the formative window. These effects cannot be explained by contemporaneous income and do not appear to be only due to increased costs from delayed driving skill acquisition. Instead, they seem to reflect the formation of preferences for driving or persistent changes in the perceived costs of driving.
Here’s a nice figure from an excellent piece covering the Severen and van Benthem paper in the Washington Post by Van Dam. Van Dam also covers a paper by Malmendier and Shen which shows how unemployment in formative years can change behavior through a lifetime even absent differences in income.
It’s well known that you can see the deleterious effects of communism from outer space but you can also learn about development, war, and international law as this video demonstrates:
Hat tip: Roman Hardgrave.
Andrew McAfee is offering to take a number of bets centered around predictions and implication from his new book More From Less. Here are a few of Andrew’s bold predictions that he is willing to bet on through the Long Bets division of the Long Now Foundation.
- In 2029, the US will consume less total energy than it did in 2019.
- In 2029, the US will produce less total CO2 emissions than it did in 2019, even after taking offshoring into account.
- Over the five years leading up to 2029, the US will use less paper in total than it did over the five years leading up to 2019.
The most famous Long Bet was between Warren Buffett and Protege Partners
- Over a ten-year period commencing on January 1, 2008, and ending on December 31, 2017, the S&P 500 will outperform a portfolio of funds of hedge funds, when performance is measured on a basis net of fees, costs and expenses.
Buffett won that bet and earned over $2 million dollars for his favorite charity.
The purpose of Long Bets is to elicit argument and debate and to better encourage long thinking. All bet winnings go to charity.
It’s an ill-wind that blows no good and in Allocating Scarce Organs, Dickert-Conlin, Elder and Teltser find that repealing motorcycle helmet laws generate large increases in the supply of deceased organ transplants. The supply shock, however, is just the experiment that the authors use to measure demand responses. It’s well known that the shortage of transplant organs has led to a long waiting-list. The waiting-list, however, is only the tip of the iceberg. Many people who could benefit from a transplant never bother getting on the list since their prospects are already so low. In addition, some people have access to substitutes for a deceased organ transplant namely a living donor. Finally, there is a quality tradeoff: as more organs become available the quality of the match may increase as people may pass on the first available organ to get a better match. The authors use the supply shock to study all these issues:
We find that transplant candidates respond strongly to local supply shocks, along two dimensions. First, for each new organ that becomes available in a market, roughly five new candidates join the local wait list. With detailed zip code data, we demonstrate that candidates listed in multiple locations and candidates living out-side of the local market disproportionately drive demand responses. Second, kidney transplant recipients substitute away from living-donor transplants. We estimate the largest crowd out of potential transplants from living donors who are neither blood relatives nor spouses, suggesting that these are the marginal cases in which the relative costs of living-donor and deceased-donor transplants are most influential. Taken together, these findings show that increases in the supply of organs generate demand behavior that at least partially offsets a shock’s direct effects. Presumably as a result of this offset, the average waiting time for an organ does not measurably decrease in response to a positive supply shock. However, for livers, hearts, lungs, and pancreases, we find evidence that an increase in the supply of deceased organs increases the probability that a transplant is successful, defined as graft survival. Among kidney transplant recipients, we hypothesize that living donor crowd out mitigates any health outcome gains resulting from increases in deceased-donor transplants.
In other words, increased organ availability increases the quality of the matches for organs that cannot be given by a living donor (hearts, lungs, pancreases, partially liver) but for kidneys some of the benefit of increased organ availability accrues to potential living donors who do not have to donate and this means that match quality does not substantially increase.
The authors also critique the geographic isolation of kidney donation regions. As I wrote when Steve Jobs received a kidney transplant:
Although there is no reason to think that Apple CEO Steve Jobs “jumped the line” to get his recent liver transplant, Jobs did have an advantage: He was able to choose which line to stand in.
Contrary to popular belief, transplant organs are not allocated solely according to medical need. Organs are allocated through a complex system of 58 transplant territories. Patients within each territory typically get first dibs on organs from that territory. That’s great if a patient happens to live in a territory with a lot of organ donors and relatively few demanders, but not so good for a patient living in New York, San Francisco or Los Angeles, where waiting lines are longest.
As a result of these “accidents of geography,” relatively healthy patients in some parts of the country get transplants while sicker patients in other parts of the country die waiting.
We investigate the effect of trade integration on interstate military conflict. Our empirical analysis, based on a large panel data set of 243,225 country-pair observations from 1950 to 2000, confirms that an increase in bilateral trade interdependence significantly promotes peace. It also suggests that the peace-promotion effect of bilateral trade integration is significantly higher for contiguous countries that are likely to experience more conflict. More importantly, we find that not only bilateral trade but global trade openness also significantly promotes peace. It shows, however, that an increase in global trade openness reduces the probability of interstate conflict more for countries far apart from each other than it does for countries sharing borders. The main finding of the peace-promotion effect of bilateral and global trade integration holds robust when controlling for the simultaneous determination of trade and peace.
From Lee and Pyun, Does Trade Integration Contribute to Peace?
It’s often said that Australia hasn’t had a recession in nearly 30 years. Paulina Restrepo-Echavarria and Brian Reinbold of the Federal Reserve Bank of St. Louis take a closer look. If a recession is defined as two quarters of negative growth in GDP then the claim is true but if you define a recession as two quarters of negative growth in GDP per capita then there have been three such recessions since 1991: circa 2000-2001, 2005-2006 and 2018-2019.
Most countries, however, have had more recessions when measured in GDP per capita than in GDP and Australia still looks comparatively good on this measure. Moreover, the official definition of a US recession is not two quarters of negative growth in real GDP it’s the more holistic
A significant decline in economic activity spread across the economy, lasting more than a few months, normally visible in real GDP, real income, employment, industrial production, and wholesale-retail sales.
Did Australia have three recession since 1991 by this measure? It’s difficult to say but I would look more to unemployment rates. The following graph shows Australian real GDP growth rates in purple measured quarterly, real GDP per capita in green measured annually and the unemployment rate in blue. (The data is not identical to Restrepo-Echavarria and Reinbold (RER) as I use FRED data and the FRED economists do not!). As per RER the purple line is generally above the green so you are more likely to see recessions in GDP per capita than in GDP. Take a look at the unemployment rate, however. The 2005-2006 Australian “recession” is completely absent in unemployment so I would rule that out. I also do not see any recession as measured by unemployment in 2018-2019, perhaps it is coming but I would rule it out as of today. The unemployment measure clearly identifies recessions circa 2001-2002 which agrees with RER and also in 2008-2009 where RER do not identify a recession!. Thus, the RER identification of recessions doesn’t work very well as it has both false positives and false negatives.
On the larger issue of Australian economic performance, at worst, I would identify two mild recessions since 1991, circa 2001-2002 and 2008-2009. Now look again at the graph. The shading is US recessions! The Australian and US economies are united enough and subject to similar enough shocks that US recession dating clearly picks out Australian recessions as measured by increases in unemployment rates.
The bottom line is that however you measure it, Australian performance looks very good. Moreover RER are correct that one of the reasons for strong Australian economic performance is higher population growth rates. It’s not that higher population growth rates are masking poorer performance in real GDP per capita, however, it’s more in my view that higher population growth rates are contributing to strong performance as measured by both real GDP and real GDP per capita.
Here’s how one MR reader spent his summer vacation:
I recently wrote a post, Short Selling Reduces Crashes about a paper which used an unusual random experiment by the SEC, Regulation SHO (which temporarily lifted short-sale constraints for randomly designated stocks), as a natural experiment. A correspondent writes to ask whether I was aware that Regulation SHO has been used by more than fifty other studies to test a variety of hypotheses. I was not! The problem is obvious. If the same experiment is used multiple times we should be imposing multiple hypothesis standards to avoid the green jelly bean problem, otherwise known as the false positive problem. Heath, Ringgenberg, Samadi and Werner make this point and test for false positives in the extant literature:
Natural experiments have become an important tool for identifying the causal relationships between variables. While the use of natural experiments has increased the credibility of empirical economics in many dimensions (Angrist & Pischke, 2010), we show that the repeated reuse of a natural experiment significantly increases the number of false discoveries. As a result, the reuse of natural experiments, without correcting for multiple testing, is undermining the credibility of empirical research.
.. To demonstrate the practical importance of the issues we raise, we examine two extensively studied real-world examples: business combination laws and Regulation SHO. Combined, these two natural experiments have been used in well over 100 different academic studies. We re-evaluate 46 outcome variables that were found to be significantly affected by these experiments, using common data frequency and observation window. Our analysis suggests that many of the existing findings in these studies may be false positives.
There is a second more subtle problem. If more than one of the effects are real it calls into question the exclusion restriction.To identify the effect of X on Y1 we need to assume that X influences Y1 along only one path. But if X also influences Y2 that suggests that there might be multiple paths from X to Y1. Morck and Young made this point many years ago, likening the reuse of the same instrumental variables to a tragedy of the commons.
Solving these problems is made especially difficult because they are collective action problems with a time dimension. A referee that sees a paper throw the dice multiple times may demand multiple hypothesis and exclusion test corrections. But if the problem is that there are many papers each running a single test, the burden on the referee to know the literature is much larger. Moreover, do we give the first and second papers a pass and only demand multiple hypothesis corrections for the 100th paper? That seems odd, although in practice it is what happens as more original papers can get published with weaker methods (collider bias!).
As I wrote in Why Most Published Research Findings are False we need to address these problems with a variety of approaches:
1) In evaluating any study try to take into account the amount of background noise. That is, remember that the more hypotheses which are tested and the less selection [this is one reason why theory is important it strengthens selection, AT] which goes into choosing hypotheses the more likely it is that you are looking at noise.
2) Bigger samples are better. (But note that even big samples won’t help to solve the problems of observational studies which is a whole other problem).
3) Small effects are to be distrusted.
4) Multiple sources and types of evidence are desirable.
5) Evaluate literatures not individual papers.
6) Trust empirical papers which test other people’s theories more than empirical papers which test the author’s theory.
7) As an editor or referee, don’t reject papers that fail to reject the null.
Walking around one of the tonier districts of Mumbai I came across a sign, “Avoid Using Plastic Carry Bags.” The sign would not have been out of place in Portland or Berkeley but less than a block away cows and people were sleeping on the street. The incongruity motivated my new paper, Premature Imitation and India’s Flailing State (with Shruti Rajagopalan). We argue that one reason that India passes laws which are incongruous with its state of development is that Indian elites often take their cues about what is normal, good and desirable from Western elites. There’s nothing wrong with imitation, of course. We hope that good policies will be imitated but imitation in India is often premature. Premature because India does not have the state capacity to enforce the edicts of a developed country.
India has essentially all the inspections, regulations, and laws a developed country such as the United States has, but at approximately $235 of federal spending per capita the Indian government simply cannot accomplish all the tasks it has assumed. Consider: U.S. federal government spending per capita was five times higher in 1902 than Indian federal government spending per capita in 2006 (Andrews, Pritchett, and Woolcock 2017, 58). Yet the Indian government circa 2006 was attempting to do much more than the U.S. government did in 1902.
Premature imitation doesn’t simply mean that proportionately less is done it results in tensions that lead to corruption and a flailing state, a state that cannot implement its own rules because it is undercut by the incentives of its own agents. Premature imitation amplifies a development trap.
What then is to be done? We argue that the ideal policy regime for a government with limited state capacity is presumptive laissez-faire.
The Indian state does not have enough capacity to implement all the rules and regulations that elites, trying to imitate the policies of developed economies, desire. The result is premature load bearing and a further breakdown in state capacity….At the broadest level, this suggests that states with limited capacity should rely more on markets even when markets are imperfect—presumptive laissez-faire. The market test isn’t perfect, but it is a test. Markets are the most salient alternative to state action, so when the cost of state action increases, markets should be used more often.Imagine, for example, that U.S. government spending had to be cut by a factor of ten.Would it make sense to cut all programs by 90 percent? Unlikely. Some programs and policies are of great value, but others should be undertaken only when state capacity and GDP per capita are higher. As Edward Glaeser quips,“A country that cannot provide clean water for its citizens should not be in the business of regulating film dialogue.” A U.S. government funded at one-tenth the current level would optimally do many fewer things. So why doesn’t the Indian government do many fewer things?
Presumptive laissez-faire is not an argument that laissez-faire is optimal but an argument that state capacity is a limited resource that must be allocated wisely. The idea runs against the “folk wisdom” of development economics. The folk wisdom says that developing countries today can leap over the laissez-faire period that most developed countries went through and instead move directly to the middle way.
In the alternative view put forward here, relative laissez-faire is a step to development, perhaps even a necessary step, even if the ultimate desired end point of development is a regulated, mixed economy. Presumptive laissez-faire is the optimal form of government for states with limited capacity and also the optimal learning environment for states to grow capacity. Under laissez-faire, wealth, education, trade, and trust can grow, which in turn will allow for greater regulation.
Read the whole thing.
Many puzzles are difficult to solve from one perspective but easy from another. A challenge on stackexchange was to find an equivalent version of the Monty Hall problem where the correct solution of switching is obvious. Joshua B. Miller has an excellent answer. To recall, in the original there is a great prize hidden behind one of three doors. You choose a door. Monty Hall then reveals a lousy prize behind one of the other two doors (it’s always a lousy prize). Do you switch doors? Most people see no reason to switch. Even Paul Erdos was a no switcher! Moreover, most of those who do switch get to that conclusion with an unintuitive Bayesian calculation.
Here’s the intuitive version.
There are three boxers. Two of the boxers are evenly matched (no draws!); the other boxer will beat either them, always.
You blindly guess that Boxer A is the best and let the other two fight.
Boxer B beats Boxer C.
Do you want to stick with Boxer A in a match-up with Boxer B, or do you want to switch?
See also Miller’s new piece in the JEP which looks at the Monty Hall problem and the Hot hand puzzle.
Dean Spears, one of the authors of Where India Goes has a new book on air pollution in India, Air. When I reviewed Where India Goes in 2017 I said it was the best social science book I had read in years. Spears is able to accurately explain academic work–much of it his own and with co-authors–in accessible language and to combine that with on-the-ground reporting to produce a book that is both informative and full of human interest. He brings the same skills to Air.
As Spears shows, pollution is killing Indians, especially babies, and those it doesn’t kill it harms as seen in statistics on stunting and respiratory disease. Spears isn’t naive, however, he knows that manufacturing is also bringing tremendous benefits. The issue, however, is that a lot of pollution in India comes from relatively low value activities like burning crops. Moreover, solar power in India is cost competitive with coal today, even before taking into account health benefits. Thus, the harms of pollution are tragic because they are unnecessary.
If the costs of pollution exceed the benefits why isn’t something being done? One of the things I like about Air is that it is clear that pollution in India is both a market failure and a government failure. The government has been slow to respond to pollution because much of the public remains unaware of pollution’s true cost and much of the true cost is born by children and future people who have no vote. In the meantime, the government enhances rational ignorance by refusing to fund even the most basic equipment to measure where and when pollution ebbs and flows. Instead the government engages in virtue-politics by banning plastic bags and creating odd-even restrictions on driving in Delhi. These activities are pointless, even counter-productive, but they are well publicized and the appearance of doing something matters more than reality.
Here’s one brilliant bit:
Just next to the Raebareli coal plant is a solar power plant. The solar plant is, in principle, capable of generating 10 MW. That capacity is 1 per cent of the 1000 MW capacity of the immediately neighbouring coal plant (which had another few hundred megawatts under construction when I talked with Gaurav).
I visited the solar plant on Independence Day. The ground around the solar panels was ﬂooded with August rain. A shoe destroying walk through the mud and water brought me to the control room in a small building. There, a cheerful young engineer from Bengaluru watched a bank of computer screens. A TV monitor reviewed a list of fifteen highlights of the Prime Minister’s holiday speech that morning. The control room was set up in a museum-like display. The apparent goal was to impress visitors with modern renewable energy and with colourful displays of General Electric–branded software. The young engineer was excited to show me the screens. He clearly wanted the message to be good.
It was not good. That cloudy day, most of the dots were red, not green. The screens reported that the solar plant was generating 60 kW. The engineer assured me that one day it had gotten up to 7500 kW. A megawatt is 1000 kilowatts. So, at 0.06 MW, the solar plant was producing less than 1 per cent of the 10 MW that the signboard at the entrance promised, which would have been 1 per cent of the coal plant.
It is not surprising that a solar plant does not generate much electricity if it is built beneath the smoke of a coal plant with 100 times the capacity. Ordinarily, one places solar plants in the path of direct sunlight. This one was placed in the path of visitors.
Addendum: Case in point. India today bans e-cigarettes because of health risks!