The use and abuse of p-values and statistical significance

Here is the American Statistical Association's (ASA) official policy statement on p-values and statistical significance.

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Introduction

Increased quantification of scientific research and a proliferation of large, complex datasets in recent years have expanded the scope of applications of statistical methods. This has created new avenues for scientific progress, but it also brings concerns about conclusions drawn from research data. The validity of scientific conclusions, including their reproducibility, depends on more than the statistical methods themselves. Appropriately chosen techniques, properly conducted analyses and correct interpretation of statistical results also play a key role in ensuring that conclusions are sound and that uncertainty surrounding them is represented properly.

Underpinning many published scientific conclusions is the concept of “statistical significance,” typically assessed with an index called the p-value. While the p-value can be a useful statistical measure, it is commonly misused and misinterpreted. This has led to some scientific journals discouraging the use of p-values, and some scientists and statisticians recommending their abandonment, with some arguments essentially unchanged since p-values were first introduced.

In this context, the American Statistical Association (ASA) believes that the scientific community could benefit from a formal statement clarifying several widely agreed upon principles underlying the proper use and interpretation of the p-value. The issues touched on here affect not only research, but research funding, journal practices, career advancement, scientific education, public policy, journalism, and law. This statement does not seek to resolve all the issues relating to sound statistical practice, nor to settle foundational controversies. Rather, the statement articulates in non-technical terms a few select principles that could improve the conduct or interpretation of quantitative science, according to widespread consensus in the statistical community.

What is a p-value?

Informally a p-value is the probability under a specified statistical model that a statistical summary of the data (for example, the sample mean difference between two compared groups) would be equal to or more extreme than its observed value.

Six principles of p-value:

1. P-values can indicate how incompatible the data are with a specified statistical model.

A p-value provides one approach to summarizing the incompatibility between a particular set of data and a proposed model for the data. The most common context is a model, constructed under a set of assumptions, together with a so-called “null hypothesis.” Often the null hypothesis postulates the absence of an effect, such as no difference between two groups, or the absence of a relationship between a factor and an outcome. The smaller the p-value, the greater the statistical incompatibility of the data with the null hypothesis, if the underlying assumptions used to calculate the p-value hold. This incompatibility can be interpreted as casting doubt on or providing evidence against the null hypothesis or the underlying assumptions

2. P-values do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone.

Researchers often wish to turn a p-value into a statement about the truth of a null hypothesis, or about the probability that random chance produced the observed data. The p-value is neither. It is a statement about data in relation to a specified hypothetical explanation, and is not a statement about the explanation itself.

3. Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold.

Practices that reduce data analysis or scientific inference to mechanical “bright-line” rules (such as “p < 0.05”) for justifying scientific claims or conclusions can lead to erroneous beliefs and poor decision-making. A conclusion does not immediately become “true” on one side of the divide and “false” on the other. Researchers should bring many contextual factors into play to derive scientific inferences, including the design of a study, the quality of the measurements, the external evidence for the phenomenon under study, and the validity of assumptions that underlie the data analysis. Pragmatic considerations often require binary, “yes-no” decisions, but this does not mean that p-values alone can ensure that a decision is correct or incorrect. The widespread use of “statistical significance” (generally interpreted as “p ≤ 0.05”) as a license for making a claim of a scientific finding (or implied truth) leads to considerable distortion of the scientific process.

4. Proper inference requires full reporting and transparency.

P-values and related analyses should not be reported selectively. Conducting multiple analyses of the data and reporting only those with certain p-values (typically those passing a significance threshold) renders the reported p-values essentially uninterpretable. Cherry-picking promising findings, also known by such terms as data dredging, significance chasing, significance questing, selective inference and “p-hacking,” leads to a spurious excess of statistically significant results in the published literature and should be vigorously avoided. One need not formally carry out multiple statistical tests for this problem to arise: Whenever a researcher chooses what to present based on statistical results, valid interpretation of those results is severely compromised if the reader is not informed of the choice and its basis. Researchers should disclose the number of hypotheses explored during the study, all data collection decisions, all statistical analyses conducted and all p-values computed. Valid scientific conclusions based on p-values and related statistics cannot be drawn without at least knowing how many and which analyses were conducted, and how those analyses (including p-values) were selected for reporting.

5. A p-value, or statistical significance, does not measure the size of an effect or the importance of a result.

Statistical significance is not equivalent to scientific, human, or economic significance. Smaller p-values do not necessarily imply the presence of larger or more important effects, and larger pvalues do not imply a lack of importance or even lack of effect. Any effect, no matter how tiny, can produce a small p-value if the sample size or measurement precision is high enough, and large effects may produce unimpressive p-values if the sample size is small or measurements are imprecise. Similarly, identical estimated effects will have different p-values if the precision of the estimates differs.

6. By itself, a p-value does not provide a good measure of evidence regarding a model or hypothesis.

Researchers should recognize that a p-value without context or other evidence provides limited information. For example, a p-value near 0.05 taken by itself offers only weak evidence against the null hypothesis. Likewise, a relatively large p-value does not imply evidence in favor of the null hypothesis; many other hypotheses may be equally or more consistent with the observed data. For these reasons, data analysis should not end with the calculation of a p-value when other approaches are appropriate and feasible.

Other approaches

In view of the prevalent misuses of and misconceptions concerning p-values, some statisticians prefer to supplement or even replace p-values with other approaches. These include methods that emphasize estimation over testing, such as confidence, credibility, or prediction intervals; Bayesian methods; alternative measures of evidence, such as likelihood ratios or Bayes Factors; and other approaches such as decision-theoretic modeling and false discovery rates. All these measures and approaches rely on further assumptions, but they may more directly address the size of an effect (and its associated uncertainty) or whether the hypothesis is correct.

Conclusion

Good statistical practice, as an essential component of good scientific practice, emphasizes principles of good study design and conduct, a variety of numerical and graphical summaries of data, understanding of the phenomenon under study, interpretation of results in context, complete reporting and proper logical and quantitative understanding of what data summaries mean. No single index should substitute for scientific reasoning.

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Key economic achievements under late PM Sushil Koirala

Former Prime Minister and President of Nepali Congress (NC) party, Sushil Koirala passed away yesterday at the age of 78. He was known for a simple lifestyle and a clean career. Here are the key economic achievements during his tenure:

• Landmark power development agreements (PDA) was singed for 900MW Upper Karnali and 900MW Arun-3 hydroelectric projects worth over $2 billion
• Power Trade Agreement (PTA) was signed with India
• 18th SAARC summit was successfully organized with agreements on vehicle movement and energy cooperation, among others.
• 6,720MW Pancheswar Multipurpose Project got some momentum
• Presided over the successful completion of post-disaster needs assessment
• Successfully organized the International Conference on Nepal's Reconstruction in March 2015 Donors committed about $4 billion for Nepal's post-earthquake reconstruction
• Successfully concluded the political transition with the promulgation of a new constitution in September 2015
• FDI commitment of about $2.7 billion in FY2015
• A Public Private Partnership (PPP) Policy was approved
• Put in good and able technocrats/policymakers at key bodies such as National Planning Commission

On the economics front, he will be remembered most for helping to lay groundwork for major investment projects and reform measures. That is quite an achievement in itself (even if the results are not quick) given the relative performance under the leadership of previous premiers. The April 2015 earthquake and subsequent aftershocks, plus the trade blockade (for four and a half month) dented growth prospects and inflicted immeasurable hardship to common folks, but these were beyond Koirala's power. With the lifting of the blockade at the borders, lets hope that economic recovery will be fast (and that the government and bureaucrats will work faster than before for that). 

For now, lets thank the former PM for his decades long service to the nation and for presiding over one of the most scandal-free governments. RIP.

Pic courtesy: Republica

Deaton vs Friedman: Is income more volatile than consumption?

Angus Deaton was awarded the 2015 Nobel Prize in economics for his "analysis of consumption, poverty and welfare."  Here you can read about his work summarized and analyzed by the Nobel committee.

One of the micro-macro issues that Deaton tackled was if income was more volatile than consumption. Milton Friedman argued that people smooth consumption over time even if they experience temporary income shock (permanent income hypothesis). In other words, consumption is not as volatile as income. However, Deaton argued that it may not be the case if people expect several bouts of income increase. In this case, consumption would not be smooth. This is the Deaton Paradox, which comes from his analysis of household's income pattern and consumption behavior. 

The Economist summarizes this:

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But Mr Deaton’s work exposed this as sloppy thinking. First, he noted that the relationship between consumption and income in Friedman’s model depended on the kinds of income shocks hitting an economy. If one pay rise acts as a signal that there are more to come, then the “rational” agent in Mr Friedman’s model should anticipate future increases, and spend even more than their initial income boost. In this case, consumption should be more volatile than income, not the other way round.

So indeed it proved. Mr Deaton examined the aggregate income data more carefully, and found that it did not seem to support the idea of consumption smoothing. His microeconomic theory, allied with his empirical observations about aggregate income, together implied that income should be smoother than consumption, in contrast to what the macroeconomists had been trying to explain in the first place. This inconsistency was the Deaton paradox.

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And some word of advise to economists:

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As well as his specific contributions to our understanding of the world, Mr Deaton offers three lessons to aspiring economists. First, the theory should tally with the data—but if not, then do not despair. Puzzles and inconsistencies help to prompt innovation. Second, the average is rarely good enough. It is only by understanding differences between people that we can understand the whole. Finally, measurement matters. In the words of Mr Deaton, “progress cannot be coherently discussed without definitions and supporting evidence”. In the words of Mr Muellbauer, Mr Deaton’s win is “a triumph for evidence-based economics”.

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Why are oil prices falling globally?

Net oil importing countries (including those who import only) are enjoying the low petroleum prices in the international market as it helps them to manage public finance by lowering fuel subsidies and strengthening balance sheet of state-backed petroleum suppliers and distributors. In the case of Nepal, Nepal Oil Corporation is seeing net losses narrow down (there is still loss in the sale of LPG cooking gas) and consumers are enjoying the declining fuel prices. Non-food inflation seems to be cooling down as well. The government recently allowed the NOC to adjust fuel prices based on international prices (basically the price IOC charges to NOC plus taxes, interest payment on past loans, transportation losses and commission). Lets hope that domestic fuel prices is also increased when international fuel prices start to climb up.

For now, what are the main causes of lower fuel prices? It has to do with a combination of demand and supply forces, along with lower cartel power of OPEC, at play. The Economist lays down the main causes as follows:

1. Low demand arising from low economic activity, increased efficiency and a switch to alternative sources of energy
2. Turmoil in Iraq and Libya has not affected their output.
3. The US has become the world’s largest oil producer (it is importing less oil now).
4. Saudi Arabia and some Gulf countries are unwilling to lower supply (and hence their share in total world output) to put upward pressure on prices.

The combined effect of these forces are lowering oil prices since the high of $115 a barrel in June 2014.

Seven years of blogging

First, Happy New Year 2015! Stay happy, healthy, wealthy and wise! Maximize and optimize = A wonderful 2015!!

Second, I started blogging on this platform (www.sapkotac.blogspot.com) on 3 February 2008. Cumulatively, I wrote 1,611 blog posts over 2008-2014. Due to workload and other time-varying interests, the number of blog posts is going down each year, registering an average annual decrease of 35.9% over the last seven years. I hope the rate of decrease will stabilize in the coming days. Furthermore, I hope that the blog posts were and are helpful to some visitors.

Blog posts have evolved drastically over the years. Initially, they were mostly a collection of articles and interesting papers (sort of extended bibliography for my own reference). Now, blog posts are analytical as well as informational. They are mostly related to contemporary as well as historical development, economic growth and trade issues. The labels next to the figure show the major topics covered (larger text indicate more number of blog posts related to the topic).

Region-wise, a large part of the blog posts are related to Nepal and South Asia (Sub-Saharan Africa as well during the initial years). Thank you for visiting the blog!

2014 Noble Prize in Economic Sciences for Jean Tirole

The 2014 Noble Prize in Economic Sciences is awarded to French economist Jean Tirole of Université Toulouse 1 Capitole “for his analysis of market power and regulation”.

Here is the Nobel committee’s brief on his work and here is the detailed explanation. His main theoretical contributions is related to the understanding and regulation of industries with a few powerful firms (oligopoly), which influence prices, volume and quality. His research has helped governments to design regulations "so that large and mighty firms will act in society's best interest" and that there is a need to ensure that different industries may require different regulations. Other important issues that has Triole’s contributions in microeconomics are market failures, oligopoly, asymmetric information, game theory, competition regulation and industrial organization, procurement theory and optimal contracts. More on the depth and breadth of his work is outlined here and here.

Excerpts from the Nobel committee's brief:

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Price caps can provide dominant firms with strong motives to reduce costs – a good thing for society – but may also permit excessive profits – a bad thing for society. Cooperation on price setting within a market is usually harmful, but cooperation regarding patent pools can benefit everyone involved. The merger of a firm and its supplier may lead to more rapid innovation, but it may also distort competition.

To arrive at these results, a new theory was needed for oligopoly markets, because not even extensive privatization creates enough space for more than a small number of firms. There was also a need for a new theory of regulation in situations of assymetric information, because regulators often have poor knowledge of firms’ conditions.

Tirole’s research would come to build upon new scientific methods, particularly in game theory and contract theory. There were great hopes that these methods would contribute to practical policy. Game theory would aid the systematic study of how firms react to different conditions and to each other’s behavior. The next step would be to propose appropriate regulation based on the new theory of incentive contracts between parties with different information. However, even though many people could see the research questions, they were difficult to solve.

Jean Tirole’s research contributions are characterised by thorough studies, respect for the peculiarities of different markets, and the skilful use of new analytical methods in economics. He has penetrated deep into the most central issues of oligopolies and assymetric information, but he has also managed to bring together his own and other’s results into a coherent framework for teaching, practical application, and continued research. Tirole’s emphasis on normative theories of regulation and competition policy has given his contributions great practical significance.

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Bill Gates on Jeff Sachs and international development

Bill Gates writes:

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In the end, I hope poverty fighters will not let what they read in this book stop them from investing and taking risks. In the world of venture capital, a success rate of 30 percent is considered a great track record. In the world of international development, critics hold up every misstep as proof that aid is like throwing money down a rat hole. When you’re trying to do something as hard as fighting poverty and disease, you will never achieve anything meaningful if you’re afraid to make mistakes.

I greatly admire Sachs for putting his ideas and reputation on the line. After all, he could have a good life doing nothing more than teaching two classes a semester and pumping out armchair advice in academic journals. But that’s not his style. He rolls up his sleeves. He puts his theories into action. He drives himself as hard as anyone I know.

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Trends shaping the world economy

At the IMF-WB Annual Meeting in Tokyo, Christine Lagarde, Managing Director, International Monetary Fund, outlined the three megatrends the world economy is witnessing right now.

 

• Demographic changes: Rising number of young people in developing countries and graying population in advanced and major emerging economies; more women participation
• Shifting economic power: Economic power is gradually shifting from west to east; the south is witnessing increasing level of prosperity
• Efficiency, productivity and connectivity with ICT: Nearly 3 billion people are connected to the internet and entrepreneurs with global reach are emerging out in developing countries

She proposed three strategies to move ahead against the backdrop of the current phase of global economic uncertainty:

 

• Looking beyond the crisis: Accommodative monetary policy; the right pace of fiscal adjustment, mindful of not undercutting growth but with solid and realistic plans to bring debt down over the medium term; finishing the banking sector clean-up; and structural reforms to boost productivity and growth
• Creating a better financial system and complying with new banking capital and liquidity requirements
• Inequality and inclusive growth: Focusing both on efficiency and equity-- fairness in sharing the burden of adjustment, and protecting the weak and vulnerable; better financial inclusion; better transparency and governance

On similar note, read World Bank Group President Jim Yong Kim's speech on the need to move forward with an aim to alleviate absolute poverty and ensure shared prosperity.

How to judge effectiveness of fiscal policy?

Here is Abba Lerner (1943):

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“The central idea is that government fiscal policy, its spending and taxing, its borrowing and repayment of loans, its issue of new money and its withdrawal of money, shall all be undertaken with an eye only to the results of these actions on the economy and not to any established traditional doctrine about what is sound or unsound."

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Source: Abba Lerner (1943). “Functional Finance and the Federal Debt.” Social Research 10(1): 38–51.

Crisis divides macroeconomic schools of thought

Here is Simon Wren-Lewis over at VoxEu:

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So why have schools of thought within mainstream macroeconomics returned? One simple story is that schools of thought are associated with macroeconomic crises, and macro synthesis follows periods of calm. Keynesian theory itself was born out of the Great Depression. The first Neoclassical Synthesis arose from the period of strong growth and low inflation in the postwar period. Monetarism gained strength from the rapid inflation of the 1970s. The more recent synthesis may be a child of the Great Moderation, and now we have the Great Recession, schools of thought have returned. Because these crises are macroeconomic, and there are no equivalent crises involving microeconomic behaviour or policy, then fragmentation of the mainstream into schools will be a macro, not micro, phenomenon.

However I think this is too simplistic a view of what is happening today. One interesting feature of the current divide is that the label ‘Keynesian’ appears to be used more by those opposed to certain policies – and in particular fiscal stimulus – than those on the other side. Typically Keynesians see themselves as putting forward synthesis analysis, without the need for branding. What has become clear is that the New Neoclassical Synthesis was in many ways a celebration of New Keynesian theory which was not shared by many freshwater departments in the US.

There may be good reasons why New Keynesian economists might have imagined that their analysis was now an uncontested part of the mainstream. In particular, it is used in nearly all central banks as their main tool in carrying out monetary policy. With monetary policy somewhat depoliticised through central bank independence, the successful implementation of New Keynesian theory during the Great Moderation allowed divisions among academic departments to remain dormant.

On the other side, there was a belief that New Classical economics had been revolutionary, ie a successful counter-revolution against Keynesian ideas.  Once again there were good reasons supporting this belief. On consumption, rational expectations, the Lucas critique and more, traditional Keynesians had unsuccessfully opposed New Classical ideas. Furthermore, many of the leaders of New Classical thought did not want to update Keynesian thinking; they wanted to destroy it. The label ‘Keynesian’ was associated with much more than a belief that prices were sticky and that therefore aggregate demand mattered. Instead it became associated with state intervention. Wikipedia, in its third paragraph on ‘Keynesian economics’, says: “Keynesian economics advocates a mixed economy – predominantly private sector, but with a significant role of government and public sector...”.

The New Classical counter-revolution failed in one respect. While Keynesian analysis may have suffered a near-death experience, it survived and subsequently prospered. New Classical critiques led to fundamental and largely progressive changes. Yet, for many reasons including ideological ones, the would-be counter-revolutionaries did not want to give up their counter-revolution. Partly as a result, the degree to which New Keynesian theory was taught to graduate students differed widely among academic departments, at least in the US.

So, perhaps unlike the first (postwar) neoclassical synthesis, the New Neoclassical Synthesis was partial in terms of its coverage among academics. This incompleteness was not apparent during the Great Moderation, because in central banks the synthesis was uncontested. The fault lines only became evident when monetary policy became relatively impotent at the zero bound after the Great Recession, and fiscal stimulus was used both in the US and UK. Once that happened, what might be called the Anti-Keynesian school re-emerged.

Using this account, it is perhaps possible to view the current emergence of schools of thought as a historical aberration. The microfoundation of macroeconomics would seem to imply that mainstream macro should be as free from fragmentation into schools as microeconomics. As it becomes clear that the New Classical counter-revolution was not successful, the New Neoclassical Synthesis may yet become complete. (For an argument along these lines, see Economist 2012) After all, New Keynesian models are essentially real business cycle models plus sticky prices, and the addition of price rigidity seems both empirically plausible and inoffensive in itself. Both sides could agree that for economies with a floating exchange-rate monetary policy is the stabilisation tool of choice, with fiscal policy only being used if monetary policy is constrained (Kirsanova et al 2009). When interest rates are stuck at the zero lower bound, synthesis models clearly show fiscal policy can be highly effective at stimulating output (Woodford 2011). What has been called ‘demand denial’ appears not to make academic sense, particularly at a zero lower bound (Wren-Lewis 2011).

This outcome may, however, represent wishful thinking by New Keynesians. An alternative reading is that the Keynesian/Anti-Keynesian division is always going to be with us, because it reflects an ideological divide about state intervention. That divide occurs all the time in microeconomics, but because it involves arguing about many different externalities or imperfections it does not lend itself to fragmentation into schools. In macro, however, there is one critical externality to do with price rigidity, and so disagreements about policy can easily be mapped into differences about theory. Demand denial is attractive because it gives a non-ideological justification for what is essentially an ideological position about economic policy. Unfortunately, there is a danger that dividing mainstream analysis this way makes macroeconomics look more like a belief system than a science.

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Nobel prize in economics to Thomas Sargent and Christopher Sims

This year's the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel  goes to Thomas J. Sargent and Christopher A. Sims "for their empirical research on cause and effect in the macroeconomy". Below is the press release:

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How are GDP and inflation affected by a temporary increase in the interest rate or a tax cut? What happens if a central bank makes a permanent change in its inflation target or a government modifies its objective for budgetary balance? This year's Laureates in economic sciences have developed methods for answering these and many of other questions regarding the causal relationship between economic policy and different macroeconomic variables such as GDP, inflation, employment and investments.

These occurrences are usually two-way relationships – policy affects the economy, but the economy also affects policy. Expectations regarding the future are primary aspects of this interplay. The expectations of the private sector regarding future economic activity and policy influence decisions about wages, saving and investments. Concurrently, economic-policy decisions are influenced by expectations about developments in the private sector. The Laureates' methods can be applied to identify these causal relationships and explain the role of expectations. This makes it possible to ascertain the effects of unexpected policy measures as well as systematic policy shifts.

Thomas Sargent has shown how structural macroeconometrics can be used to analyze permanent changes in economic policy. This method can be applied to study macroeconomic relationships when households and firms adjust their expectations concurrently with economic developments. Sargent has examined, for instance, the post-World War II era, when many countries initially tended to implement a high-inflation policy, but eventually introduced systematic changes in economic policy and reverted to a lower inflation rate.

Christopher Sims has developed a method based on so-called vector autoregression to analyze how the economy is affected by temporary changes in economic policy and other factors. Sims and other researchers have applied this method to examine, for instance, the effects of an increase in the interest rate set by a central bank. It usually takes one or two years for the inflation rate to decrease, whereas economic growth declines gradually already in the short run and does not revert to its normal development until after a couple of years.

Although Sargent and Sims carried out their research independently, their contributions are complementary in several ways. The laureates' seminal work during the 1970s and 1980s has been adopted by both researchers and policymakers throughout the world. Today, the methods developed by Sargent and Sims are essential tools in macroeconomic analysis.

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Here (also here) is technical note that further describes the new laureates contribution.

A loner Paul Krugman!

New York magazine has an interesting profile of one of my favorite economists, Paul Krugman. The author portrays Krugman as a loner trying to counter unrealistic conservative ideas and policies from the left.

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[…] For Krugman, the path forward was perfectly clear: The only way to avert a deepening crisis was massive Keynesian stimulus. During the nineties in Japan, he had seen the nightmare alternative. Officials in Tokyo, faced with a very similar scenario, had done too little to stimulate the economy, again and again, and as their nation’s recovery stumbled, they found they were toggling an unplugged joystick.

[…] Paul Krugman is a lonely man. That he is comfortable in his solitude, that he emphasizes its virtues, that his intelligence gives it a poetic gloss, none of this diminishes the poignancy of his isolation. Krugman grew up an only child and is deeply self-conscious. He will list his shortcomings as though he’d been preparing for the chance: “Loner. Ordinarily shy. Shy with individuals.” He is married but has no children nor—rare for a Nobelist—many protégés. When I asked him if there were any friends of his I could talk to in order to understand him better, he hesitated, then said, “That’s going to be hard.”

[…] Krugman had begun the work that would eventually win him the Nobel Prize—an aggressive revision of international trade theory—by the time he was in his mid-twenties, and so for nearly all of his adult life he has had good evidence for the proposition that he is smarter than just about everyone else around him, and capable of seeing things more clearly. Krugman is gleeful about being right, joyous in the revelation of his correctness, and many of his most visible early fights were with free-trade skeptics on the left. Of Robert Reich, for instance, Krugman wrote: “talented writer, too bad he never gets anything right.” He was a liberal and a Democrat, but even in 1999, when he was hired by Howell Raines to write his Times column, “I still saw equivalent craziness on both sides.”

[…] But Krugman’s writing voice—sarcastic, data-driven, flecked with just a little bit of maybe-there’s-a-bomb-in-the-wastebasket zeal—was perfect for the Internet. His self-certain empiricism matched liberal vanities as precisely as Rush Limbaugh’s stagy authenticity matches conservative ones, and he became a vehicle for the concentrating energies of the progressive generation of 2006.

[…] I ask Summers what he thinks is Krugman’s underlying complaint with the Obama administration. “Paul may be the smartest and most creative applied economic thinker of this era,” he says, “but there is some element of him that is like the guy in the bleachers who always demands the fake kick, the triple-reverse, the long bomb, or the big trade.”

The two economists have known each other since the late seventies, when they were both graduate students in Cambridge, and there were moments in conversation with Krugman that I began to suspect he viewed Summers as a one-man control group for his study of himself. They each share a high assessment of the other’s intellect (“Larry’s extremely smart—ask him and he’ll tell you,” Krugman says). Krugman’s sense of humor is built upon self-deprecation, and sometimes Summers’s sense of humor is built upon deprecating Krugman, too. In the early eighties, when the two worked together in the Reagan administration, Krugman realized that Summers had a talent for effectiveness—winning meetings, organizing subordinates, convincing economic novices of his point of view—that he himself could not hope to match. Summers became the insider and Krugman the outsider.

[…] I brought up the work of the legal scholar Cass Sunstein, now with the Obama administration, who has studied the radicalizing effects of ideological isolation—the idea, born from studies of three-judge panels, that if you are not in regular conversation with people who differ from you, you can become far more extreme. It is a very Obama idea, and I asked Krugman if he ever worried that he might succumb to that tendency. “It could happen,” he says. “But I work a lot from data; that’s enough of an anchor. I have a good sense when a claim has gone too far.”

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The political J-curve

What happens when countries move from closed to open societies? Ian Bremmer argues that you get a “powerful political phenomenon” called the J-curve.

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The theory goes like this. If you plot the relationship between a country’s stability (on the vertical axis) and its social and political openness (on the horizontal axis) the points that mark every possible combination of openness and stability will produce a pattern that resembles the letter J. Most countries start off closed and stable (think: North Korea). Many end up open and stable (like Britain). But in between there is a turbulent transition. Some governments, such as post-apartheid South Africa, survive this transition. Others – the Soviet Union, Iran under the shah and the former Yugoslavia – do not.

The J-curve is a controversial idea. When I first floated it in 2006, it was used – in some ways hijacked – by those seeking to explain the unstable postwar environment in Iraq. But an intervention bringing democracy by force was always a poor example of the theory. The current upheavals in the Middle East, the result of internal dynamics of populations trapped between economic hardship and increasing political openness, make a much better test.

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There is a J-curve in economics as well. It refers to a situation when after a currency is devalued, the short-term relatively inelastic demand for imports persists and consumers pay more for the same goods and services. Meanwhile, exports become expensive for a short time, leading to worsening balance of trade. After some adjustments, the volume of exports will start to rise because of their lower more competitive prices to foreign buyers, and domestic consumers will buy fewer of the costlier imports. Eventually, the trade balance should improve on what it was before the devaluation. If there is a currency revaluation or appreciation there may be an inverted J-curve.

The most influential economist over the past decade

Basically, Keynes’s followers prevailed. Here is more from The Economist.

An Equilibrium Model of Sex and Matching

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We develop a directed search model of relationship formation which can disentangle male and female preferences for types of partners and for different relationship terms using only a cross-section of observed matches. Individuals direct their search to a particular type of match on the basis of (i) the terms of the relationship, (ii) the type of partner, and (iii) the endogenously determined probability of matching. If men outnumber women, they tend to trade a low probability of a preferred match for a high probability of a less-preferred match; the analogous statement holds for women. Using data from National Longitudinal Study of Adolescent Health we estimate the equilibrium matching model with high school relationships. Variation in gender ratios is used to uncover male and female preferences. Estimates from the structural model match subjective data on whether sex would occur in one's ideal relationship. The equilibrium result shows that some women would ideally not have sex, but do so out of matching concerns; the reverse is true for men.

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The full paper is here. I have not read the full paper. I just posted the abstract, which itself is pretty interesting. There is economics in everything and economists are trying to go everywhere!! The main point of the paper makes me laugh: “The equilibrium result shows that some women would ideally not have sex, but do so out of matching concerns; the reverse is true for men.”

2010 Nobel prize in economics

Peter Diamond, Dale Mortensen and Christopher Pissarides share 2010 Nobel prize in economics for “their analysis of markets with search frictions”.

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Why are so many people unemployed at the same time that there are a large number of job openings? How can economic policy affect unemployment? This year's Laureates have developed a theory which can be used to answer these questions. This theory is also applicable to markets other than the labor market.

On many markets, buyers and sellers do not always make contact with one another immediately. This concerns, for example, employers who are looking for employees and workers who are trying to find jobs. Since the search process requires time and resources, it creates frictions in the market. On such search markets, the demands of some buyers will not be met, while some sellers cannot sell as much as they would wish. Simultaneously, there are both job vacancies and unemployment on the labor market.

This year's three Laureates have formulated a theoretical framework for search markets. Peter Diamond has analyzed the foundations of search markets. Dale Mortensen and Christopher Pissarides have expanded the theory and have applied it to the labor market. The Laureates' models help us understand the ways in which unemployment, job vacancies, and wages are affected by regulation and economic policy. This may refer to benefit levels in unemployment insurance or rules in regard to hiring and firing. One conclusion is that more generous unemployment benefits give rise to higher unemployment and longer search times.

Search theory has been applied to many other areas in addition to the labor market. This includes, in particular, the housing market. The number of homes for sale varies over time, as does the time it takes for a house to find a buyer and the parties to agree on the price. Search theory has also been used to study questions related to monetary theory, public economics, financial economics, regional economics, and family economics.

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The value of lobbyists

Once the politician for whom they [lobbyists] worked leaves office, their revenue falls 20%, or $177,000 per year, suggesting that lobbyists are paid more for “who they know” than “what they know”.

Very interesting finding. More here.

Understanding basic statistics for policymaking

Compilation of several brief tutorials in a single file. Original source here. Refreshing and very useful! View it on full screen for clarity.

Reading Statistics 101

Neoclassical models (plus economists) failed!

A good narrative about how neoclassicals and rational expectationists assumed general equilibrium in everything (and created an economic mess, emanating from the Wall Street):

[...] New thinkers say they are still having trouble breaking in. Among the new NSF grant awardees is J. Doyne Farmer, a physicist at the Santa Fe Institute who is trying to bring the idea of complexity back into economics by making use of advanced computing power to map human economic behavior the way weather or climate change is tracked. But Farmer says he got his $450,000 grant for a three-year study of systemic risks in markets only after a sympathetic NSF case officer overruled negative assessments by “neoclassical economists” who reject any model that doesn’t tend toward general equilibrium. “The established view just holds this stuff back,” Farmer says. “One of the dangerous cultural patterns that economics has fallen into is an excessive emphasis on theorem proof for its own sake rather than what gives you scientific results. That’s led to a disdain for computer simulation.” Johnson, who is director of the new Institute for New Economic Thinking funded by George Soros, says: “You do see some new thinking, but it doesn’t get traction in terms of policy. It’s a symptom of how far right society has gone.”

The great names in the profession have not necessarily helped. The top economists in the Obama administration—Summers; Christina Romer, the just-departed chair of the Council of Economic Advisers; and her replacement, Austan Goolsbee—are all part of the orthodoxy. Critics say Summers should know as well as anyone how the old thinking has been outstripped. As a Harvard professor, Summers wrote after the 1987 stock-market crash that it was impossible to believe any longer that prices moved in rational response to fundamentals. He even cautiously advocated a tax on financial transactions. Yet Summers, one of the world’s most astute economists, later abandoned these positions in favor of Greenspan’s view that markets will take care of themselves. And in the current era, Summers and the rest of the Obama team seem to have underestimated the depth and systemic nature of the economic crisis. Stimulus spending was timid (in deference to political antipathy to big government), mortgage workouts meager, and financial reform minimalist. The administration maintains it did as much as it could under the political constraints, but others disagree. “The financial-reform bill and other changes in the regulatory landscape are more incremental,” says MIT’s Lo. “It’s a reaction to the most immediate set of events as opposed to a more profound rethinking about the underlying causes of the crisis.”

A little history is in order here: it was largely because the field of economics came to be dominated by “neoclassical” thought—or the idea that markets are rational and can reach “equilibrium” on their own—that so-called financial innovation on Wall Street was allowed to run amok in recent decades. That led directly to the crisis of 2007–09. No matter how crazy or complex the products got, the theory was that, with little government oversight, the inherent stability of markets would keep things from getting too out of hand. It was in large part because of this way of thinking that government intervention of any kind in the markets, including regulation, came to be seen as a kind of heresy, especially after the Soviet Union collapsed and command economies and “statism” were thoroughly discredited.

The new financial-reform law has changed that to some degree, but it still leaves most of the major decisions about government oversight to the same regulators who failed last time. We are still, to a large extent, flying blind in conceptual terms. Just as the Great Depression demonstrated to John Maynard Keynes and his followers that markets often behaved badly—leading to the Keynesian reinvention of economics in the ’30s—this present crisis drove home the truth, or should have anyway, that rational models of markets don’t work well because there are too many unknowns. People most often don’t behave as rational actors. There is no real equilibrium in the real world. Above all, market economies are capable of destroying themselves. This is especially true in the world of finance, which has always worked according to different rules than other sectors of the economy and is much more prone to panics and manias. In 1983, a young Stanford economist named Ben Bernanke published the first of a series of papers on the causes of the Great Depression. The financial system, Bernanke said, was not unlike the nation’s electrical grid. One malfunctioning transformer can bring down the whole system. “I’ve never had a laissez-faire view of the financial markets,” Bernanke told me, “because they’re prone to failure.” Even Friedrich Hayek, the godfather of 20th-century laissez-faire thinking, believed that financial markets were more subject “to bouts of instability,” says one of his biographers, Bruce Caldwell of Duke University, a self-described libertarian scholar.

Yet amid the free-market triumphalism of the post–Cold War era, all this hard-won wisdom about the differences in finance was forgotten or ignored. To policymakers in Washington, it seemed silly and nitpicky to treat finance as a different animal. The dominant thinkers were the “rational expectations” economists of the Chicago school who simply assumed capital flows, no matter how open, would be stable.

Monkeynomics

This presentation is pretty interesting. Humans are not always rational, so are monkeys. Interestingly, their "irrational" behavior may be somewhat as predictable as the "rational" ones are.  Mark Buchanan argues that there is a pattern to everything. In this video, Laurie Santos looks for the roots of human irrationality by watching the way our primate relatives make decisions. A clever series of experiments in "monkeynomics" shows that some of the silly choices we make, monkeys make too.