Gordon on growth 2

PBS covers Bob Gordon's The Rise and Fall of American Growth.



[Embedded video. These aren't picked up when other sources pick up the blog, so come back to the original if you don't see the video.]

PBS and Paul Solman did a great job, especially relative to the usual standards of economics coverage in the media.  OK, not perfect -- they livened it up by tying it to partisan politics a bit more than they should have, though far less than usual.

I don't (yet, maybe) agree with Bob. I still hope that the mastery of information and biology can produce results like the mastery of electromagnetism and fossil fuels did earlier. I still suspect that slow growth is resulting from government-induced sclerosis rather than an absence of good ideas in a smoothly functioning economy.  But Bob has us talking about The Crucial Issue: long term growth, and its source in productivity. The 1870-1970 miracle was not about whether the federal funds rate was 0.25% higher or lower. And the issue is not about opinions, like the ones I just offered, but facts and research, which Bob offers.

The issue of future long-term growth is tied with the issue of measurement, something else that Bob has championed over the years. GDP is well designed to measure steel per worker. Information, health and lifespan increases are much more poorly measured. This is already a problem in long-term comparisons. In the video, Bob points to light as the greatest invention. The price of light has fallen by a factor of thousands since the age of candles, to the point where light consumption is a trivial part of GDP. It's a worse problem as all the great stuff becomes free. I suspect that we'll have to try to measure consumer surplus not just the market value of goods and services.

And congratulations to Bob. The economics profession tends to focus on the young rising stars, but he offers inspiration that economists can produce magnum opuses of deep impact at any point in a career.

Disclosure: I haven't read the book yet, but it is on top of the pile. More when I finish. Ed Glaeser has an excellent review.

Update: Tyler Cowen's review, in Foreign Affairs

Tax Oped -- full version

Source: Wall Street Journal

An Oped at the Wall Street Journal, "Here's what genuine tax reform looks like." I posted the teaser a month ago, now I can post the whole thing.

Left and right agree that the U.S. tax code is a mess. The men and women running for president in 2016 are offering reform plans, and proposals to fix the code regularly surface in Congress. But these plans are, and should be, political documents, designed to attract votes. To prevent today’s ugly bargains from becoming tomorrow’s conventional wisdom, we should more frequently discuss the ideal tax structure.

The first goal of taxation is to raise needed government revenue with minimum economic damage. That means lower marginal rates—the additional tax people pay for each extra dollar earned—and a broader base of income subject to tax. It also means a massively simpler tax code.


In my view, simplification is more important than rates. A simple code would allow people and businesses to spend more time and resources on productive activities and less on attorneys and accountants, or on lobbyists seeking special deals and subsidies. And a simple code is much more clearly fair. Americans now suspect that people with clever lawyers are avoiding much taxation, which is corrosive to compliance and driving populist outrage across the political spectrum.

What would a minimally damaging, simple, fair tax code look like? First, the corporate tax should be eliminated. Every dollar of taxes that a corporation seems to pay comes from higher prices to its customers, lower wages to its workers, or lower dividends to its shareholders. Of these groups, wealthy individual shareholders are the least likely to suffer. If taxes eat into profits, investors pay lower prices for less valuable shares, and so earn the same return as before. To the extent that taxes do reduce returns, they also financially hurt nonprofits and your and my pension funds.

With no corporate tax, arguments disappear over investment expensing versus depreciation, repatriation of profits, too much tax-deductible debt, R&D deductions, and the vast array of energy deductions and credits.

Second, the government should tax consumption, not wages, income or wealth. When the government taxes savings, investment income, wealth or inheritance, it reduces the incentive to save, invest and build companies rather than enjoy consumption immediately. Taxes on capital gains discourage people from moving or reallocating capital toward their most productive uses.

Recognizing the distortion, the federal government provides a complex web of shelters, including IRAs, Roth IRAs, 527(b), 401(k), health-savings accounts, life-insurance exemptions, and the panoply of trusts that wealthy individuals use to shelter their wealth and escape the estate tax. If investment isn’t taxed, these costly complexities can disappear.

All the various deductions, credits and exclusions should be eliminated—even the holy trinity of tax breaks for mortgage interest, charitable donations and employer-provided health insurance. The extra revenue, over a trillion dollars annually, could finance a large reduction in marginal rates. This step would also simplify the code and make it fairer.

Imagine that Congress proposed to send an annual check to each homeowner. People with high incomes, who buy expensive houses, borrow lots of money or refinance often, would get bigger checks than people with low incomes, who buy smaller houses, save up more for down payments or pay down their mortgages. There would be rioting in the streets. Yet that is exactly what the mortgage-interest deduction accomplishes.

Similarly, suppose Congress proposed to match private charitable donations. But rich people would get a 40% match, middle class people only 10%, and poor people nothing. This is exactly what the charitable deduction accomplishes.

Zeroing out deductions, credits, and corporate and investment taxes matters—for permanence, for predictability and for simplicity. If the corporate rate is drastically reduced, or if deductions are capped, it seems that the economic distortions go away. But the thousands of pages of tax code are still in place, the army of lawyers and accountants and lobbyists is still in place, and the next administration will itch to raise the caps, and the rate.

Why is tax reform paralyzed? Because political debate mixes the goal of efficiently raising revenue with so many other objectives. Some want more progressivity or more revenue. Others defend subsidies and transfers for specific activities, groups or businesses. They hold reform hostage.

Wise politicians often bundle dissimilar goals to attract a majority. But when bundling leads to paralysis, progress comes by separating the issues. Thus, we should agree to first reform the structure of the tax code, leaving the rates blank. We will then separately debate rates, and the consequent overall revenue and progressivity.

Consumption-based taxes can be progressive. A simplified income tax, excluding investment income and allowing a full deduction for savings, could tax high-income earners’ consumption at a higher rate. Low-income people can receive transfers and credits. I think smaller government and less progressivity are wiser. But we can agree on an efficient, simple and fair tax, and debate revenues and progressivity separately.

We should also agree to separate the tax code from the subsidy code. We agree to debate subsidies for mortgage-interest payments, electric cars and the like—transparent and on-budget—but separately from tax reform.

Negotiating such an agreement will be hard. But the ability to achieve grand bargains is the most important characteristic of great political leaders.

Mr. Cochrane is a senior fellow at Stanford University’s Hoover Institution.

MacDonell on QE

Gerard MacDonell has a lovely noahpinion guest post "So Much for the QE Stimulus" (HT Marginal Revolution). Some good bits here, with my bold on noteworthy zingers.

The post is unusual, because practitioners tend to regard the Fed and QE as very powerful. But here he expresses nicely the skeptical view of many academics such as myself.

the Fed leadership has now abandoned its original story about how QE affects the economy and has conceded that the tool is weak

It has long been obvious that QE operated mainly through signaling and confidence channels, which wore off on their own without any adjustment in the size or composition of the Fed’s balance sheet....

Obvious to us skeptics, not to the Fed or to the many academic papers written trying to explain the supposed powers of QE

The story initially told by the Fed leadership starts with the claim that large scale asset purchases (LSAPs) [lower interest rates]... by removing default-free interest rate duration from the capital markets. ...

Translation: buying bonds to drive up bond prices

That story does not hold much water.

 

The theoretical foundations supporting QE were invented – or really revived from the 1950s [Preferred habitat theory]– in an effort to justify a program that had been resolved upon for other reasons. 

LSAPs did not actually succeed in reducing the stock of government rates duration because they were fully offset by the fiscal deficit and the Treasury’s program of extending the maturity of the federal debt. 

Translation: The Treasury sold as much as the Fed bought.

 And while the estimated term premium and bond yields did go down during the QE era of late 2008 through late 2014, they had a disconcerting tendency to rise while LSAPs were ongoing.

 Translation: When the Fed actually bought securities, yields went up.

Peak QE gullibility seems to have been reached in the late summer of 2012, with Ben Bernanke’s presentation to the Kansas City Fed’s monetary policy conference at Jackson Hole. ...

Evidence that the Fed doesn't believe it any more

...the Fed has abandoned the flock it once led. If the leadership still believed the official story, it could not promise both to maintain the size of the balance sheet and raise rates at an historically slow pace. That would deliver far too much stimulus, particularly with the economy now near full employment. The obvious way to square this circle to recognize that the Fed does not believe the story, which is an advance.

... according to the original story, little of this presumed stimulus would unwind without asset sales or a passive shortening of maturities, both of which have largely been excluded for now.

...Readers of this comment may recall those charts circulated by Wall Street showing the fed funds equivalent going deeply and shockingly negative after 2009. In retrospect, those charts are cringe-inducing and best forgotten. It is a mercy that the Fed has participated in the forgetting. 

This is consistent with my view. The large balance sheet is a great thing. Narrow banking has arrived. We live the optimal quantity of money. Interest-paying reserves generate zero stimulus, but great liquidity. Alas, the Fed, having touted the world-saving stimulus of QE, without qualifying that effects might be temporary, now is in a tough spot to turn around and say "never mind." All it can do is be silent and wait.

...This raises the question of why the Fed initially promoted a story that so obviously would not stand the test of time. We can imagine three possibilities...

The first possibility relates to the first round of event studies, which measured the immediate effects on the term premium and bond yields of QE-related news....

Announcement effects are a poor measure of fundamental effects that will endure long enough to affect the economy... markets typically act more segmented in the short run than over time,.... But smart and credentialed people argued otherwise and the FOMC may have been comforted by that.

I have puzzled at this as well. Many studies find price impacts of large unannounced trades. But price impact melts away. Why would we treat announcement effects as permanent -- as many Fed speeches did?

The second possibility is that the Fed wanted to raise confidence in the markets and real economy and thus chose to communicate that it was wielding a new and fundamentally powerful tool, even if Fed officials had their own doubts. ...

This is the "signaling" channel.

It is best to lift confidence with tools that have a mechanical force and do not rely purely on confidence effects. But if such tools are not readily available, then it probably does not hurt to try magic tricks and pyrotechnics.

Nice phrases. But..

The problem looking forward is that people may not be so responsive to the symbolism of QE next time around. ... Moreover, the Bank of Japan has got hold of QE, which raises the odds it will be properly discredited, if history guides.

OK, not very nice, but a good snark prize, as much to the B of J as to its many critics. But far more interesting..

The third possibility ..[is] that Bernanke and his colleagues in Fed circles were durably confused by Bernanke’s early and mistaken relation of the Quantity Theory to the efficacy of LSAPs...:

"The general argument that the monetary authorities can increase aggregate demand and prices, even if the nominal interest rate is zero, is as follows:..The monetary authorities can issue as much money as they like. Hence, if the price level were truly independent of money issuance, then the monetary authorities could use the money they create to acquire indefinite quantities of goods and assets. This is manifestly impossible in equilibrium. Therefore, money issuance must ultimately raise the price level, even if nominal interest rates are bounded at zero. .."

This is indeed the crucial point. In simple quantity theory thought, MV=PY, so you can raise M even at zero rates, and eventually PY must rise. But that's wrong, alas. V becomes undefined when the interest rate is zero, or money pays interest.  As Gerard explains,

... one must wonder if this misapplication of the Quantity Theory to LSAPs created in Bernanke and associates an excessive confidence in the efficacy of the program...

...Bernanke would later argue this point himself, and demonstrate it by paying interest on excess reserves, thereby by converting them from money to debt. Bernanke’s money injection actually had ZERO maturity. Or more to the point, it did not even happen.

Stop and savor just a moment. When the government pays interest on reserves, reserves become the same thing as overnight government debt. They are held as a saving vehicle, and have no "stimulus."

To be fair, I think Bernanke's point might hold if there were a huge QE, a clear promise to leave reserves outstanding when interest rates rise above zero, and then possibly future inflation might work its way back to current inflation. But exit principles that clearly state the large reserves will pay interest so as not to give future inflation undo the possibility.

Gerard leaves out, I think, the most telling mistake in the Bernanke quote,  "monetary authorities could use the money they create to acquire indefinite quantities of goods..." Monetary policy does not buy goods; it does not drop money from helicopters. Monetary policy only gives one kind of debt in return for another kind; roughly speaking making change, giving you two 5s and a 10 for each 20. Buying goods is fiscal policy, and fiscal policy can cause inflation.

Bottom line

...The Fed leadership has come a long way from believing that QE had something to do with the power of the printing press to a recognition that the program is a combination of an indirect and transitory rates signal, a confidence game, and a duration take out that probably achieved much less than was advertised. But at least the journey has been made....

I share this view.

To be clear, both my post and Gerard's are not really critical of the Fed. If "pyrotechnics'' helped, good. If QE is not "mechanically" that powerful, great, we all learn from experience. A large interest-paying balance sheet and silence is probably the best thing for the Fed to do right now.  This question is most important to academic and historical analysis, to learn  what causal mechanisms really did play out, and what will work in the future.

Secret Data Encore

My post "Secret data" on replication provoked a lot of comment and emails,  more reflection, and some additional links.

This isn't about rules

Many of my correspondents missed my main point -- I am not advocating more and tighter rules by journals! This is not about what you are "allowed to do," how to "get published" and so forth.

In fact, this extra rumination points me even more strongly to the view that rules and censorship by themselves will not work. How to make research transparent, replicable, extendable, and so forth varies by the kind of work, the kind of data, and is subject like everything else to creativity and technical improvement.  Most of all, it will not work if nobody cares; if nobody takes the kind of actions in bullet points of my last post, and it's just an issue about rules at journals. Already, (more below) rules are not that well followed.

This isn't just about "replication." 

"Replication" is much too narrow a word. Yes, many papers have not documented transparently what they actually did, so that even armed with the data it's hard to produce the same numbers. Other papers are based on secret data, the problem with which I started.

But in the end, most important results are not simply due to outright errors in data or coding. (I hope!)

The important issue is whether small changes in instruments, controls, data sample, measurement error handling, and so forth produce different results, whether results hold out of sample, or whether collecting or recoding data produces the same conclusions. "Robustness" is a better overall descriptor for the problem that many of us suspect pervades empirical economic research.

You need replicability in order to evaluate robustness -- if you get a different result than the original authors', it's essential to be able to track down how the original authors got their result. But the real issue is that much larger one.

The excellent replication wiki (many good links) quotes Daniel Hamermesh on this difference between "narrow" and "wide" replication

Narrow, or pure, replication means first checking the submitted data against the primary sources (when applicable) for consistency and accuracy. Second the tables and charts are replicated using the procedures described in the empirical article. The aim is to confirm the accuracy of published results given the data and analytical procedures that the authors write to have used. 

Replication in a wide sense is to consider the empirical finding of the original paper by using either new data from other time periods or regions, or by using new methods, e.g., other specifications. Studies with major extensions, new data or new empirical methods are often called reproductions.

But the more important robustness question is more controversial. The original authors can complain they don't like the replicator's choice of instruments, or procedures. So "replication," which sounds straightforward, quickly turns in to controversies.

Michael Clemens writes about the issue in a blog post here, noting

...Again and again, the original authors have protested that the critique of their work got different results by construction, not because anything was objectively incorrect about the original work. (See Berkeley’s Ted Miguel et al. here; Oxford’s Stefan Dercon et al. here and Princeton’s Angus Deaton here among many others. Chris Blattman at Columbia and Berk Özlerat the World Bank have weighed in on some of these controversies.)

In a good paper, published as The meaning of failed replications in the Journal of Economic Surveys he argues for an expanded vocabulary, including "verification,"  "robustness," "reanalysis" and "extension."

"Failed replication" is a damning criticism. It implies error, malfeasance, deliberately hiding data, and so forth.  What most "replication" studies really mean is "robustness," either to method or natural fishing biases, which is a more common problem (in my view). But as Michael points out, you really can't use the emotionally charged language of failed or "discrepant" replication for that situation.

This isn't about people or past work

I did not anticipate, but should have, that the secret data post would be read as criticism of people who do large-data work, proprietary-data work, or work with government agencies that cannot currently be shared.  The internet is pretty snarky, so it's worth stating explicitly that is not my intent or my view.

Quite the opposite. I am a huge fan of the pioneering work exploiting new data sets. If these pioneers had not found dramatic results and possibilities with new data, it would not matter whether we can replicate, check or extend those results.

It is only now, that the pioneers have shown the way, that we know how important the work can be, that it becomes vital to rethink how we do this kind of work going forward.

The special problems of confidential government data

The government has a lot of great data -- IRS, and census for microeconomics, SEC, CFTC, Fed, financial product safety commission in finance. And there are obvious reasons why so far it has not been easily shared.

Journal policies allow exceptions for such data. So only a fundamental demand from the rest of us for transparency can bring about changes. And has begun to do so.

In addition to the suggestions in the last post, more and more people are going through the vetting to use the data. That leaves open the possibility that a full replication machine could be stored on site, ready for a replicator with proper access to push a button. Commercial data vendors could allow similar "free" replication, controlling directly how replicators use the data.

Technological solutions are on the way too.  "Differential privacy" is an example of a technology that allows results to be replicated without compromising the privacy of the data. Leapyear.io is an example of companies selling this kind of technology. We are not alone, as there is a strong commercial demand for this kind of data. (Medical data for example.)

Other institutions: Journals, replication journals, websites,

There is some debate whether checking "replication" should count as new research, and I argued if we want replication we need to value it. The larger robustness question certainly is "new" research. Xs result does not hold out of sample, is sensitive to the precise choice of instruments and controls, and so forth, is genuine, publishable, follow-on research.

I originally opined that replications should be published by the original journal to give the best incentives. That means an AER replication "counts" as an AER publication.

But with the idea that robustness is the wider issue, I am less inclined to this view. This broader robustness or reexamination is genuine new research, and there is a continuum between replication and the normal business of examining the basic idea of a model with new data and also some new methods. Each paper on the permanent income hypothesis is not a "replication" of Friedman! We don't want to only value as "new" research that which uses novel methods -- then we become dry methodologists, not fact-oriented economists. And once a paper goes beyond pointing out simple mistakes, to questioning specification, a question which itself can be rebutted, it's beyond the responsibility of the original journal.

Ivo Welch argues that a third of each journal should be devoted to replication and critique.  The Critical Finance Review, which he edits asks for replication papers.  The Journal of Applied Econometrics has a replication section, and now invites replications of papers in many other journals. Where journals fear to tread, other institutions step in. The replication network is one interesting new resource.

Faculties

A correspondent suggests an important additional bullet point for the "what can we do" list

• Encourage your faculty to adopt a replicability policy as part of its standards of conduct, and as part of its standards for internal and outside promotions. 

The precise wording of such standards should be fairly loose. The important thing is to send a message. Faculty are expected to make their research transparent and replicable, to provide data and programs, even when journals do not require it.  Faculty up for promotion should expect that the committee reviewing them will look to see if they are behaving reasonably. Failure will likely lead to a little chat from your department chair or dean. And the policy should state that replication and robustness work is valued.

Another correspondent wrote that he/she advises junior faculty not to post programs and data, so that they do not become a "target" for replicators. To say we disagree on this is an understatement. A clear voice on this issue is an excellent outcome of crafting a written policy.

From Michael Kiley's excellent comment below

• Assign replication exercises to your students. Assign robustness checks to your more advanced students. Advanced undergraduate and PhD students are a natural reservoir of replicators. Seeing the nuts and bolts of how good, transparent, replicable work is done will benefit them. Seeing that not everything published is replicable or right might benefit them even more.   

Two good surveys of replications (as well as journals) 

Maren Duvendack, Richard  Palmer-Jones, and Bob Reed have an excellent survey article, "Replications in Economics: A Progress Report"

...a survey of replication policies at all 333 economics journals listed in Web of Science. Further, we analyse a collection of 162 replication studies published in peer-reviewed economics journals. 

The latter is especially good, starting at p. 175. You can see here that "replication" goes beyond just can-we-get-the-author's-numbers, and maddeningly often does not even ask that question

 a little less than two-thirds of all published replication studies attempt to exactly reproduce the original findings....A frequent reason for not attempting to exactly reproduce an original study’s findings is that a replicator attempts to confirm an original study’s findings by using a different data set

"Robustness" not "replication "

Original Results?, tells whether the replication study re-reports the original results in a way that facilitates comparison with the original study. A large portion of replication studies do not offer easy comparisons, perhaps because of limited journal space. Sometimes the lack of direct comparison is more than a minor inconvenience, as when a replication study refers to results from an original study without identifying the table or regression number from which the results come.

Replicators need to be replicable and transparent too!

Across all categories of journals and studies, 127 of 162 (78%) replication studies disconfirm a major finding from the original study. 

But rather than just the usual alarmist headline, they have a good insight. Replication studies can suffer the same significance bias as original work:

Interpretation of this number is difficult. One cannot assume that the studies treated to replication are a random sample. Also, researchers who confirm the results of original studies may face difficulty in getting their results published since they have nothing ‘new’ to report. On the other hand, journal editors are loath to offend influential researchers or editors at other journals. The Journal of Economic & Social Measurement and Econ Journal Watch have sometimes allowed replicating authors to report on their (prior) difficulties in getting disconfirming results published. Such firsthand accounts detail the reticence of some journal editors to publish disconfirming replication studies (see, e.g., Davis 2007; Jong-A-Pin and de Haan 2008, 57).

Summarizing

.. nearly 80 percent of replication studies have found major flaws in the original research

Sven Vlaeminck and Lisa-Kristin Hermmann surveyed journals and report that many journals with data policies are not enforcing them. 

The results we obtained suggest that data availability and replicable research are not among the top priorities of many of the journals surveyed. For instance, we found 10 journals (i.e. 20.4% of all journals with such policies) where not a single article was equipped with the underlying research data. But even beyond these journals, many editorial offices do not really enforce data availability: There was only a single journal (American Economic Journal: Applied Economics) which has data and code available for every article in the four issues. 

Again, this observation reinforces my point that rules will not substitute for people caring about it. (They also discuss technological aspects of replication, and the impermanence and obscurity of zip files posted on journal websites.) 

Numerical Analysis

Ken Judd wrote to me,

"Your advocacy of authors giving away their code is not the rule in numerical analysis. I point to the “market test”: the numerical analysis community has done an excellent job in advancing computational methods despite the lack of any requirement to share the code....

Would you require Tom Doan to give out the code for RATS? If not, then why do you advocate journals forcing me to freely distribute my code?...

The issue is not replication, which just means that my code gives the same answer on your computer as it does on mine. The issue is verification, which is the use of tests to verify the accuracy of the answers. That I am willing to provide."

Ken is I think reading more "rule and censorship" rather than "social norms" in my views. And I think it reinforces my preference for the latter over the former.  Among other things, rules designed for one purpose (extensive statistical analysis of large data sets) are poorly adapted to other situations (extensive numerical analysis.)

Rules can be taken to extremes.  Nobody is talking about "requiring" package customers to distribute the (proprietary) package source code. We all understand that step is not needed.

For heavy numerical analysis papers, using author-designed software that the author wants to market, the verification suggestion seems a sensible social norm to me.  If I'm refereeing a paper with a heavy numerical component, I would be happy to see the extensive verification, and happier still if I could use the program on a few test cases of my own. Seeing the source code would not be necessary or even that useful. Perhaps in extremis, if a verification failed, I would want the right to contact the author and understand why his/her code produces a different result.

Some other examples of "replication" (really robustness) controversies:

Andrew Gelman covers a replication controversy, in which Douglas Campbell and Ju Hyun Pun dissect Enrico Spolaore and Romain Wacziarg's "the Diffusion of Development" in the QJE. There is no charge that the computer programs were wrong, or that one cannot produce the published numbers. The controversy is entirely over specification, that the result is sensitive to specification and controls.

Yakov Amihud and Stoyan Stoyanov Do Staggered Boards Harm Shareholders? reexamine Alma Cohen and Charles Wang's Journal of Financial Economics paper. They come to the opposite conclusion, but could only reexamine the issue because Cohen and Wang shared their data. Again, the issues, as far as I can tell, are not a charge that programs or data are wrong.

Update: Yakov corrects me:

1. We do not come to "the opposite conclusion". We just cannot reject the null that staggered board is harmless to firm value, using Cohen-Wang's experiment. 
2. Our result is also obtained using the publicly-available ISS database (formerly RiskMetrics). 
3. Why is the difference between the results? We used CRSP data and did not include a few delisted (penny) stocks that are in Cohen-Wang's sample. Our paper states which stocks were omitted and why. We are re-writing the paper now with more detailed analysis.

I think the point that replication slides in to robustness which is more important and more contentious remains clear.

Asset pricing is especially vulnerable to results that do not hold out of sample, in particular the ability to forecast returns. Campbell Harvey has a number of good papers on this topic.  Here, the issue is again not that the numbers are wrong, but that many good in-sample return-forecasting tricks stop working out of sample. To know, you have to have the data.

Secret Data

On replication in economics. Just in time for bar-room discussions at the annual meetings.

"I have a truly marvelous demonstration of this proposition which this margin is too narrow to contain." -Fermat

"I have a truly marvelous regression result, but I can't show you the data and won't even show you the computer program that produced the result" - Typical paper in economics and finance.

The problem 

Science demands transparency. Yet much research in economics and finance uses secret data. The journals publish results and conclusions, but the data and sometimes even the programs are not available for review or inspection.  Replication, even just checking what the author(s) did given their data, is getting harder.

Quite often, when one digs in, empirical results are nowhere near as strong as the papers make them out to be.

• Simple coding errors are not unknown. Reinhart and Rogoff are a famous example -- which only came to light because they were honest and ethical and posted their data. 
• There are data errors. 
• Many results are driven by one or two observations, which at least tempers the interpretation of the results. Often a simple plot of the data, not provided in the paper, reveals that fact. 
• Standard error computation is a dark art, producing 2.11 t statistics and the requisite two or three stars suspiciously often. 
• Small changes in sample period or specification destroy many "facts."  
• Many regressions involve a large set of extra right hand variables, with no strong reason for inclusion or exclusion, and the fact is often quite sensitive to those choices. Just which instruments you use and how to transform variables changes results. 
• Many large-data papers difference, difference differences, add dozens of controls and fixed effects, and so forth, throwing out most of the variation in the data in the admirable quest for cause-and-effect interpretability. Alas, that procedure can load the results up on measurement errors, or slightly different and equally plausible variations can produce very different results. 
• There is often a lot of ambiguity in how to define variables,  which proxies to use, which data series to use, and so forth, and equally plausible variations change the results.

I have seen many examples of these problems, in papers published in top journals. Many facts that you think are facts are not facts. Yet as more and more papers use secret data, it's getting harder and harder to know.

The solution is pretty obvious: to be considered peer-reviewed "scientific" research, authors should post their programs and data. If the world cannot see your lab methods, you have an anecdote, an undocumented claim, you don't have research. An empirical paper without data and programs is like a theoretical paper without proofs.

Rules

Faced with this problem, most economists jump to rules and censorship. They want journals to impose replicability rules, and refuse to publish papers that don't meet those rules. The American Economic Review has followed this suggestion, and other journals such as the Journal of Political Economy, are following.

On reflection, that instinct is a bit of a paradox. Economists, when studying everyone else, by and large value free markets, demand as well as supply, emergent order, the marketplace of ideas, competition, entry, and so on, not tight rules and censorship. Yet in running our own affairs, the inner dirigiste quickly wins out. In my time at faculty meetings, were few problems that many colleagues did not want to address by writing more rules.

And with another moment's reflection (much more below), you can see that the rule-and-censorship approach simply won't work.  There isn't a set of rules we can write that assures replicability and transparency, without the rest of us having to do any work. And rule-based censorship invites its own type I errors.

Replicability is a squishy concept -- just like every other aspect of evaluating scholarly work. Why do we think we need referees, editors, recommendation letters, subcommittees, and so forth to evaluate method, novelty, statistical procedure, and importance, but replicability and transparency can be relegated to a set of mechanical rules?

Demand

So, rather than try to restrict supply and impose censorship, let's work on demand.  If you think that replicability matters, what can you do about it? A lot:

• When a journal with a data policy asks you to referee a paper, check the data and program file. Part of your job is to see that this works correctly. 
• When you are asked to referee a paper, and data and programs are not provided, see if data and programs are on authors' websites. If not, ask for the data and programs. If refused, refuse to referee the paper. You cannot properly peer-review empirical work without seeing the data and methods. 
• I don't think it's necessary for referees to actually do the replication for most papers, any more than we have to verify arithmetic. Nor, in my view, do we have to dot is and cross t's on the journal's policy, any more than we pay attention to their current list of referee instructions. Our job is to evaluate whether we think the authors have done an adequate and reasonable job,  as standards are evolving, of making the data and programs available and documented. Run a regression or two to let them know you're looking, and to verify that their posted data actually works. Unless of course you smell a rat, in which case, dig in and find the rat. 
• Do not cite unreplicable articles. If editors and referees ask you to cite such papers, write back "these papers are based on secret data, so should not be cited." If editors insist, cite the paper as "On request of the editor, I note that Smith and Jones (2016) claim x. However, since they do not make programs / data available, that claim is not replicable."  
• When asked to write a promotion or tenure letter, check the author's website or journal websites of the important papers for programs and data. Point out secret data, and say such papers cannot be considered peer-reviewed for the purposes of promotion. (Do this the day you get the request for the letter. You might prompt some fast disclosures!)  
• If asked to discuss a paper at a conference, look for programs and data on authors' websites. If not available, ask for the data and programs. If they are not provided, refuse. If they are, make at least one slide in which you replicate a result, and offer one opinion about its robustness. By example, let's make replication routinely accepted. 
• A general point: Authors often do not want to post data and programs for unpublished papers, which can be reasonable. However, such programs and data can be made available to referees, discussants, letter writers, and so forth, in confidence. 
• If organizing a conference, do not include papers that do not post data and programs. If you feel that's too harsh, at least require that authors post data and programs for published papers and make programs and data available to discussants at your conference. 
• When discussing candidates for your institution to hire, insist that such candidates disclose their data and programs. Don't hire secret data artists. Or at least make a fuss about it. 
• If asked to serve on a committee that awards best paper prizes, association presidencies, directorships, fellowships or other positions and honors, or when asked to vote on those, check the authors' websites or journal websites. No data, no vote. The same goes for annual AEA and AFA elections. Do the candidates disclose their data and programs? 
• Obviously, lead by example. Put your data and programs on your website. 
• Value replication. One reason we have so little replication is that there is so little reward for doing it. So, if you think replication is important, value it. If you edit a journal, publish replication studies, positive and negative. (Especially if your journal has a replication policy!) When you evaluate candidates, write tenure letters, and so forth, value replication studies, positive and negative. If you run conferences, include a replication session. 

In all this, you're not just looking for some mess on some website, put together to satisfy the letter of a journal's policy. You're evaluating whether the job the authors have done of documenting their procedures and data rises to the standards of what you'd call replicable science, within reason, just like every other part of your evaluation.

Though this issue has bothered me a long time, I have not started doing all the above. I will start now.

Here, some economists I have talked to jump to suggesting a call to coordinated action. That is not my view

I think this sort of thing can and should emerge gradually, as a social norm. If a few of us start doing this sort of thing, others might notice. They think "that's a good idea," and start doing it too. They also may feel empowered to start doing it. The first person to do it will seem like a bit of a jerk. But after you read three or four tenure letters that say "this seems like fine research, but without programs and data we won't really know," you'll feel better about writing that yourself. Like "would you mind putting out that cigarette."

Also, the issues are hard, and I'm not sure exactly what is the right policy.  Good social norms will evolve over time to reflect the costs and benefits of transparency in all the different kinds of work we do.

If we all start doing this, journals won't need to enforce  long rules. Data disclosure will become as natural and self-enforced part of writing a paper as is proving your theorems.

Conversely, if nobody feels like doing the above, then maybe replication isn't such a problem at all, and journals are mistaken in adding policies.

Rules won't work without demand

Journals are treading lightly, and rightly so.

Journals are competitive too. If the JPE refuses a paper because the author won't disclose data, and the QJE publishes it, the paper goes on to great acclaim, wins its author the Clark medal and the Nobel Prize, then the JPE falls in stature and the QJE rises. New journals will spring up with more lax policies. Journals themselves are a curious relic of the print age. If readers value empirical work based on secret data, academics will just post their papers on websites, working paper series, ssrn, repec, blogs, and so forth.

So if there is no demand, why restrict supply? If people are not taking the above steps on their own -- and by and large they are not -- why should journals try to shove it down authors' throats?

Replication is not an issue about which we really can write rules. It is an issue -- like all the others involving evaluation of scientific work -- for which norms have to evolve over time and users must apply some judgement.

Perfect, permanent replicability is impossible. If replication is done with programs that access someone else's database, those databases change and access routines change. Within a year, if the programs run at all, they give different numbers. New versions of software give different results. The best you can do is to  freeze the data you actually use, hosted on a virtual machine that uses the same operating system, software version, and so on. Even that does not last forever. And no journal asks for it.

Replication is a small part of a larger problem, data collection itself.  Much data these days is collected by hand, or scraped by computer. We cannot and should not ask for a webcam or keystroke log of how data was collected, or hand-categorized. Documenting this step so it can be redone is vital, but it will always be a fuzzy process.

In response to "post your data," authors respond that they aren't allowed to do so, and journal rules allow that response. You have only to post your programs, and then a would-be replicator must arrange for access to the underlying data.  No surprise, very little replication that requires such extensive effort is occurring.

And rules will never be enough.

Regulation invites just-within-the-boundaries games. Provide the programs, but no poor documentation.  Provide the data with no headers. Don't write down what the procedures are. You can follow the letter and not the spirit of rules.

Demand invites serious effort towards transparency. I post programs and data. Judging by emails when I make a mistake, these get looked at maybe once every 5 years. The incentive to do a really good job is not very strong right now.

Poor documentation is already a big problem. My modal referee comment these days is "the authors did not write down what they did, so I can't evaluate it." Even without posting programs and data, the authors simply don't write down the steps they took to produce the numbers. The demand for such documentation has to come from readers, referees, citers, and admirers, and posting the code is only a small part of that transparency.

A hopeful thought: Currently, one way we address these problems is by endless referee requests for alternative procedures and robustness checks.  Perhaps these can be answered in the future by "the data and code are online, run them yourself if you're worried!"

I'm not arguing against rules, such as the AER has put in. I just think that they will not make a dent in the issue until we economists show by our actions some interest in the issue.

Proprietary data, commercial data, government data. 

Many data sources explicitly prohibit public disclosure of the data. Disclosing such secret data remains beyond the current journal policies, or policies that anyone imagines asking journals to impose. Journals can require that you post code, but then a replicator has to arrange for access to the data. That can be very expensive, or require a coauthor who works at the government agency. No surprise, such replication doesn't happen very often.

However, this is mostly not an insoluble problem, as there is almost never a fundamental reason why the data needed for verification and robustness analysis cannot be disclosed. Rules and censorship is not strong enough to change things. Widespread demand for transparency might well be.

To substantiate much research, and check its robustness to small variations in statistical method,  you do not need full access to the underlying data. An extract is enough, and usually the nature of that extract makes it useless for other purposes.

The extract needed to verify one paper is usually useless for writing other papers. The terms for using posted data could be, you cannot use this data to publish new original work, only for verification and comment on the posted paper.  Abiding by this restriction is a lot easier to police than the current replication policies.

Even if the slice of data needed to check a paper's results cannot be public, it can be provided to referees or discussants, after signing a stack of non-use and non-disclosure agreements. (That is a less-than-optimal outcome of course, since in the end real verification won't happen unless people can publish verification papers.)

Academic papers take 3 to 5 years or more for publication. A 3 to 5 year old slice of data is useless for most purposes, especially the commercial ones that worry data providers.

Commercial and proprietary (banks) data sets are designed for paying customers who want up-to-the-minute data. Even CRSP data, a month old, is not much used commercially, because traders need up to the minute data useful for trading.  Hedge fund and mutual fund data is used and paid for by people researching the histories of potential investments. Two-year old data is useless to them -- so much so that getting the providers to keep old slices of data to overcome survivor bias is a headache.

In sum, the 3-5 year old, redacted, minimalist small slice of data needed to substantiate the empirical work in an academic paper are in fact seldom a substantial threat to the commercial, proprietary, or genuine privacy interest of the data collectors.

The problem is fundamentally about contracting costs. We are in most cases secondary or incidental users of data, not primary customers. Data providers' legal departments don't want to deal with the effort of writing contracts that allow disclosure of data that is 99% useless but might conceivably be of value or cause them trouble.  Both private and government agency lawyers naturally adopt a CYA attitude by just saying no. 

But that can change.  If academics can't get a paper conferenced, refereed, read and cited with secret data,  if they can't get tenure, citations, or a job on that basis, the academics will push harder.  Our funding centers and agencies (NSF)  will allocate resources to hire some lawyers. Government agencies respond to political pressure.  If their data collection cannot be used in peer-reviewed research, that's one less justification for their budget. If Congress hears loudly from angry researchers who want their data, there is a force for change. But so long as you can write famous research without pushing, the apparently immovable rock does not move. 

The contrary argument is that if we impose these costs on researchers, then less research will be done, and valuable insights will not benefit society. But here you have to decide whether research based on secret data is really research at all. My premise is that, really, it is not, so the social value of even apparently novel and important claims based on secret data is not that large. 

Clearly, nothing of this sort will happen if journals try to write rules, in a profession in which nobody is taking the above steps to demand replicability. Only if there is a strong, pervasive, professional demand for transparency and replicability will things change.

Author's interest 

Authors often want to preserve their use of data until they've fully mined it. If they put in all the effort to produce the data, they want first crack at the results.

This valid concern does not mean that they cannot create redacted slices of data needed to substantiate a given paper. They can also let referees and discussants access such slices, with the above strict non-disclosure and agreement not to use the data.

In fact, it is usually in authors' interest to make data available sooner rather than later. Everyone who uses your data is a citation. There are far more cases of authors who gained notoriety and long citation counts from making data public early then there are of authors who jealously guarded data so they would get credit for the magic regression that would appear 5 or more years after data collection.

Yet this property right is up to the data collector to decide. Our job is to say "that's nice, but we won't really believe you until you make the data public, at least the data I need to see how you ran this regression." If you want to wait 5 years to mine all the data before making it public, then you might not get the glory of "publishing" the preliminary results. That's again why voluntary pressure will work, and rules from above will not work.

Service

One  empiricist who I talked to about these issues does not want to make programs public, because he doesn't want to deal with the consequent wave of emails from people asking him to explain bits of code, or claiming to have found errors in 20-year old programs.

Fair enough. But this is another reason why a loose code of ethics is better than a set of rules for journals.

You should make a best faith effort to document code and data when the paper is published. You are not required to answer every email from every confused graduate student for eternity after that point. Critiques and replication studies can be refereed in the usual way, and must rise to the usual standards of documentation and plausibility.

Why replication matters for economics 

Economics is unusual. In most experimental sciences, once you collect the data, the fact is there or not. If it's in doubt, collect more data. Economics features large and sophisticated statistical analysis of non-experimental data. Collecting more data is often not an option, and not really the crux of the problem anyway. You have to sort through the given data in a hundred or more different ways to understand that a cause and effect result is really robust. Individual authors can do some of that -- and referees tend to demand exhausting extra checks. But there really is no substitute for the social process by which many different authors, with different priors, play with the data and methods.

Economics is also unusual, in that the practice of redoing old experiments over and over, common in science, is rare in economics. When Ben Franklin stored lighting in a condenser, hundreds of other people went out to try it too, some discovering that it wasn't the safest thing in the world. They did not just read about it and take it as truth. A big part of a physics education is to rerun classic experiments in the lab. Yet it is rare for anyone to redo -- and question -- classic empirical work in economics, even as a student.

Of course everything comes down to costs. If a result is important enough, you can go get the data, program everything up again, and see if it's true.  Even then, the question comes, if you can't get x's number, why not?  It's really hard to answer that question without x's programs and data. But the whole thing is a whole lot less expensive and time consuming, and thus a whole lot more likely to happen, if you can use the author's programs and data.

Where we are 

The American Economic Review has a strong data and programs disclosure policy. The JPE adopted the AER data policy. A good John Taylor blog post on replication and the history of the AER policy. The QJE has decided not to; I asked an editor about it and heard very sensible reasons. Here is a very good review article on data policies at journals by By Sven Vlaeminck

The AEA is running a survey about its journals, and asks some replication questions. If you're an AEA member, you got it. Answer it. I added to mine, "if you care so much about replication, you should show you value it by routinely publishing replication articles."

How is it working? The Report on the American Economic Review Data Availability Compliance Project

All authors submitted something to the data archive. Roughly 80 percent of the submissions satisfied the spirit of the AER’s data availability policy, which is to make replication and robustness studies possible independently of the author(s). The replicated results generally agreed with the published results. There remains, however, room for improvement both in terms of compliance with the policy and the quality of the materials that authors submit

However, Andrew Chang and Phillip Li disagree, in the nicely titled "Is Economics Research Replicable? Sixty Published Papers from Thirteen Journals Say `Usually Not'"

We attempt to replicate 67 papers published in 13 well-regarded economics journals using author-provided replication files that include both data and code. ... Aside from 6 papers that use confidential data, we obtain data and code replication files for 29 of 35 papers (83%) that are required to provide such files as a condition of publication, compared to 11 of 26 papers (42%) that are not required to provide data and code replication files. We successfully replicate the key qualitative result of 22 of 67 papers (33%) without contacting the authors. Excluding the 6 papers that use confidential data and the 2 papers that use software we do not possess, we replicate 29 of 59 papers (49%) with assistance from the authors. Because we are able to replicate less than half of the papers in our sample even with help from the authors, we assert that economics research is usually not replicable. 

I read this as confirmation that replicability must come from a widespread social norm, demand, not journal policies.

The quest for rules and censorship reflects a world-view that once we get procedures in place, then everything published in a journal will be correct. Of course, once stated, you know how silly that is. Most of what gets published is wrong. Journals are for communication. They should be invitations to replication, not carved in stone truths.  Yes, peer-review sorts out a lot of complete garbage, but the balance of type 1 and type 2 errors will remain.

A few touchstones:

Mitch Petersen tallied up all papers in the top finance journals for 2001–2004. Out of 207 panel data papers, 42% made no correction at all for cross-sectional correlation of the errors.  This is a fundamental error, that typically cuts standard errors by as much as a factor of 5 or more. If firm i had an unusually good year, it's pretty likely firm j had a good year as well. Clearly, the empirical refereeing process is far from perfect, despite the endless rounds of revisions they typically ask for. (Nowadays the magic wand "cluster" is waved over the issue. Whether it's being done right is a ripe topic for a similar investigation.)

"Why Most Published Research Findings are False"  by John Ioannidis. Medicine, but relevant

A link on the  controversy on replicability in psychology

There will be a workshop on replication and transparency in economic research following the ASSA meetings in San Francisco

I anticipate an interesting exchange in the comments. I especially more links to and summaries of existing writing on the subject

Update On the need for a replication journal by Christian Zimmermann

There is very little replication of research in economics, particularly compared with other sciences. This paper argues that there is a dire need for studies that replicate research, that their scarcity is due to poor or negative rewards for replicators, and that this could be improved with a journal that exclusively publishes replication studies. I then discuss how such a journal could be organized, in particular in the face of some negative rewards some replication studies may elicit.

But why is that better than a dedicated "replication" section of the AER, especially if the AEA wants to encourage replication? I didn't see an answer, though it may be a second best proposal given that the AER isn't doing it.

Update 2

A second blog post on this topic, Secret Data Encore

Tax Oped

Source: Wall Street Journal

An Oped at the Wall Street Journal, "Here's what genuine tax reform looks like." With a new art style by WSJ. (Ungated via Hoover. I have to wait 30 days to post the whole thing.)

 I buried the lead, which I'll excerpt here:

"...Why is tax reform paralyzed? Because political debate mixes the goal of efficiently raising revenue with so many other objectives. Some want more progressivity or more revenue. Others defend subsidies and transfers for specific activities, groups or businesses. They hold reform hostage.

Wise politicians often bundle dissimilar goals to attract a majority. But when bundling leads to paralysis, progress comes by separating the issues. 

Thus, we should agree to first reform the structure of the tax code, leaving the rates blank. We will then separately debate rates, and the consequent overall revenue and progressivity.... we can agree on an efficient, simple and fair tax, and debate revenues and progressivity separately.

We should also agree to separate the tax code from the subsidy code. We agree to debate subsidies for mortgage-interest payments, electric cars and the like—transparent and on-budget—but separately from tax reform.

Negotiating such an agreement will be hard. But the ability to achieve grand bargains is the most important characteristic of great political leaders."

This is, I think, the most novel idea in the oped. All tax reform packages mix changes to the structure of the tax code with specific rates. Then, the wonkosphere goes on a witch hunt of who pays more and who pays less, and the attempt to fix pathological problems in the structure falls apart.

I think our politicians really could negotiate a tax code in which all the rates are left blank. Then, we have a separate debate about what those rates will be.  In fact, tax rates ought to change a lot more often than the tax code itself.

Similarly,  the key to removing the pernicious subsidies in the tax code is again to separate the issues. Taxes are for taxing, then we can debate subsidies.

We need to move from the equilibrium of, I have my subsidy/deduction/credit/special deal, so I won't complain about yours, to the equilibrium of, I gave up my subsidy/deduction/credit special deal, so I'll make darn sure you give up yours too.

Institutions and experience

These are remarks I prepared for a symposium at Hoover in honor of George Shultz on his 95th birthday. Willie Brown was the star of the symposium, I think, preceded by a provocative and thoughtful speech by Bill Bradley.

Institutions and Experience

Our theme is “learning from experience.” I want to reflect on how we as a society learn from experience, with special focus on economic affairs. Most of these thoughts reflect things I learned from George, directly or indirectly, but in the interest of time I won’t bore you with the stories.

An English baron in 1342 tramples his farmers’ lands while hunting. The farmers starve. Then, insecure in their land, they don’t keep it up, they move away, and soon both baron and farmers are poor.

How does our society remember thousands of years of lessons like these? When, say, the EPA decides the puddle in your backyard is a wetland, or — I choose a tiny example just to emphasize how pervasive the issues are — when the City of Palo Alto wants to grab a trailer park, how does our society remember the hunter baron’s experience?

The answer: Experience is encoded in our institutions. We live on a thousand years of slow development of the rule of law, rights of individuals, property rights, contracts, limited government, checks and balances. By operating within this great institutional machinery, these “structures” as senator Bradley called them last night, these “guardrails” as Kim Strassel called them in this morning’s Wall Street Journal, our society remembers Baron hunter’s experience in 1342, though each individual has forgotten it.


In particular, self-appointed technocrats — us economists — do not offer “advice” to benevolent “policymakers” to implement, though we often so flatter ourselves. Strong institutions of limited government defend against bad and transitory ideas.

Hayek told us how prices transmit information through an economy, information that no individual knows. In a similar manner, these institutions encode memories and wisdom that no individual remembers.

These great institutions do not operate of their own. They need maintenance, repair, continual improvement, and the incorporation of new experience. I am not arguing for mindless conservatism. Many of our legal structures have been, and continue to be, in need of fundamental changes.

But the mechanics who fix them, their operators, and us, their beneficiaries, need to be vaguely aware of how the machine works and why it is built the way it is. When institutions, structures, long standing traditions, rights, separations of power and so forth are abandoned or broken, when guardrails are smashed, the treasure trove of experience involved in their construction can be lost.

The Era of Forgetting

In this regard, I fear we live in an era of great forgetting.

Foreign policy increasingly seems unhinged from simplest lessons of history as well as from the carefully built institutions of the postwar order. Eisenhower and Roosevelt did not call a press conference, announce the US putting 5000 soldiers on Omaha beach, and promise the soldiers would be out by July. They set a goal, and promised to unleash whatever resources are needed for that goal. As senator Bradley reminded us, they knew that managing the peace is just as important as winning the war.

As John Taylor reminds us in his remarks today, monetary and financial policy has veered away from its traditional base in both domestic and international institutions and institutional limitations.

In economic and domestic affairs, the administration and its regulatory agencies are more and more telling people and businesses what to do, unconstrained by conventional rule-of-law restrictions and protections.

But what will happen on a change of administration? Will a new administration retreat, say we must restore rights and rule of law? Or will a new administration — once again — admire an expanded set of tools for ramming through its agenda, punishing political enemies, demanding cooperation of people and business, and set to work institutionally grabbing power for itself?

The temptation will be strong: To direct Lois Lerner’s successor to blackball different applications; to use campaign laws to persecute a different set of officials; to have its environmental, health care, and financial regulators demand the same tribute and that a different set of doors revolve; to wipe out its predecessors executive orders and issue new ones.

Or will it say, no, we eschew these methods, we will go back to respect and rebuild institutional limits, though it will take a long time and reduce our hold on power? Once the traditional restraints are broken, it’s awfully hard to go back.

The leading candidates have already promised which way they’re going. For example, Ms. Clinton, quoted by Kim Strassel, promises to use Treasury regulation to punish companies that legally reduce taxes by moving abroad. And Mr. Trump outrages the law and constitution daily.

Every society needs institutions to pass on its structures and traditions to the next generation. Grade for yourselves how well our schools and universities, even Stanford, are doing to pass on the lessons of limited government, rule of law, individual rights; the institutional wisdom of western democracy.

Our society’s premier institution for collecting, vetting, and passing on experience, science itself, is in trouble. The politicization of climate research is only the latest example.

Our policy debates are taking on a magical tone. Simple lessons of hundreds of years of experience, simple logic of cause and effect, and basic quantification, are disappearing.

Long experience tells us simple steps that encourage economic activity: Low, stable and simple taxes, good public infrastructure, an efficient legal system, predictable simple and uncorrupt regulations, and largely stay out of the way.

Long experience also teaches us many mistakes. For example, price and quantity controls induce scarcity, illegality, sclerosis and poverty. It also teaches that grand plan after grand plan for government directed growth or development has fallen apart.

But our policy debates chase ghosts instead. Rather than fix these humble and broken institutions, we are consumed whether Ms. Yellen might pay banks a quarter of a percentage more on their reserves. Action is regularly demanded over “bubbles,” “imbalances,” “reach for yield” “risk premiums” and so forth, as if anyone had any idea what these meant let alone scientific understanding of what one should do about them.

Serious people and international institutions advocate that the road to prosperity is for the government to borrow money and deliberately waste it; to confiscate wealth by extortionate taxation; to welcome natural disasters for their stimulative rebuilding opportunities; to deliberately throw sand in the gears of productivity; almost magic recommendations that ignore centuries of experience.

(To clarify: yes, we should keep our minds open new ideas. Quantum mechanics sounded like magic when introduced. I play with radical ideas too, such as the idea that higher interest rates lead to more, rather than less, inflation. The issue is, how quickly should new, revolutionary, everything you thought you knew is wrong ideas make their way to public policy? Too much economic policy jumps from "here's a cool idea I thought up on the plane" to "the US should spend a trillion bucks."  I do not advocate that the Fed should act on my latest paper!)

Our regulatory policy seems a parody of making the same mistakes over and over and refusing to learn the lessons.  The Dodd-Frank act is not a new idea. It simply tries again and bigger the same set of ideas that failed in crisis after crisis — guarantee debts, bail out banks, and add more regulators in the vain hope to stop increasingly large, politicized, too big to fail and hugely over leveraged banks from ever losing money again. The ACA/Obamacare is not a new idea. It just adds layer after layer of the same health insurance and care regulations that failed before. This time price controls will surely work to lower costs without cutting supply or innovation — let’s forget the thousands of times they have failed.

And economics is relatively sensible. Magical beliefs pervade our political system’s discussion about terrorism, migration, or the environment. No, a high speed train will not fill California’s reservoirs, or stop terrorism or refugee migration.

There is a late Roman empire feeling in the air. Conventional limitations on action are ignored. People distrust the great institutions of their society, have neglected them, and now they have forgotten how those institutions work. People follow inspirational leaders, who use any tools at their disposal to crush enemies — only to be crushed in turn. New magical faiths sweep through. I fear that our grandchildren will walk among wondrous ruins like medieval villagers, having forgotten how to make concrete.

Optimism

But I learned an important lesson from George Shultz: Any time I start down this sort of line of thought, he says, "Stop being so grumpy!"  As Ronald Reagan famously put it, there must be a pony in here somewhere.  There is.

Our society also has self-correcting institutions. You’re sitting in one, and you’re part of that process today. We’re here. The ideas that define a free — and prosperous — society are alive. The memory of a rule of law structure is alive.

We still have a free press, for now relatively free speech and most people still understand how important that is. The full potential of the regulatory and surveillance state to silence dissent has not yet been used. And in that press, and Internet, horror stories are adding up. People are getting sick of it.

Congress has noticed. There are good people who want to pass simple clear laws and bring back its rule.

For example, In November the House Judiciary Committee passed (WSJ commentary) a package of regulatory reforms. One is, to be guilty of a crime, you must have some intent to violate the law. They can’t charge you after the fact with unknowable laws or regulations, evidence such as statistical discrimination programs that you cannot see or challenge, and fine you millions or put you in jail without even claiming you intended any harm.

This principle of intent, “mens rea”, is a centuries-old bedrock of common law. It encodes a thousand years of experience. It is sad that Federal regulations forgot and trampled it. But it is great news that an effort to fix it is under way. A wider set of rights against regulators, a magna carta for the regulatory state, reestablishing the rights to know the rules ahead of time, to see and challenge evidence, to appeal, and to speedy judgment could well follow.

Financial regulators are seeing daily how ineffective the Dodd-Frank apparatus is. Slowly but surely, the realization that very simple capital standards can obviate this mess is making way. You heard it from Senator Bradley last night.

I see hope on climate. There is a small but increasing alliance between environmentalists and free-marketers. The environmentalists think carbon is such a big problem, that they want policies that will actually do something about it. Free marketers are aghast at the waste and cronyism of energy policy. They are coming together on a deal: A simple straightforward carbon tax in place of wasting money and economic capacity on tax dodges, crony subsidies and ineffective regulations. Sure, there will be a big discussion on the rate, but any conceivable rate will be a big improvement for both environment and economy.

Similar grand bargains on taxes and entitlements are sitting before us, needing only a small amount of leadership and public pressure. The experience of 1982 and 1986 is not forgotten.

A hunger for monetary policy anchored in rules or at least strong institutional traditions and constraints is palpable, even producing bills in Congress. Those may not be perfectly crafted, and may not pass. But the force for rebuilding an institutional structure for monetary policy is there.

Collegiate humanities and social science education has passed the point of the fashionable to the ridiculous, so that study of the successes of western civilization, and not just its many sins, is returning.

I don’t yet hear “it’s your property, do what you want with it” from the Palo Alto zoning board, or the citizens who elect them, but who knows, that too is possible someday.

Even the widely reported disgust with government has a silver lining. People who distrust the government are less likely to vote for the next big personality promising big new programs. Instead, they might be more attracted to candidates who promise restraint and rule of law; to administer competently and to repair broken institutions.

Our society codes its experience into its institutions; in a grand edifice we call limited government and rule of law. The old boat is rusty, but she’s not beyond hope. The bilge pumps are working. And we face no real external pressures. ISIS is the JV; compared to the Visigoths, or to Germany, Japan and the Soviet Union. A rich China should be a godsend, posing no more threat than a rich Europe and Canada. Silicon valley is full of ideas and entrepreneurs waiting to unleash prosperity on the country. If only they can get the permits. If we fail, and the grand forgetting takes over instead, the fault will only be our own.