Lottery Winners Don't Get Healthier

Alex Tabarrok at Marginal Revolution had a great post last week, Lottery Winners Don't get Healthier (also enjoy the url.)

Wealthier people are healthier and live longer. Why? One popular explanation is summarized in the documentary Unnatural Causes: Is Inequality Making us Sick?

The lives of a CEO, a lab supervisor, a janitor, and an unemployed mother illustrate how class shapes opportunities for good health. Those on the top have the most access to power, resources and opportunity – and thus the best health. Those on the bottom are faced with more stressors – unpaid bills, jobs that don’t pay enough, unsafe living conditions, exposure to environmental hazards, lack of control over work and schedule, worries over children – and the fewest resources available to help them cope. 

The net effect is a health-wealth gradient, in which every descending rung of the socioeconomic ladder corresponds to worse health.

If this were true, then increasing the wealth of a poor person would increase their health. That does not appear to be the case. In important new research David Cesarini, Erik Lindqvist, Robert Ostling and Bjorn Wallace look at the health of lottery winners in Sweden (75% of winnings within the range of approximately $20,000 to $800,000) and, importantly, on their children. Most effects on adults are reliably close to zero and in no case can wealth explain a large share of the wealth-health gradient:

In adults, we find no evidence that wealth impacts mortality or health care utilization.... Our estimates allow us to rule out effects on 10-year mortality one sixth as large as the crosssectional wealth-mortality gradient.

The authors also look at the health effects on the children of lottery winners. There is more uncertainty in the health estimates on children but most estimates cluster around zero and developmental effects on things like IQ can be rejected (“In all eight subsamples, we can rule out wealth effects on GPA smaller than 0.01 standard deviations”).

(My emphasis above)

Alex does not emphasize the most important point, I think, of this study.  The natural inference is, The same things that make you wealthy make you healthy. The correlation between health and wealth across the population reflect two outcomes of the same underlying causes.

We can speculate what those causes are.  (I haven't read the paper, maybe the authors do.) A natural hypothesis is a whole set of circumstances and lifestyle choices have both health and wealth effects. These causes can be either "right" or "left" as far as the evidence before us: "Right:" Thrift, hard work, self discipline and clean living lead to health and wealth. "Left:" good parents, good neighborhood, the right social connections lead to health and wealth.

Either way, simply transferring money will not transfer the things that produce money, and produce health.

Perhaps the documentary was right after all: "class shapes opportunities for good health."  But "class" is about more than a bank account.

Also, Alex can be misread as a bit too critical: "If this were true." It is true that health and wealth are correlated. It is not true that more wealth causes better health.  The problem is  not just "resources available to help them cope."

Why a blog post? This story is a gorgeous example of the one central thing you learn when doing empirical economics: Correlation is not causation. Always look for the reverse possibility, or that the two things correlated are both outcomes of something else, and changing A will not affect B.   We seldom get an example that is so beautifully clear.

Update:  Melissa Kearney writes,

"Bill Evans and Craig Garthwaite have an important study [AER] showing that expansions of EITC benefits led to improvements in self-reported health status among affected mothers. 

Their paper provides a nice counterpoint to the Swedish lottery study, one that is arguably more relevant to the policy question of whether more income would causally improve the health of low-income individuals in the U.S.

Thanks Melissa for pointing it out. This is interesting, but I'd rather not get in to a dissection of studies here -- just who takes advantage of EITC benefits, how instruments and differences do and don't answer these problems. The main point of my post is not to answer once and for all the question -- how much does showers of money improve people's heath -- but to point out with this forceful example for non-economists the possibility that widely reported correlations - rich people are healthier -- don't automatically mean that money showers raise health.  

4% growth

I wrote last week on the simple factual question of whether and how often the US has experienced 4% real GDP growth in the past.

The deeper question, is that growth possible again? I answered yes, it's surely possible as a matter of economics. 

A few have asked me "why do so many of your colleagues disagree?" It's a question I hate. It's hard enough to understand the economy, I don't pretend to understand how others respond to media inquiries. And I don't like the invitation to squabble in public. 

It has taken me some time to reflect on it, though, and I think I have a useful answer. I think we actually agree.

As I read through the many economists' quotes in the media, I don't think there is in fact substantial disagreement on the economic question -- is it economically possible for the U.S. to grow at 4% for a decade or more? Their caution is political. They don't think that any of the announced candidates (at least with a prayer of being elected) will advocate, let alone get enacted, a set of policies sufficiently radical to raise growth that much. 

This is a sensible position. When I answer the question, is 4% growth for a decade economically possible, my answer is whether the most extreme pro-growth policies would yield at least that result. A  short list:

• The tax code is thoroughly reformed to do nothing but raise revenue with minimal distortion -- a uniform consumption tax and no income, corporate, estate etc. taxes, or deductions.
• A dramatic regulatory reform. For example 
• Simple equity-financed banking in place of Dodd-Frank. 
• Private health-status insurance (with, if needed, on-budget voucher subsidies) in place of Obamacare. 
• An end to the mess of energy subsidies and interference. No more fuel economy standards, HOV lanes, Tesla tax credits, windmill subsidies, and so on and so on. (If you want to control carbon, a uniform carbon tax and nothing else.) 
• Many agencies cease to exist. 
• No more endless waits for regulatory decisions. 
• No more witch hunts for multibillion dollar settlements.
• Thorough overhaul of social programs to remove disincentives. Most help comes via on-budget vouchers.
• No more agricultural subsidies.
• No more subsidies, period. Fannie and Freddie closed down.  
• Unilateral free trade. 
• Essentially open immigration -- anyone can work.  
• Much labor law rolled back. Uber drivers can be contractors, thank you. Most occupational licenses removed -- anyone can work.  
• Drug legalization.
• School vouchers. 
• And so on. Essentially, every single action and policy is re-oriented toward growth. 

This program removes a lot of level inefficiencies. 10% increase in level over 10 years is 1% more growth per year. Labor force participation increases. The labor force itself grows. We get a spurt of productivity growth just from greater efficiency without needing big investments. And then innovation and new businesses, investment, technology kicks in.

There would be a lot of lawyer, accountant, lobbyist, compliance officer, and regulator unemployment. Well, Uber needs drivers.

Politically, this is free-market libertarian nirvana.

I think my fellow economists might agree that 4% growth for a decade is possible with such a program. In fact, it we can likely get to 4% with much less than all of these policies. They might complain about inequality or other objectives.  But most of all, they might say it's unlikely that the new President and Congress will enact anything like such a program.

That's a very reasonable view. I also agree that typical proposals -- a  small reduction in corporate rates, a twiddle here a tweak there, the typical small promise to improve regulation -- will not have one tenth the needed effect.

But, dear colleagues, they asked us about economic possibility, not our guess about political probability. Let's answer the question they asked us.  It would be better to say: "Sure, 4% growth is economically possible. But I don't think any politician will advocate the policies necessary to produce it."  If we were to say that more often, rather than give up at the outset, we just might get such policies and politicians.

You never know what's "politically feasible." In 1955, civil rights was "politically infeasible." In 2005 gay marriage was "politically infeasible." Politics sticks in the mud for 100 years and then changes faster than we imagine.

I think there actually are quite a few politicians who would do some of the radical things that need to be done. They need to hear from us that it could work, as a matter of economics, and let them handle the politics.

We will soon see a first test: Can any candidate show up in Iowa, and say "Ladies and Gentlemen, government subsidized corn ethanol is a rotten idea." Then, can they say something vaguely coherent on immigration and trade.  The campaign season is young. Let's not prejudge them. 

Growth is just too important to give up on so easily. Sclerotic growth is the economic issue of our time. Economists should be cheering any policy agenda focused on growth.  If you think the policies needed to give us growth are hard, and out of the current political mainstream, that's ever more reason to keep reminding people that growth is possible and needs big changes, not to confuse "it's unlikely they'll do it" with "it's economically impossible."

Update: Response to Noah Smith's comment on this post  here 

Unit roots, redux

Arnold Kling's askblog and Roger Farmer have a little exchange on GDP and unit roots. My two cents here.

I did a lot of work on this topic a long time ago, in How Big is the Random Walk in GNP?  (the first one)  Permanent and Transitory Components of GNP and Stock Prices” (The last, and I think best one) "Multivariate estimates" with Argia Sbordone, and "A critique of the application of unit root tests", particularly appropriate to Roger's battery of tests.

The conclusions, which I still think hold up today:

Log GDP has both random walk and stationary components. Consumption is a pretty good indicator of the random walk component. This is also what the standard stochastic growth model predicts: a random walk technology shock induces a random walk component in output but there are transitory dynamics around that value.

A linear trend in GDP is only visible ex-post, like a "bull" or "bear" market.  It's not "wrong" to detrend GDP, but it is wrong to forecast that GDP will return to the linear trend or to take too seriously correlations of linearly detrended series, as Arnold mentions.  Treating macro series as cointegrated with one common trend is a better idea.

Log stock prices have random walk and stationary components. Dividends are a pretty good indicator of the random walk component. (Most recently, here.)

Arnold asks  "In stock market returns, econometricians have been able to identify long-term mean reversion even though the short run is a random walk. Can something similar be done with GDP data?" Answer: Yes, and  Permanent and Transitory Components is it.

Both Arnold and Roger claim that unemployment has a unit root. Guys, you must be kidding. Actually, this makes a great test case for my point in "A critique", that it is a bad idea it is to blindly run unit root tests and then impose that structure.

A unit root means a random walk component. A random walk will eventually pass any upper and lower limit. Look at it. That's as stationary a series as you're going to find in economics. ("Look at the plot" and "think about the units" are the Cochrane unit root tests.)

Yes, unemployment like other stationary ratios in macro (consumption/GDP, hours/day, etc.)  have important and frequently overlooked low-frequency movements. But they are far from random walks, and they like unemployment have a very large transitory component at business cycle frequencies. When unemployment is above 8%, it is a good bet that it will decline over the next 5 years.

If you apply unit root tests to an hour of second by second temperature data from 9 to 10 AM you will think it has both a linear trend and a unit root. Millisecond data will not help you to detect climate change.  That's why unit root tests are a problem. You have to think, and consider the span of data you have and the frequency of mean reversion that makes economic sense in your data.

The tests are about infinite horizon behavior which you can never tell with finite horizons. However, they can alert you to low-frequency movement in your data, which can make ordinary distribution theory a bad guide. So can looking at a plot.

As far as I can tell, "Potential GDP" is equivalent to a two sided filter. It looks great ex-post. None of this is inconsistent with Arnold's view that standard calculations of potential GDP gaps do little to forecast GDP growth, especially in real time.

Jumps and diffusions

I learned an interesting continuous time trick recently. The context is a note, "The fragile benefits of endowment destruction" that I wrote with John Campbell, about how to extend our habit model to jumps in consumption. The point here is more interesting than that particular context.

Suppose one time series \(x\), which follows a diffusion, drives another \(y\). In the simplest example, \[dx_t = \sigma dz_t \] \[ dy_t = y_t dx_t. \] In our example, the second equation describes how habits \(y\) respond to consumption \(x\). The same kind of structure might describe how invested wealth \(y\) responds to asset prices \(x\), or how option prices \(y\) respond to stock prices \(x\).

Now, suppose we want to extend the model to handle jumps in \(x\), \[dx_t = \sigma dz_t + dJ_t.\] What do we do about the second equation? \(y_t\) now can jump too. On the right hand side of the second equation, should we use the left limit, the right limit, or something in between?

The usual answer is to use the left limit. We generalize the model to jumps this way: \[dx_t = \sigma dz_t+ dJ_t \] \[ dy_t = y_{t_-} dx_t = y_{t_-} \sigma dz_t + y_{t_-}dJ_t \] where \(y_{t_{-}}\) denotes the left limit.

That approach has some weird properties however. Suppose \(y_{t_-}=1\), and \(dJ_t=1\). Then \(y_t\) jumps to \(y_t=2\). But suppose there are two jumps of size 1/2, one at time \(t\) and one at time \(t+\varepsilon\). Now \(y\) jumps up to 1.5 after the first jump, and then jumps another \(1.5 \times 0.5 = 0.75\), ending up at \(y_{t+\varepsilon} =2.25\). Two half jumps have a different response than one full jump.

Suppose instead we extend the original model to jumps by taking the jump limit of a continuous process. Imagine that we observe realizations of \(\{dz_t\}\) that get closer and closer to a jump in \(dx_t\), and let's find what happens to \(y_t\). The general solution to the first set of equations is \[ y_{t+\Delta} = y_t e^{(x_{t+\Delta}-x_t - \frac{1}{2}\sigma^2\Delta)}\] so, in the limit \(\Delta \rightarrow 0\) that \(x_t\) takes a jump of size \(dJ_t\), the jump-limit of a continuous movement is \[ dy_{t} \equiv y_t -y_{t_-} = y_{t_-}(e^{dx_{t}}-1) = y_{t_-}\sigma dz_t + y_{t_-}e^{dJ_t}\] rather than \[ dy_t = y_{t_-} dx_t = y_{t_-} \sigma dz_t + y_{t_-}dJ_t \] So, the left-limit method produced a response to a jump that was different from the response to a continuous process arbitrarily close to a jump. For example, the left-limit approach can produce a negative \(y_t\), but this method, like the diffusion process, cannot fall below zero. This method also produces a response to two half jumps that is the same as the response to a full jump.

As you can see, the difference is whether the state variable \(y_t\) gets to change during the jump. In the left-limit approach, the same \(y_{t_-}\) gets applied to the whole jump. In the continuous-limit version, \(y_t\) implicitly gets to move while the jump in \(x_t\) is moving.

A nonlinear function of a jump is a little novel, but there's nothing wrong with it, and it exists in the continuous time literature. We don't see it that often, because when you're only studying one series it's easier to just change the distribution of the jump process instead. This question occurs when you can see both series x and y and you want to model the relationship between them.

Which is right?

Which extension to jumps is correct? Both are mathematically correct. There is nothing wrong with writing down a model in which the response to a jump is different from the response to continuous movements arbitrarily close to jumps. The answer depends on the economic situation.

For example, consider models with bankruptcy constraints. Agents who can continuously adjust their investments may always avoid bankruptcy in a diffusion setting. If we extend such a model to jumps with the continuous limit approach, implicitly preserving the investor's ability to trade as fast as asset prices change even in the jump limit, we will preserve bankruptcy avoidance in face of a jump in prices. However, if we model portfolio adjustment to jumps with the left-limit generalization, agents may be forced in to bankruptcy for price jumps.

Sometimes, one introduces jumps precisely to model a situation in which prices can move faster than agents can adjust their portfolios, so agents may be forced to bankruptcy. Then the left-limit generalization is correct. But if one wants to extend a model to jumps for other reasons, while avoiding bankruptcy, negative consumption, negative marginal utility (consumption below zero or below habits), violations of budget constraints, feasibility conditions, borrowing constraints, and so forth, then one should choose a generalization in which the jump gives the same result as the continuous limit.

Similarly, when extending option pricing models to jumps, one may want to model the jump in such a way that investors cannot adjust portfolios fast enough. Then the left-limit extension is appropriate, and investors must hold the jump risk. But one may wish to accommodate jumps in asset prices to better fit asset price dynamics while maintaining investor's ability to dynamically hedge. Then the nonlinear extension is appropriate, maintaining the equivalence between jumps and the limiting diffusion.

A little more general treatment

A little more generally, suppose \[ dx_t = g dt + \sigma dz_t \] \[dy_t = \mu(y_t) dt + \lambda(y_t)dx_t.\] We want to add \(dJ_t\) to the first equation. The left-limit approach is \[dy_t = \mu(y_{t_-}) dt + \lambda(y_{t_-})dx_t \] If there is a jump \(dJ_t\), \(y\) moves by an amount \[\frac{1}{\lambda(y_{t_-})}dy_t \equiv \frac{1}{\lambda(y_{t_-})}(y_t - y_{t_-}) = dx_t .\] The limit of a continuous movement solves the differential equation \[\int_{y_{t_-}}^{y_t} \frac{1}{\lambda(\xi)}d\xi = dx_t\] Again, you see the crucial difference, whether the state variable gets to move "during" the jump. We can write this as a differential, by writing the solution to this last differential equation as \[y_t-y_{t_-}=f(x_t-x_{t_-};y_{t_-})\] and then \[dy_t = \mu(y_{t_-}) dt + f(dx_t;y_{t_-})=\mu(y_{t_-}) dt + \lambda(y_{t_-})\sigma dz_t+f(dJ_t;y_{t_-})\]

So, you don't have to extend the model to jumps with the left-limit approach, and you don't have to swallow the idea that a jump has a different response than an arbitrarily close continuous-sample-path movement. The last equation shows you how to modify the model to include jumps in a way that preserves the property that the jump has the same effect as its continuous limit.

The point

Why a blog post on this? I asked a few continuous-time gurus, and none of them had seen this issue before. If someone knows where this has all been worked out with proper is dotted and ts crossed, I would like to know and cite it properly. (I would think the literature on option pricing with jumps had done it, but I couldn't find a reference.) Or perhaps it hasn't been done and someone wants to do it. I'm not good enough at the technical aspects of continuous time to write this with the right precision and generality.

And it's a cool trick that may be useful to someone outside of the narrow context that we had for it.

Update: 

Perhaps the right application is stock prices and option prices. When stock prices jump, someone must have studied the case that option prices move by the same amount the Black-Scholes formula gives for the same size stock price movement. Does anyone have a citation to that case?

Lucas and Sargent Revisited

The economics blogosphere has a big discussion going on over Bob Lucas and Tom Sargent's classic "After Keynesian Macroeconomics." You can start at Simon Wren-Lewis, Mark Thoma here and here and work back through the links.

A few thoughts here, as it bears on my WSJ oped from last week and my last post on EFG and how we do macro.

1. Views of Keynesian economics

Re-reading this paper, you will be struck about how much Lucas and Sargent praise Keynesian models, which you'd think it is their purpose to destroy.

They called the Keynesian revolution a "remarkable intellectual event." they continued

... some of its [Keynesian Revolution] most important features: the evolution of macroeconomics into a quantitative, scientific discipline, the development of explicit statistical descriptions of economic behavior, the increasing reliance of government officials on technical economic expertise, and the introduction of the use of mathematical control theory to manage an economy. 

Keynesian theory evolved from a disconnected, qualitative talk about economic activity into a system of equations that can be compared to data in a systematic way and which provides an operational guide in the necessarily quantitative task of formulating monetary and fiscal policy. 

neither the success of the Keynesisan Revolution nor its eventual failure can be understood at the purely verbal level at which Keynes himself wrote...

The Keynesian economics they are praising here is not Keynes' book -- one of those big muddy things that people are still writing "what did Keynes really mean" articles and books about nearly a century later -- but the subsequent quantification effort: Hick's creation of the ISLM model, its elaboration into computer models, estimation of those models, and the use of those models to make quantitative forecasts of the effects of policy interventions.

"Quantitative, scientific discipline," and "technical economic expertise" means we analyze policies by real models, not the opinions and judgments of famous economists turned public officials.

Yes, "mathematical control theory." Most readers will be too young to remember, but in the early 1970s academic journals were dynamic optimal control applied to simulations of large-scale Keynesian models.

Their goal was quite conservative: they wanted to preserve this great achievement:

The objectives of equilibrium business cycle theory are taken, without modification, from the goal which motivated the construction of the Keynesian macroeconometric models: to provide a scientifically based means of assessing, quantitatively, the likely effects of alternative economic policies. 

2. What was their basic criticism of Keynesian economics?

Lucas and Sargent make a two-pronged argument, one about theoretical coherence and the other about the grand econometric failure of Keynesian models.

As I see it, the main characteristic of "equilibrium" models Lucas and Sargent inaugurated is that they put people, time, and economics into macro.

Keynesian models model aggregates. Consumption depends on income. Investment depends on interest rates. Labor supply and demand depend on wages. Money demand depends on income and interest rates. "Consumption" and "investment" and so forth are the fundamental objects to be modeled.

"Equilibrium" models (using Lucas and Sargent's word) model people and technology. People make simultaneous decisions across multiple goods, constrained by budget constraints -- if you consume more and save more, you must work more, or hold less money.  Firms  make decisions across multiple goods constrained by technology.

Putting people and their simultaneous decisions back to the center of the model generates Lucas and Sargent's main econometric conclusion -- Sims' "incredible" identifying restrictions. When people simultaneously decide consumption, saving, labor supply, then the variables describing each must spill over in to the other. There is no reason for leaving (say) wages out of the consumption equation. But the only thing distinguishing one equation from another is which variables get left out.

People make decisions thinking about the future. I think "static" vs. "intertemporal" are good words to use.  That observation goes back to Friedman: consumption depends on permanent income, including expected future income, not today's income. Decisions today are inevitably tied to expectations --rational or not -- about the future.

The lack of budget constraint (or the "missing equation" and "Walras' law" issues much studied in earlier Keynesian literature) strikes me as another big conceptual and methodological difference between Keynesian models and equilibrium models, which flows from putting people rather than aggregates at the center of analysis.

Notice when you read any textbook that the microeconomic "demand" suddenly becomes the macroeconomic "plan." Why is that? Because demand curves respect budget constraints even at off-equilibrium prices. "Plans" like consumption equals c bar plus alpha times income do not respect any stated budget constraint.  You're allowed to say you want to consume and save more than income allows.

Optimization, rational expectations, and flexible prices are the ballyhooed centerpieces of the first round of equilibrium models to follow Lucas and Sargent, such as Kydland and Prescott's famous "time to build" model and the subsequent "real business cycle" models (examples: Bob King and Sergio Rebelo, John Long and Charles Plosser). But I don't think these ingredients are central to the program. The "new-Keynesian" (or, better, DSGE) school put in sticky prices with all the other Lucas-Sargent ingredients, and thus under the "equilibrium" banner.  Mike Woodford's "Interest and prices" is aimed proudly at that program. Sticky wages, distortions, and so on are just as  often included.

Simon Wren-Lewis questions  whether Lucas and Sargent were really focusing on empirical failure or methodological critique. He suggested that accelerationist Phillips curves, though adapted after the failure and thus appearing a bit as epicycles, can account for the data. (Lucas and Sargent: "In economics as in other sciences,...there is always the hope that if a particular specific models fails one can find a more successful model based on roughly the same ideas.")

I think Simon is a bit too blasé about how easy this modification is. Not only did inflation accelerate far faster than Keyensian models of the 1960s predicted, inflation dropped like a stone in 1982, far faster than  Keynesians thought possible based on adaptive expectations views. (If someone can find the quote from Samuelson in the early 1980s predicting decades of depression to wring out inflation, please add to the comments.) Proud as some self-identified Keynesians are about how well they think their unwritten, unquantified "model" fits the current recession, deep unemployment with no movement in inflation fits no Phillips curve that was actually written before the crisis. Infinite wage stickiness is an ex-post invention too, and still just a verbal debating point.

But the paper is really clear that empirical failure matters deeply to Lucas and Sargent. They said that

A key element in all Keynesian models is a trade-off between inflation and real output: the higher is the inflation rate, the higher is output (or equivalently, the lower is the rate of unemployment). For example, the models of the late 1960s predicted a sustained U.S. unemployment rate as consistent with a 4 percent annual rate of inflation. Based on this prediction, many economists at that time urged a deliberate policy of inflation. [plus ça change...]... policy in this period should, according to all of these models, have produced the lowest average unemployment rates for any decade since the 1940s. In fact, as we know, they produced the highest unemployment rates since the 1930s. This was econometric failure on a grand scale. [My emphasis]

Here as elsewhere, they said "econometric," not economic. They meant it.

Everyone was perfectly aware of the lack of "microfoundations" of Keynesian models, and the 50 year fruitless search for such foundations. But so long as the models worked, that had no real impact on their use for the "scientific" and technical policy advice Lucas and Sargent so admired.

And rightly so. Chemistry, until the last few decades, lacked "microfoundations" in quantum mechanics, first because nobody knew quantum mechanics, and later because working out how chemicals react from first principles was too hard. That did not stop chemistry from being a perfectly viable science. Biology, until the last few decades, lacked "microfoundations" in chemistry.

But chemistry and biology worked pretty well. Lucas and Sargent pointed to, and needed to point to, a grand empirical failure.

And that failure had to be accompanied not just by "well, these are reasonable rules, but they're not microfounded." The failure had to be accompanied by showing how the Keyensian model was logically flawed. That failure had to be accompanied by a better theory, which showed why the Keynesian equations were inconsistent with basic economics. That better theory had already predicted Keynesian model's  failure -- Friedman 1968, Phelps, and Lucas' prediction that the Phillips curve would shift if exploited. That is a lot more than "methodological purity."

3. So what happened?

As I survey the landscape now, it is interesting how much of the macroeconomics Lucas and Sargent praised has vanished. Quantitative, scientific discipline? Explicit statistical descriptions of economic behavior? Reliance of government officials on technical economic expertise?  The use of mathematical control theory to manage an economy? All that has vanished.

The sub-basements of central banks have big DSGE models, or combined models where you can turn Lucas and Sargent on and off. But I think it's fair to say nobody takes the results very seriously. Policy -- our stimulus, for example -- is based on back of the envelope multipliers and the authority and expertise, if you're charitable, or the unvarnished, verbal, opinions if you're not, of administration officials.

There are some large-scale empirical DSGE models left in academia too. But the vast bulk of policy analysis does not use them, as they did, say, the models of 1972. At conferences and in papers, academic work uses small scale toy models and a lot of words. Models do not seem to be cumulative. Each paper adds a little twist ignoring all the previous little twists.

A complete split occurred. "Equilibrium" models, in which I include new-Keynesian DSGE models, took over academia. The policy world stuck with simple ISLM logic -- not "models" in the quantitative scientific tradition Lucas and Sargent praised -- despite Lucas and Sargent's devastating criticism.   And, as I remarked in the earlier blog posts, the "purely verbal" or literary style of analysis is becoming more and more common now in academia as well as policy.

I'm not complaining about good vs. bad here. I write simpler and simpler models as I grow older, and spend more time thinking and writing about what those equations mean. It just is a fact about how we do things today and the "scientific," quantitative status of macroeconomics.

Lucas and Sargent's last sentence:

Unless the now evident limits of these models are also frankly acknowledged and radically different new directions taken, the real accomplishments of the Keynesian Revolution will be lost as surely as those we know to be illusory. 

Academic economics took the first half to hart. Policy economics froze in place. But Lucas and Sargent's "real accomplishments" were lost, or at least consciously abandoned, anyway.

PS: Lucas and Sargent have a delicious quote about Keynesian's loss of faith in their own model, as applicable now as then:

The current wave of protectionist sentiment directed at "saving jobs" would have been answered ten years ago with the Keynesian counterargument that fiscal policy can achieve the same end but more efficiently. Today, of course, no one would take this response seriously, so it is not offered. Indeed, economists who ten years ago championed Keynesian fiscal policy as an alternative to inefficient direct controls increasingly favor such controls as supplements to Keynesian policy.