Results for “model this” 3165 found
A simple model of lifetime happiness
Suppose that what makes a person happy is when their fortunes exceed expectations by a discrete amount (and that falling short of expectations is what makes you unhappy.) Then simply because of convergence of expectations:
- People will have few really happy phases in their lives.
- Indeed even if you lived forever you would have only finitely many spells of happiness.
- Most of the happy moments will come when you are young.
- Happiness will be short-lived.
- The biggest cross-sectional variance in happiness will be among the young.
- When expectations adjust to the rate at which your fortunes improve, chasing further happiness requires improving your fortunes at an accelerating rate.
- If life expectancy is increasing and we simply extrapolate expectations into later stages of life we are likely to be increasingly depressed when we are old.
- There could easily be an inverse relationship between intelligence and happiness.
The Solow Model
The Solow Model is a workhorse model of economic growth. Many subsequent papers in growth theory and in business cycle theory build on this model. A model of growth helps us to structure our thinking. Why is it, for example, that China is growing faster than the United States despite having much poorer institutions such as the rule of law? Surprisingly, even a simple version of the Solow model offers some useful predictions and ways to interpret aspects of the the growth data. At MRUniversity this week we have four videos on the Solow model. These videos are a bit more technical than many of our previous videos and we think they will be useful in many other classes such as macroeconomics, especially if you are using a truly excellent textbook. The videos will also be useful for anyone who wants to read more of the literature on growth theory or the empirics of growth (such as can be found, for example, in Barro and Sala-i-Martin’s Economic Growth or David Weil’s textbook Economic Growth). Even if you don’t want to study the theory in more depth, we think these videos will be useful for understanding development and how economists use theory and data to understand the sources of growth (and its absence).
Is Iceland a role model?
Or call it the wisdom of Kevin Drum:
…it’s worth pointing out a couple of things. First, Iceland has about the population of Bakersfield. So when they made foreign creditors take most of the losses in the wake of their banking failure, the rest of the world could afford to let it happen. There were no systemic risks involved. Also worth noting: the Icelandic krona got devalued a lot. In 2008 a euro bought 90 krona. Today it buys 160 krona. That means imports are a lot more expensive than they used to be. And state spending, although it went up in krona terms, was cut sharply in real terms. Iceland isn’t really an anti-austerity poster child.
Iceland is certainly an interesting example of how to handle a financial crisis, and there may even be some lessons there for the rest of us. But I’d be pretty cautious about those lessons. What worked for Iceland doesn’t necessarily scale up to work for the rest of the world.
As Krugman recognizes, Icelandic gdp is still below its previous peak. Matt offers further comment.
Bryan Caplan’s signaling model and on-line education
The very useful model is here, and there is further commentary from Bryan here. My question is this: does the model imply that on-line education should succeed, or not?
Let’s say that education signals conscientiousness. A purely on-line class, with no ogre standing over your shoulder to discipline you, should be blown off by those who are not conscientiousness. The on-line class would seem to offer a better signal and a cleaner separation of types.
Alternatively, let’s say education signals IQ or some other notion of “smarts.” On-line education would seem to offer less opportunity to get through by buttering up the teacher, spouting mumbo-jumbo in basket-weaving classes, and so on. For better or worse, a lot of on-line education seems to be based on relatively objective tests. Then on-line education would seem to offer a better signal of smarts.
One possible application of Bryan’s model might be this. Income inequality is rising, so there is greater care to get the signal, selection, and screening right for top jobs. Relatively high levels of education should be all the more discriminatory, and that may mean more on-line education. In fact, in normative terms that might well be a problem with on-line education, namely its inegalitarian nature with regard to curiosity and effort and smarts.
Oddly, the signaling model could be true, but through an invisible hand mechanism — schools competing to separate quality in the most effective ways — you can end up with a state of affairs where upfront signaling costs are fairly low. Imagine a chess school, needing to sort talent, and unable to teach its students very much, but setting up a quite cheap on-line tournament and declaring some winners. Aren’t the Khan Academy users some really talented people?
Alternatively, through an invisible hand mechanism, if the learning model is correct, you could end up with an equilibrium in which upfront signaling costs appear to be relatively high, namely that you impose “taxes” to make sure people end up learning what they need to know. Think Paris Island or KIPP schools.
It is important not to confuse “seeing high upfront signaling costs” with “the signaling model of education is essentially correct.” They sound like they should go together, but quite possibly they don’t.
Is the European banking model broken?
…a far higher proportion of U.S. loan books are funded by deposits. The U.S. market has a loan to deposit ratio of 78% compared to more than 110% in Europe. European banks have a total funding gap of $1.3 trillion ($1.72 trillion) which they need to finance in wholesale markets, estimates Simon Samuels, a banking analyst at Barclays Capital.
The article is interesting, and depressing, throughout. Here is the FT on related issues:
Describing it as a “Ponzi scheme”, Marc Chandler, currency strategist at Brown Brothers Harriman in New York, says simply: “Weak banks are buying weak sovereigns.”
A good paper and model of (part of) the financial crisis
From Gary B. Gorton and Guillermo Ordonez (pdf):
Short-term collateralized debt, private money, is efficient if agents are willing to lend without producing costly information about the collateral backing the debt. When the economy relies on such informationally-insensitive debt, firms with low quality collateral can borrow, generating a credit boom and an increase in output. Financial fragility builds up over time as information about counterparties decays. A crisis occurs when a small shock causes agents to suddenly have incentives to produce information, leading to a decline in output. A social planner would produce more information than private agents, but would not always want to eliminate fragility.
Can “education as signaling” models explain recent changes in labor markets?
Acemoglu and Autor present a few non-controversial stylized facts about labor markets, including falling wages of low-skill workers, flattening of the wage premium for workers with less education than college completion, non-monotone shifts in inequality, polarization of employment in advanced economies, and skill-replacing technologies (and don’t forget the new Brynjolfsson and MacAfee book; it is important).
The simplest model is that, because of information technology, employers demand more skills. The job market responds accordingly, and eventually the education system responds too. The major shifts are driven by changing productivities of human capital, and that is one reason why the human capital model of labor markets has proven so robust. It accounts (mostly) for the big changes in labor market returns.
What would a signalling model predict as the results of skill-biased technical change? I am never sure. Those models are tricky with comparative statics predictions for at least three reasons:
1. Multiple equilibria are common and arguably essential,
2. It is assumed that employers cannot in the short run (medium run?) observe the marginal products of workers, and
3. The (supposed) relevant factor for employers, the degree, is past history and, if not quite carved in stone, credentialed retraining remains the exception in many market segments. It hardly drives wage outcomes or observed changes in wages.
The simplest (non-signaling) model is that wages follow MP, albeit with some lag, and adjusting for a suitably sophisticated notion of marginal revenue product, including morale effects on other workers.
Again, how should skill-based technical change matter in a signaling model? In the model, no employer observes (across what time horizon?) that the MPs of some workers have gone up and that other workers’ MPs have gone down. Yet it seems that changing MPs matter at margins. And if employers can sniff out changing MPs, this implies they can sniff out large MP differences more generally, which limits the scope of educational signaling.
It is a strong result these days that occupation and also job tasks predict earnings better than before (see pp.26-27 in the first link), including relative to level of education. That also seems to run counter to what signaling theories predict. Most likely we are now better at measuring the quality of workers and their educational signals don’t matter as much as they used to. The higher returns to post-secondary education, which account for most of the recent growth in the returns to college degrees (p.145 and thereabouts), are skill-based and they are tightly connected to occupation and job tasks.
These are all reasons why the signaling model for education is not growing in popularity, namely that it does not speak well to current comparative statics and to the current big stories in labor markets.
It is an embarrassing question for signaling models to ask: with what lag do employers get a good estimate of a worker’s marginal product? If you say “it takes 37 years” it is hard to account for all the recent changes in wage rates in response to technology, as discussed above.
Alternatively, let’s say the lag is two years. There are several RCT estimates of the return to education, based on earnings profiles measured over twenty or thirty year periods. The estimated returns to education are high, and if those returns were just signaling-based you would expect the IV-elevated individuals to show up as underskilled and for the credentials-based wage gains to fall away with a few years’ time. That doesn’t happen (if you are wondering, the IV-elevated individuals are those who for essentially random reasons end up getting more education, or an instrumental variable proxies as such, without the elevation being correlated with their underlying quality as workers,).
In other words, the signaling model is caught between two core results — high long-term measured returns to the education of IV-elevated individuals, and technology drives wage changes in the medium-term. It is hard for a signaling model to explain both of those changes at the same time.
There is a way to nest signaling models within human capital models, rather than viewing them as competing hypotheses. Using matching theories, let’s say employers learn the quality of workers they have, but find it hard to estimate the quality of workers they don’t have. IV-elevated workers can’t fool the market/the employer for very long, and so their high pecuniary returns from education really do measure productivity gains. Nonetheless there can be undervalued “diamonds in the rough.” Think of them as geniuses, or at least good workers, who hate getting the education.
From the point of view of these students (or dropouts, as the case may be), the signaling model will appear to be true. They will resent the education and they won’t need the education. If it is costly enough to sample worker quality from the “outsiders bin,” it will remain an equilibrium that a degree is required to get the job, at least provided workers of this kind are not too numerous. If there were “lots and lots” of such workers, more employers would scrounge around in the outsider’s bin. In other words, the anecdotal evidence for signaling fits into a broader model precisely because such cases aren’t too common.
*In Time* (spoilers about the macroeconomic model)
It is rare to see a movie with such a perfectly realized economic model, albeit one pulled from such exotic territory. Imagine a Keynes chapter 17 world where the “own rate of interest” on time — which can be borrowed and lent — rules the roost. Many people are at or near subsistence in their time endowments, and there are economies of scale in supply, so short rates on these loans are high. Those high rates choke off other investments and a version of TGS ensues. Medium-term rates, however, are negative in real terms. Carry around too much time and it will be stolen and you die. The economy has a strongly inverted yield curve and that discourages traditional financial intermediation and investment. Wealth continues to fall, which exacerbates security problems, in turning lowering the negative medium-term real rates even further. A downward spiral ensues. The only way to make money is to buy marginal security (for time endowments) and spend less on that security than you earn on short loans of time. More and more resources go into security, again exacerbating the inverted yield curve. The economics of producing security are also the fundamental source of market power in the economy. Market segmentation reigns and the marginal rates of substitution on time loans are not equated across different social classes.
The hero has read Kalecki (1943) and he operates under the assumption that a redistribution will prove isomorphic to an “Operation Twist” and restore full employment equilibrium, and positive economic growth, by fixing the inverted yield curve. But is that policy commitment credible? Does he have the support of the heroine? You have to watch the movie to find out…
In Time also raises questions about why we find time inequality more objectionable than money inequality. You also can interpret it as a model of a world where health care really works.
This is by no means a flawless film but conceptually it was stronger than I had been expecting. Kudos again to Andrew Niccol, Gattaca is a worthwhile movie too.
Here is Robin Hanson’s review, he liked it less than I did.
Why the current revenue model of higher education is in trouble
The picture for females is also not pleasant, all from the excellent Michael Mandel. Those are simple facts, denied by some.
Non-college grads also have seen declining wages, and so one can look at the “finish college vs. finish high school only” margin and conclude that the return to higher education is robust. Another approach is to look at the “finish college and get on a real career track” vs. “finish college and hang out” margin and conclude the sector is in trouble, which indeed is the case. Don’t get stuck looking at the old margins only, the new and powerful margin, I am sorry to say, is relative to unemployment or extreme underemployment. The status and avoid-shame returns are high enough to keep a lot of people going to college, at current prices, but the falling real wages for graduates aren’t going to sustain an enormous amount of extra sectoral growth, including on the price side. Nor do I expect the preceding orgy of student debt to repeated, at that level, anytime soon.
Has the Keynesian IS-LM model made good predictions lately?
I’ll skip context and links and cut right to the chase. Reinhart-Rogoff and nominal gdp perspectives and TGS views also have been predicting a slow recovery, so while IS-LM has done OK here it wins no special prizes.
What about the “no crowding out” prediction? Since at least the early to mid 1980s, it has been well-known in macroeconomics that U.S. budget deficits do not forecast real interest rates very well and that includes under periods of full or near-full employment. Here is a brief survey by Alan Reynolds on the topic (you can follow up on his references), and he is usually considered a villain by the Keynesians and so he is hardly a Keynesian himself.
There may be a few reasons for the general lack of a connection between deficits and real interest rates in the United States:
1. The supply of capital to the United States is fairly elastic, either domestically or internationally.
2. We don’t have good identifying restrictions on the empirics in the first place. For one thing, controlling for monetary policy is tricky.
3. We haven’t yet seen budget deficits big enough to matter.
4. We are not measuring budget deficits correctly because what matters is the consolidated fiscal stance of the U.S. government, a’la Robert Eisner.
5. Ideas related to Barro’s Ricardian Equivalence hypothesis.
Anyone — Keynesian or otherwise — paying attention to the last thirty years of empirical macro never expected much crowding out of financial capital in the first place. It simply has not been in the cards.
To put it more bluntly, the “no crowding out” result is not much of a predictive victory for Keynesian economics, IS-LM, the liquidity trap, and so on, even though I have read it claimed as such many times. It is a strike against some predictors who were wrong in the first place, especially in the right-wing popular press circa 2009-2010, plus some Republicans who jumped ship on the issue, perhaps because they wanted to attack Obama.
What’s a unique prediction we might look at? It is a common Old Keynesian claim these days, at least from Krugman, that the AD curve is upward-sloping because of a liquidity trap. That would imply that harsh and binding minimum wage hikes, and other wage-propping mechanisms, should prove expansionary. That claim, at least for the Great Depression, has been knocked down fairly conclusively by Scott Sumner. If there is no comparable test on today’s data, it is because we have grown that much wiser.
A model of political corruption
Lessig takes on the model of lobbying as “legislative subsidy” developed by political scientist Richard Hall and economist Alan Deardorff as an alternative to the naive lobbying-as-bribe model. Legislators come to Washington passionate about several issues. Quickly, though, they come to depend on the economy of influence for help in advancing an agenda. They need the policy expertise, connections, public-relations machine, and all the rest that lobbyists can offer. Since this legislative subsidy is not uniformly available, the people’s representatives find themselves devoting more of their time to those aspects of their agenda that moneyed interests also support. No one is bribed, but the political process is corrupted.
That is from Matt Yglesias.
Why I do not like the IS-LM model
1. It fudges the distinction between real and nominal interest rates, so it can put the two curves on the same graph. Every time you write down an IS-LM model you should hear a clock start ticking in your head. The longer the clock ticks, you more you need to worry about this problem because the more that a) the price level may change, or b) expectations about future price level changes will start to matter.
2. It fudges the distinction between short-term interest rates (for the money market curve) and long-term interest rates (a determinant of investment). They’re not the same! Don’t assume they are the same, just to squash the two curves onto the same graph.
3. It leads you to think that the distinction between non-interest bearing currency and short-term interest-bearing securities is a critical wedge for the economy. It also implies that if all currency paid interest (a minor change, most likely, macroeconomically speaking), the economy would behave in a totally screwy way. It probably wouldn’t.
3b. The model leads you to believe that interest rates are more important than they probably are.
3c. For a while it treats “money” as the non-interest-bearing security, and then for a while it treats money as the transactions media behind AD, something closer to M2.
4. It overemphasizes flows and under-emphasizes stocks of wealth. The quantity theory approach, as wielded by Fisher and Friedman, does not induce individuals to make this same judgment. For one thing, this distinction really matters when you’re trying to predict the macro effects of “window breaking.” The flows perspective will usually be more optimistic than a perspective which recognizes both stocks and flows.
5. Those aggregate curves are not invariant with respect to expectations, including expectations of government policy. You don’t have to believe in an extreme version of the Lucas critique to worry about this one. Those curves are conditional and the ceteris paribus assumption is not to be taken lightly here.
6. In the LM curve, what is the embedded reaction function of the Fed? Good luck with that one. Pondering this issue leads you to conclude that the whole model was written for an economy fundamentally different than ours.
7. The most important points, for instance about the significance of AD, one can derive from a quantity theory or nominal gdp perspective (for the latter, see my Principles text with Alex).
Why take on all that extra baggage?
Here is why Scott Sumner does not like IS-LM. After writing this post, I remember I had an earlier 2005 post on why I do not like IS-LM; I didn’t like it back then either.
Addendum: For a few sources, here is Roubini on IS-LM. Here is Wikipedia.
A simple model of unemployment, wage stickiness and ZMP
Following up on yesterday’s discussion of wage stickiness for the unemployed and the employed (Tyler, Alex).
Imagine a farmer whose farm produces 100 bushels of wheat. He hires 10 workers to bring in the wheat, paying each of them 9 bushels. Thus, each worker carries 10 bushels, the wage is 9, the wage bill is 90, and the farmer earns 10.
Now suppose that due to climate change or a swarm of locusts the farm only produces 90 bushels of wheat. If wages were fully flexible then an equilibrium exists in which each worker is paid a wage of 8 leaving the farmer with 10 bushels as before. The farmer doesn’t want to reduce everyone’s wages, however, because that will reduce morale so he fires one worker leaving nine. Each worker now brings in 10 bushels, as before, and is paid a wage of 9, for a total wage bill of 81 leaving the farmer with 9 bushels. The unemployment rate is 10%.
The unemployed worker doesn’t want to be unemployed and offers to work for less, a lot less, say 5 bushels. Even at the lower wage, however, the farmer doesn’t want to hire the worker because the worker doesn’t generate enough additional output to justify even a low wage. In fact, in this scenario the worker has ZMP.
The best the farmer can do in response to the lower wage offer by the unemployed worker is to fire an employed worker and hire the unemployed worker at the lower wage. Eventually this will restore equilibrium but it takes time to cycle through enough firings and hirings to reach full employment. Note also that in this model the farmer only has a weak incentive to do this since in the equilibrium with 10% unemployment he earns 9, almost as much as before. As an aside, also note that in my model the unemployed workers are simply unlucky (as I argued earlier). If they were to switch places with the employed, productivity would be just as high. The unemployed worker has ZMP but is not a ZMP worker.
Since the driving shock that lowers productivity in this model is a real factor (weather, locusts), this is a real business cycle model . That raises a very interesting point. The most that wage flexibility can do in this model is to restore full employment; wage flexibility cannot restore full output. Thus, the workers in this model have a very good reason to dislike wage flexibility. In the equilibrium in which wages fall the unemployed worker is better off by a lot but the 9 employed workers are all worse off than in the unemployment equilibrium. In contrast, in a Keynesian model wage flexibility can restore full output not just full employment. Thus, and somewhat surprisingly, it’s easier to justify wage stickiness in an RBC model than in a Keynesian model since the gains from wage flexibility are so much higher in the latter!
Even in a Keynesian model along the lines of the Sweeney/Krugman babysitter model it will still be the case that lower wages by the unemployed don’t get you far enough to restore equilibrium–although as noted, we will need a coordination failure story in the Keynesian model since in principle everyone would be better off in that model with wage flexibility.
Good thing he didn’t ask Alex to explain the Solow growth model (in French)
He asked an Air Canada fight attendant for 7Up and he got Sprite.
“I’m a little bit disappointed with the lower amount awarded,” Thibodeau said. “But the positive note is that the court recognized our rights were violated on several occasions.”
…So, in 2009, when Thibodeau ordered a 7Up in French, and the English-speaking attendant brought him a — gasp! — different brand of lemon-lime soda, he sued.
“If I take a flight and I’m not served in the language of my choice, and I don’t do anything about it, then my right is basically dead,” Thibodeau told The Globe and Mail. “I was not asking for anything other than what I was already entitled to. I have a right to be served in French.”
It’s a right that Thibodeau — who is a federal employee and happens to speak perfect English — takes very, very seriously.
The full story is here. I suppose one could make a living this way. Which are the French questions most likely to be misunderstood by an English-speaking Canadian? From another article:
It is Thibodeau’s second successful legal action against the airline and its subsidiaries. In 2000, he was refused service in French when he tried to order a 7Up from a unilingual English flight attendant on an Air Ontario flight from Montreal to Ottawa.
Thibodeau filed suit in Federal Court for $525,000 in damages. The court upheld his complaint, ordered the airline to make a formal apology and pay him $5,375.95. Thibodeau was later honoured by the French-language rights group, Imperatif Francais.
For the pointers I thank Graham Rowe. Alex and I explain the Solow growth model — in English — here. Chinese, Spanish, and other editions are on the way.
Does the returns to education literature really test the signaling model?
Here is a comment by Matt, and also by Arnold. Bryan’s response argues that the returns to education tests consider “ability bias” but not “signaling.” For a lot of the tests that is a distinction without a difference, and indeed you can see this on the first two pages of Angrist and Krueger, which discuss “omitted variables that are correlated with educational attainment and with earnings capacity.” The tests still discriminate against the signaling model, even if signaling and ability bias differ in other regards. In a nutshell, artificially or randomly elevated workers fare better in the longer run than the signaling model predicts.
Here’s a parable to illustrate. Imagine a market situation with wages and different education levels observed for two classes of workers — call the locale Honduras. Now compare that to another setting — Nicaragua — where education is handed out on some subsidized, randomized basis. In the latter case some of the low ability group will be induced to get more schooling, and the pool of the educated will contain more low ability individuals in Nicaragua, compared to Honduras.
Now measure the long term earnings and compare.
If the signaling model is correct, the average long-term wage rates of return for the subsidized/elevated group in Nicaragua will be noticeably below the average wage rates of return of the educated group from the separating equilibrium in Honduras. After all, the subsidy-elevated group adds many more “low ability individuals” to the Nicaraguan mix of the educated than one would find in Honduras. According to the signaling model, in Nicaragua eventually the lower skill level of the elevated group will be discovered and their wage rates of return won’t stay so high forever.
But the wage rates of return for the elevated groups do not plummet back to earth and generally they are robust over time. That measures the real learning which went on in school, or so it would seem. Education is good for more than getting a good first job offer right off the bat.
The modern liberal interpretation (which may or may not be true) is that these poor people were waiting for a helping hand up the ladder, and then they took good advantage of it when it came. And if the elevated group in Nicaragua has higher long-term wage rates of return than the educated Hondurans (a result which does sometimes pop up in the data), that is because their lower initial margin of education made them an especially potent investment.
The actual tests are more complicated than this, and I use the country names to make the example easy to follow, not out of verisimilitude. But this example is one way to see some of the intuitions behind why the data do not treat the signaling model so kindly.
One empirical implication is that crude OLS measures of the return to education are much better than they may at first appear. These results are also one reason why most modern labor economists might object to the arguments of Charles Murray.
Here is a recent Brookings piece on the return to education, I have not had time to go through it.