Is Free Trade Good for Everyone? (Reposting, because it seems relevant right now).

Greg Mankiw implies that it is (although not anymore), and that all economists agree that it is.  But it actually isn't.  Who says so?  Economists.

In particular, the workhorse theory of International Trade, the Hecksher-Ohlin Theorem, leads to the Stolper-Samuleson Theorem, which shows that when countries start trading with each other, the relatively abundant factor of production in each country becomes better off, while the relatively scarce factor becomes worse off.   In the US context, this implies that opening up trade will leave capital better off relative to labor, and skilled labor better off relative to unskilled labor.

Does trade increase the total size of economies?  Yes--this is something that economists do agree on. But in the absence of redistribution--something that seems to be anathema to we Americans--more open trade will make low skilled laborers worse off.

In my ideal world, we would pass the Trans-Pacific Partnership (TPP), a potential [quasi]-trade agreement among the US and 11 other countries of the Pacific Rim, and redistribute its bounty such that everyone would be better off.  There is no evidence that our political system would allow this to happen.

Despite all this, I do and will continue to support trade agreements such as the TPP because that there is some evidence that they prevent wars.  Of course, as someone who has had nothing but good fortune in life, it is easy for me to think that the abstract prevention of war is more important than the tangible reduction in other people's already low wages.  

Thoughts from California about a year in Washington

The nice people I worked with at HUD asked me to write a short piece reflecting on my year there.


Five Things I have learned from a year in Government (The things I have written below represent no one’s views other than my own).


1. The federal government is staffed by some remarkable people.

I have been most fortunate throughout my career to work with intelligent, committed and

ethical people. My time at HUD has been no different. I have had the privilege of working

closely with remarkable people at HUD and at other agencies. I would name names, but worry

about leaving someone out. Many people who choose to work in government could receive far

more compensation doing something else, but are motivated by a desire to make people’s lives

better.

Government service is honorable service—this was a commonplace as recently as when I was in

college, and remains so in places like France and Japan. We as a society should respect

excellent government work more than we do right now.


2. The federal government relies too much on obsolete technology.

We see throughout the United States examples of under-maintenance of infrastructure, from a

bridge falling into the Mississippi River to tracks in metro systems catching fire. Less visible, but

just as problematic, is an unwillingness to invest in modern technology systems. Within HUD,

for example, the FHA program relies on systems that are driven by coding in COBOL, a

mainframe (!) language developed in 1959 (!!). Because almost no one uses COBOL anymore,

our university computer science departments don’t train students in its use. As COBOL

programmers retire, it will become impossible to find people to maintain the system.

On a more personal level, I was stunned to learn that my HUD PC had a 32-bit operating system

in a world where 64-bit system have been around for PCs for 13 years. As a practical matter,

32 bit systems are limited in the amount of data they can analyze, whereas 64 bit systems are

nearly unlimited. Many doing HUD work rely on large data sets (for example the Public Use

Microsamples of the Census and the American Community Survey). The current standard for

operating systems makes it relatively easy to use these datasets; the old standard requires

compromises.


3. Academics teaching policy issues should spend some time in government, if for no other

reason than to appreciate the importance of details.

Before I joined HUD, I thought I was an expert on mortgage backed securities (I even wrote a

book about them). Spending time with the good people of Ginnie Mae revealed to me that I

really wasn’t. I did understand how to evaluate cash flows from MBS, but I didn’t really

understand how Ginnie Mae operated at all. What I learned is that mortgage default risk is not

the only risk that needs to be managed; issuer risk needs to be managed as well (while

FHA/VA/Rural housing insures mortgages, Ginnie Mae insures the issuers of mortgage backed

securities that fund mortgages). Suppose a mortgage goes into default and so a Ginnie Mae

issuer needs to buy it out of a pool. That issuers needs to have enough cash on hand to survive

until it receives an insurance payment from FHA/VA/Rural Housing. For non-bank lenders, this

could be a problem in times of low liquidity.

We academics are good at thinking about analytics; we are not so good at thinking about

operations. Yet without good operations, analytics lose much of their value.


4. Regulators care too much about details

Regulations about disclosures make the point. Government sometimes worries too much

about the details of disclosures and not enough about their effectiveness.

Consider disclosures for the price of a long-term fixed rate mortgage. For consumers to be well

informed about what they are getting themselves into, they need to know two numbers: total

upfront cash payment, and the all-in interest rate (which might include a mortgage insurance

payment). Armed with these two numbers, consumers can comparison shop in a

straightforward manner.

The first page of the new TRID closing form does this well, and lenders should absolutely be

held responsible for presenting this page accurately. But the details on the following pages are

essentially irrelevant to consumers and, by increasing the length of the form five-fold, make it

more complicated and confusing than necessary.


5. Few people know who the third most powerful person is in the Federal Government.

My guess is that the name Shaun Donovan is not well known outside the Beltway. But pretty

much nothing gets done without the approval of the OMB director.

Housing now

From HUD's The Edge.


This past fall, the state of housing reached something approaching normalcy in some dimensions (new construction and price) but continued to worsen in others (rental affordability and the homeownership gap between underrepresented minorities and others).


When President Obama took office in January 2009, residential construction in the United States was at its lowest level since World War II; only 490,000 units were built that month (on a seasonally adjusted annualized basis). By November 2015, that total had risen to nearly 1.2 million units — an increase of 139 percent over the course of the administration. This total has still not, however, reached the average level of new construction over the past 55 years of 1.4 million units.


The below normal (if substantially improving) levels of construction explain why housing prices have recovered substantially from their troughs. Prices in all 20 Case-Shiller cities are well above their troughs, in part because the paucity of construction has led to falling vacancy rates nearly everywhere. In two cities, Dallas and Denver, prices are at all-time highs, and Portland, San Francisco, and Boston have recovered all of their losses. A particularly noteworthy fact is that prices have recovered while the homeownership rate has declined. The price story is an absence of supply story.


Although the demand for owner housing has been stagnant, the demand for rental housing has soared, pushing up rents even in the face of strong multifamily construction. Rental demand has risen sharply for several reasons.


First the marriage rate in the United States has been falling steadily. According to Pew, 65 percent of the “greatest generation” were married by the age of 35; among millennials, the marriage rate is only 26 percent. After taking into account age, education, race, ethnicity, and geography, married couples are 22 percentage points more likely to be owners than singles. If millennials continue to postpone (or avoid) marriage, the ownership rate will continue to fall.


Second, racial and ethnic minorities, again after taking into account the standard list of demographic and economic characteristics, have lower ownership rates than non-Hispanic whites. The population of African Americans, Asians, and Hispanics is growing much faster than the population of non-Hispanic whites. African Americans, for instance, have a homeownership rate that is 17 percentage points lower after controls than it is for non-Hispanic whites. If homeownership rates among the groups whose population is growing fastest continue to lag, the pressure on the rental market will become even greater.


The reasons for lagging ownership among minorities are doubtless varied and complex, but part of the gap almost certainly results from continued discrimination in housing markets and issues with access to credit. Turner and Yinger have demonstrated the continued existence of discrimination, but we will say a few words about access to credit here.


One group of Americans, now very large, does not have access to mortgage credit at the moment: those whose homes went into foreclosure during the global financial crisis. RealtyTrac puts the number of homeowners who were foreclosed upon at [nearly 7] million, or about 5.5 percent of U.S. households. These households overwhelmingly became rental households (some doubled up with other families or moved back to their parents’ homes), and this phenomenon alone put sudden pressure on rental markets.


They also became ineligible for mortgage debt for at least 3 years (the number of years the Federal Housing Administration requires to have followed foreclosure for borrowers to become eligible for a loan) to 7 years (the minimum number of years post-foreclosure government-sponsored enterprises require before issuing a loan). Many of these potential borrowers are about to become eligible again for mortgages, and should thus relieve pressure a bit from rental markets. But many, having been traumatized by the homeowning experience, might decide to remain renters. As it happens, minorities bore a disproportionate share of the foreclosure burden.


The other access to credit issue involves access to wealth and credit scoring. Many researchers have shown that children’s wealth is highly correlated with parents’ wealth. African Americans, who as a group were stripped of wealth and who were over generations systematically denied access to credit, have less wealth than non-Hispanic whites even after controlling for income and education. The absence of wealth among older generations means that it is more difficult for younger generations to accumulate downpayments and establish excellent credit scores. This puts generation after generation of minorities at a disadvantage when it comes to owning a home.


The combination of diminishing numbers of married couples, the fallout from the recession, and access to credit issues have pushed rental demand and therefore rents as well. While there are many methods for measuring rental affordability, perhaps the most telling is that in the vast majority of American metropolitan areas, median-income renter households must spend more than 30 percent of their gross income on the median rental unit. Economists like to talk about “choice,” suggesting that people “choose” to live in expensive housing. But both within and across our cities, affordable rental housing is not a choice that is available to the median-income renter.

A book that changed my life

"We believe that the confounding of the aggregate with the individual is as dangerous as it is pervasive...."  Page 81.

Apartments, Energy and Bad Incentives

I am spending this academic year working in Washington working at HUD, so I am renting an apartment here.  When I signed the lease, I understood that I would pay utilities; what I didn't understand, because I did not read the lease carefully enough, is how I would be charged for utilities.

Even though I live in a professionally managed building that has something like 400 units, and even though each unit has its own circuit breaker the units are not metered individually.  Instead, utility costs are allocated on a pro rate basis based on unit square footage and number of residents in the unit.

Since moving into this place, I have been very conscientious about setting the air conditioner at 80 degrees F. before leaving the apartment for the day.  I will do my best to continue to do this, but the fact is, my principal motivation for doing so was thinking that I could reduce the very expensive cost of cooling an apartment in the hot DC summer.

Of course, as one of 400 units, my influence on electricity usage for the complex is small.   There is essentially no financial reason to avoid blasting the AC all day long.  It must be the case that how one consumes air conditioning has a large impact on how much one spends on air conditioning.  In fact, people who like Bikram Yoga could drive their air conditioning costs to nothing.

A Google search on the cost of individual metering implies that the cost of installing meters for electricity would be about $300-$500 per unit.  Summer electricity costs in my unit are about $100 per month, so if price incentives lead to a 10 percent saving, each unit could save about $60 per year on electricity (I am applying the savings to six months).  Beyond this, of course, energy use produces negative externalities, so the social benefit of metering would be greater than the private benefit. This implies the benefits of metering exceed the costs. [If I am completely wrong about either the cost of metering or the savings arising from it, I would be happy to hear about it].

So why don't landlords do this?  The answer might be that they can't get enough extra net rent from tenants to justify paying for metering.  The only private cost they bear is not being able to charge as much rent as they otherwise might.  It might be worth doing serious analysis to determine the social benefits of metering in the context of individual apartments, and whether such metering should consequently be subsidized.

   

LA has zoned itself out of the ability to house its residents (h/t Matthew Glesne)

Once upon a time, the zoning in Los Angeles would have allowed for 10 million residents to live within its municipal boundaries.  Greg Morrow, in his UCLA dissertation, "Homeowner Revolution: Democracy, Land Use and the Los Angeles Slow Growth Movement 1965-1992," documents how this was eroded over time:

So LA really did create a moat around itself and pulled up the drawbridge.  For those of us who think the blessings of cities should be shared widely, this is a shame.

It is hard to feel urban form sometimes.

I have spent a fair amount of time in Sao Paulo over the past 3-4 years, and always thought it sprawled more than LA, because it takes forever to get from one side of the place to the other.  So was I surprised when I went to Google Earth and looked at both of them from the same elevation.

Here is LA:

Now here is SP:

It is far more compact.  Metro LA has about 18 million people; SP has about 20 million. But it takes about 2 hours to get from Santa Clarita in the west to San Bernardino in the east--the distance between the two is 85 miles; it can take four hours to go just 30 kilometers in SP.  Sao Paulo feels much larger to me.

Should Finance Departments Pay Pigou Taxes?

The Economist leads me to an article by Stephen G Cecchetti and Enisse Kharroubi:

 

The purpose of this paper is to examine why financial sector growth harms real growth. We begin by constructing a model in which financial and real growth interact, and then turn to empirical evidence. In our model, we first show how an exogenous increase in financial sector growth can reduce total factor productivity growth.2 This is a consequence of the fact that financial sector growth benefits disproportionately high collateral/low productivity projects. This mechanism reflects the fact that periods of high financial sector growth often coincide with the strong development in sectors like construction, where returns on projects are relatively easy to pledge as collateral but productivity (growth) is relatively low.  

 Next, we introduce skilled workers who can be hired either by financiers to improve their ability to lend, increasing financial sector growth, or by entrepreneurs to improve their returns (albeit at the cost of lower pledgeability). We then show that when skilled workers work in one sector it generates a negative externality on the other sector. The externality works as follows: financiers who hire skilled workers can lend more to entrepreneurs than those who do not. With more abundant and cheaper funding, entrepreneurs have an incentive to invest in projects with higher pledgeability but lower productivity, reducing their demand for skilled labour. Conversely, entrepreneurs who hire skilled workers invest in high return/low pledgeability projects. As a result, financiers have no incentive to hire skilled workers because the benefit in terms of increased ability to lend is limited since entrepreneurs’ projects feature low pledgeability. This negative externality can lead to multiple equilibria. In the equilibrium where financiers employ the skilled workers, so that the financial sector grows more rapidly, total factor productivity growth is lower than it would be had agents coordinated on the equilibrium where entrepreneurs attract the skilled labour. Looking at welfare, we are able to show that, relative to the social optimum, financial booms in which skilled labour work for the financial sector, are sub-optimal when the bargaining power of financiers is sufficiently large. 

Maybe the lesson is that finance departments should subsidize physics/chemistry/engineering departments.

One reason to worry about US inequality...it is really bad for our babies.

My colleague Alice Chen, along with Emily Oster and Heidi Williams, have a new paper that explains differences in the infant mortality rate in the United States and other OECD countries. Despite its affluence, the US ranks 51st in the world in infant mortality, which puts it at the same level as Croatia.

One reason the US performs poorly on the infant mortality measure actually reflects differences in measurement between it and other countries--babies born very prematurely in the United States are recorded as live births, but in other countries might be reported as miscarriages.  Because extremely premature babies have higher mortality rates, their inclusion in the US birth and mortality rate makes the US look relatively worse.

Nevertheless, when Chen, Oster and Williams control for reporting differences, and focus on microdata from the US, Austria and Finland, they find that the US continues to lag the others in terms of first year survival.  What is particularly interesting is that the difference between the US and other countries accelerates over the course of the first year of life--as neonatal threats recede, the position of the US worsened relative to Austria and Finland.

Here is where inequality comes in--if when Chen and co-authors look at children born to advantaged individuals (meaning married, college-educated and white) in the US, they survive at the same rates as their counterparts in Austria and Finland.  But the trio find that children of disadvantaged parents in the US have much lower survival rates than children of disadvantaged parents in the other countries.  This may well be because Europe's safety nets make the disadvantaged less disadvantaged.

(Dylan Matthews blogs on this paper also).

No comment necessary

The Violence Policy Center put out a press release this morning relating gun ownership rates to gun death rates.  I wrote to them asking for the complete data, and plotted it.  Here is the plot.

In case you're interested, the bivariate regression's R-squared is .6.

Cities and the Environment--A first order effect?

I was reading a story about peak driving over the weekend.  In the course of reading the story, I discerned that we here in California drive far less than the average American.  In fact, California ranks 41st among the states in per capita driving:

Date are from the Insurance Institute for Highway Safety.

Given the stereotype about California (as a place where everyone drives, always), this was a surprise to me.  But then it dawned on me--when one excludes the District of Columbia (which is kind of like a state, just without representation), California is the most urbanized state in the country.  And so I drew a scatter plot of VMT per capita against urbanization by state:

The negative correlation is quite apparent. To anyone who might be interested, here is the bivariate regression:

       mpc |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       var4 |  -81.73815    14.1223    -5.79   0.000     -110.118   -53.35832
       cons |   15994.33   1066.959    14.99   0.000      13850.2    18138.47
------------------------------------------------------------------------------

So a one percentage point increase in urbanization is associated with an 81 mile per year reduction in driving.  I think the direction of causality is not too big a problem here (it is hard to tell a story that more driving causes a reduction in urbanization).  So Matt Kahn, Ed Glaeser and Richard Florida are all right--cities are environmentally friendly!

[BTW, a little Googling led me to a paper that relates to all this].

How choosing the right discount rate matters to Max Scherzer

My student Hyojung Lee sent me to a cute article about how Max Scherzer's $210 million contract is not really a $210 million contract.  Because Scherzer is getting $15 million per year over 14 years, the present value of the contract is substantially less than $210 million; it is also worth less than a contract that pays $30 million per year over the seven years he is expected to pitch.

But Dave Cameron (the author of the piece) assigns a 7 percent discount rate to the contract.  The present value of $15 million per year over 14 years at a 7 percent discount rate is about $131 million. He chose 7 percent as the discount rate because that is the expected long run return of investing in the stock market.

A contract is not, however, like a stock.  It is a bond--contracts have seniority to equity, and guarantee a particular cash flow.  I would guess the Nats (unlike the Expos) are something like a BBB company.  The current bond yield on BBB issues is currently about 3.6 percent.  Discounting the value of the Scherzer contract at 3.6 percent produces a present value of $163 million.  Not that $131 million isn't nothing, but $163 million is a lot more.

[Update: Adam Levitin says that MLB teams are more like AAA (in bond rating, not playing quality, except, perhaps for the Diamondbacks last year), because all of baseball backs team contracts (when the Rangers went bankrupt, all players got paid).  That would drive the discount rate to 2.8 percent, and raise the value of the contract to $172 million.]

Is Houston really vulnerable to recession? {Updated answer: maybe}

So after reading Paul Krugman's prediction that Texas was vulnerable, I did two things I should have done before.

First, I looked to see whether the share of jobs in the mineral industry in Houston now are any lower than they were in 1986 (the first year for which I could easily download data).  The answer is that, if anything, it is slightly more reliant now.

Second, I plotted the unemployment rates for Houston and the US against real oil prices.  This is what I found:

Two things: Houston's unemployment moves with the business cycle (so the stronger US economy should help it), but also that the relative unemployment rate of Houston fell as the real price of oil rose between 2000 and 2007.

We can summarize this in a regression:

HOUE - USUE = -1.5 -.83 ln(real oil price).

The t-stat on the coefficient on real oil price is 11.  So what this approximately means is that a 50 percentage point drop in real oil price will produce a 0.41 percentage point increase in Houston's unemployment rate.  Now this is all descriptive, and is not a serious model of the region, but it nevertheless provides evidence that Houston's relative employment performance has been affected by oil prices. Given that Houston it is as reliant on energy for jobs as ever, it probably will continue to be affected as well.

The limits of knowledge in economics, Part V

How much does it cost you to live in your house?  If you are a renter, the answer to that question is fairly straightforward (although if your rent includes a gym membership and heat, it is not clearcut what the simple cost of occupying your place is).

But suppose you are an owner.  What is it costing you every month or every year to live in your house?  The truth is, you don't know with a great deal of precision, and neither does anyone else.

There are two ways to look at the issue.  One is to look at something called owner's equivalent rent. In principle, one could determine owner's equivalent by offering her house for rent, and seeing what it would fetch in the rental market.  Needless to say, owners don't do this very often.  Another way to calculate owner's equivalent rent is to find a perfect comparable for an owned house in the renter market, and impute the rent for the owner.  In the next episode of this series, we will discuss the problems with doing that.  It should be fairly obvious that they are large.

The alternative to owner's equivalent rent is user cost, which seeks to compute the cost of owning to those who live in their own houses.  [Pat Hendershott is the guru of user cost.  See a typical paper of his here]. The formula for the user cost of housing is

uct = Vt[((1-m)rt + mit)(1-ty) + τp(1-ty) + d - π 

where uct is user cost at time t, Vt is the value of the house at time t, m is the loan-to-value ratio, rt is the opportunity cost of equity, it is the mortgage interest rate, ty is the marginal income tax rate, τp is the property tax rate, d is depreciation and π is expected house price appreciation.  This formula calculates the after-tax cash-flow cost of owning (including the opportunity cost of equity), adds depreciation and subtracts expected appreciation.

Of all these elements (and this formula doesn't incorporate everything, but it is close enough), the only thing we know with near certainty is the marginal income tax rate: once one calculates reported taxable income, one can know the marginal tax, which is set by statute, with certainty.

Everything else?  Up in in the air.  On a year-to-year basis, we don't know the value of our houses with certainty.  We don't know with certainty the opportunity cost of equity. While we know the coupon rate of a mortgage, we don't always know its total cost until we extinguish it, because mortgages with fees and points (and sometimes, even prepayment penalties) are amortized over time, and the life of a mortgage is generally considerably shorter than its term, as households refinance their mortgages or sell their houses.  We don't know property taxes until an assessor determines assessed value, which is usually at least a little different from market value.  Depreciation is difficult to measure.  Finally, we are pretty lousy at forecasting the values of our houses.

But let's say we are good at forecasting house prices, and you think the value of your house is going to increase by $5,000 over the next year.  Is this the same as being handed a check for $5,000?  No, because you still need to live somewhere.  If your house goes up by $5,000 in value, so to does your neighbor's.  The only way to cash in on your $5,000 is to downsize.  Pocketing the $5,000 and downsizing may leave you better off, but not as well off as just having $5,000.  So the user cost formula does not exactly get user cost right.

Is Houston really vulnerable to recession?

My inbox is filling up with dire warnings about the near-term future of Houston's economy.  After all, the price of oil has dropped by 50 percent, and we know how reliant Houston is on energy.  Except, perhaps, it is not.  Let's look at a Bureau of Labor Statistics chart that gives the composition of employment in Harris County, Texas at the end of 2013:

On the one hand, what the chart does't show is that the location quotient for natural resources and mining in Harris County is 2.78, meaning that it is almost three times more reliant on the sector as the rest of the country.  Despite this, however, only about five percent of jobs in Harris County are in that sector.  The county in which Houston sits is actually very well diversified, with 75 percent of its jobs being in the service sector.  Put another way, over the past several years, Harris County has been creating more total jobs every two years as there are jobs in the entire natural resources and mining sector.

NRS jobs do pay well, which means that any reductions in these jobs would have a multiplier effect (but we are generally terrible at estimating regional multipliers).  But clearly something is happening in Houston that makes it attractive to employers that have nothing to do with the energy sector.  My suspicion is that inexpensive housing is one of those things.

I could be completely wrong about this, but it seems to me that the decline in oil prices will more likely bring slower growth--as opposed to recession--to Harris County. 

The limits of knowledge in economics: Part IV

The workhorse model of international trade is the Heckscher-Ohlin Model.  It is essentially the model that formalizes the Ricardian model of trade students in principles of economics learn.  It demonstrates that countries export goods in which they have a comparative advantage; it moreover shows that it is comparative advantage, rather than absolute advantage determines patterns of trade.

In the context of the HO model, comparative advantage is defined by the relative abundance of a production factor.  Let's say country A has 10 units of labor and 10 units of capital, which country B has 8 units of labor and 4 units of capital.  Country A has an absolute advantage in both labor and capital, but B has a comparative advantage in labor, because its labor to capital ratio (i.e., 2) is higher than  country A's (1).

The HO model thus predicts that country A will export a good that needs relatively more capital for production to B, and that country B will export a good that needs relatively more capital for production to A.

Everything works beautifully in a world with two countries, two goods and two factors.  But the world is nothing like that--it has many more countries, goods and factors than 2.  Is this a big deal?

The whole point of economic modeling is to isolate the impact of a particular phenomenon, ceteris paribus.  Ceteris paribus is a favorite phrase in the economist's lexicon, and means "all other things being equal or held constant."  Sometimes asserting ceteris paribus is innocuous; often it is not.

A small change in the HO model creates serious problems.  As Alan Deardorff showed, if the number of goods is greater than the number of factors of production, patterns of trade become indeterminate. The mechanics of the problem are simple: when the numbers of factors equal the number of goods, solving the pattern of trade problem involves equal numbers of equations and unknowns.  This equality disappears when there are more goods than factors, we become unable to determine what is produced where.  Allowing more goods than factors is not a trivial change to the model--it has an enormous impact on the analytical outcome.  It is also a change that better reflects the reality of the world.

This is a fundamental problem with HO that is easiest to explain.  Deardorff takes us through more sophisticated argues that show other problems with the predictions of the HO model.  He expresses concerns that the model:

(1) implies fractions of good produced or trade routes utilized that are (unrealistically?) low;
(2) has a solution that is hypersensitive to [trade costs].

So the fact is we really don't have a theoretical model that predicts patterns of trade well.  Yet we for years made lots of policy decisions based on a model that has lots of limitations. 

[Update in response to comment: Krugman and Helpman are great in reconciling how interindustry trade happens and why countries with similar factors trade with each other.  But the problems outlined by Deardorff about developing a robust general equilibrium model that predicts patterns of trade remain.]

    

The limits of knowledge in economics, Part III.

Economists think a lot about preferences, and are more interested in what people do, instead of what people say they will do. When people make work-leisure decisions, consumption decisions, and investment decisions, they are revealing their preferences, and we economists attempt to gather information about the broader economy based on these revelations.

Drawing inferences from revealed preferences can work if individual preferences meet four seemingly simple assumptions (Hal Varian's Microeconomics Analysis provides the clearest exposition I know of micro theory.  There is also a nice discussion here).

(1) Preferences are complete.  This simply says that if I am faced with a choice of two consumption bundles, I can always say that one is at least as good as the other.

(2) Preferences are reflexive.  This simply says that any bundle is always as good as itself.

(3) Preferences are transitive.  This simply says that if bundle A is better than bundle B, and bundle B is better than bundle C, then bundle A is better than bundle C.

(4) Preferences are strongly monotonic.  This simply says I never prefer less to more.

These may seem like mild assumptions, and they would be, except that people change their mind.

Perhaps there is a restaurant you go to on a regular basis.  Its menu stays the same, and the prices stay the same over a reasonable length, so your choice set remains constant.  Yet this week you might have the beef burger, next week the veggie-burger, and the following week the cajun chicken sandwich.  In doing so, you have violated assumptions (1) and (3).

Except that, in a sense, you haven't.  Suppose an element of the choice set is the time at which you eat your sandwich.  Under these circumstances, the menu does change because it is offered at three different times.  So we can preserve our theoretical assumptions.

But now if we want to estimate preferences with precision, we have a difficult problem, because we have to estimate in far more dimensions than the data can support.  So anything we infer about preferences will necessarily be approximations.

This is not to say that the general axiom of revealed preferences isn't a powerful tool to learn things about the economy: Varian's lecture on the subject makes a pretty compelling case that it is.  But it will always be an imprecise powerful tool.

The limits of knowledge in economics, Part II

Everyone--and I mean everyone--who does empirical analysis should read Charles Manski's Identification Problems in the Social Sciences (I am happy to say I took advanced econometrics from Manski when I was a Ph.D. student at Wisconsin).  The book reminds us that we are constantly relying on unstated assumptions when we do statistical analysis, and we need to do a better job of stating them.

The focus of this post involves a simple issue: extrapolation.  Let me show two graphs from Manski's chapter:

Suppose one wanted to infer y based on x.  Obviously, as the sample size gets larger, the confidence interval gets smaller, for the set of x that we are able to observe.  Note that in this instance, however, no x between 4 and 6 is sampled.

Most empirical analysis would simply assume that E(y|x=5) is some smooth function that gives weights to E(y|x=4) and E(y|x=6).  Put in English, one would just draw some sort of line between the x,y relation at x=4 and the x,y relation at x=6, and read off an x,y relation for x=5.

But doing this involves an important assumption: that y doesn't go flying off in one direction or another at x=5.  We actually cannot know this, because we have no observations at x=5; indeed, maybe the reason we never observe x=5 is because y is highly unstable at that point.  Just as problematic (perhaps more so) is predicting y when x > 9.

I am pretty sure that it is hard to go a day without reading something that involves someone extrapolating outside the support of observed data.  Sometimes it is necessary to do this, but when we do, we should always say so.

The limits of knowledge in economics, Part I.

I rather like this paragraph from Ed Leamer's Journal of Economic Perspectives piece on the utility and limits of econometrics:

Finally, I think that Angrist and Pischke are way too optimistic about the
prospects for an experimental approach to macroeconomics.  Our understanding
of causal effects in macroeconomics is virtually nil, and will remain so.  Don’t we
know that?  Though many members of our profession have jumped up to support
the $787 billion stimulus program in 2009 as if they knew that was an appropriate
response to the Panic of 2008, the intellectual basis for that opinion is very thin,
especially if you take a close look at how that stimulus bill was written.  

The economists who coined the DSGE acronym combined in three terms the
things economists least understand:  “dynamic,” standing for forward-looking decision
making; “stochastic,” standing for decisions under uncertainty and ambiguity; and “general equilibrium,” standing for the social process that coordinates and
influences the actions of all the players...that’s what we
do.  We seek patterns and tell stories [italics added].

The point is correct: among other things, we have nothing like the necessary degrees of freedom (not exactly the same as observations, but to a lay person, close enough) necessary to identify causal relationships in the macroeconomy with anything like certainty. We are now in our 11th business cycle since World War II, and as much as we like using high frequency data (I, for one, am guilty as charged), that really means in an important sense we have 11 observations about the post-War macroeconomy.

All that said, I did support the stimulus, because I have a Bayesian prior that Keynes was basically right.  In particular, the ideas that high unemployment can result from inadequate aggregate demand, and that in turn that high unemployment is an unrecoverable waste of resources, and, finally, that deficit spending can spur aggregate demand all make sense to me.

At the same time, George Akerlof lists example after example of how new classical macro-theory is rejected by empirical evidence (such as it is), while also showing that the empirical evidence, such as it is, is consistent with Keynesian predictions.  But this is still different from confirming that Keynes was right, something we are unable to do with data.

So back to Leamer. Elsewhere in the paper, he talks about three-valued logic: the ability to use evidence to come to a yes, and no, or an I don't know.   The honest thing to say about macroeconomics is "I don't know."  Alas, while the world is uncertain, policymakers still need to make policy decisions.

If you think we're post-racial, read the lead article from the November 2014 American Economic Review

Alesina and La Ferrara conclude:

This paper proposes a test for racial bias in capital sentencing in the US over the period 1973-1995. We use the share of judicial errors in rst degree sentencing as an indicator of racial bias of such courts. Using an originally collected dataset, we uncover a bias against minority defendants killing white victims. The bias is present, according to our test, only in Southern States. More precisely, according to our interpretation rst degree courts tend to place less weight on the possibility of condemning an innocent in cases of minority defendants with one or more white victims relative to minority defendants who did not kill whites. The same does not hold for white defendants. This result is not explained by differences in observable characteristics of the crime or of the trial, nor by the ideological orientation of appeal courts.

The paper is really well done.