The Year I Beat Bill Gates

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Shane Greenstein writes,

Gates misinterpreted the value of the Internet’s commercial prospects. This error would take three interrelated forms in its conventional assessment:

1. Underestimating the Internet’s value to users;
2. Underestimating the myriad and clever ways entrepreneurs and established firms would employ Internet and web technologies to provide that value for users;
3. Underestimating the ability of Internet firms to support applications that substituted for Microsoft’s in ther marketplace.

This is from How the Internet Became Commercial, Greenstein’s new book. I started my business on the Internet in April of 1994. Gates did not become a believer in the Internet until a year later.

Greenstein offers a well-judged analysis of the business strategy and Internet governance issues during the first decade of the Internet’s commercialization, starting in 1994. However, I think that there is still plenty of room for someone to write another book on this historical episode. I would like to see a book that makes the dynamics more vivid.

In the introduction, Greenstein sketches a few timelines on which he lists events. I am not clear whether he chooses the events for their significance or to try and help the reader understand the order in which certain developments occurred. In any case, his choices are mostly very different from what mine would have been.

I actually would include several timelines:

–the release date and processing speed of Intel’s chips. Another would show

–the amount of hard disk storage on a top-selling personal computer each year.

–the speed of the most commonly used Internet connection each year. I remember when 28.8 Kilobytes per second was an upgrade.

–the number of people with Web access each year. When I started my business, unbeknownst to me that figure was less than a million. I had read, correctly, that there were 20 million Internet users in the U.S., and I very naively figured that this was approximately the number of people with Web access. The Web did not become a mass-market phenomenon until the fall of 1995, when AOL began offering Web access and Microsoft released Windows 95.

–the total number of web sites and the top five web sites in terms of traffic each year.

–well-hyped businesses that failed, such as MecklerWeb, Web TV, and PointCast Network.

–buzzwords that no longer have meaning, such as portal and push technology.

–creation of important software and protocols, such as JavaScript, Java, Flash, MP3, JPEG, and Linux.

–appearance of iconic web sites, such as Yahoo, Amazon and Google

–fading of once-iconic web sites, such as AltaVista, the NCSA home page, and the Netscape home page.

–Internet IPOs, by year

My point is that the environment evolved very rapidly. Your business strategy could not be based on what was there at the time. It had to be based on a guess about what was coming.

I describe my business experience in those days as a sequence of miscalculations, because I got so many things wrong. But I made some fundamentally good guesses about what was coming, and that was sufficient.

How Bad is Financialization?

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Noah Smith writes,

For a long time, and especially since the financial crisis, many people have suspected that financialization is bad for an economy. There is something unsettling about watching the financial sector become a bigger and bigger part of what people do for a living. After all, finance is all about allocation of resources — pushing asset prices toward their correct value so businesses can know what projects to invest in. But when a huge percent of a country’s effort and capital are put into finance, there are less and less resources to reallocate. We can’t all get rich trading houses and bonds back and forth.

Pointer from Mark Thoma.

1. Economists have no idea how to measure the value created by the financial sector. Ask any economist the following question: how should we define/measure the output of a commercial bank? You will hear the sound of crickets–even among economists who purport to study economies of scale in banking! An even more difficult question is how to measure the output of an investment bank.

2. Mathematical economics, notably the Arrow-Debreu general equilibrium model, implies that the value produced by the financial sector is exactly zero. Note Smith’s phrase “pushing asset prices toward their correct value.” This strikes me as a very truncated view of the role of financial institutions, but even so it is ruled out by Arrow-Debreu, in which prices are determined by a set of equations without any agent in the economy doing any “pushing.”

What should we conclude from (1) and (2)? One possibility is that the value of the financial sector is close to zero. The other possibility is that the cult of mathematical modeling has left economists unable to describe the role of financial institutions in the economy. My money is on this latter possibility.

Economists’ analysis of the financial sector is close to 100 percent mood affiliation. You will find many economists who are convinced that the failure of Lehman Brothers had major economic effects. You will not find a carefully worked-out verbal description of this, much less a mathematical model.

Note that I do not cheer for large banks or for mortgage securitization. My thinking on the financial sector is spelled out more in the Book of Arnold.

Here, the point I am trying to make is that not having a grasp on what financial institutions do should be an indictment of economists, not of the financial sector.

Another AS-AD Anomaly

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Timothy Taylor writes,

[Alan] Krueger argues that the patterns of wage changes and unemployment are roughly what one should expect. He focuses only on short-term employment (that is, employment less than 27 weeks), on the grounds that the long-term unemployed are more likely to be detached from the labor force and thus will exert less pressure on wages. Increases in real wages are measured with the Employment Cost Index data collected by the US Bureau of Labor Statistics, and then subtracting inflation as measured by the Personal Consumption Expenditures price index. In the figure below, the solid line shows the relationship between short-term unemployment and changes in real wages for the period from 1976-2008. (The dashed lines show the statistical confidence intervals on either side of this line.) The points labelled in blue are for the years since 2008. From 2009-2011, the points line up almost exactly on the relationship predicted from earlier data. For 2012-2014, the points are below the predicted relationship, although still comfortably within the range of past experience (as shown by the confidence intervals). For the first quarter of 2015, the point is above the historical prediction.

As an aside, note the particular selection of data series. I am not saying that Krueger is wrong for choosing short-term unemployment, the employment cost index, and the PCE deflator. In fact, I think he shows good taste here. But there are other choices available, and I can think of economists who have defiantly done so, cheered on by other prominent economists.

What I wish to point out is that the relationship as depicted is an anomaly with respect to textbook AS-AD, including both Keynesian economics and Sumnernomics. Timothy Taylor refers to the relationship as a Phillips Curve. However, the Phillips Curve relates nominal wages to unemployment, and the chart shows real wages and unemployment. Although in standard macro nominal wages may rise as the unemployment rate falls, real wages are supposed to move in the opposite direction. In standard macro, aggregate supply is derived from movement along the demand curve for labor. When real wages rise by less than productivity increases, demand for labor rises and output goes up. When real wages rise by more than productivity increases, demand for labor falls and output goes down.

Thus, rather than confirming conventional macroeconomic analysis, Krueger’s chart demonstrates an anomaly. In fact, this is hardly a new anomaly. The procyclical behavior of real wages was something that I had observed when I was in graduate school more than 40 years ago.

Of course, you can modify the Keynesian model to accommodate procyclical real wages. Or, you can find data that you believe demonstrate countercyclical real wages (I think that Sumner would try this latter approach). But that is because Keynesian economics is what I call an interpretive framework. How many anomalies you can tolerate before you discard an interpretive framework is a matter of choice. For me, the AS-AD paradigm has too many anomalies to live with.

The WaPo on Government Workers

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One one page, Joe Davidson writes,

[Congressman Paul Ryan] viewed federal employees as a privileged class.

On the facing page, Lisa Rein writes,

A year after auditors documented tens of thousands of federal workers on paid leave for at least a month and longer stretches that exceed a year, close to 100 Department of Homeland Security employees still are being paid not to work for more than a year.

The large number persists even after the Obama administration urged agencies in June to curtail their reliance on what is known as administrative leave, the government’s go-to strategy for dealing with employees facing allegations of misconduct.

While employees stay home, they not only collect paychecks but also build their pensions, vacation and sick days and move up the federal pay scale.

No comment.

John Cochrane on Public Finance

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And other things. But on public finance, he writes,

The central goal of a growth-oriented tax system is to raise the revenue needed to fund necessary government spending at minimal distortion to the economy, and in particular minimizing the sorts of distortions that impede the growth process.

This is a very basic statement, and I believe that it would be difficult to come up with a good economic argument against it. What it implies is that using the tax code for social engineering is entirely wrong. Subsidies should be subsidies, not tax credits or tax deductions.

But the implications go further. Poor people face very high implicit marginal tax rates, because of the hard cut-offs on benefit programs. As you know, I favor consolidating all benefit programs into a single flexible benefit taxed at about a 20 to 25 percent rate. Also, taxes discourage work (the payroll tax) and thrift (the corporate income tax and other taxes on capital).

There is still room for disagreement about the trade-off between redistribution through taxation and economic growth. But there are many tax reforms on which economists could agree, politics aside.

Idiosyncratic Charts

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Kevin Erdmann writes,

there was little change in the share of securitized mortgages during any of the boom years from the mid-1990s to the height of the boom. The share of these pools was 57% in 1995 when rent inflation began to rise, it peaked at 62% by 2002 before the steepest moves in home prices, and then declined back to 59% at the end of 2005 when housing starts and home prices peaked.

Within this group, there was a shift to private pools, much of which were subprime. But, as we can see in the graph, there was a gradual shift from Ginnie Mae to private pools from about 1990 to 2003.

…After 2003, the GSE’s began to decline as a portion of the market also. It was during this period that private pools shot from about 10% to about 20% of the market, until the private pool market collapsed in 2007. This period was not associated with a rise in homeownership, and included the last period of sharply rising prices followed by two years of flat prices.

What I find idiosyncratic about the chart is that it is based (I think) on total mortgage debt outstanding. Also, he charts the share of mortgages, rather than total amounts. Both of those factors tend to make the chart tamp down changes in dollar mortgage flows.

One point is that the issuance of mortgages by agencies was affected by loan limits interacting with higher house prices. My guess is that the substitution of private mortgages for agency mortgages took place in locations where house prices were rising faster than the loan limits adjusted.

Yet another point is that a lot of lending was in the form of cash-out refinances (people using their homes as ATMs). I may be wrong, but I don’t think that FHA was in that business.

Another chart shows the increase in mortgage debt by income class. Kevin writes,

The proportion of mortgage debt held by the bottom 80% of households declined during this period [2004 to 2007].

What I would want to see is the behavior of the ratio of debt to equity by income class. Suppose that everybody is using their homes as ATMs. If a rich guy with a million dollar home raises refinances his $400,000 mortgage for $500,000 and a poor guy with a $100,000 home refinances his $90,000 mortgage for $100,000, then most of the new mortgage debt goes to the rich guy. But it’s the poor guy whose equity is disappearing.

Paging Daniel Klein

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Don Boudreaux writes,

The bad news is that 74 percent of these surveyed economists either disagreed, were “uncertain,” or expressed no opinion that such a huge hike in the minimum wage would cause substantial shrinkage of low-skilled workers’ job prospects.

My stream-of-consciousness reaction was this:

1. These economists must be mood-affiliating with sociologists, or other left-wing academics.

2. I’ll bet that non-academic economists would think about this question in a more detached, business-informed way.

3. This sounds like a project for Daniel Klein. Conduct a large survey of economists affiliated with academia and economists affiliated with businesses, and find out questions on which they differ. Interesting questions would include the one on the minimum wage, whether Obamacare is lowering health care costs, whether more inflation would be lead to better economic growth, . . .

Null Hypothesis Watch

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David Autor and co-authors write,

Although family disadvantage is strongly correlated with schools and neighborhood quality, the SES gradient in the sibling gender gap is almost as large within schools and neighborhoods as it is between them.

Read the whole paper, which focuses on the question of why males from low-income families do poorly–even more poorly than females from low-income families. I view the quoted sentence as throwing some cold water on Raj Chetty’s view that neighborhoods make a big difference.

Null Hypothesis Watch

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A reader points me to a piece by Dale C. Farran and Mark W. Lipsey, which studies the long-term effects of the TNVPK pre-kindergarten program.

As is evident, pre-K and control children started the pre-K year at virtually identical levels. The TNVPK children were substantially ahead of the control group children at the end of the pre-K year (age 5 in the graph). By the end of kindergarten (age 6 in the graph), the control children had caught up to the TNVPK children, and there were no longer significant differences between them on any achievement measures. The same result was obtained at the end of first grade using two composite achievement measures (the second created with the addition of two more WJIII subtests appropriate for the later grades). In second grade, however, the groups began to diverge with the TNVPK children scoring lower than the control children on most of the measures. The differences were significant on both achievement composite measures and on the math subtests. Differences favoring the control persisted through the end of third grade.

The null hypothesis is that educational interventions make no difference. Technically, the last two sentences suggest that the null hypothesis is rejected here. The intervention of sending kids to pre-K made their outcomes worse in a statistically significant way.

Read the whole article. This will create some cognitive dissonance for progressives who have faith in universal pre-K and also believe in using rigorous social science to guide policy.

And some cognitive dissonance for James Heckman. He argues that measurements at third grade are noisy, but lifetime outcomes favor pre-school education. Pointer from Mark Thoma. I think Heckman is really reaching.

Solution Disconnected from Problem

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From a WSJ profile of Raj Chetty.

High-mobility metro areas have a combination of greater economic and racial integration, better schools and a smaller fraction of single-parent families than lower-mobility areas. Integration is lagging in Atlanta, he said. “The strongest predictors of upward mobility are measures of family structure,” Mr. Chetty said.

His proposal: move poor children to high-mobility communities and remove the impediments to mobility in poor-performing neighborhoods. He now is working with the Obama administration on ways to encourage landlords in higher-opportunity neighborhoods to take in poor families by paying landlords more or guaranteeing rent payment.

Pointer from Tyler Cowen.

The problem is family structure. The solution is engineering the spatial/income distribution of households. The connection is not there for me.

And if the problem is a need to improve teacher quality, then the solution is not for economists to run regressions on test scores. The solution is to put the power in the hands of people who care about quality and are close to the situation (i.e., parents), not in the hands of teachers’ unions.