Relevant and even prescient commentary on news, politics and the economy.

Real business fixed investment.

Yesterday I compared real private GDP in cycles.

Today I would like to look at  real business fixed investment.

If you listened to CNBC — otherwise known as the Republican Propaganda Channel — or the campaign claims you would think that all the uncertainty created by Obama was destroying business confidence and that real business fixed investment was collapsing.

But the data shows a different story.  Three years from the economic bottom  real business fixed investment is up some 18.0%, or about the same as during the Clinton years.  In contrast, during  the
“Bush Investment lead Boom” it was up only up 6.9%, or about a third of the current cyclical rise.

(Update…slightly edited for readability…Dan)

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Economics Cannot Find Racism; Just Move Along

One of my favorite paper presentations ever was by Daniel Parent, who is a good enough reason in himself for pending Labor Economists to apply to HEC. He was trying to present data on income inequalities in the Financial Services industry and was forced to note—all right, I asked—that they didn’t have the data to determine if there was a racial difference in earnings because there wasn’t enough data on high-earning Blacks in the sample to be “statistically significant.” Since the sample used IRS data, among other sources, the answer was clear.

Now (via Tyler Cowen), I see that “not statistically significant” is not just for Financial Services Executives; the WSJ’s markets blog notes:

On average, Republican professors gave black students grades that were .2 of a grade point lower than their Democratic colleagues, or about two-thirds of the distance between a B and a B-minus.

(Among eleven black professors in the sample, there were no Republicans, and the Democrats appeared to grade white and black students as their white-Democratic peers did. But there were too few black professors to make that finding statistically significant.)

Again, the finding may not be statistically significant, but the sample, er, complection is.

Their data set is available here.

UPDATE: Thoreau riffs on the subject and finds a link to the paper.

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FICO Scores and Mortgage Payment Performance

I had an informal discussion with a manager in an MBS IT area last month. Just a general conversation about the field and the data people check.  He mentioned FICO scores and I noted that I’m not fond of using them to evaluate a mortgage, especially for first-time homebuyers.

Part of this is simple: it’s relatively easier—even in the densely-populated metropolitan areas (e.g., NYC, SF), and certainly in sub- and exurban areas—to maintain a good credit rating if you don’t own a residence.  No property taxes, no major repairs, no appliance replacement, no general maintenance, no landscaping, no snow shoveling.  And it’s very easy, especially the first time, to underestimate just how much those expenses will be.  Looking at just the cost of commuting, renting, storage, parking, etc. makes homeownership appear to be a better economic decision than it is.*

Well, the Federal Reserve Bank of New York recently released some data on mortgage payments by type. It’s not directly comparable—the subprime and Alt-A loans have a more granular level of data, most especially with respect to late and current payments—but there are some interesting relationships.

I looked at the data for States where the subprime loans are current for either (1) more than 55% of the borrowers or  (2) less than 45% of the borrowers, which includes 24 states and the District of Columbia.  The overall breakdown was 16 states in the first group and eight states and the District of Columbia in the second.

Of the six states that have more than 100,000 subprime loans outstanding, three—Illinois, Florida, and California—are in the More Delinquent category, while only one (Texas) is in the “so far, so good” realm.**

So I ran a regression on those states and the District, using as factors the percent of the subprime loans that were not Owner-Occupied, the Average FICO score for the state, the percent of subprime loans issued to borrowers with a FICO below 600, and the percent of subprime loans issued to borrowers with a FICO score above 660.  The result was

PctwithCurrPymt = –1.18*(FICO>660) + .292*(FICO<600) + .266*(Average FICO Score) –0.9*(Pct Not Owner-Occupied) –93.66

R-squared = 0.4213  (Adjusted R-squared= .3056) F = 3.64  (Prob > F = 0.0220)

However, none of the coefficients passes the t-test.

If we assume that there is a solid distinction between a FICO score below 600 and one above 660, then we must note that the signs of this regression are precisely the opposite of what we should expect.  The more loans with an initial FICO score above 660, the fewer the number of households that are expected to be current in their payment. Conversely, the more households with a FICO score below 600, the better the Current Payment Performance should be expected to be.

This would seem to be a Very Bad Regression—both methodologically, since it takes two separate sets of data and treats them as if they are part of the same set and intuitively, since it produces results that are not compatible with rational assumptions—but that may not be so.

California, for instance, has the third-highest percentage of Owner-Occupied Properties, the highest Average FICO Score, the lowest percentage of subprime loans to borrowers with FICO scores below 600 and the highest percentage of subprime loans to borrowers with a FICO score above 660.  But it falls into the group where fewer than 45.0% of the borrowers are current.***

Which means that, were you to use FICO scores as an input to your model for buying Whole Loans to securitize, you would likely have bought more currently-dicey CA paper than not.

But, as noted, we may believe this to be a Very Bad Regression. The greatest likelihood is that there is/are (an) excluded variable(s) in the equation.  If we consider the entire set of data, this becomes clearer.  The regression equation for all of the states and the District of Columbia is:

PctwithCurrPymt = –1.019*(FICO>660) + .6118*(FICO<600) + .7685*(Average FICO Score) –0.38*(Pct Not Owner-Occupied) –422.80

R-squared = 0.1471  (Adjusted R-squared= .0730) F = 1.98  (Prob > F = 0.1128)

The signs remain consistent—and counterintuitive—but there is a much lower explanatory power and it is much more likely that the regression fails the F-test.  And again, none of the coefficients passes a t-test.

Adding variables whose signs are more likely to produce indeterminate results—the Average Age and the Average Interest Rate of the Loans—corrects the two original signage issues, but produced a third (and possibly a fourth):

PctwithCurrPymt = 1.375546*(FICO>660) –1.639*(FICO<600) – 1.3423*(Average FICO Score) –0.223*(Pct Not Owner-Occupied) + 16.5340 AvgInterestRate + 0.2632 AvgLoanAge + 775.9700

R-squared = 0.4661  (Adjusted R-squared= .3991) F = 6.40  (Prob > F = 0.0001)

The additional variables have significantly raised the explanatory power of the model, and we now see that the FICO scores point in the intuitive directions. But the Average FICO score has ceased to be a positive contributor to the model, and the Average Interest Rate—the only variable that passes a t-test for significance—indicates that the higher the rate, the higher the likelihood of payment.

So we are left suspecting that the initial FICO score does not significantly affect the ability of the borrower to keep their loan payment(s) current.  This also seems intuitive, since a FICO score is a stock variable, while mortgage payments are flow variables.

But, as with credit ratings, good FICO scores can only go downward.  And it is very rare—especially in an environment in which there is downward pressure on wages—for a good FICO score to go upward.  Indeed, dropping the positive FICO score and the Average FICO score as a variables makes for a better regression:

PctwithCurrPymt = –0.663*(FICO<600)  –0.238*(Pct Not Owner-Occupied) + 15.4976 AvgInterestRate + 0.1469 AvgLoanAge – 47.325

R-squared = 0.4466  (Adjusted R-squared= .3985) F = 9.28  (Prob > F = 0.0000)

While the Average Interest Rate still has a counterintuitive sign, we should note that the Averages range from 6.69 to 8.66%—even the high end is neither an overwhelming burden for subprime borrowers nor a level from which it is likely to have been worth refinancing. Additionally, while AvgInterestRate remains the only coefficient that completely passes a t-test, both FICO<600 (-3.17) and the constant (-2.54) are negative for all values within a 95% confidence interval. Dropping Non-Owner-Occupied from the equation sharpens matters even more:

PctwithCurrPymt = –0.6747*(FICO<600)  + 15.7738 AvgInterestRate + 0.1400 AvgLoanAge – 50.7117

R-squared = 0.4407  (Adjusted R-squared= .4050) F = 12.34  (Prob > F = 0.0000)

With the t-values for both FICO<600 (-3.25) and the constant (-2.83) now both more than 99% probable and, again, the values being negative for the entirety of a 95% confidence interval. In summary, the use of FICO scores as a predictor of mortgage repayments appears to be questionable at best, for the same reason that “junk” bonds tended to outperform high-grade securities on a risk-adjusted basis: it is much easier for a rating to decline than it is for it to improve.  The value of a FICO score as a predictor of loan performance appears to be much more for lower scores than it is for higher ones.  Whether there is greater value on a risk-adjusted basis, as there legendarily has been for corporate bonds, is left for further, more detailed research. *None of which is to suggest that the non-economic reasons aren’t valid.  But credit scores deal with how you manage credit, and how you manage credit has to do with the options you have as much as the choices you make.  Homeowners have fewer options on the allocation of funds to lodging than renters do. **New York State and Ohio are in the middle range.with 46.8% and 52.0% current, respectively. ***Only Hawaii had tighter FICO standards than California—and they have the second-highest (worst) level of non Owner-Occupied Subprime loans (and the worst of any area with more than 10,000 subprime loans outstanding), while California is fifth-best (lowest) in that metric.

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Barro on Keynes Barro and Grossman

Robert Waldmann

Robert Barro wrote an op-ed in The Wall Street Journal. The substance of the op-ed is to report an estimate of the Fiscal multiplier 0.8 which is less than one. Thus, according to Barro, a stimulus will partially crowd out of investment, consumption or net exports and not just reduced leisure. Paul Krugman took Barro to task for using the huge WWII stimulus in his estimates, since the economy was at full employment during WWII. So have Matthew Yglesias using his Harvard BA in philosophy from Harvard and Kevin Drum using his BA in Communications from The California State University in Long Beach.

I might want to reassess Long Beach State, but I think the reason that Yglesias and Drum immediately make the same argument is Krugman is that Yglesias and Drum don’t know about modern econometrics. Barro is using an instrumental variables regression in which wartime military spending is considered to be an exogenous variable which is correlated with government consumption. The implicit assumption is that we can safely assume that the fiscal multiplier today is identical to the fiscal multiplier during World War II, because the economy is basically similar. Without training in modern econometrics it is simply impossible to assume something that stupid.

There is also a severe gap in economic theory, at least as remembered by Robert Barro. Wouldn’t one think that there must be some model in which correlations vary depending on the general conditions of the economy — say like whether at current prices there is excess demand for goods or excess supply of goods.

Of course, no one could expect Barro to know that there is a vaguely Keynesian model, which differs from the neoclassical model only because of rigid nominal wages and prices, in which the economy can be in one of three different regimes, Keynsian (with insufficient aggregate demand), Classical (firms can sell as much as they want but real wages are too high so workers are unemployed) and repressed inflation (excess supply of labor and goods).

I’m mean who’s ever heard of the Barro-Grossman model (A General Disequilibrium Model of Income and Employment Barro, Robert J.; Grossman, Herschel I.; American Economic Review, March 1971, v. 61, iss. 1, pp. 82-93 [stable JSTOR link added for those with access])? Certainly not Robert Barro.

The passage quoted by Krugman about what Keynes thought is inconsistent with The General Theory. However, it can be corrected easily. The accurate description of the history of economic thought is “John Maynard Keynes Robert Barro and Herschel Grossman thought that the problem lay with wages and prices … will mean that wages and prices do not have to fall.”

Look I sympathise. Like Barro, when I was young and reckless I did embarrassing things which I have tried to cancel from my memory. I really wish I could do that as well as he has.

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