Estimating Household Debt Service back to 1947

The other day I showed a graph of the components of household debt service, the interest and the principal:

Graph #1: Components of Household Debt Service: Principal (blue) and Interest (red) as % of DPI

The data on interest paid goes back to the 1940s. (Same for disposable income and household debt.) But the debt service data only goes back to 1980, so that's as far back as the blue line can go. I sure would like to see the behavior of that blue line in the earlier years.

I wonder if you could use interest cost and debt-to-income in a regression, to find an equation that simulates the debt service data since 1980, and then use that equation to estimate debt service back to the 1940s. I know: It's not as good as actually having the data would be. But it might be better than not having the data.

I must have been out the day they taught regression in school. The only thing I know about it is, it's way more complex and sophisticated than I am. So if you want to grab this idea and run with it, that would be great. Meanwhile, I do know how to find the "Data Analysis" window in Excel, click on "Regression", and plug numbers in. Let's see where that gets us.

At FRED I picked out some relevant data and saved it as my Early Years Debt Service Dataset #1.

When I think on TDSP, household debt service, I think it has to be related to the rate of interest and the level of debt. For the interest rate I'll figure the "effective" rate: household interest paid, as a percent of household debt. For the level of debt I'll go with household debt relative to disposable personal income, the same context used for TDSP.

From Jim Frost at Statistics by Jim:

Use regression analysis to describe the relationships between a set of independent variables and the dependent variable. Regression analysis produces a regression equation where the coefficients represent the relationship between each independent variable and the dependent variable. You can also use the equation to make predictions.

Sounds good. I'm thinkin I want to "predict" values for debt service:

• first, to see if my "independent variables" give me good estimates since 1980;
• then, to create estimates for the years before 1980.

Debt-to-income and the interest rate are my "independent" variables. They go in Excel's "X value" range as inputs for the regression. The FRED Debt Service data (which begins with 1980) goes in the "Y value" range. My "prediction" -- my calculated values based on the regression results -- should be a pretty good match to the Debt Service data. That's my plan so far.

//

Okay, I got results from Excel. The regression gave me coefficients I can multiply by the input values, giving me numbers I can compare to the original TDSP data. And since both the interest rate values and the debt-to-income values are available back to the 1940s, my estimate of debt service can be calculated back that far as well:

Graph #2: Regressing Household Debt and Interest to Approximate Debt Service

Not a bad match between red and blue. The biggest gap between red and blue is about a one percentage point difference near the value 12, so my numbers are off by less than one in 12, less than 10%. Excel's "Summary Output" gives me an R Squared of 0.844, if that's important for a regression. And my P-Values are suspiciously low (but I don't know what that means).

Let me be the first to say it: Past results are no guarantee of more remote past performance. But let me also say I'll use this estimate when I have a use for it, as a way to test whether it seems to make sense.

I did find an old article, "Recent Financial Behavior of Households" by Charles Luckett, from the Federal Reserve Bulletin of June 1980 (vol.66). Here's Luckett's Figure 6:

Graph #3: Luckett's 6th

His numbers are far higher than FRED's. The highest point in the FRED data is 13.22% of DPI in 2007. The low edge of Luckett's plot window is the 14% level!

I copied some of his gray background down (to duplicate his line spacing), down to the 6% level. Here (blue) is my result for 1973-1981, overlaid on Luckett' graph:

Graph #4: Comparison 1973-1980

The blue line going back to 1973  is from my calculation based on the regression. The short green line, down around the 10% level, shows FRED's Debt Service data for 1980 and '81. The black lines show Luckett's numbers. Mine are low. But so are FRED's.

Eh. I added 12 to all my numbers to bring them up near Luckett's. Adding 12 to the blue line gives me the double red line, which looks very much like a smoothed path for Luckett's numbers. Mine go a little low where his go low in 1974 and '75. Mine go a little high where his go high in 1978 and '79. The general trend of mine looks about right, assuming Luckett's numbers are right. That's at least a little bit interesting.

But why are my numbers so much lower than his?

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Another old article: "Household debt burden: how heavy is it?" by Carl J Palash, from 1979. Here is Chart 1:

Graph #5: Carl Palash's First

I'll take the lower chart there, enlarge it, and overlay my numbers on it as before. But I can see already that these numbers also are far higher than mine. The overlay:

Graph #6: Comparison 1960-1979

Again, the blue line is my calculation. The red this time is twice the blue. Multiplication gives my red line more variation than the blue has, relative to Palash's, making the path of my data somewhat more similar in shape to the path of his black line: We both show flatness for a decade beginning in the mid-1960s; we both show increase before the flatness and after; and after 1975 our increases run parallel. But all of this is based on my calculated numbers being doubled.

Again I have to wonder why my numbers are so low. But so far, the only answer I have is that the numbers I was trying to duplicate are low, FRED's household debt service numbers.

Doesn't really answer the question.

//

Based on the two comparisons, I can say that my numbers show less variation than Luckett's or Palash's, and that mine and FRED's are a lot lower. I don't know why the old numbers are so high and the recent ones are so low. But in another article, "Recent Changes to a Measure of U.S. Household Debt Service" by Dynan, Johnson, and Pence, from 2003, I found this graph, showing the revised data lower than the older vintage:

Graph #7

The "Financial Obligations Ratio" they show here does go up above 18% of DPI, in the neighborhood of the older data. But the Financial Obligations Ratio includes other things in addition to Debt Service. And anyway in the FRED data today, the corresponding peak comes in at less than 18%. The numbers are still going down.

Factors presented in the article which would tend to lower the Debt Service Ratio include

• the "time to maturity" of loans, because "longer-maturity loans have lower payments"
• the failure to make payments: "According to the SCF, at any given time, payments are not being made on one-quarter to one-half of student loans. To account for the deferral of student loans, we adjusted the stock of loans to reflect only those loans on which payments are currently being made."
• mis-estimating interest rates: "we replaced the previously used proxies with [the average interest rate offered by banks on 48-month new car loans], which is 3 to 4 percentage points lower than the proxies we had been using."

One factor that would seem to raise the Debt service Ratio is

• broadening the measurement of debt: "Sallie Mae's student loans since 1977 were added to the Federal Reserve's G.19 consumer credit statistics beginning with the October 2003 release. Their inclusion did not materially change the growth rate of consumer credit, but it has raised the level an average of 2 1/2 percent since 1977."

"On net," they write, "changes to the source data led to a downward revision to the DSR of about 1½ percentage points from 1980 through 2002..." And "the lion's share of this revision [was due to] the lengthening of our assumptions about remaining maturity on these loans."

Even so, the Debt Service Ratio today is half or less than half what it was back when that data was still called the Debt Service Burden. That's a very large decrease, especially given the very large increase in outstanding debt which occurred during that same period.

Whatever. I got the result I got, and that's what I got. Do with it what you will.

by ArtS	EDSR47 dataset	Regression Result
Year	TDSP (% of DPI)	My Estimate
1947		5.696
1948		5.887
1949		6.118
1950		6.276
1951		6.337
1952		6.502
1953		6.739
1954		6.899
1955		7.071
1956		7.289
1957		7.429
1958		7.543
1959		7.688
1960		7.938
1961		8.023
1962		8.134
1963		8.333
1964		8.423
1965		8.519
1966		8.519
1967		8.449
1968		8.467
1969		8.582
1970		8.508
1971		8.521
1972		8.678
1973		8.796
1974		8.887
1975		8.750
1976		8.875
1977		9.152
1978		9.506
1979		9.921
1980	10.478	10.312
1981	10.348	10.559
1982	10.435	10.934
1983	10.383	11.021
1984	10.671	11.185
1985	11.463	11.483
1986	11.883	11.620
1987	11.936	11.522
1988	11.687	11.355
1989	11.709	11.510
1990	11.609	11.509
1991	11.362	11.400
1992	10.633	10.948
1993	10.387	10.668
1994	10.532	10.623
1995	11.095	10.963
1996	11.300	11.076
1997	11.321	11.179
1998	11.170	11.124
1999	11.455	11.255
2000	11.766	11.506
2001	12.373	11.612
2002	12.369	11.390
2003	12.246	11.436
2004	12.206	11.638
2005	12.583	12.424
2006	12.737	13.027
2007	13.033	13.495
2008	12.907	13.157
2009	12.331	12.663
2010	11.289	11.878
2011	10.622	11.076
2012	10.063	10.453
2013	10.113	10.362
2014	9.905	10.044
2015	9.927	9.896
2016	9.998	9.864
2017	9.934	9.827
2018	9.717	9.758