Total US Debt to GDP Ratio – Deleveraging Analysis
A secular trend of borrowing
Many people may be aware of the ballooning US government debt, which is now approaching $20 trillion in 2017. What may not be obvious, however, is that since 2009 the total debt outstanding in the US (including consumer, business, and government debt) has actually dropped when compared to GDP. In fact, the ratio of total us debt to GDP peaked in 2009/Q1 around the 400% mark and has since steadily decreased.
I began researching this ratio after studying Ray Dalio’s theory on how the economy works. In his 30 minute video about “The Economic Machine,” he explains that the economy follows recurring cycles:
- a 5-8 year short term debt cycle which leads to economic expansions and recessions
- a 75-100 year long term debt cycle characterized by large swings in the ratio of total debt to GDP.
Completing the long-term debt cycle
Dalio believes that the long term cycle peaked in 2009. Debt burdens, he reasoned, had grown too high relative to income and were no longer sustainable. To resolve such a situation and purge unsustainable levels of debt, our economy had to (and still does) go through a period of deleveraging. This can happen in one of three ways:
- Deflationary deleveraging is characterized by tight credit and defaults. This is what happened during the Great Depression.
- Inflationary deleveraging occurs when prices rise rapidly while the country’s currency depreciates, eventually wiping away debts through hyperinflation. Germany experienced this in the 1920s after World War I.
- Beautiful deleveraging occurs when the government balances the deflationary tendencies of a deleveraging by borrowing and spending credit-based stimulus. GDP growth slows down as businesses and households consume less and use savings to pay down debt (thus reducing the debt to GDP ratio).
Tracking the ratio
In 2012, Dalio claimed that the US was executing a beautiful deleveraging and I wanted to confirm the validity of this statement. The only problem was that I couldn’t find any free, real-time analysis of such a the Total US Debt to GDP ratio on Google. Furthermore, I wanted to be able to analyze where the debt was concentrated. Obviously much of America’s debt is public government debt, but what about the proportion of mortgages, student loans, and corporate debt?
I took matters into my own hands by creating this analysis. It allows the user to easily track total debt in the US economy broken down by the main categories (Government, Household + Non-Profit, Business). Furthermore, it tracks the total US debt per dollar of GDP over time and shows how each sector contributed to the change in Debt:GDP.
Conclusions and Takeaways
As Ray Dalio describes in his video, the US has well-timed the increase in government debt to soften the blow of consumer/business deleveraging from 2009-2012.
Non-financial corporate debt has continued to grow. At ~$9 trillion, this is a similar scale as compared to the total value of mortgages outstanding when the housing bubble collapsed ($13.6t). It’s especially significant because corporations are issuing debt to buy back stocks that are overvalued and could be forced to deleverage their balance sheets quickly if future cash flows are lower than expected.
As time goes on, monitor the debt to GDP ratio to see whether is continues to drop steadily while the economy grows slowly. This would indicate a healthy deleveraging, regardless of the rhetoric coming from the media.
To create this dashboard I scoured the Federal Reserve Economic Data repository (FRED) to identify all the necessary debt-related economic data series. It’s relatively easy to calculate a bottom line number – just combine two series (ASTLL and ASTDSL) to get the sum of all debt – but it’s significantly more complicated to find the break down of the various child series for government debt, household debt, and business debt.
Once I identified the pieces of the puzzle, I used PowerQuery (the ETL module in PowerBI) to UNION the datasets for each series into a single fact table. I then added two dimension tables (dates and series) which I used for segmenting the data.
Because most of the data is available online, this report can be easily configured for auto refresh. New data is released by the government by the 3rd week of Mar, Jun, Sep, and Dec.