Written by Steve Keen, Steve Keen’s Debtwatch

Minsky’s Financial Instability Hypothesis (FIH) is an emergent property of macroeconomic models derived directly from macroeconomic definitions.

This is Part 3 of a paper presented at the International Conference Minsky at 100 Revisiting Financial Instability, December 16-17 2019 – Universita Cattolica del Sacro Cuore Milano.

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This paper will be posted in five parts:

Part 1: Deriving a Minsky Model

Part 2: Simulating Loanable Funds and BOMD

Part 3: Accounting For The Great Moderation & The Great Recession (this article)

Part 4: Nonlinearity and Realism

Part 5: Appendix and References

Accounting for the Great Moderation & the Great Recession

The models and logical analysis of the previous sections provide a causal argument for a relationship between the levels of debt and credit and macroeconomics, and in particular the experience of severe economic crises like the 2008 “Great Recession”. A rising level of debt relative to GDP, and a rising significance of credit relative to GDP, are Minskian warnings of a crisis, while the crisis is caused by a plunge in credit from strongly positive to strongly negative. The plunge in credit from a peak of 15% of GDP in late 2006 to a depth of -5% in late 2009 was the first experience of negative credit since the end of WWII, and this was the cause of the Great Recession.

Figure 14: The “Great Recession” was the first negative credit event in post-WWII economic history

The empirical relationship between credit and the level of unemployment rises as the level of private debt rises, and by the time of the recovery from the 1990s recession, it is overwhelming: in a ridiculously strong contrast to Bernanke’s Neoclassical a priori dismissal of Fisher’s Debt Deflation explanation for the Great Depression on the grounds that

“Absent implausibly large differences in marginal spending propensities among the groups, it was suggested, pure redistributions should have no significant macroeconomic effects” ( – Bernanke 2000, p. 24),

the correlation between credit and unemployment since 1990 is a staggering -0.85 (see Figure 15).

Figure 15: Credit and Unemployment. Correlation -0.53 since 1970, -0.85 since 1990

Credit and Asset Markets

Though credit does finance corporate investment on the one hand ( – Fama and French 1999; – Fama and French 2002) and consumer purchases on the other, the majority of credit today is created not to finance economic activity but for speculation on asset markets: house purchases by the household sector, margin lending, and (particularly since the 2008 crisis) share buybacks by the corporate sector. These credit flows both drive asset prices, and have spillover effects onto the macroeconomy via the income of participants in the FIRE sectors, the partial expenditure of the proceeds of asset sales on goods and service, and relatively trivially, the impact of trends in asset prices on consumption ( – Zhou and Carroll 2012). Sensible monetary economics thus breaks the hermetic seal that Neoclassical economics pretends exists between macroeconomics and finance.

Therefore, as well as being a source of aggregate demand and income in macroeconomics, credit is a major determinant of asset prices. Extending the preceding logical analysis of the role of credit in aggregate demand, a logical link can be derived between household credit and house prices by starting from the simplifying assumption that all monetary demand for housing is financed by new household debt DHH. We can then relate the flow of demand for houses (HD denominated in “standard houses”) per year to the change in household debt divided by the price index for houses PH:

The flow of supply of housing for sale can be treated as a fraction of the existing housing stock , plus the rate of change of that stock due to net construction:

If prices adjusted instantaneously to match the physical flows of supply and demand, we could write:

So that PH would be given by:

Since prices do not adjust instantly, we can relate the change in the price level to the change in the relative flows of demand and supply by differentiation (13):

(13) The use of bidirectional arrows (represented above – and below – by the rectangular box) rather than equals signs is to acknowledge that causation potentially flows both ways.

Converting this into a percentage rate of change and expanding out terms yields:

Equation implies that as a simplification we can write

This implication of a relationship between the second derivative of household debt (or the first derivative of household credit) and change in house prices is strongly supported by the data – see Figure 16 and also ( – Biggs, Mayer et al. 2010; – Schularick and Taylor 2012; – Constantinescu and Lastauskas 2018). (14)

(14) I normalize the credit change data by dividing it by GDP, simply because the supply of housing data is not as readily available, nor as well calculated.

Figure 16: Change in Household Credit and Change in House Prices since 1990 (Correlation 0.67)

Credit thus plays a pivotal role in determining both macroeconomic activity and asset prices, in keeping with Minsky’s FIH, and in stark contrast to Neoclassical macroeconomics ( – Krugman 2012) and finance theory ( – Sharpe 1964).

The role of negative credit in the USA’s major economic crises

Since credit has no role in mainstream economic theory, the collection of data on private debt and credit has been sporadic, depending more on the initiative of statisticians than the expressed needs of economists for data. This situation has improved dramatically in recent years thanks to the work of the Bank of International Settlements ( – Borio 2012; – Dembiermont, Drehmann et al. 2013), the Bank of England ( – Hills, Thomas et al. 2010) and various non-mainstream economists ( – Jorda, Schularick et al. 2011; – Schularick and Taylor 2012; – Vague 2019), but much remains to be done to provide the comprehensive time series data that the significance of debt and credit warrants.

However, some data can still be retrieved that helps make sense of past economic crises ( – Vague 2019). In particular, a long term debt series can be derived for the USA from three wildly different time series: the post-1952 Federal Reserve Flow of Funds data; Census data for debt between 1916 and 1970; and a series on loans by selected banks between 1834 and 1970 ( – Census 1949; – Census 1975). (15)

(15) The source of the pre-Flow of Funds data is the US Census publication Historical Statistics of the United States Colonial Times to 1970, series X393-409: Net public and private debt by sector, p. 989, and X-580-587. All banks: principal assets, p. 1019

Figure 17: Debt to GDP data from the BIS & US Census

Fortunately, the data series overlap, and the trends in the data show that, though the definitions differed, the same fundamental processes were being tracked by these three data series. This allows a composite time series to be assembled by rescaling the two Census data series to match the current BIS/Federal Reserve data set. When credit data is derived from this composite series, two phenomena stand out: firstly, America’s greatest economic crises coincide with sustained periods of negative credit; and secondly, the post-WWII regime has only one negative credit event – the “Great Recession” – while the pre-WWI regime had frequent, though smaller, negative credit experiences (see Figure 18). The two greatest were the Great Depression, and the “Panic of 1837” ( – Roberts 2012).

While Great Depression and the Great Recession are etched into our collective memories, I was personally unaware of the “Panic of 1837” until this credit data alerted me to the scale of negative credit at that time. Though the recorded level of private debt was low compared to post-WWII levels, the rate of decline of debt – the scale of negative credit – was both enormous and sustained.

Credit was negative between mid-1837 and 1844, and hit a maximum rate of decline of 9% per annum. It is little wonder that the “Panic of 1837” was described as “an economic crisis so extreme as to erase all memories of previous financial disorders” ( – Roberts 2012, p. 24).

Figure 18: Composite time series for private debt and credit derived from the data in Figure 17.

See Part 4: Nonlinearity and Realism.

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