Credit market distress arises in its more virulent form only in certain monetary environments, and has its most extreme effects when it exacerbates a business cycle downturn. Policy questions about a central bank’s role as lender of last resort or regulator must be seen in the context of monetary policy.

The relatively infrequent nature of major credit distress events makes an historical approach to these issues particularly useful. Using a combination of historical narrative and econometric techniques, we identify major periods of credit distress 1875 to 2007, examine the extent to which credit distress arises as part of the transmission of monetary policy, and document the subsequent effect on output.

These issues involve relationships between policy rates (monetary aggregates) credit spreads, and GDP growth. Using turning points defined by the Harding?Pagan algorithm, we compare the timing, duration, amplitude and co?movement of cycles in money, credit and output. For the period since the 1920s, this is most easily done with a risk spread between corporate and Treasury bonds, the discount rate, and real GDP. This allows us to pick out and compare periods of tight credit that result from tight monetary policy and those that have a more exogenous cause. For the period from 1875 to 1920 credit spreads are measured by differences between yields on different rail road bonds, and the conditions in the money market are measured by commercial paper yields. We also examine the patterns for real stock prices since stock market crashes also can act as an exacerbating factor in credit turmoil.

Literature review

The effect of credit on the broader economy has been of concern to economists since the early days of the profession. Nineteenth century authors often spoke of “discredit,” a term Kindleberger (2000) adopts for the later phase of a financial crises. Mitchell (1913) was an early expositor of the credit channel as was Hansen (1921), and J. Laurence Laughlin (1913) testified that “the organization credit is more important than the question of bank notes.” Disentangling the impact of credit supply from changes in demand as well as from the myriad channels of monetary policy remains a challenging empirical (and theoretical!) exercise even today. The importance of expectation in forward looking financial markets indeed for economic behavior in general further compounds the problem.

Much work has focused on isolating the “credit channel” of monetary policy from other transmission mechanisms. Bernanke and Gertler (1995) review the ways monetary policy can affect the “external finance premium” and the cost and amount of credit obtained by firms. The balance sheet (or broad lending) channel affects firm (and individual) credit worthiness by changing the value of available collateral (Bernanke and Gertler, 1989 Mishkin, 1978) The bank lending (or narrow lending) channel works by restricting banks’ ability to borrow and subsequently lend to smaller firms (Bernanke, and Blinder, 1988). Earlier, of course, Brunner and Meltzer (1972) emphasized the importance of distinguishing between money and bank credit.

Though agency problems, credit rationing, and other deviations from the Modigliani?Miller paradigm provide a basis for financial accelerator models that transmit the effects of monetary policy, such frictions also mean that credit markets can produce as well as transmit shocks. Rajan (1994) shows how banks can transmit business cycle shocks independently of monetary policy, as reputational concerns induce herding in credit availability. Gorton and He (2008) view credit tightening as an increase in monitoring by banks resulting from the need to enforce collusive behavior over time.

Empirically there have been several approaches to identifying credit effects. One looks at particular historical episodes for evidence, and our historical narrative takes a closer look at this section of the literature. A second strand uses microeconomic data of particular industries, often looking at regulatory or other changes that shift bank portfolios (Haubrich and Wachtel 1993, Beatty and Gron, 2001) or demonstrate that financial constraints affect firm investment (Fazzari, Hubbard and Petersen 1988, Lamont 1997). A third strand examines the relationship between bank lending standards as a measure of the credit cycle. See eg. Asea and Blomberg ( 1998), Lown and Morgan (2006) and Dell’Arricia and Marquez (2006). And a fourth strand has worked to calibrate general equilibrium models with explicit financial frictions, looking to obtain tighter bounds of credit effects (Carlstrom and Fuerst 1997, Christiano, Motto and Rostogno 2008), building on the earlier work of Bernanke, Gertler and Gilchrist (1996).

More recently, there has been renewed interest in observing correlations between macro variables across broad ranges of countries and time periods, as in Reinhart and Rogoff (2009) and Claessens, Kose, and Terrones (2008). Our work is closer to these latter papers and the related work of Mishkin (1990). Relative to Mishkin and Reinhart and Rogoff, we give greater attention to all business cycles, not just those associated with crises, giving a broader picture of the relations between money, credit and output. Relative to Claessens, Kose and Terrones and Reinhart and Rogoff, we look at one country, and are able to give greater detail on the institutional and historical factors at work in the economy. For example, we can compare how contemporary account of credit conditions compare with empirical measures of credit tightness.

Section II presents an historical narrative, providing descriptive evidence on the incidence of policy tightening, banking and stock market crashes, and credit market turmoil across 27 U.S. business cycles. This narrative is designed to complement the empirical evidence in the rest of the paper where we use empirical methods to discern significant patterns in the data. We focus on the relationship between monetary policy, credit cycles, asset busts and real GDP. Section III discusses our methodology. We use the Harding and Pagan ( 2002) algorithm to identify cycles in money credit, stock prices and real GDP and then examine the concordance of these cycles.

Section IV presents the empirical results, first comparing the duration, timing, and amplitude of cycles in money, credit, and output. Several sets of regressions then compare the depth of recessions to the cyclical movements in other variables. Section V concludes.

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