Abstract
The USA in the 1930s experienced unprecedented uncertainty. Uncertainty shocks buffeted the economy during recessionary periods, but these shocks receded during the recovery periods of the Great Depression. Using vector autoregressions on monthly data for 1919–1941, I show that a one standard deviation increase in uncertainty decreased investment, GDP, industrial output, employment, hours worked, wages, and the price level. I perform a historical decomposition simulation to see how much uncertainty shocks mattered for explaining movements in major variables during the Depression. Roughly 40–70% of the simulated decline in output can be explained by uncertainty shocks in the Great Depression.
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Notes
See Mathy (2016) for a description of the events that correspond to uncertainty shocks in this period. After the shock of the Crash of October 1929, the recession worsened, with an unprecedented wave of bank failures. This meant that no one’s life savings were safe. In September 1931, the United Kingdom left the gold standard, which generated massive uncertainty about monetary policies across the world and what the future of international exchange rates would be and how long the USA would remain on gold. In his memoirs, Hoover said the USA almost left the gold standard due to this shock (Hoover 1952) in late 1931, but the USA would remain on the gold standard for a couple more years in a very uncertain environment. While it might seem clear in retrospect, Roosevelt did not campaign on ending the gold standard, and gold outflows from New York also forced his hand during the Bank Holiday at the start of his term (Wigmore 1987). After about a year of policy experimentation with respect to the gold standard, the exchange rate, and the rest of the New Deal, the level of uncertainty died down, though uncertainty would rise again around the 1937–1938 as events in Europe and Asia moved the USA closer to isolation or war.
Precautionary savings theories, based on a literature begun by Leland (1968), would predict that households increase savings and corresponding reductions in consumption to guard against the possibility of a bad future outcome. The focus on 1930 by Romer is meant to address the finding in Temin’s book that 1930 was the most puzzling year of the Depression as it saw an abnormally large drop in consumer durable purchases. Romer uses the case of 1930 to show that uncertainty can explain the remaining decline in consumer durable purchases which remained unexplained by Temin’s analysis.
See Bloom et al. (2007, 2012), Bloom (2007, 2009, 2014), Gilchrist et al. (2014), Caggiano et al. (2014), Fernández-Villaverde et al. (2015), Basu and Bundick (2017), Leduc and Liu (2016), Nakamura et al. (2017), and, though Knotek and Khan (2011), and Bachmann and Bayer (2013) find that uncertainty, working through the wait-and-see effect, is not important for the business cycle.
Historical decompositions of this type generally assign a large fraction of the decline to lagged values of the simulated variable (e.g., lagged industrial production), which makes these results even more striking.
The monetarist position that monetary policy was tight would imply that interest rates were rising, but in fact, interest rates were falling as money demand fell pari passu with declining output.
Note that news about future productivity in the early 30s would have been good news as productivity growth through 1941 was very strong Field (2003), and thus the news shocks theory would have predict a boom, not a deep recession during this period. Nevertheless, L’Huillier and Yoo (2017) find that bad news was a significant factor in the U.S. Great Depression.
The newspaper index does rise during the (mild and forgettable) 1923–1924 recession, but this seems to be largely noise and the other measures remain relatively low during this episode.
Lopez and Mitchener (2018) used an exchange rate volatility measure to measure policy uncertainty as a driver of hyperinflations in post-World War I Europe.
Naturally, these errors must be highly skewed to match these large shocks in the data.
The Dow Jones Industrial Average is used here as it is available prior to 1926, unlike the S&P 500, and the resulting stock return volatility for the Dow is very close to that of the S&P 500 in any case. For the modern period, a volatility index such as the VIX or VOX is often used, as they are a forward-looking measure of stock return volatility based on the implied volatility derived from option prices. As stock options are not available for the interwar period, I use observed stock volatility as it is available. Observed volatility is not very different than the implied volatility, so this should not affect the results much.
More specifically, the correlation between return volatility and the newspaper index is 0.549, the correlation between credit spreads and return volatility is 0.66, and the correlation between the newspaper index and credit spreads is 0.814. More correlations can be found in Table 1 of online appendix.
Most recessions would not be characterized as having a large uncertainty component, though uncertainty is often pointed to as being important in the recessions of 2007–2009 in the USA and recession to tend to be more uncertain on average (Bloom 2014). The 1973–1975 recession is also one that is often pointed to as having a significant uncertainty component (Jurado et al. 2015). Even when one looks at economic policy uncertainty, which is a slightly different concept that the one studied in this paper, the 1930s stands out as particularly uncertain among recessions in the last century (Baker et al. 2016).
Without restrictions, the system is underdetermined, so structural assumptions informed by economic theory must be used for identification. The Cholesky decomposition assumes that anterior variables can affect posterior variables contemporaneously, but posterior variables cannot affect anterior variables contemporaneously. This framework assumes that uncertainty cannot be measured directly, but that the uncertainty shock measures should rise on impact of an uncertainty shock.
The preponderance of the lag length tests found that three lags were optimal for the VAR that uses quarterly data and 11 lags for the monthly var, so I use 3 quarterly lags and 11 monthly lags. To check for robustness, later I use 1 lag, which does not affect the result much and, if anything, strengthens the results. The Johansen cointegration test finds that there we can reject at most 1 cointegration relationship between both the monthly set of variables and the quarterly variables so there is the potential for some cointegrating relationships. To ensure stationarity and to remove seasonal factors, I regress all variables on a time trend and four quarterly dummies, then detrend all variables using their corresponding trend series. The KPSS (Kwiatkowski–Phillips–Schmidt–Shin) test is then run on all variables, and none can reject the null hypothesis of stationarity.
The price level is ordered after the uncertainty shock but before the economic activity measures as a robustness check. This is discussed in the next paragraph.
If there is a large drop in the stock market level, this will appear as both a first-moment and second-moment shock. This control for the first-moment shock is intended to more cleanly identify the second-moment shocks.
The AIC test found that three lags gave the largest test statistic to reject a unit root, so I use 3 quarterly lags. To decide whether to perform the VAR in levels or first differences, I perform a Johansen test on the variables in levels. This test can reject the hypothesis of at most 10 cointegrating vectors out of 11 variables, so due to cointegration, I perform the test in levels. To ensure stationarity and to remove seasonal factors, I regress all variables on a time trend and four quarterly dummies, then detrend all variables using their corresponding trend series.
The reader will note that this corresponds to the upper end of a 90% confidence interval.
Bloom’s VAR’s resemble those in my second VAR specification, and I have followed his techniques whenever possible for this empirical exercise.
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Acknowledgements
I have benefited from fruitful conversations with Gustavo Cortes, Chris Meissner, Ellis Tallman, Nicholas Bloom, and many others, and have had many useful discussions at seminars and conferences. All errors remain my own.
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Appendix
Appendix
See Figs. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 and 32.
Graph of industrial production, CPI, personal income, and wages during the Great Depression
Notes Left axis corresponds to credit spreads, newspaper index, and high stock volatility indicator. Right axis is stock volatility. Credit spreads are the difference between BAA and AAA interest rates, the newspaper index is the number of articles mentioning economic uncertainty normalized by the number of articles, and stock return volatility is the annualized quarterly standard deviation of daily returns in percent
Four uncertainty measures: 1919–1941.
Source: ProQuest Historical Newspapers
Newspaper indexes formed from number of articles contained the terms “economy” or “economic” and “uncertain” or “uncertainty.” The number of articles per month is then divided by the number of days in that month, and multiplied by the average number of days in an average month (30.25). A 7-month moving average is applied using the 3 months before and after, and then all series are normalized such that the trough of the Great Depression is March 1933 \(=\) 100.
Reverse bivariate specification of GDP on three uncertainty shocks. Blue line is point estimate and red lines are 95% confidence intervals (color figure online)
Impulse response simulation of four uncertainty shocks on wages. VAR is uncertainty shocks, investment, GDP, the consumer price index, and wages with 1 lag instead of 3. Blue line is point estimate and red lines are 95% confidence intervals (color figure online)
Impulse response simulation of four uncertainty shocks on investment. VAR is uncertainty shocks, investment, GDP, the consumer price index, and wages with 1 lag instead of 3. Blue line is point estimate and red lines are 95% confidence intervals (color figure online)
Impulse response simulation of four uncertainty shocks on GDP. VAR is uncertainty shocks, investment, GDP, the consumer price index, and wages with 1 lag instead of 3. Blue line is point estimate and red lines are 95% confidence intervals (color figure online)
Impulse response simulation of four uncertainty shocks on the CPI. VAR is uncertainty shocks, investment, GDP, the consumer price index, and wages with 1 lag instead of 3. Blue line is point estimate and red lines are 95% confidence intervals (color figure online)
Impulse response simulation of four uncertainty shocks on wages. VAR has an alternative ordering with uncertainty shocks first, then the consumer price index, investment, GDP, and wages. Blue line is point estimate and red lines are 95% confidence intervals (color figure online)
Impulse response simulation of four uncertainty shocks on investment. VAR has an alternative ordering with uncertainty shocks first, then the consumer price index, investment, GDP, and wages. Blue line is point estimate and red lines are 95% confidence intervals (color figure online)
Impulse response simulation of four uncertainty shocks on GDP. VAR has an alternative ordering with uncertainty shocks first, then the consumer price index, investment, GDP, and wages. Blue line is point estimate and red lines are 95% confidence intervals (color figure online)
Impulse response simulation of four uncertainty shocks on the CPI. VAR has an alternative ordering with uncertainty shocks first, then the consumer price index, investment, GDP, and wages. Blue line is point estimate and red lines are 95% confidence intervals (color figure online)
Impulse response simulations of industrial output to one standard deviation shock to stock volatility, high-volatility indicator, newspaper index, and credit spreads. VAR estimated with 1 lag. Cholesky ordering is stock return level, uncertainty shock, industrial output, and CPI. Blue line is point estimate and red dotted lines are 95% confidence interval over 24 months (color figure online)
Impulse response simulations of manufacturing employment to one standard deviation shock to stock volatility, high-volatility indicator, newspaper index, and credit spreads. VAR estimated with 1 lag. Cholesky ordering is stock return level, uncertainty shock, industrial output, and CPI. Blue line is point estimate and red dotted lines are 95% confidence interval over 24 months (color figure online)
Impulse response simulations of hours worked in manufacturing to one standard deviation shock to stock volatility, high-volatility indicator, newspaper index, and credit spreads. VAR estimated with 1 lag. Cholesky ordering is stock return level, uncertainty shock, industrial output, and CPI. Blue line is point estimate and red dotted lines are 95% confidence interval over 24 months (color figure online)
Impulse response simulations of industrial output to one standard deviation shock to stock volatility, high-volatility indicator, newspaper index, and credit spreads. Cholesky ordering is stock return level, uncertainty shock, CPI, and industrial output. Blue line is point estimate and red dotted lines are 95% confidence interval over 24 months (color figure online)
Impulse response simulations of manufacturing employment to one standard deviation shock to stock volatility, high-volatility indicator, newspaper index, and credit spreads. Cholesky ordering is stock return level, uncertainty shock, CPI, and industrial output. Blue line is point estimate and red dotted lines are 95% confidence interval over 24 months (color figure online)
Impulse response simulations of hours worked in manufacturing to one standard deviation shock to stock volatility, high-volatility indicator, newspaper index, and credit spreads. Cholesky ordering is stock return level, uncertainty shock, CPI, and industrial output. Blue line is point estimate and red dotted lines are 95% confidence interval over 24 months (color figure online)
2-way VAR between stock volatility, the newspaper index, and credit spreads. Only the three significant dyads from a Granger causality test are displayed. Blue line is point estimate and red lines are 95% confidence intervals (color figure online)
Impulse response simulation of four uncertainty shocks on investment. VAR is uncertainty shocks, investment, GDP, the consumer price index, and wages. Blue line is point estimate and red lines are 95% confidence intervals (color figure online)
Impulse response simulation of four uncertainty shocks on GDP. VAR is uncertainty shocks, investment, GDP, the consumer price index, and wages. Blue line is point estimate and red lines are 95% confidence intervals (color figure online)
Impulse response simulation of four uncertainty shocks on the consumer price index. VAR is uncertainty shocks, investment, GDP, the consumer price index, and wages. Blue line is point estimate and red lines are 95% confidence intervals (color figure online)
Impulse response simulation of four uncertainty shocks on wages. VAR is uncertainty shocks, investment, GDP, the consumer price index, and wages. Blue line is point estimate and red lines are 95% confidence intervals (color figure online)
Impulse response simulations of industrial output to one standard deviation shock to stock volatility, high-volatility indicator, newspaper index, and credit spreads. Cholesky ordering is stock return level, uncertainty shock, industrial output, and CPI. Blue line is point estimate and red dotted lines are 95% confidence interval over 24 months (color figure online)
Impulse response simulations of manufacturing employment to one standard deviation shock to stock volatility, high-volatility indicator, newspaper index, and credit spreads. Cholesky ordering is stock return level, uncertainty shock, industrial output, and CPI. Blue line is point estimate and red dotted lines are 95% confidence interval over 24 months (color figure online)
Impulse response simulations of hours worked in manufacturing to one standard deviation shock to stock volatility, high-volatility indicator, newspaper index, and credit spreads. Cholesky ordering is stock return level, uncertainty shock, industrial output, and CPI. Blue line is point estimate and red dotted lines are 95% confidence interval over 24 months (color figure online)
Impulse response function of four uncertainty shocks and two monetary shocks on industrial production. VAR run on stock returns, uncertainty shocks, monetary base, discount rate, industrial production, wages, and the CPI. Blue line is point estimate and red lines are 95% confidence intervals (color figure online)
Impulse response function of four uncertainty shocks and two monetary shocks on wages. VAR run on stock returns, uncertainty shocks, monetary base, discount rate, industrial production, wages, and the CPI. Blue line is point estimate and red lines are 95% confidence intervals (color figure online)
Impulse response function of four uncertainty shocks and two monetary shocks on CPI. VAR run on stock returns, uncertainty shocks, monetary base, discount rate, industrial production, wages, and the CPI. Blue line is point estimate and red lines are 95% confidence intervals (color figure online)
Historical decomposition based on VAR run four times, once for each of the four uncertainty shocks. Ordering is uncertainty shock, industrial production, CPI, and wages
Historical decomposition based on VAR run four times for four uncertainty shocks. Ordering is uncertainty shock, industrial production, CPI, and wages
Historical decomposition based on VAR run four times for four uncertainty shocks. Ordering is uncertainty shock, industrial production, CPI, and wages
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Mathy, G.P. How much did uncertainty shocks matter in the Great Depression?. Cliometrica 14, 283–323 (2020). https://doi.org/10.1007/s11698-019-00190-1
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DOI: https://doi.org/10.1007/s11698-019-00190-1