Macroeconomic Crises since 1870

We build on the Maddison GDP data to assemble international time series from before 1914 on real per capita personal consumer expenditure, C. We also improve the GDP data in many cases. The C variable comes closer than GDP to the consumption concept that enters into usual asset-pricing equations. We have essentially full annual data on C for 24 countries and GDP for 36 countries. For samples that start as early as 1870, we apply a peak-to-trough method for each country to isolate economic crises, defined as cumulative declines in C or GDP by at least 10%. The principal world economic crises ranked by importance are World War II, World War I and the Great Depression, the early 1920s (possibly reflecting the influenza epidemic of 1918-20), and post-World War II events such as the Latin-American debt crisis and the Asian financial crisis. We find 95 crises for C and 152 for GDP, implying disaster probabilities around 3-1/2% per year. The disaster size has a mean of 21-22% and an average duration of 3-1/2 years. A comparison of C and GDP declines shows roughly coincident timing. The average fractional decline in C exceeds that in GDP during wartime crises but is similar for non-war crises. We simulate a Lucas-tree model with i.i.d. growth shocks and Epstein-Zin-Weil preferences. This simulation accords with the observed average equity premium of around 7% on levered equity, using a “reasonable” coefficient of relative risk aversion of 3.5. This result is robust to a number of perturbations, except for limiting the sample to non-war crises, a selection that eliminates most of the largest declines in C and GDP. Acknowledgements The National Science Foundation has supported this research. We thank for suggestions Olivier Blanchard, John Campbell, George Constantinides, Emmanuel Farhi, Xavier Gabaix, Claudia Goldin, Rustam Ibragimov, Dale Jorgenson, Greg Mankiw, Emi Nakamura, and Jón Steinsson. We appreciate help with the financial data from Bryan Taylor of Global Financial Data. On the construction of the data base on GDP and personal consumer expenditure, we are grateful for comments and contributions from many people worldwide. A non-exhaustive list includes Roberto Cortés (Argentina); Felix Butschek, Anton Kausel, Felix Rauscher, Marcus Schleibecker, and Rita Schwarz (Austria); Frans Buelens, Erik Buyst, Jean-Jacques Heirwegh, Yves de Lombaerde, Kim Oosterlinck, Peter Scholliers, Yves Segers, Eric Vanhaute, and Guy Vanthemsche (Belgium); Claudio Haddad (Brazil); José Diaz and Eric Haindl (Chile); Adolfo Meisel, Carlos Posada, and Miguel Urrutia (Colombia); Jakob Madsen (Denmark); Riitta Hjerppe and Visa Heinonen (Finland); Claude Diebolt, Thomas Piketty, Gilles Postel-Vinay, and Pierre Villa (France); Carsten Burhop, Davide Cantoni, Nicola Fuchs-Schündeln, Albrecht Ritschl, Mark Spoerer, Beatrice Weder, and Guntram Wolff (Germany); Violetta Hionidou, George Kostelenos, and George Manolas (Greece); Guðmundur Jónsson (Iceland); Mausumi Das, Ramesh Kolli, Bharat Ramaswami, Bhanoji Rao, Partha Sen, S. L. Shetty, Rohini Somanathan, and Nittala Subrahmanyasastry (India); Ann Booth, Pierre van der Eng, and Kees van der Meer (Indonesia); Stefano Fenoaltea (Italy); Yana Kandaiya, H.R.H. Raja Nazrin, and Wan Rahim Wan Ahmad (Malaysia); Aurora Gómez, Stephen Haber, Jaime de la Llata, and John Womack (Mexico); Marjan Balkestein, Ferry Lapré, Herman de Jong, Hein Klemann, Jan-Pieter Smits, and Jan Luiten van Zanden (Netherlands); Brian Easton, Anthony Endres, Les Oxley, Andrew Petty, Jakob Preston, Keith Rankin, Grant Scobie, and John Singleton (New Zealand); Ola Grytten and Karin Snesrud (Norway); José Robles (Peru); Ricardo Jose and Richard Hooley (Philippines); Luzia Estevens, Pedro Lains, and José Tavares (Portugal); Paul Gregory (Russia); Ichiro Sugimoto (Singapore); Olu Akimboade and Jon Inggs (South Africa); Myung-Soo Cha, Nak-Nyeon Kim, Mitsuhiko Kimura, Jong-Wha Lee, and Dwight Perkins (South Korea); Leandro Prados (Spain); Rodney Edvinsson (Sweden); Felix Andrist, Philippe Bacchetta, Stefan Gerlach, and Stefanie Schnyder (Switzerland); Şevket Pamuk (Turkey); and Jorge Alvarez and Inés Morales (Uruguay). Many other researchers provided invaluable contributions through their published work. All errors remain our own. An earlier study (Barro [2006]) used the Rietz (1988) insight on rare economic disasters to explain the equity-premium puzzle introduced by Mehra and Prescott (1985). Key parameters were the probability, p, of disaster and the distribution of disaster sizes, b. Because large macroeconomic disasters are rare, pinning down p and the b-distribution from historical data requires long time series for many countries, along with the assumption of rough parameter stability over time and across countries. Barro (2006) relied on long-term international GDP data for 35 countries from Maddison (2003). Using the definition of an economic disaster as a peak-to-trough fall in per capita GDP by at least 15%, 60 disasters were found, corresponding to p=1.7% per year. The average disaster size was 29%, and the empirical size distribution was used to calibrate a model of asset pricing. The underlying asset-pricing theory relates to consumption, rather than GDP. This distinction is especially important for wars. For example, in the United Kingdom during the two world wars, GDP increased while consumer expenditure fell sharply—the difference representing mostly added military spending. Maddison (2003) provides national-accounts information only for GDP. Our initial idea was to add consumption, which we approximate by real personal consumer expenditure, C, because of difficulties in most cases in separating durables from nondurables. (We discuss later the breakdown of C into durables versus non-durables for a sub-set of countries with available data for crisis periods.) We have not assembled data on government consumption, some of which may substitute for C and, thereby, affect asset pricing. However, this substitution is probably unimportant for military expenditure, which is the type of government spending that moves a lot during some disaster events.