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A New Monthly Index of Industrial Production, 1884–1940

Published online by Cambridge University Press:  03 March 2009

Jeffrey A. Miron  and
Christina D. Romer
Christina D. Romer
Affiliation:
The authors are Professor of Economics, Boston University, Boston, MA 02215 and Acting Associate Professor of Economics, University of California, Berkeley, CA 94720, respectively.
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Abstract

The article derives a new monthly index of industrial production for the United States for 1884 to 1940. This index improves upon existing measures of industrial production by excluding indirect proxies for industrial activity, using only component series that are consistent over time, and not making ad hoc adjustments to the data. Analysis of the new index shows that it has more within-year volatility than conventional indexes, has relatively unimportant seasonal fluctuations, and has cyclical turning points that are grossly similar to but subtly different from existing series.

Type
Papers Presented at the Forty-Ninth Annual Meeting of the Economic History Association
Information
The Journal of Economic History , Volume 50 , Issue 2 , June 1990 , pp. 321 - 337
Copyright
Copyright © The Economic History Association 1990

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References

We thank Todd Clark and David Bowman for excellent research assistance and David Romer for helpful comments and suggestions. Financial support was provided by the National Bureau of Economic Research, the John M. Olin Fellowship at the NBER (awarded to Miron), the National Science Foundation (grants SES-8710140 and SES-8896257), and the Institute of Business and Economic Research at the University of California at Berkeley.Google Scholar

1 De Long, J. Bradford and Summers, Lawrence, “The Changing Cyclical Variability of Economic Activity in the United States,” in Gordon, Robert J., ed., The American Business Cycle: Continuity and Change (Chicago, 1986), pp. 679–734Google Scholar; Miron, Jeffrey A., “The Founding of the Fed and the Destabilization of the Post-1914 Economy,” in de Cecco, Marcello and Giovannini, Alberto, eds., A European Central Bank? Perspectives on Monetary Unification after Ten Years of the E.M.S. (Cambridge, 1989)Google Scholar; Romer, Christina D., “Is the Stabilization of the Postwar Economy a Figment of the Data?” American Economic Review, 76 (06 1986), pp. 314–34Google Scholar; Romer, Christina D., “The Cyclical Behavior of Individual Production Series, 1889–1984” (unpublished manuscript, University of California at Berkeley, 1989)Google Scholar; Romer, Christina D., “The Prewar Business Cycle Reconsidered: New Estimates of Gross National Product, 1869–1908,” Journal of Political Economy, 97 (02. 1989), pp. 1–37CrossRefGoogle Scholar; Gordon, Robert J., “Price Inertia and Policy Ineffectiveness in the United States, 1890–1980,” Journal of Political Economy, 90 (12. 1982), pp. 1087–117CrossRefGoogle Scholar; Calomiris, Charles and Hubbard, R.Glenn, “Price Flexibility, Credit Availability, and Economic Fluctuations: Evidence from the United States, 1894–1909,” Quarterly Journal of Economics, 104 (08. 1989), pp. 429–52CrossRefGoogle Scholar; and Schwert, G.William, “Why Does Stock Market Volatility Change Over Time?” Journal of Finance (forthcoming).Google Scholar

2 Babson, Roger W., Business Barometers and Investment (New York, various annual issues).Google ScholarThis series is used by Dominguez, Kathryn, Fair, Ray, and Shapiro, Matthew, “Forecasting the Great Depression: Harvard Versus Yale,” American Economic Review, 78 (09. 1988), pp. 595–612; and Schwert, “Why Does Stock Market Volatility Change Over Time?”Google Scholar

3 Macaulay, Frederick R., The Movement of Interest Rates, Bond Yields, and Stock Prices in the United States Since 1856 (New York, 1938).Google ScholarThis series is used by Calomiris and Hubbard, “Price Flexibility” Victor Zarnowitz, “The Regularity of Business Cycles” (NBER Working Paper No. 2381, 1987)Google Scholar; and Gorton, Gary, “Banking Panics and Business Cycles,” Oxford Economic Papers, 40 (12. 1988), pp. 751–81.CrossRefGoogle Scholar

4 Persons, Warren M., Forecasting Business Cycles (New York, 1931). This series is used in Gordon, “Price Inertia.”Google Scholar

5 For 1877 to 1902 the Persons Index is based only on bank clearings in seven cities and pig iron production. After 1902 the index includes data on merchandise imports, railroad earnings, and employment, as well as bank clearings and pig iron production (see Persons, Forecasting Business Cycles, pp. 111, 131).Google Scholar

6 A more detailed description of our procedures is contained in Miron, Jeffrey A. and Romer, Christina D., “A New Monthly Index of Industrial Production, 1884–1940” (NBER Working Paper No. 3172, 1989).Google Scholar

7 Several of these series are available for at least part of the period from the records of the NBER that are on a tape deposited at the Inter-university Consortium for Political and Social Research. However, we have checked all data from the NBER tape against published original sources. When there was a discrepancy between the NBER tape and the original sources, we have resolved the difference in favor of the primary sources unless there was evidence of a typographical error or other obvious flaw in the primary source.Google Scholar

8 This adjustment for production days is currently done by the Federal Reserve in the derivation of its seasonally unadjusted index of industrial production.Google Scholar

9 The original data on value added are from the Census of Manufactures or the Census of Mines and Quarries. We use the version of these data given by Solomon Fabricant and in Historical Statistics of the United States. Fabricant, Solomon, The Output of Manufacturing Industries, 1899–1937 (New York, 1940)Google Scholar; and U.S. Bureau of the Census, Historical Statistics of the United States (Washington, DC, 1975).Google Scholar

10 See Romer, “Is the Stabilization of the Postwar Economy a Figment of the Data?” for a more thorough discussion of this bias.Google Scholar

11 The exact sources of the alternative series used are the following. For the Babson index we use the version given in Moore, Geoffrey, Business Cycle Indicators (Princeton, 1961), vol. 2, pp. 130–31.Google Scholar We splice ratios to the two variants of the index in Jan. 1933. For the Persons index we use the Index of Production and Trade given in Persons, Forecasting Business Cycles, pp. 91–167. The pig iron series is from Macaulay, The Movement of Interest Rates, pp. A252–A270, table 27.Google ScholarThe FRB series is from U.S. Board of Governors of the Federal Reserve System, Industrial Production (Washington, DC, 1986), p. 303.Google Scholar

12 Table I also shows the behavior of the widely used Frickey series that is available only on an annual basis. These data are from Frickey, Edwin, Production in the United States, 1860–1914 (Cambridge, 1947), p. 54, table 6.Google Scholar

13 Specifically, the FRB index in this period is based on approximately 80 series, whereas our index includes only 13.Google Scholar

14 It is also important to note that for the interwar period the FRB index includes several employment series. If there is labor hoarding, then employment will tend to be less volatile than output. This feature of the FRB index could explain some of its relative stability.Google Scholar

15 The fact that the Persons series is more volatile in the prewar era may be due to the interaction of the use of bank clearings data and the frequency of financial panics in the period before 1914.Google Scholar

16 The standard deviation of our new seasonally unadjusted index for 1922 to 1928 is 10.72. The fact that the interwar period is more volatile than the pre-World War I period has been emphasized by Miron, “The Founding of the Fed.” The results presented here verify those findings with new monthly data.Google Scholar

17 The R 2 of the regression of log growth rates on seasonal dummy variables is 0.05 for the prewar pig iron series, 0.05 for the interwar pig iron series, and 0.29 for the interwar FRB series.Google Scholar

18 See Beaulieu, J. Joseph and Miron, Jeffrey, “Seasonality in U.S. Manufacturing” (unpublished manuscript, Boston University, 1989).Google Scholar

19 It is important to note that these differences in timing are not due to the fact that our index is seasonally unadjusted. The same patterns emerge when our series is adjusted using a regression against seasonal dummy variables and a linear trend.Google Scholar