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Political Shocks and Investment: Some Evidence from the 1930s

Published online by Cambridge University Press:  03 March 2009

Thomas Mayer  and
Monojit Chatterji
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Abstract

There has been some dispute about the impact of New Deal policies on the business climate. Some historians have argued that the New Deal frightened business, and others that it encouraged business. This paper analyzes the impact of the New Deal on business investment by looking at the response of investment to the outcome of the 1934, 1936, and 1938 elections, as well as the passage of certain New Deal legislation. The data reject the hypothesis that the New Deal frightened off business investment.

Type
Articles
Information
The Journal of Economic History , Volume 45 , Issue 4 , December 1985 , pp. 913 - 924
Copyright
Copyright © The Economic History Association 1985

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References

1 See Stein, Herbert, The Fiscal Revolution in America (Chicago, 1969), p. 89;Google ScholarKrooss, Herman, Executive Opinion (New York, 1970), pp. 189–90 and 199.Google Scholar

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13 Moreover, unfilled orders also take into account the possibility that supplying firms, afraid that the political shock will cause the cancellation of previously-placed orders, speed up their shipments.Google Scholar

14 For electrical motors no data on unfilled orders are available; hence data on shipments were used instead. For machine tools only new orders could be used. Since as discussed below, in some regressions one lagged dependent variable and in other regressions three lagged dependent variables were used, the actual period for which the regressions were run starts either one quarter or three quarters later.Google Scholar

15 The data come from U.S. Department of Commerce, Commerce Yearbook, (Washington, D.C., 1932); and Survey of current Business; Supplement (Washington, D.C., 1936, 1938, 1942). There is, of course, a danger of too much aggregation. However given our autogressive approach monthly data are not practicable. To generate lagged variables covering one year, we would need 12 lagged (monthly) variables, and this could produce too much multicollinearity. Aggregation of quarterly data is simple way of reducing severe multicollinearity. Furthermore the use of quarterly data allows for short (3 months) lags in the effect of political shocks should they exist.Google Scholar

16 There is no reason to assume that a regression that explains price expectations in the postwar period also explains price expectations in the 1930s. See Temin's, Peter largely qualitative discussion of price expectations in his Did Monetary Forces Cause the Great Depression? (New York, 1976), pp. 160–64.Google Scholar

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18 The coefficients of the lagged dependent variables pick up the impacts of those variables that affect investment for more than one period. The impact of those that affect investment in only a single quarter becomes part of the error term.Google Scholar

19 The choice of third-order equations is arbitrary, but the results are similar if first-order equations are used instead.Google Scholar

20 An excellent discussion of the proper use of time-series models is in Anderson, O.D., Time Series Analysis and Forecasting (Borough, Green, Kent, England, 1977). More recently the use of time series methods has been given prominence in macroeconomic modeling.Google Scholar Thus Sims, Christopher in “Macroeconomics and Reality,” Econometrica, 48 (01 1980), pp. 1–49 says “… one can obtain macroeconomic models with useful descriptive characteristics, within which tests of econonucally meaningful hypotheses can be executed, without as much of a burden of maintained hypotheses as is usually imposed in such modeling.”CrossRefGoogle Scholar

21 Since there were many shocks during the 1930s there is a danger that the political-shock hypothesis could be force-fitted by experimenting with enough sets of shocks until one set happens to fit. To avoid this the political shocks used were selected without looking at the data and before running any of the regressions. The only regressions that were run are those shown, except for some runs that contained errors.Google Scholar

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33 In principle not all six shocks are independent. Suppose a negative shock in, say 1936-IV, reduces investment for that quarter only. In the following quarter, in the absence of new shocks, investment returns to its previous level but the autoregressive method used here predicts that investment will be lower. Suppose further that there is another negative shock in 1937–1, which does affect investment in that quarter. Since investment is underpredicted by the autoregressive terms, the coefficient of the dummy variable for the 1937–1 shock is then biased. Fortunately this is not a problem here since it turns out that none of the shocks—with the possible exception of the last one—had any effect on investment.Google Scholar

34 In a previous test of the political-shock hypothesis Smithies, Arthur, “The American Economy in the Thirties,” American Economic Review, 36 (05 1964), pp. 11–27, looked at the residuals from regressions for plant and equipment investment and from residential construction regressions. He too concluded that these residuals give no support to the shock hypothesis. However, such an approach is model-specific, and the models used by Smithies in 1946 are now outdated.Google Scholar

35 Both significant results are for nonresidential building contracts, and may be due to a special situation in the nonresidential building industry.Google Scholar

36 Since our results reject the null hypothesis, we are not subject to a standard criticism of econometric results; the improper use of significance tests whereby failure to reject the null hypothesis is seen as accepting the null hypothesis as true. Of course in alternative specifications the test outcome could have been different. However, our procedure is sufficiently general to encompass a wide variety of alternative specifications. This problem, known as Duhem's irrefutability thesis is unavoidable in scientific work. See Blaugh, Mark, Methodology in Economics (Cambridge, 1980), pp. 17–18.Google Scholar