This study is an attempt to employ some simple statistical models, motivated by certain assumptions about voting akin to those discussed by Downs and others, in an attempt to explain short-term fluctuations in the division of the national vote for the U. S. House of Representatives, over the period 1896–1964. The models will yield quantitative estimates of the impact of economic conditions on congressional elections, and of the effects of incumbency and presidential “coattails” as well.
The notion that a vote represents a decision or rational choice between alternatives is an important theme in democratic theory. However, this rationality hypothesis has proved to be difficult to test empirically, particularly with survey data, from which most of our recent knowledge of individual voting behavior is drawn. The present study is an attempt to put a modified form of the rationality hypothesis to a different and in some respects more direct test than is readily possible with survey data.
The analysis bears directly on the substantive question of the relationships between economic conditions and U. S. national election results.
1 Downs Anthony, An Economic Theory of Democracy (New York: Harper, 1957).
2 Though see Key V. O. Jr., The Responsible Electorate (Cambridge: Belknap Harvard, 1966).
3 Kerr W. A., “A Quantitative Study of Political Behavior, 1840–1940”, Journal of Social Psychology, 19 (1944), 273–281.
4 Pearson F. A. and Myers W. I., “Prices and Presidents”, Farm Economics (Ithaca, N.Y.: New York State College of Agriculture, Cornell University), 163 (September 1948), 4210–4218, at p. 4210.
5 Bean Louis H., Ballot Behavior (Washington, D.C.: American Council on Public Affairs, 1940), p. 63n.
6 Wilkinson T. and Hart H., “Prosperity and Political Victory”, Public Opinion Quarterly, 14 (1950), 331–335, at pp. 332, 334.
7 Ibid., p. 334.
8 Tibbits Clark, “Majority Votes and the Business Cycle”, American Journal of Sociology, 36 (1931) 596–606.
9 Gosnell H. F. and Coleman W. G., “Politica Trends in Industrial America: Pennsylvania as an Example”, Public Opinion Quarterly, 4 (1940), 473–484, at p. 475.
10 Ogburn W. F. and Coombs L. C., “The Economic Factor in the Roosevelt Elections”, this REVIEW, 34 (1940), 719–736, at pp. 719–720.
11 Ibid., p. 724.
12 Rees Albert, Kaufman H., Eldersveld S. J., and Freidel F., “The Effect of Economic Conditions on Congressional Elections, 1946–58”, Review of Economics and Statistics, 44 (1962), 458–465.
13 Clark Wesley C., Economic Aspects of a Presidents Popularity (unpublished Ph.D. dissertation, University of Pennsylvania, 1943).
14 Durant Henry, “Indirect Influences on Voting Behavior”, Polls, 1 (1965), 3–11.
15 Campbell Angus, Converse Philip E., Miller Warren E., and Stokes Donald E., The American Voter (New York: Wiley, 1960), pp. 381–440.
16 For discussion of the identification problem in a somewhat different context, see Koopmans Tjalling C., “Identification Problems in Economic Model Construction”, in Hood W. C. and Koopmans T. C. (eds.), Studies in Econometric Method (New York: Wiley, 1953).
17 Stokes Donald E. and Miller Warren E., “Party Government and the Saliency of Congress”, Public Opinion Quarterly, 26 (1962).
19 Stokes Donald E., “A Variance Components Model of Political Effects”, in Bernd J. L. (ed.), Mathematical Applications in Political Science (Dallas, Tex.: Southern Methodist University Press, 1965).
20 It would also be natural to add a third disturbance, say z., to represent the net effect of the personal characteristics of individual congressional candidates. However, as we have already noted, congressional candidates are relatively anonymous to most of their constituents, and congressional campaign efforts are typically much less extensive than those of either statewide or local races; furthermore, this “personal” effect would be averaged over some 400-odd individual races, so the final net effect would be smalt indeed. Hence we shall ignore it.
21 Rees et al, op. cit.
22 Durant, op. cit.
23 Cummings Milton C. Jr., Congressmen and the Electorate (New York: The Free Press, 1966), pp. 135ff.
24 The other parameter estimates are close to those reported in Table 1, though the goodness of fit is somewhat poorer.
25 Key, op. cit.
26 See Freeman Harold, Introduction to Statistical Inference (Reading, Pa.: Addison-Wesley, 1963), pp. 69ff; and Mood A. M. and Graybill F. A., Introduction to the Theory of Statistics (New York: McGraw-Hill, 1963), pp. 220ff.
27 See Freeman, op. cit., pp. 253c; and Mood and Graybill, op. cit. pp. 178ff.
28 See Freeman, op. cit., pp. 300ff; and Mood and Graybill, op. cit., pp. 297ff.
* The research described in this article was performed at the Cowles Foundation for Research in Economics at Yale University, under grants from the National Science Foundation and the Ford Foundation.
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