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A Rational Choice Model for the Dakota Effect

Published online by Cambridge University Press:  01 October 2008

Jac C. Heckelman
Jac C. Heckelman
Affiliation:
Wake Forest University
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Extract

In a recent issue, Young and Sigelman (2008) present evidence of a “Dakota effect,” in which persons born in the Dakotas are disproportionately likely to represent their home state, other states, and also generate government spending directed toward the Dakotas. These authors are unable to explain the causal underpinnings for overrepresentation in Congress or the Dakotan natives' keen ability to direct pork back to these two states.

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Forum
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PS: Political Science & Politics , Volume 41 , Issue 4 , October 2008, pp. 677 - 678
Copyright
Copyright © The American Political Science Association 2008

In a recent issue, Young and Sigelman (Reference Young and Sigelman2008) present evidence of a “Dakota effect,” in which persons born in the Dakotas are disproportionately likely to represent their home state, other states, and also generate government spending directed toward the Dakotas. These authors are unable to explain the causal underpinnings for overrepresentation in Congress or the Dakotan natives' keen ability to direct pork back to these two states.

As is now well established, rational choice modeling can be successfully employed to explain every political phenomenon under the sun, as well as some over the sun. Even the harshest critics of rational choice analysis, such as Green and Shapiro (Reference Green and Shapiro1994), have recently admitted this.Footnote 1 As such, a rational choice model will be employed here, and using fictitious data, empirical analysis will be shown to corroborate the model. We believe this to be a major breakthrough.Footnote 2

The Rational Choice Model

As with most rational choice models, the equations are too complex to be understood by mere rank-and-file political scientists. Thus, the model will not be presented but merely summarized. In sum, the model predicts the Dakota effect.

Empirical Support

Having stated the model is correct, no empirical evidence should be needed. But we recognize some of the readers of this journal may not be convinced by the theoretical model alone. To placate such luddites, we now present direct evidence of the model's veracity.

We supplement Young and Sigelman's (Reference Young and Sigelman2008) specification, which supplemented Balla et al.'s (Reference Balla, Lawrence, Maltzman and Sigelman2002) original specification with a Dakota dummy variable, by introducing a New Variable. Data for the New Variable is generated internally from a non-existent source,Footnote 3 and the regression is estimated using the Heckelman Non-Parametric Inference Procedure (HNPIP).Footnote 4

As clearly shown in the first column of Table 1, the New Variable is statistically significant, within a 45% error bound. None of the other variables, including the Dakota variable, come close to this, as predicted by the model. Additional estimations do not support the model and are therefore not reported.Footnote 5

Table 1.

Importance of the New Variable (HNPIP estimates)

* Significant at p < .45;

** significant at p < .00

To further show the power of the model, the New Variable is also used in the final column to explain a totally unrelated result, namely the heretofore previously undescribed “Dragonfly effect.”Footnote 6 Because there is no dependent variable to relate this to, the estimated coefficient is difficult to interpret. However, the New Variable alone explains 137% of the variation.Footnote 7

Conclusion

Once again, rational choice analysis has been used to explain an effect that traditional political science methods could not. Because the model was not presented and the New Variable was not identified, such flexibility will be exploited in future work to explain other important findings in the field of political science. One example is why the two major party labels in the United States, Democratic and Republican, both contain 10 letters. In support of the model, it is found in preliminary analysis that the number of letters in the candidate's party label perfectly explains the winner of each of the last 20 presidential elections. Future work in this area appears promising.

We conclude by quoting Adam Smith: “I'm right because I'm bigger than you.”Footnote 8

Footnotes

1 Actually, they have done no such thing.

2 Following convention of the literature, the term we will be used throughout the text, in an effort to fool the reader into believing the perspective presented here represents the views of more than just one person.

3 On the use of fictitious data from non-existent sources, see the following fictitious studies that appeared in non-existent journals. These sources are not presented below.

4 HNPIP estimates represent the researcher's estimates of how an estimated equation should look in order to support the hypotheses in question. For additional details, reread the previous sentence.

5 These additional results are not available, even upon request.

6 For a completely unrelated paper, see Heckelman (Reference Heckelman2007). This note serves as the obligatory self-reference.

7 Without any dependent variable, the R-square measure does not retain its normal interpretation.

8 Spoken by Smith to the author during fourth-grade recess, Thursday, April 15, Reference Smith1976.

References

Balla, Steven, Lawrence, Eric, Maltzman, Forrest, and Sigelman, Lee. 2002. “Partisanship, Blame Avoidance, and the Distribution of Legislative Pork.” American Journal of Political Science 46 (July): 515–25.CrossRefGoogle Scholar
Green, Donald P., and Shapiro, Ian. 1994. Pathologies of Rational Choice Theory: A Critique of Applications in Political Science. New Haven: Yale University Press.Google Scholar
Heckelman, Jac C. 2007. “Explaining the Rain: The Rise and Decline of Nations after 25 Years.” Southern Economic Journal 74 (July): 18–33.Google Scholar
Smith, Adam K. 1976. Personal correspondence to author.Google Scholar
Young, Garry, and Sigelman, Lee. 2008. “The Dakota Effect.” PS: Political Science and Politics 16 (April): 349–53.Google Scholar
Figure 0

Table 1.Importance of the New Variable (HNPIP estimates)