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The Quality of Political Leadership: A Case Study of John Major

Published online by Cambridge University Press:  27 January 2009

Philip Jones  and
John Hudson
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Abstract

Perceptions of leadership quality depend upon personality traits. With John Major as a case study, we demonstrate that the most important personality attributes are those which contribute to the belief that a leader can govern in a business-like fashion. Changes in personality traits affect the rating of a leader and ratings affect a party's electoral support. Using this approach, perceptions of John Major's personality are changed continuously to illustrate the impact on voters. A Monte Carlo simulation is introduced to allow for the likelihood that not everyone would perceive a personality change. This approach is preferable to alternatives and can explain why, on occasion, there has been dispute over the importance of party leadership in British general elections.

Type
Articles
Information
British Journal of Political Science , Volume 26 , Issue 2 , April 1996 , pp. 229 - 244
Copyright
Copyright © Cambridge University Press 1996

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References

1 See, for example, Mughan, A., ‘Party Leaders and Presidentialism in the 1992 Election: A Post War Perspective’, in Denver, D., Norris, P., Railings, C. and Broughton, D., eds, British Elections and Parties Yearbook 1993 (Hemel Hempstead, Herts: Harvester Wheatsheaf, 1993), pp. 193–204.Google Scholar

2 For example, Brown, J. A., ‘The Major Effect: Changes in Party Leadership and Party Popularity’, Parliamentary Affairs (1993), 549–64.Google Scholar

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7 Crewe, and King, , ‘Did Major Win? Did Kinnock Lose?’ p. 144.Google Scholar

8 Crewe, and King, , ‘Did Major Win? Did Kinnock Lose?’ p. 127.Google Scholar

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11 Although there are similarities, the approach is different to the two strategies outlined in Crewe and King – the improved prediction strategy and the thought experiment strategy.

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14 This would have been possible in this article.

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17 The figures in parentheses in Equation (2) represent standard errors and the chi-squared figure relates to the likelihood ratio test for the significance of the regression as a whole. Chi-squared (10) reflects the insignificance, at the 5 per cent level, of a vector of long-term variables: age; age squared; educational age; gender; owner of a car; owner of a phone; owner of a home; an occupational business class variable (constructed by Gallup) and two regional dummy variables (one for Wales and one for Scotland).

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22 This can be illustrated by reference to column 3 of their Table 4. Someone who rated Michael Foot positively on all characteristics but did not rate Thatcher would have a ‘probability’ of voting Conservative of – 0.72. This may be exaggerated due to the non-reporting of a positive constant term. If such a constant term were sufficient to transform this into a non-negative number then the probability of someone voting Conservative who rated Thatcher positively on all characteristics, but did not rate Foot, would substantially exceed one. Clearly, the predicted values from these coefficients cannot be regarded as probabilities.

23 See, for example, Hudson, J., ‘The Relationship between Government Popularity and Approval for the Government's Record in the United Kingdom’, British Journal of Political Science, 15 (1985), 165–86CrossRefGoogle Scholar; Hibbs, D. A. Jr, ‘Economic Outcomes and Political Support for British Governments among Occupational Classes: A Dynamic Analysis’, American Political Science Review, 76 (1982), 259–84CrossRefGoogle Scholar; and Sanders, D., Ward, H. and Marsh, D., ‘Government Popularity and the Falklands War: A Reassessment’, British Journal of Political Science, 17 (1987), 281–313.CrossRefGoogle Scholar

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25 The figures in parenthesis in Equation (3) are again standard errors and the right-hand side variables are defined as in Table 1. Chi-squared (10) reflects the insignificance, at the 10 per cent level, of a vector of long-term variables: age; age squared; educational age; gender; owner of a car; owner of a phone; owner of a home; an occupational business class variable (constructed by Gallup) and two regional dummy variables (one for Wales and one for Scotland).

26 The Monte Carlo algorithm was originally written in LIMDEP but for reasons of expediency was subsequently transferred to RATS. A copy of either program is available from the authors on request.

27 A discussion of two-stage estimation techniques within a limited dependent variable context and their properties can be found in Maddala, , Limited Dependent and Qualitative Variables in EconometricsGoogle Scholar; Mallar, C. D., ‘The Estimation of Simultaneous Probability Models’, Econometrica, 45 (1977), 1717–22CrossRefGoogle Scholar, was the first to use the two-stage approach for two discrete variables.

28 This was used as a proxy for income.

29 Crewe, and King, , ‘Did Major Win? Did Kinnock Lose?’Google Scholar