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Political Competition and the Study of Public Economics

Published online by Cambridge University Press:  07 September 2022

Stanley L. Winer
Affiliation:
Carleton University, Ottawa and CESifo
J. Stephen Ferris
Affiliation:
Carleton University, Ottawa

Summary

Why is an understanding of political competition essential for the study of public economics and public policy generally? How can political competition be described and understood, and how does it differ from its strictly economic counterpart? What are the implications of the fact that policy proposals in a democracy must always pass a political test? What are the strengths and weaknesses of electoral competition as a mechanism for the allocation of economic resources? Why are tax structures in democratic polities so complicated, and what implications follow from this for normative views about good policy choice? How can we measure the intensity of political competition, why and how does it vary in mature democracies, and what are the consequences? This Element considers the approach to answer these questions, while also illustrating some of the interesting theoretical and empirical work that has been done on them. This title is also available as open access on Cambridge Core.

Information

Figure 0

Figure 1 The common pool problem in public finance

Note: n denotes a particular electoral district in a majoritarian electoral system, or a particular party in a governing coalition under PR. Marginal social cost (n) is the full marginal social cost of supplying good G to the nth district, or to supporters of the nth member of a governing coalition. λ∙ Marginal social cost (n), 0 is the marginal cost of benefits to voters in district n, or to supporters of the nth coalition member, when the full cost is spread over all taxpayers through the national tax system.
Figure 1

Figure 2 Alternative measures of the closeness of Indian state elections 1967–2009

Source: Dash, Ferris, and Winer (2019). The fourteen major states constitute about 85 percent of the Indian population. The first versus second volatility-adjusted margin = (v1v2)/volatility. The multiparty volatility-adjusted index is normalized to equal 1 at the constituency level if all party-specific vote share margins included are smaller than the corresponding vote volatility. Indexes are interpolated linearly between elections.
Figure 2

Figure 3 Change in marginal and asymmetry-adjusted marginal seats in fourteen Indian states, 1972–2009

Source: Dash, Ferris, and Winer (2019). A safe or non-marginal seat is one for which an incumbent’s volatility-adjusted margin of victory over the nearest rival is one standard deviation above the mean of a rolling three past-election sample of such margins. Indexes are interpolated linearly between elections.
Figure 3

Figure 4 Loss Probabilities for Canada: 1896–2011

Source: Splinter (2019). Thesis data spreadsheet: columns AL and AM. Historical data refers to the use of actual rather than estimated swing ratios and, in this case, estimated seat swings are based on rolling estimates that use eight to eleven previous elections.
Figure 4

Figure 5 Effective number of political parties: 1962–2017

Source: Gallagher and Mitchell (2009) Election indices dataset online: www.tcd.ie/Political_Science/people/michael_gallagher/ElSystems/index.php, Accessed 11/1/2021.

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