เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| Vote Buying Analysis× | Clientelism Analysis× | Distributive Politics Analysis× | |
|---|---|---|---|
| สาขาวิชา | Political Economy | Political Economy | Political Economy |
| ตระกูล≠ | Regression model | Process / pipeline | Regression model |
| ปีกำเนิด≠ | 2008 | 2005 | 1986 |
| ผู้ริเริ่ม≠ | Simeon Nichter; Ezequiel Gonzalez-Ocantos et al. | Susan C. Stokes; Herbert Kitschelt & Steven Wilkinson | Gary Cox & Mathew McCubbins (core); Avinash Dixit & John Londregan (swing) |
| ประเภท≠ | Measurement-and-targeting regression model | Network-and-exchange pipeline for clientelist accountability | Regression analysis of electorally motivated spending allocation |
| แหล่งต้นตำรับ≠ | Nichter, S. (2008). Vote Buying or Turnout Buying? Machine Politics and the Secret Ballot. American Political Science Review, 102(1), 19-31. DOI ↗ | Stokes, S. C. (2005). Perverse Accountability: A Formal Model of Machine Politics with Evidence from Argentina. American Political Science Review, 99(3), 315-325. DOI ↗ | Cox, G. W., & McCubbins, M. D. (1986). Electoral Politics as a Redistributive Game. The Journal of Politics, 48(2), 370-389. DOI ↗ |
| ชื่อเรียกอื่น | Electoral Clientelism Measurement, Turnout Buying Analysis, Vote-Buying Targeting Model, List-Experiment Vote Buying | Machine Politics Analysis, Contingent Exchange Analysis, Broker-Mediated Clientelism, Party Machine Network Analysis | Electoral Targeting Analysis, Swing versus Core Voter Analysis, Pork Barrel Politics Analysis, Tactical Redistribution Analysis |
| ที่เกี่ยวข้อง | 3 | 3 | 3 |
| สรุป≠ | Vote buying analysis is the measurement-and-modeling toolkit for studying the distribution of private goods or cash in exchange for electoral support. Two problems define the field. First, who is being paid: Simeon Nichter's 2008 article showed that machines frequently engage in turnout buying — paying their own already-loyal supporters to show up — rather than the classic vote buying of swing voters, a distinction with sharp implications for who gets targeted. Second, how to measure something respondents are reluctant to admit: Gonzalez-Ocantos and colleagues' 2012 study demonstrated that direct survey questions sharply understate vote buying because of social-desirability bias and that list experiments recover far higher, more credible prevalence. Vote buying analysis combines such debiased measurement with regression models of targeting. | Clientelism analysis studies the contingent, direct exchange of material benefits for political support and the broker-mediated networks that make such exchange enforceable. Susan Stokes's 2005 formal model of machine politics, built on evidence from Argentina, showed that clientelism inverts normal democratic accountability: instead of voters holding politicians to account, the party machine holds voters to account, rewarding compliance and punishing defection through brokers who can monitor behavior. Kitschelt and Wilkinson's 2007 comparative volume situated this contingent linkage alongside programmatic competition and mapped its variation across democracies. The analysis combines a network view of the party-broker-client machine with a model of how monitoring through dense social ties sustains the bargain. | Distributive politics analysis studies how governments allocate divisible public spending — grants, transfers, projects, and pork — across districts and groups to maximize electoral support. Two competing theories anchor the field. The swing-voter logic, formalized by Avinash Dixit and John Londregan in 1996 (building on Lindbeck and Weibull), holds that parties target marginal districts where votes are most responsive to spending. The core-voter logic, associated with Gary Cox and Mathew McCubbins's 1986 redistributive-game model, holds that parties instead reward loyal supporters whose preferences and reliability they know best. The empirical method is a regression of observed transfers on electoral characteristics — district marginality and partisan alignment — to test which targeting strategy the data reveal. |
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