Electoral Imbalances and Their Consequences

Abstract

It is widely believed that competitive elections are required for good democratic performance. Many races, however, see little electoral competition, due to asymmetries in voters' evaluation of candidates' quality (due, for example, to incumbency) and party labels (due, for example, to ideology). We study the consequences of both types of imbalances in a unified theoretical framework building on the notion that voters are rationally ignorant and need to pay costly attention to learn about candidates. Our paper rationalizes key empirical regularities such as the existence of large incumbency spending and electoral advantages or the heterogeneous effect of incumbency. Further, we highlight that properly accounting for voter attention is critical to interpreting empirical estimates of key determinants of electoral success, the sources of the incumbency advantage, and the causal effect of incumbency status. We also show that while depressing electoral competition, imbalances nonetheless improve voter welfare.

Phantom Counterfactuals

“…Second, my findings expand a growing literature on the ‘theoretical implications of empirical models’ (TIEM) (e.g. Ashworth and de Mesquita 2014; Eggers 2017; Gailmard and Patty 2018; Izzo, Dewan, and Wolton 2020; Prato and Wolton 2018). In particular, the identification problems posed by phantom counterfactuals constitute a new class of commensurability problems in which analysts aim to estimate a quantity that is theoretically undefined (Bueno de Mesquita and Tyson 2020).…”

Quality-Based Explanations of Incumbency Effects

“…Along with other recent theoretical work (e.g. Meirowitz, 2008;Ashworth and Bueno De Mesquita, 2008;Caselli et al, 2013;Muthoo and Shepsle, 2014;Prato and Wolton, 2016), this paper has added to the list of possible mechanisms that could contribute to incumbency effects. To an empiricist, the growing list of explanations for incumbency effects can be viewed as a mixed blessing.…”

The Commensurability Problem: Conceptual Difficulties in Estimating the Effect of Behavior on Behavior

“…Fudenberg and Levine (2019) model research designs, such as RD or difference-in-differences, in a model where information feedback affects effort that endogenously determines treatment. Other work, somewhat further afield, uses theory to think about how to optimally design treatments to maximize learning (Banerjee, Chassang, and Snowberg 2017; Chassang, Padró i Miquel and Snowberg 2012) or to question the normative or positive interpretation of well-identified empirical findings (Ashworth and Bueno de Mesquita 2014; Ashworth, Bueno de Mesquita, and Friedenberg 2018; Fowler 2018; Izzo, Dewan, and Wolton 2018; Prato and Wolton 2018; Sun and Tyson 2019; Wolton 2019).…”