Democracy, Information, and Audience Costs (Previously circulated as - PowerPoint PPT Presentation

Democracy, Information, and Audience Costs (Previously circulated as “Informational Effects of Audience Costs”) Shuhei Kurizaki & Taehee Whang Waseda University Yonsei University American Political Science Association, Philadelphia, September 1-4, 2016

Research Program on Audience Costs Audience costs can make the decision to go to war rational (Fearon 1994) A set of conjectures to be substantiated ◮ Audience costs exist ◮ Audience costs ∝ democracy ◮ Audience costs → bargaining power

Research Program on Audience Costs Audience costs can make the decision to go to war rational (Fearon 1994) A set of conjectures to be substantiated ◮ Audience costs exist � Tomz 2007, K+W 2015 ◮ Audience costs ∝ democracy � K+W 2015 ◮ Audience costs → bargaining power � “Democratic Advantage”

Research Program on Audience Costs Audience costs can make the decision to go to war rational (Fearon 1994) A set of conjectures to be substantiated ◮ Audience costs exist � Tomz 2007, K+W 2015 ◮ Audience costs ∝ democracy � K+W 2015 ◮ Audience costs → bargaining power � “Democratic Advantage” � But this causal effect depends on a learning mechanism: Audience costs help to send credible signals and learn each other’s resolve

Research Program on Audience Costs Audience costs can make the decision to go to war rational (Fearon 1994) A set of conjectures to be substantiated ◮ Audience costs exist � Tomz 2007, K+W 2015 ◮ Audience costs ∝ democracy � K+W 2015 ◮ Audience costs → bargaining power � “Democratic Advantage” � But this causal effect depends on a learning mechanism: Audience costs help to send credible signals and learn each other’s resolve ◮ Audience costs → information ⇐ This paper

What We Do: Objectives 1. Test Whether Audience Costs Facilitate Learning ◮ We model learning as belief-updating in a crisis ◮ We measure the prior and posterior beliefs

What We Do: Objectives 1. Test Whether Audience Costs Facilitate Learning ◮ We model learning as belief-updating in a crisis ◮ We measure the prior and posterior beliefs � This allows us to test another outstanding question in the literature on democracy and conflict.

What We Do: Objectives 1. Test Whether Audience Costs Facilitate Learning ◮ We model learning as belief-updating in a crisis ◮ We measure the prior and posterior beliefs � This allows us to test another outstanding question in the literature on democracy and conflict. 2. Test Among Informational Mechanisms of Democracy Institutional constraints Democratic Institutions Transparency Information Democratic revelation Signaling via Advantage audience costs Schultz (1999 IO)

How Do We Do This? Structural Approach ◮ We measure learning itself as it is defined in audience costs theory, rather than its effect. ◮ Signaling and learning are modeled as beliefs and their changes ◮ Belief-updating and audience costs are both estimated based on the estimates of underlying payoffs and outcome probabilities in international conflict data

How Do We Do This? Structural Approach ◮ We measure learning itself as it is defined in audience costs theory, rather than its effect. ◮ Signaling and learning are modeled as beliefs and their changes ◮ Belief-updating and audience costs are both estimated based on the estimates of underlying payoffs and outcome probabilities in international conflict data ↑ These are already done in Shuhei Kurizaki & Taehee Whang (2015) “Detecting Audience Costs in International Disputes” International Organization

How Do We Do This? Structural Approach ◮ We measure learning itself as it is defined in audience costs theory, rather than its effect. ◮ Signaling and learning are modeled as beliefs and their changes ◮ Belief-updating and audience costs are both estimated based on the estimates of underlying payoffs and outcome probabilities in international conflict data ↑ These are already done in Shuhei Kurizaki & Taehee Whang (2015) “Detecting Audience Costs in International Disputes” International Organization ◮ What’s left for this paper to do: ◮ We estimate prior beliefs and posterior beliefs using the estimates of the payoffs (and audience costs) ◮ We demonstrate that audience costs improve the amount of belief-updating

� � � � � � � � � � Common Theoretical Model of Audience Costs State 1 State 2 State 1 Resist Fight Challenge Stand Firm u ( SF ) w ~Challenge ~Resist ~Fight 1 1 u ( SF ) w 1 2 Status Quo Concession Back Down u ( SQ ) 0 ( ) 1 ( ) u CD u BD a 1 1 1 1 u ( SQ ) 1 u ( CD ) a u ( BD ) 1 1 2 2 2 Definition Audience costs for State 1 exist iff u 1 ( BD ) < u 1 ( SQ )

Beliefs and Belief-Updating in a Model of Audience Costs Singling and Learning (Theoretical Definition) Belief updating = S 2 ’s posterior minus prior beliefs. 1 S 2 ’s subjective Prior beliefs probability that Posterior (45°) S 1 is resolved beliefs ( q ) Belief updating ( � ) 0 a 1 ~ ˆ a a a 1 1 1 S 1 ’s audience costs

Beliefs and Belief-Updating in a Model of Audience Costs Measuring beliefs requires estimating the payoffs in the underlying game. ◮ Prior Belief Ex ante probability that State 1 fights Pr( SF ) = Pr( u 1 ( SF ) ≥ u 1 ( BD )) ◮ Posterior Belief Conditional probability that State 1 fights, given the challenge � u 1 ( SF ) ≥ u 1 ( BD ) � Pr( SF | CH ) = Pr E [ u 1 ( CH )] ≥ u 1 ( SQ )

� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � Statistical Model of Audience Costs in Kurizaki & Whang (2015) State 1 State 2 State 1 Challenge Resist Fight Pr(CH) Pr(RS|CH) Pr(F|CH) Stand Firm u ( SF ) SF 1 ~Challenge ~Resist ~Fight 1 SF 1 Pr(~CH) Pr(~RS|CH) Pr(~F|CH) X SF SF SF 1 1 1 u ( SF ) SF 2 2 SF 2 Status Quo Concession Back Down X SF SF SF 2 2 2 u ( SQ ) SQ u ( CD ) CD u ( BD ) BD 1 1 1 1 SQ 1 CD 1 BD 1 1 1 X X X SQ SQ SQ CD CD CD BD BD BD 1 1 1 1 1 1 1 1 1 u ( CD ) CD u ( BD ) BD 2 2 2 CD 2 BD 2 2 X X CD CD CD BD BD BD 2 2 2 2 2 2 Observable payoffs: mean payoffs + unobservable noise u 1 ( SF ) = SF 1 + ǫ SF 1 = X SF 1 β SF 1 + ǫ SF 1 where ǫ SF 1 ∼ N (0 , Var ( ǫ SF 1 ))

Modeling Beliefs: Empirical Specification of Payoffs Empirical specifications are true to those in theoretical model. War Payoff: u 1 ( SF ) = p − c 1 p : Prob that State 1 wins in a war ◮ Balance of power: Capabilities ratio c 1 : Cost of war ◮ Material cost: Economic development ◮ Political will to incur the cost: Democracy Specifications of other payoffs are given in Kurizaki & Whang (2015) ◮ Concession payoffs; Status-Quo payoffs; Back-Down payoffs

Data - Dependent Variable Coercive Diplomacy Database (Lewis, Schultz, Zucco 2012) ◮ Unit of analysis: a military challenge case, plus SQ cases ◮ 93 dyadic crisis cases ranging from 1919 to 1939 ◮ Integrate both Militarized Interstate Dispute data (MID) and International Conflict Behavior data (ICB) ◮ N = 2187 with the addition of SQ cases Outcome ICB MID Total SQ 2094 CD 28 16 44 BD 5 7 12 SF 33 4 37

Estimation Results Main Status Quo Second AC Democracy Payoff Variable Est (SE) Est (SE) Est (SE) Est (SE) u 1 ( SQ ) Constant 0 0 0 0 0 . 58 ∗∗ 0 . 36 ∗∗ 0 . 14 ∗∗ MaxAge (0.14) (0.14) (0.05) Democracy 1 Alliance u 1 ( CD ) Constant − 1 . 47 (1.11) 0.98 (0.91) 1.76 (1.90) 1 . 59 ∗∗ (0.42) Alliance − 2 . 52 (1.37) − 3 . 51 ∗∗ (1.16) − 2 . 48 ∗∗ (1.04) − 1 . 00 ∗∗ (0.30) CivilWar 2 4.07 (2.13) 4 . 46 ∗∗ (1.45) 1.95 (1.82) 2 . 06 ∗∗ (0.60) Contiguity 1.13 (0.78) 3 . 16 ∗∗ (0.90) 1.09 (1.02) 0 . 99 ∗∗ (0.36) Democracy 1 0 . 82 ∗∗ (0.19) − 0 . 40 ∗∗ − 1 . 40 ∗∗ − 1 . 31 ∗∗ − 1 . 43 ∗∗ u 2 ( CD ) Constant (0.39) (0.56) (0.67) (0 . 27) 0 . 67 ∗∗ Alliance (0.35) 0.48 (0.33) 0.41 (0.33) − 0 . 07 (0 . 10) CivilWar 2 − 1 . 43 (0.37) 0.18 (0.21) 0.20 (0.32) − 0 . 03 (0.07) − 0 . 11 ∗ Contiguity − 0 . 17 (0.26) − 0 . 37 (0.23) − 0 . 02 (0.20) (0.06) Democracy 2 0 . 04 (0.04) u 1 ( BD ) Constant − 5 . 98 ∗∗ (1.57) − 4 . 09 ∗∗ (0.82) − 3 . 65 ∗∗ (0.99) − 4 . 19 ∗∗ (0.36) Democracy 1 − 0 . 32 ∗∗ (0.10) − 0 . 41 ∗∗ (0.10) − 0 . 25 ∗∗ (0.09) − 0 . 67 ∗∗ (0.11) u 2 ( BD ) Constant 0 0 0 0 u 1 ( SF ) Constant − 3 . 33 ∗∗ (1.25) − 4 . 62 ∗∗ (0.79) − 3 . 48 ∗∗ (0.75) − 3 . 78 ∗∗ (0.24) − 1 . 30 (0.80) 0 . 95 ∗∗ (0.47) 0.84 (0.53) 0 . 69 ∗∗ (0.17) CapShare 1 − 0 . 09 ∗∗ (0.04) − 0 . 37 ∗∗ (0.09) − 0 . 19 ∗∗ (0.08) − 0 . 68 ∗∗ (0.11) Democracy 1 0.10 (0.06) 0.09 (0.05) 0.06 (0.05) 0.01 (0.01) Develop 1 u 2 ( SF ) Constant − 1 . 06 ∗∗ (0.39) − 2 . 73 ∗∗ (0.79) − 1 . 90 ∗∗ (0.74) − 2 . 70 ∗∗ (0.33) CapShare 1 0 . 50 (0.34) 0.61 (0.42) 0 . 41 ∗ (0.25) 1 . 00 ∗∗ (0.21) Democracy 2 0.01 (0.01) 0.01 (0.01) 0.06 (0.05) 0.00 (0.00) Develop 2 − 0 . 01 (0.02) − 0 . 02 (0.02) − 0 . 01 (0.02) − 0 . 01 (0.01) ∗∗ p < 0 . 05 , ∗ p < 0 . 1 (two-tailed)