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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
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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
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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
X Tomz 2007, K+W 2015
X K+W 2015
X “Democratic Advantage”
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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
X Tomz 2007, K+W 2015
X K+W 2015
X “Democratic Advantage”
But this causal effect depends on a learning mechanism: Audiencecosts help to send credible signals and learn each other’s resolve
![Page 5: Democracy,Information,andAudienceCostsDemocracy,Information,andAudienceCosts (Previously circulated as “Informational Effects of Audience Costs”) Shuhei Kurizaki & Taehee Whang](https://reader033.vdocuments.mx/reader033/viewer/2022042207/5ea973876e3b2330da143df2/html5/thumbnails/5.jpg)
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
X Tomz 2007, K+W 2015
X K+W 2015
X “Democratic Advantage”
But this causal effect depends on a learning mechanism: Audiencecosts help to send credible signals and learn each other’s resolve
◮ Audience costs → information ⇐ This paper
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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
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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 literatureon democracy and conflict.
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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 literatureon democracy and conflict.
2. Test Among Informational Mechanisms of Democracy
Democratic
Institutions
Institutional
constraints
Democratic
AdvantageSignaling via
audience costs
Transparency Information
revelation
Schultz (1999 IO)
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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 outcomeprobabilities in international conflict data
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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 outcomeprobabilities in international conflict data↑ These are already done in
Shuhei Kurizaki & Taehee Whang (2015) “Detecting Audience Costs inInternational Disputes” International Organization
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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 outcomeprobabilities in international conflict data↑ These are already done in
Shuhei Kurizaki & Taehee Whang (2015) “Detecting Audience Costs inInternational Disputes” International Organization
◮ What’s left for this paper to do:
◮ We estimate prior beliefs and posterior beliefs using theestimates of the payoffs (and audience costs)
◮ We demonstrate that audience costs improve the amount ofbelief-updating
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Common Theoretical Model of Audience Costs
Resist
Back Down
1)(
)(
2
11
BDu
aBDu
~Challenge
Status Quo
1)(
0)(
1
1
SQu
SQu
Stand Firm
21
11
)(
)(
wSFu
wSFu
Challenge
~Resist
Fight
~Fight
State 1 State 2 State 1
Concession
22
1
)(
1)(
aCDu
CDu
Definition
Audience costs for State 1 exist iff u1(BD) < u1(SQ)
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Beliefs and Belief-Updating in a Model of Audience Costs
Singling and Learning (Theoretical Definition)
Belief updating = S2’s posterior minus prior beliefs.
a10
S1’s audience costs
1a1a
Prior beliefs
(45°)Posterior
beliefs
(q)Belief updating
( )
1
1~a
S2’s subjective
probability that
S1 is resolved
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Beliefs and Belief-Updating in a Model of Audience Costs
Measuring beliefs requires estimating the payoffs in the underlyinggame.
◮ Prior BeliefEx ante probability that State 1 fights
Pr(SF ) = Pr(u1(SF ) ≥ u1(BD))
◮ Posterior BeliefConditional probability that State 1 fights, given the challenge
Pr(SF |CH) = Pr
(
u1(SF ) ≥ u1(BD)
E [u1(CH)] ≥ u1(SQ)
)
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Statistical Model of Audience Costs in Kurizaki & Whang (2015)
ResistPr(RS|CH)
Back Down
111
111 )(
BDBDBD
BD
X
BDBDu
222
222 )(
BDBDBD
BD
X
BDBDu
~ChallengePr(~CH)
Status Quo
111
111 )(
SQSQSQ
SQ
X
SQSQu
Stand Firm
111
111 )(
SFSFSF
SF
X
SFSFu
222
222 )(
SFSFSF
SF
X
SFSFu
ChallengePr(CH)
~ResistPr(~RS|CH)
FightPr(F|CH)
~FightPr(~F|CH)
State 1 State 2 State 1
Concession
111
111 )(
CDCDCD
CD
X
CDCDu
222
222 )(
CDCDCD
CD
X
CDCDu
Observable payoffs: mean payoffs + unobservable noise
u1(SF ) = SF1 + ǫSF1
= XSF 1βSF 1
+ ǫSF1where ǫSF1
∼ N(0,Var(ǫSF1))
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Modeling Beliefs: Empirical Specification of Payoffs
Empirical specifications are true to those in theoretical model.
War Payoff: u1(SF ) = p − c1
p: Prob that State 1 wins in a war
◮ Balance of power: Capabilities ratio
c1: 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
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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) andInternational Conflict Behavior data (ICB)
◮ N = 2187 with the addition of SQ cases
Outcome ICB MID Total
SQ 2094CD 28 16 44BD 5 7 12SF 33 4 37
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Estimation Results
Main Status Quo Second AC DemocracyPayoff Variable Est (SE) Est (SE) Est (SE) Est (SE)u1(SQ) Constant 0 0 0 0
MaxAge 0.58∗∗ (0.14) 0.36∗∗ (0.14) 0.14∗∗ (0.05)Democracy1Alliance
u1(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)CivilWar2 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)Democracy1 0.82∗∗ (0.19)
u2(CD) Constant −0.40∗∗ (0.39) −1.40∗∗ (0.56) −1.31∗∗ (0.67) −1.43∗∗ (0.27)Alliance 0.67∗∗ (0.35) 0.48 (0.33) 0.41 (0.33) −0.07 (0.10)CivilWar2 −1.43 (0.37) 0.18 (0.21) 0.20 (0.32) −0.03 (0.07)Contiguity −0.17 (0.26) −0.37 (0.23) −0.02 (0.20) −0.11∗ (0.06)Democracy2 0.04 (0.04)
u1(BD) Constant −5.98∗∗ (1.57) −4.09∗∗ (0.82) −3.65∗∗ (0.99) −4.19∗∗ (0.36)Democracy1 −0.32∗∗ (0.10) −0.41∗∗ (0.10) −0.25∗∗ (0.09) −0.67∗∗ (0.11)
u2(BD) Constant 0 0 0 0u1(SF ) Constant −3.33∗∗ (1.25) −4.62∗∗ (0.79) −3.48∗∗ (0.75) −3.78∗∗ (0.24)
CapShare1 −1.30 (0.80) 0.95∗∗ (0.47) 0.84 (0.53) 0.69∗∗ (0.17)Democracy1 −0.09∗∗ (0.04) −0.37∗∗ (0.09) −0.19∗∗ (0.08) −0.68∗∗ (0.11)Develop1 0.10 (0.06) 0.09 (0.05) 0.06 (0.05) 0.01 (0.01)
u2(SF ) Constant −1.06∗∗ (0.39) −2.73∗∗ (0.79) −1.90∗∗ (0.74) −2.70∗∗ (0.33)CapShare1 0.50 (0.34) 0.61 (0.42) 0.41∗ (0.25) 1.00∗∗ (0.21)Democracy2 0.01 (0.01) 0.01 (0.01) 0.06 (0.05) 0.00 (0.00)Develop2 −0.01 (0.02) −0.02 (0.02) −0.01 (0.02) −0.01 (0.01)
∗∗p < 0.05,∗ p < 0.1 (two-tailed)
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Estimates of the Prior and Posterior Beliefs
−10 −5 0 5 10
0.0
0.4
0.8
Main Model
−10 −5 0 5 100.0
0.4
0.8
Status Quo Model
−10 −5 0 5 10
0.0
0.4
0.8
Second AC Model
−10 −5 0 5 10
0.0
0.4
0.8
Democracy Model
−10 −5 0 5 10
0.0
0.4
0.8
Sunk Cost Model
Posterior Belief, Pr(SF|CH)Prior Belief, Pr(SF)Belief Updating, Λ
− Legend −
x−Axis: Democracy Level
y−Axis: Probability
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Findings: Prior Beliefs
S2’s Prior Beliefs
◮ Increasing as AC for S1
increases in 3 of 5 models
◮ Democracy Model: Concave inAC
◮ Sunk-Cost Model: independentof AC for S1
S2’s Posterior Beliefs
◮ Increasing as AC for S1
increases in all models
◮ Statistically different than fullseparation
◮ Sunk-Cost Model: Posteriorincreases in AC
S2’s Belief Updating
◮ Learning is statistically significant
◮ Lower bounds of 95% CI don’t includezero
◮ Who updates? Everybody
◮ Except for the least democraticregimes (Democracy1 = −10)
Effect of S1’s AC on belief-updating
◮ Learning without AC forDemocracy1 < −5
◮ Increasing as AC for S1 increase in allmodels
◮ Is the effect significant?
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Estimates of the Prior and Posterior Beliefs
−10 −5 0 5 10
0.0
0.4
0.8
Main Model
−10 −5 0 5 100.0
0.4
0.8
Status Quo Model
−10 −5 0 5 10
0.0
0.4
0.8
Second AC Model
−10 −5 0 5 10
0.0
0.4
0.8
Democracy Model
−10 −5 0 5 10
0.0
0.4
0.8
Sunk Cost Model
Posterior Belief, Pr(SF|CH)Prior Belief, Pr(SF)Belief Updating, Λ
− Legend −
x−Axis: Democracy Level
y−Axis: Probability
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Statistical Test of Fully-Separating Signals
Posterior belief Belief Updatingat Democracy1 = 10 at Democracy1 = −10
Models [Lower, Upper] [Lower, Upper]
Main [0.589, 0.999] [0.003, 0.361]Status Quo [0.788, 0.962] [-0.066, 0.293]Second AC [0.618, 0.999] [0.040, 0.355]Democracy [0.625, 0.990] [0.000, 0.163]Sunk Cost [0.482, 0.904] [0.002, 0.142]
Bootstrapped 95% Confidence Intervals of the Beliefs and Belief-Updating
(Two-tail)
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Findings: Posterior Beliefs
S2’s Prior Beliefs
◮ Increasing as AC for S1
increases in 3 of 5 models
◮ Democracy Model: Concave inAC
◮ Sunk-Cost Model: independentof AC for S1
S2’s Posterior Beliefs
◮ Increasing as AC for S1
increases in all models
◮ Statistically different than fullseparation
◮ Sunk-Cost Model: Posteriorincreases in AC
S2’s Belief Updating
◮ Learning is statistically significant
◮ Lower bounds of 95% CI don’t includezero
◮ Who updates? Everybody
◮ Except for the least democraticregimes (Democracy1 = −10)
Effect of S1’s AC on belief-updating
◮ Learning without AC forDemocracy1 < −5
◮ Increasing as AC for S1 increase in allmodels
◮ Is the effect significant?
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Estimates of the Prior and Posterior Beliefs
−10 −5 0 5 10
0.0
0.4
0.8
Main Model
−10 −5 0 5 100.0
0.4
0.8
Status Quo Model
−10 −5 0 5 10
0.0
0.4
0.8
Second AC Model
−10 −5 0 5 10
0.0
0.4
0.8
Democracy Model
−10 −5 0 5 10
0.0
0.4
0.8
Sunk Cost Model
Posterior Belief, Pr(SF|CH)Prior Belief, Pr(SF)Belief Updating, Λ
− Legend −
x−Axis: Democracy Level
y−Axis: Probability
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Statistical Significance of Belief-Updating
Posterior belief Belief Updatingat Democracy1 = 10 at Democracy1 = −10
Models [Lower, Upper] [Lower, Upper]
Main [0.589, 0.999] [0.003, 0.361]Status Quo [0.788, 0.962] [-0.066, 0.293]Second AC [0.618, 0.999] [0.040, 0.355]Democracy [0.625, 0.990] [0.000, 0.163]Sunk Cost [0.482, 0.904] [0.002, 0.142]
Bootstrapped 95% Confidence Intervals of the Beliefs and Belief-Updating
(Two-tail)
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Findings: Belief-Updating
S2’s Prior Beliefs
◮ Increasing as AC for S1
increases in 3 of 5 models
◮ Democracy Model: Concave inAC
◮ Sunk-Cost Model: independentof AC for S1
S2’s Posterior Beliefs
◮ Increasing as AC for S1
increases in all models
◮ Statistically different than fullseparation
◮ Sunk-Cost Model: Posteriorincreases in AC
S2’s Belief Updating
◮ Learning is statistically significant
◮ Lower bounds of 95% CI don’t includezero
◮ Who updates? Everybody
◮ Except for the least democraticregimes (Democracy1 = −10)
Effect of S1’s AC on belief-updating
◮ Learning without AC forDemocracy1 < −5
◮ Increasing as AC for S1 increase in allmodels
◮ Is the effect significant?
![Page 27: Democracy,Information,andAudienceCostsDemocracy,Information,andAudienceCosts (Previously circulated as “Informational Effects of Audience Costs”) Shuhei Kurizaki & Taehee Whang](https://reader033.vdocuments.mx/reader033/viewer/2022042207/5ea973876e3b2330da143df2/html5/thumbnails/27.jpg)
Estimates of the Prior and Posterior Beliefs
−10 −5 0 5 10
0.0
0.4
0.8
Main Model
−10 −5 0 5 100.0
0.4
0.8
Status Quo Model
−10 −5 0 5 10
0.0
0.4
0.8
Second AC Model
−10 −5 0 5 10
0.0
0.4
0.8
Democracy Model
−10 −5 0 5 10
0.0
0.4
0.8
Sunk Cost Model
Posterior Belief, Pr(SF|CH)Prior Belief, Pr(SF)Belief Updating, Λ
− Legend −
x−Axis: Democracy Level
y−Axis: Probability
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Findings: Effect of AC on Belief-Updating
S2’s Prior Beliefs
◮ Increasing as AC for S1
increases in 3 of 5 models
◮ Democracy Model: Concave inAC
◮ Sunk-Cost Model: independentof AC for S1
S2’s Posterior Beliefs
◮ Increasing as AC for S1
increases in all models
◮ Statistically different than fullseparation
◮ Sunk-Cost Model: Posteriorincreases in AC
S2’s Belief Updating
◮ Learning is statistically significant
◮ Lower bounds of 95% CI don’t includezero
◮ Who updates? Everybody
◮ Except for the least democraticregimes (Democracy1 = −10)
Effect of S1’s AC on belief-updating
◮ Learning without AC forDemocracy1 < −5
◮ Increasing as AC for S1 increase in allmodels
◮ Is the effect significant?
![Page 29: Democracy,Information,andAudienceCostsDemocracy,Information,andAudienceCosts (Previously circulated as “Informational Effects of Audience Costs”) Shuhei Kurizaki & Taehee Whang](https://reader033.vdocuments.mx/reader033/viewer/2022042207/5ea973876e3b2330da143df2/html5/thumbnails/29.jpg)
Findings: Effect of AC on Belief-Updating
S2’s Prior Beliefs
◮ Increasing as AC for S1
increases in 3 of 5 models
◮ Democracy Model: Concave inAC
◮ Sunk-Cost Model: independentof AC for S1
S2’s Posterior Beliefs
◮ Increasing as AC for S1
increases in all models
◮ Statistically different than fullseparation
◮ Sunk-Cost Model: Posteriorincreases in AC
S2’s Belief Updating
◮ Learning is statistically significant
◮ Lower bounds of 95% CI don’t includezero
◮ Who updates? Everybody
◮ Except for the least democraticregimes (Democracy1 = −10)
Effect of S1’s AC on belief-updating
◮ Learning without AC forDemocracy1 < −5
◮ Increasing as AC for S1 increase in allmodels
◮ Is the effect significant? Need to regress the amount ofupdating with Democracy1
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Illustrating the Effects of AC on Belief-Updating
●
●
Prior Posterior
0.4
0.6
0.8
1.0
●
●
Main Model
Democracy, +0.224
Non−Democracy, +0.182
●
●
Prior Posterior0.4
0.6
0.8
1.0
●
●
Status Quo Model
Democracy, +0.324
Non−Democracy, +0.114 ●
●
Prior Posterior
0.4
0.6
0.8
1.0
●
●
Second AC Model
Democracy, +0.178
Non−Democracy, +0.197
●
●
Prior Posterior
0.4
0.6
0.8
1.0
●
●
Democracy Model
Democracy, +0.245
Non−Democracy, +0.082
●
●
Prior Posterior
0.4
0.6
0.8
1.0
●
●
Sunk Cost Model
Democracy, +0.096
Non−Democracy, +0.070
Non−Democracy(Democracy1=−10)Democracy(Democracy1=10)
− Legend −
y−Axis: Probability
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Implications
The results substantiate the causal mechanism of audience costs model
◮ “Audience costs improve crisis communication through signals”
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Implications
The results substantiate the causal mechanism of audience costs model
◮ “Audience costs improve crisis communication through signals”
Results allow us to test why democracies can reveal information
1. Transparency of democratic processes reveals government’sintentions apart from conflict processes → Common Priors
◮ Bueno de Mesquita & Lalman (1992)
2. Audience costs improve government’s ability to reveal intentionsthrough conflict behavior → Belief Updating
◮ Fearon (1994), Schultz (1999)
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Do Democracies Inform or Constrain, and How?
“Do Democratic Institutions Inform or Constrain?” (Schultz 1999 IO)
Democratic
Peace
Institutional
constraints
Democratic
Advantage
Democratic
Prudence
Informational
effects
Democratic
Institutions
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Do Democracies Inform or Constrain, and How?
“Do Democratic Institutions Inform or Constrain?” (Schultz 1999 IO)
Democratic
Peace
Institutional
constraints
Democratic
Advantage
Democratic
Prudence
Informational
effects
Democratic
Institutions
This Paper! How do Democratic Institutions Inform?
Democratic
Institutions
Institutional
constraints
Democratic
AdvantageSignaling via
audience costs
Transparency Information
revelation
Schultz (1999 IO)
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Hypotheses on the Informational Effects of Democratic Institutions
Two Mechanisms for Informational Effects of Democratic Institutions
Signaling and Learning Institutional Transparency(Fearon 1994, Schultz 1999) (Bueno de Mesquita
and Lalman 1992)
S2’s Resistance∗
− −
Prior Belief + +Posterior Belief + +Belief Updating + 0
◮ Existing research design suffers from observational equivalence (*)
◮ Hypotheses on the effect of democracy on beliefs avoid this problem
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Hypotheses on the Informational Effects of Democratic Institutions
◮ Existing research design suffers from observational equivalence (*)
◮ Hypotheses on the effect of democracy on beliefs avoid this problem
Two Mechanisms for Informational Effects of Democratic Institutions
Signaling and Learning Institutional Transparency(Fearon 1994, Schultz 1999) (Bueno de Mesquita
and Lalman 1992)
S2’s Resistance∗
− −
Prior Belief + +Posterior Belief + +Belief Updating + 0
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Testing the “Institutional Transparency” Mechanism
Least Most Effect ofDemocratic Democratic Democracy
(Democracy1 = −10) (Democracy1 = 10)
Prior belief 40% 53% +13%
Posterior belief 60% 85% +25%
Belief updating +20% +32% +12%
Effect of Transparency How common prior changes as S1 becomesmore democratic
◮ 53%− 40% = 13% increase
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Belief-Updating without Audience Costs
Least Most Effect ofDemocratic Democratic Democracy
(Democracy1 = −10) (Democracy1 = 10)
Prior belief 40% 53% +13%
Posterior belief 60% 85% +25%
Belief updating +20% +32% +12%
Effect of “Democratic” Signaling Signaling with AC
◮ 32%− 20% = 12% increase
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Testing the Informational Effects of Democracy
Least Most Effect ofDemocratic Democratic Democracy
(Democracy1 = −10) (Democracy1 = 10)
Prior belief 40% 53% +13%
Posterior belief 60% 85% +25%
Belief updating +20% +32% +12%
Effects of a Threat
◮ 60%− 40% = 20% increase (Effect of Signaling w/out AC)
◮ 85%− 53% = 32% increase (Effect of Signaling w/out AC)
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Robustness Check and Illustration
●
●
Least democratic Most democratic
0.0
0.2
0.4
0.6
●
●
Main Model
Transparency, +0.047
Signaling, +0.043 ●
●
Least democratic Most democratic
0.0
0.2
0.4
0.6
●
●
Status Quo Model
Transparency, +0.145
Signaling, +0.210
●●
Least democratic Most democratic
0.0
0.2
0.4
0.6
●
●
Second AC Model
Transparency, +0.138
Signaling, −0.019
●
●
Least democratic Most democratic
0.0
0.2
0.4
0.6
●
●
Democracy Model
Transparency, −0.048
Signaling, +0.164 ●●
Least democratic Most democratic
0.0
0.2
0.4
0.6 ● ●
Sunk Cost Model
Transparency, 0
Signaling, +0.025
Signaling(Belief−updating)Transparency(Prior belief)
− Legend −
y−Axis: Increase in Probability
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Conclusion
1. We find that audience costs do enhance learning in crises.◮ We estimated audience costs◮ We estimated belief-updating◮ Then, we show that belief-updating is statistically significant
and increasing in audience costs
2. We distinguish and test two mechanisms of informationaleffects of democratic institutions in crises.
◮ We find evidence consistent both with the “signaling andlearning” mechanism and the “institutional transparency”mechanism
◮ We also find evidence against the “institutional transparency”mechanism
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Appendix
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Empirical Strategy: Intuition
◮ Theory: mapping from preferences to outcomes.
Preference
relations
Choices &
Outcomes
Equilibrium
Deduction
Given by assumption
◮ Empirics: mapping from outcomes to preferences.
Preference
relations
Choices &
Outcomes
Statistical Equilibrium
Estimation
Given by data
◮ We ask: “given the observation of outcomes, what prefenreces makethese observed outcomes most likely according to the PBE?”
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Statistical Model of Audience CostsEstimation 1 of 2
ln L =N∑
i=1
[YSQi lnPSQi + YCDi lnPCDi + YBDi lnPBDi + YSFi lnPSFi ] ,
◮ We estimate a log-likelihood function of equilibrium outcomeprobabilities, covariates, payoff specification
◮ Maximization of ln L yields the vector of MLE of β’s.
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Statistical Model of Audience CostsEstimation 2 of 2
◮ Estimate var-cov matrix to estimate belief updating correctly◮ Identification◮ Seven additional parameters
◮ Correct estimation of belief updating
◮ Previous models as special cases (Lewis and Schultz 2003;Wand 2006; Signorino and Whang 2009)
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Testing Conjecture about Association with Democracy
Audience costs ∝ democracy
Audience costs of some form exist: u1(BD) < u1(SQ).
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Testing Conjecture about Association with Democracy
Outcomes Payoffs Variables Est. (SE)
Status Quo SQ1 Constant 0MaxAge 0.575** 0.135
Back Down BD1 Constant -4.09** 0.820Democracy1 -0.411** 0.104
∗∗ = p < .01, ∗ = p < .05 (two-tailed)
◮ Fearon’s conjecture is confirmed◮ First evidence that audience costs increase with democracy
score◮ Support for existing applied work that attributes democratic
uniqueness to audience costs.
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A note on the signaling value of audience costs
In the Sunk Cost model, the coefficient on Democracy1 is positive andsignificant. This also indicates the signaling value of audience costs.
◮ Recall audience costs ∝ democracy. Thus, this result indicates thestates with higher audience costs are less likely to issue a threat.
◮ The signaling value of audience costs stems not only from thehand-tying effects but also from the fact that leaders with higheraudience costs would be unwilling to make an explicit threat (due toother kinds of costs associated with a public commitment).