Democracy,Information,andAudienceCostsDemocracy,Information,andAudienceCosts (Previously circulated as “Informational Eﬀects of Audience Costs”) Shuhei Kurizaki & Taehee Whang

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







X Tomz 2007, K+W 2015

X K+W 2015

X “Democratic Advantage”







X Tomz 2007, K+W 2015

X K+W 2015


But this causal effect depends on a learning mechanism: Audiencecosts help to send credible signals and learn each other’s resolve







X Tomz 2007, K+W 2015

X K+W 2015


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

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.





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)

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




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




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

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)

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

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)

)

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))

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

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

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)

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

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


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?


−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


− Legend −



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)

Findings: 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


− Legend −



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)

Findings: Belief-Updating





















−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


− Legend −



Findings: Effect of AC on Belief-Updating




















Findings: Effect of AC on Belief-Updating



















◮ Is the effect significant? Need to regress the amount ofupdating with Democracy1

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


●

●

Prior Posterior

0.4

0.6

0.8

1.0

●

●

Democracy Model

Democracy, +0.245


●

●

Prior Posterior

0.4

0.6

0.8

1.0

●

●

Sunk Cost Model

Democracy, +0.096


Non−Democracy(Democracy1=−10)Democracy(Democracy1=10)

− Legend −


Implications

The results substantiate the causal mechanism of audience costs model

◮ “Audience costs improve crisis communication through signals”

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)

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

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)

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

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

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

Belief-Updating without Audience Costs






Effect of “Democratic” Signaling Signaling with AC

◮ 32%− 20% = 12% increase

Testing the Informational Effects of Democracy






Effects of a Threat

◮ 60%− 40% = 20% increase (Effect of Signaling w/out AC)

◮ 85%− 53% = 32% increase (Effect of Signaling w/out AC)

Robustness Check and Illustration

●

●

Least democratic Most democratic

0.0

0.2

0.4

0.6

●

●

Main Model

Transparency, +0.047

Signaling, +0.043 ●

●


0.0

0.2

0.4

0.6

●

●

Status Quo Model


Signaling, +0.210

●●


0.0

0.2

0.4

0.6

●

●

Second AC Model


Signaling, −0.019

●

●


0.0

0.2

0.4

0.6

●

●

Democracy Model

Transparency, −0.048

Signaling, +0.164 ●●


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

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

Appendix

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?”

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.

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)

Testing Conjecture about Association with Democracy

Audience costs ∝ democracy

Audience costs of some form exist: u1(BD) < u1(SQ).

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.

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).

Documents

Democracy,Information,andAudienceCostsDemocracy,Information,andAudienceCosts (Previously circulated as “Informational Eﬀects of Audience Costs”) Shuhei Kurizaki & Taehee Whang