GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No.203

Media, Democracy, and Government Action:
Prevention vs. Palliation in the Time of Cholera

Chongwoo Choe and Paul A. Raschky

July 2011

GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

Media, Democracy, and Government Action:
Prevention vs. Palliation in the Time of Cholera∗
Chongwoo Choe †Paul A. Raschky ‡July 2011

Abstract
This paper studies how media and democracy influence government action taken before and after a natural disaster. The key elements in this relationship are the media’srole as the provider of information to voters about government action and the quality of democracy that pertains to how relevant election results are. We show that
more media activity and more democratic institutions both contribute positively to
the government’s palliative effort after the disaster. However, the effects of media anddemocracy on the government’s preventive effort before the disaster are negative. We
provide empirical evidence based on major cholera epidemics around the world, whichlends some support to these hypotheses.
JEL classification: D23, H40, L82
Keywords: Media, democracy, corruption, natural disaster∗We thank helpful comments from Eric Chou, Hideshi Itoh, and seminar participants at Kyoto University.
The first author gratefully acknowledges the warm hospitality at the Institute of Social and Economic
Research, Osaka University, where this research was conducted. The usual disclaimer applies.
†
Department of Economics, Monash University; email: chongwoo.choe@monash.edu.
‡
The corresponding author. Department of Economics, Monash University, PO Box 197, Caulfield East,
VIC 3145, Australia; email: paul.raschky@monash.edu.

11Introduction

Free press is considered to be one of the main pillars of a modern democratic society. The
media feed information to citizens about otherwise opaque political processes, and informed
voters are better able to hold elected officials accountable. A growing body of empiricalevidence shows that media access increases citizens’ political knowledge (Coyne & Leeson
2009, Snyder & Strömberg 2010), affects voter turn-out (Gentzkow 2006, Oberholzer-Gee &Waldfogel 2009), and can influence voting decisions (DellaVigna & Kaplan 2007, Enikolopov,
Petrova & Zhuravskaya 2010, Chiang & Knight 2011). Moreover, related studies report theeffect of media on actual policy outcomes. For example, Besley & Burgess (2002) develop atheoretical model and provide empirical evidence from India showing that more media activity increases government responsiveness to disaster relief. A cross-country study by Brunetti& Weder (2003) suggests that more press freedom leads to less corruption. Strömberg (2004)shows that U.S. counties with higher radio penetration received more public funding under
the New Deal. Finally, Eisensee & Strömberg (2007) examine U.S. government emergency
aid response to worldwide natural disasters and show that relief decisions are driven by news
coverage of disasters.
For several reasons, however, the media are no panacea to inefficient or corrupt behaviorof elected officials. First, the media in some societies can be subject to political influenceor censorship, and captured media cannot function as a provider of transparent information(Besley & Prat 2006). Second, the physical and financial constraints facing the demandand supply side of the media market imply that only limited areas of government activities
can be covered by media reports. On the demand side, consumers allocate only a fraction of
their time to the consumption of news and, as argued by Mullainathan & Shleifer (2005) andGentzkow & Shapiro (2006), they would prefer reading news that is more likely to confirm
their prior beliefs. On the supply side, profit-maximizing media companies allocate limited
budget, air time or newspaper pages to news that is more likely to attract a larger audience.
As a result, only limited areas of government activities can be covered by news, and media
presence is more likely to improve policy outcomes in those areas that receive more media
coverage. Indeed Snyder & Strömberg (2010) show that local jurisdictions in the U.S. thatreceive less media coverage have less informed voters, which leads to political representatives
that exert less effort on federal level and ultimately receive less federal funding.
The purpose of this paper is to extend the above literature by looking at the differential
effects of media coverage and the quality of democracy on different types of government
activities. Given that media coverage is selectively based on the media’s profit-maximizing
motives that may not be consistent with overall efficiency, more media attention in one areaof government activity may shift more resources to that area even though it may not besocially efficient to do so.1 To the extent that voters are informed of government action
1

A somewhat related story can be found in Jayasuriya & McCawley (2010). During the 2004 Asiantsunami, the western media tended to focus on stories about the plight of western tourists caught up in thetsunami although less than one percent of those who died were tourists. It also meant that popular tourist
locations in Thailand received extensive media coverage while far-flung places in Indonesia and Sri Lanka
that were much more severely affected by the tsunami received less attention. Thus Thailand was inundatedwith offers of assistance from governments, multilateral donor agencies, corporate and community groups,
and individuals.

2through the media, more developed democracy can exacerbate this problem since election
results matter more in a more democratic society. Thus a democratic society where votersform their political views mainly through the media is potentially susceptible to inefficient
resource allocation. The inefficiency worsens as the media’s objectives deviate further awayfrom social welfare, as more voters are informed through the media, and as election results
matter more in the ultimate change of government.
We choose a natural disaster, epidemics in particular, as a basis for our model andempirical analysis. It is well-known that the effort to mitigate the damage from a natural
disaster can be put in both before - called the preventive effort - and after - called thepalliative effort - the disaster. In some natural disasters such as epidemics, efficiency may
require more resources be allocated to the preventive effort;2 in others such as earthquakes,
speedy and organized palliative effort can be more crucial. Our model allows us to consider
two different types of government effort around the time of natural disaster. In addition, anatural disaster is also likely to be a major media event, which enables us to examine how
the interplay between media and democracy affects the government’s resource allocation to
the two types of effort.
Our theoretical model extends Besley & Burgess (2002) to the case where the incumbent
politician with re-election concerns chooses both types of effort, and the quality of democracy
is proxied by the extent to which election results are relevant. The incumbent’s preventive
effort can reduce the likelihood of epidemic outbreak while the palliative effort can reduce
the number of fatalities from the epidemic. Voters are informed of the incumbent’s effort
only through the media, and profit-maximizing media choose to report news that is morenewsworthy. Given that urgency matters in news report, we assume that the media are
more likely to cover the incumbent’s palliative effort than the preventive effort. It follows
then that more media activity increases the incumbent’s palliative effort since it raises the
chance the incumbent wins the election. More democratic institutions imply election results
are more relevant, which again raises the marginal value of palliative effort. On the other
hand, the effect of media and democracy on the incumbent’s choice of preventive effort
runs in the opposite direction. This is because the preventive effort lowers the likelihoodof epidemic outbreak and, since more media activity increases the marginal value of theincumbent’s palliative effort which is relevant only in the event of epidemic outbreak, more
media activity lowers the marginal value of the incumbent’s preventive effort. Because media
activity and the quality of democracy are complementary to the incumbent, more democratic
institutions also lead to a lower level of preventive effort.3
We test the hypotheses derived from the model using the data on major cholera epidemics
around the world during the period of 1976-2006. However the existing data on natural
disasters do not contain enough information on the government’s preventive and palliative
effort across countries. Thus we adopt an outcome-based approach whereby the incidence of
epidemic outbreak is used as a proxy for the preventive effort while the number of fatalities
2

For example, the provision of safe drinking water can all but prevent the outbreak of cholera epidemic.
Although our model is based on an epidemic where the two types of effort interact in a specific, temporal
fashion, the main logic continues to hold in other types of natural disasters. For example, suppose theprobability of disaster is exogenous and both types of effort can only reduce the size of damage. Then if
more media activity increases the palliative effort, it will increase (decrease) the preventive effort if the two
types of effort are complements (substitutes).
33

is used as a proxy for the palliative effort. We use three media variables to measure both
media penetration and the quality of media. The proxy for the quality of democracy is fromthe Polity IV database. Our model is estimated using Heckman’s two-stage procedure after
controlling for various geographic, climatic and socio-economic variations across countries.
The results partially confirm our hypotheses. As for the first-stage estimation, democracy
is shown to have a positive effect on epidemic outbreak in some specifications although theeffect of media on epidemic outbreak is not significant. The results from the second stage
provide stronger support for our hypotheses: both democracy and media have a significant
negative effect on cholera deaths in all specifications. We also conduct several further tests
to check the robustness of our results and find that they stay qualitatively the same.
Our empirical analysis is most closely related to a study by Anbarci, Escaleras & Register(2006). They focus on annual cholera cases and deaths instead of only large scale epidemics.
They provide separate panel estimates for the determinants of access to clean water andannual cholera cases and deaths. They suggest that economic development and income
inequality4 reduce the number of cholera deaths through the channel of clean water supply.
Therefore our study is also related to empirical papers that analyze the determinants of cleanwater supply (e.g. Anbarci, Escaleras & Register 2009, Cole & Neumayr 2006).
The remainder of the paper is organized as follows. In the next section, we present amodel and derive testable hypotheses. Section 3 describes the data and empirical strategy
while Section 4 reports the empirical findings. Section 5 concludes the paper.

2

Model

Our model extends Besley & Burgess (2002) by introducing three additional ingredients thatallow us to study the interplay among the quality of institutions, democracy, and disastermitigation effort at both prevention and palliation stages. It comprises an incumbent politician, citizens and the media.5 At the beginning of period 1, some citizens may experiencea negative shock, which can be mitigated by the incumbent’s effort exerted either before orafter the shock arrives. Citizens do not directly observe the incumbent’s effort, but only
through the media. The incumbent is motivated to exert effort partly because the media’sreports affect citizens’ voting behavior at the end of period 1. Those citizens affected by
the negative shock vote for the incumbent or the challenger based on their expectation of
who is likely to exert more effort in period 2. The quality of institutions pertains to how
effectively the incumbent’s effort is translated into disaster mitigation, while the quality of
democracy concerns how citizens’ voting outcome is reflected in who holds power. In what
follows, we detail our model and derive the main comparative statics results relating various institutional features to the incumbent’s effort, which form the basis for our empirical
exercise.
There are two types of citizens, vulnerable and nonvulnerable, the former comprising afraction γ < 1/2 of the population.6 Vulnerable citizens may experience a negative shock4

In our case, income inequality does not yield a statistically significant coefficient.
We use the male gender pronoun for the incumbent politician.
6
As will become clear below, γ < 1/2 guarantees that the probability the incumbent wins the election
does not exceed 1.
5

4

which causes damage equal to k. To relate our model to the empirical part of this paper,
we will call the negative shock an epidemic. The damage can be mitigated by two typesof public action. First, the incumbent politician can exert effort e1 ∈ [0, E1 ] to reduce
the likelihood of epidemic outbreak, which we call the preventive effort.7 We assume thatthe probability of epidemic outbreak is given by a twice-differentiable function α(e1 ) with
0 < α(E1 ) < α(0) < 1, α0 (e1 ) < 0 and α00 (e1 ) > 0. Thus more preventive effort reduces
the likelihood of epidemic outbreak but its marginal effect is decreasing. After the epidemic
breaks out, the incumbent politician can further exert effort e2 ∈ [0, E2 ] to reduce the sizeof damage, which we call the palliative effort. The effect of palliative effort is given by atwice-differentiable function β(e2 ) with 0 = β(0) < β(E2 ) < γk, β 0 (e2 ) > 0 and β 00 (e2 ) < 0.
Thus the palliative effort can reduce the size of damage but not entirely, and its marginal
effect is decreasing. Given (e1 , e2 ), the net expected damage from the epidemic is thenα(e1 )[γk − β(e2 )].
The incumbent politician is one of the three types. The altruistic incumbent always
exerts the maximal effort level in both stages: e1 = E1 , e2 = E2 . The selfish (and myopic)
incumbent never exerts any effort in either stage: e1 = e2 = 0. The opportunistic incumbent is a rational economic agent, optimally choosing the preventive and palliative effort
to maximize his payoff, which depends on the utility from holding office, the costs of effort
provision, and the disutility that he attaches to citizens’ suffering from the epidemic. The
exact form of the opportunistic incumbent’s objective will be spelt out as we go on. Citizens’
prior beliefs assign a strictly positive probability on each type of incumbent.
Citizens do not directly observe the incumbent’s effort but only through the media.
As in Besley & Burgess (2002), we assume that the media’s report is imperfect in that it
only informs citizens whether positive effort has been exerted. The media’s report may
cover both types of effort. But the palliative effort is more likely to be newsworthy thanthe preventive effort in that urgency matters in news report. Therefore we assume thatthe media’s report covers only the palliative effort.8 Given the extent of media activity
m, let q(e2 , m) denote the fraction of vulnerable citizens who are informed. We assumeq(0, m) = 0, qm (e2 , m) > 0, qe2 (e2 , m) > 0, qe2 m (e2 , m) > 0 and qe2 e2 (e2 , m) < 0 wheresubscripts denote partial derivatives. Thus vulnerable citizens are more likely to learn theincumbent’s positive palliative effort if media activity increases and the incumbent puts inmore effort although the marginal effect of the latter decreases. Moreover the positive crosspartial derivative implies that media activity increases the marginal effect of the incumbent’s
7
In case of epidemics, examples of preventive effort include investment in sanitation and sewage system,and the provision of safe drinking water. In case of other natural disasters such as earthquakes or volcanic
eruptions, the probability of disaster is more or less exogenous, hence the preventive effort also contributes
to reducing the size of damage rather than the likelihood of disaster. This is the case considered by Cohen& Werker (2008) although they do not model the political economy aspect of natural disaster. Our model
can be easily modified to account for this case, as shown in Appendix A.
8Although we do not explicitly model the media’s objective, it is straightforward to formalize whenreporting only the palliative effort can be optimal for the media. For example, suppose the media’s revenue
depends on the subscription rate and that citizens demand more news after the epidemic breaks out than
before. Then, for some cost function, reporting only the palliative effort can be profit-maximizing for the
media. Moreover, insofar as the probability citizens learn positive preventive effort through the media is lessthan the probability citizens learn positive palliative effort, our qualitative results hold. Thus we focus only
on the media’s report of palliative effort.

5effort on the fraction that is informed.
After citizens learn the incumbent’s palliative effort at the end of period 1, there is anelection in which the incumbent faces a randomly selected challenger. Citizens have the sameprior beliefs about the challenger and the incumbent. Since vulnerable citizens may again
face a negative shock in period 2, they would want to vote for the politician who is likely toexert more effort in period 2. The probability the incumbent is altruistic conditional on theobservation of positive palliative effort is larger than the prior belief of the randomly selectedchallenger being altruistic. Thus all the vulnerable citizens who learned e2 > 0 through themedia vote for the incumbent. The uninformed vulnerable citizens, a fraction 1 − q(e2 , m),have their prior beliefs about the incumbent’s type unchanged. Thus they are indifferent
between the incumbent and the challenger and, without loss of generality, we assume thata half of them vote for the incumbent. As in Besley & Burgess (2002), we assume that
nonvulnerable citizens’ votes are random: the fraction of nonvulnerable citizens who votefor the incumbent is given by v which is uniformly distributed on [0, 1]. Given e2 > 0, theincumbent then wins the election ifh
1 − q(e2 , m) i
+ (1 − γ)v ≥ 1/2.
γ q(e2 , m) +2
Focusing on the interior solution, the probability the incumbent wins the election is then9π(e2 ; m, γ) :=1 γq(e2 , m)+.
22(1 − γ)As is clear from the above, π(e2 ; m, γ) ∈ [1/2, 1), π(e2 ; m, γ) increases in q(e2 , m), and the
maximum winning probability for the incumbent is 1/(2(1 − γ)) which is less than 1 sinceγ < 1/2.
How the election result is translated into change of government depends on how democratic political institutions are. Similar to Bhattacharyya & Hodler (2010), we measure thequality of democracy by a parameter δ ∈ [0, 1], which is the probability the incumbent holds
on to power even after losing the election. Thus δ = 0 corresponds to full democracy, δ = 1
corresponds to dictatorship, and political institutions are more democratic as δ decreases.
We assume that the incumbent who wins the election remains in power for certain.10 Thenthe incumbent who chooses e2 > 0 can expect to remain in office with probability
P (e2 ; m, δ, γ) := π(e2 ; m, γ) + δ[1 − π(e2 ; m, γ)].

(1)

The following lemma is straightforward from (1) and the assumptions on q.
LEMMA. If the incumbent exerts positive palliative effort, then the probability he remains
9
We focus on the interior solution since our primary interest is in comparative statics of (e1 , e2 ) withrespect to various parameters.
10
Bhattacharyya & Hodler (2010) model the quality of democracy as the difference between the probability
the incumbent stays in power after winning the election and the probability the incumbent stays in power
after losing the election. Our parameterization is a special case where the first probability is 1.

6

in office increases in the level of palliative effort (Pe2 > 0), the extent of media activity(Pm > 0), and decreases in the quality of democracy (Pδ > 0). The marginal effect of theincumbent’s palliative effort on the probability of holding office decreases in the level of palliative effort (Pe2 e2 < 0), and increases in the extent of media activity (Pe2 m > 0) and thequality of democracy (Pe2 δ < 0).
We now turn to the opportunistic incumbent’s objective function. He derives utility fromholding office, denoted by Ω, and disutility from exerting effort. We proxy the quality of
institutions by the extent to which the incumbent’s effort is translated into effective disastermitigation. Specifically we assume that, in order to exert one unit of effective preventive
effort, the incumbent has to put in 1+τ units of effort where τ ≥ 0.11 Then larger τ implies aless effective channel through which the incumbent’s effort is reflected in disaster mitigation.
For example, this may be due to corruption down in the policy hierarchy, red tapes, slower
administrative processes, etc. As for the incumbent’s palliative effort, we assume that one
unit of effective effort requires 1 + (m)τ units of effort where (0) = 1, 0 (m) < 0 and(m) approaches zero as m increases. That is, the quality of institutions becomes less of
an issue in the post-epidemic stage as media activity increases. This is because, during amajor disaster, a lot of attention is focused on government response, where the media playa major role. Thus media scrutiny will make it more difficult for corruption to persist or
bureaucrats to delay the relief process.12 Finally the incumbent also derives disutility fromcitizens’ suffering from the epidemic and we denote the welfare weight by θ > 0.
Given preventive effort e1 , the epidemic breaks out with probability α(e1 ). In this event,the above discussions lead to the incumbent’s expected utility from choosing e2 equal to
P (e2 ; m, δ, γ)Ω − θ[γk − β(e2 )] − [1 + (m)τ ]e2 . If the epidemic does not break out withprobability 1 − α(e1 ), then the incumbent wins the election with probability 1/2 and stays
in power with probability (1 + δ)/2. Thus his expected utility in this case is ((1 + δ)Ω)/2.
Putting all these together, the opportunistic incumbent chooses (e1 , e2 ) to maximize his
objective function13
V (e1 , e2 ) := α(e1 ) P (e2 ; m, δ, γ)Ω − θ[γk − β(e2 )] − [1 + (m)τ ]e2
h (1 + δ)Ω i− (1 + τ )e1 .
(2)+ [1 − α(e1 )]2
We assume that the incumbent’s problem has an interior solution. Then the first-order11
An alternative way to model this is that one unit of incumbent’s effort is translated into 1/(1 + τ ) unitsof effective effort. These two approaches are equivalent after suitable normalization.
12
In this sense, the quality of institutions refers to the more malleable part such as corruption rather
than established bureaucracy. Public attention during the disaster relief may stop corruption temporarily
although it may return afterwards. On the other hand, it is harder and takes longer to change establishedbureaucracy. In our empirical analysis, we proxy the quality of institutions by a measure of corruption
control.
13This objective function can be suitably modified to justify the assumed behavior by other types ofincumbent. For example, the altruistic type does not derive any utility from holding office, nor disutility
from exerting effort, and assigns the welfare weight θ = 1. The selfish (and myopic) type does not care aboutstaying in power, nor citizens’ suffering, hence θ = 0.

7

conditions are
Ve1 = α0 (e1 )∆ − (1 + τ ) = 0,

(3)

Ve2 = α(e1 )[Pe2 Ω + θβ 0 (e2 ) − (1 + (m)τ )] = 0(4)where ∆ := [P −(1+δ)/2]Ω−θ[γk−β(e2 )]−[1+(m)τ ]e2 . Observe first that (3) implies ∆ < 0,or the incumbent’s utility in the epidemic event is smaller than that in the non-epidemic
event so that he would optimally choose e1 to reduce the probability of epidemic. Otherwise,
Ve1 < 0, hence e1 = 0. Next we note from (4) that Pe2 Ω + θβ 0 (e2 ) = 1 + (m)τ , which
implies that the incumbent’s optimal palliative effort equates its marginal cost 1 + (m)τ
to the marginal benefit, the latter being the sum of utility from holding office throughthe increase in the probability of staying in power and the reduction in citizens’ suffering.
Given these first-order conditions, it is easy to check that V (e1 , e2 ) is strictly concave in(e1 , e2 ): Ve1 e1 = α00 (e1 )∆ < 0, Ve1 e2 = Ve2 e1 = 0, Ve2 e2 = α(e1 )[Pe2 e2 Ω + θβ 00 (e2 )] < 0, and|H| := Ve1 e1 Ve2 e2 − Ve21 e2 > 0. These observations and Lemma lead to our main comparative
statics results.
PROPOSITION. As voters have greater media access, the opportunistic incumbent exerts less preventive effort (∂e1 /∂m < 0) and more palliative effort (∂e2 /∂m > 0). As
the quality of democracy improves, the opportunistic incumbent exerts less preventive effort(∂e1 /∂δ > 0) and more palliative effort (∂e2 /∂δ < 0). As the quality of institutions improves, the opportunistic incumbent exerts more preventive effort (∂e1 /∂τ < 0) if and only
if 1 + α0 (e1 )(m)e2 > 0, and more palliative effort (∂e2 /∂τ < 0) although the latter effectapproaches zero as media activity increases (limm→∞ ∂e2 /∂τ = 0).
Proof. For the comparative statics with respect to m, total differential of (3) and (4)leads to
−1
−1
∂e2
∂e1
=
[Ve2 e2 α0 (e1 )(Pm Ω − 0 (m)τ e2 )] < 0,=
[Ve1 e1 α(e1 )(Pe2 m Ω − 0 (m)τ )] > 0
∂m
|H|
∂m
|H|
where the inequalities follow from α0 (e1 ) < 0, 0 (m) < 0 by assumption, |H| > 0, Ve2 e2 < 0,Ve1 e1 < 0 as shown previously, and Pm > 0, Pe2 m > 0 by Lemma. Similarly we have
−1
∂e2
−1
∂e1
=
[Ve2 e2 α0 (e1 )(Pδ − 1/2)Ω] > 0,=
[Ve e α(e1 )Pe2 δ Ω] < 0
∂δ
|H|
∂δ
|H| 1 1since Pδ − 1/2 = 1/2 − π < 0 and Pe2 δ < 0 by Lemma. For τ , similar steps lead to∂e1
1=[Ve e (1 + α0 (e1 )(m)e2 )] < 0 iff 1 + α0 (e1 )(m)e2 > 0,
∂τ
|H| 2 2∂e2
1=[Ve e α(e1 )(m)] < 0.
∂τ
|H| 1 1Since (m) approaches zero as m increases, ∂e2 /∂τ approaches zero as m increases.

8

The above proposition shows how the incumbent’s effort responds to various institutionalfeatures. As for the palliative effort, the intuition is quite clear. Given that only the palliative
effort attracts media attention, more media activity increases the marginal value of palliative
effort as it increases the chance the incumbent wins the election and stays in power. Moredemocratic institutions imply that election results are more relevant, which again raises themarginal value of palliative effort. Better quality of institutions implies lower marginal costof palliative effort, hence has a positive effect on the palliative effort. However more media
activity in the post-epidemic stage makes corruption more difficult to thrive, which makes
the marginal cost of palliative effort independent of the quality of institutions.
The channel through which these institutional features affect the preventive effort is via
the incumbent’s cost-benefit comparison of the epidemic versus non-epidemic event. If theincumbent puts in more preventive effort, then the likelihood of epidemic event decreases,
and so is the likelihood the incumbent is subject to public scrutiny through the media. On theother hand, the media are irrelevant in the non-epidemic event. Therefore, as media activity
increases, the incumbent benefits more in the epidemic event than in the non-epidemic
event provided that he subsequently chooses the optimal palliative effort. This implies thathigher media activity leads to less preventive effort as it makes the epidemic event more
likely. A similar reasoning can be applied to the relation between the incumbent’s choice ofpreventive effort and the quality of democracy. As the quality of democracy deteriorates, theincumbent benefits more in the non-epidemic event than in the epidemic event. It is because
the change in the probability the incumbent stays in power is Pδ = 1 − π in the epidemic
event, which is less than 1/2, the change in the probability the incumbent stays in power in
the non-epidemic event. The flip side is that the incumbent benefits more in the epidemic
event when the quality of democracy improves, in which case the incumbent will put in lesspreventive effort. As for the effect of institutional quality, we note that Ve1 τ = −1 − α0 (e1 )e2 .
Thus the condition 1 + α0 (e1 )(m)e2 > 0 implies that the incumbent’s marginal cost of e1
increases as τ increases, or the quality of institutions deteriorates.

3

Empirical Analysis

Our main theoretical findings of the previous section concern the differential effects of media
and democracy on government action before and after a natural disaster. That is, more media
activity and more developed democracy both have positive effects on government response
to a natural disaster, but negative effects on the preventive measures government can takebefore the disaster. On the other hand, better institutional quality has a positive effect on
government action both before and after the disaster, although its effect on post-disaster
response decreases in media activity. We empirically examine these implications below.
An empirical test of our model is not straightforward for two reasons. First, the existingdata on natural disasters do not contain enough comparable information on the preventive
and palliative effort across countries. Therefore, the existing empirical literature on the crosscountry analysis of the fatalities from natural disasters (e.g. Anbarci, Escaleras & Register
2005, Kahn 2005, Anbarci et al. 2006) uses an outcome-based approach. For example, insteadof collecting data on prevention and relief expenses, Anbarci et al. (2006) use the probabilityof an event or the magnitude of a disaster (e.g. death toll) to infer a country’s preventive
9

effort. This approach builds the basis for our empirical analysis. The second one is the twostage feature of our model. The studies using natural disaster data have their main focus on
the determinants of preventive measures. Testing our theoretical predictions using typicalnatural disaster data is not possible because the data do not allow us to distinguish between
the preventive and palliative effort. The occurrence and magnitude of a natural disasterare mainly influenced by preventive measures and most of the fatalities occur from theevent itself. Palliative action comprises mainly post-disaster relief such as building shelters,
rebuilding infrastructure and housing, and providing financial assistance. Although some
information on financial relief by the government is available, it is impossible to retrieve all
the information on the total palliative effort. In addition, using only the data on financial
relief would be prone to a measurement error and bias our results. Thus we need to choosea natural disaster where an outcome-based approach can be applied for inference on thepreventive as well as palliative effort. An example of such a natural disaster is large-scalediarrhoeal epidemics.

3.1

Data and Variables

Diarrhoeal epidemics include diseases such as cholera, dysentery, and schistosomiases. The
major cause is the lack of safe drinking water caused by insufficient infrastructure and bad
hygiene as well as large-scale natural disasters. In contrast to natural disasters such as
earthquakes and storms, diarrhoeal epidemics allow us to apply an outcome-based approachto analyze the effect of preventive as well as palliative effort. Controlling for a country’s
predisposition to diarrhoeal diseases via a number of geographic, climatic and socio-economic
variables, we are able to identify the impact of media activity, institutional quality anddemocracy on preventive measures by comparing the probability that an epidemic occurs.
Once an epidemic occurred, the outcome of the epidemic (e.g. the number of fatalities)
depends on the government’s palliative effort (e.g. providing safe drinking water to preventa further spread of the epidemic, medical assistance).
Our data on cholera epidemics are from the most comprehensive data set on disasters
and humanitarian catastrophes, EM-DAT by the Centre for Research on the Epidemiology ofDisasters (CRED). EM-DAT has collected around 12,000 reports on the comprehensive listof natural disasters divided into five subgroups of geophysical, meteorological, hydrological,
climatological, and biological disasters. A natural disaster has to meet at least one of thefollowing criteria in order to be included in the database: 10 or more people reported killed;
100 or more people reported affected; declaration of a state of emergency; call for international assistance. We use information from EM-DAT to construct two dependent variables.
The first one is a dummy variable, Epidemic, that switches to 1 if a country suffered fromat least one major cholera outbreak in a given year and zero otherwise. The second one isa magnitude variable that accounts for the number of fatalities from a given cholera event,Ln(1 + f atalities). Our data set contains information on 324 major cholera epidemics in 91
countries from 1979 to 2006. The full list is reported in Table 1. Given that we were notable to collect data for all the explanatory variables for all country-year observations usedin this study, our baseline estimation uses information on about 200 epidemics. This leads
to a total number of country-year observations in the first stage estimation of around 1,960,
where 201 observations are coded with Epidemic = 1 and the remaining 1,759 are coded10with Epidemic = 0.
— Table 1 goes about here. —Our variable for the quality of democracy, Democracy, is constructed from the Polity
IV database by Marshall & Jaggers (2005). Polity IV defines democracy and autocracy
along a line of different indicators such as the competitiveness and regulation of politicalparticipation, the openness and competitiveness of executive recruitment, and the constraints
on the executive. We re-scale the original indicator such that it ranges from 0 to 1, with higher
values implying more democratic institutions. To account for the quality of institutions in the
public sector, we define a variable Corruption Control, which is taken from the “Control
of Corruption” variable from the governance database developed by Kaufmann, Kraay &Mastruzzi (2008). This indicator reflects perceptions of corruption, conventionally definedas the exercise of public power for private gain. It combines corruption in different areas
such as the business environment, political and public area.
Existing empirical studies on the effect of media on economic outcomes use several different variables for media. Besley & Burgess (2002) and Strömberg (2004) use measures of
media penetration (e.g. radios or newspapers per capita), while Brunetti & Weder (2003)
and Coyne & Leeson (2009) use media freedom variables. Although more recent studies use
more detailed information such as actual media content (Snyder & Strömberg 2010), theaccess to independent media (Enikolopov et al. 2010), or availability of news in the local
language (Oberholzer-Gee & Waldfogel 2009), such detailed information is not available atthe cross-country level. Thus we use a set of three media variables to proxy both media
penetration and media freedom. The first two are quantitative measures of media activity
in the country, the number of televisions per 1,000 inhabitants, T V , and daily newspaper
circulation per 1,000 inhabitants, N ewspapers.14 The data come from Banks (2004) international database. The third variable is a qualitative measure, F reedom of the press,
and we use the most widely used measure, the ‘Freedom of the Press’ indicator by Freedom
House. This is a composite indicator that combines three sub-components: press laws andregulations; political pressures and controls on the media; economic influences and repressive
actions against the press.
The first set of additional control variables accounts for geographic and climatic variations
that explain a country’s predisposition to diarrhoeal diseases. Given that most diarrhoeal
diseases are waterborne, we use the country’s mean distance to the nearest inland navigable river (Gallup, Sachs & Mellinger 1999), Distance to river, and GIS-information on the
country’s exposure to flood risk (Dilley et al. 2005), F lood Distr. In addition, we accountfor continent specific fixed effects.15 Our set of socio-economic controls contains the naturallog of GDP per capita (in constant US dollar), population density, government expenditure
as a fraction of GDP, and the proportion of population with access to safe drinking water.
All of these variables are taken from the World Development Indicators (World Bank 2008).
The definition of all the variables used in this study and their data sources are summarized
14

We also used another media variable, radios per 1,000 inhabitants, but did not obtain any significant
results.
15
Alternative geographical and climatic variables such as absolute latitude, fraction of population livingin the tropics, precipitation and mean temperature are not statistically significantly different from zero.

11

in Appendix B. Table 2 presents the descriptive statistics.
— Table 2 goes about here. —3.2

Empirical Strategy

Our model consists of two stages. The first stage (outbreak stage) defines the cases where an
actual cholera epidemic broke out. The selection variable is a latent variable y1 that equals
one if at least one cholera epidemic was reported.

1 if
Epidemic,
y1 =(5)0 if no Epidemic.
The second stage (magnitude stage) specifies that fatalities are observed only if an epidemic
broke out.
F atalities if
Epidemic,
y2 =
(6)
−
if no Epidemic.
Based on the above, we have the following system of equations:y1 = X01 β + u1 ,(7)

y2 = X02 γ + u2

(8)

where X0i , i = 1, 2, are vectors of explanatory variables, β and γ are vectors of coefficientsto be estimated, and ui , i = 1, 2, are the error terms for the first and the second stage,
respectively. As shown in our theoretical model, the outcomes in the outbreak and magnitude
stage are correlated: the first-order conditions in (3) and (4) show that e1 and e2 both dependon the same set of parameters and the choice of e1 depends on the subsequent choice of e2 .
This suggests that the error terms u1 and u2 are correlated as well. We make the standard
assumption that the errors are jointly normally distributed and homoscedastic so that  

u1
0
1 ρ
∼N
,
(9)
u2
0
ρ 1
where ρ denotes the correlation between the first and second stage errors. For example, atthe beginning of a rainy season, a country may experience above-average rainfall and, as aresult, its government anticipates a higher likelihood of a cholera outbreak. Based on thisexpectation, the government may decide on additional palliative measures such as increased
supply of bottled water or medicine to mitigate the effects of an epidemic. This unobserved
government expectation is captured by the error terms u1 and u2 . If the unobserved, unusual
rainfall patterns push the government to stock up additional palliative measures and the government’s anticipation about a higher likelihood of cholera outbreak turns out to be correct,
the error terms will be negatively correlated as more palliative effort reduces fatalities. This12

would result in ρ < 0. Alternatively, cholera outbreaks and resulting fatalities are often
driven by unique combinations of local socio-economic conditions and regional short-termclimatic conditions that we are unable to control for. An example would be an outbreak ofregional violence combined with heavy rainfall in the region. In this case ρ would have apositive sign. In either case, ρ 6= 0 indicates that OLS yields inconsistent estimates of γ.
We follow the two-stage procedure of Heckman (1976) and Heckman (1978) to overcome
this problem. In the first stage, we estimate eq. (7) using a probit estimator. The predictedvalues of β, β̂, are then used to calculate the inverse Mills ratio, λ = φ(X01 β̂)/Φ(X01 β̂),
where φ denotes the pdf and Φ, the cdf. In the second stage, we augment eq. (8) by λ andestimate the following equation using OLS:
y2 = X02 γ + ρλ + .

(10)

The application of a sample selection model requires unique information in the explanatory variables X01 and X02 to separately identify the parameters in the outbreak and magnitude stage. A major trigger for cholera epidemics is an external shock to a country’s supplyof safe drinking water. Natural disasters, floods in particular, are considered to be one ofthese shocks. In order to account for the magnitude of a flood, we use the number of fatalities from a flood and take its natural log, Ln(F lood Kills). Major floods can damage
water and sanitation infrastructures causing a contamination of drinking water and food.
Flood victims then run the risk of cholera infection from ingesting contaminated water andfood. Cholera has a rather short incubation period of between 1 and 5 days, which makes it
hard for the government to control the outbreak of an epidemic. However, quick palliative
effort such as the distribution of potable water and immunization can limit further transmission of the disease. In addition, emergency disease surveillance helps to decrease thefatality rate (Woodruff, Toole, Rodrigue, Brink, Mahgoub, Ahmed & Babikar 1990). Therefore, one could argue that Ln(F lood Kills) fulfills the exclusion criteria. In addition, using
Ln(F lood Kills) in the second stage regressions shows no correlation between the selection
variable and number of fatalities from cholera.16

4

Results

The results from the first-stage estimation are presented in Table 3. Columns (1) - (6)show separately the estimated coefficients for each of the three key variables, Democracy,
Corruption Control as well as the set of media variables TV, Newspapers, Freedom of thePress. Columns (7) and (8) present the estimates for the full specifications.
The coefficient estimates for Democracy and Corruption Control in columns (1) and (2)are consistent with our theoretical predictions. That is, more democratic countries appear toexperience more cholera epidemic17 while countries with better quality of institutions are lesslikely to have cholera outbreak. The coefficients for T V and N ewspapers are not significantly
16

The results are available upon request.
Another possible interpretation is that the observed negative association between democracy and cholera
outbreak could reflect the likelihood that an epidemic is actually reported and hence recorded on EM-DAT.
For example, some autocratic regimes might be reluctant to admit that their countries are experiencing an
epidemic.
17

13

different from zero (columns (3) and (4)), which does not change even after controlling for
freedom of the press (columns (5) and (6)). The results from the full specifications (columns(7) and (8)) show that the effect of democracy is not significant once we control for variations
in media and institutional quality. Among the control variables, Access to drinking water
has a statistically strong and negative coefficient. Government Expenditure also reduces thelikelihood of epidemic outbreak, while a country’s exposure to flood risk weakly increases
the likelihood of epidemic outbreak. Our selection variable Ln(F lood Kills) is strongly significant and has the expected sign.
— Table 3 goes about here. —Let us now turn to the second-stage estimation where the dependent variable is basedon the number of fatalities from the epidemic, Ln(1 + F atalities). The results are reported
in Table 4. Once more, columns (1) - (6) show separately the estimated coefficients for eachof the three key variables while columns (7) and (8) show the results for the full specifications. The results are mostly consistent with our theoretical predictions. The coefficientfor Democracy is negative and significant at the 10% level (column (1)). In contrast to
the outbreak stage, less corruption does not have a significant effect on cholera fatalities.
More media access has a significant negative effect on cholera fatalities: the coefficient for
TV is negative and always statistically significant at the 1% level, while the coefficient for
Newspapers is significant at the 5% and 10% level, depending on the specification. On theother hand, freedom of the press does not have a significant impact on cholera fatalities.
This could be because the quality of media, like the quality of institutions, could be less of
an issue at the time of crisis.
Similar to the results from the first-stage regression, the coefficient for Democracy becomes insignificant once we control for corruption and media as shown in columns (7) and (8).
This suggests that variations in media access dominate the effect of democracy on palliative
effort and hence cholera fatalities. This is consistent with the general thrust of our argument.
The incumbent’s choice of palliative effort is partly driven by re-election concerns. However,re-election concerns matter only if voters are informed about the incumbent’s effort; facing
re-election does not drive the incumbent to exert positive effort if the electorate remains
uninformed. Given that the media are one of the major channels that inform voters about
the provision of public goods, they provide a necessary condition for re-election concerns tomotivate the incumbent. The Wald test rejects the null hypothesis that ρ = 0 indicatingthat sample selection is a problem in our case. In addition, ρ is negative which suggests thatthe unobservables in the outbreak and magnitude stage are negatively correlated.
— Table 4 goes about here. —Next we report the results from additional tests. First, we present the second-stage OLS
results that do not account for sample selection. They are shown in the first two columns inTable 5. Second, we apply a negative binomial (NegBin) model for the second stage where
the dependent variable is F atalities. In our baseline model, we have transformed the originalvariable F atalities to Ln(1 + F atalities), which can lead to biased results. Therefore ourfirst robustness check is to estimate the second stage using a negative binomial estimator14and F atalities as the dependent variable. The results are presented in the last two columns
in Table 5. Although the size of the coefficients changes slightly, the sign and significancestay the same or even improve.
— Table 5 goes about here. —As the second robustness check, we replace the variables for democracy and corruption
control by alternative institutional variables to check whether our findings are sensitive tothe choice of these variables. The results are reported in Table 6. In columns (1) and (2),
Democracy is replaced by the number of veto players, Checks & Balances, developed by
Beck, Clarke, Groff, Keefer & Walsh (2001). In columns (3) and (4), the Heritage Foundation’s index, Freedom from Corruption, is used instead of Corruption Control. All of ourresults stay qualitatively the same although our original choice of institutional variables yields
stronger coefficients in terms of statistical significance. We further check the sensitivity of
our results to the choice of the first-stage selection variable by replacing the number of flood
kills by the number of people affected by floods. The results in columns (5) and (6) showthat this selection variable also has a positive coefficient, albeit significant only at the 5 %and 10% level depending on the specification. Once again, our main results remain the same.
— Table 6 goes about here. —5Conclusion

This paper has studied how media and democracy influence government action around thetime of a natural disaster. The central elements in this relationship are the media’s role as
the provider of information to voters about government action and the quality of democracythat pertains to how relevant election results are. Our theoretical model has shown that
more media activity and more democratic institutions both contribute positively to thegovernment’s palliative effort exerted after the disaster. However, the effects of media anddemocracy on the government’s preventive effort made before the disaster are negative. We
have provided empirical evidence based on major cholera epidemics around the world, which
partially supports these hypotheses.
Although we have not provided a normative analysis in this paper, we argue that public
policy influenced by media coverage can be susceptible to inefficiency insofar as the media’s
objectives deviate from social welfare. Even when efficiency requires more preventive effort asis the case in some epidemics, the government with re-election concerns may divert resourcesaway from the preventive effort if the palliative effort receives more media coverage and istherefore more likely to be a vote winner. The situation worsens in a society where election
results are more relevant to who holds power, which we interpret in a narrow sense as a moredemocratic society. To the extent that voters gather information mainly through the media,
and profit-maximizing media selectively report news that may not be in the best interest of
the society as a whole, the perils of media-based democracy are unlikely to disappear. We
conjecture that media diversity in response to reader heterogeneity is one possible way out ofthe problem since, as shown by Mullainathan & Shleifer (2005), reader heterogeneity leads15

to segmented media content which, in the aggregate, provides a balanced perspective. We
leave this for future research.

16

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18

World Bank (2008), World Development Indicators, Cd-rom, Washington, D.C.

19

Table 1: List of countries and number of epidemics
Country
Afghanistan
Algeria
AngolaArgentina
Bangladesh
Benin
BhutanBolivia
Botswana
BrazilBurkina Faso
BurundiCambodia
Cameroon
Cape VerdeCentral African Republic
Chad
Chile
China
Colombia
Comoros
Congo. Dem. Rep.
Congo. Rep.
Cote d’Ivoire
Djibouti
EcuadorEl Salvador
Equatorial Guinea
Ethiopia
FranceGabon
Ghana
GuatemalaGuineaGuinea-Bissau
Honduras
India
Indonesia
Iran. Islamic Rep.
Iraq
JamaicaJapan
Jordan
Kenya
Korea. Dem. Rep.
Korea. Rep.

Epidemics
6
2
3
1
14
4
1
3
1
4
4
5
3
8
1
1
6
1
2
2
3
10
3
5
3
3
2
1
3
1
1
5
3
6
6
1
9
7
1
1
1
1
1
5
1
1

20

Country
Lao PDR
LatviaLesothoLiberia
Madagascar
MalawiMalaysia
Mali
Marshall Islands
Mauritania
Mauritius
MexicoMicronesia. Fed. Sts.
Mongolia
Mozambique
Nepal
Nicaragua
Niger
Nigeria
Pakistan
PanamaPeru
Philippines
Russian Federation
Rwanda
Sao Tome and Principe
SenegalSierra Leone
SomaliaSouth Africa
Sri Lanka
Sudan
Swaziland
SwedenTajikistan
Tanzania
Togo
TurkeyUgandaUkraineUnited Kingdom
United States
Venezuela. RB
ZambiaZimbabwe
Total

Epidemics
3
1
2
8
1
6
1
4
1
3
1
1
1
1
11
4
2
12
10
3
2
4
2
1
5
1
5
5
9
3
1
5
2
1
4
10
4
1
8
1
1
1
2
7
7
324

Table 2: Descriptive statistics
Obs.
Ln(1+Fatalities)
Democracy
Corruption Control
TV
Newspapers
Freedom of the Press
Ln(GDP p.c.)
Population Density
Access to drinking waterDistance to river
Government Expenditure
Flood Distr.
Ln(Flood Kills)

Variable
324
271
223
320
315
231
281
299
280
316
279
314
324

Mean Std. Dev. Min.
3.741
2.052
0.000
0.515
0.294
0 .000-0.8240.595-2.0906.668710.765
0.000
0.277
0.608
0.000
60.36819.4788.000
10.0722.758
3.576
110.348180.8861.471
62.05419.08319.0001049.170714.69155.171
12.687
5.790
0.000
4.434
2.706
0.000
1.505
2.196
0.000

21

Max.
9.183
1.000
2.250
90.3805.788
100.000
16.169990.366
100.000
3227.860
36.50110.000
7.894

22

(3)

(4)

(5)

(6)

(7)

(8)

0.392
0.377
(0.277) (0.278)−0.354∗∗∗−0.362∗∗∗(0.131) (0.136)−0.003−0.0020.005(0.004)(0.005)(0.006)−0.042−0.0220.080(0.072)(0.084)(0.077)−0.001 −0.001 −0.001 −0.001(0.003) (0.003) (0.005) (0.005)−0.0190.003 −0.011 −0.012 −0.010 −0.011 −0.001 −0.008(0.018) (0.019) (0.017) (0.017) (0.020) (0.020) (0.021) (0.022)
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)−0.010∗∗∗−0.008∗∗ −0.007∗∗∗−0.008∗∗∗−0.009∗∗∗−0.010∗∗∗−0.008∗∗ −0.009∗∗(0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.004) (0.004)
0.000
0.000 −0.000 −0.000
0.000
0.000
0.000
0.000
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)−0.030∗∗∗−0.017∗∗ −0.029∗∗∗−0.028∗∗∗−0.029∗∗∗−0.025∗∗ −0.015 −0.013(0.008) (0.008) (0.007) (0.008) (0.008) (0.010) (0.010) (0.012)
0.049∗∗ 0.055∗
0.042∗
0.047∗∗ 0.045∗
0.052∗∗ 0.071∗∗ 0.076∗∗(0.023) (0.028) (0.022) (0.023) (0.025) (0.026) (0.029) (0.031)
0.082∗∗∗ 0.082∗∗∗ 0.080∗∗∗ 0.073∗∗∗ 0.083∗∗∗ 0.077∗∗∗ 0.089∗∗∗ 0.084∗∗∗(0.020) (0.024) (0.019) (0.020) (0.023) (0.024) (0.024) (0.024)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
1984
1438
2213
2146
1592
1545
1247
1215

(2)

0.434∗∗∗(0.153)−0.259∗∗(0.112)(1)

Notes: Robust standard errors are reported in the parentheses. ***, **, * indicate significance at the
1, 5 and 10% level, respectively.

Continent FE
N

Ln(Flood Kills)

Access todrinking waterDistance
to riverGovernment
Expenditure
Flood Distr.

Population Density

Freedom of
the press
Ln(GDP p.c.)

Newspapers

Corruption
ControlTV

Dependent variable:
P (Epidemicit = 1)
Democracy

Table 3: Outbreak Stage - Heckman First Stage

23

(2)

(3)

(4)

(5)

(6)

(7)

(8)

−0.219−0.238(0.928)(0.954)
0.230
0.810
0.516
(0.349)(0.564)(0.477)−0.045∗∗∗−0.038∗∗∗−0.051∗∗∗(0.012)(0.012)(0.017)−0.352∗−0.411∗∗−0.462∗∗(0.201)(0.194)(0.219)
0.001
0.001
0.007
0.002
(0.009)(0.009)(0.017)(0.016)−0.058−0.037−0.066−0.037−0.027−0.007−0.056−0.024(0.065)(0.068)(0.059)(0.062)(0.062)(0.064)(0.069)(0.072)−0.0010.000−0.002−0.002−0.000−0.000−0.0000.000(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)(0.002)−0.013−0.009−0.004−0.0170.002−0.005−0.004−0.009(0.013)(0.012)(0.012)(0.012)(0.012)(0.012)(0.014)(0.015)−0.001∗∗ −0.000−0.000−0.001∗∗ −0.000−0.0000.000−0.000(0.000)(0.000)(0.000)(0.000)(0.000)(0.000)(0.000)(0.000)−0.029−0.024−0.009−0.015−0.004−0.012−0.039−0.044(0.032)(0.024)(0.030)(0.031)(0.026)(0.026)(0.029)(0.029)−0.063−0.185∗∗ −0.120−0.058−0.194∗∗ −0.160∗ −0.256∗∗ −0.201∗(0.088)(0.090)(0.086)(0.089)(0.084)(0.090)(0.106)(0.103)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
−0.773∗∗∗ −0.847∗∗∗ −0.816∗∗∗ −0.801∗∗∗ −0.866∗∗∗ −0.838∗∗∗ −0.872∗∗∗−0.849∗∗∗(0.126)(0.070)(0.089)(0.109)(0.065)(0.078)(0.089)(0.095)10.68*** 24.96*** 18.43*** 13.08*** 25.49*** 21.39*** 12.83***
13.35
201
170
224
222
176
174
148
146

−1.281∗(0.691)(1)

Notes: a The Wald test is for the null hypothesis that ρ = 0. Robust standard errors are reported in theparentheses. ***, **, * indicate significance at the 1, 5 and 10% level, respectively.

Wald Testa
N

Continent FEρAccess todrinking waterDistance
to riverGovernment
Expenditure
Flood Distr.

Population Density

Freedom of
the press
Ln(GDP p.c.)

Newspapers

Corruption
ControlTV

Dependent variable:
Ln(1 + F atalities)
Democracy

Table 4: Magnitude Stage - Heckman Second StageTable 5: Robustness Test - Alternative Estimators
Dependent variable:
Democracy
Corruption
ControlTV

OLS
Ln(1+Fatalities)
0.648(0.808)0.084(0.353)−0.039∗∗∗(0.014)OLS
Ln(1+Fatalities)
0.551(0.854)−0.112(0.335)−0.333∗(0.177)−0.005(0.013)−0.065(0.075)0.001(0.001)−0.021(0.013)0.000(0.000)−0.070∗∗(0.032)−0.114(0.086)
Yes
146

Newspapers
Freedom of
the press
Ln(GDP p.c.)
Population
Density
Access todrinking waterDistance
to riverGovernment
Expenditure
Flood Distr.
Continent FE
N−0.003(0.013)−0.090(0.070)0.001(0.001)−0.016(0.011)0.000(0.000)−0.074∗∗(0.032)−0.150∗(0.081)
Yes
148

NegBinNegBinFatalities Fatalities
1.061
0.893
(0.908)(0.892)−0.175−0.699(0.548)(0.560)−0.090∗∗∗(0.030)−0.897∗∗∗(0.327)0.002−0.012(0.019)(0.019)−0.135∗∗ −0.100∗(0.059)(0.057)−0.001−0.001(0.001)(0.002)0.000−0.018(0.015)(0.015)0.000
0.000(0.000)(0.000)−0.073∗∗∗ −0.081∗∗∗(0.023)(0.022)−0.0650.029(0.122)(0.155)
Yes
Yes
148
146

Notes: Robust standard errors are reported in the parentheses. ***, **, * indicate
significance at the 1, 5 and 10% level, respectively.

24

Table 6: Robustness Test - Alternative Political, Corruption and Dependent Variables
Dependent variable:
Ln(1 + F atalities)
Checks &Balances
Democracy
Freedom from
Corruption
Corruption
ControlTV
Newspapers
Freedom of
the press
Other ControlsρWald Testa
N
P (Epidemicit = 1)
Checks &Balances
Democracy
Freedom from
Corruption
Corruption
ControlTV
Newspapers
Freedom of
the press
Ln(Flood Kills)
Ln(Flood Aff.)
Other Controls
N

(1)

(2)

(3)

(4)

(5)

(6)

Magnitude - Second Stage
0.119(0.077)0.241
0.082−0.097−0.122(0.842) (0.884)(0.899)(0.934)0.016
0.022∗(0.011) (0.012)
0.853∗
0.575
0.720
0.424
(0.458)(0.412)(0.492)(0.442)−0.047∗∗∗−0.050∗∗∗−0.050∗∗∗(0.015)(0.014)(0.016)−0.500∗∗−0.604∗∗∗−0.431∗∗(0.201)(0.219)(0.208)
0.016
0.012
0.012
0.014
0.003
−0.001(0.012)(0.011)(0.015) (0.015)(0.016)(0.016)
Yes
Yes
Yes
Yes
Yes
Yes
−0.863∗∗∗ −0.813∗∗∗−0.746∗∗∗−0.724∗∗∗−0.853∗∗∗−0.822∗∗∗(0.086)(0.107)(0.139) (0.165)(0.096)(0.106)14.97*** 12.98*** 9.20*** 9.09*** 13.73*** 8.84***
164
163
145
143
148
146
Outbreak - First Stage
0.051∗
0.045(0.030)(0.031)
0.284
0.269
0.396
0.390
(0.272) (0.273)(0.278)(0.279)−0.007∗ −0.007∗∗(0.003) (0.003)−0.375∗∗∗ −0.384∗∗∗−0.356∗∗∗−0.368∗∗∗(0.128)(0.133)(0.130)(0.136)
0.004
0.004
0.005
(0.006)(0.005)(0.006)
0.078
0.039
0.080
(0.079)(0.079)(0.076)−0.005−0.0040.001
0.001−0.001−0.001(0.004)(0.004)(0.004) (0.005)(0.005)(0.005)0.073∗∗∗ 0.068∗∗∗ 0.089∗∗∗ 0.089∗∗∗(0.025)(0.026)(0.027) (0.026)
0.022∗∗ 0.019∗(0.010)(0.010)
Yes
Yes
Yes
Yes
Yes
Yes
1390
1363
1292
1266
1247
1215
0.078
(0.080)Notes: a The Wald test is for the null hypothesis that ρ = 0. Robust standard errors arereported in the parentheses. ***, **, * indicate significance at the 1, 5 and 10% level,
respectively.

25

Appendix A
In our model, the preventive effort can reduce the likelihood of disaster while the palliative
effort can reduce the size of actual damage. We now discuss briefly the case where theprobability of disaster α is exogenous and the incumbent’s effort in both stages reduces thesize of actual damage. The net damage from disaster is then γk − f (e1 , e2 ) where f is strictlyincreasing in each argument and strictly concave. To simplify notation, we assume (m) = 1
for all m. Since the probability of disaster is exogenous and so is the incumbent’s utility inthe non-disaster event, the opportunistic incumbent chooses (e1 , e2 ) to maximize V (e1 , e2 ) =α P (e2 ; m, δ, γ)Ω − θ[γk − f (e1 , e2 )] − (1 + τ )e2 − (1 + τ )e1 . Then Ve1 e1 = αθfe1 e1 < 0,Ve2 e2 = α(Pe2 e2 Ω + θfe2 e2 ) < 0 and Ve1 e2 = αθfe1 e2 . Thus Ve1 e2 is positive (negative) if e1 ande2 are complements (substitutes) in the sense that fe1 e2 > (<) 0. Following similar steps as2= (−αVe1 e1 Pe2 m Ω)/|H| > 0, hence more media
in the proof of Proposition, we can show ∂e∂mactivity unambiguously increases the palliative effort. For the preventive effort, we have∂e1
= (αVe1 e2 Pe2 m Ω)/|H|. Thus the preventive effort increases (decreases) in media activity∂m
if e1 and e2 are complements (substitutes). Comparative statics results with respect to δ are
1
2= (Ve2 e2 − αVe1 e2 )/|H| and ∂e= (αVe1 e1 − Ve1 e2 )/|H|. Thus bothsimilar. For τ , we have ∂e∂τ∂τe1 and e2 decrease in τ if they are complements. When they are substitutes, the sign of each
derivative depends on the relative magnitude of Ve1 e1 , Ve2 e2 and Ve1 e2 . For example, if theutility from holding office (Ω) is large enough and |fe1 e2 | is large relative to |fe1 e1 | and |fe2 e2 |,then better institutions (low τ ) can increase the palliative effort but reduce the preventive
effort. In sum, the incumbent’s effort choice in this case hinges on the interaction between
the preventive and palliative effort.

26

Appendix B
Variable

Description

SourceEpidemic

Dummy variable. 1 if country i
experienced at least one cholera outbreakin year t; 0 otherwise

EM-DAT, CRED (2008)

F atalities

Total number killed by cholera epidemic

EM-DAT, CRED (2008)

Democracy

Revised Combined Polity Score (Polity2)
Higher values indicate better political institutions.

Marshall & Jaggers (2005)

Control ofcorruption

Perception of corruption, defined as
the exercise of public power for private gain.

Kaufmann et al. (2008)

TV

Televisions per capita (in ’000)

Banks (2004)

N ewspapers

daily newspaper circulation
per 1,000 inhabitants

Banks (2004)

Freedom of the press

Freedom house press freedom index
0 (most free) to 100 (least free)
Real GDP per capita (US dollars in 2000 prices)www.freedomhouse.org

P opulation
Density

Population per square km

World Bank (2008)

Distance
to riverMean distance to nearest inland navigable river (in km)

Gallup et al. (1999)

Access todrinking water

Percentage of population with accessto safe drinking waterWorld Bank (2008)

Government expenditure

Ratio of government expenditure to GDP

World Bank (2008)

F lood distribution

GIS-DATA on spatial flood risk
Country mean, higher values indicate higher risk
Total number killed by floods

Dilley et al. (2005)

Checks &Balances

Number of veto players

Beck et al. (2001)

F reedom f rom
corruption

Freedom from corruption based on
International Corruption Perception Index

http://www.heritage.org/index/

F lood af f.

Total number affected by floods

EM-DAT, CRED (2008)

GDP

F lood Kills

27

World Bank (2008)

EM-DAT, CRED (2008)