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The Political Economy of Scientific Uncertainty: how communication campaigns are affected by scientific uncertainty? February 1, 2013 Abstract We consider an industry organized as a lobby group whose activity generates pollution. This pollution may create damages. Scientists are unsure about the level of harm it might cause, and they can do research to reduce their uncertainty. The government may be benevolent, but populist. The industry wants to avoid being regulated. It can engage in costly communication efforts to affect public beliefs on the negative outcome. However, the effectiveness of the communication efforts depends on the level of scientific uncertainty. We find that there exists a certain level of scientific uncertainty above which the lobby communicates in order to influence the people’s beliefs, and so the government’s regulation. The more uncertain the science, the easier it is for the lobby to affect people’s beliefs. Finally, we discuss about the possibility of government’s research subvention to decrease this impact. Keywords: lobby, communication campaign, scientific uncertainty. JEL Classification: D72, D82, D83, Q58 . 1 Introduction For contemporary societies, an environmental policy on global warming is an important challenge. This policy depends on public attitudes but also on communication campaign which has an influence on public’s beliefs. Actually, according to several studies, most people acquires scientific knowledge through communication campaign coverage (Ader, 1995; Ball-Rokeach and DeFleur, 1976; Cabecinhas, Lazaro and Carvalho, 2008; Corbett and Durfee, 2005; Corner, 2011; Gonzenbach and Hester, 1997; McCombs and Shaw, 1972; Nelkin, 1987). The communication campaigns then play an important role in the risk perception of global climate change (Kasperson, Pidggon and Slovic, 2003; Palfreman, 2006). Kahneman (2003) states that under uncertainty individual reacts differently to the way a message is framed. Dramatic and alarming messages on climate change, such as those presenting climate change as an extreme threat1 (Pandora’s Box, Polar bear extinction), a complex problem, or increasing public uncertainty (stressing conflicts between scientists), may have a distancing effect of public from global warming (Corbett and Durfee, 2004; Dilling and Moser, 2004; Ereaut and Segnit, 2006; Lowe, 2006; Rogers, 1983; Shanahan, 2007). On the other hand, a message based on financial aspect or growth of the economy (innovative energy technology, sustainable economic prosperity) may motivate actions (Comby, 2009; Nordhaus and Schellenberger, 2007). There is a clear divergence of causes, and so, on recommended actions for global warming (Boykoff and Boykoff, 2004; Ereault and Segnit, 2006; Shanahan, 2007). Global warming is either anthropogenic (IPCC, 2007) or is purely caused by natural fluctuations2 (IPSOS/Mori, 2007) implying that voluntary actions would suffice as well as mandatory action would be needed. Hence, different ideological lines on climate change have appeared (Nisbet, 2009; Painter, 2011; Ward, 2008). For example, in America, a majority of Republicans are sceptics, they refuses climate science and dismisses the urgency of the problem, while a majority of Democrats accept this science and feels concern about the issue. So, under uncertainty, how communication campaigns will change people’s beliefs? How these campaigns will influence environmental and research policies? To address these questions, we consider an economic activity (pollution emissions) with potentially harmful consequences on people’s health and to the environment. Scientists are unsure about the level of harm it might cause, and they can do research, through experiments to learn about the truth, to reduce their uncertainty. The industry is organized as a lobby group who seeks to maximize profit. This lobby group may notably try to influence people’s beliefs about the issue, through communication efforts. The government may regulate its activity. The gov1 See: ”An Inconvenient Truth”, Gore’s documentary on climate change’s effects, dramatized climate change as an environmental Frankenstein’s monster; ”Global Warming: Be Worried. Be VERY Worried,” Time, 3 April 2006, cover, http://www.time.com/time/covers/0,16641,20060403,00.html 2 See: ”Weather Wars”, Fox & Friends (2007) 2 ernment may be benevolent, but populist, seeking to maximize median’s voter welfare.3 Lobby may then be heavily engaged in communication campaigns to affect public perception about the positive effects of his activity. However, the government may also decide to decrease lobby’s communication impact on people by giving a subsidize to research. We examine the literature on the effects of mass media on public policies (Besley and Burgess, 2001; Sobbrio, 2011). Besley and Burgess (2002) and Stromberg (2003) examine the effects of mass media on the government responsiveness to the public needs in India and on New Deal relief program in the U.S. in the 1930s, respectively. While these studies show how the presence of mass media could affect government policy, the focus of our paper is on how scientific committee through experiments and lobby’s communication campaigns may influence people beliefs, and so a popular government’s choices. Furthermore, our paper also investigate the literature relying political and the environmental economics. 4 Yu (2005) studies the complementarity and the substitution between ”direct” (lob- bying the government) and ”indirect” (persuading the public) competition for political influence on environmental policy. In our paper, we introduce experiments (research) and we focus on the ”indirect” competition for persuading people between the scientific committee (which may be subsidized by the government) and the lobby. Yu (2006) finds that when the effectiveness of anti-smoking campaign increases, the government will increase its effort in campaigns, and will extract more political contributions from the tobacco lobby. We then propose to study the impact of the level of uncertainty on the lobby’s contributions, which are, here, communication campaigns. In our model, scientists’ beliefs are defined by the results of experiments (research). This characterization implies that both more research leads to more dispersion in scientists’ beliefs, and when the number of experiments becomes arbitrarily large, scientists can be sure, ex post, of which theory is true. In addition, people’s beliefs depends on the interaction between the scientists’ beliefs and the lobby’s communication campaign. This yields to the more certain the science the more difficult it is for the lobby to affect people’s beliefs. Under this characterization, we analyse lobby’s communication efforts and government’s choices on the pollution regulation with different types of regimes: a benevolent government who measures the social welfare using scientific committee’s beliefs; a populist government who only cares about being re-elected by people, and then specifies the social welfare using people’s beliefs. Moreover, we introduce the possibility that the government decides to subsidizes research (endogenous science) or not (exogenous science). First, we present the result under exogenous science. We find that whatever the regimes 3 As in Salanié and Treich (2009), we consider two kinds of regime: benevolent based on scientists’ beliefs, called paternalist approach in Salanié and Treich, and populist approach.) 4 See Persson and Tabellini (2000) and Mueller (2003) for more details on political economy and public choice. 3 (benevolent or populist) when scientists’ belief on harm increases, the government regulates more, i.e., reduces emissions more. In contrast, the populist government increases emissions as communication campaign increases. Moreover, we obtain that communication campaign decision depends on both the scientific belief and the cost of the campaign. The lobby will never make communication with a cost which leads him to a non profitable profit. However, with a cost of communication arbitrarily small, the lobby will always make communication. Finally, for some levels of cost communication, there exists a threshold of scientific committee’s beliefs which leads people to the highest uncertainty on the dangerousness or not of the industry’s pollution. People are then more ductile yielding the lobby to act and make communication campaign. Then, under exogenous science, we show that a populist government benefits from people being lied to and relieved of their worries. We get that when peoples beliefs for the dangerousness emerging from the industrys pollution are higher than one half, then the lobby communicates more for reducing the dispersion in people belief. In addition, when peoples beliefs for the dangerousness emerging from the industrys pollution are lower than one half, then more dispersion in people belief increases the lobby’s communication effort. Furthermore, ex-ante, the lobby prefer to encourage research when the expected scientists’ beliefs are under a certain threshold. Finally, when the lobby distorts information as much as it can, given the constraint that it cannot fabricate false information, the lobby prefers to encourage research. The remainder of paper is organized as follows. Section 1 presents the model. Section 2 investigates the optimal decision-making according to the government’s regime (benevolent and populist) and the exogenous or endogenous science. Finally, section 3 concludes. I The model We consider an industry organized as a lobby group whose activity generates pollution. This pollution may create damages to people’s health and to the environment. There are two possible states of the world, θ̃ = 0 if the activity has no harmful consequences and θ̃ = 1 if it has. Society (government, industry (lobby), scientific committee and people) has limited initial knowledge on the dangerousness of pollution, it is unsure about the level of harm it might cause. The scientific committee can do research to reduce its uncertainty. The prior beliefs of scientific committee are p0 on state θ̃ = 1, and 1 − p0 on state θ̃ = 0. It can run n experiments to learn about the truth. Each experiment provides a noisy signal on the true state of the world. Each experiment is correct with probability P > 1/2, the same for both states of the world. Denote by k the number of experiments saying that θ̃ = 1 is the true state of the world, i.e., k experiments among n showing that environmental damages are real. Bayes’ rule tell us that: p = p(θ̃ = 1|k) = p(k|θ̃ = 1)p(θ̃ = 1) p(k|θ̃ = 1)p(θ̃ = 1) + p(k|θ̃ = 0)p(θ̃ = 0) 4 Here p(θ̃ = 1) = p0 and p(k|θ̃ = 1) = Cnk P k (1 − P )n−k while p(θ̃ = 0) = 1 − p0 and p(k|θ̃ = 0) = Cnk (1 − P )k P n−k . This yields p= p0 P k (1 − P )n−k p0 P k (1 − P )n−k + (1 − p0 )(1 − P )k P n−k Introduce α = P/(1 − P ) > 1 the relative precision of any experiment. Scientists’ belief is p(p0 , k, n) = p0 p0 + (1 − p0 )αn−2k (1) and clearly, p(p0 , k, n) > p0 if k > n/2 while p(p0 , k, n) < p0 if k < n/2. In addition, the ex-ante probability that p = p(p0 , k, n) is equal to p(p̃(n) = p(k, n)) = p(k̃ = k) = p(k̃ = k|θ̃ = 1)p(θ̃ = 1) + p(k̃ = k|θ̃ = 0)p(θ̃ = 0) = p0 Cnk P k (1 − P )n−k + (1 − p0 )Cnk P n−k (1 − P )k and we check that E(p̃(n)) = p0 . We note that equation (1) satisfies the following Bayesian: p(p0 , k, n) = p(p(p0 , k ′ , n′ ), k − k ′ , n − n′ ) for any k ′ ≤ k and n′ ≤ n. This simply says that the order of experimentations does not matter. Observe from (1) that p̃(n) dominates in the sense of second order stochastic dominance p̃(n′ ) if n < n′ . In other words, more research leads to more dispersion in scientists’ beliefs. Moreover, we get that as n tends to +∞, p̃ converges to the following random variable: p = 1 with probability p0 and p = 0 with probability 1 − p0 (both in distribution and in probability). So when the number of experiments become arbitrarily large, scientists can be sure, ex post, of which theory is true. In order to avoid a decrease of consumer’s demand about its products, the industry can engage in costly communication efforts on the safety of its production, m ≥ 0, to affect people’s beliefs on the negative outcome. By considering that each scientific experiment corresponds to a different scientist, industry (lobby) may hire a scientist with experiments signalling θ̃ = 0 at cost c. In other word, m is the number of experiments showing that there is no damage that the lobby can present as if they were independently ran. The industry then presents information biased in his favour in the media. We suppose that media fairly represent all sides in the debate, i.e., they fairly represent scientific committee’s view as well as the lobby’s view. Hence, according to the communication effort m and the scientific committee’ beliefs p(p0 , k, n), the beliefs of people are q on state θ̃ = 1, and 1 − q on state θ̃ = 0. 5 If n experiments are conducted with k signaling θ̃ = 1, and if the lobby cannot directly lie about evidence but can only present existing evidence as if it were independently drawn, communication effort m is naturally bounded m ≤ n − k. This means that people’s belief is determined as if there were n + m experiments overall, hence citizen’s belief is q= p0 p = . n−2k+m p0 + (1 − p0 )α p + (1 − p)αm (2) Hence, we can write q = q(p, m). Study how q depends on p and m. We have qp = αm >0 [p + (1 − p)αm ]2 so q is increasing from q(0, m) = p = 0 to q(1, m) = p = 1. Then qpp = − −2αm (1 − αm ) < 0. [p + (1 − p)αm ]3 So the scientific belief have an increasing marginal impact on citizen belief. Moreover, we have qm = −p(1 − p) ln(α) αm <0 [p + (1 − p)αm ]2 so q is decreasing from q(p, 0) = p to q(p, ∞) = 0. Then qmm = −p(1 − p) (ln(α))2 αm p − (1 − p)αm [p + (1 − p)αm ]3 and hence qmm = 0 ⇔ p = (1 − p)αm ⇔ p = αm /(1 + αm ) ⇔ q = 1/2. In addition qmm > 0 ⇔ q < 1 1 and qmm < 0 ⇔ q > . 2 2 Thus q is everywhere convex if p ≤ 1/2. In that case, any new ”fake” experiment has a decreasing marginal impact on citizen belief. In contrast, if p > 1/2, q is first concave and then convex. The first few ”fake” experiments have an increasing marginal impact on citizen belief. Then, we get that qpm = αm ln(α) (p − (1 − p)αm ) [p + (1 − p)αm ]3 and hence qpm = 0 ⇔ p = (1 − p)αm ⇔ p = αm /(1 + αm ) ⇔ q = 1/2. In addition qpm > 0 ⇔ q > 1 1 and qpm < 0 ⇔ q < . 2 2 Thus, the more certain the science - when scientific committee’s beliefs for the dangerousness emerging from the industry’s pollution are closer to zero or one - the more difficult it is for the 6 lobby to affect people’s beliefs. So there is no incentive for the lobby to spend money in a failed communication campaign. The industry benefits from pollution. Let e be the level of pollution emissions. These benefits are captured in the function b(e). We define by e0 the level of emissions with business as usual and assume that for e < e0 then b is an increasing and concave function. Hence e0 solves maxe≥0 b(e). However, if the activity is harmful, Society suffers from damage d(e) with d defined as an increasing and linear function. The industry’s activity being able to generate damages on people health and the environment, the government may want to regulate the level of emissions, e. The government may be benevolent, but populist, seeking to maximize median’s voter welfare. In other words, if the government is benevolent, it measures social welfare in using scientific committee’s beliefs, p(k, n); while if the government is populist, it uses the people’s belief, q(p(k, n), m). So, the expected welfare of a benevolent and a populist government are recursively: W (e) = b(e) − p(k, n)d(e) and W (e) = b(e) − q(p(k, n), m)d(e). Finally, we use quadratic benefits and linear damages: 1 b(e) = be0 e − be2 2 d(e) = de II The optimal decision-making In this section, we present the government’s and the industry (lobby)’s optimal decision-making. We propose to study four situations in which the government is either benevolent (beliefs depend on p) or populist (beliefs depend on q), the lobby either engages in a campaign of communication (m > 0) or not (m = 0), and the government either subsidizes research (endogenous science) or not (exogenous science). This study allows us to analyse the influence of the level of uncertainty on the communication campaign. Moreover, this will also tell us whether, ex-ante, the industry (lobby) prefers to encourage or discourage the research. We use the backward induction method in order to characterize the government’s and the industry (lobby)’s optimal decisions. A Benevolent government, no lobby and exogenous science. A government which is benevolent measures the social welfare using scientific committee’s beliefs. Hence, given scientific committee’s beliefs, p, the government regulates by setting e in order to maximize welfare. So the government solves the following problem in order to define its optimal 7 decision. max W (e) = b(e) − pd(e). e≥0 We easily verify that this problem is concave, so the first order condition is as follows: b′ (e) = pd′ (e). (3) We note eA the optimal level of pollution emissions. From equation (3), we may write that eA = eA (p). We derive equation (3) with respect to p, this yields eA p = d′ (e) <0 b′′ (e) − pd′′ (e) with eA the optimal level of pollution emissions. As scientists’ belief on harm increases, the government regulates more, i.e., reduces emissions more. From the form, we get that: d eA (p) = e0 − p b under the assumption that d < be0 which guarantees that there is still positive emission in the first best. So effort decreases linearly with belief. More the scientific committee believes that the pollution creates by the lobby’s activities is dangerous for people’s health and the environment, more the government will favour a reduction of emission. B Populist government, lobby, exogenous science. A populist government only cares about being re-elected by people. He then specifies the social welfare using people’s beliefs, q(p, m), rather than the beliefs of scientific committee. Hence, given people’s beliefs the government regulates by setting e in order to maximize welfare. So the government solves the following problem in order to define its optimal decision. max W (e) = b(e) − q(p, m)d(e). e≥0 We easily verify that this problem is concave, so the first order condition is as follows: b′ (e) = q(p, m)d′ (e). (4) We note eB the best response for the level of pollution emissions. From equation (4), we may write that eB = eB (p, m). We derive equation (4) with respect to p and m, this yields eB p = qp d′ (e) qm d′ (e) B < 0 and e = > 0. m b′′ (e) − q(p, m)d′′ (e) b′′ (e) − q(p, m)d′′ (e) 8 As scientists’ belief on harm increases, the government regulates more, i.e., reduces emissions more. In contrast, the government increases emissions as communication campaign increases. From the form, we get that: d eB (p, m) = e0 − q(p, m) b under the assumption that d < be0 . Next, the lobby then chooses its optimal level of communication, m, in order to maximize its profit. The lobby’s maximization problem is: max π(m) = b(eB (p, m)) − cm m≥0 This problem is not always concave. Let then turn to the form. So industry profit is quadratic in citizen belief. The lobby’s maximization problem is: 1 1 d2 max π(m) = be20 − q(p, m)2 − cm. m≥0 2 2 b Substituting equation (2) in π(m) and examining the derivatives, we find: π(m) = π ′ (m) = π ′′ (m) = 1 2 1 d2 p2 be0 − − cm 2 2 b [p + (1 − p)αm ]2 αm d2 2 p (1 − p) ln(α) −c b [p + (1 − p)αm ]3 d2 p2 (1 − p) (ln(α))2 αm [p − 2(1 − p)αm ] b [p + (1 − p)αm ]4 Thus, if p < 2/3, π is concave and the optimal communication effort is given by mB = 0 if π ′ (0) = d2 1−p b p ln(α) − c < 0 and mB is such that π ′ (mB ) = 0 otherwise. In contrast, if p > 2/3, π is first convex and then concave. π ′′ = 0 for m∗ such that q = 2/3 implying that p p+(1−p)αm = 32 . This is equivalent to π ′ (m∗ ) = 1−p m p α = 12 . We have: 4d2 ln(α) −c 27b p so we easily verify that π ′ (m∗ ) > π ′ (0). Thus, if p > 2/3, the optimal communication effort is given by mB = 0 if π ′ (m∗ ) ≤ 0; otherwise if π ′ (m∗ ) > 0 then mB is such that π ′ (mB ) = 0. So the communication campaign decision depends on both the scientific belief and the cost of the message mB . If c ≥ 4d2 ln(α) 27b p >c≥ d2 1−p b p 4d2 ln(α) 27b p then the lobby will never make communication, m = 0; If ln(α) then there exists a threshold p = 2/3 under which the lobby will not communicate, while above it, the lobby send a message to the population. Hence, p = 2/3 may be the level of scientific committee’s beliefs which lead people to the highest uncertainty on the dangerousness or not of the industry’s pollution. People are then more ductile yielding the lobby to act. Finally, if d2 1−p b p ln(α) > c then the communication is always profitable for the lobby which will always communicate. 9 C Benevolent government, no lobby, endogenous science. We come back to the case of a benevolent government without lobby and we add that the number of experiments is not given but it is chosen by the government. Scientific beliefs p then depends on n and k, so p = p(k, n). We first analyse the impact of the dispersion of the scientific committee’s belief, p, on the welfare, W (p) = W (eA (p)) = b(eA (p)) − pd(eA (p)). We get: W ′ (p) = −d(eA (p)) < 0 and W ′′ (p) = −d′ eA p > 0. Hence W is convex, by Jensen welfare is then always higher when facing a more dispersed repartition of beliefs (in the sense of second order stochastic dominance). Now, we introduce the possibility the government can subsidize scientists at cost C per experiment. The government’s best response for the level of emission, eC , is characterize by equation (3) with p = p(k, n). The government seeks to maximizes W (n) = Ek̃ W (eC (p(k̃, n))) − Cn with respect to n. We easily verify that this problem is concave, so the first order condition is as follows: −Ek̃ pn d(eC (p(k̃, n))) = C. (5) With p = p(k, n) and from equation (3) we obtain that eC n = pn d′ (eC (p(k̃, n))) > 0. b′′ (eC (p(k̃, n))) − p(k, n)d′′ (eC (p(k̃, n))) (6) We derive pn d(eC (p(k̃, n))) with respect to n, we obtain pnn d(eC ) + pn d′ (eC )eC n which is negative if p0 ≤ αn α2k +αn and otherwise the sign is ambiguous. So if p0 ≤ αn α2k +αn then more dispersion in scientists’ belief leads the government to make lower number of experiments. On the other hand, more dispersion on scientists’ belief leads the government to regulates less, i.e., increases emissions more. From the form, we get that the best response for the level of emission is: d eC (p(k, n)) = e0 − p(k, n) b under the assumption that d < be0 . Then the optimal number of experiment chosen by the government is such that: dp0 (1 − p0 )lnααn−2k Ek̃ (p0 + (1 − p0 )αn−2k )2 ( dp0 e0 − p0 + (1 − p0 )αn−2k 10 ) = C. D Populist government, lobby, endogenous science. We turn to the case of a populist government which only cares about the social welfare using people’s beliefs, q(p, m), rather than the beliefs of scientific committee. We add that the number of experiments is chosen by the government. Scientific beliefs p then depends on n and k, so p = p(k, n). Hence, people’s beliefs q(p, m) = q(p(k, n), m). Moreover, we introduce the possibility the government can subsidize scientists at cost C per experiment. The government’s best response for the level of emission, eD , is characterize by equation (4) with p = p(k, n). Remark that any distribution of scientists’ beliefs p̃ gets transformed by lobbies’ actions into a distribution of people’s beliefs q̃. We know that E(q̃) < E(p̃) since the lobby can only lead to a lowering of belief. This then increases welfare for a populist government: a populist government benefits from people being lied to and relieved of their worries. Observe from (2) that q̃(n) dominates in the sense of second order stochastic dominance q̃(n′ ) if n < n′ . In other words, more research leads to more dispersion in people’s beliefs. If the government anticipates the distortion of scientific beliefs generated by the lobby’s efforts, how does this affect his incentives to subsidize research? The government seeks to maximizes W (n) = Ek̃ W (eD (q(p(k̃, n)), m)) − Cn with respect to n. We easily verify that this problem is concave, so the first order condition is as follows: −Ek̃ qp pn d(eD (q(p(k̃, n)), m)) = C. From the derivation of equation (7) with respect to m, we get that if q ≥ (7) 1 2 then the communi- cation effort reduces the number of experiments. On the other hand, the effect is ambiguous. So when peoples beliefs for the dangerousness emerging from the industrys pollution are higher than one half, then the lobby communicates more for reducing the dispersion in people belief. This tells us that in this situation, more communication efforts reduces the government’s incentives to subsidize research. Now, we are looking for the optimal effort of communication. The lobby’s maximization problem is: max π(m) = Ek̃ b(eD (q(p(k̃, n)), m)) − cm m≥0 This problem is not always concave. Let then turn to the form. So industry profit is quadratic in citizen belief. The lobby’s maximization problem is: 1 1 d2 max π(m) = be20 − E q(p(k̃, n)), m)2 − cm. m≥0 2 2 b k̃ We then summarize the solutions: If c ≥ munication, mD = 0; If 4d2 ln(α) 27b E p(k̃,n) k̃ > c ≥ 4d2 ln(α) 27b E p(k̃,n) then the lobby will never make comk̃ d2 1−Ek̃ p(k̃,n) b E p(k̃,n) ln(α) then there exists a threshold k̃ 11 Ek̃ p(k̃, n) = 2/3 under which the lobby will not communicate, while above it, the lobby sends a message to the population, mD which is defined such that π ′ (mD ) = 0. Hence, Ek̃ p(k̃, n) = 2/3 may be the level of scientific committee’s beliefs which lead people to the highest uncertainty on the dangerousness or not of the industry’s pollution. People are then more ductile yielding the lobby to act. Finally, if d2 1−Ek̃ p(k̃,n) b E p(k̃,n) k̃ ln(α) > c then the communication is always profitable for the lobby which will always communicate, mD which is defined such that π ′ (mD ) = 0. So now we analyse the impact of the number of experiments on the optimal communication effort. When mD = 0 then there is no impact, so we focus on mD defined such that π ′ (mD ) = 0. We then get mD > 0 such that: Ek̃ b′ (eD (q(p(k̃, n)), m))eD q qm = c. From the derivation of equation (8) with respect to m, we get that if Ek̃ q ≤ (8) 1 2 then the number of experiments increases the lobby’s communication effort. On the other hand, the effect is ambiguous. So when peoples beliefs for the dangerousness emerging from the industrys pollution are lower than one half, then more dispersion in people belief increases the lobby’s communication effort. We now study how q̃(n) vary with n, under the assumption that the lobby exerts the optimal amount of communication effort. We rewrite q̃(n) = q(p(k̃, n)), m(n)) and we derive it with respect to n. We obtain qp pn + qm mn which is negative if Ek̃ p ≤ threshold, Ek̃ p = αm 1+αm , αm 1+αm , and its sign is ambiguous otherwise. Hence, there exists a under which the number of experiments decreases the people’s beliefs. This tells us, ex-ante, the lobby prefer to encourage research when the expected scientists’ beliefs are under this threshold. Finally, we propose to analyse the impact of the number of experiments on the people’s beliefs when the lobby make the maximum communication effort. We characterize the maximum 12 communication effort as follows: 1−p = p 1−p = αn−2k = n − 2k = k = n−k = (1 − p0 )αn−2k p0 + (1 − p0 )αn−2k p0 α−(n−2k) 1 − p0 p0 1 − p 1 − p0 p p0 1−p ln[ 1−p ] 0 p ln[α] ln(p0 ) − ln(1 − p0 ) − [ln(p) − ln(1 − p)] 1 [n − 2 ln(α) 1 ln(p0 ) − ln(1 − p0 ) − [ln(p) − ln(1 − p)] [n + 2 ln(α) In this case, the maximum communication effort is 1 ln(p0 ) − ln(1 − p0 ) − [ln(p) − ln(1 − p)] mmax = [n + 2 ln(α) In that case, we get q(mmax ) = q(mmax ) = p p + (1 − p0 1−p 1/2 n/2 p)[ 1−p ] α 0 p 3/2 p (1 − p0 )1/2 1/2 p3/2 (1 − p0 )1/2 + (1 − p)3/2 p0 αn/2 So when the lobby makes the maximum communication effort, the number of experiments decreases the people’s beliefs. This tells us, when the lobby distorts information as much as it can, given the constraint that it cannot fabricate false information, the lobby prefer to encourage research. III Conclusion In this paper, we wonder how communication campaigns are affected by scientific uncertainty. We find that there exists a certain level of scientific uncertainty above which the lobby communicates in order to influence the people’s beliefs, and so the government’s regulation. Moreover, we get that more dispersion in people belief increases the lobby’s communication effort. Then we also show that when the lobby distorts information as much as it can, given the constraint that it cannot fabricate false information, the lobby prefer to encourage research. Nevertheless, the linearity of the damage function is a restrictive simplification. It is applied for the sake of mathematical simplicity. However, Germain, Tulkens, and Zeeuw (1998), Tol (1996) and Tahvonen (1995) have also used this form to study global warming and pollution issues. 13 References 1. Ader, C. R. (1995), ”A Longitudinal Study of Agenda Setting for the Issue of Environmental Pollution”, Journalism and Mass Communication Quarterly, Vol. 72, 300-11. 2. Ball-Rokeach, S. and DeFleur, M. 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