Social interaction and conformism in a random utility model

Barucci, Emilio; Tolotti, Marco

doi:10.1016/j.jedc.2012.06.005

Cited by 12 publications

(9 citation statements)

References 21 publications

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“…In this case, we have that . Arguing similarly as in Barucci & Tolotti (2012), it can be shown that, under the assumptions of Proposition 1,…”

Section: Research Activities and Intra-industry Interactionsmentioning

confidence: 79%

“…The former case represents the so-called "standing-on-the-shoulder effect", that is innovation by some firms increases the possibility of further innovation by others, while the latter case the "fishing-out effect", that is innovation by some firms decreases the possibility of further innovation by others (Jones (2005)). Formally, we model the individual firm research profits as in random utility models (see Brock & Durlauf (2001) and Barucci & Tolotti (2012)). Each firm is thus characterized by its specific innovative attitude ω i,t ∈ {0; 1}, where ω i,t = 1 (ω i,t = 0) denotes that firm i is (is not) innovating at time t. The decision to engage in research activities to produce innovation is based on the following profit structure:…”

Section: Research Activities and Intra-industry Interactionsmentioning

confidence: 99%

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Endogenous Growth and Technological Progress with Innovation Driven by Social Interactions

Marsiglio

Tolotti

2015

SSRN Journal

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We analyze the implications of innovation and social interactions on economic growth in a stylized endogenous growth model with heterogeneous research firms. A large number of research firms decide whether to innovate or not, by taking into account what competitors (i.e., other firms) do. This is due to the fact that their profits partly depend on an externality related to the share of firms which actively engage in research activities. Such a share of innovative firms also determines the evolution of technology in the macroeconomy, which ultimately drives economic growth. We show that when the externality effect is strong enough multiple BGP equilibria may exist. In such a framework, the economy may face a low growth trap suggesting that it may end up in a situation of slow long-run growth; however, such an outcome may be fully solved by government intervention. We also show that whenever multiple BGP exist, they are metastable meaning that the economy may cyclically fluctuate between the low and high BGP as a result of shocks affecting the individual behavior of research firms. Forthcoming in Economic Theory AbstractWe analyze the implications of innovation and social interactions on economic growth in a stylized endogenous growth model with heterogenous research firms. A large number of research firms decide whether to innovate or not, by taking into account what competitors (i.e., other firms) do. This is due to the fact that their profits partly depend on an externality related to the share of firms which actively engage in research activities. Such a share of innovative firms also determines the evolution of technology in the macroeconomy, which ultimately drives economic growth. We show that when the externality effect is strong enough multiple BGP equilibria may exist. In such a framework, the economy may face a low growth trap suggesting that it may end up in a situation of slow long run growth; however, such an outcome may be fully solved by government intervention. We also show that whenever multiple BGP exist, they are metastable meaning that the economy may cyclically fluctuate between the low and high BGP as a result of shocks affecting the individual behavior of research firms.

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“…In this case, we have that . Arguing similarly as in Barucci & Tolotti (2012), it can be shown that, under the assumptions of Proposition 1,…”

Section: Research Activities and Intra-industry Interactionsmentioning

confidence: 79%

Section: Research Activities and Intra-industry Interactionsmentioning

confidence: 99%

Endogenous Growth and Technological Progress with Innovation Driven by Social Interactions

Marsiglio

Tolotti

2015

SSRN Journal

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“…In our setting, this suggests that it may be possible that the majority may be divided into multiple subgroups in which individuals are characterized by a different value of the social externality parameter. In order to keep the analysis as simple as possible, similar to Barucci and Tolotti (2012), let us assume that there exist two subpopulations of whites, W 1 and W 2 , in which the social externality parameter is given by J 1 and J 2 , respectively. Without loss of generality, we assume that J 1 ≤ J 2 , that is subpopulation 1 is less concerned than subpopulation 2 about the eventual presence of blacks in the neighborhood.…”

Section: Two White Subpopulationsmentioning

confidence: 99%

Social interactions, residential segregation and the dynamics of tipping

et al. 2021

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We develop an analytically tractable population dynamics model of heterogeneous agents to characterize how social interactions within a neighborhood determine the dynamic evolution of its ethnic composition. We characterize the conditions under which integration or segregation will occur, which depends on the majority’s social externality parameter and net benefit from leaving, and the minority’s leaving probability. Minority segregation may result from the process of tipping, which may arise from three possible channels: two are related to exogenous shocks (migration flows and changes in tipping points) and one is related to the endogenous probabilistic features of our framework (endogenous polarization). This characterization of integration and segregation conditions yields interesting policy implications for social and urban planning policies to mitigate segregation.

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“…This means that the effective magnitude of the social externality in the utility function depends on the number of households adhering to the norm, that is, on the number of households actively adopting the green technology: The eventual adoption by other households reinforces the social component positively affecting individual utility. Formally, we model the individual household's utility as in random utility models (see Barucci & Tolotti, 2012;Brock & Durlauf, 2001). Eventually, the adoption decision is based on the following utility function:…”

Section: The Laissez-faire Outcomementioning

confidence: 99%

“…Mathematically, it is given by

J x_{t}^{N}

, where the size of such a social externality is measured by

J > 0

, while

x_{t}^{N} = \frac{1}{N} \sum_{j} ω_{j, t}

represents the time‐varying fraction of green households (i.e., households adopting the green technology); for the sake of expositional simplicity, we will refer to

x_{t}^{N}

as the “ green share .” This means that the effective magnitude of the social externality in the utility function depends on the number of households adhering to the norm, that is, on the number of households actively adopting the green technology: The eventual adoption by other households reinforces the social component positively affecting individual utility. Formally, we model the individual household's utility as in random utility models (see Barucci & Tolotti, 2012; Brock & Durlauf, 2001). Eventually, the adoption decision is based on the following utility function:

u_{i, t} (ω_{i, t}) = ω_{i, t} true(b + {italicε}_{i} - c + J x_{t}^{N} true) .

If the agent

i

does not adopt the green technology (

ω_{i, t} = 0

) his utility is simply null,

u_{i, t} (0) = 0

.…”

Section: The Laissez‐faire Outcomementioning

confidence: 99%

Motivation crowding‐out and green‐paradox‐like outcomes

Marsiglio

Tolotti

2020

J Public Economic Theory

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We analyze the effectiveness of environmental policy in a framework in which households’ utility is determined by both private and social components, representing their extrinsic and intrinsic motivations to undertake green actions, respectively. Environmental policy, in the form of a subsidy aiming to incentivize the adoption of a green technology, on the one hand, directly increases households’ extrinsic motivation, while, on the other hand, indirectly decreases their intrinsic motivation. We show that, provided that the indirect effect dominates, the policy leads to crowding‐out of intrinsic motivation which ultimately undermines the effectiveness of the policy itself. Specifically, despite its positive effect on environmental outcomes in the short run, the policy will lead to a deterioration in long‐run environmental outcomes, giving rise to a reverse green‐paradox‐like outcome. Moreover, even in the case in which the direct effect dominates, provided that the indirect effect is large enough, the policy will generate a deterioration in short‐run environmental outcomes. These results clearly suggest that the optimal design of environmental policy is particularly complicated since it requires to take into account also its effects on intrinsic motivation.

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Social interaction and conformism in a random utility model

Cited by 12 publications

References 21 publications

Endogenous Growth and Technological Progress with Innovation Driven by Social Interactions

Endogenous Growth and Technological Progress with Innovation Driven by Social Interactions

Social interactions, residential segregation and the dynamics of tipping

Motivation crowding‐out and green‐paradox‐like outcomes

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