Visibility and Peer Influence in Durable Good Adoption

Bollinger, Bryan; Gillingham, Kenneth; Kirkpatrick, A. Justin; Sexton, Steven

doi:10.2139/ssrn.3409420

Cited by 10 publications

(8 citation statements)

References 29 publications

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“…They found that soda taxes lead to a 34% retail price increase and a drop of 46% in demand. Similar to Bollinger and Sexton (2018), Seiler, Tuchman, and Yao (2019) also found that consumers shifted to buying SSBs outside the city.…”

Section: Empirical Applicationmentioning

confidence: 52%

“…Rojas and Wang (2017) and Bollinger and Sexton (2018) found that the effect of the SSB tax in Berkeley on prices and consumption was limited. Bollinger and Sexton (2018) further identified the reasons for this limited effect; the tax can be easily avoided by buying SSBs from retail stores outside the taxed jurisdiction; also, chain retailers tend to pursue uniform local pricing whereby all stores in large geographic regions charge a common price. Seiler, Tuchman, and Yao (2019) analyzed the effect of an SSB tax in Philadelphia and surrounding areas.…”

Section: Empirical Applicationmentioning

confidence: 99%

“…The synthetic control method (SCM) proposed by Abadie and Gardeazabal (2003) and Abadie, Diamond, and Hainmueller (2010) generalizes the DID methods by providing an explicit data-driven control unit selection procedure. The SCM moves away from using a single control or a simple average of control units; instead, it uses a weighted average of the set of controls as a “synthetic control.” Because of its clarity and simplicity of control unit selection, Athey and Imbens (2017, p. 9) stated that synthetic control is “arguably the most important innovation in the policy evaluation literature in the last 15 years.” In the marketing literature, SCM has been applied to understand the impact of offline television advertising on multiple dimensions of online chatter (Tirunillai and Tellis 2017), to examine how consumers’ search and click activities changed when search engines banned foreign pharmacies from sponsored search (Chesnes, Dai, and Jin 2017), and to investigate the effect of a soda tax on firms’ and consumers’ behaviors in Berkeley, California (Bollinger and Sexton 2018; Rojas and Wang 2017).…”

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confidence: 99%

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Bayesian Synthetic Control Methods

Kim¹,

Lee²,

Gupta³

2020

Journal of Marketing Research

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The authors propose a new Bayesian synthetic control framework to overcome limitations of extant synthetic control methods (SCMs). The proposed Bayesian synthetic control methods (BSCMs) do not impose any restrictive constraints on the parameter space a priori. Moreover, they provide statistical inference in a straightforward manner as well as a natural mechanism to deal with the “large p, small n” and sparsity problems through Markov chain Monte Carlo procedures. Using simulations, the authors find that for a variety of data-generating processes, the proposed BSCMs almost always provide better predictive accuracy and parameter precision than extant SCMs. They demonstrate an application of the proposed BSCMs to a real-world context of a tax imposed on soda sales in Washington state in 2010. As in the simulations, the proposed models outperform extant models, as measured by predictive accuracy in the posttreatment periods. The authors find that the tax led to an increase of 5.7% in retail price and a decrease of 5.5%∼5.8% in sales. They also find that retailers in Washington overshifted the tax to consumers, leading to a pass-through rate of approximately 121%.

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Section: Empirical Applicationmentioning

confidence: 52%

Section: Empirical Applicationmentioning

confidence: 99%

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confidence: 99%

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Bayesian Synthetic Control Methods

Kim¹,

Lee²,

Gupta³

2020

Journal of Marketing Research

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“…Although we find evidence that social norms play a role in the adoption of solar panels and battery storage, norms are clearly not the only relevant factor, and the effect of norms may be moderated by other influences (e.g., Wolske, Gillingham, and Schultz 2020). For example, the effect of norms may be weaker when the behavior is difficult or costly or when people have strong preexisting opinions (Wolske, Gillingham, and Schultz 2020; see also Bollinger et al 2021). In the case of solar panels and battery storage, in particular, research suggests that costs are an important consideration (e.g., Agnew and Dargusch 2017; Balcombe, Rigby, and Azapagic 2014).…”

Section: Discussionmentioning

confidence: 99%

Norms, Norm Sets, and Reference Groups: Implications for Household Interest in Energy Technologies

Horne

Familia

2021

Socius: Sociological Research for a Dynamic World

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Household adoption of rooftop solar panels and battery storage can potentially reduce the negative environmental impacts of the electric grid. We argue that the number of socially close (friends and family) and geographically close (neighbors) others who have rooftop solar panels will affect norm expectations regarding how much others approve of solar panels and battery storage and expectations about how personally beneficial those technologies are likely to be. We test our hypotheses using survey data collected from California homeowners and find partial support. We find evidence for the significance of socially close others (rather than neighbors), highlighting the importance of identifying the appropriate reference groups when studying norms. Our results also provide insight into how one category of behavior (adoption of solar panels) can influence norm expectations about another less visible behavior (battery storage), suggesting a mechanism that may contribute to norms regulating private behaviors.

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“…Although these models do not achieve perfect recognition accuracy (nor do humans, usually, e.g., [7], [15]), they are often sufficiently accurate to support downstream applications, including energy applications. Some examples include estimating the electricity consumption of buildings, [16], estimating the electricity generation capacity of residential solar arrays [17], and studying the peer effects of residential solar array ownership [18].…”

Section: A Mapping the Grid Using Overhead Imagerymentioning

confidence: 99%

GridTracer: Automatic Mapping of Power Grids Using Deep Learning and Overhead Imagery

Huang

Yang

Streltsov

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Energy system information for electricity access planning such as the locations and connectivity of electricity transmission and distribution towers-termed the power grid-is often incomplete, outdated, or altogether unavailable. Furthermore, conventional means for collecting this information is costly and limited. We propose to automatically map the grid in overhead remotely sensed imagery using an deep learning approach. Toward this goal, we develop and publicly release a large dataset (263 km 2 ) of overhead imagery with ground-truth for the power grid-to our knowledge, this is the first dataset of its kind in the public domain. Additionally, we propose scoring metrics and baseline algorithms for two grid-mapping tasks: 1) tower recognition and 2) power line interconnection (i.e., estimating a graph representation of the grid). We hope the availability of the training data, scoring metrics, and baselines will facilitate rapid progress on this important problem to help decision-makers address the energy needs of societies around the world.

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Visibility and Peer Influence in Durable Good Adoption

Cited by 10 publications

References 29 publications

Bayesian Synthetic Control Methods

Bayesian Synthetic Control Methods

Norms, Norm Sets, and Reference Groups: Implications for Household Interest in Energy Technologies

GridTracer: Automatic Mapping of Power Grids Using Deep Learning and Overhead Imagery

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