Demand Estimation with Social Interactions and the Implications for Targeted Marketing

Hartmann, Wesley R.

doi:10.2139/ssrn.1339763

Cited by 26 publications

(37 citation statements)

References 35 publications

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“…Indeed, we show in the supplementary appendix, via a simple example, that the joint distribution of unobservables cannot be uniquely recovered without the excluded variables, at least in our general formulation of the model. These results clarify the critical, if not surprising, need for choice-specific exclusion restrictions, something that has been exploited in some applications (e.g., Liu, Chintagunta, and Zhu (2010)) but is absent in others (e.g., Kretchsmer, Miravete, and Pernias (2012) and Hartman (2010)).…”

Section: Introductionsupporting

confidence: 57%

A note on identification of discrete choice models for bundles and binary games

Fox

Lazzati

2017

Quantitative Economics

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We study nonparametric identification of single‐agent discrete choice models for bundles (without requiring bundle‐specific prices) and of binary games of complete information. We show that these two models are quite similar from an identification standpoint. Moreover, they are mathematically equivalent when we restrict attention to the class of potential games and impose a specific equilibrium selection mechanism in the data generating process. We provide new identification results for the two related models.

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Section: Introductionsupporting

confidence: 57%

A note on identification of discrete choice models for bundles and binary games

Fox

Lazzati

2017

Quantitative Economics

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“…The extant literature in this area primarily focuses on disentangling the causal social interactions from the confounding effects, such as endogenous group formation, correlated unobservables, and simultaneity problems (Blume and Durlauf 2006;Hartmann 2010;Hartmann et al 2008;Manski 1993;Nair, Manchanda, and Bhatia 2010). The extant literature in this area primarily focuses on disentangling the causal social interactions from the confounding effects, such as endogenous group formation, correlated unobservables, and simultaneity problems (Blume and Durlauf 2006;Hartmann 2010;Hartmann et al 2008;Manski 1993;Nair, Manchanda, and Bhatia 2010).…”

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confidence: 99%

The Impact of Sampling and Network Topology on the Estimation of Social Intercorrelations

Chen

Xiao

2013

Journal of Marketing Research

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With the growing popularity of online social networks, it is becoming more important for marketing researchers to understand and measure social intercorrelations among consumers. The authors show that the estimation of consumers' social intercorrelations can be significantly affected by the sampling method used in the study and the topology of the social network. Through a series of simulation studies using a spatial model, the authors find that the magnitude of social intercorrelations in consumer networks tends to be underestimated if samples of the networks are used (rather than using the entire population of the network). The authors further demonstrate that sampling methods that better preserve the network structure perform best in recovering the social intercorrelations. However, this advantage decreases in networks characterized by the scale-free power-law distribution for the number of connections of each member. The authors discuss the insights they glean from these findings and propose a method to obtain unbiased estimation of the magnitude of social intercorrelations.

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“…Treatments have costs which might need to be weighed and nature of social spillovers means that treating a friend of an individual can be a substitute or a complement for treating that individual directly. Ryan and Tucker (2012) report policy simulations, employing models containing economic structure based on assumptions about individual behavior in the presence of peer effects (Hartmann, 2010). The key idea behind the policy simulation approach is that the experiment is used to estimate parameters of the model and then the model can be used to extrapolate the findings to more complex or interesting policies than those randomly set in the original data set.…”

Section: Policy Simulationsmentioning

confidence: 99%

Randomized Experiments to Detect and Estimate Social Influence in Networks

Taylor

Eckles

2018

Computational Social Sciences

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Estimation of social influence in networks can be substantially biased in observational studies due to homophily and network correlation in exposure to exogenous events. Randomized experiments, in which the researcher intervenes in the social system and uses randomization to determine how to do so, provide a methodology for credibly estimating of causal effects of social behaviors. In addition to addressing questions central to the social sciences, these estimates can form the basis for effective marketing and public policy.In this review, we discuss the design space of experiments to measure social influence through combinations of interventions and randomizations. We define an experiment as combination of (1) a target population of individuals connected by an observed interaction network, (2) a set of treatments whereby the researcher will intervene in the social system, (3) a randomization strategy which maps individuals or edges to treatments, and (4) a measurement of an outcome of interest after treatment has been assigned. We review experiments that demonstrate potential experimental designs and we evaluate their advantages and tradeoffs for answering different types of causal questions about social influence. We show how randomization also provides a basis for statistical inference when analyzing these experiments.

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Demand Estimation with Social Interactions and the Implications for Targeted Marketing

Cited by 26 publications

References 35 publications

A note on identification of discrete choice models for bundles and binary games

A note on identification of discrete choice models for bundles and binary games

The Impact of Sampling and Network Topology on the Estimation of Social Intercorrelations

Randomized Experiments to Detect and Estimate Social Influence in Networks

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