Abstract
This study investigates the voluntary allocation of monetary resources to future food consumption by customers of campus cafeterias. A rich dataset allows us to infer social proximity of cafeteria customers and to measure social spillovers in allocation decisions. We show that individuals tend to imitate directly observed behavior and that close social proximity further encourages imitation.
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Notes
On this issue, Brock and Durlauf (2001) show that generalized linear models impose an implicit restriction on the linear dependence of explanatory variables and thus provide some advantages when compared to linear models. Nevertheless, models of this class do not provide a general remedy for complications encountered in the empirical estimation of social effects.
Each student is provided with a personal ID card. The name and the picture of the owner are printed on the card. The card provides the owner with the right to access various facilities at the campus.
In more detail, following the temporal order in the database, each individual was kept as a target, and individuals preceding him at the cash desk by k seconds and individuals following him by k seconds were recorded in an array. The same operation was repeated every time the target ID showed up in the database. Thus, all the meetings of the target ID were recorded in the array. Two maximum intervals between the operations were considered (i.e., k = 60 and k = 120). The regression analysis reported in Section 3.4 considers the Index of Social Connection computed with k = 60.
The average time interval between consecutive choices in the empirical distribution is equal to 76.5 s. This implies that the likelihood of a meeting between two subjects is higher in the simulated data than in the real data. We adopted these specifications to obtain a conservative estimate of mutual acquaintance in the real data.
To improve the presentation, Fig. 1 omits the frequencies of single meetings in the actual dataset. These frequencies amount to 0.7752 for the distribution with k = 60 and 0.7461 for the distribution, with k = 120.
In an exploratory investigation, the quantitative analysis was implemented for several specifications of the time intervals over which the deposit rate was computed. The outcomes of the analysis did not overall differ across alternative specifications of the deposit rate and, thus, are not reported here.
The results do not substantially change when considering the time interval k = 120 and, thus, are not reported here.
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Acknowledgements
I thank Dominique Cappelletti, for her continuous assistance and support. I thank Roberto Pallanch of Opera Universitaria di Trento, for providing the data. I thank Anthony Ziegelmeyer, Birendra Kumar Rai, and Mahalakshmi Natarajan, for useful suggestions. I thank Markus Mobius, for support in the early stage of this work. The paper has benefited from comments by seminar participants and anonymous referees. The usual caveats apply, however.
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Ploner, M. Peer effects at campus cafeterias. J Evol Econ 23, 61–76 (2013). https://doi.org/10.1007/s00191-012-0276-2
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DOI: https://doi.org/10.1007/s00191-012-0276-2