Abstract
Search and switching costs are two market frictions that are well known in the literature for preventing people from switching to a new and cheaper provider. Previous experimental literature has studied these two frictions in isolation. However, field evidence shows that these two frictions frequently occur together. Recently, a theoretical framework has been developed (Wilson in Eur Econ Rev 56(6):1070–1086) which studies the interplay between these two costs. We report on an experiment testing this theory to see if individual behaviour with search and switching costs is in line with the theoretical predictions derived from the optimal choice rule of Wilson. The results show the crucial role of the search strategy: not only, according to Wilson model, the search cost has a greater deterrent impact on search than the switching costs, but also the sub-optimality of the search strategy is the major source of sub-optimality in the switching behaviour.
Highlights
As an illustration of the kind of problem, we investigate in this paper, consider a householder who uses gas for heating and cooking
We report on an experiment testing this theory to see if individual behaviour with search and switching costs is in line with the theoretical predictions derived from the optimal choice rule of Wilson
The results show the crucial role of the search strategy:, according to Wilson model, the search cost has a greater deterrent impact on search than the switching costs, and the sub-optimality of the search strategy is the major source of sub-optimality in the switching behaviour
Summary
As an illustration of the kind of problem, we investigate in this paper, consider a householder who uses gas for heating and cooking. We experimentally investigate the role of search and switching costs as determinants for non-switching and sub-optimal switching: one intention is to determine the relative importance of each cost in preventing switching in general; for this purpose, we test the comparative static predictions of a model of search and switch (Wilson 2012). Rather than field data, we can manipulate the costs of searching and switching, and measure the direction and strength of their effect on decision-making; the experimental setting allows us to isolate search and switching costs from other sources of non-switching that we can find in the field, and to compute the theoretical optimal choice to check if non-switching occurs optimally or not. To the best of our knowledge, none of the previous experimental studies analysed search and switching costs together, these costs usually co-occur in the field, allowing a comparison with theoretical predictions.
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