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Deep habits in consumption: a spatial panel analysis using scanner data

Benjamin Verhelst (UGent) and Dirk Van den Poel (UGent)
(2014) EMPIRICAL ECONOMICS. 47(3). p.959-976
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Abstract
Using scanner data from a large European retailer, this paper empirically assesses deep habit formation in consumption. Deep habit formation constitutes a possible source of price stickiness and helps to mimic procyclical labour and real wage dynamics that are present in macrodata. To gauge the existence and the extent of deep habits in consumption, we estimate a dynamic time–space simultaneous model for consumption expenditure at different levels of product aggregation. This spatial panel model enables us to test for both internal and external deep habit formation at the same time. The former captures inertia or persistence in consumption and is included in the empirical specification as a time lag. The latter captures preference interdependence across households and is captured by a spatial lag. Our results show mixed evidence with respect to internal habit formation, whereas the external habit effect is almost always positive and significant.
Keywords
TESTS, PREFERENCES, Deep habits, MODELS, ERROR-COMPONENTS, IDEAL DEMAND SYSTEM, SEEMINGLY UNRELATED REGRESSIONS, Preference interdependence, Spatial panel

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MLA
Verhelst, Benjamin, and Dirk Van den Poel. “Deep Habits in Consumption: A Spatial Panel Analysis Using Scanner Data.” EMPIRICAL ECONOMICS, vol. 47, no. 3, Springer, 2014, pp. 959–76, doi:10.1007/s00181-013-0776-4.
APA
Verhelst, B., & Van den Poel, D. (2014). Deep habits in consumption: a spatial panel analysis using scanner data. EMPIRICAL ECONOMICS, 47(3), 959–976. https://doi.org/10.1007/s00181-013-0776-4
Chicago author-date
Verhelst, Benjamin, and Dirk Van den Poel. 2014. “Deep Habits in Consumption: A Spatial Panel Analysis Using Scanner Data.” EMPIRICAL ECONOMICS 47 (3): 959–76. https://doi.org/10.1007/s00181-013-0776-4.
Chicago author-date (all authors)
Verhelst, Benjamin, and Dirk Van den Poel. 2014. “Deep Habits in Consumption: A Spatial Panel Analysis Using Scanner Data.” EMPIRICAL ECONOMICS 47 (3): 959–976. doi:10.1007/s00181-013-0776-4.
Vancouver
1.
Verhelst B, Van den Poel D. Deep habits in consumption: a spatial panel analysis using scanner data. EMPIRICAL ECONOMICS. 2014;47(3):959–76.
IEEE
[1]
B. Verhelst and D. Van den Poel, “Deep habits in consumption: a spatial panel analysis using scanner data,” EMPIRICAL ECONOMICS, vol. 47, no. 3, pp. 959–976, 2014.
@article{4210640,
  abstract     = {{Using scanner data from a large European retailer, this paper empirically assesses deep habit formation in consumption. Deep habit formation constitutes a possible source of price stickiness and helps to mimic procyclical labour and real wage dynamics that are present in macrodata. To gauge the existence and the extent of deep habits in consumption, we estimate a dynamic time–space simultaneous model for consumption expenditure at different levels of product aggregation. This spatial panel model enables us to test for both internal and external deep habit formation at the same time. The former captures inertia or persistence in consumption and is included in the empirical specification as a time lag. The latter captures preference interdependence across households and is captured by a spatial lag. Our results show mixed evidence with respect to internal habit formation, whereas the external habit effect is almost always positive and significant.}},
  author       = {{Verhelst, Benjamin and Van den Poel, Dirk}},
  issn         = {{0377-7332}},
  journal      = {{EMPIRICAL ECONOMICS}},
  keywords     = {{TESTS,PREFERENCES,Deep habits,MODELS,ERROR-COMPONENTS,IDEAL DEMAND SYSTEM,SEEMINGLY UNRELATED REGRESSIONS,Preference interdependence,Spatial panel}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{959--976}},
  publisher    = {{Springer}},
  title        = {{Deep habits in consumption: a spatial panel analysis using scanner data}},
  url          = {{http://dx.doi.org/10.1007/s00181-013-0776-4}},
  volume       = {{47}},
  year         = {{2014}},
}
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