Identifying the Presence and Cause of Fashion Cycles in Data
Foster School of Business, University of Washington
January 21, 2016
Fashions and conspicuous consumption play an important role in marketing. In this paper, we present a three-pronged framework to analyze fashion cycles in data -- a) algorithmic methods for identifying cycles, b) statistical framework for identifying cycles, and c) methods for examining the drivers of such cycles. In the first module, we identify cycles based on pattern-matching the amplitude and length of cycles observed to a user-specified definition. In the second module, we define the Conditional Monotonicity Property, derive conditions under which a data generating process satisfies it, and demonstrate its role in generating cycles. A key challenge that we face in estimating this model is the presence of endogenous lagged dependent variables, which we address using system GMM estimators. Third, we present methods that exploit the longitudinal and geographic variations in agents' economic and cultural capital to examine the different theories of fashion. We apply our framework to data on given names for infants, show the presence of large amplitude cycles both algorithmically and statistically, and confirm that the adoption patterns are consistent with Bourdieu's theory of fashion as a signal of cultural capital.
Number of Pages in PDF File: 56
Keywords: Fashion, Social Influence, Panel Data, Marketing, GMM Estimators
JEL Classification: C33, C52, C32, M31, D71
Date posted: November 20, 2012 ; Last revised: January 22, 2016