Template-type: ReDIF-Paper 1.0
Author-Name: Ángel Valarezo
Author-Workplace-Name: Instituto Complutense de Análisis Económico (ICAE), Universidad Complutense de Madrid (UCM).
Author-Name: Rafael López
Author-Workplace-Name: Instituto Complutense de Análisis Económico (ICAE), Universidad Complutense de Madrid (UCM).
Author-Name: Teodosio Pérez-Amaral
Author-Workplace-Name: Instituto Complutense de Análisis Económico (ICAE), Universidad Complutense de Madrid (UCM).
Title: Adoption of e-commerce by individuals and digital divide: Evidence from Spain
Abstract: E-commerce penetration rates are distant among those groups of individuals with the lowest and the highest levels of online shopping adoption. This 
	is an indicator of digital divide, having negative effects in terms of untapped opportunities for people, companies and the whole economy. Key 
	socioeconomic and demographic determinants of adoption of ecommerce are explored, analyzing a dataset of 174,776 observations for the period 2008-2017 
	in Spain. The empirical analysis is based on a standard neoclassical utility maximization framework. Linear probability model, logistic regression, 
	and Heckman’s sample selection correction model have been used. The results suggest that e-commerce adoption is positively related with being male, 
	having higher levels of education, income and digital skills, being Spanish, and being employed; while being female, older and belonging to a household 
	of two or more members have negative effects. An interaction between digital skills and age has been introduced in the model, where high digital skills 
	seem to have a positive influence, partly counteracting the lower odds for some age groups. Policy recommendations related to demand and supply 
	measures are suggested to foster the adoption of e-commerce.
Classification-JEL: C25, D11, O33.
Keywords: E-commerce; Digital divide; Linear probability model; Logistic regression; Heckman’s sample selection correction; Polychoric correlation; Digital 
	skills; Time and regional dummies; Pool data; Utility maximization framework.
Length: 32 pages 
Creation-Date: 2019-03
Number: 2019-19
X-File-Ref: http://america.sim.ucm.es/repec/ucm/ref/doicae1919.txt
File-URL: https://eprints.ucm.es/id/eprint/55421/1/1919.pdf
File-Format: Application/pdf
Handle: RePEc:ucm:doicae:1919