Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/294998 
Year of Publication: 
2022
Citation: 
[Journal:] Junior Management Science (JUMS) [ISSN:] 2942-1861 [Volume:] 7 [Issue:] 3 [Year:] 2022 [Pages:] 643-667
Publisher: 
Junior Management Science e. V., Planegg
Abstract: 
Worldwide the demand for solar photovoltaics (PV) has increased significantly over the past decades. This was driven by a price reduction for solar PV systems. A two-stage least squares linear regression yields insights into the price sensitivity for residential customers in the U.S., and California in particular. The specification includes instrumental variables as well as fixed effects to account for the common issues of endogeneity and data heterogeneity in demand estimation problems, respectively. The variation in the sales tax rate on solar PV and the movements of polysilicon spot prices are used to instrumentalise PV price changes. The regression results imply an inelastic demand with a long-term price elasticity of -0.443, accounting for differences over state and time. Investigating price elasticities for various income groups shows that lower-income customers react more strongly to price changes compared to those with relatively high income (-0.521 vs. -0.195). Likewise, regions with lower population density are more sensitive to price changes (-0.473 vs. -0.338). Besides price, installation costs and technological efficiency majorly impact the system size installed. Results of this study can provide data-driven guidance to efficient policy design and pricing strategies.
Subjects: 
Price elasticity
solar photovoltaic
instrumental variables
demand estimation
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
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