Economics at your fingertips  

Estimating the degree of firms’ input market power via data envelopment analysis: Evidence from the global biotechnology and pharmaceutical industry

Hirofumi Fukuyama, Roman Matousek and Nickolaos G. Tzeremes

European Journal of Operational Research, 2023, vol. 305, issue 2, 946-960

Abstract: This study estimates input market power by introducing an input-market-based Lerner index based on data envelopment analysis (DEA). This index is an extension to the DEA-based Lerner index. In doing so, we develop a revenue function, which is based on minimum distance framework. DEA based-Lerner indexes are commonly based on efficiency measures aiming at finding the “farthest” strongly efficient point. However, if firms operate in non-optimal scales, then such a methodological treatment can be restrictive. In our case, we adopt the minimum distance. We utilize a strongly revenue-efficient frontier and model a DEA based Lerner index which is less restrictive. We apply our index to a sample of global biotechnology and pharmaceutical (B&P) firms over the period 2015–2019. We further investigate the effect of firms’ research and development (R&D) on firms’ market power estimated by the input-market DEA-based Lerner indexes. Our findings suggest that the investment on R&D has a nonlinear effect on firms’ market power. Finally, our finding suggest that the positive effect of R&D is more pronounced on the estimated firms’ employees-based market power compared to the asset-based market power index.

Keywords: Data envelopment analysis; Minimum distance models; Lerner index; Biotechnology and pharmaceutical companies; Input Market power (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

DOI: 10.1016/j.ejor.2022.06.023

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

Page updated 2023-02-18
Handle: RePEc:eee:ejores:v:305:y:2023:i:2:p:946-960