Productivity Measurement in a Service Industry: Plant-Level Evidence from Gambling Establishments in the United Kingdom
David Paton (),
Donald Siegel () and
Leighton Vaughan Williams ()
Rensselaer Working Papers in Economics from Rensselaer Polytechnic Institute, Department of Economics
Gambling is one of the fastest growing service industries. Unfortunately, there have been no studies of total factor productivity (TFP) in this sector. The purpose of this paper is to fill this gap, based on an analysis of U.K. establishment-level data. These data are derived from the Annual Respondents Database (ARD) file, constructed by the U.K. Office for National Statistics, consisting of individual establishment records from the Annual Census of Production. The ARD file contains detailed data on output, materials, energy, employment, and numerous plant and firm characteristics and is quite similar to the U.S.-based Longitudinal Research Database (LRD). This information can be used to construct measures of TFP. We also construct estimates of labour productivity, since TFP is may be measured with error. We use these data to estimate labour and total factor productivity equations based on a stochastic frontier production function framework. The latter approach enables us to assess whether investment in information technology enhances relative productivity. Our preliminary results suggest that the production function models fit well, generating plausible elasticity estimates and indicating constant returns to scale. While investment in computers per se does not appear to have a productivity enhancing effect, gambling establishments that use Internet-based technology appear to be closer to the frontier.
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (2) Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:rpi:rpiwpe:0413
Access Statistics for this paper
More papers in Rensselaer Working Papers in Economics from Rensselaer Polytechnic Institute, Department of Economics Contact information at EDIRC.
Series data maintained by Shawn Kantor (). This e-mail address is bad, please contact .