Measuring the effect of management on production: a two-tier stochastic frontier approach
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Measuring the effect of management on production: a two-tier stochastic frontier approach Alecos Papadopoulos1 Received: 21 March 2020 / Accepted: 17 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract We revisit the production frontier of a firm, and we examine the effects that the firm’s management has on output. In order to estimate these effects using a cross-sectional sample while avoiding the costly requirement of obtaining data on management as a production factor, we develop a two-tier stochastic frontier model where management is treated as a latent variable. The model is consistent with the microeconomic theory of the firm, and it can estimate the effect of management on the output of a firm in monetary terms from different angles, separately from inefficiency. The approach can thus contribute to the cost–benefit analysis related to the management system of a company, and it can facilitate research related to management pay and be used in studies of the determinants of management performance. We also present an empirical application, where we find that the estimates from our latent-variable model align with the results obtained when we use the World Management Survey scores that provide a measure of management. Keywords Management · Two-tier stochastic frontier · Efficiency · Endogeneity · Copula JEL Classification: D24 · C21
1 Introduction It is a widely circulated maxim in the business world that, in order to manage something, you must be able to measure it. It is therefore a bit ironic that management itself has for a long period of time resisted quantification, a situation helped by the fact that
This article is partly based on research included in the Ph. D thesis of the author.
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Alecos Papadopoulos [email protected] Athens University of Economics and Business, Athens, Greece
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A. Papadopoulos
the very definition of management appears to remain unsettled, since it is a multidimensional ensemble of planning designs, processes, monitoring, decision making, hardware, software, and people. In economics, we know how to measure such complex entities—we do it by using their monetary value, with the most prominent example being the capital production factor. So, in principle, we could collect all the costs related to management in a company (like salaries of positions with supervising duties, monitoring software costs, etc.) and use it as a quantitative measure of the management factor. The problem is that such a decomposition of costs is not usually done by many companies, and in any case it is not available to outside researchers. Faced with such hurdles, scholars that were determined to do quantitative research on management have experimented with many different methods, which we can group into three main approaches: one that uses other variables as proxies for management, another that attempts to obtain a measure of management through the quantification of survey responses, and a third that treats management as a latent variable and focuses mainly on measu
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