Abstract and Keywords
The focus of this chapter is to move the measurement of efficiency and productivity from a static to a dynamic approach using distance functions. Since distance functions represent technology, the authors first specify that technology in a dynamic framework is amenable to data envelopment analysis (DEA)–type estimation, explicitly allowing current (or past) decisions to affect future production possibilities. This includes notions of intermediate products, investment, time substitution, supply chain, networks and possible reallocations across time. The chapter shows how to estimate dynamic distance functions and specify a multi-period dynamic model in the spirit of Ramsey (1928), as well as an adjacent-period model familiar from the Malmquist productivity literature, providing an empirical illustration of the former. Extensions of these dynamic models is relatively straightforward for other distance function–based productivity indices, both parametric and nonparametric, as well as for production in the presence of good and bad outputs.
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