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Published in: Journal of Quantitative Economics 4/2020

30-11-2019 | Original Article

A Comparative Study of Alternative Approaches to Estimate Productivity

Authors: Saleem Shaik, Joseph Atwood

Published in: Journal of Quantitative Economics | Issue 4/2020

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Abstract

Theoretically, for single output-single input, annual productivity are expected to be identical across index, non-parametric programming and parametric statistical approaches. The following models within each approach is considered—index (Tornqvist-Theil and Ideal Fisher), the non-parametric programming (Malmquist input, output and graph; Malmquist total factor productivity) and parametric (Input and Output; total factor productivity) regression. Empirically, for single output-single input, this research show differences in annual productivity and productivity growth rate between and within each of the three approaches using Nebraska agriculture data from 1936 to 2004. The annual productivity growth rate from 1936 to 2004 was identical across non-parametric Malmquist output, input, graph and Malmquist total factor productivity, and parametric Malmquist total factor productivity. However annual productivity estimated by parametric Malmquist total factor productivity is identical to Ideal Fisher productivity.

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Footnotes
1
Due to discrete annual approximations, the index and the programming approaches are traditionally categorized as non-parametric techniques of productivity estimation.
 
2
Recently, there is integration of the distance function with econometric estimation of production, cost, or profit functions. However, this is more reflective of efficiency rather than productivity. So, we did not explore this approach in the productivity framework.
 
3
The index approach uses exogenous output and input market prices as share weights to construct aggregate index. These shares used as weights are the partial derivatives and equivalent to one under constant returns to scale technology. The shares used are always greater than zero but less than or equal to one. In contrast, the non-parametric programming approach use shadow prices implicit in the linear programming constraints. These shadow prices are recovered from the dual values of the constraint row in the programming problem, since the gradient of the technology frontier is the marginal product and equal to price under perfect competition. The shares in the programming approach range between zero and one, both included. The rate of change in the variables under consideration influences the weights as defined by the implicit shadow prices due to its piece wise linear approximation of the productivity measures.
 
4
For a single economic unit like firm, farm, state or country over time, the technology T represents all the years, i.e., \(t=1,...,T\)
 
5
Rate of growth can be calculated either as rate of change concept or chained logarithmic changes. The logarithmic change is considered as a close discrete approximation to rate of change concept for a continuous change. The difference between the two is quite remarkable if the changes are large enough. Conceptually productivity measurement is built on the rate of change concept, but for discrete yearly data, logarithmic change is used in the computation of output and input indexes.
 
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Metadata
Title
A Comparative Study of Alternative Approaches to Estimate Productivity
Authors
Saleem Shaik
Joseph Atwood
Publication date
30-11-2019
Publisher
Springer India
Published in
Journal of Quantitative Economics / Issue 4/2020
Print ISSN: 0971-1554
Electronic ISSN: 2364-1045
DOI
https://doi.org/10.1007/s40953-019-00191-x

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