Estimating the productivity of US agriculture: The Fisher total factor productivity index for time series data with unknown prices
Loading...
Date
2024-05-10
Open Access Location
Journal Title
Journal ISSN
Volume Title
Publisher
John Wiley and Sons Australia, Ltd on behalf of Australasian Agricultural and Resource Economics Society Inc.
Rights
(c) The author/s
CC BY-NC-ND
CC BY-NC-ND
Abstract
In this paper, we propose a straightforward way to estimate the Fisher ideal total factor productivity (TFP) index (FI) in cases where price information is unavailable, using ‘shadow prices’ derived from data envelopment analysis (DEA). A Monte Carlo experiment shows that the shadow price Fisher ideal TFP index (SPFI) can effectively estimate the ‘true’ FI with relatively small (and stable) errors. The empirical application to the US agriculture sector (1948–2017) further suggests that the SPFI is a (superior) alternative to the traditional Malmquist DEA, especially in dealing with unbalanced panel or time series data when price data are unknown.
Description
Keywords
data envelopment analysis, Fisher index, Monte Carlo simulation, total factor productivity, US agriculture
Citation
Ngo T, Tripe D, Nguyen DK. (2024). Estimating the productivity of US agriculture: The Fisher total factor productivity index for time series data with unknown prices. Australian Journal of Agricultural and Resource Economics. Early View. (pp. 1-12).