Nonparametric Estimates Of The Components Of Productivity And Profitability Change In U.S. Agriculture
Profitability change can be decomposed into the product of a total factor productivity (TFP) index and an index of relative price change. Oâ€™Donnell (2008) shows that the TFP index can be further decomposed into an index of technical change and various indexes of efficiency change â€“ these indexes measure changes in productivity resulting from movements in the production frontier, movements by firms towards the frontier, and movements by firms around the frontier to capture economies of scale and scope. The Oâ€™Donnell decomposition methodology can be applied in any multiple-input multiple-output setting, it makes no assumptions concerning the optimising behaviour of firms or the degree of competition in product markets, and it only involves components that can be unambiguously interpreted as measures of either technical change or efficiency change. This paper uses the methodology to decompose spatially - and temporally-transitive Lowe indexes of TFP change in U.S. agriculture for the period 1960-2004. To implement the methodology, data envelopment analysis (DEA) is used to estimate separate production frontiers for each of the ten farm production regions identified by the USDA Economic Research Service (ERS). California and Florida are found to be the most profitable and productive states. In most states, the main drivers of TFP change over the 45-year study period appear to have been technical change and scale and mix efficiency change. For example, Texas is found to have experienced a 40% increase in productivity due to technical change and a 32% increase in productivity due to economies of scale and scope, resulting in an overall productivity increase of 1.40 ! 1.32 â€“ 1 = 85%; in Tennessee, the combined effects of technical progress (122%), technical efficiency improvement (1%) and diseconomies of scale and scope (-24%) resulted in an net productivity increase of 2.22 ! 1.01 ! 0.76 â€“ 1 = 70%.
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