How weather affects the decomposition of total factor productivity in U.S. agriculture

This study illustrates and quantifies how overlooking the impact of weather shocks can affect measurement decomposition agricultural total factor productivity (TFP) change. The underlying technology is represented by a flexible input distance function with quasi-fixed inputs estimated Bayesian methods. Using production data for 16 states in Pacific Region, Central Southern Plains United States, we estimate TFP change as direct sum multiple components, including net effect. To assess role weather, conduct comparative analysis based on two distinct sets output variables. A traditional set variables that ignore variations, new “weather-filtered” represent levels would have been chosen under average conditions. From this analysis, derive biases growth from omission shocks. We find accelerated 12 out equivalent 11.4% their group-average growth, but slowed down 6.5% other four (located Northern-most part country). also substantial contribution technical change, scale effects, efficiency allocation effects to (varying magnitude direction across regions) when are excluded model. first present estimates those counterfactual analysis. One major implication our official USDA's measures appear overestimate rate U.S. agriculture stemming market forces, policies, nonweather drivers.