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

中文翻译：

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天气如何影响美国农业中全要素生产率的分解

这项研究说明并量化了忽略天气冲击的影响如何影响农业全要素生产率（TFP）变化的度量和分解。基础技术由灵活的输入距离函数表示，该函数具有通过贝叶斯方法估算的准固定输入。利用美国太平洋地区，中部地区和南部平原的16个州的农业生产和天气数据，我们估算了全要素生产率变化是多个因素的直接总和，其中包括净天气效应。为了评估天气的作用，我们基于两组不同的输入和输出变量进行比较分析。一组传统的变量会忽略天气变化，而一组新的“经过天气过滤”的变量则代表了在平均天气条件下可能会选择的输入和输出水平。从这一比较分析中，我们从天气冲击的遗漏中得出了TFP增长分解中的偏差。我们发现，天气冲击加速了16个州中的12个州的生产率增长，相当于其团体平均TFP增长的11.4％，但使生产率降低了相当于团体平均TFP的6.5％。其他四个州（位于该国最北部）的经济增长。当从模型中排除天气影响时，我们还发现在技术变化，规模效应，技术效率变化和产出分配效应对TFP增长的估计贡献（各地区的大小和方向均发生变化）方面存在重大偏差。这是基于反事实分析提出这些偏见估计的第一项研究。我们的研究的一个主要含义是，美国农业部官方对全要素生产率的衡量指标似乎高估了由于技术变革，市场力量，农业政策和其他非天气因素导致的美国农业生产率的增长速度。