A novel modeling framework for quantifying spatial spillovers on total factor productivity growth and its components
提出一个单阶段随机参数前沿模型，同时量化企业全要素生产率增长及其组成部分的空间溢出效应，并应用于荷兰奶牛场数据，发现邻近集约化农场带来正向技术溢出。
Abstract This article presents a novel modeling framework that quantifies spatial spillovers on firm total factor productivity (TFP) growth and its components in a single‐stage setting. A random parameters frontier model is specified to measure firm efficiency and calculate TFP growth and its components while allowing for the random parameters and the inefficiency term to be functions of individuals' and neighbors' characteristics. In this manner, the dependence of TFP growth and its components on these characteristics is built into the model, and the corresponding marginal effects are calculated. The empirical application concerns specialized Dutch dairy farms observed over the 2009–2016 period. Apart from the conventional input–output quantities, information on farms' latitudes and longitudes is available, thus allowing the identification of neighboring producers and testing for the existence of spatial spillovers. The empirical findings suggest that farms surrounded by more intensive neighbors experience faster technical progress and TFP growth, which highlights the existence of positive spatial spillovers in Dutch dairy farming.