Latent vs. Observed Variables: Analysis of Irrigation Water Efficiency Using SEM and SUR
比较了将灌溉用水效率视为潜变量或观测变量的两种方法，发现结构方程模型优于似不相关回归，并识别出水资源稀缺感知是效率的最重要正向决定因素。
Abstract In this paper we compare conceptualising single factor technical and allocative efficiency as indicators of a single latent variable, or as separate observed variables. In the former case, the impacts on both efficiency types are analysed by means of structural equation modeling ( SEM ), in the latter by seemingly unrelated regression ( SUR ). We compare estimation results of the two approaches based on a dataset on single factor irrigation water use efficiency obtained from a survey of 360 farmers in the Guanzhong Plain, China. The main methodological findings are that SEM allows identification of the most important dimension of irrigation water efficiency (technical efficiency) via comparison of their factor scores and reliability. Moreover, it reduces multicollinearity and attenuation bias. It thus is preferable to SUR . The SEM estimates show that perception of water scarcity is the most important positive determinant of both types of efficiency, followed by irrigation infrastructure, income and water price. Furthermore, there is a strong negative reverse effect from efficiency on perception.