Applying a population flow-based spatial weight matrix in spatial econometric models : conceptual framework and application to COVID-19 transmission analysis

Abstract

This article proposes a novel method for constructing an asymmetric spatial weight matrix and applies it to improve spatial econometric modeling. As opposed to traditional spatial weight matrices that simply consider geographic or economic proximity, the spatial weight matrix proposed in this study is based on large-volume daily population flow data. It can more accurately reflect the socioeconomic interactions between cities over any given period. To empirically test the validity and accuracy of this proposed spatial weight matrix, we apply it to a spatial econometric model that analyzes COVID-19 transmission in Mainland China. Specifically, this matrix is used to address spatial dependence in outcome and explanatory variables and to calculate the direct and indirect effects of all predictors. We also propose a practical framework that combines instrumental variable regressions and a Hausman test to validate the exogeneity of this matrix. The test result confirms its exogeneity; hence, it can produce consistent estimates in our spatial econometric models. Moreover, we find that spatial econometric models using our proposed population flow–based spatial weight matrix significantly outperform those using the traditional inverse distance weight matrix in terms of goodness of fit and model interpretation, thus providing more reliable results. Our methodology not only has implications for national epidemic control and prevention policies but can also be applied to a wide range of research to better address spatial autocorrelation issues. Key Words: COVID-19 transmission, endogeneity, population flow, spatial dependence (autocorrelation), spatial weight matrix. 本文提出了构建非对称空间权重矩阵的新方法，并用于改进空间计量经济建模。不同于传统的仅考虑地理或经济邻近性的空间权重矩阵，本文提出的空间权重矩阵以海量的日人口流量数据为基础，能更准确地反映任何时期城市之间的社会经济互动。为了验证本文的空间权重矩阵的有效性和准确性，我们将其应用于中国大陆COVID-19疾病传播分析的空间计量模型。具体的，该矩阵考虑了结果变量和解释变量的空间相关性，计算了各预测因子的直接和间接影响。我们还提出了一个应用框架，可以结合工具变量回归和Hausman检验，验证该矩阵的外生性。验证结果证实了矩阵的外生性，并能够在空间计量经济模型中提供稳定的估计。此外，我们发现，在拟合优度和模型解释方面，采用基于人口流动的空间权重矩阵的空间计量模型，明显优于基于传统的反距离权重矩阵的空间计量模型，能提供更可靠的结果。我们的方法不仅有益于国家流行病控制和预防政策，也可用于其它研究，能更好地解决空间自相关性的问题。