ABSTRACT

This paper explores the impact of COVID-19 on shopping behavior in two neighboring developing economies: Bangladesh and India. While the previous studies investigating the impact of COVID-19 on shopping behavior have relied on Revealed Preference (RP) data, this paper combines RP and Stated Preference (SP) data to develop joint RP-SP discrete choice models. This makes it possible to quantify the relative impact of the situational contexts on the choice of shopping modes of households and to capture the associated heterogeneity arising from the characteristics of the households. Further, comparison of the data and the estimated model parameters of the two countries with substantial socio-cultural similarities provide insights about how differences in the state of e-commerce can lead to different levels of inertia in continuing the pre-COVID behavior. The results will be useful to planners and policymakers for predicting the shopping modes in different future scenarios and formulating effective restriction measures.

摘要

本文探讨了 COVID-19 对两个邻近发展中经济体购物行为的影响：孟加拉国和印度。虽然之前调查 COVID-19 对购物行为影响的研究依赖于显示偏好 (RP) 数据，但本文结合 RP 和陈述偏好 (SP) 数据来开发联合 RP-SP 离散选择模型。这使得量化情境背景对家庭购物模式选择的相对影响和捕捉由家庭特征引起的相关异质性成为可能。更多，对具有重大社会文化相似性的两国的数据和估计模型参数进行比较，可以深入了解电子商务状态的差异如何导致在继续 COVID 之前的行为时产生不同程度的惯性。研究结果将有助于规划者和决策者预测未来不同情景下的购物模式并制定有效的限制措施。