Estimating Case‐based Individual and Social Learning in Corporate Tax Avoidance
构建基于案例决策理论的计量学习模型，分析中国制造业企业如何通过自身和邻居经验进行避税学习，发现邻居经验权重约为自身经验的65%，且提高审计率或罚款能显著抑制避税。
Abstract We build an econometric learning model based on case‐based decision theory to analyse tax avoidance by Chinese manufacturing firms. In our model, firms forecast the consequences of tax avoidance by judging the similarity between present and remembered circumstances. We allow firms to consider not only their own experiences but also those of neighbouring firms. This is the first empirically fitted model of case‐based individual and social learning. Our measure of tax avoidance is based on underreported profits, which we measure using difference‐in‐difference (DID) and propensity score matching (PSM) with a case‐based similarity function between non‐state and state firms. We find that firms learn from their past and neighbours' experiences weighted as about 65% as important as their own. We also find that the average tax audit rate for non‐state firms is less than 2% and that more than half of non‐state firms practice tax avoidance. Our government policy simulations suggest that increasing the tax audit rate or fine would significantly deter tax avoidance.