调查工资中报告偏差的程度、相关因素与后果
On the Extent, Correlates, and Consequences of Reporting Bias in Survey Wages
AI总结：
本研究利用SOEP-ADIAB数据链接比较自我报告与行政工资，发现约7.3%的低报偏差，且该偏差显著影响性别工资差距和工资-满意度关系等实证结果。
摘要：
调查是应用经济研究不可或缺的数据来源；然而，其对自我报告信息的依赖可能引入偏差，尤其是当个人收入等核心变量被错误报告时。为了评估这种错误报告偏差的程度和影响，我们将德国社会经济小组（SOEP）中自我报告的工资与同一批个体的社会保障记录（IEB）中的行政工资进行了比较。利用一项新颖且独特的数据链接（SOEP-ADIAB），我们识别出一个适度但具有经济意义的报告偏差，SOEP受访者将其行政工资低报了约7.3%。这种错误报告随个人、家庭，尤其是工作和企业特征而系统性变化。在重复工资作为因变量或自变量的常见实证分析中，我们发现错误报告对某些估计关系有影响，但对其他关系则无影响。事实证明，错误报告对教育回报的考察没有影响，但对性别工资差距的分析却很重要。此外，我们发现当工资作为自变量时，错误报告偏差可能显著影响结果。具体而言，基于调查数据估计的工资-满意度关系被大幅高估，尽管当关注人际变化时，这种偏差会有所缓解。我们的研究结果强调，基于调查的个人工资测量可能显著偏差常见估计的实证关系。它们也展示了链接行政-调查数据的巨大研究潜力。
英文摘要：
Surveys are an indispensable source of data for applied economic research; however, their reliance on self-reported information can introduce bias, especially if core variables such as personal income are misreported. To assess the extent and impact of this misreporting bias, we compare self-reported wages from the German Socio-Economic Panel (SOEP) with administrative wages from social security records (IEB) for the same individuals. Using a novel and unique data linkage (SOEP-ADIAB), we identify a modest but economically significant reporting bias, with SOEP respondents underreporting their administrative wages by about 7.3%. This misreporting varies systematically with individual, household, and especially job and firm characteristics. In replicating common empirical analyses in which wages serve as either dependent or independent variables, we find that misreporting is consequential for some, but not all estimated relationships. It turns out to be inconsequential for examining the returns to education, but relevant for analyzing the gender wage gap. In addition we find that misreporting bias can significantly affect the results when wage is used as the independent variable. Specifically, estimates of the wage-satisfaction relationship are substantially overestimated when based on survey data, although this bias is mitigated when focusing on interpersonal changes. Our findings underscore that survey-based measures of individual wages can significantly bias commonly estimated empirical relationships. They also demonstrate the enormous research potential of linked administrative-survey data.
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