标题： 关于调查工资报告偏倚的范围、相关因素和后果 显示英文标题

标题： On the Extent, Correlates, and Consequences of Reporting Bias in Survey Wages

摘要： 调查是应用经济学研究中不可或缺的数据来源；然而，它们依赖于自我报告的信息可能会引入偏差，尤其是在核心变量如个人收入被错误报告的情况下。 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面板（SOEP）中自我报告的工资与同一人群的社会保障记录（IEB）中的行政工资进行了比较。 使用一种新颖且独特的数据链接（SOEP-ADIAB），我们发现了一个适度但具有经济意义的报告偏差，SOEP受访者报告的行政工资大约低了7.3%。 这种错误报告在个体、家庭以及特别是工作和公司特征方面系统性地变化。 在复制常见的实证分析中，工资作为因变量或自变量时，我们发现错误报告对某些但不是所有估计的关系是有影响的。 结果表明，对于研究教育回报来说，这种错误报告并不重要，但对于分析性别工资差距则相关。 此外，我们发现当工资作为自变量使用时，错误报告偏差可能会显著影响结果。 具体而言，基于调查数据估计的工资与满意度之间的关系会被大大高估，尽管当关注人际间的变动时，这种偏差会有所缓解。 我们的研究结果强调了基于调查的个人工资测量可能显著偏误常用的实证关系。 它们还展示了链接的行政-调查数据的巨大研究潜力。 显示英文摘要

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.