通过虚拟变量测量结果差异的偏差:注

Q3 Social Sciences

Journal of Economic and Social Measurement Pub Date : 2014-01-01 DOI:10.3233/JEM-140390

Shawn W. Ulrick

{"title":"通过虚拟变量测量结果差异的偏差:注","authors":"Shawn W. Ulrick","doi":"10.3233/JEM-140390","DOIUrl":null,"url":null,"abstract":"Disparity in an outcome between two groups is often measured via the coefficient of a dummy variable in a regression that pools both groups. The dummy is interpreted as the disparity. A casual search of the literature in economics and other social sciences reviews far too many examples of this method to catalog. Unfortunately, if the impact of one (or more) of the control variables differs between the two groups, the measured disparity (i.e., the coefficient on the group dummy) will be biased. We illustrate and derive this bias. Given the bias, we believe that one is better running separate regressions for each group and then implementing decomposition methods or predicting adjusted gaps in outcome (i.e., predicting the but-for world that would exist if the two groups had identical characteristics).","PeriodicalId":53705,"journal":{"name":"Journal of Economic and Social Measurement","volume":"39 1","pages":"153-161"},"PeriodicalIF":0.0000,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JEM-140390","citationCount":"3","resultStr":"{\"title\":\"The bias in measuring disparity in outcomes via a dummy variable: A note\",\"authors\":\"Shawn W. Ulrick\",\"doi\":\"10.3233/JEM-140390\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Disparity in an outcome between two groups is often measured via the coefficient of a dummy variable in a regression that pools both groups. The dummy is interpreted as the disparity. A casual search of the literature in economics and other social sciences reviews far too many examples of this method to catalog. Unfortunately, if the impact of one (or more) of the control variables differs between the two groups, the measured disparity (i.e., the coefficient on the group dummy) will be biased. We illustrate and derive this bias. Given the bias, we believe that one is better running separate regressions for each group and then implementing decomposition methods or predicting adjusted gaps in outcome (i.e., predicting the but-for world that would exist if the two groups had identical characteristics).\",\"PeriodicalId\":53705,\"journal\":{\"name\":\"Journal of Economic and Social Measurement\",\"volume\":\"39 1\",\"pages\":\"153-161\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.3233/JEM-140390\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Economic and Social Measurement\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3233/JEM-140390\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Social Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Economic and Social Measurement","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/JEM-140390","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Social Sciences","Score":null,"Total":0}

引用次数: 3

摘要

两组之间结果的差异通常通过将两组合并的回归中虚拟变量的系数来衡量。假人被解释为差距。随便搜索一下经济学和其他社会科学的文献，就会发现这种方法的例子太多了，难以分类。不幸的是，如果一个(或多个)控制变量的影响在两组之间不同，则测量的差异(即组虚拟的系数)将有偏差。我们举例说明并推导出这种偏见。考虑到偏差，我们认为最好对每组分别进行回归，然后实施分解方法或预测调整后的结果差距(即，如果两组具有相同的特征，则预测不存在的世界)。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

The bias in measuring disparity in outcomes via a dummy variable: A note

Disparity in an outcome between two groups is often measured via the coefficient of a dummy variable in a regression that pools both groups. The dummy is interpreted as the disparity. A casual search of the literature in economics and other social sciences reviews far too many examples of this method to catalog. Unfortunately, if the impact of one (or more) of the control variables differs between the two groups, the measured disparity (i.e., the coefficient on the group dummy) will be biased. We illustrate and derive this bias. Given the bias, we believe that one is better running separate regressions for each group and then implementing decomposition methods or predicting adjusted gaps in outcome (i.e., predicting the but-for world that would exist if the two groups had identical characteristics).

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Journal of Economic and Social Measurement Social Sciences-Social Sciences (all)

CiteScore

1.60

自引率

0.00%

发文量

期刊介绍： The Journal of Economic and Social Measurement (JESM) is a quarterly journal that is concerned with the investigation of all aspects of production, distribution and use of economic and other societal statistical data, and with the use of computers in that context. JESM publishes articles that consider the statistical methodology of economic and social science measurements. It is concerned with the methods and problems of data distribution, including the design and implementation of data base systems and, more generally, computer software and hardware for distributing and accessing statistical data files. Its focus on computer software also includes the valuation of algorithms and their implementation, assessing the degree to which particular algorithms may yield more or less accurate computed results. It addresses the technical and even legal problems of the collection and use of data, legislation and administrative actions affecting government produced or distributed data files, and similar topics. The journal serves as a forum for the exchange of information and views between data producers and users. In addition, it considers the various uses to which statistical data may be put, particularly to the degree that these uses illustrate or affect the properties of the data. The data considered in JESM are usually economic or social, as mentioned, but this is not a requirement; the editorial policies of JESM do not place a priori restrictions upon the data that might be considered within individual articles. Furthermore, there are no limitations concerning the source of the data.