| dc.contributor | 社會系 | |
| dc.creator (作者) | 林季平 | |
| dc.creator (作者) | Lin, Ji-Ping;Liaw, Kao-Lee | |
| dc.date (日期) | 2014-08 | |
| dc.date.accessioned | 24-May-2024 14:24:26 (UTC+8) | - |
| dc.date.available | 24-May-2024 14:24:26 (UTC+8) | - |
| dc.date.issued (上傳時間) | 24-May-2024 14:24:26 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/151361 | - |
| dc.description.abstract (摘要) | In explaining wage or income by personal attributes (e.g. educational attainment, age, and ethnicity) in a regression model, many researchers choose to use the log of wage or income as the dependent variable and then to estimate the unknown coefficients by some version of the least-squares method. We call this approach the conventional approach. Using the micro data of the 2005-2007 American Community Survey and Taiwan's 2001-2010 Manpower Utilization Survey, we show that the conventional approach has the serious shortcoming of under-predicting the observed wage structure in the space spanned by the values of the explanatory variables. In addition to revealing the reason for the under-prediction problem and linking the severity of this problem to wage variability, we present a nonlinear approach that does not have this shortcoming. We also offer a SAS module for carrying out the estimation task in the nonlinear approach. | |
| dc.format.extent | 107 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | 中研院 | |
| dc.subject (關鍵詞) | uneven distortions; log-transformation of dependent variable; regression model; wage structure; income; nonlinear estimation | |
| dc.title (題名) | A shortcoming of the conventional approach to statistical explanation for wage or income variation: offering a better alternative | |
| dc.type (資料類型) | report | |
