Description

Wooldridge Source: Thada Chaisawangwong, a former graduate student at MSU, obtained these data for a term project in applied econometrics. They come from the Material Requirement Planning Survey carried out in Thailand during 1998. Data loads lazily.

Usage

Format

A data.frame with 403 observations on 17 variables:

• male: =1 if male

• educ: highest grade completed

• wage: monthly salary, Thai baht

• swage: starting wage

• exper: years on current job

• pexper: previous experience

• lwage: log(wage)

• expersq: exper^2

• highgrad: =1 if high school graduate

• college: =1 if college graduate

• grad: =1 if some graduate school

• polytech: =1 if a polytech

• highdrop: =1 if no high school degree

• lswage: log(swage)

• pexpersq: pexper^2

• mleeduc: male*educ

• mleeduc0: male*(educ - 14)

Notes

This is a nice change of pace from wage data sets for the United States. These data are for engineers in Thailand, and represents a more homogeneous group than data sets that consist of people across a variety of occupations. Plus, the starting salary is also provided in the data set, so factors affecting wage growth – and not just wage levels at a given point in time – can be studied. This is a good data set for a common term project that tests basic understanding of multiple regression and the interpretation of models with a logarithm for a dependent variable.

Used in Text: not used

Source

https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781111531041

Examples