fringe

Description

Wooldridge Source: F. Vella (1993), “A Simple Estimator for Simultaneous Models with Censored Endogenous Regressors,” International Economic Review 34, 441-457. Professor Vella kindly provided the data. Data loads lazily.

Usage

data('fringe')

Format

A data.frame with 616 observations on 39 variables:

• annearn: annual earnings, $

• hrearn: hourly earnings, $

• exper: years work experience

• age: age in years

• depends: number of dependents

• married: =1 if married

• tenure: years with current employer

• educ: years schooling

• nrtheast: =1 if live in northeast

• nrthcen: =1 if live in north central

• south: =1 if live in south

• male: =1 if male

• white: =1 if white

• union: =1 if union member

• office:

• annhrs: annual hours worked

• ind1: industry dummy

• ind2:

• ind3:

• ind4:

• ind5:

• ind6:

• ind7:

• ind8:

• ind9:

• vacdays: $ value of vac. days

• sicklve: $ value of sick leave

• insur: $ value of employee insur

• pension: $ value of employee pension

• annbens: vacdays+sicklve+insur+pension

• hrbens: hourly benefits, $

• annhrssq: annhrs^2

• beratio: annbens/annearn

• lannhrs: log(annhrs)

• tenuresq: tenure^2

• expersq: exper^2

• lannearn: log(annearn)

• peratio: pension/annearn

• vserat: (vacdays+sicklve)/annearn

Notes

Currently, this data set is used in only one Computer Exercise – to illustrate the Tobit model. It can be used much earlier. First, one could just ignore the pileup at zero and use a linear model where any of the hourly benefit measures is the dependent variable. Another possibility is to use this data set for a problem set in Chapter 4, after students have read Example 4.10. That example, which uses teacher salary/benefit data at the school level, finds the expected tradeoff, although it appears to less than one-to-one. By contrast, if you do a similar analysis with FRINGE.RAW, you will not find a tradeoff. A positive coefficient on the benefit/salary ratio is not too surprising because we probably cannot control for enough factors, especially when looking across different occupations. The Michigan school-level data is more aggregated than one would like, but it does restrict attention to a more homogeneous group: high school teachers in Michigan.

Used in Text: page 624-625

Source

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

Examples

 str(fringe)