fringe | R Documentation |
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.
data('fringe')
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
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
https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781111531041
str(fringe)
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