fringe: fringe

fringeR 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.




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




wooldridge documentation built on May 3, 2023, 5:06 p.m.