# fringe: fringe In wooldridge: 115 Data Sets from "Introductory Econometrics: A Modern Approach, 7e" by Jeffrey M. Wooldridge

 fringe R Documentation

## 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, \$

• 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

### Examples

`````` str(fringe)
``````

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