fringe: fringe

Description Usage Format Notes Source Examples

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

1
data('fringe')

Format

A data.frame with 616 observations on 39 variables:

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

1

Example output

'data.frame':	616 obs. of  39 variables:
 $ annearn : num  15000 6500 6909 5512 7800 ...
 $ hrearn  : num  7.81 5 2.35 4.5 3.75 ...
 $ exper   : int  14 7 22 2 19 24 42 21 22 40 ...
 $ age     : int  36 23 38 18 35 40 58 37 37 59 ...
 $ depends : int  2 0 3 0 0 3 0 1 5 0 ...
 $ married : int  1 0 1 0 0 1 1 1 0 1 ...
 $ tenure  : num  15 8 0.5 0.5 2 8 8 0.5 15 8 ...
 $ educ    : int  18 10 6 12 12 13 12 13 10 6 ...
 $ nrtheast: int  1 0 0 0 0 1 1 0 1 0 ...
 $ nrthcen : int  0 0 0 0 0 0 0 1 0 0 ...
 $ south   : int  0 1 1 1 1 0 0 0 0 1 ...
 $ male    : int  1 1 1 1 1 1 1 1 1 1 ...
 $ white   : int  1 1 1 1 0 1 1 1 1 1 ...
 $ union   : int  0 0 0 0 0 0 1 0 1 1 ...
 $ office  : int  1 0 0 0 0 0 0 0 0 0 ...
 $ annhrs  : num  1920 1300 2940 1225 2080 ...
 $ ind1    : int  1 0 0 0 0 0 0 0 0 0 ...
 $ ind2    : int  0 1 1 1 1 1 1 1 1 1 ...
 $ ind3    : int  0 0 0 0 0 0 0 0 0 0 ...
 $ ind4    : int  0 0 0 0 0 0 0 0 0 0 ...
 $ ind5    : int  0 0 0 0 0 0 0 0 0 0 ...
 $ ind6    : int  0 0 0 0 0 0 0 0 0 0 ...
 $ ind7    : int  0 0 0 0 0 0 0 0 0 0 ...
 $ ind8    : int  0 0 0 0 0 0 0 0 0 0 ...
 $ ind9    : int  0 0 0 0 0 0 0 0 0 0 ...
 $ vacdays : num  975 0 0 0 0 ...
 $ sicklve : num  421 0 0 0 0 ...
 $ insur   : num  618 0 0 0 0 ...
 $ pension : num  1368 0 0 0 0 ...
 $ annbens : num  3381 0 0 0 0 ...
 $ hrbens  : num  1.76 0 0 0 0 ...
 $ annhrssq: num  3686400 1690000 8643600 1500625 4326400 ...
 $ beratio : num  0.225 0 0 0 0 ...
 $ lannhrs : num  7.56 7.17 7.99 7.11 7.64 ...
 $ tenuresq: num  225 64 0.25 0.25 4 64 64 0.25 225 64 ...
 $ expersq : int  196 49 484 4 361 576 1764 441 484 1600 ...
 $ lannearn: num  9.62 8.78 8.84 8.61 8.96 ...
 $ peratio : num  0.0912 0 0 0 0 ...
 $ vserat  : num  0.0931 0 0 0 0 ...
 - attr(*, "datalabel")= chr ""
 - attr(*, "time.stamp")= chr "25 Jun 2011 23:03"
 - attr(*, "formats")= chr  "%9.0g" "%9.0g" "%9.0g" "%9.0g" ...
 - attr(*, "types")= int  254 254 251 251 251 251 254 251 251 251 ...
 - attr(*, "val.labels")= chr  "" "" "" "" ...
 - attr(*, "var.labels")= chr  "annual earnings, $" "hourly earnings, $" "years work experience" "age in years" ...
 - attr(*, "version")= int 10

wooldridge documentation built on June 24, 2021, 9:07 a.m.