injury | R Documentation |
Wooldridge Source: B.D. Meyer, W.K. Viscusi, and D.L. Durbin (1995), “Workers’ Compensation and Injury Duration: Evidence from a Natural Experiment,” American Economic Review 85, 322-340. Professor Meyer kindly provided the data. Data loads lazily.
data('injury')
A data.frame with 7150 observations on 30 variables:
durat: duration of benefits
afchnge: =1 if after change in benefits
highearn: =1 if high earner
male: =1 if male
married: =1 if married
hosp: =1 if inj. required hosp. stay
indust: industry
injtype: type of injury
age: age at time of injury
prewage: previous weekly wage, 1982 $
totmed: total med. costs, 1982 $
injdes: 4 digit injury description
benefit: real dollar value of benefit
ky: =1 for kentucky
mi: =1 for michigan
ldurat: log(durat)
afhigh: afchnge*highearn
lprewage: log(wage)
lage: log(age)
ltotmed: log(totmed); = 0 if totmed < 1
head: =1 if head injury
neck: =1 if neck injury
upextr: =1 if upper extremities injury
trunk: =1 if trunk injury
lowback: =1 if lower back injury
lowextr: =1 if lower extremities injury
occdis: =1 if occupational disease
manuf: =1 if manufacturing industry
construc: =1 if construction industry
highlpre: highearn*lprewage
This data set also can be used to illustrate the Chow test in Chapter 7. In particular, students can test whether the regression functions differ between Kentucky and Michigan. Or, allowing for different intercepts for the two states, do the slopes differ? A good lesson from this example is that a small R-squared is compatible with the ability to estimate the effects of a policy. Of course, for the Michigan data, which has a smaller sample size, the estimated effect is much less precise (but of virtually identical magnitude).
Used in Text: pages 458-459, 475-476
https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781111531041
str(injury)
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