Wooldridge Source: Economic Report of the President, 1989, Table B-47. The data are for the non-farm business sector. Data loads lazily.
data('earns')
A data.frame with 41 observations on 14 variables:
year: 1947 to 1987
wkearns: avg. real weekly earnings
wkhours: avg. weekly hours
outphr: output per labor hour
hrwage: wkearns/wkhours
lhrwage: log(hrwage)
loutphr: log(outphr)
t: time trend: t=1 to 47
ghrwage: lhrwage - lhrwage[_n-1]
goutphr: loutphr - loutphr[_n-1]
ghrwge_1: ghrwage[_n-1]
goutph_1: goutphr[_n-1]
goutph_2: goutphr[_n-2]
lwkhours: log(wkhours)
https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781111531041
These data could be usefully updated, but changes in reporting conventions in more recent ERPs may make that difficult.
Used in Text: pages 363-364, 398, 407
#> 'data.frame': 41 obs. of 14 variables: #> $ year : int 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 ... #> $ wkearns : num 124 123 128 134 135 ... #> $ wkhours : num 40.3 40 39.4 39.8 39.9 ... #> $ outphr : num 51.4 53.3 54.2 57.7 59.4 ... #> $ hrwage : num 3.07 3.09 3.24 3.36 3.38 ... #> $ lhrwage : num 1.12 1.13 1.18 1.21 1.22 ... #> $ loutphr : num 3.94 3.98 3.99 4.06 4.08 ... #> $ t : int 1 2 3 4 5 6 7 8 9 10 ... #> $ ghrwage : num NA 0.00674 0.05022 0.03569 0.00523 ... #> $ goutphr : num NA 0.0363 0.0167 0.0626 0.029 ... #> $ ghrwge_1: num NA NA 0.00674 0.05022 0.03569 ... #> $ goutph_1: num NA NA 0.0363 0.0167 0.0626 ... #> $ goutph_2: num NA NA NA 0.0363 0.0167 ... #> $ lwkhours: num 3.7 3.69 3.67 3.68 3.69 ... #> - attr(*, "time.stamp")= chr "25 Jun 2011 23:03"