Wooldridge Source: Statistical Abstract of the United States, 1990 and 1994. (For example, the infant mortality rates come from Table 113 in 1990 and Table 123 in 1994.) Data loads lazily.
data('infmrt')A data.frame with 102 observations on 12 variables:
year: 1987 or 1990
infmort: deaths per 1,000 live births
afdcprt: afdc partic., 1000s
popul: population, 1000s
pcinc: per capita income
physic: drs. per 100,000 civilian pop.
afdcper: percent on AFDC
d90: =1 if year == 1990
lpcinc: log(pcinc)
lphysic: log(physic)
DC: =1 for Washington DC
lpopul: log(popul)
https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781111531041
An interesting exercise is to add the percentage of the population on AFDC (afdcper) to the infant mortality equation. Pooled OLS and first differencing can give very different estimates. Adding the years 1998 and 2002 and applying fixed effects seems natural. Intervening years can be added, too, although variation in the key variables from year to year might be minimal.
Used in Text: pages 330-331, 339
str(infmrt)
#> 'data.frame': 102 obs. of 12 variables:
#> $ year : int 1987 1990 1987 1990 1987 1990 1987 1990 1987 1990 ...
#> $ infmort: num 8.3 6.2 7.8 7.1 8.5 ...
#> $ afdcprt: int 52 62 11 21 20 25 234 282 42 52 ...
#> $ popul : int 1186 1228 1056 1109 547 563 5856 6016 986 1003 ...
#> $ pcinc : int 13996 17125 18083 21051 14267 17630 19131 22558 15683 18771 ...
#> $ physic : int 173 178 186 200 244 253 322 337 244 254 ...
#> $ afdcper: num 4.38 5.05 1.04 1.89 3.66 ...
#> $ d90 : int 0 1 0 1 0 1 0 1 0 1 ...
#> $ lpcinc : num 9.55 9.75 9.8 9.95 9.57 ...
#> $ lphysic: num 5.15 5.18 5.23 5.3 5.5 ...
#> $ DC : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ lpopul : num 7.08 7.11 6.96 7.01 6.3 ...
#> - attr(*, "time.stamp")= chr "25 Jun 2011 23:03"