Wooldridge Source: Source: Statistical Abstract of the United States, 1990, 1993, and 1994. Data loads lazily.
data('lowbrth')
A data.frame with 100 observations on 36 variables:
year: 1987 or 1990
lowbrth: perc births low weight
infmort: infant mortality rate
afdcprt: # participants in AFDC, 1000s
popul: population, 1000s
pcinc: per capita income
physic: # physicians, 1000s
afdcprc: percent of pop in AFDC
d90: =1 if year == 1990
lpcinc: log of pcinc
cafdcprc: change in afdcprc
clpcinc: change in lpcinc
lphysic: log of physic
clphysic: change in lphysic
clowbrth: change in lowbrth
cinfmort: change in infmort
afdcpay: avg monthly AFDC payment
afdcinc: afdcpay as percent pcinc
lafdcpay: log of afdcpay
clafdcpy: change in lafdcpay
cafdcinc: change in afdcinc
stateabb: state postal code
state: name of state
beds: # hospital beds, 1000s
bedspc: beds per capita
lbedspc: log(bedspc)
clbedspc: change in lbedspc
povrate: percent people below poverty line
cpovrate: change in povrate
afdcpsq: afdcper^2
cafdcpsq: change in afdcpsq
physicpc: physicians per capita
lphypc: log(physicpc)
clphypc: change in lphypc
lpopul: log(popul)
clpopul: change in lpopul
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This data set can be used very much like INFMRT.RAW. It contains two years of state-level panel data. In fact, it is a superset of INFMRT.RAW. The key is that it contains information on low birth weights, as well as infant mortality. It also contains state identifies, so that several years of more recent data could be added for a term project. Putting in the variable afcdprc and its square leads to some interesting findings for pooled OLS and fixed effects (first differencing). After differencing, you can even try using the change in the AFDC payments variable as an instrumental variable for the change in afdcprc.
Used in Text: not used
#> 'data.frame': 100 obs. of 36 variables: #> $ year : int 1987 1990 1987 1990 1987 1990 1987 1990 1987 1990 ... #> $ lowbrth : num 8 8.4 4.8 4.8 6.4 ... #> $ infmort : num 12.2 10.8 10.4 10.5 9.5 ... #> $ afdcprt : int 132 132 19 24 91 144 67 73 1708 2023 ... #> $ popul : int 4084 4041 524 550 3400 3665 2388 2351 27653 29760 ... #> $ pcinc : int 12039 14899 18461 20867 14322 16169 11421 14037 17770 20547 ... #> $ physic : int 151 158 138 146 191 197 144 150 242 244 ... #> $ afdcprc : num 3.23 3.27 3.63 4.36 2.68 ... #> $ d90 : int 0 1 0 1 0 1 0 1 0 1 ... #> $ lpcinc : num 9.4 9.61 9.82 9.95 9.57 ... #> $ cafdcprc: num NA 0.0344 NA 0.7377 NA ... #> $ clpcinc : num NA 0.213 NA 0.123 NA ... #> $ lphysic : num 5.02 5.06 4.93 4.98 5.25 ... #> $ clphysic: num NA 0.0453 NA 0.0564 NA ... #> $ clowbrth: num NA 0.4 NA 0 NA ... #> $ cinfmort: num NA -1.4 NA 0.1 NA ... #> $ afdcpay : int 114 115 567 651 268 268 185 190 557 637 ... #> $ afdcinc : num 0.947 0.772 3.071 3.12 1.871 ... #> $ lafdcpay: num 4.74 4.74 6.34 6.48 5.59 ... #> $ clafdcpy: num NA 0.00873 NA 0.13815 NA ... #> $ cafdcinc: num NA -0.1751 NA 0.0484 NA ... #> $ stateabb: chr "AL" "AL" "AK" "AK" ... #> $ state : chr "alabama" "alabama" "alaska" "alaska" ... #> $ beds : num 24.2 23.5 2 2 13.4 ... #> $ bedspc : num 0.00593 0.00582 0.00382 0.00364 0.00394 ... #> $ lbedspc : num -5.13 -5.15 -5.57 -5.62 -5.54 ... #> $ clbedspc: num NA -0.0188 NA -0.0484 NA ... #> $ povrate : num 21.3 19.2 12 11.4 12.8 ... #> $ cpovrate: num NA -2.1 NA -0.6 NA ... #> $ afdcpsq : num 10.45 10.67 13.15 19.04 7.16 ... #> $ cafdcpsq: num NA 0.224 NA 5.894 NA ... #> $ physicpc: num 0.037 0.0391 0.2634 0.2655 0.0562 ... #> $ lphypc : num -3.3 -3.24 -1.33 -1.33 -2.88 ... #> $ clphypc : num NA 0.0559 NA 0.00793 NA ... #> $ lpopul : num 8.31 8.3 6.26 6.31 8.13 ... #> $ clpopul : num NA -0.0106 NA 0.0484 NA ... #> - attr(*, "time.stamp")= chr "25 Jun 2011 23:03"