| lowbrth | R Documentation |
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
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
https://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781111531041
str(lowbrth)