pder (version 1.0-1)

GiantsShoulders: Impact of Institutions on Cumulative Research

Description

yearly observations of 216 articles from 1970 to 2001

number of observations : 4880

number of time-series : 32

country : United States

JEL codes: D02, D83, I23, O30

Chapter : 08

Usage

data(GiantsShoulders)

Format

A dataframe containing:

pair

the pair article index

article

the article index

brc

material of the article is deposit on a Biological Ressource Center

pubyear

publication year of the article

brcyear

year of the deposit in brc of the material related to the article

year

the year index

citations

the number of citations

References

Furman, Jeffrey L. and Scott Stern (2011) “Climbing Atop the Shoulders of Giants: the Impact of Institutions on Cumulative Research”, American Economic Review, 101(5), 1933-1963, 10.1257/aer.101.5.1933 .

Examples

# NOT RUN {
#### Example 8-6

## ------------------------------------------------------------------------
data("GiantsShoulders", package = "pder")
head(GiantsShoulders)

## ------------------------------------------------------------------------
library("dplyr")
GiantsShoulders <- mutate(GiantsShoulders, age = year - pubyear)
cityear <- summarise(group_by(GiantsShoulders, brc, age), 
                     cit = mean(citations, na.rm = TRUE))

## ------------------------------------------------------------------------
GiantsShoulders <- mutate(GiantsShoulders,
                          window = as.numeric( (brc == "yes") & 
                                               abs(brcyear - year) <= 1),
                          post_brc = as.numeric( (brc == "yes") & 
                                                 year - brcyear > 1),
                          age = year - pubyear)
GiantsShoulders$age[GiantsShoulders$age == 31] <- 0
#GiantsShoulders$year[GiantsShoulders$year %in% 1970:1974] <- 1970
#GiantsShoulders$year[GiantsShoulders$year %in% 1975:1979] <- 1975
GiantsShoulders$year[GiantsShoulders$year < 1975] <- 1970
GiantsShoulders$year[GiantsShoulders$year >= 1975 & GiantsShoulders$year < 1980] <- 1975



## ------------------------------------------------------------------------
library("pglm")
t3c1 <- lm(log(1 + citations) ~ brc + window + post_brc + factor(age), 
           data = GiantsShoulders)
t3c2 <- update(t3c1, . ~ .+  factor(pair) + factor(year))
t3c3 <- pglm(citations ~ brc + window + post_brc + factor(age) + factor(year),
           data = GiantsShoulders, index = "pair", 
           effect = "individual", model = "within", family = negbin)
t3c4 <- pglm(citations ~ window + post_brc + factor(age) + factor(year),
             data = GiantsShoulders, index = "article", 
             effect = "individual", model = "within", family = negbin)
## screenreg(list(t3c2, t3c3, t3c4),
##           custom.model.names = c("ols: age/year/pair-FE", 
##                                  "NB:age/year/pair-FE", "NB: age/year/article-FE"),
##           omit.coef="(factor)|(Intercept)", digits = 3)
# }

Run the code above in your browser using DataCamp Workspace