Journals {AER}

Economics Journal Subscription Data

Description

Subscriptions to economics journals at US libraries, for the year 2000.

Usage

data("Journals")

Format

A data frame containing 180 observations on 10 variables.

title

Journal title.

publisher

factor with publisher name.

society

factor. Is the journal published by a scholarly society?

price

Library subscription price.

pages

Number of pages.

charpp

Characters per page.

citations

Total number of citations.

foundingyear

Year journal was founded.

subs

Number of library subscriptions.

field

factor with field description.

Details

Data on 180 economic journals, collected in particular for analyzing journal pricing. See also https://econ.ucsb.edu/~tedb/Journals/jpricing.html for general information on this topic as well as a more up-to-date version of the data set. This version is taken from Stock and Watson (2007).

The data as obtained from the online complements for Stock and Watson (2007) contained two journals with title “World Development”. One of these (observation 80) seemed to be an error and was changed to “The World Economy”.

Source

Online complements to Stock and Watson (2007).

References

Bergstrom, T. (2001). Free Labor for Costly Journals? Journal of Economic Perspectives, 15, 183–198.

Stock, J.H. and Watson, M.W. (2007). Introduction to Econometrics, 2nd ed. Boston: Addison Wesley.

See Also

StockWatson2007

Examples

## data and transformed variables
data("Journals")
journals <- Journals[, c("subs", "price")]
journals$citeprice <- Journals$price/Journals$citations
journals$age <- 2000 - Journals$foundingyear
journals$chars <- Journals$charpp*Journals$pages/10^6

## Stock and Watson (2007)
## Figure 8.9 (a) and (b)
plot(subs ~ citeprice, data = journals, pch = 19)
plot(log(subs) ~ log(citeprice), data = journals, pch = 19)
fm1 <- lm(log(subs) ~ log(citeprice), data = journals)
abline(fm1)

## Table 8.2, use HC1 for comparability with Stata 
fm2 <- lm(subs ~ citeprice + age + chars, data = log(journals))
fm3 <- lm(subs ~ citeprice + I(citeprice^2) + I(citeprice^3) +
  age + I(age * citeprice) + chars, data = log(journals))
fm4 <- lm(subs ~ citeprice + age + I(age * citeprice) + chars, data = log(journals))
coeftest(fm1, vcov = vcovHC(fm1, type = "HC1"))
coeftest(fm2, vcov = vcovHC(fm2, type = "HC1"))
coeftest(fm3, vcov = vcovHC(fm3, type = "HC1"))
coeftest(fm4, vcov = vcovHC(fm4, type = "HC1"))
waldtest(fm3, fm4, vcov = vcovHC(fm3, type = "HC1"))

## changes with respect to age
library("strucchange")
## Nyblom-Hansen test
scus <- gefp(subs ~ citeprice, data = log(journals), fit = lm, order.by = ~ age)
plot(scus, functional = meanL2BB)
## estimate breakpoint(s)
journals <- journals[order(journals$age),]
bp <- breakpoints(subs ~ citeprice, data = log(journals), h = 20)
plot(bp)
bp.age <- journals$age[bp$breakpoints]
## visualization
plot(subs ~ citeprice, data = log(journals), pch = 19, col = (age > log(bp.age)) + 1)
abline(coef(bp)[1,], col = 1)
abline(coef(bp)[2,], col = 2)
legend("bottomleft", legend = c("age > 18", "age < 18"), lty = 1, col = 2:1, bty = "n")