R: Economics Journal Subscription Data

Journals {AER} | R Documentation

## 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 http://www.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")

[Package AER version 1.2-5 Index]