GrowthDJ {AER}

Determinants of Economic Growth

Description

Growth regression data as provided by Durlauf & Johnson (1995).

Usage

data("GrowthDJ")

Format

A data frame containing 121 observations on 10 variables.

oil

factor. Is the country an oil-producing country?

inter

factor. Does the country have better quality data?

oecd

factor. Is the country a member of the OECD?

gdp60

Per capita GDP in 1960.

gdp85

Per capita GDP in 1985.

gdpgrowth

Average growth rate of per capita GDP from 1960 to 1985 (in percent).

popgrowth

Average growth rate of working-age population 1960 to 1985 (in percent).

invest

Average ratio of investment (including Government Investment) to GDP from 1960 to 1985 (in percent).

school

Average fraction of working-age population enrolled in secondary school from 1960 to 1985 (in percent).

literacy60

Fraction of the population over 15 years old that is able to read and write in 1960 (in percent).

Details

The data are derived from the Penn World Table 4.0 and are given in Mankiw, Romer and Weil (1992), except literacy60 that is from the World Bank's World Development Report.

Source

Journal of Applied Econometrics Data Archive.

http://qed.econ.queensu.ca/jae/1995-v10.4/durlauf-johnson/

References

Durlauf, S.N., and Johnson, P.A. (1995). Multiple Regimes and Cross-Country Growth Behavior. Journal of Applied Econometrics, 10, 365–384.

Koenker, R., and Zeileis, A. (2009). On Reproducible Econometric Research. Journal of Applied Econometrics, 24(5), 833–847.

Mankiw, N.G, Romer, D., and Weil, D.N. (1992). A Contribution to the Empirics of Economic Growth. Quarterly Journal of Economics, 107, 407–437.

Masanjala, W.H., and Papageorgiou, C. (2004). The Solow Model with CES Technology: Nonlinearities and Parameter Heterogeneity. Journal of Applied Econometrics, 19, 171–201.

See Also

OECDGrowth, GrowthSW

Examples

## data for non-oil-producing countries
data("GrowthDJ")
dj <- subset(GrowthDJ, oil == "no")
## Different scalings have been used by different authors,
## different types of standard errors, etc.,
## see Koenker & Zeileis (2009) for an overview

## Durlauf & Johnson (1995), Table II
mrw_model <- I(log(gdp85) - log(gdp60)) ~ log(gdp60) +
  log(invest/100) + log(popgrowth/100 + 0.05) + log(school/100)
dj_mrw <- lm(mrw_model, data = dj)
coeftest(dj_mrw) 

dj_model <- I(log(gdp85) - log(gdp60)) ~ log(gdp60) +
  log(invest) + log(popgrowth/100 + 0.05) + log(school)
dj_sub1 <- lm(dj_model, data = dj, subset = gdp60 < 1800 & literacy60 < 50)
coeftest(dj_sub1, vcov = sandwich)

dj_sub2 <- lm(dj_model, data = dj, subset = gdp60 >= 1800 & literacy60 >= 50)
coeftest(dj_sub2, vcov = sandwich)