GrowthMP {WALS}

Determinants of Economic Growth

Description

Growth regression data used in Masanjala and Papageorgiou (2008).

Usage

GrowthMP

Format

A data frame with 37 observations on 25 variables:

gdpgrowth

Average growth rate of GDP per capita from 1960 - 1992 at purchasing power parity.

lgdp60

Logarithm of GDP per capita in 1960.

yrsopen

Fraction of years economy open from 1960 - 1990.

mining

Fraction of GDP in mining.

primexp70

Share of exports of primary products in GDP in 1970.

invest

Ratio of real domestic investment (public and private) to real GDP.

rerd

Real exchange rate distortion.

school60

Average years of primary schooling for population over 25 years of age in 1960.

life60

Life expectancy at age 0 in 1960.

popgrowth

Average growth rate of population from 1960 - 1990.

war

factor. "yes" if country participates in at least one external war from 1960 to 1985. "no" else.

revcoup

Average number of revolutions and coups per year from 1960 - 1990.

rights

Index of political rights ranging from 1 (most restrictive) to 7 (most freedom)

civil

Index of civil liberties ranging from 1 (most restrictive) to 7 (most freedom)

out

Index of outward orientation.

capitalism

Degree of capitalism.

colony

factor. Shows if the country used to be "british" or "french" colony. If neither of them applies, then "none".

english

Fraction of English speakers.

foreign

Fraction speaking foreign language.

frac

Probability that two random people are from different ethnolinguistic groups.

protestant

Fraction of population Protestant.

catholic

Fraction of population Catholic.

muslim

Fraction of population Muslim.

area

Size of country in millions of square kilometers.

abslat

Distance from the equator.

Details

The dataset of Masanjala and Papageorgiou (2008) is a subset of sub-Sahara African countries from the data used in Sala-I-Martin (1997). See Table A2. in Masanjala and Papageorgiou (2008) for the original sources of the variables. This dataset is also used for replication purposes in Amini and Parmeter (2012).

To replicate the WALS estimates in Amini and Parmeter (2012), use all variables except for a constant as auxiliary regressors and divide all regressors by their in-sample maximum before running wals(..., prescale = FALSE) (NOTE: It is not recommended to use prescale = FALSE as this runs an old version of the WALS estimator, prescale = FALSE should only be used for replication purposes). The resulting coefficients and standard errors have to be divided by the maximum of the regressors again to get the values presented in Table I of the paper.

Source

Journal of Applied Econometrics Data Archive. The data was taken from the archive entry of Amini and Parmeter (2012) for replication purposes but they can also be found in the archive entry of Masanjala and Papageorgiou (2008).

https://journaldata.zbw.eu/dataset/comparison-of-model-averaging-techniques-assessing-growth-determinants

References

Amini SM, Parmeter CF (2012). “Comparison of model averaging techniques: assessing growth determinants.” Journal of Applied Econometrics, 27(5), 870-876. doi:10.1002/jae.2288.

Masanjala WH, Papageorgiou C (2008). “Rough and lonely road to prosperity: a reexamination of the sources of growth in Africa using Bayesian model averaging.” Journal of Applied Econometrics, 23(5), 671-682. doi:10.1002/jae.1020.

Sala-I-Martin X (1997). “I Just Ran Two Million Regressions.” The American Economic Review, 87(2), 178–183.

Examples

## Replicate second panel of Table I in Amini & Parmeter (2012)
## NOTE: Authors manually scale data, then rescale the resulting coefs and se.
X <- model.matrix(gdpgrowth ~ ., data = GrowthMP)
scaleVector <- apply(X, MARGIN = 2, max)
Xscaled <- apply(X, MARGIN = 2, function(x) x/max(x))
Xscaled <- Xscaled[,-1]
datscaled <- as.data.frame(cbind(gdpgrowth = GrowthMP$gdpgrowth, Xscaled))

fitMP <- wals(gdpgrowth ~ 1 | ., data = datscaled, prescale = FALSE,
              prior = laplace(), eigenSVD = FALSE)
tableMP <- cbind("coef" = coef(fitMP)/scaleVector,
                 "se" = sqrt(diag(vcov(fitMP)))/scaleVector)
printVars <- c("(Intercept)", "lgdp60", "yrsopen", "mining", "primexp70",
               "invest")
print(round(tableMP[printVars,], 4))