*! version 17Mar2017

help for prody

Calculation of factor intensity and sophistication indicators such as the PRODY index by Hausmann et al. (2007).

prody
[if exp] [in range] using filename ,
trade(varname) gdp(varname) id(varname)
product(varname) [time(varname)
version(version_options) balance(balance_options)
sample(sample_file) replace]

Related

expy -- Calculation of the EXPY index as proposed by Hausmann et al. (2007)

Description

prody allows to calculate variations of factor intensity and sophistication indicators such as the PRODY index, which was proposed by Hausmann et al. (2007).
The procedure is based on unilateral and disaggregated trade data and a country-specific indicator, like the GDP per capita. Data can be either a cross-section or a panel.
The result is stored in an external file which is specified by using filename. For a more detailed description I refer to Huber (2016).

Options

trade(varname) specifies the variable containing unilateral disaggregated trade flows.

gdp(varname) specifies any type of country-specific indicator, for example the GDP per capita.

id(varname) specifies the variable identifying countries.

product(varname) specifies the variable classifying trade.

time(varname) specifies the time variable. This must be specified in case of a panel dataset.

version(version_options) specifies the version(s) that should be calculated, where version_options is one or more of the following:

timevarying: takes the time-varying trade and the time-varying gdp information for calculation.

mean1: takes the average of gdp and trade over time.

mean2: takes the average of the time-varying PRODY over time. This version was used by Hausmann et al.(2007) in their cross-section.

meangdp: takes the average of gdp over time and the time-varying trade information for calculation.

meantrade: takes the time-varying gdp and the average of trade over time for calculation.

lall: takes the time-varying gdp and trade information for calculation, whereby countries are grouped into ten income groups as proposed by Lall et al.(2006).

mic1: takes the time-varying gdp and trade information for calculation of the Michaely (1984) index with simple trade share as weights.

mic2: takes the average of gdp and trade over time for calculation of the alternative Michaely (1984) index, which takes the estimated coefficient of a simple linear regression of the country's trade share on their gdp.

If version() is not specified, all variations are calculated. Due to the fact of the versions time-varying, mean1, mean2, meangdp, and meantrade being identical in a cross-section, I only report the mean1 indicator.

balance(balance_options) specifies how your data should be balanced, where balance_options is one of the following:

none should be specified, if you wish to use the unbalanced full sample.

weak drops all observations of those countries which exhibit no entries for trade in one period of time.

strong drops those product observations for each country which are not reported over all periods of time.

If your data are not balanced, this option is required.

sample(sample_file) saves the sample used in the calculation. The identifying variables of the sample_file are id, product, and time (in the case of panel data).

Examples

use http://www.uni-regensburg.de/wirtschaftswissenschaften/vwl-moeller/medien/prody/data_prody.dta, clear
*Please note: this are no real data
prody using output_prody, trade(value) gdp(gdppc) time(year) id(country_desc) prod(indicator_desc) bal(none)
use output_prody, clear

References

Stephan Huber (2016): "Factor Intensities and Product Sophistication: Measurement Matters". Downloadable under:
http://dx.doi.org/10.2139/ssrn.2843713

Ricardo Hausmann, Jason Hwang and Rodrik Dani (2007). "What You Export Matters". Journal of Economic Growth 12(1), 1-25

Author

Stephan Huber
Email: stephan.huber@wiwi.uni-regensburg.de