Input data for the Resources for the Future Socioeconomic Projections (RFF-SPs)

This repository contains large input data files for generating the RFF-SPs, and small accompanying files with data necessary for interpreting the larger files.

--------------------------------------------------------------------------------
/MSW_GDP/
--------------------------------------------------------------------------------

-MSW.mat is a MATLAB data file based on Müller, Stock, and Watson (forthcoming) containing two series:
    1) Year-by-sample draws "F-paths" representing frontier values of ln(GDP/capita) for the OECD, and 
    2) Year-by-country-by-sample deviations from those F-paths, denoted as the U-path.

A country's ln(GDP/capita) is equal to the sum of the sample's F-path and the country-sample's U-path. There are 418 years representing 1900-2317, 113 countries with names in country_names.txt, and 2000 sample trajectories. GDP/capita is measured in 2011$.

-country_names.txt contain iso codes for the 113 countries in the order in which they appear in MSW.mat's U-path object. OECD countries are indicated with an "x" for reference.

This information was transmitted via email from Ulrich Mueller (Princeton) to Kevin Rennert (RFF) on January 28, 2020, with a link to the file on Mueller's website at http://www.princeton.edu/~umueller/matlabJan2020.mat.

--------------------------------------------------------------------------------
/Raftery population/
--------------------------------------------------------------------------------

-pop_trajectories_probmig.csv contains country-by-year-by-sample (here, a sample is labelled a "Trajectory") population estimates based on Raftery and Ševčíková (2021). Population is in units of thousands of people, with 1000 samples. This data was transmitted via email by Hana Sevcikova (University of Washington) to Kevin Rennert (RFF) on August 11, 2021.

-death_rates.csv contains country-by-year-by-sample death rates in units of annual deaths per one thousand people, with 1000 samples corresponding to the sample order of samples in pop_trajectories_probmig.csv. This data was transmitted via email by Hana Sevcikova (University of Washington) to Kevin Rennert (RFF) on October 7, 2021.

-iso_key.csv contains a key mapping ISO alpha-3 country codes ("ISO3") to ISO numeric-3 country codes (denoted "NumericCode" in this file and "LocID" in pop_trajectories_probmig.csv). This data was acquired from a search on https://www.iso.org/obp/ui#search in 2021. The "country codes" box was checked and no keywords were used. The list was trimmed to the 184 countries included in the current version of the GIVE model.

--------------------------------------------------------------------------------
Information
--------------------------------------------------------------------------------

The probabilistic economic and emissions projections are from Müller, Stock, and Watson (forthcoming). 

The probabilistic population projections were produced by Adrian E. Raftery and Hana Ševčíková (University of Washington), using the methods described by Raftery and Ševčíková (2021). Please cite this reference in any publications using these projections. Their research was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) under NIH grant number R01 HD-070936.  

--------------------------------------------------------------------------------
References  
--------------------------------------------------------------------------------

Müller, U.K, Stock, J.H., and Watson, M.W. (forthcoming). An Econometric Model of International Growth Dynamics for Long-Horizon Forecasting. The Review of Economics and Statistics, available online 30 October 2020. URL: https://direct.mit.edu/rest/article-abstract/doi/10.1162/rest_a_00997/97738/An-Econometric-Model-of-International-Growth 

Raftery, A.E. and Ševčíková, H. (2021). Probabilistic population forecasting: Short to very long-term. International Journal of Forecasting, available online 7 October 2021. URL: https://www.sciencedirect.com/science/article/pii/S0169207021001394