This repository refers to the codes, data and procedures for constructing the dataset on the Skills in Literacy Adjusted Mean Years of Schooling for 185 countries in the period 1970-2015.
More details on the methodology can be found at:
Reiter, C., Özdemir, C., Yildiz, D., Goujon, A., Guimaraes, R., & Lutz, W. (2020). The Demography of Skills-Adjusted Human Capital [Working paper]. International Institute for Applied System Analysis. http://pure.iiasa.ac.at/id/eprint/16477/
Wolfgang Lutz
Anne Goujon
Claudia Reiter
Caner Özdemir
Dilek Yildiz
Raquel Guimaraes
This repository contains two folders:
- SLAMYS_empirical
- adj_fact
Estimation of the complete dataset starts with the SLAMYS_empirical, and then follows to the calculation of the adjustment factor.
The user can replicate estimation of empirical SLAMYS using "SLAMYS_empirical.R" in "SLAMYS_empirical" folder. Estimation includes four steps:
- Estimation of standard of comparison
- Calculation of base year SLAMYS
- Estimation of standard ageing pattern
- Reconstruction of SLAMYS 1970-2015
Original data can be found in Input folder. Large files are stored with Git LFS.
The output file is "slamys_1970-2015.csv" providing SLAMYS data in quinquennial time intervals for all 44 countries for the population aged 20-64.
Attention: Data preparation files are renamed with the initial word "prep_"
The user can replicate SLAMYS calculations with the codes listed in "Estimation" section We also provide additional code used for data preparation and visualisation
The R Code for SLAMYS estimation can be found on the script "./code/slamys_estimation_1970_2015.R"
The data required are available "./data" folder, and regional averages are calculated in the R-script "./code/regional_averages_1970_2015.R"
We provide also additional files, as follows
- R Code to compute the QEI regression model: "./code/qei_estimation.R"
- Literacy is estimated in six steps starting with "prep_literacy_1_merge_unesco.R"
- Literacy data preperation also includes a Stata code at step 5
- Stata code for teacher-pupil ratio: "prep_tp_ratio.do"
- Stata code for education expenditure: "prep_edu_exp.do"
Folder description
./code: code for data preparation and for regression models ./data: raw and manipulated data ./figures ./results