Most of the code is generic and thus it can be easily adapted to other programs that can take advantage of parallelism, for example Monte Carlo simulations. The procedure uses a Python script to run the ’FIT’ procedure, either sequentially or in parallel. It is run for each of the countries/regions in the build, but there is no interaction across countries/regions and thus can be run in parallel. The input to ’FIT’ is a balanced input-output table (IOT), which is adjusted to a number of exogenous elements including aggregate domestic absorption and import and export vectors.

It describes running one of the processes in the GTAP build procedure called ’FIT’. This short note describes one way of taking advantage of the multiple cores on most desktop computers. Using Python for Parallelization by van der Mensbrugghe, Dominique Cameroon, Cote d'Ivoire, the Democratic Republic of Congo, Ethiopia, Ghana, Kenya and Sudan are the new I-O tables that have been contributed by African economists who were awarded scholarships to contribute this data and pursue research on African economic issues. The GTAP Africa 1 Data Base includes data for 39 regions (30 African regions and 9 other aggregated regions) and the 57 sectors of the GTAP 6 Data Base. More info GTAP Africa 1 Data Base for free The GTAP Africa 2 Data Base is packaged with the GTAPAgg aggregation software which is freely available for download after placing an order on the GTAP website. New and updated I-O tables have been contributed with the help of African economists who were awarded scholarships to contribute this data and pursue research on African economic issues.

The GTAP Africa 2 Data Base includes data for 42 regions (32 African regions plus 10 other aggregated regions) and the 57 sectors of the GTAP 8.1 Data Base. GTAP Data Base GTAP Africa 2 Data Base for free

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