IMF World Economic Outlook (WEO) database. The IMF World Economic Outlook is a twice-yearly survey by IMF staff that presents IMF staff economists' analyses of global economic developments during th...

Imports of low carbon technology products comprise all low carbon technology products entering the national territory. A relatively high share of low carbon technology products imports indicates that an economy purchases a significant share of low carbon technology products from other economies. Exports of low carbon technology products comprise all low carbon technology products leaving the national territory. A relatively high share of low carbon technology products exports indicates that an economy produces and sells a significant share of low carbon technology products to other economies. An economy’s trade balance in low carbon technology products is the difference between its exports and imports of low carbon technology products.Comparative advantage is a measure of the relative advantage or disadvantage a particular economy has in a certain class of goods (in this case, low carbon technology products), and can be used to evaluate export potential in that class of goods. A value greater than one indicates a relative advantage in low carbon technology products, while a value of less than one indicates a relative disadvantage.Sources: Department of Economic and Social Affairs/United Nations. 2022. United Nations Comtrade database. https://comtrade.un.org. International Monetary Fund (IMF) Direction of Trade Statistics (DOTS). https://data.imf.org/dot. World Economic Outlook (WEO) Database. https://www.imf.org/en/Publications/WEO/weo-database/2022/April. IMF staff calculations.Category: Mitigation,Transition to a Low-Carbon EconomyData series: Comparative advantage in low carbon technology productsExports of low carbon technology productsExports of low carbon technology products as percent of GDPExports of low carbon technology products as share of total exportsImports of low carbon technology productsImports of low carbon technology products as percent of GDPImports of low carbon technology products as share of total importsTotal trade in low carbon technology productsTotal trade in low carbon technology products as percent of GDPTrade balance in low carbon technology productsTrade balance in low carbon technology products as percent of GDPMetadata:Sources: Trade data from UN Comtrade Database (https://comtrade.un.org/). Harmonized Commodity Description and Coding System (HS) 2017. Trade aggregates from IMF Direction of Trade Statistics (DOTS) (data.imf.org/dot). GDP data from World Economic Outlook.Methodology:Low carbon technology products are estimated by aggregating HS 6-digit commodities identified as low carbon technology products based on Pigato, Miria A., Simon J. Black, Damien Dussaux, Zhimin Mao, Miles McKenna, Ryan Rafaty, and Simon Touboul. 2020. Technology Transfer and Innovation for Low-Carbon Development. International Development in Focus. Washington, DC: World Bank, and IMF research. Trade balance in low carbon technology products is calculated as low carbon technology products exports less low carbon technology products imports. A positive trade balance means an economy has a surplus in low carbon technology products, while a negative trade balance means an economy has a deficit in low carbon technology products.Total goods are estimated by aggregating all commodities. Comparative advantage is calculated as the proportion of an economy’s exports that are low carbon technology products to the proportion of global exports that are low carbon technology products. Total trade in low carbon technology products is calculated as the sum of low carbon technology products exports and low carbon technology products imports. National-accounts basis GDP at current prices from the World Economic Outlook is used to calculate the percent of GDP. This measure provides an indication of an economy’s involvement (openness) to trade in low carbon technology products, which is important for understanding how these technologies can be transferred between economies.Methodology Attachment Low Carbon Technology Harmonized System Codes

Description: Raw text database of roughly 23,000 documents - country reports and program related - covering the whole IMF membership throughout the period 1950-2019. Extraction performed using Google Cloud Vision (See paper for more info). Structure: Each .RDS file contains a named list with the individual documents for the country (ISO3 Code) as character string. String numbering corresponds to page numbering. We also provide an S3 method (print.corpusTM) to not overcrowd the console: run the script before loading the RDS files. Instead of the whole country corpus, it will display the number of docs in the corpus and the name of the first and last document.

These codes use the original sources of data to add in replicating the JBR article. It uses data from Chinn and Ito 2006, Fernandez et al. 2016, Alam et al 2019, Laeven and Valencia 2020, to document financial crises and macroprudential policies in the Middle East and North Africa (MENA) relative to other countries.

References Alam, Z., A. Alter, J. Eiseman, G. Gelos, H. Kang, M. Narita, E. Nier and N. Wang (2019), "Digging Deeper-Evidence on the Effects of Macroprudential Policies from a New Database," IMF WP/19/66. Chinn, M. and H. Ito (2006), "What matters for financial development? Capital controls, institutions, and interactions," Journal of Development Economics, 81(1), 163-192. Fernandez, A., M. Klein, A. Rebucci, M. Schindler and M. Uribe (2016), "Capital Control Measures: A New Dataset," IMF Economic Review, 64(3), 548-574. Laeven, L. and F. Valencia (2020), "Systemic Banking Crises Database II," IMF Economic Review, 68(2), 307-361.

The SPIN covid19 RMRIO dataset is a time series of MRIO tables covering years from 2016-2026 on a yearly basis. The dataset covers 163 sectors in 155 countries.

This repository includes data for years from 2016 to 2019 (hist scenario) and the corresponding labels.
Data for years 2020 to 2026 are stored in the corresponding repositories:

• covid: 10.5281/zenodo.5713825
• counterfactual: 10.5281/zenodo.5713839

Tables are generated using the SPIN method, based on the RMRIO tables for the year 2015, GDP, imports and exports data from the International Financial Statistics (IFS) and the World Economic Outlooks (WEO) of October 2019 and April 2021.

From 2020 to 2026, the dataset includes two diverging scenarios. The covid scenario is in line with April 2021 WEO's data and includes the macroeconomic effects of Covid 19. The counterfactual scenario is in line with October 2019 WEO's data and simulates the global economy without Covid 19. Tables from 2016 to 2019 are labelled as hist.

The Projections folder includes the generated tables for years from 2016 to 2019 (hist scenario) and the corresponding labels.
The Sources folder contains the data records from the IFS and WEO databases. The Method data contains the data files used to generate the tables with the SPIN method and the following Python scripts:

• SPIN_covid19_MRIO_files_preparation.py generates the data files from the source data.
• SPIN_covid19_RMRIO runs.py is the command to run the SPIN method and generate the dataset.
• figures.py is a script to produce figures reflecting the consistency of the projected tables and the evolution of macroeconomic figures in the 2016-2026 period for a selection of countries.

All tables are labelled in 2015 US$ and valued in basic prices.