{"type":"script","doc_desc":{"producers":[{"name":"Reproducibility WBG","abbr":"DIME","affiliation":"World Bank - Development Impact Department","role":"Verification and preparation of metadata"}],"prod_date":"2024-09-03","version":"1"},"project_desc":{"title_statement":{"idno":"FR_WLD_2024_175","title":"Reproducibility package for Climate Risk Scorecard Vision Indicator 2021"},"authoring_entity":[{"name":"Minh Cong Nguyen","email":"mnguyen3@worldbank.org","affiliation":"World Bank"},{"email":"bbrunckhorst@worldbank.org","name":"Ben James Brunckhorst","affiliation":"World Bank"},{"name":"Esther G. Naikal","email":"elee@worldbank.org","affiliation":"World Bank"},{"name":"Nisan Gorgulu","affiliation":"World Bank","email":"ngorgulu@worldbank.org"},{"name":"Ruth Vargas Hill","affiliation":"World Bank","email":"rhill@worldbank.org"},{"name":"Stephane Hallegatte","email":"shallegatte@worldbank.org","affiliation":"World Bank"}],"output":[{"type":"Dataset","title":"Climate Risk Scorecard Vision Indicator 2021 dataset","authors":"Minh Cong Nguyen; Ben James Brunckhorst; Esther G. Naikal; Nisan Gorgulu; Ruth Vargas Hill; Stephane Hallegatte "}],"software":[{"name":"Stata","version":"18 MP"},{"name":"R","version":"4.3.2"}],"scripts":[{"file_name":"FR_WLD_2024_175.zip","zip_package":"FR_WLD_2024_175.zip","title":"Reproducibility package (partial data and code) for Climate Risk Scorecard Vision Indicator 2021","date":"2024-09","dependencies":"All dependencies are stored in the ado folder, except for datalib web, which can only be run from WB computers. ","notes":"Computational reproducibility verified by Development Impact (DIME) Analytics team, World Bank."}],"production_date":"2024-09","geographic_units":[{"name":"World","code":"WLD","type":"Region"}],"language":[{"name":"English","code":"EN"}],"repository_uri":[{"name":"Reproducible Research Repository (World Bank)","uri":"https:\/\/reproducibility.worldbank.org"}],"technology_environment":"Paper exhibits were reproduced on a computer with the following specifications:\n\u2022 OS: Windows 10 Enterprise\n\u2022 Processor: Intel(R) Xeon(R) Gold 6226R CPU @ 2.90GHz, 2900 Mhz, 16 Core(s), 16 Logical Processor(s)\n\u2022 Memory available: 147 GB\n\u2022 Software version: Stata 18 MP, R 4.3.2","technology_requirements":"~48 hours runtime","contacts":[{"affiliation":"World Bank","name":"Minh Cong Nguyen","email":"mnguyen3@worldbank.org"},{"name":"Reproducibility WBG","affiliation":"World Bank","email":"reproducibility@worldbank.org"}],"disclaimer":"The materials in the reproducibility packages are distributed as they were prepared by the staff of the International Bank for Reconstruction and Development\/The World Bank. The findings, interpretations, and conclusions expressed in this event do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank, or the governments they represent. The World Bank does not guarantee the accuracy of the materials included in the reproducibility package.","license":[{"name":"Modified BSD3","uri":"https:\/\/opensource.org\/license\/bsd-3-clause\/"}],"datasets":[{"name":"Fathom Global Flood Hazard","note":"Source: Sampson et al. (2015). Date Accessed: November 2023.\nLocation: 02.input\/flood_fathom\/. \nThis data is provided for World Bank staff only. Per the license terms, the data may not be made publicly available. Third parties should check the FATHOM website (www.fathom.global) or contact info@fathom.global.\n","access_type":"Restricted and not included in the package","uri":"https:\/\/datacatalog.worldbank.org\/int\/search\/dataset\/0021728\/Global-Flood-Hazard--FATHOM-","license":"Official Use Only","license_uri":"https:\/\/datacatalog.worldbank.org\/int\/search\/dataset\/0021728\/global-flood-hazard--fathom-"},{"name":"Deltares Global Flood Map","note":"Source: Deltares (2021).\nDate Accessed: November 2023.\nLocation: 02.input\/flood_deltares\/. The data is publicly available and can be downloaded from the provided links below. For detailed instructions please see the README file. ","license_uri":"https:\/\/cdla.dev\/permissive-1-0\/","access_type":"Public, but due to its large size and processing requirements, the dataset is not included in the replication package, but intermediate data is provided.","license":"Community Data License Agreement - Permissive, Version 1.0","uri":"https:\/\/deltaresfloodssa.blob.core.windows.net\/floods\/v2021.06\/global\/MERITDEM\/90m\/GFM_global_MERITDEM90m_2018slr_rp0005_masked.nc https:\/\/deltaresfloodssa.blob.core.windows.net\/floods\/v2021.06\/global\/MERITDEM\/90m\/GFM_global_MERITDEM90m_2018slr_rp0010_masked.nc https:\/\/deltaresfloodssa.blob.core.windows.net\/floods\/v2021.06\/global\/MERITDEM\/90m\/GFM_global_MERITDEM90m_2018slr_rp0025_masked.nc https:\/\/deltaresfloodssa.blob.core.windows.net\/floods\/v2021.06\/global\/MERITDEM\/90m\/GFM_global_MERITDEM90m_2018slr_rp0050_masked.nc https:\/\/deltaresfloodssa.blob.core.windows.net\/floods\/v2021.06\/global\/MERITDEM\/90m\/GFM_global_MERITDEM90m_2018slr_rp0100_masked.nc"},{"name":"FAO Historic Drought Frequency","access_type":"Public, but due to its large size and processing requirements, the dataset is not included in the replication package, but intermediate data is provided.","note":"Source:  FAO (2023). Date Accessed: August 2023. \nLocation: 02.input\/drought_fao\/. \nThe cropland and pasture Historic Drought Frequency data for season 1 and season 2, covering the period from 1984 to 2022, were obtained from the FAO Agricultural Stress Index System. These data, including thresholds for >30% and >50% land affected, were downloaded via Google Earth Engine after requesting access by following the instructions provided in the following link. \n\n","license_uri":" https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/","uri":"https:\/\/www.fao.org\/giews\/earthobservation\/access.jsp?lang=en","license":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0"},{"name":"CCKP Environmental Stress Index (ESI) max 5-day heatwave","note":"Source: World Bank, Climate Change Knowledge Platform (2024). Date Accessed: April 2024. Location: 02.input\/heatwave_cckp\/. \nThe \"CCKP ESI max 5-day heatwave\" data was obtained from the World Bank Climate Change Knowledge Portal (CCKP) team, who produced the dataset specifically for this project. These ESI data files will be provided by the project team on request and are not available from the CCKP platform, but intermediate data necessary to run the package is provided. ","access_type":"The dataset is not publicly available and is therefore not included in the replication package. Intermediate data necessary to run the package is provided."},{"name":"STORM Tropical Cyclone Wind Speed Return Periods","note":"Source: Russell (2024); Bloemendaal et al. (2023). Date Accessed: April 2024. \nLocation: 02.input\/cyclone_storm\/. \n\nThe \"STORM tropical cyclone wind speed return periods\" data was obtained from Zenodo (Version v2) in the following link.\n\n\n","license":"CC0 1.0 Universal","license_uri":"https:\/\/creativecommons.org\/publicdomain\/zero\/1.0\/","access_type":"Public, but due to its processing requirements, the dataset is not included in the replication package. Intermediate data required to run the package is provided.","uri":"https:\/\/doi.org\/10.5281\/zenodo.10931452"},{"name":"Global Human Settlement population grid (R2023)","uri":"https:\/\/human-settlement.emergency.copernicus.eu\/download.php?ds=pop","license":" Creative Commons Attribution 4.0 International (CC BY 4.0) licence","license_uri":"https:\/\/commission.europa.eu\/legal-notice_en","access_type":"Public, but due to its size, the dataset is not included in the replication package. ","note":"Source: European Commission. Date Accessed: August 2023. Location: 02.input\/population_ghsl. \nData can be downloaded at the following link. The \u201c2020\u201d epoch global data file with \u201c3 arcsec\u201d resolution (\u201cWGS84\u201d coordinate system) is required.  \nThe required files should be saved here: 02.input\/population_ghsl, with the name GHS_POP_E2020_GLOBE_R2023A_4326_3ss_V1_0.tif in order to run the package from its intermediate data option.\n"},{"name":"Global Human Settlement Model grid (R2023)","note":"Source: European Commission. Date Accessed: August 2023. \nLocation: 02.input\/population_ghsl.  \nThe \"GHS-SMOD - R2023A\" gridded degree of urbanization data for the \"2020\" epoch, with a 1 km resolution (Mollweide coordinate system), was obtained from the European Commission\u2019s Joint Research Centre (JRC) Global Human Settlement Layer (GHSL). The data is available for download at the following link.\n\n","license":" Creative Commons Attribution 4.0 International (CC BY 4.0) licence","license_uri":"https:\/\/commission.europa.eu\/legal-notice_en","uri":"https:\/\/human-settlement.emergency.copernicus.eu\/download.php?ds=smod","access_type":"Public, but due to its processing requirements, the dataset is not included in the replication package. Intermediate data required to run the package is provided."},{"name":"World Bank AM24 VUL Boundaries","note":"Source: World Bank. Date Accessed: July 2024. Location: 02.input\/boundaries\/. \nData were produced by the project team. The dataset is derived from administrative boundary data sources including Global Administrative Unit Layers (GAUL) 2015, Nomenclature of Territorial Units for Statistics (NUTS), GADM, United Nations Common Operational Datasets, and National Statistical Offices (NSOs).","access_type":"Published with the package","license_uri":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/","license":"Creative Commons Attribution 4.0 International License"},{"note":"Source: UN Sustainable Development Solutions Network (SDSN). Date Accessed: June 2024. \nLocation raw: 02.input\/rai_sdsn\/911RAI_InaccessibilityIndex2023.tif. Location intermediate provided: 03.intermediate\/RAI\/GHS-POP2020_SDSN-RAI_3arcsec.tif.\nData is obtained from the UN Sustainable Development Solutions Network (SDSN). The dataset is produced as part of tracking SDG Indicator 9.1.1 Rural Access Index by the UN SDSN and provided directly to the project team.  The data can be accessed through Google Earth Engine using the code and instructions at the following link.","access_type":"Public, but due to its processing requirements, the dataset is not included in the replication package. Intermediate data required to run the package is provided.","name":"Rural Access Index (RAI)","uri":"https:\/\/gee-community-catalog.org\/projects\/rai\/","license":" Creative Commons Attribution 4.0 International (CC BY 4.0) licence","license_uri":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/legalcode"},{"note":"Source: WHO\/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP). Date Accessed: April 2024.\nLocation: 02.input\/jmp\/data\/.\nThe JMP dataset provides information on access to drinking water. The data was obtained from the JMP website and downloaded using R scripts included in the replication package.","license":"Creative Commons Attribution 4.0 International License (CC BY 4.0)","license_uri":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/","uri":"https:\/\/washdata.org\/data\/downloads#WLD","name":"Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP)","access_type":"Published with the package"},{"name":"Global Electricity Database","access_type":"Published with the package","note":"Source: IEA, IRENA, UNSD, World Bank, WHO (2023). Date Accessed: March 2024. \nLocation: 02.input\/GED\/sdg7.1.1-access_to_electricity.xlsx.\nThis dataset contains data on global electricity access, derived from the 2023 Tracking SDG 7: The Energy Progress Report. The data is publicly available and was downloaded from the official SDG7 platform, available below. Follow the link below>datasets>SDG 7.1.1 Electrification Dataset.","uri":"https:\/\/trackingsdg7.esmap.org\/downloads","license":"Creative Commons Attribution 4.0 International License (CC BY 4.0) ","license_uri":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"uri":"https:\/\/apiportal.uis.unesco.org\/bdds","license":"Attribution-Sharealike 3.0 Intergovernmental Organization (CC BY-SA 3.0 IGO) ","license_uri":"https:\/\/creativecommons.org\/licenses\/by-sa\/3.0\/igo\/","name":"Education Completion Data","note":"Source: UNESCO Institute for Statistics (UIS) (2024). Date Accessed: April 2024.\nLocation: 02.input\/UNESCO_Education_CompletedPrimaryorHigher.xlsx.\nThe dataset provides global education statistics and was obtained from the UIS.Stat Bulk Data Download Service. It includes indicators related to education completion rates.","access_type":"Published with the package"},{"name":"Global Financial Inclusion (Global Findex) Database 2021","note":"Source: World Bank. Date Accessed: March 2024. Location: 02.input\/Findex\/.  The dataset provides insights into global financial inclusion based on the 2021 Findex survey. The data was obtained from the World Bank Microdata Library and is publicly available for registered users. Interested replicators must follow the link below, log in, download the database, rename it as WLD_2021_FINDEX_v03_M.dta, and add it to the 02.input\/Findex\/ folder. ","access_type":"Public but does not allow republication. Publicly accessible through the World Bank's Microdata Library.","license_uri":" https:\/\/microdata.worldbank.org\/index.php\/terms-of-use","uri":"https:\/\/microdata.worldbank.org\/index.php\/catalog\/4607","license":"Public Use Files"},{"name":"World Development Indicators (WDI)","access_type":"Data is public and the code to load it is included in the reproducibility package","note":"Source: World Bank. Date Accessed: March 2024. Location: 02.input\/WDI_elec_water.dta. Data was obtained from the World Bank, available using the API through the Stata package wbopendata and R package wbstats. Data for the following variables are called from the code: EG.ELC.ACCS.ZS, SP.POP.TOTL. The code used to retrieve these indicators is included in the replication package. However, please note that updates to these indicators may occur, which could slightly affect the final coefficients.","license":"Creative Commons Attribution 4.0 International License (CC BY 4.0) ","license_uri":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"name":"Luxembourg Income Study Database (LIS)","note":"Source: Luxembourg Income Study Database (LIS). Date Accessed: June 2024. Location: 03.intermediate\/LISoutput\/LIS nat 2021 listing_job_1245084.txt, LIS nat 2021 listing_job_1245381.txt, LIS subnat 2021 listing_job_1245388.txt.\nThe data is available through the following link, but interested users need to request access to the LIS portal first. After getting access the do files required to access these data are included in the reproducibility package. ","access_type":"Restricted and not included in the package","uri":"https:\/\/www.lisdatacenter.org\/data-access\/lissy\/","license_uri":"LIS Microdata User Agreement"},{"name":"Poverty and Inequality Platform (PIP) Data","note":"Source: World Bank\nDate accessed: August 2024\nLocation: 03.intermediate\/PIPinput\/PIP_2021_215, pip_2021_365, PIP_2021_685 (version: 20240627_2017_01_02_PROD).\n\nThe data was obtained from the World Bank's Poverty and Inequality Platform (PIP) through the Stata package pip, which was used to access the PIP API. The documentation for the pip package can be found at the following link.\n\nThe Stata do-file used to generate the datasets is titled \"0-1 Get PIP nat lineup number\". It is important to note that the data may change over time, but this version is included for reproducibility purposes.","access_type":"Data is public and the code to load it is included in the reproducibility package","license":"Creative Commons Attribution 4.0 International License (CC BY 4.0) ","uri":"https:\/\/worldbank.github.io\/pip\/","license_uri":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"uri":"https:\/\/www.oecd.org\/en\/about\/members-partners.html","name":"OECD Members","note":"Source: OECD.\nLocation: 02.input\/oecd_list.xlsx, oecd_list.dta","access_type":"Published with the package"},{"name":"SPID boundaries","note":"Source: Authors\u2019 own elaboration\nLocation: 02.input\/SPID boundaries.xlsx.\n\nThe SPID boundaries data was generated by the authors using input datasets. This intermediate data was developed as part of the research process and is included in the reproducibility package to facilitate the replication of results. The data includes modified and missing boundaries and relevant administrative codes.","access_type":"Published with the package"},{"note":"Source: Authors\u2019 own elaboration.\nLocation: 02.input\/Subnational list - 0.xlsx.\n\nThe data includes information on region codes, country lists, and subnational identifiers for various countries and years.","name":"Subnational list ","access_type":"Published with the package"},{"access_type":"Published with the package","note":"Source: Authors\u2019 own elaboration using World Bank classification data.\nLocation: 02.input\/CLASS.dta","name":"World Bank Classification Data","uri":"https:\/\/datahelpdesk.worldbank.org\/knowledgebase\/articles\/906519-world-bank-country-and-lending-groups"},{"name":"ASPIRE Database","note":"Source: World Bank. \nLocation: 02.input\/ASPIRE\/ASPIRE_data_touse.xlsx, BSC_ASP_ASPIRE_152countries.xlsx.","access_type":"Published with the package","license_uri":"https:\/\/www.worldbank.org\/en\/about\/legal\/terms-of-use-for-datasets","uri":"https:\/\/www.worldbank.org\/en\/data\/datatopics\/aspire\/documentation","license":"Creative Commons Attribution 4.0 International License (CC BY 4.0)"},{"name":"Global Monitoring Database (GMD)","access_type":"Restricted and not included in the package","note":"Source: World Bank\nDate Accessed: August 2024\nLocation: 02.input\/repo_AM24all.dta, 02.input\/Final_CPI_PPP_to_be_used.dta, Survey_price_framework.dta, 03.intermediate\/Sim\/2021\/.\n\nThe Global Monitoring Database (GMD) contains micro-level survey data, with access restricted to World Bank staff using Bank-managed computers. External access is not available. While most surveys in the GMD are accessible internally, some require special access requests. The code necessary to access these data is included in the reproducibility package, with instructions in the README file for the first three data sources. Codes 1.2 and 2.3 are used to generate the files located at 03.intermediate\/Sim\/2021. For more details, please refer to the README file."}],"data_statement":"Some data is restricted and has not been included in the reproducibility package. For more details, please refer to the README file.","reproduction_instructions":"Due to the high computational requirements, the replicators did not process the spatial data themselves. Instead, the authors provided intermediate datasets to simplify replication. These are included in the package under `03.intermediate\/RAI` and `03.intermediate\/Hazard` and are used by the R code to produce the necessary inputs for the Stata portion of the code.\n\n1. **Missing Datasets**  \n   Some datasets required to run this package are not included. As a replicator, your first task is to obtain these missing files. Detailed instructions for accessing each file are provided in the README file.\n\n2. **Run the R Code First**  \n   - The R portion of the code processes the spatial data. The only dataset you need to download separately to run the R code from the intermediate data is the **Global Human Settlement Population Grid (R2023)**. Instructions for downloading this dataset are in the README file and the specific data entry.\n   - If you prefer, you can choose to download and process all spatial data from scratch. However, this requires over **1TB and 14 days of processing** and includes a dataset that is not publicly available (you can request access from the replicators). For this reason, intermediate data spatial data is provided by the authors to make the replication more feasible.\n\n3. **Prepare the Data**  \n   After downloading the required datasets, place them in the designated folders as outlined in the README file.\n\n4. **Run the R Script**  \n   Run the R script `00_MASTER` to process the first portion of the data.\n\n5. **Move to the Stata Code**  \n   - The Stata portion of the code relies on some datasets not included in the package. The publicly available but not included datasets are **Findex** and **LISSY**. Follow the instructions in the README file and place these datasets in the appropriate folders.\n   - Additionally, **GMD** data is required and can only be accessed by World Bank users via Datalibweb. External replicators will not have access to these files. If you're a World Bank user, ensure you have access to all the GMD surveys.\n\n6. **Run the Stata Code**  \n   Run the Stata `MASTER` file. The code will pause at a certain point because specific steps need to be run on the **LISSY platform**.\n\n7. **Steps on the LISSY Platform**  \n   On the LISSY platform, run the following scripts:\n   - `2-1a Estimate national vulnerability rate for LISSY data`\n   - `2-1b Estimate vulnerability rate for LISSY data`\n\n8. **Complete the Stata Code**  \n   Once the LISSY steps are completed, continue running the `MASTER.do` file to complete the process.\n\n9.  **Final Output**\n- Given the complexity and restricted access to some data, even with the intermediate provided for the R portion, external replicators may not be able to reproduce the full process. However, the final **Climate Risk Scorecard Vision Indicator** output has been included in the package under `04.output\/For CSC`.\n- In addition, the **Get Microdata** tab also allows you to download the indicator, which includes information for 103 countries. This enables users to review the results produced by the replicators' code.\n","abstract":"The World Bank Group introduced a Scorecard Vision Indicator in 2024 to track progress on climate adaptation: the percentage of global population at high risk from climate-related hazards. The indicator is estimated by combining geospatial and household survey data. People at high risk from climate-related hazards are defined as those exposed to any hazard and vulnerable on any dimension, based on specific thresholds. Exposure to four climate related hazards is considered: agricultural drought, flood, heatwave, and tropical cyclone. Household level vulnerability is assessed on seven dimensions: income, education, financial inclusion, access to social protection, access to electricity, access to improved drinking water, and access to services and markets. The global indicator is calculated for 2021 based on a sample of 103 countries with complete data. "},"tags":[{"tag":"DOI"}],"schematype":"script"}