Simulation of subnational projections of income distribution and poverty to support climate risk assessments

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Koopman, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6762519/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract The global poor are expected to suffer most from the impact of climate change, in particular the increasing frequency of extreme weather events, such as floods, heat waves and fores fires as well as changes in food supply. These events are very uncertain as they depend on different climate scenarios and are highly local, only affecting a certain part of the population. To develop targeted climate adaptation strategies national decision makers need to have detailed information on the quantity, location and profile of the people that are most vulnerable to climate impacts. This study presents an innovative spatial microsimulation modelling framework for projecting subnational income distribution and poverty trends under different scenarios that can be combined with spatial data sets on extreme climate events to support climate risk assessments. The Microsimulation of Income DynamicS (MIDS) model, combines household survey data with subnational projections on key drivers of income, including demographic change, urbanization and shifts in skills and occupation, to simulate how the distribution of income changes as a consequence of economic development and structural transformation. To account for impact of global linkages and shocks, MIDS incorporates labor income projections from a global general computable equilibrium model. To illustrate the model we provide an application to Ethiopia, one of the largest countries in Africa in terms of population size, which is characterized by high poverty levels and deep inequality. We used MIDS to project changes in income distribution and the poverty headcount for 60 different zones (administrative level 2 regions) and three different Shared Socio-economic Pathways (SSP) scenarios for the period 2018-2050. We extended the SSPs with additional assumptions on regional development to make them suitable for subnational assessments. We combined the subnational income projections with heat stress maps to identify the share of the population that is most vulnerable to climate change. We found that, depending on the scenario, between 1.4 and 9.4 million people will be at risk of heat stress in 2050. Development Economics Climate Analysis and Modeling microsimulation spatial Ethiopia macro-micro CGE Full Text Additional Declarations The authors declare no competing interests. Supplementary Files si.pdf Simulation-based subnational projections of income distribution and poverty to support climate risk assessments - Supplementary Information Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6762519","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463390200,"identity":"d93dfd3c-8a87-4e3c-8e52-7ea95331062d","order_by":0,"name":"Michiel van Dijk","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYDACZgY2hgQGCzBbIoEnQQ4iXEBQiwRcizFE2ACvPWwgxRAtDAwJiQ2EtOi2Mz978KBCgsHg+NmDNx7IpKVvuJH7gLkAjxazw2zmBglngFrO5CVbJPDk5G64kW7APAOvFh42icQ2CQbJhhwzoF8qcrfdSGNg5iGo5R9QS/8bsJZ0M+K0NEgw8EuAbclJIEILG1DlMQkefok3xkC/pBnuP/OM4TBeLecPP5P8UWMjx8afY3jzZ0+yvGR7GuNjngrcWmCAB0wy9kB4BwhrgIMfJKgdBaNgFIyCEQMApD9EtY37jjkAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-5207-7304","institution":"Wageningen Social \u0026 Economic Research","correspondingAuthor":true,"prefix":"","firstName":"Michiel","middleName":"van","lastName":"Dijk","suffix":""},{"id":463390201,"identity":"dd533392-f8b7-4c57-a49f-4801126fd3da","order_by":1,"name":"Marijke Kuiper","email":"","orcid":"https://orcid.org/0000-0002-2218-0212","institution":"Wageningen Social \u0026 Economic Research","correspondingAuthor":false,"prefix":"","firstName":"Marijke","middleName":"","lastName":"Kuiper","suffix":""},{"id":463390202,"identity":"9d39a841-6ecc-4b4d-9385-98101c00e7b1","order_by":2,"name":"Thijs de Lange","email":"","orcid":"https://orcid.org/0000-0003-1599-7037","institution":"Wageningen Social \u0026 Economic Research","correspondingAuthor":false,"prefix":"","firstName":"Thijs","middleName":"","lastName":"de Lange","suffix":""},{"id":463390203,"identity":"fe6c16ba-c9d0-47e5-996a-faf456f1c0c2","order_by":3,"name":"Jason F.L. 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These events are very uncertain as they depend on different climate scenarios and are highly local, only affecting a certain part of the population. To develop targeted climate adaptation strategies national decision makers need to have detailed information on the quantity, location and profile of the people that are most vulnerable to climate impacts. This study presents an innovative spatial microsimulation modelling framework for projecting subnational income distribution and poverty trends under different scenarios that can be combined with spatial data sets on extreme climate events to support climate risk assessments.\u003c/p\u003e\n\u003cp\u003eThe Microsimulation of Income DynamicS (MIDS) model, combines household survey data with subnational projections on key drivers of income, including demographic change, urbanization and shifts in skills and occupation, to simulate how the distribution of income changes as a consequence of economic development and structural transformation. To account for impact of global linkages and shocks, MIDS incorporates labor income projections from a global general computable equilibrium model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo illustrate the model we provide an application to Ethiopia, one of the largest countries in Africa in terms of population size, which is characterized by high poverty levels and deep inequality. We used MIDS to project changes in income distribution and the poverty headcount for 60 different zones (administrative level 2 regions) and three different Shared Socio-economic Pathways (SSP) scenarios for the period 2018-2050. We extended the SSPs with additional assumptions on regional development to make them suitable for subnational assessments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe combined the subnational income projections with heat stress maps to identify the share of the population that is most vulnerable to climate change. We found that, depending on the scenario, between 1.4 and 9.4 million people will be at risk of heat stress in 2050. \u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Simulation of subnational projections of income distribution and poverty to support climate risk assessments","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2026-03-04 19:13:23","doi":"10.21203/rs.3.rs-6762519/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2025-05-29 04:28:58","doi":"10.21203/rs.3.rs-6762519/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4a10bdfa-b3ac-49d4-be94-4cebf0865f64","owner":[],"postedDate":"March 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49204340,"name":"Development Economics"},{"id":49204341,"name":"Climate Analysis and Modeling"}],"tags":[],"updatedAt":"2025-05-29T04:28:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-04 19:13:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-6762519","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6762519","identity":"rs-6762519","version":["v2"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}