[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-108507-en":3,"doc-seo-108507-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":11,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},108507,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Peru - Background note on microsimulations - Poverty forecasting: volatility, climate change, and welfare in Peru","This paper develops a framework to quantify income and welfare forecasts under plausible macroeconomic development trajectories, then applies it to Peru to identify correlates and drivers of poverty. It evaluates the likely distributional impacts of two climate shocks: moderate increases in food prices and decreases in agricultural earnings. The model estimates about a 1 percentage point rise in extreme income poverty incidence, roughly a 30% increase today, with stronger sensitivity in urban/coastal areas for food-price shocks and in rural highland/Amazon areas for earnings shocks. It also informs ShockWaves country-level analysis for the Peru Country Climate & Development Report.","Pub lic Disclosure Authorized Pub lic Disclosure Authorized Pub lic Disclosure Authorized Pub lic Disclosure Authorized  \nLATIN AMERICA AND CARIBBEAN  \nPeru  Background note on  \n microsimulations  \n World Bank Group  \nPrepared 29 September 2022  \n Contact [bwalsh1@worldbank.org](bwalsh1@worldbank.org)  \nPoverty forecasting: volatility, climate change, and welfare in Peru  \nBrian Walsh, Francis Dennig, Luciana de la Flor, Julie Rozenberg, & Joaquin Urrego *  \nSeptember 29, 2022  \nAbstract  \nThis paper presents a framework for quantifying income and welfare forecasts, conditional on plausible macroeconomic development trajectories. We use this framework to identify correlates and drivers of poverty in Peru, and to describe the distribution and severity of two likely climate shocks: moderate increases in food prices, and decreases in agricultural earnings. At the upper limit, our model anticipates a 1 percentage point increase in extreme income poverty incidence by these effects alone. Today, this would represent a 30% increase in extreme poverty in Peru. More usefully, we find that poverty in urban and coastal areas is relatively more sensitive to food price shocks, as compared to agricultural earnings shocks. Among rural households in highland and Amazonian areas, the converse is true: poverty is more sensitive to agricultural earnings than to food prices. This contrast implies that anti-poverty programs should seek food security in urban areas, and job security in rural areas. This work has been prepared as a background note to the Peru Country Climate & Development Report. It is the second adaptation to country level of the ShockWaves methodology, which has previously been used to inform the Pacific Poverty Assessment (2022), and in flagship reports from the World Bank’s Climate Change (ShockWaves 2016) and Poverty (Poverty & Shared Prosperity 2020) practices.  \nJEL C11, C31, C54, D10, D31, D60, D81, I32, J11, J31, O21, Q54  \nList of Figures  \n1 Labor earnings and food consumption, descriptive statistics ............................ 2  \n2 Partial estimate of climate-induced poverty..................................... 3  \n3 Poverty forecasts................................................... 4  \n4 GDP and population forecasts   5  \n5 Employment, historical relationship to value add and forecast   6  \n6 Macroeconomic input parameters, & possible values   7  \n* Contact [bwalsh1@worldbank.org](bwalsh1@worldbank.org)  \nContext  \nClimate change is best understood as an intensification of threats familiar to the poor. Households move in and out of monetary poverty because of disruptions to their incomes and consumption habits. Workers’ earnings are dependent on working conditions, market access, their health, and labor demand. Subsistence farmers and the food-insecure are squeezed when crops are destroyed, and when food prices rise. Small businesses and the uninsured struggle with unexpected losses and health expenditures. These are familiar daily challenges expected to be intensified and compounded by climate change.  \nFuture macro losses to climate change are uncertain. The eventual consequences of climate change are tightly coupled with uncertain macro trends. Economic growth, labor demand, social protection and other infrastructures, and migration remain unpredictable, and conditional on policy choices made today. Some uncertainties could be reduced with better data and deeper analysis, including the consequences of assumptions and methodological choices, especially built into leading macroeconomic models. Other uncertainties including decarbonization scenarios, commodity prices, technological breakthroughs, political instabilities are irreducible and in many cases linked to unpredictable crises (COVID-19 being an extreme example) .  \nClimate change is likely to affect welfare in Peru via agricultural earnings and food prices. The poorest households by income earn 40% of total income from agricultural labor (cf. Figure 1) . Am","cbCaio2KxcVxKeap","https://ap.wps.com/l/cbCaio2KxcVxKeap","pdf",1088870,4,1,"English","en",105,"# Abstract\n# Context\n## Poverty and climate-related vulnerabilities\n## Uncertainty in macroeconomic losses\n## Transmission channels in Peru\n# Findings\n## Poverty impacts by 2030 from food and earnings shocks\n## Sensitivity to macroeconomic scenarios\n# Figures and references","[{\"question\":\"What framework does the background note use to forecast poverty and welfare in Peru?\",\"answer\":\"It presents a framework that quantifies income and welfare forecasts conditional on plausible macroeconomic development trajectories, then uses it to identify poverty correlates and drivers in Peru.\"},{\"question\":\"Which two climate-related shocks are analyzed, and what are their modeled poverty effects?\",\"answer\":\"The note analyzes moderate increases in food prices and decreases in agricultural earnings. Combined shocks are estimated to raise extreme poverty incidence by about 1 percentage point by 2030, corresponding to over 300,000 individuals in the marginal effect estimate.\"},{\"question\":\"How does the sensitivity of poverty differ between urban/coastal areas and rural highland/Amazon regions?\",\"answer\":\"Urban and coastal poverty is relatively more sensitive to food price shocks, while rural households in highland and Amazonian areas are more sensitive to agricultural earnings shocks. The contrast informs program priorities for food security in urban areas and job security in rural areas.\"}]","Peru - Background note on microsimulations - Poverty forecasting: volatility, climate change, and welfare in Peru | PDF",1784471927,20,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"peru-background-note-on-microsimulations-poverty-forecasting-volatility-climate-change-and-welfare-in-peru","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":20},"https://docshare.wps.com/document/peru-background-note-on-microsimulations-poverty-forecasting-volatility-climate-change-and-welfare-in-peru/108507/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-30","2026-07-19",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What framework does the background note use to forecast poverty and welfare in Peru?","Question",{"text":75,"@type":76},"It presents a framework that quantifies income and welfare forecasts conditional on plausible macroeconomic development trajectories, then uses it to identify poverty correlates and drivers in Peru.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which two climate-related shocks are analyzed, and what are their modeled poverty effects?",{"text":80,"@type":76},"The note analyzes moderate increases in food prices and decreases in agricultural earnings. Combined shocks are estimated to raise extreme poverty incidence by about 1 percentage point by 2030, corresponding to over 300,000 individuals in the marginal effect estimate.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the sensitivity of poverty differ between urban/coastal areas and rural highland/Amazon regions?",{"text":84,"@type":76},"Urban and coastal poverty is relatively more sensitive to food price shocks, while rural households in highland and Amazonian areas are more sensitive to agricultural earnings shocks. 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