[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120667-en":3,"doc-seo-120667-105":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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"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},120667,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Age, Wealth, and the MPC in Europe - A Supervised Machine Learning Approach","Supervised machine learning is used to analyze consumption patterns across Europe and to quantify how age and (liquid) wealth shape the marginal propensity to consume (MPC) differently by country. Using 2017 data from the Eurosystem’s Household Finance and Consumption Survey (HFCS), the study focuses on the Netherlands, Germany, France, and Italy, building on Christelis et al. (2019). Decision trees generate data-driven age and wealth splits that improve regression fit, and random forest and XGBoost results—supported by SHAP—inform policy discussions on MPC heterogeneity.","Satyajit Dutt | Jan W. Radermacher  \nAge, Wealth, and the MPC in Europe A Supervised Machine Learning Approach  \nSAFE Working Paper No. 383 | February 2023  \nElectronic copy available at: [https://ssrn.com/abstract=4360002](https://ssrn.com/abstract=4360002)  \nAge, Wealth, and the MPC in Europe A Supervised Machine Learning Approach∗  \nSatyajit Dutt† Jan W. Radermacher‡  \nFebruary 13, 2023  \nAbstract  \nWe investigate consumption patterns in Europe with supervised machine learning methods and reveal differences in age and wealth impact across countries. Using data from the third wave (2017) of the Eurosystem’s Household Finance and Consumption Survey (HFCS), we assess how age and (liquid) wealth affect the marginal propensity to consume (MPC) in the Netherlands, Germany, France, and Italy. Our regression analysis takes the specification by Christelis et al. (2019) asa starting point. Decision trees are used to suggest alternative variable splits to create categorical variables for customized regression specifications. The results suggest an impact of differing wealth distributionsand retirement systems across the studied Eurozone members and are relevant to European policy makers due to joint Eurozone monetary policy and increasing supranational fiscal authority of the EU. The analysis is further substantiated by a supervised machine learning analysis using a random forest and XGBoost algorithm.  \n∗We thank Andreas Hackethal, Nathanael Vellekoop, Gyozo Gyngysi, Fabian Nemeczek, and the participants of the EFL Jour Fixe at Goethe University Frankfurt on 18 January, 2021 for helpful comments and suggestions. Financial support from the Leibniz Institute for Financial Research SAFE is gratefully acknowledged. This paper uses data from the Eurosystem’s Household Finance and Consumption Survey (HFCS) .  \n†[sunnydutt@gmail.com](sunnydutt@gmail.com)  \n‡SAFE, GSEFM, and Goethe University, radermacher@safe-frankfurt.de  \n1  \nElectronic copy available at: [https://ssrn.com/abstract=4360002](https://ssrn.com/abstract=4360002)  \n1 Introduction  \nIn the EU, pension systems differ between the member states but monetary policy is conducted jointly. It is therefore important to understand differences in consumption patterns-particularly, as the group of retirees is relatively growing in most societies.  \nSpecial emphasis lies on the marginal propensity to consume (MPC), i.e. the consumption rate/fraction out of a change in income. Because pension amounts are often at the discretion of governments, policy makers are particularly concerned about their ramifications on macroeconomic variables such as aggregate demand. But also additional/removed taxes, introduced/ -suspended transfers, rising/falling inflation, or appreciating/depreciating foreign exchange rates can pose income shocks that affect the purchasing power of individuals and are therefore at the concern of policy makers.  \nThis study analyzes consumption patterns in four of the five largest Eurozone economies: Germany, France, Italy, and the Netherlands.1 Because traditional consumption theory (Modigliani and Brumberg, 1954; Friedman, 1957; Hall, 1978; Deaton, 1991; Carroll, 1997) does not account for countryspecific differences, we ask: how does the MPC differ per Eurozone country? What are the impacts of important demographic variables such as age and wealth? Do country differences in pension schemes, life expectancy, age or wealth distributions matter for consumption behavior?  \nWe follow Christelis et al. (2019) in their basic econometric set-up and use data from the third wave (2017) of the Eurosystem’s Household Finance and Consumption Survey (HFCS) (HFCN, 2020b) . Utilizing decision trees, a corner stone of supervised machine learning, we derive age and wealth dummies for regression specifications that outperform the specifications suggested by Christelis et al. (2019) . Decision trees are discussed as simple, effective, and interpretable tools to deal with non-linear data and we","cbCail3vdxZKtpOA","https://ap.wps.com/l/cbCail3vdxZKtpOA","pdf",1826539,1,42,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"Which countries and dataset are analyzed to study MPC differences in Europe?\",\"answer\":\"The analysis covers Germany, France, Italy, and the Netherlands using 2017 data from the Eurosystem’s Household Finance and Consumption Survey (HFCS).\"},{\"question\":\"How does the study use supervised machine learning to model the MPC?\",\"answer\":\"Decision trees are used to derive alternative age and (liquid) wealth dummy splits for customized regression specifications, and random forest and XGBoost provide further supervised learning analysis support.\"},{\"question\":\"Why are age and wealth important for policymakers in this context?\",\"answer\":\"Because pension systems and other income shocks influence individuals’ purchasing power and thereby affect aggregate demand, understanding how age and wealth affect MPC is relevant for European policy makers.\"}]","Age, Wealth, and the MPC in Europe - A Supervised Machine Learning Approach | PDF",1785731255,106,{"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},"age-wealth-and-the-mpc-in-europe-a-supervised-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"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":53},"https://docshare.wps.com/document/age-wealth-and-the-mpc-in-europe-a-supervised-machine-learning-approach/120667/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",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},"Which countries and dataset are analyzed to study MPC differences in Europe?","Question",{"text":75,"@type":76},"The analysis covers Germany, France, Italy, and the Netherlands using 2017 data from the Eurosystem’s Household Finance and Consumption Survey (HFCS).","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study use supervised machine learning to model the MPC?",{"text":80,"@type":76},"Decision trees are used to derive alternative age and (liquid) wealth dummy splits for customized regression specifications, and random forest and XGBoost provide further supervised learning analysis support.",{"name":82,"@type":73,"acceptedAnswer":83},"Why are age and wealth important for policymakers in this context?",{"text":84,"@type":76},"Because pension systems and other income shocks influence individuals’ purchasing power and thereby affect aggregate demand, understanding how age and wealth affect MPC is relevant for European policy makers.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]