[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126552-en":3,"doc-seo-126552-105":31,"detail-sidebar-cat-0-en-105":92},{"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":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126552,13056712833777,"Logic","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","What drives SME formalization in Tanzania - An assessment using machine learning techniques","Identifying the motives for business formalization is essential for policy coordination of entrepreneurs in Tanzania. This study applies four machine learning models to examine how contingent and institutional factors affect SME formalization, using an updated World Bank Enterprise Survey dataset (2013) with 698 firm-level observations and 37 explanatory variables. Feature importance via SHAP values highlights firm location in a business city, sales revenue, full-time employees, sector experience, and internal/external finance as key positive drivers. The findings support stakeholders crafting policies to accelerate formalization.","Research in Business & Social Science IJRBS VOL 12 NO 1 (2023) ISSN: 2147-4478  \nAvailable online [at www.ssbfnet.com](at www.ssbfnet.com)  \nJournal homepage: [https://www.ssbfnet.com/ojs/index.php/ijrbs](https://www.ssbfnet.com/ojs/index.php/ijrbs)  \n\n|  |  |  |  |\n| --- | --- | --- | --- |\n| What drives SME formalization in Tanzania? An assessment using the |  |  |  |\n| machine learning techniques\u003Cbr> Frank A. Mwombeki (a) *\u003Cbr>(a) Assistant lecturer, College of Business Education (CBE), Tanzania |  |  |  |\n| ARTICLE INFO\u003Cbr>Article history:\u003Cbr>Received 28 October 2022\u003Cbr>Received in rev. form 19 Dec. 2022 Accepted 22 January 2023\u003Cbr>Keywords:\u003Cbr>SMEs, Formalization, Contingent Factors, Institutional Factors, And Machine Learning Techniques\u003Cbr>JEL Classification:\u003Cbr> G38, O17, O49  |  | A B S T R A C T\u003Cbr>Identifying the motives for business formalization is important for policy-making and the smooth coordination of entrepreneurs in Tanzania. This paper employs four machine learning (ML) models to investigate the effects of several contingents and institutional factors on SME formalization in Tanzania. Using an updated large dataset of the World bank enterprise survey in 2013, this research relies on 698 firms level data and 37 explanatory variables. The feature importance through SHAP values analysis proves that firm location in a business city, sales revenue, number of full-time employees, firm experience in the sector, and internal and external finance are the most significant factors that positively affect SME's formalization. This paper will be a reference for the actors and stakeholders in business and entrepreneurship to make good policies to drive business formalization in Tanzania. |  |\n|  |  | © 2023 by the authors. Licensee SSBFNET, Istanbul, Turkey. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)). |  |\n|  |  |  |  |\n\nIntroduction  \nIn recent years, the formalization of small and medium enterprises has increased. The report from Ishengoma (2018) indicated the increase of SMEs being formalized by registering to respective government authorities. Government organs also take different initiatives to mobilize the formalization. For instance, the government program of property and business formalization and the government vision 2025 aim to push the registration of businesses currently operating informally (Hamisi, 2021) . A higher rate of business formalization indicates the business owner's understanding of registering their business and the economic advantages they will earn for their business being registered at the firm and national levels (Rand & Torm, 2012) . Business formalization can be the action of a firm to graduate from being informal to being formal. In Tanzania, a firm or business is considered formally registered if it attains the business license from the local authority and is registered as a taxpayer at the revenue authority. Subject to the nature and size of the firms, especially medium and large firms, the law wants to register the firm to Business Registration and Licensing Agency (BRELA) as a sole proprietor, company, partnership, or trust (Ishengoma, 2018). That is different from informal businesses, which operate in unknown areas, don't pay taxes or levies, and so they termed tobe out of the government business system (Ishengoma & Kappel, 2007) . Accordingly, following recent studies on formalization, such as Cling et al. (2012) and Ishengoma (2018): this paper explains informal SMEs as an entity functioning without being registered by government authorities and with no legal permission to run the business.  \nThis paper investigates the likely features that may influence the formalization of small and medium enterprises in Tanzania. I examine the contingent and institutional factors in predicting the formalization of S","cbCaigrkI4B4V7zq","https://ap.wps.com/l/cbCaigrkI4B4V7zq","pdf",534185,3,1,10,"English","en",105,"# Introduction\n## Defining SME formalization in Tanzania\n## Machine learning approach and baseline models\n## Related research on formalization determinants","[{\"question\":\"What does the paper mean by SME formalization in Tanzania?\",\"answer\":\"A firm is considered formally registered when it obtains the business license from the local authority and is registered as a taxpayer at the revenue authority. Medium and large firms are expected to register with BRELA under specific legal forms.\"},{\"question\":\"Which method does the study use to analyze SME formalization?\",\"answer\":\"The paper uses four machine learning models and compares them with a logit baseline model. It also mentions ensemble approaches including Random Forest, Decision Tree, and XGBoost, plus recursive feature elimination to identify key variables.\"},{\"question\":\"What factors show the strongest positive association with SME formalization?\",\"answer\":\"SHAP-based feature importance indicates that firm location in a business city, sales revenue, number of full-time employees, firm experience in the sector, and internal and external finance are among the most significant positive factors.\"}]","What drives SME formalization in Tanzania - An assessment using machine learning techniques | PDF",1785933283,25,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"what-drives-sme-formalization-in-tanzania-an-assessment-using-machine-learning-techniques","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/what-drives-sme-formalization-in-tanzania-an-assessment-using-machine-learning-techniques/126552/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-26","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What does the paper mean by SME formalization in Tanzania?","Question",{"text":76,"@type":77},"A firm is considered formally registered when it obtains the business license from the local authority and is registered as a taxpayer at the revenue authority. Medium and large firms are expected to register with BRELA under specific legal forms.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which method does the study use to analyze SME formalization?",{"text":81,"@type":77},"The paper uses four machine learning models and compares them with a logit baseline model. It also mentions ensemble approaches including Random Forest, Decision Tree, and XGBoost, plus recursive feature elimination to identify key variables.",{"name":83,"@type":74,"acceptedAnswer":84},"What factors show the strongest positive association with SME formalization?",{"text":85,"@type":77},"SHAP-based feature importance indicates that firm location in a business city, sales revenue, number of full-time employees, firm experience in the sector, and internal and external finance are among the most significant positive factors.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":22,"slug":134},"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]