[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"page:what-we-do\u002Fdata-and-services\u002Fsurveys\u002Fgarment-supply-chain":3},{"id":4,"slug":5,"title":6,"short_title":7,"intro_text":8,"meta_description":8,"seo_title":8,"path":9,"content_type":10,"locale":11,"go_live_at":7,"first_published_at":12,"page_created_at":13,"published_at":12,"edit_url":14,"breadcrumbs":15,"seo":26,"body_blocks":34,"call_to_action":97,"categories":104,"owner":7,"authors":117,"related_pages":118,"related_sites":119,"in_subsite":120,"contact_page_url":121,"banner_message":122},21680,"garment-supply-chain","Garment Supply Chain - Data Collection Overview",null,"","\u002Fwhat-we-do\u002Fdata-and-services\u002Fsurveys\u002Fgarment-supply-chain","pages.contentpage","en","2022-01-27T09:22:00+00:00","2026-04-22T14:18:45.956347+00:00","\u002Fcms\u002Fpages\u002F21680\u002Fedit\u002F",[16,19,22,25],{"title":17,"slug":18},"What We Do","what-we-do",{"title":20,"slug":21},"Data and Services","data-and-services",{"title":23,"slug":24},"Surveys","surveys",{"title":6,"slug":5},{"title":6,"description":8,"image":27,"canonical":28,"robots":29,"og_type":30,"twitter_card":31,"locale":11,"created_at":32,"last_modified_at":33},"https:\u002F\u002Fwageindicator.org\u002Fmedia\u002Fimages\u002FSocial_media_preview_image_-_2025.2e16d0ba.fill-1200x630.png","https:\u002F\u002Fwageindicator.org\u002Fwhat-we-do\u002Fdata-and-services\u002Fsurveys\u002Fgarment-supply-chain\u002F","index, follow","website","summary_large_image","2022-01-27T10:22:00+01:00","2026-04-22T16:18:46.102701+02:00",[35,72],{"type":36,"data":37},"rich_text_table_block",{"variant":38,"table_data":39,"caption":70,"column_headings":71},"light",[40,43,46,49,52,55,58,61,64,67],[41,42],"\u003Cp>\u003Cb>Full name of the database\u003C\u002Fb>\u003C\u002Fp>","\u003Cp>WageIndicator Garment Supply Chain Database 2018\u003C\u002Fp>",[44,45],"\u003Cp>\u003Cb>Aims\u003C\u002Fb>\u003C\u002Fp>","\u003Cp>The project aimed to build an extensive database covering the supply chains of 24 major garment and footwear-selling firms or brands drawing on useful recent information they disclosed on the Internet. The companies covered are: 14 European firms\u002Fbrands: Adidas; Amer Sports; ASOS; Bestseller; C&A; Debenhams; G-Star; H&M; KappAhl; M&S; Pentland; Primark; Puma, and Tesco; seven US-based firms\u002Fbrands: Gap Inc; Levi Strauss; New Balance; Nike; PVH Corp; Under Armour, and VF Corp; 2 Japanese brand\u002Ffirms: ASICS and Uniqlo; 1 mixed Asian\u002FEuropean firm\u002Fbrand: Esprit.\u003C\u002Fp>",[47,48],"\u003Cp>\u003Cb>Data used for...\u003C\u002Fb>\u003C\u002Fp>","\u003Cp>Research\u003C\u002Fp>",[50,51],"\u003Cp>\u003Cb>Countries covered\u003C\u002Fb>\u003C\u002Fp>","\u003Cp>25 garment production countries, namely: 16 Asian countries: Bangladesh; Cambodia; China; India; Indonesia; South Korea; Malaysia; Myanmar; Pakistan; Philippines; Singapore; Sri Lanka, Taiwan; Thailand, Turkey, and Vietnam; 4 African countries: Egypt; Ethiopia; Morocco, and Tunisia; 5 Latin-American countries: El Salvador; Guatemala; Honduras; Mexico, and Peru.\u003C\u002Fp>",[53,54],"\u003Cp>\u003Cb>Languages\u003C\u002Fb>\u003C\u002Fp>","\u003Cp>English\u003C\u002Fp>",[56,57],"\u003Cp>\u003Cb>Data collection method\u003C\u002Fb>\u003C\u002Fp>","\u003Cp>Desk research\u003C\u002Fp>",[59,60],"\u003Cp>\u003Cb>Data time frame\u003C\u002Fb>\u003C\u002Fp>","\u003Cp>2018\u003C\u002Fp>",[62,63],"\u003Cp>\u003Cb>Data updating policy\u003C\u002Fb>\u003C\u002Fp>","\u003Cp>Data collection is closed.\u003C\u002Fp>",[65,66],"\u003Cp>\u003Cb>Sampling Frame\u003C\u002Fb>\u003C\u002Fp>","\u003Cp>The largest 24 garment and footwear-selling firms or brands worldwide.\u003C\u002Fp>",[68,69],"\u003Cp>\u003Cb>Nr. of Observations\u003C\u002Fb>\u003C\u002Fp>","\u003Cp>8,100 factories\u003C\u002Fp>","General Database Information",[8,8],{"type":36,"data":73},{"variant":38,"table_data":74,"caption":95,"column_headings":96},[75,78,81,84,87,89,92],[76,77],"\u003Cp> \u003Cb>Data archive\u003C\u002Fb> \u003C\u002Fp>","\u003Cp> The 2018 dataset can be downloaded from \u003Ca href=\"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.4783320\" rel=\"nofollow noopener\" target=\"_blank\">Zenodo.\u003C\u002Fa> Interested in the larger dataset from 2017 - present? Please \u003Ca href=\"https:\u002F\u002Fwageindicator.org\u002Fabout\u002Fcontact\">contact us\u003C\u002Fa>. \u003C\u002Fp>",[79,80],"\u003Cp> \u003Cb>DOI code\u003C\u002Fb> \u003C\u002Fp>","\u003Cp> DOI: 10.5281\u002Fzenodo.4783320 \u003C\u002Fp>",[82,83],"\u003Cp> \u003Cb>Data visuals\u003C\u002Fb> \u003C\u002Fp>","\u003Cp>-\u003C\u002Fp>",[85,86],"\u003Cp> \u003Cb>Codebook\u002FManual\u003C\u002Fb> \u003C\u002Fp>","\u003Cp> Van Klaveren, M. & Tijdens, K.G. (2018). \u003Ca href=\"https:\u002F\u002Fwageindicator.org\u002Fwhat-we-do\u002Fpublications\u002F2018\u002Fvan-klaveren-m-tijdens-k-g-2018-codebook-wageindicator-garment-supply-chain-database-2018-wageindicator-foundation-amsterdam\">Codebook WageIndicator Garment Supply Chain Database 2018.\u003C\u002Fa> WageIndicator Foundation, Amsterdam. \u003C\u002Fp>",[88,83],"\u003Cp> \u003Cb>Long list variables\u003C\u002Fb> \u003C\u002Fp>",[90,91],"\u003Cp> \u003Cb>Formats\u003C\u002Fb> \u003C\u002Fp>","\u003Cp> SPSS \u003C\u002Fp>",[93,94],"\u003Cp> \u003Cb>Citation\u003C\u002Fb> \u003C\u002Fp>","\u003Cp> Maarten van Klaveren, & Kea Tijdens (2021). WageIndicator Garment Supply Chain Database 2018. \u003C\u002Fp>","Data Access",[8,8],{"text":98,"link":99},"Contact us",{"title":98,"url":100,"description":98,"rel":101,"type":102,"id":103},"\u002Fabout\u002Fcontact","follow","internal",24590,[105,109,113],{"id":106,"slug":107,"name":108},5,"collective-agreements","Collective Agreements",{"id":110,"slug":111,"name":112},2,"labour-law","Labour Law",{"id":114,"slug":115,"name":116},1,"salary","Salary",[],[],[],false,"\u002Fwork\u002Fliving-wages\u002Fcontact-us","\u003Cp>Welcome to WageIndicator. Same organisation, same information, new look!\u003C\u002Fp>"]