| A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | AA | |
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1 | Source Link | Source Name | Description | Relevance | Ease of access | ||||||||||||||||||||||
2 | https://gender-pay-gap.service.gov.uk/ | Gender Pay Gap Data Service | Gov.uk's gender pay gap service. Contains stats overview of mean, median, quartiles & bonus pay. Also contains links to each company's individual report, which is typically a PDF or webpage hosted on the employer's own website. The full data set can be downloaded in one go. | High | Easy | Easy = multiple points of structured data in an accessible data format ready to be extracted. Medium = only one data point accessible at a time; ??? Difficult = unstrutured data in varying formats that must be tracked down & obtained before it can be used. | |||||||||||||||||||||
3 | Obtain f/ pay gap service | Each individual company's report. | Each reporting in Gov.uk contains a link to the employers' report. This data can be on a webpage or in a downloadable PDF. Could be valuable for free-text analysis of bias in how the employer speaks about itself. | High | Difficult | ||||||||||||||||||||||
4 | Reed? Indeed? [monki gras contact] | Vacancies listings of companies in the GPG data set. | Can we find a programmatic way to scrape these from employer websites? | High | Difficult | ||||||||||||||||||||||
5 | https://www.gov.uk/get-information-about-a-company | Companies House Data Service | Freely available information about public companies. This includes the 'People with Significant Control' info which Global Witness previously used to expose shell companies funneling money around. Is it possible to extract a large chunk of data at once? I [Caitlin] could only figure out how to search for companies one at a time. | Medium | Medium | Further info on what Global Witness did: https://www.globalwitness.org/en/blog/what-does-uk-beneficial-ownership-data-show-us/ | |||||||||||||||||||||
6 | https://greatripoffmap.globalwitness.org/#!/explore/companies | Global Witness 'Anonymous Companies' interactive map. | Easy to extract data; relevance to our project is pretty low. | Low | Easy | ||||||||||||||||||||||
7 | http://www.advertisingarchives.co.uk/en/pages/picture-research.html | Advertising Archives | Picture research archive containing over 1 million images. We'd have to contact them to get access to the data. | Medium | Difficult | ||||||||||||||||||||||
8 | https://github.com/awesomedata/awesome-public-datasets#finance | Financial public data sets | These need individually listed & assessed | ||||||||||||||||||||||||
9 | https://github.com/awesomedata/awesome-public-datasets#socialnetworks | Social Network data sets | These need individually listed & assessed | ||||||||||||||||||||||||
10 | http://business.data.gov.uk/companies/ | Linked Data Service for Company Registrations | This looks like a downloadable version of the Companies House Data Service, w/o the 'Significant Control' data. Easy to use; less info included. | High | Easy | ||||||||||||||||||||||
11 | http://esriuk-govportal.opendata.arcgis.com/ | ESRI UK Government Open Data Demo Portal | Stuff with maps. Easy to download; not sure how relevant this is. | Low | Easy | ||||||||||||||||||||||
12 | https://www.gov.uk/government/statistics?keywords=&topics%5B%5D=business-and-enterprise&departments%5B%5D=all&from_date=&to_date= | ONS data (filtered for "Business & Enterprise") | Easy enough to get data sets out, but this is a huge grab-bag of stuff from insolvency rates to pesticide usage to export licences. Not sure we can get much relevant info here. | Medium | Easy | ||||||||||||||||||||||
13 | http://landregistry.data.gov.uk/app/ppd | Land Registry Data | Property transactions in the UK. Not sure about the relevance to gender pay. | Low | Easy | ||||||||||||||||||||||
14 | https://data.london.gov.uk/ | London Data Store | Has a couple of APIs that could be used to get the data out. Includes data on London Businesses like survival rates, international trade, VAT enterprises, etc. | Medium | Easy | ||||||||||||||||||||||
15 | https://data.gov.uk/search?q=&filters%5Bpublisher%5D=&filters%5Btopic%5D=Business+and+economy&filters%5Bformat%5D=&sort=best | Open Government Data Portal (filered for "Business & economy") | All the open government data in one handy searchable portal. Big grab back of stuff, but examples of interesting things include corporation tax receipts, Employer compliance (includes PAYE data) & Monthly Differences Data (differences between VAT returns & receipts). Note that not all the data listed is published. | Medium | Easy | ||||||||||||||||||||||
16 | https://www.personalgroup.com/resources/blog/it-s-not-me-it-s-the-gap | Gender Happiness Gap data | Employee engagement services providers the Personal Group did research on the 'Gender Happiness Gap' at companies (women are happier despite being paid less). Report is linked at left. We mgiht be able to ask them for a cut of the data that would let us see happiness/seniority scores by company (if captured) & correlate that with the GPG data. | High | |||||||||||||||||||||||
17 | https://github.com/lovedaybrooke/gender-decoder | Gender bias detector tool for job ads (Kat Matfield) | Uses a word list based on research published in 2011 in the Journal of Personality and Social Psychology. Handily available code from github. | High | Easy | ||||||||||||||||||||||
18 | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2572075/ | Gender bias word list for recommendation letters | 2007 research from the National Institute of Health. Limitation: this is specifically focused on biology & chemistry recommendation letters so may not be comprehensive enough to generalise to other industries. That hasn't stopped this guy from building a tool based on it, though: https://www.tomforth.co.uk/genderbias/ | Medium | Easy | ||||||||||||||||||||||
19 | https://www.totaljobs.com/insidejob/gender-bias-decoder/ | Gender biast detector tool for job ads (totaljobs) | Totaljobs appears to draw from multiple studies for its list of words, including the same one Kat Midfield cites in her tool. As well as the list of words from the original study, totaljobs' analysis includes some facts about the UK that might be useful for creating new models. | High | Easy | ||||||||||||||||||||||
20 | Data.gov API | Chiin??? | |||||||||||||||||||||||||
21 | McKinsey data | ||||||||||||||||||||||||||
22 | https://www.weforum.org/reports/the-global-gender-gap-report-2017 | WEF global data gender gap | |||||||||||||||||||||||||
23 | UN data | ||||||||||||||||||||||||||
24 | https://data.bathhacked.org/ | Bath city data | |||||||||||||||||||||||||
25 | https://genderize.io/ | Gender first-name predictor | Brigitte???? | ||||||||||||||||||||||||
26 | http://apps.trustpilot.com/apis/ | Trustpilot | |||||||||||||||||||||||||
27 | https://www.gov.uk/government/publications/women-in-finance-charter | Women in Finance charter | |||||||||||||||||||||||||
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