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WageIndicator’s volunteer web-survey on work and wages

WageIndicator's volunteer web-survey collects global work and wage data to understand country differences and the effects of wage-setting institutions. It recruits survey respondents from high-traffic national websites using a salary check and teasers.

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WageIndicator’s volunteer web-survey on work and wages

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  1. WageIndicator’svolunteer web-survey on work and wages InGRID2 dataforumKea Tijdens, University of Amsterdam/AIAS 31 – 8 - 2017

  2. Introduction What is WageIndicator? Recruiting WageIndicator survey respondents Why is thisfun? Reflecting on volunteer surveys Outline

  3. Introduction What is WageIndicator? Recruiting WageIndicator survey respondents Why is thisfun? Reflecting on volunteer surveys Outline

  4. Report on Online Panels (2010) “Researchers should avoid non-probability online panels when one of the research objectives is to accurately estimate population values.” However... “There are times when a non-probability online panel is an appropriate choice. […] there may be survey purposes and topics where the generally lower cost and unique properties of web data collection is an acceptable alternative to traditional [...] methods.”

  5. In a globalising economy … we need worldwide comparative work & wage data to understand country differences, and this data needs to be up-to-date we need micro-level data as to capture the effects of national wage-setting institutions and industrial relations Internet allows for global data-collection global surveys, managed from one IT system local data entry in global databases on a budget far less than random sampled surveys with f2f interviews WageIndicator web-survey on work and wages teaser in the national WageIndicator websites in 92 countries: “please complete survey” >>multi-country, multilingual, continuous, volunteer web-survey Need for global data

  6. Introduction What is WageIndicator? Recruiting WageIndicator survey respondents Why is thisfun? Reflecting on volunteer surveys Outline

  7. WageIndicator websites • In 2000 started with one website forworkandwages in the Netherlands • In 2004 expansion to neighbouring countries andother continents • In 2017 national websites in 92 countries

  8. Content of the websites: Home page Paywizard.uk

  9. And even in Chinese …..

  10. Introduction What is WageIndicator? Recruiting WageIndicator survey respondents Why is thisfun? Reflecting on volunteer surveys Outline

  11. Websites with high web traffic The national WageIndicator websites publish information thatpeople are searching for on the Internet Thisresults in millions of webvisitors (2016: 40 mln) Thishuge pool of visitors is usedtorecruit survey respondentsforthe WageIndicator web-survey The Salary Check All websites post a “Salary Check”, a web-tool thatindicatesa referencewage per occupation The Salary Check is populatedwith data fromthe web-survey andfromnational random sampledsurveys The Salary Check is a ‘mini-survey’ of the web-survey, andalsousedfor data-collection Recruiting survey respondents

  12. Recruiting 1 - Salary check

  13. Recruiting 2 – Jobs & salary pages • WageIndicator websites provide matches formanytermsused in search engines, e.g. “Earningswindow cleaner UK” • The national websites have 430 landing pages, one page for each 4-digit occupation

  14. Recruiting 3 – Teasers • On many WageIndicator webpages teasers pop up

  15. Need for global data What is WageIndicator? Recruiting WageIndicator survey respondents Why is thisfun? Reflecting on volunteer surveys Outline

  16. Why is thisfun - Content andsize of web-survey • Web-survey has questionsabout • wages, workingcondions, job satisfaction, migration, household, etc • questions are organised in pages, each page addresses a specific topic • Analysis otherwiseimpossible, because of • country coverage: Health workforce remuneration: comparing wage levels, ranking and dispersion of 16 occupational groups in 20 countries using survey data (In Human Resources for Health 2013) • omnibus survey, squeezing in survey questionsforspecific time period: Wage and workforce adjustments in the economic crisis in Germany and the Netherlands (European Journal of Industrial Relations 2014) • large dataset allowing follow-up tasklists per occupation (433 occ’s): Task implementation heterogeneity and wage dispersion (IZA Journal of Labour Economics 2015) • large datasets: Employees’ Preferences for longer or shorter working hours (Van der Lippe, T., Peters, P. (eds.), Competing Claims in Work and Family Life 2007)

  17. Why is thisfun - Technology • One single excel file to run the survey • Oneexcel file withall survey questionsandanswersandtheirtranslations is usedtogenerate country/languagespecificsurveysforall 92 countries, with a plan to go to 150 countries • Thistableincludesvariable/value labels plus routing • API’swithtranslationandmappingtables are usedfor survey questions • Occupation, industry, education, city • XML and Python scripts • One XML file to run the survey • Program to run thecommunicationbetween user and server • One Python survey2csv script togenerate a monthlycsv datafile • Responsive design • Facilitates survey completion on a smart phone

  18. Why is thisfun - Technology 2 • Language selector • Respondentscan select theirprefered survey language (47 languages) • Facilitatesother survey modes • Previously we have conducted face-to-face surveys in developpingcountriesusingprinted questionnaires • Now F2F survey available in an app for tablets • We canusethe app to target toveryspecificpopulations • Random survey pages • Dueto large number of respondents, thebattlebetweenthedesiretoaskmany survey questionsandthelimitationsduetorespondent’s time couldbesolvedbyshowingsome pages randomly (of 6 pages a rotating 2 are shownto a rspondent)

  19. Occupation database with 1,700 occupational titles English master label and ‘translated’ into approx 40 languages -->> Coded ISCO-08, in SERISS project extended to 4,000 titles Industry database with 320 industries English master label and translated into approx 40 languages -->> Coded NACE_rev.2.0 Education database (country specific) 10-40 educational categories per country in national language -->> Coded ISCED–97, in SERISS updated to ISCED-2011 Region database (country specific) Cities nested in regions in nationallanguage -->> includes an urbanisationvariable Why is thisfun - APIsfor long-list questions

  20. We run experiments in the web-survey How is job satisfactionaffectedwhenrespondentsreceive different messageshowthey are positioned in thewage ranking? (no effect ) Currently in the field A face-to-face survey of garmentworkers in Indonesia, usingan app on a tablet Respondents are invitedtoanswerthe survey questions, which is a customizedversion of the web-survey Respondentstendto co-operatewiththe interview because interviewers are associatedwithtradeunionsandbecausethequestionsinstantly test whetherthe job complieswiththenationallabourlawand minimum wages Why is thisfun? - - Technology 3

  21. Introduction What is WageIndicator? Recruiting WageIndicator survey respondents Why is thisfun? Reflecting on volunteer surveys Outline

  22. Messy web data Volunteer survey => a non-random sample Self selection => over-representation young & higher educated,more so in countries with low Internet access Substantial drop out rates during survey completion But … No other continuous global data collection on wages available The survey allows for explorative modelling without the need for expensive field surveys Due to large samples => it allows for analysis of subpopulations Flexible survey => allows for follow-up questions for specific groups, for experimental approaches, for omnibus survey Surveysforface-to-face interviews canbegenerated(in 25+ African, Asian and LATAM countries, now in an app) Social media usedtoreach out toveryspecificpopulations Reflecting on volunteer surveys

  23. Advantages of volunteer web-surveys increasing importance of web-surveys increasing problems with other survey modes (rising non response; no telephone book) web-surveys can easily be held continuously & worldwide large sample sizes allow for analyses of small/rare populations large sample sizes allow for follow-up questions (screening device) particularly important when sampling frames are absent Disadvantages of volunteer web-surveys: self-selection self-selection: the results are not representative for population sources of error: non-coverage due to lack of Internet access, no sampling frame due to lack of list of Internet users drop out rates much higher compared to other survey modes Advantages & disadvatages

  24. Volunteer survey data allows for explorative analysis Providing insights otherwise not traced Recruitement of respondents-->> ‘Putting a survey on the web’ is notenough in case of volunteer web-surveys Conclusions

  25. Thank you for your attention   Useful links http://www.wageindicator.org/main http://www.wageindicator.org/main/Wageindicatorfoundation/publications

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