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Web data and Applied Economics

Web data and Applied Economics. Pablo de Pedraza Lisbon 26 th March 2013. Web data & Applied Economics. 1.- Why quick, reliable and internationally comparable data 1.1.- The Globalization Trilemma ( Rodrik 2002) 2.- Types and examples of web data

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Web data and Applied Economics

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  1. Web data and Applied Economics Pablo de Pedraza Lisbon 26th March 2013

  2. Web data & Applied Economics 1.- Why quick, reliable and internationally comparable data 1.1.- The Globalization Trilemma (Rodrik 2002) 2.- Types and examples of web data 2.1.- Web surveys: the Wage Indicator data 2.2.- Non reactive data: Google econometrics 2.3.- Non reactive data: the twitter miner 3.- The Webdatanet scientific structure & activities 4.- Proposal

  3. Web data & Applied Economics 1.- Why quick, reliable and internationally comparable data 1.1.- The Globalization Trilemma (Rodrik 2002) 2.- Types and examples of web data 2.1.- Web surveys: the Wage Indicator data 2.2.- Non reactive data: Google econometrics 2.3.- Non reactive data: The twitter miner 3.- The Webdatanet scientific structure & activities 4.- Proposal

  4. 1.- Why quick, reliable and internationally comparable data 1.1.- The Globalization Trilemma (Rodrik 2002) Economic Integration GLOBALIZATION TRILEMMA (Rodrik 2002) National politics Welfare system

  5. 1.- Why quick, reliable and internationally comparable data 1.1.- The Globalization Trilemma (Rodrik 2002) Economic Integration Markets without governance National politics Welfare system

  6. 1.- Why quick, reliable and internationally comparable data 1.1.- The Globalization Trilemma (Rodrik 2002) Economic Integration Markets without governance National politics Welfare system Protectionism

  7. 1.- Why quick, reliable and internationally comparable data 1.1.- The Globalization Trilemma (Rodrik 2002) Economic Integration • Global Federalism • Non-market global institutions • Tremendous difficulties • Variety of systems, views, regulations. • European experience • WB, ILO, WTO… • Political Sciences, sociology, economy, psychology • Central role of web data collection experts bc Markets without governance Global comparable data: WEB DATA Global Federalism National politics Welfare system Protectionism

  8. Web data & Applied Economics 1.- Why quick, reliable and internationally comparable data 1.1.- The Globalization Trilemma (Rodrik 2002) 2.- Types and examples of web data 2.1.- Web surveys: the Wage Indicator data 2.2.- Non reactive data: Google econometrics 2.3.- Non reactive data: the twitter miner 3.- The Webdatanet scientific structure & activities 4.- Proposal

  9. 2.- Types and examples of web data 2.1.- Web surveys: the Wage Indicator data • Wage Indicator - 80 countries (Wages, labor conditions & preferences) - quick & cheap access (IZA Institute, Bonn) • large and growing amount of data • Traditional flow is too slow • Special campaigns aiming at specific groups under-represented • Good qualities (Pedraza 2010, Pedraza & Martin 2013)

  10. 2.- Types and examples of web data 2.1.- Web surveys: the Wage Indicator data CVWS process

  11. 2.- Types and examples of web data 2.1.- Web surveys: the Wage Indicator data • WI Wages > SES Wages → Education • Same salary determinants • Good special campaigns • Good performance of PS weights • (Pedraza et al. 2010) • Compare WI & SES • Subjective job insecurity • Happiness determinants Theoretical model of SJI Corroborated for five EU countries (Pedraza & Bustillo 2009) • - Corroborate happiness literature • New findings regarding • Labour • Crisis impact on H determinants • Forthcoming as IZA Discussion Paper

  12. Web data & Applied Economics 1.- Why quick, reliable and internationally comparable data 1.1.- The Globalization Trilemma (Rodrik 2002) 2.- Types and examples of web data 2.1.- Web surveys: the Wage Indicator data 2.2.- Non reactive data: Google econometrics 2.3.- Non reactive data: the twitter miner 3.- The Webdatanet scientific structure & activities 4.- Proposal

  13. 2.- Types and examples of web data2.2.- Non reactive data: Google econometrics -Askitas & Zimmerman (2009) Google search data -Choi & Variant (2012) -Other sources of data on real time economic activity • - Available: http://www.google.com/trends/correlate • Timely and at continual basis • Countries & sometimes regions • -Find: • Strong correlation bt: search keywords & unemployment rates • Internet activity help to predict complex and fast changing conditions • Econometrics not yet tap into amount of info • Search engines forecast other economic indicators: • Automobiles sales, unemployment claims, travel destinations, comsumers confidence MasterCard, Federal Express, UPS, Intuit…

  14. Web data & Applied Economics 1.- Why quick, reliable and internationally comparable data 1.1.- The Globalization Trilemma (Rodrik 2002) 2.- Types and examples of web data 2.1.- Web surveys: the Wage Indicator data 2.2.- Non reactive data: Google econometrics 2.3.- Non reactive data: the twitter miner 3.- The Webdatanet scientific structure & activities 4.- Proposal

  15. 2.- Types and examples of web data2.3.- Non reactive data: twitter miner -Reips and Garaizar (2011) • http://maps.iscience.deusto.es/ • iScience Maps • Allows to test the effect of an specific event in twitter • Not yet tested it correlation with economic variables

  16. Web data & Applied Economics 1.- Why quick, reliable and internationally comparable data 1.1.- The Globalization Trilemma (Rodrik 2002) 2.- Types and examples of web data 2.1.- Web surveys: the Wage Indicator data 2.2.- Non reactive data: Google econometrics 2.3.- Non reactive data: the twitter miner 3.- The Webdatanet scientific structure & activities 4.- Proposal

  17. Web data & Applied Economics 3.- The Webdatanet scientific structure & activities 3.1.- Scientific Goal 3.2.- Members 3.3.- Organization: WorkingGroups and Taskforces (WGs & TFs) 3.4.- Activities 3.5.- Nextmeetings

  18. Web data & Applied Economics 3.- The Webdatanet scientific structure & activities 3.1.- Scientific Goal 3.2.- Members 3.3.- Organization: WorkingGroups and Taskforces (WGs & TFs) 3.4.- Activities 3.5.- Nextmeetings

  19. 3.- The Webdatanet scientific structure & activities3.1.- Scientific Goal Scientific goal -Address methodological issues of web-based data collection (surveys, experiments, tests, non-reactive data collection, and mobile Internet research) and foster its scientific usage. -Contribute to the theoretical and empirical foundations of web-based data collection, stimulate its integration into the entire research process (i-science), and enhance the integrity and legitimacy of these new forms of data collection.

  20. Web data & Applied Economics 3.- The Webdatanet scientific structure & activities 3.1.- Scientific Goal 3.2.- Members 3.3.- Organization: WorkingGroups and Taskforces (WGs & TFs) 3.4.- Activities 3.5.- Nextmeetings

  21. 3.- The Webdatanet scientific structure & activities3.2.- Members (Researchers from EU but also outside the EU) • Universities • Data collection Institutes • Research Institutes • Private firms • Statistical Institutes • 80 members, 29 countries

  22. Web data & Applied Economics 3.- The Webdatanet scientific structure & activities 3.1.- Scientific Goal 3.2.- Members 3.3.- Organization: WorkingGroups and Taskforces (WGs & TFs) 3.4.- Activities 3.5.- Nextmeetings

  23. WGs & TFs WG1 Quality WG2 Innovation WG3 Implementation TF1Measuring wages via web surveys (S. Steinmetz) TF2 Evaluating questionnaire quality (A. Slavec) TF3Mixed modes & representativ. (A.Jonsdottir & K. Kalgraff) TF4Internet Panels Europe (A. Scherpenzeel) TF6 New types of measurement (U. Reips) TF7Webdatametrics Workshops (U. Reips & K. Kissau) TF8Dissemination WG2 (U. Reips & A. Selkala) TF9iScienceportals (U. Reips) TF15Non-reactive data (N. Fornara) TF19 Mobile research ( R. Pinter & A. Wijnant) TF20 Paradata (I. Andreadis) TF22 Elections, Facebook & Twitter (R. Vatrapu, L. Kaczmirek) TF10TSE Categorization (F. Thorsdottir & S. Biffignandi) TF 11 How web change empirical world (S. Steinmetz & K. Manfreda) TF16Selecting surveys(M. Revilla) TF17 Web data & Official Statistics (S. Biffignandi) TF21 GenPopWeb (G.Nicolas) TF23 Applied Economics and web data (P. Pedraza) TF14 Development & supervision of the web (F. Serrano & C. Zimmerman) TF12 Master in webdatametrics (Alberto Villacampa) TF18 Organization of Iceland meeting (F. Thorsdottir, A. Jonsdottir & V. Vesteinsdottir)

  24. 3.- The Webdatanet scientific structure & activities3.3.- Organization: WorkingGroups and Taskforces (WGs & TFs) http://www.ijis.net/ijis7_1/ijis7_1_supplement.pdf

  25. Web data & Applied Economics 3.- The Webdatanet scientific structure & activities 3.1.- Scientific Goal 3.2.- Members 3.3.- Organization: WorkingGroups and Taskforces (WGs & TFs) 3.4.- Activities 3.5.- Nextmeetings

  26. WGs & TFs WG1 Quality WG2 Innovation WG3 Implementation TF1Measuring wages via web surveys (S. Steinmetz) TF2 Evaluating questionnaire quality (A. Slavec) TF3Mixed modes & representativ. (A.Jonsdottir & K. Kalgraff) TF4Internet Panels Europe (A. Scherpenzeel) • WGs & TFs can use: • -Meetings • -STSMs (2500€) • -Training Schools (TS) (Ljubljana 10-12 of April) • -WebdatametricsWorkshops (WW) • Bergamo Webdatametrics Workshop I (WG2 & WG3), 22 and 23 January 2013 • -Workshops (GOR workshops) • -Involvement of ESR & PhD students (STSM, TS, WW, TFs ...) • -AIAS-WEBDATANET Working papers TF6 New types of measurement (U. Reips) TF7Webdatametrics Workshops (U. Reips & K. Kissau) TF8Dissemination WG2 (U. Reips & A. Selkala) TF9iScienceportals (U. Reips) TF15Non-reactive data (N. Fornara) TF19 Mobile research ( R. Pinter & A. Wijnant) TF20 Paradata (I. Andreadis) TF22 Elections, Facebook & Twitter (R. Vatrapu, L. Kaczmirek) TF10TSE Categorization (F. Thorsdottir & S. Biffignandi) TF 11 How web change empirical world (S. Steinmetz & K. Manfreda) TF16Selecting surveys(M. Revilla) TF17 Web data & Official Statistics (S. Biffignandi) TF21 GenPopWeb (G.Nicolas) TF23 Applied Economics and web data (P. Pedraza) TF14 Development & supervision of the web (F. Serrano & C. Zimmerman) TF12 Master in webdatametrics (Alberto Villacampa) TF18 Organization of Iceland meeting (F. Thorsdottir, A. Jonsdottir & V. Vesteinsdottir)

  27. WGs & TFs WG1 Quality WG2 Innovation WG3 Implementation 1.- Increase interaction, communication and understandingbetween web surveyors, other web based data collection experts and analyses. 2.- State of the art from a multidisciplinary perspective. 3.- Identify frontiers of knowledge 4.- Creative thinking 5.- Theoretical foundations of web surveys take into account innovations INCREASE interaction, communication and understanding across WEBDATANET disciplines TF1Measuring wages via web surveys (S. Steinmetz) TF2 Evaluating questionnaire quality (A. Slavec) TF3Mixed modes & representativ. (A.Jonsdottir & K. Kalgraff) TF4Internet Panels Europe (A. Scherpenzeel) • WGs & TFs can use: • -Meetings • -STSMs (2500€) • -Training Schools (TS) (Ljubljana 10-12 of April) • -WebdatametricsWorkshops (WW) • Bergamo Webdatametrics Workshop I (WG2 & WG3), 22 and 23 January 2013 • -Workshops (GOR workshops) • -Involvement of ESR & PhD students (STSM, TS, WW, TFs ...) • -AIAS-WEBDATANET Working papers TF6 New types of measurement (U. Reips) TF7Webdatametrics Workshops (U. Reips & K. Kissau) TF8Dissemination WG2 (U. Reips & A. Selkala) TF9iScienceportals (U. Reips) TF15Non-reactive data (N. Fornara) TF19 Mobile research ( R. Pinter & A. Wijnant) TF20 Paradata (I. Andreadis) TF22 Elections, Facebook & Twitter (R. Vatrapu, L. Kaczmirek) TF10TSE Categorization (F. Thorsdottir & S. Biffignandi) TF 11 How web change empirical world (S. Steinmetz & K. Manfreda) TF16Selecting surveys(M. Revilla) TF17 Web data & Official Statistics (S. Biffignandi) TF21 GenPopWeb (G.Nicolas) TF23 Applied Economics and web data (P. Pedraza) WEBDATAMETRICS “General concept that emerges from the existing diverse variety of disciplines related to web data collection methods and analyses. Putting this knowledge together webdatametrics aim to generate new knowledge to take advance of ICT to collect data for scientific proposes” TF12Master in webdatametrics (Alberto Villacampa) TF14 Development & supervision of the web (F. Serrano & C. Zimmerman) TF12 Master in webdatametrics (Alberto Villacampa) TF18 Organization of Iceland meeting (F. Thorsdottir, A. Jonsdottir & V. Vesteinsdottir)

  28. Web data & Applied Economics 3.- The Webdatanet scientific structure & activities 3.1.- Scientific Goal 3.2.- Members 3.3.- Organization: WorkingGroups and Taskforces (WGs & TFs) 3.4.- Activities 3.5.- Nextmeetings

  29. Webdatanetscientificcoordination 4.- Nextmeeting and events • Mannheim 7th, 8th March 2013 - 1st TrainningSchool: Implementinghighquality web survyes, Ljubljana 10-12 April 2013 -CoreGroup Meeting, Salamanca 18-19 April 2013 (maybealsosomeTFs) -IcelandSeptember 2013 (TF meetings, Webdatametrics Workshop, Key note speaker) • Greece, Spring 2014 • Cypus,Autum 2014

  30. Web data & Applied Economics 1.- Why quick, reliable and internationally comparable data 1.1.- The Globalization Trilemma (Rodrik 2002) 2.- Types and examples of web data 2.1.- Web surveys: the Wage Indicator data 2.2.- Non reactive data: Google econometrics & the twitter miner 2.3.- Testing and experimenting 3.- The Webdatanet scientific structure & activities 4.- Proposal

  31. Proposal WG1 Quality WG2 Innovation WG3 Implementation • Start testing simple models using Google & Wage Indicator • Organize a training school on Google data & others • STSM (PhD) • Join proposals • Participate in our meetings • Help in organizing: • http://www.iza.org/conference_files/worldb2013/call_for_papers • http://openeconomics.net/events/workshop-june-2013/ TF1Measuring wages via web surveys (S. Steinmetz) TF2 Evaluating questionnaire quality (A. Slavec) TF3Mixed modes & representativ. (A.Jonsdottir & K. Kalgraff) TF4Internet Panels Europe (A. Scherpenzeel) TF6 New types of measurement (U. Reips) TF7Webdatametrics Workshops (U. Reips & K. Kissau) TF8Dissemination WG2 (U. Reips & A. Selkala) TF9iScienceportals (U. Reips) TF15Non-reactive data (N. Fornara) TF19 Mobile research ( R. Pinter & A. Wijnant) TF20 Paradata (I. Andreadis) TF22 Elections, Facebook & Twitter (R. Vatrapu, L. Kaczmirek) TF10TSE Categorization (F. Thorsdottir & S. Biffignandi) TF 11 How web change empirical world (S. Steinmetz & K. Manfreda) TF16Selecting surveys(M. Revilla) TF17 Web data & Official Statistics (S. Biffignandi) TF21 GenPopWeb (G.Nicolas) TF23 Applied Economics and web data (P. Pedraza) TF14 Development & supervision of the web (F. Serrano & C. Zimmerman) TF12 Master in webdatametrics (Alberto Villacampa) TF18 Organization of Iceland meeting (F. Thorsdottir, A. Jonsdottir & V. Vesteinsdottir)

  32. Visit www.webdatanet.eu& contact uspablodepedraza@usal.es Pablo de Pedraza Lisbon March 26th, 2013

  33. Muito Obrigado Lisbon March 26th, 2013

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