Wage Indicator, WEBDATANET & eduworks

Eduworks kick off meeting, Amsterdam December 11th, 2013.

Pablo de Pedraza

1.- Wage Indicator: Quick, reliable and internationally comparable data

1.1.- Continuous Voluntary Web Surveys (CVWS): Drawbacks

1.2.- Methodological approaches and research examples

2.- Webdatanet

1.-Wage Indicator: Quick, reliable and internationally comparable data

The current economic crisis

requires fast information for quick reaction

to predict economic behavior early

difficult at times of structural changes.

Quick & reliable data

Web-based data collection methods

1.-Quick, reliable and internationally comparable data

Web vs traditional Labour Surveys

CURRENT CONTEXTGlobal EconomyQuick changes

1.-Quick, reliable and internationally comparable dataWeb vs traditional Labour Surveys

CURRENT CONTEXT

Global EconomyQuick changes

Traditional Surveys

Slow

National/regional coverage

International comparisons

1.-Quick, reliable and internationally comparable dataWeb vs traditional Labour Surveys

CURRENT CONTEX

Global EconomyQuick changes

Traditional Surveys

Slow

National/regional coverage

International comparisons

Web surveysFast

(collecting & processing)

Multi-country/Multi-lingual homogenized surveys (75 countries)

International comparisons

HOWEVER…

1.- Wage Indicator: Quick, reliable and internationally comparable data

1.1.- Continuous Voluntary Web Surveys (CVWS): Drawbacks

1.2.- Methodological approaches and research examples

2.- Webdatanet

2.- CVWS drawbacks

CVWS process

2.- Advantages and drawbacks

CVWS process

TRADITIONAL CONCEPTS OF SURVEY METHODOLOGY:

- Coverage- Non-response…

Total Survey Error APPROACH

And other surveys…

1.- Wage Indicator: Quick, reliable and internationally comparable data

1.1.- Continuous Voluntary Web Surveys (CVWS): Drawbacks

1.2.- Methodological approaches and research examples

2.- Webdatanet

1.2.- Methodological approaches and research examples

1.2.a.- Bias description

1.2.b.- Design base approach

1.2.c.- Model base approach: Calculate and test weights

1.2.d.- Test innovations and use paradata

1.2.e.- Wage Indicator Research examples

1.2.- Methodological approaches: present & future

1.2.a.- Bias description– National (Labour Force Survey & Structures of Earnings S.)

Bias description: National Reference Surveys vs Wage Indicator sample

Reference Survey proportions vs Wage Indicator proportions(using demographic variables)

3.- Methodological approaches: present & future

1.2.a- Bias description

1.2.b.- Design based approach

1.2.c- Model base approach: Calculate and test weights

1.2.d.- Test innovations and use paradata

1.2.e.- Wage Indicator Research examples

MARKETING MEASSURES- Attract large masses of visitors - Address underrepresented groups

Able to correct socio-demographic bias

Ex. Spain, Germany

Provide internet access

Costs (LISS PANEL)

But less and less

Mixed modes

Offline questionnaires

Low Income Countries

& Middle-Low Income Countries

1.2.- Methodological approaches and research examples

1.2.a.- Bias description

1.2.b.- Design base approach

1.2.c.- Model base approach: Calculate and test weights

1.2.d.- Test innovations and use paradata

1.2.e.- Wage Indicator Research examples

1.2.- Methodological approaches: present & future

1.2.c- Model base approach: Calculate and test weights

-Post-stratification: weight=npopulation / nsample

1.2.- Methodological approaches: present & future

1.2.c.- Model base approach: Calculate and test weights

-Post-stratification: weight=npopulation/nsample

-Probability functions predicted probability weight=1/calc.prob.

1.2.- Methodological approaches: present & future

1.2.c.- Model base approach: Calculate and test weights

-Post-stratification: weight=npopulation/nsample

-Probability functions predicted probability weight=1/calc.prob.

example

1.2.- Methodological approaches: present & future

SES

Structures of

Earnings Survey

WI

Wage Indicator

Proportional

Wage Indicator

PSW

Wage Indicator

Mean salary

(standard error)18 888.18€

(33.46)

22 902.81€

(212.63)

21 903.06€

(251.95)

21 288.67€

(351.81)Wage-Gini-index

0.3687 0.3596 0.3593 0.3645

- WI Wages > SES Wages → Education- Same salary determinants- Good special campaigns- Good performance of Propensity Score Weights

(REIS, Pedraza et al. 2010)

Theoretical model of Subjective Job InsecurityCorroborated for five EU countries(EJIR, Pedraza & Bustillo 2009)

- Corroborate Life Satisfaction literature (IZA DP)- New findings regarding

- Employment status- Crisis impact on Life Satisfaction determinants

1.2.- Methodological approaches and research examples

1.2.a.- Bias description

1.2.b.- Design base approach

1.2.c.- Model base approach: Calculate and test weights

1.2.d.- Test innovations and use paradata

1.2.e.- Wage Indicator Research examples

1.2.- Methodological approaches: present & future

1.2.d.- Test innovations and use paradata

Dynamic testing for Occupational questions(Ulf D. Reips)

Study of paradata to improve quality Ex. study drop out

(AIAS Working Paper, K.Tijdens, 2011)

Other web based data collection methods

1.2.- Methodological approaches and research examples

1.2.a.- Bias description

1.2.b.- Design base approach

1.2.c.- Model base approach: Calculate and test weights

1.2.d.- Test innovations and use paradata

1.2.e.- Wage Indicator Research examples

1.2.- Wage Indicator content research examples and opportunities1.2.e.- Bias study→ weights→ efficiency of w.→ content research

Spain: Job Insecurity, Life Satisfaction

Brazil: Life satisfaction

International comparisons (BRICS)

- National: LFS

- International: ILO LFS,

World Values Survey;

European Social Survey.

1.- Wage Indicator: Quick, reliable and internationally comparable data

2.- Continuous Voluntary Web Surveys (CVWS): Drawbacks

3.- Methodological approaches and research examples

4.- Webdatanet

4.- Webdatanet: Who we are? What are our goals? How? Why?

Who Sociologists, Psychologists, Economists, Media researchers, Computer scientists…

- Universities- Data collection Institutes - Research Institutes- Companies- Statistical Institutes

We are researchers from EU but also outside the EU (80 members, 30 countries)

Webdatanet is a Multidisciplinary Network of web-based data collection experts funded by the European Commission

Internationalization of the network

and find resources to do it

4.- Webdatanet: Who we are? What are our goals? How? Why?Webdatanet is a Multidisciplinary Network of web-based data collection

experts funded by the European Commission

Scientific goal

- Foster scientific usage of web-based data: Surveys, Experiments, Tests, Non-reactive data collection, Mobile Internet research.

- Benefit society giving behavioral and social scientist high quality web data

4.- Webdatanet: Who we are? What are our goals? How? Why?Webdatanet is a Multidisciplinary Network of web-based data collection

experts funded by the European Commission

How

- Enhancing quality, integrity and legitimacy of these new forms of data collection,

- Methodological issues: Theoretical and empirical foundations,

- Stimulating its integration into the entire research process (i-science),

- Increasing interaction and communication across disciplines,

4.-Webdatanet: Scientific Structure (WGs & TFs).WG1 Quality WG2 Innovation WG3 Implementation

TF1 Measuring wages via web surveys (S. Steinmetz)

TF2 Evaluating questionnaire quality (A. Slavec)

TF3 Mixed modes & representativ.(A.Jonsdottir & K. Kalgraff)

TF4 Internet Panels Europe (A. Scherpenzeel)

TF24 Experiments on students samples (K. L. Manfreda)

TF6 New types of measurement(U. Reips)

TF7 Webdatametrics Workshops(U. Reips & K. Kissau)

TF8 Dissemination WG2 (U. Reips & A. Selkala) TF9 iScience portals (U. Reips) TF15 Non-reactive data (N. Fornara)

TF19 Mobile research (R. Pinter & A. Wijnant)

TF20 Paradata (I. Andreadis)

TF22 German Elections, Facebook & Twitter (R. Vatrapu, L. Kaczmirek)

TF10 TSE Categorization (F. Thorsdottir & S. Biffignandi)

TF 11 How web change empirical world (S. Steinmetz & K. Manfreda)

TF16 Selecting surveys (M. Revilla)

TF17 Web data & Official Statistics (S. Biffignandi)

TF21 GenPopWeb (G.Nicolas)

TF25 Applied Economics and web data (P. Pedraza)

TF26 Web data journal (Konstantinos T.)

TF14 Development & supervision of the web (F. Serrano & C. Zimmerman)TF12 Master in webdatametrics (Alberto Villacampa)TFs for Meetings, training schools, workshops, WebSM (TF18, TF13...) SGs (Small Group meetings)

2.-Scientific Structure (WGs & TFs).WG1 Quality WG2 Innovation WG3 Implementation

TF1 Measuring wages via web surveys (S. Steinmetz)

TF2 Evaluating questionnaire quality (A. Slavec)

TF3 Mixed modes & representativ.(A.Jonsdottir & K. Kalgraff)

TF4 Internet Panels Europe (A. Scherpenzeel)

TF24 Experiments on students samples (K. L. Manfreda)

TF6 New types of measurement(U. Reips)

TF7 Webdatametrics Workshops(U. Reips & K. Kissau)

TF8 Dissemination WG2 (U. Reips & A. Selkala) TF9 iScience portals (U. Reips) TF15 Non-reactive data (N. Fornara)

TF19 Mobile research (R. Pinter & A. Wijnant)

TF20 Paradata (I. Andreadis)

TF22 German Elections, Facebook & Twitter (R. Vatrapu, L. Kaczmirek)

TF10 TSE Categorization (F. Thorsdottir & S. Biffignandi)

TF 11 How web change empirical world (S. Steinmetz & K. Manfreda)

TF16 Selecting 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)TFs for Meetings, training schools, workshops, WebSM (TF18, TF13...) SGs (Small Group meetings)

WGs & TFs: www.webdatanet.eu

- Conferences & Meetings

- STSMs (2500€)

- Training Schools (TS) (Ljubljana April 2013)

- Webdatametrics Workshops (WW)Bergamo, January 2013

- Involvement of ESR & PhD students (STSM, TS, WW, TFs ...)

- AIAS-WEBDATANET Working papers (IJIS)

4.- Webdatanet: Some Examples of TFs:

- TF1.- Measuring wages in web surveys

- TF17.- Web data & official statistics

- TF23.- Web data and Applied Economics

- TF12.- Master in Webdatametrics

4.- Some Examples of TFs: TF 1.- Measuring wages in web surveys

www.wageindicator.orgMeasurement & comparability

70 countriesILO and Decent Work Projects

Also labor conditions and satisfaction variablesParadata (Quality of data)

2.- Webdatanet scientific structure (WGs & TFs). TF 17.- Integrating web data with Official Statistics

ESSNetEurostat & Statistical Institutes

Contribute web data to expansion to:ILO

UN www.unglobalpulse.orgWorld Bank

4.- Some Examples of TFs: TF 12.- Master in webdatametrics Multidisciplinary Academic Board

September 2014Online & F2F teachings

Core: 5 types of web base data Elective: implementation to specific disciplines

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”

TF12 Master in webdatametrics (Alberto Villacampa)

4.- Some Examples of TFs: TF 25.- Web data & Applied Economics

- Systematically explore all the possibilities web data Applied Economic research;

- identify & classify limits of any kind -scientific, ethical, legal, institutional, related to data access...

- work overcame those limits and open new research opportunities aiming to benefit society;

- foster the Webdatanet international multidisciplinary networking process with leading academics, companies and national and international institutions;

- Apply for the necessary institutional and private support for all the above.

THANK YOU Amsterdam, December 11th, 2013.