The e-census challenge: how web affects quality

1. The e-census challenge: how web affects quality The Italian experience with a local focus on Tuscany L. Porciani; L. Faustini; A. Valentini; B.M. Martelli Istat Session 31 - Quality Aspects when using Administrative Sources 04 June 2014, Vienna

2. Summary Officialstatistics and web data collection in Italy Population and HousingCensus & Enterprises Census The Italianresponse to web data collection mode Data quality management in e-census Discussion and future research 1 2 3 4 5 The e-census challenge: how web affects quality. The Italian experience with a local focus on Tuscany

3. 1 Official statistics and web data collection Social environment Statistical environment • Cutting down expenses (mainly human costs) • Decreasing statistical burden • Increasing timeliness • Reducing workload of the local actors • Evolution of technology • Changes in lifestyles • Widespread use of Internet by people and entreprises • Development of e-Government Promotion of web data collection tecniques in Official Statistics The e-census challenge: how web affects quality. The Italian experience with a local focus on Tuscany

4. 2 2011 Italian CensusPopulation and Housing / Enterprises Census • Maininnovations: • From a totally door-to-door census to an assisted [by censuslists] e-census • Mixed- mode channel of restitution [paper, web, enumerator and postal office] • Samplingstrategy • Survey Management System [SGR] The e-census challenge: how web affects quality. The Italian experience with a local focus on Tuscany

5. 2 2011 Italian CensusPopulation and Housing / Enterprises Census National Local • Local mass media communication [ethnic journal/radio/television] • Web promotional contest [I-phone prize] • Open informative meeting • Mass media communication [Journal/Radio/Television] • Promotional contest [for children and teenagers] • Differentiatedpaymentstrategies+ web response - > + payment The e-census challenge: how web affects quality. The Italian experience with a local focus on Tuscany

6. 3 The Italian response to web DCM The e-census challenge: how web affects quality. The Italian experience with a local focus on Tuscany

7. 3 The Italian response to web DCM WRR = web response rate • Observation units are Italian NUTS-2 (regions: [20 units]) • Covariates (x) are: • 6 for Population and Housing Census; • 3 for Enterprises Census • Dichotomization of covariates [based on median values] • Model and all covariates are statistically significant (p< 0.0001) The e-census challenge: how web affects quality. The Italian experience with a local focus on Tuscany

8. 3 The Italian response to web DCM Enterprises Census Population and HousingCensus • Ageing Index [Age] • Foreigner quota [Foreigners] • Average family size [Family] • Quota of municipalities over than 20.000 inhab. - [Large Munic] • ICT users quota [ICT] • Ex-post evaluation level of web mode data collection [Evaluation] • Foreigner quota of entrepreneur [Foreigners] • Quota of enterprises with 10 or more persons employed [Large Enter] • ICT users quota [ICT] The e-census challenge: how web affects quality. The Italian experience with a local focus on Tuscany

9. 3 The Italian response to web DCM Population and HousingCensus The e-census challenge: how web affects quality. The Italian experience with a local focus on Tuscany

10. 3 The Italian response to web DCM Enterprises Census The e-census challenge: how web affects quality. The Italian experience with a local focus on Tuscany

11. Web response analysis: main results 3 • Internet penetration is not the only factor which affects the web response [coverage issue] • Socio-demographic characteristics seems to be more crucial to determine the web response quota • Regarding population, an aged sociey with smaller families settled in smaller cities is less confortable with web tools • Regarding enterprises, the habits of using Internet and working in largeenterprises push them towards a wider use of web response • Ex post evaluation instruments confirm their central role for future surveys and census planning The e-census challenge: how web affects quality. The Italian experience with a local focus on Tuscany

12. 3 Focus on Tuscany PHC web response Regional web response rates distribution 24.3% Tuscany PHC web responseis the lowest in the whole country, the ICT penetration rate isabove the averagenationallevel (54.7% versus 52.5%) The e-census challenge: how web affects quality. The Italian experience with a local focus on Tuscany

13. 3 Focus on Tuscany PHC web response WRR = quota of web respondent • Observation units are Municipalities of Tuscany [287 units] – LAU2 • 4 covariates • Ageing Index [Age] • Foreigner quota [Foreigners] • Average family size [Family] • Municipalities size [Size Munic] • Dichotomization of covariates [based on median values] • Model and all covariates are statistically significant (p< 0.0001) The e-census challenge: how web affects quality. The Italian experience with a local focus on Tuscany

14. Focus on Tuscany PHC web response 3 The e-census challenge: how web affects quality. The Italian experience with a local focus on Tuscany

15. 3 The Italian response to web DCM • Web responses are linked to some characteristics of population and enterprises • The presence of foreignershas a negative impact both for population and enterprises. On the other side, the higheris the habit to use ICT the higheris the propensity to web response. • And specifically • in case of population, a more traditional society (aged and with bigger family size) living in smallermunicipalitiesislessattracted by web tools. Nevertheless, agoodappreciation of web instruments by censusoperatorspromotes a better performance of e-census • in case of enterprises, largerenterprises are more confortableto work with web tools The e-census challenge: how web affects quality. The Italian experience with a local focus on Tuscany

16. Data quality management in e-census 4 Field work Top-down approach Researcher selects DQ attributes Intuitive Paradatatool Top-down approach How data become weak during data collection process Theoretical Ad hoc survey on operators Bottom up approach Investigation of users/researcher opinions about DQ attributes Empirical The e-census challenge: how web affects quality. The Italian experience with a local focus on Tuscany

17. 4 Thereotical approach: paradata Road-mapindicators Dashboard indicators Real time -> to realize an immediate set of interventions to increase general census performance Ex post indicator -> actions planned to prevent critical situations and to reengineer the process • Rate of postal delivery (percentages) • Questionnaires by channel of answer (percentages) The e-census challenge: how web affects quality. The Italian experience with a local focus on Tuscany

18. 5 Discussion and proposal • The set of tools introduced is valid to • easily point out the criticalities through simple indicators • promote best practices in data collection • more consciously monitor the field work and, finally • assess data and process quality Ex post and in itinere analysis of results could be a key factor for better planning and realizing the continuous [rolling] censuses, which will be paperless The e-census challenge: how web affects quality. The Italian experience with a local focus on Tuscany

19. 5 Discussion and proposal • Availability of censusmicro datacouldrepresent a goodopportunity to include othercovariates in the web response rate model [age; work activities and level of education of the family head; innovationpropensity, localization, outsourcing quota of enterprises…] • Detailed information about the responseprocess [place of compilation? help from who?] couldrepresentuseful information to enrich the understanding of web data collection mode The e-census challenge: how web affects quality. The Italian experience with a local focus on Tuscany

20. Thanks for your attention! Linda Porciani, porciani@istat.it The e-census challenge: how web affects quality. The Italian experience with a local focus on Tuscany