Almiro Moreira Statistics Portugal « 25 September 2013 Geneva, Switzerland UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE CONFERENCE OF EUROPEAN STATISTICIANS Topic (ii): Centralising data collection CREATING A DATA COLLECTION DEPARTMENT: STATISTICS PORTUGAL'S EXPERIENCE Paulo Saraiva dos Santos Statistics Portugal

Sharing eight years of experience in centralising data collection, implementing an integrated and process driven approach to change the statistical production, improving its efficiency and flexibility. Motivation 2

1.Background and context; 2.Reengineering the production; 3.Centralised Data Collection; 4.Administrative Sources & registers; 5.Data Collection infrastructure 6.Benefits of a Centralised Approach; 7.The future of Data Collection. Outline 3

Central national authority for the production of official statistics; Aims at developing and supervising the national statistical system; Created in 1935, has its head office in Lisbon with delegations in Porto, Coimbra, Évora and Faro. Statistics Portugal (INE) 4

Madeira Azores Oporto Lisboa Coimbra Évora Faro Geographical dispersion 5 Common technical requirements, methods and infrastructure

Statistics Portugal is a public institution which has legal personality, administrative autonomy and technical independence in the exercise of its official statistical activity; It is a special public institution integrated within indirect State administration; The Statistical Law confers on Statistics Portugal statistical authority and legal obligation to confidentiality. Statistics Portugal 6

European and National Statistics (2013): European scope 7 Statistical Operations (2013)EuropeanNational / OtherTotal Statistics Portugal15382%3318%186100% Quantity of Statistical Operations

Timeline 8 From 1989 to 2003: –Headquarters & Regional Directorates; Regional Directorates: –Firstly acting as dissemination and data collection center for the region (NUTS II); –Gradually assumed active role in statistical production and regional studies; –Its organization and resources have increased fast. From 2003: –Proactive evaluation of the existing model; –Reorganization not guided by resources constraints.

Reorganization in New Executive board in 2003; Hired external advisory company (international strategy consultants); Request a Peer Review in 2004: –Mr. Ivan Fellegi Former Chief Statistician of Canada from 1985 to 2008; –Mr. Jacob Ryten Former Assistant Chief Statistician of Canada from 1969 to A proactive action: to create a new structure.

Former organization (2003) 10 Executive Board Lisbon and Tagus Valle North CenterAlentejo Algarve Dissemination Finance Human Resources Planning and International Legal SupportMethodology Information Systems National Accounts Agriculture Population and Census Business Industry and Services Social Short Term and Forecast Regional DirectoratesSupportSubject Matter

Regional Directorates (2003) 11 Regional Directorate SocialBusinessStudies RH & ResourcesIT Support Dissemination Regional Directorate (Department) Unit Section Three hierarchical levels An example of the organization of a former RD.

Local 1 Survey 2 Survey n 12 Difusão Tratamento Recolha Difusão Tratamento Recolha Difusão Tratamento Recolha... Former architecture Difusão Tratamento Recolha Difusão Tratamento Recolha Difusão Tratamento Recolha Dissemination Treatment Collection Dissemination Treatment Collection Dissemination Treatment Collection Local 2 Local n Survey 1... Stovepipe systems Complex, inefficient and not flexible

Former organization (2003) 13 Heavy and costly organization –788 workers: 37% in Regional Directorates. –195 managers (25%): 14 Departments, 5 Regional Directorates, 48 Units, 128 sections Duplication of work, procedures and tools; Not flexible enough for the future. Need to be reorganized

Fellegi & Ryten’s Peer Review Objective: to review the Portuguese statistical system and produce recommendations; Main results: The diagnosis; Structural problems and remedies; Recommendations

Started in 2004 and based on the Peer Review´s recommendations; Internal reorganization: –A central data collection department was created; –Regional directorates were extinct; –Domain departments have been merged into three units: economics, social and national accounts; –Methods and information system were merged into one department. It was a successful challenge, although some resistances and constraints. 15 Production re-engineering

New organization (2004  2013) 16 Executive Board Porto Finance & HR Inf Systems Methodology Data Collection National Accounts Economics Social DelegationsSupport Statistical Production Coimbra Évora Faro Subject Matter Staff Dissemination Planning Legal Support International Communication L1: Department L2: Unit L3: Section Three hierarchical levels

Production architecture 17 Social and Demographics Economics National Accounts Data Collection Methods and Information Systems

18 Impact of the reorganization % Diference L1: Departments197-63% L2: Units % L3: Sections % Managers % Workers / Managers4,010,9173% Workers % Lisbon % Regions % Staff reduction without firing anyone

19 Human Resources Distribution (by macro process)

Centralised Data Collection

Survey’s data collection: –40% budget & 30% human resources. Data Collection at Statistics Portugal 21 Survey Data Collection is a core function

A Data Collection department assures the collection, processing and analysis of collected microdata, covering all business and social surveys;  HR  ~ 200 workers freelance interviewers Data Collection 22  120 surveys  105 business (self-completed)  15 by interview (CAPI and CATI).  companies (99% SME);  dwellings;  farms. Annual figures

Data Collection Department 23 Data Collection Self-completed Surveys Interview Surveys Data Collection Processes Lisbon 1 Lisbon 4 Lisbon 3 Lisbon 5 Lisbon 6 Lisbon 7 Coimbra Porto 1 Porto 3 ÉvoraFaro Porto 2

Data Collection Department Human Resources by Unit 24

Data Collection Department Organization by Unit 25 Self-completed surveys: –By project or statistical operation; –National management of each project; Interview surveys: –Sections work with the same projects; –Share same methods, procedures and tools. Data collection processes; –National coordination of interview surveys; –CATI national coordination.

Management within DC 26 Decentralized managed but centrally controlled; One overall budget distributed through each management level; Autonomy with responsibility; Objective definition in “cascade”; –Department  Unit  Section  worker HR: matrix management;

Interview Management System 27 Interview Management System supports all the processes related with social statistics and the price collection; The Survey Management System has several components: team management and the tools used by the interviewers to collect data, transfer them to Statistics Portugal, allowing them to work both in face-to-face and telephone interviews..

HR and costs control 28 Assiduity control  WebRH app; Accounting to projects  Factiv app –Project codes and Task codes; –Individually daily allocation of the working time to each project code and tasks; Direct HR costs are monthly calculated to each project, according to individual wages and social costs; The same with other costs and indirect costs; Transfers can be made between projects.

2 Design 3 Build 4 Collect 5 Process 6 Analyse 7 Disseminate 1 Specify Needs 3.5 Test statistical business process 3.4 Test production system 3.3 Configure workflows 3.2 Build or enhance process components 3.1 Build data collection instrument 1.6 Prepare business case 1.5 Check data availability 1.3 Establish output objectives 1.2 Consult and confirm needs 1.1 Determine needs for information 2.6 Design production systems and workflow 2.5 Design statistical processing methodology 2.4 Design frame and sample methodology 2.3 Design data collection methodology 2.2 Design variable descriptions 2.1 Design outputs 4.4 Finalize collection 4.3 Run collection 4.2 Set up collection 4.1 Select sample 5.1 Integrate data 5.2 Classify and code 5.3 Review, validate, edit and analyze microdata 5.4 Impute 5.5 Derive new variables and statistical units 5.6 Calculate weights 5.7 Calculate aggregates 6.1 Prepare draft outputs 6.2 Validate outputs 6.3 Scrutinize and explain 6.4 Apply disclosure control 6.5 Finalize outputs 7.5 Manage user support 7.4 Promote dissemination products 7.3 Manage release of dissemination products 7.2 Produce dissemination products 7.1 Update output systems 8 Archive 9 Evaluate 8.2 Manage archive repository 8.1 Define archive rules 8.3 Preserve data and associate metadata 8.4 Dispose of data and associated metadata 9.1 Gather evaluation inputs 9.2 Conduct evaluation 9.3 Agree action plan Levels 1 and 2 GSBPM, version Identify concepts 3.6 Finalize production system 5.8 Finalize data files Data Collection Subject Matter Shared DC/SM IS & Methods Dissemination Quality Control Division of work

Relationship between DC & Subject Matter (1) 30 One major issue at the beginning; There were a negative perception of the DC tasks  “a low profile work …” Conversely, subject matter statisticians were very “data collection oriented”; But expectations are always high! –“You have to do better than me (when I was responsible for DC) …”

Relationship between DC & Subject Matter (2) 31 Solution Service Level Agreements (SLA) to manage expectations and to build trust; It was used a step-by-step approach, from a simplified version and increasing gradually the complexity.

Administrative Sources & Registers

Administrative Sources (ADS) 33 ADS are not (still) in the scope of the DC Department; It is managed by Subject Matter departments, supported by IS & Methods; Statistics Portugal is still very “survey oriented”. Thus, ADS are not well developed; But there one remarkable initiative: –IES: Simplified Business Information

Data Collection Infrastructure

Survey Management System (SIGINQ); Other Data Collection Systems: –Datawahouse; –HomeCATI; –Interview Management System; –Telephone Data Entry; Outline 35

Survey Management System Survey Management

Design a new approach of production based on a broad integration with process and tools standardization; Use of an internal reference model to describe the statistical business processes (SPPM); 37 Re-engineering Working Group Survey Management Business AgricultureSocial

Management and control of all data collection processes, including information about respondents and paradata; Supported by the Metadata System; Process Management System Business AgricultureSocial SAGR Similar features, but adapted by statistical unit. 38

GPap is the core for Business Surveys, linked with: –Questionnaires and Capture (WebInq and WebReg); –Respondent Management (GRESP), –Business Register (FUE), Transfers validated microdata to Datawarehouse. Process Management System 39

SurveyUnitOccurCollectReport Analysis UpdateManag.Help Errors Status Validations Primary Val Upload Insert Manage entries Data Entry Method Specific Generic SIGUA block prop Manage Cross Specific Supplement Launch By mode Consult Open / Close Specific Tables Generic Tables Specific Reports Generic Reports Consult transfers Transfer to analysis Consult Analysis GPap Survey Register Sample Respondent Batch update Table Manag. Common process Specific process GPap components 40

41 BEA FNA Survey Management Contact Centre Business AgricultureSocial SAGR Interviewer Management

HomeCATI 42 HomeCATI is an infrastructure which allows freelance interviewers work at home, integrated in a virtual contact centre and based on a voice over Internet protocol (VoIP) solution; This solution has many advantages, but there are many challenges to deal with, like the interview supervision and monitoring.

Interview Management System 43 Interview Management System supports all the processes related with social statistics and the price collection; The Survey Management System has several components: team management and the tools used by the interviewers to collect data, transfer them to Statistics Portugal, allowing them to work both in face-to-face and telephone interviews..

Telephone Data Entry (1) 44 Telephone Data Entry (TDE), which is a solution by which respondents can return their data using the keypad on their telephone; Respondents are sent a letter which informs them of the free phone telephone number to call, their unique respondent identification key number, and the data required. On calling the telephone number, the respondent can choose the appropriated survey, and a recording of the survey questions is heard and the respondent enters their data using their telephone keypad.

Automated Data Collection 45 INE is developing and implementing Automated Data Collection Methods for Business Surveys; It aims to reduce the reporting burden businesses, to improve the timeliness and to promote a more efficient way of collection data; Based on XML, it is already available for two surveys.

Benefits of Centralised Data Collection

1.Development and management of a common infrastructure, both intellectual and operational, which could only be duplicated geographically; 2.Creation of a flexible, dynamic and responsive production architecture tied to the common services provided by shared means of production (sampling frames, classifications and standards, questionnaire designs, methods and tools, etc.); 47 Benefits of centralised DC (1)

3.Creation of right means of coordination to make our design work in order to face future (but now present) budgetary cuts; 4.Adoption of a cost-effective approach that makes the most effective use of regional and central resources. It was possible to do more with the same. 5.Reduction of the data collection cycle, specially the time to deliver statistical results; 48 Benefits of centralised DC (2)

6.Assistance to develop a steady culture based on efficiency and innovation, considering the full in-house design and development approach; 7.Development of analytic competences in order to improve the quality of the information (more reviewing and validation tools); 49 Benefits of centralised DC (2)

8.Creation of an integrated Survey Management System as well as other Data Collection tools; 9.Reduction of respondent burden: –Avoiding duplication of variables and offering easy and multiple ways to provide data; 10.Reduction of production costs; –Estimated in 27.2% (business surveys; 2005 – 2012). 50 Benefits of centralised DC (2)

Cost reduction Business data collection 51 Total costs Base 2005 = 100% %

Electronic Data Collection 52 Visits % electronic collection Questionnairs 2013 – 100%

Electronic Data Collection Avoid variable duplication 53 Common Variables Easy update

Future of Data Collection

Increase the use of administrative sources; Extend Integrated Production Systems; Improve Automated Data Collection and the use of Scanner Data on price collection; Increase the multimodal collection capability (web based); Improve the use of paradata to support the quality processes; Create new processes to better understand respondent's behavior in order to motivate their collaboration. 55 Future of Data Collection

Almiro Moreira Statistics Portugal « 25 September 2013 Geneva, Switzerland UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE CONFERENCE OF EUROPEAN STATISTICIANS Topic (ii): Centralising data collection CREATING A DATA COLLECTION DEPARTMENT: STATISTICS PORTUGAL'S EXPERIENCE Paulo Saraiva dos Santos Statistics Portugal Thank you for your attention!