Thursday, 10 July, 2008Q2008, 8-11 July 2008, Rome1 Getting Data for Business Statistics: A Response Model for Business Surveys Ger Snijkers Statistics Netherlands Utrecht University

Thursday, 10 July, 2008Q2008, 8-11 July 2008, Rome2 Getting Data for Business Statistics How do we get the data we need for business statistics? Yesterday, today, tomorrow Data In time Complete Correct Statistical picture of a country NSI Survey Parameters in and out of control Respondent Parameters

Thursday, 10 July, 2008Q2008, 8-11 July 2008, Rome3 Getting Data for Business Statistics Over the years: 1.The day before yesterday:ICES-I * Yesterday:ICES-II Today:ICES-III Tomorrow * International Conference on Establishment Surveys

Thursday, 10 July, 2008Q2008, 8-11 July 2008, Rome4 Getting Data for Business Statistics The day before yesterday ICES-I (1993): 1. Surveying various branches of industry: agriculture, energy, health care, trade, finance, education, manufacturing industry 2. Quality of business frames & sampling 3. Data analysis & Estimation 4.Data collection methodology: data quality, registers, non-response, Q-design  ‘Stove-pipe’ approach  The ‘one-size-fits-all’ survey design

Thursday, 10 July, 2008Q2008, 8-11 July 2008, Rome5 Response In time Complete Correct Decision to participateAnswering behaviourMotivation Respondent burden De facto Perception Internal business factors Policy Data Resources Market position Informant: Mandate Data knowledge Job priority External business factors Econ. climate Regulatory requirements Political climate The survey: Topic Population and sample Sponsor / Survey organisation Resources Planning Authority/confidentiality The survey design Contact strategy Questionnaire Modes of data collection NSI Black box A business Paper Data WE want Letters: Mandatory Survey designs not coordinated: ‘Stove-pipe’ approach NSI ‘One-size-fits-all’

Thursday, 10 July, 2008Q2008, 8-11 July 2008, Rome6 Getting Data for Business Statistics Yesterday ICES-II (2000): Issues in government surveys Data collection modes & non-response The response process Use of register data Sampling Editing and Data Quality Data analysis, estimation and dissemination

Thursday, 10 July, 2008Q2008, 8-11 July 2008, Rome7 Getting Data for Business Statistics Today ICES-III (2007): 1.Survey data collection methodology: questionnaire design & pre-testing survey participation: non-response reduction, response burden, bias mixed-mode designs & e-data collection understanding the response process in bus’s 2.Using administrative data 3. Business frames & Sampling 4.Weighting, Outlier detection, Estimation & Data analysis

Thursday, 10 July, 2008Q2008, 8-11 July 2008, Rome8 Getting Data for Business Statistics Today International Workshop on Business Data Collection Methodology 1.London, 2006: ONS 2.Ottawa, 2008: Statistics Canada Organising Committee: Ger Snijkers (Stats Netherlands) Gustav Haraldsen (Stats Norway) Jacqui Jones (ONS) Diane Willimack (US Census Bureau) Practices, developments, research issues

Thursday, 10 July, 2008Q2008, 8-11 July 2008, Rome9 Getting Data for Business Statistics Today International Workshop on Business Data Collection Methodology 1.Primary data collection: questionnaire design & pre-testing survey participation: non-response reduction, response burden & bias, contact strategies mixed-mode designs & e-data collection understanding the response process in bus’s 2.Secondary data collection: use of registers 3.Multi-source designs: combining survey and administrative data

Thursday, 10 July, 2008Q2008, 8-11 July 2008, Rome10 Getting Data for Business Statistics Over the years General picture: 1993: 2007: ‘Stove-pipe’ approach One-size-fits-all Survey organisation is central Systematisation and standardisation of methods Mixed-mode, multi-source Respondent is central: tailoring 2000: Transition

Thursday, 10 July, 2008Q2008, 8-11 July 2008, Rome11 Getting Data for Business Statistics The data collection design today Challenge: Good statistics: relevant more & integrated information faster Less money Less compliance costs: providing data only once to government Consequences for the data collection …

Thursday, 10 July, 2008Q2008, 8-11 July 2008, Rome12 Getting Data for Business Statistics Consequences for data collection 1.Using more and more register data: Definitions of variables Definitions of units Timeliness of register Quality of register data Combining register and survey data ○Managing integrated sets of statistics using various data sources Not: Managing stove-pipes (a survey and related statistics)

Thursday, 10 July, 2008Q2008, 8-11 July 2008, Rome13 Getting Data for Business Statistics Consequences for data collection 2.Additional data collection: When register data are not available: ○Not in time ○Additional information needed: -variables -target population ○Quality is not good

Thursday, 10 July, 2008Q2008, 8-11 July 2008, Rome14 Getting Data for Business Statistics Consequences for data collection 3.Sample design: Controlling for overlap across surveys Controlling for rotation over time To avoid this:

Thursday, 10 July, 2008Q2008, 8-11 July 2008, Rome15 Getting Data for Business Statistics Consequences for data collection 4.Survey design: Mode of data collection: ○EDI: XBRL ○Mixed-mode designs: Internet, paper, telephone (CATI) Questionnaire design: ○Tailored to information bus’s have in their records ○Controlling for overlap across questionnaires ○Pre-tested for Q-A process and usability Contact strategy: ○When data are available (not when we need them) ○Motivating and stimulating respondents: - Compliance principles → To avoid these reactions:

Thursday, 10 July, 2008Q2008, 8-11 July 2008, Rome16 Getting Data for Business Statistics Consequences for data collection Reactions by businesses: “What is the use of this survey?” “It is pointless!” “There is no connection with my business activities.” “It only costs money and time!” “The costs outweigh the added value.” “There is no added value.” “Pick someone else. Although you say it is a sample, I am in it every time.”

Thursday, 10 July, 2008Q2008, 8-11 July 2008, Rome17 Getting Data for Business Statistics The data collection design today More complex than yesterday: More data sources Dependent on providers of registers Mixed-mode designs Coordinated development over modesTailored to mode Tailoring to subgroups Tailored to target populations -opening the black box: the response process Coordinated over surveys (ask only once) Tailored multi-source/mixed-mode design

Thursday, 10 July, 2008Q2008, 8-11 July 2008, Rome18 Response In time Complete Correct Decision to participateAnswering behaviourMotivation Respondent burden De facto Perception More than one survey More than once In other ways: ○ Registers ○ EDI Internal business factors Policy Data Resources Market position Informant: Mandate Data knowledge Job priority External business factors Econ. climate Regulatory requirements Political climate Image The survey: Topic Population and sample Sponsor / Survey organisation Resources Planning Authority/confidentiality The survey design Contact strategy Questionnaire Modes of data collection NSI Black box A business

Thursday, 10 July, 2008Q2008, 8-11 July 2008, Rome19 Getting Data for Business Statistics The data collection design today Tailored multi-source/mixed-mode design: Small businesses: register data (+ survey data) Middle-sized businesses: register data + survey data Large businesses: consistent data collection for: - all businesses - all variables It is our job to make statistics out of these data

Thursday, 10 July, 2008Q2008, 8-11 July 2008, Rome20 Getting Data for Business Statistics Tomorrow Improving the tailored multi-source/mixed-mode design Advanced statistical modelling: Estimations based on multiple sources and mixed-mode surveysManaging integrated sets of statistics (not stove-pipes) Opening the businesses: Insight in the response processTailored surveys to the internal business’s processes Opening the survey process: Improved relationships with businesses: - What surveys, when, feedback, involving bus’s Systematisation and standardisation of survey designs: - Survey parameters in control

Thursday, 10 July, 2008Q2008, 8-11 July 2008, Rome21 Response In time Complete Correct Decision to participateAnswering behaviourMotivation Respondent burden De facto Perception More than one survey More than once In other ways: ○ Registers ○ EDI Internal business factors Policy Data Resources Market position Informant: Mandate Data knowledge Job priority External business factors Econ. climate Regulatory requirements Political climate Image The survey: Topic Population and sample Sponsor / Survey organisation Resources Planning Authority/confidentiality The survey design Contact strategy Questionnaire Modes of data collection Statistical picture of a country NSI A business Register data

Thursday, 10 July, 2008Q2008, 8-11 July 2008, Rome22 References American Statistical Association, Proceedings of ICES-I (1993), ICES-II (2000) and ICES-III (2007). Alexandria (Virginia). Groves, R.M., and M.P. Couper (1998), Nonresponse in Household Interview Surveys. Wiley, New York. Hedlin, D., T. Dale, G. Haraldsen, and J. Jones (2005), Developing Methods for Assessing Perceived Response Burden. Statistics Sweden, Stockholm, Statistics Norway, Oslo, and UK Office for National Statistics, London. Snijkers, G. (2007), Between Chaos and Creation. Inaugural lecture Utrecht University (in Dutch). Statistics Netherlands, Heerlen. Snijkers, G. (2007), Collecting Data for Business Statistics: Yesterday, Today, Tomorrow. Presentation at 56th Meeting of the ISI, August 2007, Lisbon, Portugal. Snijkers, G. (2007), Collecting Data for Business Statistics: A Response Model. Proceedings of the 56th Meeting of the ISI (CD-rom), August 2007, Lisbon, Portugal. Willimack, D.K., E. Nichols, and S. Sudman (2002), Understanding Unit and Item Nonresponse in Business Surveys. In: Groves, R., D. Dillman, J. Eltinge, and R. Little (eds.), Survey Nonresponse, pp Wiley, New York.