National Instituteof Economic and Social Research

An Introduction to the Analysis of WERS 2004

John Forth & Lucy StokesWERS 2004 Information & Advice Service

Aims

• Introduce the publicly-available data files– Content– Availability and access procedures– Key features

• Analytical issues– First steps– Weighting and statistical inference– Linking data files

• Where to get help and advice

Assumptions

• Know something about nature and content of WERS 2004

• Yet to obtain or use the data, or

• At the very early stages in your analysis

Existing research using WERS

• Primary analysis:– 40-page booklet of First Findings (July

05): http://www.dti.gov.uk/employment/research-evaluation/wers-2004/index.html

– 400-page sourcebook (July 06): www.routledge.com/textbooks/0415378133

– 120-page report on SMEs (July 06): http://www.dti.gov.uk/employment/research-

evaluation/wers-2004/index.html

– Compendium of regional tabulations (Oct 06)

Existing research using WERS

• Secondary analysis:– Bibliography of research using WERS

1980-1998– Searchable on-line database of research

using WERS 2004

Both available at:http://www.wers2004.info/research/search.php

Overview of WERS 2004 data

WERS 2004

2004 Cross-section 1998-2004 Panel Survey

Survey of Managers

Survey of Employee Representatives

Survey of Employees

Financial Performance Questionnaire

2,295 (64%)

985 (82%)

22,451 (61%)

1,070 (47%)

Annual Business Inquiry

932 (47%; 51% in trading sector)

Cross-Section: Managers• Workforce composition • Management of personnel and employment

relations • Recruitment and training • Workplace flexibility and the organisation of

work • Consultation and information • Employee representation • Payment systems and pay determination • Grievance, disciplinary and dispute procedures • Equal opportunities, work-life balance • Workplace performance

Cross-Section: Employee Reps• Structure of representation at the workplace • Time spent on representative duties • Means of communication with employees • Incidence of negotiation and consultation

over pay and other matters • Involvement in redundancies, discipline and

grievance matters • Incidence of collective disputes and

industrial action • Relations with managers • Union recruitment

Cross-Section: Employees• Working hours • Job influence • Job satisfaction • Working arrangements • Training and skills • Information and consultation • Employee representation • Pay

Cross-Section: FPQ / ABI• Turnover• Employment costs• Purchases• Capital stocks• Capital expenditure (acquisitions and

disposals)• R&D activity

Longitudinal analysis

• Each XS: independent samples – no overlap between surveys

• Time-series with 5 data points– Changes to questionnaires over time– 1998 a major break point– Expansion of population in 1998 and

2004 to include smaller workplaces

Longitudinal analysis

• Two-wave Panel Surveys provide longitudinal data on individual workplaces– 1984-1990 (trading sector only)– 1990-1998 (all workplaces with 25+)– 1998-2004 (all workplaces with 10+)

• Survival status of original x-section• Changes in practice in continuing

workplaces (headline practices only)

Overview of WERS 2004 data

1998-2004 Panel Survey

WERS 1998 Cross-Section2,192 (80%)

1242 (99%)

67% random sample of continuing workplaces Survival status of all other workplaces

Survey of Managers

Annual Business Inquiry

938 (76%)

166 matched for 1998 and 2004 (18%)

Data availability

• General release data• Restricted until April 2007

– Region identifiers– Detailed industry (below SIC(2003) Section

level)– Financial Performance Questionnaire

• Permanently restricted– Annual Business Inquiry

• Not available– Names & addresses of respondents /

workplaces and the wider organisation

Obtaining general release data• Where:

– UK Data Archive (http://www.data-archive.ac.uk)

– Study Number 5295

• What:– Data and core documentation

(questionnaires, codebooks, technical report, introductory note)

• How:– Athens ID required– Download or CD– Local use

Obtaining financial data• Where:

– ONS Virtual Micro-data Lab (http://www.statistics.gov.uk/about/bdl/)

– London, Newport, Titchfield, Southport

• What:– General release data + FPQ + ABI – Core documentation (limited for ABI)

• How:– Application to ONS Micro-data Release Panel– Site access only– Withdrawal limited to non-disclosive results

Timed release of restricted data• In April 2007:

– FPQ to the UK Data Archive– Region codes and detailed industry

codes to UKDA and ONS

Analysis – first steps

• Read core documentation– Survey questionnaires– Technical report– Introductory note

• Check latest WIAS guidance– http://www.wers2004.info– Variable notes– Derived variables– Errata in primary analysis– FAQs

Key features of the data files

• Layout:

• Variable naming convention:– SqnameN (e.g. ASTATUS1)– S = Section letter– Qname = descriptive name– N = numbered response

SERNO Questionnaire items (in

order)

XCODEs WEIGHT(s)

12345 …. …. ….

Key features of the data files

• Multiple-response sets (e.g. CFACTORS)

CFACTORS*^ Which of the following factors are important when recruiting new employees? PROBE: Which others? UNTIL 'None':

1) References, 2) Availability, 3) Recommended by another employee, 4) Skills5) Age6) Qualifications, 7) Experience, 8) Motivation, 9) Other (please specify CFACTOTH)

Key features of the data files

• Multiple-response sets– CFACTOR1 = 1st response given– CFACTOR2 = 2nd response given, etc– XCFACT1-3 = codes for verbatim

responses using ‘other, please specify’ code

– Convert to dummies using ANY command (SPSS) or EGEN command with EQANY option (Stata)

Key features of the data files

•T variables (e.g. FMEASPR, FMEASPRT)FMEASPR* What proportion of non-managerial employees at this workplace have their performance formally appraised? INTERVIEWER: If respondent gives answer as an exact number you can code “97” here and record the number of the next question 1) All (100%), 2) Almost all (80-99%), 3) Most (60-79%), 4) Around half (40-59%), 5) Some (20-39%), 6) Just a few (1-19%), 97) Number

{If giving exact number } FMEASPRT How many non-managerial employees here have their performance formally appraised? ENTER NUMBER

Key features of the data files

• In this case, code FMEASPRT into FMEASPR using total non-managerial employees (ZALLEMPS – ZMNG_TOT)

• Syntax available for all T variables at:

http://www.wers2004.info/FAQ.php#syntax

Key features of the data files

• Missing values:-9 = Not answered / refused-8 = Don’t know-1 = Not applicable

• Treatment in data files:– SPSS: Assigned as user-missing values– Stata: Not assigned as missing values

(valid values)

Producing reliable estimates

• Sample bias Weights

• Less precision than SRS Survey-adjusted variance estimation

Importance of weighting

• Sample of workplaces not SRS• Unequal probabilities of selection by

workplace size and industry (IDBR)• Large workplaces and small industries

over-represented vs population• Also: variations in response rates by size

and industry (at least)• Weight = 1 / p(selection and response)• Weighted estimates free of known

biases (i.e. representative of wider population)

Correctly estimating variances• Textbook formulae assume SRSWR• WERS not sampled according to SRSWR• Unequal p(selection) & clustering of

employee sample larger standard errors than SRSWR (50-60% larger, on average)

• Textbook formulae Type I or II errors• Linearization or replication methods

SEs that account for the survey design

Software options

• Stata version 5 onwards:– ‘svy’ suite of commands (included)– svyset informs Stata about the sample

design– svy: prefix can be used with wide range

of statistical procedures– iweights will remove bias but

incorrectly estimate variances (SEs)– Syntax examples at:

http://www.wers2004.info/FAQ.php#stata

Software options

• SPSS version 12 onwards:– Complex Samples module (add-on)– CSPLAN ANALYSIS informs SPSS about

the sample design– Limited range of CS procedures then

available (descriptives, x-tabs, logit, ordinal, GLM)

– WEIGHT BY will remove bias but incorrectly estimate variances (SEs)

– Syntax examples at: http://www.wers2004.info/FAQ.php#spss

Linking data files

• Combining data from different questionnaires for linked analysis

• Examples:– Using data on payment practices from

MQ in analysis of employees’ wages– Comparing managers’ and employee

representatives’ ratings of climate– Linking 1998 and 2004 observations in

Panel

• Link via unique workplace identifier (SERNO)

Linking data files (cross-section)• One-to-one match: FPQ MQ

Master (FPQ) Secondary (MQ)

SERNO SERNO

12345 12345

12346

12347 12347

12348

Linking data files (cross-section)• One-to-many match: SEQ, ERQ MQ

Master (SEQ)

Master (SEQ)

Secondary (MQ)

SERNO PERSID SERNO

12345 1 12345

12345 2 12345

12346

12347 1 12347

Software options

• SPSS:MATCH FILES FILE=master file

/TABLE=secondary file/BY serno

• Stata:get file=master file

merge serno using secondary file

drop _merge==2

Linking data files (cross-section)• Many-to-one match: MQ SEQ, ERQ

Master (MQ)

Secondary (SEQ)

Secondary (SEQ)

SERNO SERNO PERSID

12345 12345 1

12345 2

12345 3

Software options

• SPSS:AGGREGATE then MATCH FILES

• Stata:collapse then merge

• Issue:– summary data item from SEQ may be

measured with error (sampling error)– errors in variables regression?

Linking data files (panel)

• One-to-one match: 1998 2004• Wide form: one record per workplace

Master (1998)

Secondary (2004)

SERNO Xvar1 Xvar2 SERNO Yvar1 Yvar2

12345 1 2 12345 2 1

12346 1 1 12345 1 2

Linking data files (panel)

• Long form: one record per workplace per year

• Syntax for wide and long forms available at: http://www.wers2004.info/FAQ.php#construct

SERNO year var1 var2

12345 1998 1 2

12345 2004 2 1

12346 1998 1 1

12346 2004 1 2

Aims

• Introduce the publicly-available data files– Content– Availability and access procedures– Key features

• Analytical issues– First steps– Weighting and statistical inference– Linking data files

• Where to get help and advice

Further info and advice

WERS 2004 Information and Advice Service

Website: http://www.wers2004.info

Email: wers2004@niesr.ac.uk

Telephone: +44 (0) 20 7654 1933