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Management Practices Survey (MPS) Microdata Metadata

28th February 2017

Edition: 1

Reference period: 2015

Office for National Statistics

UK Data Archive Study Number 8182 - Management Practices Survey: Secure Access

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Table of Contents

Basic Information ....................................................................................................................... 4

1 Introduction to Management Practices Survey ................................................................. 5

1.1 Aim of survey ............................................................................................................... 5

1.2 Background of survey .................................................................................................. 5

1.3 Main themes / key words............................................................................................ 5

1.4 Relevance .................................................................................................................... 5

1.5 Longitudinal ................................................................................................................. 5

1.6 Geography ................................................................................................................... 5

1.7 Status of the data in the VML ..................................................................................... 6

2 Sample Design .................................................................................................................... 7

2.1 Target population ........................................................................................................ 7

2.2 Sample size .................................................................................................................. 7

2.3 Sample frames and sample selection .......................................................................... 7

2.4 Stratification and sample design ................................................................................. 7

3 Estimation ........................................................................................................................... 7

3.1 Weighting .................................................................................................................... 8

3.1.1 Construction of strata .......................................................................................... 8

3.1.2 Two-phase sample design .................................................................................... 9

3.1.3 MPS Design weights ........................................................................................... 10

3.2 Calibration weights .................................................................................................... 11

3.3 Overview of MPS weighting ...................................................................................... 11

3.4 Imputation ................................................................................................................. 12

3.5 Outliers ...................................................................................................................... 12

4 Questionnaire and Data Collection .................................................................................. 12

4.1 Questionnaire design and structure ......................................................................... 12

4.2 Data collection........................................................................................................... 12

4.3 Response and attrition rate ...................................................................................... 13

5 Quality Assurance and Validation .................................................................................... 13

5.1 Accuracy .................................................................................................................... 13

5.2 Sampling error ........................................................................................................... 13

5.3 Non-sampling error ................................................................................................... 13

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5.4 Comparability ............................................................................................................ 13

6 Datasets ............................................................................................................................ 14

6.1 Types of microdata produced ................................................................................... 14

6.2 Changes to the dataset ............................................................................................. 14

6.2 Related datasets ........................................................................................................ 14

7 Variables ........................................................................................................................... 14

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Basic Information

Title

The Management Practices Survey (MPS)

Previous titles

Topics Covered/ key words

Management practices, labour productivity, output per worker, manufacturing businesses.

Time Covered

2015

Data Source

Office for National Statistics (Management Practices Survey, Annual Business Survey, Inter Departmental Business Register)

Geographic Coverage

Great Britain

Lowest level of Geography

Country

Other Breakdowns such as SIC code can be added here

The survey covered businesses with employment of ten or more, within manufacturing industries in section C (divisions 10 to 33) of the 2007 Standard Industry Classification (SIC2007).

Sample Size

Selected sample: 1,026 Responses: 702

Frequency of Release

Ad-hoc

Revision Policy

N/A

Next Publication

October 2017 (provisional) – International comparisons between Germany and Great Britain

Data Owner and Supplier

Gaganan Awano

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1 Introduction to Management Practices Survey

1.1 Aim of survey

o The primary purpose of the survey is to provide greater insight into variations in

productivity across businesses, and whether management practices play a role in

these variations.

o International developments in academic research and literature have supported a

strong link between management practices and productivity performance.

o Furthermore, since 2008 the UK has experienced a period of slow growth in

productivity, often called the ‘Productivity Puzzle’.

o Therefore, the MPS was created to gather data on management practices in British

manufacturing firms and allow for analysis against productivity data obtained using

linked data from the Annual Business Survey (ABS).

1.2 Background of survey

o The MPS is a voluntary pilot survey of British manufacturing businesses launched in

2016 to gather information on the use of structured management practices.

o The survey is designed to produce data which can be compared with a subset of the

data collected in the US Census Bureau’s Management and Organizational Practices

survey.

1.3 Main themes / key words

o Management practices

o Labour productivity

o Manufacturing industries

1.4 Relevance

o Key users of this dataset include policy-makers and academic researchers.

o It meets users’ needs as it includes data on the management practices of

businesses, along with other characteristics of interest, and includes weights for

linking with the Annual Business Survey for analysis of productivity and performance.

1.5 Longitudinal

o As a pilot survey, this is currently the only wave of MPS planned in its current form,

so longitudinal analysis may not be possible. Future vintages of management surveys

are likely to vary from this version.

1.6 Geography

o The sample covers Great Britain.

o There is currently no equivalent survey in Northern Ireland.

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o Information on region is included in the dataset but analysis at this level is not

recommended, due to the relatively small sample and the nature of the sample

design. Further information is available in Section 2.4 below.

1.7 Status of the data in the VML

o Currently no previous data from this survey are held in the VML as this is the first

wave of data collection. This dataset is finalised and no updates or revisions are

expected.

Microdata and Publications Produced:

o Our publications so far have included ‘Experimental data on management practices

of manufacturing businesses in Great Britain: 2016’ (October 2016), and (January

2017).

o The next planned publication is an international comparison of management

practices between Germany and Great Britain. The expected date of publication is

October 2017. The timeline for the MPS 2015 was as follows:

Chapter 2 4 5 6

March 2016

Sample selection Survey

dispatched

April

Editing and validation

May

Reminder dispatched

June

Telephone response chasing

July

August

Analysis, disclosure

control, quality assurance

September

October

Initial results and analysis

published

November

Analysis, disclosure

control, quality assurance

December

January 2017

Further analysis published

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2 Sample Design

2.1 Target population

o The target population covers Reporting Units in Great Britain with employment of

ten or more, within manufacturing industries in section C (divisions 10 to 33) of the

2007 Standard Industry Classification (SIC2007).

o Employment data is obtained from the Inter Departmental Business Register (IDBR)

at the time of sample selection and includes both employees and working

proprietors.

2.2 Sample size

o Selected sample of 1,026 Reporting Units, with response from 702 Reporting Units

(response rate of 68%).

2.3 Sample frames and sample selection

o We used the 2015 sample of the Annual Business Survey (ABS) as the sample frame.

This allows us to link to the financial data collected by the ABS to produce firm-level

productivity estimates for as many firms as possible. Please refer to the ABS

documentation for further details.

o MPS can be linked to the Inter Departmental Business Register (IDBR) and ABS data

through Reporting Unit (RU) reference numbers.

o The sample was selected on 1 March 2016.

2.4 Stratification and sample design

o The strata, or cells, are defined by 2-digit SIC code (divisional level), using SIC 2007,

and employment using bands of 10 to 49, 50 to 99, 100 to 249, 250 and over.

o Strata are not based on geography.

o Within strata, Reporting Units were randomly selected. In some cells all Reporting

Units were selected, due to low cell counts. Where possible a minimum of 12

reporting units were selected per stratum.

o Please note that this differs from the sample design of the ABS, for which more

information is available in the ABS documentation.

3 Estimation

o Microdata provided here are unweighted, with weights provided for producing

estimates for the target population. Below is included a full explanation of the

formulation of weights.

o The appropriate weights to use depend on whether you are analysing MPS data only,

or linking the MPS data with the ABS for 2015.

o The essential information on the usage of weights is available below in Section 3.3.

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3.1 Weighting

This section includes a detailed description of the weighting used for the MPS. Please see

Section 3.2 for the essential information on using weights in practice.

Design weights were used to produce population estimates using responses to the MPS

exclusively. As the respondents for the MPS were a sub-sample of the ABS, a two-phase

sampling design has been used. Two sets of weights were used to produce estimates for the

population. One set of weights were produced to weight the MPS respondents to the ABS

selection/respondents and a second set of weights (based on the ABS sample design) were

then used to produce final population estimates.

Figure 1: Weighting

3.1.1 Construction of strata

Due to the fine level of stratification used for ABS, strata were combined across region,

industry and in some cases employment size band to construct strata for MPS. An example

of this is shown in Figure 2, for the MPS stratum representing businesses in division 15, in

employment size band 10-49, in Great Britain.

Figure 2: Construction of MPS stratum for Division 15 in Great Britain, with employment size band 10-49

This figure shows that 8 ABS strata have been combined to produce one stratum for MPS.

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3.1.2 Two-phase sample design

As the sample for MPS is a sub-sample of those businesses in the ABS, this is known as a

two-phase sample design. In order to produce design weights, inclusion probabilities must

be calculated at both the first and second phase.

The two-phase inclusion probability for the MPS is denoted by:

is the first-phase inclusion probability that unit k is in the ABS sample. This is calculated

using the number of businesses in the ABS universe in ABS stratum h, and using the number

of businesses selected/or have responded to ABS in ABS stratum h:

is the inclusion probability at the second-phase, conditional on the realized first-phase

sample . This realized first-phase sample could be the number of businesses selected for

ABS, or those that have responded to ABS, given the information available at the time of

calculating these weights.

represents the number of businesses that were sampled or had responded to the ABS,

within each MPS stratum g. Note: MPS strata are constructed by aggregating ABS stratum

and so this number will not be the same as , used in the first-phase inclusion probability

calculation. represents the number of businesses that responded to MPS in each MPS

stratum g.

To calculate the inclusion probability that businesses k would be included in the MPS

sample, the first and second-phase inclusion probabilities can be combined:

Design weights are the inverse of the inclusion probability and so the design weight for a

particular business in the MPS sample would be as follows:

In order to construct an average score for a select sub-population, the following formula

should be used:

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∑

The table below indicates the variable names for and in each of these two cases

(please note that and , i.e. population and cell counts for ABS cells, are not available).

Table 1: Variables containing cell counts for samples and populations

Using MPS data only Using MPS and ABS data,

linked at Reporting Unit (RU)

level

(Number of ABS observations

in MPS cell)

MPS_N_g MPS_N_g_post

(Number of MPS observations

in MPS cell)

MPS_n_g MPS_n_g_post

3.1.3 MPS Design weights

Two slightly different sets of weights have been provided for both MPS and ABS for the use

in two different circumstances:

i. In order to replicate published results in ONS’s first MPS publication (Awano

and Robinson, 2016), the first set of design weights should be used. The MPS

respondents are weighted to the number of businesses selected for the ABS.

Information on the number of ABS respondents was not available at the time

of the MPS publication.

ii. The second set of design weights should be used when making links to ABS

data. These weights are based on the number of respondents to the ABS.

These weights were also used in the second ONS publication on MPS (Awano

et al., 2017).

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Table 2: Two sets of design weights used for MPS

Based on number of businesses selected for ABS (will replicated first published analysis)*

Based on the number of businesses that responded to ABS (when linking with ABS data)**

MPS weight 2nd phase

MPS_a_weight MPS_a_weight_post

ABS weight 1st phase

ABS_a_weight ABS_a_weight_post

Combined weight (So MPS sample represents MPS target population)

weight_MPS weight_MPSABS

Note: The ABS_a_weight_post will not match the final design weight calculated for the ABS (which may be found on the ABS dataset), because it is based on provisional, rather than final, ABS data. ABS_a_weight_post differs from ABS_a_weight in two ways: it adjusts for non-response to ABS and it adjusts for business births and deaths in the ABS target population. *This replicates the analysis in Awano and Robinson, 2016 ** This replicates the analysis in Awano et al., 2017

3.2 Calibration weights

o No calibration weights have been included.

3.3 Overview of MPS weighting

o The MPS sample is a sub-sample taken from the ABS and therefore has a two-

phase sampling design.

o The MPS sample is stratified by industry (at the 2-digit SIC level) and

employment size band (10-49, 50-99, 100-249 and 250+). ABS strata have

been aggregated to form MPS strata.

o In order to replicate published MPS results, use weight_MPS. This uses on the

number of businesses sampled for the ABS.

o If linking the MPS data to ABS data, use weight_MPSABS. This weight uses

the number of respondents to the ABS.

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o These weights will produce estimates for the MPS target population, i.e.

businesses in Great Britain in manufacturing industries with employment of

at least 10.

3.4 Imputation

o No observations have been imputed in this dataset. A set of impute marker variables

has been included to indicate item non-response.

3.5 Outliers

o MPS data are categorical. As such outlier detection is less applicable so outliers have

not been identified or treated in this dataset. We have checked the data for internal

consistency, including the following of routing instructions.

4 Questionnaire and Data Collection

4.1 Questionnaire design and structure

o The MPS used one type of questionnaire for all businesses. As this is the pilot, there

have been no changes in the questionnaire thus far, but this may be a possibility in

the future if the survey is run again.

o There are 8 core questions relating to management practices that have been

inspired by the US version of this survey to allow for comparability. It is hoped that in

the future, international comparison may be possible using similar data from other

countries.

o The questionnaire was sent for completion in March 2016, to be returned by end of

April. Telephone response chasing took place in May/June and responses closed in

August. This allowed for initial analysis of results to take place in October.

o The data refer to the calendar year (2015).

4.2 Data collection

o This is a voluntary survey with no incentives provided for respondents.

o The main method of data collection was a paper questionnaire which respondents

could return by post of fax.

o To maximise the response rate, members of the sample who did not respond were

also telephoned. At this point they were given the option to complete an interview

over the telephone.

o The method (paper or telephone) by which a response was recorded is indicated by

the RespondentStatus variable (see Variable catalogue for more details).

o Data for businesses are also linked in from the ABS and the IDBR, to avoid

repetitiveness of questions and increase response rate due to a shorter survey.

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4.3 Response and attrition rate

o The survey’s response rate was 68%.

o The attrition rate is not relevant as this is the first collection for this survey.

5 Quality Assurance and Validation

5.1 Accuracy

o Accuracy is defined as the degree of closeness between an estimate and the true

value. The difference between the estimate and the true value is the sum of the

sampling and non-sampling error.

5.2 Sampling error

o Sampling error is due to collecting information for only a selection, and not the

whole population.

o Standard errors indicating the degree of sampling error are not currently available

for this survey.

o In a general sense, we expect the sampling error to be relatively large for this survey

as the sample size is relatively small.

5.3 Non-sampling error

o Non-sampling errors include errors caused by coverage issues, measurement,

processing and non-response.

o Coverage - by design, the sample has no coverage of micro businesses (businesses

with employment of less than 10), so we recommend against drawing any inferences

about this group.

o Response accuracy - The questionnaire was tested using cognitive interviewing, and

the questionnaire reviewed to minimise the risk of errors.

o Non-response – some observations include item non-response. These are clearly

flagged by indicator variables. For more details please see the variable catalogue.

5.4 Comparability

o This is the only wave of this survey, so it is not possible to compare results over time.

o Similar surveys have been run in other countries. Exact questionnaire content differs

across countries and it may be necessary to restrict data to a subset of questions to

provide comparable estimates.

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6 Datasets

6.1 Types of microdata produced

o There is only one version of the same dataset produced, and is thus the only version

held in the VML, therefore, there is only one release of the dataset.

o There is only one file of this data at RU level.

6.2 Changes to the dataset

o There have been no changes to the dataset over time as this is the initial collection

of this dataset.

6.2 Related datasets

o The ABS and the IDBR are related to this dataset. The ABS was used to determine

our sample as we used information from both the ABS and IDBR to create a more in

depth profile for each business without having to make the survey longer than

necessary and affect the response rate.

o The MPS survey provides a MPS score which can be linked with the ABS and IDBR

data to analyse productivity.

7 Variables

Details of all variables are available in the Variable Catalogue Excel workbook. Further

details of the schedule used to score responses to each question are available in the table

below.

Table 3: Scoring schedule for the management score

Question Score

1 In 2015, what generally best describes what happened at this business when a production problem arose?

a We fixed it but did not take further action 1/3

b We fixed it and took action to make sure that it did not happen again 2/3

c We fixed it and took action to make sure that it did not happen again, and had a continuous improvement process to anticipate problems like these in advance

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d No action was taken 0

2 In 2015, how many key performance indicators were monitored at this business?

a 1 to 2 key performance indicators 1/3

b 3 to 9 key performance indicators 2/3

c 10 or more key performance indicators 1

d No key performance indicators 0

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Question Score

31 In 2015, how frequently were the key performance indicators reviewed at this business?

a Annually 1/6

b Quarterly 1/3

c Monthly 1/2

d Weekly 2/3

e Daily 5/6

f Hourly or more frequently 1

g Never 0

4 In 2015, what best describes the time frame of production targets at this business?

a Main focus was on short-term (less than one year) production targets 1/3

b Main focus was on long-term (more than one year) production targets 2/3

c Combination of short-term and long-term production targets 1

d No production targets 0

52 In 2015, how easy or difficult was it for this business to achieve its production targets?

a Possible to achieve without much effort 0

b Possible to achieve with some effort 1/2

c Possible to achieve with normal amount of effort 3/4

d Possible to achieve with more than normal effort 1

e Only possible to achieve with extraordinary effort 1/4

6 In 2015, how were employees usually promoted at this business?

a Promotions were based solely on performance and ability 1

b Promotions were based partly on performance and ability, and partly on other factors, such as tenure

2/3

c Promotions were based mainly on factors other than performance and ability, such as tenure

1/3

d Employees are normally not promoted 0

7 In 2015, when was an under-performing employee moved from their current role?

a Within 6 months of identifying employee under-performance 1

b After 6 months of identifying employee under-performance 1/2

c Rarely or never 0

8 In 2015, who made decisions over the hiring of permanent full-time employees?

a Only the owner(s) 0

b Mostly the owner(s) with some input from other employees 1/3

c Jointly the owner(s) and other employees 2/3

d Other employees 1

1 For Question 3 respondents were asked to mark all options which applied. The score was determined by the most frequent option selected. Where respondents marked for Question 2 that they had no key performance indicators, they were given a score of zero. 2 Where respondents indicated in question 4 that they did not use targets, they were given a score of zero for question 5.

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