04 October 2013
Micro-data Perspectives on the UK
Productivity Conundrum - An Update
Author Name(s): Simon Field and Mark Franklin (ONS)
Abstract
This article reports some new perspectives on UK productivity up to 2010, using a large dataset
assembled from firm-level micro-data. A central finding is that productivity performance over
2008-10 has varied widely, by industry, firm size and a range of other firm-level characteristics.
Our evidence suggests that the productivity conundrum is more pronounced in services (excluding
the financial and communications sectors) than in manufacturing, and that labour productivity
performance in 2010 was weaker among smaller firms than larger firms across all sectors. This is
also the case using a broader measure of productivity, taking account of capital inputs at the firm
level. Other things equal, firms that export, or are part of a multi-national enterprise, or report higher
levels of ICT maturity demonstrate systematically stronger performance over the recession across
all sectors than firms which do not have these characteristics. However, we find less evidence that
recent productivity performance is related to prior growth rates of firm employment or a measure
of firm-level innovation. In common with other micro-data research we find evidence of large and
persistent variation in productivity across firms. Our results suggest that the impact of the economic
downturn in 2008-09 has been more apparent among high productivity than low productivity service
sector firms, while the reverse is the case in the high-tech sector, where high productivity firms have
been little affected. There is also some evidence that the level of productivity below which firms exit
the industry has fallen over the recession.
Acknowledgements
1.
We are grateful for financial support from the European Commission (Grant
50721.2013.001-2013.082), to the other 13 national statistical institute consortium members
(listed in Appendix A) for data access and to the academic advisers to the consortium: Eric
Bartelsman of the Free University of Amsterdam and Patricia Kotnik of the University of
Ljubljana and our ONS colleagues John Allen, Joseph Murphy and Bhavik Patel for technical
and analytical support.
Office for National Statistics | 1
04 October 2013
Introduction
This article updates an earlier article published in January 2013 which used firm-level micro data
to throw some light on to the productivity conundrum. In particular we have made a number of
improvements to the underlying dataset and extended the dataset by one year to 2010.
Our point of departure is a unique dataset built from business micro-data as part of the 14-country
EU-funded ESSlait project (www.esslait.eu). This is a very large dataset (the UK component alone
runs to 24MB of data) with hundreds of thousands of separate estimates. This article reports a
tiny fraction of the outputs, and it is perfectly feasible that we have overlooked some valuable
information. The aim of the article is to raise awareness, to encourage feedback and to promote the
use of linked micro-data in economic analysis.
The primary focus of the ESSlait project is to examine the impact of information and communication
technologies (ICT) on firm performance. However, with some small amendments to the project
coding, we can use this dataset to explore a number of lines of enquiry that cannot be addressed
1
2
using conventional macro-level statistics . The ESSlait project is an ESSnet project on linking of
micro-data to analyse ICT impact, and continues work carried out over 2010-12 (Eurostat 2012)
and 2006-2008 (Eurostat 2008). The underlying methodological approach is to link multiple microdata sources to compile ‘micro-aggregated statistics’ (sometimes referred to as meso statistics)
designed to inform policy-makers and researchers. Examples of meso statistics are where firm-level
productivity estimates are aggregated by two or more categories over time (such as industry and
a measure of the firm’s ICT maturity) and statistics on the distribution of firm-level productivity by
industry, such as quartile averages. Additionally, the project dataset can be used to analyse firmlevel demographics such as the characteristics of firms entering and exiting particular industries,
and reports regression results for a standardised set of productivity specifications. An important
feature of the ESSlait project and its predecessors is the comparability of outputs across consortium
members. This approach is also designed to comply with confidentiality and disclosure control
regimes governing the use of micro-data.
The layout of the rest of the article is as follows. Section 1 provides descriptive statistics of the
dataset and some notes on interpretation of results.
Section 2 reports time series for labour productivity and total factor productivity up to 2010 by
filtering the dataset in different ways, for example by size of firm, and - by merging information from
different business surveys – considering interactions between productivity and (a) ICT usage and
(b) innovativeness. This section also compares UK micro-aggregated productivity indicators with
comparable indicators from other ESSlait consortium countries and reports some regression results.
Section 3 turns the focus to measures of the distribution of productivity across firms. This is one of
the central benefits of using micro-data, and allows us to investigate heterogeneity between firms at
a point in time and over time. This section also looks at measures of industry dynamics, focusing in
particular on reallocation, the dynamic process by which resources flow from less productive to more
productive firms.
Office for National Statistics | 2
04 October 2013
There are 3 appendices: a list of consortium members, an overview of the ESSlait dataset and data
developments since our previous article, and a list of the full industry breakdown.
Notes
1.
As in our previous article, our definition of ‘macro’ includes components within the
national accounts framework, such as industry level measures of output, employment and
productivity. micro-data refers to data at the level of individual firms and enterprises, typically but
not exclusively in the form of their responses to ONS surveys.
2.
ESSnet projects are consortia of national statistical institutes (NSIs) and are aimed at providing
results beneficial to the European Statistical System, see http://epp.eurostat.ec.europa.eu/
portal/page/portal/pgp_ess/partners/european_union/eurostat/tab_statistics#7
Section 1: Descriptive statistics
Table 1 provides some basic summary statistics in terms of five broad industry aggregates. We
focus on these broad sectors partly for reasons of brevity and partly to minimise risks of disclosure.
The full dataset contains results at the 2-digit (SIC03) industry breakdown shown in Appendix C.
Table 1: Sector shares
Period averages, 2001-2010
memo:
nv
e
pay
k
nobs
pay/nv
%
%
%
%
%
%
MexElec
18
13
16
13
15
53
EleCom
6
4
5
6
3
55
50
53
48
45
56
57
NonMar
8
19
17
20
10
129
OtherG
18
11
13
16
16
43
MServ
Table source: Office for National Statistics
Table notes:
1. n/v is nominal value added; e is employment; pay is wage costs; k is real capital stock; nobs is number of
observations. Weights are approximate inverse sample probabilities (except nobs which is unweighted)
2. Sector classification (SIC03, 2-digit) and description:
MexElec (15-29, 33-37) Manufacturing (excluding electrical machinery)
EleCom (30-32, 64) Electrical machinery, Telecommunication services
MServ (50-63, 71-73, 90-93) Market services (excluding telecommunication services)
Office for National Statistics | 3
04 October 2013
NonMar (70, 75, 80, 85) Non-market services
3.
OtherG (01-14, 40-41, 45) Other goods
Financial services (65-67) are not included in the dataset
Download table
XLS format
(85 Kb)
As in our January article, the focus of this article will be on MexElec (that is, SIC03 manufacturing
other than electrical machinery), EleCom (an amalgam of electrical machinery plus post and
telecommunication services and designed to proxy the 'hi-tech' industries) and MServ (market
services excluding post and telecommunication services, and excluding the financial sector which is
excluded from the survey sample frame). These three sectors respectively account for 18%, 6% and
50% of weighted nominal value-added in the whole project sample. For MexElec and EleCom the
shares of value-added are a little larger than their shares of employment, whereas the employment
share of MServ is a little larger than its share of value-added. The distribution of the capital stock
is broadly consistent with the distribution of value-added and employment. The relatively low share
of capital in MexElec probably reflects the disproportionate share of structures in micro-data capital
stocks, which is also a feature of macro capital measures. See Appendix B for more information.
Table 1 also shows shares of pay in value-added, which averages 53-57% for the three featured
industries. One reason why this is lower than labour’s share of national income recorded in the
National Accounts is that the micro-data do not include non-wage labour costs such as employers’
social security and pension contributions. The pattern broadly reflects a priori assumptions about
labour and capital shares across the three industries, recalling that – under SIC03/NACE1, EleCom
includes labour intensive postal services. However, the pay share is greater than 100% in NonMar
(public administration, education and health) reflecting measurement issues for value-added in
the surveyed components of these industries. Thus, although the ESSlait dataset contains some
interesting information on ICT usage in this sector, we do not report productivity estimates in this
article.
Similarly the pay share of the OtherG sector (comprising agriculture, extractive industries, utilities
and construction) is rather low. But this aggregate is very heterogeneous, and cutting the microaggregated data into its separate constituent parts is beyond the scope of this article. Again we do
not report productivity estimates for this aggregate.
Office for National Statistics | 4
04 October 2013
Table 2: Sample coverage
Period averages, 2001-2010
All
SZ1
SZ2
SZ3
SZ4
MexElec
N_BR
000s
133.2
114.7
10.2
6.7
1.7
N_PS
% of BR
6.2
2.2
16.5
40.1
85.0
N_PSEC
% of BR
0.6
0.0
0.1
3.1
35.2
N_PSIS
% of BR
1.2
0.2
1.8
7.2
40.8
N_BR
000s
30.3
27.2
1.6
1.1
0.3
N_PS
% of BR
4.8
1.9
15.4
37.3
84.7
N_PSEC
% of BR
0.6
0.0
0.1
3.6
39.3
N_PSIS
% of BR
1.0
0.1
2.5
7.7
41.7
N_BR
000s
1234.6
1179.2
34.8
16.6
3.9
N_PS
% of BR
2.4
1.6
11.9
27.2
79.9
N_PSEC
% of BR
0.1
0.0
0.1
2.7
30.8
N_PSIS
% of BR
0.2
0.0
0.9
4.5
40.0
EleCom
MServ
Table source: Office for National Statistics
Table notes:
1. For row headings, see text
2. SZ1: Size class 1: employment<20
SZ2: Size class 2: 20<=employment<50
SZ3: Size class 3: 50<employment<250
SZ4: Size class 4: 250<=employment
Download table
XLS format
(85 Kb)
Table 2 provides summary information on sample size and overlaps between samples for
each of the three sectors. N_BR is the number of firms in the sample universe (BR stands for
business register), averaged over the sample period 2001-10. N_PS is the number of firms in
the annual production survey (PS) datasets. And N_PSEC and N_PSIS are numbers of firms
Office for National Statistics | 5
04 October 2013
in the intersections of the PS and E-Commerce (EC) survey, and PS and Innovation Survey
(IS) respectively, all expressed as percentages of N_BR.
On average there are around 1.4m firms in the sample frame for these three sectors, but only some
30,000 (2%) in EleCom and 133,000 (10%) in MexElec. More than 1.3m (94%) are in the smallest
size class, and less than 6,000 (0.4%) are in the largest size class.
After outlier filtering and other deletions there are about 54,000 observations in the annual PS
datasets, of which around 40,000 are in the three sectors which are the focus of this article. Of
these, around 4% are in EleCom and 21% in MexElec. Differences in sampling probabilities across
the size classes means that the share of the smallest size class falls to 54% while the share of the
largest size class rises to 12%.
The EC and IS sample sizes are much smaller than the Annual Business Survey (ABS, which is the
main source for our PS datasets, see Appendix B), and in addition, ONS sampling policy means that
small firms selected for one survey are automatically not selected for any other survey for several
years. This means that the sample size of the PS-EC intersection is much smaller (around 2,700
firms on average), with only tiny numbers of firms in the lowest two size classes. Moreover, since
smaller firms are disproportionately found in services, the rate of attrition is larger for the MServ
sector.
Sample attrition is not quite as severe for the PS-IS intersection, with slightly better representation
1
for the smaller size classes. On average there are around 2,900 firms in this matched dataset .
Table 3: Weighted employment shares
Period averages, 2001-2010
Size Class
Exporter
MNC
HGE
FO
1
2
3
4
%
%
%
%
%
%
%
%
MexElec
18
13
28
41
58
51
31
60
EleCom
11
7
16
65
61
74
23
80
MServ
29
8
12
50
15
42
37
62
Table source: Office for National Statistics
Table notes:
1. Weights are approximate inverse sample probabilities
2. SZ1/2/3/4: as Table 2; Exporter: firm is exporter; MNC: Firm is Multi-national; HGE: firm is high growth enterprise;
FO: firm is foreign owned
Download table
XLS format
(85 Kb)
Office for National Statistics | 6
04 October 2013
Table 3 focuses on the PS datasets and reports weighted shares of employment in terms of various
firm characteristics. Here and elsewhere in this article, weights are approximate inverse sample
probabilities, so these are estimates of the sample universe rather than the selected sample. This
is a development from our previous article, when inconsistencies between the ABS sample and the
register population meant that some of the sample re-weighted estimates were unreliable.
Although, as shown in Table 2, there are many more small firms than large firms, employment
is heavily weighted towards large firms, especially in EleCom. Weighted employment shares of
exporters may seem surprisingly high for MexElec and EleCom – a recent ONS article reported that
only 11% of registered businesses in the non-financial business sector were engaged in exporting
of goods or services. However, the same ONS article reports that larger firms are much more likely
to be engaged in exporting than smaller firms. Unsurprisingly the employment share of exporters in
MServ is much smaller.
Equally, employment shares of multi-national firms may seem surprisingly high – these firms make
up less than 2% of all firms on the business register, and 4-5% of firms in MexElec and EleCom.
But as for exporters, larger firms are much more likely to be multi-nationals than smaller firms.
The employment share of high growth enterprises (defined as those with more than 10% annual
employment growth for three years) is comparatively large in MServ and comparatively small in
EleCom.
The employment share of high growth enterprises (defined as those with more than 10% annual
employment growth for three years) is comparatively large in MServ and comparatively small in
EleCom.
Table 4: Headline micro-aggregated statistics
Average compound growth, 2001-2010
memo:
LPV
TFP
E
K
Pay/E
%
%
%
%
%
MexElec
5.3
4.0
-2.4
-1.6
3.5
EleCom
6.2
7.3
-2.8
-0.2
3.2
MServ
2.2
1.3
1.1
-0.5
3.8
Table source: Office for National Statistics
Table notes:
1. LPV and TFP weighted by product of inverse sample probabilities and employment; E and K weighted by inverse
sample probabilities; Pay/E derived from weighted pay and employment
Download table
XLS format
(85 Kb)
Office for National Statistics | 7
04 October 2013
Table 4 provides some broad information on growth rates of key variables pertaining to productivity.
We focus on period averages to highlight differences between the sectors; time series for these
series are reported in the following section. Productivity growth has on average been strongest for
firms in the EleCom sector, followed by MexElec. Productivity growth has been considerably slower
in MServ. This ranking holds both in terms of LPV and TFP, although the dispersion is wider in terms
of TFP which has grown faster than labour productivity in EleCom but slower than labour productivity
in the other two sectors. Employment growth has been negative over the 2001-10 period in MexElec
and EleCom, and the real value of the capital stock has fallen in all three sectors. Average pay has
grown at similar rates across the three sectors, although fastest in MServ and least in EleCom.
Since growth in labour productivity can be decomposed as growth of real value added minus growth
of employment, these estimates imply growth of real value added of 2.8%, 3.4% and 3.3% for
MexElec, EleCom and MServ respectively.
When interpreting the ESSlait productivity statistics it should be borne in mind that micro-data
productivity estimates cannot be directly compared with productivity estimates derived from macro
data, such as those in ONS’s quarterly Labour Productivity statistical release, for a raft of reasons,
including:
•
•
•
•
•
•
micro-aggregated estimates of value added and employment will differ from those reported from
the ABS and BRES due to differences in weighting and outlier filtering among other reasons.
Users should also recall that ABS and BRES have moved to SIC07 with effect from 2008
although ABS is an important source for benchmarking value added in the UK National Accounts,
it is by no means the only source. And in any event, timing lags mean that ABS is only used in
benchmarking annual estimates around 18 months after the year in question
ONS labour productivity statistics make use of employee estimates that are benchmarked to
BRES but again BRES is not the only source
both ABS and BRES survey only the corporate sector and do not capture information on nonincorporated businesses and most of the self-employed
macro statistics are subject to balancing adjustments to produce a coherent picture of the
economy across the output, expenditure and income approaches
the relationship between chained volume and current price measures of value added is much
2
more complex in the macro environment than the approach taken in this article . Thus even in
sectors where there is a close correspondence between ABS measures and macro current price
measures of value added, in practice there will be differences in the volume series between the
macro and micro-level data.
There are also some important differences between micro-data based estimates of TFP and ONS
3
estimates of multi-factor productivity (MFP) . The principal differences are that the micro-data
measures labour input simply in terms of headcount employment, whereas ONS MFP estimates
measure labour input in hours and account for changes in labour quality. And on the capital
side, the micro-data measure is a crude aggregation of real stocks of structures, equipment and
vehicles, whereas ONS MFP estimates attempt to measure flows of capital services using a more
disaggregated asset breakdown.
Office for National Statistics | 8
04 October 2013
Notes
1.
Questions in the biennial IS refer to innovation activity over the previous 3 years, so for years
when IS is not conducted, such as 2009, we match firms in the PS with firms in the 2010 vintage
of the innovation survey.
2.
UK macro statistics do not currently have a chained volume dimension in terms of gross output,
but only in terms of value-added. Whereas the ESSlait database reports productivity both gross
and net of firm-level intermediate consumption.
3.
In the ESSlait dataset, MFP refers to a measure of gross output divided by a weighting of labour,
capital and intermediate inputs.
Section 2: Micro-aggregated productivity statistics
This section examines time series of productivity according to various firm level characteristics
that are identified within the ESSlait dataset. Unless otherwise stated the data are weighted to
provide estimates of productivity for the population of firms with the named characteristics. This is an
improvement on the weighting methodology used in our January article, and reflects developments
in achieving closer consistency between the continuous business register and the annual PS
datasets.
Office for National Statistics | 9
04 October 2013
Figures 1-3: Labour productivity by size class
Source: Office for National Statistics
Download chart
XLS format
(385.5 Kb)
Figures 1-3 show time series of labour productivity for MexElec, EleCom and MServ differentiated
by size class, as defined in Table 2 above. For MexElec (Figure 1) there is a very clear relationship
Office for National Statistics | 10
04 October 2013
between size class and labour productivity, and equally clear evidence of a recession effect in 2009,
with productivity in all size classes picking up in 2010. However, only in the largest size class did
productivity in 2010 return to something like the pre-recession trend.
A somewhat similar picture is apparent for the two largest size classes in the EleCom sector (Figure
2), in terms of outperforming smaller size classes and recovering from a downturn in 2009. In
fact, there is little evidence of a recession-induced downturn in labour productivity in these size
classes. By contrast labour productivity fell sharply among firms in the two smaller size classes
of the EleCom sector in 2008, and while there has been a decent recovery in size class 2, labour
productivity was flat in size class 1 in 2009 and fell further 2010.
In MServ (Figure 3) there is no clear relationship between size class and level of labour productivity,
although the ranking is fairly consistent over time, and the dispersion in productivity is less than
for the other two sectors. Remarkably, firms in the largest size class are consistently at the bottom
of the ranking. This pattern may reflect fewer opportunities to exploit economies of a scale in
services, compared with manufacturing. The recession-induced downturn in productivity in 2009
is most pronounced for size classes 2 and 3. But while 2010 witnessed a recovery in productivity
for size class 3 (and a muted upturn for size class 4) productivity continued to fall across the two
smaller size classes. With the possible exception of size class 4 (where the pre-recession trend was
weakest) productivity in 2010 was well below the level implied by the pre-recession trend.
Office for National Statistics | 11
04 October 2013
Figures 4-6: Total factor productivity by size class
Source: Office for National Statistics
Download chart
XLS format
(385.5 Kb)
Allowing for the distribution of capital across firms changes the picture considerably. In MexElec
(Figure 4) the ranking is completely reversed compared with Figure 1, suggesting that larger firms
Office for National Statistics | 12
04 October 2013
use more capital per person employed but extract less value added from their combined labour and
capital inputs than do smaller firms. As in Figure 1 there is a clear recession effect in 2009 and a
clear rebound in 2010, but unlike Figure 1 none of the size classes have fully recovered ground lost
since 2008 in terms of TFP. A recurring pattern is that the steepest downturn in productivity was in
the smallest size class.
Larger firms are also bottom of the TFP ranking in EleCom (Figure 5), but the time profile is similar
to LPV, with little evidence of a recession-induced downturn. TFP for the middle size classes
is remarkably similar, much more so than labour productivity (Figure 2), with modest falls in
productivity in 2008 and 2009 but a reasonable recovery in 2010. Once again size class 1 stands out
as experiencing the steepest fall in TFP, albeit with a slight recovery in 2010.
As for labour productivity, there is only mixed evidence of a recovery in TFP in MServ in 2010
(Figure 6). In contrast to the MexElec and EleCom sectors there is relatively little difference between
productivity in terms of TFP compared with LPV.
Productivity by other firm-level characteristics
One advantage of the linked micro-data approach is that it allows ABS data to be analysed by
other firm-level attributes from other sources which can be matched at the firm level using unique
firm identifiers. Here we examine productivity trends up to 2010 in terms of a range of firm-level
characteristics that can be identified in the ESSlait dataset.
(i) Multi-national corporations
In our previous article we showed that labour productivity was systematically higher for foreignowned firms than for domestically-owned firms. As discussed in the previous section, for the current
round of work we have compiled a panel of multi-national firms, some of which are foreign-owned
and some of which are UK-owned.
Office for National Statistics | 13
04 October 2013
Figures 7-9: Labour productivity by multi-national status
Source: Office for National Statistics
Download chart
XLS format
(385.5 Kb)
Office for National Statistics | 14
04 October 2013
Labour productivity is also systematically higher for multi-national firms (MNCs) than non-MNCs
across all three sectors (Figures 7-9). In general, falls in LPV in 2009 and recoveries in 2010 are
both more pronounced for MNCs than for non-MNCs.
Corresponding estimates of TFP are available in the chart download component (385.5 Kb Excel
sheet) of this release, which can be accessed by clicking on the link below Figures 7-9.
(ii) Exporters
Here we use an export flag to examine the relationship between export status and firm level
productivity. As noted above, export status has not until very recently been captured by ABS so we
use pooled information from monthly business surveys to populate an export panel which is then
merged into our PS annual datasets. For further information see Appendix B. The ESSlait dataset
also includes a variable on export intensity (export value as a share of turnover). Further information
on export intensity is available from the analytical lead, [email protected].
Office for National Statistics | 15
04 October 2013
Figures 10-12: Labour productivity by exporter status
Source: Office for National Statistics
Download chart
XLS format
(385.5 Kb)
In the MexElec sector labour productivity is higher among exporting firms than non-exporters (Figure
10), although the difference is not as pronounced as the productivity gaps in terms of size class and
Office for National Statistics | 16
04 October 2013
MNC status noted above. Another notable feature is that there was no recovery in productivity in
2010 across the non-exporting cohort.
As in our previous article we find that labour productivity is higher among non-exporters than
exporters in EleCom (Figure 11), although the gap is narrower on our current dataset and appears to
be narrowing over time. We also continue to see a considerable productivity advantage for exporters
in MServ (Figure 12). In all three industries, labour productivity in 2010 is stronger for exporters than
non-exporters.
Corresponding estimates of TFP are available in the chart download component (385.5 Kb Excel
sheet) of this release (click on the link below Figures 10-12). One feature of interest in that the
ranking in EleCom reverses in 2008 and later years, that is, exporting firms outperform nonexporters in terms of TFP.
(iii) High-growth enterprises
High-growth enterprises are defined as firms where employment has grown by at least 10% per year
for the previous three years.
Office for National Statistics | 17
04 October 2013
Figures 13-15: Labour productivity by high-growth status
Source: Office for National Statistics
Download chart
XLS format
(385.5 Kb)
Figure 13 shows that there is little difference in labour productivity in MexElec between highgrowth firms and other firms, either in levels or growth rates. By contrast, the set of high-growth
Office for National Statistics | 18
04 October 2013
firms in EleCom (Figure 14) has demonstrated consistently higher labour productivity than their
counterparts, and LPV grew recovered more strongly in 2010 across these firms. High-growth firms
also outperform in terms of LPV in MServ (Figure 15). Corresponding estimates of TFP are available
in the chart download component (385.5 Kb Excel sheet), accessible via the link below Figures
13-15.
(iv) ICT intensity
Under this heading we examine the intersection between the PS and EC datasets. The EC survey
collects a large amount of information on business use of ICT. One of the challenges for analysts
is to identify summary ICT indicators which are consistently related to productivity. Here we focus
on such a summary variable - BROADCAT – which takes different values depending on the
proportion of workers that have access to the internet over a high speed connection. Specifically,
BROADCAT=1 when this proportion is between 10% and 40%, and BROADCAT=2 when the
percentage is between 40% and 90%.
The following results are slightly different from those previously reported. The survey base has been
reduced because the BROADCAT variable is derived from the E-commerce survey meaning that
that the sample size is smaller than in the previous characteristics which are drawn exclusively from
the PS dataset. This accounts for the greater volatility of these estimates.
Office for National Statistics | 19
04 October 2013
Figures 16-18: Labour productivity by ICT maturity status
Source: Office for National Statistics
Download chart
XLS format
(385.5 Kb)
Nevertheless it is apparent from Figures 16 to 18 that labour productivity is consistently higher
among firms where more workers have access to high speed internet. LPV is also considerably
Office for National Statistics | 20
04 October 2013
more volatile for the BROADCAT=2 cohort of firms, or to put it another way. LPV is less volatile
among firms with a lower level of ICT maturity as measured by this summary indicator. A similar
pattern holds in terms of TFP, as can be seen in the chart download component (385.5 Kb Excel
sheet), accessible via the link below Figures 16-18.
Note that these measures of ICT maturity are not exhaustive – some firms lie below 10% and some
lie above 90%. Moreover, the average level of ICT maturity has increased dramatically over the
period 2001-10, such that some firms in the lower maturity cohort in one year will have moved into
the higher category in the following year. The section on regression results later in this article reports
positive and significant co-efficient on a continuous version of this ICT maturity indicator in OLS
estimates of production function specifications, after taking account of other measurable inputs.
(v) Innovation
The ESSlait dataset also provides productivity estimates broken down in terms of firm-level
characteristics taken from the biennial Innovation Survey (IS). As with the E-Commerce survey
there is a wealth of information in the IS and part of the challenge to analysts is identifying summary
indicators which demonstrate consistent relationships with firm performance. This is work in
progress.
Figure 19: Labour productivity by innovation status
Source: Office for National Statistics
Download chart
XLS format
(385.5 Kb)
Here we are using the PS-IS intersection, and the small sample size of the overlap produces
extreme volatility of some of the weighted productivity estimates in EleCom and MServ. Figure 19
Office for National Statistics | 21
04 October 2013
shows labour productivity of the MexElec firms in the overlap, split between those firms that report a
new or significantly improved product or service (INPD=1) and those firms which do not (INPD=0).
Over the sample period the respective proportions are around 40% and 60%. It is perhaps surprising
that there is no evidence of a link between innovative firms on this measure and LPV. Indeed, noninnovators have experienced faster productivity growth over this period. There is also no evidence
of different productivity performance during the recession and its aftermath. Neither is a relationship
apparent in terms of TFP, as can be seen in the chart download component (385.5 Kb Excel sheet),
accessible via the link below Figure 19.
Comparisons with other countries
Figures 20 to 22 show UK labour productivity compared with weighted average productivity for
the rest of the ESSlait consortium over the period 2001 to 2010. (See Appendix A for a list of
consortium members. Note that at the time of writing, not all ESSlait member countries have
delivered outputs to 2010.) For ease of exposition we have indexed productivity estimates for each
country to 2007=100. Weights are based on employment shares across the consortium, by sector.
Office for National Statistics | 22
04 October 2013
Figures 20-22: Labour productivity, UK and non-UK average
Office for National Statistics | 23
04 October 2013
Source: Office for National Statistics
Download chart
XLS format
(259 Kb)
For MexElec (Figure 20), UK labour productivity has grown consistently faster than the average
across the rest of the ESSlait consortium and especially in 2008, when UK labour productivity
increased while labour productivity fell across the rest of the consortium. Perhaps reflecting this,
productivity fell more steeply in the UK in 2009, and the recovery in 2010 was less pronounced than
across the rest of the consortium.
For EleCom (Figure 21) there is more evidence of a recession effect on labour productivity outside
the UK. While for MServ (Figure 22), strong UK labour productivity growth up to 2007 contrasts
sharply with much weaker growth elsewhere. But the corollary is that the fall in labour productivity in
2009 was much more pronounced in the UK.
A broadly similar story holds in terms of TFP, as can be seen in the <<chart download as can be
seen in the chart download component (259 Kb Excel sheet), accessible via the link below Figures
20-22. Note that some members of the consortium are unable to provide estimates of TFP due to
the absence of firm level measures of capital inputs.
Regression results
The ESSlait coding generates a large volume of OLS regression results. The main aim is to
compare regression coefficients between different project countries (since co-ordinated micro-data
regression analysis is very scarce). The regression results for a single country are not intended as
causal models – for example they take no account of endogeneity between the dependent variable
and regressor variables. Nevertheless the multivariate structure can provides some insights.
Office for National Statistics | 24
04 October 2013
Table 5: Sample Regression Output
DEPVAR
LNV
LNV
LNV
IND
EleCom
MexElec
MServ
LNK
0.233
***
0.286
***
0.246
***
LNE
0.722
***
0.757
***
0.697
***
HKITPCT
0.518
**
-0.014
0.414
***
HKNITPCT
0.524
**
0.215
**
0.182
***
AGE
-0.004
0.012
**
0.033
***
AGE2
0.000
0.000
***
-0.001
***
BROADPCT
0.543
0.549
***
0.524
***
-0.068
-0.116
***
-0.182
***
EXPORT0
0.113
-0.037
-0.181
***
R-SQD
0.790
0.780
0.776
918
4456
9201
MNC0
NOBS
***
Table source: Office for National Statistics
Table notes:
1. Significance levels- ***=0.1%, **=1%, *=5%
LNV
Dependent Variable, log value added
LNK
Log capital stock
LNE
Log employment
HKITPCT
Proportion of workers with higher IT qualifications
HKNITPCT Proportion of workers with higher non-IT qualifications
AGE
Firm age
AGE2
Firm age squared
BROADPCT Proportion of Broadband enabled employees in firms
MNC0
Firm is not a multinational
EXPORT0
Firm does not export
R-SQD
Goodness of fit measure defined as Explained Sum of Squares/Total Sum of Squares
NOBS
Number of observations
Fixed effects (industry, size class and year) included but not reported
Office for National Statistics | 25
04 October 2013
Download table
XLS format
(85 Kb)
Table 5 shows the results of one fixed effects panel regression model which can be interpreted as
a generalised production function, with the log of real value added as the dependent variable. The
table illustrates the different parameter results obtained for the industry categories EleCom, MexElec
and MServ for a number of different variables.
The results of table 5 indicate that across the three industries, capital and labour are the strongest,
most significant predictors of output, their coefficients summing roughly equal to one in the case
of each industry category, consistent with constant returns to scale. This finding (which generally
holds across a number of regression specifications) provides some informal support for the quality of
the micro-data estimates for employment and firm-level capital. Moreover the size of the respective
coefficients on labour and capital seem plausible.
Having a high proportion of employees with post upper secondary ICT education appears to have a
large and significant effect on production in the EleCom sector, which is likely due to reliance of its
constituents on IT ability. In the MexElec category, only a very small, statistically insignificant value
could be found. In the case of MServ, another industry category where constituents rely on IT ability,
the coefficient is highly significant, and only a little smaller than that of EleCom.
The results above contrast with the results for the proportion of employees with post secondary
but not IT related qualifications. In that case, while a significant and large parameter is found
for EleCom, comparatively small values are found for MexElec and MServ, all three results are
significant. This indicates that in the EleCom category, having a high proportion of highly skilled
workers with and without advanced IT skills have significant effects on production. For MexElec,
while having higher IT skills is deemed insignificant, having non-IT post secondary education does
significantly correlate with output. In MServ, while the figure is significant, it is the smallest of the
three coefficients, and compared to the figure with IT skills it shows that in MServ, having more
employees with higher IT skills contributes to output much more than having higher non-IT skills.
Both the firm age and age squared variables produce relatively small parameters. In the case of
those in EleCom, they are both statistically insignificant. However, for MexElec and MServ the
parameters are highly significant for both variables, and they do have measurable effect on output.
This could be indicative of a “Horndal effect” (Lazonik and Brush, 1985), whereby firms maintain
assets and workers for a long period of time, becoming more productive over this period simply
by gaining experience. The age coefficient is almost 3 times larger in MServ than in MexElec,
suggesting that a typical service firm can produce some 3% more value added simply by being a
year older, with no change in factor inputs. The negative sign on the age squared variable suggests
that this effect decays over time, but the coefficient is tiny. For MexElec firms, the age squared
variable suggests that the impact of aging gets fractionally (but significantly) greater over time.
Having a high proportion of broadband enabled employees seems to positively and highly
significantly affect production across all three industry categories. The parameter values are very
similar, which is likely to reflect a strong reliance on broadband access across the three industry
Office for National Statistics | 26
04 October 2013
categories. These coefficients and the units of measurement imply that an increase of 1 percentage
point in the share of workers with broadband access is associated with an increase of over 0.5% in
real value added. This is an interesting finding, although more work is needed to establish causality.
Being a non-multinational company (note that the regression coding creates a dummy variable for
non-MNCs, and non-exporters) appears to have a small negative effect on production. For EleCom
this result is statistically insignificant, however for the other categories, it is highly significant. This is
consistent with the TFP relationships noted above.
The effect of being a non-exporting company appears to be insignificant for categories other
than MServ. For MServ, the value is negative and a reasonable size, meaning that for MServ
constituents, being a non exporter will negatively affect output. Again this is consistent with the
relationships between TFP and export status noted above.
Section 3: Productivity distributions and market dynamics
This section explores the distribution of firm-level productivity a little further. We begin with a
summary measure of industry dynamics known as the Olley-Pakes (OP) coefficient (Olley &
Pakes, 1996). We then look at quartiles of productivity, which provides an indication of the overall
distribution of productivity across industries, and extend this analysis further to summary statistics of
other firm characteristics (such as employment and pay growth) in terms of the firm’s position in the
productivity distribution. Lastly we examine productivity in terms of firm-level demographics over the
recession.
OP coefficients
The intuition behind OP is that the observed heterogeneity of firm-level productivity reflects a
dynamic process (‘reallocation’) whereby increases in productivity at the aggregate (industry) level
reflect at least in part a reallocation of output from less productive firms to more productive firms
(as opposed to an increase in average productivity of all firms). This implies that, at any point in
time, productivity should be correlated with firm size. Olley & Pakes pointed out that this can be
quantified very simply, by subtracting an unweighted measure of productivity from a comparable
measure weighted by firm size. Effectively, OP extends the analysis of productivity by size class in
the previous section.
Office for National Statistics | 27
04 October 2013
Figures 23-25: Labour productivity, Olley-Pakes coefficients
Office for National Statistics | 28
04 October 2013
Source: Office for National Statistics
Download chart
XLS format
(259 Kb)
Figures 23-25 show the OP coefficient for labour productivity, computed as the difference
between weighted and unweighted LPV, both expressed in logs. For MexElec and EleCom the
OP coefficients are positive, consistent with the interpretation that reallocation of output from less
productive to more productive firms is occurring in these industries. Moreover, the OP coefficient in
MexElec increased sharply in 2010, suggestive of a pick up in the reallocation process as the sector
recovered from the recession.
The OP coefficient also increased in the EleCom sector but earlier, in 2008 and 2009, and the
movement was reflected more in the unweighted productivity measure than the weighted measure.
The OP coefficient is consistently negative in MServ (Figure 25), consistent with earlier evidence
(Figure 3) showing lower productivity among firms in the largest size class. This implies that
reallocation works to lower aggregate productivity in this sector, possibly reflecting weak competitive
pressures in MServ, though it may also reflect other factors such as measurement error and
compositional changes. It is also worth noting that the OP coefficient in MServ in 2010 was sharply
less negative than in 2009.
OP coefficients can also be computed in terms of TFP, although in this case the weighted
productivity aggregate is weighted by both labour and capital inputs. These results (available in
the link below Figures 23-25) suggest that once capital is accounted for, the reallocation effect
of output going to more productive firms disappears. Across all 3 sectors there appears to be
a negative redistribution; output seems to be redistributed to smaller firms. And evidence of a
recession-induced change in OP coefficients is less apparent. One possible explanatory factor for
the difference between OP coefficients in terms of LPV and TFP is that capital is less mobile than
labour across firms in the same sector.
Productivity Quartiles
Another perspective on the distribution of productivity across firms is provided by summary statistics
of ranked quartiles. Here we rank firms in an industry by (unweighted) productivity and report
summary statistics for each quartile. In the figures in this section, the top and bottom points of
each line represent the average of the highest and lowest quartile of firms in that industry and year
respectively, with the (unweighted) average for the whole sample shown for comparison purposes.
The height of the lines can be seen as a crude summary of dispersion of productivity across firms.
Office for National Statistics | 29
04 October 2013
Figures 26-28: Labour productivity, highest and lowest quartiles
Office for National Statistics | 30
04 October 2013
Source: Office for National Statistics
Download chart
XLS format
(259 Kb)
Alongside the evidence in the previous section, Figures 26-28 provide strong evidence of wide
heterogeneity in firm-level productivity performance. Remember that the high and low points are
averages of productivity in the top and bottom quartiles; a cursory analysis of standard deviations of
productivity within these quartiles (a full analysis is beyond the scope of this article) shows that there
is a much wider gap between the very highest and lowest performing firms in all sectors.
The three sectors display different reactions to the 2008-09 recession. The least affected industry
seems to be MexElec, where the productivity dispersion widened gradually up to 2007 and rather
more sharply in 2008, but has then more or less moved in step with the overall average. This
suggests that the variance between the most and least productive firms has not changed as a result
of the recession.
EleCom firms in the highest productivity quartile do not seem to have been affected by the recession
at all. The effect of the recession is more visible in the lowest quartile, with the average productivity
of the bottom quartile of firms turning negative in 2008, and some sign of a double dip in productivity
in 2010 after a small recovery in 2009.
By contrast the effect of the recession in MServ is most apparent at the top of the distribution, with
a clear narrowing apparent since 2008. As in our previous article we find that average productivity
among the least productive quartile of firms is consistently negative in this sector. There was some
deterioration in 2008 and 2009, but nothing like as much movement as at the top of the productivity
distribution.
Similar trends in productivity distributions are also apparent in terms of TFP (available in the link
below Figures 26-28).
Further characteristics of productivity distributions
In addition to summary statistics of quartile distributions of productivity (as above) we can examine
cross relationships with other variables of interest, along the productivity distribution. Specifically, we
can investigate changes in employment and wages in each of the above productivity quartiles. Some
caution needs to be exercised in interpreting these estimates. To be able to report changes requires
that firms are present in both period t and t-1, leading to considerable sample attrition compared with
the simple productivity distribution. For example, for the purpose of reporting LPV quartiles there are
approximately 6,000 firms in MexElec in 2010. But of these, changes in employment and changes in
wages can be computed for less than 3,000 firms.
Office for National Statistics | 31
04 October 2013
Figures 29-31: Employment growth by quartile of labour productivity
Office for National Statistics | 32
04 October 2013
Source: Office for National Statistics
Download chart
XLS format
(259 Kb)
Figures 29-31 show average percentage changes in persons employed for each of the labour
productivity quartiles. There is no obvious relationship between employment changes and
productivity quartiles for either MexElec or EleCom. Employment fell across all productive quartiles
in MexElec in 2009 and for all but the lowest quartile in 2010. By contrast there was a large fall in
employment among less productive firms in the EleCom sector in 2009 before a belated drop in
employment across all quartiles in 2010.
But the most interesting sector is MServ, where 2010 witnessed a clear break in the previous
pattern of exceptionally strong employment growth over the whole of the sample period, albeit that
employment growth remained positive for all but the most productive quartile. The resilience of
employment among less productive MServ firms is a consistent feature of the micro-data and a likely
contributory factor in the ‘productivity conundrum’.
Office for National Statistics | 33
04 October 2013
Figures 32-34: Wage growth by quartile of labour productivity
Office for National Statistics | 34
04 October 2013
Source: Office for National Statistics
Download chart
XLS format
(259 Kb)
Analysing average percentage changes in wages by labour productivity quartiles (Figures 32-34)
suggests a number of interesting trends. Firstly there is a very clear positive relationship between
wage growth and productivity quartiles in MexElec and MServ over the whole period. This is much
less evident in EleCom, suggesting that labour productivity is less of a driver of wage growth in this
sector. (Note however that EleCom looks more like the other sectors if we use TFP as the metric for
productivity quartiles).
Secondly, average wage growth is negative among the least productive firms in MexElec and
MServ in all years, which may account for some of the resilience of employment growth noted
above. Thirdly, there is clear evidence of a recession effect in 2009 in MexElec (where wages fell
on average across all but the most productive quartile of firms) and MServ (all quartiles). In both
sectors this was a short lived phenomenon – wage growth resumed in 2010 and was particularly
strong in MexElec.
Productivity by demographic status
We conclude this section of the article with a brief look at firm-level demographics. Over time, entry
and exit plays a major role in movements in productivity, see Dunne et al (1988) for a quantitative
analysis of US manufacturing. Here we report simple averages of labour productivity, distinguishing
between continuing firms (CO), entrants (EN) and exiting firms (EX). Note that these characteristics
are taken from the continuous business register. Exiting firms cannot be identified for the last year
of the sample, and entrants cannot be identified in the first year. For context, in a typical year more
than 90% of firms are continuers, with entrants and exiting firms being a few percentage points
each.
Office for National Statistics | 35
04 October 2013
Figures 35-37: Labour productivity by demographic status
Source: Office for National Statistics
Download chart
XLS format
(259 Kb)
Figure 35 shows that in MexElec, the average productivity of exiting firms and entrants is
consistently lower than that of continuing firms. For entrants this partly reflects the fact that new
Office for National Statistics | 36
04 October 2013
firms are typically smaller than incumbents. However, drilling down in the ESSlait dataset reveals
that the average size of exiting firms is broadly comparable with the average size of incumbents.
The drop in productivity of exiting firms in 2008 and 2009 is somewhat counter-intuitive. Other things
equal one would expect a recessionary shock to drive better performing firms out of the market
compared with normal turnover in the industry, whereas these estimates suggest that exiting firms
during the recession were on average markedly less productive than exiting firms in earlier years.
This finding is even more apparent for EleCom and MServ, perhaps suggesting that competitive
pressure to exit (sometimes referred to as ‘creative destruction’) has weakened in recent years,
despite the recession. This is turn may reflect increased forbearance by lenders. However, the
sample size for exiting firms is relatively small in all sectors so caution needs to exercised in
interpreting these results.
Background notes
1.
Details of the policy governing the release of new data are available by visiting
www.statisticsauthority.gov.uk/assessment/code-of-practice/index.html or from the Media
Relations Office email: [email protected]
Copyright
© Crown copyright 2013
You may use or re-use this information (not including logos) free of charge in any format
or medium, under the terms of the Open Government Licence. To view this licence, visit
www.nationalarchives.gov.uk/doc/open-government-licence/ or write to the Information Policy Team,
The National Archives, Kew, London TW9 4DU, or email: [email protected].
This document is also available on our website at www.ons.gov.uk.
Office for National Statistics | 37
04 October 2013
References
1.
Appleton J and Franklin M, 2012, ‘Multi-factor Productivity – Indicative Estimates, 2010’,
Available at: http://www.ons.gov.uk/ons/rel/icp/multi-factor-productivity--experimental-/
index.html
2.
Bartelsman E, Haltiwanger, J and Scarpetta, S, 2009, ‘Cross-country differences in productivity:
the role of allocation and selection’, NBER Working Paper 15490, November 2009.
3.
Crawford C, Jin W and Simpson H, 2013, ‘Productivity, Investment and Profits during the Great
Recession: Evidence form UK Firms and Workers’, Fiscal Studies, vol. 34, no. 2, March 2013
4.
Criscuolo C, Haskel J and Martin R, 2003, ‘Building the evidence base for productivity policy
using business data linking’, Economic Trends, volume 600, November 2003
5.
Dunne T, Roberts M and Samuelson L, 1988, ‘Patterns of firm entry and exit in US
manufacturing industries’, Rand Journal of Economics, Vol 19, No 4, Winter 1988
6.
Eurostat (2008), ‘ICT impact assessment by linking data from different sources – Final
Report’, Available at: http://epp.eurostat.ec.europa.eu/portal/page/portal/information_society/
methodology
7.
Eurostat (2012), ‘Final Report: ESSnet on Linking of Micro-data on ICT Usage’, Available
at: http://www.scb.se/Grupp/Hitta_statistik/Forsta_Statistik/Metod/_Dokument/final-reportall-121130.pdf
8.
Field S and Franklin M, 2013, ‘Micro-data perspectives on the UK productivity conundrum’,
Available at: http://www.ons.gov.uk/ons/rel/icp/microdata-perspectives-on-the-uk-productivityconundrum/index.html
9.
Lazonick, W and Brush, T (1985), ‘The “Horndal Effect” in Early US Manufacturing’, Explorations
in Economic History, Vol 22, pp 53-96
10. OECD, 2013, ‘Indicators of productivity and competitiveness: Improving coherence and applying
micro-data sources’, STD/CSTAT(2013)2.
11. Olley G and Pakes A, 1996, ‘The Dynamics of Productivity in the Telecommunications
Equipment Industry, Econometrica, Vol 64, No 6
12. Robjohns J, (2006). ‘ARD2: the new Annual Respondents Database’, Economic Trends, No 630,
May.
Appendix A: List of participants in ESSLAIT project
UK Office for National Statistics
Sweden Statistics Sweden
Office for National Statistics | 38
04 October 2013
Netherlands Statistics Netherlands
Norway Statistics Norway
Italy National Statistical Institute of Italy
Germany Statistisches Bundesamt
France Institut National de la Statistique et des Études Économiques
Denmark Statistics Denmark
Ireland Central Statistics Office
Slovenia Statistical Office of the Republic of Slovenia
Austria Statistics Austria
Finland Statistics Finland
Luxembourg Service Central de la Statistique et des Études Économiques
Poland Central Statistical Office of Poland
Appendix B: The ESSlait dataset
The primary source of the data used in this article is the annual structural business survey, which
since 2008 has been known as the Annual Business Survey (ABS) in the UK. ABS collects
information from approximately 50 thousand firms on a number of economic variables (e.g. turnover,
value-added, employment costs, capital investment etc). Additionally, the ESSlait project draws
on firm level data from other business surveys, principally an annual survey of business use of
ICT, known as the E-commerce (EC) survey, and the Community Innovation Survey (IS) which is
conducted every two years and collects a range of information on innovation, such as whether firms
have introduced new products or processes.
In addition to these primary sources, the ESSlait dataset merges in other firm-level information
which is not collected in any of ABS, EC or IS. Chief among these is employment, which plays a key
role in the ESSlait project framework (as a core measure of firm size) and, from 2008, comes from
1
the annual Business Register and Employment Survey (BRES) . Additionally, two export variables
(an exporter/non-exporter flag and a measure of export sales as a share of turnover) are merged
in from a panel built from successive editions of the monthly business survey (MBS) and we also
merge in a dataset on firm-level capital stocks. The derivation of these datasets is described further
below. We use questions on the IS to populate variables on firm-level skills. The combined dataset
of annual firm-level economic variables is referred to in this article as the PS dataset.
The ESSlait project architecture also utilises a continuous register dataset containing basic
information (on employment, firm age, and 1/0 flags for whether the firm is foreign owned and/or
part of a multi-national enterprise) on the universe of firms of which the ABS and its predecessors
are samples. This register is derived from similar sources, but is not identical to either the InterDepartmental Business Register (IDBR) or the Business Structure Dataset (BSD), both of which
have been used by other micro-data researchers. The principal differences are that the IDBR covers
all parts of the economy, whereas our register is confined to those sectors that are sampled in ABS
(and its predecessors), and the BSD is available in terms of enterprise units and local units, whereas
2
our register is, like ABS, structured in terms of reporting units .
Office for National Statistics | 39
04 October 2013
The register dataset serves two main functions. First, to provide information on firm demographics
(entry and exit). And second, to allow weighted estimates to be computed for any sub-category of
micro-aggregated data, including data from two or more merged datasets. It is important to realise
that the ESSlait dataset does not use reported sample weights for ABS or any other source dataset,
but rather endogenous weights computed within the project coding.
Information on whether the firm is part of a multi-national enterprise is merged into the register
dataset from a panel of multi-national firms constructed from the Annual Survey of Foreign Direct
Investment (AFDI). AFDI is also used to supplement administrative data on foreign ownership, such
that firms in receipt of inward direct investment are deemed to be foreign owned. The addition of a
multi-national flag is a development from the dataset used in our previous article.
In the final register dataset there is some overlap between multi-national and foreign ownership flags
but not an exact correspondence – some UK-owned firms are multi-nationals and many firms
recorded as foreign owned are not recorded as multi-nationals. In the results sections below we
report estimates in terms of the multi-national flag rather than (as in our January article) the foreign
ownership flag because we believe the new variable to be a more robust indicator.
Transition from ABI1/2 to ABS/BRES
In the ONS micro-data research environment (known as the virtual micro-data laboratory, VML)
ABI1 and ABI2 survey results up to 2007 were used to compile a set of annual datasets known as
the Annual Respondents Database (ARD) (Robjohns 2006). This process used coding developed
over a number of years by VML researchers to map survey questions to a standardised set of
ARD variables and carry out other tasks such as data-cleaning, assignment of industry codes and
so forth. Extracts from the ARD datasets were used to compile PS datasets up to 2007 in earlier
phases of ESSnet work. But the ARD coding is not robust to the transition from ABI1/2 to ABS and
BRES so we (and other micro-data researchers) have had to do our own processing to create ARDlike datasets from 2008 onwards from the ABS and BRES source data.
Parallel to this, the VML team compiled a register specific to the ARD, known as the ARD Register
Panel. Again these datasets were used to compile the register datasets for the previous phases
of micro-data linking work, and again compilation of these datasets broke down after 2007. For
2008 and later years we have carried out work in the VML to replicate the ARD Register Panel from
the ABS and BRES sample frames.
One specific issue is that whereas ABI1 and ABI2 were drawn from the same sample frame,
ABS and BRES have slightly different sample frames and are drawn from the IDBR at different
times. BRES datasets for 2008 and later were not available in the VML at the time of compiling the
register dataset for our January paper. For this update we have converted BRES to a reporting unit
basis and used this as source for firms that are in both the ABS universe and the BRES survey
sample. We have also gone back to fix missing records on foreign ownership, fixed a discontinuity
between the universe and the (ABI) sample in 2006, and made a continuous age variable for all
years.
A further issue concerns outlier filtering. In ABS there is an explicit outlier marker (used to suppress
observations in weighting ABS responses to population totals), but investigation showed that many
of the records marked as outliers had perfectly plausible productivity characteristics, while other
firms not marked as outliers had implausible characteristics. So a pragmatic approach of selective
Office for National Statistics | 40
04 October 2013
outlier filtering was used, in which records with nominal productivity (value-added and turnover
based) more than 6 standard deviations from the sample mean were deleted, plus further deletions
where quality assurance of the micro-aggregated results revealed implausible time series properties
of productivity or other metrics.
Capital stocks
Coding to build a dataset of firm-level capital stocks has been completely re-run since our January
article. In particular, we have benchmarked starting values to 2-digit capital stock estimates derived
from an exercise in 2012 to derive volume indices of capital services for the purpose of estimating
3
multi-factor productivity (MFP), see (Appleton & Franklin 2012) . This has led to a completely
revised set of firm-level capital stock data, but one where the distribution of capital across industries
is a priori more plausible than previously (see next section on descriptive statistics). Building firmlevel capital stock dataset is not a trivial task and requires a number of assumptions to be made.
Accordingly, we judge these estimates to be less robust than our measure of firm-level employment.
And more work remains to be done. In particular, we would like to expand the asset breakdown to
reflect more closely the breakdown used in ONS’s MFP framework (and note that this differs from
the asset breakdown used to compile capital stock estimates in the current National Accounts).
Secondly, we are planning to introduce more “triangulation” between asset stocks at the micro-data
level and the equivalent series at the macro level. Thirdly, we plan to combine micro-data capital
stock estimates with information on firm demographics to derive estimates of capital scrapping.
Industry classification
Like other international datasets such as EUKLEMS and the OECD STAN database, the ESSlait
industry taxonomy is classified in NACE1, equivalent to SIC03 in UK terms. ABS and other ONS
business surveys moved to the SIC07 industry taxonomy in 2008. The ABS survey data for that year
and for 2009 are dual coded, but for 2010 (and later years) ABS contains only SIC07 industry codes.
The project uses dual industry coding to analyse mappings between industry classifications and
derive weights (based on employment) where SIC07 codes are mapped one-to-many from SIC03.
These weights are used to proportion new firms back to SIC03. Continuing firms in such industries
are coded to their original SIC03 coding.
Export variables
Perhaps surprisingly, ABS and its predecessor surveys have only included questions on exports
(and imports) of services since 2007, and only introduced questions on exports (and imports) of
goods since 2011. This means that we have had to look for other sources to populate the export
variables used in the project architecture. Following user feedback after the January article, we have
reviewed the compilation method for creating the export flag, as described below. This has meant
that the number of firms classified as exporters differs from our previous article.
There are two main difficulties in deriving a panel of exporting firms from monthly business surveys.
Firstly the MPI (Monthly Production Inquiry) was replaced in 2009 with the MBS (Monthly Business
Survey), this has led to some inconsistencies in survey questions. Secondly, only large firms are
continually surveyed, small and medium sized firms are surveyed sporadically. This can cause
problems when deciding whether or not a firm is an exporter in years when the firm has not been
surveyed.
Office for National Statistics | 41
04 October 2013
To create a consistent export panel, questions 40 (“what is this firm’s turnover?”) and 49 (“value
of exports included in turnover”) are taken from the MPI and MBS. This creates a panel of firms
showing turnover and exports for each month of the year. We create a yearly turnover to export ratio
by taking the sum of responses to both questions throughout the year and then taking the ratio of
exports to turnover. This is done for all years, the individual years panels are merged to create a
complete panel. This shows two things; the yearly ratio of firms’ exports to turnover, and whether a
firm has been surveyed in any given year.
The complete panel also demonstrates the sporadic nature of small and medium sized firms being
surveyed. To create an export flag for small and mid size firms, we calculate how many observations
show that the firm is exporting (by having an export ratio which is not equal to zero). We then divide
the number of observations for which a firm is exporting by the total number of observations on the
firm. This creates an exporter probability.
If this probability equals one then it suggests that the firm is an exporter, therefore for any years for
which data are missing we replace with the average export ratio. Any firms for which the exporter
probability is zero we interpret as indicating that the firm is not an exporter, and so we replace
missing observations with zero. If the probability is between zero and one we cannot conclude
whether the firm exports. Therefore, we do not alter the observations for which the firm has reported
being an exporter and do not replace any of the missing observations.
Firm-level productivity measures
As in our previous article, in this article we focus on two measures of productivity at the firm level:
labour productivity and total factor productivity. Labour productivity is denoted as LPV and is
computed from firm-level measures of real value-added and employment. Note that real valueadded is derived using industry value-added deflators, since no firm-level deflators are available.
Value added is recorded in the UK micro-data in £k, employment in actuals and deflators are
indexed to 2005=100, so LPV in the figures and tables in this article are £k per person employed in
prices of 2005.
Firm-level total factor productivity (TFP) is computed by dividing real value-added by a weighted
index of employment and capital, where the weights are derived from industry shares of labour
and capital remuneration as computed from the micro-data. Units of TFP in the figures and tables
have no direct intuition because the units of labour and capital are not comparable. Increases in
TFP imply that real value-added is rising more (or falling less) than weighted factor inputs and vice
4
versa .
Office for National Statistics | 42
04 October 2013
Notes
1.
Prior to 2008 the information collected in ABS and BRES was collected through two versions
of the Annual Business Inquiry, known as ABI1 and ABI2. See below for more on data issues
resulting from the move from ABI1/2 to ABS and BRES.
2.
For more information on the differences between enterprise units, local units and reporting units,
see Criscuolo et al, 2003. For more information on coverage of the ABS, go to the ABS index
page on the ONS website.
3.
Capital services and MFP are on SIC07, so benchmarks have been converted back to SIC03.
4.
The project database also contains results for two further productivity metrics, where real value
added is replaced with real turnover. For more information on results using these alternative
productivity metrics, please contact the authors.
Appendix C: Full industry breakdown
EUK Industry Definitions:
EUK
Description
TOT
Total Economy
1t5
_AGRICULTURE, HUNTING, FORESTRY AND
FISHING
10t4
_MINING AND QUARRYING
15t37
_Manufacturing
15a6
__Food, Beverages and Tobacco
17t9
__Clothing
20
__WOOD AND OF WOOD AND CORK
21a2
__Pulp, paper, publishing
21
___Pulp, paper and paper
22
___Publishing and Printing
23t25
__Refining, chemicals, and rubber
23a4
___Refining and chemicals
25
___Rubber and plastics
26
__OTHER NON-METALLIC MINERAL
27a8
__Metals and Machinery
27
___Basic metals
28
___Fabricated metal
29t33
__Machinery and Equipment
Office for National Statistics | 43
04 October 2013
29
___MACHINERY, NEC
30t3
___Equipment
30a3
____Office, accounting, computing and scientific
machinery
31
____Electrical Equipment
32
____Electronic Equipment
34a5
__Motor Vehicles and Transport Equipment
34
___Motor vehicles, trailers and semi-trailers
35
___Transport Equipment
36a7
__Misc Manufacturing
40a1
_ELECTRICITY, GAS AND WATER SUPPLY
45
_CONSTRUCTION
50t74
_Market Services
50t5
__Trade, Hotels, Restaurants
50t2
___Trade, Hotels, Restaurants
50
____Sale, and repair of motor vehicles and
motorcycles; retail sale of fuel
51
____Wholesale trade , except of motor vehicles
and motorcycles
52
____Retail trade, except of motor vehicles;
repair of household goods
55
___HOTELS AND RESTAURANTS
60t4
__Transport and Communications
60t3
___Transport
64
___POST AND TELECOMMUNICATIONS
65t7
__Banking
70t4
__Real Estate and Bus Services
70
___Real estate activities
71a4
___Renting of machinery and other Bus services
72
___Computer and related activities
73
___Research and development
75t99
_Social Services
75
__PUBLIC ADMIN AND DEFENCE;
COMPULSORY SOCIAL SECURITY
80
__EDUCATION
85
__HEALTH AND SOCIAL WORK
Office for National Statistics | 44
04 October 2013
90t3
__Personal Services
90t3x
___Personal Services excl media
921t2
___Media activities
Office for National Statistics | 45