Office for National Statistics
Identifying Sources on Entrepreneurship and the Informal Economy Final Report on Identifying Data Sources on Entrepreneurship
and the Informal Economy
15 July 2005
Contents
Sections
1
2
3
4
Executive Summary
Introduction
The Informal Economy
Data Sources on the Informal Economy
5
6
Data Sources on Entrepreneurship
Conclusion
Annexes
A
Informal Economy Measurement Methods
B
GEM Methodology
C
The BETA Model
D
List of Councils Responding to Queries
E
Publications Reporting Results of English Localities Survey
F
Bibliography
G
Contributors
1
Section 1 – Executive Summary
Executive Summary
•
Identifying data sources on both subjects is a difficult task. The review has produced a wide range of
sources, which provide varying degrees of detail, but above all, highlight the lack of consistent data
collection.
•
There are inherent problems in investigating the informal economy, because of varying degrees of non
compliance. There are fewer data sources identified on entrepreneurship, but the data collection in
some cases is more consistent.
•
The best data sources for the informal economy comprise UK locality studies conducted by academics
and collated in the English Localities Survey; the Small Business Survey; the Family Expenditure
Survey; Inland Revenue data on under-reporting of tax returns; DWP data on 'working whilst
claiming' and HM Customs and Excise data on VAT compliance.
•
Discussion of data sources on the Informal Economy has identified two main reasons for identifying
informal economic activity – the pursuit of non-compliance, and the understanding of informal activity in
the local economy.
•
The best data sources for measuring entrepreneurship comprise the GEM survey of UK entrepreneurial
activity; data from the Small Business Service including the Household Panel Survey, SME statistics
and VAT registrations/de-registrations; the Beta Model which measures relative change over time of
business formation, employment and enterprise; the IDBR register on enterprise development for policy
assessment, the Yellow Pages which contains regional databases of and for businesses across the UK
and an EU/SBS 2005 survey containing questions on tracking entrepreneurship. Not all of these data
sources are in the public domain, however.
•
Definitions. The appropriate data source will depend to a substantial degree on the definition of
entrepreneurship that is used. The GEM data set defines entrepreneurship in terms of the ‘ newness’
of the activity and has this definition built into its measure. Alternative data sources do not build a
definition into their data collection processes.
•
Overlap with Informal Economy. Since the GEM dataset and the SBS Household Survey a) Ask about
start up intentions and b) Find median business turnovers below the VAT threshold, much of the activity
captured may be unregistered or informal. Given the size of the GEM data set it is possible to isolate the
smaller, lower turnover business from the larger ones.
•
District and Parish Councils hold data on allotment farming, market trading and unlicensed businesses
though gathering such data is difficult since Councils rarely compile such information in a systematic
form.
•
Further work should be undertaken to measure the often subtle relationship between the informal
economy and entrepreneurship. This could be done by adding questions to the SBS Household panel
survey, commissioning an academic study of how start-ups might become integrated into the formal
economy or examining the data collected by Regional Development Agencies, which they use to
evaluate the success of interventions, if this could be made available.
2
Section 2 - Introduction
Introduction This is the final report on identifying data sources on entrepreneurship and the informal economy. It
was undertaken by Hedra in accordance with terms of reference issued in April 2004 by the Office for
National Statistics.
To understand why data sources on these two subjects are investigated together, one needs only to
pose two questions:
* how many entrepreneurs start-up their ventures conducting a portion or all of their trade on an informal or off-the-books basis?; and
* how many continue to conduct a portion of their transactions in such a manner once they become
more established?
It is becoming widely recognised that not all entrepreneurs start-off and continue operating on a totally
legitimate basis and also that not all informal workers are employees and that many work on a self-employed
own account basis (see Small Business Council, 2004; Williams, 2004).
Definitions of entrepreneurship and the informal economy have been reviewed as well as the varying
measurement methods that have been previously employed, along with current data sources. Our
finding is that on the whole very few data sources provide either SIC-level and/or neighbourhood
statistics on either area of research. This report thus provides evidence of the need for
neighbourhood-level and SIC-coded data to be consistently collected, and offers some ideas and on
how this could be systematically and effectively collected.
The purpose of this Final Report, as set out in the tender specification, is ‘to show the scope of the
sources identified and the results of research or fieldwork to date’. Two requirements are listed:
* To identify data that will inform the Neighbourhood Statistics Service (NeSS), an internet facility
aimed at meeting the needs of the National Strategy for Neighbourhood Renewal and other
area-based policies in both central and local government;
* A secondary objective is to use such sources to identify data that will enhance the Authorities
Inter-Departmental Business Register (IDBR), which is the comprehensive register of UK
businesses used by the government for statistica
We will begin by discussing the informal economy and then move onto entrepreneurship.
3
Section 3 - The Informal Economy
The Informal Economy
Defining the informal economy
It is important to be clear that the ‘informal economy’ is often taken to refer to all work that is not formal
employment. As such, in many analyses, three broad varieties of informal work are identified
according to the socio-economic relations within which the work is conducted:
Unpaid domestic work - Unpaid work conducted by a household member either for themselves or for some
other member of the household. This ranges from routine housework, child-minding, through to do-it-yourself
work.
Unpaid community work - Unpaid work conducted by a household member for members of households other
than their own. This ranges from one-way giving (voluntary work) that may be conducted either on a one-to-one
basis (informal volunteering) or through groups (formal volunteering) to reciprocal (two-way) exchange such as
between kin, friends and neighbours.
Paid informal work - The paid production and sale of goods and services that are unregistered by, or hidden
from the state for tax, benefit and/or labour law purposes but which are legal in all other respects.
In this report, however, and in accordance with a long-standing stream of thought, the ‘informal
economy’ here refers only to paid informal work, that is, the paid production and sale of goods and
services that are unregistered by, or hidden from the state for tax, benefit and/or labour law purposes
but which are legal in all other respects (European Commission, 1998; Feige, 1990; Marcelli et al, 1999;
Portes, 1994; Thomas, 1992; Williams and Windebank, 1998).
Informal work is thus composed of three types of activity:
• evasion of both direct (i.e. income tax) and indirect (e.g. VAT, excise duties) taxes;
• benefit fraud where the officially unemployed are working whilst claiming benefit; and
• avoidance of labour legislation, such as employers’ insurance contributions, minimum wage agreements
or certain safety and other standards in the workplace (e.g. hiring labour off-the-books or sub-contracting
work to small firms and the self-employed asked to work for below-minimum wages).
This definition of the scope of informal work is the standard one used and one on which there is
widespread consensus in both academic and policy-making circles. On the one hand, it excludes
unpaid work, including the production of goods and services for a family’s own consumption or as an
unpaid favour for friends, neighbours or one’s community, as well as barter and time banks. On the
other hand, it explicitly denotes that the only criminality about informal work is the fact that the
production and sale of the goods and services are not registered for tax, social security and/or labour
law purposes (e.g., Feige, 1990; Portes, 1994; Thomas, 1992). Criminal activities where the goods and
services themselves are illegal are not included (e.g. drug trafficking). In other words, informal work
covers only activities where the means do not comply with regulations but the ends (goods and
services) are legitimate (Staudt, 1998).
Although there is a strong consensus with regard to the type of activity involved (e.g. Feige, 1990;
Leonard, 1998a,b; Pahl, 1984; Portes, 1994; Thomas, 1992; Williams and Windebank, 1998), there is no such
consensus when it comes to deciding on the adjectives and nouns used to denote such work. As Table 1
displays some 35 adjectives and 7 nouns have been so far variously combined when denoting
such activity. For example, ‘cash-in-hand work’, ‘black market’, ‘shadow sector’, ‘underground economy’).
4
Section 3 - The Informal Economy
Table 1: Alternative adjectives and nouns used to denote informal economy
Adjectives
Nouns
Black
Informal
Clandestine
Concealed
Activity
Dual
Everyday
Ghetto
Grey
Economic activity
Hidden
Invisible
Irregular
Marginal
Work
Moonlight
Non-observed
Non-official
Occult
Employment
Off-the-books
Other
Parallel
Peripheral
Economy
Precarious
Second
Shadow
Submerged
Sector
Subterranean
Twilight
Underground
Unexposed
Market
Unobserved
Unofficial
Unorganised
Unrecorded
Unregulated
Untaxed
Underwater
Source: Williams (2004a)
What is important to recognise here is that a ‘task-based’ definition of the informal economy is not
possible. For example, cooking or window cleaning cannot be allocated to some particular type of
work. They can be conducted as unpaid domestic work (e.g. where one cooks for oneself and/or
one’s family), as unpaid community work (e.g. where one cooks for neighbours or friends on an
unpaid basis), (paid) informal work (e.g. where one cooks in a restaurant on a off-the-books basis for
‘informal’ payments that are not declared to the government for tax, benefit or labour law purposes) or
formal employment (e.g. where one is a formally employed chef either registered self-employed or
paid on a PAYE basis). Neither can an ‘occupation-based’ definition be employed.
Window-cleaners, market traders and allotment farmers can work either formally or informally. And
finally, nor can a ‘sectoral definition’ be adopted (e.g. antique shop retailers, zero-rated food shops,
hairdressers, market traders). The informal economy crosscuts all sectors and even if recent studies
show that it is considerably more prevalent in some trade sectors than others, it cannot be wholly
defined in this manner. While some activities are considered to be more likely to be conducted in the
informal economy than others, this does not mean that one can reliably use a task, occupation and/or
trade sector as a ‘proxy indicator’ of the informal economy.
To identify neighbourhood-level data sources on the informal economy, a wide range of research
methods have been used:
• the existing academic literature has been extensively reviewed in order to identify where such data
has been collected. A bibliographic search of the International Bibliography of the Social Sciences
(IBSS) reveals that in the 18 months from January 2003 to June 2004, 173 peer-reviewed academic
journal articles were published on the informal sector, following on from 405 articles between 2000 and
2002, 295 between 1997-99 and 183 between 1994-96. These 1,000 journal articles have been
reviewed to identify;
(a) sources of UK data on the informal economy; and
(b) good practice on measurement methods.
• . international organisations have been reviewed which either collect data or evaluate measurement
.....................methods.
5
Section 3 - The Informal Economy
• Interviews / meetings have taken place with civil servants in UK central government departments with
responsibility for various aspects of the informal economy. Furthermore, recent reports produced for
them on the informal economy have been reviewed in order to identify data sources and methods.
This has included attending a meeting of the cross-Government Informal Economy Steering Group
(November 2004); holding an interview with the head of the Cross-Cutting Policy Team at the Inland
Revenue (November 2004), and reviewing research conducted by the Office of the Deputy Prime
Minister (ODPM), Small Business Service and Small Business Council, and discussing data sources
with the DWP and the Home Office. Any public reports produced for these teams have been scoped
out for both their emergent approaches and the evidence used to inform their approach.
• Regional and local government agencies have been contacted for data on the informal economy. This
has involved contacting the relevant officials and asking them for information on:
o
Data on the amount of street trading done in an area (e.g. number of market stalls, frequency of
local markets)
o
Data on the amount of allotment farming in an area (number of allotments, etc.)
o
Data on avoidance of Business Rates
o
Data on avoidance of a range of licensing requirements (e.g. road hauliers, non-licensed
child-minders, non-licensed street trading). 6
Section 4 - Data sources on the informal economy
Data Sources on the Informal Economy
The tender specification for this project requests that ‘As a minimum, the data must include an
industry (SIC) breakdown’ and include the spatial level at which the source collects the data (e.g.
district, ward, postcode, etc.). Below, we review the sources of UK data currently available on firstly,
spatial variations in the informal economy and secondly, sectoral variations, to show that no sources
sources currently match up to all of these requirements and that some serious work needs to take
place on considering how such data might be collected in the future.
Spatial data on the informal economy
Until recently, the only means of assessing the intra-national geographical variations in informal work
in the UK was to compare the direct empirical studies of informal work conducted in specific locations
that used different methods to collect data at different times. The UK main locality studies of the
informal economy that have been conducted are:
Isle of Sheppey Study
• Pahl, R.E. (1984) 'Divisions of Labour', Blackwell, Oxford
• Pahl, R.E. (1988) ‘Some remarks on informal work, social polarization and the social structure’, International
Journal of Urban and Regional Research, 12, 2: 247--67.
Cleveland study
• MacDonald, R. (1994) ‘Fiddly jobs, undeclared working and the something for nothing society’, Work, Employment and Society, 8, 4: 507--30.
Belfast study
• Leonard, M. (1994) Informal Economic Activity in Belfast, Aldershot: Avebury.
• Leonard M. (1998a) ‘The long-term unemployed, informal economic activity and the underclass in Belfast:
rejecting or reinstating the work ethic’, International Journal of Urban and Regional Research, 22,1: 42--59.
• Leonard, M. (1998b) Invisible Work, Invisible Workers: the informal economy in Europe and the US, London:
Macmillan.
• Leonard, M. (2000) ‘Coping Strategies in developed and developing Societies: the workings of the informal
economy’, Journal of International Development, 12,8: 1069-85.
• Howe, L. (1988) ‘Unemployment, doing the double and local labour markets in Belfast’, in C. Cartin and T. Wilson (eds.) Ireland from Below: social change and local communities in modern Ireland, Dublin: Gill and Macmillan. • Howe, L. (1990) Being Unemployed in Northern Ireland: an ethnographic study, Cambridge: Cambridge University Press.
Hartlepool Study
• Morris L. (1987), 'Local social polarisation: a case study of Hartlepool', International Journal of Urban and Regional Research, vol. 11, pp.351-62. • Morris, L. (1993), 'Is there a British underclass?', International Journal of Urban and Regional Research, vol.17, pp.404-13. • Morris, L. (1994), 'Informal aspects of social divisions', International Journal of Urban and Regional Research, vol.18, pp.112-26.
• Morris, L. (1995), Social Divisions: economic decline and social structural change. UCL Press, London
South-West of England
• Jordan, B. and Redley, M. (1994) ‘Polarisation, underclass and the study welfare state’, Work, Employment
and Society, 8,2: 153--76.
• Jordan, B. and Travers, A. (1998) ‘The informal economy: a case study in unrestrained competition’, Social
Policy and Administration, 32,3: 292--306.
• Jordan, B., James, S., Kay, H. and Redley, M. (1992) Trapped in Poverty? Labour-market decision in low-
income households, London: Routledge.
7
Section 4 - Data sources on the informal economy
In recent years, however, three empirical studies have been undertaken that more directly interrogate
the intra-national geographical variations in the informal economy. None of these studies, it should be
noted, provide any indication of the industry (by SIC code) in which informal work takes place.
English localities survey (academic survey)
How data Authority could obtain from the source
might be relevant to the objectives of the contract.
Only UK study of the comparative extent and nature of informal work in deprived and affluent urban and
rural localities. Based on 11 localities.
How the source obtains its data (e.g., by estimation,
survey, administrative record). Where appropriate,
description of IT systems involved.
Face-to-face household interviews on ‘coping
practices’ used to conduct 46 tasks and also whether
household members have engaged in these activities
for others on an informal basis, along with openended questions on other paid informal work received
and supplied.
Purpose for which the source collects data; and, where the data is shared with other parties, for what
purposes these parties use the data.
Academic enquiry into the nature of the informal economy. Data is disseminated via academic
publications. How the source updates the data and the frequency of updates.
No up-dates planned.
Breakdown of the elements that make up the data (including the spatial level at which the source
collects the data, e.g. district, ward, postcode. As a minimum the data must include an industry (SIC) breakdown. Southampton (one affluent and two deprived
neighbourhoods), Sheffield (one affluent and two
deprived neighbourhoods), two affluent rural localities
and three deprived rural localities. No SIC code
breakdown of informal work.
The quality checks that are applied to the data
Responses of respondents as suppliers and
customers are compared on a neighbourhood-level
as check on validity of supplier responses.
How the source manages, cross-references and
combines different data sets in order to ensure uniformity and consistency of data Not relevant
A description of any data protection, confidentiality,
ownership (IPR), or disclosure issues related to the data and the source
Respondents were informed that individual records
would not be available to anybody other than the
researcher. As such, the dataset is not available to
third parties.
The charges related to acquisition of the data from
the source
Dataset not available for interrogation by other
agencies due to confidentiality promises made to
respondents. Data only available through academic
publications.
Method, frequency and timings of delivery of the
data from the source and the formats in which the
data is available
Only available via academic publications.
Contact: Prof. Colin C. Williams, Management Centre, University of Leicester, Leicester, LE1 7RH
e-mail address: [email protected]
8
Section 4 - Data sources on the informal economy
Study of Bassetlaw, North Nottinghamshire (academic survey)
How data Authority could obtain from the source
might be relevant to the objectives of the contract
Comparative study of the extent and nature of
informal work in a deprived, middle-ranking and
affluent ward in Bassetlaw, North Nottinghamshire.
How the source obtains its data (e.g., by estimation,
survey, administrative record). Where appropriate,
description of IT systems involved.
Face-to-face household interviews on ‘coping
practices’ used to conduct 46 tasks and also whether
household members have engaged in these activities
for others on an informal basis, along with openended questions on other paid informal work received
and supplied.
Purpose for which the source collects data; and,
where the data is shared with other parties, for what
purposes these parties use the data
Academic enquiry into the nature of the informal
economy. Data is disseminated via academic
publications.
How the source updates the data and the frequency
of updates
No up-dates planned.
Breakdown of the elements that make up the data
(including the spatial level at which the source
collects the data e.g. District, Ward, Postcode. As a
minimum the data must include an industry (SIC)
breakdown.
Data on three wards chosen using maximum
variation sampling using the Index of Multiple
Deprivation (IMD). No SIC-level data.
The quality checks that are applied to the data
Responses of respondents as suppliers and
customers compared on a ward-level as check on
validity of supplier responses.
How the source manages, cross-references and
combines different data sets in order to ensure
uniformity and consistency of data
Not relevant
A description of any data protection, confidentiality,
ownership (IPR), or disclosure issues related to the
data and the source
Respondents were informed that individual records
would not be available to anybody other than the
researcher. As such, the dataset is not available to
third parties.
The charges related to acquisition of the data from
the source
Dataset not available for interrogation by other
agencies due to confidentiality promises made to
respondents. Data only available through academic
publications.
Method, frequency and timings of delivery of the
data from the source and the formats in which the
data is available
Only available via Williams, C.C. (2004) "Cash-inhand work: unravelling informal employment from the
moral economy of favours", Sociological Research
On-Line, 9, 1
Contact: Prof. Colin C. Williams, Management Centre, University of Leicester, Leicester, LE1 7RH.
e-mail address: [email protected]
9
Section 4 - Data sources on the informal economy
Leicester Study
How data Authority could obtain from the source
might be relevant to the objectives of the contract
Compares level and nature of informal work in a
deprived and affluent ward in Leicester chosen using
the ward-level Index of Multiple Deprivation (IMD)
How the source obtains its data (e.g., by estimation,
survey, administrative record). Where appropriate,
description of IT systems involved.
Face-to-face interviews. Collated using SPSS.
Purpose for which the source collects data; and,
where the data is shared with other parties, for what
purposes these parties use the data
PhD thesis.
How the source updates the data and the frequency
of updates
Snapshot study
Breakdown of the elements that make up the data
(including the spatial level at which the source
collects the data e.g. District, Ward, Postcode. As a
minimum the data must include an industry (SIC)
breakdown.
Comparison of a deprived and affluent ward in
Leicester. SIC data not gathered.
The quality checks that are applied to the data
Not known
How the source manages, cross-references and
combines different data sets in order to ensure
uniformity and consistency of data
Not known
A description of any data protection, confidentiality,
ownership (IPR), or disclosure issues related to the
data and the source
Respondents were informed that individual records
would not be available to anybody other than the
researcher. As such, the dataset is not available to
third parties.
The charges related to acquisition of the data from
the source
Not relevant
Method, frequency and timings of delivery of the
data from the source and the formats in which the
data is available
Only available through PhD thesis.
Contact: Richard White, Warwick Business School
e-mail address: [email protected]
10
Section 4 - Data sources on the informal economy
Sectoral data on the informal economy
Although there are a number of small-scale studies of the prevalence of the informal economy in
specific UK sectors and occupations, such as the hospitality industry (Williams and Thomas, 1996),
the restaurant and clothing sectors (e.g., Ram et al, 2002) and taxi drivers (Jordan and Travers,
1998), little overarching evaluation has taken place of the sectors in which it is concentrated.
In recent years, a very partial bridging of this gap has begun. As Table 3 demonstrates, Pedersen
(2003) has surveyed the sectors in which informal work takes place in the UK as part of a four-nation
study (albeit based on a small sample). This shows that just under half of all UK informal work (46.7
per cent) takes place in the ‘construction sector’ and another quarter (23.8 per cent) in a range of
consumer services.
Table 3: Sectoral distribution of informal work, by type of activity, Britain, 2000
Sector
% of all informal work
Agriculture, forestry & fishing
(1.1)
Manufacturing
14.0
Trade
(0.7)
Construction
46.7
- painting & decorating
17.2
- carpentry & joinery
11.1
- bricklaying
1.3
- other (e.g., electrical & plumbing)
17.2
Service Functions
23.8
- repair work
11.6
- other services (e.g., hairdressing, cleaning)
12.2
Other (e.g., transport)
(4.8)
Activity not stated
8.9
Total
100.0
Source: Rockwool Foundation Research Unit, 2003 (cited in Renooy et al, 2004: Table 4.4)
Note: In parentheses when respondents in a cell are less than 20.
Focusing upon the domestic services realm, the English Localities Survey has identified
remarkably similar results (see Table 4). Examining eleven affluent and deprived urban and rural
localities, it finds that some 43 per cent of all informal work is concentrated in the home repair and
maintenance/construction sector (46.7 per cent in the Pedersen study) and a quarter in domestic
service activities.
Indeed, the tasks identified as most frequently using informal rather than formal labour are: making /
repairing garden equipment such as repairing lawnmowers or sharpening tools (one in three of such
tasks are conducted on an informal basis); attic conversions (one in four are primarily done informally);
installing a bathroom (24 per cent); car repairs (19 per cent); plumbing (13 per cent); electrical
work (12 per cent); plastering (12 per cent); baby-sitting (12 per cent); maintaining appliances
(11 per cent); outdoor painting (10 per cent); and window cleaning (10 per cent).
11
Section 4 - Data sources on the informal economy
Table 4: Tasks conducted in the informal economy in England
Task
House maintenance
- Outdoor painting
- Indoor painting
- Wallpapering
- Plastering
- Mending broken window
- Maintenance of appliances
Home improvement
- Double glazing
- Plumbing
- Electrical work
- House insulation
- Put in bathroom
- Build a garage
- Build an extension
- Convert attic
- Put in central heating
- Carpentry
Routine housework
- Do housework
- Clean the house
- Clean Windows
- Spring cleaning
- Do the shopping
- Wash clothes/sheets
- Ironing
- Cook the meals
- Wash dishes
- Hairdressing
- Administration
Making & repairing goods
- Make/repairing clothes
- Knitting
- Repair clothes
- Make/repairing furniture
- Make/repairing garden equipment
- Make curtains
Car maintenance
- Wash car
- Repair the car
- Car maintenance
Gardening
- Indoor plants
- Outdoor borders
- Outdoor vegetables
- Lawn mowing
Caring
- Baby sitting (day)
- Baby sitting (night)
- Courses (e.g. Piano lessons)
- Pet care
% conducted
in informal economy
8
10
7
6
12
7
11
10
7
13
12
1
24
0
0
25
9
9
3
2
2
10
2
1
1
1
1
1
8
1
2
0
0
0
6
33
8
9
6
19
4
2
0
3
0
4
8
11
12
0
2
Share of informal
economy (%)
24
4
4
4
4
2
6
19
2
6
4
0
2
0
0
0
1
4
28
2
2
9
2
1
1
1
1
1
8
1
3
0
0
0
1
0
2
12
3
8
2
3
0
2
0
2
11
5
5
0
1
Source: English Localities Survey (n=861 households) (see Williams, 2004a: Table 5.2)
12
Section 4 - Data source on the informal economy
This survey, however, concentrates on domestic service provision. It is 19 not based on a business
survey but rather on the reports of households both as suppliers and customers concerning the
activities in which they engage.
The finding that the informal economy is concentrated in the construction sector and a range of
consumer services has important consequences for designing policy initiatives to tackle this sphere. If
policy measures were to deal with the realms of home maintenance and repair (43 per cent of all
informal work), routine housework (28 per cent), gardening services (3 per cent) and caring (11 per
cent), they would cover some 85 per cent of all informal work.
Reviewing the data available in central governments departments, it is apparent that Her Majesty’s
Customs and Excise concentrate their investigations on 15 SIC sectors and the Inland Revenue on 10
SIC sectors. For good reasons, these are not made public and are not here made available. There
are two primary reasons for concentrating on these sectors: custom and practice; and on the basis of
‘risk assessment’ procedures (which are again not available). There is a widespread
acknowledgement in these departments; however, that they are working with less than perfect
knowledge about the prevalence of the informal economy in different sectors and that much better
SIC level data would be useful to help them in deciding their priorities.
Information held by District Councils
Our ongoing survey of district councils for information on market-stalls, street-trading and allotment
farming as potential indicators of the size of the informal economy has so far provided little
information.
District Councils do have information on street trading and specific individuals within the council
can often provide details of the number of stalls and frequency of local markets etc. Most compile
data on these topics and maintain it by means of administrative record, with manual updating as and
when necessary. The breakdown of the data tends to comprise the names/addresses of stallholder
and fee income. The spatial level at which the data is collected is usually "Market Town" based as far
as street traders go, and Market based for the Markets. The quality checks applied to the data would
be manual verification of details supplied and invoice/payment auditing.
Other Licensing - given the breadth of licensing responsibilities of local authorities and other
agencies, identifying the sources of data on licensing requirement avoidance is difficult. Some District
Councils are aware of different types of avoidance.
Examples include licenses CDC issues and to some extent OCC (child-minders) but there is only a
patchy maintenance of records of instances of avoidance.
In general though, there is no single agency, let alone officer, within a District Council who can
provide the data. Officers may have individual areas of concern on avoidance, and abuse, and data
could be collected on this, but it may vary from Council to Council, depending both on the officer, and
the arrangements in that Authority.
13
Section 4 - Data sources on the informal economy
Chester-le-Street example:
Information from Chester-le-Street District Council on allotment farming has been identified, prepared
for an ODPM 2004 survey on allotment farming. The results still have to be properly processed but they
indicate:
Within the Chester-le-Street District there are allotments belonging to Private Allotment Associations,
Durham County Council, Parish Councils and Chester-le-Street District Council.
At present, all allotments belonging to Chester-le-Street District Council are managed by Leisure
Services. Administrative records are kept detailing names, number of sites, rental charges and tenant’s
names and addresses. On behalf of Chester-le-Street District Council an extensive allotment policy
questionnaire for every site was completed for every site managed. Details given were:
• name and address of site, size of site and number of plots ,rental income; and
•
livestock, site facilities and security, visits and other uses of allotment sites, management of sites,
relocation of sites, grid references and funding.
Future sources of data
The above review of currently available data on the informal economy displays the need for spatial as
well as sectoral data to be collected, and demonstrates the fragmented nature of current collection.
We thereby propose a number of ways in which data could, in future, be collated.
RATIONALE 1: Creating a cross-departmental Data Bank
If the primary rationale underpinning data collection and collation on the informal economy is to
pinpoint and prosecute those avoiding taxation and regulation, then in line with the modernisation
programme across government, there is a much greater need for cross-departmental data-sharing. At
present, there is only limited sharing of data by central government departments on the informal
economy. This, moreover, appears to take place on the level of specific individuals in which a specific
government department might have an interest. On the whole, inter-departmental data sharing is the
exception rather than the rule.
This is not the case in other countries. In Belgium, for instance, it is our understanding that there is a
shared ‘data bank’ bank that collates data from departments and which any government department
can use to investigate specific individuals. One way forward, therefore, is to further develop the InterDepartmental Business Register (IDBR) by government departments adding additional data
(notwithstanding the changes in legislation that might be necessary).
Utilising the IDBR
Dugmore (2004) has already started to make considerable progress in this direction by evaluating the
future potential of the IDBR and what data sets might be additionally included. It is here contended
that if the multiplicity of data sets that are suggested in this report were to be incorporated into the
IDBR - notably data from the Valuation Office (rateable value, floor space and land use), new
company details and company tax returns, the data from yell directories, VAT records, PAYE and
income tax records - then this database could represent a potentially valuable tool to enable
government departments to seek out individual businesses in need of further investigation. It would
greatly facilitate data sharing across central government departments with an interest in the informal
economy and represent a major advance in facilitating the identification of enterprise ventures in need
of further interrogation. See Dugmore (2004) for further discussion of how the database could be
developed.
14
Section 4 - Data sources on the informal economy
If it is to be used as a tool for identifying individual instances of informal work, in order, for example,
to better understand the extent of economic activity taking place in the UK, considerable advances in
its coverage and the timeliness of the data are required. The IDBR covers businesses in all parts of
the economy, other than some very small businesses (self-employed without employees, and low
turnover) and some non-profit making organisations. There is a real need to broaden this coverage to
those small (but very numerous) businesses that fall beneath the current VAT and PAYE radar. Take,
for example, PAYE. According to the 2003 SBS Annual Survey of Small Businesses, some 69.5 per
cent of all businesses with a headcount of less than 250 employees have no employees (2003 SBS
Annual Survey of Small Businesses). To miss these businesses is to omit a very large share of all
small businesses.
Timeliness of the data is also crucial if this is to be used in the manner suggested above to identify
individual sources of informal work. Dugmore (2004), for example, suggests that the IDBR could be used
to generate a list of self-employed people that could be checked against the Inland Revenue’s National
Insurance file (self-employed people have to pay a flat rate Class 2 contribution) and each person should
have a unique NI number. NI Class 2 Contributions file records, however, are out-of-date, so it would be
difficult to decipher what was due to the out-datedness of the records and what due to the informal
economy. In addition, there are serious problems with NI numbers. Sometimes NI numbers might be
used by people (e.g. illegal immigrants) who pay their taxes as required.
As such, these people are not an IR problem although they are a problem for other departments.
RATIONALE 2: Identifying spatial data on the informal economy - next steps
If the primary objective is to improve spatial data on the informal economy, then there are
numerous ways forward, some of which are presently occurring and some of which will need to be
further explored in the forthcoming months. Here, a list is provided of the various possibilities for
future data collection in this regard.
2004 Small Business Service (SBS): Annual Survey of Small Businesses
The Small Business Service (SBS) Annual Survey of Small Businesses covers some 8,693 small
businesses with less than 250 employees (2003). The 2004 Survey (preliminary results due in 2005)
will include the following questions:
Question:
Is your business affected by the existence of others who are working ‘off-the-books’? (prompt if
required: "is your business affected by the existence of others who are doing ‘cash-in-hand’ work?")
□ □ □ Yes
No
don’t know
If YES, (a) is your business affected:
□ □ □ □ □ Very significantly
Significantly
Neither significantly nor insignificantly
Not very significantly
Not at all significantly
(b) What % of trade in your sector of business would you say is conducted "off-the-books"?
15
Section 4 - Data sources on the informal economy
Although the SBS Annual Survey of Small Businesses does not currently provide ward-level
data (but usually only regional data), it might be possible to request SBS to arrange the data
into Index of Multiple Deprivation (IMD) deciles so as to provide comparative spatial data or to
use some other spatial scaling.
Prevalence of the Informal Sector: Perception of small businesses
How data Authority could obtain from the source might
be relevant to the objectives of the contract
Provides indication of prevalence of informal
economy both by SIC and spatially, according to
small businesses
How the source obtains its data (e.g., by estimation,
survey, administrative record). Where appropriate,
description of IT systems involved.
Survey of 8,693 small businesses
Purpose for which the source collects data; and, where
the data is shared with other parties, for what purposes
these parties use the data
Annual Survey of Small Businesses
How the source updates the data and the frequency of
updates
Annual survey (questions asked for first time in 2004)
Breakdown of the elements that make up the data
(including the spatial level at which the source collects
the data e.g. District, Ward, Postcode. As a minimum
the data must include an industry (SIC) breakdown.
SIC code of businesses; location; firm size; etc
The quality checks that are applied to the data
Not currently known.
How the source manages, cross-references and
combines different data sets in order to ensure
uniformity and consistency of data
Not currently known.
A description of any data protection, confidentiality,
ownership (IPR), or disclosure issues related to the
data and the source
Not currently known.
The charges related to acquisition of the data from the
source
Not currently known.
Method, frequency and timings of delivery of the data
from the source and the formats in which the data is
available
2005 Annual survey dataset available in 2005.
Caroline Berry, Analytical Division, Small Business Service, St Mary’s Gate, Sheffield S1;
E-mail address: [email protected]
16
Section 4 - Data sources on the informal economy
Family Expenditure Survey (FES) Household Income / Expenditure
Discrepancies
It is possible to interrogate on a regular basis a sample of records from the Family Expenditure
Survey using the income/expenditure discrepancies method. To represent this data spatially, the
individual records from the FES will need to be coded by ward-type in which it is collected (e.g., by
IMD ranking, region) so that a spatially stratified sample of households can be investigated.
Examples of how this can be undertaken are to be found in Dilnot and Morris (1981), Macafee (1980),
O’Higgins (1981) and Smith (1986). Dilnot and Morris (1981), for example, compare household
income and expenditure in 1,000 out of the 7,200 households surveyed for the 1977 FES so as to
examine whether some households appear to live beyond their means. They employ a variety of
‘traps’ to exclude discrepancies that might be explained by factors other than informal work (e.g., high
expenditure due to an unusual major purchase or to the running down of accumulated wealth).
It is important to recognise that data on the informal economy is not immediately extractable from
the FES. It requires permission to interrogate individual records and then for researchers to work
through these individual records.
Household Income / Expenditure Discrepancies: Family Expenditure Survey
Details (i.e., location and contact details)
Family Expenditure Survey
How data Authority could obtain from the source might be
relevant to the objectives of the contract
Provides indication of prevalence of informal
economy both by occupation, household
type and spatially
How the source obtains its data (e.g., by estimation, survey,
administrative record). Where appropriate, description of IT
systems involved.
Survey
Purpose for which the source collects data; and, where the
data is shared with other parties, for what purposes these
parties use the data
N/A
How the source updates the data and the frequency of
updates
Not currently known
Breakdown of the elements that make up the data (including
the spatial level at which the source collects the data e.g.
District, Ward, Postcode. As a minimum the data must
include an industry (SIC) breakdown.
Requires permission to take a spatially
stratified sample of individual records from
the FES returns and then the investigation of
these individual records.
The quality checks that are applied to the data
Not currently known.
How the source manages, cross-references and combines
different data sets in order to ensure uniformity and
consistency of data
Not currently known.
A description of any data protection, confidentiality,
ownership (IPR), or disclosure issues related to the data and
the source
Not currently known.
The charges related to acquisition of the data from the
source
Not currently known.
Method, frequency and timings of delivery of the data from
the source and the formats in which the data is available
Not currently known.
17
Section 4 - Data sources on the informal economy
Inland Revenue data on under-reporting on tax returns
The IR is sub-divided into 64 geographical areas that have considerable discretion in who and what
they pursue in their investigations. Providing data for these 64 areas would be problematic because
this might reflect the differing effectiveness and strategies of the local offices rather than the
prevalence of the informal economy in these areas. One way forward might be for the IR to provide
spatial data on the results of its Random Enquiry Programme. This would provide a measure of the
number of cases of ‘tax evasion’ by moonlighters and could be provided at an IMD level such as in
decile categories, although the IR has stated that it does not have the resources to produce the tables
itself. There would also have to be some care over how the data was presented. This would provide a
measure of the spatially variable extent of ‘under-reporting’. A ward-by-ward analysis would not be
feasible.
Under-reporting on tax returns
How data Authority could obtain from the source
might be relevant to the objectives of the contract
Provides data on identified ‘under-reporting’
How the source obtains its data (e.g., by estimation,
survey, administrative record). Where appropriate,
description of IT systems involved.
Conducts a Random Enquiry Programme of tax
payers
Purpose for which the source collects data; and,
where the data is shared with other parties, for what
purposes these parties use the data
Taxation compliance purposes
How the source updates the data and the frequency
of updates
Not known
Breakdown of the elements that make up the data
(including the spatial level at which the source
collects the data e.g. District, Ward, Postcode. As a
minimum the data must include an industry (SIC)
breakdown.
Collected at postcode level. Could be analysed and
presented using Index of Multiple Deprivation (IMD)
deciles.
The quality checks that are applied to the data
Not available.
How the source manages, cross-references and
combines different data sets in order to ensure
uniformity and consistency of data
Not available.
A description of any data protection, confidentiality,
ownership (IPR), or disclosure issues related to the
data and the source
This could only be presented at aggregate level.
Contact Inland Revenue for further information.
The charges related to acquisition of the data from
the source
Not known.
Method, frequency and timings of delivery of the
data from the source and the formats in which the
data is available
Not known.
Keith Moore Head, Cross-Cutting Policy Team, Inland Revenue
18
Section 4 - Data sources on the informal economy
DWP Identified cases of ‘working whilst claiming’
How data Authority could obtain from the source
might be relevant to the objectives of the contract
Provides data on numbers caught ‘working whilst
claiming’ (i.e., earnings-related benefit loss)
How the source obtains its data (e.g., by estimation,
survey, administrative record). Where appropriate,
description of IT systems involved.
Administrative records
Purpose for which the source collects data; and,
where the data is shared with other parties, for what
purposes these parties use the data
Compliance purposes
How the source updates the data and the frequency
of updates
Not known
Breakdown of the elements that make up the data
(including the spatial level at which the source
collects the data e.g. District, Ward, Postcode. As a
minimum the data must include an industry (SIC)
breakdown.
Could be potentially analysed and presented using
Index of Multiple Deprivation (IMD) deciles.
The quality checks that are applied to the data
Not available.
How the source manages, cross-references and
combines different data sets in order to ensure
uniformity and consistency of data
Not available.
A description of any data protection, confidentiality,
ownership (IPR), or disclosure issues related to the
data and the source
This could only be presented at aggregate level.
Contact DWP for further information.
The charges related to acquisition of the data from
the source
Not known.
Method, frequency and timings of delivery of the
data from the source and the formats in which the
data is available
Not known.
Employment, Jobs & Vacancies Analysis, Department for Work and Pensions;
e-mail: [email protected] Tel: 0207 712 2276
19
Section 4 - Data sources on the informal economy
HM Customs and Excise ensure compliance in the area of VAT
How data Authority could obtain from the source
might be relevant to the objectives of the contract
Data on VAT fraud and prosecutions
How the source obtains its data (e.g., by estimation,
survey, administrative record). Where appropriate,
description of IT systems involved.
Administrative records
Purpose for which the source collects data; and,
where the data is shared with other parties, for what
purposes these parties use the data
Compliance purposes
How the source updates the data and the frequency
of updates
Not known
Breakdown of the elements that make up the data
(including the spatial level at which the source
collects the data e.g. District, Ward, Postcode. As a
minimum the data must include an industry (SIC)
breakdown.
Could be analysed and presented by SIC (currently
use 60 trade sectors) and using Index of Multiple
Deprivation (IMD) deciles.
The quality checks that are applied to the data
Not currently available.
How the source manages, cross-references and
combines different data sets in order to ensure
uniformity and consistency of data
Not currently available.
A description of any data protection, confidentiality,
ownership (IPR), or disclosure issues related to the
data and the source
Not currently known.
The charges related to acquisition of the data from
the source
Not currently known.
Method, frequency and timings of delivery of the data
from the source and the formats in which the data is
available
Not currently known.
Contact: David Lambert, VAT Risk Management, HM Customs & Excise
20
Section 4 - Data sources on the informal economy
Other future possible data sources
Explore whether future ‘time-budget surveys’ – introduced to produce ‘satellite accounts’ following the
UN declaration to which the UK became a signatory - can be designed so that data collation can
occur in a manner that identifies time spent engaged in informal work, and that this provide ward,
postcode, IMD ranking, etc data so that geographical variations can be studied;
Request in selected future business surveys (e.g. SBS Annual Small Business Survey) data on the
extent to which the informal economy affects business performance so as to provide longitudinal data;
Introduce questions into the Small Business Service (SBS) Household Panel Survey to investigate the
extent and character of the informal economy so as to explore the relationship between the informal
economy and entrepreneurship.
Questions u nder the section on ‘Do ers’ might inc lude:
Whether people have a bank account in the name of their business; use a personal account; or both. If
they use the business account for all of their transactions, if no, what portion of revenue is put in their
business account. Wh ether they k eep no records; ba sic le vel re cords; or higher-le vel a ccounts
for their business. What proportions of their business is currently cash revenue rather than invoiced
revenues; and whether they have public liability insurance, employers’ liability insurance, all known
licences and permits to operate their businesses.
When cross-tabulated with variables such as firm size, SIC code, post code, etc, this would provide
a detailed spatial and sectoral business-level account of the extent and nature of the informal economy
both in relation to entrepreneurship and at different stages of business formation. The questions
below might be used.
Q.1. Do you have a bank account in the name of your business, do you use a personal current
account or do you have both? SINGLE CODE ONLY
Business Account
Personal Account
Both
Other (SPECIFY)
Don’t know
1
2
3
4
5
• If '1', do you use your Business Account for all of your transactions? Yes No Don't Know
• If 'NO', what proportion of your revenue would you say you put in your Business Account? ……….
• If '3', what proportion of your total revenue do you put in your Business Account? …………..
Q.2. Would you say that you keep no records for your business at present, basic level records or
higher-level accounts
No records
1
Basic level records
2
Higher-level accounts
3
Don’t know
4
Q.3. Many businesses in their early stages tend to gradually move from having majority cash
revenues to majority invoiced revenues. At present, what percentage of your business would you say
is cash revenue? ………
Q.4. Do you have:
• Public liability insurance
• Employers’ liability insurance
• All known licenses and permits to operate the
business (e.g., health and safety inspection certificates) 21
Y N D/K
Y N D/K
Y N D/K Section 4 - Data sources on the informal economy
• Request inclusion in future household/population surveys (e.g. General Household Survey,
Home Office Citizenship Survey; British Crime Survey) questions on whether informal economy
has been used by households to source goods and/or domestic services, and which goods
and/or services;
• Request inclusion in Labour Force Survey questions on participation in informal economy;
• Design and conduct a spatially stratified direct survey to measure the extent and character of
the informal economy. This could be a household and/or a business survey.
• In the summer of 2004, the Government, through the Office of the Deputy Prime Minister,
commissioned the University of Derby to carry out important research on allotments in England.
When the results of this research are complied and collated, it should provide valuable
information on the extent of informal economic activity though allotments. As of July 2005 this
research has yet to be published.
22
Section 5 - Data sources on entrepreneurship
Data sources on entrepreneurship
Finding data sources on entrepreneurship has proved more difficult than finding sources on the
informal economy. In part this is due to the difficulty of defining and measuring entrepreneurial activity
but it is also a result of fewer academic research projects on the topic. There is however a clear
overlap between the informal economy and entrepreneurship, as not all enterprises necessarily start
up in the formal economy, although the thrust of much business advice encourages business start-up
to place themselves on a fully legal footing from the outset. There is little known about whether this
approach correlates in any way with entrepreneurial success rates.
Defining Entrepreneurship
For the purposes of this report, entrepreneurial activity relates to new business start-up, or a wholly
new activity within a business. As such it is different from examining on-going small business activity,
which may be of a long term nature: many businesses start small and stay small.
Investigating the informal economy is fraught with difficulties relating to its status. Entrepreneurship
does not carry this stigma, and so it is perhaps easier to rely on systematic data collection of the type
described below, while recognising its inherent limitations at the ward level. Having said that, as
noted above and in the section on the Informal Economy, considerable entrepreneurial activity takes
place in the informal economy and at that stage may not reliably be captured by any statistical survey.
Both the extent and the ways in which entrepreneurial activity taking place in the informal economy
transforms to the formal economy are not well understood. It is beyond the scope of this report, but
the drivers for formalizing this entrepreneurial activity require further study. By remaining ‘informal’
businesses may be inhibited from opportunities for growth, but the reasons for remaining in the
informal economy may seem compelling to the small scale entrepreneur.
For the purposes of this interim report, the following activities have been followed to look at some of
the obvious sources of entrepreneurial data in the UK.
Global Entrepreneurship Monitor - UK
GEM collects data on start-up activity, businesses 0-42 months and older than 42 months on the
basis of a random adult population survey. The survey is of adults of working age NOT of businesses.
GEM UK is part of a wider international study using a consistent methodology (37 countries, but not
all of the EU 15 + accession); http:www.gemconsortium.org
GEM UK is the largest study and is based on survey of 25,000 adults. Sample collected by postcode
– allows analysis by SDI, urban-rural, county, region etc.
The Small Business Service
The Small Business Service has three sources of data on small businesses:
• Household Panel Survey (not publicly available)
• SME statistics for the UK (available online, compares size of firms for the whole economy, legal
status, employment and turnover. Does not cover regions).
• VAT registrations/de-registrations: available online; broken down regionally but is only really covering
those firms over £54k in annual turnover. This is much bigger than the median turnover of businesses
in the UK (£40K according to GEM).
Other government sources
The principle source for data used for economic development in the UK is the government. The data
is taken from a variety of sources – Inland Revenue, various departments as well as external sources
– and compiled and managed by the Office of National Statistics. This results in the Inter-Departmental
Business Register (IDBR).
23
Section 5 - Data sources on entrepreneurship
The Inter-Departmental Business Register (IDBR)
The IDBR is comprised of businesses that have registered for VAT or operate a PAYE account for
employees. Much of the data on IDBR is annualised as it coincides with compulsory requirements for
taxation or administrative and regulatory notifications. However, to reduce the administrative burden
on businesses it is limited to essential data that is fit for purpose. In addition, it is recognised that this
is not a complete record, and parts of the IDBR are imputed from other sources. Further information is
collated from survey work that uses the IDBR as the sample frame.
However, use of the register is highly restricted as it contains commercially sensitive information.
ONS and other departments, most notably the Small Business Service within the DTI, produce non
disclosive analytical output.
There are currently two main problems with this source. Firstly, the available information is often quite
old. In most cases the point between data being captured and it use to support policy definition will be
more than year and often nearer to two years and by the time the policy is being implemented it can
be three or four years out of date. Secondly, much of the analysis is imputed and aggregated from
surveys that are representative of the national business population and often this does not reflect
local demographics or the dynamics of change in the economy.
There is an ongoing policy problem in economic development concerning the use of business
population data. The data used to determine policy and manage interventions are subject to issues of
accuracy, confidentiality and currency. Firstly, when dealing with any large data set the collation and
management of data is susceptible to a range of errors that arise for many reasons including the way
it is handled and the how it is analysed. Secondly, many data sets have extensive limitations on
disclosure to preserve commercial confidentiality. Finally, there is always an interval between the
capture of data, when it is used to plan or measure interventions and the actual delivery of activity.
We can summarise the problem as ‘what we know’, ‘who we know it about’ and ‘when we know it’.
Unfortunately, there are no solutions that can resolve these issues at once. Whilst solutions exist
for resolving each of the issues, they remain hypothetical or impractical because of the scale and
nature of the problem. So we exist in a paradigm in which the main objective is improvement. The
focus is twofold: reducing the number of errors and reducing the interval between capture and use;
and reducing the impact of errors and time through refined methodologies and analytical tools.
Confidentiality will remain an issue and it is often the nature of the data set that determines this. The
most common source is government data which is captured for administrative purposes and is highly
restricted, where as commercial data available for marketing purposes has few restrictions but is often
less complete.
The Beta Model
This is the paradigm in which the BETA Model service was established.
By necessity third party data has to be used and this therefore has a limited effect on accuracy and
currency – control of this is out of its scope, although it remains an important element of the choice
and where possible we can use our purchasing power to positive effect. It was accepted as a first
principle that all available data would contain errors. We wanted to move away from an ‘inventorial’
understanding towards a more plausible appreciation of the underlying dynamics and how these
changed over time. An important assumption in our thinking is that, on the whole, errors would be
maintained over time and add little disturbance to the dynamic.
24
Section 5 - Data sources on entrepreneurship
The data solution was the Business Database from Yell Data (now part of Experian). Firstly, it offered
UK coverage. Yell Data maintain a record of all business tariff telephone contracts supplied by
licensed telecom operators. Capture of data is event driven from new contracts, terminated contracts
and change to contracts such as relocations and this is used to inform the annual geography-based
Yellow Pages directories. The micro data is granular to the level of business sites, which is
maintained and updated on an annualised rotating scheme. Secondly, through a unique arrangement
The BETA Model was able to access historical archives back to 1999. Thirdly, The Business
Database offers a list rental service for direct communication campaigns and around 20% of
businesses request not have their details passed on for this purpose. In another unique arrangement,
all non-sensitive suppressed records were accessed for analytical purposes.
The following tables were constructed to begin a comprehensive survey of where available data
sources on small business and entrepreneurship were, how accessible they were and the extent to
which they met the needs of the client. Three sets of data were examined:
*
There is a substantial amount of data on small business measured through VAT registrations and
de-registrations. However, many of the more entrepreneurial businesses at start-up stage will fall outside
of the VAT threshold and, as a result, this data does not necessarily represent a measure of
entrepreneurial activity. Similar concerns exist with the Beta model and Yell data sources (based on
telephone numbers) although these are, for a fee, available at ward level.
* A second set of data is collected (although not publicly available) through the banks. However, much
of this data is based on bank account registrations and not the newness of the idea or activity.
Further, as this data is not publicly available, it is not usable either by government or by researchers.
* GEM (UK) is a major household survey collected by postcode and representative at a regional level
by age, gender and ethnicity. The way it formulates its survey ensures that it is only capturing the
newest of businesses at both the earliest (start-up) phase and once more established. The median
turnover for the businesses within the survey is £40,000 which means that it is a more representative
of UK entrepreneurial business than VAT registrations.
The tables really highlight the limitations of publicly available data on entrepreneurial activity,
particularly at the level of wards within the multiple deprivation index.
GEM Database
This illustrates quite graphically the difficulty in collecting reliable data on entrepreneurship.
On the one hand VAT registrations collect robust small business data, but do not capture
“entrepreneurial activity” as such. The Labour Force survey similarly captures self-employment
but can’t tell which of these are “entrepreneurial”. GEM, in contrast, lets the data itself define who
the entrepreneurs are from a general adult population survey and records an overall level for the
population as a whole. It is not a survey of businesses as such. For full details on GEM methodology
see Appendix B.
25
Section 5 - Data sources on entrepreneurship
Global Entrepreneurship Monitor UK:
Global Entrepreneurship Monitor UK (GEM UK), Foundation for Entrepreneurial Management, London
Business School (http://www.gemconsortium.org/)
How data Authority could obtain from the source might
be relevant to the objectives of the contract
Data provides comparative levels of Total, Social,
Ethnic, Regional Entrepreneurship activity over
time. Assessment of UK policy on levels of general
business activity, specific sectors, ethnic minorities,
gender bias, social and community activities at
national and regional levels.
How the source obtains its data (e.g., by estimation,
survey, administrative record). Where appropriate,
description of IT systems involved.
24,000 quantitative surveyed. Qualitative interviews.
The data can give an accurate picture of all
business start ups and incipient businesses at
national, regional, local scales plus gender and
ethnic minorities. Social and Community businesses
are identified.
Purpose for which the source collects data; and, where
the data is shared with other parties, for what purposes
these parties use the data
To measure UK Entrepreneurship. Data is used by
RDAs, Small Business Service, Barclays, Ernst and
Young and various investment funds.
How the source updates the data and the frequency of
updates
Annually. Electronically centrally held database.
Breakdown of the elements that make up the data
(including the spatial level at which the source collects
the data e.g. District, Ward, Postcode. As a minimum
the data must include an industry (SIC) breakdown.
Postcodes to street level. Ethnicity, gender, age
bands, profession (GEM classification)
The quality checks that are applied to the data
Small Business Service statisticians check the
robustness of the methodology. Also checked
against Barclays data
How the source manages, cross-references and
combines different data sets in order to ensure
uniformity and consistency of data
Start-ups and e-businesses are defined as anybody
setting up a new business entity, by themselves or
with others that hasn’t paid salaries for more than
42 months. These can be independent or jobrelated.
A description of any data protection, confidentiality,
ownership (IPR), or disclosure issues related to the
data and the source
Not publicly available. Data is protected by IP
agreement with GERA, London Business School
and Babson College. Data sets for more than 3
years ago are publicly available
The charges related to acquisition of the data from the
source
This is currently under review. Access to current
data set is through sponsorship.
Method, frequency and timings of delivery of the data
from the source and the formats in which the data is
available
Annual adult population survey. SPSS. Subject to
sponsorship.
26
Section 5 - Data sources on entrepreneurship
The Beta Model Ltd:
The Beta Model Ltd http://www.betamodel.com
How data Authority could obtain from the source
might be relevant to the objectives of the contract
Measures relative changes over time of business
formation, employment and enterprise. Baselines,
trends and performance data for business.
How the source obtains its data (e.g., by estimation,
survey, administrative record). Where appropriate,
description of IT systems involved.
Business entries in Yellow Pages and Thomson
Local. Targeted contact lists, market analysis,
profiling, online access. Activity descriptions, SIC,
Clusters and Groups.
Purpose for which the source collects data; and,
where the data is shared with other parties, for what
purposes these parties use the data To measure changes over time of business formation,
employment and enterprise.
How the source updates the data and the frequency
of updates
The Business Database (TBD) from Yell (now
operated by Experian) and Thomsons Local
directories. Yell and Thomsons call 450,000 per
quarter to update and find primary classification and
employee size. Web sites add further entries.
Breakdown of the elements that make up the data
(including the spatial level at which the source
collects the data e.g. District, Ward, Postcode. As a
minimum the data must include an industry (SIC)
breakdown.
Market oriented business group listing. SIC data held
but not implemented. Possible source of errors for
VAT due to ‘free text’ descriptions from VAT forms.
Postcode via ONS Postcode File (AFPD)
The quality checks that are applied to the data
Incorrect data entry in directory attracts a refund.
Data is tested against external sources (ONS’s IDBR)
How the source manages, cross-references and
combines different data sets in order to ensure
uniformity and consistency of data
All output data from the same source, standardised
by analysis type. BETA generate the output data so
no issues with uniformity or consistency.
A description of any data protection, confidentiality,
ownership (IPR), or disclosure issues related to the
data and the source
Businesses with a TBD entry can elect to be excluded
from the ‘rental list’ to prevent ‘junk mailing’. BETA
have access to this data for non-disclosure purposes.
The charges related to acquisition of the data from the source
Variable according to business customer
requirements, i.e. number of users and level of
access.
Method, frequency and timings of delivery of the
data from the source and the formats in which the
data is available
Electronically and hard copy if required. Quarterly
updated.
27
Section 5 - Data sources on entrepreneurship
Inter-Departmental Business Register:
UK Government Inter-Departmental Business Register (IDBR) ,
Office of National Statistics
http://www.statistics.gov.uk/idbr/idbr.asp
How data Authority could obtain from the source might be
relevant to the objectives of the contract
Data provides information on enterprise
development for policy assessment.
Small Businesses/ Entrepreneurship is
calculated by VAT registration numbers
and users of PAYE.
How the source obtains its data (e.g., by estimation, survey,
administrative record). Where appropriate, description of IT
systems involved.
VAT registrations for formations from
HM Customs and Excise, PAYE records
from the Inland Revenue; Incorporated
Businesses from Companies House.
Purpose for which the source collects data; and, where the data
is shared with other parties, for what purposes these parties use
the data
All Government Department s share the
data though the harmonisation of returns
is not yet up and running.
How the source updates the data and the frequency of updates
Data is updated as new registrations
occur for VAT.
Breakdown of the elements that make up the data (including the
spatial level at which the source collects the data e.g. District,
Ward, Postcode. As a minimum the data must include an
industry (SIC) breakdown.
Elements comprising the data are:
Name, Address, Classification
(Industrial/economic activity),
Employment, Employees, Turnover,
Legal status, Enterprise group links,
Country of ownership, Enterprise zone
markers, Company number, Value of
goods traded (from Intrastat) Data is
held at mailing address level, i.e.
Postcode and for local units, i.e. factory
or shop etc.
The quality checks that are applied to the data
There are serious gaps in the data.
Many businesses are not VAT
registered. Not all businesses use
PAYE.
How the source manages, cross-references and combines
different data sets in order to ensure uniformity and consistency
of data
Complies with the European Union
regulation 2186 – 93 on harmonisation
of business registers for statistical
purposes.
A description of any data protection, confidentiality, ownership
(IPR), or disclosure issues related to the data and the source
Restricted Commercial (confidential)
The charges related to acquisition of the data from the source
Produced for Government departments,
Local authorities, Government
contractors. Also for analysis by the
general public provided they are not
disclosive.
Method, frequency and timings of delivery of the data from the
source and the formats in which the data is available
Hard copy and electronically
downloadable, annually. Usually in
May/June
28
Section 5 - Data sources on entrepreneurship
YELL – Yellow Pages:
Yellow Pages http://www.yellgroup.com (now operated by Experian)
How data Authority could obtain from the source
might be relevant to the objectives of the contract
Contains databases of and for businesses across UK.
Types of businesses are classified as per telephone
book group listing.
How the source obtains its data (e.g., by estimation,
survey, administrative record). Where appropriate,
description of IT systems involved.
Classified advertising by payment by businesses for
an individual entry in the listings. The data is online
via the web, mobile telecommunications and in book
form. It is based on telephone number entry
expanded to include Business
Classification (market oriented classes by
Yellow Pages) and address. Postcodes
are available separately through additional financial
arrangements..
Purpose for which the source collects data; and,
where the data is shared with other parties, for what
purposes these parties use the data
To allow business to advertise. To provide data for
targeted mail shots, general marketing and for in
depth analysis of the data set.
How the source updates the data and the frequency
of updates
Books updated yearly, data set updated quarterly.
Based on all new business tariff telephone contracts
supplied by licensed telecom operators. Data capture
from new and terminated contracts and changes i.e.
relocations.
Breakdown of the elements that make up the data
(including the spatial level at which the source
collects the data e.g. District, Ward, Postcode. As a
minimum the data must include an industry (SIC)
breakdown.
Business classification (by Yellow Pages), street, town. Not postcode.
Postcodes available to purchase separately. Industry
SIC codes are known and held but only used in additional requests.
The quality checks that are applied to the data
Hundreds of thousands of calls are made every
quarter to check accuracy of held data. Additional
questions asked include employee numbers and
primary classification. Changes to the data set are
checked by the business entry representative and
errors attract a refund.
How the source manages, cross-references and
combines different data sets in order to ensure
uniformity and consistency of data
Majority of data from the Yell method as described.
Additional data collected from online sources but
using the same method.
A description of any data protection, confidentiality,
ownership (IPR), or disclosure issues related to the
data and the source
SIC data held and can be used. Some (20%) of
businesses opt out of rental lists for direct marketing
campaigns.
The charges related to acquisition of the data from
the source
Basic Yell data set available free in book form and
online. Postcodes available to
purchase separately
Method, frequency and timings of delivery of the
data from the source and the formats in which the
data is available
1999-2003 Annually based on full
database for April of each year
July 2003 onwards: Quarterly updates
29
Section 5 - Data sources on entrepreneurship
Regional Intelligence Network Regional Intelligence Unit (RIU)
http://www.nwriu.co.uk/researchframework.asp
How data Authority could obtain from the source might be relevant to the objectives of the contract
The RIU aims to improve access to intelligence and
information at a regional level. However, it provides
no full breakdown of entrepreneurial activity across
sectors and no assessment of the informal economy.
Start-ups are measured on a sectoral basis
depending on the project focus.
How the source obtains its data (e.g., by estimation,
survey, administrative record). Where appropriate,
description of IT systems involved.
Individual projects. Varied methods including use of
beta model data. Some searchable databases,
qualitative surveys, benchmarking and general
assessments.
Purpose for which the source collects data; and, where
the data is shared with other parties, for what purposes
these parties use the data
The data is used by regionally based organisations in
the public and private sectors. There are a variety of
collaborators
How the source updates the data and the frequency of
updates
As projects are funded and as information is made
available. The data is partially integrated into one site.
Breakdown of the elements that make up the data
(including the spatial level at which the source collects
the data e.g. District, Ward, Postcode. As a minimum
the data must include an industry (SIC) breakdown.
Spatial level is varied according to project and source
of information. There is no systematic data based on all business activities. .
The quality checks that are applied to the data
See Beta model: RIN is one of Beta model’s largest
clients
How the source manages, cross-references and
combines different data sets in order to ensure
uniformity and consistency of data
Consistency where possible from Beta model, but no
full breakdown of entrepreneurial activity and no
assessment of the informal economy
A description of any data protection, confidentiality,
ownership (IPR), or disclosure issues related to the data
and the source
Client based
The charges related to acquisition of the data from the
source
Available to network members
Method, frequency and timings of delivery of the data
from the source and the formats in which the data is
available
Project-by-project updates
30
Section 5 - Data sources on entrepreneurship
Small Firms Consultation Database: Inland Revenue
http://www.sbs.gov.uk/regulations/smallfirmsdatabase.php
How data Authority could obtain from the source
might be relevant to the objectives of the contract
Contains details of businesses submitting VAT
registrations and those using the PAYE system. Can
be used to estimate levels of start-up activity.
How the source obtains its data (e.g., by estimation,
survey, administrative record). Where appropriate,
description of IT systems involved.
VAT registration and PAYE records.
Purpose for which the source collects data; and,
where the data is shared with other parties, for what
purposes these parties use the data
Monitors the collection of due monies to HM Treasury.
All VAT registered businesses provide the data as do
all companies using PAYE. The source omits nonVAT businesses and those not using PAYE (e.g. most
of the informal economy)
How the source updates the data and the frequency
of updates
At every new VAT registration. At every PAYE use.
Breakdown of the elements that make up the data
(including the spatial level at which the source
collects the data, e.g. District, Ward, Postcode. As a
minimum the data must include an industry (SIC)
breakdown.
Self limiting by size of businesses for VAT. Choice of
using PAYE for other business.
The quality checks that are applied to the data
NOT CURRENTLY KNOWN
How the source manages, cross-references and
combines different data sets in order to ensure
uniformity and consistency of data
Internal checks not known, but data is collected
through online survey – self selecting. Does not
target informal economy and not systematic – not
strictly comparative
A description of any data protection, confidentiality,
ownership (IPR), or disclosure issues related to the
data and the source
NOT CURRENTLY KNOWN
The charges related to acquisition of the data from
the source
NOT CURRENTLY KNOWN
Method, frequency and timings of delivery of the data
from the source and the formats in which the data is
available
Updated as entries are inputted
31
Section 5 - Data sources on entrepreneurship
High Street Banks: High Street Banks.
Bank of England reports on Small Business start-up statistics. British Bankers’ Association
How data Authority could obtain from the source
might be relevant to the objectives of the contract
Provides information on start-ups registering business
bank accounts. Entrepreneurship is calculated by
the number of ‘start-up’ bank accounts which are only
business related and run solely for the business.
How the source obtains its data (e.g., by estimation,
survey, administrative record). Where appropriate,
description of IT systems involved.
Data from high street banks is customer driven and is
obtained whenever a new account has been opened.
Purpose for which the source collects data; and,
where the data is shared with other parties, for what
purposes these parties use the data
Tracks all new business bank accounts. Misses non
business bank account operations. The data is also
amalgamated into Bank of England report on Small
Businesses and start-up statistics. British Bankers’
Association has an aggregated data set of all small
business bank accounts compiled from all high street
banks but it is limited (not broken down into gender,
ethnicity, regions etc). Barclays estimate whole UK
business start-up s from their market share not from
the BBA data.
How the source updates the data and the frequency
of updates
As new accounts are opened. But note
inconsistencies of definitions according to each bank.
Breakdown of the elements that make up the data
(including the spatial level at which the source
collects the data e.g. District, Ward, Postcode. As a
minimum the data must include an industry (SIC)
breakdown.
Data is made up of individual business accounts.
Defined by Barclays, for example, as ‘ A newly
registered bank account not previously registered for
that business.’ Note the business could have been
operational for years before registration. Postal
addresses given and held.
The quality checks that are applied to the data
.Lack of common definitions means the data which is
used by the BBA is not capable of major analyses.
For example, the CO-OP has no specifically start-up
products.
How the source manages, cross-references and combines different data sets in order to ensure
uniformity and consistency of data Due to differences in high street banks methods of definitions and collecting criteria, the common
denominator of the BBA data makes Barclays check their data with GEM UK and Thomsons Local Business directories.
A description of any data protection, confidentiality, ownership (IPR), or disclosure issues related to the data and the source
The BBA data is aggregated, not traceable to
individuals and is freely available. The other high
street bank data is not freely available and thus no
disclosure issues are relevant.
The charges related to acquisition of the data from
the source
The high street banks contribute to the BBA
aggregated data set which is free. Their own data is
not generally available outside of the bank.
Method, frequency and timings of delivery of the data
from the source and the formats in which the data is
available
From aggregated statistics from all high street banks
from new business bank accounts, quarterly,
electronically and in hard copy.
32
Section 5 - Data sources on entrepreneurship
The Phoenix Fund: Small Business Service
http://www.sbs.gov.uk/phoenix
How data Authority could obtain from the source
might be relevant to the objectives of the contract
The data promotes innovative ways to develop
entrepreneurship in deprived areas. It assesses the
impact of regeneration of entrepreneurial activity.
How the source obtains its data (e.g., by estimation,
survey, administrative record). Where appropriate,
description of IT systems involved.
The data is demand led and is sourced from the
processing of claims and application forms.
Purpose for which the source collects data; and,
where the data is shared with other parties, for what
purposes these parties use the data
The data is a joint venture between the Business
Volunteer Mentoring Association, Community
Development Finance Institutions, the Community
Development Venture Fund, City Growth Strategies
and the Development Fund for Rural Renewal.
However, there is no strategy for supply-side
targeting. There is no access to data to target
possible applicants. It is not strictly a comparable
database.
How the source updates the data and the frequency
of updates
Data is updated as applicants apply.
Breakdown of the elements that make up the data
(including the spatial level at which the source
collects the data e.g. District, Ward, Postcode. As a
minimum the data must include an industry (SIC)
breakdown.
Individual applicants, joint applicants, group initiatives
seeking funding.
The quality checks that are applied to the data
SBS statisticians
How the source manages, cross-references and
combines different data sets in order to ensure
uniformity and consistency of data
SBS statisticians
A description of any data protection, confidentiality,
ownership (IPR), or disclosure issues related to the
data and the source
NOT CURRENTLY KNOWN
The charges related to acquisition of the data from
the source
NOT CURRENTLY KNOWN
Method, frequency and timings of delivery of the data
from the source and the formats in which the data is
available
NOT CURRENTLY KNOWN
33
Section 5 - Data sources on entrepreneurship
Annual SBS Questionnaire 72
The SBS questionnaire consists of 230 questions altogether (in addition to business ID). Sixty-five of
these questions are directed to all respondents with the potential for a further 80/90 to be asked,
dependent upon the answers selected. Estimated completion time is 20-25 minutes.
Some questions are specific to the location of the business (Wales, Scotland, Northern Ireland and
England) and there are a series of approximately 20 questions that are posed to a random half of
respondents in all countries, covering such things as environmental issues, discrimination against
small businesses, crime, cash-in-hand trade and the provision of staff development/training.
Annual Small Business Survey
http://www.sbs.gov.uk/default.php?page=/analytical/default.php
How data Authority could obtain from the source might be relevant to the objectives of the contract
Survey includes data on some businesses not registered for
VAT or PAYE.
How the source obtains its data (e.g., by estimation, survey,
administrative record). Where appropriate, description of IT
systems involved.
Data obtained from survey of approx 8000 SMEs across the
UK, including boost samples for Wales and Scotland in
2003 and 2004, and 2005 survey also likely to include a
boost for N. Ireland.
Purpose for which the source collects data; and, where the data is shared with other parties, for what purposes these parties use the data
Data is used to monitor achievement of some SBS PSA
targets and is also a ‘listening exercise’ to monitor the
concerns of SMEs and their reaction to government policies.
Data is published in report form and also available in
response to ad hoc queries.
How the source updates the data and the frequency of
updates
Data is updated annually by means of repeat survey.
Breakdown of the elements that make up the data
(including the spatial level at which the source collects the
data e.g. District, Ward, Postcode. As a minimum the data
must include an industry (SIC) breakdown.
Analysis is at level of region/devolved administration, and
also by a raft of variables including SIC, turnover, number of
employees, rural/urban, women/ethnic minority led, and age
of business.
The quality checks that are applied to the data
The survey is carried out by a reputable market research
company in line with industry standards, and all data has
been fully checked before the report is produced.
How the source manages, cross-references and combines
different data sets in order to ensure uniformity and
consistency of data
There is only one data set involved. The questionnaire,
sampling methodology and weightings are consistent from
year to year to ensure comparability.
A description of any data protection, confidentiality,
ownership (IPR), or disclosure issues related to the data
and the source
Data is owned by SBS and governed by Data Protection –
no individual data can be released. The survey asks
whether participants would be willing to be re-contacted for
additional research, but under this details could only be
released to a contractor acting as an agent on SBS’s behalf.
The charges related to acquisition of the data from the
source
SBS commissions the survey as a bespoke piece of work
and pays for it out of the SBS research budget.
Method, frequency and timings of delivery of the data from
the source and the formats in which the data is available
Survey work takes place annually in the autumn and the
results are published the following spring/summer in report
form, both in hard copy and on the web. There is potential
eventually to place the dataset on the SBS website to allow
some scope for tailored analysis as required by members of
the public.
34
Section 5 - Data sources on entrepreneurship
In addition to these data-sources:
EU Questionnaire: A questionnaire administered periodically by the EU consists of 30 questions,
divided into three distinct sections (in addition to business ID) with estimated average completion time
15-20 minutes. The three areas of questioning cover:
• Condition of enterprise at start up and profile of original entrepreneur (16 related questions
only to be answered if respondent is one of the original entrepreneurs)
• The enterprise’s present situation as at survey date (11 related questions)
Questions covered exclusively by the EU survey include:
• Future plans (3 related questions)
• What type of start up it was i.e. new/merger/take-over etc (no mention of family business - generational
change)
• How the enterprise was originally financed
• Previous experience of the entrepreneur in business activity/ business in general
• Training given
• Whether the entrepreneur is gainfully employed in addition to running the business
• Impediments to development (SBS form concentrated on impediments to business in general not directly
related to development)
• The number of customers from which the business' turnover originates
• Co-operation with other enterprises e.g. joint venture, subcontractor, franchise
• Forecast of areas for future development
• Forecast of profit and if increased, how that profit would be spent
Observations on the EU and SBS surveys
Styles of questioning
• Both questionnaires investigate obstacles to success but in the SBS form the answers chosen
often lead to more probing and detailed questions on the specific problems.
• The questions relating to turnover in the EU form require a specific answer, whereas the SBS
form is multiple choice within given size bands.
• The questions relating to difficulties and incentives in the EU form require graded comments to
be applied to a given list (high degree/some degree/not at all or don't know) whereas the SBS
form asks the respondent to choose the 'most important factors'.
SBS Household Survey: Panel study of attitudes to entrepreneurship; identifies “thinkers”, “doers”
and “avoiders” and the barriers they face in setting up a business. The survey is the base of Public
Service Agreement Targets for entrepreneurship. Data is not widely available. Tenders for survey to
ensure robustness of sampling and cost effectiveness; quality checked by SBS statisticians.
Labour force survey: collects data on self employment.
Locality studies: local authorities collect data on start-up activity but definitions are not consistent
and the comparability is questionable.
Academic sources of entrepreneurship research and/or data: University of Durham; University of
Strathclyde, Hunter Centre for Entrepreneurship (GEM Scotland); University of Glamorgan (GEM
Wales), Kingston University (GEM Northern Ireland) – all part of GEM UK team run through London
Business School; Cass Business School: Centre for New Technology, Innovation and
Entrepreneurship; Essex University Data Archive and new entrepreneurship centre at Southend;
Centre for Business Research, University of Cambridge; Said Business School, Oxford University.
35
Section 5 - Data sources on entrepreneurship
Summary conclusions of data sources on entrepreneurship
GEM arguably represents the most systematic comparison of “entrepreneurial activity” in that it can
both identify the “newness” of a business and its scale. It collects data internationally, nationally and
regionally and can assess Total Entrepreneurial Activity at a postcode level which gives it the capacity
to look at the index of multiple deprivation, and by wards or unitary authorities. It can also distinguish
between urban and rural businesses as well as identify specific groups and sectors.
The Beta Model is based to a large extent on new telephone lines and does not assume a definition of
entrepreneurship for its data collection. It can, however, isolate new telephone lines by postcode and
therefore has the same degree of spatial granularity that GEM has. However, it is not internationally
comparable, not does it identify all entrepreneurial businesses, as some may well start up without a
separate telephone line.
Data-sources based on VAT registrations and de-registrations have the advantage of being able to
measure business churn by region and nationally. However, since many businesses operate below
the VAT threshold (for example, GEM identifies median business turnover at £40,000), VAT
registrations do not necessarily capture a substantial amount of start-up activity. Further, VAT
registrations are unlikely to capture the informal economy”
36
Section 6 - Conclusion
Conclusion This report has attempted to give an overview of work undertaken to identify sources of data on
entrepreneurship and the informal economy. Specifically, the report found that:
• Identifying data sources on both subjects is a difficult task. The informal economy review has
produced a wide range of sources, which provide varying degrees of detail, but above all,
highlight the lack of consistent data collection. As noted above, there are inherent problems in
investigating the informal economy, because of varying degrees of non-compliance. There are
fewer data sources identified on entrepreneurship, but the data collection in some cases is more
consistent.
• The best data sources for the informal economy comprise UK locality studies conducted by
academics and collated in the English Localities Survey; the Small Business Survey; the Family
Expenditure Survey; Inland Revenue data on under-reporting of tax returns; DWP data on
‘working whilst claiming’ and HM Customs and Excise data on VAT compliance.
• Discussion of data sources on the Informal Economy has identified two main reasons for
identifying informal economic activity – the pursuit of non-compliance, and the understanding of
informal activity in the local economy.
• The best data sources for measuring entrepreneurship comprise the GEM database of UK
Start-Up activity; data from the Small Business Service including the Household Panel Survey,
SME statistics and VAT registrations/de-registrations. The Beta Model which measures relative
change over time of business formation, employment and enterprise; the IDBR register on
enterprise development for policy assessment and the Yellow Pages which contains regional
databases of and for businesses across the UK. A recent EU survey also contains some useful
questions designed to measure entrepreneurship.
• Local Authorities hold data on allotment farming, market trading and unlicensed businesses
though gathering such data is difficult since Councils rarely compile such information in a
systematic form.
The purposes for which data are required crucially affect how it is collected, collated and
disseminated. Data collection for enforcement necessitates a different approach (and has different
sources) from data collection which is designed to understand the dynamics of business formation
and growth. Relatively little is known about how the informal economy relates and translates into the
formal economy, and the measurement of entrepreneurial activity across this divide may yield insights
into how policy measures militates for or against the expansion of businesses.
Suggestions for further work
• Explore in more detail the link between the informal economy and entrepreneurship. As the
report attempted to make clear, the link between the two is more subtle than is often supposed.
There are a number of possible approaches to this, and some of these are set out below.
• To measure the relationship between the informal economy and entrepreneurship, one way is to
extend the questions on the Small Business Service Household Panel Survey, to investigate in
the first instance the extent to which businesses use business accounts and/or personal
accounts, and at what stage in different sectors and places, all of their transactions are put into
their business accounts. In addition, the extent to which businesses maintain records for their
business could be investigated, the degree to which cash revenues and invoiced revenues are
used in different sectors and places at different stages in the development of businesses, and
the degree to which there is compliance with licenses, permits and insurances required for the
operation of businesses. This snapshot would provide a first attempt at establishing how
businesses at different stages of their development (in varying sectors and places) operate on
an informal basis.
37
Section 6 - Conclusion
• Such a measure of the extensiveness of informal economy in relation entrepreneurship,
however, does not explain why it occurs. As the report attempted to make clear, the link
between the two is subtler than is often supposed. To understand why entrepreneurs start-up
conducting some/all trade off-the-books and continue to do so once they become more
established, an academic study might be commissioned that provides a more qualitative study
of this subject. In particular, the study should examine the extent to which informal start-ups
become integrated into the formal economy, and what the mechanism for this is. It is possible
that policies that ‘capture’ activity in the informal economy may deter business start-up. Part of
this work could include understanding why people avoid regulation. This may be due to
ignorance, problems with dealing with officials, including lack of literacy, a desire to avoid
income tax and VAT or simply the desire to compete on price by undercutting legitimate
businesses.
• Research also suggests that business operating in the formal economy also undertake a part of
their activity in the informal economy. While the reasons for this can be guessed at, the extent
of the activity is not known. One approach to this would be a representative survey which asks
householders what work they have received ‘informally’ in the last year. If anonymity is
assured, sampling experience in other areas (for example driving without insurance) suggests
that most respondents are remarkably open about their participation in activity which they know
to be informal.
• In addition to the known data sources on entrepreneurial activity, there is scope for examining
the data collected by Regional Development Agencies, which they use to evaluate the success
of interventions, if this could be made available.
• As part of the Phoenix Fund, and Regional Development activity, some local areas have
produced Business Directories. The number and extent of these is not known, but this could be
systematically investigated, and the results compared with more official sources, such as
Thompsons and Yell. The results would describe Business Activity, which would then have to
disaggregated into various categories.
38
Annex A - Informal economy measurement methods
Annex A - Informal economy measurement methods
Given that the informal economy is by its very nature hidden from, or unregistered by, the state,
estimating its size is a perplexing and difficult task. Methods used until now range from techniques
that employ direct survey methods to those that attempt to indirectly measure its size by seeking
statistical traces of the informal economy in data collected for other purposes (see Bajada, 2002;
Thomas, 1992; OECD, 2002; Renooy et al, 2004; Williams, 2004a; Williams and Windebank, 1998).
These indirect methods use various measures ranging from non-monetary proxy indicators (e.g.,
based on labour force estimates, the number of very small enterprises, electricity demand), through
monetary indicators (e.g., of the number of large denomination notes in circulation, the cash-deposit
ratio or level of money transactions) to income/expenditure discrepancies either at the household
and/or national level.
Those assuming that research participants will not be forthcoming about 91 whether or not they
engage in informal work have tended to seek evidence indirectly in macroeconomic data collected
and/or constructed for other purposes. The belief is that even if informal workers wish to hide their
incomes, their work will be nonetheless revealed at the macroeconomic level and it is these statistical
traces of their informal work that are examined by indirect approaches. At the other end of the
spectrum are those who assume that despite the illicit nature of informal work, reliable data can be
directly collected from research participants. These analysts thus conduct mostly intensive
investigations on small samples, such as through locality studies, of the nature and extent of informal
work using direct survey methods. Here, a review is provided of measurement methods used to date
throughout the western world so as to provide an overview of how data might be collected and the
problems involved.
At the outset, however, it is important to stress that the emerging 92 consensus is that indirect
methods are very limited in their usefulness. This is the conclusion of the OECD when compiling their
handbook on measurement methods (OECD, 2002), the recent European Commission ‘good practice’
report on undeclared work (Renooy et al, 2004) and many academic reviews (e.g., Thomas, 1992;
Williams, 2004a).
Indirect methods
Indirect methods are of three kinds:
* firstly, there are those that seek statistical traces of such work in non-monetary indicators
such as the discrepancies in labour supply figures or the number of very small firms;
* secondly, there are those that track down evidence of informal work in monetary indicators
collected for other purposes; and
* third and finally, there are those that investigate discrepancies between income and
expenditure levels either at the aggregate or household level.
Each is here considered in turn.
The problem with all of these approaches, as will be shown, is that these so-called ‘traces’ of informal
work not only provide an unreliable and inaccurate proxy of the volume of this work but also provide
very little information on the nature of such work (e.g., SIC industry-level or spatial data). Instead, in
most cases, they are driven by some very crude assumptions concerning its character that are far
from proven.
Indirect non-monetary methods
Two of the most common indirect approaches that use non-monetary surrogate indicators to
estimate the extent of informal work in advanced economies are firstly, those that seek out traces of
informal employees in formal labour force statistics and secondly, those that use very small
enterprises as a proxy for the existence of informal work.
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Annex A - Informal economy measurement methods
Labour force estimates
Methods that seek to measure informal work from formal labour force statistics are of two types.
Firstly, there are those that identify various types of employment (e.g. self-employment, second-job
holding) as proxy indicators of the existence of informal work and then look for unaccountable
increases in the official labour force statistical data on the numbers employed in these categories
(e.g., Alden, 1982; Crnkovic-Pozaic, 1999; Del Boca and Forte, 1982; Hellberger and Schwarze,
1986). The problem, however, is that the idea that informal work prevails in these categories of
employment is an assumption, rather than a finding, of the technique, and there is no way of
discerning the extent to which it is informal work, rather than other factors, that has led to an increase
in these categories of employment. The growth of self-employment, for instance, is not simply a result
of the rise in informal work. Since the early 1980s when most studies using this method were
undertaken, it has also been due to such trends as the rise of an enterprise culture, increased sub
contracting in the production process and the advent of other forms of flexible production
arrangement. Second-job holding, furthermore, is by no means directly a result of informal work
except if such job holding is illegal per se. It is also in part the combined result of broader economic
and cultural restructuring processes such as the demise of the ‘breadwinner wage’ and the
proliferation of part-time work. To identify the proportion of growth in either self-employment or
multiple-job holding attributable to such processes and the share attributable to informal work at a
particular moment is thus a difficult if not impossible task.
Secondly, therefore, those who analyse official employment data for evidence of informal work
have sought discrepancies in the results of different surveys used to compile official employment
statistics. In the USA, for example, the Census Bureau’s Current Population Survey (CPS) has been
compared with the Bureau of Labor Statistics (BLS) survey of firms. The CPS includes a monthly
sampling of about 60,000 households that asks questions about the work status of their occupants
and everyone is classified as employed, unemployed or not in the labour force, whilst the BLS survey
examines establishments to determine the number on the payroll. The comparison of the two data
sets has been premised on the assumption that those working on a informal basis would declare
themselves, or be declared as job holders, in the household survey but would not show up on the
books of business enterprises. The discrepancy in the numbers between the two surveys has been
thus taken as the number employed on a informal basis, with changes in the difference between the
two sets of figures seen as a measure of its growth or decline (e.g., Denison, 1982; Mattera, 1985; US
Congress Joint Economic Committee, 1983).
However, many problems exist with this approach. In terms of its relevance for measuring the
volume of informal work, the first problem as both Bajada (2002) and Williams and Windebank (1998)
highlight, is that it erroneously assumes that each individual is either a formal or informal worker and
in so doing, misses the vast majority of informal work that is conducted by those who have a formal
job (Williams, 2004a). Secondly, by analysing only those employed in businesses, it assumes that all
informal work is conducted on an ‘organised’ basis and misses both more autonomous forms of
informal work and informal work conducted for households. Thirdly, there is no reason to assume that
an informal worker will describe him/herself as employed in a household survey whilst the employer
will not in a business survey. And fourth and finally, the fact that such analyses have resulted in
contradictory results, with some studies showing no change in the size of informal work in the post
war years (e.g. Denison, 1982) and others showing growth (US Congress Joint Economic Committee,
1983) intimates the need for great caution. Identifying the magnitude of informal work through formal
labour force statistics, in sum, is beset by problems that cannot be easily transcended and this
method has waned in popularity since its heyday in the early 1980s.
Indeed, Eurostat (2003) in a note to the informal Council of Ministers of Employment and Social
affairs on undeclared work, conclude that ‘a comparison of the labour force survey results with
administrative sources or register data on an aggregate level does not yield an estimate of undeclared
work’ (cited in Renooy et al, 2004: 99).
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Annex A - Informal economy measurement methods
Very small enterprise (VSE) approach
For many commentators, very small enterprises (VSEs) represent a useful alternative non-monetary
proxy (e.g. Fernandez-Kelly and Garcia, 1989; International Labour Office, 2002; Portes and Sassen-Koob,
1987; Sassen and Smith, 1992; US General Accounting Office, 1989). The assumption is that in advanced economies, most informal work takes place in smaller enterprises
because of their reduced visibility, greater flexibility and better opportunities to escape state controls.
Larger firms, meanwhile, are viewed as subject to more state regulation and risk-averse to the potential penalties so will be less likely to directly employ informal workers, although they are
purported to subcontract to smaller firms who use such labour. As an indicator of informal work, however, the VSE approach is subject to two contradictory assumptions. On the one hand, not all VSEs engage in informal practices, which could lead to an
overestimate. On the other hand, fully informal VSEs will escape government record keeping that could lead to an underestimate (Portes, 1994). Moreover, it seems likely that the extent to which
VSEs participate either wholly or partly in informal practices will vary according to the geographical context in which they are operating (Williams and Windebank, 1998). As such, estimates of both the
size and growth/decline of informal work using this proxy indicator can only be very approximate.
More importantly, such a proxy totally ignores more individualised forms of informal work conducted
by people on a one-to-one basis to meet final demand. As Portes (1994, p. 440-1) thus concludes.
By themselves,... such series represent a very imperfect measure of the extent of informal
activity. It is impossible to tell from them which firms actually engage in irregular practices and the character of these practices. All that can be said is that small firms, assumed to be the principal locus of informality, are not declining fast and actually appear to increase significantly during periods of
economic recession. Yet despite the inappropriateness of this indicator, it is still widely used. The International Labour Office, for example, collects data from 54 countries on ‘informal sector enterprise’ (International Labour Office, 2002). Despite the existence of an internationally agreed definition adopted by the
Fifteenth International Conference of Labour Statisticians (ICLS), most of these 54 countries still adhere to their own national definitions, 21 of which use the criterion of non-registration of the
enterprise, either alone or in combination with other criteria such as small size or type of workplace location, while 33 countries use small size as a criterion, either alone or in combination with non
registration or workplace location. In consequence, variants of the VSE approach are still in common
usage and it should not be thought that this indirect non-monetary approach is in abeyance. If
anything, and as shown by the International Labour Office, quite the opposite is the case, despite its inappropriateness to understanding the full range of informal work in contemporary societies. Although the labour force and VSE measures are the most popular non-monetary indicators used
to measure informal work, they are not the only one’s employed. Recent years have seen various other non-monetary indicators adopted to measure informal work, such as electricity demand (e.g., Friedman et al, 2000; Lacko, 1999). Ultimately, however, and whichever indicator is employed,
indirect non-monetary proxy measures all suffer from the same problems. They provide little more
than a crude indicator of the extent of informal work and little, if any, information about the nature of informal work (e.g., SIC-level data). As such, other analysts have turned to monetary indicators in the
belief that this will enable a much closer estimate of informal work. Indirect monetary methods
Methods seeking evidence of informal work in indirect monetary indicators have concentrated
largely on three proxies, namely large denomination notes, the cash-deposit ratio and money
transactions.
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Annex A - Informal economy measurement methods
Large denomination notes approach
In this approach, the circulation of high denomination bank notes is seen as a key indicator of the
prevalence of informal work (Carter, 1984; Freud, 1979; Henry, 1976; Matthews, 1982). This
methodology, used principally during the 1970s and early 1980s, but now falling out of favour, is
embedded in a portrayal that views informal workers carrying around a fat roll of bank notes. It
assumes not only that they use cash exclusively in their transactions but also that they handle large
quantities of cash and exchange high denomination bank notes.
However, this method is problematic for at least three reasons:
1. it cannot separate the use of large denomination notes for crime from their use in informal
work, meaning that one has no way of knowing the proportion used for crime and the share for
informal work;
2. there is little evidence that those engaged in informal work use large denomination notes.
Indeed, many informal transactions are for relatively small amounts of money (e.g. Cornuel and
Duriez, 1985; Evason and Woods, 1995; Tanzi, 1982) and do not necessarily even involve the
use of cash in transactions (see below). Using high denomination bank notes, therefore, seems
to be not only a poor proxy indicator of the level of informal work but also grounded in a
conceptualisation of its nature that is, at the very best, a description of only a very small
segment of all informal work; and
3. there are a multitude of other factors that may account for the increased use of large
denomination bank notes in contemporary society. On the one hand, inflation needs to be
taken into account. Porter and Bayer (1989) in the US present strong econometric evidence for
a relationship between per capita holdings of $100 bills and the price level. In the UK,
meanwhile, the increase in large denomination notes has been less than the rate of inflation.
Between 1972 and 1982, the retail price index rose by 290 per cent whilst the average value of
the denomination of bank notes only rose by 120 per cent, meaning that the average
denomination has declined by 40 per cent over this period when inflation is taken into account
(Trundle, 1982). In Canada, moreover, Mirus and Smith (1989) note little change in real terms.
On the other hand, since this approach was propagated, profound transformations have taken
place in both attitudes and behaviour towards cash payments. Firstly, major alterations in
modes of payment (e.g., credit and debit cards, store cards) have occurred resulting in a
decline in cash usage. Secondly, a restructuring of formal financial services in the advanced
economies, reflected in the ‘flight of financial institutions’ from poorer populations (Collard et al,
2001; Kempson and Whyley, 1999; Leyshon and Thrift, 1994), has necessitated increased
cash usage amongst the financially excluded. These counter-tendencies thus make it difficult to
discern whether alterations in cash usage are due to the restructuring of formal financial
services, shifts in attitudes and behaviour, or the growth/decline of informal work.
The large denomination notes method, in sum, represents a very unreliable indicator of the extent
and changing magnitude of informal work and makes some heroic and erroneous assumptions about
the overall character of this work.
The cash-deposit ratio approach
Another indirect monetary method is to examine the ratio of currency in circulation to demand
deposits. Again grounded in the assumption that in order to conceal income, illegitimate transactions
will occur in cash, this approach seeks an estimate of the currency in circulation required by legal
activities and subtracts this figure from the actual money in circulation. The difference, multiplied by
the velocity of money, is an estimate of the magnitude of informal work. The ratio of this figure to the
observed GNP then provides a measure of the proportion of the national economy represented by
informal work (Atkins, 1999; Caridi and Passerini, 2001; Cocco and Santos, 1984; Gutmann, 1977,
1978; Matthews, 1983; Matthews and Rastogi, 1985; Meadows and Pihera, 1981; Santos, 1983;
Tanzi, 1980).
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Annex A - Informal economy measurement methods
However, it is a method that suffers from serious problems,
• cash is not always the medium for undeclared monetised exchange. There is plenty of evidence
that undeclared work utilises cheques and credit cards as well (see below). Indeed, in some
countries such as Italy, laws have precluded the disclosure of information concerning bank
accounts, so it is unnecessary to use only cash in informal work (Contini, 1982). Moreover, and
as Smith (1985) identifies in the US, whether an undeclared payment is made in cash or by
cheque depends on the same factors as determine the mode of payment in formal employment
(i.e., the size of the transaction and the seller’s confidence in the purchaser’s cheque);
• this approach again has no way of distinguishing what share of the illegitimate cash circulation
is due to informal work and what proportion is due to crime, nor how it is changing over time;
• the choice of the cash-deposit ratio as a measure of informal work is an arbitrary one that is not
derived from economic theory (e.g., Trundle, 1982) and it is not clear why this was chosen
rather than others;
• similar to the high denomination notes approach, the cash-deposit ratio is influenced not only by
the level of informal work but a myriad of other tendencies, often working in opposite directions
to one another. As already stated, whilst methods of payment have changed, with credit cards
and new interest-bearing assets reducing cash usage (e.g., Bajada, 2002), increasing financial
exclusion (e.g., refusal of credit cards and cheque accounts to the poor) resulting from the
banks ‘flight’ to affluent markets, has increased the use of cash amongst some populations.
Mattera (1985) echoes these criticisms arguing that Gutmann fails to take account of factors
other than those to do with informal work, which might have contributed to the decline of the
currency ratio. To take into account these factors, some commentators have refined this
method. Tanzi (1980), Matthews (1983) and Matthews and Rastogi (1985), rather than
attributing the entire increase in cash usage to greater levels of informal work, instead focus
upon only the proportion of the increase that can be shown to result from informal work. These
approaches, although more sophisticated, are not without their critics. Smith (1986) for instance,
questions the appropriateness and value of the statistical variables employed by Matthews
(1983), such as the identification of a positive causal relationship between rises in
unemployment levels and the growth of informal work. Even using Matthews’ own figures,
informal work expanded most rapidly at a time when unemployment increased comparatively
little (Smith, 1986). Thomas (1988), moreover, details some stark differences between the
results obtained in Matthews (1983) and Matthews and Rastogi (1985), which neither study
attempted to explain;
• the choice of a base period when informal work supposedly did not exist is problematic,
especially given the sensitivity of the results to which base year is chosen. O’Higgins (1981)
shows in the UK that if 1974 is taken as the base year, 16.5 per cent of the currency in
circulation was fuelling informal work in 1978. However, if 1963 is taken as the base year,
informal work became negative in 1978. Thomas (1988) highlights the problems associated with
the need to locate a year when informal work did not exist. For him, the choice seems to be
determined more by the availability of data than by any other factor and is somewhat ad hoc.
Indeed, there is little evidence that some period exists in history where informal work was zero.
Such work has been in existence as long as there have been rules and regulations with regard
to employment. Henry (1978), for example, cites examples of fiddling and tax evasion from the
time of Aristotle, whilst Houghton (1979, p. 91) shows that in 1905, when tax was at a uniform
rate of less than one shilling (5 pence) in the pound in the UK, a departmental committee
reported that ‘In the sphere in which self-assessment is still requisite, there is a substantial
amount of fraud and evasion’. Smithies (1984), moreover, in a detailed case study of informal
work in five towns (Barnsley, Birkenhead, Brighton and Hove, Walsall and part of North London)
between 1914 to 1970, clearly demonstrates a continuity in the prevalence of such activity. Any
method which measures the size of informal work based on the assumption that there was a
time when it did not exist is thus founded on suspect grounds (see Henry, 1978);
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Annex A - Informal economy measurement methods
• to convert the estimates of undeclared cash into undeclared income, it is necessary to know the
velocity of cash circulation in the informal sphere. Given the lack of data, the standard approach
assumes the same velocity as in the formal sphere. However, there is no evidence that the two
velocities are the same (Frey and Weck, 1983);
• it is impossible to determine how much of the currency of a country is held domestically and
how much abroad. Some of the cash which the cash-deposit ratio assumes is held domestically
will be doubtless located abroad causing an exaggeration in the estimate of informal work; and
• this approach does not allow one to consider the nature of informal work in terms of either the
economic relations within which the work is conducted or the motives of the participants. In a bid
to overcome one of problems with this approach (i.e., that informal transactions are assumed to
occur in cash), the next approach relaxes this assumption.
The money transactions approach
Recognising that cheques as well as cash are used in informal transactions, Feige (1979)
measures its magnitude by estimating the extent to which the total quantity of monetary transactions
exceeds what would be predicted in the absence of informal work. As evidence that cheques as well
as cash are used in undeclared transactions in the US, Feige (1990) quotes a study by the Internal
Revenue Service (IRS) showing that between a quarter and third of unreported income was paid by
cheque rather than currency. Many other studies identify similar tendencies. For example, Isachsen et
al (1982) find that in 1980, about 20 per cent of informal services were paid for by cheque, whilst in
Detroit, Smith (1985) provides a higher estimate in the realm of informal home repair, displaying that
bills were settled roughly equally in cheques and cash.
By relaxing this cash-only assumption, the unsurprising result is that monetary-transaction
approaches generally produce much higher estimates of the size of informal work than other
approaches. Before accepting such findings, however, it must be recognised that this approach
suffers exactly the same problems as the cash-deposit approach. The only problem it overcomes is
the acceptance that cheques can be used in undeclared transactions. Here, in consequence, the
criticisms are not repeated. Instead, and to conclude this review of the indirect monetary methods,
two key points are made. Firstly, there are inherent problems with all of these indirect methods for
evaluating the volume of informal work that raise grave doubts about the validity of their findings (see
Tanzi, 1999; Thomas, 1999; Williams and Windebank, 1998). Yet despite this, such approaches
continue to be used to measure its magnitude (e.g., Bhattacharya, 1999; Dixon, 1999; Gadea and
Serrano-Sanz, 2002; Giles, 1999, 2001, 2002; Hill, 2002; OECD, 1997, 2000, 2002). Secondly, and
most importantly for the purposes of this report, none of these approaches appear capable of
providing information on the nature of informal work. Given that ‘the methodology underlying the
monetary approaches ... rests upon questionable and generally un-testable assumptions and ... the
estimates they have generated are of dubious validity’ (Thomas, 1988, p. 180), the only conclusion
that can be reached is that ‘Estimates of the size of the black economy based on cash indicators are
best ignored’ (Smith, 1986, p. 106). Here, therefore, another method of assessing informal work that
again uses monetary methods but in a more direct manner is evaluated.
Income/expenditure discrepancies
Another way of measuring the volume and nature of informal work is to analyse differences in
expenditure and income either at the aggregate national level or through detailed microeconomic
studies of different types of individuals or households. This approach is premised on the assumption
that even if informal workers can conceal their incomes, they cannot hide their expenditures. An
assessment of income/expenditure discrepancies is thus considered to reveal the extent of informal
work and where it is to be found.
Firstly, there are aggregate level studies that analyse the discrepancy between national expenditure and
national income so as to estimate the size of informal work. Such studies have been conducted in Germany
(e.g., Langfelt, 1989), Sweden (e.g., Hansson, 1982; Park, 1979), the UK (O’Higgins, 1981) and the US
(e.g., Macafee, 1980; Paglin, 1994), In the US, for example, Paglin (1994) examines the discrepancy
between household expenditure and income surveys published
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Annex A - Informal economy measurement methods
annually in the Bureau of Labor Statistics Consumer Expenditure Survey (CES). He finds that
between 1984 and 1992, informal work declined from 12.4 per cent of personal income in 1984 to 9.6
per cent in 1992, or from 10.2 per cent to 8.1 per cent of GDP over this period. This, he asserts, is
principally due to the growth of formal employment during the 1980s in the US. Nevertheless, he finds
that in 1992, 10.2 per cent of households were income-poor but consumption-rich and views this to be
a product of the existence of informal work. Paglin (1994) finds that the poorest 20 per cent of
households had an average after-tax income of US$5,648 in 1991 but an average expenditure level of
US$13,464. He then takes a major logical leap to conclude that a sizeable number of the income-poor
households are engaged in informal work, failing to consider whether this could be due to other
factors (e.g., retirement household spending, households between jobs, major one-off expenditures
on costly items).
Secondly, others study income/expenditure discrepancies at the household level. In the UK, the
Family Expenditure Survey (FES) has been analysed (see Dilnot and Morris, 1981; Macafee, 1980;
O’Higgins, 1981). Comparing households’ income and expenditure in 1,000 out of the 7,200
households surveyed for the 1977 FES so as to examine whether some households appear to live
beyond their means, Dilnot and Morris (1981) employ a variety of ‘traps’ to exclude discrepancies that
might be explained by factors other than informal work (e.g., high expenditure due to an unusual
major purchase or to the running down of accumulated wealth). After all adjustments, Dilnot and
Morris (1981), assuming that tax evasion existed in any household whose expenditure exceeded its
reported income by more than 15 per cent, derived upper and lower estimates of its extent. They
reveal that 9.6-14.8 per cent of households evaded taxes and that tax evasion was equivalent to 2.3
3.0 per cent of the GNP in 1977. Such evasion, moreover, was found to be more prevalent amongst
the self-employed (who understate their income by between 10-15 per cent) and part-time employees
than those in full-time employment. Smith (1986) further reinforces this in a study of the 1982 FES,
concluding that the self-employed understate their income by between 10 and 20 per cent.
O’Higgins (1981), however, casts doubt over the accuracy of this method. He suggests that it could
be an underestimate because 30 per cent of households refuse to participate in the FES, and it is
possible and plausible both that a greater proportion of non-respondents participate in informal work
than the 9.6 per cent of respondents suggested by the lower-bound estimate of Dilnot and Morris
(1981) and probably to a greater extent than the identified average weekly figure of £31. As O’Higgins
(1981) argues, even if as few as 25 per cent of non-respondents engage in informal work to the extent
of £31 weekly, the lower-bound estimate would be raised by almost half, yielding an adjusted lower
estimate of 3.5 per cent of GNP.
Although this method has advantages over other indirect monetary methods, not least its reliance on
relatively direct and statistically representative survey data, its problems remain manifold (see
Thomas, 1988, 1992; Smith, 1986). For the discrepancy to represent a reasonable measure of the
level of informal work, one has to make a number of assumptions about the accuracy of the income
and expenditure data. Take, for example, the expenditure side of the equation. Estimates such as
those made by Dilnot and Morris (1981) depend upon the accurate declaration of expenditure to
government interviewers by respondents. Mattera (1985) suggests that it is somewhat naive to
assume that this is the case. Equally convincing is the criticism that, for most people, spending is
either over- or under-estimated during a survey because records are kept by few members of the
population compared with income, which for employees comes in regular recorded uniform
installments. Moreover, at an aggregate level at least, such expenditure will omit informal purchases
by both declared and undeclared incomes (Dallago, 1991). Household level expenditure studies also
suffer from the fatal flaw of only examining final demand (i.e., consumer expenditure), not
intermediate demand (i.e., business expenditure) for informal goods and services. As such, it ignores
informal produced intermediate demand, such as informal sub-contracting as well as off-the-books
employment by formal enterprises (Portes, 1994).
On the income side, meanwhile, these studies cannot decipher whether the income derives from
criminal or informal activities, or even whether it derives from wealth accumulated earlier such as
money savings. In addition, and so far as studies such as the FES are concerned, there are problems
of non-response as well as under-reporting (Thomas, 1992).
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Annex A - Informal economy measurement methods
Consequently, it is difficult to accumulate accurate data on the extent of informal work using this
method. Weck-Hannemann and Frey (1985) in Switzerland bring such problems to the fore when they
report that the national income tends to be larger than expenditure. According to this method, Swiss
informal work is negative. This is nonsensical and reveals that the discrepancy does not display the
level of informal work but is due to other factors. For this reason, more direct approaches to
investigating informal work have been recently explored as a way forward (e.g., OECD, 2002; Renooy
et al, 2004; Williams, 2004a).
Direct survey methods
Direct survey methods have been employed to evaluate the magnitude and/or character of
informal work in many nations, including Belgium (Pestieau, 1983; Kesteloot and Meert, 1999),
Canada (Fortin et al, 1996), Germany (Frey et al, 1982), Italy (Censis, 1976), Norway (Isachsen and
Strom, 1985), the Netherlands (e.g., van Eck and Kazemier, 1988; Renooy, 1990), the United
Kingdom (e.g., Leonard, 1994; Pahl, 1984; Williams and Windebank, 2003a) and the United States
(Ross, 1978; Jensen et al, 1996; Nelson and Smith, 1999; Tickamyer and Wood, 1998). To survey
directly the magnitude and character of informal work, one can in theory ask suppliers and/or
purchasers of informal work about the volume or the value of their exchanges, what they did/received,
for/from whom and why.
Starting with the volume of informal work, studies can directly or indirectly ask either households or
businesses whether they have used informal work to acquire specific goods and/or services.
Alternatively, one can ask people about their supply of informal work in specific activities. In practice,
much of the research to assess the volume of informal work has been conducted on a household
level and has requested information from respondents both as suppliers and purchasers of informal
work (e.g. Leonard, 1994; Pahl, 1984; Warde, 1990). To explore the value of undeclared purchases
and/or sales, meanwhile, the amount of money earned by sellers, or spent by consumers, with regard
to informal work can be examined. Again, most studies have tended to investigate respondents as
both purchasers and sellers (e.g. Fortin et al, 1996; Isachsen et al, 1982; Lemieux et al, 1994),
although some examine respondents only as purchasers (e.g. McCrohan et al, 1991; Smith, 1985).
This data can be collected, moreover, through either mail-shot questionnaires (e.g. Fortin et al,1996)
or through face-to-face interviews that range from the relatively unstructured (e.g. Howe, 1988) to the
relatively structured variety (e.g. Williams and Windebank, 2001a) using either open - and / or
closed-ended questions. On the whole, and perhaps reflecting the lack of data available on this
subject, most studies have used relatively quantitative approaches composed largely of closed-ended
questions and then frequently employed a variety of more open-ended questions and/or qualitative
methods for in-depth exploration of the findings (e.g. Leonard, 1994; Pahl, 1984). Indeed, even
studies relying primarily on ethnography, such as that by Howe (1988), conduct some interviews as a
quantitative precursor for their ethnographic material. Pahl (1984), meanwhile, used follow-up
in-depth interviews with a limited number of households to explore specific issues.
Although direct studies could be carried out on either national, regional or local population samples, in
most instances they have tended to be applied to particular localities (e.g. Barthe, 1985; Fortin et al,
1996; Leonard, 1994; Pahl, 1984; Renooy, 1990; Warde, 1990; Williams and Windebank, 2003a),
socio-economic groups such as home-workers (e.g. Phizacklea and Wolkowitz, 1995) or industrial
sectors such as garment manufacturing (e.g. Ram et al, 2003). Indeed, unless governments decide to
invest in conducting such direct studies, it seems unlikely that direct studies will move beyond the
case study approach in the near future.
Examining criticisms of these direct survey methods, it is telling that these derive almost exclusively
from users of indirect approaches. Their major criticism is that researchers naively assume that
people will reveal to them, or even know, the character and magnitude of informal work in their lives. It
is intimated on the one hand, that purchasers may not even know if such work is being offered
informally or formally and on the other hand, that sellers will be reticent about disclosing the nature
and extent of their informal work since it is illegal activity.
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Annex A - Informal economy measurement methods
The former point might be correct. For example, if a purchaser has his/her external windows cleaned
or purchases some goods from a market stall, s/he might assume that this money is not declared
when this is not necessarily the case, or vice versa. In other words, although consumers may often
assume that goods and services bought in certain contexts are purchased on a informal basis whilst
in other contexts they are not, their assumptions might not be correct. Goods acquired in formal retail
outlets, for example, may not only have been produced on an informal basis but may even be sold in
such a manner (e.g., in illegally inhabited shop premises) without the knowledge of the consumer. Not
all those dealing in cash meanwhile, may necessarily be working on an informal basis (e.g., they may
not have confidence in the purchaser’s cheque), just as some accepting cheques may be tax evaders.
On the whole, therefore, although people who purchase goods and services may be more willing to
reveal whether they think it has been bought on an informal basis, they cannot necessarily be sure
whether this is indeed the case unless the supplier informs them that this is so.
Despite the assertions of those critical of direct surveys, however, it is not necessarily the case that
those supplying informal work will be untruthful in their dealings with researchers. Indeed, such a
criticism has been refuted many times although it continues to be raised. Pahl (1984), in his study of
the Isle of Sheppey, questioned people both as suppliers and purchasers. He found that when the
results from individuals as suppliers and purchasers were compared, the same level of informal work
was discovered. The implication, therefore, is that individuals are not so secretive as sometimes
assumed about their informal work. Just because it is activity hidden from or unregistered by the state
for tax, social security and/or labour law purposes does not mean that people will hide it from each
other or even from academic researchers. It also intimates that customers are not as wrong as might
be considered about whether or not a supplier is working on an informal basis.
Similar conclusions have been drawn concerning the openness of research participants in Canada
(Fortin et al, 1996) and the UK (Evason and Woods, 1995; Leonard, 1994; MacDonald, 1994). As
MacDonald (1994) reveals in his study of informal work amongst the unemployed, ‘fiddly work’ was
not a provocative subject from their perspective. They happily talked about it in the same breath as
discussing, for instance, their experiences of starting up in self-employment or of voluntary work. This
willingness of people to talk about their informal work was also identified by Leonard (1994) in Belfast.
This willingness was also identified in Belfast (Leonard, 1994). Indeed, a survey of the informal
economy in UK deprived urban neighbourhoods (Williams and Windebank, 2001a) finds that the total
amount customers reported spending on informal work was £23,354 and this was near enough
exactly the same amount that informal workers asserted that they had received (£22,986). So too was
the mean price customers paid (£90.24) broadly equitable with the mean price suppliers asserted that
they received (£84.48). Similar results were found when affluent suburbs and rural areas were
analysed (Williams, 2004a; Williams and Windebank, 2003). No evidence was thus found either that
suppliers greatly under-report such work or their income, or that customers falsely allocate economic
activity to informal work when this is not the case. Given that the order of magnitude of customer and
supplier responses are approximately the same, there are few grounds for believing that direct
surveys under-report either the level of this work or the income from such activity.
Perhaps a more salient criticism is that the direct approaches have so far largely investigated only
informal work used in relation to final demand (spending by consumers on goods and services), not
intermediate demand (spending by businesses). Final demand, however, accounts for only some twothirds of total spending in most advanced economies. Such direct methods are thus missing the
informal work that takes place in the other third of the economy. This is a valid criticism of those
studies that focus upon only households as customers of informal work. Where respondents as
suppliers of informal work are considered, however, it is to be expected that this would gather data on
work that not only met final but also intermediate demand.
In summary, there is emerging a strong consensus that indirect measurement methods are very limited in
their usefulness. Indeed, this is the conclusion of both OECD experts in their handbook on
measurement methods (OECD, 2002) and the most recent European Commission report on the
informal economy (Renooy et al, 2004) who find that direct survey methods are more valid and
reliable (see also Thomas, 1992; Williams, 2004a; Williams and Windebank, 1998).
47
Annex B - GEM methodology
Annex B - GEM Methodology What is GEM?
The Global Entrepreneurship Monitor (GEM) started in 1999. Now in its fifth year, this world-wide
project involves around 80 researchers in 28 countries. This is slightly smaller than the 2002 study,
but GEM nevertheless still represents the largest and most rigorous longitudinal study of
entrepreneurship in the world.
GEM defines entrepreneurship as:
“Any attempt at new business or new venture creation, such as self-employment, a new business
organization, or the expansion of an existing business by an individual, teams of individuals, or
established business.”
This is a sufficiently broad definition to include anyone who is adding value to the work they do by
acting entrepreneurially, although too narrow to identify those enterprise that fulfill a not-for-profit or
specific social purpose.
GEM’s core research questions remain those that were first set at the start of the programme:
• How much entrepreneurial activity is taking part in the world?
• Why do levels of entrepreneurial activity differ between countries?
• What are the links between entrepreneurial activity and national economic growth and productivity?
Equally as interesting, especially to national policy and practitioner audiences, however, are a further
set of questions that are focused on the cultural and labour market contexts in which entrepreneurship
thrives. More specifically these questions centre around;
•
• • • • Individual motivations;
The demographic profile of entrepreneurs;
The types of entrepreneurial businesses being created;
The political, economic, social and technological drivers of entrepreneurship; and
The role of government in stimulating entrepreneurship.
How does GEM measure entrepreneurial activity?
Each of the countries in the study has a team of researchers who use a standardised questionnaire
survey of the adult population to create the Total Entrepreneurial Activity (TEA) index. This random
adult population survey is conducted by telephone during June and October of each year and, on the
basis of the 18-64 year olds within the population it is used to identify:
Nascent ventures:
These are the firms that would be called start-ups by most analysts. Anyone in the survey who said
they were actively involved in creating a new business that they would own all or part of and had not
paid any salaries or wages to anyone for more than three months fell into this category.
Baby businesses:
These are the more established, owner-manager, businesses that have been running for up to 42
months and have not paid salaries for longer than that.
There will be some double counting between these two groups – serial entrepreneurs may be setting
up and running businesses simultaneously. This problem is overcome by allocating these individuals
either to nascent or to baby businesses, but not to both. Adding together the two categories of people
makes the TEA index that can then be used to illustrate differences and similarities between
countries, regions, types of people and types of entrepreneurship.
48
Annex B - GEM methodology
Since 2001, GEM has distinguished between two types of entrepreneurship:
• Necessity entrepreneurship: These are the people who have no better choices for work.
• Opportunity entrepreneurship: These are the people who perceive a business opportunity and take
advantage of it, either independently or from paid employment.
In addition to this, the adult population survey is supported by a practitioner survey of experts involved
with policy formulation and delivery, small business support, small business finance and
entrepreneurs themselves. This gives the study a richness and allows each country team to be able
to make specific and evidence-based policy recommendations to their national governments.
What’s different about GEM UK 2003?
GEM UK is the largest ever single country study of entrepreneurship within the GEM project. The
2001 dataset contained 7000 cases, 2002: 16,000, 2003: 22,500; 2004: 24,000; 2005 (projected):
34,000. Additional questions to those in the GEM Global survey on finance, technology and turnover,
and social entrepreneurship are included, as well as some of the SBS household survey questions to
identify “thinkers”, “avoiders” and “doers”. Results of GEM UK are annually checked against Barclay’s
data for consistency.
The expanded sample size allows us to provide reliable and robust inter-regional comparisons of
entrepreneurial activity and to have a large and representative sample of the UK’s entrepreneurial
businesses, including an attempt to understand entrepreneurship in the most deprived wards of the
UK. We have asked additional questions on turnover and employment, as well as the postcode
locations of the businesses in order to examine more closely:
•
•
•
•
•
The relationships between entrepreneurship and employment growth (and ultimately productivity).
The role of different types of finance for start up businesses.
Technology networks and intrapreneurship.
Social entrepreneurship.
Barriers to entrepreneurship.
GEM UK represents a first attempt to measure social entrepreneurial activity in a systematic way.
This is an important area of investigation for policy makers and businesses alike yet definitions and
data are illusive. We have taken GEM’s broad approach to defining entrepreneurship and have
adapted it to ask similar questions about social enterprise businesses to create a Social Enterprise
Activity Index (SEA). Like TEA it is the sum (minus double counting) of those answering positively to
one of the following two questions:
* Are you, alone or with others, currently trying to start any kind of social, voluntary or community service,
activity or initiative? This might include providing subsidised or free training, advice or support to
individuals or organisations; profit making activity, but where profits are used for socially orientated
purpose; or self-help groups for community action; or
* Are you, alone or with others, currently managing any such social, voluntary or community service,
activity or initiative?
The SEA index is based on as broad a definition of social entrepreneurship as possible, derived from
the GEM definition of entrepreneurship as follows:
Any attempt at new business or enterprise or new venture creation, such as self-employment, a new
business organisation, or the expansion of an existing business by an individual, teams of individuals,
or established businesses with social or community goals as its base.
49
Annex B - GEM methodology
Interpreting GEM data
GEM captures a larger proportion of entrepreneurial activity than business or household surveys
since it measures entrepreneurial behaviour as well as actual businesses established1. This is
particularly useful for understanding entrepreneurial potential (for example in different demographic
groupings, such as ethnic minorities), as well as total entrepreneurial activity. Effectively it establishes
the extent to which people are likely to be entrepreneurial if the entrepreneurial drivers in the
economy are effective (for example, government policy, innovation, finance, education and training
and culture).
As a result of this, the data presented in this text should not be interpreted as an accurate
comparative measure of actual numbers of businesses in particular regions, communities or sectors,
particularly where the sample size is small. Instead it should be taken as a measure of the number of
businesses that are likely to exist if appropriate drivers are in place.
GEM UK, however, as the largest sample in the whole global survey does have a substantial number
of actual businesses within it, as well as a representative sample of the UK’s adult population
according to 2001 census classifications. As a result, the margins of error2 are relatively small and
the degree of statistical inference possible from the data high. All data shown within this text are
3
shown with their levels of significance for ease of interpretation.
1
This approach is also taken by the Small Business Service’s household survey
Defined as one standard deviation either side of the mean
3
Defined at the 1% (***), 5% (**) or 10% level (*)
2
GEM: Note on sampling and survey techniques
GEM interviews were conducted using a sampling method known as RDD (Random Digit Dialling).
This works as follows:
1.
GEM UK specified a target number of interviews within each of the UK GORs (Government Office Regions). These
varied from a maximum of 5000 (Northern Ireland, including a boost within Belfast), to a minimum of 1000 (South
East, London, Eastern, North East, South West, West Midlands). We also conducted 3000 interviews in each of the
North West (including a boost of 2000 interviews within Merseyside), Yorkshire and Humberside and the East
Midlands. 2000 interviews were also conducted in each of Scotland and Wales, making a total of 24,000 interviews
within the UK.
2.
Random telephone numbers were generated by computer within specified UK area codes or STDs (Standard Trunk
Dialling), predicted to be within the GORs. These were sampled in proportion to the total number of interviews
required for each GOR.
3.
The sampled numbers were then "pinged" to remove unallocated lines. This technique involves an auto-dialer ringing
each of the sampled numbers for a split second (hence emitting a "ping" sound) which confirms the presence of a
receiver at the other end. This process removes approximately 30% of randomly generated numbers. The technique
cannot, however, identify extension numbers within offices, lines used solely for the purpose of computers/the
internet, or fax numbers.
4.
With domestic interviewing it is normal to screen the ensuing pinged sample against electronic versions of business
directories, and so remove business numbers. This was not the case with GEM as many small business numbers
listed in directories might also be home telephone numbers, and were thus included.
5. The RDD sampling was conducted by a specialist telephone number company called UK Changes. The market
research agency, IFF Research Ltd, was provided with raw numbers in a ratio of approximately 6:1 to the target
number of interviews.
6.
On calling, the potential respondents were asked whether the number called was their home telephone number.
Anybody who was answering the telephone from their workplace, or who was visiting somebody else's house, were
thus excluded from the research. This was so we could be sure that respondents lived within the region/area for
which they were sampled, and so the sample is based upon individuals within households.
7.
The sample provided was a representative random selection of telephone lines within specified regions, but not of
individuals. Therefore, for 75% of interviews attempted we asked to speak to the "person available in your household
at the moment aged between 16 and 80 with the next birthday coming up?"
50
Annex B - GEM methodology
8.
Because younger people are historically less likely to want to take part in most market research surveys, in 25% of
attempted calls we asked to speak to anybody aged 16-34 year old in the first instance.
9.
All respondents were required to be between the aged of 16 and 80. Other than the attempt to interview 16-35 year
olds, and the regional sampling, there were no other quotas on type of respondent.
10. 24,006 respondents aged 16-80 were interviewed using Computer Assisted Telephone Interviewing (CATI) between
24th May and 16th October 2004. Please note that for reasons of comparability, data for GEM will normally be
shown as 18-80.
11. The full breakdown of call outcomes was as follows:
• • • • • • • • • • Completed interviews
Screened out on age
Respondent did not speak English
Fax number Work number
Dead number
No reply after seven attempts
Refused to take part
Quit interview/other outcome
Total number of outcomes
= 24,006 (19.0% of all outcomes)
= 1036 (0.8%)
= 219 (0.2%)
= 5820 (4.6%)
= 8541 (6.8%)
= 13137 (10.4%)
= 665 (0.5%)
= 71142 (56.4%)
= 1558 (1.2%)
= 126,123 (100%)
12. The method of ensuring regional coverage through Area Code 167 sampling is as accurate as is available. However,
sub-area codes are not always exclusive within area, and it may also be that a domestic line can be transferred in its
entirety to a nearby area. It is also the case that Area Codes transcend regional boundaries, and this is especially an
issue in areas of dense population near a regional border. For this reason we asked respondents to give us both their
home postcode and county/Unitary Authority, and from this we reallocated some of the home regions for certain
records. The net result was that we achieved the following number of achieved interviews according to GOR (based
on all 16-80):
• • • • • • • • • • • • South West
South East
London
Eastern Wales West Midlands
East Midlands
Yorks/Humberside
North West
North East
Scotland
Northern Ireland
= 1019 (+19 on target)
= 991 (-9)
= 1013 (+13)
= 1004 (+4)
= 1985 (-15)
= 1015 (+15)
= 2969 (-31)
= 2984 (-16)
= 3034 (+34)
= 996 (-4)
= 1998 (-2)
= 4998 (-2)
13. At the analysis stage IFF weighted the data by age, gender, 168 ethnicity and region to remove the regional sampling
and any other response imbalances. The weighted numbers for each region are as follows:
• • • • • • • • • • • • South West
South East
London
Eastern Wales
West Midlands
East Midlands
Yorks/Humberside
North West
North East
Scotland
Northern Ireland
= 2018
= 3262
= 2956
= 2199
= 1180
= 2137
= 1707
= 2020
= 2733
= 1036
= 2094
= 664
14. The weighting targets derived by IFF are based on 16-80 year olds 169 only, from the 2001 census.
51
Annex C - The BETA model
Annex C - The BETA Model
Keeping account of the variations in the business population is important from a strategic and
operational perspective for both business and government. Over the past decade the UK economy
has seen a fundamental structural transformation of the business population and The BETA Model
offers a valuable insight into the changes that have taken place.
For commercial organisations it is crucial to understand how a changing business population affects
market opportunities. In recent years the UK has seen a phenomenal growth in the number of new
businesses forming in existing and newly emerging sectors. These modifications are re-shaping both
the business-to-business and consumer markets. Early intelligence can make the difference between
success and failure in increasingly competitive and dynamic environments.
For public organisations it is essential to know if programmes to stimulate market activity and manage
structural change are having the desired impact. Public interventions need effective feedback to
ensure that restructuring does not skew markets and that competition is enhanced without reinforcing
existing disparities. Changes in the business population profile are indicative of structural
transformation and can be used to regulate programme activity and calibrate strategic objectives
during a programme life-cycle.
The BETA Model was developed to ensure that commercial and government organisations have
access to useful business population metrics in near real-time. As a specialist Business Population
Statistics service we provide an in-depth understanding of changing economies.
The service is underpinned by an innovative longitudinal economic database that profiles the
characteristics of the UK business population. The database incorporates data for approximately 95%
of organisations in the UK providing for higher levels of confidence than survey-based inquiry. Our
unique methodologies offer highly focused segmentation by location, sector and key life-events:
formation, deformation, relocation and employment change proving a more detailed view of the
dynamics in the population profile.
The data has a complete times-series from April 1999 and is updated quarterly from Experian’s
National Business Database – the most comprehensive record of the UK business Population on the
market. The primary output from The BETA Model provides the ultimate building blocks for
geographic and/or sectoralised analysis and secondary data sources can be combined to provide
superior depth.
The BETA Model offers the most flexible approach for profiling discreet economies including
enterprise density levels, growth ratios, shift-share analyses, location quotients and other standard
methodologies for exploring micro-economic change. Output can offer a detailed reconciliation of
change for a single location/sector or combine parallel indicators to facilitate benchmarking and
indexation. The BETA Model’s unique functionality also offers the ability to define and develop
bespoke methodologies that are primed for specific strategic requirements.
The pioneering data-mining techniques employed by the BETA Model exploits the atomised nature of
the National Business Database. The key source of the NDB is Yellow Pages and Thompsons Local
directories – the two foremost business directories in the UK. In the BETA model data universe every
business site is treated separately, this way we can provide a localised understanding of a given
economy.
This level of atomisation gives the BETA Model unique access to the smallest trading organisations
that would not ordinarily be captured by other sources, especially government statistics that rely on
the VAT registration process. Further more, this methodology also reduces ‘noise’ from self-employed
who are not openly trading.
52
Annex C - The BETA model
The directory classification system from the NDB also provides a powerful market segmentation
based on a businesses identified primary market – and this may differ across sites and branches. This
segmentation can provide a much more sensitive understanding of clusters and sector groupings.
The BETA Model is widely used to profile markets and evaluate economic impact. Our clients have
used the service for baselines, mapping, customer acquisition and economic impact assessments.
Many of the briefs we have been given allow us to develop an insight previously unavailable. The
BETA Model offers the most comprehensive view of the UK Business population and our clients are
confident that our skills, knowledge and in-sight make us the first choice for understanding markets.
A range of recent assignments for the Beta Model
Feb 2005 – ongoing
Title:
Profiling Supported Business in Objective 1 Merseyside
Client:
GONW/Merseyside Objective 1 Programme
TBM took the companies beneficiary database of the O1 programme and compiled a profiled of the supported to
benchmark their levels of employment change in comparison to other businesses on Merseyside, the NW and
the UK. The preliminary results suggest that companies supported by Objective 1 are twice as likely to grow than
the non-supported businesses.
Jan 2005 – February 2005
Title:
Metrics for Strategic Investment Areas
Client:
Liverpool City Council
The brief is to provide population metrics including dynamic changes for the 10 SIC categories and TMP
Growth Sectors for the 5 Strategic Investment Areas as defined in the RES. The population metrics are being
used by the Regeneris consultancy as part of Liverpool’s contribution to the new RES.
Oct 2004 – ongoing
Title:
Investigation into the impact of the Congestion Charge on Business Dynamics
Client:
Greater London Authority/Transport for London
The brief is to investigate if there any significant differences in the dynamics of business population in the
congestion area that cannot be explained other phenomena such as economic or structural changes.
July 2004 - Oct 2004
Title:
Benchmarking the National Business Database
Client:
Experian PLC
Experian provide one of the most comprehensive B2B information services in the UK. TBM were asked to
develop a benchmark for Experian’s National Business Database product. A benchmark was developed using
available IDBR data and additional modelling techniques to compare the NBD against the UK Business
Population.
April 2004 – July 2004
Title:
Investigation in the Dynamics of the Business Population in Deprived Wards
Client:
DTI/Small Business Service/Inter Departmental group on deprivation
As part of the HMGs Neighbourhood Renewal Strategy TBM were ask to provide an analysis of the
differences in Business Dynamics in the 20% Most Deprived Wards in England and in the Norm.
53
Annex C - The BETA model
April 2001 – Ongoing
Title:
Company Monitoring Activity Programme
Client:
GONW/NWDA
To provide an online support tool for assessing the business changes in the NW and benchmarking them
against the UK. The service is provided to over 100 regional organisations involved in the regeneration of the
NW. Users have access to reports from ward level upwards, and can segment by size and sector. This project is
used in all of the sector development work in the Merseyside Objective One programme.
April 2001 – Ongoing
Title:
Provision of bespoke Analysis
Client:
Various
The TBM is used by a wide range other clients who have used our data in the preparation of various
strategies, policies and publications. These include;
• • • • • • ‘Merseyside Travel-To-Work Strategy’ - Mott McDonald/MIS;
‘Merseyside Economic Review 2003’ - Pion Economics;
‘200 Outstanding SMEs on Merseyside’ - Trinity Mirror News Group;
‘Top 100 Firms on Merseyside’ - Trinity Mirrors News Group;
‘Property requirements for SMEs in Berkshire’ Clive Davey Commercial;
‘SME Business and Movement Since 1999’ – KnowledgeCall/DBM.
54
Annex D - List of councils responding to queries
Annex D – List of Councils responding to queries • Blyth Valley
• Bridgend
• Breckland
• Braintree
• Broadland
• Bassetlaw
• Cannock Chase
• Cherwell DC
• Chester-le-Street
• Dorset County Council
• Northampton
• Leicester City Council
• Lancaster County Council
• Oxford City Council
• Suffolk County Council
55
Annex E – Publications reporting results of English
Localities Survey
Books:
Williams, C.C. and Windebank, J. (2001)
Revitalising Deprived Urban Neighbourhoods: an assisted self-help approach, Ashgate, London.
Williams, C.C. (2004)
Cash-in-Hand Work: the underground sector and the hidden economy of favours, Palgrave Macmillan, Basingstoke. Reports:
Evans, M., Syrett, S. and Williams, C.C. (2004)
The Informal Economy and Deprived Neighbourhoods: a systematic review, ODPM, London
Williams, C.C. (2004)
Small Businesses in the Informal Economy: making the transition to the formal economy, SBS, London. Academic Journal Articles:
Williams, C.C. (2001)
"Tackling the participation of the unemployed in paid informal work: a critical evaluation of the deterrence approach", Environment and Planning C, vol. 19, no.5, pp.729-749. Williams, C.C. and Windebank, J. (2001)
"Beyond profit-motivated exchange: some lessons from the study of paid informal work", European Urban and
Regional Studies, vol. 8, no. 1, pp.49-61
Williams, C.C. and Windebank, J. (2001)
"Paid informal work in deprived urban neighbourhoods: exploitative employment or co-operative self-help?" Growth and Change, vol. 32, no. 4, pp 562-571. Williams, C.C. and Windebank, J. (2001)
"Reconceptualising paid informal exchange: some lessons from English cities", Environment and Planning A, vol. 33, no. 1, pp. 121-140. Williams, C.C. and Windebank, J. (2002)
"The uneven geographies of informal economic activities: a case study of two British cities", Work, Employment and Society, vol. 16, no. 2, pp. 231-250. Williams, C.C. and Windebank, J. (2002)
"Why do people engage in paid informal work? a comparison of affluent suburbs and deprived urban
neighbourhoods in Britain", Community, Work and Family, vol. 5, no.1, pp.67-83.
Williams, C.C. (2002)
"Beyond the commodity economy: the persistence of informal economic activity in rural England", Geografiska
Annaler B, vol. 83, no. 4, pp. 221-233. Williams, C.C. and Windebank, J. (2003)
"Reconceptualizing women’s paid informal work: some lessons from lower-income urban neighbourhoods", Gender, Work and Organisation, vol.10, no.3, pp.281-300.
Williams, C.C. and Windebank, J. (2004)
"The heterogeneity of cash-in-hand work", International Journal of Sociology and Social Policy, vol. 24, no.1/2, pp. 124-140.
Williams, C.C. (2004)
"Geographical variations in the nature of undeclared work", Geografiska Annaler B, vol. 86, no.3, pp. 187 - 200.
Williams, C.C. (forthcoming June 2005) "Unraveling the meanings of underground work", Review of Social Economy, vol.
Williams, C.C. (forthcoming September 2005)
"The undeclared sector, self-employment and public policy", International Journal of Entrepreneurial Behaviour
and Research, vol. 11, no. 4, pp.
Williams, C.C. and Windebank, J. (forthcoming) "Refiguring the nature of undeclared work: some evidence from England", European Societies
56
Annex F – Bibliography
Annex F - Bibliography
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Bajada, C. (2002) Australia’s Cash Economy: a troubling issue for policymakers, Aldershot: Ashgate.
Barthe, M.A. (1985) ‘Chomage, travail au noir et entraide familial’, Consommation 3: 23--42.
Barthelemy, P. (1991) ‘La croissance de l’economie souterraine dans les pays occidentaux: un essai d’interpretation’, in J-L. Lespes (ed.) Les
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Bhattacharyya, D.K. (1990) ‘An econometric method of estimating the hidden economy, United Kingdom (1960-1984): estimates and tests’, The
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Caridi, P. and Passerini, P. (2001) ‘The underground economy, the demand for currency approach and the analysis of discrepancies: some
recent European experience’, The Review of Income and Wealth, 47, 2: 239--50.
Carter, M. (1984) ‘Issues in the hidden economy: a survey’, Economic Record, 60, 170: 209--21.
CENSIS (1976) ‘L’occupazione occultra-carratteristiche della partecipazione al lavoro in Italia’, cited in Frey, B.S. and
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Cocco, M.R. and Santos, E. (1984) ‘A economia subterranea: contributos para a sua analisee quanticacao no caso Portugues’, Buletin Trimestral
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Contini, B. (1982) ‘The second economy in Italy’, in V.V. Tanzi (ed.) The Underground Economy in the United States and Abroad, Lexington, MA:
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Dixon H. (1999) ‘Controversy: on the use of the "hidden economy" estimates’, The Economic Journal, 109, 456: 335--7.
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Evason, E. and Woods, R. (1995) ‘Poverty, deregulation of the labour market and benefit fraud’, Social Policy and Administration, 29,1: 40--55.
57
Annex F – Bibliography
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Annex G – Contributors
Annex G - Contributors
This report was compiled by:
Dr. Simon King
Senior Consultant, Hedra, PLC.
Angela Zvesper
Research Director, Social Research Associates
Professor Colin Williams
Professor of Work Organisation, Leicester University
Dr. Rebecca Harding
Associate Director of Research at Deloitte
and until December 2004 Chief Economist at The Work
Foundation.
Disclaimer:
As of December 2004, Rebecca Harding has not been paid by either GEM UK or GEM global so has
had no vested financial or personal interest in her interpretation of GEM data. Similarly, while she was
at London Business School, she was a Senior Fellow of the School and not an employee of GEM UK
or GEM global. There are two key points:
a. GEM UK does not exist and has never existed as a legal entity, only as a research project
within an academic setting; and
b. GEM global is a legal affiliation of national teams as of January 2005 and only pays its acting
executive director who was from March 2004-January 2005 Pia Arenius (when the budget was
held with LBS and Babson College) and from February 2005 has been Mick Hancock. Before
then the global project was run by Paul Reynolds as a Professor of the two schools. Rebecca
Harding has never had any affiliation to it except as a national unpaid ambassador representative
(unpaid) for GEM.
62