05
SECTION 5 | SUPPORTING INFORMATION
Primary data was collected directly from representatives of community businesses through a survey and follow-up in-depth interviews. This data was analysed and compared with results from previous years to assess how views change over time. Case studies were also created from information provided during interviews.
Secondary research data was sourced from a range of different organisations that support community businesses. This data was used directly to understand the size of different community market sub-sectors. Secondary data was also combined with this year’s survey responses to estimate how many community businesses operate in England, their economic contribution and staffing levels.
The purpose of this Technical Appendix is to explain how data is collected and used to derive our estimates. The Technical Appendix in three parts:
- The survey method outlines the design, sampling, implementation and analysis of survey data.
- The qualitative method describes our approach to interview fieldwork, coding and analysis.
- We also undertook a market size estimate this year. This section describes the approach and the series of assumptions made during the estimation process.
There are several supporting resources for this report are available to view and download:
COMMUNITY BUSINESS MARKET SURVEY 2022
Fieldwork
We continued to develop the Community Business Market survey first employed in 2016. The key changes we made were to insert additional questions for new topics of interest, namely the cost of living, the profile of customers by different characteristics and views on the high street and community technology. As in the 2021 iteration of the survey, the trend questions closely matched those used in surveys before the pandemic.
The survey was disseminated online late in June and remained open until late July. In total, we received 1,015 complete and valid survey responses from operational businesses – almost double those received in 2021 (548).
The survey was issued to a much larger sample of community businesses than in previous years. For example, in previous years we only contacted organisations who had received funding from Power to Change. This year’s sample included community businesses who applied for, but did not receive, funding from Power to Change, and was also distributed via partners and newsletters. This year, the survey featured a question giving participants the option to enter a prize draw, with the chance to win one of five £900 prizes.
Participants only needed to complete this question to enter the draw and were not required to complete the whole survey. Winners were chosen randomly by CFE Research in September 2022 and prizes awarded shortly afterwards. To select winners, the research team assigned a random number to each eligible contact. A random number generator was then used to determine the winners of the draw, who were then contacted via email and telephone.
As the composition of the community business sample surveyed differed this year (i.e. the significant increase in responses), we cannot say with certainty whether any changes recorded in longitudinal questions are evidence of changing views or experiences, or reflect differences in the participant profile. For example, it may be that changes are simply a more accurate representation of the whole community business market, rather than trends/changes in the market itself.
For insight, we present comparisons between the survey findings from different years of the study where they indicate a noteworthy change in the structure or behaviour of community businesses. Tests for statistical significance are used to compare data. However, readers should note differences are illustrative because of the difference in the profile of businesses between this wave of the survey and the last in 2021.
Statistical analysis techniques
Two methods of analysis were used on the survey data. Firstly, a basic frequency analysis was used to show what all respondents thought. This includes deriving statistics on the proportion of respondents answering items in questions and the mean or median results for measures like community business income, staffing and volunteer numbers. We also include some bivariate or cross break analysis comparing answers from one question by a feature of the community business such as its sector, size or location.
Secondly, regression analysis was also used because we wanted to interrogate the data to answer some specific questions like:
- What factors influence a community businesses confidence in the future? or
- Are there any features of a community business that make it more or less likely to report rising concern about energy costs from its customers?
Multiple linear regression
Two types of regression were used depending on the way a question was structured. We used multiple linear regression to assess whether answers to questions in the survey can predict the answer to another specific question, or outcome variable, of interest. In this case, our two outcome variables were:
- How confident are you in the financial prospects of your community business over the next 12 months, compared to the previous 12 months? (Table 1) and
- To what extent do you agree or disagree that a high street is, or would be, a suitable location for our business (Table 2).
Both these questions use a five-point response scale. The regression model compares responses to selected survey responses to the outcome variable and identifies statistically significant relationships. Significant relationships are those with p values lower than 0.05 and are highlighted with red text in each table’s “Sig.” column. A positive t value indicates a positive relationship. For example, confidence increases with expectations of increased trading in Table 1. A negative t value shows an inverse relationship. Table 2 shows agreement that the high street is a suitable location for their business decreases as social advantage increases.
| Tested questions | t | Sig. |
| (Constant) | -1.823 | 0.069 |
| Whether they expect income from trading / contracts to increase or decrease (q29a) | 10.384 | 0 |
| Whether they expect income from grants to increase or decrease (q29b) | 8.375 | 0 |
| If the businesses took action to boost trading revenue from existing sources in the last 12 months (q32a) | 2.449 | 0.015 |
| Whether the business improves buildings (q14a) | 2.347 | 0.019 |
| Whether people who are economically or educationally disadvantaged benefit from the business’s services (q20f) | -2.212 | 0.027 |
| The year the community business started operating (q4) | 2.049 | 0.041 |
| If the businesses took action to increase efficiency / reduce costs in the last 12 months (q33c) | -1.974 | 0.049 |
| Index of Multiple Deprivation (IMD) Rank (where 1 is most deprived) in the location in which the business is based derived from the postcode. | 1.824 | 0.069 |
| Whether the business does waste management and/or resource consumption (q14f) | 1.684 | 0.093 |
| Whether young people (aged 35 or younger) benefit from the business’s services (q20i) | 1.647 | 0.1 |
| Reported impact on greater community cohesion (q12f) | -1.531 | 0.126 |
| Whether refugees and migrants benefit from the business’s services (q20g) | 1.241 | 0.215 |
| Whether the business says it has some or a lot of environmental impact (q12e) | -1.003 | 0.316 |
| Total trading income plus grant income (derived) | -0.524 | 0.6 |
| Whether or not the business owns or manages assets (q6) | 0.435 | 0.664 |
| Number of paid staff (q15) | -0.32 | 0.749 |
| Whether customers have seen increased energy costs (q13b) | 0.265 | 0.791 |
| Whether people with a disability benefit from the business’s services (q20a) | -0.235 | 0.814 |
| Tested questions | t | Sig. |
| (Constant) | -1.769 | 0.077 |
| Whether the business improves buildings (q14a) | 3.316 | 0.001 |
| Whether or not the business owns or manages assets (q6) | -2.587 | 0.01 |
| Index of Multiple Deprivation (IMD) Rank (where 1 is most deprived) | -2.2 | 0.028 |
| Whether refugees and migrants benefit from the business’s services (q20g) | 2.123 | 0.034 |
| The year the community business started operating (q4) | 2.093 | 0.037 |
| Whether older people (aged 60 or more) benefit from the business’s services (q20g) | 1.942 | 0.053 |
| Total number of volunteers (derived) | -1.553 | 0.121 |
| Whether they expect income from grants to increase or decrease (q29b) | -1.271 | 0.204 |
| Businesses which main activity is a venue (q11) | 1.237 | 0.217 |
| Whether customers have seen increased energy costs (q13b) | -1.072 | 0.284 |
| Whether the business says it has some or a lot of environmental impact (q12e) | -0.962 | 0.337 |
| Total number of staff (derived) | 0.956 | 0.34 |
| Whether young people (aged 35 or younger) benefit from the business’s services (q20i) | -0.917 | 0.36 |
| Whether the business does waste management and/or resource consumption (q14f) | 0.863 | 0.389 |
| Whether they expect income from trading / contracts to increase or decrease (q29a) | 0.846 | 0.398 |
| If the businesses took action to increase efficiency / reduce costs in the last 12 months (q33c) | 0.83 | 0.407 |
| Whether people with a disability benefit from the business’s services (q20a) | -0.783 | 0.434 |
| Whether the business created or adapted new technologies to support their community in the last 12 months (q36c) | 0.78 | 0.436 |
| Whether the business opened up a new line of trading activity / diversified services (q32b) | 0.721 | 0.471 |
| Whether women and girls benefit from the business’s services (q20h) | 0.665 | 0.506 |
| Whether the business developed new partnerships / collaborations with other organisations in the past 12 months (q33e) | 0.631 | 0.528 |
| Reported impact on greater community cohesion (q12f) | 0.591 | 0.555 |
| Businesses which offer public facing support (q11) | -0.589 | 0.556 |
| Whether people who are economically or educationally disadvantaged benefit from the business’s services (q20f) | 0.557 | 0.578 |
| Total trading income plus grant income (derived) | -0.309 | 0.757 |
| Whether the business took action to boost trading revenue from existing sources in the past 12 months (q32a) | 0.235 | 0.814 |
Logistic regression
Logistic regression (logit) is a predictive method of analysis. It compares a binary dependent variable (yes / no; present / not present, etc.) against other independent variables. In this case, the logit analysis considers what other factors relate to three questions:
- Respondents that said support associated with the costs of energy increased over the last 12 months compared to all other respondents (Table 3)
- Respondents that said they had at least some impact[1] on improving the environment and taking climate action compared to all other respondents (Table 4), and
- Respondents who said their community business create or adapted new technologies to support your community over the last 12 months compared to all other respondents (Table 5).
Respondents who are the target for each analysis are assigned a value of 1 versus a 0 for all other values. The key output of logistic regression model is a likelihood ratio (a chi-squared test) which predicts how the target group of respondents answer a question compared to the non-target group. The output is an odds ratio which shows whether the target groups are more or less likely than others to think in a certain way or perform an action.
This statistic is the Exp(B) ratio in Tables 3 to 5. Ratios greater than one show the target group is more likely to do something or act a certain way. For example, community businesses that experienced increased demand for support from customers on the costs of energy are nearly twice as likely (Exp(B) of 1.889) as other businesses to offer public facing support. Ratios lower than 1 mean the target group is less likely than others on that measure. For example, Table 4 shows community businesses that report at least some positive impact from their services on improving the environment and taking climate action are a third less likely (Exp(B) of 0.652) to expect income from trading / contracts to increase.
| Tested question | Sig. | Exp(B) |
| Whether the business took action to boost trading revenue from existing sources in the past 12 months (q32a) | 0.001 | 0.548 |
| Businesses which offer public facing support (q11) | 0.003 | 1.889 |
| Whether the business does energy collection or supply (q14b) | 0.003 | 2.212 |
| Whether people who are economically or educationally disadvantaged benefit from the business’s services (q20f) | 0.006 | 1.345 |
| Index of Multiple Deprivation (IMD) Rank (where 1 is most deprived) | 0.012 | 1 |
| Whether refugees and migrants benefit from the business’s services (q20g) | 0.019 | 1.259 |
| Whether the business does waste management and/or resource consumption (q14f) | 0.048 | 1.452 |
| Whether the business developed new partnerships / collaborations with other organisations in the past 12 months (q33e) | 0.059 | 1.42 |
| Whether they expect income from grants to increase or decrease (q29b) | 0.142 | 0.864 |
| Businesses which main activity is a venue (q11) | 0.206 | 1.314 |
| Whether older people (aged 60 or more) benefit from the business’s services (q20g) | 0.22 | 1.128 |
| Whether the business improves buildings (q14a) | 0.428 | 1.166 |
| Whether the business says it has some or a lot of environmental impact (q12e) | 0.455 | 1.162 |
| If the businesses took action to increase efficiency / reduce costs in the last 12 months (q33c) | 0.464 | 1.144 |
| Total number of volunteers (derived) | 0.569 | 1.001 |
| Total number of staff (derived) | 0.596 | 1.005 |
| Whether they expect income from trading / contracts to increase or decrease (q29a) | 0.625 | 0.944 |
| Whether or not the business owns or manages assets (q6) | 0.765 | 0.944 |
| Whether the business opened up a new line of trading activity / diversified services (q32b) | 0.8 | 1.046 |
| Total trading income plus grant income (derived) | 0.978 | 1 |
| Constant | 0.022 | 0.265 |
| Tested question | Sig. | Exp(B) |
| Whether the business improves buildings (q14a) | 0 | 2.819 |
| Whether the business does waste management and/or resource consumption (q14f) | 0 | 3.639 |
| Whether they expect income from trading / contracts to increase or decrease (q29a) | 0.004 | 0.652 |
| Whether the business does energy collection or supply (q14b) | 0.017 | 3.01 |
| Total number of volunteers (derived) | 0.017 | 1.009 |
| Whether refugees and migrants benefit from the business’s services (q20g) | 0.028 | 1.297 |
| Whether people with a disability benefit from the business’s services (q20a) | 0.071 | 1.256 |
| Whether women and girls benefit from the business’s services (q20h) | 0.071 | 0.732 |
| Ta Whether the business took action to boost trading revenue from existing sources in the past 12 months (q32a) | 0.157 | 1.357 |
| Whether they expect income from grants to increase or decrease (q29b) | 0.161 | 1.179 |
| In which year did your community business start operating | 0.286 | 1.007 |
| Index of Multiple Deprivation (IMD) Rank (where 1 is most deprived) | 0.398 | 1 |
| Level of agreement that a high street is, or would be, a suitable location for their business (q35c) | 0.528 | 0.956 |
| Total number of staff (derived) | 0.605 | 0.995 |
| Total trading income plus grant income (derived) | 0.691 | 1 |
| Whether people who are economically or educationally disadvantaged benefit from the business’s services (q20f) | 0.714 | 1.052 |
| Whether customers have seen increased energy costs (q13b) | 0.943 | 0.985 |
| Constant | 0.306 | 0 |
| Tested question | Sig. | Exp(B) |
| Reported impact on greater community cohesion (q12f) | 0.004 | 1.753 |
| Whether refugees and migrants benefit from the business’s services (q20g) | 0.004 | 1.319 |
| Whether they expect income from grants to increase or decrease (q29b) | 0.006 | 1.308 |
| Whether the business opened up a new line of trading activity / diversified services (q32b) | 0.008 | 1.577 |
| Whether the business developed new partnerships / collaborations with other organisations in the past 12 months (q33e) | 0.019 | 1.571 |
| Whether people with a disability benefit from the business’s services (q20a) | 0.023 | 1.27 |
| Whether customers have seen increased energy costs (q13b) | 0.028 | 1.489 |
| Whether they expect income from trading / contracts to increase or decrease (q29a) | 0.074 | 0.816 |
| Index of Multiple Deprivation (IMD) Rank (where 1 is most deprived) | 0.096 | 1 |
| Businesses which main activity is a venue (q11) | 0.125 | 0.738 |
| Whether the business says it has some or a lot of environmental impact (q12e) | 0.143 | 1.348 |
| The year the community business started operating (q4) | 0.21 | 0.994 |
| Total trading income plus grant income (derived) | 0.414 | 1 |
| Total number of volunteers (derived) | 0.565 | 0.999 |
| Whether people who are economically or educationally disadvantaged benefit from the business’s services (q20f) | 0.665 | 0.951 |
| Total number of staff (derived) | 0.82 | 1.002 |
| Whether or not the business owns or manages assets (q6) | 0.971 | 0.993 |
| Constant | 0.406 | 2608.961 |
IN-DEPTH INTERVIEWS WITH COMMUNITY BUSINESS
Sub samples were selected to gain insight from community businesses who:
- Had moved, or were considering moving, to the high street or similar areas
- Were working towards climate action directly or indirectly through the community
- Had created and/or adapted technology as part of their work.
Potential interviewees were identified via a consent to re-contact question in the survey. With interviewees’ permission, the in-depth interviews were recorded to allow for full transcription. We then analysed these transcripts and coded them thematically.
All 15 interviewees consented to possible participation in a themed case study. The themes these focussed on were:
- High-streets
- Community technology
- Environmental impact
- Asset management and ownership
- The cost-of-living crisis
A draft of each organisation’s case study was shared with the corresponding interviewee who were provided the opportunity to provide feedback. Each organisation was also contacted with the opportunity to review quotes used in the summary of interview findings.
One of the interviewees requested that their community business remain anonymous during the research. The remaining 14 interviewee organisations are as follows:
- Hour Community
- Success4All CIO
- Kingsclere Community Association (The Fieldgate Centre)
- Walthamstow Toy Library and Play Centre
- The Anstice Community Trust
- Ware Arts Centre Limited (Southern Maltings)
- Kimberworth Park Community Partnership
- Stamford Villa Ltd (Cornerstone Place)
- Grange Welfare Association
- Community Care Connect CIC
- Oxfordshire Community Land Trust
- Port Bannatyne Development Trust
- Digital Woodoo
- Hale Village Hall New Forest
MARKET ESTIMATION ANALYSIS
Main principles
No population-level data collection exists that accurately lists all community businesses in England. Since its inception, the Community Business Market survey estimated the overall number of community businesses. CFE Research began refining the process for estimating market size from different data sources in 2018 and used to provide an annual update. Since 2020, we moved towards conducting this analysis every two years as this frequency better reflects changes in some of the available background data.
The broad principle is to combine data collected in the survey with data produced by Power to Change and other organisations which support community businesses. We then make a series of assumptions to calculate how many community businesses operate in England. With each iteration, we continue to refine our approach to estimating the size of the market. This Technical Appendix is intended to provide transparency about the approach to the market estimation, with the emphasis on estimate. This modelling is not an exact science.
Using data from the CBM 2022
We were delighted to receive more than 1,000 surveys this year. The questionnaire we use includes a screener question to only include businesses meeting three of five criteria. To take part in the survey, all businesses must agree that their “business is led by members of the local community”. They must also agree with two of the following four criteria:
- My business was started by members of the local community
- My business exists to meet a local need
- My business is defined by its link to a local area
- My business’s primary purpose is generating economic and social and/or environmental benefit in the local community
As a result, we can be confident that all surveyed businesses meet the main definition of a community business.
Furthermore, we added data from all businesses that were surveyed in 2021 who did not participate in 2022. We can use older data because this is contemporaneous with the secondary data we use (explained later). This means we have data from 1,340 community businesses to use in an estimation model.
We calculate several metrics from this data for 19 sector categories (See Table 9 later). These metrics are a count of community businesses per sector and then means and medians for total income, fixed assets, paid staff and volunteer numbers. These estimates are useful on their own and are used with other data in an estimation model for the overall market size, explained later.
Twine data from the Institute for Community Studies
In prior estimates, Power to Change provided data collected from certain fund applicants and grantees to estimate market size. These funding programmes are no longer live, so we turned to the Institute for Community Studies’ (ICS) Community Business Sector Overview[2] data collection (formerly known as Twine) to use in the model. The Twine dataset was originally created from Power to Change grantee and application data, so is a good proxy in the absence of the grantee application dataset.
The ICS dashboard uses data collected between 2015 and 2020. We received the underlying data so we could strip out only that collected in 2020 and then run a similar by-sector analysis as used on the Community Business Market survey data. The 14 sectors used by ICS do not exactly match the 19 used in the survey, but there is broad alignment. For the purpose of the model, the 14 ICS sectors were assumed to match the equivalent used in the survey. Five sectors used in the survey were therefore not matched.
Further, not all ICS data was assigned a sector category. We therefore coded businesses with a sector to improve the number of businesses from which we could calculate the same financial and staff market estimates derived from the CBM survey. Sector was assigned by visiting the websites of uncoded community businesses. The most valuable aspect of the ICS data is extensive coverage of fixed assets.[3]
Data from other sources
Data from seven sectors is derived from research conducted by other organisations. The frequency by which this data is collected varies. Some data is new or updated from that used when we last estimated market size in 2020. For example, The Plunkett Foundation has revised the number of community pubs, shops and cafes in operation in 2022. Other data has not been updated, but still represents a better estimate of sector size compared to that collected via the survey or by ICS. For example, the number of community-run libraries and independent community libraries was last updated in 2018 (Department for Digital, Culture, Media & Sport, 2017[4]). This source has been retained this year as we made the subjective judgement that the number of libraries listed in this data is more accurate than the number completing the survey.
| Main activity (Sector category) | 2022 and 2021 survey | ICS data | Secondary sources | |||
| n | % | n | % | n | % | |
| Arts centre or facility | 59 | 4% | 43 | 6% | – | – |
| Business support, employment, IAG, training and education | 254 | 19% | 74 | 11% | – | – |
| Cafes and shops | 120 | 9% | 46 | 7% | 413 | 8% |
| Community hubs | 346 | 26% | 227 | 34% | – | – |
| Childcare | 16 | 1% | 27 | 4% | – | – |
| Craft, industry and production | 14 | 1% | 11 | 2% | – | – |
| Digital services, consultancy or products | 17 | 1% | 8 | 1% | – | – |
| Energy | 27 | 2% | 11 | 2% | 323 | 6% |
| Environment or nature conservation | 46 | 3% | 30 | 4% | – | – |
| Finance | 16 | 1% | – | – | – | – |
| Food, catering and production | 46 | 3% | 37 | 6% | 272 | 5% |
| Health, social care and wellbeing | 110 | 8% | 63 | 9% | – | – |
| Housing | 43 | 3% | 14 | 2% | 550 | 11% |
| Libraries | 6 | 0% | – | – | 350 | 7% |
| Pubs | 64 | 5% | 22 | 3% | 123 | 2% |
| Sports and leisure | 78 | 6% | 57 | 9% | – | – |
| Transport | 20 | 1% | – | – | 350 | 7% |
| Village halls | 25 | 2% | – | – | 2,700 | 53% |
| Other | 33 | 2% | – | – | – | – |
| Total | 1,340 | 670 | 5,081 |
External data describing the composition of a sector from other sources is particularly useful in the model, as it provides a foundation upon which estimates for other sectors can be calculated. Collectively, the seven sectors in Table 7 represent those for which more reliable external estimates exist. They are referred to as ‘known’ sectors for the purpose of explaining the estimation model.
The relative size of each of the seven known sectors acts as an initial distributive template for the wider market. For example, it is known that the sizes of the housing, transport and libraries sectors are roughly the same. This can therefore be used to calibrate survey returns from the CBMS19 and Social Enterprise UK (SEUK) 2018 results (described in more detail later, see SEUK, 2018).
| Sector | Sources | Businesses (n) | Income (£ per CB) | Staff (n per CB) |
Volunteers (n per CB) |
| Housing[5] | Community Land Trusts (2022)
Cohousing (2022) |
550 | n/a | n/a | n/a |
| Transport[6] | Department for Transport (2020) | 350 | £6,500 | n/a | n/a |
| Libraries[7] | Department for Digital, Culture, Media & Sport listing (2018) | 350 | n/a | n/a | n/a |
| Pubs[8] | Plunkett Foundation (2022) | 123 | n/a | n/a | n/a |
| Cafés and shops[9] | 413 | £156,000 | 1.0 | 14.4 | |
| Food[10] | Community Supported Agriculture (2022)
Sustainweb (2022) |
272 | n/a | n/a | n/a |
| Energy[11] | Community Energy Scotland (2022) | 323 | £116,000 | 1.3 | 5.8 |
| Total of known businesses | 2,381 | ||||
The issue of village halls
There are a lot of village halls and the survey collects data from few of them. Later, we explain why we deal with village halls separately in the model because they exert strong influence on the estimate.
Action with Communities in Rural England (ACRE) assessed the impact of Covid-19 on village halls[12] which updated some figures in their 2019 survey. We adopted the 2020 method of estimating which village halls operated as community businesses using three criteria:
- Excluding those operating as church halls or rooms, parish or town council halls, halls used jointly with a school, reading rooms, Women’s Institute halls or Scout/Guide halls.
- Excluding all run by ‘members or trustees with no power for user groups to appoint trustees or a parochial church council’.
- Including halls whose annual income from ‘hiring charges’ was £10,000 or greater, or who derived £5,000 or more from ‘renting for public services’, ‘renting to private or commercial concerns’ or from ‘enterprise and trading’.
A quarter (25%) of surveyed village halls met all these criteria. Applying this proportion[13] to ACRE’s estimate of 10,700 village halls in England gives a rounded total of 2,700 operating as community businesses.
Estimating the size of the sector
The size of the sector is estimated in several stages.
Table 6 provides sector counts for the survey and, where present, the ICS data. If there are counts for the survey and the ICS data, an average is taken for that sector. Where there is only data from the survey, that count is used to represent that sector. These sectors are identified by an * in Table 8’s second column. This creates a new estimated count and distribution for each sector.
- Table 7 shows there are 2,381 businesses in our seven known sectors. The survey included 320 businesses from these sectors, representing an estimated response rate of 13.4% (or 0.1344).
- We assume that this response rate applies to all other sectors except village halls. The average number of businesses in Stage 1 is then divided by the response rate to create the sector estimate. For example, the average number of arts centres or facilities is 51 which, when divided by 0.1344, is an estimate of 379. The sector estimate is the number of businesses from the secondary data for the seven known sectors.
- Village halls are excluded from the calculation because it has a very large influence on the response rate. Including village halls would result in an estimated response rate of 6.8% (345 / 5,081). This would more than double sector estimates. For example, the estimate for arts centres or facilities would increase to 751. Instead, we add village halls to the estimate after all the other calculations are made.
| Main activity (Sector category) | Average of survey and ICS | Secondary data | Sector Estimate | ||
| N | % | n | n | % | |
| Arts centre or facility | 51 | 5% | – | 379 | 3% |
| Business support, employment, IAG, training and education | 164 | 15% | – | 1,220 | 11% |
| Cafes and shops | 83 | 8% | 413 | 413 | 4% |
| Community hubs | 287 | 30% | – | 2,132 | 19% |
| Childcare | 22 | 1% | – | 160 | 1% |
| Craft, industry and production | 13 | 1% | – | 93 | 1% |
| Digital services, consultancy or products | 13 | 1% | – | 93 | 1% |
| Energy | 19 | 2% | 323 | 323 | 3% |
| Environment or nature conservation | 38 | 4% | – | 283 | 3% |
| Finance | 16* | 1% | – | 119 | 1% |
| Food, catering and production | 42 | 4% | 272 | 272 | 2% |
| Health, social care and wellbeing | 87 | 9% | – | 644 | 6% |
| Housing | 29 | 3% | 550 | 550 | 5% |
| Libraries | 6* | 0% | 350 | 350 | 3% |
| Pubs | 43 | 4% | 123 | 123 | 1% |
| Sports and leisure | 68 | 7% | – | 502 | 5% |
| Transport | 20* | 1% | 350 | 350 | 3% |
| Village halls | 25* | 2% | (2,700) | 2,700 | 25% |
| Other | 33* | 1% | – | 246 | 2% |
| Total | 1,055 | 2,381 (5,081) | 10,952 | ||
Estimating financial and staffing data
For total income, staffing and volunteers estimates, the Community Business Market survey is usually the best data source available. The only exceptions are estimates for the café and shops, energy and village hall sectors, which are drawn from secondary data.
Sector estimates for total income, staff and volunteers are calculated through multiplying the estimated number of businesses in Table 8 by the median income, staff numbers or volunteers reported in the survey. For the café and shops, energy and village hall sectors, the average reported in the secondary data is used instead of the survey median.
The only different calculation is for fixed assets. The ICS data contains more observations for median assets than the survey so that data is used instead. Note the data from the housing sector is based on data from a low base of 11 community businesses.
As the sector size estimates are reliant on a series of assumptions, total numbers in Table 9 are rounded to the nearest hundred for business, staff and volunteer estimates, and to the nearest million for total income and assets.
| Main activity (sector category) | Number of businesses | Sector income | Sector fixed assets | Sector staffing | |||||||
| n (2022) | N (2020) | % share | % share – VH | (£m) | Median | (£m) | Median | Paid staff (s) | Volunteers (v) | ratio v:s | |
| Arts centre or facility | 400 | 600 | 3% | 5% | £59 | £155,000 | £15 | £40,000 | 1,900 | 4,600 | 2.4 |
| Business support, employment, IAG, training & education | 1,200 | 1,200 | 11% | 15% | £122 | £100,000 | £24 | £20,000 | 6,100 | 9,800 | 1.6 |
| Cafes and shops | 400 | 400 | 4% | 5% | £64 | £156,000 | £13 | £30,800 | 400 | 5,900 | 14.8 |
| Community hubs | 2,100 | 2,300 | 19% | 26% | £234 | £110,000 | £229 | £107,400 | 10,700 | 40,500 | 3.8 |
| Childcare | 200 | 100 | 1% | 2% | £15 | £93,800 | £4 | £24,800 | 3,000 | 1,600 | 0.5 |
| Craft, industry and production | 100 | 100 | 1% | 1% | £1 | £11,200 | 100 | 600 | 6.0 | ||
| Digital services, consultancy or products | 100 | 0 | 1% | 1% | £7 | £80,000 | 400 | 800 | 2.0 | ||
| Energy | 300 | 300 | 3% | 4% | £37 | £116,000 | 400 | 1,900 | 4.8 | ||
| Environment or nature conservation | 300 | 400 | 3% | 3% | £18 | £62,500 | £4 | £14,000 | 800 | 4,200 | 5.3 |
| Finance | 100 | 100 | 1% | 1% | £15 | £122,700 | 400 | 1,000 | 2.5 | ||
| Food, catering and production | 300 | 300 | 2% | 3% | £17 | £61,300 | £7 | £25,200 | 800 | 5,400 | 6.8 |
| Health, social care and wellbeing | 600 | 700 | 6% | 8% | £129 | £200,000 | £15 | £24,000 | 5,800 | 8,400 | 1.4 |
| Housing | 600 | 300 | 5% | 7% | £22 | £39,800 | £295 | £537,000 | 600 | 4,400 | 7.3 |
| Libraries | 400 | 400 | 3% | 4% | £12 | £35,000 | 700 | 8,800 | 12.6 | ||
| Pubs | 100 | 100 | 1% | 1% | £6 | £46,300 | £45 | £362,600 | 800 | 1,500 | 1.9 |
| Sports and leisure | 500 | 600 | 5% | 6% | £39 | £77,500 | £93 | £184,500 | 1,000 | 7,500 | 7.5 |
| Transport | 400 | 500 | 3% | 4% | £105 | £300,600 | 2,100 | 4,900 | 2.3 | ||
| Village halls | 2,700 | 2,700 | 25% | £47 | £17,500 | 5,400 | 10,800 | 2.0 | |||
| Other | 200 | 300 | 2% | 3% | £10 | £40,000 | 400 | 3,700 | 9.3 | ||
| Total (may not sum due to rounding) | 11,000 | 11,300 | 8,300 | £959 | £744 | 41,800 | 126,200 | 3.0 | |||
Caveats based on assumptions
The model is reliant on strong assumptions.
Response rate: The strongest is applying the overall response rate from all known sectors to all other sectors. The individual response rates from known sectors (excluding village halls) varies between 2% for libraries to 52% for pubs. The research data on pubs and shops is recent whereas that for libraries has not been updated for some time. Applying a flat percentage as a response rate to all estimated sectors therefore assumes a great deal.
The model is also very sensitive to reported response rate as per the reason for removing village halls. A percentage point difference roughly changes estimates by 500 businesses. For example, a response rate of 12.4% would increase the overall market estimate to 11,500 community businesses.
Limited data: The model’s sensitivity is also influenced by the small number of observations available to estimate the income, asset and staffing and volunteer numbers for some sectors (such as the assets for the 11 housing community businesses noted earlier). This is one reason why the median rather than average figure is used in the model. Nevertheless, the median is still affected by small numbers of observations.
Total market estimates are calculated by summing all data from sectors where an estimate is present. These totals are therefore influenced by any changes in the underlying data within each sector. When this data changes a lot, so does the model.
Coding: Coding businesses to a sector can also be subjective, especially when a business delivers multiple services. The prime example is the community hubs sector. For example, the way the survey participant classes the business may differ from that given by another representative from the same business. Similarly, each Power to Change grantee dataset codes main business sector differently (if at all). While a consistent manual code was applied as part of the data processing method, there is room for misclassification depending on who completes a Power to Change grant application or monitoring form.
We continue to refine and improve the model whenever possible.
Supporting resources
- Market sizing tool
- Full anonymous survey data set
- Survey questions
- Interview discussion guide copy
If you would like to reference this report, please use the following citation: ‘Community Business Market Report 2022, CFE Research and Power to Change (2022) www.powertochange.org.uk/market-reports/market-report-2022‘
For further information or any questions about the research, you can contact Chloe.Nelson@powertochange.org.uk, Head of Impact and Learning at Power to Change, and John.Higton@cfe.org.uk, Research Director at CFE Research.
Footnotes
[1] Aggregated together those recording some impact and a lot of impact
[2] https://icstudies.org.uk/dashboards
[3] Data from Twine is taken from balance sheets submitted by community businesses, from which the value of fixed assets in the sector is calculated. When we state ‘fixed assets owned by the sector’ in the research, we are making an assumption that any fixed assets included on balance sheets are owned rather than managed as long-term leases.
[4] See https://www.gov.uk/government/publications/public-libraries-in-england-basic-dataset, (accessed on 24 October 2022).
[5] Derived from counts in England on two websites: http://www.communitylandtrusts.org.uk/what-is-a-clt/about-clts and https://cohousing.org.uk/members-directory/ (accessed 22 September, 2022)
[6] Community bus and transport operators derived from https://www.gov.uk/government/publications/bus-service-operators-grant-payments-to-english-operators-from-2010-onwards (accessed 22 September, 2022)
[7] DCMS figures last updated in January 2018. https://www.gov.uk/government/publications/public-libraries-in-england-basic-dataset, (accessed on 24 October 2022)
[8] Plunkett Foundation Report on Community Pubs. https://plunkett.co.uk/wp-content/uploads/Plunkett-Urban-Pubs-report.pdf, (accessed 22 September, 2022)
[9] Plunkett Foundation Report on Community Shops. https://plunkett.co.uk/wp-content/uploads/Plunkett_BB-2020_Shops.pdf, (accessed 22 September, 2022). Café numbers are estimated from Plunkett’s total of shops in England. The survey returns show that the number of sole cafés is around 30% that of shops. This percentage is applied to the shops numbers to derive an estimate for both.
[10] Estimates drawn from maps on two websites: https://communitysupportedagriculture.org.uk/ and https://www.sustainweb.org/foodcoops/finder/
[11] https://communityenergyscotland.org.uk/news/community-energy-state-of-the-sector-2022-report-read-it-now/. Although now produced by Community Energy Scotland, the data presented is for England.
[12] Archer, T., and Skropke, C. (2021) The Impact of Covid-19 on Village and Community Halls in England. ACRE. https://acre.org.uk/wp-content/uploads/impact-of-covid-on-village-halls-final-report-june-2021.pdf (accessed 22 September, 2022)
[13] To one decimal place, the proportion is 25.3%
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