Two-thirds of sharing-economy businesses that responded to the Business Impact of Coronavirus (COVID-19) Survey (BICS) for the period 27 July to 9 August 2020 reported a decrease in turnover compared to normal expectations for the time of year; this can be compared with 50.8% of all businesses.
The proportions of sharing-economy businesses that applied for, received and used financial support from government schemes and initiatives for the coronavirus (COVID-19) appears similar to the proportion of businesses that applied for, received and used this support across all industries.
The proportion of employees on furlough leave in the period 27 July to 9 August 2020 was similar for the sharing-economy businesses responding to BICS, compared to all industries responding; however, the proportion working remotely was much higher for sharing-economy businesses with just under four-fifths of sharing-economy employees working remotely, compared with 39% of employees across all industries.
Data from the Annual Business Survey (ABS) show that sharing-economy businesses appear to have higher median purchasing costs than non-sharing businesses; higher median advertising and marketing costs contribute to this difference.
E-commerce Survey results show that sharing-economy businesses are more likely to generate turnover via their own website or app than non-sharing-economy businesses.
The Office for National Statistics's (ONS’s) definition of what should be included in the sharing economy is still under discussion; we currently include companies where there is a digital platform and people make use of under-utilised assets, for example, sharing car journeys, renting out parking or accommodation space, and sharing of skills such as DIY.
This article presents new analysis from the Business Impact of Coronavirus (COVID-19) Survey (BICS) for the period 27 July to 9 August 2020 giving an early indication of how the coronavirus pandemic may be affecting sharing-economy businesses.
Our previous article presented analysis from 2016 from the E-commerce Survey and Annual Business Survey (ABS). The Office for National Statistics (ONS) has continued to sample known sharing-economy businesses for these surveys, and this article presents descriptive statistics from 2016 and 2018 (see Section 4: Comparison of sharing-economy businesses with non-sharing-economy businesses in annual surveys). The responses from these businesses have been compared with responses from non-sharing-economy businesses with similar levels of employment and industrial classification. More detail about this can be found in the previous article and in Section 8: Data sources and quality.
There is no universally accepted definition of the sharing economy. Although we continue to use our working definition that was developed in 2016 in this analysis, we discuss changes in the sharing economy, and we are collecting more evidence to understand whether our definition should be updated.
It is important to note that the findings presented in this article are based on a small number of responses from known sharing-economy businesses. These findings provide us with a basis for further research. Users should refer to the summary of strengths and limitations in Section 8: Data sources and quality to understand how to interpret these results.Back to table of contents
The Office for National Statistics (ONS) sampled known sharing-economy businesses as part of the E-commerce Survey and Annual Business Survey (ABS) from 2016 onwards to produce some descriptive statistics. The latest results are for the 2018 reporting year. Results from 2016 were presented in our previous article; we have made some small changes in methodology, which are described in Section 8: Data sources and quality.
The results from the 2016 and 2018 ABS show that median turnover of known sharing-economy businesses was £1 million in 2016 and £1.4 million in 2018. For equivalent non-sharing-economy businesses of the same Standard Industrial Classification (SIC) 2007 and employment size, the median turnover was £0.7 million in 2016 and £0.8 million in 2018.
Sharing-economy businesses generate a bigger proportion of their turnover via their own website or app than equivalent non-sharing businesses; data from the 2018 E-commerce Survey suggest that around two-thirds (67%) of sharing-economy businesses received orders from customers for goods and services via a website or app compared with 16% of equivalent non-sharing businesses. Businesses may generate income from digital trade by connecting users with goods and services as captured within the E-commerce Survey. They may also monitise other aspects of digital trade, for example, using consumer information to target advertising or by matching users and receiving a fee or transaction share. This indirect monitisation presents a measurement challenge, as primary and secondary income and transactions may involve different actors. For instance, intermediaries who transact with consumers may also generate income by allowing businesses to advertise to its users via the platform (Gonzalez and Jouanjean, 2017).
Over three-fifths (64%) of turnover generated by the sharing-economy businesses responding to the 2018 E-commerce survey (n=30) was reported to be from orders received via a website or app. Over four-fifths (85%) of this turnover (or 54% of total turnover) was generated through business-to-consumer transactions. Almost all (99%) of the turnover generated via the website or app (or 63% of total turnover) was from the businesses’ own websites or apps (rather than a third-party marketplace). These figures are apportioned using the turnover recorded for each reporting unit on the Inter-Departmental Business Register (IDBR).
The results from the 2016 and 2018 ABS show that median employment costs of known sharing-economy businesses were £0.8 million in 2016 and £1.1 million in 2018. Generally, the sharing-economy businesses with more employees had higher employment costs. The median number of employees in the sharing-economy businesses was 13 in 2016 and 19 in 2018.
For equivalent non-sharing-economy businesses of the same SIC and employment size, the median employment cost was £0.25 million in 2016 and £0.29 million in 2018.
“Employment costs” here relates to employees who work for the platform, rather than individuals who find work through the platform; ABS defines an employee as anyone aged 16 years or over that an organisation pays directly from its payroll(s), in return for carrying out a full-time or part-time job or being on a training scheme. This also includes those temporarily absent but still being paid, for example, those on maternity leave. Voluntary workers, self-employed workers, subcontractors and working owners not paid under Pay As You Earn (PAYE) are beyond the scope of ABS.
Employees of the sharing economy that are not paid directly via PAYE are not included in the estimates of employment within the current analysis; however, this is something that we hope to explore further within our future research. Intermediaries provide a unique opportunity for individuals to work flexibly, seek supplementary or additional income, and have freedom to choose work, but these flexible arrangements also lead to concerns about labour market protection against low wages and poor job quality. With a diverse range of new forms of employment, robust classification of employment is required to aid the measurement of platform work to support policy.
It is also worth noting that ABS uses the IDBR as its sampling frame. Work is ongoing to address under-coverage of micro-businesses in the IDBR, which should improve future estimates of employee costs. More detail can be found in Section 8: Data sources and quality.
The results from the 2016 and 2018 ABS suggest that sharing-economy businesses have higher purchasing expenditure than equivalent non-sharing-economy businesses. Median purchasing expenditure of known sharing-economy businesses was £0.64 million in 2016 and £0.60 million in 2018. Median purchasing expenditure of equivalent non-sharing-economy businesses was £0.07 million in 2016 and £0.08 million in 2018.
Our previous article explains that “Advertisement and marketing costs, energy and water costs, subcontractor costs and other services costs, are included within total purchases. Many sharing-economy businesses have relatively high subcontractor costs. This could also be explained by the fact that these businesses are in their infancy, or that they are more likely to hire IT/Tech contractors.” Subcontractor costs have not been investigated in detail in this statistical analysis; however, the next section investigates advertising and marketing costs.
Advertising and marketing
Advertising and marketing costs are included within purchases, and we have seen that purchases costs are higher for sharing-economy businesses than non-sharing-economy businesses.
The emergence of new economies that have overarching characteristics, such as digitalisation and connecting users, can make it a difficult to gain a full understanding of the complex layers and unique characteristics that differentiate them. Businesses that may be considered as part of the sharing economy may also share characteristics with businesses that are considered part of the gig economy, digital economy, platform economy or circular economy. Furthermore, for the sharing economy there is no universal definition that makes the task of gaining a full understanding arduous for both consumers and industry people.
Sharing-economy businesses may use advertising and marketing to promote goods and services to users much like traditional businesses, but they may also use advertising and marketing to educate users or investors about how the sharing economy works and how they may best utilise goods, services and assets that the business has to offer. (Coyle and O’Connor, 2019) suggests that smaller sharing-economy businesses may even utilise other sharing-economy businesses to do this. Our previous research suggests that most sharing-economy businesses are startups, which may be less well-known and further require the use of advertising and marketing to promote their business compared with established businesses.
The results from the 2016 and 2018 ABS suggest that sharing-economy businesses have higher advertising and marketing expenditure than equivalent non-sharing-economy businesses. Median advertising and marketing expenditure of known sharing-economy businesses was £0.07 million in 2016 and £0.11 million in 2018. Median advertising expenditure of equivalent non-sharing-economy businesses was £0.01 million in 2016 and £0.008 million in 2018.
Advancements in digital technology provide businesses with an opportunity to adapt advertising and marketing strategies, with many businesses now opting to utilise media platforms and digital channels to advertise their business or goods and services. When compared with traditional methods of advertising and marketing, media platforms and digital channels offer businesses competitive pricing and easy access to a broad audience.
The previous publication showed that sharing-economy businesses were more connected than non-sharing-economy businesses, with sharing-economy businesses more likely to have multimedia content-sharing websites, business blogs or microblogs, social networks and websites, and this trend continues for the 2018 data.
Businesses may use a range of media platforms to reach and engage users with their business; however, the limitation is that as platforms increase or decrease popularity, advertising and marketing strategies require evolution to remain relevant and continue reaching the intended audience.
Figure 11 suggests that the use of blogs among sharing-economy businesses has reduced between 2016 and 2018. This pattern can also be seen in the results for all businesses in Section 8 of the E-commerce and ICT activity, UK, 2018 bulletin; the use of blogs among all businesses has reduced from 41.9% in 2016 to 32.4% in 2018.
However, a possible explanation for this may be the term “micro blogs” used within the question asked. Broad, intangible or new terms require consensual understanding between individuals for complete understanding. Absence of this common knowledge can lead to mis-interpretation or mis-representation of what is being asked. Our research suggests that respondents would more commonly use the terms “social media” or “Twitter” than the phrase “microblogs”.Back to table of contents
Our working definition of the “sharing economy” was adopted in 2016 and was developed following consultation with businesses and individuals participating in the sharing economy. In addition to the definition, a decision tree was developed to help identify sharing-economy businesses based on a set framework. The Office for National Statistics (ONS) sampled sharing-economy businesses as part of the E-commerce Survey and Annual Business Survey (ABS) from 2016 onwards to produce some descriptive statistics.
In this article, we define the sharing economy as “the sharing of under-used assets through completing peer-to-peer transactions that are only viable through digital intermediation, allowing parties to benefit from usage outside of the primary use of that asset.”
Digital intermediation via a digital platform enables efficient matching of multiple suppliers and customers, for example, ride-sharing platforms like BlaBlaCar match drivers and passengers. However, a distinction is made between existing car owners filling the spare seats in their car on their regular journeys (and therefore sharing an under-used asset) and a supplier who purchases a vehicle in response to demand for rides, making journeys that they would not otherwise have made. Within the UK, Uber is a private hire transport business and under our definition is not part of the sharing economy as cars are bought for commercial use and not considered an under-used asset.
The sharing economy is evolving
The previous publication noted that the definition was likely to evolve alongside our understanding of how to measure sharing-economy activities. Recent advancements with sharing-economy research have highlighted some larger companies that fell within our definition in the past now contain business-to-consumer as well as consumer-to-consumer transactions. We refer to these businesses as hybrid platforms. Our definition limits the scope of the sharing economy to peer-to-peer transactions, connected via digital platforms, where users gain temporary access to the goods without any transfer of ownership.
A consequence of this decision is that the market for second-hand goods is excluded from the scope of the current definition (for example, eBay and Etsy), as are hybrid platforms. The exclusion of hybrid platforms also means that businesses like Airbnb that are widely used when describing the sharing economy are not be included in these measurements of the sharing economy. This is as the primary function of the under-used asset changes from private to commercial use.
Other definitions of the sharing economy
Although there are many different definitions of the sharing economy, operating through a website or app using technology to match users with providers is commonly accepted as a requirement.
Research conducted by Diane Coyle and Shane O’Connor reveals “that smaller platforms also transact via larger platforms and this activity is seen by members as an inherent part of the sharing economy ecosystem”. This research notes that while business-to-consumer transactions alone capture final demand for national accounts, for other measurement reasons a broader definition is appropriate. Furthermore, understanding the composition of income for sharing-economy and digital businesses will provide further insight into digital trade activities and the relationship between different income sources and net revenues.
Debate continues regarding the inclusion of intermediaries or platforms whereby the ownership of the under-used asset transfers between users (for example, eBay, Etsy or marketplace). The importance of capturing these transactions to UK taxation is highlighted by HM Revenue and Customs (HMRC); however, the prerequisite to distinguish between personal and “for profit” transactions would require a distinction between commercial and personal use of the asset. This is complex and requires classification of the intended use of the asset.
We have started to explore the potential impact of updating the definition to include hybrid and transfer ownership platforms upon the current data. Within our initial exploration, only businesses that were previously known to the researcher as hybrid or transfer of ownership platforms and sampled in ABS could be included to reflect the changes to the definition, resulting in only a small number of additional businesses being added to the sample influencing the data for each condition. To effectively compare the economic impact of changes to the current sharing-economy definition, it is first necessary to compile a census of platforms. This would provide an opportunity to capture all platforms that meet the characteristics of the current and new definitions or to design a sampling frame allowing more robust measurement and comparisons.
Identifying digital platforms is also challenging as these businesses fit into many Standard Industrial Classification (SIC) 2007 codes. A variety of methods have been employed by National Statistical Institutes (NSIs) attempting to identify and survey digital platforms. Recently, Statistics Netherlands (CBS) have conducted a study of online platforms, using text mining and existing populations to identify platforms. CBS are using this initial study to refine their approach. Questions within such a survey could be used to identify whether businesses were a part of the sharing-economy. This is an approach that we could consider in the future, but our first step before deciding the best approach will be to investigate how to define the population. More information about our future developments can be found in Section 9: Future developments.Back to table of contents
Business Impact of COVID-19 Survey (BICS) results
Dataset | Released 27 August 2020
Responses from the new voluntary fortnightly business survey, which captures businesses’ responses on how their turnover, workforce prices, trade and business resilience have been affected in the two-week reference period.
Non-financial business economy, UK: Sections A to S
Dataset | Released 15 May 2020
Annual data on size and growth within the UK non-financial business sectors as measured by the Annual Business Survey (ABS), broken down to four-digit Standard Industrial Classification (SIC) 2007.
E-commerce and ICT activity
Dataset | Released 29 November 2019
Use of information and communication technology (ICT) and e-commerce activity by UK businesses. Annual data on e-commerce sales and how businesses are using the internet.
There is no universally accepted definition of the sharing economy. In this article, we define the sharing economy as “the sharing of under-used assets through completing peer-to-peer transactions that are only viable through digital intermediation, allowing parties to benefit from usage outside of the primary use of that asset.” Further information about this definition can be found in Section 8: Data sources and quality.
A digital platform is a digital service that facilitates interactions between two or more distinct but interdependent sets of users who interact through the service via the internet.
An e-commerce transaction is defined as the sale or purchase of goods or services, conducted over computer networks by methods specifically designed for the purpose of receiving or placing of orders. It is important to note, under this definition, that the goods or services are ordered by those methods, but the payment and the ultimate delivery of the goods or services do not have to be conducted online.Back to table of contents
Summary of strengths and limitations
It is important to note that this is a sample of sharing-economy businesses and not a comprehensive list, as we are not aware of all the businesses in the sharing economy.
There is no universal definition of the sharing economy; these are businesses that we have identified as likely to be part of the sharing economy using our decision tree.
The results are based on a small number of responses. Results reflect the characteristics of those who responded and not necessarily the wider business population.
The 2016 Annual Business Survey (ABS) data contain responses from 38 sharing-economy businesses out of 69 businesses included in the sample. The 2018 ABS data contain responses from 34 sharing-economy businesses out of 66 businesses included in the sample.
The 2016 E-commerce Survey data contain responses from 22 sharing-economy businesses out of 63 businesses included in the sample. The 2018 E-commerce Survey data contain responses from 30 sharing-economy businesses out of 64 businesses included in the sample. We have used imputation methods to estimate missing data in our E-commerce Survey results.
The Business Impact of Coronavirus (COVID-19) Survey (BICS) results are based on 12 sharing-economy businesses that responded to Wave 11 out of 60 included in the sample. There were 21 out of the 60 businesses that responded to any wave from Wave 7 to 12, and qualitative analysis was conducted on these data.
This analysis cannot be considered to be representative of the sharing economy as a whole. However, it provides a picture that can be further developed with future research. These initial findings help us to explore how to measure the sharing economy and to invite discussion and collaboration with other agencies.
Definition and classification challenges
Our working definition of the “sharing economy” was adopted in 2016 and was developed following consultation with businesses and individuals participating in the sharing economy. In addition to the definition, a decision tree was developed that helps identify sharing-economy businesses based on a set framework. This decision tree helps to determine whether a given business is likely to be part of the sharing economy. Classification is still subjective despite the definition and decision tree.
Another challenge comes from how we classify activities and businesses. As previously mentioned, the sharing-economy platforms have an innovative business model that facilitates peer-to-peer transactions, complicating how we classify businesses. The Office for National Statistics (ONS) uses the Standard Industrial Classification (SIC) 2007 as a basis for collecting data and publishing official statistics on business. Sharing-economy platforms do not easily fit this classification system, as it is possible for a business in any given industry to contribute to the sharing economy. Similarly, the ONS uses the Standard Occupational Classification (SOC) 2010, which might not always be representative of some sharing-economy occupations.
Without a SIC to identify businesses, we rely on research to identify a list of businesses to sample. Further research is planned to improve our methods of identifying sharing-economy businesses.
Our sample is selected from businesses on the Inter-Departmental Business Register (IDBR). The IDBR provides the main sampling frame for surveys of businesses carried out by the ONS and other government departments and covers around 2.7 million businesses in all sectors of the economy. The sources of input for the IDBR are Value Added Tax (VAT) and Pay As You Earn (PAYE) records from HM Revenue and Customs (HMRC). Additional information comes from Companies House. Therefore, small and micro-businesses do not need to register as a company, and those businesses that have turnover below the thresholds are less likely to appear on the IDBR. The ONS is working to improve this and when this is rectified, it might impact our understanding of sharing-economy businesses.
Business Impact of Coronavirus (COVID-19) Survey
BICS is voluntary and responses are qualitative, which should be treated with caution; results reflect the characteristics of those businesses that responded and not necessarily the wider business population. The survey was designed to give an indication of the impact of the coronavirus pandemic on businesses and a timelier estimate than other surveys.
The results presented in this article are final results from Wave 11 of BICS are for the period 27 July to 9 August 2020, which closed on 23 August 2020. All sharing-economy businesses responding to Wave 11 were trading in the reference period. Analysis of the free-text comments was conducted for the 21 businesses that responded to any wave from Waves 7 to 12. These responses relate to the period from 1 June to 23 August 2020. Some businesses responded to multiple waves; their responses were treated as one combined response. Themes were identified among the survey responses.
The findings are based on a small number of responses; 60 known sharing-economy businesses were included in the BICS sample and 12 businesses returned responses for Wave 11. This corresponds to a response rate of 20%. The overall response rate for all businesses sampled for BICS Wave 11 was 24.8%.
Figures 1, 3 and 4 show a comparison of sharing-economy businesses with all businesses from BICS. The all-businesses category includes sharing-economy businesses, but sharing-economy businesses only account for 0.2% of the overall responses. Sharing-economy businesses from the following sectors responded:
- accommodation and food service activities
- administrative and support service activities
- information and communication
- professional, scientific and technical activities
- real-estate activities
Annual Business Survey (ABS)
ABS covers the non-financial business economy. Known sharing-economy businesses were added to the sample of ABS for the 2016 reporting year onwards.
ABS provisional national results are published 11 months after the end of the calendar year reference period, revised national results are published 16 to 17 months after the end of the reference period, and the final revised results are published 28 to 29 months after the end of the reference period. Planned revisions usually arise from either the receipt of additional data from late-responding businesses or the correction of errors to existing data by businesses responding to ABS. We are using final revised results for 2016 and provisional results for 2018. The previous publication used an earlier version of the 2016 data.
We have 69 businesses on our 2016 reference list, 38 of which returned responses. This is fewer businesses than in our previous publication, as we have revised the list of businesses according to our evolving knowledge of the sharing economy. We have also removed businesses that were dead or out of scope of ABS. The 2018 ABS data contain responses from 34 sharing-economy businesses out of 66 businesses included in the sample. We also had 16 sharing-economy businesses return data for 2017, but the number of returns was lower than 2016 and 2018 and the proportion of missing data was much higher, and we felt that there was too much uncertainty in the results. We have conducted some preliminary research into appropriate imputation methods to account for non-response and will continue to investigate this as we collect more data.
Responses from equivalent non-sharing-economy businesses were analysed for comparison. Businesses with the same SIC and employment group were identified, and 7,267 and 7,803 businesses were selected from the 2016 and 2018 ABS data respectively.
The E-commerce Survey of UK businesses covers the manufacturing, production, construction and distribution sectors as well as parts of the service sectors. Known sharing-economy businesses were included in the sample of this survey for the 2016 survey onwards.
In our previous article, the findings relating to the sharing economy from the E-commerce Survey were based on a total of 81 businesses; 24 of these responded, and data for the remaining businesses were imputed manually using research to estimate the “yes” or “no” responses. When data could not be imputed manually (when uncertain), the response was left empty.
Our knowledge of sharing-economy businesses has evolved, and we have revised the original list so that it now contains 63 businesses, including 22 that responded (2016 data). This means that the proportion of imputed data is smaller, but the results follow the same pattern and the findings remain the same. There were 64 known sharing-economy businesses included in the 2018 E-commerce Survey sample and 30 of these responded. Our analysis of the 2017 and 2018 responses took place in 2020, which made research using businesses’ websites to impute the missing data less reliable. For this reason, we used a different method to impute non-response for 2017 and 2018.
Non-response for 2017 was imputed using the responses for each business to 2016 and 2018 to estimate a response for 2017. If no information was available, the response was left blank. For 2018, we used data from 2016 and 2017 and some research conducted by visiting the companies’ websites. Again, uncertain cells remained blank. Figure 11 does not contain 2017 results as most of the variables were not asked on the 2017 survey.
A sample of non-sharing-economy businesses were selected from the E-commerce Survey for comparison with the sharing-economy businesses. In this article, we adjusted the selection method slightly to make it more easily reproducible. This follows the same principle that all businesses with the same SIC and similar employments were selected. For each sharing-economy business, we selected every business with the same SIC and a similar number of employees (within plus or minus 40 employees). We selected 878 similar businesses from the 2018 survey using this method.Back to table of contents
Classification and alternative data sources
We have started to conduct preliminary research to attempt to identify additional sharing-economy businesses on the Inter-Departmental Business Register (IDBR) and plan to consolidate this research over the next year.
Our current data sources cover business statistics. It is also important to understand who the individuals participating in the sharing economy are and whether they are being disadvantaged or vulnerable. Likewise, it is important to understand how much individuals are benefitting from the sharing economy by paying lower prices for services, earning subsidiary income from making money from their under-used assets and saving time because of platform technology efficiently matching them with services. However, these are long-term ideals outside of the scope of our current research.
Pilot questions have previously been added to the Living Costs and Food Survey (LCF) and to the Labour Force Survey (LFS). However, analysis of the responses to the pilot questions showed that questions needed further development and extensive testing to ensure that the concepts are universally understood by respondents.
Time-use surveys are used to collect information on how much time individuals spend undertaking different activities. A 2020 time-use study was carried out exclusively for adults aged 18 years and over under coronavirus (COVID-19) restrictions (between 28 March and 26 April 2020). Time measurements are compiled from activities that have been recorded by respondents in a time-diary study. A range of time categories have been defined to report on how people use their time.
These categories have been developed to capture activities performed for sharing-economy purposes and other aspects of the modern digital economy. For sharing-economy activities, a time-use survey might identify how much time respondents take preparing their spare room or how long they spend using sharing-economy websites.
The information about time spent on the activities could be used as the basis for estimates of the value of the sharing economy. The viability of this approach will be explored over the coming months. The dates of the survey correspond to dates when there were restrictions because of the coronavirus pandemic, which mean that results are likely to be atypical, but the data collection gives us the opportunity to explore the practicality of collecting the information.
We are planning to review our definition by engaging with stakeholders to determine whether our current definition remains in-line with users’ expectations and understanding of the sharing economy.Back to table of contents
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