Figure 1.
Figure 2.
Figure 3.
Selected methods for measuring the scale of activity in the gig economy (Source: Own compilation based on: Murtin, 2021)
| Research method | Short description | Strengths | Weaknesses |
|---|---|---|---|
| Information and communication technologies (ICT) research (ICT usage surveys) | Computer, personal, or phone surveys |
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| Web scraping | The process of data extraction which involves collecting information from online resources for later analysis. This data can be processed using big data techniques |
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| Tax data | Analysis of data collected by public administration, which is facilitated by the use of ICT systems |
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| Big data analysis | Analysis of large data sets enables ex post research and ex ante estimation of future phenomena, thanks to usage of forecasting methods and technique known as data science |
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Correlation and R2 coefficients between replies (Source: Own research)
| Question | 1 | 2 | 3 | 4 | 5 | 14a | 14b | 14c | 14d | 14e | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1.00 | - | - | - | - | - | - | - | - | - | ||||
| 2 | 0.14 | 1.00 | - | - | - | - | - | - | - | - | ||||
| 3 | 0.06 | 0.69 | 1.00 | - | - | - | - | - | - | - | ||||
| 4 | 0.13 | 0.44 | 0.29 | 1.00 | - | - | - | - | - | - | ||||
| 5 | 0.03 | –0.01 | –0.08 | 0.00 | 1.00 | - | - | - | - | - | ||||
| 14a | –0.01 | 0.26 | 0.27 | 0.35 | 0.25 | 1.00 | - | - | - | - | ||||
| 14b | 0.05 | 0.10 | 0.46 | 0.11 | –0.04 | 0.37 | 1.00 | - | - | - | ||||
| 14c | 0.02 | 0.47 | 0.48 | 0.25 | 0.14 | 0.53 | 0.29 | 1.00 | - | - | ||||
| 14d | 0.02 | 0.42 | 0.47 | 0.29 | –0.03 | 0.71 | 0.44 | 0.71 | 1.00 | - | ||||
| 14e | –0.07 | –0.34 | –0.04 | 0.09 | 0.15 | 0.14 | 0.42 | -0.18 | 0.06 | 1.00 | ||||
| R2 coefficients | ||||||||||||||
| 1 | - | - | - | - | - | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ||||
| 2 | - | - | - | - | - | 0.07 | 0.01 | 0.22 | 0.18 | 0.12 | ||||
| 3 | - | - | - | - | - | 0.07 | 0.21 | 0.23 | 0.22 | 0.00 | ||||
| 4 | - | - | - | - | - | 0.12 | 0.01 | 0.06 | 0.08 | 0.01 | ||||
| 5 | - | - | - | - | - | 0.06 | 0.00 | 0.02 | 0.00 | 0.02 | ||||
Criteria for freelancer membership: author's proposal (Source: Own compilation)
| Question | Replies | |||
|---|---|---|---|---|
| 1 | Mean: 37.30 | |||
| 2 | Micro enterprise (up to nine employees): 12.90% | |||
| 3 | Mean: 5.58 | |||
| 4 | Secondary: 3.23% | |||
| 5 | Yes: 58.06% | |||
| 6 | Mean: 2.35 | |||
| 7, 8 | Mean: 0.68 | Mean: 8.29 | ||
| 9 | Yes | Yes – this is possible through state institutions that already have the appropriate data (e.g., tax data) | 23.26% | |
| Yes – but this will only be possible when a law is developed that clearly defines who is a giger | 16.28% | |||
| Yes – the scale of the market can be assessed based on existing data from the statistical office and private sector entities that research the labor market | 16.28% | |||
| No | It is not possible because it is impossible to clearly define who is a freelancer | 44.19% | ||
Data regarding gig economy and gigers in making managerial decisions (Source: Own compilation based on: Freelancing w Polsce 2023, Useme Report; UK HM Government, The experiences of individuals in the gig economy; Ernst&Young, GIG on, Nowy Ład na rynku pracy)
| Entity | Where data can be used by management staff? |
|---|---|
| Useme | HR planning, adaptation to project requirements |
| Ernst&Young | Information useful for shaping the company's legal strategy |
| UK Government | Evaluation of gigers’ skills |
Data regarding gig economy in Europe – variance (Source: Own compilation based on Piasna, Zwysen and Drahokoupil, 2022, p_16)
| Country | Antytime (a) | At least once during the last year (b) | At least once during the last month (c) | At least once during the last week (d) | Part of total income € |
|---|---|---|---|---|---|
| Austria | 28.10% | 17.10% | 10.80% | 5.10% | 2.30% |
| Bulgaria | 31.20% | 19.10% | 9.80% | 5.40% | 2.90% |
| The Czech Republic | 33.80% | 20.10% | 13.60% | 8.80% | 3.60% |
| Estonia | 24.40% | 15.00% | 8.60% | 4.90% | 2.30% |
| France | 25.90% | 16.10% | 11.50% | 6.90% | 2.60% |
| Germany | 30.50% | 16.90% | 11.20% | 5.70% | 2.30% |
| Greece | 27.50% | 15.70% | 9.90% | 3.50% | 2.50% |
| Hungary | 20.90% | 13.30% | 9.60% | 3.20% | 4.60% |
| Ireland | 31.40% | 18.70% | 13.20% | 6.50% | 4.30% |
| Italy | 25.00% | 12.40% | 8.90% | 5.30% | 2.40% |
| Poland | 37.30% | 19.40% | 7.80% | 5.20% | 4.10% |
| Romania | 19.20% | 9.90% | 4.90% | 3.30% | 1.50% |
| Slovakia | 43.30% | 25.20% | 14.30% | 10.00% | 3.60% |
| Spain | 33.60% | 18.60% | 10.40% | 5.10% | 2.50% |
| - | - | - | - | - | - |
| Average | 29.44% | 16.96% | 10.32% | 5.64% | 2.96% |
| Variance | 0.0041481 | 0.0014172 | 0.0006074 | 0.0003756 | 0.0000839 |
| Std deviation | 0.0644057 | 0.0376453 | 0.0246457 | 0.0193812 | 0.0091619 |
Examples of measuring the size of the gig economy (Source: Own compilation based on: Ostoj, 2020, pp_34-35)
| Year | Research subject | Research area | Pros and cons of the chosen method |
|---|---|---|---|
| 2009–2010 | Activity of freelancers registered on a selected digital platform (Amazon Mechanical Turk) | Amazon Mechanical Turk (digital platform) | Amazon Mechanical Turk (digital platform) |
| 2010 | Individual interviews conducted with analysts, journalists, managers, entrepreneurs | Gig economy in IT and internet marketing | Pros: In-depth individual interviews |
| 2009–2012 | Activity of freelancers registered on a selected digital platform (Upwork) | Upwork (digital platform) | Pros: Study conducted in different countries |
| 2013 | Expert interviews with representatives of firms offering online outsourcing services | Freelancers working online | Pros: Interviews conducted with experts |
| 2012–2015 | Study of large datasets from various online platforms | 30 English-language digital platforms | Pros: Large research sample (study included about 1 million service buyers and about a quarter of a million performers) |
| 2015 | Survey conducted on freelancers as part of Research ANd Development (RAND) the Rise and Nature of Alternative Work Arrangements in the USA) | Freelancers | Pros: Coverage of offline work in the study; |
| 2015 | Activity of freelancers registered on a selected digital intermediary platform (Up-work) | Upwork (digital platform) | Pros: Big data analysis |
| 2016–2017 | Survey of freelancers from seven European countries | Digital platforms | Pros: Analysis covering gig workers engaged in both online and offline activities from various countries |