Overview of Attempts to Measure The Gig Economy with Considering The Role of Data in Making Managerial Decisions
Emil ZELMA
Warsaw University of Technology, College of Economic and Social SciencesWarsaw, POLAND
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31 dic 2024
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Pubblicato online: 31 dic 2024
Pagine: 359 - 378
DOI: https://doi.org/10.2478/fman-2024-0022
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© 2024 Emil ZELMA, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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 |
|---|---|---|---|
| Computer, personal, or phone surveys |
Good data comparability Research conducted mainly in countries where the gig economy market is highly developed |
Small size of the research sample Much of the existing data comes from the USA, which is quite unique in terms of its labor market, characterized by relatively low levels of employment stability and a large number of gig workers. As a result, attempting to apply the results obtained for the USA to the global labor market may lead to incorrect conclusions Research implementation is limited to a few selected countries Risk of subjective replies from free lancers |
|
| The process of data extraction which involves collecting information from online resources for later analysis. This data can be processed using big data techniques |
Possibility of collecting data in real time Possibility of comparative analysis of data in time |
The issue of ethics is debatable Legal data collection via web scraping requires consent from individual users. This means that research conducted using this method may not include freelancers who do not consent to the analysis of their data |
|
| Analysis of data collected by public administration, which is facilitated by the use of ICT systems |
Generally, the study applies to all participants earning income in a given country (although it does not include people operating in the gray market) |
Focusing solely on numerical data, completely disregarding qualitative data. Such a study does not take into account, for example, issues related to the type of platforms used or technological development Differences between countries resulting from different tax solutions. This factor may seriously hamper the comparative analysis of data between individual countries |
|
| Analysis of large data sets enables |
Wide possibilities of data analysis and prediction |
Difficulties in obtaining complete data The data analysis process may be time-consuming Focus on quantitative data, difficulty in analyzing qualitative information |
Correlation and R2 coefficients between replies (Source: Own research)
| 1.00 | - | - | - | - | - | - | - | - | - | |||||
| 0.14 | 1.00 | - | - | - | - | - | - | - | - | |||||
| 0.06 | 0.69 | 1.00 | - | - | - | - | - | - | - | |||||
| 0.13 | 0.44 | 0.29 | 1.00 | - | - | - | - | - | - | |||||
| 0.03 | –0.01 | –0.08 | 0.00 | 1.00 | - | - | - | - | - | |||||
| –0.01 | 0.26 | 0.27 | 0.35 | 0.25 | 1.00 | - | - | - | - | |||||
| 0.05 | 0.10 | 0.46 | 0.11 | –0.04 | 0.37 | 1.00 | - | - | - | |||||
| 0.02 | 0.47 | 0.48 | 0.25 | 0.14 | 0.53 | 0.29 | 1.00 | - | - | |||||
| 0.02 | 0.42 | 0.47 | 0.29 | –0.03 | 0.71 | 0.44 | 0.71 | 1.00 | - | |||||
| –0.07 | –0.34 | –0.04 | 0.09 | 0.15 | 0.14 | 0.42 | -0.18 | 0.06 | 1.00 | |||||
| - | - | - | - | - | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |||||
| - | - | - | - | - | 0.07 | 0.01 | 0.22 | 0.18 | 0.12 | |||||
| - | - | - | - | - | 0.07 | 0.21 | 0.23 | 0.22 | 0.00 | |||||
| - | - | - | - | - | 0.12 | 0.01 | 0.06 | 0.08 | 0.01 | |||||
| - | - | - | - | - | 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 |