key: cord-0193130-3ss5myzt authors: Berkesewicz, Maciej; Nikulin, Dagmara title: COVID-19 and the gig economy in Poland date: 2021-07-23 journal: nan DOI: nan sha: 56f0cc3734037bd4ab1c31d65ada8052a6f7f4e4 doc_id: 193130 cord_uid: 3ss5myzt We use a dataset covering nearly the entire target population based on passively collected data from smartphones to measure the impact of the first COVID-19 wave on the gig economy in Poland. In particular, we focus on transportation (Uber, Bolt) and delivery (Wolt, Takeaway, Glover, DeliGoo) apps, which make it possible to distinguish between the demand and supply part of this market. Based on Bayesian structural time-series models, we estimate the causal impact of the first COVID-19 wave on the number of active drivers and couriers. We show a significant relative increase for Wolt and Glover (15% and 24%) and a slight relative decrease for Uber and Bolt (-3% and -7%) in comparison to a counterfactual control. The change for Uber and Bolt can be partially explained by the prospect of a new law (the so-called Uber Lex), which was already announced in 2019 and is intended to regulate the work of platform drivers. The COVID-19 pandemic has significantly influenced many areas of life, including the labor market situation. Periods of domestic isolation and lockdown-induced job losses may have contributed to the development of the platform economy, which is an increasingly large part of the labor market. The platform economy (hereafter also gig economy) can be defined as "non-standard work facilitated by online platforms, which use digital technologies to 'mediate' between individual suppliers (platform workers) and buyers of labour" (Hauben et al., 2020, p. 98) . However, existing studies report that COVID-19 has had a varying impact on the extent of the gig economy. At the same time, many empirical studies are based on online surveys and do not differentiate between specific types of gig work, such as, crowd workers, who perform tasks online, highly skilled gig workers, such as architects or software engineers, and low skilled gig workers, like drivers and deliverers (Spurk and Straub, 2020) . Importantly, if the separate sectors of the gig economy are taken into account, the impact of COVID-19 may prove to be differential (Cao et al., 2020) . In this article we focus on gig jobs performed by drivers and couriers, so we narrow down the scope of the gig economy to the transportation and delivery sectors. In this field recent evidence suggests an increase in tasks posted and filled within the platform economy during the pandemic, as a result of many people working remotely and the closure of restaurants. In particular, if one considers the sector of food delivery, it is possible to observe an increasing trend, see among others Batool et al. (2020) ; Raj et al. (2021) . As far as transportation services are concerned, the pandemic may have had a negative impact, which is documented by Batool et al. (2020) using Google Trends for Uber and Lyft. Similarly, due to the social distancing measures and lockdowns earnings of drivers and couriers have decreased. As regards Poland, Polkowska (2021) analysed the impact of the first wave of COVID-19 on the work of Glovo couriers. Based on 20 semi-structured interviews and 1300 posts in the Internet forum, she concludes that during the pandemic, working as a courier is perceived as a good occupation, which can offer opportunities for those who lost their job during the lockdown. Moreover, the popularity of such activities is increasing following a growing number of orders. Similarly, Muszyński et al. (2021) find an increasing interest in food delivery platforms (Glovo, Takeaway and Stava), which reflects the surge in demand for such services. The main limitations of the above-mentioned studies are their qualitative nature and the fact they do not attempt to estimate the effect of COVID-19 on this sector. In this article we take a different approach using a dataset that covers over 21 million smartphone users in Poland (Poles and foreigners) to study the impact of COVID-19 1 . Our main research question is: What is the size of the effect of the first COVID-19 wave on the gig economy, in particular on the transportation and food delivery services?. Our results contribute significantly to the existing knowledge by providing empirical quantitative evidence about the impact of the COVID-19 pandemic on the gig economy. Unlike previous studies, which are mostly based on surveys or interviews, our study makes it possible to track changes on the gig labour market more precisely. The article has the following structure. In Section 2 we describe trends in the number of active users of 6 apps used in the study. In section 3 we briefly describe a Bayesian structural time-series model proposed by Brodersen et al. (2015) , which we use to estimate the causal effect of the first COVID-19 wave. Section 4 presents the results and is followed by conclusions. To distinguish between the demand and the supply part of the gig economy we focused only on apps specially created for workers i.e. drivers and couriers. We identified the following mobile apps for couriers: Takeaway, Glover, Wolt and DeliGoo and for drivers: Bolt, Uber. However, in the latter case, it is not possible to distinguish between drivers and couriers, since the same app is used to provide two kinds of services: Uber Driver and Uber Eats. A detailed description of the data can be found in Beręsewicz et al. (2021) . For Glover and Wolt we observe a sharp increase after the beginning of the first wave. For Takeaway there is a slight increase but it is not significantly different from the pre-COVID-19 linear trend. The number of active users of DeliGoo varied over the reference period and we can observe an increase starting at the beginning of 2020, which may be connected with the company's advertising campaign. For Bolt and Uber the trend is different: there is a decrease, which is larger for Bolt. This may be due to the character of the Uber app, which includes drivers and couriers. The decline for Bolt and Uber seems to start at the end of 2019 and the beginning of 2020, which may be the result of the prospect of a new law intended to regulate the work of platform drivers. In order to estimate the causal effect of the first COVID-19 wave we use Bayesian structural timeseries models proposed by Brodersen et al. (2015) . This method involves a diffusion-regression statespace model to predict the counterfactual response in a synthetic control that would have occurred had no intervention taken place. The underlying model consists of three parts: the local linear trend, seasonality and contemporaneous covariates with static or dynamic coefficients. We did not assume the seasonal component as our time series is too short (2.5 years) and its deviations from the linear trend are negligible. The estimation of a causal effect requires covariate(s) that will be used for counterfactual analysis. As COVID-19 impacted almost all monthly macroeconomic indicators, we decided to use the number of active Takeaway couriers as a control. The motivation for this choice is twofold: first, it does not significantly differ from the pre-COVID-19 trend and despite a slight increase after the first wave, it quickly returned to the pre-COVID trend; second, Takeaway had the same popularity during the first wave 2 and couriers may in fact be restaurant employees rather than freelance workers. Table 2 in the Supplementary Materials provides background information about gig workers, which indicates that there is no significant difference between those who worked before and after the first wave. The purpose of this article was to analyse the impact of the first COVID-19 wave on the gig economy, (Batool et al., 2020; Katta et al., 2020) . At the same time, the extraordinary situation where restaurants and bars were closed and many people had to self-isolate in homes has triggered a growing demand for food delivery services, which led to a sharp rise in the number of couriers, as confirmed by recent evidence provided by Batool et al. (2020) and Raj et al. (2021) . Our study has a number of potential policy implications. First, we contribute to the existing knowledge by providing evidence about the varying impact of the pandemic on separate sectors of the gig economy. It supports the hypothesis that the gig economy is very heterogeneous and cannot be analysed as a whole. Moreover, given the increase in the popularity of services offered by platform economy, the crisis caused by the pandemic highlighted the uncertain and unstable situation of gig workers, who cannot rely on a steady source of income. As incomes of gig workers are unstable and workers are often not covered by the labor protections offered to employees, the pandemic crisis has been exacerbated as a result of these precarious employment types. However, it is worth mentioning that the social reaction including pressure from regulators, driver advocates, and the media has helped to improve the social protection of Uber drivers in terms of safety and the mitigation of health risks Katta et al. (2020) . A COVID-19 in Poland Important dates regarding the first lockdown in Poland: • 12 March -closure of all schools until March 25, which was then extended until April 10classes were held remotely, universities switched to distance learning, the activity of cultural institutions, i.e. philharmonics, operas, theaters, museums, cinemas, was suspended. • 14 March -the functioning of shopping centers and galleries was limited. Only grocery stores, pharmacies, drugstores, and laundries remained open. • 15 March -borders closed for air and rail traffic. • 25 March -limitation of assemblies to max. 2 people (except for families, religious gatherings and workplaces), only essential travel was permitted, people were allowed to go outside only in essential situations, e.g. shopping, to buy medicines, visit a doctor, walk the dog, physical activity in the fresh air; bars, restaurants, pubs, casinos, cinemas, theaters and other entertainment venues remained closed. • 31 March -children under the age of 18 were not allowed to leave homes without a guardian; parks, boulevards and beaches were closed, as well as hairdressing, beauty, tattoo and piercing salons; hotels could only operate if guests were quarantined or were staying on business, such as medics or construction workers; the obligation to keep a distance of 2 meters from each other was introduced, except for caretakers of children under the age of 13 and disabled persons. Note that in Polish the search word "Wolt" may also refer to a unit for electric potential. C Background information about apps users How COVID-19 has shaken the sharing economy? An analysis using Google trends data The gig economy in Poland: evidence based on mobile big data Inferring causal impact using bayesian structural time-series models The Impact of COVID-19 Pandemic on Gig Economy Labor Supply Publication for the committee on Employment and Social Affairs Dis)embeddedness and (de)commodification: COVID-19, Uber, and the unravelling logics of the gig economy Coping with precarity during COVID-19: A study of platform work in Poland Platform work during the COVID-19 pandemic: a case study of Glovo couriers in Poland COVID-19 and Digital Resilience: Evidence from Uber Eats Flexible employment relationships and careers in times of the COVID-19 pandemic The study was conducted as part of the research project Economics in the face of the New Economy, financed under the Regional Initiative for Excellence programme of the Minister of Science and Higher Education of Poland, years 2019-2022, grant no. 004/RID/2018/19, financing 3,000,000 PLN (for Maciej Beręsewicz). Data were obtained from the Selectivv company (https://selectivv. com/en/). The views expressed in this article are those of the authors and do not necessarily reflect the official policy or position of Statistics Poland or the Statistical Office in Poznań. The authors would like to thank Jakub Sawulski for his valuable comments that led to the creation of this article; We also thank Marcin Augustyniak for explaining the data and Robert Pater for his comments on the early version. All data and codes are available at https://github.com/ DepartmentOfStatisticsPUE/rid-gig-economy-covid19.