MONITORING THE CROWDWORKING SECTOR IN GERMANY

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1 MONITORING THE CROWDWORKING SECTOR IN GERMANY Determinants of size and reach of crowd- and gig-work, motivation and socio-demographic characteristics of crowdworker in Germany Prof. Dr. Oliver Serfling Rhine-Waal University of Applied Sciences, Kleve, Germany InGRID-2 Expert Workshop, 9-10 April 2018, Brussels

2 OUTLINE Research Project Partners and Research Questions Crowdworking Forms and Definiton Survey Methodology Results Socio-Demographics Platforms CW-Volume Project Outlook 2

3 CROWDWORKING MONITOR Joint Research Project 2 years: December 2017 November 2019 continuous online-data collection (and identification of active, past, future CWs) through web-access panel of Civey GmbH funded by The German Federal Ministry of Labor and Social Affairs RESEARCH GOALS Identification and monitoring of crowd- and gig- workers sociodemographic characteristics and motivation aspects of their material and non-material well-being, Testing common hypotheses of the literature, e.g. taylorization, polarization, segregation. Ultimately, contribution to the empirical database on Crowdworking in Germany 3

4 CROWDWORKING MONITOR Research Questions: What are the socio-demographics of CWs? Are there different types of CWs? Motivation of CWs? Remuneration and Income inequality? What is the size of the Crowdworking Market? Changes across regions and time? Crowdworking working definition: includes: gig- and platform-work excludes: internal CW Working-age persons that earn at least a part of their income by execution of paid, temporary, work-assingments, intermediated via internet platforms or smartphone apps. 4

5 SURVEY METHODOLOGY (1) Data is being surveyed by Civey GmbH, Berlin Start-up focussing on public opinion polls and market-research Web-access panel with 1 mio. active, verified users in Germany Polling-widget is imprinted in websites, producing ½ mio. votes per day, i.e. 15 mio. per month; avg. active user: 50 votes per month. newspapers and blogs: Spiegel Online, Welt, Wirtschaftswoche, Cicero, T-Online Identification of CW: Do you execute paid work assingments intermediated via online-platforms or market places? (Poll #1043) Figure 1: Screenshot of Civey Widget for Poll No Source: 10

6 SURVEY METHODOLOGY (2) Non-Probability Samples have to deal with sampling and selection bias 1. Riversampling Polling-widget is imprinted in a variety of websites, with different audiences (socio-demography, attitudes) Polls are directed by a relevance algorithm to users to reduce bias Votes are only counted after login (results are shown) 2. Poststratified Quota Sample and weighting Usually a quota sample of is drawn Population weights (for the german federal electorate) account for remaining biases in user sociodemographics Lack of a statistical grid on Crowdworker (not included in census), thus weighting bases on sociodemographic characteristics (observables) Remaining bias in unobservable characteristics, possible! 11

7 FIRST RESULTS Sample size by March, 24th 2018: 374,468 respondents 14,007 Active CWs (= 3.7% weighted, thereof: 1.4 % (+) 1.6 % (=) 0.7 % (-) 8,857 Future CWs (= 2.4% weighted) 9,761 Past CWs (= 2.6% weighted) 32,625 (8.7%) crowdworking affinity in the population of internet users in Germany 341,843 (91.3%) Non-CW 12

8 SOCIO-DEMOGRAPHICS 4,2% higher CW-affinity with men compared to women Negative linear trend with respect to age Higher CW-affinity in West-Germany, in the South and the urban regions / cities Higher CW-affinity for singles and divorced Higher participation of CWs with no school leaving certificate and high-school diploma (12-13yrs. schooling) ~40% of Crowdworkers are self-employed 6,8% students, 4% unemployed Lower participation of full-time employees, however: 2,8% of FTE are actively Crowdworking 13

SOCIO-DEMOGRAPHICS: Gender, Age 4,2% more CW affinity among men compared to women (confirms conclusions of Leimeister et al. 2016; Kuek et al. 2015; Bonin, Rinne 2017.

9 SOCIO-DEMOGRAPHICS: Gender, Age 4,2% more CW affinity among men compared to women (confirms conclusions of Leimeister et al. 2016; Kuek et al. 2015; Bonin, Rinne Exact the same percentage as in Huws, Joyce 2016) 100% 87,9% 8,7% 3,7% 2,4% 2,6% 2,6% 1,9% 3,4% 1,6% -10,1% Linear trend of affinity with age above avg. for the younger (+3,6%) declining to -5% for 65+ yrs. Old. In line with Kuek et al. 2015: Millennials born between ; Huws, Joyce 2016: Young people between yrs old; Bonin, Rinne 2017: yrs old. +3,6% among yrs old the highest increase if CW affinity compared to the sample population 14

10 SOCIO-DEMOGRAPHICS: Region Highest CW-affinity in South and West-Germany 100% 87,9% 8,7% 3,7% 2,4% 2,6% CW is more an urban issue (as there is high affinity in City-States and densely populated areas) East Germany the lowest % of active, future, past CWs in comparison with representation of the region in the sample population 15

100% 87,9% 8,7% 3,7% 2,4% 2,6% 9,4% Singles incline the most towards CW in the future, with respect to the marginal distributions in the

11 SOCIO-DEMOGRAPHICS: Marital Status The most of CW affine are married (results in line with their representation in the sample). In contrast to Leimeister et al. 2016: 53% CWs are single; Bertschek et al. 2016: 76 % CWs have single status. 100% 87,9% 8,7% 3,7% 2,4% 2,6% 9,4% Singles incline the most towards CW in the future, with respect to the marginal distributions in the sample - 10% Future CWs with status married. The most significant gap if future CW are compared with the representation in the sample population or CW affinity (-5,7%) 16

SOCIO-DEMOGRAPHICS: Education The most of CWs (active, future, past) have 12-13 yrs of education in line with Leimeister et al. 2016: 48% CWs with degree; 27% Uni degree; Bertschek et al.

12 SOCIO-DEMOGRAPHICS: Education The most of CWs (active, future, past) have yrs of education in line with Leimeister et al. 2016: 48% CWs with degree; 27% Uni degree; Bertschek et al.: 2016: 41% CWs have University degree; Bonin, Rinne 2017: Most of CWs graduation or University degree. 100% 87,9% 8,7% 3,7% 2,4% 2,6% The largest CW affinity increase 1,6% have participants without graduation in comparison to their representation in the sample population. The lowest CW affinity (-3,1%) have participants with 10 yrs of education in comparison to their representation in the sample population. 17

SOCIO-DEMOGRAPHICS: Employment 100% 87,9% 8,7% 3,7% 2,4% 2,6% Full-time workers are the most CW affine 30%, but if we compare their affinity with their distribution in

The same trend as among full-time workers is valid also for pensioners, who claim declines of -7,6% Increasing CW affinity is visible among self-employed (14,8%) and

13 SOCIO-DEMOGRAPHICS: Employment 100% 87,9% 8,7% 3,7% 2,4% 2,6% Full-time workers are the most CW affine 30%, but if we compare their affinity with their distribution in the sample population it shows significant decrease! The same trend as among full-time workers is valid also for pensioners, who claim declines of -7,6% Increasing CW affinity is visible among self-employed (14,8%) and students (2,4%) with remarkable gains in active and future CWs (if sample population is taken as comparison unit) Leimeister et al. 2016: 38% self-employed, only 6% unemployed; Bertschek et al. 2016: 39% dependent employment, 31% students; Huws, Joyce 2016: Only 10% CWs are students 18

homeoffice in their mainjob 51,9 % Work on tasks that are

14 The active Crowdworker 10,4 % Live in households with at least one other Crowdworker >52% Would like to work more in the homeoffice in their mainjob 51,9 % Work on tasks that are remunerated with money 37 % Use crowdwork for generating an extra income 23

The active Crowdworker 56,3% executed tasks from another OP than 99 Designs (2,2%) Clickworker (1,7%) Crowdflower (1,4%) Freelancer (6,65%) Guru (2,9%), Upwork (1,3%) (=22,2% coverage) 64,4% executed

15 The active Crowdworker 56,3% executed tasks from another OP than 99 Designs (2,2%) Clickworker (1,7%) Crowdflower (1,4%) Freelancer (6,65%) Guru (2,9%), Upwork (1,3%) (=22,2% coverage) 64,4% executed tasks from another service OP than Deliveroo (1,2%), Foodora (1,3%) Helpling (1,5%) Lieferando (3,4%) Mila (1,5%), MyHammer (3,22%) Streetspotr (0,86%) (=16,7% coverage) CWs on Platforms 59,3% can decide freely on work time 2.8 platforms Avg. Number CW get tasks from. 33,3% get tasks from only 1 platform 3.5 yrs. ago Avg. time of first working task. 46% got it more than 5 years ago; 26% consulting services, design (6%), crafts (12%), programming (8%), writing (9%) or testing (6%); (34,2 % other) 30,2 % execute their work solely in the real world, compared to 18,2% only online 50,2 % Need specific knowledge, compared to 10,9% only general 24

The active Crowdworker 27 weeks per year on average CW working. Most work btw. 42-48 in the last year 14.2 Avg. Number of assignments per week.

16 The active Crowdworker 27 weeks per year on average CW working. Most work btw in the last year 14.2 Avg. Number of assignments per week. 43% < 5 tasks 13,2% of CW yield their main income with CW; 37% mostly additional income. 32 mins. Avg. search time. Mostly < 15 min. Crowdworking Volume 732 avg. gross earnings per week. 38% obtain > 1, hrs. Avg. time to complete one task. 22% need a week or more. 19% < 15 mins. 51,9 % The most working tasks of CWs were remunerated with money 30,7% executed in the last half year less than 5 working tasks per week (28,6% DK) 25

The active Crowdworker MOTIVATION The Main reasons for executing paid working tasks via online-platforms are: Fast mediation, short running time 16,6% You can execute tasks from OP next to another

17 The active Crowdworker MOTIVATION The Main reasons for executing paid working tasks via online-platforms are: Fast mediation, short running time 16,6% You can execute tasks from OP next to another tasks (as side-activity) SATISFACTION 15% Flexible working time 12,2% Other reasons than those mentioned in our questionnaire 20% With overall work at the OP: - satisfied more than 34,6% of CWs - not satisfied 12,8% of CWs More than 40% of CWs are satisfied with the payment for working tasks More than 40% of CWs satisfied with their overall employment situation More than 55% of clients were satisfied with the work of CWs 26

18 NEXT STEPS Waiting for additional data to flow in. Other descriptive analysis with general attitudes of CWs (up to items available) Design and Test of CW-Sentiment Indicator ( Crowdworking Climate ) Transaction-volume (conversion), Income, Education, Health, Flexibility, Satisfaction, Social security. Continuous Data Quality Assessment Plausibility checks Respondent behavior Bias reduction 27

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(32.2% after weighting) said they had tried to find work, managed via socalled sharing economy platforms such as Upwork, Uber or Handy.

(32.2% after weighting) said they had tried to find work, managed via socalled sharing economy platforms such as Upwork, Uber or Handy. First survey results reveal high levels of crowd work in Switzerland Authors: Ursula Huws, Neil H. Spencer, Dag S. Syrdal The so-called gig economy has attracted increasing attention in recent years, with