Digital Labour Platforms in Europe : numbers, profiles, and employment status of platform workers.
Date: 2019-07-23
Institutions: Joint Research Centre
Subjects: digital technology, job vacancy, labour market, online platform worker, organisation of work, outsourcing, research report

Digital Labour Platforms in 
Europe: Numbers, Profiles, and 
Employment Status of Platform 
Workers 

Cesira Urzì Brancati 

Annarosa Pesole 

Enrique Fernández-Macías 

2019  

EUR 29810 EN 

 
 
 
  
 
 
 
This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science 
and knowledge service. It aims to provide evidence-based scientific support to the European policymaking 
process. The scientific output expressed does not imply a policy position of the European Commission. Neither 
the European Commission nor any person acting on behalf of the Commission is responsible for the use that 
might be made of this publication. 

Contact information  
Name: Cesira Urzì Brancati 
Address: C/ Inca Garcilaso, 3; 41092 Seville/Spain 
Email: Cesira.URZI-BRANCATI@ec.europa.eu 
Tel.: +34 9544-88278 

EU Science Hub 
https://ec.europa.eu/jrc 

JRC117330 

EUR 29810 EN 

PDF 

ISBN 978-92-76-08955-1 

ISSN 1831-9424 

doi:10.2760/16653 

Luxembourg: Publications Office of the European Union, 2019 

© European Union, 2019 

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How to cite this report: Urzì Brancati, C., Pesole, A., Fernández-Macías, E. (2019), Digital Labour Platforms in 
Europe: Numbers, Profiles, and Employment Status of Platform Workers, EUR 29810 EN, Publications Office 
of the European Union, Luxembourg, 2019, ISBN 978-92-76-08955-1, doi:10.2760/16653, JRC117330. 

All content © European Union 2019 

Contents 

Acknowledgements ................................................................................................ 2 

Abstract ............................................................................................................... 3 

1  Introduction ...................................................................................................... 4 

2  Estimating the number of platform workers across Europe ...................................... 6 

2.1  A quick summary of previous attempts ........................................................... 6 

2.2  A conceptual framework to measure platform work as a form of employment ...... 6 

2.3  An empirical demonstration on how to classify platform work as a form of 
employment...................................................................................................... 8 

3  What do platform workers actually do? A look at the type of tasks carried out ......... 10 

3.1  Socio-demographic profiling by task type: an empirical analysis ...................... 12 

3.2  Results ..................................................................................................... 12 

4  The actual or perceived employment status of platform workers .............................. 2 

4.1  Legal issues about the employment status of platform workers ......................... 2 

4.2  An empirical assessment of the employment status of platform workers ............. 3 

5  Discussion and conclusions ................................................................................. 6 

i 

Acknowledgements 

The  authors  acknowledge  Miia  Rossi-Gray  (DG  EMPL),  Nicholas  Costello  (DG  EMPL)  and 
Ioannis Maghiros (JRC B4) for their support, as well as PPMI for providing the data. 

Authors 

Cesira Urzí Brancati 

Annarosa Pesole 

Enrique Fernández-Macías 

2 

Abstract 

This  report  explores  three  issues  related  to  the  growing  phenomenon  of  Digital  Labour 
Platforms: firstly, how to measure platform work as a form of employment incorporating 
elements  such  as  regularity  of  provision,  time  allocated  and  income  generated;  on  this 
basis,  and  drawing  on  a  new  dedicated  survey  (COLLEEM),  the  article  quantifies  and 
categorises  platform  work  into  sporadic,  secondary  and  main.  Secondly,  it  provides  an 
empirical  investigation  of  the  association  between  individual  characteristics,  such  as 
gender,  age,  family  composition,  education  and  motivation,  and  the  probability  of 
carrying  out  particular  types  of  platform  work,  such  as  microtasking,  creative  services, 
software  development,  transportation  and  so  on.  The  analysis  highlights  substantial 
heterogeneity in the attributes and motivations of the workers performing different tasks. 
Finally,  it  discusses  the  employment  status  of  platform  workers  and  provides  some 
descriptive  statistics  suggesting  that  a  large  share  of  platform  workers  perceive 
themselves  as  employees,  even  though  they  are  in  most  cases  legally  considered  self-
employed.  

3 

 
 
1  Introduction 

Digital  labour  platforms  (DLP)  are  a  new  form  of  coordinating  the  provision  of  labour 
services and arguably one of the most visible results of the latest technological revolution 
in  the  world  of  work.  Because  they  are  relatively  new,  researchers  strive  to  define  and 
measure  them,  while  policy-makers  struggle  to  regulate  them.  Pesole  et  al.  (2018) 
defines  DLP  as  “digital  networks  that  coordinate  labour  service  transactions  in  an 
algorithmic  way”.  De  Groen  et  al  (2018)  provide  a  longer,  more  detailed  definition  of 
platform  work  as  “an  employment  form  in  which  organisations  or  individuals  use  an 
online platform to access other organisations or individuals to solve specific problems or 
to  provide  specific  services  in  exchange  for  payment.”  Berg  et  al.  (2018)  draws  a 
distinction  between  “web-based  platforms,  where  work  is  outsourced  through  an  open 
call  to  a  geographically  dispersed  crowd  (“crowdwork”),  and  location-based  applications 
(apps)  which  allocate  work  to  individuals  in  a  specific  geographical  area”.  All  these 
definitions  share  the  underlying  notion  that  DLPs  act  as  intermediaries,  hence  not  as 
conventional employers; they also imply that the object of each mediation is not a job in 
the  traditional  sense,  but  a  single  task  or  service  –  in  this  they  differ  from  temporary 
agencies. Furthermore, they assume that management is carried out by algorithms, that 
is,  step  by  step  sets  of  instructions  to  automate  the  optimal  allocation,  monitoring  and 
evaluation of jobs.  

DLPs  have  attracted  a  lot  of  attention,  both  from  the  media  and  from  policy-makers, 
because  of  their  potential  to  disrupt  labour  markets.  A  priori,  the  impact  of  DLPs  on 
employment  is  ambivalent:  if  on  the  one  hand  they  can  have  a  positive  effect  by 
increasing  participation  in  the  labour  market  through  better  matching  procedures  and 
more  flexible  working  conditions,  on  the  other,  they  can  have  a  negative  effect  by 
circumventing  regulation  and  lowering  the  quality  of  employment.  If  DLPs  are  to  be 
considered  as  mere  intermediaries,  then  it  follows  that  they  are  not  required  to 
guarantee  basic  employment  rights  to  the  people  whose  services  they  coordinate,  for 
instance a minimum wage, holiday pay, maternity leave or sick leave, and social security, 
as well as the right to organise into unions. If the size of the platform economy were to 
grow,  as  many  predict,  and  DLPs  kept  on  being  considered  as  merely  intermediaries, 
then  a  large  proportion  of  the  workforce  would  risk  being  excluded  from  employment 
protection and even some types of welfare provision. 

However,  DLPs  have  the  potential  to  affect  society  not  just  by  improving  access  to  the 
labour market or damaging working conditions: if we conceive jobs as bundles of tasks, 
and  understand  that  DLPs  allow  the  unbundling  of  such  tasks  into  atomized  units,  then 
we  may  conclude  that  DLPs  could  shatter  the  very  notion  of  jobs  by  reducing  them  to 
single  tasks.  Such  a  dissolution  of  jobs  would  have  consequences  that  go  beyond  work 
organization,  as  jobs  are  not  only  contracts  for  the  provision  of  labour  services,  but,  in 
Durkheim’s  words,  they  are  a  crucial  anchor  of  “organic  solidarity”,  a  system  of 
representations  rooted  in  and  reflective  of  concrete  social  groups  (Lincoln  and  Guillot 
2004).  

However, while it is true that, at least in theory, DLPs can disrupt labour markets and the 
social structures in which they are embedded, their transformative power hinges on how 
widespread they are today and how widespread they are likely to become in the future. 
In  other  words,  to  assess  the  potential  impact  of  DLPs,  we  need  precise  and  reliable 
estimates  of  the  number  of  people  who  spend  a  significant  amount  of  time  and  gain 
substantial income via DLPs, because they are the ones who will be affected by platform 
regulation or the lack of it. At the same time, when we talk about the impact of DLPs on 
working  conditions,  it  is  imperative  to  understand  whether  platform  work  is  just  a  side 
gig  as  it is  often  claimed,  or  whether  it  represents  a  major  source  of  income.  Thus,  we 
need  to  ascertain  the  regularity,  time  allocated  and  income  generated  from  platform 
work,  as  well  as  the  employment  status  of  platform  workers,  since  this  will  provide 
information  as  to  whether  they  have  other  forms  of  social  protection  from  other  jobs. 
Finally,  to  investigate  the  extent  to  which  the  unbundling  of  tasks  is  feasible,  we  need 
have detailed information of what type of tasks platform workers actually carry out. 

4 

 
In this paper, we address these questions by reviewing the academic and grey literature 
as  well  as  by  conducting  an  empirical  analysis.  The  analysis  draws  from  a  new  and 
unique  source  of  data:  the  JRC  COLLEEM  survey  carried  out  in  2017,  which  aimed  to 
identify  platform  workers,  their  characteristics,  the  nature  of  their  work,  and  their 
working conditions (Pesole et al. 2018). 

The remainder of this paper is organised as follows: section 2 provides a quick review of 
previous  attempts  to  measure  platform  work,  then  proposes  a  methodology  and 
illustrates it with empirical estimates. Section 3 focuses on the types of tasks performed 
by  platform  workers.  Section  4  discusses  issues  related  to  the  employment  status  of 
platform workers, and section 5 concludes.    

5 

 
 
 
 
 
2  Estimating the number of platform workers across Europe 

2.1  A quick summary of previous attempts  

A few studies have tried to estimate the size of the platform economy in the last couple 
of years. In general, they follow two alternative approaches: one approach is to infer the 
size of the DLPs workforce through the use of publically available data, such as company 
reports,  blogs,  and  so  on.  This  approach  often  results  in  very  low  estimations.  For 
instance, according to McKinsey (2015), approximately 1% of the US population provides 
services via DLPs; they reached this figure by simply summing up the number of people 
active on 10 platforms and dividing it by the US working age population. Farrel and Greig 
(2016)  report  a  very  similar  figure  for  the  prevalence  of  platform  work  in  the  US, 
between approximately 0.5% and 1.5%. To derive this figure, the authors calculated the 
proportion  of  JPMorgan  Chase  customers  (nearly  6  million people)  who  received  income 
at  least  once  over  the  36  months  preceding  the  study  from  at  least  one  of  42  online 
platforms.  A  study  by  the  European  Commission  (2017)  presents  a  similar  figure, 
estimating  that  approximately  1.79%  of  European  workers  provide  services  via  online 
platforms. However, even in this case the estimate is indirect and results from combining 
Eurostat aggregate data on sectoral employment with information on platforms revenues. 

The second main approach found in the literature is to estimate the number of platform 
workers  on  the  basis  of  a  dedicated  survey,  generally  ad  hoc,  and  directly  asking 
respondents  whether  they  provide  services  via  DLPs  (Katz  and  Krueger,  2016;  Robles 
and  McGee,  2016;  Flash  Eurobarometer,  2016;  Bonin;  2017;  Huws  et  al.  2017; 
Lepanjuuri  et  al.,  2018;  Pesole  et  al.,  2018).  In  this  case,  respondents  who  answer 
positively  to  a  screener  question  are  subsequently  asked  about  frequency  of  provision, 
regularity,  income  earned  and  so  on.  Estimates  coming  from  surveys  generally  yield 
much  higher  figures.  It  should  be  noted  that  drawing  comparisons  between  these 
different  studies  is  not  that  straightforward,  because  each  one  measures  the 
phenomenon  in  a  slightly  different  way;  in  addition,  there  is  substantial  heterogeneity 
across countries, so estimates at the aggregate level vary widely.  

According  to  Huws  et  al  (2017)  about  2.9%  of  the  people  in  their  sample  of  seven 
European countries earned at least 50% of their income from platform work. Lepanjuuri 
et al. (2018) estimate that approximately 4.4% of people aged 18 over and in the UK did 
platform  work  during  the  previous  12  months.  Pesole  et  al  (2018)  estimate  that 
approximately  2.3%  of  the  respondents  in  their  sample  of  14  European  member  states 
provide services via platforms as their main job.  

Katz and Krueger (2016) estimate that only 0.5% of people aged 18 and over in the US 
do  platform  work  as  a  main  job.  However,  it  would  be  hasty  to  say  that  DLPs  are  less 
widespread in the US than in Europe because the authors adopt a particularly restrictive 
definition,  by  only  considering  people  who  did  platform  work  in  the  previous  week;  in 
addition,  they  define  platform  work  as  "any  gigs,  HITs  or  other  small  paid  jobs  […] 
through an online app, such as TaskRabbit or Uber”. Similarly, the estimates for Canada 
(Statcan, 2017) are particularly low: 0.3%; however, the scope is limited to people who 
provide services such as "rides" facilitated by online platforms such as Uber or Lyft. 

2.2  A  conceptual  framework  to  measure  platform  work  as  a  form 

of employment 

To assess the impact of DLPs on employment and on working conditions we need precise 
estimates of the number of people doing platform work; in addition we need information 
on what type of services they provide, how frequently these services  are provided, how 
much money is earned as a result of this provision and so on. Most of the studies carried 
out  until  now  provide  only  limited  information,  and  often  lack  a  conceptual  framework 
guiding the analysis.  

6 

 
This section fills a gap in the literature by providing a framework on how to elicit all the 
necessary  information  so  that  an  appropriate  measure  of  platform  work  as  a  form  of 
employment  can  be  retrieved  from  the  data.  It  should  be  noted  that,  because  of  the 
inherent difference between traditional employment and platform work, the latter cannot 
be quantified using exactly the same indicators and methodology of the European Union 
Labour  Force  Survey1  (EU  LFS)  -  the  reference  statistical  source  to  assess  labour 
participation  in  Europe.  While  the  object  of  the  LFS  is  regular  employment,  where  work 
activity  generally  corresponds  to  a  job,  the  object  of  a  survey  aimed  at  estimating  the 
prevalence  of  platform  work  is  to  assess  how  many  people  perform,  more  or  less 
regularly, atomised tasks via DLPs. The basic LFS question used to ascertain participation 
in  the  labour  market  –  whether  the  respondent  did  any  paid  work  as  employee  or  self-
employed in the 7 days before the interview – assumes that a person who answers “yes” 
has  a  job,  and  therefore  carries  out  work  activity  with  some  regularity.  However,  if  a 
respondent were asked if they did any paid work on a DLP and answered “yes”, we would 
only  know  that  the  person  has  carried  out  a  one-off  task,  without  further  indication  on 
how regularly the task was performed.  

This has two important corollaries for the purposes  of measuring platform work:  first of 
all, the identification of platform work as a form of employment requires that we assess 
not  only  whether  someone  has  done  some  work  via  platforms  on  a  particular  reference 
period,  but  also  the  regularity,  intensity  and  significance  of  that  work;  secondly,  any 
measure of the attributes of the work performed or the type of labour relationship should 
refer to a specific task. 

In  practical  terms,  we  propose  the  following  principles  to  measure  platform  work  as  a 
form of employment - assuming that the measurement is implemented through a survey 
aimed at the working age population. 

First  of  all,  it  is  necessary  to  identify  whether  the  respondent  is  part  of  the  platform 
economy with a screener question. The screener question must include all the elements 
of  the  definition  of  platform  worker,  i.e.  someone  who  provides  services  mediated  by  a 
DLP  and  gets  paid  for  it.  The  question  should  be  worded  in  such  a  way  as  to  exclude 
platforms  of  a  different  nature,  such  as  online  marketplaces  (e.g.  eBay)  or  asset  based 
platforms to rent out accommodation (e.g. Airbnb). Additionally, the following attributes 
of this work should be measured: 

  Locus (online or in-person provision) 

  Regularity (how often) over a specific reference period (e.g. last month) 

  Time allocated (working hours) in the reference period.  

 

Income generated (in monetary terms and as a share of total personal income) 

Information on the regularity, time allocated and income generated via DLPs, can then be 
used by the researcher to set thresholds to classify platform work as: 

a)  Main  -  equivalent  to  a  regular  job:  regular,  enough  hours,  enough  income 

(whereby enough means more than a reasonable threshold) 

b)  Secondary  –  whereby  the  employment  is  somewhat  regular,  but  less  the 
respondents does not work enough hours (below a given threshold) and/or earns 
enough income through DLPs as to be considered her main job; 

c)  Sporadic - infrequent and inconsequential in terms of time or income. 

1   The European Union Labour Force Survey (EU LFS) is conducted in the 28 Member States of the European 
Union,  2  candidate  countries and  3 countries of  the  European  Free  Trade  Association  (EFTA).  The  Labour 
Force Surveys are conducted by the national statistical institutes across Europe and are centrally processed 
by Eurostat. http://ec.europa.eu/eurostat/web/microdata/european-union-labour-force-survey  

7 

 
 
                                           
Estimates  of  the  size  of  the  platform  economy  in  terms  of  employment  should  focus  on 
the categories “secondary” and “main”. 

Secondly,  a  survey  should  identify  the  characteristics  of  the  work  itself  by  eliciting 
information  on  the  specific  tasks  performed  over  the  reference  period.  If  respondents 
worked  on  multiple  task  categories,  the  time  worked  in  each  task  category  in  the 
reference period should be asked.  

More  specifically,  for  the  main  task  category  over  the  reference  period,  the  following 
information  could  be  collected:  number  of  transactions;  number  of  platforms  used; 
number of clients; income generated; time spent; typical location; and general conditions 
(physical and psychological risks, social interaction, intrinsic rewards and satisfaction). 

2.3  An  empirical  demonstration  to  classify  platform  work  as  a 

form of employment 

The JRC COLLEEM survey 2017 can be used as a source of data to illustrate how platform 
work  can  be  measured  as  a  form  of  employment.  The  JRC  COLLEEM  2017  is  an  online 
survey representative of all internet users between 16 and 74 years  old in 14 European 
countries.  When  compared  to  data  from  official  statistics,  the  original  sample  of  the 
survey  appears  to  underrepresent  certain  profiles  of  respondents,  therefore  calibration 
weights should be used to present adjusted estimates.2  

The  JRC  COLLEEM  survey  contains  a  screener  question  to  identify  platform  workers  as 
the one suggested in section 2.2. It asks whether the respondent has ever gained income 
from  different  online  sources,  among  which  there  are  two  corresponding  to  DLPs 
(online/web-based or in-person): "providing services via online platforms, where you and 
the client are matched digitally, payment is conducted digitally via the platform and the 
work  is  location-independent,  web-based"  and  "providing  services  via  online  platforms, 
where  you  and  the  client  are  matched  digitally,  and  the  payment  is  conducted  digitally 
via  the  platform,  but  work  is  performed  on-location  (i.e.  in-person)".  Given  that  the 
definition  appears  to  be  rather  lengthy,  examples  of  typical  platforms  are  provided  to 
help respondents.  

The  share  of  the  JRC  COLLEEM  sample  that  responded  positively  to  either  of  those 
questions is reported in the first columns of panel 1 in  Table 1. All categories in Table 1 
refer  to  services  provided  either  online  or  in  person  without  differentiating,  since  the 
locus of service provision will be analysed in the next section. Because COLLEEM tends to 
overestimate  the  proportion  of  high  frequency  internet  users,  estimates  have  to  be 
adjusted using information from Eurostat’s ICT survey.  

Adjusted  estimates  range  from  6.9%  in  Finland  to  12.5%  in  Spain.  However,  as  we 
already  mentioned,  knowing  that  someone  provided  services  via  DLPs  at  least  once  in 
their  life-time  is  not  a  particularly  useful  piece  of  information  to  infer  the  size  of  the 
platform  workforce,  therefore  in  the  second  column  of  panel  1,  we  report  the  share  of 
people  who  provided  services  at  least  monthly.  Predictably,  the  proportions  drop  for  all 
countries,  but  not  necessarily  evenly  or  proportionately:  for  instance,  the  proportion  of 
people who provide services via DLPs monthly is 4.6 percentage points smaller than the 
proportion  of  those  who  have  ever  provided  services  in  Portugal,  but  less  than  1 
percentage point in the UK. In the  second panel, we combine information on regularity, 
time allocated, and income, as suggested above, to show the proportion of respondents 
who  provide  services  via  DLPs  sporadically,  as  a  secondary  activity,  and  as  a  main  job. 
More  specifically,  the  column  “sporadic”  identifies  people  who  work  between  0  and  10 
hours  a  week  on  platforms  and  earn  less  than  25%  of  their  total  income  from  platform 
work;  the  column  “Secondary”  identifies  respondents  who  earn  at  least  25%  of  their 
income via platforms (but less than 50%) and/or work via platforms between 10 and 20 

2   Calibration weights were computed using raking ratio estimation. Raking consists of adjusting proportions 
of certain variables in the sample to given population proportions. The survey was adjusted by country and 
the raking procedure included the following three variables: level of formal education, frequency of internet 
use and employment status. 

8 

 
                                           
hours  per  week;  the  column  “main”  identifies  respondents  who  earn  50%  or  more  of 
their  income  via  platforms  and/or  work  via  platforms  more  than  20  hours  a  week. 
Unsurprisingly,  the  proportion  of  people  doing  platform  work  as  main  or  secondary 
activity is significantly lower than the proportion of those who do it sporadically. The UK 
has the largest proportion of Platform workers who do it as a main job (3.6%), followed 
by  the  Netherlands  (2.8%)  and  Spain  (2.7%).  At  the  bottom  of  the  ranking  we  find 
Finland (0.9%) and Slovakia (0.9%).   

Table 1: An estimation of platform work as a form of employment (adjusted for 
high frequency internet use) 

Panel 1: screener questions 

Panel 2: core measures 

Ever  
platform 
work 

At least  
monthly 
platform work 

Sporadic  Secondary  Main 

Obs. 

Spain 
UK 
Portugal 
Germany 
Lithuania 
Netherlands 
Italy 
Romania 
Croatia 
France 
Sweden 
Slovakia 
Hungary 
Finland 

Total 

12.5% 
12.0% 
11.5% 
10.6% 
10.3% 
10.2% 
9.5% 
8.8% 
7.9% 
7.6% 
7.6% 
6.9% 
6.9% 
6.5% 

10.5% 

10.2% 
9.9% 
7.8% 
8.3% 
6.5% 
9.0% 
7.5% 
7.0% 
5.2% 
6.0% 
5.5% 
5.0% 
5.2% 
4.6% 

8.0% 

6.3% 
4.3% 
6.4% 
4.2% 
4.0% 
4.1% 
4.2% 
4.8% 
3.7% 
2.8% 
3.7% 
4.0% 
2.8% 
3.4% 

4.8% 

2.6% 
3.2% 
1.8% 
2.5% 
1.9% 
2.9% 
2.5% 
2.0% 
1.7% 
2.3% 
1.2% 
1.1% 
1.6% 
1.3% 

2.3% 

2.7% 
3.6% 
2.1% 
2.6% 
2.4% 
2.8% 
2.4% 
1.4% 
1.4% 
1.9% 
1.7% 
0.9% 
1.8% 
0.9% 

2,331 
2,320 
2,305 
2,319 
2,308 
2,314 
2,317 
2,307 
2,300 
2,315 
2,321 
2,313 
2,309 
2,310 

2.3% 

32,389 

Source: authors’ elaborations using COLLEEM 2017; weighted data. To adjust for high frequency 
internet use, we calculated the share of daily and weekly internet users by country in COLLEEM, 
divided it by the share of daily and weekly internet users by country in the ICT survey in 2017 
(isoc_ci_ifp_fu) and applied it to the estimates of platform workers in COLLEEM. Note: results are 
slightly different from Pesole et al (2018) because in this paper we use 2017 ICT survey data as 
opposed to 2016. 

As  previously  mentioned,  the  information  on  regularity,  time  allocated  and  income 
generated  by  platform  work  should  be  collected  by  keeping  in  mind  that  the  unit  of 
analysis is the task, or at least a task-type.  

The next section discusses how tasks in DLPs can be classified within a typology defined, 
among other things, by locus (online or in-person provision) and skills required. 

9 

 
 
 
 
 
 
 
3  What do platform workers actually do? A look at the type 

of tasks carried out  

Labour  services  provided  via  DLPs  can  be  broadly  characterised  as  services  performed 
online  or  via  web  and  services  performed  in  person  or  on  location.  This  basic 
categorisation  can  be  broken  down  further;  for  instance,  Eurofound  (2018)  sketches  a 
classification  of  platforms  which  takes  into  account  skills  required,  scale  of  the  task, 
selection  process,  and  the  form  of  matching  as  well  as the locus  of  provision.  However, 
combining  all  these  elements  would  result  in  120  combinations,  so  the  authors  focused 
on  a  smaller  subset  of  27.  De  Groen,  Maselli  and  Fabo  (2016)  provide  a  much  simpler 
classification  resulting  in  four  groups  which  combine  skill  (high/low)  and  locus  of 
provision (in person/online). The JRC COLLEEM 2017 survey  elicits information on types 
of task, which implicitly combines information on locus and skill level; Pesole et al (2018) 
use the information on tasks provided by COLLEEM to classify platform workers into three 
categories:  professional,  non-professional  and  on-location.  The  classification  was  used 
mainly to look at potential skill mismatch within each broader category.  

For  the  purposes  of  this  section,  we  will  analyse  the  10  different  task  types  in  the 
COLLEEM  survey  without  grouping  them  into  smaller  sets,  so  as  to  exploit  even  minor 
differences. 

The 10 tasks types defined by the COLLEEM survey are the following3:  

  Online  clerical  and  data-entry  tasks  (e.g.  customer  services,  data  entry, 

transcription, and similar);  

  Online  professional  services  (e.g.  accounting,  legal,  project  management  and 

similar) 

  Online  creative  and  multimedia  work  (e.g.  animation,  graphic  design,  photo 

editing and similar) 

  Online sales and marketing support work (e.g. lead generation, posting ads, social 

media management, search engine optimisation and similar) 

  Online  software  development  and  technology  work  (e.g.  data  science,  game 

development, mobile development and similar) 

  Online writing and translation work (e.g. article writing, copywriting, proofreading, 

translation and similar) 

  Online  micro  tasks  (e.g.  object  classification,  tagging,  content  review,  website 

feedback and similar) 

 

Interactive services (e.g. language teaching, interactive online lessons, interactive 
consultations and similar) 

  Transportation and delivery services (e.g., driving, food delivery, moving services 

and similar) 

  On-location 

services 

(e.g.  housekeeping,  beauty 

services,  on-location 

photography services and similar) 

Respondents could choose one or more tasks; it must be noted that in many cases there 
is  not  a  one-one  association  between  a  specific  DLP  and  a  type  of  task.  For  instance,  a 
person  who  provides  services  via  “Freelancer”  may  be  carrying  out  online  clerical  and 
data-entry tasks, as well as online professional or creative services.  

3   The  survey  also  contains  information  on  an  11th  type  of  task,  called  on-location  ancillary  services,  (e.g. 
housekeeping, cleaning) specifically to short-term rental accommodation (i.e. apartments listed on Airbnb 
and similar); however, the authors of the survey claim that it was put there just as control, and therefor 
ewe can disregard it.  

10 

 
 
                                           
Pesole et al. (2018) report that nearly 40% of the respondents who have ever provided 
services  via  DLPs  carry  out  only  one  type  of  task;  23%  claim  to  have  carried  out  two 
types  of  task;  16.2%  three;  while  the  remaining  20%  claim  to  have  performed  four  or 
more types of tasks. This illustrates the problem of studying platform work as if it were a 
regular  form  of  employment:  whereas  in  the  regular  labour  market  it  can  be  assumed 
that  anyone  employed  can  easily  answer  questions  referring  to  their  main  job,  people 
providing services via DLPs are likely to carry out many different tasks, linked to different 
characteristics  and  working  conditions,  and  therefore  may  struggle  to  answer  a  generic 
question  on  the  nature  or  conditions  of  their  work.  In  theory,  the  problem  could  be 
overcome by asking the question on working conditions and characteristics only to those 
respondents  who  carry  out  just  one  type  of  task;  however,  this  would  result  in  a 
significant loss of information.  

As  per  the  relationship  between  the  type  of  work  carried  out  and  the  frequency  or 
regularity with which it is done, one would expect to find significant differences between 
the categories identified in the previous section. Figure 1 shows the share of respondents 
who  provide  services  sporadically,  as  a  secondary  activity  and  as  a  main  job  for  each 
type of task, with the task types ordered according to the proportion of platform workers 
who performs them overall (number in red).  

The  category  “sporadic”  is  almost  always  the  dominant  one,  except  among  respondents 
who  carry  out  professional  tasks,  which  are  more  often  likely  to  do  it  as  a  main  job. 
Relatively medium skilled task types, such as translation, micro tasks or interactive tasks 
are  the  ones  in  which  the  proportion  of  platform  workers  who  do  it  as  a  main  job  is 
lower.   

It is also worth noticing the lack of clear correlation between how widespread a task type 
is  and  the  proportion  of  people  who  do  it  sporadically,  which  suggests  that  the 
prevalence of a task type is not driven by the presence of people simply trying out.  

Figure 1: Regularity of platform work by task type 

Clerical

Creative

Translation

Professional

Sales

Microtasking

44.2% 

36.1% 

46.5% 

34.5% 

39.7% 

42.6% 

33.7% 

25.8% 

30.3% 

27.6% 

25.8% 

30.5% 

26.6% 

34.9% 

33.7% 

30.0% 

45.7% 

31.5% 

29.9% 

28.6% 

28.5% 

30.5% 

26.9% 

24.7% 

Software

34.5% 

33.0% 

32.6% 

17.9% 

Transportation

44.3% 

25.8% 

29.9% 

17.0% 

Interactive

38.2% 

34.4% 

27.4% 

16.2% 

On location

38.4% 

28.1% 

33.6% 

14.2% 

Sporadic

Secondary

Main

% of PW sample

Source: authors’ elaborations using COLLEEM 2017; weighted data.  

Even  in  this  case,  any  further  analysis  of  task  types  should  be  limited  to  the  sample  of 
platform workers who provide services as a secondary or main activity, because they are 
likely to be more relevant from a policy perspective.  

11 

 
 
 
3.1  Socio-demographic  profiling  by  task  type:  an  empirical 

analysis 

The  empirical  analysis  of  COLLEEM  conducted  by  Pesole  et  al.  (2018)  revealed  that,  in 
general, platform workers tended to be young, highly educated males; when the authors 
regressed the three broad groups of task types – professional, non-professional and on-
location  –  on  the  socio-demographic  characteristics  and  country  of  residence  of  the 
workers, they found similar coefficients across all types, except for a higher probability of 
providing on-location services among the low educated. The study did not however look 
at the socio-demographic determinants of choosing each specific task type, which is what 
we are going to do in this section. Besides the usual socio-demographic control variables, 
such  as  age,  gender,  household  composition  and  educational  attainment,  we  add  a  few 
indicators  relating  to  motivations  provided  for  working  in  the  platform  economy.  In  a 
recent study carried out for the British Government, Broughton et al. (2018) report that 
flexibility  of  the  work  –  both  in  terms  of  hours  worked,  location  and  work-life  balance, 
was one of the main reasons for participating in the platform economy, based a series of 
qualitative  interviews.  We  therefore  create  a  few  simple  indicators  (more  details  below) 
to  investigate  empirically  their  association  with  the  choice  to  perform  a  given  task.  In 
addition,  we  include  an  indicator  representing  another  typical  motivation  for  working  as 
self-employed  which  can  be  applied  to  platform  workers,  that  is  the  desire  to  be  one’s 
own  boss.  Finally,  as  mentioned  above,  the  sample  will  be  limited  to  respondents  who 
provide  services  via  platforms  either  as  a  main  or  as  a  secondary  job,  hence  excluding 
those who do it sporadically.  

Econometric specification: 

Let (yn) be a binary variable equal to 1 if the respondent carries out a specific task type: 
clerical,  creative,  professional,  translation,  interactive  services,  software  development, 
microtasking, sales, transport and on-location; in this case, the probability of choosing a 
specific  type  of  task  (yn)  can  be  estimated  with  a  discrete  choice  model  by  maximum 
likelihood.  

We run a probit model that includes as a vector of regressors: age, gender (1 = female), 
education  (1  =  low  education,  2  =  medium,  and  3=high  education),  household 
composition (1 = couple), four indicators related to motivation (binary variables equal to 
1  if  respondents  find  important  or  very  important  flexibility  over  workplace,  flexibility 
over hours, work-life balance and being one’s own boss), and country level fixed effects, 
while ε is the error term.  

We  report  average  marginal  effects,  that  is,  how  the  probability  of  carrying  out  a  task 
changes as the independent variables change in Table 2.  

3.2  Results  

Estimates in Table 2 show that the sign and coefficient of some of the socio-demographic 
characteristics associated with platform work vary significantly across tasks, while others 
remain remarkably similar. For instance, females are 5.0 percentage points less likely to 
perform  creative  tasks,  3.9  percentage  points  less  likely  to  do  microtasking,  4.1 
percentage points less likely to do transportation and a 14.3 percentage points less likely 
to do software development; by  contrast, they are 6.4 percentage points more likely to 
engage in translation work via DLPs. Interpreting the coefficient of the age variable is not 
as straightforward when estimating a probit model, and ideally we would have to look at 
average marginal effects at representative ages to get a more detailed picture. However, 
it is  still  possible  to  say  that  while in  general  age  shows  a  negative  correlation  with  the 
probability  of  being  a  platform  worker,  Table  2  suggests  that  the  relationship  is 
significant only for some types of tasks, namely clerical, creative, software development, 
translation  and  micro-tasking.  Being  part  of  a  couple  raises  the  probability  of  doing 
professional  tasks  by  5  percentage  points  and  of  doing  software  development  by  3.7 

12 

 
 
percentage  points,  while  has  no  correlation  with  the  probability  of  carrying  out  other 
tasks. Having children appears to have a positive association with the probability of doing 
all tasks except for translation, microtasking and on location, which is consistent with the 
findings  of  Ipeirotis  (2010)  on  a  sample  of  microtasking  workers  (Amazon  Mechanical 
Turk).  High education appears to  be positively correlated with all types of tasks, except 
for microtasking, transportation and on location; in particular, respondents with a degree 
are 8.8 percentage points more likely to perform professional tasks than those who have 
medium  education,  and  6.3  percentage  points  more  likely  to  carry  out  software 
development  tasks.  On  location  tasks  are  the  only  ones  that  appear  to  be  predicted  by 
medium  education  given  that  both  the  coefficient  on  high  education  and  the  coefficient 
on low education have a negative sign.  

The motivation related indicators are significantly associated only with the probability of 
carrying  out  a  few  specific  tasks.  For  instance,  flexibility  to  choose  the  place  of  work 
appears  to  be  positively  associated  only  with  the  probability  of  doing  software 
development  and  interactive  tasks,  while  flexibility  over  hours  worked  appears  to  have 
the  strongest  association  with  translation  and  microtasking  (+6.9  percentage  points) 
followed by creative, clerical and sales tasks.  

Respondents who claim that an important motivation to do platform work is that it “offers 
a  type  of  work  that  is  more  compatible  with  [their]  family  commitments  than  standard 
employment” (indicator=balance) are 6.5 percentage point more likely to do professional 
tasks than those for whom work life balance is not an issue, 4.0 percentage points more 
likely to do transportation tasks, but there is no significant association with other tasks. 
Finally,  those  who  claim  that  the  desire  to  be  their  own  boss  has  been  an  important 
driver  to  choose  platform  work  are  more  likely  to  carry  out  creative,  translation  or  on 
location tasks.  

13 

 
 
 
 
 
Table 2: Average marginal effects of the probability of choosing a task-type 

Prob (y=1): 

Female (d) 

Age 

Couple (d) 

Children  

Highedu (d) 

Low educ (d) 

flexi_time 

flexi_place 

balance 

ownboss 

Country FE 
P>χ2 
Pseudo R2 
Observations 

(1) 
clerical 
0.445 
AME 
0.023 
(0.024) 
-0.002** 
(0.001) 
-0.018 
(0.026) 
0.032** 
(0.012) 
0.053** 
(0.025) 
-0.073* 
(0.044) 
0.055* 
(0.030) 
0.018 
(0.031) 
0.017 
(0.026) 
0.005 
(0.026) 
Yes 
0.0000 
0.0236 
1,933 

(2) 
professional 
0.295 
AME 
-0.015 
(0.022) 
0.000 
(0.001) 
0.050** 
(0.023) 
0.035*** 
(0.011) 
0.088*** 
(0.023) 
0.026 
(0.040) 
-0.044 
(0.028) 
0.034 
(0.028) 
0.065*** 
(0.024) 
0.011 
(0.024) 
Yes 
0.000 
0.0411 
1,933 

(3) 
creative 
0.330 
AME 
-0.050** 
(0.022) 
-0.003*** 
(0.001) 
0.011 
(0.024) 
0.029** 
(0.012) 
0.033 
(0.024) 
-0.051 
(0.042) 
0.061** 
(0.029) 
0.016 
(0.029) 
0.015 
(0.024) 
0.074*** 
(0.025) 
Yes 
0.000 
0.0295 
1,933 

(5) 

(4) 
sales  Software 
0.199 
0.288 
AME 
AME 
-0.143*** 
0.005 
(0.019) 
(0.021) 
-0.002*** 
-0.000 
(0.001) 
(0.001) 
0.037* 
0.011 
(0.023) 
(0.020) 
0.021*  0.029*** 
(0.011) 
(0.010) 
0.056**  0.063*** 
(0.020) 
(0.023) 
0.002 
0.062 
(0.036) 
(0.039) 
-0.014 
0.051* 
(0.024) 
(0.028) 
0.074*** 
-0.007 
(0.025) 
(0.028) 
0.010 
0.023 
(0.021) 
(0.023) 
0.010 
0.029 
(0.021) 
(0.024) 
Yes 
Yes 
0.0000 
0.0011 
0.0633 
0.0214 
1,933 
1,933 

(6) 

(7) 

translation  microtasking 

0.281 
AME 
0.065*** 
(0.021) 
-0.001* 
(0.001) 
-0.033 
(0.023) 
0.008 
(0.011) 
0.049** 
(0.022) 
-0.103** 
(0.042) 
0.071** 
(0.028) 
-0.025 
(0.028) 
-0.014 
(0.023) 
0.067*** 
(0.024) 
Yes 
0.0000 
0.0272 
1,933 

0.240 
AME 
-0.039* 
(0.020) 
-0.002** 
(0.001) 
0.016 
(0.022) 
-0.000 
(0.011) 
-0.012 
(0.021) 
-0.130*** 
(0.041) 
0.069*** 
(0.026) 
-0.009 
(0.026) 
-0.028 
(0.022) 
-0.006 
(0.022) 
Yes 
0.0002 
0.0243 
1,933 

(8) 
interactive 
0.168 
AME 
0.004 
(0.018) 
-0.001 
(0.001) 
-0.016 
(0.019) 
0.024*** 
(0.009) 
0.028 
(0.019) 
-0.076** 
(0.036) 
-0.034 
(0.023) 
0.057** 
(0.024) 
0.026 
(0.020) 
-0.015 
(0.020) 
Yes 
0.0085 
0.0238 
1,933 

(9) 
transportation 
0.164 
AME 
-0.041** 
(0.018) 
0.001 
(0.001) 
0.004 
(0.019) 
0.019** 
(0.009) 
-0.020 
(0.018) 
-0.003 
(0.032) 
-0.002 
(0.022) 
-0.010 
(0.023) 
0.040** 
(0.019) 
-0.017 
(0.020) 
Yes 
0.0008 
0.0289 
1,933 

(10) 
onlocation 
0.139 
AME 
0.003 
(0.016) 
-0.000 
(0.001) 
0.012 
(0.018) 
0.007 
(0.009) 
-0.032* 
(0.017) 
-0.059* 
(0.031) 
-0.030 
(0.021) 
0.001 
(0.021) 
0.023 
(0.018) 
0.055*** 
(0.019) 
Yes 
0.0421 
0.0215 
1,933 

Standard errors in parentheses are robust to heteroscedasticity 

*** p<0.01, ** p<0.05, * p<0.1 

14 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Most  of  results  are  fairly  intuitive  and  appear  to  be  consistent  with  previous  findings. 
What is remarkable is that by looking at task types separately rather than grouped as in 
Pesole et al (2018), different determinants emerge. For instance, the coefficient on being 
female is not significant in any of the regressions when the dependent variable is a group 
of tasks defined by high-low skill and locus of provision, where in in our analysis we find 
a  significant  association  with  five  task  types.  This  can  be  explained  by  the  fact  that 
creative, software development and translation tasks are grouped in the same category – 
i.e. professional – however the first two tasks are significantly and negatively associated 
with being a woman, while the association with the last type of task is positive. Similarly, 
being highly educated appears to be positively correlated not only with what Pesole  et al 
term professional tasks, but also with some non–professional task types such as clerical 
or sales services.  

Furthermore,  some  of  the  measures  for  motivation  to  participate  in  labour  platforms 
appear  in  line  with  common  knowledge  while  others  seem  more  counterintuitive:  for 
instance while it seems reasonable that people who are driven by the desire  for flexible 
hours  will  opt  for  creative,  translation  or  microtasking,  the  positive  association  between 
those who rate highly being one’s own boss and on-location tasks appear less obvious, in 
the  case  of,  for  instance  food  delivery  riders,  and  more  acceptable  in  the  case  of  ride 
hailing  apps.  This  leads  to  the  topic  explored  in  the  next  section,  the  labour  markets 
status of platform workers.  

 
 
 
4  The actual or perceived employment status of platform 

workers  

4.1  Legal issues about the employment status of platform workers  

The  classification  of  platform  workers,  the  delineation  of  the  relevant  market,  and  the 
nature  of  the  services  provided  by  DLPs  are  some  of  the  complex  challenges  that  the 
platform  economy  poses  to  the  legal  system.  Both  Pesole  et  al  (2018)  and  Huws  et  al 
(2017) highlight that the labour market status of platform workers is a challenging issue 
and  arguably  one  of  the  most  important  from  a  policy  perspective.  The  2016 
Communication  of  the  European  Commission  on  the  Collaborative  Economy  calls  for 
solutions that protect service providers (platform workers) without limiting the innovative 
potential  of  the  platform  economy  with  onerous  regulation  (European  Commission, 
2016).  DLPs  tend  to  qualify  platform  workers  as  independent  contractors  -  a  sub-
category of the self-employed.  

Even  though  some  platform  workers  may  display  characteristics  that  are  typical  of 
independent  contractors,  others,  for  example  those  who  carry  out  transportation  or  on-
location tasks are more reminiscent of employees (De Stefano and Aloisi, 2018).  

To fill what they perceived as a gap in employment legislation, Harris and Krueger (2015) 
proposed to create an intermediary, third category of workers: the Independent Worker, 
which would be specifically suited for (microtasking) platform workers. According to their 
proposal,  such  independent  workers  would  gain  rights  to  organise  and  bargain 
collectively  and  would also  gain  anti-discrimination  protections  in  the  US.  However,  this 
proposal would exclude payment for overtime and minimum wage arrangements. 

The  proliferation  of  individual  lawsuits  and  grassroots  organisations  asking  for  a  more 
accurate  classification  of  platform  workers  supports  the  claim  that  not  all  platform 
workers  should  be  classified  as  self-employed.  However,  the  lack  of  a  unified  legal 
framework often results in uneven court rulings. For instance, in the case of Aaslam Y. & 
Farrar J. against Uber4, the US employment tribunal ruled in their favour by agreeing that 
they  were  "workers"  within  the  definition  in  s.230(3)(b)  of  the  Employment  Rights  Act 
1996,  and  were  thus  entitled  to  the  minimum  wage  and  holiday  pay.  However,  the 
tribunal did not settle the issues of the employment status as it did not  specify whether 
the  claimants  were  also  employees.  Another  example  in  the  US  is  the  case  of  Otey  vs. 
Crowdflower  in  2013,5  where  the  court  identified  violations  of  the  Fair  Labor  Standards 
Act  (FLSA)  and  ruled  in  favour  of  a  minimum  wage  for  Crowdflower  workers  who 
performed microtasks via Amazon Mechanical Turk.  

In  France,  a  ruling  of  the  Conseil  des  Proud’hommes  de  Paris  in  December  2016  has 
qualified  the  relation  between  a  private-hire  driver  (“voiture  de  transport  avec 
chauffeur”) and a transportation DLP (Le Cab) as an employment contract, in light of the 
exclusivity clause preventing the driver from using other platforms or serving a customer 
on his own. In addition, the French social security institution has demanded contributions 
by  Uber,  therefore  qualifying  its  “partner-drivers”  as  employees  and  not  self-employed 
(De Stefano and Aloisi, 2018). In the UK grassroots organisations, such the “Independent 
Workers  Union  of  Great  Britain”,  have  started  promoting  lawsuits  to  overturn  the 
improper  classification  of  platform  workers  and  achieving  remarkable  victories,  such  as 
the right to a minimum wage and holiday pay. However, they did not manage to win the 
right  to  collective  bargaining  for  on-location  platform  works  who  delivered  food,  as  the 
High Court ruled in favour of the food delivery service app Deliveroo.6 

In  Italy,  six  platform  workers  providing  on-location  services  (food  delivery)  through  the 
DLP  “Foodora”  filed  an  employment  claim  before  the  Turin  Employment  Tribunal  at  the 

4   Employment Tribunal, Mr Y Aslam, Mr J Farrar and Others v Uber, Case Numbers: 2202551/2015 & Others, 

28 October 2016. 

5   Otey v. CrowdFlower, Inc., No. 12-cv-05524 JST, 2013 WL 4552493, at *3 (N.D. Cal. Aug. 27, 2013). 
6   https://www.ft.com/content/51fb5da8-f879-11e8-af46-2022a0b02a6c.  

 
 
                                           
end  of  2017  asking  that  their  contracts  be  reclassified  as  employment  contracts,  and 
therefore their dismissal ruled as unfair. On 11 April 2018 the tribunal ruled in favour of 
Foodora claiming that “riders” were completely free to accept or refuse any meal delivery 
requests  from  the  platform,  and  therefore  fit  within  the  category  of  freelance,  not 
subordinate  employment.7  Similarly,  the  British  High  Court  judgment  backed  an  earlier 
ruling  by  the  Central  Arbitration  Committee  (CAC),  which  found  that  Deliveroo  riders 
cannot  be  classed  as  "workers"  because  they  have  the  option  of  asking  other  riders  to 
take their deliveries. 

A  recent  study  focusing  on  a  specific  type  of  on-location  platform  workers  in  Italy, 
specifically those providing services through a food delivery app, investigated the extent 
to  which  the  characteristics  associated  with  this  type  of  work  made  it  more  similar  to 
subordinate  employment  or  self-employment  (Giorgiantonio  and  Rizzica,  2018).  The 
authors  claim  that  to  understand  the  extent  to  which  platform  workers  should  be 
classified  as  employee  as  opposed  to  self-employed  it  is  necessary  to  have  information 
on  regularity,  time  allocated  and  income  earned.  In  other  words,  only  platform  workers 
who  provide  services  as  a  main  job  should  be  classified  as  employees.  Albeit  related 
because  of  policy  implications,  the  employment  status  of  platform  workers  is  a  distinct 
issue from the classification in terms of  regularity, time allocated and income; a worker 
may be earning all of his or her income by providing professional services via DLPs and 
yet  not  fit  within  the  category  of  employee.  Furthermore,  as  Figure  1  showed,  the 
proportion  of  platform  workers  who  provide  services  via  DLPs  as  a  main  job  does  not 
differ exceedingly across task types.   

Pesole et al. (2018) tried to address this issue by classifying platform workers according 
to  whether  they  claimed  to  have  a  side  gig  as  self-employed.  The  authors  initially 
comment  on  the  problem  of  retrieving  information  on  employment  status  by  directly 
eliciting  from  platform  workers,  since  workers  themselves  may  not  be  aware  of  what 
their  legal  status  is.  They  explain  that  the  fact  that  many  platform  workers  identify  as 
employees  does  not  imply  that  they  have  other  employment  contracts  besides  platform 
work,  because  they  may  be  simply  considering  the  provision  of  services  via  DLPs  as 
subordinate  employment.  For  this  reason,  Pesole  et  al.  combine  information  on  main 
employment  status  and  additional  self-employment  provided  by  the  COLLEEM  survey  to 
split the population of platform workers into the following five categories: Self-employed; 
Employees;  Employee  +  self-employed  (platform  workers  who  are  employees  as  main 
activity,  but  also  self-employed  as  additional  activity);  Not  employed  (unemployed, 
retirees, students, and homemakers); and Not employed on the main but self-employed 
on  the  side  (platform  workers  who  claim  to  be  mainly  unemployed,  retirees,  students, 
and homemakers, but also have additional activity as self-employed).  

The  authors  found  that  the  proportion  of  platform  workers  who  claimed  to  have  self-
employment  as  secondary  activity  was  substantially  higher  among  those  who  provided 
services  more  regularly.  However,  the  study  did  not  look  at  how  different  categories  of 
employment status interacted with the types of tasks that platform workers provided, as 
we are doing in this section. A priori, we would expect a higher proportion of respondents 
who identify as self-employed among those who provide typically freelance services, such 
as  creative,  translation  or  software  development  tasks.  Similarly,  it  would  not  be 
surprising  to  find  a  high  proportion  of  platform  workers  providing  transportation  or  on 
location services who claim to be employees.  

4.2  An empirical assessment of the employment status of platform 

workers  

This  section  builds  on  the  work  by  Pesole  et  al  (2018)  and  looks  at  the  self-declared 
employment status of platform workers with a specific focus on task types.  

7 https://www.lexology.com/library/detail.aspx?g=9f42d61e-31a8-464c-83c2-7c6c4ba3670b . 

 
 
 
                                           
We grouped the  employment status  of platform workers defined by Pesole  et al. in four 
categories  by  focusing  on  the  main  issue,  trying  to  assess  whether  platform  workers 
consider the provision of services via DLPs as a form of self-employment (either main or 
on the side).  

Table  3  report  some  simple  descriptive  statistics  of  employment  status  for  sporadic, 
secondary and main platform workers.  

Table 3: Employment status of platform workers 

Self-employed 

Employee 

Side-gig as self-employed 

Not employed 

Observations 

Sporadic 
7.0% 

Secondary 
9.9% 

48.0% 

27.83 

17.3% 

1,693 

38.5% 

38.52 

13.1% 

405 

Main 
13.8% 

37.8% 

42.32 

6.1% 

376 

Source: authors’ elaborations using COLLEEM 2017; weighted data.  

From Table 3 two things emerge: first of all, the proportion of self-employment as a main 
and  as  a  side  gig  increases  with  the  intensity  of  platform  work.  Secondly,  even  among 
secondary and main platform workers there is still a significant proportion of respondents 
(approx.  38%)  who  claim  to  be  an  employee.  Respondents  who  claim  to  be  self-
employed  and  provide  services  via  platforms  as  a  secondary  or  main  source  of  income 
are likely to consider their work via DLPs as a form of self-employment. Similarly, those 
who claim to have another main employment status, be it employee, student, retired or 
other,  but  also  have  a  side  gig  as  self-employed,  probably  consider  platform  work  as 
their side gig. The fact that even allowing for a secondary status as self-employed we still 
find  a  substantial  proportion  of  platform  workers  who  claim  to  be  an  employee  suggest 
that  many  of  them  probably  consider  their  work  via  platforms  as  subordinate 
employment  –  which  would  not  be  entirely  surprising,  given  the  aforementioned  court 
cases.   

We  further  investigate  whether  the  relationship  between  the  self-reported  employment 
status of platform workers as employee may depend on the task they perform in  Figure 
2.  The  sample  is  limited  to  platform  workers  who  provide  services  as  a  secondary  and 
main job. 

Figure  2  illustrates  the  relationship  between  the  four  categories  of  employment  status 
and the type of task platform workers  carry out (sample limited to secondary and main 
platform workers). Because the emphasis is placed on respondents who claim to be only 
employees  (with  no  secondary  self-employment),  the  chart  is  sorted  so  that  tasks  with 
the highest proportion of only employees are at the top.   

Platform  workers  carrying  out  professional,  interactive  or  transportation  tasks  are  the 
ones  who  report  more  frequently  that  they  are  only  employees  (42.3%,  40.2%and 
39.7%  respectively),  while  those  who  provide  on-location  services  report  being  an 
employee  with  the  lowest  frequency  (31.6%).  In  addition,  on  location  platform  workers 
report more frequently that they are self employed, both as a main job (14.5%) and as a 
side gig (45.6%). The highest proportion of platform workers who claim to be only self-
employed occurs among those who carry out microtasking (15.5%) as well as on-location 
workers  (14.5%),  while,  somewhat  predictably,  the  same  is  true  for  a  remarkably  low 
proportion of platform workers who provide transportation services (9.6%).  

 
 
 
 
 
Figure 2: Self-reported employment status by task type – only main and 
secondary platform workers 

Professional

Interactive

Transportati…

Software

Creative

Sales

Microtasking

Translation

Clerical

On location

42.3% 

40.2% 

39.7% 

39.5% 

38.7% 

38.4% 

38.0% 

37.8% 

37.5% 

9.9% 

12.0% 

9.6% 

11.8% 

13.6% 

10.2% 

15.5% 

10.9% 

13.5% 

31.6% 

14.5% 

42.2% 

40.1% 

5.7% 

571 

7.7% 

327 

39.5% 

11.2% 

319 

41.4% 

40.5% 

41.6% 

36.0% 

40.9% 

37.2% 

45.6% 

7.9% 

385 

7.3% 

640 

9.8% 

559 

10.5% 

465 

10.4% 

546 

11.81 

862 

8.3% 

271 

Employees

Self-employed

Side gig as self-employed

Not employed

# obs.

Source: authors’ elaborations using COLLEEM 2017; weighted data.  

 
 
 
 
5  Discussion and conclusions 

This  article  explored  some  key  issues  related  to  the  emerging  phenomenon  of  digital 
labour  platforms:  firstly,  how  to  measure  platform  work  as  a  form  of  employment 
incorporating  elements  such  as  regularity  of  provision,  time  allocated  and  income 
generated;  secondly,  how  to  investigate  the  services  provided  via  DLPs,  by  focusing  on 
task  types  as  the  unit  of  analysis;  and  finally  how  to  classify  the  employment  status  of 
platform workers, given the current gap in legislation and unevenness in the literature.  

Accurate  measurement  is  crucial  for  understanding  the  current  magnitude  and  potential 
growth  of  the  platform  economy.  This  article  identified  some  necessary  information  to 
classify platform workers, and therefore be able to produce reliable estimates of the size 
of the DLP workforce. Ideally, future surveys investigating this subject should include all 
the  basic  elements  identified  in  this  paper:  frequency  of  provision,  time  allocated  and 
income  generated,  as  well  as  characteristics  linked  to  the  task  performed,  such  as 
number of transactions, platform used, time spent carrying out a task, and other general 
conditions.  However,  collecting  information  on  regularity,  time  allocated  and  income 
generated via DLPs has implications beyond the need to estimate precisely the size of the 
platform  economy:  according  to  Broughton  et  al  (2018),  the  experience  of  platform 
workers  depends  heavily  on  whether  they  are  providing  services  via DLPs  as  their  main 
source of income. If this is the case, they are more vulnerable to fluctuations in working 
time  and  therefore  pay  levels,  short  notice  of  working  schedules,  and  suffer  from  a 
degree of precariousness in terms of a lack of employment rights. In addition, if platform 
work  represents  their  main  source  of  income,  individuals  may  not  have  a  real  choice 
about which work to accept. By contrast, individuals providing services via platforms as a 
secondary  or  sporadic  activity  in  order  to  top  up  their  income  are  less  vulnerable  to 
fluctuations in the amount of work available and typically have more freedom to choose 
which  jobs  to  accept  and  reject.  The  notion  that  a  worker  who  provides  services  via 
platforms as a main job is not free to reject a job he or she does not want is in complete 
antithesis  with  the  Foodora  or  Deliveroo  court  rulings,  which  was  based  on  the  formal 
characteristics of the work, rather than the substantial ones.  

This  highlights  the  importance  to  come  up  with  a  harmonized  way  to  measure  platform 
work,  which  is  able  to  disentangle  sporadic  from  main/secondary  service  provision,  and 
regulate them accordingly. Furthermore, it is imperative to look at task types separately, 
and  gather  information  directly  linked  to  single  tasks  carried  out  rather  than  platform 
work  in  general,  given  the  substantial  heterogeneity  not  only  in  the  intrinsic 
characteristics of different tasks, but also in the attributes and motivations of the workers 
performing them. 

Finally,  our  findings  suggest  that  even  though  a  very  large  proportion  of  platform 
workers  consider  their  work  through  platforms  as  a  form  of  self-employment  (either 
primary  or  as  side  activity)  across  all  different  types  of  tasks,  a  significant  share  still 
perceive themselves as employees, probably because they lack the kind of freedom and 
control over the work they do that is typically associated with self-employment. For these 
workers,  a  clarification  of  their  employment  status  and  a  proper  protection  of  their 
employment rights and entitlements is an urgent necessity. 

 
 
 
 
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