Benefits and protection of american workers in the age of on demand and multi-sided platform

3
1 Acknowledgements
First we would like to thank my professor Zehra Gulen Sarial Abi for her
attention, corrections and care she devotes to this work.
Then to professor Paula Semenzato, for her language adjustments and
review of this thesis.
After to my family, my mother, my father and my brother, the best
investors every entrepreneur of life can hope to have, who believe in me
beyond every expectation.
To Giulia Girardi, and Egemen Korkusuz, my best friends and colleagues in
this experience, who make me growing both as a person and both as a
professional future marketing manager.
To Saadet Okur, dear companion and advisor, who with affection and care
drives my experience in Milan into something special.
To my friends from Padua, indivisible companions of adventures,
experience and maturation.
To my colleagues and travel companions in Los Angeles, and my dear flat
mates in Canada Elisa Villa and Jordan Perrier for making my traveling
experience complete, amazing and unforgettable.
To my friends from Trent, and my Erasmus ones from Poland who make
magic all my studying experience.
And lastly to Bocconi, that gives me amazing opportunities, broadens my
horizons and aspirations, and makes me grow in behavior, personality and
knowledge.

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2 Table of Contents
1 Acknowledgements ...................................................................................................... 3
2 Table of Contents ......................................................................................................... 5
3 Introduction ................................................................................................................. 8
4 The rise of the Sharing Economy ................................................................................. 12
4.3 Collaborative consumption: A new concept of economy .................................................................... 12
4.4 Main data about the sharing economy and Airbnb and Uber case .................................................. 14
4.5 The creation and the rise of sharing economy ......................................................................................... 17
5 The “supply side” between opportunities and challenges ........................................... 22
5.3 The boom of freelance economy .................................................................................................................... 22
5.4 Sharing economy, gig economy, on-demand economy and its “employees” .............................. 24
5.5 The advantages of the new working models ............................................................................................ 28
5.6 The “independent contractors” and the (missed) liability of companies .................................... 30
6 Research and analysis section ..................................................................................... 36
6.3 The aim of the questionnaire ........................................................................................................................... 36
6.4 Data audit and data cleaning ........................................................................................................................... 40
6.5 Univariate analysis ............................................................................................................................................... 45
6.5.1 Main implications and conclusion from univariate analysis .......................................................... 61
6.6 Bivariate analysis .................................................................................................................................................. 63
6.6.1 Main implications from bivariate analysis ............................................................................................. 97
Conjoint analysis ............................................................................................................................................................... 99
6.6.2 Conjoint analysis implications ................................................................................................................... 102
6.7 Factor and cluster analysis ............................................................................................................................ 103
6.7.1 Factor and cluster analysis implications .............................................................................................. 116
6.8 Multiple linear regression analysis ............................................................................................................ 118
1.3.5 Multiple linear regression implication .................................................................................................. 121
7 Concluding section ................................................................................................... 122
7.3 Main implication of the analysis: our contributions to the current research ......................... 122
7.4 Conclusion ............................................................................................................................................................ 134
8 Limitations ............................................................................................................... 139
8.3 The choice of the topic and its continuous updating .......................................................................... 139
8.4 General observation and data audit limitations ................................................................................... 141
8.5 Univariate and bivariate analysis limitations ....................................................................................... 144
8.6 Conjoint analysis limitations ........................................................................................................................ 147
8.7 Factor and cluster analysis limitations .................................................................................................... 148
8.8 Multiple linear regression limitations ...................................................................................................... 150
9 Bibliography ............................................................................................................. 151
10 Spss survey: benefits and protections for American workers .................................. 161
11 Appendix ................................................................................................................ 167
11.3 Analysis of outliers ......................................................................................................................................... 167
11.4 Who is their target? ........................................................................................................................................ 170

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11.5 How is sharing economy differentiated? .............................................................................................. 173

7
“Without us noticing, we are entering the post-capitalist era(..). At the
heart of further change to come, is information technology, new ways of
working and the sharing economy. The old ways will take a long while to
disappear, but it’s time to be utopian.” (Mason, 2015)

8
3 Introduction
Sharing economy is the next big challenge for existing business
worldwide. Every year since the word and the concept started appearing
in different newspaper, publishers, journalists, economists, and most of all
employers, have been betting as to when the disruption in their market
will take place. Wired wrote that 2015 would have been the year of
sharing economy (Cosimi, 2014) and the Times inserted it into the idea
that would change the world in the next decade (Walsh, 2011).
This year is not finished yet, and certainly some sectors have been
disrupted by this concept, but others are still waiting for this
“earthquake”. Companies analyze data, create possible scenarios, and
prepare possible strategies waiting for the time when the movement will
take place among their consumers (PWC, 2014). So businesses wait
fearing that this new disruptive concept will eventually erode their income
and profits; some of introducing new offer and adapting their business
model to the upcoming trend, others react when it is too late.
Meanwhile little firms, startups that understand the upcoming change in
the consumers mind, take advantage of this; with their decision-making
process more flexible and effective, they manage to grow at an
astonishing rate, and they soon become big enough to displace the old
incumbent in a typical and old business cycle that repeats itself over and
over (Rosenberg, 2015).
We believe that the disruption is close in many sectors but we do not
believe that this is the year of the sharing economy. However we think
that this is the time when curiosity toward this movement has boomed
and its awareness has spread so much from a niche concept started in

9
2010, to a mainstream one. Simply looking at the Google history of the
term, we can see how even in Italy the searching of this term has
increased by 9900% since October 2013; worldwide speaking from
February 2013 till today has risen by more than 1000% (Google, 2015).
The rule of the “business game” has been changed particularly in the US,
where this movement was created and developed faster than in any other
country.
For this reason we have decided to focus my thesis in this geographical
area. Here the sharing economy has found its fertile ground, together with
the on-demand business model. On-demand means that businesses are
able to provide products or services to customers in real time, thanks to
technological improvements and smartphone-based applications (Jaconi,
2014).
Here companies such as Uber combine the sharing economy concept with
the on-demand one, creating an innovative business model and basing
their success on independent contractors; employees depend on
companies to find jobs, but they are left with no guaranteed protections or
benefits. In fact, self-dependent contractors have only the aim to make a
salary on the basis of the contingent works they find through the
platform.
Little selection, quick application, quick response and so many common
people were able to gain some extra money at the end of the day.
Independent contractors were indeed a cost revolution for many
companies, which are able to save a lot of money on benefits, protections,
training and paid dismissals. On the other side the more this model
spread, the more traditional companies were threatened in their core

10
business and activities, by on-demand platforms, which often were able to
provide the same level of services at a cheaper cost.
To protect themselves traditional companies have appealed to the courts
in several states in order to force on-demand companies to change their
business structure. Others adapt their business model to the competitors’
one. However many issues remain on the supply side for both sharing
economy and on-demand ones, which involve non-professional workers.
First of all: do American workers really prefer to work inside these
platforms? Are they satisfied with an employment contract, which
guarantees them neither benefits nor protections?
Therefore the first part of my thesis will focus on the defining what the
sharing economy is, its numbers, which factors make it successful, and
which sectors are affected by this business idea.
In the second part, we will deal more specifically with on-demand
companies, their business models and the consistent role of the supply
side in shaping the success and the future of these platforms. Moreover
we will define terms such as gig economy and on-demand economy,
identify the benefits and advantages of this model for companies, and all
the challenges and problems faced by their “employees”.
In the third part we will define our research as to the importance of
benefits and protections for US workers; we then present our survey and
comment our analysis.
We will conduct a univariate and bivariate analysis, a conjoint analysis, a
factor and cluster analysis, and a multiple linear regression. For every
examination, we will outline the main implications.

11
In the fourth part we will summarize the results of our findings, and we
will combine them together with secondary data and previous research.
Lastly we will draw our conclusion based on these results.

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4 The rise of the Sharing Economy
4.3 Collaborative consumption: A new concept of
economy
What is the sharing economy? As mentioned earlier the sharing economy
is not a temporary trend, a business idea, or a management approach,
but a global movement, which is rising at stunning rate and could change
capitalism, as we know it today. Other names have been given to this
movement beginning in 2009-2010 such as new economy, mesh
economy, peer-to-peer economy…
Rachel Bootsman is probably the most important researcher in this topic
and was among the first to identify this new trend, to study it and assign
a proper definition in 2010. He described this sharing economy as “an
economic model based on sharing underutilized assets from spaces to
skills to stuff for monetary or non-monetary benefits” (Botsman, 2013).
The sharing economy is indeed a revolution that changes the typical
capitalistic model of creation and ownership, into a share and access
model. The consumption process made of buy, use, and discharge, is
transformed into accessing and reutilizing underused resources. Moreover,
the most innovative feature is that it is all managed through community
system put together through an online channel, so that this “spontaneous
rise of collaborative production no longer respond to the dictates of the
market. (Denning, 2015)”. Community in the sense that professional
workers are not include in this process.
This was made possible through the Internet connection that solves the
so-called problem of the “coincidence of want”. Let’s assume first that in a
disconnected world, A wants X and B is willing to offer that, but if B is

13
living far away from A, the exchange of item or service has a low
probability to take place. In a connected world this rate is much higher
due to the internet consent to connect different and separated individuals
all over the world (Rachel Bootsman, 2010), and making the match of
offer and demand as quick and effective as never before.
On the other side the consumers experiment the so-called Collaborative
Consumption which allows people to get the benefits of accessing products
instead of owning them, or get quick services instead of solving problems
by themselves (Rachel Bootsman, 2010), so that they extract value from
already existing resources.
Maybe we are at the end of the capitalism era, or maybe it is only a new
form of capitalism, but what is certain is that a new “wind” is coming and
“almost unnoticed in the niches and hollows of the market system, whole
swaths of economic life are beginning to move to a different rhythm.”
(Mason, 2015).”

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4.4 Main data about the sharing economy and Airbnb and
Uber case
A sampling of 200 startups involved in the sharing economy was
estimated to have collected 2 billion $ in venture funding in 2014. The
total funding received by these startups was worth $110bn in 2013
(Gravitytank, 2013).
This sharing economy till this year has received $15 billion in funding,
which is more than the entire social networking sector that includes
Facebook, Twitter, Snapchat, and more (Koetsier J. , 2015). This funding
can represent a solid base for the future growth of these companies.
Today, we estimate that the top five main sharing economy sectors
generate $15bn in global revenues. However, by 2025, these same five
main sharing economy sectors could generate a potential revenue
opportunity worth $335bn (Wosskow, 2014).
The sharing economy now is valued for £9 billion will be valued £230bn in
2025 (Wosskow, 2014). According to Jeremiah Owyang, leader in
research of the sector at VB profile, a market intelligence firm, there were
17 billion-dollar companies with 60,000 employees two years ago
(Owyang, The Collaborative Economy , 2013).
Among the multibillion dollar companies we found Ola ($1b), Instacart
($2b), TranferWise ($1b), Lyft($2.5b), We work ($5b), Kuadi Dache
($8.8b) and lastly Airbnb and Uber (Koetsier J. , 2015).
These last two have been considered the pillars of the sharing economy
and for many reasons. Their astonishing rate of growth brought their

15
evaluation up to $24 and $50 billions respectively in recent estimates in
June 2015.
Thanks to this, Airbnb surpassed the Marriott chain, one of biggest hotel
chain worldwide that manages more than 4,000 hotels, got $13.8 billion in
revenue in 2014, and was valued at about $21 billion the same year. Its
main Airbnb’s rival is the travel site Expedia Inc. has been doubled thanks
to its growth rate at about 90% projected over the past two years (Rolfe
& Macmillan, 2015) though its current evaluation is “only” $15.60 billions
(Trefis, 2015).
On the other side Uber Technologies Inc. has recently completed a new
round of funding that valued the company to almost $51 billion,
reaching Facebook Inc.’s record for a private, venture-backed startup.
Moreover Uber reached this target two years faster than Facebook thanks
to its rapid global expansion into more than 300 cities, a growing success
among customers, and popularity among millions of daily riders (Douglas
Macmillan, 2015). Thanks to Uber and other car sharing services,
AlixPartners, a consulting firm has forecasted that by 2020 more than 4
million vehicles would disappear from the European and American streets
(Fuhrmans, 2013).
Like Uber, 12 out of 17 of these multibillion-dollar companies were in the
US, but meanwhile the European sharing companies also kept on
expanding (Koetsier J. , 2015). Bla bla car, the Paris-based company
raised $110 million from venture capital backers in July 2014 beating a
record of funding for a French VC company (Dillet, 2014), and today it is
searching new financing opportunities that would value the company at
more than $1 billion (Picker & Davis, 2015).

16
London, Paris and Berlin are already leader in the sharing mobility sector
with the latter one counting 200K subscribers to car sharing services
(Nieuwenhuis & Wells, 2015). Milan follows right behind with new car
sharing services offered in the city every day (Borella, 2015).
The more this sharing economy expands the more money goes into the
pockets of private entrepreneurs. Forbes indeed estimated that the
revenue for these independent workers would surpass $3.5 billion in
2013, with growth exceeding 25% (Geron, 2013).
If we consider the drivers of success of sharing economy we will
understand how this movement is destined to grow.

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4.5 The creation and the rise of sharing economy
In the same period sharing economy started spreading, in October 2009
Elinor Ostrom won the Nobel Memorial Prize in Economic Sciences,
together with Oliver E. Williamson demonstrating how commons-based
societies can work and be efficient.
She started from the “tragedy of commons” problem, where the bad
behaviors of single individuals, the so-called free-riders can affect the
entire community in a negative way (Hardin, 1968). Her research has
demonstrated that certain rules applied to a society can make a self-
organized community work, and all individuals cooperate for the social
well-being (Ostrom, 1999). This is mainly the concept that many sharing
economy platforms are trying to apply.
We can claim that the idea of a sharing economy was structured in this
period, but on the other hand studying the origin of sharing economy
platforms could be extremely complicated. There is no single company
considered as the first creator of this kind of businesses.
Many suggest that the sharing economy is an evolution of the so-called
“user contribution system”; a method “for aggregating and leveraging
people’s contributions or behaviors in ways that are useful to other
people” (Cook, 2008). Through this system, contributions (such as videos,
information, knowledge) are gathered together and automatically
transformed into something useful to others. This has been the case for
many successful companies such as Wikipedia, YouTube, Facebook, and
MySpace and many others.
The more these platforms have been able to manage efficiently the control
they hand over to their users efficiently, the more they have been able to

18
develop self-regulating ecosystem, and lead their market against
competitors.
The crowdsourcing and open-source models have been such a valuable
option for many successful companies such as Linux or Mozzilla Firefox in
2002.
On the other side the first types of sharing websites have born thanks to
non-profit initiatives, such as Couchsurfing and Freecycle (both created in
2003). The first form of sharing economy was developed by a public
institution and concerns the bike sharing system created in Lyon, France
in 2005 (Rachel Bootsman, 2010). This revolution went to Paris a couple
of year after (Lanyado, 2007), and from there it expands to the US and
the rest of the world (Penn & Wihbey, 2015).
Researchers underline a mix of several reasons behind the creation of this
movement. For disadvantaged communities there are mainly economic
forces, which let them move towards this new form of earning. The crisis
of 2008 represented a big turnover for many people, and a change in their
consumption habit. The recession creates unemployment and a consistent
debt load also for the middle class; therefore many of these job seekers
were forced to seek alternative solutions in order to save money.
(Dillahunt & Malone, 2015). However these are not the only reasons.
Rachel Botsman identifies five main causes such as a renewed belief in the
importance of community, a torrent of peer to peer social networks real
time technologies’ development, pressing unresolved environmental
concerns, and a global recession that shocked consumer behaviors into
cost-consciousness solutions. Behind these causes there is also the
loneliness of the contemporary society, experienced especially by
Millennials, which creates the need for establishing different forms of

19
community. In this sense the information technology offer many points of
inspiration (What's mine is yours: the rise of collaborative consumption,
2010).
The Cambell Mithun research differentiates between rational and
emotional benefits: among the first kind, there are financial issues of
saving the money due to the crisis, environmental ones, to help the
environment, and lastly the need for flexibility which comes when
assessing goods and services. For what concerns emotional benefits there
is a renewed sense of generosity, the desire to be part of a community, to
conduct a responsible and sustainable lifestyle, and the need to be part of
this movement (National study quantifies reality of the ‘sharing economy’
movement, 2012).
In the Altimeter research (Owyang, The Collaborative Economy , 2013)
the rise of the Collaborative Economy is driven by different market forces,
which are societal, economic, and technological. Among the first ones,
there is an increasing population density and urbanization, the drive for
sustainability, and a renewed desire for community, a generational
altruism.
Indeed if we look at the data worldwide, the rate of urbanization of the
population has increased by 2.05% annually in the 2000-2015 period (The
World factbook, 2015).
Secondly among the economic reason the capitalism has created a
monetize excess and an idling inventory, a desire towards financial

20
flexibility, a preference of access towards ownership, and the increase of
venture capital funding.
Self-storage is now a $22 billion-per-year industry in the U.S.A and its
value has overcome the American Hollywood box-office sales. On the
other side rentable storage indeed has risen up by 740% between 1990
and 2010. The executive director of Greenpeace USA, declared that the
percentage of total material still in production or use 6 months after their
sale in North America is only 1% (Leornard, 2011).
Thirdly there are technological improvements, such as the creation of SNs
websites, the increase of mobile devices and alternative platforms, and
the invention of flexible and various online payment systems. For instance
among 30 top sharing startups, 74% have social profile identification’s
systems, and more than half (54%) have integrated Facebook Connect
(Owyang, The Collaborative Economy , 2013).
The smartphone number instead is expecting to reach over 2 billions
customers by 2016 (eMarketer, 2014). Moreover the next year the sales
of these items are expecting to grow of 12.6% after 8 years, in which this
number has decupled itself (Statista, Number of smartphones sold, 2015).
In the meanwhile tablet sales are expecting to grow as well by 11% in
comparison with a 12% increase in sales of the past year (Rivera, 2015).
This data suggest that there is a huge fertile ground for the growth of the
sharing economy in the next decade.
However many threats and challenges exist in the future of sharing
economy especially on the supply side.

21
The “independent contractors” have often determined the success of these
kinds of companies, but it is unclear whether this business model is
sustainable for them or not.

22
5 The “supply side” between opportunities and challenges
5.3 The boom of freelance economy
At the center of this sharing economy revolution first of all there are
freelancers (Horowitz, 2013). In fact one of the main reasons why sharing
economy expands so quickly is also because it managed to attract the
supply side in a massive scale.
In the U.S.A, at the moment there are 53 million freelancers, or workers
in some sort of contingent arrangements, who counts for approximately
34% of the total workforce. This number is expected to rise to 50 percent
by 2020, and freelancing is now the fastest growing segment in the labor
market (Berland, 2014).
The U.S. Government Accountability Office reports that "contingent
workers" (part-timers, contract workers and the self-employed) has
increased by 36% in the past ten years (Pofeldt, 2015).
At the beginning this increase was mainly driven by small companies,
which started employing freelancing professionals to solve the tasks that
their own workers sometimes cannot solve. Step by step however every
kind of business is finding the advantages of relying on freelance labor
(Chase, 2015).
In this way companies often save money and increase their efficiency
(Flexibility drives productivity, 2012). One of the most difficult tasks for
firms is to motivate their recruiters to work consistently at high
productivity levels (Bagai, 2014); with a more flexible employment model,
businesses search task-solvers instead of long term workers, and they
gain a more adaptable cost structure. As a matter of fact 71% of
respondents declare that their company is more productive nowadays, as
a result of more flexible working (Flexibility drives productivity, 2012).

23
In this way, the assignments are measured in hours instead of weeks or
months, creating a new form of “variable labor services”. Looking at the
data, “it’s reasonable to assume that, in our collective lifetimes, freelance
or contractual work will be the fundamental core of our global labor
market” (Ellison, 2015).

24
5.4 Sharing economy, gig economy, on-demand economy
and its “employees”
Not only are freelancers spreading among all the types of businesses, but
also among every company and private individual’ activity. If freelancers
once used to do the tasks that nobody else in the company wanted to do,
now entrepreneurs use this workforce for all sort of specialized projects or
tasks (Karpie, 2013).
Expert workforce becomes cheaper and cheaper especially thanks to the
sharing economy platforms that replace temporary agencies, often too
expensive for SMEs.
For instance online staffing platform started spreading, making it easier
and quicker for the employers to meet these freelancers at a lower cost.
In short the middleman, in this case the traditional staff companies have
been removed from the economic transactions between the freelance and
the business; all these websites manage the payments, pre-select the
workers, so that employers then could choose their workers basing their
judgment on previous feedbacks and ratings. In other words, the
companies that embrace this revolutionary concept, besides bringing up
new ways of value creation change “the structure of business itself"
(Thompson, 2015).
After, what started as a company’s problem-solving revolution spread
among ordinary people and their everyday tasks. Independent workers
started to be used to solve ordinary problems, and personal service
platforms relying on casual non-professional staff, began to increase.
That is how the “gig economy” was created. “Gig economy” because it is
indeed composed of platforms, which connect workers, who have free
time and resources and end-users for a particular defined task (called
“gigs”) for a limited time (Friedman, 2014). In this model the word

25
workers may sound an exaggeration because most of the times, these are
ordinary people who perform ordinary tasks.
A good example of a gig economy platform is Amazon Turk, mostly
famous for its users as Mturk. It is a crowdsourcing platform developed by
Amazon, that matches individuals and businesses to perform micro tasks,
or better HITs (Human intelligence tasks), that is little jobs that
computers or other machineries are unable to do.
Mturk is not an isolated case. Nowadays there is Handy Homejoy for
cleaning services, Instacart for grochery delivery, Washio for laundry
services, BloomThat for flowers transport, or Spoon Rocket, for a
restaurant quality meal distribution (Bacon, 2015). All these services have
created a “generation that missed out on secretaries” who “now has a
surfeit of support staff at hand” (Gregg, 15).
At the same time the more the market for these platforms expands, the
more the need among consumers for rapidity and efficiency grows. The
companies, which were providing services quicker than the other
competitors, soon outperformed the market.
This brought to the development of the on-demand economy, an
“economic activity created by technology companies that fulfill consumer
demand via the immediate provisioning of goods and services” (Jacony,
2014). On-demand economy is able to provide goods and solve
customers’ needs in a very short time. Many sharing economy platforms,
and gig economy ones are also on-demand: for instance Uber, Task
rabbit, Handy, Instacart…
However an on-demand platform is not necessarily a sharing economy
one. My Taxy for instance is a platform that through its app provides on-
demand fast regular taxy drivers to its customers. As well a gig economy

26
platform should not necessarily be on-demand, as in the case of Mturk,
where you post deadlines for your task and you wait for someone to
perform it.
Nevertheless our thesis will focus precisely on on-demand sharing
economy, where the employees are not professional workers but common
people. In fact professional workers working for on demand companies
tend to have more benefits and protections guaranteed than non-
professional ones (U.S. department of labor, 2014).
For simplicity of the analysis we will just call it on demand economy,
aware of the fact that there are different types.
In general all these types of platform are growing. Research study counts
that “over $4.8 billion in capital has been invested in on-demand
companies, with $2.2 billion invested in the last 12 months alone (Jaconi,
2014)”. Data from CB Insights instead said that the billion invested were
$9.4 (Isaac & Singer, 2015).
Gig economy firms or on-demand companies rely on self employed
contractors, or independent contractors as their employers call them.
They are also called 1099 employees, since the 1099 form is the tax form
that independent contractors have to fill out in order to work for these
kinds of platforms. At the moment the on-demand economy see the
involvement of 3.2 million American contingent workers, but it is expected
to grow by 18.5% up to 7.6 million in 2020 (Kessler, 2015).
This on-demand economy has been grown thanks to an internet effect
that “makes human desires more easily attainable. In other words, it
offers convenience. Convenience on the Internet is basically achieved by
two things: speed, and cognitive ease” (Jaconi, 2014).
Internet, together with the “smartphone revolution” has made
convenience, efficiency, and simplicity, at the hands of everybody and
these are key elements in purchasing decision. In a near future “every

27
need we have, every car we take, every purchase we make, will be
available at the tap of a button (Jaconi, 2014)”.
Clearly, the investors push for the growth of these startups, and this
requires more and more on-demand workers; however, it is not clear how
many will be willing to join these companies at the current employment
conditions.

28
5.5 The advantages of the new working models
Many workers embrace the changes in the job market and its
consequences, but they are not only the unemployed ones or those
dissatisfied with the current job offers. Far from being desperate one the
main reasons why someone adheres to this this freelancing service, is
because “the nature of the work, the flexibility and the compensation
appeals to them” (Ferla, 2015). Working for these platforms means “to
work when they want and how they want” (Lever, 2015).
Moreover it is valuable. Indeed, a report by the Altimeter Group found
that 72% of American workers seek to “quit their jobs to be independent
and would use online freelancing services” as a way to earn money and
move forward (Owyang, The Collaborative Economy , 2013). Freelancing
used to be seen as a temporary occupation before finding a long-term job;
today instead consulting or solving tasks for many companies at the same
time can be synonymous of a respected, learned and wealthy worker.
This dynamic and flexible on-demand model can also be efficient; the
employee can “shop their skills to the highest bidder in real time, take as
much time as they want for themselves, and never feel locked in to a
dysfunctional relationship with their boss (Salmon, 2015).” In this system
human biases and long hiring process are removed and the recruitment
becomes more efficient and less time consuming. All kind of discrimination
based on race, gender, age, sexual orientation, faith, or other believes are
removed and illegal questions easily tracked down (Salmon, 2015).
The millennials are the ones who embrace the change most. 69% of
independent contractors are between the ages of 18 and 34 years
(Houston, 2015). They like the idea that results and productivity is
measured through the output of the work performed and not by the
number of hours at the office (PWC, 2012). Moreover they appreciate the
opportunity to shift hours (66% of them), and work from home (64%).

29
But they are not the only ones: there is a vast range of people of all the
ages who enjoy the benefits of this model: students who want to
supplement their incomes, fired workers in a transition period, bohemians,
who can afford to enter in and out of the labor market, young mothers
with children, the semi-retired, or other people that for personal reasons
cannot have a long-term agreement (Micha, 2013).
Moreover this platform can become a lifetime choice for workers who lack
skills for other employed jobs, or for the ones who do not want to commit
too heavily to a certain company.
Surely many people seem to appreciate this model. Many are now taking
entrepreneurial risks that they would never have taken before (Fowler,
2015). Many freelancers like their freedom and self-reliance and the
possibility that they could become wealthy, quicker than expected.
Estimates claim that only 7.5% of self-employed people and 9.4% of
independent contractors will like a different type of employment.
However, on the other hand 48.3% of on-call workers, and 59.3% of
agency temps stated that they would prefer some changes (Duwar,
2015).
In fact aside from the enthusiasts many problems on the supply side of
sharing economy and on demand platforms certainly persist.

30
5.6 The “independent contractors” and the (missed)
liability of companies
Michel Bauwens declared that this sharing economy should be called
“selling economy instead, since what is being done by Uber and AirBnb,
has nothing to do with mutualizing resources, but only with selling and
renting”.
Most of these sharing economy’s companies are accused to monetize “the
desperation of people in the post-crisis economy”, leveraging ” a fantasy
of community in an atomized population.” In the end the real revolution
fostered by independent contractors (Zhuo, 2015), may not create for
them all the advantages that have been created for consumers. If you
use Uber as a customer you must be conscious that a significant
percentage of the revenues go to the company’s headquarters in Sylicon
Valley (Gurley, 2014).
On the other hand, Uber drivers get a low (and declining) pay and almost
null protections. Full-timers who work for sixty hours a week gain only
$12 after expenses (Griswold, In Search of Uber’s Unicorn, 2014) or even
$10 (Bloom, 2015). The 17 dollars the company is claiming its drivers to
gain is only an optimistic estimate that does not count all their other
expenses such as fuel or maintenance costs of the car (Mims C. , 2015).
In general “contingent workers had median hourly earnings in 2012 of
$11.95, compared to $17 for workers with standard full-time jobs”, that is
roughly 10.6% less paycheck per hour (Pofeldt, 2015).
However the main issue here is that all these workers are left with big
insecurities compared to workers in traditional jobs, and lack of many
benefits and protections. Health care insurance, minimum wage
protection, overtime pay, offshore regulation, retirement saving, in
company training, are some of the benefits the freelancers of sharing
economy platforms are missing.

31
There is a huge risk that these businesses will “inevitably exacerbate the
trend towards enforced self-reliance (Bacon, 2015)”. In the end people
could work over 40 hours per week but not getting all the benefits that
this type of workload would have implied before (Fox, 2015).
The America affordable Care Act guarantees low cost individual access to
health insurance (Habert, 2015), but independent contractors still have a
higher reliance on public assistance and cannot access the government
safety net (Pofeldt, 2015). Furthermore as result of a more reduced and
variable salary only 61% of contingent workers were covered by a form of
insurance plan in comparison with 77.9% of standard employees (Pofeldt,
2015). In 1982 84% of workers had a full time contract with several
benefit plans in medium-large sized companies and by 2013 this figure
decreased to 27% (Fox, 2015). Moreover college graduates with a
received employer-provided health insurance, went from 53% in 2000 to
31% in 2014. (Habert, 2015).
In the end sharing economy risks becoming a Wal-Mart-style economy
where contingent, low-paid workers have reduced power (Mims C. ,
2015).
On the other side the employers, are celebrating; the companies that rely
on contractors or part-timers can save up between 25 to 35% in their
total payroll (Salmon, 2015). A lot of companies are intentionally starting
to misclassify workers in order “to cut costs and avoid compliance with
labor laws (Xia, 2015).”
The sharing economy companies only accrued this trend. On-demand
platforms such Uber, Amazon Inc’s Mechanical Turk, Handy, and Task
rabbit often define their workers as independent contractors assuming no
labor-related compliance responsibilities.

32
At the same time, these platforms want to control the contractors as
employees, giving them guidelines and directions they are forced into
accepting, if they want to get the job. In the end freelancers and buyers
are promised to meet in a secure marketplace that “offer identity and
profile management, skills searching, resume matching, ratings and
references, payment terms or negotiations, and more (Bagai, 2014).”
However in the end the contractual arrangement the personal, financial
risks or any other job-related issue is left to the parties involved: the
freelancer and the buyer. In short, what remains to the employee is a
variable and limited salary, and a consistent part of it is given to the
platform’s management. The degree of liability of the platform will be the
main issue in determining whether the workers can be defined as
independent contractors or not.
Recently, Hillary Clinton criticized the sharing economy for its poor
workers’ protections and the harsh conditions imposed by businesses
(Daly, 2015). Senators Al Franken and Bob Casey in a letter to the Labor
Department raise the issue whether the on-demand economy can
potentially misclassify workers and how this system is going to evolve
(Singer, 2015).
She is not the only one with this opinion. In early June the California
Labor Commission sentenced that an Uber driver must be considered an
employee, not an independent contractor. This decision only concerns one
driver and forced “Uber to reimburse Barbara Ann Berwick $4,152.20 in
expenses” for the period the woman was working for the platform (Isaac
& Singer, 2015). More importantly, this fact was one of the “first official
recognition of misclassification”, and creates an important precedent for
the mobility service company (Fernholz, 2015). After this decision, last
September three employees decide to appeal the court against Uber for
the same reason; their aim is to create a class action from the case,
gathering consensus and union among the 160’000 workers who have
signed a contract with the Californian platform (Huet, 2015).

33
The case could have enormous implications for all the sharing companies
in America (Daly, 2015). If the ruling is upheld, Uber could be forced to
pay social security, healthcare, overtime pay and other benefits and
compensations to its employees and so many sharing economy businesses
will adapt their model consequently to avoid jurisdictional litigations.
Moreover this could really affect Uber profit (Alba, 2015), forcing the
company to change its revenue model (Rosenblatt & Newcomer, 2015).
Can this renovation destroy the competitive price for the end users of
these sharing economy platforms? Not necessarily, according to Arun
Sundarayan, professor at NYU’s School of Business, as one of the first
problem of these businesses lies in training, managing and motivating the
workers. Classifying drivers as employee, for instance in the case Uber,
will result in higher tariff for customers, better conditions for workers but
very little impact on company’s global revenue (Rosenblatt & Newcomer,
2015).
Surely Uber is not the only one among the sharing economy companies to
be sued in the name of its employee’s rights. Homejoy, an on-demand
house-cleaning business, shut down due to litigation threat (Rosenblatt &
Newcomer, 2015). Feeling the threats many other companies have
introduced some changes.
TaskRabbit has started granting its independent contractors health
insurance with discounts and accounting systems. Lyft has signed a
partnership with Freelancers Union, offering its group health plan and
other benefits, Instacar is granting part time employee status to some of
its workers, and My Clean started using only permanent stuff.
Many sharing economy companies will likely adapt themselves, increasing
protections and benefits for their independent contractors.
The key concept of the sharing economy is that common people provide
services and tasks for other ordinary people, but economic transactions
and different prices always create different expectations. Very few people

34
expect an excellent service when this latter one is provided for free or
very little money, but they demand efficiency and high satisfaction when
high fees are at stake (Grimmon, 2008). In the end both demand and
supply search for this last scenario, but some tasks imply well-trained and
efficient workers to be performed and to achieve a sufficiently satisfactory
results. So if the company trains, educates and sets guidelines and rules
to its independent contractors, can the business still be called a sharing
economy one? And once again, can these workers still be called
independent contractors? The national employment labor project found
that in 2012 between 10% and 30% of employers were incorrectly
classified (White, 2015).
Companies such as Hello Alfred decide to adopt a middle way model.
Working 30 hours a week or more guarantees the workers the status of
employee; this status comes with protections and benefits. The other part
timers are treated like independent contractors but with training programs
and clear instructions to follow. In this model the workers are let free to
decide which status they want to maintain. As result, this training
program has reduced the turnover of workers typically high in these 1099
platforms (Abello, 2015).
On the other side, Hanauer-Rolf claims that a sharing economy of the 21st
century with micro or null benefits, could need a 21st century social
contract (Hanauer & Rolf, 2015). The two researchers propose a security
contract called Shared Security Account “encompassing all of the
employment benefits traditionally provided by a full-time salaried job”,
managed by a third party public or no profit institution. In addition, this
account must be prorated in the sense that it must weighted for the
working hours an employee is doing. Then portable in the sense that the
benefits gathered will follow the workers from contract to contract. In the
end universal in the sense that a basic set of this protection must be for
all employees and all types of employment.

35
Moreover, a minimum worker protection, such as a minimum shared
security standard must always be guaranteed (for instance “a minimum of
five days a year of paid sick leave, 15 days a year of paid vacation leave,
a matching 401 contribution”) (Hanauer & Rolf, 2015).
These are only possible solutions for sharing economy companies to
implement their business model, and limit their employee dissatisfactions.
There are a lot of uncertainties as to whether this on-demand model will
be appropriate for the future (Pofeldt, 2015). Overall, this change in the
employment standard implies many risks, and can weaken traditional
working conditions in many ways. The rewards of new forms of
employment contracts accrue to a minority, while “others lose out”
(Gapper, 2015). If freelancing salaries can be higher than regular full time
jobs, there are still risks for many to work for a little paycheck (Golden,
Flexibility and Overtime Among Hourly and Salaried Workers, 2014).
In addition on the demand side consumers can become more concerned
about workers’ conditions and treatment of employees inside a company.
Therefore even if having a W-2 employee (an employee with heath care
benefits and social protections) is costly, it can be a very good marketing
strategy for companies (CBS, 2015). The protection of the workers must
be guaranteed and the innovation’s wave of the sharing economy should
follow, but there should not be a trade-off between innovation and
worker’s protection (Xia, 2015).
In the end, labor law may be adapted according to the evolution of the
labor market, providing “a wider variety of work arrangements (Abello,
2015)”.
My research is trying to establish which model, benefits and protections
within a work agreement can maximize employees’ satisfactions.

36
6 Research and analysis section
6.3 The aim of the questionnaire
The sharing economy in the U.S.A. will likely expand in the near future,
and so will do the 1099 economy creating major dissatisfactions on the
supply side.
Many businesses are slightly adapting their models to the workers’
requests. However many others are resilient to change, fearing that they
could lose their competitive advantages. Suitable solutions that combine a
flexible agreement with securities and protections are often difficult to be
found.
In the meanwhile, many workers go on the street, on strike or even in
court to fight for their rights and securities. Nevertheless many millennials
keep on working for these companies enjoying the flexibility this model is
granting them.
On the other side, many companies start to adopt the same business
model since it turns out to be very profitable and less difficult to manage.
The main purpose of this research is to find out whether an on-demand
economy model is really attractive for American workers.
In other words we are questioning whether or not a job which lacks of
main benefits and protections can be really attractive for American
workers; in this case, these elements include sick pay leave, a good
health care insurance, overtime work paid, liability of the companies in
issues at workplace together with the lack of a stable and fixed salary.
Secondly, we want to discover to which extent American workers are
willing to give up part of their salary to have their benefits and protections
guaranteed, or if a really high salary with flexible timetable would be
preferable.

37
In other terms we want to find out if American workers approve the on-
demand platform model, and to what extent they are willing to adopt it.
Thirdly, we want to determine if American workers think that these
benefits and protections should belong to all type of worker or they should
be restricted according to the employment status and number of hours
worked.
Lastly, we want to see if the level of instruction, age or family income, can
affect the responses.
Since our research is pertinent to the U.S.A., only Americans compose our
target population. Due to logical and geographical problems we choose to
interview people in U.S.A. through a web-based quantitative survey.
However, aware of the limited spread of on-demand platforms, we decide
to enlarge our target population, to the all-American active population,
and interview them about the most critical element of these job
agreements.
Our sample is a simple random one, with no reintroduction from the
target population. We decide to consider all the American active
populations, not only because the age of on-demand workers is really
heterogeneous, but also because we want to study the ideas and positions
of all American people capable of working.
Our respondents’ ages vary and are quite distributed between 18 to 65
years old workers (Pinsker, What Does the On-Demand Workforce Look
Like?, 2015). The silent generation is not considered, due to its limited
level of engagement with the sharing economy platforms.
Due to the heterogeneity of the target population, we decided to reach at
least 130 participants in order to obtain a significant level of precision in
the answers.

38
We then write my survey including 151
questions. The first four are
behavioral, and aim at analyzing the general interests and the importance
perceived of benefits and protections. Question number five is
motivational and tries to reveal the general perception and attitude of
Americans towards universal benefits and protections of workers.
Question number six is a conjoint analysis and investigates the preference
towards different employment agreement that includes the most critical
elements of on-demand platforms.
Question seven, eight and nine are also behavioral but are restricted to
the ones who are working or have worked inside the on-demand
economy. The last six questions are mostly demographical and investigate
the occupation, the level of instruction, age, sex and family income of our
respondents.
Before the launch, we tested the survey in advance with 5 respondents.
We changed question Q4; the question used to be “which percentage of
your salary would you give up to obtain the following benefits?” putting
inside the question only the main critical elements of a contract we study,
and giving the opportunity to allocate up to 50% of their hypothetical
salary to these components.
After the change, the question became “how would you allocate your
salary?”; in fact, the “net salary” element is inserted, and therefore also
the total percentage required is modified from 50 to 100.
Most of the respondents found difficult to understand the meaning of
“legal protection and liability of the company”, therefore we include a
definition of it. We confronted the same problem with terms such as
independent contractors, temporary work, and moonlight freelance work,
and we solved the same way.

1 The survey is actually composed by 16 questions, but the questiona t the beginning is the
welcome message.

39
In some questions we introduced a change from 1st
to 3rd
person; in order
to obtain more effective results and more sincere responses, we changed
all the questions into 1st
person. In the end, we adapted all the demands
to facilitate answers from smartphones and tablets.

40
6.4 Data audit and data cleaning
It took one working week to collect all the respondents. In the end we
managed to collect 154 respondents.
However, several cases have missing answers, because some of them
started, but they did not finish the questionnaire. We eliminate the invalid
results and in the end we reduce the amount of complete and valuable
answers to 132. In this case we also erase the respondents who claim not
to work neither at the moment nor before in Q10 and Q11.
In the end the sample size appears to be coherent with the aims of our
research.
However the results do not respect gender distribution of the American
population, where 49.6% of US people are male, and 50.4% are female
(Worldbank, 2014).
It is advisable in this kind of study to have a heterogeneous sample that
follows or is similar to the original distribution of the population.
Nevertheless in our case 65.9% of respondents are female and 34.1 are
male.
Additionally, we must consider that previous research discovered how the
majority of workers actively involved in the on-demand business are male
(72%) (Pinsker, What Does the On-Demand Workforce Look Like?, 2015),
so our results about on-demand economy may be affected by these
differences.
Afterwards we must also consider a significant age difference in
comparison with official data regarding the population distribution in the
U.S.A. In fact, we divide our respondents in Q13 into different groups as
to official data (United States Department of Labor, 2014). After we

41
compare the results (see graph below); in the range of age considered2
we register more respondents than the actual value in the second and
third bands, which means that we have more respondents in ages
between 25-44 than the normal distribution of the American population.
In the other groups, especially in the first (age: 18-24) and last one (age:
55-64) we record significantly less respondents.
Graph number 1

2 To be noticed that we left out from our analysis 2 respondents of age 65 that accounts for 1.5%
of our total respondents in our sample. We did it in order to make an effective comparison
between the official data and our observed ones.
19%
35%
24%
14%
7%
32%
17% 16%
18% 16%
0%
5%
10%
15%
20%
25%
30%
35%
40%
18-24 25-34 35-44 45-54 55-64
Age comparison between of2icial data
and our respondents
Age of respondets Of[icial values

42
Another sample error that may be considered is the wealth of the
respondents. Indeed, we put into a comparison the actual official data
(Census, 2014) and our registered ones (see graph below). As we can
observe our respondents are on average less wealthy than what
Americans in fact are. We definitely have more respondents on the first
two bands (53% of total), which are considered the poorer parts of the
population, and less people in the wealthiest sections. At the point of
actual fact, the American wealth is quite well distributed among the 5
bands.
Graph number 2
12% 12%
22%
30%
24%
20% 19% 20% 21% 20%
0%
5%
10%
15%
20%
25%
30%
35%
Over 129,007$ 82,033$ -
129,006$
52,698$ -
82,032$
29,101$ -
52,697$
$0-29,100$
Wealth comparison between of2icial data
and our respondents
Registered percententages Real percentages

43
Lastly we must mention the education distribution of our respondents. On
average, they are in fact way more educated than the average American.
Indeed, the most staggering difference results in high school level, which
is the maximum level of education reached by more than 40% of all the
Americans.
In our case almost 40% of our sample finished her/his college and almost
35% got a bachelor degree in contrast with 20% of US citizens (see graph
3.3).
In addition, we notice significant and valuable dissimilarities in the
bachelor degree values where our observed percentage is much higher
than official values.
Graph number 3
14%
39%
33%
11%
2%
42%
28%
19%
9%
2%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
High school or
less
College Bachelor
degree
Master degree PHD or more
Education comparison between
of2icial data and our respondents
Observed percents Real percents

44
In general in the end of our analysis, we must take into account that in
major parts, females compose our sample, on average our respondents
are younger, wealthier, and more educated in comparison with the normal
distribution of Americans.
As to the analysis of outliers, several studies have been conducted, but
lastly we choose to not consider any of the cases as outliers, due to the
fact that almost the totality of the variables analyzed are quantitative
discrete variables, or in other terms, they are defined in a range of
answers (for further explanation please see the Appendix section). The
only variable, which may have the problem of outliers, is Q13 (age).
However, in this case all the respondents are within the age of my target
population, which vary from 18 to 65. Therefore there was no need to
perform any outlier detection method for this variable.
I start my analysis considering individually the most important variables in
our research, conducting the so-called univariate analysis.

45
6.5 Univariate analysis
The variable Q2 in our survey analyses the importance for the
respondents from 1 to 10, of several conditions in a job contract such as
sickness protection Q2.1, a stable income Q2.2, a health care insurance
Q2.3, payment of overtime work Q2.4, flexible working hours Q2.5, and
legal protection of companies Q2.6.
As we can see from the graph below the variable with the highest mean is
Q2.2, “a stable income always guaranteed” (mean =8,77), then “a good
health insurance” Q2.3 follows (mean =8.47). In the end there are the
legal protection from the company Q2.6 and a flexible working hours
timetable Q2.5.
Overall speaking, all the values are quite high and all the means vary from
7.17 to 8.77, a sign that indicates the remarkable importance of all these
aspects. The 3 central statistical measures are fairly equal in all the cases
considered; only the modes of Q2.2 and Q2.4 are significantly higher. The
variability of the results is also quite reduced since the standard
deviations of the variables vary from 1.49 to 2.32 of Q2.4.

46
Graph number 4
Q2.1 A
salary
guaranteed
in case of
sickness
Q2.2 A
stable
income
always
guaranteed
Q2.3A good
health
insurance
Q2.4
Overtime
work
always paid
Q2.5
Flexible
working
hours
timetable
Q2.6 Legal
protection +
liability of
company in
issues in
workplace
Mean 7.7803 8.771 8.4733 7.8788 7.1667 7.5227
Median 8 9 9 8 7 8
Mode 8 10 9 10 7 0
Std. Deviation 1.71795 1.49133 1.64236 2.315 1.93863 1.97876
0
2
4
6
8
10
12
Values
Q2 Importance given to each element

47
In question number 3 (see graph below) the respondents are asked to
allocate their salary as employees among their net salary, and different
benefits and protections. Here the means are significantly different in
comparison with Q2.
The net salary as we expected, shows the highest central statistical
measures (around45%), but in comparison with mean and median the
mode is significantly lower (40%). As the standard deviation suggests, the
dispersion of respondent’s data is quite high with almost 20 points of
percentages’ variation.
The second most important element for our sample is a good health
insurance (mean=19.83) followed by illness protection (mean=15.05),
overtime work always paid (mean=10.28) and legal protection in
workplace (mean=8.92). Among this latter one the dispersion of the data
is fairly similar among 10 points of percentage. Besides, its mean
overtime work paid has a mode around 0, which indicates that many
people perceive this variable as not valuable at all.

48
Graph number 5
Q3.1
Allocate
your salary
%: Illness
protection
Q3.2
Allocate
your salary
%: A good
health
insurance
Q3.3
Allocate
your salary
%: Overtime
work paid
Q3.4
Allocate
your salary
%: Legal
protection
and liability
in workplace
Q3.5
Allocate
your salary
%: Net
salary
Mean 15.0455 19.8333 10.2803 8.9167 45.9242
Median 12 20 10 5.5 45
Mode 10 20 0 5 40
Std. Deviation 10.00905 10.96014 7.80714 9.61157 19.67702
0
5
10
15
20
25
30
35
40
45
50
Values
Q3 Salary allocation %

49
In Q4 we ask the respondents to rank the elements of a job contract in
terms of importance. The question is very similar to Q2 in fact was
created to also confirm our previous results.
In this case the questions with the lowest value are the preferred ones
(see graph below). Once again “a stable good salary” (Q4.1) is the
preferred variable with similar values of mean median and mode.
Secondly comes “a good health insurance” (Q4.2) most of the times listed
in the second position (mode=2). Thirdly, there is “a good illness
protection”(Q4.3) according to the mean’s value. However the median and
the mode are quite similar to “a quite flexible working timetable”(Q4.4)
that comes later. In any case, this variable is more dispersed than the
previous one as the standard deviation suggests.
Slightly after there is overtime work always paid (Q4.5), which has a
higher mode in comparison with the previous two variables analyzed. In
the end, there is in the last position legal protection of the company in
issues raised at workplace (Q4.6).
The standard deviation of all the variables is mostly around 1 that
suggests a variation of +/-1 in all the positions considered.

50
Graph number 6
Q4.1 A
stable
good
salary
Q4.2 A
good
health-
care
insurance
Q4.3 A
good
illness
protection
Q4.4 A
quite
[lexible
working
timetable
Q4.5
Overtime
work
always
paid
Q4.6 A
suf[icient
legal
protection
and
liability of
company
in case of
problems
Mean 1.3409 2.5 3.7197 4.0833 4.1136 5.3106
Median 1 2 4 4 4 6
Mode 1 2 4 4 5 6
Std. Deviation 0.93962 1.12907 1.07231 1.28433 1.25794 1.26088
0
1
2
3
4
5
6
7
Rank position Q4 Rank in your job contract

51
In Q5 we analyze the agreement with some statements regarding benefits
and protections of part time and full time employees (see graph below).
Surprisingly this time the statement regarding liability of company in
issues raised at workplace (Q5.4) obtains very high values with a mean
and median close to 8 and a mode of 10. We can confirm now that our
respondents still value this item consistently even if they rank and
attribute less importance in comparison with other elements.
The second most rated statement claims that an employer who does not
guarantee a sufficient level of benefits and protections is exploiting his
workers (Q5.5), with almost similar values.
Thirdly there is the declaration stating that benefits and protections should
also belong to independent contractor (Q5.1), which has mean and
median around 6 and a mode of 10.
After, there is the statement “benefits and protections should be
proportional to the hours worked”(Q5.3) which has central statistical
measures almost equal.
At almost the same level is “benefits and protection only to full time
employee (Q5.2)”, and lastly the one concerning the fact that a high
salary can compensate benefits and protections.
In general, in this variable the dispersion of data are much higher than
the previous results; in all cases, in fact the standard deviation is almost
the double (=2) than in Q4. For instance in Q5 (6) the mode is 1 whereas
the mean is almost 4 and the median 3.

52
Graph number 7
Q5.1
Independe
nt
contractor,
same
protection
s than full
time
employees
Q5.2
Bene[its
+protectio
ns only to
full time
employees
Q5.3
Protection
s
proportion
al to hours
worked
Q5.4
Company
liable and
responsibl
e in
problems
and issues
connected
to job
Q5.5
Employer
who do not
guarantee
minimum
protection
s bene[its
is
exploiting
Q5.6 A
very high
salary
compensat
e
protection
s and
bene[its
Mean 6.3923 5.4231 5.7252 7.9091 7.2045 3.8939
Median 6 6 6 8 8 3
Mode 10 7 6 10 10 1
Std. Deviation 2.65452 2.7019 2.51168 1.92792 2.40781 2.41894
0
2
4
6
8
10
12
Values
Q5 Agreement with following statement

53
In Q7 we asked if the respondents are or used to work for an on-demand
platform.
About two thirds of the respondents answer no (see graph below), 4.5%
declare they are, or have applied for it, 16% they are actively involved,
and lastly 13% have been for a short period. We choose to insert “yes”
and “no but I have for a short time” aware of the fact that this industry is
characterized by a high turnover. We also choose to put “no, but I was
applying to” to find out how many people are interested in becoming
independent contractors in this sector.
In total we can say that one third (33.3%) of the respondents are, have
been, or they wish to be, active parts of this on-demand economy.
Graph number 8
16%
67%
13%
5%
Q7 Working or worked for 1099
platforms
Yes
No
No but I have for a short
time
No but I was applying to

54
In Q8 we ask the people who have worked or are working inside these
platforms their level of satisfaction. The respondents appear to be quite
satisfied with their professional relationship (see graph 3.6), since the
average value registered is 6, like the median, and the most frequent
response is 7.
However, we can notice a consistent dispersion of the data since the
standard deviation is more than 2 points of percentage. In any case this
data must be interpreted very carefully since only 38 people answer these
questions, and for every level of satisfaction there might be only 1 or 2
respondents.
Graph number 9
5.55
6
7
2.11
0 1 2 3 4 5 6 7 8
Mean
Median
Mode
Std. Deviation
Q8 Level of satisfaction with the
platforms
Values

55
In Q9 we ask respondents whether these companies should guarantee
their employees more benefits and protections (see graph 3.7). Only
about 10% of respondents answer negatively; 11% show to be uncertain
about the answers but the large majority is in between “probably
yes”(31.6%) and “definitely yes” (47.4%).
Also in this case the data must be considered with attention due to the
limited number of answers recorded.
Moreover, we are aware of the fact that this is a general question aiming
at understanding the attitudes of people towards the independent workers
model, and further research may be needed.
Graph number 10
5%
5%
11%
32%
47%
Q9 Should platform garantuee more
bene2its and protections
De[initely not
Probably not
Maybe
Probably yes
De[initely yes

56
In Q10 we ask our sample the type of jobs they were currently involved
in. In this case it was possible to select more than one answer. From the
data we can conclude as the secondary data suggest, that the US job
market is extremely variegated (see graph 3.8). Only 50% percent
declare to be engaged also in full time jobs. Almost 30% of respondents
have a part time or independent contractor job. 16.7% of them is also
involved in temporary work, almost 16% do some sort of moonlight
freelance activity, whereas a minority (5.3%) is also doing a diversified
type of work.
The total sum of percentage of answers collected amount to 155.3%,
which means that more than one out of two is doing two types of jobs at
the same time.
Graph number 17
49.20%
16.70%
28.80% 29.50%
15.90%
5.30%
9.80%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
Q10 On-going type of work
Percentages

57
In Q11 we ask the previous type of job done by the current unemployed.
In Q10 only 9.8%, 12 of our respondents, declared not working at the
moment. Q11 was available only to these respondents and “12” was
considered an insufficient number to perform any analysis and the results
were really approximate (see limitations section). This however does not
influence the value of our analysis in a consistent way.
From Q12 to Q15 we mainly ask demographical questions to better
understand the level of our respondents.
In Q12 we analyze the level of education of our sample (see graph below).
As we previously discussed in our research introduction (see data error
section), the majority of them happen to be very learned; indeed 40% of
them have a college degree. About 54% of respondents have obtained a
college or a lower level of education, and 87% if them have reached a
bachelor degree or an inferior certificate.
On the other side, we must consider that only 15 of our respondents
obtained a master degree and 3 of them a PhD. In order for a cluster to
be considered significant, it should contain at least 5 people so we may
take this into considerations in the development of our analysis.

58
Graph number 18
2%
11%
33%
40%
14%
Q12 Level of education obtained
PHD or more
Master degree
Bachelor degree
College
High school or less

59
In Q13 we analyze the age of our respondents. We change the written
answers from string data to numerical ones and then we divide the
answers into 5 different groups (see graph below). We use a slightly
different division than the one we use before integrating all the
respondents and all the named age3
.
The majority of the respondents are quite young since 58.3% of them are
between 18 and 35 years old, almost 80% are under 45. The median
value is 34.
Graph number 19

3 Before the range went from 18-64, now we consider ages that vary from 18 to 64.
22%
37%
21%
12%
8%
Q13 Age
18-25
26-35
36-45
46-55
56-65

60
In Q15 we ask our respondents their family income. The percentages
obviously decrease the higher the level of income is (see graph below). As
we discussed earlier, this variable does not mirror the real distribution of
the income of the U.S.A. population. In general however, our sample is
not that poor; more than two thirds of the respondents (77%) has a
salary of 52.697$ or lower, but the rest has an income of 82,033$ or
more.
Graph number 20
12%
12%
22%
30%
24%
Q15 Family income
Over 129,007$
82,033$ - 129,006$
52,698$ - 82,032$
29,101$ - 52,697$
$0-29,100$

61
6.5.1 Main implications and conclusion from
univariate analysis
From this first analysis we can conclude that a stable salary is still very
important for Americans since in Q2, Q3, Q4, this element registers the
highest value. Though we still do not answer if Americans will give up
benefits and protections for a high salary, we now are aware of the fact
that they prefer a stable salary among these categories.
Secondly, a good health care insurance is also considered very important;
this is probably due to the fact that the health care system in the U.S.A. is
private and most of the time very expensive if you do not have a
insurance, or a job providing you this kind of protection (see main
implications of research). Together with health insurance, sickness
protection is placed in high position in all the three questions considered.
Indeed “flexible working hours” is also placed in last positions in Q2 and
Q4.
On the other side Americans do not consider “overtime work always paid”,
and “legal protection of the company” as most important variables. They
register moderately high value in Q2, but in the end of actual fact they are
placed in last positions in Q4 and they have low values in Q3. As we notice
from the very low standard deviation we can conclude that the results are
fairly similar among the respondents.
Overall, we conclude from Q2 that all the benefits and protections
considered are recognized in terms of importance. Indeed in Q5 many
respondents strongly agree on the fact that an employer who does not
guarantee any benefits or protections is exploiting workers.
Yet it seems like most of the US workers are not used to the independent
contractors’ model. Indeed on-demand economy does not seem so spread

62
and common. Less than 30% are or have been involved actively in this
economy (Q7), and the almost half of respondents declare to have a full
time jobs (Q10), which means that the traditional forms of employment
are still largely spread.
In any case the satisfaction towards the on-demand model is sufficiently
high (around 6) besides its lack of protections and benefits (Q8). However
78% of our respondents agree on the fact these businesses should
definitely or probably guarantee more protections and benefits to their
employees (Q9).
Besides on-demand platforms, the types of job’s occupations are very
different and most Americans seem to be involved in more than one type
of job; 44% of them are also working as independent contractors or
temporary work, while a consistent 28% has a part time contract (Q10).
Lastly, we analyze the demographical questions. We discover our sample
is quite well educated with 74% with a college or bachelor degree. In
addition, our respondents are quite young with 80% of them under 45
years old, and lastly they are moderately wealthy with more than half of
the respondents (52%) with a family income level of 82’000 or less.

63
6.6 Bivariate analysis
Firstly, as pointed out in the univariate, it must be mentioned that only 12
people answered Q11; therefore we decided not to correlate this variable
with any questions. Q12, due to the nature of its variable (multiple
answers question), was difficult to relate to any other questions. Lastly,
for Q13 we consider the numerical variable and not the grouped one,
previously created by us for the univariate.
As a general rule, we highlight and comment only the most relevant
variables with a level of correlation above 0.35. Moreover, we consider
significant correlation with a level of confidence of both 95 and 99%.
We first start our bivariate analysis finding correlations between all the
variables in Q2 and Q4. We expect to find high correlation among the
variables since some of them are very similar, and in the univariate we
find analogous results.
On one side, all the variables in Q2 are correlated to each other to a
different extent (see table below). Q2.1 is also correlated with Q2.2 and
Q2.3, and to a minor extend to Q6; in other terms workers who want a
salary guaranteed in case of sickness also want a certain minimum level of
income and a good health insurance. Q2.2 is also slightly correlated with
Q2.4, to the same extend Q2.5 is connected to Q2.6. So we can say that
those who value high a stable income, also recognize the importance of
paid overtime work and the importance attributed to a flexible working
hour timetable is correlated to the legal protection and liability of the
company.
Q2.2 is also slightly correlated with Q2.5 and Q2.6, so respondents who
rank a stable income highly, tend to put in low position overtime work
always paid and the liability of the company in issues at workplace.

64
Table number 1
Q2.
1
Q2.
2
Q2.
3
Q2.
4
Q2.
5
Q2.
6
Q2.1 A salary guaranteed
in case of sickness
Pearson
Correlat
ion
1 .43
9**
.52
1**
.21
4*
.28
8**
.35
1**
Sig. (2-tailed) 0 0 0.0
14
0.0
01
0
Q2.2 A stable income
always guaranteed
Pearson
Correlat
ion
.43
9**
1 .25
2**
.38
7**
.30
5**
.32
9**
Sig. (2-
tailed)
0 0.0
04
0 0 0
Q2.3 A good health
insurance
Pearson
Correlat
ion
.52
1**
.25
2**
1 0.1
24
.33
8**
.25
3**
Sig. (2-
tailed)
0 0.0
04
0.1
57
0 0.0
04
Q2.4 Overtime work
always paid
Pearson
Correlat
ion
.21
4*
.38
7**
0.1
24
1 .26
5**
.28
9**
Sig. (2-
tailed)
0.0
14
0 0.1
57
0.0
02
0.0
01
Q2.5 Flexible working
hours timetable
Pearson
Correlat
.28
8**
.30
5**
.33
8**
.26
5**
1 .38
9**

65
ion
Sig. (2-
tailed)
0.0
01
0 0 0.0
02
0
Q2.6 Legal protection of
company in issues in
workplace
Pearson
Correlat
ion
.35
1**
.32
9**
.25
3**
.28
9**
.38
9**
1
Sig. (2-
tailed)
0 0 0.0
04
0.0
01
0

66
Thereafter we analyze Q2 in correlation with Q4. Also in this case we
expect to find many associations due to the similar elements in the
variables. Q2.3 and Q4.2, and 2.5 with Q4.4 are the only real valuable
correlation (see table 3.2); this means that those who rate heath
insurance or a flexible working timetable (Q2.5) as very important (Q2.3)
tend also to include these as one of his/her first elements inside a
contract. To notice that also in this case the correlation is negative due to
the fact that Q4 is a ranking variable, therefore the more importance you
give to the element the more we expect that element to rank lower, and
so to have a reduced value.
Table number 2
Q4
.1
Q4.2 Q4
.3
Q4.4 Q4.
5
Q4.
6
Q2.1 A salary
guaranteed in case of
sickness
Pearso
n
Correla
tion
-
0.0
05
-
.199
*
-
.25
3*
*
.188* 0.1 0.0
81
Sig.
(2-
tailed)
0.9
52
0.02
2
0.0
03
0.031 0.2
54
0.3
55
always guaranteed
Pearso
n
Correla
tion
-
.22
8*
*
0.10
5
0.0
16
0.085 -
0.0
55
0.0
74
Sig.
(2-
tailed)
0.0
09
0.23
1
0.8
58
0.332 0.5
3
0.4
Q2.3 A good health Pearso - - - 0.165 .21 .21

67
insurance n
Correla
tion
0.0
62
.510
**
0.1
29
1* 5*
Sig.
(2-
tailed)
0.4
81
0 0.1
42
0.06 0.0
16
0.0
14
Q2.4 Overtime work
always paid
Pearso
n
Correla
tion
-
0.1
11
0.01
2
0.0
85
0.086 -
.33
6**
.22
7**
Sig.
(2-
tailed)
0.2
06
0.89
4
0.3
35
0.329 0 0.0
09
Q2.5Flexible working
hours timetable
Pearso
n
Correla
tion
-
0.0
02
-
0.09
4
0.0
45
-
.364*
*
.18
0*
.17
9*
Sig.
(2-
tailed)
0.9
81
0.28
3
0.6
11
0 0.0
39
0.0
41
Q2.6 Legal protection +
liability of company in
issues in workplace
Pearso
n
Correla
tion
-
0.0
56
0.00
2
-
0.1
21
0.166 0.0
5
-
0.0
9
Sig.
(2-
tailed)
0.5
27
0.98
4
0.1
67
0.057 0.5
73
0.3
04

68
We then analyze the correlation among the variables in Q3. As in Q4 all
the correlations were negative since all the variables inside the question
were mutually exclusive (see table 3.3). Q3.6, equal to “Net salary” is
strongly negative correlated with all the other variables, so we can state
that this is the variable that determines the allocation of salary to other
elements.
Table number 3
Q3.1 Illness protection Pearson
Correlatio
n
1 -
0.10
8
0.00
1
-
0.11
-
.395
**
Sig. (2-tailed) 0.21
8
0.99
5
0.20
9
0
Q3.2 A good health
insurance
Pearson
Correlatio
n
-
0.10
8
1 .203
*
0.03 -
.598
**
Sig. (2-
tailed)
0.21
8
0.01
9
0.72
9
0
Q3.3 Overtime work paid Pearson
Correlatio
n
0.00
1
.203
*
1 0.11
4
-
.566
**
Sig. (2-
tailed)
0.99
5
0.01
9
0.19
3
0
Q3.4 Legal protection and
liability in workplace
Pearson
Correlatio
n
-
0.11
0.03 0.11
4
1 -
.495
**
Sig. (2-
tailed)
0.20
9
0.72
9
0.19
3
0
Q3.5 Net salary Pearson
Correlatio
n
-
.395
**
-
.598
**
-
.566
**
-
.495
**
1
Sig. (2-
tailed)
0 0 0 0

69
Lastly we analyze the correlation between Q2 and Q3(see table 3.4) but
we find that only health care insurance values (Q2.3 and Q3.2) and
overtime work paid (Q2.4 and Q3.3) are correlated, and not even so
strongly (pearson correlation value<0.4). In other terms the more
importance is attributed to a good health insurance (Q2.3) or an overtime
work always paid (Q2.4), the more percentage of salary is allocated
respectively to a good health insurance (Q3.2), or an overtime work
always paid (Q3.3).
However we find no other variable in Q2 correlated with its correspondent.

70
Table number 4
Q3.1
Illnes
s
prote
ction
Q3.2
A
good
healt
h
insur
ance
Q3.3
Overt
ime
work
paid
Q3.4
Legal
protecti
on and
liability
in
workpl
ace
Q3.5
Net
salar
y
Q2.1 A salary
guaranteed in
case of sickness
Pearson
Correlatio
n
.193* 0.031 0.148 0.02 -
.184*
Sig. (2-
tailed)
0.027 0.725 0.09 0.823 0.035
Q2.2 A stable
income always
guaranteed
Pearson
Correlatio
n
-
0.067
-0.08 0.072 0.058 0.021
Sig. (2-
tailed)
0.444 0.361 0.411 0.512 0.813
Q2.3 A good
health insurance
Pearson
Correlatio
n
-
0.098
.360*
*
0.107 -0.003 -
.197*
Sig. (2-
tailed)
0.265 0 0.223 0.974 0.024
Q2.4 Overtime
work always paid
Pearson
Correlatio
n
0.013 -0.04 .366*
*
-0.071 -
0.095
Sig. (2-
tailed)
0.885 0.65 0 0.418 0.281
Q2.5 Flexible
working hours
timetable
Pearson
Correlatio
n
0.131 0.08 0.052 0.062 -
0.162
Sig. (2-
tailed)
0.136 0.36 0.555 0.482 0.064

71
Q2.6 Legal
protection +
liability of
company in
issues in
workplace
Pearson
Correlatio
n
0.053 0.022 0.128 0.12 -
0.148
Sig. (2-
tailed)
0.545 0.802 0.144 0.172 0.09

72
We then analyze the correlation of Q2 with Q7, Q8, Q9 but we did not find
any valuable relationship (See appendix).
Therefore the importance attributed to the main benefits of protections did
not change among the people actively involved in the on-demand
economy, and the ones who are not. Moreover, their level of satisfaction
with this platform do not affect their value attribution to this contract’s
elements, and these importance levels remain stable also among the ones
who declare they want these on-demand businesses to guarantee more
value more benefits and protections.
We do find a significant correlation between Q2.6 and Q12, where the
level of education clearly affects the value attributed to “legal protection
and liability of the company in issues raised at workplace” (see table 3.5).
The higher the level of education the lower the value attributed to the
importance of this element. Not only is the F test significant
(p.value<0.01) but also Eta measures suggest a strong correlation
(eta>0.3). Observing the means (see graph below), we notice how the
value given to the liability of the company decreases as the level of
instruction is higher. However, the trends are not so clear since the value
is pretty similar in the first three bands.
We may assume that since the job position increases with the level of
education, the higher the job position the less this attribute is becomes
important. For manual workers and low employees this element can be
extremely meaningful.
Table number 5
ANOVA Table Q2*Q12
Measures of
Association
Sig. Eta Eta
Square
d
Q2.1 A salary
guaranteed in case of
Betwee
n
(Combine 0.35 0.18 0.034

73
sickness * Q12 Level of
instructions obtained
Groups d) 6 4
always guaranteed *
Q12 Level of
Betwee
n
Groups
(Combine
d)
0.62
7
0.14
2
0.02
Q2.3 A good health
insurance * Q12 Level
of instructions obtained
Betwee
n
Groups
(Combine
d)
0.48
4
0.16
4
0.027
Q2.4 Overtime work
always paid * Q12 Level
of instructions obtained
Betwee
n
Groups
(Combine
d)
0.37
3
0.18
1
0.033
Q2.5 Flexible working
hours timetable * Q12
Level of instructions
obtained
Betwee
n
Groups
(Combine
d)
0.67
6
0.13
4
0.018
Q2.6 Legal protection +
liability of company in
issues in workplace *
Q12 Level of
Betwee
n
Groups
(Combine
d)
0.01 0.31
4
0.099
Graph number 21
0
1
2
3
4
5
6
7
8
9
High school College Bachelor
degree
Master
degree
PHD or
more
Q3.3 *Q12 Comparison between
means
Mean
Median
Std. Deviation

74
In addition, we find a significant positive correlation between Q2.3 and
Q13, the age of our respondents. As we expect, the higher is the age of
our respondents the higher is the importance attributed to a good health
care insurance.
Table number 6
CORRELATIONS Q2*Q13
Q2.1 Q2.2 Q2.3 Q2.4 Q2.5 Q2.6
Q13 Age Pearson Correlation 0.287 0.03 0.351 -0.071 0.063 -0.007
Sig. (2-tailed) 0.001 0.733 0 0.418 0.476 0.939
Moreover, we find a strong correlation (eta>0.3) between Q2.4 the
importance attributed to “overtime work always paid” and Q15, which is
the level of annual family income (see table 3.7). The higher the family
income, the lower weight is given to overtime work paid.
Table number 7
ANOVA Table Q2*Q15
Sig. Eta Eta
Squared
Q2.1 A
salary
guaranteed
in case of
sickness *
Q15 Family
annual
income?
Between
Groups
0.321 0.19 0.036
Q2.2 A
stable
income
always
guaranteed
Between
Groups
0.847 0.104 0.011

75
* Q15
Q2.3 A
good health
insurance *
Q15
Between
Groups
0.054 0.266 0.071
Q2.4
Overtime
work
always paid
* Q15
Between
Groups
0.007 0.322 0.104
Q2.5
Flexible
working
hours
timetable *
Q15
Between
Groups
0.076 0.253 0.064
Q2.6 Legal
protection
+ liability
of company
in issues in
workplace
* Q15
Between
Groups
0.448 0.169 0.028

76
After analyzing Q2, we move on to considering the correlation of Q3 with
other variables. We first start with Q3 and Q4 to see if the ranking given
to the variable also reflected the allocation of employees’ salary.
Surprisingly, not so many valuable and significant correlations were found
(see table 3.8). Only Q3.1 and Q4.3 seem to be connected by a strong
negative relationship. However 3.1 and 4.1 are correlated in a moderate
way. The higher the percentage of salary allocated to net salary, the lower
the position given in the rank to “a good illness protection”, and the
higher one to a stable good salary.
Table number 8
Q4.1 Q4.2 Q4.3 Q4.
4
Q4.5 Q4.6
Q3.1 Illness
protection
Pearson
Correlati
on
.349
**
-
0.05
8
-
.378
**
-
0.12
1
0.10
3
0.10
7
Sig. (2-
tailed)
0 0.50
8
0 0.16
5
0.24
1
0.22
1
Q3.2 A good health
insurance
Pearson
Correlati
on
-0.02 -
.313
**
-
0.043
0.03
7
0.16
4
0.13
6
Sig. (2-
tailed)
0.81
7
0 0.625 0.67
5
0.06 0.11
9
Q3.3 Overtime
work paid
Pearson
Correlati
on
-
0.01
5
-
0.00
2
0.049 0.16 -
.299
**
0.05
6
Sig. (2-
tailed)
0.86
3
0.98 0.579 0.06
7
0.00
1
0.52
2
Q3.4 Legal
protection and
liability in
workplace
Pearson
Correlati
on
0.12
7
0.02
4
0.117 0.03 0.13
2
-
.293
**

77
Sig. (2-
tailed)
0.14
5
0.78
2
0.182 0.73
1
0.13
1
0.00
1
Q3.5 Net salary Pearson
Correlati
on
-
.222
*
.193
*
0.14 -
0.03
7
-0.09 -0.01
Sig. (2-
tailed)
0.01 0.02
7
0.11 0.67
4
0.30
6
0.91
4

78
We then correlate Q3 with Q4, Q5, Q7, Q8, Q13, Q15 but we do not find
any both significant and valuable statistical correlations.
Indeed, also the salary allocated to “overtime work always paid”
decreases with the level of instruction obtained as the correlation Q3.3
and Q12 outline. It is not a strong one (eta<0.3) but it is significant (see
table 3.9). This can be explained with the same motivation we give to
explain the correlation between Q2.6 and Q12. The higher the level of
education is, the higher the job position people occupy on average, which
could often be managerial ones for higher levels of education; when
people are managers they can decide more freely their timetables and
those of others, in general of employees.
If we look at the graph below we notice how the salary allocated to this
element steadily decrease on master degrees and PhD students.
We must take into consideration however that only few people reach this
latter level of education.
Table number 9
ANOVA Table Q3*Q12 Measures of
Association
Q12 Level of instruction
obtained
Sig. Eta Eta
Squared
Q3.1 Illness protection * Q12
Level of instructions obtained
Between
Groups
0.718 0.2
15
0.046
Q3.2 A good health insurance
* Q12
Between
Groups
0.329 0.1
84
0.034
Q3.3 Overtime work paid *
Q12
Between
Groups
0.049 0.1
85
0.034
Q3.4 Legal protection and
liability in workplace * Q12
Between
Groups
0.927 0.0
88
0.008
Q3.5 Net salary * Q12 Between
Groups
0.507 0.1
86
0.035

79
Graph number 22
12.00
11.60
9.91
5.00
9.00
10.5
10 10
5
6
6.48
9.00
6.87
5.84
5.20
0
2
4
6
8
10
12
14
High school College Bachelor
degree
Master
degree
PHD or
more
Mean
Median
Std. Deviation

80
After Q3 we start analyzing Q4. We correlate Q4 with Q5, Q8, Q9, Q12,
Q13, Q14, but unfortunately we do not find any significant and
considerable correlations.
Very interestingly, we find that people who are working or have been
working in on-demand platforms (Q7) are placing a good health care
insurance on a lower ranking (Q4.2, see table 3.10). According to the
data, the relationship is significant at a 99% level, and quite strong
(eta>0.3).
We can deduce that these independent workers prefer on-demand
platforms because they offer a job contract stripped off of the most
important benefits, but with a good salary. If they highly valued health
care insurance they would likely not work in these kinds of businesses. If
we observe graph below, we see how the means vary in Q7, and between
“yes” and “no” there is a difference of almost 1 point (1 position).
Table number 10
ANOVA Table Q4*Q7
Sig. Eta Eta
Squared
Q4.1 Importance in your job
contract: A stable good salary
* Q7 Working or worked for
1099 platforms
Betwee
n
Groups
(Combine
d)
0.40
4
0.15 0.022
contract: A good health-care
insurance * Q7
Betwee
n
Groups
(Combine
d)
0.00
5
0.30
8
0.095
contract: A good illness
protection * Q7
Betwee
n
Groups
(Combine
d)
0.77 0.09
4
0.009
contract: A quite flexible
Betwee
n
(Combine 0.87 0.07 0.005

81
working timetable * Q7 Groups d) 9 2
contract: Overtime work
always paid * Q7
Betwee
n
Groups
(Combine
d)
0.32
3
0.16
3
0.027
contract: A sufficient legal
protection and liability of
company in case of problems
* Q7
Betwee
n
Groups
(Combine
d)
0.46
2
0.14
1
0.02
Graph number 23
3.29
2.34
2.47
2.17
3
2 2 2
1.55
1.04
0.62
0.75
0
0.5
1
1.5
2
2.5
3
3.5
Yes No Yes for a short
time
No
Q7*Q4.2 Compare means
Mean
Median
Std. Deviation

82
We then move on consider the variables in Q4, and discover that they are
only correlated negatively among each other due to the ranking type of
variable. Interestingly, Q4.3 is correlated with Q4.4 (see table below),
from which can deduce that who put first a flexible working timetable first
tends to dislike illness protection among other variables. On the other
hand, there is a quite strong negative correlation between Q4.2, the rank
given to health care insurance with the position attributed to overtime
work and a sufficient legal protection. Indeed these are the last variables
in terms of ranking position (see Q4 univariate).
Table number 11
Q4.1 Q4.2 Q4.3 Q4.4 Q4.5 Q4.6
Q4.1 A
stable
good
salary
Pearso
n
Correla
tion
1 -
.255**
-0.154 -0.169 -0.007 -0.167
Sig.
(2-
tailed)
0.003 0.935 0.077 0.052 0.935 0.055
Q4.2 A
good
health
-care
insura
nce
Pearso
n
Correla
tion
-
.255**
1 .199* -0.124 -
.363**
-
.362**
Sig. (2-
tailed)
0.003 0.022 0.158 0 0
Q4.3 A
good
illness
protec
tion
Pearso
n
Correla
tion
-0.154 .199* 1 -
.415**
-
.327**
-0.099
Sig. (2-
tailed)
0.077 0.022 0 0 0.259

83
Q4.4 A
quite
flexibl
e
workin
g
timeta
ble
Pearso
n
Correla
tion
-0.169 -0.124 -
.415**
1 -
.237**
-0.11
Sig. (2-
tailed)
0.052 0.158 0 0.006 0.208
Q4.5
Overti
me
work
always
paid
Pearso
n
Correla
tion
-0.007 -
.363**
-
.327**
-
.237**
1 -0.152
Sig. (2-
tailed)
0.935 0 0 0.006 0.081
Q4.6 A
suffici
ent
legal
protec
tion
Pearso
n
Correla
tion
-0.167 -
.362**
-0.099 -0.11 -0.152 1
Sig. (2-
tailed)
0.055 0 0.259 0.208 0.081

84
We then analyze the correlation among the variables in Q5. We notice
correlations between Q5.1 and Q5.2, Q5.4 and Q5.5, Q5.6. Indeed the
first strong correlation sounds reasonable since the Q5.1 statement is in
sharp contrast with Q5.2 (see table 3.12).
It is also rational that who agrees with the Q5.5 statement about a
minimum level of protections and benefits required by the employees, also
wants his/her company liable in issues raised at workplace Q5.4, with a
strong correlation of more than 0.4. Finally, the negative correlation
between Q5.4 and Q5.6 is also logical because people convinced of a
necessity of a business liable in issues at workplace, could not give up
benefits and protections for a high salary.
Lastly there is a strong significant correlation between Q5.2 and Q8. The
more someone agree that benefits and protections should belong only to
full time employees the more he/she seems to be satisfied with on-
demand platform (see table 3.12).

85
Table number 12
Correlations Q8*Q5
Q8 Q
5.
1
Q
5.
2
Q
5.
3
Q
5.
4
Q
5.
5
Q
5.
6
1
-
0.
02
0.
47
1
0.
21
1
-
0.
21
3
-
0.
17
5
0.
22
Sig.
(2-
tailed)
0.
90
6
0.
00
3
0.
20
3
0.
19
9
0.
29
2
0.
18
4
Q5.1 Independent contractor,
same protections+ benefits
full time employees
Pearso
n
Correla
tion
-
0.0
2
1 -
.3
95
**
-
0.
08
3
.2
87
**
.2
92
**
-
.2
07
*
Sig.
(2-
tailed)
0.9
06
0 0.
34
6
0.
00
1
0.
00
1
0.
01
8
Q5.2 Benefits+ protections
only to full time employees
Pearso
n
Correla
tion
.47
1*
*
-
.3
95
**
1 .3
01
**
-
.2
15
*
-
.2
80
**
.2
21
*
Sig.
(2-
tailed)
0.0
03
0 0.
00
1
0.
01
4
0.
00
1
0.
01
2
Q5.3 Protections proportional
to hours worked
Pearso
n
Correla
tion
0.2
11
-
0.
08
3
.3
01
**
1 -
0.
04
6
0.
00
1
.2
88
**
Sig.
(2-
tailed)
0.2
03
0.
34
6
0.
00
1
0.
60
3
0.
99
3
0.
00
1
Q5.4 Company liable and Pearso - .2 - - 1 .4 -

86
responsible in problems and
issues connected to job
n
Correla
tion
0.2
13
87
**
.2
15
*
0.
04
6
07
**
.3
59
**
Sig.
(2-
tailed)
0.1
99
0.
00
1
0.
01
4
0.
60
3
0 0
Q5.5 Employer who do not
guarantee minimum
protections benefits is
exploiting
Pearso
n
Correla
tion
-
0.1
75
.2
92
**
-
.2
80
**
0.
00
1
.4
07
**
1 -
.2
39
**
Sig.
(2-
tailed)
0.2
92
0.
00
1
0.
00
1
0.
99
3
0 0.
00
6
Q5.6 A very high salary
compensate protections and
benefits
Pearso
n
Correla
tion
0.2
2
-
.2
07
*
.2
21
*
.2
88
**
-
.3
59
**
-
.2
39
**
1
Sig.
(2-
tailed)
0.1
84
0.
01
8
0.
01
2
0.
00
1
0 0.
00
6
Q8 Level of satisfaction with
1099 platforms
Pearso
n
Correla
tion
1 -
0.
02
.4
71
**
0.
21
1
-
0.
21
3
-
0.
17
5
0.
22

87
We then find a correlation with a 99% level of significance between Q5.1,
Q5.5 and Q12 (see table 3.13). The level of agreement on protection and
benefits also for independent contractors (Q5.1) decreases with the
increase of the level of education (Q12). On the other side, the agreement
on “exploitation of workers in case of lack of protections and benefits
(Q5.5)” increase with the level of education.
If we look at the eta value of both variables Q5.1 and Q5.5, they are all
higher than 0.3, which mean that the correlation is strong.
While observing the trends of their means in the graph below, we must
bear in mind that only few people reach this latter level of education (see
univariate analysis).
Table number 13
ANOVA Table Q5*Q12
F Si
g.
Et
a
Q5.1 Independent contractor, same
protections+benefits full time employees * Q12
Level of instructions obtained
Betwee
n
Groups
3.
92
8
0.
00
5
0.
33
4
Q5.2 Benefits+protections only to full time
employees * Q12
Betwee
n
Groups
2.
17
8
0.
07
5
0.
25
5
Q5.3 Protections proportional to hours worked *
Q12 Level of instructions obtained
Betwee
n
Groups
1.
36
8
0.
24
9
0.
20
4
Q5.4 Company liable and responsible in problems
and issues connected to job * Q12
Betwee
n
Groups
0.
30
6
0.
87
4
0.
09
8
Q5.5 Employer who do not guarantee minimum
protections benefits is exploiting * Q12
Betwee
n
Groups
4.
41
2
0.
00
2
0.
34
9
Q5.6 A very high salary compensate protections Betwee 0. 0. 0.

88
and benefits * Q12 n
Groups
30
8
87
2
09
8
Graph number 24
7.00 6.90
6.37
4.07
6.00
7.28 7.37
7.68
4.93
8.33
0
1
2
3
4
5
6
7
8
9
Mean Mean Mean Mean Mean
High
school
College Bachelor
degree
Master
degree
PHD or
more
Mean values
Compare means Q5.1/Q5.5*Q12
Q5.1 Independent
contractor, same protections
+bene[its full time
employees
Q5.5 Employer who do not
guarantee minimum
protections bene[its is
exploiting

89
Afterwards we consider the correlations of Q5 with Q7, Q9, Q12, Q13, Q15
but we do not find any significant and considerable correlation.
We then study Q7, and from a demographical perspective, which kinds of
people are working for an on-demand platform. Surprisingly the
involvement and participation in these businesses is not affected by the
level of instruction obtained (Q12), the age (Q13) nor the family income
(Q15) as we can see from tables 3.14 and 3.15. Moreover, participation
with these businesses for a short or long time (Q7) is not correlated with
the level of satisfaction of the workers (Q8), or their desire to increase
their benefits and protections (table 3.16, 3.17).
Table number 14
Chi-Square Tests Q7 and Q12
Value df Asymp. Sig. (2-
sided)
Pearson Chi-
Square
12.480a 12 .408
Note: p.value>0.5. We accept the Null hypothesis of no correlation.
Table number 15
Value df Asymp. Sig. (2-
sided)
Pearson Chi-
Square
13.726a 12 .319

90
Value df Asymp.
Sig. (2-
sided)
Value
Pearson
Chi-
Square
1.821a 4 .769 Pearson
Chi-
Square
1.821a
Table number 16
ANOVA Table Q7*Q8
Sum of
Square
s
d
f
Mean
Squar
e
F Sig.
Q8 Level of
satisfaction
with 1099
platforms * Q7
Working for
1099 platform
s
Betwee
n
Groups
(Combined
)
3.098 1 3.098 .68
7
.41
3

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