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The development of online communication platforms has given rise to the phenomenon of the gig economy. A new economic model that embraces a variety of forms of short-term employment is rapidly spreading around the world, becoming an everyday reality and transforming the labor market. The article analyzes the factors influencing the dynamics of this process and its main effects. Testing the main hypothesis showed that the development of technological infrastructure, despite its importance, does not fully explain the unevenness of the penetration of the gig economy and the variations in its impact upon different sectors, professions, and skill levels. Gig economy drivers are subject to further study, but already now we can state the need for targeted measures to adapt the economy to the new model, including retraining or creating alternative employment opportunities for “traditional” workers giving up jobs in favor of gig-employed ones.
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e Spread of Gig Economy: Trends and Eects
The development of online communication platforms
has given rise to the phenomenon of the gig econo-
my. A new economic model that embraces a variety
of forms of short-term employment is rapidly spreading
around the world, becoming an everyday reality and trans-
forming the labor market. e article analyzes the factors
inuencing the dynamics of this process and its main eects.
Testing the main hypothesis showed that the development
of technological infrastructure, despite its importance, does
not fully explain the unevenness of the penetration of the
gig economy and the variations in its impact upon dierent
sectors, professions, and skill levels.
Gig economy drivers are subject to further study,
but already now we can state the need for targeted mea-
sures to adapt the economy to the new model, including
retraining or creating alternative employment opportuni-
ties for “traditional” workers giving up jobs in favor of gig-
employed ones.
Nilanjan Banik
Keywords: gig economy; technology index; income distribution;
digital platforms; labor markets; corporate strategies
Citation: Banik N., Padalkar М. (2021) e Spread of Gig
Economy: Trends and Eects. Foresight and STI Governance,
15(1), 28–38. DOI: 10.17323/2500-2597.2021.1.19.29
Milind Padalkar
Bennett University, Plot Nos 8-11, TechZone II, Greater Noida 201310,Uttar Pradesh, India
1 e Cambridge dictionary denes ‘gig’ as ‘a single performance by a musician or a group of musicians’. See:
english/gig; accessed on 23 July 2020.
2 Named by the title of the 1099-MISC tax form, which any American company is required to issue for a freelance employee whose income exceeds
Following the 2008 global nancial crisis and the
resultant unemployment, many professionals
and skilled workers began performing short-
term jobs to earn their livelihood. is phenomenon
was described as the ‘gig economy’, a metaphor drawn
from the music industry where artists performed gigs.1
e spread of gig work was initially driven by skilled IT
professionals who began using online digital platforms
to search for such opportunities. Gig work is emerging
as a livelihood option for job seeking students, retirees,
low- and high-skilled workers. Working with US em-
ployment data, Collins et al. [Collins et al., 2019] nd
virtually all expansion of the gig workforce since 2011
has come from online platform work. e expansion
of the gig phenomenon has attracted the interest of
researchers. Dierent descriptions have been oered
towards a clearer understanding of this phenomenon.
ere are various denitions that do not always coin-
cide with practical approaches when it comes to the
gig economy. Scholars also note the denitional di-
culties associated with the platform economy – a term
which is close to gig economy [Frenken, Schor, 2019].
Based on the existing literature, we draw upon the ma-
jor characteristics of the gig economy, comment on
its implications for labor productivity, employment
growth, income distribution, and corporate strategies.
en, we discuss the legal implications of the rise of the
gig economy in India. Also, we examine the hypothesis
that Information and Communications Technology
(ICT) infrastructure plays a positive role in the spread
of gig work by constructing a Technology Index (TI)
encompassing mobile, internet, broadband connectiv-
ity, and electricity connections. Finally, based on our
results we conclude with some policy recommenda-
e report by the World Bank [World Bank, 2015]
categorizes the gig economy into three types of out-
sourcing activities such as Microwork, Freelancing,
and Business Process Outsourcing. Meanwhile, draw-
ing from the empiricist tradition the term ‘gig econ-
omy’ exhibits a few other characteristics. First is that
gig work tends to be on-demand and short-term [Berg,
2016; Van Doorn, 2017]. It is priced by pre-dened
outcomes and depending upon how much one earns,
gig work is also referred to as the 1099 economy2
[Kalleberg, Dunn, 2016].
ere are no clear denitions for “short term” and
“short-term contract”. Gig workers may work for one
year or more, under serially renewed xed-term con-
tracts, and yet fall under the classication of short-
term contracts [Connelly, Gallagher, 2006].
A characteristic feature of the gig economy is that it
is platform-enabled [Kenney, Zysman, 2016]. e gig
economy uses technology platforms as conduits to
connect the workers to the hirers, and the owners of
assets to the customers. e rst category is the trans-
action happening using a labor platform and the sec-
ond is the transaction happening through a capital
platform [Farrell, Greig, 2016].
Examples of labor market platforms are Uber, Task
Rabbit, Swiggy, Zomato, among others. As the work
is job-specic, workers using these platforms have
the exibility to work for more than one contractor.
A food delivery person can work for both Swiggy and
Zomato, and yet can drive Uber during some other
time. Similarly, the aggregators may provide more than
one type of service. For example, Uber which is gener-
ally known as a taxi service aggregator also has Uber
Eats which is a food delivery and online take-out ser-
vice app. e examples for capital market platforms are
service providers such as Airbnb, which serves house
owners in renting out temporally free living space.
Similar is the case with car rental service platforms
such as Zipcar and Hertz.
e next characteristic is about scalability and the ab-
sence of entry barriers. e platform-enabled gig econ-
omy can accommodate a large number of buyers and
sellers. e cost of entering a platform-enabled market
is minimal. According to [Drahokoupil, Fabo, 2016],
digital platforms have lowered the transaction cost of
labor outsourcing and temporary access to goods and
services. e gig economy has helped to reduce infor-
mation asymmetry associated with job search costs
[Zhao, 1999]. In India, for example, before the advent
of the digital world, job seekers regularly waited in line
‒ sometimes all day ‒ to register at national employ-
ment exchanges for their job search. At present, digital
platforms allow the job seekers to conduct most of the
search and inquiry processes online. Finally, the gig
economy usually operates on the basis of ‘standardized’
outcomes. As the job performed isoutcome-based, the
risks associated with moral hazard or asymmetric in-
formation are mitigated. For instance, in the case of a
long-term contract, persons once hired cannot be red
without serving a notice period or trade unions agree-
ing to such a decision. e onus of risk associated with
employee’s output falls upon the employers. In the gig
economy driven by task-based jobs, problems associat-
ed with information asymmetry and/or moral hazard
generally do not arise. e rating systems on platforms
for task-based services also ensure that only the most
standardized and ecient suppliers get rewarded in
the long-run.
Given these aforementioned characteristics and based
on the work arrangement that the gig economy has
to oer, it is spreading fast. According to US Bureau
Banik N., Padalkar М., pp. 19–29
of Labor Statistics, there are 1.6 million gig economy
workers working for services such as Uber, TaskRabbit,
and others.3 Worldwide, major demand for gig work
arises from Information Technology (IT), IT-enabled
services, e-commerce, retail, hospitality, and the fast-
moving consumer goods (FMCG) sector, wherein sud-
den and short-duration workers at the lateral levels
are very much in demand [AfDB et al., 2018]. During
2015, some of the in-demand jobs dealt with internet
marketing, blogs, and e-commerce jobs. ere were
about 26,000 open jobs paying hourly rates between
$16 and $22 on average [World Bank, 2015]. e digi-
tal platforms are creating additional job opportunities
for the employees working in the traditional brick-and-
mortar economy. Collins et al. [Collins et al., 2019] nd
that in the US, the share of the workforce with income
from gig work has grown by 1.9 percentage points of
the workforce between 2000 and 2016. In the overall
gig economy, about 60% of the workforce also have a
full-time salaried income over the course of year.
e Impact of Gig Economy
e rise of technology, cheap labor, and entrepreneur-
ial spirit is aiding the growth of the gig economy. e
platforms enable workers to connect across geographi-
cal boundaries. Consequently, the outcomes are rais-
ing productivity, optimizing employment and income
distribution. In this section, we consider these dimen-
sions in detail.
Productivity and Specialization
e rise of the gig economy is likely to increase overall
productivity due to increase in labor force participation
rates and improved access to lower-wage workers from
abroad, leading to more specialization and standard-
ization of work. For instance, over the last few decades,
Europe has been witnessing an ageing society and a
fall in labor productivity. With falling birth rates, an
ageing population, it is dicult to increase productiv-
ity through traditional labor force participation meth-
ods. e population growth rates in many Eurozone
countries have fallen below the required replacement
rate threshold of 2.1. For instance, the net population
growth rates are 1.38 for Greece, 1.39 for Spain, 1.41 for
Italy, and 1.94 for the UK. It is estimated that for Spain
and Greece, the over-65-year-old population will in-
crease from around 17% to 25% by 2030 [Banik, 2019].
An ageing society with strong trade unions nds it dif-
cult to increase worker productivity [Sherk, 2009].4
However, this is likely to change with the spread of gig
work which increases productivity by increasing labor
participation through digital platforms. Rather than
hiring one generalist to complete all tasks, companies
can designate tasks to various freelancers specialized
in that area. Workers are also more accountable as per-
formance standards dictate future incomes. Connected
global labor markets will lead to a rise in economic
productivity even in countries in Europe which now
have a shortage in the supply of labor. Workers from
labor-abundant developing countries are likely to gain.
Owing to the standardized rules, in a gig-world, low-
salaried service workers from developing countries
can now earn more by engaging in similar job proles
in established economies. ere are no entry barriers
and all that is needed is access to mobile/internet and
electricity connections. e rise in labor productivity,
as well as an increase in per-capita income can hap-
pen not only because of presence in gig work but also
from the structural transformation brought in through
technological innovation [Bassanini, Scarpetta, 2002].
(Figure 1)
Employment and Labor Participation
Labor participation in a gig world comes from a va-
riety of sources. e lower-income individuals are
more likely to participate on labor platforms than
higher-income counterparts [Farrell, Greig, 2016].
As of 2016, 0.6% of the people in the lowest income
quintile earned income from labor platforms such as
Upwork and Uber, whereas the remaining 0.4% is de-
pendent on capital platforms like Airbnb. is lower
income group is also more persistent in using the labor
platform: 56% of the participants in the lowest income
Source: [AfDB et al., 2018].
Within-sector productivity
Structural change
Figure 1. Source of Labor
Productivity Growth (%)
Sri Lanka
3 See:,According%20to%20the%20Bureau%20of%20Labor%20
Statistics%2C%20there%20are%201.6,1%25%20of%20the%20US%20workforce; accessed 27 July 2020.
4 An ageing country is one with 10% or more of its population above 60 years of age. See [Sherk, 2009].
bracket continued accessing the platform within 12
months compared to 47% of the middle-income par-
ticipants, and 40% from the highest income quintile.
ere are no barriers based on caste, religion, gender,
and location. Women comprise more than a third of
15,000 users of the digital platform Souktel in the West
Bank and Gaza region, but only 19% of the entire labor
force in the same area.5 In the US, before the advent
of Airbnb, African American rental hosts were get-
ting 12% less rent than their white American counter-
parts for the same type of house in the same location
[Edelman, Luca, 2014].6 Spatial location of workers
whether urban, rural, or small towns does not matter.
Online labor markets such as Freelancer and Upwork
are likely to substitute for physical labor migration and
hence the uptake in working opportunities on digital
platforms. e gig jobs have a spill-over eect not only
on labor markets [World Bank, 2015; Gomez-Herrera
et al., 2017]. For example, aer the introduction of
taxi services by Uber and Ola, taxi fares were reduced
in major cities in India [Pandya et al., 2017]. e gig
economy has other societal benets such as a reduc-
tion in motor vehicle accidents and trac congestion
[Greenwood, Wattal, 2017] and the improvement of air
quality [Tran, Sokas, 2017].
Income Distribution
e benets from the advent of the gig economy as
a complex and ambiguous phenomenon are not uni-
formly distributed. Full-time employment in the gig
economy may lead to lower-income and economic
vulnerability of lower-skilled workers in developed
countries [Bergman, Jean, 2016]. As the workers from
less developed countries get connected to the poten-
tial recruiters in developed regions, their wage rates
are likely to increase at a faster rate. Similarly, a low-
skilled worker from the developed country is likely to
lose out in the presence of global competition. ings
may get more dicult for these unskilled laborers in
the presence of technological innovation. is leads
to a skewed income distribution globally. For exam-
ple, a rise in wage inequality in Germany results from
rms paying more to their high-skilled workers in
comparison to the others [Card et al., 2013]. As the
availability of high-skilled and talented workers are
limited, wage premium increases. In the US, the rea-
son for wage inequality has to do with more competi-
tive rms tending to keep their high-skilled laborers
as full-time workers, by paying a wage premium. e
low-skilled work is outsourced, both in the US, as well
as in other developed countries including Sweden,
Japan, and the United Kingdom, typically as gig work.
An International Labor Organization report suggests
that gig workers are making less than the government-
mandated minimum wage rate [ILO, 2018]. About
two-thirds of the US workers using the Amazon plat-
form made less than the federal minimum wage rate
of $7.25 an hour and only 7% of Germans on the Click
worker platform made the statutory minimum wage
of 8.84 Euros ($10.40) an hour.7 Virtual “sweatshops”
created by technology platforms are largely unregu-
lated with no oor on minimum wage rates. e work-
ers do not have access to other fringe benets such
as health insurance, sick leaves, working hours, the
continuation of contracts, and settlement of disputes
[Chandy, 2017]. Currently platform services are com-
ing under increasing pressure to adhere to the rules
that are applicable to traditional service providers in
those elds. e city of Seattle has passed a law permit-
ting Uber and Ly drivers to unionize and the driv-
ers receive unemployment benets.8 In a court ruling
against a garment manufacturer in India, the Supreme
Court of India passed a judgment stating that female
contractual laborers who are working from home
doing piece work would be considered “employees”
of the company who has engaged them to do work
[Kumar, 2019].
Another possible source of unequal income distri-
bution arises from ownership of capital platforms.
Although platform soware has become ubiquitous, the
market valuation of companies such as Uber, Airbnb,
Facebook, and Amazon, put together, may in fact be
higher than the GDP of many low-income countries.
e drivers hired by Uber in the US were embroiled in
a long-drawn legal battle, arguing they should be treat-
ed as employees and not as an independent contrac-
tors, with a better non-pecuniary benet [Lobel, 2016].
For instance, although drivers using the Uber platform
are paid by the job and have control over their work
hours and geographical preference for operation, Uber
set the passenger pay-rate and displaced the drivers
falling below a minimum rating point. Drivers led a
class-action suit during 2013, with Uber nally agree-
ing to pay $20 million to settle the case in 2019.9
Exogenous shocks, such as COVID-19, can also
change distribution of income. In a survey conducted
by APPJOBS, comprising of 1,400 workers from 58
dierent countries, the study nds the sectors which
benetted from the pandemics includes delivery, con-
sulting, freelancing, and online surveys. Whereas the
in-person sectors, such as house sitting, babysitting,
driving, and hospitality (hotel and tourism) industries,
got negatively impacted [AppJobs, 2020].
5, accessed 19.02.2021
6 However, the organized labour market comes with a tag of equal opportunity employer, wherein the employer agrees not to discriminate against employ-
ee or job applicants because of race, national origin, and gender. See [Edelman, Luca, 2014].
7 Accessed 21 April 2020.
8 See:; accessed 20 April 2020
9 See:; accessed 19 June 2020
Impact of the Gig Economy on the
Organizational Environments
e largest impact of the gig economy occurs in the
areas of corporate strategies and performance manage-
ment. In the traditional organizational forms, whether
hierarchical, matrix, or network, the work is broken
down in discrete units and allocated to workers in
logical sequences of assembly-type dependency struc-
tures to ensure a swi, even ow [Schmenner, Swink,
1998]. However, the real-life organizations experience
sub-optimal performance due to structural imperfec-
tions, incomplete specications of work elements, co-
ordination delays, and ambiguity experienced by the
human element. Organizations compensate for such
imperfections by building buers of extra manpower
and skills by calibrating the processes of worker selec-
tion and allocation. e dynamic of work expansion
or contraction leads to the uneven absorption of such
extra manpower adding to the coordination problems.
In practice, therefore, the work environment of most
organizations is plagued by overstang and under-
stang at dierent stages of work cycles. e net eect
of such a dynamic is a patchwork of idle time within
the organizational environments. e idle time occurs
both during the switchover between the tasks as well
as endogenously within the tasks due to workers pac-
ing their work dierently under dierent conditions
[Gevers et al., 2006, 2015; Brodsky, Amabile, 2018].
Such idle time in an unevenly overstaed organization
is of serious concern to the management teams, who
oen employ various methods to plan work to maxi-
mize throughput.
e advent of the gig economy and availability of gig
workers or freelancers represents an opportunity for
managers to package work dierently to assign it to gig
workers through online platforms. While such assign-
ments take the form of short-term engagements, they
are dierent from outsourcing which generally are
semi-permanent arrangements of non-core activities
performed through business-to-business contracts and
paid on the basis of dened inputs or outcomes. Gig
work on the other hand involves the element of choice
on part of the gig worker, short-term contracts, and
payment on the basis of pre-dened outcomes and typ-
ically are covered by person-to-person, or by business-
to-person contracts. For instance, the consulting rms
working on complex contracts require specic subject
matter experts. Such experts are rarely employed with
anyone on a full-time basis; instead, the rms obtain
them through the gig channels. us, the gig work can
consist of high expertise as well as commodity services
such as canteen work, security, courier, transportation,
and so on. Such a broad scope poses both challenges
as well as opportunities for the operating management,
who must develop the organizational capability to plan,
decouple, and dene the work packages, participate on
the digital platform to select the gig workers and assign
the work, and control the performance. Such a capabil-
ity remains weak within the traditional organizations.
is implies that organizations wishing to leverage the
benets of the gig economy must develop the process-
es to codify the work packages and the matching skills
to be sourced from the gig platforms. Evidently, the or-
ganizations investing in such capabilities benet from
greater exibility, scalability, and agility.
Another aspect of the gig economy is its retarding eect
on the career and skill development of the gig worker
[Kost et al., 2020]. As the organizations adapt their pro-
cesses to integrate gig work, the skill proles of their
full-time employees must change from generalists to
specialists skilled in controlling the outsourced work
and managing the arms-length relationships with the
gig workers. Traditional organizations structure the
roles of their employees in accordance with the princi-
ples of division of labor, repetitive tasks, and hierarchi-
cal control. Integrating gig work implies considerable
changes in the managerial and interpersonal skills of
the full-time employees, and corresponding changes in
the processes of selection, tment, training and perfor-
mance management [Meijerink, Keegan, 2019].
Finally, it should be quite evident that the applicabil-
ity of gig work will be non-uniform within the value
chains. Areas such as new product development, prod-
uct strategy, or branding maybe less amenable to plat-
form-type gig outsourcing, compared to the relatively
standardized and non-critical areas such as employee
benets, payroll, transportation, warehousing, website
development, etc. Traditional organizations following
a ‘one size ts all’ design philosophy will nd it chal-
lenging to switch to more exible and agile designs for
themselves, as they must overcome the hurdles posed
by generating the consensus and the action within the
existing person-organization ts.
Gig Economy in India
India has recently witnessed a rapid rise in the gig econ-
omy and gig work in the recent years as evidenced by
the mounting anecdotal evidence. India has emerged
as the h largest country in the world for exi-
stang behind US, China, Brazil, and Japan, and had
about 3 million gig workers in 2018. It estimates that
this gure will rise to 6 million by end of 2021.10 It lists
Banking, Financial Services, Insurance, Information
Technology, and Retail as the major sectors absorbing
the gig work. e growth of gig work is increasingly
driven by large corporate companies who have begun
to leverage independent consultants and freelancers to
drive high-priority strategic projects and to test new
product or service models [FlexingIt, 2019]. ere has
show/69854133.cms; accessed 21.07.2020
Banik N., Padalkar М., pp. 19–29
been a sharp growth in the registration of freelancers
on the job portals, with 73% of the freelancers indicat-
ing that they do not intend to return to 9-to-5 full-time
jobs [AppJobs, 2020]. While the emergence of the gig
phenomenon is too recent to be comprehensively sur-
veyed or studied by the researchers, the media articles
suggest that the growth in the gig economy is driven
by strong positive trends on both demand and supply
From a demand perspective, gig work involves parcel-
ling out short pieces of work with predened outcomes
by engaging workers on a non-permanent basis and
paying them on the basis of the achievement of the
outcomes. us, the ability to spin o work packages is
key to the demand for gig work. Most of the gig work
until 2015 came from start-ups and small early-stage
entrepreneurs. e recent entry of large corporate or-
ganizations trying systemically reengineer the work
processes imparts sustainability and robustness to the
demand. Such reengineering involves partitioning of
all work into routine and non-routine categories, the
careful reassessment of the work processes, and devel-
opment of managerial systems for engaging/outsourc-
ing gig labor. Examples of routine work include pro-
cesses related to ongoing functions such as production,
sales, inventory management, preventive plant main-
tenance, etc. ese activities require steady manpower
to be engaged on a full-time employment basis. On
the other hand, special projects or sporadic, one-time
work do not require permanent manpower. Examples
of such non-routine activities include the design of
new products or services, market surveys and analysis,
soware development, process consultancy, occasional
breakdown of specialized machinery, infrastructure or
layout changes, and so on. In general, the organiza-
tions nd it economical to engage the gig labor either
because they do not possess the required expertise for
such activities or do not have the economic justica-
tion to engage such expertise on a full-time basis; or
the tasks being assigned for gig work are deemed suf-
ciently non-critical and low-valued [Howcro et al.,
Coming to the supply side of the labor economy, India
historically has had a large workforce in the unorga-
nized (also called informal) sectors. e informal sec-
tor employs more than 90% of the labor and contrib-
utes 50% to the GDP of the country [Government of
India, 2012]. Agriculture and Forestry, Fishing, Trade,
Hospitality, Community, Social and Personal Services,
Real Estate and Construction, and Manufacturing are
the leading sectors for absorbing unorganized labor.
According to 2015 data, nearly 85% of the workforce
were engaged without job contracts or contracts of
less than one year [Government of India, 2014]. Given
the large size of India’s unorganized economy, it is no
surprise that it has continued to draw attention from
diverse interests such as policymakers, legislators,
economists, lawyers, and tax authorities and has gen-
erated extensive studies. While specic surveys and
studies about the gig economy remain sparse, available
reports indicate that it is fairly sizeable and is experi-
encing rapid growth. It is believed that workers in an
unorganized economy have a low-lever or no quali-
cation. Extant literature on the gig economy however
cites choice and exibility as key qualifying attributes
to be a gig worker [Rosenblat, 2016]. Initially, gig work-
ers were characterized as highly skilled professionals
doing multiple short assignments as a way of earning
their livelihoods.11 Since then, several authors have re-
tained the attributes of diversity and skills and added
the positive mediating eect of technology platforms
on the gig phenomenon [Lepanjuuri et al., 2018; Gleim
et al., 2019; Wood et al., 2019]. We argue that choice,
exibility, and intermediation by technology platforms
are the key attributes of the gig work. Consequently,
those parts of the informal economy which lack the
elements of voluntary choice and platform intermedia-
tion must be excluded from the gig phenomenon.
Since its independence, India’s public policies have had
a strong socialist orientation, and this has reected
in its labor laws. Present-day India has well-invested
structures of labor laws for the protection of the work-
ers from unfair and exploitative practices of employers.
ese laws were enacted in the times when industrial
manufacturing was the dominant part of the formal
economy, and the service sector was miniscule in size.
With the passage of time, the manufacturing sector has
contracted from 40% to less than 20%, while services
have grown to more than 50% of the country’s GDP.
However, the labor laws have not kept pace with the
changing times, and face criticism from several quar-
ters that they are excessively restrictive, and their pro-
labor orientation is choking investments as well as
growth in organized employment. Taking cognizance
of such criticism, the government has tried to bring in
reforms in the labor laws, however this remains a work
in progress, with experts claiming these attempts to be
at best anaemic.12 e issue of informal sector workers
is lost in the political cross winds of change. All regula-
tory frameworks apply to the organized sector workers,
leaving the very large informal sector workers unpro-
tected against adverse practices by the employers.
e rapid rise of the gig economy is occurring in the
legal landscape that has no regulation whatsoever, and
this exacerbates the issue of worker rights, protections,
and social security. In 2018, the drivers of the ride-
hailing services in India went on a strike protesting the
compensation structures of Ola and Uber – two rms
operating the intermediating technology platforms.13
In a paper on this issue, Surie [Surie, 2018] analyzes
11, accessed 20.02.2020.
12 https://www.; accessed 26.07.2020
13; accessed 26.07.2020
the nature of the engagement of these gig workers and
argues for regulatory frameworks and bodies noting
the potential for the exploitation of these workers.
e rapid rise of technology platforms and the gig
economy has amplied the inequities in the labor situ-
ation. Unequal access to the internet and gender dis-
parity in labor participation rates imply that several
sections of the population have not been able to benet
from the gains of modern technologies. First, despite
the rapid penetration of mobile telephony in India, the
rural populations have generally not moved on from
2G telephony and thus lack good quality or high-speed
access to internet. is severely restricts their ability
to engage in complex transactions. Second, the female
populations have not been able to participate in the gig
economy, owing to multiple factors such as poorer lit-
eracy rates and technology illiteracy, familial responsi-
bilities, and gender-determined social constraints. e
issues of social security, workplace harassment, and
contract enforcement transcend all segments of gig
workers. A paper by the Indian think tank Observer
Research Foundation notes dispute redressal, ombuds-
man of platforms, protection against workplace ha-
rassment, emergency button for physical safety, social
security, and contractual protection as key areas for
regulatory interventions [Kasliwal, 2020].
In summary, the growth of the gig economy in India
holds considerable potential to address the endemic
problem of employment generation and provides an
impetus to the stalled process of reforms in Indias
labor laws. However, the promise of the gig phenom-
enon is unlikely to be delivered without enacting the
necessary regulatory structures and legal frameworks.
Model Development and Analysis
Hypothesis Development
We developed a model to relate the size of the gig
economy in terms of the contextual macroeconomic
variables. e platform-based economy is creating new
value by monetizing economic resources such as as-
sets and labor. We anticipate the availability of mobile,
internet and broadband connectivity, and electricity
connections to aid the gig labor economy. Accordingly,
we motivate the following hypothesis:
H1: e number of gig workers is positively inuenced by
the availability of internet, mobile, broadband subscrip-
tions, and electricity connections.
Further, we posit that workers in the low skill catego-
ries face high search costs for work and continued un-
certainty in accessing opportunities. e emergence of
technology platforms will induce such workers to join
the gig economy by leveraging the technology infra-
structure. We therefore hypothesize that the supply of
gig workers would be higher when per-capita income
levels are lower. Accordingly, we motivate the follow-
ing hypothesis:
H2: Rising per capita income negatively inuences the
number of gig workers.
Dependent Variable
e dependent variable is the number of gig workers in
the country. ILOSTAT published by the International
Labor Organization (ILO) provides employment data
by occupation and gender, segregated by dierent oc-
cupation categories.14 ILO estimates of employment by
occupation categorize skills on four levels from level 1
(Low skilled) to level 4 (Professionals). ILOSTAT data
suers from several limitations. First, it reports data
from conventional labor markets such as manufactur-
ing and construction; and does not cover gig workers.
Second, a large proportion of professional workers
such as university professors, doctors, and engineers
are part of the organized labor markets and do not par-
ticipate in the gig economy. Hence the ILOSTAT un-
derstates the estimates of professionals doing gig work.
ird, ILO denes employment as worker employed
for at least one hour in a week or a day [Hussmanns,
2007]. Such a measure fails to capture any collateral
wage-earning work. For instance, if a worker employed
full-time performs additional job(s), then such addi-
tional work is not counted in the employment statistics.
Fourth, it is dicult to capture the value of gig work in
areas such as product development, design, and mar-
keting in published macro-economic data [Corrado,
Hulten, 2010]. In general, the published macroeco-
nomic data does not capture the online gig workers,
even though such workers are large in number, espe-
cially in the developing countries.
To overcome these limitations, we use the Online
Labor Index (OLI).15 is dataset oers gig economy-
equivalent of the conventional labor market statis-
tics. OLI tracks workers using labor market platforms
across countries and occupations posted on major on-
line gig platforms in near real time and provides the
counts of workers engaged in gig labor. OLI is based
on data by accessing websites through collection tech-
nologies such as application programming interface,
scraping, or downloading the data from the digital
platforms [Kässi, Lehdonvirta, 2018]. It uses data from
unique visitor counts on leading gig platforms from
Alexa,16 and surveys of top-ve gig platforms: Upwork.
and It includes the following occupation
SD1!595095360?_afrLoop=1828381741967760&_afrWindowMode=0&_afrWindowId=null; accessed on 14 May 2020
15 Published by the Oxford Internet Institute. See: accessed on 26 June 2020.
16 Alexa’s site popularity trac rankings are based on the anonymous usage patterns of one of the largest and most global samples of internet users available
in the world. See:; accessed on 02 January 2021
17 Our data set is based on OLI surveys conducted in July 2016 and again in February 2017.
Banik N., Padalkar М., pp. 19–29
classes: Professional services (such as accounting, con-
sulting, legal, etc.), clerical and data entry, creative
and multimedia (such as animation, logo design, etc.),
sales and marketing support, soware development
and technology, and writing and translation. e OLI
database is more exhaustive with many countries in
the sample, and to our knowledge is the rst database
to give a comprehensive estimate of the number of gig
Explanatory Variables
For explanatory variables, we propose ICT components
namely: mobile telephony, internet access, broadband
connectivity, and electricity connection. Other studies
also point to the pivotal role of ICT in the gig economy
[De Stefano, 2016; Aubert-Tarbey et al., 2018]. For in-
stance, a joint study by the Foundation for European
Progressive Studies and UNI Europa reports that 42%
of the respondents had used online platforms to nd
services, including taxi drivers, builders, graphic de-
signers, and accountants.18
To rule out the multicollinearity objections, we con-
structed a new variable, TI, by merging these four
ICT variables. We take TI as an explanatory variable.
Drawing from an earlier discussion in this paper, we
expect the gig economy to positively aect the incomes
of the gig workers. Gomez-Herrera et al. [Gomez-
Herrera et al., 2017] nd that workers from low-income
countries are likely to participate in jobs oered by the
high-income countries.19 Accordingly, we include log
of per-capita income as an explanatory variable.
e data on mobile, internet and broadband connec-
tivity, electricity connections, and per-capita income
is taken from World Development Indicators [World
Bank, 2017], detailing data about 139 countries.20
We follow Ordinary Least Squares method to estimate
the following equation:
OLIi = α + βTi + γPCi ,
Where, OLI refers to the online labor index, TI is the
technology index, and PC refers to log of per-capita
income. Subscript i refers to the country.
Results and Analysis
Using Principal Component Analysis [Mardia et al.,
1979], we construct TI as vector X (X = X1, X2,…, X4)
where, X1 = mobile, X2 = internet, X3 = broadband sub-
scription, and X4 = electricity connections. Before con-
structing TI, we standardize the data to ensure unit-free
comparability among the data. e rst principal com-
ponent shows maximal variance of 1.94 and accounts
for 48.5% variation among all regressors (Figure 2). It
assigns weights of 0.65, 0.35, 0.66, and 0.05 to X1, X2,
X3, and X4 respectively. e high weights of broadband
and internet connectivity indicate the high relative im-
portance within the technology infrastructure, while
connectivity to mobile telephony and to electricity
have moderate and low importance, respectively. e
second principal component with a variance of 1.09
however accounts for only 27.3% of the total variation.
We therefore retain TI with its weights as the variable
for the regression. For each country, we then compute
TI using soware package EViews 11.
Table 1 reports the ndings from the regression analy-
sis. e results support the hypothesis that technology
infrastructure is signicantly positively related to the
number of gig workers. e signicant negative per
capita income suggests that workers from low-income
countries are induced to participate in the gig econo-
my. e employers and the rms contracting out gig
work are predominantly located in high-income coun-
tries, while the gig work can be outsourced to low-
18 accessed on 26 June 2020
19 Per-capita income follows log-normal distribution, with a vast majority of people earning low incomes.
20 e World Bank classies countries into three groups: low income, middle income and high income. As of 1 July 2018, low-income economies are dened
as those with a gross national income (GNI) per-capita of $995 or less in 2017; lower middle-income economies are those with a GNI per capita between
$996 and $3,895; upper middle-income economies are those between $3,896 and $12,055; high-income economies are those with a GNI per capita of
$12,055 or more. For data source, see: accessed on 26 June 2020.
Source: compiled by the authors.
Mobile Internet Broadband
1 2 3 4
Figure 2. Cumulative Proportions
of Eigen Values
Vari abl e Coecient
Constant 0.036** (0.017)
Technology Index 0.008* (0.002)
Per capita Income -0.003*** (0.001)
R-squared 0.14
Adjusted R-squared 0.13
No. of Observations 139
Note: Robust Standard Errors in Parenthesis; *p<0.01, **p<0.05, ***p<0.1.
Source: authors.
Table 1. Gig Worker Index (Base Regression)
income countries [Gomez-Herrera et al., 2017; Song et
al., 2019]. Within a particular country, as the evidence
from the US suggests, high-skilled workers who al-
ready have traditional jobs are less likely to alter their
behavior to search for gig work [Collins et al., 2019].
For robustness checks of the results, we perform sen-
sitivity analysis [Levine, Renelt, 1992] by including
additional explanatory variables to our base regres-
sion model: Manufacturing value-added to GDP and
Service value-added to GDP. e results from the aug-
mented regression show that the coecients of manu-
facturing value-added to GDP and service value-add-
ed to GDP are insignicant.
e gig economy complements the traditional brick-
and-mortar economy by creating markets to exploit
spaces that have remained inaccessible. e paper ex-
plores the drivers of the gig economy phenomenon and
discusses its implications for labor productivity, em-
ployment, income distribution, and corporate strate-
gies. As a case in point, we motivate the hypothesis that
the economics and the availability of ICT infrastruc-
ture moderate the supply of gig labor. We nd that ICT
infrastructure plays a pivotal role in the spread of the
gig economy.
Given its ability to connect workers across the national
boundaries, we nd that such transnational reach does
not lead to wage equalization. Rather, we nd evidence
of rising income disparity across low-skilled and high-
skilled gig labor, indicating that the phenomenon
impacts the dierent skill groups dierently. At its in-
tersection points with the traditional economy, busi-
nesses in sectors such as transportation, health, edu-
cation, personal services, and the gig economy have
caused displacement of brick-and-mortar workers.
Given our nding about the unequal benets of the gig
economy across activities and skill classes, the policy-
makers should evaluate appropriate regulatory or tax
interventions. e policymakers also need to design
interventions to address the needs of such displaced
workers through retraining or through alternative em-
ployment opportunities.
A few limitations of our work can be readily acknowl-
edged. First, while we nd that technology infra-
structure plays a signicant positive role in the gig
economy, there are empirical reports about the un-
even spread of the phenomenon. It is necessary to
go beyond the infrastructure and examine whether
societal variables especially related to the ability to
access and use such technology infrastructure exist.
Second, the demand for gig work is inuenced by
governmental policies related to unemployment ben-
ets. Cross-sectional analysis falls short of studying
the policy impacts and will require longitudinal stud-
ies. ird, the dierential absorption of technology
infrastructure across occupations and age groups will
require further studies to elicit the workings of the
phenomenon. Finally, further research is necessary to
understand how the skill levels of workers aect their
participation in the gig economy.
is work is supported by the National Research Foundation
of Korea Grant funded by the Korean Government (NRF-
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... We share the view of Nilanjan Banik and Milind Padalkar that the development of technological infrastructure, while important, does not fully explain the uneven penetration of the gig economy and the variations in its impact across sectors, professions, and skill levels [24]. Indeed, digital platforms play a crucial role in the rapid growth of the gig economy. ...
... According to the U.S. Bureau of Labor Statistics, 1.6 million gig workers work for services such as Uber, TaskRabbit, and others. India, the Philippines, and the United States are the three most significant centers of contingent labor [24]. The US Federal Reserve believes anyone can be a gig worker, from a nanny to an Uber driver. ...
... The industries with the highest demand for gig workers, as practice shows, include information technology (IT), IT-enabled services, e-commerce, retail, hospitality, and the consumer goods (FMCG) sector, where temporary workers are in high demand [24]. ...
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Gig economy is being increasingly popular globally across the sectors, positions and types of jobs. Despite the growing awareness of the benefits and challenges associated with gig jobs, little is known about what drives the younger generation to take up gig careers. The present study attempts to address this gap and better explain gig work intentions through extended TPB theory. The study rests on the premise that job seekers’ attitude towards gig jobs, belief in self-efficacy and the prevailing social norms influences the decision to make a gig career choice. Apart from these basic constructs of TPB model, impact of two additional factors (gig personality traits and gig work environment consisting of gig job demands and gig job resources based on JD-R model) on gig job intentions was measured to get a deeper understanding on predictors of gig jobs choices. Based on analysis of 471 responses from business management students studying in NIRF listed business schools in India, the study empirically reveals that all three basic constructs-gig job attitude, gig self-efficacy and social norms significantly influence management student’s gig job intentions. Gig job demands had a significant negative whereas gig job resources had a positive relationship with gig job intentions. The three personality traits did not have any direct significant relationship with gig job intentions. But the mediating role of gig job attitude between gig personality traits was also confirmed. The findings of the study contribute to literature on factors driving choice of gig jobs. It has huge implications for policy makers, organizations, business schools as well as society in general.
This study focuses on working life, which has an important place in the center of social changes after COVID-19. In the working life, where digital transformation has an application area, different concepts have taken their place in the literature in the important change observed with COVID-19. Digitalization made its presence felt in a process that accelerated with COVID-19. Concepts and applications that were not included in daily life practices until a few years ago are experienced today. In the study, an evaluation was made on crowdworking and related concepts, which are considered one of these concepts. In this evaluation, digital transformation and the post-COVID-19 process were highlighted, and the motivation sources of crowdworker and crowdsourcer were analyzed through in-depth interview method.
The shocks of the pandemic and the economic downturn became factors that challenged, first of all, new market structures in the national economy, the formation of which was associated with the development of information and communication technologies and the globalization of the information space. It was they who had to offer new ways to adapt to stress factors and find new areas for development. One of such structures in the Russian Federation is the virtual labor market. The lack of publications on the mechanism of its functioning in a pandemic made the research topic proposed by the authors relevant and innovative. The article proposes criteria for adapting the virtual labor market in a pandemic, on their basis, non-price tools for its adaptation are identified, and the prospects for cooperation between transport enterprises and the virtual labor market. The identified trends can be assessed as an innovative adaptation to the conditions of uncertainty associated with the active restructuring of virtual interaction channels. Of particular importance is the growth in intensity and expansion of communication channels for transport enterprises, since the solution of their systemic problems is possible only through the use of a wide range of intellectual and information products and services. The materials can be of practical importance for the creation of the main institutions of the virtual labor market and the development of strategic decisions for transport enterprises.
Цель статьи – показать, что развитие цифровых технологий, осуществляемое в развитых странах по преимуществу силами частного наукоемкого бизнеса, привело к формированию крупных монополий, определяющих ряд видов деловой и социальной активности, прямо связанных с реализацией ряда важных государственных функций. В этих условиях государственные власти США, Евросоюза и Китая сформировали комплекс мер, ограничивающих национальное и глобальное функционирование крупнейших цифровых компаний. В центре этих мер – антимонопольное регулирование, что в перспективе может существенно повлиять на модель инновационного развития и изменить сравнительные позиции современных технологических лидеров. Методологически статья основана на сопоставлении данных о финансировании научных исследований в разных отраслях и корпорациях США и других стран, изучении процессов монополизации в цифровой сфере и анализе мер государственной политики в направлении усиления конкуренции. Автор показывает, что цифровой бизнес быстро опередил все остальные комплексы отраслей по масштабам научных исследований. Опираясь на собственные ресурсы, цифровой бизнес проводит технологическую и инновационную политику, которая все чаще приходит в противоречие с государственными интересами. Формирование крупных цифровых монополий отличается от процессов монополизации в других отраслях, так как формирует платформы с широким участием индивидуальных предпринимателей и малого бизнеса. Одновременно их деятельность в сфере массовых коммуникаций создает новые политические проблемы, рождает новые формы регулирования в разных по уровню развития странах, в том числе вызывает подъем технологического протекционизма, новые законодательно оформленные формы борьбы с цифровой глобализацией.
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Manufacturing is the primary engine of economic growth in Malaysia. This study uses data on 14,705 manufacturing firms in Malaysia to reveal that technology and labour-intensive firms have significant negative and positive effects, respectively, on gig employment. Furthermore, firm size and growth are negatively associated with gig employment, while firm age has a positive association with such employment. Interestingly, the location variable indicated that firms in highly industrialised and relatively developed states in Malaysia (e.g. Selangor) are less inclined towards gig worker recruitment. This study provides an essential input to the dearth of literature on the gig economy, especially from the firm perspective. Also, it guides policymakers in designing industrial policies in line with changing employment trends, thereby reducing labour market disruptions.
Purpose The growing popularity of gig and sharing economy changes not only consumption models but also employment patterns. This study aims to analyze the potential entrepreneurial nature of gig and sharing economy initiatives. As such, the authors compare the entrepreneurial intentions of gig and sharing economy workers to the general population. Further, the authors consider commonalities and differences in terms of the driving forces of both intentions to start-up and participation in gig and sharing economy, treating them as connected phenomena. Finally, the authors look into gig and sharing economy experience as a direct antecedent to entrepreneurial intentions formation. Design/methodology/approach The empirical settings for this study are derived from the sample of 1,257 individuals who participated in the Global Entrepreneurship Monitor Adult Population Survey 2018 in Russia. Methodologically, the authors rely on analysis of variance-test and binary logistic regression analysis to test the study hypotheses. Findings The results indicate that entrepreneurial intentions of gig and sharing economy workers are significantly higher when compared to the general population. In terms of antecedents to gig and sharing economy participation and startup intentions, similar effects of age, entrepreneurial social capital, prior entrepreneurial exit and intrapreneurial experience were revealed, while perceived self-efficacy was associated only with engagement into digital platforms. Finally, gig and sharing economy experience showed significant and positive effect on entrepreneurial intentions formation. Originality/value This study represents a first substantive effort to systematize antecedents to gig and sharing economy participation through an entrepreneurship perspective. Beyond that, this research adds to the contextualization of entrepreneurship literature stream, further defining the mechanism of entrepreneurial intentions formation in empirical settings of an emerging market with a relatively low propensity of population to develop intentions to start-up.
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Advocates of the boundaryless career perspective have relied to a great extent on the assumption that actors take responsibility for their own career development and that they consequently take charge of developing their career competencies. In this provocation piece, we debate the obstructions to and potential ways to promote boundaryless careers in the gig economy, which—despite appearing on the surface to offer suitable conditions for boundaryless careers—suffers from numerous conditions that hinder such careers. Thus, boundaryless careers in the gig economy could be an oxymoron. In particular, we conjecture that intraorganisational and interorganisational career boundaries restrict gig workers' development of relevant career competencies and thus limit their mobility. We then put forward the notion that we have to consider moving away from traditional, employer‐centric human resource management and introduce new forms of network‐based and self‐organised human resource management practices (in the form of collaborative communities of practice) in order to diminish these boundaries.
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The binary of employment versus entrepreneurship that forms the framework indicates a failure of public policy to keep abreast of changing times.
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The increased usage and proliferation of businesses entering the gig economy has meant more employment options for individuals wishing to participate in the gig economy. However, not all gig employment opportunities are the same. Typically, gig employment opportunities fall into one of two categories: the sharing economy or direct selling. These two types of gig employment are unique in the perceptions of those that choose to engage in them. This research seeks to provide insights into the drivers of gig worker perceptions of the product, organizational trust, job outcome status and satisfaction. Results suggest that direct sales workers have higher levels of self-congruence, and lower levels of perceived commerciality, leading to positive evaluations of the product offered, organizational trust and job satisfaction. Conversely, sharing economy workers have much lower levels of self-congruence, and higher levels of perceived commerciality, leading to a more complicated relationship with the outcome variables.
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Purpose Although it is transforming the meaning of employment for many people, little is known about the implications of the gig economy for human resource management (HRM) theory and practice. The purpose of this paper is to conceptually explore the notion of HRM in the gig economy, where intermediary platform firms design and implement HRM activities while simultaneously trying to avoid the establishment of employment relationships with gig workers. Design/methodology/approach To conceptualize HRM in the gig economy, the authors offer a novel ecosystem perspective to develop propositions on the role and implementation of HRM activities in the gig economy. Findings The authors show that HRM activities in the gig economy are designed to govern platform ecosystems by aligning the multilateral exchanges of three key gig economy actors: gig workers, requesters and intermediary platform firms, for ensuring value co-creation. The authors argue that the implementation of HRM activities in the gig economy is contingent on the involvement and activities of these gig economy actors. This means that they are not mere recipients of HRM but also actively engaged in, and needed for, the execution of HRM activities. Originality/value The study contributes to research by proposing a theoretical framework for studying the design of HRM activities, and their implementation, in the gig economy. From this framework, the authors derive directions for future research on HRM in the gig economy.
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This article evaluates the job quality of work in the remote gig economy. Such work consists of the remote provision of a wide variety of digital services mediated by online labour platforms. Focusing on workers in Southeast Asia and Sub-Saharan Africa, the article draws on semi-structured interviews in six countries (N = 107) and a cross-regional survey (N = 679) to detail the manner in which remote gig work is shaped by platform-based algorithmic control. Despite varying country contexts and types of work, we show that algorithmic control is central to the operation of online labour platforms. Algorithmic management techniques tend to offer workers high levels of flexibility, autonomy, task variety and complexity. However, these mechanisms of control can also result in low pay, social isolation, working unsocial and irregular hours, overwork, sleep deprivation and exhaustion.
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Although both media commentary and academic research have focused much attention on the dilemma of employees being too busy, this paper presents evidence of the opposite phenomenon, in which employees do not have enough work to fill their time and are left with hours of meaningless idle time each week. We conducted six studies that examine the prevalence and work pacing consequences of involuntary idle time. In a nationally representative cross-occupational survey (Study 1), we found that idle time occurs frequently across all occupational categories; we estimate that employers in the United States pay roughly $100 billion in wages for time that employees spend idle. Studies 2a–3b experimentally demonstrate that there are also collateral consequences of idle time; when workers expect idle time following a task, their work pace declines and their task completion time increases. This decline reverses the well-documented deadline effect, producing a deadtime effect, whereby workers slow down as a task progresses. Our analyses of work pace patterns provide evidence for a time discounting mechanism: workers discount idle time when it is relatively distant, but act to avoid it increasingly as it becomes more proximate. Finally, Study 4 demonstrates that the expectation of being able to engage in leisure activities during posttask free time (e.g., surfing the Internet) can mitigate the collateral work pace losses due to idle time. Through examination and discussion of the effects of idle time at work, we broaden theory on work pacing.
We use a massive, matched employer-employee database for the United States to analyze the contribution of firms to the rise in earnings inequality from 1978 to 2013. We find that one-third of the rise in the variance of (log) earnings occurred within firms, whereas two-thirds of the rise occurred due to a rise in the dispersion of average earnings between firms. However, this rising between-firm variance is not accounted for by the firms themselves but by a widening gap between firms in the composition of their workers. This compositional change can be split into two roughly equal parts: high-wage workers became increasingly likely to work in high-wage firms (i.e., sorting increased), and high-wage workers became increasingly likely to work with each other (i.e., segregation rose). In contrast, we do not find a rise in the variance of firm-specific pay once we control for the worker composition in firms. Finally, we find that two-thirds of the rise in the within-firm variance of earnings occurred within mega (10,000+ employee) firms, which saw a particularly large increase in the variance of earnings compared with smaller firms.
Labour markets are thought to be in the midst of a dramatic transformation, where standard employment is increasingly supplemented or substituted by temporary work mediated by online platforms. Yet the scale and scope of these changes is hard to assess, because conventional labour market statistics and economic indicators are ill-suited to measuring this “online gig work”. We present the Online Labour Index (OLI), an experimental economic indicator that approximates the conventional labour market statistic of new open vacancies. It measures the utilization of online labour across countries and occupations by tracking the number of projects and tasks posted on major online gig platforms in near-real time. The purpose of this article is to introduce the OLI and describe the methodology behind it. We also demonstrate how it can be used to address previously unanswered questions about the online gig economy. To benefit policymakers, labour market researchers and the general public, our results are published in an interactive online visualisation which is updated daily.
Based on an exhaustive dataset of all journalists in France, this article investigates the impact of digitisation on the employment of journalists in the press industry. In particular, focus is put on the effect played by the level of digitisation of newspapers and magazines, some of which have resisted digitisation, while others have embraced it. We find that greater levels of digitisation tend to increase the likelihood of job creation and reduce the probability of job destruction. Likewise, higher level of digitisation leads, on average, to higher earnings for journalists. At the same time, though, higher digitisation also increases sharply the likelihood that jobs created are of casual contractual natures, as opposed to regular permanent contracts. Yet, we find that digitisation also has a positive impact on the earnings of journalists on a casual contact (though, far less than for ‘tenured’ journalists). More surprisingly, we show that digitisation also reduces job instability of those journalists on a casual contract, as a greater level of digitisation reduces the likelihood of job destruction, even for casual jobs. Though, while digitisation tends to change the contractual nature of job created, embracing digitisation appears to be a ‘lesser evil’ than resisting technological change.