.tocmtchapter

Nik Dawson

# Layoffs, Inequity and COVID-19: A Longitudinal Study of the Journalism Jobs
Crisis in Australia from 2012 to 2020

Nik Dawson11affiliationmark:  22affiliationmark:  Sacha
Molitorisz33affiliationmark:  Marian-Andrei Rizoiu44affiliationmark:  and
Peter Fray55affiliationmark:  11affiliationmark:  Centre for Artificial
Intelligence, University of Technology Sydney
22affiliationmark:  OECD Future of Work Research Fellow
33affiliationmark:  Centre for Media Transition, University of Technology
Sydney
44affiliationmark:  Data Science Institute, University of Technology Sydney
55affiliationmark:  Private Media<EMAIL_ADDRESS>
###### Abstract

In Australia and beyond, journalism is reportedly an industry in crisis, a
crisis exacerbated by COVID-19. However, the evidence revealing the crisis is
often anecdotal or limited in scope. In this unprecedented longitudinal
research, we draw on data from the Australian journalism jobs market from
January 2012 until March 2020. Using Data Science and Machine Learning
techniques, we analyse two distinct data sets: job advertisements (ads) data
comprising 3,698 journalist job ads from a corpus of over 8 million Australian
job ads; and official employment data from the Australian Bureau of
Statistics. Having matched and analysed both sources, we address both the
demand for and supply of journalists in Australia over this critical period.
The data show that the crisis is real, but there are also surprises. Counter-
intuitively, the number of journalism job ads in Australia rose from 2012
until 2016, before falling into decline. Less surprisingly, for the entire
period studied the figures reveal extreme volatility, characterised by large
and erratic fluctuations. The data also clearly show that COVID-19 has
significantly worsened the crisis. We then tease out more granular findings,
including: that there are now more women than men journalists in Australia,
but that gender inequity is worsening, with women journalists getting younger
and worse-paid just as men journalists are, on average, getting older and
better-paid; that, despite the crisis besetting the industry, the demand for
journalism skills has increased; and that, perhaps concerningly, the skills
sought by journalism job ads increasingly include social media and generalist
communications.

###### keywords:

Journalism Jobs, Skills, Demand

##  1 Introduction

Globally, the news about the news is not good. This was true before 2020, but
COVID-19 has only made matters worse. Take Australia. In March 2020, newswire
service the Australian Associated Press announced it would be shutting down
its operations after 85 years (Samios, 2020). In June, a last-minute
consortium of investors and philanthropists saved the day – but salvation was
merely partial, with only 85 of the company’s 180 journalists, photographers
and other staff retained (Wahlquist, 2020). Meanwhile, News Corp has been
closing scores of regional titles (see below). In the US, the news about the
news is just as bad, if not worse. In February, the country’s No. 2 newspaper
chain (McClatchy) declared bankruptcy (Benton, 2020). Amid widespread pay
cuts, furloughs and layoffs, US newsrooms reportedly shed more than 11,000
jobs in the first half of 2020 (Willens, 2020).

Even before COVID-19, digital technology upended journalism’s advertising-
driven business model (ACCC, 2019). As the Nieman Lab notes:

> The Internet has brought forth an unprecedented flowering of news and
> information. But it has also destabilised the old business models that have
> supported quality journalism for decades. Good journalists across the
> country are losing their jobs or adjusting to a radically new news
> environment online (Nieman-Lab, 2020).

But is journalism in crisis? A wealth of research in Australia, the US and
comparable countries suggests yes. Profits have been hard, if not impossible,
to come by; many firms were struggling or collapsing; and layoffs and
redundancies were the norm (ACCC, 2019). As Fenton (2011) wrote in a paper
centred on the UK, ‘News media are in crisis. The crisis is being managed by
closing papers or shedding staff [and] these cuts are having a devastating
effect on the quality of the news.’ That was nearly a decade ago. Subsequent
research suggests the situation has worsened significantly. In Australia, the
commonly cited figure based on research by the journalists’ union is that
3,000 journalism positions have been lost since 2011 (Ricketson et al., 2020).
For instance, it is estimated that in 2011 news publisher Fairfax Media
employed about 1,000 editorial staff across the Sydney Morning Herald, The
Age, The Australian Financial Review, and its Sunday papers, The Sun Herald
and The Sunday Age. By mid-2017, however, half of those jobs were gone (Zion
et al., 2018) (including the job of one of this paper’s authors). And then the
coronavirus wielded its scythe. Already, the reported impact of COVID-19 on
journalism jobs has been devastating, with widespread closures and job losses,
particularly in regional areas (Crerar, 2020).

This research assesses the extent of the claimed ‘journalism crisis’ in
Australia by analysing labour market data from January 2012 to March 2020. To
do this, we performed a quantitative analysis of two longitudinal data sets:
job advertisements (ads) for journalism jobs and the official Australian
employment statistics. This allowed us to measure longitudinally the demand
for and supply of journalism jobs in Australia. Further, the breadth and
detail of these data provided us with the opportunity to comprehensively
assess the quality and characteristics of these journalism jobs. Not only did
we examine how key features of journalism jobs have changed – such as
salaries, location or years of experience – but also how journalism skills
required in Australia have evolved. Additionally, the available data enabled
us to measure the early effects of COVID-19 on journalism jobs.

Our findings confirm that there is a crisis in Australian journalism; a crisis
that appears to have worsened during the early stages of the COVID-19
pandemic. However, the data also yields more granular findings, including
three surprise findings. The first finding is that advertised journalism jobs
only started to decline from 2016, not before. The second finding is that as
the journalism jobs market became more volatile, gender inequity worsened:
women journalists who remained were younger and worse paid than the men. And
the third finding is that, according to our skill similarity calculations,
generalist skills such as ‘Communications’, ‘Public Relations’, and ‘Social
Media’ became more important to journalism, as opposed to traditionally
specialist journalism skills such as ‘Reporting’, ‘Editing’, and
‘Investigative Journalism’. These findings, together with others, reveal that
the crisis in journalism is not only real, but in some ways more complex than
was previously understood.

By implementing a data-driven methodology, we provide a comprehensive and
longitudinal assessment of journalism jobs in Australia from January 2012 to
March 2020. We tease out granular and specific trends, including the early
impacts of the COVID-19 pandemic, the contrasting effects on regional and
urban journalism jobs, and the gendered nature of ongoing impacts. And we
analyse the underlying skills data to identify the skills sought in journalism
jobs, and where people with journalism skills are likely finding alternate
career paths.

##  2 Relevant Literature & Background

Journalism jobs in crisis. If there is a crisis, the simple explanation is the
Internet. (Putting aside COVID-19, to which we will return.) While digital
channels have given journalism bigger audiences, they have also strangled
income. Once, advertising funded journalism, but now advertising has largely
migrated online. As the Australian Competition and Consumer Commission (ACCC)
found in 2019, in the Final Report of its Digital Platforms Inquiry, ‘The
reduction in advertising revenue over the past 20 years, for reasons including
the rise of online advertising, appears to have reduced the ability of some
media businesses to fund Australian news and journalism’. The ACCC cited
Census data showing that ‘from 2006 to 2016, the number of Australians in
journalism-related occupations fell by 9 per cent overall, and by 26 per cent
for traditional print journalists (including those journalists working for
print/online news media businesses)’. Further, the ACCC cited data provided by
leading media companies showing that the number of journalists in traditional
print media businesses fell by 20 per cent from 2014 to 2018 – a time of
growth for Australia’s population and economy (ACCC, 2019).

However, the pressures on news media were not spread evenly. For instance,
local news in particular bore the brunt. Between 2008 and 2018, 106 local and
regional newspaper titles closed across Australia, representing a 15 per cent
decrease in the number of such publications. As a result, 21 local government
areas previously served by a newspaper were now without coverage, including 16
local government areas in regional Australia (ACCC, 2019). These figures are
mirrored in the US. In 2018, Abernathy (2018) from the Hussman School of
Journalism and Media at UNC released a report, ‘The Expanding News Desert’,
which found that the US had lost almost 1800 papers since 2004, with 7112
remaining (1283 dailies and 5829 weeklies). This meant that the US lost
roughly 20 per cent of its newspapers between 2004 and 2018. These closures
included large dailies such as the Tampa Tribune and the Rocky Mountain News,
but also many newspapers that had circulations of fewer than 5000 and served
small, impoverished communities.

As the above research reveals, news media companies were under pressure, and
journalism jobs were being cut. There was some hope in the shape of new
players entering the market and hiring journalists, including digital natives
such as Vice and Buzzfeed. However, in 2019 these two companies were among the
many that announced significant staff layoffs (Goggin, 2019). Worse, in 2020
Vice cut a further 155 jobs and Buzzfeed furloughed many of its workers
without pay (Izadi, 2020). Furthermore, as Australia’s ACCC notes, these
publications ‘tend to employ relatively few journalists’ (ACCC, 2019). Even
accounting for new arrivals, the number of journalism jobs in Australia
continued to fall (our own analysis in Jobs Data Analysis and Results also
shows this trend), and as a result there were areas (including local
government, local court, health and science issues) that journalism no longer
covered adequately (ACCC, 2019).

Further research has also revealed a clearer profile of the typical
journalist, and also the typical journalist who loses his/her job. Drawing on
2017 data, one study found that journalism jobs internationally were largely
filled by a young, inexperienced and itinerant workforce (Josephi and Oller
Alonso, 2018). Meanwhile, research suggests that it was journalists with
extensive experience who were losing their jobs (at least in Australia)
(Sherwood and O’Donnell, 2018). And those who lost their jobs faced decidedly
uncertain futures. In longitudinal research tracking the post-journalism
careers of Australian journalists who had been made redundant, many of those
surveyed revealed they were experiencing job precarity (Zion et al., 2018).
Further, a significant minority had moved into strategic communications or
public relations (Zion et al., 2018). However, the flow of journalists into PR
(and sometimes back again) is not new (Carey, 1965; Fisher et al., 2014;
Macnamara, 2014, 2016) and our analysis also supports these previous results.

The nature of ‘journalism work’ has also changed. Increasingly, scholars have
sought to theorise journalism in terms of boundaries and blurring (Carlson and
Lewis, 2015; Loosen, 2015; O’Regan and Young, 2019; Maares and Hanusch, 2020).
The idea of blurred boundaries is intended to capture the ways in which
journalism is increasingly difficult to define, and how traditional notions of
journalism have been upended in the digital age (Loosen, 2015). Empirical work
suggests that journalism is a fluid concept that now means many things, and
that the definition of journalism is changing over time (Bögenhold and
Fachinger, 2013). For example, many contributors to social networks, including
Instagrammers, can be considered to be creating work that is journalism
(Maares and Hanusch, 2020). As such, there is now no such thing as a typical
journalist; rather, journalism is marked by diversity and heterogeneity rather
than any unifying concept (Bögenhold and Fachinger, 2013). The notion of
blurred boundaries aligns with our findings regarding the way journalism jobs,
and journalism skills, have been shifting. Indeed, Carlson (2016) argues that
journalism is uncertain, which means that scholars and audiences need to work
towards clarifying both the value of journalism, and its meaning. Our research
has been data-driven, analysing journalism jobs data according to the
Australian occupational classifications of journalists (see Supplemental
Material Appendix (2021)) and based on their underlying skills in job ads
data. Nonetheless, we suggest that our findings, coupled with previous
research, have the potential to further inform how precisely journalism jobs
in Australia have changed during this tumultuous period for the media
industry.

The impacts of COVID-19. There is a growing body of research into the impacts
of COVID-19 on news and its audiences. Unsurprisingly, the research reveals
that the outbreak of the global pandemic was accompanied by a marked upswing
in news consumption in Australia (Park et al., 2020), the US (Casero-Ripollés,
2020) and the UK (Kalogeropoulos et al., 2020). In Australia in 2019, 56 per
cent of Australians accessed news more than once a day; by April 2020, three
months after the first local case of COVID-19 was confirmed, that figure had
jumped to 70 per cent (Park et al., 2020). Among other things, this increase
involved audiences returning to television and legacy media in greater numbers
(Park et al., 2020; Kalogeropoulos et al., 2020). Soon, however, many people
started avoiding news – and especially news about coronavirus - because it
made them anxious (Park et al., 2020; Kalogeropoulos et al., 2020). As
Kalogeropoulos et al. (2020) wrote following a survey of UK audiences
conducted in May, ‘After an initial surge in news use, there has been a
significant increase in news avoidance.’

Ultimately, COVID-19 gave rise to a paradox. The above surveys show that, as
audiences sought out information to stay safe, there was a dramatic surge in
the consumption of news - at least initially. At the same time, however, news
outlets found it even harder to make money, as advertising dried up even
further (Radcliffe, 2020; Doctor, 2020; Olsen et al., 2020). With concerts
cancelled and restaurants shuttered, promoters and restaurateurs had nothing
to advertise, and the impacts on local and regional news were especially harsh
(Doctor, 2020). On March 25, 2020, The Atlantic ran a story under the
headline, ‘The coronavirus is killing local news’ (Waldman and Sennott, 2020).
The author urged people to subscribe: ‘Among the important steps you should
take during this crisis: Wash your hands. Don’t touch your face. And buy a
subscription to your local newspaper.’ As one US media expert noted in late
March, ‘Advertising, which has been doing a slow disappearing act since 2008,
has been cut in half in the space of two weeks’ (Doctor, 2020).

Even before COVID-19, the advertising crisis for journalism has been described
not as a single black swan, but as a flock of black swans (Doctor, 2020). By
one estimate, from 2006 to 2020, US newspapers lost more than 70 percent of
their advertising dollars (Doctor, 2020). COVID-19 further cruelled
advertising, compounding the strain on news media and the journalists they
employ (Olsen et al., 2020).

In Australia, there were widespread closures and job losses before the
pandemic, but COVID-19 compounded the problem. In late March, Rupert Murdoch’s
publishing business News Corp warned of ‘inevitable’ job cuts and the closure
of regional titles (Meade, 2020c). Soon afterwards, News Corp – Australia’s
biggest publisher - suspended the print editions of 60 Australian newspapers,
including the Manly Daily and Wentworth Courier in Sydney, the Brisbane News
and the Mornington Peninsula Leader in Victoria (Meade, 2020c). In May, News
Corp confirmed that more than 100 of its local and regional mastheads would
either switch to digital only or disappear completely (Meade, 2020b). These
cuts came in the wake of a dramatic drop in advertising from the
entertainment, restaurant and real estate industries, the titles’ main revenue
sources. The global pandemic is ongoing, and its lasting impact on journalism
remains to be seen. Our findings, drawn from data that runs until March 2020,
are early and indicative rather than definitive.

Job ads as a proxy for labour demand. Job ads provide ‘leading’ indicators of
shifting labour demands as they occur, as opposed to the ‘lagging’ indicators
from labour market surveys. Consequently, job ads are increasingly used as a
data source for analysing labour market dynamics (Markow et al., 2017; Blake,
2019). For instance, job ads data have also been used to assess labour
shortages. Dawson et al. (2019) defined a range of indicators to evaluate the
presence and extent of shortages, such as posting frequency, salary levels,
educational requirements, and experience demands. They also built a metric
based on the forecasting error from Machine Learning models trained to predict
posting frequency. Intuitively, occupations experiencing high posting
volatility are difficult to predict. Subsequent work showed these indicators
to be predictive of labour shortages in the Australian Labour Market (Dawson
et al., 2020). In the present research, in Jobs Data Analysis and Results, we
use a similar set of indicators to analyse labour demand for journalists.
Further details on job ads data are provided in the Supplemental Material
Appendix (2021).

Analysing journalism jobs. Journalism jobs have previously been analysed using
job ads. Young and Carson (2018) collected and assessed how Australian media
outlets defined journalism job positions when hiring journalists from November
2009 to November 2010. The authors used a content analysis methodology and
manually labelled data fields, such as employer, educational qualifications,
job responsibilities, experience requirements, location, work hours, media
platform, skill demands, job title, and any other miscellaneous information.
The authors found that journalism was not a high priority during this period;
instead employers advertised four times as many job ads for sales, marketing,
and advertising positions.

More recently, Guo and Volz (2019) conducted content analysis on 669
journalist job announcements from US media organisations from 1 July to 31
December 2017. The authors’ objective was to define, compare, and analyse the
journalists’ expertise requirements as expressed through job ads. To achieve
this objective, the authors manually reviewed and codified job vacancies. This
research found that ‘multi-skilled’ journalists are experiencing higher levels
of demand. The authors also found that journalists’ ability to flexibly adapt
to changing situations was a characteristic of growing importance. These
studies, while significant, are relatively limited in scope. In this paper, we
analyse a nine-year dataset of job ads which allows us to uncover longitudinal
dynamics of journalism jobs.

Historic employment levels of journalists in Australia have also been analysed
by O’Regan and Young (2019). The authors used five-yearly census data and
found that not only has the advent of digital platforms coincided with the
decline of many types of journalists (for example, ‘Print’, ‘Radio’,
‘Television’ and ‘Editors’), but employment has shifted into related
professions, such as ‘Authors’ and ‘Public Relations’. O’Regan and Young’s
paper built on earlier research by Higgs and Cunningham (2007). Our research
complements the findings of O’Regan and Young (2019), providing additional
labour demand detail from job ads data while also matching it with labour
supply data from employment statistics.

Limitations of job ads data. Job ads data are an incomplete representation of
labour demand. Some employers use traditional forms of advertising for
vacancies, such as newspaper classifieds, their own hiring platforms, or
recruitment agency procurement. Furthermore, anecdotal evidence reveals that
some vacancies are filled informally, using channels such as word of mouth,
professional networks and social media. Job ads data also over-represent
occupations with higher-skill requirements and higher wages, colloquially
referred to as ‘white collar’ jobs (Carnevale et al., 2014). Finally, just
because a job is advertised, does not mean that the position will be, or has
been, filled. Despite these shortcomings, job ads provide extremely rich
information for what employers are demanding in near real-time; including
information that cannot be gathered from employment statistics. Given the
sample size of journalism job ads available and the detailed skills extracted
in the data set, we are confident that the journalism job ads used for this
research provide a useful indication of journalism labour demand.

Employment statistics and occupational standards. Employment statistics
provide data on populations employed in standardised occupational classes.
Occupations in Australia correspond to their respective occupational classes
according to the Australian and New Zealand Standard Classification of
Occupations (ANZSCO) (Australian Bureau of Statistics, 2013).

There are significant shortcomings to analysing occupations within ANZSCO
categories. Official occupational taxonomies (like ANZSCO) are often static
and are rarely updated, therefore failing to capture emerging skills, which
can misrepresent the true labour dynamics of particular jobs. For example, the
occupational class of ‘Print Journalist’ has been a constant in Australian
occupational statistics. Yet, the underlying skills of a ‘Print Journalist’
have changed dramatically in recent decades.

To overcome the above-stated limitations, in our data construction, we
leveraged the Burning Glass Technologies (BGT – the job ads data source)
occupational ontology together with the ANZSCO ontology. We also used the rich
skill-level information from job ads that are missing from occupational
employment statistics to build an encompassing journalism job ads dataset.

##  3 Data & Methods

###  3.1 Data Sources

This research used both labour demand and labour supply data to analyse
journalism jobs. On the labour demand side, we used a detailed dataset of over
8 million Australian job ads, spanning from January 2012 to March 2020. These
data were generously provided by Burning Glass Technologies111BGT is a leading
vendor of online job ads data. https://www.burning-glass.com/ (BGT). For
labour supply data, we leveraged official employment statistics (Australian
Bureau of Statistics, 2019a) and salary levels (Australian Bureau of
Statistics, 2019b) provided by the Australian Bureau of Statistics (ABS) over
the same period. These data sources provide longitudinal employment and salary
information that have been disaggregated by gender, location, and types of
employment (full-time and part-time). Further details of data sources and data
construction are provided in the Supplemental Material Appendix (2021). While
there are nuances to ‘journalism work’ and the requirements of journalism jobs
have evolved over time (Macnamara, 2016; O’Regan and Young, 2019; Maares and
Hanusch, 2020), this research defines journalism jobs by the official ANZSCO
standards (ABS, 2019).

###  3.2 Skill Similarity

To analyse the underlying journalism skills within occupations, we implemented
a skill similarity methodology adapted from Alabdulkareem et al. (2018) and
then by Dawson et al. (2019) to calculate the pairwise similarities between
skills from job ads.

Two skills are similar when the two are related and complementary, i.e. the
two skills in a skills-pair support each other. For example, ‘Journalism’ and
‘Editing’ have a high pairwise similarity score because together they enable
higher productivity for a journalist; whereas ‘Journalism’ and ‘Oncology’ have
a low similarity because they are seldom required together. We measured the
similarity of skill-pairs based on their co-occurrence patterns in job ads,
while accounting for skill ubiquity and specialisation. To capture how
journalism skills have changed over time, we measured skill similarity during
calendar years.

Formally, given $J$ as the set of job ads posted during a specific calendar
year, we measured the similarity between two skills $s$ and $s^{\prime}$ as:

$\theta(s,s^{\prime})=\frac{\mathop{\sum}\limits_{j^{\prime}\in
J}e(j,s)e(j,s^{\prime})}{max\left(\mathop{\sum}\limits_{j^{\prime}\in
J}e(j,s),\mathop{\sum}\limits_{j^{\prime}\in J}e(j,s^{\prime})\right)}$ (1)

where $j$ and $j^{\prime}$ are individuals jobs ads from the set $J$, and
$e(s,j)\in\\{0,1\\}$ measures the importance of skills $s$ for job $j$ using
theory from Trade Economics (Hidalgo et al., 2007). Skills $s$ and
$s^{\prime}$ are considered highly complementary if they commonly co-occur and
are both ‘important’ for the same job ads. Finally,
$\theta(s,s^{\prime})\in[0,1]$, a larger value indicates that $s$ and
$s^{\prime}$ are more similar, and it reaches the maximum value when $s$ and
$s^{\prime}$ always co-occur (i.e. they never appear separately).

We build the top yearly lists of journalism skills by computing
$\theta(Journalism,s)$ – i.e. the similarity between the skill ‘Journalism’
and each unique skill that occurs for each year from 2014-2018. The yearly top
50 skills most similar to ‘Journalism’ are shown in the Supplemental Material
Appendix (2021) together with the full details of the $\theta$ measure.

Finally, we determined the occupations with the highest levels of skill
similarity to the top journalism skills uncovered from above. We propose
$\eta$, the _‘Journalism Skill Intensity’_ , for each standardised BGT
occupation, defined as percentage of journalism skills relative to the total
skill count for the job ads related to an occupation $o$. Formally:

$\eta(o,\mathcal{D})=\frac{\mathop{\sum}\limits_{j\in\mathcal{O},s\in\mathcal{D}}x(j,s)}{\mathop{\sum}\limits_{j\in\mathcal{O},s^{\prime}\in
S}x(j,s^{\prime})}$ (2)

where $\mathcal{D}$ is the set of journalism skills, and $\mathcal{O}$ is the
set of job ads associated with the occupation $o$. This method allowed us to
adaptively select occupations based on their journalism skill intensities.

##  4 Jobs Data Analysis and Results

In this section, we conducted a data-driven analysis of journalism jobs in
Australia based on job ads data and official occupational statistics. First,
we longitudinally examined key features of jobs data, such as employment
levels, job ads posting frequency, salaries, and posting frequency growth and
predictability level. We also analysed how the underlying skills of
journalists had changed over time, and which skills and occupations grew in
similarity to journalism.

###  4.1 Posting Frequency & Employment levels

Figure 1:  Quarterly posting frequency of journalism job ads (see Sec. Data &
Methods) and employment levels of ‘Journalists & Other Writers’ at the ANZSCO
Unit level (000’s) from Jan 2012 to Mar 2020.

In Australian journalism, 2012 is considered a watershed year. An estimated
1,500 journalists were made redundant, the majority of those from Australia’s
two largest print companies, Fairfax Media (now Nine Entertainment) and News
Limited (now News Corp Australia) (Zion et al., 2016). The severity of this
industrial shock can be observed in Fig. 1. Against the left y-axis, the blue
line shows quarterly job ads posting frequency for journalism jobs. As the
graph depicts, posting frequency for journalism job ads experienced extremely
low levels in 2012 until 2013, when they began to increase. The volume of
vacancies increased until mid-2014, before plummeting in late-2014 to the
levels last seen in 2012. From 2015, journalism job ads experienced strong
growth, reaching a peak in mid-2016. Since then, journalism job ads have
trended downward until the first quarter of 2020 (end of available data for
job ads), albeit with volatile peaks and troughs. In summary, the data shows
that journalism job ads had not been in freefall since 2012. Rather, there was
erratic growth in journalism job ads until a peak in 2016, followed by erratic
decline.

Similarly, employment levels underwent immense volatility from 2012 to 2013.
Against the right y-axis of Fig. 1, the orange line shows the number of
quarterly employed for ‘Journalists & Other Writers’ at the ANZSCO Unit level.
Employment levels peaked in mid 2012, before dramatically dropping in early
2013. This is an effect of the mass journalist redundancies made in 2012,
given that employment statistics are ‘lagging indicators’ and it takes time
for labour markets to reflect changes in occupational statistics. Early 2013
marked the lowest point of journalist employment seen in this time-series. As
also observed in job ads data, journalist employment levels grew until
2016-2017 and has since trended downwards, exhibiting volatile quarterly
changes through to the first quarter of 2020.

Figure 2:  Posting frequency for journalism jobs during the early stages of
the COVID-19 crisis in Australia and its major cities: (a) Weekly posting
frequency volumes for journalists and all Australian job ads between April
2019 and March 2020. Both decreased as the early stages of the COVID-19 crisis
hit; (b) Monthly posting frequency for journalists were down 63 per cent when
comparing March 2019 to March 2020. This was significantly higher than all
Australian job vacancies, which was down 37 per cent over the same period.

COVID-19 and journalism jobs. The early effects of COVID-19 were apparent in
the posting frequency of job ads in Australia. This was the case for most
occupations, including journalists. Higher vacancy rates typically mean higher
levels of labour demand by employers, which is a critical component of healthy
labour markets. As Fig. 2 highlights, vacancy volumes declined for both
journalism jobs and at aggregate levels in Australia. Since mid-February 2020,
weekly posting frequency had decreased across all Australia job ads, as seen
in Fig. 2a. Such a decline this early in the year is atypical. As Dawson and
Rizoiu (2020) show, the frequency of job ad postings follow a yearly seasonal
pattern, with late February and early March typically being a period of upward
trend growth. However, late February and early March 2020 coincided with the
international outbreak of COVID-19. During this period, the Australian
government instituted widespread quarantine and social distancing measures,
which significantly constrained economic activity (Boseley and Knaus, 2020).
The impacts of these COVID-19 containment laws are starkly apparent in Fig.
2b. Posting frequency for journalism jobs were down 63 per cent when comparing
March 2019 volumes to March 2020. This was significantly higher than the
aggregate market of all Australian job ads, which was down 37 per cent over
the same period. Fig. 2b shows that Melbourne appeared to be the city hardest
hit, recording no journalism job ads in March 2020 and only 3 posts for the
first quarter of 2020, even before the major lock-downs instituted for
Melbourne in August 2020. Clearly the pandemic had an early and damaging
effect on the journalism jobs market.

Figure 3:  Journalist salaries (solid blue line) increased according to job
ads data, but remained below market average levels (dashed blue line).
However, according to ABS data, ‘Journalists & Other Writers’ (ANZSCO Unit
level, solid orange line) earned a growing wage premium above the market
average (dashed orange line).

###  4.2 Salaries

We compared salaries extracted from job ads with ABS reported wage data for
‘Journalists and Other Writers’222ABS wage data is reported biennially, with
the latest reporting year being 2018. Therefore, wage values in the ‘odd’
years in between the reporting periods were interpolated, calculated as the
mean of the previous and the subsequent years.. Fig. 3 reveals two main
findings regarding journalist salaries. First, according to job ads data,
journalists attracted considerably lower annual wage levels (solid blue line)
than the market average (dashed blue line). As of 2018, job ads indicated that
journalists earned approximately AU$10,000 less than the market average. These
findings, however, are somewhat contrary to the wage earnings data collected
by the ABS (Australian Bureau of Statistics, 2019b), according to which
‘Journalists and Other Writers’ (solid orange line) had been earning a growing
wage premium over the market average (dashed orange line) since 2014. This
discrepancy can be explained by the fact that job ads data tend to over-
represent occupations in the ‘Professional’ and ‘Manager’ classes (Carnevale
et al., 2014), which typically attract higher wages. As a result, the average
salary levels from job ads data (dashed blue line) were about AU$20,000 higher
than average salary levels from ABS data (dashed orange line), from 2014 to
2018. However, the salary levels for journalists were very similar when
comparing across the two data sources.

Fig. 3 yields a second observation: journalist salary levels increased in both
absolute and relative terms compared to average market levels, between 2012 to
2018 in both data sources. More importantly, the relative salary growth of
journalists exceeded the market averages, during the period studied.

Figure 4:  Trend lines of posting frequency for ‘Journalists’, ‘Data
Scientists’, ‘Data Analysts’, and ‘All Australian job ads’. Posting frequency
for ‘Journalists’ trended downwards since 2016.

###  4.3 Trend Analysis & Predictability

Posting trends. We constructed an auto-regressive Machine Learning model to
predict posting frequency of journalism job ads in Australia (Taylor and
Letham, 2018). The model accounts for long term trends, seasonality patterns
and external events (see the Supplemental Material Appendix (2021) for
technical details). We isolated the posting frequency trend component and, in
Fig. 4, plotted it comparatively for ‘Journalists’ against two occupations
that have experienced high levels of labour demand, ‘Data Scientists’ and
‘Data Analysts’, as well as against the aggregated market trend. Visibly,
journalism jobs experienced varying degrees of growth until mid 2016, at which
point growth plateaued, and started to decline. From the end of 2017 until
2019, the trend for journalism job ads has heavily decreased, even when
compared to the aggregate market, which also shows a more modest decrease
during the same period. ‘Data Scientists’ and ‘Data Analysts’ consistently
grew throughout the entire period.

Figure 5:  (a) Predictability comparison of temporal posting frequency
highlighting the difficulties of predicting journalism job ads and their
volatility.

(a)

(b)

Figure 6:  Journalist employment levels and salaries by Gender: (a) Since
2015, the employment ratio of female-to-male journalists increased; (b) Wage
inequality increased between males and females in the ‘Journalists & Other
Writers’ Unit group. This was at the same time that the average age of
journalists decreased for females and increasing for males since 2014.

Quantify labour demand volatility. When constructing Machine Learning models,
it is standard procedure to use error metrics to evaluate the prediction
accuracy. Volatility in posting volumes inherently lead to lowered prediction
performance and higher error values. Here we use the prediction error measured
using the ‘Symmetric Mean Absolute Percentage Error’ (Scott Armstrong, 1985;
Makridakis, 1993) as a proxy for the volatility of labour demand for different
occupations (see the technical section in the Supplemental Material Appendix
(2021) for more details).

Fig. 5 shows the prediction performance for three occupations (‘Journalists’,
‘Data Scientists’, ‘Data Analysts’) and for the volume of ‘All Australian job
postings’. We used a sliding window approach to obtain multiple predictions
(see the Supplemental Material Appendix (2021)) that we aggregated as
boxplots. The higher the error score on the vertical axis, the lower the
predictive abilities for that occupation. As Fig. 5 reveals, predicting the
daily posting frequency of journalism jobs was consistently more difficult
than for the other occupations, and the market as whole. ‘Data Scientists’, an
occupation undergoing strong relative growth, is also showing a high
prediction error compared to the market as a whole, indicative of experiencing
a degree of volatility. However, it was not nearly commensurate to the
predictive difficulties, and volatility, of journalists. This was true from
2012 to 2019, and has become worse in 2020 with the spread of COVID-19.

(a)

(b)

Figure 7:  Location of journalists in Australia: (a) Posting frequency for
journalism jobs decreased in major Australian cities, in relative terms; (b)
As of 2019, the majority of journalists in Australia were employed in New
South Wales, Victoria, and Queensland, respectively.

(a)

(b)

Figure 8:  (a) Years of Education demanded by employers from job ads were
consistent with the market average; (b) Years of Experience required by
employers consistently remained below the market average, according to job
ads. However, this gap had closed since 2014.

###  4.4 Gender

There have been growing gender differences of employed journalists in
Australia (North, 2016, 2009) and across the world (Hanitzsch et al., 2019);
the data presented in this research reinforces these previous findings. Fig.
6a shows that the ratio of female employed journalists increased relative to
male journalists (ANZSCO Unit Level) (Australian Bureau of Statistics, 2019a).
In 2014, the female-to-male employment ratio was 0.7. In 2018, the proportion
more than doubled, with almost 1.8 female journalists employed for every male
journalist. It then declined in 2019 to 1.35, but this proportion was still
almost double that of 2014.

Fig. 6b also shows that wage inequality between female and male journalists
worsened from 2014 to 2018 (Australian Bureau of Statistics, 2019b). Since
2014, the annual salaries for female journalists increased by only AU$3,000,
whereas annual salaries for male journalists increased by more than AU$30,000.
Male journalists thus experienced an average wage growth that was ten times
greater than female journalists from 2014 to 2018.

There were also changing age demographics of employed journalists during the
studied period. The markers on Fig. 6b highlight the average age of
journalists by gender, per year. Male journalists were getting older, their
average age increasing by two years from 2014 to 2018. Female journalists,
however, were steadily getting younger. The average age for female journalists
decreased by more than four years from 2014 to 2018.

###  4.5 Location

Fig. 7 plots the location and volume of employed journalists in Australia.
Fig. 7a shows the absolute and relative number of job ads posted for each of
the capital cities, and outside them, and Fig. 7b shows the location of
employed journalists per state. Unsurprisingly, Sydney and Melbourne, the
respective capital cities of New South Wales (NSW) and Victoria (VIC),
consistently had the highest job ad posting frequencies. However, the relative
share of job ad posting frequency in Australian capital cities had shrunk in
later years, with Fig. 7a showing an increase outside of major cities, both in
relative and absolute terms. This trend reached a peak in 2017, when less than
50 per cent of all journalist job ads were for positions inside capital
cities. A small rebound followed, and in 2019 Sydney commanded approximately
one-third of all journalism job ads.

###  4.6 Education & Experience

Figs. 8a and 8b show respectively the number of years of formal education
required for journalists, and the experience requirements (both per year,
extracted from job ads data). The education requirements consistently remained
at market average levels, with journalists required to possess a Bachelor-
level degree (approximately 16 years of education).

By contrast, the experience requirements were more variable. Since 2012,
employers required fewer years of experience from journalists than was
required in the market generally. However, the gap narrowed. In 2019,
employers demanded of journalists, on average, half of an additional year of
experience compared to 2014. This countered the general market, where
employers’ demands trended downward from 2012 to 2019.

###  4.7 Employment Type

Casual and temporary work have become more commonplace in Australia
(Gilfillan, 2018), and we study if this is also the case for Australian
journalism jobs. In Fig. 9 we plot the number of permanent and temporary
journalism jobs, per calendar year – in the job ads data, jobs are classified
as either ‘Permanent’, ‘Temporary’, or not specified according to the content
of the job descriptions. The number of ‘Temporary’ journalism jobs had
increased in absolute terms since 2012, which made up the majority of all
journalism ads in every year. It is noteworthy too that the share of
‘Permanent’ journalism vacancies had also increased since 2012. However, this
trend should be interpreted with a degree of scepticism as only $\sim$ 50 per
cent of all journalism job ads specified whether the roles advertised were
permanent or temporary.

Figure 9:  Temporary positions represented the majority of journalism job ads
in Australia.

###  4.8 Journalism Skills

Growing demand for journalism skills.

Here, we analysed how the demand for some fundamental journalism skills
changed over time. First, we selected three traditionally important skills to
journalists that appear in job ads: (1) ‘Journalism’, (2) ‘Editing’, and (3)
‘Writing’. These skills were then counted across all job ads in Australia,
regardless of their occupational class. While Fig. 4 shows that labour demand
for journalists has decreased since 2016, Fig. 10a presents the more nuanced
story, showing that the posting frequency for each of these core journalism
skills increased from 2012 to 2019, with 2018 to 2019 being the first yearly
decline.

The relative rankings of these three skills also increased. For each year, we
counted the posting frequency of each unique skill that appears in job ads. We
then ranked these skills by posting frequency as a proxy for labour demand.
Fig. 10b shows that the rankings of all three of these fundamental journalism
skills had improved from 2012 to 2019. In other words, not only did the
posting frequency of these three journalism skills increase in job ads over
these eight years, but their importance relative to all other skills also
increased.

Figure 10:  The absolute posting frequency (a) and relative yearly rank (b) of
three major journalism skills increased between 2012 and 2019.

Changing importance of journalism skills. We wanted to determine whether the
relative importance of the Journalism skill changed over time, using a skill
similarity approach. Given the dynamics of skill requirements in job ads,
skills can become increasingly more (or less) similar over time. We used the
similarity measures in Eq. 1 to identify the skills that are becoming more
relevant to being a journalist (see Data & Methods for details, and the
Supplemental Material Appendix (2021) for the top 50 skills for each from 2014
to 2018). The higher the similarity score, the more likely the skills pair
will complement and support each other in a given job. Fig. 11a shows the
changes in similarity scores between the skill ‘Journalism’ and each of the
eight other top journalism skills (as per the top yearly journalism skills
lists in the Supplemental Material Appendix (2021)). The greater the area
covered in the radar chart, the greater the similarity score, with the blue
area representing 2014 and the red area 2018.333At the time of analysis, 2018
was the final full year available of access to the required skills-level data.
Visibly in Fig. 11a, ‘Social Media’ related skills became increasingly
relevant for journalists, with the relative ratio of more traditional skills
such as ‘Editing’ and ‘Copy Writing’ diminishing with respect to ‘Social
Media’, from 2014 to 2018.

Occupations that require journalism skills. Here, we studied which occupations
most required journalism skills, and their dynamics over time (according to
the BGT occupational taxonomy). Given the yearly lists of top journalism
skills (described in Skill Similarity), we used Eq. 2 to determine the
occupations with the highest intensities of journalism skills, for each year
from 2014 to 2018. Intuitively, this allows us adaptively to identify
occupations that become more or less similar to ‘Journalism’, based on their
underlying skill usage. It also provides a means to assess likely transitions
between occupations, as workers are more likely to transition to occupations
where the underlying skill requirements are similar (Bechichii et al., 2018).
Higher similarity lowers the barriers to entry from one occupation to another.

Fig. 11b highlights eight top occupations and their journalism skill intensity
scores for 2014 and 2018. ‘Reporter’, ‘Editor’, and ‘Copywriter’ cover the
highest percentage of journalism jobs in the dataset, respectively. While the
journalism skill intensities of these occupations were relatively high in
2018, their growth since 2014 was relatively low. In comparison,
‘Photography’, ‘Communications’, ‘Social Media’, and ‘Public Relations’
experienced higher journalism skill intensity growth from 2014 to 2018. This
provides insights as to where workers with journalism skills might have found
employment outside of journalism.

(a)

(b)

Figure 11:  Skill and occupational similarity analyses: (a) The changing
similarity (or relative importance) of specific skills compared to the skill
‘Journalism’; (b) Eight occupations that had the highest similarity to the
‘Top Yearly Journalism Skills’.

##  5 Discussion

###  5.1 Volatility of journalism jobs

Drawn from job ads and employment statistics, our findings reveal the highly
volatile nature of the journalism industry. Compared to other occupations and
the aggregate labour market, journalism experiences dramatic fluctuations that
are unpredictable and irregular (see Fig. 5). The data also confirmed that
journalism is an industry in crisis, worsened in the early stages of COVID-19.
However, the data also reveals surprises, including that the number of
journalism jobs ads and employment levels increased from 2012 until 2016.
Since then, though, journalism jobs in Australia declined.

The volatility of journalism jobs in Australia was clearly apparent in Posting
Frequency & Employment levels. Posting frequency of job ads ranged from near
zero levels in 2012 and 2014 to more than 200 posts per quarter in 2016. These
violent swings are also apparent in the quarterly employment statistics of
‘Journalists and Other Writers’. Following the mass redundancies of 2012,
employment levels plummeted, reaching their lowest levels in 2013. They then
increased before falling again into the beginning of the COVID-19 pandemic.
However, the data confirms that volatility of employment has been a constant
for journalism, and that this has worsened during COVID-19.

Fig. 5 reveals this extreme volatility. The error metrics from the Machine
Learning model used to predict daily posting frequencies of job ads (as
detailed in Trend Analysis & Predictability) highlight the difficulties of
making predictions about journalism employment. This lack of predictability is
indicative of volatility. The higher the error scores for a given occupation,
the higher the likelihood that the occupation is experiencing significant
disruption. This becomes apparent when we compare journalism to other
occupations. For example, the volatility of ’Journalists’ dwarfs that of ‘Data
Scientists’, an occupation experiencing significant demand and volatility in
Australia (Dawson et al., 2019).

The volatility of journalism jobs was further revealed by a time series
analysis of journalism compared to other occupations (Fig. 4), a gender-based
analysis (Fig. 6), a geographical analysis (Fig. 7) and an analysis of the
temporary nature of journalism jobs (Fig. 9).

What is indisputably clear is that the advertising market for news and
journalism collapsed, and, at the time of writing, continues to collapse
(Shirky, 2009; ACCC, 2019; Doctor, 2020). Meanwhile, consumers have tended to
show an unwillingness to pay for digital journalistic content: in 2019,
Australian news consumers admitted they would much would rather subscribe to a
video streaming service such as Netflix (34 per cent), than pay for online
news (9 per cent) (Fisher et al., 2019). Admittedly, during COVID-19 some
subscription rates have risen (Edmonds, 2020). Clearly, however, the Internet
has detonated the advertising model that once sustained journalism, and
simultaneously re-adjusted consumer expectations on the monetary value of
journalism content. The fact that journalism is struggling is confirmed in
several ways by the data, including by the unpredictability of job ads posting
frequency and the clear shifts in employment levels, as shown in Fig. 1. To
say that journalism has been disrupted is an understatement.

Volatility exacerbated by COVID-19. In a fragmenting news ecosystem, consumer
demand for news and journalism is difficult to quantify. The Digital News
Report: Australia 2019 has found that many consumers are disengaging, with the
proportion of Australians avoiding news increasing from 57 per cent in 2017 to
62 per cent in 2019 (Fisher et al., 2019). Demand for ‘quality’ and ‘public
interest’ journalism is even harder to quantify, given ongoing debates as to
what exactly constitutes ‘quality’ and ‘public interest’ (Wilding, D., Fray,
P., Molitorisz, S. & McKewon, E, 2018). Nonetheless, demand for journalism has
surged dramatically since the outbreak of COVID-19.

The irony of the coronavirus pandemic is that even as it has been killing off
journalism jobs, it has also created a heightened demand for, and appreciation
of, journalism among the general public. As news analyst Doctor (2020) wrote
of the US situation in late March, ’The amount of time Americans spend with
journalists’ work and their willingness to pay for it have both spiked, higher
than at any point since Election 2016, maybe before … [but] how many
journalists will still have jobs once the initial virus panic subsides?’. In
the UK in March, The Guardian received 2.17 billion page views, an increase of
more than 750 million above its previous record, set in October 2019
(Bedingfield, 2020).

Since the outbreak of COVID-19, the volatility of the journalism jobs market
has worsened dramatically. We noted above that in May News Corp ended the
print run of more than 100 newspapers nationally In April, Australian
Community Newspapers, which publishes 170 community titles, said it was
suspending publication of some of its non-daily newspapers; as a result, four
printing presses were closed and an unspecified number of staff were stood
down (Meade, 2020a). Also in April, the federal government announced a
AU$50million package to support public interest journalism across TV,
newspapers and radio in regional and remote Australia (Hayes and Rubbo, 2020).
And on April 20, the Australian government announced that digital platforms
including Google and Facebook would be forced to pay for content as the
Internet advertising business would be overhauled to help local publishers
survive the economic fallout of the coronavirus crisis (Crowe, 2020). The
scheme, which would involve a mandatory code imposed on digital giants, would
potentially set a global precedent. The combined and ongoing impact on
journalism jobs of these sudden, cumulative developments are hard to predict,
but will no doubt be profound.

###  5.2 Gender Wage Gap

At first glance, the data seems to suggest that gender equity is finally
arriving in Australia for journalism – an industry that has traditionally been
male-dominated – as more women than men are employed. As the data shows, in
2014 there were 0.7 female journalists employed for every male Journalist, but
by 2018 the proportion of female-to-male employment more than doubled, with
almost 1.8 female journalists employed for every male Journalist. It then
declined in 2019 to 1.35, a proportion still almost double that of 2014.

However, further detail reveals that equity remained elusive. Specifically,
wage inequality worsened. Since 2014, annual salaries for female journalists
increased by AU$3,000, compared with an increase for male journalists of over
AU$30,000 over the same period. From 2014 to 2018, average wage growth for
Male journalists was more than ten times greater than for female journalists.
Meanwhile, the average male Journalist was getting older, while the average
female Journalist was getting younger. In 2014, the average age for a
Journalist, whether male or female, was roughly the same: late 30s. By 2018,
the average age for a male journalist was 42, whereas for a female journalist
it was 34. These results support previous findings on the changing demographic
characteristics of journalists in Australia. In a survey of female journalists
in Australia, North (2016) found gendered divisions of tasks associated with
reporting, where the majority of female reporters were assigned ‘soft-news’
areas, such as arts, education, and health. These gender and age inequities
for journalists were also present in other countries (Hanitzsch et al., 2019).
The wage and age discrepancies between female and male journalists observed in
the employment statistics are consistent with the surveyed experiences of
female journalists in Australia by North (2016).

The potential impacts of this worsening disparity are concerning. It is
possible that senior positions responsible for major editorial decisions were
increasingly being dominated by men, whereas junior roles were being filled by
women who are younger and worse-paid.

Further research is needed into related issues of the industry’s composition,
including, for instance, the ethnicity of journalists. A vast body of
literature exists regarding the importance of diversity in news (Rodrigues and
Paradies, 2018; Budarick and Han, 2017). Further work is needed into diversity
(and its various sub-categories), and what effect diversity has, for instance,
on the proportion of people who are actively avoiding the news.

###  5.3 Location

As discussed above, the sustained pressures on regional and local journalism
have led to a worrying growth of ‘news deserts’ in countries including
Australia and the US. This trend alarmingly accelerated in the early stages of
COVID-19, leaving many areas without any regional or local news coverage. For
instance, as of October 2020, ‘Public Interest Journalism Initiative’ had
documented a net decline of 124 newsrooms from January 2019 (PIJI, 2020).
Hence, we might assume that journalism jobs in regional and local areas were
been drying up, and that an ever-increasing proportion of journalism jobs were
in urban centres.

The data, however, were not so clear until the end of 2019. As Fig. 7a shows,
in 2012 fewer than a quarter of Australia’s journalism job ads were for jobs
outside Sydney, Melbourne, Brisbane, Canberra and the ACT or Perth. In every
subsequent year, the proportion of job ads for journalism positions outside
these urban centres was considerably higher. The peak came in 2017, when
nearly half of all job ads were for positions outside the major cities. Does
this suggest that in 2017 there were as many jobs for journalists in the
regions as in the centres? Surely not. The explanation, we suggest, lies in
various factors. These include that regional journalism jobs are hard to fill,
perhaps because they offer relatively low salaries, and are hence re-
advertised. It is also possible that there is a high turnover for some
regional positions. In short, the job ads data may simply be an indication
that the journalism industry is even more volatile in the regions than in
major urban centres.

Research consistently and emphatically reveals that regional and local
journalism have been suffering, with an increasingly bleak prognosis of cuts
and closures (Abernathy, 2018; ACCC, 2019; Doctor, 2020). While the data shows
a surprisingly high proportion of journalism job ads for positions outside the
main metropolitan centres, this cannot be taken to suggest that journalism is
holding steady in these areas.

###  5.4 Evolving journalism skills

Skills are the building blocks of jobs and standardised occupations. In this
regard, occupations can be characterised as ‘sets of skills’. Intuitively,
skills that are similar can be interpreted as complementary when they are
paired together or relatively easy to acquire when one skill is already
possessed.

This intuition provides insight into how journalism skills are evolving and
where journalists might be finding alternate career paths. As Fig. 1 shows,
both the demand for and supply of journalists have been declining in Australia
since 2016. Therefore, a growing number of former journalists, who presumably
possess an assortment of journalism skills, needed to transition between
occupations to find new work. There are, however, significant transition costs
moving between jobs (Bechichii et al., 2018; Bessen, 2015). These costs can
come in the form of education, training, physically moving for new employment
and other barriers. To reduce the friction of these transition costs, workers
tend to leverage their extant skills, in concert with acquiring new skills, to
make career transitions.

As seen in Fig. 11a, the skill ‘Journalism’ became more similar to ‘Social
Media’ and more ‘generalist’ communications skills. After applying the Skill
Intensity formula from Eq. 2, we identified the top occupations with highest
intensities of journalism skills from 2014-2018 – these normalised measures
from Eq. 1 and Eq. 2 take into account newly emerging and redundant skills.
The Fig. 11b chart reinforces that top journalism skills were becoming more
important to other occupations, such as ‘Photographers’, ‘Social Media
Strategists’, ‘Public Relations Professionals’, and ‘Communications
Specialists’. While it is certainly possible that journalism tasks were being
performed in these different occupations, it nonetheless highlights the
changing nature of journalism work and the occupations where journalism skills
were of growing importance.

From the data, we suggest, three conclusions can be drawn, which supports
previous research (Young and Carson, 2018; O’Regan and Young, 2019; Macnamara,
2014, 2016). First, to be hired, journalists are required to have a wider
array of skills, such as photography and social media aptitude. Second, jobs
requiring journalism skills were increasingly occupations in social media,
generalist communications, and public relations rather than in reporting and
editing. And third, we see hints as to where onetime journalists are finding
alternate career paths. As employment conditions progressively worsen,
journalists are seemingly pursuing new careers in the occupational areas seen
in Fig. 11b, such as photography or public relations.

At a time of great uncertainty, with employment prospects deteriorating, it is
no wonder that journalists look beyond traditional journalism for their
futures. For society, however, the implications are significant. In this time
of economic instability and polarising politics, the people who possess the
journalism skills required to keep the public informed and hold leaders to
account are, in many cases, employing their talents elsewhere. This places
enormous strain on the health and quality of journalism in Australia.

##  6 Conclusion

The data reveals a contradiction: demand for journalism skills increased at
the same time that demand and employment for journalists declined. Indeed,
this is one of several contradictions in a volatile industry. For an
increasing number of news media organisations, a sustainable business model
remains elusive.

Our findings give a clearer outline of the problem. Unfortunately, the
solutions remain less clear. Quality journalism is expensive. Good reporting
is often slow and laborious, fixed to the unfolding story. What is required of
quality journalism is, therefore, at odds with the prevailing employment
conditions. The declines in employment of traditional journalists could have
serious implications if the media produce poorer quality materials.

This paper highlights the stresses experienced by journalism in Australia by
analysing jobs data. We observed the volatility and downward trajectory of the
occupation both in job ads and employment statistics. These unfavourable
employment conditions were worsened by the unfolding COVID-19 crisis. Our
longitudinal analysis also yields important findings regarding gender
inequity. While women represented a greater share of employed journalists,
they earned less, and the wage gap grew.

Further, this paper also identified top journalism skills. Adopting a data-
driven method, we described which skills are most similar to ‘Journalism’. We
then used these yearly skill sets to adaptively select similar occupations.
This enabled us to quantitatively show that the skill demands of journalists
became increasingly similar to those of ‘Social Media Strategists’, ‘Public
Relations Professionals’, ‘Communications Specialists’, and others. This
suggests where people with journalism skills were likely finding alternate
career paths, but also raises a related concern. On the face of it, the
journalism jobs data we have analysed does not look so bad after all. On
reflection, however, it suggests that the thinning ranks of ‘journalism’ are
populated by fewer journalists, and more public relations specialists.

Future research could compare these results to other labour markets in
different countries to assess and compare the validity of these findings. For
example, the skill similarity methodology could be applied in other labour
markets to compare the resulting top journalism skills in different locations.
Additionally, labour demand analyses could be conducted on occupations most
similar to journalists to better understand the incentives to transition to
other vocations. This could provide insights into the boundaries of
‘journalism work’ and analyse the relative demand for different types of
journalism. Further work could also examine the implications of changing
journalism skill demands for journalism schools. This research demonstrated
that not only have the skills demanded of journalists evolved, but the
occupations that require journalism skills have broadened. The extent to which
journalism schools are adequately preparing their students for the quickly
changing labour demands of journalists is a rich area of inquiry.

The results from this research both reinforce the well-documented difficulties
of journalism in Australia and provide granular details that isolate and
reveal these challenges. The hope is that these analytical methods and
insights can contribute to the health and well-being of the Fourth Estate, and
hence to the health and well-being of society.

We would like to thank Burning Glass Technologies for generously providing the
job advertisements data that has enabled this research.

The authors disclosed receipt of the following financial support for the
research, authorship, and/or publication of this article: Marian-Andrei Rizoiu
was partially supported by Facebook Research under the Content Policy Research
Initiative grants and the Commonwealth of Australia (represented by the
Defence Science and Technology Group).

Nikolas Dawson is a Labour Economist and PhD candidate at the University of
Technology Sydney. His doctoral research at UTS is on the changing labour
market dynamics in Australia, where he analyses issues such as skill
shortages, job transitions, and emerging technology adoption in the Australian
labour market. In his research, Nikolas applies data science and machine
learning techniques to draw insights from large datasets, including online job
advertisements, employment statistics, and longitudinal household survey data.
During his Doctoral studies, Nikolas was selected as an OECD Future of Work
Research Fellow and has also worked with agencies of the United Nations in
Geneva, Switzerland researching the ‘Economic Impacts of Artificial
Intelligence’.

Sacha Molitorisz is a lecturer at the Centre for Media Transition with the
University of Technology Sydney, employed jointly by the Law Faculty and the
Faculty of Arts and Social Sciences. In May 2020, his book Net Privacy, How we
can be free in an age of surveillance, was published by New South Books
(Australia) and McGill-Queens University Press (Canada). He has co-authored
com-missioned reports for bodies including the Australian Competition and
Consumer Commission and the Australian Communications and Media Authority, he
has worked as a consultant for the Public Interest Journalism Initiative on
the development of Australia’s world-first news media bargaining code, and he
has been published in mainstream outlets such as The Conversation and The
Sydney Morning Herald as well as in leading academic journals. He is also a
regular media commentator. Previously, he spent nearly 20 years on staff at
the Sydney Morning Herald and smh.com.au as a writer, editor, and blogger.

Marian-Andrei Rizoiu is a lecturer with the University of Technology Sydney,
where he leads the Behavioral Data Science group, studying human attention
dynamics in the online environment. His research has made several key
contributions to applications such as online popularity prediction and real-
time tracking and countering disinformation campaigns. For the past four
years, he has been developing theoretical models for online information
diffusion, which can account for complex social phenomena, such as the rise
and fall of online popularity, the spread of misinformation, or the adoption
of disruptive technologies. He approached questions such as “Why did X become
popular, but not Y?” and “How can problematic content be detected based solely
on how it spreads?” with implications in detecting the spread of conspiracy
theories and disinformation campaigns. Marian-Andrei also works in
understanding shortages and mismatches in labour mar-kets. Together with his
collaborators, he built a real-time occupation transition recommender system
usable in periods of massive disruptions (such as COVID-19). He also has shown
that social-media predicted personality profiles are linked with worker
occupation. Marian-Andrei has published in the most selective venues, such as
the PNAS, WWW, WSDM, ICWSM, and CIKM. His work has received significant media
attention—including Bloomberg Business Week, Nature Index, BBC, and The
Conversation—, and Marian-Andrei served as an expert for the NSW government’s
Defamation Law Reform. See more at http://www.rizoiu.eu.

Peter Fray is managing editor of Private Media, an independent Australian news
media company. He was formerly a professor of journalism practice at
University Technology Sydney, the founder of PolitiFact Australia, and the
editor of The Sydney Morning Herald, the Canberra Times, and the Sunday Age.

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.tocmtappendix mtchapternone mtappendixsubsection 1Contents (Appendix)

###### Contents

  1. 1 Introduction
  2. 2 Relevant Literature & Background
  3. 3 Data & Methods
    1. 3.1 Data Sources
    2. 3.2 Skill Similarity
  4. 4 Jobs Data Analysis and Results
    1. 4.1 Posting Frequency & Employment levels
    2. 4.2 Salaries
    3. 4.3 Trend Analysis & Predictability
    4. 4.4 Gender
    5. 4.5 Location
    6. 4.6 Education & Experience
    7. 4.7 Employment Type
    8. 4.8 Journalism Skills
  5. 5 Discussion
    1. 5.1 Volatility of journalism jobs
    2. 5.2 Gender Wage Gap
    3. 5.3 Location
    4. 5.4 Evolving journalism skills
  6. 6 Conclusion
  7. A Technical Appendix
    1. A.1 Data Sources
    2. A.2 Skill Similarity
    3. A.3 Trend Analysis & Predictability
    4. A.4 Quantify Labour Demand Volatility
    5. A.5 Top Journalism Skills by Year

This document is accompanying the submission Layoffs, Inequity and COVID-19: A
Longitudinal Study of the Journalism Jobs Crisis in Australia from 2012 to
2020. The information in this document complements the submission, and it is
presented here for completeness reasons. It is not required for understanding
the main paper, nor for reproducing the results.

##  Appendix A Technical Appendix

Here, we describe the data sources we used to analyse journalism jobs. We also
outline the skill similarity methodology that enables us to construct temporal
(yearly) sets of top journalism skills. Lastly, we describe how these temporal
sets of top journalism skills then allow us to adaptively identify occupations
that are ‘most similar’ to journalism, at the granular skill level.

###  A.1 Data Sources

Journalism job ads. This research draws on more than 8 million Australian
online job ads from 2012-01-01 until 2019-02-28, courtesy of data provided by
Burning Glass Technologies444BGT is a leading vendor of online job ads data.
https://www.burning-glass.com/ (BGT). BGT also granted access to the
aggregated job ads data from 2019-03-01 to 2020-03-31, allowing us to address
the early impacts of the unfolding coronavirus pandemic (COVID-19) on
journalism jobs in Australia. BGT collected the job ads data via web scraping
and systematically processed it into structured formats. The dataset consists
of detailed information on individual job ads, such as location, salary,
employer, educational requirements, experience demands, and more. The skill
requirements have also been extracted (totalling $>11,000$ unique skills) and
each job ad is classified into its relevant occupational and industry classes.
There are two occupational ontologies in the job ads dataset. The first is
ANZSCO, which is the official occupational classification standard in
Australia and New Zealand. The other is the BGT occupational ontology, which
has been developed due to shortcomings of official occupational standards (as
described in Relevant Literature & Background).

To ensure selection accuracy, we instituted the following search query
conditions over the dataset:

  1. 1.

All job ads with ANZSCO Occupation labels of ‘Newspaper or Periodical Editor’,
‘Print Journalist’, ‘Radio Journalist’, ‘Television Journalist’, and
‘Journalists and Other Writers nec’ (where ‘nec’ stands for ‘not elsewhere
classified’).

  2. 2.

OR All job ads with the BGT Occupation label of ‘Journalist / Reporter’ and
‘Editor’ (the two primary BGT occupational classes for journalists);

  3. 3.

OR All job ads with the ‘Journalist’, ‘News’, or ‘Editor’ in any part of the
job title.

After manually reviewing the returned job ad features for accuracy, the
selection process resulted in a sample of 3,231 Australian journalism job ads
from 2012-01-01 until 2019-02-28. We used the same search query and approach
for the 2019-03-01 to 2020-03-31 period to supplement this sample. This
returned 467 journalism job ads, amounting to a total of 3,698 journalism job
ads from 2012-01-01 to 2020-03-31. The job ads during the period are observed
aggregated daily, with limited skill level details. However, much of the
analysis that follows requires access to the features within individual job
ads, so only Fig. 2 leverages the 2020 data.

Further details on job ads data. It is estimated that approximately 60% of
Australian job ads are posted online (Department of Employment, Skills, Small
and Family Business, 2019), which grew quickly in the early 2000’s before
plateauing in recent years (Carnevale et al., 2014). At aggregate levels,
online job advertisements (ads) provide valuable indicators of relative labour
demands. This includes demand features, such as salaries, educational
requirements, years of experience, and, most importantly, skill-level
information. Here, a distinction must be made between skills, knowledge,
abilities, and occupations. ‘Skills’ are the proficiencies developed through
training and/or experience (OECD, 2019); ‘knowledge’ is the theoretical and/or
practical understanding of an area; ‘ability’ is the competency to achieve a
task (Gardiner et al., 2018); and ‘occupations’ are standardised jobs that are
the amalgamation of skills, knowledge, and abilities used by an individual to
perform a set of tasks that are required by their vocation. Throughout this
paper, the term ‘skill’ will incorporate ‘knowledge’ and ‘ability’. Skills, in
this sense, are the constituent elements that workers use to perform tasks,
which ultimately define jobs and occupations. While it is possible that the
ways skills are described could evolve over time, it is unlikely that their
meanings materially changed over the nine year period analysed in this
research.

Advantages of job ads data. Understanding how the composition of skill sets
evolve within an occupation is essential to understanding trends in that
occupation. However, occupational data rarely captures skill-level data. Most
often, official occupational standards are static, rarely updated
classifications, which fail to capture the changing skill demands of
occupations, or to detect the creation of new types of jobs.

Example of journalism job ad titles. The table below illustrates a random
sample of job ad titles classified as journalists in the BGT dataset.

Table 1: Random sample of journalism job ad titles Job Ad Title
---
News And Features Journalist - The Courier
Journalist/Copywriter
Senior Finance Journalist/Content Manager
ABC Rural Reporter
Rural Reporter, ABC Local Radio
Newspaper Journalist
Journalist Radio News - Illawarra
News Writer
Editor, News
Senior Digital News Editor - Leading Digital Media Company

Journalist employment statistics. Employment data (labour supply) were
collected from the ‘Quarterly Detailed Labour Force’ statistics by the ABS
(Australian Bureau of Statistics, 2019a). These employment data are organised
into standardised occupations called the Australia and New Zealand Standard
Classification of Occupations (ANZSCO). ANZSCO provides a basis for the
standardised collection, analysis and dissemination of occupational data for
Australia and New Zealand. The structure of ANZSCO has five hierarchical
levels - major group, sub-major group, minor group, unit group and occupation.
The categories at the most detailed level of the classification are termed
’occupations’.

A shortcoming, however, is that the lowest level of occupational employment
data available by the ABS is at the 4-digit Unit level, which is one
hierarchical level above specific occupations. As our research is focused on
the employment Unit class of ‘Journalists and Other Writers’, all ABS
employment statistics cited in this research include the following
occupations: ‘Copywriter’, ‘Newspaper or Periodical Editor’, ‘Print
Journalist’, ‘Radio Journalist’, ‘Technical Writer’, ‘Television Journalist’,
and ‘Journalists and Other Writers nec’. While the inclusion of the
‘Copywriter’ and ‘Technical Writer’ occupations in these statistics could
distort results pertaining to ‘Journalists’ to an extent, we consider this
impact to be limited in scope. As we describe in Jobs Data Analysis and
Results, the employment statistics highlight important trends in journalism
occupations, which are confirmed by findings from the job ads data.

Another shortcoming of employment statistics is their ‘lagging’ nature. The
inertia of labour markets means that it takes time for changes to materialise
in employment statistics. Additionally, the official reporting of employment
statistics takes time. Employment statistics are often published several
months or years after the reported period. As a result, these ‘lagging’
characteristics are not available for the most recent periods in our work
(such as for the second half of 2019 and later.)

###  A.2 Skill Similarity

In this section, we detail the methodology previously employed in
(Alabdulkareem et al., 2018; Dawson et al., 2019) to measure skill similarity
dynamically. Here, we present the building blocks for this method, applying it
for journalism related skills and occupations.

Intuition. Two skills are similar when the two are related and complementary,
i.e. the two skills in a skills-pair support each other. For example,
‘Journalism’ and ‘Editing’ have a high pairwise similarity score because
together they enable higher productivity for the worker, and because the
difficulty to acquire either skill when one is already possessed by a worker
is relatively low.

Our goal, therefore, is to calculate the similarity of each unique skill
relative to every other unique skill in the dataset. Such a measure allows us
to identify which skills have the highest pairwise similarities to a specific
skill or set of skills. We also want to identify how skill similarity evolves
over time. To achieve this, we have instituted a temporal split of a calendar
year. This enables us to assess yearly changes to the underlying skill demands
of journalism jobs.

The Revealed Comparative Advantage of a skill. We implement a data-driven
methodology to measure the pairwise similarity between pairs of skills that
co-occur in job ads. One difficulty we encounter is that some skills are
ubiquitous, occurring across many job ads and occupations. We address this
issue by using the _Revealed Comparative Advantage_ (RCA), which maximises the
amount of skill-level information obtained from each job ad, while minimising
the biases introduced by over-expressed skills in job ads. Formally, RCA
measures the relevance of a skill $s$ for a particular job ad $j$ as:

$RCA(j,s)=\frac{x(j,s)/\mathop{\sum}\limits_{s^{\prime}\in\mathcal{S}}x(j,s^{\prime})}{\mathop{\sum}\limits_{j^{\prime}\in
J}x(j^{\prime},s)/\mathop{\sum}\limits_{j^{\prime}\in\mathcal{J},s^{\prime}\in\mathcal{S}}x(j^{\prime},s^{\prime})}$
(3)

where $x(j,s)=1$ when the skill $s$ is required for job $j$, and $x(j,s)=0$
otherwise; $\mathcal{S}$ is the set of all distinct skills, and $\mathcal{J}$
is the set of all job ads in our dataset.
$RCA(j,s)\in\left[0,\mathop{\sum}\limits_{j^{\prime}\in J,s^{\prime}\in
S}x(j^{\prime},s^{\prime})\right],\forall j,s$, and the higher $RCA(j,s)$ the
higher is the comparative advantage that $s$ is considered to have for $j$.
Visibly, $RCA(j,s)$ decreases when the skill $s$ is more ubiquitous (i.e. when
$\mathop{\sum}\limits_{j^{\prime}\in J}x(j^{\prime},s)$ increases), or when
many other skills are required for the job $j$ (i.e. when
$\mathop{\sum}\limits_{s^{\prime}\in S}x(j,s^{\prime})$ increases). $RCA$
provides a method to measure the importance of a skill in a job ad, relative
to the total share of demand for that skill in all job ads. It has been
applied across a range of disciplines, such as trade economics (Hidalgo et
al., 2007) (Vollrath, 1991), identifying key industries in nations (Shutters
et al., 2016), and detecting the labour polarisation of workplace skills
(Alabdulkareem et al., 2018).

Measure skill similarity. The next step is measuring the complementarity of
skill-pairs that co-occur in job ads. First, we compute the ‘effective use of
skills’ $e(j,s)$ defined as $e(j,s)=1\text{ when }RCA(j,s)>1$ and $e(j,s)=0$
otherwise. Finally, we compute the skill complementarity (denoted $\theta$) as
the minimum of the conditional probabilities of a skills-pair being
effectively used within the same job ad. Skills $s$ and $s^{\prime}$ are
considered as highly complementary if they tend to commonly co-occur within
individual job ads, for whatever reason. Formally:

$\theta(s,s^{\prime})=\frac{\mathop{\sum}\limits_{j^{\prime}\in
J}e(j,s).e(j,s^{\prime})}{max\left(\mathop{\sum}\limits_{j^{\prime}\in
J}e(j,s),\mathop{\sum}\limits_{j^{\prime}\in J}e(j,s^{\prime})\right)}$ (4)

Note that $\theta(s,s^{\prime})\in[0,1]$, a larger value indicates that $s$
and $s^{\prime}$ are more similar, and it reaches the maximum value when $s$
and $s^{\prime}$ always co-occur (i.e. they never appear separately).

Top journalism skills. Following the procedure outlined in (Dawson et al.,
2019) for building sets of highly complementary skills, we use the $\theta$
function together with ‘Journalism’ as the ‘seed’ skill to create top yearly
lists of journalism skills. More precisely, we compute $\theta(Journalism,s)$
– i.e. the similarity between the skill ‘Journalism’ and each unique skill
that occurs during a given year. Skills on each yearly list are ordered by
their descending pairwise skill similarity scores. When inspecting the yearly
skill lists, we make two observations. First, the skills in 2012 and 2013
appear of notably lower quality than from 2014 onward. We posit that this has
to do with imperfect skills extraction methods during the early years of the
BGT dataset. As a result, we decided to measure the top yearly journalism
skill sets from 2014 to 2018 (the last available full year of data for which
we had access). 555We did not notice a deterioration of quality regarding
other features, such as salaries, education, experience etc. Therefore, these
2012 and 2013 will be used for parts of the analysis. Second, we decided to
retain only the top 50 skills on each yearly list. Through qualitative
analysis, we determined that this threshold of 50 is both sufficiently
exclusive for defining journalism skills and reasonably inclusive for
detecting the evolution of new, emerging skills in journalism. The purpose of
these top journalism skills lists is to capture journalism labour trends; it
is not intended to represent a complete taxonomy of journalism skills. The
yearly lists of top journalism skills, and their similarity scores, can be
observed in the Supplemental Material Sec. Top Journalism Skills by Year.

Compute journalism skill intensity. For the occupational similarity analysis
in Sec. Journalism Skills, we decided to use the BGT occupational ontology as
opposed to ANZSCO. This is because the BGT occupational classes appear more
reflective of current job titles. For example, a job title advertised for a
‘Social Media Manager’ is classified by BGT as a ‘Social Media Strategist /
Specialist’. Whereas the same job title would be classified by ANZSCO as an
‘Advertising Specialist’ or ‘Marketing Specialist’.

###  A.3 Trend Analysis & Predictability

We use the Prophet time-series forecasting tool developed by Facebook Research
(Taylor and Letham, 2018). Prophet is an auto-regressive tool that fits non-
linear time-series trends with the effects from daily, weekly, and yearly
seasonality, and also holidays. The main model components are represented in
the following equation:

$y(t)=g(t)+s(t)+h(t)+\epsilon_{t}$ (5)

where $g(t)$ refers to the trend function that models non-periodic changes
over time; $s(t)$ represents periodic changes, such as seasonality; $h(t)$
denotes holiday effects; and $\epsilon_{t}$ is the error term and represents
all other idiosyncratic changes.

###  A.4 Quantify Labour Demand Volatility

We evaluate the forecasting performance using a temporal holdout setup. That
is, we split the available time-series into a training part (the first part of
the sequence) and a testing part (the latter part of the sequence). We train
the Prophet model on the training part, and we generate job ad posting
forecasts by “running time forward” in Eq. 5 for time $t$ in the testing
period. Finally, we measure the accuracy of the forecast against the observed
posting volumes using the Symmetric Mean Absolute Percentage Error (SMAPE)
(Scott Armstrong, 1985; Makridakis, 1993). SMAPE is formally defined as:

$SMAPE(A_{t},F_{t})=\frac{200}{T}\sum_{t=1}^{T}\frac{|F_{t}-A_{t}|}{(|A_{t}|+|F_{t}|)}$
(6)

where $A_{t}$ denotes the actual value of jobs posted on day $t$, and $F_{t}$
is the predicted value of job ads on day $t$. SMAPE ranges from 0 to 200, with
0 indicating a perfect prediction and 200 the largest possible error. When
actual and predicted values are both 0, we define SMAPE to be 0. We selected
SMAPE as an alternative to the more widely used MAPE because it is (1) scale-
independent and (2) robust to actual or predicted zero values. To evaluate the
uncertainty of the forecast, we adopt a ‘sliding window’ approach. This
consists of using a constant number of training days (here $1,186$ days) to
train the model, and we test the forecasting performance on the next $365$
days. We then shift both the training and the testing periods right by one
day, and the process is repeated. Consequently, we train and test the model
365 times, and we obtain 365 SMAPE performance values.

###  A.5 Top Journalism Skills by Year

Top journalism skills calculated by skill similarity methodology in Sec. Skill
Similarity.

Table 2: Top journalism skills calculated by skill similarity methodology in
Skill Similarity

Rank 2014 2015 2016 2017 2018 1 Journalism Journalism Journalism Journalism
Journalism 2 Editing Editing Editing Editing Editing 3 Media Relations Media
Relations Copy Writing Content Management Content Management 4 Corporate
Communications Copy Writing Media Relations Social Media Media Relations 5
Copy Writing Content Management Content Management Copy Writing Copy Writing 6
Content Management Copywriting Social Media Media Relations Social Media
Platforms 7 Public Relations Social Media Social Media Platforms Corporate
Communications Social Media 8 Social Media Public Relations Copywriting Social
Media Platforms Content Development 9 Content Management Systems (CMS) Social
Media Platforms Corporate Communications Content Development Corporate
Communications 10 Multimedia Corporate Communications Public Relations Social
Content Public Relations 11 Copywriting Content Development Content
Development Public Relations Social Media Tools 12 Content Development Content
Management Systems (CMS) Social Media Tools Copywriting Copywriting 13
Strategic Communications Strategic Communications Digital Marketing Facebook
Content Management Systems (CMS) 14 Facebook Social Media Tools Online
Marketing Strategic Communications Social Content 15 Social Media Platforms
Multimedia Multimedia Social Media Tools Strategic Communications 16 Marketing
Communications Facebook Strategic Communications Marketing Communications
Social Media Strategy 17 Media Coverage Marketing Communications Market
Research Content Management Systems (CMS) Content Marketing 18 Publicity
Social Content Marketing Communications Multimedia Facebook 19 Proofreading
Digital Communications Content Management Systems (CMS) Proofreading Digital
Communications 20 Social Media Tools Publicity Writing Content Marketing Media
Coverage 21 Digital Communications Media Production Content Marketing Digital
Journalism Publicity 22 Crisis Management Social Media Strategy Photography
Digital Communications Proofreading 23 Adobe Photoshop Communications
Programmes Instagram Publicity Multimedia 24 Communications Programmes Media
Coverage Publicity Media Coverage Instagram 25 Digital Journalism Internal
Communications Digital Communications Digital Marketing Video Production 26
Community Relations Content Marketing Media Coverage Writing Marketing
Communications 27 Photography Proofreading Social Content Video Production
Adobe Photoshop 28 Social Media Strategy Writing Social Media Strategy Graphic
Design Content Curation 29 Graphic Design Adobe Photoshop Media Production
Media Production Video Editing 30 Youtube Brand Awareness Generation
Proofreading Communications Programmes Adobe Indesign 31 Media Strategy Adobe
Indesign Facebook Instagram Adobe Creative Suite 32 Brand Management Marketing
Materials Event Planning Social Media Strategy Adobe Acrobat 33 Web Content
Management Digital Marketing Adobe Photoshop Video Editing Brand Awareness
Generation 34 Adobe Indesign Video Editing Meeting Deadlines Adobe Photoshop
Adobe Illustrator 35 Social Content Adobe Creative Suite Self-Starter Self-
Starter Google Analytics 36 Marketing Materials Adobe Acrobat Marketing
Breaking News Coverage Press Releases 37 Event Planning Graphic Design
Creativity Creativity LinkedIn 38 Digital Marketing Video Production Adobe
Indesign Adobe Illustrator Digital Marketing 39 Writing Instagram Adobe
Creative Suite Event Planning Digital Journalism 40 Instagram LinkedIn Adobe
Illustrator Adobe Indesign Media Production 41 Online Research Media Strategy
Community Relations Adobe Creative Suite Communications Programmes 42 Adobe
Acrobat Photography Adobe Acrobat Adobe Acrobat Crisis Management 43 LinkedIn
PR Agency Press Releases Promotional Materials Media Strategy 44 Google
Analytics Meeting Deadlines Internal Communications Photography Writing 45
Video Editing Digital Journalism Campaign Management Content Curation
Photography 46 Website Production Event Planning Creative Writing Marketing
Blog Posts 47 Proofing Google Analytics Video Production Meeting Deadlines
Internal Communications 48 Video Production Media Campaigning Blog Posts
Google Analytics Event Planning 49 Media Planning Press Releases Crisis
Management Media Strategy Creative Problem Solving 50 Campaign Management
Crisis Management Youtube Business-to-Business Creativity