econ essay
400 words included introduction and arguments
Whatdo we know about Australia’s self-employed?
Introduction
In Australia, the self-employed make up a small but significant part of the workforce. In
2013, the self-employment rate was 10.4 per cent (World Bank 2016). This represents
approximately 1.2 million Australians (Australian Bureau of Statistics 2016a). There are two
reasons to study the self-employed. Firstly, many countries view self-employment as a
positive force in the economy, with many European governments using self-employment as
an active labour market policy (European Foundation for the Improvement of Living and
Working Conditions 2011). Secondly, the face of self-employment is likely to be changing in
Australia. Traditionally the self-employed were tradespeople, retail owners and professionals.
However, with companies re-hiring ex-workers as contractors, Australians adopting the
sharing economy as a source of income, and an aging workforce, the demographics of self-
employment are now likely to have changed. This review examines what self-employment
looks like in Australia, how it is changing and whether it is beneficial for the economy. It
concludes there is little available literature on self-employment in the Australian context and
that as such, both academia and governments would benefit from further research in this area.
What does self-employment look like in Australia?
The Australian Bureau of Statistics (ABS) classifies self-employed workers as either:
‘employer’-an owner of an unincorporated firm hiring employees; ‘own account worker’- an
owner of an unincorporated firm who hires no employees; and ‘contributing family worker’.
Of note, owners of incorporated firms are counted as ‘employees’ rather than self-employed
(Australian Bureau of Statistics 2013).
According to the ABS these groups now make up the following proportion of employed
Australians:
‘employer’: 2 per cent
‘own account worker’: 3 per cent
‘contributing family member’: 0.2 per cent
‘owner of an unincorporated enterprise’: 7 per cent (Australian Bureau of Statistics
2016b).
Atalay et al. (2014) used data from another source-the Household, Income and Labour
Dynamics in Australia (HILDA)-and estimated that the self-employed are likely to be older
and male. The self-employment rate in older workers (aged more than 55 years) was around
double that of younger workers as was the self-employment rate of males compared to
females (Atalay, Kim, and Whelan 2014, 477). Beyond this we know little else about the self-
employed in Australia because most literature focuses on the United States, Canada and the
European Union. For example we have no indication of Australia’s self-employed work
motivation, average earnings, main industries and education levels. In contrast, data from the
US suggests that the relationship between education and self-employment is U shaped; the
least educated and most educated have a higher propensity to be self-employed
(Blanchflower 2000, 488). The top two industries for self-employment in the US are
‘Professional and business services’ and ‘Construction’ (Goetz, Fleming, and Rupasingha
2012, 317). This suggests the self-employed are a heterogeneous group, which is also likely
to be the case in Australia.
Trends in Australia’s self-employment rate
What we do know is that the self-employment rate in Australia has been decreasing since its
most recent peak of 16.3 per cent in 1993 (Atalay, Kim, and Whelan 2014, Harrison 2015,
World Bank 2016). Authors suggest different causes for this decline. Atalay et al. (2014)
attribute it to a combination of two factors. The first is increased labour force participation,
particularly by older females. This means that the pool of people in the workforce has
increased, driving down the self-employment rate. Increased labour force participation of
older females is caused in turn by increased employer demand for this demographic and an
increase in the age of accessing the pension (Atalay, Kim, and Whelan 2014, 486). The
second factor is decreased entry into self-employment from both unemployment and paid
employment pools (Atalay, Kim, and Whelan 2014, 479-480). Harrison (2015) instead bases
the decline on small business consolidation. The premise behind this is that most
self-employed workers are small business owners. Hence, the decline in small business
ownership in Australia has led to a corresponding decrease in self-employment rates.
This decline in self-employment is not unique to Australia. Blanchflower (2000) notes that
with the exception of the UK, Portugal and New Zealand, decreases have occurred in the
majority of OECD countries including the United States. In contrast, Goetz et al. (2012) note
that the United States has experienced a continuous increase in self-employment since the
1960s and that since 2000, there has been a ‘surge’ in self-employment growth. This suggests
firstly that there are variations in self-employment trends amongst countries and that
secondly, declines in self-employment are not widespread nor are they a hallmark of
developed economies.
Differences in countries’ institutional, labour and tax structures are likely to be behind these
differing self-employment trends. Indeed the literature suggests that there are many elements
influencing or associated with self-employment rates. These include: labour market
conditions, an individual’s access to capital and education, institutional factors such as tax
rates, business conditions, and structural changes (Burke, Fitzroy, and Nolan 2002,
Blanchflower 2000, Harrison 2015, Parker and Robson 2004). However, Australia’s tax
system is unlikely to account for the recent declines in self-employment since taxes have not
changed significantly in recent times nor do they differentiate between employees and the
self-employed (Atalay, Kim, and Whelan 2014, 485).
Unfortunately, there appears to be no research internationally or in Australia on how the
nature of self-employment itself is changing. That is, whether there are changes or transitions
within the underlying groups which constitute the self-employment pool and the reasons
behind these changes.
Self-employment and the economy
It is unclear whether the decline in the self-employment rate has been beneficial or
detrimental to the Australian economy. Goetz et al. (2012) argue there has been little research
on the relationship between self-employment rates and the performance an economy because
anecdotal evidence is often enough to prove the benefits of successful entrepreneurship. More
bluntly Blanchflower (2000) states that ‘probably the greatest interest in entrepreneurship
springs from a belief that small businesses are essential to the growth of a capitalist
economy’.
There is a mixed opinion on whether high levels of self-employment help economies or not.
On the one hand as Congregado et al. (2010) state, boosting self-employment should have
positive effects on unemployment through two means. Firstly by removing an individual
from the unemployment pool and secondly by the eventual creation of additional jobs when
the firm grows. Rupasingha and Goetz (2011) and Goetz et al., (2012) confirm that in US
counties, higher self-employment rates are associated with lower unemployment, and go
further, showing that they also improve per capita income growth (including employee
wages) and reduce poverty (in non-metropolitan counties).
On the other hand Blanchflower (2000) found no relationship between a country’s level of
self-employment and its GDP growth, nor any consistent relationship between
self-employment and unemployment rates. But it is important to consider that Goetz et al.
(2012) found a lag between self-employment increases and subsequent employment growth.
Additionally, a measure such as GDP may be too blunt a measure for determining the local
impacts of self-employment.
At the individual level at least, self-employment may have a significant non-market value;
those who are self-employed have a higher job satisfaction (Blanchflower 2000, 502).
Hamilton (2000) observed that many self-employed remained in self-employment despite
lower earnings than their employee counterparts because of their ‘non-pecuniary benefits’.
Even if international studies were in agreement on the relationship between self-employment
and economic performance, it is not necessarily the case that this relationship would hold in
Australia. This is because each country has different institutional and demographic
differences which are likely to affect the relationship between the self-employed and other
parts of the economy. This justifies the need for an Australian based study, similar to Goetz et
al. (2012) to determine how changes to the self-employment rate affect regional or state
economic indicators.
Conclusion
The self-employed make up a significant proportion of the Australian labour force, but the
scarcity of literature means that we know little about them, a sentiment which is shared by
Atalay et al. (2014). The rate of self-employment in Australia has been declining, and there
have been studies hypothesising why this may be the case. But it is unclear the effects that
this decline has had or will have because there is no consensus, even internationally, about
the role that self-employment plays in the economy.
There is therefore a need for future research into the state of self-employment in Australia.
This research can be categorised into three areas. Firstly, further characterising self-employed
workers, focusing on motivation, average earnings, and industry of work and education
levels. Secondly, investigating the trends in self-employment in Australia with a focus on
transitions amongst the individual subgroups comprising the self-employed and the possible
effects of demographic and structural changes on these transitions. And thirdly, investigating
the effect that the decline in self-employment rates is having on the economy. Combined, the
results of this research will form a strong evidence base on which Australian governments
can develop sound entrepreneurship policy.
References
Atalay, Kadir, Woo‐Yung Kim, and Stephen Whelan. 2014. “The Decline of the Self‐
Employment Rate in Australia.” Australian Economic Review 47 (4):472-489.
Australian Bureau of Statistics. 2013. Labour Statistics: Concepts, Sources and Methods. Cat
no. 6102.0.55.001.
Australian Bureau of Statistics. 2016a. Australian Labour Force, Cat no. 6202.0.
Australian Bureau of Statistics. 2016b. Labour Force, Australia, Detailed, Quarterly, Feb
2016. Cat. no. 6291.0.55.003, table 13.
Blanchflower, David G. 2000. “Self-employment in OECD countries.” Labour economics 7
(5):471-505.
Burke, Andrew E, Felix R Fitzroy, and Michael A Nolan. 2002. “Self-employment wealth
and job creation: The roles of gender, non-pecuniary motivation and entrepreneurial
ability.” Small business economics 19 (3):255-270.
Congregado, Emilio, Antonio A Golpe, and Mónica Carmona. 2010. “Is it a good policy to
promote self-employment for job creation? Evidence from Spain.” Journal of Policy
Modeling 32 (6):828-842.
European Foundation for the Improvement of Living and Working Conditions. 2011. Public
measures to support self-employment and job creation on one-person and
microenterprises.
Goetz, Stephan J, David A Fleming, and Anil Rupasingha. 2012. “The economic impacts of
self-employment.” Journal of Agricultural and Applied Economics 44 (03):315-321.
Hamilton, Barton H. 2000. “Does entrepreneurship pay? An empirical analysis of the returns
to self‐employment.” Journal of Political economy 108 (3):604-631.
Harrison, Anthony H. 2015. Declining self-employment: examining evidence and labour
market impacts of business consolidation. Department of Industry Innovation Science
and Research.
Parker, Simon C, and Martin T Robson. 2004. “Explaining international variations in self-
employment: evidence from a panel of OECD countries.” Southern Economic
Journal:287-301.
Rupasingha, Anil, and Stephan J Goetz. 2013. “Self‐employment and local economic
performance: Evidence from US counties.” Papers in Regional Science 92 (1):141-
161.
World Bank. 2016. Self-employed as a percentage of total employed.
ECON5004: Communication in Economics
Semester 1, 2021
Instructor: Denny Lie
1
Assignment: Academic Writing Project
Assignment Outline
Students are required to draft an academic essay over the course of the first 8-9 weeks of the
semester. The paper will be drafted in phases, with opportunities for detailed feedback, revision
and improvement at each phase. Since it is not possible to write a full academic paper in 1500
words (and the research required for a full academic paper would exceed the workload
requirements for this course), the essays will follow one of the following formats:
1. Literature review. Students can write a review of the literature in a specific topic area.
This will need to be more than just a series of paragraphs summarizing various papers – it
will need to have a central thesis (argument) and cite academic literature to support that
argument (most likely, arguing what the main takeaway of the literature is). So, ideally,
for this kind of essay the student would identify an area of literature where there is some
debate, with multiple papers taking different sides of an issue. The student’s essay would
then argue in support of one side of the debate, citing and acknowledging literature on
both sides of the debate, and in most cases, ultimately coming down in support of one
side.
2. Research proposal. Students can write a proposal for a specific research project. You can
think about this in a number of ways – as the narrative section of a research grant, as a
proposal that one would write to receive permission to conduct research in a public sector
or professional context, or as the proposal for a masters or PhD thesis.
3. Persuasive essay. Students can use academic literature to backup an argument about an
issue of economic interest. This could be a public policy issue, a business issue, or a more
academic issue. In any case, the essay should be grounded in supporting a particular
argument using economic reasoning and supported by credible academic economic
literature.
Students are encouraged to select an essay format that best fits their personal and professional
interests. The potential subject matter is broad. The core requirement is that the essay use
economic reasoning and argumentation to make its main points, and be based on credible
academic economics literature.
In general the essays will have the following format: introduction, evidence/exposition,
conclusion. The introduction will motivate the central argument (the “hook”), outline the main
aspects of the essay, and argue for the importance of the essay. The evidence/exposition is the
meat of the essay – it will bring the student’s research to bear, in outlining the evidence for the
main argument or point of the essay, while the conclusion will succinctly tie up the essay.
The final submission will be capped at 1500 words, exclusive of literature references. Format:
double spaced, 12-point font, 2.5 cm margins.
Phases
The assignment will be completed in phases. The phases are meant to build on each other.
Phase 1 (400-750 words; 3/37 percent): due Thursday 25 March, 6pm (Week 4)
Students will write about a quarter to half of the essay for a first draft. This should involve a first
attempt at an introductory section, and at least one section of the supporting evidence. It is
ECON5004: Communication in Economics
Semester 1, 2021
Instructor: Denny Lie
2
understood that students may not have completed all the background research by this stage, so
the evidence/exposition section may be incomplete. However it will present a first attempt to
giving the hook for the essay and outlining the main arguments.
The soft copy of the draft must be submitted online through Canvas (by Thursday 25 March,
6pm) so the course instructor can give feedback particularly on the economic topic and reasoning
in the essay. Phase 1 will be capped by having students participate in an in-class peer feedback
session (3%) during Week 5 lecture on 29 March.
Phase 2 (1500 words; 14/37 percent): due Thursday 15 April, 6pm (Week 6)
Students will fill out the full essay for the second draft.
Phase 2 will be capped by (i) students participating in an in-class peer feedback session (4%) and
(ii) having their essay read and receiving detailed written feedback from the course tutor through
submitting the essay in soft copy through Canvas (10%). Students are expected to revise their
essay in light of the tutor’s feedback and will have an opportunity to meet the tutor to discuss
their revision and any further changes.
Phase 3 (1500 words; 20/37 percent): due Thursday 6 May, 6pm (Week 9)
This is the final submission of the essay, to be submitted in soft copy online through Canvas, to
be graded by the course instructor.
Due Date
Due dates are as listed above. Several adjustments on the dates (by the instructor) are possible as
the semester progresses.
Grading Weight
The academic essay carries 37% of the grading weight for the course.
Tips
How do you come up with new research ideas or areas to explore in economics? Well, if it were
easy we would all have a shot at a Nobel Prize. Unfortunately it is not so easy, but there are
certain patterns we can follow:1
• Be curious and critical. Don’t take what you read at face value; always ask questions. Just
because someone wrote something doesn’t mean it’s “right”. Sharpen your filter for ideas
by learning from others, but also learn to trust your instincts and unique perspective.
• Gather new information. While you can get new research ideas from reading the
academic literature, unless you work on methodology this is often not a great source of
new ideas. Much more interesting can be broad reading and exposure – to what’s going
on in the world, what the media is chattering about (in op-eds, etc.), what’s happening in
1 Here’s useful advice from some other academic economists on how to come up with research ideas: Marc
Bellemare, UMN (http://marcfbellemare.com/wordpress/wp-content/uploads/2014/08/BellemareHowtoPublish );
Amy Finkelstein, MIT (http://econ.lse.ac.uk/staff/spischke/phds/Amy%20Finkelstein%20IAP%20talk%2007.ppt);
Steve Pischke, LSE (http://econ.lse.ac.uk/staff/spischke/phds/How%20to%20start ).
http://marcfbellemare.com/wordpress/wp-content/uploads/2014/08/BellemareHowtoPublish
http://econ.lse.ac.uk/staff/spischke/phds/Amy%20Finkelstein%20IAP%20talk%2007.ppt
http://econ.lse.ac.uk/staff/spischke/phds/How%20to%20start
ECON5004: Communication in Economics
Semester 1, 2021
Instructor: Denny Lie
3
other academic fields, etc. What are interesting new trends/phenomena that aren’t easily
explained with current results and knowledge?
• Write it down! Things that seem intuitive (including others’ arguments) sometimes don’t
hold water. Check for yourself!
• Eventually we need to think about feasibility – can an idea work? Don’t push this filter
too early, but eventually it must come into play.
1
Is the Use of Big Data with Smart Cities in the U.S economically Effective?
Yinfan Li
500707676
ECON5004: Communication in Economics
Semester 1, 2021
Instructor: Denny Lie
Is the Use of Big Data with Smart Cities in the U.S economically Effective?
Introduction
There has been a heated debate on the significance of big data analytics and how it promotes economic growth. Some sources have argued that big data analytics in the management of cities has enhanced economic growth by improving service delivery and a clean environment. Other sources have claimed that the application of data analytics with smart cities has led to an economic downturn because many operations have been automated, leading to massive loss of jobs (Allam & Dhunny, 2019). This essay will analyze how the U.S has influenced from applying big data analytics in developing its major cities and what’s advantages and disadvantages of widespread applying big data analytics for economic growth.
What’s the benefit of applying big data analytic
Hashem et al (2016) has linked the application of big data analytics in smart cities with the advancement in technology. According to these sources, the rise in technology, globalization, and rapid population increase has influenced big data with smart cities. In the U.S, bid data analytics has helped improve urban mobility by eliminating traffic, reducing carbon footprint, and managing infrastructure in a sustainable, cost-effective, and secure manner.
The traffic in major cities in the U.S has reduced significantly over the past decade since the adoption of data analytics with smart cities. According to Kitchin (2014), the reduction of traffic has helped enhance movement and has helped save time and resources. Some sources argue that before adopting big data analytics in the transport sector, the U.S was losing over $2 billion every year on traffic gum. The waiting time was approximated to be about 3 hours. Over 60,000 liters of gas were wasted in the traffic during this period. Many employees were arriving 2 hours late at workplaces. The approximation of the money lost through arriving late at work was over $6 billion annually. However, with the adoption of big data analytic, all this money has been saved. These measures have influenced the past decade’s economic increase that the government undertook to reduce and eliminate traffic gum and congestion in big and small cities using big data analytics.
Other sources have indicated how the use of big data analytics has reduced pollution in the cities. According to Lim, Kim & Maglio (2018), big data analytics have enhanced environmental conservation. The report shows that reducing traffic gum in cities has led to less pollution in the cities. Big data analytics to purify the air in companies has also helped reduce pollution in big cities significantly.
What’s the disadvantages of applying big data analytic
Despite big data analytic economic achievements in cities’ development, some sources have argued that big data analytics have led to the economic downturn. According to these sources, many people have been rendered jobless. In contrast, others have been denied job opportunities. Because of the development of big data, AI with more widespread. The use of IT and robots in various sectors to offer services rendered by man has led to a heated debate. Some sources have claimed robots have contributed immensely to the current joblessness and the increasing unemployment rate in the country. Research conducted by Psomakelis et al (2016) that over one million jobs are currently done using robots. Other sources have argued that the use of robots undermines the education system and the skills students acquire in schools because they are not given a platform to exercise their skills since most jobs are done using robots.
Summary
This paper has presented various arguments on the significance of big data analytics in developing cities and how the use of big data analytics has affected the economy. This paper has addressed the areas that have rendered many people jobless and the importance of using big data analytics to reduce traffic and reduce pollution in major cities.
References
Allam, Z., & Dhunny, Z. A. (2019). On big data, artificial intelligence and smart cities. Cities, 89, 80-91.
Hashem, I. A. T., Chang, V., Anuar, N. B., Adewole, K., Yaqoob, I., Gani, A., … & Chiroma, H. (2016). The role of big data in smart city. International Journal of Information Management, 36(5), 748-758.
Kitchin, R. (2014). The real-time city? Big data and smart urbanism. GeoJournal, 79(1), 1-14.
Lim, C., Kim, K. J., & Maglio, P. P. (2018). Smart cities with big data: Reference models, challenges, and considerations. Cities, 82, 86-99.
Psomakelis, E., Aisopos, F., Litke, A., Tserpes, K., Kardara, M., & Campo, P. M. (2016). Big IoT and social networking data for smart cities: Algorithmic improvements on Big Data Analysis in the context of RADICAL city applications. arXiv preprint arXiv:1607.0050