âThis is an economic tsunami,â Mark Zandi, chief economist at Moodyâs Analytics, told me. Social
distancing is economic distancing. We are telling people to cease going to stores, to restaurants, to
workplaces. We are insisting they stop supplying their labour, making their goods. To slow a pandemic, we
are forcing a recession, perhaps a depression. [Source: Ezra Klein, Vox, 23 Mar 2020]
Discuss the likely economic impact of COVID-19 pandemic on the Australian economy by using
recent macroeconomic indicators whilst critically evaluating the role of fiscal policy as a stabilization
tool in a crisis like this.
Background Information
Australia has experienced an unprecedented economic growth, achieving the longest economic expansion on
record (28 years) without a recession. It also escaped the Global Financial Crisis at the end of the mining
boom relatively unscathed. This unprecedented economic story is about to topple by an unprecedented health
crisis that could drag the Australian economy into a recession for the first time in 28 years. Or will it extend
to a depression, as the economist, Mark Zandi is alerting? Australian economy has been facing several
structural issues such as the stagnant wage growth rate, a sluggish economy, housing affordability, rising
household debt, a rapidly declining manufacturing sector, and concerns of job security caused by mass
automation. How does a pandemic impact the households, businesses, government spending and foreign
trade? What does this impact mean for GDP, unemployment rate, and the budget balance? In this economic
environment, the role of macroeconomic policy cannot be ignored. Particularly, the role of recent stimulus
packages to combat the effects of Corona virus pandemic. In this essay, you will discuss the impact of the
pandemic on the economy by assessing macroeconomic indicators and critically evaluating the role of fiscal
policy as a stabilization tool of a modern economy.
Sample Solution
1.2 Research question Given the importance of movie trailers in the consumer decision process and the large budget all, it is relevant to research how to make this advertising medium more relevant to consumers personally. Bigger, more relevant datasets mean that there is more insight to consumer preferences, which provides ample opportunity to target audiences highly specifically. This thesis proposes a personalized movie trailer based on such consumer preferences. Such a system would help movie studios to target different audiences more effectively, as each consumer will be shown a movie trailer highly relevant to their own personal preferences. For instance, if data shows that user X will watch anything that features actor Y, a trailer that heavily features that actor should be more effective in persuading that user to watch the movie. Central to linking consumer preferences data to movie trailers are recommendation systems. Recommendation systems (RS) are software tools and techniques that provide suggestions for items that are most likely to be of interest to a user (Ricci, Rokach, & Shapira, 2015). Content-based RSs generate recommendations by combining user feedback on items with the content (i.e., features) associated with them (Lops, De Gemmis, & Semeraro, 2011). Operating by the logic that users will prefer the same features in movies as they do in movie trailers, the following research question is proposed:>
1.2 Research question Given the importance of movie trailers in the consumer decision process and the large budget all, it is relevant to research how to make this advertising medium more relevant to consumers personally. Bigger, more relevant datasets mean that there is more insight to consumer preferences, which provides ample opportunity to target audiences highly specifically. This thesis proposes a personalized movie trailer based on such consumer preferences. Such a system would help movie studios to target different audiences more effectively, as each consumer will be shown a movie trailer highly relevant to their own personal preferences. For instance, if data shows that user X will watch anything that features actor Y, a trailer that heavily features that actor should be more effective in persuading that user to watch the movie. Central to linking consumer preferences data to movie trailers are recommendation systems. Recommendation systems (RS) are software tools and techniques that provide suggestions for items that are most likely to be of interest to a user (Ricci, Rokach, & Shapira, 2015). Content-based RSs generate recommendations by combining user feedback on items with the content (i.e., features) associated with them (Lops, De Gemmis, & Semeraro, 2011). Operating by the logic that users will prefer the same features in movies as they do in movie trailers, the following research question is proposed:>
