Your browser doesn't support javascript.
A cross-country analysis of macroeconomic responses to COVID-19 pandemic using Twitter sentiments.
Nia, Zahra Movahedi; Ahmadi, Ali; Bragazzi, Nicola L; Woldegerima, Woldegebriel Assefa; Mellado, Bruce; Wu, Jianhong; Orbinski, James; Asgary, Ali; Kong, Jude Dzevela.
  • Nia ZM; Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Toronto, Canada.
  • Ahmadi A; Laboratory for Industrial and Applied Mathematics, York University, Toronto, Canada.
  • Bragazzi NL; Faculty of Computer Engineering, K.N. Toosi University, Tehran, Iran.
  • Woldegerima WA; Advanced Disaster, Emergency and Rapid-response Simulation (ADERSIM), York University, Toronto, Ontario, Canada.
  • Mellado B; Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Toronto, Canada.
  • Wu J; Laboratory for Industrial and Applied Mathematics, York University, Toronto, Canada.
  • Orbinski J; Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Toronto, Canada.
  • Asgary A; Laboratory for Industrial and Applied Mathematics, York University, Toronto, Canada.
  • Kong JD; Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), York University, Toronto, Canada.
PLoS One ; 17(8): e0272208, 2022.
Article in English | MEDLINE | ID: covidwho-2002303
ABSTRACT
The COVID-19 pandemic has had a devastating impact on the global economy. In this paper, we use the Phillips curve to compare and analyze the macroeconomics of three different countries with distinct income levels, namely, lower-middle (Nigeria), upper-middle (South Africa), and high (Canada) income. We aim to (1) find macroeconomic changes in the three countries during the pandemic compared to pre-pandemic time, (2) compare the countries in terms of response to the COVID-19 economic crisis, and (3) compare their expected economic reaction to the COVID-19 pandemic in the near future. An advantage to our work is that we analyze macroeconomics on a monthly basis to capture the shocks and rapid changes caused by on and off rounds of lockdowns. We use the volume and social sentiments of the Twitter data to approximate the macroeconomic statistics. We apply four different machine learning algorithms to estimate the unemployment rate of South Africa and Nigeria on monthly basis. The results show that at the beginning of the pandemic the unemployment rate increased for all the three countries. However, Canada was able to control and reduce the unemployment rate during the COVID-19 pandemic. Nonetheless, in line with the Phillips curve short-run, the inflation rate of Canada increased to a level that has never occurred in more than fifteen years. Nigeria and South Africa have not been able to control the unemployment rate and did not return to the pre-COVID-19 level. Yet, the inflation rate has increased in both countries. The inflation rate is still comparable to the pre-COVID-19 level in South Africa, but based on the Phillips curve short-run, it will increase further, if the unemployment rate decreases. Unfortunately, Nigeria is experiencing a horrible stagflation and a wild increase in both unemployment and inflation rates. This shows how vulnerable lower-middle-income countries could be to lockdowns and economic restrictions. In the near future, the main concern for all the countries is the high inflation rate. This work can potentially lead to more targeted and publicly acceptable policies based on social media content.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Social Media / COVID-19 Type of study: Observational study / Randomized controlled trials Limits: Humans Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2022 Document Type: Article Affiliation country: Journal.pone.0272208

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Social Media / COVID-19 Type of study: Observational study / Randomized controlled trials Limits: Humans Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2022 Document Type: Article Affiliation country: Journal.pone.0272208