ASSESSING THE IMPACT OF COVID-19 ON MONETARY POVERTY: A REVIEW OF INDIVIDUAL PRACTICES

Keywords: poverty, COVID-19, asessment methods, nowcasting methods, subjective poverty

Abstract

The article is dedicated to the analysis of poverty assessment methods during the COVID-19 pandemic. The social and economic challenges and transformations resulting from the COVID-19 pandemic had wide-ranging negative economic consequences and a global impact on the standard of living for the population. The unprecedented nature of the COVID-19 pandemic has resulted in various long-term consequences for the economy and society, with the direct impact of COVID on the rise in poverty being evident. The category most at risk accumulates the population living in poverty, which is disproportionately affected by the economic consequences of the coronavirus. The poor have a lower level of social and economic resilience, meaning they have less ability to cope with the consequences of natural disasters and recover from them independently. Therefore, it is important to identify successful practices for assessing the impact of the COVID pandemic on the poverty level. An objective understanding of the vulnerability of impoverished individuals in non-standard, shock situations that arise suddenly requires the establishment of somewhat different mechanisms for rapid response and the provision of social support. However, first and foremost, there is a need for the operational assessment of the consequences of non-standard situations on the welfare and living conditions of the poor. Rapidly assessing the social vulnerability and poverty of the population as a result of epidemics is often a challenging task, as there is a lack of systematic and official information about the social and economic status of the population. The monetary approach dominates in poverty assessment because precise measurement is a prerequisite for the formation and implementation of government policies to reduce poverty. Using only one poverty criterion does not allow for a correct assessment of the scale, level, and depth of such a multidimensional phenomenon. Only the use of a whole system of criteria will allow for an assessment of the impact of different processes, including crises, on the scale, level, depth, and profiles of poverty. The combination of multiple methods for determining poverty provides critically important information for assessing the impact of various crisis situations on the well-being of the poor.

References

Jinjing Li, Yogi Vidyattama, Hai Anh La, Riyana Miranti, Denisa M. Sologon (2021) Estimating the Impact of Covid-19 and Policy Responses on Australian Income Distribution Using Incomplete Data. URL: https://link.springer.com/article/10.1007/s11205-021-02826-0#Sec4

Mike Brewer, Iva Tasseva (2020) Did the UK Policy Response to COVID-19 Protect Household Incomes? URL: https://www.researchgate.net/publication/346120762_Did_the_UK_Policy_Response_to_COVID-19_Protect_Household_Incomes

Cathal O'Donoghue, Denisa M. Sologon, Iryna Kyzyma, John McHale (2020) Modelling the Distributional Impact of the COVID‐19 Crisis. URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7323411/

Cathal O’Donoghue, Denisa M. Sologon, Iryna Kyzyma, John McHale (2021) A Microsimulation Analysis of the Distributional Impact over the Three Waves of the COVID-19 Crisis in Ireland. URL: https://arxiv.org/ftp/arxiv/papers/2103/2103.08398.pdf

Figari, Francesco, V. Fiorio, Carlo Welfare resilience in the immediate aftermath of the COVID-19 outbreak in Italy. URL: https://ideas.repec.org/p/ese/emodwp/em6-20.html

Patryk Bronka, Diego Collado and Matteo Richiardi (2020) The Covid-19 Crisis Response Helps the Poor: The Distributional and Budgetary Consequences of the UK lock-down. URL: https://www.inet.ox.ac.uk/files/Bronka-et-al-COVID-Crisis-Response-Consequences-UK.pdf

Seung-Pyo Jun, Hyoung Sun Yoo, San Choi (2018) Ten years of research change using Google Trends: From the perspective of big data utilizations and applications. URL: https://www.sciencedirect.com/science/article/pii/S0040162517315536

Coronavirus reveals need to bridge the digital divide. URL: https://unctad.org/en/pages/newsdetails.aspx?OriginalVersionID=2322.

N. Rohmah Mayasari, Dang Khanh Ngan Ho, David J. Lundy and others (2020) Impacts of the COVID-19 Pandemic on Food Security and Diet-Related Lifestyle Behaviors: An Analytical Study of Google Trends-Based Query Volumes. URL: https://www.mdpi.com/2072-6643/12/10/3103

Francesco D’Amuri, Juri Marcucci (2017) The predictive power of Google searches in forecasting US unemployment. URL: https://www.sciencedirect.com/science/article/abs/pii/S0169207017300389

M. Fajar, O.Rizky Prasetyo (2020) Forecasting Unemployment Rate in the Time of COVID-19 Pandemic Using Google Trends Data (Case of Indonesia). URL: https://www.researchgate.net/publication/346525612_Forecasting_Unemployment_Rate_in_the_Time_of_COVID-19_Pandemic_Using_Google_Trends_Data_Case_of_Indonesia

Yongming Xu,Yaping Mo, Shanyou Zhu (2021) Poverty Mapping in the Dian-Gui-Qian Contiguous Extremely Poor Area of Southwest China Based on Multi-Source Geospatial Data. URL: https://www.mdpi.com/2071-1050/13/16/8717

Yusuke Tateno, Zakaria Zoundi (2021) Estimating the Short-term Impact of the COVID-19 Pandemic on Poverty in Asia-Pacific LDCs. URL: https://www.unescap.org/sites/default/d8files/2021-03/Technical%20note_Estimating%20COVID%20impact%20on%20poverty%20in%20APLDCs_final.pdf

Christoph Lakner, Daniel Gerszon Mahler, Espen Beer Prydz Mario Negre (2022) How much does reducing inequality matters for global poverty? URL: https://link.springer.com/content/pdf/10.1007/s10888-021-09510-w.pdf

D.Laborde, W. Martin, R. Vos (2020) Estimating the Poverty Impact of COVID-19 The MIRAGRODEP and POVANA frameworks. URL: http://surl.li/mlzen

Data from PovcalNet can be accessed at URL: http://iresearch.worldbank.org/PovcalNet/home.aspx or directly through Stata or R (Castaneda et al., 2019a).

M.Haziq Adli Zamzuri, N. Sofian, R. Hassan (2023) The Forecasting of Poverty using the Ensemble Learning Classification Methods.URL: https://journals.iium.edu.my/kict/index.php/IJPCC/article/view/326

C. Altshuler, D. Holland, P. Hong, Hung-Yi Li (2016) The World Economic Forecasting Model at the United Nations. URL: https://www.un.org/development/desa/dpad/wp-content/uploads/sites/45/publication/2016_Apr_WorldEconomicForecastingModel.pdf

The MIRAGRODEP Model by International Food Policy Research Institute (IFPRI). URL: https://www.ifpri.org/publication/miragrodep-model

Household Surveys in POVANA dataset by David Laborde. URL: https://public.tableau.com/app/profile/laborde6680/viz/POVANA_Surveys/POVANA

N. Rohmah Mayasari, Dang Khanh Ngan Ho, David J. Lundy and others (2020) Impacts of the COVID-19 Pandemic on Food Security and Diet-Related Lifestyle Behaviors: An Analytical Study of Google Trends-Based Query Volumes. URL: https://www.mdpi.com/2072-6643/12/10/3103

Aleksandra Łuczak, Sławomir Kalinowski (2023) The Measurement of Subjective Household Poverty: Concepts and Application. URL: https://www.researchsquare.com/article/rs-3159844/v1.pdf?c=1690984384000

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Published
2023-09-26
How to Cite
Reut , A., Zaiats, V., & Klymenko, Y. (2023). ASSESSING THE IMPACT OF COVID-19 ON MONETARY POVERTY: A REVIEW OF INDIVIDUAL PRACTICES. Economy and Society, (55). https://doi.org/10.32782/2524-0072/2023-55-52
Section
ECONOMICS