an increase in the poverty rate and a closed society are a matter of great concern. This article briefly analyzes these 2 aspects not from a socio-economic point of view but by reasoning on the data using some quantitative methods: data analysis and markov process simulation.
Global extreme poverty rose in 2020 for the first time in over 20 years as the disruption of the COVID-19 pandemic compounded the forces of conflict and climate change, which were already slowing poverty reduction progress. About 120 million additional people are living in poverty as a result of the pandemic, with the total expected to rise to about 150 million by the end of 2021. World Bank: Understanding Poverty
The Organisation for Economic Co-operation and Development (OECD), an intergovernmental economic organisation with 38 member countries, founded in 1961 to stimulate economic progress and world trade, publishes the poverty rate. As defined at OECD data org site it is the ratio of the number of people (in a given age group) whose income falls below the poverty line; taken as half the median household income of the total population. It is also available by broad age group: child poverty (0-17 years old), working-age poverty and elderly poverty (66 year-olds or more). However, two countries with the same poverty rates may differ in terms of the relative income-level of the poor. Data are available for 44 countries: Australia, Austria, Belgium, Brasil, Bulgaria, Canada, Chile, Cina, Costa Rica, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Japan, Iceland, India, Ireland, Israel, Italy, Korea, Latvia, Lithuania, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Romania, Russia, Slovak Republic, Slovenia, South Africa, Spain, Switzerland, Sweden, Turkey, United Kingdom, United States.
A rapid spike in the poverty rate form 2020 due to COVID pandemic can be seen. Also note a slow but steady growth in all age groups at least from 2000 onwards.
As expected, comparing different countries before the COVID epidemic, some were recovering while others were worsening their poverty rate.
For example, Italy and Britain were doing badly while Canada, Greece and Turkey were recovering. Not all countries, in fact, follow an effective process of poverty reduction and certainly not the country where the author lives.
two concepts summary
Assuming that quality of life largely depends on wealth conditions, the poverty rate measures the percentage of people who have unsatisfactory lives while the social mobility score measures the hope that people, in a given context, can improve their sense of accomplishment.
As we have seen, the relationship between poverty (or wealth) and social mobility is strong even if the concepts are very different.
Through above markov process simulation it emerged that investing in social mobility can be highly inefficient.
On the contrary, policies of redistribution of wealth risk limiting the possibilities of improving one’s living conditions.
Choices aimed at reducing poverty are clearly influenced by the economic and social theories to which decision makers refer.
From the standpoint of Bayesian thinking this is not necessarily bad as long as you are able to update your decisions based on new information and knowledge.
Feel free to email me if you would like to go deeper in the analysis, thanks for reading!
The analysis shown in this post have been executed using R as main computation tool together with its gorgeous ecosystem (tidyverse included).