My goal is to understand how government's responses to the COVID-19 pandemic varied between female led and male led countries.

GitHub Repository Link: https://github.com/Erum505/Capstone-Data-2020

Notebook: https://github.com/Erum505/Capstone-Data-2020/blob/master/Capstone%20Phase%203%20by%20Erum%20Khan.ipynb

HTML: https://github.com/Erum505/Capstone-Data-2020/blob/master/Capstone%20Phase%203%20by%20Erum%20Khan.html

Video Presentation: https://youtu.be/qHxGqb00wvk

main dataset: The Oxford COVID-19 Government Response Tracker

The dataset I am using is the Oxford COVID-19 Government Response Tracker (OxCGRT). This dataset "systematically collects information on several different common policy responses that governments have taken to respond to the pandemic on 18 indicators such as school closures and travel restrictions" [8]. It has data from more than 180 countries [8].

This codebook contains the definitions of each of the variables in the dataset: https://github.com/OxCGRT/covid-policy-tracker/blob/master/documentation/codebook.md

The primary indicators I'll be comparing are the health system policies, listed below. I will also be comparing the overall indexes in the dataset which combine individual factors. These indices are:

• StringencyIndex

• GovernmentResponseIndex

• ContainmentHealthIndex: focusing on Containment and closure policies

• EconomicSupportIndex: focusing on Economic policies

The main dataset can be found here: https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker

Along with the data on a government's COVID response, I wanted to compare counties that are similar in terms of GDP and country attributes. To get this, I loaded up data from the World Bank on World_Development_Indicators for each country [10]. I will use the data for 2019 since 2020 is not yet over. The data can be found at https://databank.worldbank.org/source/world-development-indicators

I then grouped countries based on whether they are male vs female led, as well as based on their GDP size and Human Capital Index values.

GDP was used as this is a known factor in a country's ability to allocate resources in order to respond to the COVID-19 pandemic [6]. The Human Capital Index is a measure of "the contribution of health and education to the productivity of the next generation of workers" [1].

I looked at the following metrics:

Economic Support Index

This index focuses on a government's economic policies around COVID-19.

This includes:

• Income support (direct welfare payments to people who lost jobs)

• Debt/contract relief

• Fiscal measures (ex: stimulus spending)

• Giving international support to other countries

Government Response Index

This index gives an overall synthesis of the Government's response to COVID-19 across all metrics (including all the metrics included in the previous indices such as Economic Support Index and Containment Health Index

Conclusions

• Our data shows an overall greater COVID-19 response from the governments of low to medium GDP women-led countries, especially regarding providing economic support to citizens. This suggests that good leadership that takes quick and decisive action may enable countries to overcome some structural barriers that are found in countries with lower GDP. Contrastingly, weak leadership, may not use all of a country’s resources and therefore hinder a timely response.

• The male-led countries with the highest GDPs had an overall greater government COVID-19 response. This could be due to these countries being better able to more quickly implement healthcare as well as containment and closure policies when compared to countries with lower GDPs.

The PCA analysis shows that the variance between government COVID-19 response in female-led countries is less than the variance between male-led countries. Hopefully, if in the future we will have more female leaders and can perform analysis to determine if there would be more variable government responses had there been a larger number of countries with current female heads of state or government.

Note: We only used a dataset of 21 countries where women are currently heads of state or government compared to 160 male-led countries, so this analysis is possibly skewed due to the disproportional data between the two populations.

Visualizations