|DO FEMALE POLITICIANS
CONTROLLING COVID-19 MORTALITY?
Most of the studies tested this based on correlation relationships. In our paper, we provide now evidence on the link between the gender of the policymaker and their policy outcome. Furthermore, with method of PSM(propensity score matching method), we intend to get a causal relationship between the policy outcome and leader´s gender.
- We use 50 states of America as our sample, and the dependent variable of our model is the mortality rate of Covid-19 in 2020. The independent variables are going to be the total population of each state, the number of medical institutions(hospitals) each state, the ratio of the number of citizens more than 60 years old to the total population, economic variables like GDP, and income ,and gender of the governor in each state.
- In order to get causal relationship between gender of governor and the rate of mortality, propensity score matching is used. This method allows us to keep only those states with similar characteristics. The only difference of those kept states is the gender of the governor.
- Covid-19 cases by city and town(released by Massachusetts government)
- data released by The New York Times captures number of deaths caused by COVID-19 in the United States in 2020(from January 1, 2020 to December 30,2020).
- we could combine the data from both websites and American map to subtract covid-19 deaths in 2020 by cities. The population information also released on it.(yearly data, by city, deaths)
- Women Mayors and population of cities in U.S. Cities 2020((cities over 30,000, listed by population & by state)
- As of June 2020, per CAWP research and population data from the S. Census, of the 1,621 mayors of U.S. cities with populations over 30,000, 378, or 23.3%, were women.
- Age and sex data can be found on the website below, (United States Census). This website is also doing household and business survey, so we can also download employment and personal income from it one by one and combine them finally.
- we are going to use the number of beds in hospitals to measure the level of medical infrastructure in each city. The link (HEALTHCARE dive) below provides us with this information.
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Why we choose the experiment