**Cross posted from r/skeptic [here.](https://np.reddit.com/r/skeptic/comments/hpds9c/femaleled_countries_versus_maleled_countries_in/)** *(reddit link)* ---------- **EDIT August 21, 2020: mid-August update to the analyses can be found [here.](https://np.reddit.com/r/theydidthemath/comments/idz5ih/self_according_to_european_cdc_and_oxford/)** *(reddit link)* **EDIT August 21, 2020: commentary on articles claiming superior responses and outcomes of female-led countries can be found [here.](https://www.reddit.com/r/skeptic/comments/ie5zng/fundamental_analysis_flaws_underlying_just_about/)** *(reddit link)* I'm sure some of you have seen the articles making claims such as how female-led countries are faring better under COVID-19 like this one, that a lot of male leaders are downplaying the severity of the pandemic and dragging their feet. Articles like these appeared several times on my Facebook feeds. However, I couldn't find any analyses assessing these claims using data from all countries (or at least all developed countries). Most of these articles were primarily narrative, comparing countries like New Zealand and Taiwan to countries like United States and Brazil. I decided to do my own investigation when I got ahold of COVID-19 data from the European CDC and the Oxford Government Response Tracker. Full details of my analysis are provided at the end of this post. Yes, there is probably a better use of my time, but I was curious. Plus, the data were not difficult to obtain and the analyses were straightforward and quick to program. I'm sure some of you have seen the articles making claims such as how female-led countries are faring better under COVID-19 like [this one](https://archive.vn/MNdt2), that a lot of male leaders are downplaying the severity of the pandemic and dragging their feet. Articles like these appeared several times on my Facebook feeds. However, I couldn't find any analyses assessing these claims using data from all countries (or at least all developed countries). Most of these articles were primarily narrative, comparing countries like New Zealand and Taiwan to countries like United States and Brazil. I decided to do my own investigation when I got ahold of COVID-19 data from the European CDC and the Oxford Government Response Tracker. Full details of my analysis are provided at the end of this post. Yes, there is probably a better use of my time, but I was curious. Plus, the data were not difficult to obtain and the analyses were straightforward and quick to program. #Results# **The data I used were current as of July 10, 2020.** I will present the results with no commentary or opinion from me. From my preliminary analyses, I found that: - Among the 119 countries with a high or very high Human Development Index (index value of 0.7 or higher), the **median death rate among male-led countries was 28.4 deaths per million** (range: 0 to 1238 deaths per million; first quartile: 4.4 deaths per million; third quartile: 81.2 deaths per million), whereas the **median death rate among female-led countries was 55.6 deaths per million** (range: 0.29 to 844 deaths per million; first quartile: 28.1 deaths per million; third quartile: 116 deaths per million). The p-value of a Mann-Whitney U test comparing the death rates per million between male and female-led countries was 0.155. - The Oxford Government Response Tracker defines a stringency index based on factors such as school closures, workplace closures, restriction of international travel, and cancellation of public events; values range from 0 to 100 (higher values mean more stringency). Define the degree of stringency as the maximum value this metric has reached up to this point. Among these 119 countries, the **median degree of stringency among male-led countries was 87.0** (range: 19.4 to 100; first quartile: 80.3; third quartile: 92.6), whereas the **median degree of stringency among female-led countries was 74.5** (range: 30.6 to 100; first quartile: 69.2; third quartile: 92.7). The p-value of a Mann-Whitney U test comparing the degree of stringency between male and female-led countries was 0.087. - The Oxford Government Response Tracker defines a government response index based on factors such as imposing restrictions on school and workplace openings, public events, and travel, as well as efforts to contain the spread and to communicate with the public; values also range from 0 to 100 (higher values mean more involvement). Define the degree of response as the maximum value this metric has reached up to this point. Among these 119 countries, the **median degree of response among male-led countries was 81.1** (range: 26.9 to 96.2; first quartile: 74.4; third quartile: 85.3), whereas the **median degree of response among female-led countries was 75.0** (range: 34.0 to 89.1; first quartile: 66.7; third quartile: 79.2). The p-value of a Mann-Whitney U test comparing the degree of response between male and female-led countries was 0.061. - Define the time until any meaningful government response as the number of days between the appearance of the first case in a country and the day the government response index hits 30; note this value can be negative if countries start taking precautions before the appearance of the first case. Among these 119 countries, the **median time until response among male-led countries was 11 days** (range: -43 to 118 days; first quartile: three days; third quartile: 18 days), whereas the **median time until response among female-led countries was 13 days** (range: one to 42 days; first quartile: 9.5 days; third quartile: 20.5 days). The p-value of a Mann-Whitney U test comparing the time until response between male and female-led countries was 0.375. - If the time until response was defined as the number of days between the appearance of the first case in a country and the day the government response index hits 50, then among these 119 countries, the **median time until response among male-led countries was 17 days** (range: -3 to 124 days; first quartile: eleven days; third quartile: 30 days), whereas the **median time until response among female-led countries was 18.5 days** (range: six to 156 days; first quartile: 13.8 days; third quartile: 40.8 days). The p-value of a Mann-Whitney U test comparing the time until response between male and female-led countries was 0.585. - Among all countries, the **median death rate among male-led countries was 8.53 deaths per million** (range: 0 to 1238 deaths per million; first quartile: 1.21 deaths per million; third quartile: 44.9 deaths per million), whereas the **median death rate among female-led countries was 51.7 deaths per million** (range: 0.29 to 844 deaths per million; first quartile: 24.4 deaths per million; third quartile: 108 deaths per million). The p-value of a Mann-Whitney U test comparing the death rates per million between male and female-led countries was 0.011. #Remarks# First, I know what some of you are going to ask. **Yes, the contents of the results section are correct as far as I know.** Believe me, I checked the data, my code, and my results very thoroughly to make sure I didn't make a mistake somewhere. If you still don't believe me, you're welcome to analyze the data yourself; my methodology should be fleshed out enough to allow anyone with sufficient statistical proficiency to reproduce the analysis. I even include where I got the data. The reason I analyzed the 119 countries with high or very high Human Development Index separately was because I was concerned lumping all the countries together would be too much mixing apples and oranges. Countries with moderate or low Human Development Indices have had much lower death rates per million so far, and many of them might not have the means to respond to the pandemic the way more developed countries have been able to. Please do not take these results as some anti-feminist rant about how we don't need to have female leaders. I simply wanted to investigate the data to see if they supported a hypothesis. Having said that, leaders like Jacinda Ardern and Tsai Ing-Wen did a lot of things right during this pandemic, and I would much rather have them leading things than Donald Trump. It's just that apparently, a lot of the male leaders didn't screw up too badly either. Then there is that often-repeated caveat that causation is not correlation. I'll have to do a more in-depth multivariate analyses of the factors associated with deaths per million attributable to COVID-19. If I get around to doing that, that will have to come later; that analysis is much more complicated. Later, I might also compile some results about other factors that are associated with deaths per million (e.g. stringency of international travel restrictions, stringency of school closures, investment in vaccine research, and so on). Finally, some other random findings. The death rate per million is very strongly associated with what continent a country is on (p-value < 0.0001). South America has the highest median death rate per million (65.2 deaths per million), followed very closely by Europe (61.2 deaths per million). Oceania is the clear winner here (close to zero deaths per million); it says something when New Zealand has the highest death rate per million in Oceania (for now). #Methodology# The analysis took place at the level of individual countries. The countries in this analysis do not include dependent territories that do not have full sovereignty (see list [here](https://en.wikipedia.org/wiki/Dependent_territory)). A female-led country was defined as one where the person whose office is primarily responsible for the executive and legislative duties (as opposed to a largely ceremonial head of state with limited powers or does not in practice exercise executive or legislative power such as the Presidents of Greece or Iceland) is a woman. I got my information from [this list.](https://en.wikipedia.org/wiki/List_of_current_heads_of_state_and_government) By this definition, **thirteen countries are female-led: Bangladesh, Barbados, Belgium, Bolivia, Denmark, Finland, Germany, Iceland, New Zealand, Norway, Serbia, Switzerland, and Taiwan.** The data I used are publicly available [here](https://ourworldindata.org/coronavirus-data) (European CDC data) and [here](https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker#data) (Oxford data). I used the most up-to-date Human Development Indices, available [here.](http://hdr.undp.org/en/data) I filled in missing values using information from [en.populationdata.net/rankings/hdi/.](https://en.populationdata.net/rankings/hdi/) Univariate associations between deaths per million, stringency, and response metrics and gender of the country's leader were performed using a Mann-Whitney U test. The Mann-Whitney U-test is an assessment of whether the gender of the country's leader is associated with any of these outcomes based on whether, when countries are ranked by value of the outcome, male-led countries consistently rank higher than female-led countries or vice versa (e.g. MMMMMMMFMFFF would be evidence of association; FMMFMMMMFMMF would not). For those of you without a working knowledge of statistics, a p-value of 0.05 or less is traditionally accepted as evidence of an association.