Hello everyone,

I have panel data with countries and daily data on covid-19 outcomes at the country level (such as covid-19 cases, deaths due to covid, hospitalization, etc...). I am trying to do a preliminary analysis to see if the political system of a country (federal vs unitary states) leads to worse and better covid outcomes. Here the country political system is identified by the variable country_system = 1 if the country is a federal state, 0 otherwise. This is a data sample:


Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input str32 country str10 date double(total_cases_per_million new_deaths_per_million hosp_patients_per_million) float country_system
"France"         "2020-03-04"   4.226    0      . 0
"Australia"      "2020-03-04"   2.039 .039      . 1
"United States"  "2020-03-04"    .323 .012      . 1
"Netherlands"    "2020-03-05"   4.786    .  3.793 0
"Italy"          "2020-03-05"  63.809 .678 35.411 0
"Belgium"        "2020-03-05"   4.314    .      . 1
"India"          "2020-03-05"    .022    .      . 1
"South Africa"   "2020-03-05"    .017    .      . 1
"South Korea"    "2020-03-05" 118.746    0      . 0
"Luxembourg"     "2020-03-05"   1.598    .      . 0
"Brazil"         "2020-03-05"    .019    .      . 1
"Japan"          "2020-03-05"   2.886    0      . 0
"Israel"         "2020-03-05"   2.426    .  1.502 0
"Greece"         "2020-03-05"   2.974    .      . 0
"Spain"          "2020-03-05"    5.54 .021      . 1
"United States"  "2020-03-05"    .556 .003      . 1
"China"          "2020-03-05"  55.955 .022      . 0
"Lithuania"      "2020-03-05"    .367    .      . 0
"Russia"         "2020-03-05"    .027    .      . 1
"Saudi Arabia"   "2020-03-05"    .144    .      . 0
"Chile"          "2020-03-05"     .68    .      . 0
"Germany"        "2020-03-05"   5.753    .      . 1
"New Zealand"    "2020-03-05"    .622    .      . 0
"Denmark"        "2020-03-05"   1.899    .      . 0
"Portugal"       "2020-03-05"    .785    .      0 0
"Finland"        "2020-03-05"   2.166    .      . 0
"Canada"         "2020-03-05"     .98    .      . 1
"Austria"        "2020-03-05"   4.552    .      . 1
"Latvia"         "2020-03-05"     .53    .      . 0
"Mexico"         "2020-03-05"    .039    .      . 1
"Estonia"        "2020-03-05"   2.262    .   .754 0
"Indonesia"      "2020-03-05"    .007    .      . 0
"Iceland"        "2020-03-05"  99.634    .   2.93 0
"Switzerland"    "2020-03-05"  13.172 .116      . 1
"Hungary"        "2020-03-05"    .207    .      . 0
"Slovenia"       "2020-03-05"    .962    .      . 0
"United Kingdom" "2020-03-05"   4.346    .      . 0
"Poland"         "2020-03-05"    .026    .      . 0
"France"         "2020-03-05"   6.251 .044      . 0
"Sweden"         "2020-03-05"   8.614    .   .594 0
"Norway"         "2020-03-05"  16.048    .      . 0
"Argentina"      "2020-03-05"    .022    .      . 1
"Ireland"        "2020-03-05"   1.215    .      . 0
"Australia"      "2020-03-05"   2.157    0      . 1
"Czechia"        "2020-03-05"   1.121    .      0 0
"Israel"         "2020-03-06"   4.275    .  1.849 0
"New Zealand"    "2020-03-06"    .829    .      . 0
"Australia"      "2020-03-06"   2.353    0      . 1
"Ireland"        "2020-03-06"   3.645    .      . 0
"Japan"          "2020-03-06"   3.321    0      . 0
"China"          "2020-03-06"  56.061  .02      . 0
end

At this stage of the research, I would like to investigate the correlation between the covid-19 outcomes and the dummy variable country_system. I saw in previous posts that - pwcorr - is an excellent command to do that. However, it is not clear to me how to see the correlation differentiated for the two dummy variable conditions (namely country_system == 1 and country_system ==0) in a separate way, but in the same table. In case it is not possible to distinguish the correlation for country_system ==1 and ==0 separately, I would ask how to interpret the correlation coefficients with respect to the dummy variable.
Thank you in advance for your time
Best regards

Alessio Lombini