Table 1 Description of independent variables
From: Older Europeans’ experience of unmet health care during the COVID-19 pandemic (first wave)
Variables | Description |
|---|---|
Demographic | |
male | Dummy variable. Takes value 1 if male; 0 if female. |
age | Number of years old in 2020. |
education | Number of years of education. |
Economic | |
income | Natural logarithm of monthly household income per person before the pandemic. The survey question was “How much was the overall monthly income, after taxes and contributions, that your entire household had in a typical month before Corona broke out?” |
dif_makends | Dummy variable. Takes value 1 if respondent says it is difficult to make ends meet with the household monthly income; 0 otherwise. The survey question was “Thinking of your household’s total monthly income since the outbreak of Corona, would you say that your household is able to make ends meet with great difficulty, with some difficulty, fairly easily, or easily.” |
unemployment | Dummy variable. Takes value 1 if respondent is got unemployed during the pandemic; 0 otherwise. The survey question was “Due to the Corona crisis have you become unemployed, were laid off or had to close your business”. |
Health | |
SHA− | Self-assessed health before pandemic is a categorical variable. Ranges from 1 to 5, where 1 is excellent and 5 is poor. The survey question was “Before the outbreak of Corona, would you say your health was excellent, very good, good, fair, or poor?” |
worse_health | Dummy variable. Takes value 1 if health got worse during the pandemic; 0 otherwise. The survey question was “If you compare your health with that before the outbreak of Corona, would you say your health has improved, worsened, or stayed about the same.” |
chronic | Number of chronic diseases provided in Wave 7 of SHARE. |
Health System | |
beveridge | Dummy variable. Takes value 1 if health system is Beveridge type; 0 otherwise. Beveridge Health Systems: Sweden, Spain, Italy, Denmark, Portugal, Cyprus, Finland, Latvia, Malta. |
high_OOP | Dummy variable. Takes value 1 if the level of Out-Of-Pocket payments are above the EU average in 2018; 0 otherwise. Countries with high OOP level are Bulgaria, Cyprus, Estonia, Greece, Hungary, Italy, Latvia, Lithuania, Malta, and Portugal. (Source Eurostat [36]). |
high_unmetneeds | Dummy variable. Takes value 1 if the level of unmet health needs (no matter the reason) for people older than 65 is above the EU average in 2019; 0 otherwise. Countries with high level of unmet health needs are Estonia, Finland, Greece, Latvia, Poland, Romania, Slovakia and Slovenia. (Source Eurostat [36]). |
high_doctors | Dummy variable. Takes value 1 if the number of doctors per 100,000 people is above the sample average in 2019 (or 2018 in case of missing value) equal to 233.43; 0 otherwise. Countries with high number of doctors: Bulgaria, Czechia, Denmark, France, Germany, Lithuania, Malta, Portugal, Spain. (Source Eurostat [36]). |
high_nurses | Dummy variable. Takes value 1 if the number of nurses and midwives per 100,000 people is above the sample average in 2019 (or 2018 in case of missing value) equal to 463.12; 0 otherwise. Countries with high number of nurses and midwives: Belgium, Czechia, Cyprus, Denmark, Finland, France, Germany, Lithuania, Luxemburg, Malta, Slovakia, Switzerland (Source Eurostat [36]). |
beds | Hospital beds per 100,000 people in 2019. Average number in the sample of countries is equal to 509.3. (Source Eurostat [36]). |
Government Response | |
no_lockdown | Dummy variable. Takes value 1 if government response to COVID-19 during the first wave of the pandemic in 2020 does not include a national lockdown; 0 otherwise. Countries with no lockdown: Malta, Latvia, Hungary and Sweden. This information is provided by Coronavirus Government Response Tracker [37]. |
Country controls | Set of dummy variables for each country. |