Skip to main content

Table 1 Selected literature on adverse selection variables

From: Health Uninsurance in rural America: a partial equilibrium analysis

Authors (Year) “Country of study” Socioeconomic Variables Adverse Selection Variables
Studies with Pre-existing condition variables
 Cardon and Hardel [7] “US” Age, Sex, Income, Region, Self-reported heath state, Health Care Cost
 Zhang and Wang [34] “China” Age, Sex, Marital status, Education, Family size, Income, Type of house Existing chronic condition
 Gao et al. [13] “China” Age, Education, Sex, Marital Status, Occupation Wealth
 Resende and Zeidan [25] “Brazil” Sex, Age, Income, Number of dependents and Highest Educational Level Occurrence of illness
Occupational Groups
 Bolhaar et al. [4] “Ireland” Age, Sex, Educational Level, Employment Status, Household size, Number of children, Marital status, Habitation, Income, Insurance option from Employer, GP visits, Specialist visits, Hospital nights, Women that gave birth, Poor mental health, Existing Health Problem, Obese, Daily smoker
 Spenkuch [29] Mexico” Age, Sex, habitation, education, household size, household assets, household expenditure, healthcare expenditure. Self-rated health status, BMI, Blood pressure, Preventive care, medical utilization
 Dardanoni and Donni [11] “United States” Age, Sex, Education, Wealth, Employment, income Hospital admission, average number of disease
Studies with variables representing Risk Attitudes
 Schmitz [26] “Germany”   11-point scale on willingness to take risk
 Johar and Savage [15] “Australia” Age, Education, Income, Cognition, Expectation Risk Tolerance
Studies with variables representing both Pre-existing Conditions and Risk attitudes
 Buchmueller et al. [6] “Australia” Age, Sex, Income, Highest Educational Level Risk Attitudes
Smoker Status, Level of activity (Exercises)
Checks of Freckles and Moles, Kessler PDS
Pre-existing Condition
Inpatient Stay in the past 12 months
Self-reported health condition
 Keane and Stavrunova [16]. “United States” Age, Sex, Race, Marital Status, Highest Level of Education, Income Pre-existing Condition
Health factor, Total medical expenditure
Subjective probability to live to 75 years or more
Risk Attitude
Risk tolerance, Financial planning Horizon
 Polyakova [24] “Germany” Age, Sex, Income, Worktime, Educational Level, Marital Status, Number of Children, House size, Occupation of spouse, Employs house help Pre-existing Condition
BMI, annual no. of outpatient visits, inpatient stays, smoker status, Self-reported suffering from diseases (asthma, cancer, cardiac, dementia, depression, diabetes, high blood pressure, migraine, stroke)
Risk Attitude
Question on desire to take health risks