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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