Can we predict how enrollment may change if eligibility floor is

raised to 200% of FPL?Test health insurance policy

optionDetermine typical characteristics of low-income residents that are linked to their having health insurance in

regression model

Substitute target group into this model

Compare predicted rate of insurance with observed rate

Where we are now: Distribution of Insurance in 2001, BRFSS Survey

Class Num. Percent

Uninsured 134 7.84

Insured 1470 85.96

Medicaid 106 6.2

Key questions for test

• Which model? That is, what behavior pattern is the appropriate one to expect?

• Which target population?

• What is the base of comparison?

Critical preconditions

• Quest (Medicaid) beneficiaries pay nothing for their insurance.– Zero prices are very different experiences

from the money prices faced by other people in the society

– The enrollment and administrative experience for new Quest beneficiaries will be similar to that experienced by current members

– Elements of the experience can be captured by the regression coefficients

Important limitations of models

• Predictive models must generally have real data for each variable, for each person in the sample

• The income question is unanswered by about 20% of respondents in the BRFSS

• Children are not included in the study—results must be interpreted for adults only

Two models

• Compute a predictive equation on the 0% to 100% FPL population – Estimate the distribution of insured among the

100%-200% FPL population using this equation

• Compute a predictive Equation on the entire sample– Estimate the distribution of insured among the

100% -200% FPL population

Predictions of changing Medicaid ceiling to 200% of FPL

Predicted vs. Actual Rates of Coverage

(pooled 2000 and 2001 data, BRFSS)

method 1* 2** Actual

Coverage  

Uninsured 8.9 3 7.84

Insured 81.2 92 85.96

Medicaid 9.9 5 6.2

*Method 1: Fit model over the population <100% FPL, then predict over the means of the 100%-200% FPL population.

**Method 2: Fit model over the entire population and predict over the means of the 100%-200% FPL population.

Implications: Model 1

• Lower income residents in the 100% to 200% FPL range will increase their use of Quest, if Model 1 governs their behavior– There will be some substitution out of private

health insurance– The uninsured population may rise slightly,

based on the behavior norms of the low income group used to estimate the model

Implications: Model 2

• Low income residents in the 100% to 200% FPL range will increase their privately insured status if Model 2 behavioral norms affect this population– The uninsured rate will fall dramatically to

about 3%– The Quest enrollment will fall to 5%– This population will be fully absorbed by the

private insurance system

Which rationality should we believe?

• The critical drivers of the prediction equations are being male and being unemployed. Both lead to lower insurance levels.

• Poverty is obviously directly linked to unemployment.

• Rational persons attending to price differences should not pick costly insurance over free insurance

Where does irrationality arise?

• Modeling the whole population– Estimates the behavior of the large block of

folks who go to work every day, file paperwork on time, and handle bureaucracies

– May impute to the poor the characteristics which would make them non-poor if they had them

– May assume other experiences and advantages (aside from money) which the poor do not have

Open Questions

• Can we separate the economic decision making of the 100% to 200% FPL person from other motivations captured in the variables of the model?

• Is there any sign that interacting with socials service agencies may not be perceived as a benefit?– If so, which data set will allow us to address this

question?– Should experience with bureaucratic agencies be

examined as one of the potential inhibitors of health care coverage?