The Effects of Expansion of Oregon Health Plan on Health Insurance

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The Effects of Expansion of Oregon Health Plan
on Health Insurance
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Plan of the Presentation
Introduction Literature Review Hypotheses and Ou
r Contributions to the Existing Research
Methodologies and Data Empirical Results Conclus
ions Q A
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Abstract

• In this presentation, we examine how Medicaid
  expansions affect public insurance take-up and
  private insurance drop-off
  
• As an extension of this topic, we also look at
• -Oregon-specific effects
• -Effects on different income groups
• -How the changes in the Oregon Health Plan in
  2003 affect our estimates

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State Medicaid Spending as a Percent of U.S.
Gross Domestic Product, 2003
SOURCE National Association of State Budget
Officers, 2003 State Expenditure Report, October
2004.

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Principal Health Coverage of the U.S. Population,
2003
Note Data do not total 100 due to rounding.
SOURCE KCMU and Urban Institute estimates based
on March 2004 Current Population Survey.

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Implementation of the Oregon Health Plan

• Started in 1994
• Purpose provide coverage to the uninsured
  population below 100 of Federal Poverty Line
  
• Means federal waivers and creation of the
  Prioritized List
  
• Groups covered children, pregnant females and
  other low-income adults
  

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Oregon and OHPIn 2003 the state of Oregon had
13th highest uninsurance rates in U.S.
Source http//egov.oregon.gov/DAS/OPB/2005report/
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Trends in Oregon Insurance Coverage
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Examining the Trends

• The cost of expanding public sector health
  programs depends critically on the extent to
  which public eligibility will cover just the
  uninsured, or will crowd out existing private
  insurance coverage.
• Expansions impact on health insurance poses
  two questions
  
• 1) The level of the TAKE UP RATE
• the rate at which eligible population is
  enrolled in the program
  
• and
• 2) To what extent the expanded public insurance
  caused CROWDING OUT of private coverage.

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Principal Health Coverage of the U.S. Population,
2003
Note Data do not total 100 due to rounding.
SOURCE KCMU and Urban Institute estimates based
on March 2004 Current Population Survey.

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Literature References

• (From left) Jonathan Gruber, Robert LaLonde,
• and IPR's Bruce Meyer at the health conference

The issue of private coverage crowd out is a
extensively studied area. Most recognized wor
k Cutler and Gruber (1995), Does Public Insu
rance Crowd Out Private Insurance?
Eugene Garfield, David Cutler,
FDA Commissioner Mark McClellan
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Cutler and Grubers Regression

• Covgi a0 bEligi Xic Sasstatei Satyeari
  Saaagei ei
  
• Variables Used
• Covgi insurance coverage status (Medicaid,
  private, or no insurance)
  
• Eligi dummy indicating eligibility for
  Medicaid
  
• Xi demographic controls (race, sex, marital
  status, etc.)
  
• Note
• When Medicaid is the dependent variable, b
  measures the take-up rate. When private coverage
  is the dependent variable, b measures the crowd
  out.

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Literature Critique

• Lara Shore-Sheppard (2005) produces much smaller
  estimates of marginal take-up rates and no
  statistically significant evidence of crowding
  out due to
• Problems that she found in the Cutler/Gruber
  model
  
• Omitted variable biases state by year effects,
  age by year effects age by state effects.
  
• Our critique of Shore-Sheppard
• Inclusion of these interaction variables
  over-specified the model
  
• (i.e. degrees of freedom problem).

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Our Contributions to the Existing Research

• Expand existing studies to include the most
  current data (19902004)
  
• For the first time research the adult populations
  of males and non-pregnant females
  
• Focus on the state of Oregon
• Use the state of Washington and California as the
  control groups
  
• Estimate whether Oregon take up and crowd out
  rates are different from the other states in the
  sample
  
• Determine the effects of the restructuring of OHP
  (due to the introduction of co-pays after 2003
  etc.)
  
• Look at effects on the different income level
  groups
  

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Data Used

• Current Population Survey (CPS) data
• -Monthly survey of 50,000 U.S. households
  conducted by U.S. Census Bureau
  
• -Includes data on usual demographics, labor
  force data, income sources, household
  composition, participation in government programs
  including Medicaid
• Federal Poverty Level (FPL) from the Census
  Bureau
  
• Eligibility Requirements as percentages of FPL
  for the states of Oregon, Washington, and
  California.
  

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Determining Eligibility Algorithm
Eligibility depends on State Year Age of
FPL
For example, for the year of 2001 Oregon adults
between the ages 19 to 64 with income within
100 of FPL will have the following code up
recode elig2 01 if state92 age18 age faminc 17
Empirical Results for Adults
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Empirical Results for Children
Our findings for the children of Oregon
take up rate 29.5 crowd out rate -3.9 Cu
tler Gruber findings for the children
nationwide take up 23.5 crowd out -7.4
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Empirical Results for Pregnant Women
Our findings for the women of Oregon
take up rate 19.3 crowd out rate -29.2 Cut
ler Gruber findings for the women nationwide
take up 0.8 crowd out -4.5
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Oregon-Specific Variable Effect

• We included Oregon-specific interaction variable
  to show how the estimates differ in Oregon vs.
  Washington California.
  
• Results
• Children
• -0.27 take-up, -2 crowd-out relative to other
  states
  
• Pregnant Women
• -13 take-up, 22 crowd-out
• No results for adults due to the issues with data
  from WA CA.

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OHP Changes after 2002

• Effects of increased co-pays, higher premiums,
  and stricter enrollment requirements
  
• Children
• No statistically significant changes in take-up
  rate,
  
• Negative 3.5 crowd-out
• Pregnant women
• Take-up changes -31 crowd-out 16.7
• Other adults
• Take-up decrease -6 crowd-out -1.

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Crowd Out and Take Up Effects on the Different
Income Level Groups

• Arbitrary income cutoffs 0-50 FPL 50-100
  FPL.
  
• stateinsi B0 B1a_eligi B2a_elig50i B3Xi
  ui?,
  
• where a_elig50 stands for those adults with
  family income below 50 of FPL.
  

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Concluding Thoughts

• We found significant evidence of the
  substitutions effect (i.e. crowd-out)
  
• Future research could calculate the implications
  of our findings on the state budget
  
• Should Medicaid be expanded?

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Questions?!