Evaluating the Effect of Automatic Plan Assignment and Enhanced Outreach of Marketplace Take-up
Phase: NA
Conditions: Outreach Intervention
Sponsor: Covered California

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Evaluating the Effect of Facilitated Enrollment and Enhanced Outreach Strategies on 
Marketplace Health Insurance Take-up 
 
Emory Wolf, MPP, Covered California 1601 Exposition Blvd Sacramento CA 95815 
Emory.wolf@covered.ca.gov 
 
Marina Lovchikova, PhD, Covered California, 1601 Exposition Blvd Sacramento CA 95815 
Marina.lovchikova@covered.ca.gov 
 
Isaac Menashe, MPP, Covered California 1601 Exposition Blvd Sacramento CA 95815 
Isaac.menashe@covered.ca.gov  
 
Katie Ravel, MPH, Covered California 1601 Exposition Blvd Sacramento CA 95815 
Katie.ravel@covered.ca.gov  
 
Valerie York, Covered California 1601 Exposition Blvd Sacramento CA 95815 
Valerie.york@covered.ca.gov  
 
 
Funding 
None 
 
Conflict of Interest 
None  
 
Date of the document creation: May 2, 2024 
 
 
 

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Design Document and Analysis Plan 
 
Project Objective 
 
For individuals who do not have coverage through their employers or state Medicaid programs, 
health insurance take-up remains incomplete, despite significantly reduced premiums, and even 
free coverage for many of these uninsured. Facilitated enrollment offers a potential solution to 
some administrative burdens and choice frictions that produce this incomplete take-up of 
marketplace coverage. Because Covered California, California’s ACA individual marketplace, 
and Medi-Cal, California’s Medicaid program, share an eligibility system, Covered California has 
the ability to identify individuals who are eligible for marketplace coverage upon losing Medicaid 
eligibility.  
 
This evaluation will compare the effectiveness of facilitated enrollment strategies on 
marketplace take-up among individuals losing Medi-Cal coverage, by comparing outcomes for 
personalized plan pre-selection and personalized quotes of net premiums, to other more 
general outreach tactics (such as reminder nudges).  
 
Target population 
The study population includes single-member households who are discontinued from Medi-Cal 
and found eligible for Covered California. The study leverages application differences between 
the Medi-Cal and Covered California programs, the latter of which requires that individuals 
attest to plan to file taxes in order to receive premium subsidies, in the form of advanced 
premium tax credits (APTCs). Individuals who did not answer this question on their Medi-Cal 
application are found ineligible for marketplace premium subsidies when discontinued from 
Medi-Cal. Thus, many individuals are missing out on financial assistance that they are likely 
eligible for because of these discrepancies in program requirements.  
 
The study population will focus on this subset of consumers, rerunning their program eligibility to 
check for subsidy eligibility after suggesting changes to the tax filing questions within the 
application. The exact inclusion criteria are: 
• 
Individual in a single-member household 
• 
Discontinued from Medi-Cal 
• 
Eligible for Covered California program 
• 
Ineligible for APTCs 
• 
Did not attest to planning to file taxes within their Covered California application 
• 
Do not meet any of the exclusion criteria:  
o Consumer case is no longer active within the application system 
o Consumer is already actively enrolled in a marketplace plan 
o Consumer has already restored Medi-Cal eligibility 
o A consumer’s Special Enrollment Period has ended 
o Consumer is no longer listed as a single-member household 
o Consumer was procedurally denied from Medi-Cal coverage due to no response 
o Consumers has a MAGI Medi-Cal income less than 138% of the Federal Poverty 
Level (FPL) 
 
Interventions and Randomization 
 
The project's research design is a randomized intervention among single-member households 
who have recently lost Medi-Cal coverage, are found eligible for marketplace coverage, but are 

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ineligible for subsidies due to incomplete application information regarding planning to file taxes. 
After meeting the inclusion criteria above, individuals are randomly assigned to one of three 
treatment arms:     
1. A control group that receives a standard eligibility notice informing them of their eligibility 
for marketplace coverage. 
2. A treatment group that receives a standard eligibility notice informing them of their 
eligibility for marketplace coverage and an informational flyer describing what Covered 
California is, the availability of financial assistance, and information on benefit design 
and private insurance terminology, such as deductibles and copays.  
3. A treatment group that receives a modified eligibility notice that includes information on 
the pre-selected lowest-cost silver health insurance plan with a personalized quote for 
their net premium amount, as well as the informational flyer.  
 
The evaluation will run for two years and include approximately 30,000 individuals who have 
been discontinued from Medi-Cal coverage. An estimated 6,000 individuals will be assigned to 
the control and estimated 12,000 individuals will be assigned to one of the two treatment arms. 
 
 
Power: 
To arrive at an estimate for the minimum detectable effect (MDE) for our primary outcome, we 
assume a baseline health insurance take up of 6 percent. The baseline rate was determined 
with the historical data on the plan effectuation of single-member households disenrolled from 
Medi-Cal in 2018-2019 years. The intervention has 80% power to detect a 1.1 percentage point 
increase in effectuation1. 
 
 
Likely Effect Size: 
Based on prior personalized outreach campaigns carried out by Covered California, we would 
expect to observe an ITT effect between 0 and 4 percentage points with lower bound point likely 
detected for the enhanced outreach group and upper bound detected for the treatment group 
with personalized quote information.   
 
Data and Data Structure 
Outcomes:  
We use Covered California administrative data to examine health insurance take-up behavior 
and health care utilization among households included in the study and found eligible for 
premium subsidies at the time of the intervention.  
 
The primary outcome of interest will be a binary indicator for whether a consumer has at least 
one month of effectuated coverage a Covered California plan within the 90 days of their Special 
Enrollment Period (SEP) following loss of Medi-Cal coverage.   
 
Our secondary outcomes will be measured 1-2 years after the intervention when data becomes 
available will include: 
1. Tenure with Covered California (measured in months); 
2. A binary indicator for whether someone self-reported being uninsured while filing their 
taxes; 
 
1 Authors’ calculations based on a two-sample proportion Pearson’s chi-squared test using Stata 18 SE 
software 

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3. A binary indicator for consumer engagement (including calls to service center, logins to 
user account, agent delegation after eligibility determination);  
4. A binary indicator for whether someone had an office visit during 1-2 years since the 
intervention; 
5. A binary indicator for whether someone had a prescription drug fill during 1-2 years since 
the intervention; 
6. A binary indicator for whether someone had an emergency room visit during 1-2 years 
since the intervention; 
7. A binary indicator for whether someone had a hospital admission 1-2 years before and 
during 1-2 years since the intervention. 
8. Total out-of-pocket spending for the 1-2 years since the intervention  
 
 
 
Data: 
We will use Covered California administrative data to obtain enrollment outcomes and baseline 
demographics for our sample and Covered California’s health care claims database to obtain 
health care utilization and financial outcomes for individuals who enroll in coverage. 
Administrative data from pilot months (March-May 2024) of program implementation have been 
reviewed to provide insight on sample size estimation. Data from these pilot months will be 
included in the analysis.  
 
We will use data from the Department of Health Care Access and Information for hospital 
admissions data for all individuals in the intervention. We will use data from the California 
Franchise Tax Board to measure which individuals reported not having Minimum Essential 
Coverage (MEC) while filing their state taxes.  
 
 
Quality Control Checks: 
After carrying out the randomization, we will check for balance across several observable 
covariates (e.g., gender, presence of email address on the application, age, race/ethnicity, 
language, and income as a percent of the federal poverty level), which could indicate that there 
would be no significant dissimilarities across treatment arms.  
 
We will segment and separately review data collected for cases with eligibility determinations for 
program start dates October through December, as many of these consumers will have a 
different enrollment experience than the main analytic sample.     
 
Statistical Models 
Intention-to-treat estimation: To estimate treatment effects, our primary analysis will be an 
intent-to-treat (ITT) specification, examining the effect of treatment assignment. We will estimate 
the effect of the treatment using ordinary least squares (OLS) regression. Covariates identified 
during quality control checks will be included in the model. We will regress the outcome of 
interest (e.g., take-up) for household i on the treatment indicator:  
outcomei = ∝+ β1 1(Treatment 1=1)i + β2 1(Treatment 2= 1)i + εi 
 
The coefficients β1 and β2 will be the intent-to-treat estimate of the causal effect of the outreach.   
 
Complier average causal effect estimation: We will examine the effects of health insurance 
(among households induced to enroll in a Covered California as a result of random assignment) 
on our four utilization outcomes. We anticipate a significant share of noncompliers among those 

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households assigned to receive outreach (intervention) as they may have opted out of assigned 
plan or not enroll into another Covered California plan. Thus, to augment our analysis of 
insurance take-up, we will also estimate treatment effects based on treatment receipt 
(enrollment into Covered California Plan), using two-stage least squares regression (2SLS).  
 
Pre-Specified Subgroup Analyses 
Same subgroup analyses will be performed for our complier average causal effect analysis.  
 
Inference Criteria, Including Any Adjustments for Multiple Comparisons: 
We will not perform any corrections for multiple hypothesis testing, and we will use two-tailed 
tests with p-values <= 0.05 to denote statistically significant effects.