The Effect of SCHIP Expansions on Health The Effect of SCHIP Expansions on Health Insurance Decisions by EmployersInsurance Decisions by Employers

Thomas Buchmueller (UC Irvine and NBER)Thomas Buchmueller (UC Irvine and NBER)Phil Cooper (AHRQ)Phil Cooper (AHRQ)

Kosali Simon (Cornell University and Kosali Simon (Cornell University and NBER)NBER)

and Jessica Vistnes (AHRQ)and Jessica Vistnes (AHRQ)

SCHIP Expansions SCHIP Expansions

In 2002, SCHIP covered 5 million childrenIn 2002, SCHIP covered 5 million children (Almost) all children in families under 200% of the Federal Poverty Level (Almost) all children in families under 200% of the Federal Poverty Level

are now eligibleare now eligible– Compared to earlier federal minimums of 185% for infants, 133% for children 1-6, Compared to earlier federal minimums of 185% for infants, 133% for children 1-6,

100% for children born after 1983100% for children born after 1983– 32 states changed eligibility for older children (17-18) from <100% to at least 200% 32 states changed eligibility for older children (17-18) from <100% to at least 200%

FPLFPL

Expansions occurred late 1998/early 1999Expansions occurred late 1998/early 1999– Variation in the timing and extent of SCHIP adoption across statesVariation in the timing and extent of SCHIP adoption across states

Did SCHIP affect employers’ health Did SCHIP affect employers’ health insurance decisions?insurance decisions?

Did employers cut back on offers of health insurance Did employers cut back on offers of health insurance in low and middle wage firms as a result of SCHIP?in low and middle wage firms as a result of SCHIP?

Did employers encourage workers to drop family Did employers encourage workers to drop family benefits?benefits?– By increasing employee contributions to family health By increasing employee contributions to family health

insurance plans?insurance plans? Did workers drop family benefits?Did workers drop family benefits?

Anecdotal EvidenceAnecdotal Evidence

RWJ outreach programsRWJ outreach programs Evidence from state benefit managersEvidence from state benefit managers Roll-backsRoll-backs

HypothesesHypotheses

Reasons to expect an effectReasons to expect an effect– Family health insurance premiums were $7500 in 2001, with Family health insurance premiums were $7500 in 2001, with

employees contributing $1740 of the costemployees contributing $1740 of the cost– In 1996 , many children who would later become eligible for In 1996 , many children who would later become eligible for

SCHIP were covered by private insurance and had working SCHIP were covered by private insurance and had working parentsparents

– Administrative data show several million children served by Administrative data show several million children served by SCHIPSCHIP

HypothesesHypotheses

Reasons to expect no effectReasons to expect no effect– SCHIP has measures designed to prevent crowd outSCHIP has measures designed to prevent crowd out– Competitive labor markets of the late 1990s, even for low-Competitive labor markets of the late 1990s, even for low-

wage employerswage employers– Take-up of SCHIP has been low, stigma costs may existTake-up of SCHIP has been low, stigma costs may exist– Employers might not know employees’ total family income Employers might not know employees’ total family income

and employee eligibility for SCHIPand employee eligibility for SCHIP

Importance of using employer Importance of using employer data to answer these questionsdata to answer these questions

Employer decisions plausibly driven by the median Employer decisions plausibly driven by the median worker, and individual level data do not identify worker, and individual level data do not identify employer workforce characteristicsemployer workforce characteristics– Problems in identifying ‘affected’ group in individual dataProblems in identifying ‘affected’ group in individual data

Employer decisions such as premium contributions Employer decisions such as premium contributions are not captured well in individual level surveysare not captured well in individual level surveys

Findings from previous studiesFindings from previous studies

Household-based studiesHousehold-based studies– Cutler and Gruber (1996)Cutler and Gruber (1996)– Dubay and Kenney (1997)Dubay and Kenney (1997)– Shore-Sheppard (1997)Shore-Sheppard (1997)– Yazici and Kaestner (1998)Yazici and Kaestner (1998)– Thorpe and Florence (1998/1999)Thorpe and Florence (1998/1999)– Blumberg, Dubay and Norton (1999)Blumberg, Dubay and Norton (1999)

– Cunningham, Hadley, ReschovskyCunningham, Hadley, Reschovsky (2002)(2002)

– LoSasso and Buchmueller (forthcomming)LoSasso and Buchmueller (forthcomming)

EEmployer-based studiesmployer-based studies– Shore-Sheppard, Buchmueller and Jensen (2000)Shore-Sheppard, Buchmueller and Jensen (2000)

MethodsMethods

Dependent variables: employer and employee level health Dependent variables: employer and employee level health insurance decisions and outcomesinsurance decisions and outcomes– offer insuranceoffer insurance– offer family coverageoffer family coverage– cost of familycost of family

Also, cost relative to single coverageAlso, cost relative to single coverage

– Enrollment rateEnrollment rate– Family enrollment rateFamily enrollment rate

‘‘Affected’ firms: those with substantial share of workers Affected’ firms: those with substantial share of workers newly eligible for SCHIP (other firms serve as controls)newly eligible for SCHIP (other firms serve as controls)

Econometric SpecificationEconometric Specification

Identifying sources of variation: Identifying sources of variation: – Exploiting time and state variation in eligibility Exploiting time and state variation in eligibility

expansionsexpansions

Model: Model: YY = = BB11 * Eligibility * Eligibility + + BB22 * Percent LOW + * Percent LOW + BB33 * *

(Percent Low * Eligibility) +(Percent Low * Eligibility) +BB55 * Other Characteristics * Other Characteristics

Where Y = different health insurance outcomesWhere Y = different health insurance outcomes

Data Data MEPS-Insurance Component (MEPS-IC) MEPS-Insurance Component (MEPS-IC)

– List Sample of Private EstablishmentsList Sample of Private Establishments– 1997-20011997-2001– Roughly 25,000 private sector establishments and their plans Roughly 25,000 private sector establishments and their plans

in each yearin each year Employers asked: Employers asked:

– Whether they offer insuranceWhether they offer insurance– Fraction of eligible active workers that enrollFraction of eligible active workers that enroll

At allAt all In single/non-single coverageIn single/non-single coverage

– Premiums and contributions for single and family coveragePremiums and contributions for single and family coverage– Workforce characteristics, including information on the percent Workforce characteristics, including information on the percent

of workers in three different wage rangesof workers in three different wage ranges

Data (continued)Data (continued)

Wages: 1997-1999 <$6.50, $6.50-$15.00, >$15Wages: 1997-1999 <$6.50, $6.50-$15.00, >$15 Wages: 2000-2001 <$9.50, $9.50-$21.00, >$21Wages: 2000-2001 <$9.50, $9.50-$21.00, >$21

IndustryIndustry Age of firmAge of firm Establishment sizeEstablishment size Percent of workforce that are women, union members, over age Percent of workforce that are women, union members, over age

50, part time50, part time For profit statusFor profit status Organization type (i.e. corporation)Organization type (i.e. corporation) State dummiesState dummies Year dummiesYear dummies

Data (continued)Data (continued) We merge in data on:We merge in data on:

– SCHIP/Medicaid EligibilitySCHIP/Medicaid EligibilityComputed from running detailed simulation Computed from running detailed simulation

programs (created by Gruber and Simon) on programs (created by Gruber and Simon) on March CPS respondents as in Cutler and Gruber March CPS respondents as in Cutler and Gruber (1996)(1996)

We take We take allall March CPS children in 1996, and March CPS children in 1996, and calculate the weighted fraction of them that would calculate the weighted fraction of them that would be eligible for Medicaid or SCHIP in a particular be eligible for Medicaid or SCHIP in a particular state in a particular year. That fraction is used as state in a particular year. That fraction is used as our measure of Medicaid/SCHIP generosity. The our measure of Medicaid/SCHIP generosity. The measure varies by state and year.measure varies by state and year.

Data (continued)Data (continued)

Workers most likely to affected by SCHIP Workers most likely to affected by SCHIP expansions.expansions.– Using PUMS we construct a proxy measure of the Using PUMS we construct a proxy measure of the

percent of families with children under 200 % FPL percent of families with children under 200 % FPL by industry and state (LOW).by industry and state (LOW).

Mean of Eligibility Measure from CPSMean of Eligibility Measure from CPS

0

5

10

15

20

25

30

35

40

45

50

1996 1997 1998 1999 2000

Eligiblity

Data (continued)Data (continued)

Other data merged onto the MEPS-IC:Other data merged onto the MEPS-IC:– Area Resource File Area Resource File

Unemployment rateUnemployment ratePer capita incomePer capita incomeNumber of active doctors in areaNumber of active doctors in areaNumber of HMOs in countyNumber of HMOs in county

– County Business Pattern DataCounty Business Pattern DataPercent of establishments with < 10 employees, > 1000 employees, in manufacturingPercent of establishments with < 10 employees, > 1000 employees, in manufacturingHerfindahl indexHerfindahl index

ResultsResults

Offers and Family OffersOffers and Family Offers– No significant results on eligibility in either equationNo significant results on eligibility in either equation– Employers are not reacting to SCHIP through Employers are not reacting to SCHIP through

offers of coverage or family coverageoffers of coverage or family coverage

ResultsResults Take-up Take-up

– For hypothetical establishments with 50 percent of For hypothetical establishments with 50 percent of the workforce under 200 percent of the poverty line, the workforce under 200 percent of the poverty line, a 0.188 increase in ELIG results in a:a 0.188 increase in ELIG results in a: 4 percentage point decrease in overall take-up rates.4 percentage point decrease in overall take-up rates. 5 percentage point decrease in family take-up.5 percentage point decrease in family take-up.

ResultsResults

Marginal Cost of Family Coverage (family – single).Marginal Cost of Family Coverage (family – single).– For hypothetical establishments with 50 percent of the For hypothetical establishments with 50 percent of the

workforce under 200 percent of the poverty line, a 0.188 workforce under 200 percent of the poverty line, a 0.188 increase in ELIG results in a:increase in ELIG results in a: $378 increase in the marginal cost of family coverage$378 increase in the marginal cost of family coverage All workers under 200 FPL - $800 increase in marginal cost of All workers under 200 FPL - $800 increase in marginal cost of

family coveragefamily coverage

ImplicationsImplications

If employers are reacting to SCHIP If employers are reacting to SCHIP expansions, what will happen with rollbacks?expansions, what will happen with rollbacks?

What is the effect of a sluggish economy?What is the effect of a sluggish economy?

Future plansFuture plans

Include other measures of SCHIP: outreach, Include other measures of SCHIP: outreach, waiting periods, paperwork simplificationwaiting periods, paperwork simplification

Use more worker eligibility indices (<100 FPL, Use more worker eligibility indices (<100 FPL, 200 – 300 FPL, etc.)200 – 300 FPL, etc.)

Additional yearsAdditional years