A Burden or Merely a Load: New Empirical Estimates of Health Insurance Loading Fees by Group Size

Transcription

1 A Burden or Merely a Load: New Empirical Estimates of Health Insurance Loading Fees by Group Size Pinar Karaca-Mandic, University of Minnesota Jean M. Abraham, University of Minnesota Charles E. Phelps, University of Rochester 1

2 What is a Load? 2

3 In the context of health insurance Expected Insurance Benefits + Loading Fee = Premium Portion of the premium that is above and beyond the expected amount of medical expenditures to be paid by the insurance company 3

4 What does the loading fee cover? Loading Fee Administrative Costs Insurer Profits Taxes Costs associated with Selling insurance Collecting premiums Processing claims 4

5 In summary, loading fee is the relevant price of insurance Expected Insurance Benefits ($1) + Loading Fee ($0.25) = Premiu m ($1.25) 5

6 What do we know about loads? Non group policies: 60-80%, sometimes up to 100% Group policies: on average 15-20% varies by group size By how much? We know very little. No published academic research Some historical estimates cited from 1980s era: can be as high as 40% for small firms 6

7 Why do we care about loading fees? They represent the price of health insurance, and we care about price They tell us about the extent of administrative costs and profits Their magnitude has implications for Both for the demand and supply of health insurance Affordability of health insurance 7

8 Why care how loads vary with group size? For small businesses, high loads may be a serious issue. Too small to self insure Small firms repeatedly report barriers to afford health insurance. Only 45% offer health insurance If loads contribute a lot, could policy and reform proposals address this issue? National or regional insurance exchange proposals How to attract small businesses Whether/how to subsidize small businesses Assessing cost savings from reductions in loads Implications for employer sponsored insurance 8

9 Our objective To provide new estimates on the size and nature of loading fees and how they differ between small and larger groups 9

10 Summary findings Small firms pay a lot more in administrative costs relative to large employer groups. Relative to firms with less than 100 employees, loading fees are smaller by 20% for firms with employees 60% for firms with 5000 or more employees At this point, we estimate the following lower and upper bounds for loading fees 100 or fewer employees : 27% - 47% 1000 or fewer employees: 24% - 43% Across all firm sizes : 13% - 30% 10

11 Data Source Medical Expenditure Panel Survey (MEPS), the Household Component Insurance Component Linked File for the years 1997, 1998, 1999, 2001 Access at the AHRQ data center Household Component (HC) Demographic, health and expenditure data on all individuals in the household Insurance Component (IC) Information on employers, health insurance offerings, benefit designs, premiums for a subset of the HC individuals 11

12 Data Extraction Used the Person-Round-Plan files Identified individuals covered by ESI either as the policy holder or dependent during the first round of their MEPS interview (IC collects data on the employer reported in the first round) Require that individuals stay with the same employer throughout the whole year and do not change insurance plans Exclude individuals with multiple private plans 22,663 individuals 12

13 Merge the sample to employers and plan Merge individuals to the employer and held plan in MEPS-IC MEPS-IC linkage is available only for a subset Focus on individuals for whom there was an exact match between the health plan reported by the individual and the employer. 7,875 individuals Exclude those with missing key info (firm size, premiums etc). 6,116 individuals Supplement with additional data Small group regulations by business state/year InterStudy data on HMO penetration rate in MSA/year Area Resource File data on relevant market conditions in county/year 13

14 Econometric framework individual firm size fixed component, varies by firm size, captured with firm size indicators Expected Insurance Payment Firm attributes other than firm size State group regulations Other market 14

15 Estimating Expected Insurance Payment Two Part Model Part I: Probability of Positive Private Insurance Payment Predict probability of any private pay Part II: Amount of Private Insurance Payment Conditional on Positive Private Pay Predict conditional private pay In both parts, controls include individual demographics, health insurance benefit design and scope of policy Expected Insurance Payment = probability of any spending x conditional private payment 15

16 Caution: Expenditures in MEPS are biased down Private insurer payments are 21% greater in National Health Expenditure Accounts than in MEPS (Sing et al, 2006) Selden and Sing (2008) construct NHEA aligned private insurance payments by adjusting them up 24.4% Zuvekas et al, 2005 compare mean health expenditures in MEPS to those in MarketScan data for large self insured employers. Again 21%-29% lower private health insurance payments in MEPS. Zuvekas and Olin (2009) compare MEPS expenditures and claims of the Medicare beneficiaries 50% of the discrepancy due to missing data for people with more than $100,000 in annual total expenditures 50% due to underreporting The bias is uniform across population 16

17 Our approach for adjusting MEPS Want to make sure MEPS is aligned to other employer sponsored insurance Inflate MEPS private payments to match those in MarketScan by age and gender (provided by Zuvekas and colleagues) Female Male Age Age MEPS MarketScan Adjustment Factor

18 Select Summary Statistics of Individuals Individual characteristics Mean Std % msa 81 % age % age % age % age % female 50 % white 83 % black 12 % hispanic 18 % married 46 total household income ($1000), 2007 dollars num kids fam size num years of education Health Conditions % cancer 1.26 % diabetes 2.39 % high cholesterol 1.95 % heart disease 1.68 % hypertension 5.8 % asthma 3.34 % depression, anxiety disorder

19 Select Summary Statistics of Employers/Plans Employer's workforce composition Mean Std % female workers % workers over age % union representation % workers earning $15 per hour or more % Government owned 9 % Nonprofit 20 % in business 0-5 years 3 % in business 6-10 years 3 % in business years 18 % in business more than 50 years 20 Benefit Design Features % Exclusive provider organization 44 Individual deductible % zero individual deductible 70 Ind. deductible among positive deductible plans % with OOP maximum 63 Total premium for single coverage Employee contribution to single coverage % Plan includes: Outpatient prescription 70 Dental 19 Inpatient mental illness coverage 79 Outpatient mental illness coverage 67 Alcohol/substance abuse 60 19

20 Firm size distribution, raw data MarketScan Adjusted Private Ratio of Mean Premiums to Mean Adjusted Prv. Ins. Pay Firm Size Number Any Spending Unadjusted Private Insurance Pay Insurance Pay Single Premiums (%) mean st dev mean st dev mean st dev > Mean across all size categories

21 Two-part model for predicting insurance payments Part I Part II Coef. Est Std. Err. stat. sig. Coef. Est Std. Err. stat. sig. Demographic/Socio-Economic age between * ** age between *** age between *** female *** *** number of children ** *** number of children squared *** *** family size *** ** family size squared ** ** # years of schooling * *** # years of schooling squared * *** Total household income ($1000) *** ** Total household income ($1000) squared *** ** work less than 20 hours *** ** work between hours ** * work between hours ** ** job tenure under 1 year ** * job tenure 1-5 years ** ** job tenure 6-10 years ** *** Health cancer *** *** chol/diabetes/heart *** *** asthma *** *** pregnant/delivered *** *** depression/anxiety *** *** muscle disorders *** *** oth_chronic ** *** Benefit Features family coverage *** ** exclusive provider org *** ** individual deductible 0-$ ** ** individual deductible $300-$ ** ** 21

22 Premium Equation (1) Coef. Est Std. Err. stat. sig. Firm Size cat 1:1-10 reference cat 2:11_ cat 3:26_ cat 4: 51_ * cat 5:101_ cat 6:500_ ** cat 7:5000_ ** cat 8:> ** Expected Insurer Payment and Firms Size Interactions Expected Ins. Pay ** Expected Ins. Pay x cat *** Expected Ins. Pay x cat ** Expected Ins. Pay x cat Expected Ins. Pay x cat * Expected Ins. Pay x cat ** Expected Ins. Pay x cat * Expected Ins. Pay x cat ** 22

23 Premium Equation (2) Coef. Est Std. Err. stat. sig. Workforce Composition Less than 25% female ** Between 26%-50% female Less than 25% over age *** More than 50% unionized *** More than 50% earning $15/hour or more *** Industry of the Business Retail * Personnel Services *** Business Services ** Other Services ** Manufacturing * Wholesale Trade * Finance/Insurance *** Transportation ** Construction Agriculture Mining * Other Business Characteristics Government owned Nonprofit ** In business 0-5 years In business 6-10 years In business years Market Conditions MSA Indicator mean nurse wage rate *** average malpractice payment in state ($1000) ** percapita hospitals * % over age * HMO penetration rate x MSA indicator

24 Predicted Insurer Payments and Premiums $ (in 2007) >10000 Predicted Insurer Payments Number of employees Predicted Premiums Predicted Loading Fees >10000 Number of employees 24

25 These estimates differ from other reports We find that loading fees are similar for firms under 100 employees Previous reports indicate a downward gradient 30-40% up to 10 employee, 20% employees Our estimates are higher for all firm size categories Even for the largest, we estimate loads around 17%, other reports indicate 10% or lower 25

26 Aggregate implications from MEPS (1) Extract the nationally representative number of enrollees by group size from MEPS-IC summary tables Information available for various firm size categories Number of employees % employees in firms that offer coverage % employees that enroll in ESI fin forms that offer coverage Estimate primary vs. dependent population from HC-IC linked data by firm size Estimate total # enrollees in ESI by firm size 26

27 Aggregate implications from MEPS (2): overall loading fee of 30% Inferred Number of Employees and Dependents Covered by ESI Expected Insurer Payments Premiums for Single Coverage Ratio of Premiums to Expected Insurer Payments Total Premiums Paid Total Insurer Payments Firm Size ,689, E E ,684, E E ,272, E E ,657, E E+10 > ,663, E E+11 Ratio Total 135,966, E E These figures are for 2001, with 2007 dollars 27

28 NHEA Estimates of Private Insurance Costs Table 1: Private Health Insurance Premiums Paid and Benefits Received Private Health Insurance Premiums Private Health Insurance Benefits Net Cost Net Cost as Percent of Benefits Source: (Table 12) NHEA estimates suggest an overall loading fee of 13-15%, much lower than our 30% 28

29 Why are our estimates higher? Although we inflated the MEPS private payments to match other ESI, maybe it is still not sufficient, and we need to inflate more - coming up next NHEA estimates include many other components besides ESI. We need to sort out and understand the portion of NHEA figures attributable to ESI work in progress We are working with single coverage premiums and expected payments for individuals Our estimates may well be lower when we distinguish single coverage and family coverage work in progress 29

30 Estimates Comparable to NHEA Further inflate insurance benefits paid out by 10%: Obtain an overall loading fee of 19% by 15%: Obtain an overall loading fee of 13% (the magic number) status quo 10% adjustment 15% adjustment Number of employees >

31 Range of Estimates (lower bound: status quo, upper bound: 15% inflation of private payment) Across all firms, average loading fee (total premiums/total insurer payments) ranges between 13% to 30% 100 or fewer employees : 27% - 47% 1000 or fewer employees: 24% - 43% Assuming 136 million covered under ESI, loading costs are between $55 - $108 billion (in 2007 dollars 100 or fewer employees : $24 - $36 billion 1000 or fewer employees: $39 - $60 billion 31

32 Implications 15% reduction in loading costs would save (assuming no other changes) $4-5 billion annually for firms with 100 or fewer employees $6-9 billion annually for firms with 1000 or fewer employees If loading fees were reduced to 15%, annual savings would be (assuming no other changes) $11-24 billion annually for firms with 100 or fewer employees $15-$39 billion annually for firms with 1000 or fewer employees. 32

33 Next Steps More refined aligning MEPS private payments to match NHEA Take into account different points in expenditure distribution Take into account adjustments by sevrice categories Estimate separate models for those in single coverage and family coverage 33