Do Workers Bear the Cost of Rising
Health Insurance Premiums through
Lower Wage Raises?
Bradley Herring, Ph.D. Johns Hopkins University
M. Kate Bundorf, Ph.D. Stanford University
Mark V. Pauly, Ph.D. University of Pennsylvania
AcademyHealth’s ARM: June 28, 2009
Funding Source: RWJF’s HCFO Initiative
Overview
• Policy and Economic Motivation
• Empirical Methods:
– Effect of changes in premiums on changes in wages
– Sample of insured workers in 2004-2007 SIPP
– Average premiums for state/industry from MEPS-IC
• Preview of Results:
– 70% of premium growth passed on to wages in short term
– Smaller effect for workers with low wages (“stickiness”)
– No difference between low and high spending workers
Health Policy Motivation
• Who pays the cost in employment-based insurance?
– Employer profit margins and/or prices? Employee wages?
• Some claim that U.S. competitiveness in the global
economy is hampered by high healthcare spending
• What are the distributional effects of impending
healthcare reform?
– Who will benefit from reductions in medical spending?
– Effect of “play or pay” employer mandates?
– Effect of firms changing coverage after Medicaid expansion,
subsidies to Exchange, tax reform, and public plan option?
Taken from: Kaiser Family Foundation, 2008, Costs/Insurance Fast Facts.
Economic Motivation
• Economic theory on the tradeoffs between benefits
costs and wages is pretty straightforward:
– productivity = total compensation = wages + benefits
• But the supporting empirical evidence is limited:
–
–
–
–
–
Many studies document positive correlation or no effect
Gender: Gruber, 1994
Age: Sheiner, 1999; Pauly and Herring, 1999; Adams, 2007
Obesity: Bhattacharya and Bundorf, 2005
Changes: Goldman et al., 2005; Baicker and Chandra, 2006
Common Approach
Workers at
firm offering
insurance:
Worker at firm
not offering
insurance:
Low Spending
Worker
High Spending
Worker
Difference:
WOL
WOH
WOH – WOL
WNL
WNH
WNH – WNL
Net Effect
on Wages:
(WOH – WOL)
– (WNH – WNL)
Concerns With This Approach
• Are jobs at employers that don’t offer health benefits
really a good control for those that do?
– Couldn’t the proxy for high spending have a different
relationship in “good” jobs versus “bad” jobs?
• This approach assumes that premiums affect wages
differentially by spending level, rather than testing for
differential effects empirically
– If there is only “average incidence” across all workers, this
difference-in-difference approach won’t observe an effect.
Our Approach
Firm facing
high growth
in premiums:
Firm facing
low growth
in premiums:
Worker i
Last Year
Worker i
This Year
Difference:
WH1
WH2
WH2 – WH1
a smaller raise?
WL1
WL2
WL2 – WL1
a larger raise?
Net Effect
On Wages:
(WH2 – WH1)
– (WL2 – WL1)
Methods
• Sample: Longitudinal sample of workers from 2004
SIPP merged with average premiums from MEPS-IC
• OLS regression model for wages, w:
w2 = a + w1 + b (p2 – p1) + c x + e
• Hypothesize that b = -1 with full incidence on wages
• Test for differences in b across different subsamples:
– Lower wage vs. higher wage, if wages constrained for former
– Lower vs. higher covered spending, based on age and gender
Methods: SIPP Data
• Survey of Income and Program Participation (SIPP):
– Large, rich nationally-representative household survey data
– 2004 Panel for years 2004 through 2007 (Waves 1, 4, 7, & 10)
• Sample:
– Full-time, private-sector ESI policyholders staying in job
– Yields three one-year changes in wages (January to January)
– N = 24,133 (representing over 8,000 workers)
• Control variables, x:
– Age, gender, marital status, race/ethnicity, education, union
status, firm size, industry indicators (18), region, and time
Methods: MEPS-IC Data
• Medical Expenditure Panel Survey’s Insurance
Component’s (MEPS-IC):
– Large nationally-representative employer survey data
– Tables with average premiums by state posted online
• Data Used:
– Premium changes: 2003 to 2004, 2005 to 2006, 2006 to 2007
– Five groupings of industry classifications within each state
– Employer share of the premium, single/family pooled
• Merge the MEPS-IC’s average premiums to the SIPP
sample of workers by worker i’s state-industry.
Main Results
• OLS coefficient for the one-year change in premiums:
– Wage regression using a sample of covered workers; the
control variables behave as expected (r-square = 0.72)
– Standard errors clustered by worker and by state/industry
Sample:
All insured workers
Coefficient
for
p2 – p1
-0.712
Robust
Standard
Error
0.344**
• Implication: About 70% of premium increase passed
onto wages in the short term
Results By Wage Level
• A binding constraint of non-negative nominal wage
increases for lower-wage workers?
– At $35K: 10% family premium growth = 3% wage growth
Sample:
Below $35K
Above $35K
Coefficient
for
p2 – p1
-0.180
-1.051
Robust
Standard
Error
0.173
0.576*
• Implications: consistent with “stickiness” at low wages,
and full incidence for the others where applicable
Results By Low vs. High Spending
• Is this wage tradeoff larger for high-spending workers?
– If so, expect the coefficient b to be larger for that sample
• Spending for covered unit, by age/gender (MEPS-HC)
– Low Spending: $3,675 (average age of 36, 39% female)
– High spending: $5,882 (average age of 49, 25% female)
Sample:
Low Spending, Above $35K
High Spending, Above $35K
Coefficient
for
p2 – p1
-0.918
-1.042
Robust
Standard
Error
0.901
0.750
Conclusions and Implications
• Examine the tradeoff between wages and premiums:
– 70% of premium growth passed on to wages in short term
– Nominal wage constraints at low end; full incidence for rest
– No support for differential incidence by expected spending
• Implications for Reform: Workers pay most of the costs
– Workers will benefit from efforts to reduce medical spending
– Workers will ultimately “pay” under reforms geared to
incentivize or require employers to start offering coverage
– Workers won’t necessarily “lose” in transitions away from
employment-based coverage (i.e., they’d get wage raises)